From 4b7cb696b8045c8f4cdace363671104f03d82be7 Mon Sep 17 00:00:00 2001
From: seokho92 <seokho9208@gmail.com>
Date: Fri, 12 Sep 2025 16:43:28 +0900
Subject: [PATCH 01/34] Update Session1_Sequencing.md

---
 Session1_Sequencing.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Session1_Sequencing.md b/Session1_Sequencing.md
index e5ecf2f..bb8783e 100644
--- a/Session1_Sequencing.md
+++ b/Session1_Sequencing.md
@@ -376,6 +376,9 @@ gatk --java-options "-Xms4g" HaplotypeCaller \
 -ERC GVCF \
 -O sample03_20.g.vcf
 ```
+#### What GVCF provides us : SNV
+
+#### What GVCF provides us : Insertion/Deletion
 
 #### Combine individual `GVCF` files
 
@@ -417,6 +420,3 @@ DeepVariant is a deep learning-based variant caller that takes aligned reads (in
 
 The simple concept of Deepvariant is explained and visualized in following post:
 [Looking Through DeepVariant's Eyes](https://google.github.io/deepvariant/posts/2020-02-20-looking-through-deepvariants-eyes/)
-
-### 9. [Geneformer](https://github.com/jkobject/geneformer) & [Enformer](https://github.com/lucidrains/enformer-pytorch)
-Gene Expression prediction models or single-cell resolution foundation model via transformer.

From 4cccace7421a59c4a116e168c63ab4d61ad58ecc Mon Sep 17 00:00:00 2001
From: seokho92 <seokho9208@gmail.com>
Date: Mon, 15 Sep 2025 13:58:05 +0900
Subject: [PATCH 02/34] Update Session1_Sequencing.md

---
 Session1_Sequencing.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Session1_Sequencing.md b/Session1_Sequencing.md
index bb8783e..8688f17 100644
--- a/Session1_Sequencing.md
+++ b/Session1_Sequencing.md
@@ -1,4 +1,4 @@
-# Practice Session #1: Sequencing (Mar. 20, 2025)
+# Practice Session #1: Sequencing (Sep. 16, 2025)
 
 In this session, we will learn how to convert raw unmapped read files (`FASTQ`) to analysis-ready files (`VCF`). \
 The overall process in this session is mainly based on the [GATK Best Practice](https://gatk.broadinstitute.org/hc/en-us/categories/360002302312-Getting-Started). \

From b54cd989de34049ece5d3738f11fbf983ff658f1 Mon Sep 17 00:00:00 2001
From: seokho92 <seokho9208@gmail.com>
Date: Tue, 16 Sep 2025 10:02:43 +0900
Subject: [PATCH 03/34] Update Session1_Sequencing.md

---
 Session1_Sequencing.md | 98 +++++++++++++++++++++++++-----------------
 1 file changed, 58 insertions(+), 40 deletions(-)

diff --git a/Session1_Sequencing.md b/Session1_Sequencing.md
index 8688f17..9f8fda0 100644
--- a/Session1_Sequencing.md
+++ b/Session1_Sequencing.md
@@ -3,7 +3,7 @@
 In this session, we will learn how to convert raw unmapped read files (`FASTQ`) to analysis-ready files (`VCF`). \
 The overall process in this session is mainly based on the [GATK Best Practice](https://gatk.broadinstitute.org/hc/en-us/categories/360002302312-Getting-Started). \
 This document was created on Mar. 19, 2025 and the following contents were tested on local WSL (Ubuntu 22.04.1 LTS) + GSDS Cluster.
-### 0. Installing Linux and Anaconda in Windows
+### 0. Installing Linux and Mamba(Anaconda) in Windows
 Using Linux has become easy in Windows with WSL. \
 To start, launch windows powershell in administration mode and run following. 
 ``` 
@@ -24,10 +24,18 @@ ID: gcda_<xxx>
 PW (default): gcda_<xxx>
 
 ```
-### Use designated node for computation: leelabsg15
-After login, use ssh to log on to designated node for GCDA 2025 Spring
+#### Installing mamba for faster conda usage
+Follow the instruction from https://github.com/conda-forge/miniforge 
+
 ```
-ssh leelabsg15
+bash Miniforge3-Linux-x86_64.sh -b -p "${HOME}/conda"
+conda/bin/conda init
+```
+
+### Use designated node for computation: leelabsg11
+After login, use ssh to log on to designated node for GCDA 2025 Fall
+```
+ssh leelabsg11
 ```
 
 ### 1. Setting up the environment
@@ -35,23 +43,26 @@ ssh leelabsg15
 We will use the Anaconda environment on the GSDS cluster. \
 It is already created on the GSDS cluster, but you can create the environment on your local machine with the following command \
 In this session, OpenJDK, samtools, GATK and BWA are installed in creation of conda environment and Picard is downloaded as java package \
-All files and tools are included in '~/GCDA/1_sequencing/' folder
+All files and tools are included in '~/1_sequencing/' folder
 ```
-# Create conda environment and install softwares 
-conda create -n SEQ samtools bwa -c anaconda -c bioconda
+# Create conda/mamba environment and install softwares 
+# conda create -n SEQ samtools bwa -c anaconda -c bioconda
+mamba create -n SEQ samtools bwa -c anaconda -c bioconda
+
 conda activate SEQ
 
 # Install jdk 17 version (Required after picard 3.0.0)
 ## wget https://download.java.net/java/GA/jdk17.0.2/dfd4a8d0985749f896bed50d7138ee7f/8/GPL/openjdk-17.0.2_linux-x64_bin.tar.gz
-tar xvf ~/GCDA/1_sequencing/utils/openjdk-17.0.2_linux-x64_bin.tar.gz
+tar xvf ~/1_sequencing/utils/openjdk-17.0.2_linux-x64_bin.tar.gz
 export JAVA_HOME=$JAVA_HOME/:~/jdk-17.0.2/
 export PATH=$JAVA_HOME/bin:$PATH
 alias java17="~/jdk-17.0.2/bin/java"
 
 # install gatk4 
 conda install gatk4 -c bioconda
+mamba install gatk4 -c bioconda
 # Download Picard (Find Latest Release: https://github.com/broadinstitute/picard/releases/latest)
-## cd ~/GCDA/1_sequencing/utils
+## cd ~/1_sequencing/utils
 ## wget https://github.com/broadinstitute/picard/releases/download/3.0.0/picard.jar
 
 ```
@@ -62,9 +73,10 @@ To map our raw unmapped reads, we need the reference panel and the information f
 Here, we will use the `FASTA` file of 1000 Genome Phase 3 (GRCh37 build) and the `VCF` file for known variants. \
 You can browse [FTP server](ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/) of 1000 Genome Project.
 
+#### Already done in your user account
 ```
-mkdir -p ~/GCDA/1_sequencing/reference
-cd ~/GCDA/1_sequencing/reference
+mkdir -p ~/1_sequencing/reference
+cd ~/1_sequencing/reference
 # Download 1000 Genome reference panel
 ## wget http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/human_g1k_v37.fasta.gz
 ## gzip -d human_g1k_v37.fasta.gz
@@ -97,7 +109,7 @@ gatk CreateSequenceDictionary -R human_g1k_v37.fasta
 And we need a sequence read file (`FASTQ`) for the sample individual (HG00096).
 This can be downloaded from ftp server wih project description : [1000 Genome Project Phase 3](https://www.internationalgenome.org/1000-genomes-summary)
 ```
-cd ~/GCDA/1_sequencing/raw_reads
+cd ~/1_sequencing/raw_reads
 # Download sequence read file from 1000 Genome
 # sample HG00096
 ## wget http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/HG00096/sequence_read/SRR062634.filt.fastq.gz
@@ -175,11 +187,11 @@ This practice session consists of 4 steps.
 Using `FastqToSam` function of Picard, we can convert the `FASTQ` file to an unmapped `BAM` file.
 
 ```
-mkdir -p ~/GCDA/1_sequencing/data/
+mkdir -p ~/1_sequencing/data/
 SID=HG00096
-java17 -jar ~/GCDA/1_sequencing/utils/picard.jar FastqToSam \
-F1=~/GCDA/1_sequencing/raw_reads/SRR062634.filt.fastq \
-O=~/GCDA/1_sequencing/data/fastq_to_bam_${SID}.bam \
+java17 -jar ~/1_sequencing/utils/picard.jar FastqToSam \
+F1=~/1_sequencing/raw_reads/SRR062634.filt.fastq \
+O=~/1_sequencing/data/fastq_to_bam_${SID}.bam \
 SM=${SID}
 ```
 
@@ -188,8 +200,8 @@ SM=${SID}
 We can add read groups in `BAM` file by the following command:
 
 ```
-cd ~/GCDA/1_sequencing/data/
-java17 -jar ~/GCDA/1_sequencing/utils/picard.jar AddOrReplaceReadGroups \
+cd ~/1_sequencing/data/
+java17 -jar ~/1_sequencing/utils/picard.jar AddOrReplaceReadGroups \
 I=fastq_to_bam_${SID}.bam \
 O=add_read_groups_${SID}.bam \
 RGID=4 \
@@ -204,7 +216,7 @@ RGSM=20
 Using `MarkIlluminaAdapters`, we can mark adapter sequences.
 
 ```
-java17 -Xmx8G -jar ~/GCDA/1_sequencing/utils/picard.jar MarkIlluminaAdapters \
+java17 -Xmx8G -jar ~/1_sequencing/utils/picard.jar MarkIlluminaAdapters \
 I=add_read_groups_${SID}.bam \
 O=mark_adapter_${SID}.bam \
 M=mark_adapter_${SID}.metrics.txt
@@ -213,7 +225,7 @@ M=mark_adapter_${SID}.metrics.txt
 #### Convert the preprocessed `BAM` file to a `FASTQ` file
 
 ```
-java17 -Xmx8G -jar ~/GCDA/1_sequencing/utils/picard.jar SamToFastq \
+java17 -Xmx8G -jar ~/1_sequencing/utils/picard.jar SamToFastq \
 I=mark_adapter_${SID}.bam \
 FASTQ=fastq_input_${SID}.fq \
 CLIPPING_ATTRIBUTE=XT \
@@ -229,7 +241,7 @@ NON_PF=true
 GATK's variant discovery workflow recommends Burrows-Wheeler Aligner's maximal exact matches (BWA-MEM) algorithm.
 
 ```
-bwa mem -M -t 7 -p ~/GCDA/1_sequencing/reference/human_g1k_v37.fasta ~/GCDA/1_sequencing/data/fastq_input_${SID}.fq > aligned_${SID}.sam
+bwa mem -M -t 7 -p ~/1_sequencing/reference/human_g1k_v37.fasta ~/1_sequencing/data/fastq_input_${SID}.fq > aligned_${SID}.sam
 ```
 
 You can check the contents of the `SAM` file:
@@ -263,8 +275,8 @@ The information of some columns are as follows:
 #### Add information to `BAM` file using `MergeBamAlignment`
 
 ```
-java17 -Xmx10G -jar ~/GCDA/1_sequencing/utils/picard.jar MergeBamAlignment \
-R=~/GCDA/1_sequencing/reference/human_g1k_v37.fasta \
+java17 -Xmx10G -jar ~/1_sequencing/utils/picard.jar MergeBamAlignment \
+R=~/1_sequencing/reference/human_g1k_v37.fasta \
 UNMAPPED=add_read_groups_${SID}.bam \
 ALIGNED=aligned_${SID}.sam \
 O=preprocessed_${SID}.bam \
@@ -281,7 +293,7 @@ ATTRIBUTES_TO_RETAIN=XS
 #### Mark Duplicates
 
 ```
-java17 -jar ~/GCDA/1_sequencing/utils/picard.jar MarkDuplicates \
+java17 -jar ~/1_sequencing/utils/picard.jar MarkDuplicates \
 I=preprocessed_${SID}.bam \
 O=mark_dup_${SID}.bam \
 M=mark_dup_${SID}.metrics.txt
@@ -290,7 +302,7 @@ M=mark_dup_${SID}.metrics.txt
 #### Sort, index and convert alignment to a BAM using SortSam
 
 ```
-java17 -jar ~/GCDA/1_sequencing/utils/picard.jar SortSam \
+java17 -jar ~/1_sequencing/utils/picard.jar SortSam \
 I=mark_dup_${SID}.bam \
 O=sorted_${SID}.bam \
 SO=coordinate 
@@ -301,8 +313,8 @@ SO=coordinate
 ```
 gatk --java-options '-Xmx10g' BaseRecalibrator \
 -I sorted_${SID}.bam \
--R ~/GCDA/1_sequencing/reference/human_g1k_v37.fasta \
---known-sites ~/GCDA/1_sequencing/reference/ALL.wgs.mergedSV.v8.20130502.svs.genotypes.vcf.gz \
+-R ~/1_sequencing/reference/human_g1k_v37.fasta \
+--known-sites ~/1_sequencing/reference/ALL.wgs.mergedSV.v8.20130502.svs.genotypes.vcf.gz \
 -O recal_data_${SID}.table
 ```
 
@@ -311,7 +323,7 @@ gatk --java-options '-Xmx10g' BaseRecalibrator \
 ```
 gatk --java-options '-Xmx10g' ApplyBQSR \
 -I sorted_${SID}.bam \
--R ~/GCDA/1_sequencing/reference/human_g1k_v37.fasta \
+-R ~/1_sequencing/reference/human_g1k_v37.fasta \
 --bqsr-recal-file recal_data_${SID}.table \
 -O bqsr_${SID}.bam
 ```
@@ -319,7 +331,7 @@ gatk --java-options '-Xmx10g' ApplyBQSR \
 ```
 for SID in HG00096 HG00097 HG00098
 do
-bash ~/GCDA/1_sequencing/bam_proc.sh ${SID}
+bash ~/1_sequencing/bam_proc.sh ${SID}
 done
 ```
 
@@ -330,7 +342,7 @@ We can visualize the aligned `BAM` file with the [IGV viewer](https://software.b
 
 For example, we can observe high coverage around SUMO1P1 gene. (`HG00096.chrom20.ILLUMINA.bwa.GBR.exome.20120522.bam`)
 ```
-cd ~/GCDA/1_sequencing/raw_reads
+cd ~/1_sequencing/raw_reads
 ## wget http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/HG00096/exome_alignment/HG00096.chrom20.ILLUMINA.bwa.GBR.exome.20120522.bam
 ## wget http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/HG00096/exome_alignment/HG00096.chrom20.ILLUMINA.bwa.GBR.exome.20120522.bam.bai
 ## wget http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/HG00097/exome_alignment/HG00097.chrom20.ILLUMINA.bwa.GBR.exome.20130415.bam
@@ -347,11 +359,11 @@ In this section, we will use the real data of 3 individuals in 1000 Genome Proje
 We can convert these `BAM` files to `GVCF` files.
 
 ```
-cd ~/GCDA/1_sequencing/data
+cd ~/1_sequencing/data
 # Convert BAM for the first individual
 gatk --java-options "-Xms4g" HaplotypeCaller \
--R ~/GCDA/1_sequencing/reference/human_g1k_v37.fasta \
--I ~/GCDA/1_sequencing/raw_reads/HG00096.chrom20.ILLUMINA.bwa.GBR.exome.20120522.bam \
+-R ~/1_sequencing/reference/human_g1k_v37.fasta \
+-I ~/1_sequencing/raw_reads/HG00096.chrom20.ILLUMINA.bwa.GBR.exome.20120522.bam \
 -L 20 \
 -ERC GVCF \
 -O sample01_20.g.vcf
@@ -360,8 +372,8 @@ gatk --java-options "-Xms4g" HaplotypeCaller \
 ```
 # Convert BAM for the second individual
 gatk --java-options "-Xms4g" HaplotypeCaller \
--R ~/GCDA/1_sequencing/reference/human_g1k_v37.fasta \
--I ~/GCDA/1_sequencing/raw_reads/HG00097.chrom20.ILLUMINA.bwa.GBR.exome.20130415.bam \
+-R ~/1_sequencing/reference/human_g1k_v37.fasta \
+-I ~/1_sequencing/raw_reads/HG00097.chrom20.ILLUMINA.bwa.GBR.exome.20130415.bam \
 -L 20 \
 -ERC GVCF \
 -O sample02_20.g.vcf
@@ -370,21 +382,27 @@ gatk --java-options "-Xms4g" HaplotypeCaller \
 ```
 # Convert BAM for the third individual
 gatk --java-options "-Xms4g" HaplotypeCaller \
--R ~/GCDA/1_sequencing/reference/human_g1k_v37.fasta \
--I ~/GCDA/1_sequencing/raw_reads/HG00099.chrom20.ILLUMINA.bwa.GBR.exome.20130415.bam \
+-R ~/1_sequencing/reference/human_g1k_v37.fasta \
+-I ~/1_sequencing/raw_reads/HG00099.chrom20.ILLUMINA.bwa.GBR.exome.20130415.bam \
 -L 20 \
 -ERC GVCF \
 -O sample03_20.g.vcf
 ```
 #### What GVCF provides us : SNV
-
+```
+bcftools view -v snps sample03_20.g.vcf | grep -v '##' | head
+```
 #### What GVCF provides us : Insertion/Deletion
+```
+bcftools view -v indels sample03_20.g.vcf | grep -v '##' | head
+```
+<img width="850" height="578" alt="image" src="https://github.com/user-attachments/assets/89a3c5c8-39e2-44fa-af8f-ac40344431ab" />
 
 #### Combine individual `GVCF` files
 
 ```
 gatk CombineGVCFs \
--R ~/GCDA/1_sequencing/reference/human_g1k_v37.fasta \
+-R ~/1_sequencing/reference/human_g1k_v37.fasta \
 --variant sample01_20.g.vcf \
 --variant sample02_20.g.vcf \
 --variant sample03_20.g.vcf \
@@ -395,7 +413,7 @@ gatk CombineGVCFs \
 
 ```
 gatk --java-options "-Xmx4g" GenotypeGVCFs \
--R ~/GCDA/1_sequencing/reference/human_g1k_v37.fasta \
+-R ~/1_sequencing/reference/human_g1k_v37.fasta \
 -V sample_all.g.vcf.gz \
 -O sample_all.vcf
 ```

From 1b2488461617c7c51f3232ab99e0cee19887096b Mon Sep 17 00:00:00 2001
From: seokho92 <seokho9208@gmail.com>
Date: Tue, 16 Sep 2025 10:07:22 +0900
Subject: [PATCH 04/34] Update Session1_Sequencing.md

---
 Session1_Sequencing.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Session1_Sequencing.md b/Session1_Sequencing.md
index 9f8fda0..ea7dfb3 100644
--- a/Session1_Sequencing.md
+++ b/Session1_Sequencing.md
@@ -2,7 +2,7 @@
 
 In this session, we will learn how to convert raw unmapped read files (`FASTQ`) to analysis-ready files (`VCF`). \
 The overall process in this session is mainly based on the [GATK Best Practice](https://gatk.broadinstitute.org/hc/en-us/categories/360002302312-Getting-Started). \
-This document was created on Mar. 19, 2025 and the following contents were tested on local WSL (Ubuntu 22.04.1 LTS) + GSDS Cluster.
+This document was created on Sep. 16, 2025 and the following contents were tested on local WSL (Ubuntu 22.04.1 LTS) + GSDS Cluster.
 ### 0. Installing Linux and Mamba(Anaconda) in Windows
 Using Linux has become easy in Windows with WSL. \
 To start, launch windows powershell in administration mode and run following. 
@@ -47,7 +47,7 @@ All files and tools are included in '~/1_sequencing/' folder
 ```
 # Create conda/mamba environment and install softwares 
 # conda create -n SEQ samtools bwa -c anaconda -c bioconda
-mamba create -n SEQ samtools bwa -c anaconda -c bioconda
+mamba create -n SEQ samtools bwa bcftools -c anaconda -c bioconda
 
 conda activate SEQ
 
@@ -64,7 +64,7 @@ mamba install gatk4 -c bioconda
 # Download Picard (Find Latest Release: https://github.com/broadinstitute/picard/releases/latest)
 ## cd ~/1_sequencing/utils
 ## wget https://github.com/broadinstitute/picard/releases/download/3.0.0/picard.jar
-
+mamba install bioconda::bcftools
 ```
 
 ### 2. Preparing data

From 0e6500113d03a1b1a747e0c014a0c6b5192055db Mon Sep 17 00:00:00 2001
From: seokho92 <seokho9208@gmail.com>
Date: Tue, 16 Sep 2025 10:37:01 +0900
Subject: [PATCH 05/34] Update Session1_Sequencing.md

---
 Session1_Sequencing.md | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/Session1_Sequencing.md b/Session1_Sequencing.md
index ea7dfb3..c4a0971 100644
--- a/Session1_Sequencing.md
+++ b/Session1_Sequencing.md
@@ -64,7 +64,7 @@ mamba install gatk4 -c bioconda
 # Download Picard (Find Latest Release: https://github.com/broadinstitute/picard/releases/latest)
 ## cd ~/1_sequencing/utils
 ## wget https://github.com/broadinstitute/picard/releases/download/3.0.0/picard.jar
-mamba install bioconda::bcftools
+
 ```
 
 ### 2. Preparing data
@@ -196,7 +196,7 @@ SM=${SID}
 ```
 
 #### Add read group information in `BAM` file
-
+read group is a set of sequences (in one or more fastq files) having a common set of metadata. This metadata generally includes patient/sample ID, library ID (the library is the preparation of the patient/sample DNA.
 We can add read groups in `BAM` file by the following command:
 
 ```
@@ -215,6 +215,8 @@ RGSM=20
 
 Using `MarkIlluminaAdapters`, we can mark adapter sequences.
 
+Adapter sequences are short, synthetic DNA segments attached to the ends of fragmented DNA or RNA libraries for next-generation sequencing (NGS)
+
 ```
 java17 -Xmx8G -jar ~/1_sequencing/utils/picard.jar MarkIlluminaAdapters \
 I=add_read_groups_${SID}.bam \
@@ -273,7 +275,7 @@ The information of some columns are as follows:
 * Column 11: ASCII representation of phred-scale base quality
 
 #### Add information to `BAM` file using `MergeBamAlignment`
-
+the alignment information from the mapped file (bwa) + the metadata  → one standardized, analysis-ready BAM.
 ```
 java17 -Xmx10G -jar ~/1_sequencing/utils/picard.jar MergeBamAlignment \
 R=~/1_sequencing/reference/human_g1k_v37.fasta \
@@ -309,7 +311,7 @@ SO=coordinate
 ```
 
 #### Create Recalibration Table using `BaseRecalibrator`
-
+[BQSR Explaination](https://gatk.broadinstitute.org/hc/en-us/articles/360035890531-Base-Quality-Score-Recalibration-BQSR)
 ```
 gatk --java-options '-Xmx10g' BaseRecalibrator \
 -I sorted_${SID}.bam \
@@ -409,7 +411,7 @@ gatk CombineGVCFs \
 -O sample_all.g.vcf.gz
 ```
 
-### 7. Converting `GVCF` to `VCF`
+### 7. Converting `GVCF` to `VCF` (Genotyping)
 
 ```
 gatk --java-options "-Xmx4g" GenotypeGVCFs \

From f6aafa9ba9782dd73f2a3617ed442dd094e6e4cc Mon Sep 17 00:00:00 2001
From: seokho92 <seokho9208@gmail.com>
Date: Tue, 16 Sep 2025 11:02:58 +0900
Subject: [PATCH 06/34] Update Session1_Sequencing.md

---
 Session1_Sequencing.md | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/Session1_Sequencing.md b/Session1_Sequencing.md
index c4a0971..1016abc 100644
--- a/Session1_Sequencing.md
+++ b/Session1_Sequencing.md
@@ -243,7 +243,7 @@ NON_PF=true
 GATK's variant discovery workflow recommends Burrows-Wheeler Aligner's maximal exact matches (BWA-MEM) algorithm.
 
 ```
-bwa mem -M -t 7 -p ~/1_sequencing/reference/human_g1k_v37.fasta ~/1_sequencing/data/fastq_input_${SID}.fq > aligned_${SID}.sam
+bwa mem -M -t 7 -p /data/GCDA_2025_2/1_sequencing/reference/human_g1k_v37.fasta ~/1_sequencing/data/fastq_input_${SID}.fq > aligned_${SID}.sam
 ```
 
 You can check the contents of the `SAM` file:
@@ -315,8 +315,8 @@ SO=coordinate
 ```
 gatk --java-options '-Xmx10g' BaseRecalibrator \
 -I sorted_${SID}.bam \
--R ~/1_sequencing/reference/human_g1k_v37.fasta \
---known-sites ~/1_sequencing/reference/ALL.wgs.mergedSV.v8.20130502.svs.genotypes.vcf.gz \
+-R /data/GCDA_2025_2/1_sequencing/reference/human_g1k_v37.fasta \
+--known-sites /data/GCDA_2025_2/1_sequencing/reference/ALL.wgs.mergedSV.v8.20130502.svs.genotypes.vcf.gz \
 -O recal_data_${SID}.table
 ```
 
@@ -325,7 +325,7 @@ gatk --java-options '-Xmx10g' BaseRecalibrator \
 ```
 gatk --java-options '-Xmx10g' ApplyBQSR \
 -I sorted_${SID}.bam \
--R ~/1_sequencing/reference/human_g1k_v37.fasta \
+-R /data/GCDA_2025_2/1_sequencing/reference/human_g1k_v37.fasta \
 --bqsr-recal-file recal_data_${SID}.table \
 -O bqsr_${SID}.bam
 ```
@@ -364,7 +364,7 @@ We can convert these `BAM` files to `GVCF` files.
 cd ~/1_sequencing/data
 # Convert BAM for the first individual
 gatk --java-options "-Xms4g" HaplotypeCaller \
--R ~/1_sequencing/reference/human_g1k_v37.fasta \
+-R /data/GCDA_2025_2/1_sequencing/reference/human_g1k_v37.fasta \
 -I ~/1_sequencing/raw_reads/HG00096.chrom20.ILLUMINA.bwa.GBR.exome.20120522.bam \
 -L 20 \
 -ERC GVCF \
@@ -374,7 +374,7 @@ gatk --java-options "-Xms4g" HaplotypeCaller \
 ```
 # Convert BAM for the second individual
 gatk --java-options "-Xms4g" HaplotypeCaller \
--R ~/1_sequencing/reference/human_g1k_v37.fasta \
+-R /data/GCDA_2025_2/1_sequencing/reference/human_g1k_v37.fasta \
 -I ~/1_sequencing/raw_reads/HG00097.chrom20.ILLUMINA.bwa.GBR.exome.20130415.bam \
 -L 20 \
 -ERC GVCF \
@@ -384,7 +384,7 @@ gatk --java-options "-Xms4g" HaplotypeCaller \
 ```
 # Convert BAM for the third individual
 gatk --java-options "-Xms4g" HaplotypeCaller \
--R ~/1_sequencing/reference/human_g1k_v37.fasta \
+-R /data/GCDA_2025_2/1_sequencing/reference/human_g1k_v37.fasta \
 -I ~/1_sequencing/raw_reads/HG00099.chrom20.ILLUMINA.bwa.GBR.exome.20130415.bam \
 -L 20 \
 -ERC GVCF \
@@ -404,7 +404,7 @@ bcftools view -v indels sample03_20.g.vcf | grep -v '##' | head
 
 ```
 gatk CombineGVCFs \
--R ~/1_sequencing/reference/human_g1k_v37.fasta \
+-R /data/GCDA_2025_2/1_sequencing/reference/human_g1k_v37.fasta \
 --variant sample01_20.g.vcf \
 --variant sample02_20.g.vcf \
 --variant sample03_20.g.vcf \
@@ -415,7 +415,7 @@ gatk CombineGVCFs \
 
 ```
 gatk --java-options "-Xmx4g" GenotypeGVCFs \
--R ~/1_sequencing/reference/human_g1k_v37.fasta \
+-R /data/GCDA_2025_2/1_sequencing/reference//human_g1k_v37.fasta \
 -V sample_all.g.vcf.gz \
 -O sample_all.vcf
 ```

From 0bdbfd6afd701aebc72f1aeef9d447d4b957e086 Mon Sep 17 00:00:00 2001
From: seokho92 <seokho9208@gmail.com>
Date: Tue, 16 Sep 2025 19:36:20 +0900
Subject: [PATCH 07/34] Update Session1_Sequencing.md

---
 Session1_Sequencing.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Session1_Sequencing.md b/Session1_Sequencing.md
index 1016abc..20da1a4 100644
--- a/Session1_Sequencing.md
+++ b/Session1_Sequencing.md
@@ -278,7 +278,7 @@ The information of some columns are as follows:
 the alignment information from the mapped file (bwa) + the metadata  → one standardized, analysis-ready BAM.
 ```
 java17 -Xmx10G -jar ~/1_sequencing/utils/picard.jar MergeBamAlignment \
-R=~/1_sequencing/reference/human_g1k_v37.fasta \
+R=/data/GCDA_2025_2/1_sequencing/reference//human_g1k_v37.fasta \
 UNMAPPED=add_read_groups_${SID}.bam \
 ALIGNED=aligned_${SID}.sam \
 O=preprocessed_${SID}.bam \

From 4b34f2d1170de3c5905148a13b3f8174635df4c4 Mon Sep 17 00:00:00 2001
From: seokho92 <seokho9208@gmail.com>
Date: Thu, 2 Oct 2025 13:40:52 +0900
Subject: [PATCH 08/34] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index ca460a0..b7aaa0a 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # GCDA_Tutorial_2025
 
-This repository is for the Genomic & Clinical Data Analysis course for the Spring 2025 semester. 
+This repository is for the Genomic & Clinical Data Analysis course for the Fall 2025 semester. 
 
 Processes and codes for various practice sessions will be shared here. Please note, the schedule is subject to change.
 

From 0277fcfdc07fe6d4ba9daa19e225a0246565b7e1 Mon Sep 17 00:00:00 2001
From: nayeonkim93 <88818872+nayeonkim93@users.noreply.github.com>
Date: Thu, 2 Oct 2025 01:00:08 -0400
Subject: [PATCH 09/34] Update Session3_PRS.md

---
 Session3_PRS.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Session3_PRS.md b/Session3_PRS.md
index d2bd1c3..a9946f0 100644
--- a/Session3_PRS.md
+++ b/Session3_PRS.md
@@ -1,4 +1,4 @@
-# Practice Session #3: Polygenic Risk Score (May 1, 2025)
+# Practice Session #3: Polygenic Risk Score (October 21, 2025)
 
 In this session, we are going to construct polygenic risk score using PRS-CS. \
 References : [PRS-CS github](https://github.com/getian107/PRScs), [PRS-CS paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6467998/). \

From 83e394050c7bcd4e6637342b9fcf94e6baec0747 Mon Sep 17 00:00:00 2001
From: Kisung Nam <63442596+namks@users.noreply.github.com>
Date: Fri, 3 Oct 2025 14:45:29 +0900
Subject: [PATCH 10/34] Update GWAS session details and commands

Updated the date and environment details, modified Docker commands to use the latest SAIGE version, and revised section headings for consistency.
---
 Session2_GWAS.md | 135 +++++++++++++++++++++++++++++++----------------
 1 file changed, 90 insertions(+), 45 deletions(-)

diff --git a/Session2_GWAS.md b/Session2_GWAS.md
index f75d548..1e5930c 100644
--- a/Session2_GWAS.md
+++ b/Session2_GWAS.md
@@ -1,17 +1,18 @@
-# Practice Session #2: Genome-wide Association Studies (GWAS) (April 3, 2025)
+# Practice Session #2: Genome-wide Association Studies (GWAS) (October 3, 2025)
 
 In this session, we will learn how to conduct the genome-wide association analysis using SAIGE. \
 (References: [Paper](https://www.nature.com/articles/s41588-018-0184-y), [Github](https://github.com/saigegit/SAIGE), [Documentation](https://saigegit.github.io/SAIGE-doc/)) \
-This document was created on April 3, 2025 and the following contents were tested on the Lee Lab cluster (Ubuntu 24.04.1 LTS).
+This document was created on October 3, 2025 and the following contents were tested on the Lee Lab cluster (Ubuntu 18.04.4 LTS).
 
-### 1. Setting up the environment
+## 1. Setting up the environment
 
-We will use [Docker](https://www.docker.com/) on the Lee Lab cluster (`leelabsg15` node). \
-It is already created on the GSDS cluster, but you can create the environment on your local machine with the following command:
+We will use [Docker](https://www.docker.com/) on the Lee Lab cluster (`leelabsg11` node). \
+You can find the latest version of SAIGE docker image on [Docker Hub](https://hub.docker.com/). \
+It is already created on the cluster, but you can create the environment on your local machine with the following command:
 
 ```
 # Pull SAIGE docker image
-sudo docker pull wzhou88/saige:1.4.4
+sudo docker pull wzhou88/saige:1.5.0.2
 
 # Give previlege to use docker without sudo
 sudo usermod -aG docker $USER
@@ -20,17 +21,19 @@ sudo usermod -aG docker $USER
 You can test if it works:
 
 ```
-docker run --rm -it -v /data/home/gcda_XXX/GCDA/2_GWAS:/data/home/gcda_XXX/GCDA/2_GWAS wzhou88/saige:1.4.4 /bin/bash
+docker run --rm -it \
+    -v /data/home/gcda_XXX/2_GWAS:/data/home/gcda_XXX/2_GWAS \
+    wzhou88/saige:1.5.0.2 /bin/bash
 ```
 
-### 2. Preparing data
+## 2. Preparing data
 
 We need (individual-level) genotype and phenotype files to conduct GWAS. \
 However, access to these files is strictly restricted.
 
 Let's take a quick look at what the real data (UK Biobank) looks like.
 
-#### Phenotype file
+### Phenotype file
 
 The phenotype file of UK Biobank looks like the following:
 
@@ -52,14 +55,14 @@ You can find the information of each phenotype column in [UK Biobank showcase](h
 * Field ID 46 means 'hand grip strength (left)'
 * Field ID 47 means 'hand grip strength (right)'
 
-#### Genotype file
+### Genotype file
 
-##### Variant Call Format (`vcf`)
+#### Variant Call Format (`vcf`)
 
 It contains meta-information lines, a header line, and then data lines each containing information about a position in the genome.
 
 
-##### PLINK binary (`bed`, `bim`, `fam`)
+#### PLINK binary (`bed`, `bim`, `fam`)
 
 PLINK binary files are the binary version of PLINK files (`ped`, `map`). \
 PLINK binary genotype data consists of 3 files:
@@ -98,7 +101,15 @@ PLINK binary genotype data consists of 3 files:
 1a10	1a10	0	0	0	-9
 ```
 
-`bed` file contains the genotype information (in binary format). \
+`bed` file contains the genotype information (in binary format).
+
+|     | 1a1 | 1a2 | 1a3 | 1a4 | ... |
+|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
+| rs1 | 0   | 0   | 0   | 2   | ... |
+| rs2 | 1   | 0   | 1   | 0   | ... |
+| rs3 | 0   | 1   | 0   | 0   | ... |
+| ... | ... | ... | ... | ... | ... |
+
 We cannot easily check the genotype information, but we can see the bitwise 
 
 ```
@@ -117,7 +128,7 @@ We cannot easily check the genotype information, but we can see the bitwise
 You can find more details on [PLINK website](https://zzz.bwh.harvard.edu/plink/binary.shtml).
 
 
-#### Example Data
+### Example Data
 
 We will use `pheno_example.txt` as a phenotype file.
 
@@ -135,62 +146,62 @@ IID	x1	                x2	y_binary  y_quantitative
 ```
 
 
-### 3. Run GWAS using SAIGE
+## 3. Run GWAS using SAIGE
 
-#### What is SAIGE?
+### What is SAIGE?
 
 * Mixed effect model-based method
 * Score test to compute test statistics
 * Several techniques for fast computation
 For detailed explanation, please refer the [Notion page](https://admitted-guan-7d5.notion.site/SAIGE-10ea25444f7a802ea917d4aad44f2ce0).
 
-#### Why SAIGE?
+### Why SAIGE?
 
 * Can account for related individuals
 * Fast computation
 * Prevent type I error inflation in case of case-control imbalance
 
-#### Process
+### Process
 
-##### Step 0. (Optional) Create sparse GRM
+#### Step 0. (Optional) Create sparse GRM
 
 This sparse GRM only needs to be created once for each data set, e.g. a biobank, and can be used for all different phenotypes as long as all tested samples are in the sparse GRM.
 
 ```
-docker run -v /data/home/gcda_XXX/GCDA/2_GWAS:/data/home/gcda_XXX/GCDA/2_GWAS \
-        wzhou88/saige:1.4.4 createSparseGRM.R \
-        --plinkFile=/data/home/gcda_XXX/GCDA/2_GWAS/genotype/nfam_100_nindep_0_step1_includeMoreRareVariants_poly_22chr \
+docker run -v /data/home/gcda_XXX/2_GWAS:/data/home/gcda_XXX/2_GWAS \
+        wzhou88/saige:1.5.0.2 createSparseGRM.R \
+        --plinkFile=/data/home/gcda_XXX/2_GWAS/genotype/nfam_100_nindep_0_step1_includeMoreRareVariants_poly_22chr \
         --nThreads=4 \
-        --outputPrefix=/data/home/gcda_XXX/GCDA/2_GWAS/sparseGRM \
+        --outputPrefix=/data/home/gcda_XXX/2_GWAS/sparseGRM \
         --numRandomMarkerforSparseKin=2000 \
         --relatednessCutoff=0.125
 ```
 
-##### Step 1. Fitting the null (logistic/linear) mixed model
+#### Step 1. Fitting the null (logistic/linear) mixed model
 
-###### Binary trait example
+##### Binary trait example
 
 ```
-docker run -v /data/home/gcda_XXX/GCDA/2_GWAS:/data/home/gcda_XXX/GCDA/2_GWAS \
-        wzhou88/saige:1.4.4 step1_fitNULLGLMM.R \
-        --plinkFile=/data/home/gcda_XXX/GCDA/2_GWAS/genotype/nfam_100_nindep_0_step1_includeMoreRareVariants_poly_22chr \
-        --phenoFile=/data/home/gcda_XXX/GCDA/2_GWAS/phenotype/pheno_example.txt \
+docker run -v /data/home/gcda_XXX/2_GWAS:/data/home/gcda_XXX/2_GWAS \
+        wzhou88/saige:1.5.0.2 step1_fitNULLGLMM.R \
+        --plinkFile=/data/home/gcda_XXX/2_GWAS/genotype/nfam_100_nindep_0_step1_includeMoreRareVariants_poly_22chr \
+        --phenoFile=/data/home/gcda_XXX/2_GWAS/phenotype/pheno_example.txt \
         --phenoCol=y_binary \
         --covarColList=x1,x2 \
         --sampleIDColinphenoFile=IID \
         --traitType=binary \
-        --outputPrefix=/data/home/gcda_XXX/GCDA/2_GWAS/example_binary \
+        --outputPrefix=/data/home/gcda_XXX/2_GWAS/example_binary \
         --nThreads=8 \
         --IsOverwriteVarianceRatioFile=TRUE
 ```
 
-###### Quantitative trait example
+##### Quantitative trait example
 
 ```
-docker run -v /data/home/gcda_XXX/GCDA/2_GWAS:/data/home/gcda_XXX/GCDA/2_GWAS \
-        wzhou88/saige:1.4.4 step1_fitNULLGLMM.R \
-        --plinkFile=/data/home/gcda_XXX/GCDA/2_GWAS/genotype/nfam_100_nindep_0_step1_includeMoreRareVariants_poly_22chr \
-        --phenoFile=/data/home/gcda_XXX/GCDA/2_GWAS/phenotype/pheno_example.txt \
+docker run -v /data/home/gcda_XXX/2_GWAS:/data/home/gcda_XXX/2_GWAS \
+        wzhou88/saige:1.5.0.2 step1_fitNULLGLMM.R \
+        --plinkFile=/data/home/gcda_XXX/2_GWAS/genotype/nfam_100_nindep_0_step1_includeMoreRareVariants_poly_22chr \
+        --phenoFile=/data/home/gcda_XXX/2_GWAS/phenotype/pheno_example.txt \
         --phenoCol=y_quantitative \
         --invNormalize=TRUE \
         --covarColList=x1,x2 \
@@ -203,26 +214,60 @@ docker run -v /data/home/gcda_XXX/GCDA/2_GWAS:/data/home/gcda_XXX/GCDA/2_GWAS \
 
 You can find more example usages in [SAIGE Documentation page](https://saigegit.github.io/SAIGE-doc/docs/single_example.html).
 
-##### Step 2. Performing single-variant association tests
+#### Step 2. Performing single-variant association tests (SAIGE)
 
-###### Quantitative trait example
+##### Quantitative trait example
 
 ```
-docker run -v /data/home/gcda_XXX/GCDA/2_GWAS:/data/home/gcda_XXX/GCDA/2_GWAS \
-        wzhou88/saige:1.4.4 step2_SPAtests.R \
-        --vcfFile=/data/home/gcda_XXX/GCDA/2_GWAS/genotype/genotype_100markers.vcf.gz \
-        --vcfFileIndex=/data/home/gcda_XXX/GCDA/2_GWAS/genotype/genotype_100markers.vcf.gz.csi \
+docker run -v /data/home/gcda_XXX/2_GWAS:/data/home/gcda_XXX/2_GWAS \
+        wzhou88/saige:1.5.0.2 step2_SPAtests.R \
+        --vcfFile=/data/home/gcda_XXX/2_GWAS/genotype/genotype_100markers.vcf.gz \
+        --vcfFileIndex=/data/home/gcda_XXX/2_GWAS/genotype/genotype_100markers.vcf.gz.csi \
         --vcfField=GT \
         --chrom=1 \
         --minMAF=0 \
         --minMAC=10 \
-        --GMMATmodelFile=/data/home/gcda_XXX/GCDA/2_GWAS/example_quantitative.rda \
-        --varianceRatioFile=/data/home/gcda_XXX/GCDA/2_GWAS/example_quantitative.varianceRatio.txt \
-        --SAIGEOutputFile=/data/home/gcda_XXX/GCDA/2_GWAS/step2_quant.txt
+        --GMMATmodelFile=/data/home/gcda_XXX/2_GWAS/example_quantitative.rda \
+        --varianceRatioFile=/data/home/gcda_XXX/2_GWAS/example_quantitative.varianceRatio.txt \
+        --SAIGEOutputFile=/data/home/gcda_XXX/2_GWAS/step2_quant.txt
+```
+
+#### Step 2. Performing gene-level association tests (SAIGE-GENE+)
+
+Gene-based test requires a group file, which defines the annotations for variants within the region.
+
 ```
+GENE1 var 1:1:A:C 1:2:A:C 1:3:A:C 1:4:A:C 1:5:A:C 1:6:A:C 1:7:A:C 1:8:A:C 1:9:A:C 1:10:A:C 1:11:A:C 1:12:A:C 1:13:A:C 1:14:A:C 1:15:A:C 1:16:A:C 1:17:A:C 1:18:A:C 1:19:A:C 1:20:A:C 1:21:A:C 1:22:A:C 1:23:A:C 1:24:A:C 1:25:A:C 1:26:A:C 1:27:A:C 1:28:A:C 1:29:A:C 1:30:A:C 1:31:A:C 1:32:A:C 1:33:A:C 1:34:A:C 1:35:A:C 1:36:A:C 1:37:A:C 1:38:A:C 1:39:A:C 1:40:A:C 1:41:A:C 1:42:A:C 1:43:A:C 1:44:A:C 1:45:A:C 1:46:A:C 1:47:A:C 1:48:A:C 1:49:A:C 1:50:A:C
+GENE1 anno lof lof lof lof lof lof lof lof lof lof missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof
+GENE3 var 1:51:A:C
+GENE3 anno intergenic
+GENE2 var 1:51:A:C 1:52:A:C 1:53:A:C 1:54:A:C 1:55:A:C 1:56:A:C 1:57:A:C 1:58:A:C 1:59:A:C 1:60:A:C 1:61:A:C 1:62:A:C 1:63:A:C 1:64:A:C 1:65:A:C 1:66:A:C 1:67:A:C 1:68:A:C 1:69:A:C 1:70:A:C 1:71:A:C 1:72:A:C 1:73:A:C 1:74:A:C 1:75:A:C 1:76:A:C 1:77:A:C 1:78:A:C 1:79:A:C 1:80:A:C 1:81:A:C 1:82:A:C 1:83:A:C 1:84:A:C 1:85:A:C 1:86:A:C 1:87:A:C 1:88:A:C 1:89:A:C 1:90:A:C 1:91:A:C 1:92:A:C 1:93:A:C 1:94:A:C 1:95:A:C 1:96:A:C 1:97:A:C 1:98:A:C 1:99:A:C 1:100:A:C
+GENE2 anno missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense missense lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof lof
+```
+
+##### Quantitative trait example
 
+```
+docker run -v /data/home/gcda_XXX/2_GWAS:/data/home/gcda_XXX/2_GWAS \
+        wzhou88/saige:1.5.0.2 step2_SPAtests.R \
+        --bgenFile=/data/home/gcda_XXX/2_GWAS/genotype/genotype_100markers.bgen    \
+        --bgenFileIndex=/data/home/gcda_XXX/2_GWAS/genotype/genotype_100markers.bgen.bgi \
+        --SAIGEOutputFile=/data/home/gcda_XXX/2_GWAS/step2_gene_quant.txt \
+        --chrom=1 \
+        --AlleleOrder=ref-first \
+        --minMAF=0 \
+        --minMAC=0.5 \
+        --LOCO=FALSE \
+        --sampleFile=/data/home/gcda_XXX/2_GWAS/genotype/samplelist.txt \
+        --GMMATmodelFile=/data/home/gcda_XXX/2_GWAS/example_quantitative.rda \
+        --varianceRatioFile=/data/home/gcda_XXX/2_GWAS/example_quantitative.varianceRatio.txt \
+        --groupFile=/data/home/gcda_XXX/2_GWAS/genotype/group_new_chrposa1a2.txt    \
+        --annotation_in_groupTest=lof,missense:lof,missense:lof:synonymous        \
+        --maxMAF_in_groupTest=0.0001,0.001,0.01 \
+        --is_fastTest=TRUE
+```
 
-### 4. Drawing plots
+## 4. Drawing plots
 
 Using `qqman` package in R, we can draw manhattan plot and Q-Q plot with the GWAS result (summary statistics). ([Documentation](https://cran.r-project.org/web/packages/qqman/qqman.pdf))
 

From 43b8b67385b8a5ccd697782ba9222e0bcca6cdde Mon Sep 17 00:00:00 2001
From: exagorazo <68946473+exagorazo@users.noreply.github.com>
Date: Thu, 23 Oct 2025 12:28:42 +0900
Subject: [PATCH 11/34] Update Session4_Mendelian_Randomization.Md

---
 Session4_Mendelian_Randomization.Md | 48 ++++++++++-------------------
 1 file changed, 16 insertions(+), 32 deletions(-)

diff --git a/Session4_Mendelian_Randomization.Md b/Session4_Mendelian_Randomization.Md
index 08811eb..7f37c28 100644
--- a/Session4_Mendelian_Randomization.Md
+++ b/Session4_Mendelian_Randomization.Md
@@ -1,4 +1,4 @@
-# Practice Session #4: Two-sample Mandelian Randomization (May 8, 2025)
+# Practice Session #4: Two-sample Mandelian Randomization (Oct 28, 2025)
 In this session, we will learn how to perform two Sample MR with publicly available GWAS datasets. \
 The overall process in this session is mainly based on the [TwoSampleMR in R vignette](https://mrcieu.github.io/TwoSampleMR/).
 
@@ -6,16 +6,16 @@ The overall process in this session is mainly based on the [TwoSampleMR in R vig
 
 ### 0. Installing twoSampleMR in R using Conda 
 To avoid conflicts in dependecies, we will create new environment for two sample MR (twoMR) in this session.
-First, lets install R and devtools in new conda environment (Already installed as twoMR under leelab server)
+First, lets install R and devtools in new conda environment:
 ```
 conda create -n twoMR r-base r-essentials r-devtools r-remotes r-gmp -c conda-forge -c R 
 conda activate twoMR ; R --no-save
 ```
-TwoSampleMR pacakge is installed from github 
+Install TwoSampleMR pacakge from github: 
 ```
 remotes::install_github("MRCIEU/TwoSampleMR")
 ```
-Next, Load the necessary packages
+Load the necessary packages:
 ```
 library(TwoSampleMR)
 ```
@@ -36,8 +36,10 @@ The workflow for performing MR is as follows:
 
 
 ### 2. IEU GWAS database
-The [IEU](https://gwas.mrcieu.ac.uk/) GWAS database (IGD) contains complete GWAS summary statistics from a large number of studies. You can browse them through the website. \
-<img width="746" alt="image" src="https://user-images.githubusercontent.com/22064612/235642892-b283511b-6e2e-410a-9f7b-3c0611b325f7.png">
+The [IEU Open GWAS project](https://opengwas.io/datasets/) contains complete GWAS summary statistics from a large number of studies. You can browse them through the website. 
+
+To use the API provided by IEU, you must log in with your school account, apply for a JSON Web Token (JWT), and then register the generated token. \
+All necessary steps for this process can be found in the [API Tutorial](https://api.opengwas.io/).
 
 From the package, available_outcomes() will list up outcomes obtainable from IEU.
 ```
@@ -58,28 +60,11 @@ bmi_exp_dat <- extract_instruments(outcomes = 'ieu-a-2')
 head(bmi_exp_dat)
 ```
 
-The output from this function is a new data frame with standardized column names:
-
-- SNP 
-- exposure 
-- beta.exposure 
-- se.exposure 
-- effect_allele.exposure 
-- other_allele.exposure 
-- eaf.exposure 
-- mr_keep.exposure 
-- pval.exposure 
-- pval_origin.exposure 
-- id.exposure 
-- data_source.exposure 
-- units.exposure 
-- gene.exposure 
-- samplesize.exposure 
 
 (Optional) You can import your own GWAS summary for exposure data. \
 Example:
 ```
-bmi_file <- system.file("extdata", "bmi_exp.txt", package = "TwoSampleMR")
+bmi_file <- system.file("extdata/bmi.csv", package = "TwoSampleMR")
 
 bmi_exp_dat2 <- read_exposure_data(
     filename = bmi_file,
@@ -95,10 +80,11 @@ bmi_exp_dat2 <- read_exposure_data(
     gene_col = "Gene",
     samplesize_col = "n"
 )
+
+head(bmi_exp_dat2)
 ```
-For standard two sample MR, it is important to ensure that the **instruments for the exposure are independent**. If you are going to extract exposure variables from GWAS summary of your own or other studies, it is necessary to perform **clumping** to ensure independence between instrumental variables. \
-\
-*Additional note after 5/8 class* \
+For standard two sample MR, it is important to ensure that the **instruments for the exposure are independent**. If you are going to extract exposure variables from GWAS summary of your own or other studies, it is necessary to perform **clumping** to ensure independence between instrumental variables. 
+
 For LD clumpig, you can use [clump_data() function](https://mrcieu.github.io/TwoSampleMR/reference/clump_data.html) provided by the "TwoSampleMR" package. 
 
 ### 4. Outcome Data Example
@@ -114,7 +100,7 @@ head(chd_out_dat)
 ```
 chd_out_dat2 <- read_outcome_data(
     snps = bmi_exp_dat$SNP,
-    filename = "/data/GCDA/4_MR/26343387-GCST003116-EFO_0000378.h.tsv",
+    filename = "/data/GCDA_2025_2/4_MR/26343387-GCST003116-EFO_0000378-build37.f.tsv",
     sep = "\t",
     snp_col = "variant_id",
     beta_col = "beta",
@@ -131,7 +117,6 @@ head(chd_out_dat2)
 
 ### 5. Harmonizing Data
 Harmonizing data step is required because there are mismatches between alleles in exposure and outcome. Recent GWASs typically present the effects of a SNP in reference to the allele on the forward strand but not necessarily.
-![image](https://user-images.githubusercontent.com/22064612/235645818-0c25fa91-7ba8-4dcf-9066-406e2db0eb9e.png)
 
 ```
 dat <- harmonise_data(
@@ -152,7 +137,7 @@ Before we perform MR, lets find out which method is available in the package
 ```
 mr_method_list()
 ```
-Let's try MR with Egger, two sample maximum likelihood,simple median, and IVW. You can add list of methods or methods set by default.
+Let's try MR with Egger, weighted median, and IVW. You can add list of methods or methods set by default.
 
 ```
 res <- mr(dat, method_list = c("mr_egger_regression","mr_weighted_median","mr_ivw"))
@@ -177,7 +162,7 @@ ggplot2::ggsave(p1[[1]], file = <FILE_PATH>, width = 7, height = 7)
 ```
 In case that you want to download the plot to local computer, you can use the script below on your local terminal.
 ```
-scp -P 22555 <USER_ID>@147.47.200.131:<FROM_FILE_PATH> <TO_FILE_PATH>
+scp -P 22555 -J <USER_ID>@147.47.200.131 <USER_ID>@leelabsg11:<FROM_FILE_PATH> <TO_FILE_PATH>
 ```
 ![filename](https://user-images.githubusercontent.com/22064612/235652335-b57f0381-b820-47e0-a07f-da09564f9c0b.png)
 
@@ -205,4 +190,3 @@ ggplot2::ggsave(p4[[1]], file = <FILE_PATH>, width = 7, height = 7)
 
 
 
-

From f8c37602f0985f87fc6f6e06aaa243388231a36c Mon Sep 17 00:00:00 2001
From: exagorazo <68946473+exagorazo@users.noreply.github.com>
Date: Thu, 23 Oct 2025 12:29:34 +0900
Subject: [PATCH 12/34] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b7aaa0a..6bc8646 100644
--- a/README.md
+++ b/README.md
@@ -22,7 +22,7 @@ Processes and codes for various practice sessions will be shared here. Please no
 **TA:** Bo-Eun Ahn ([email](mailto:ahnbo@snu.ac.kr))
 
 ### Session 5 - EHR
-**Date:** TBA   
+**Date:** October 28, 2005   
 **TA:** Junhyeong Lee ([email](mailto:lrainsoul@snu.ac.kr))
 
 ### Session 6 - Medical Image Analysis  (HW)

From c0dcefadfc64ee837bd0265779e7b47434a6d42b Mon Sep 17 00:00:00 2001
From: exagorazo <68946473+exagorazo@users.noreply.github.com>
Date: Thu, 23 Oct 2025 12:30:12 +0900
Subject: [PATCH 13/34] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 6bc8646..6cd582a 100644
--- a/README.md
+++ b/README.md
@@ -18,11 +18,11 @@ Processes and codes for various practice sessions will be shared here. Please no
 **TA:** Na Yeon Kim ([email](mailto:nayeonkim1@snu.ac.kr))
 
 ### [Session 4 - Mendelian Randomization](Session4_Mendelian_Randomization.Md)
-**Date:** TBA   
+**Date:** October 28, 2025    
 **TA:** Bo-Eun Ahn ([email](mailto:ahnbo@snu.ac.kr))
 
 ### Session 5 - EHR
-**Date:** October 28, 2005   
+**Date:** TBA  
 **TA:** Junhyeong Lee ([email](mailto:lrainsoul@snu.ac.kr))
 
 ### Session 6 - Medical Image Analysis  (HW)

From 3bc019997c4c7cc2707c3fd7fce4189e8035c7ca Mon Sep 17 00:00:00 2001
From: Peter Kang <71389955+chkang96@users.noreply.github.com>
Date: Thu, 23 Oct 2025 12:35:10 +0900
Subject: [PATCH 14/34] Update session dates and titles in README.md

---
 README.md | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 6cd582a..58826c6 100644
--- a/README.md
+++ b/README.md
@@ -10,23 +10,27 @@ Processes and codes for various practice sessions will be shared here. Please no
 **TA:** Seokho Jeong ([email](mailto:seokho92@snu.ac.kr))
 
 ### [Session 2 - GWAS](Session2_GWAS.md) (HW)
-**Date:** TBA  
+**Date:** October 7, 2025  
 **TA:** Kisung Nam ([email](mailto:kisung.nam@snu.ac.kr))
 
 ### [Session 3 - PRS](Session3_PRS.md)  (HW)
-**Date:** TBA  
+**Date:** October 17, 2025  
 **TA:** Na Yeon Kim ([email](mailto:nayeonkim1@snu.ac.kr))
 
 ### [Session 4 - Mendelian Randomization](Session4_Mendelian_Randomization.Md)
 **Date:** October 28, 2025    
 **TA:** Bo-Eun Ahn ([email](mailto:ahnbo@snu.ac.kr))
 
-### Session 5 - EHR
-**Date:** TBA  
+### [Session 5 - Prediction using EHR (RNN)]
+**Date:** November 18, 2025  
 **TA:** Junhyeong Lee ([email](mailto:lrainsoul@snu.ac.kr))
 
-### Session 6 - Medical Image Analysis  (HW)
-**Date:** TBA   
+### [Session 6 - Medical Note and LLM] 
+**Date:** November 25, 2025   
+**TA:** Wonyoung Jang ([email](mailto:jwy4888@snu.ac.kr))
+
+### [Session 7 - Medical Image Data Analysis (CNN)]  (HW)
+**Date:** December 2, 2025   
 **TA:** Wonyoung Jang ([email](mailto:jwy4888@snu.ac.kr))
 
 *For more details on each session, please refer to the specific folder within this repository.*

From f4825c2056639fbc0df61cd7c8c93942fb9458c1 Mon Sep 17 00:00:00 2001
From: exagorazo <68946473+exagorazo@users.noreply.github.com>
Date: Fri, 24 Oct 2025 10:10:16 +0900
Subject: [PATCH 15/34] Update Session4_Mendelian_Randomization.Md

---
 Session4_Mendelian_Randomization.Md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Session4_Mendelian_Randomization.Md b/Session4_Mendelian_Randomization.Md
index 7f37c28..f56c50b 100644
--- a/Session4_Mendelian_Randomization.Md
+++ b/Session4_Mendelian_Randomization.Md
@@ -6,7 +6,7 @@ The overall process in this session is mainly based on the [TwoSampleMR in R vig
 
 ### 0. Installing twoSampleMR in R using Conda 
 To avoid conflicts in dependecies, we will create new environment for two sample MR (twoMR) in this session.
-First, lets install R and devtools in new conda environment:
+First, let's install R and devtools in new conda environment:
 ```
 conda create -n twoMR r-base r-essentials r-devtools r-remotes r-gmp -c conda-forge -c R 
 conda activate twoMR ; R --no-save

From b20dba1f75e5d4ea8a297d6dc2b3b4a561ed8350 Mon Sep 17 00:00:00 2001
From: exagorazo <68946473+exagorazo@users.noreply.github.com>
Date: Mon, 27 Oct 2025 14:14:31 +0900
Subject: [PATCH 16/34] Update Session4_Mendelian_Randomization.Md

---
 Session4_Mendelian_Randomization.Md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Session4_Mendelian_Randomization.Md b/Session4_Mendelian_Randomization.Md
index f56c50b..f101127 100644
--- a/Session4_Mendelian_Randomization.Md
+++ b/Session4_Mendelian_Randomization.Md
@@ -162,7 +162,7 @@ ggplot2::ggsave(p1[[1]], file = <FILE_PATH>, width = 7, height = 7)
 ```
 In case that you want to download the plot to local computer, you can use the script below on your local terminal.
 ```
-scp -P 22555 -J <USER_ID>@147.47.200.131 <USER_ID>@leelabsg11:<FROM_FILE_PATH> <TO_FILE_PATH>
+scp -P 22555 <USER_ID>@147.47.200.131:<FROM_FILE_PATH> <TO_FILE_PATH>
 ```
 ![filename](https://user-images.githubusercontent.com/22064612/235652335-b57f0381-b820-47e0-a07f-da09564f9c0b.png)
 

From 3cc4ecf690eb86f9a889af50a6a586f40f4e510c Mon Sep 17 00:00:00 2001
From: exagorazo <68946473+exagorazo@users.noreply.github.com>
Date: Mon, 27 Oct 2025 18:49:18 +0900
Subject: [PATCH 17/34] Update Session4_Mendelian_Randomization.Md

---
 Session4_Mendelian_Randomization.Md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Session4_Mendelian_Randomization.Md b/Session4_Mendelian_Randomization.Md
index f101127..d75dfc7 100644
--- a/Session4_Mendelian_Randomization.Md
+++ b/Session4_Mendelian_Randomization.Md
@@ -38,7 +38,7 @@ The workflow for performing MR is as follows:
 ### 2. IEU GWAS database
 The [IEU Open GWAS project](https://opengwas.io/datasets/) contains complete GWAS summary statistics from a large number of studies. You can browse them through the website. 
 
-To use the API provided by IEU, you must log in with your school account, apply for a JSON Web Token (JWT), and then register the generated token. \
+To use the API provided by IEU, you must log in with your school email, apply for a JSON Web Token (JWT), and then register the generated token. \
 All necessary steps for this process can be found in the [API Tutorial](https://api.opengwas.io/).
 
 From the package, available_outcomes() will list up outcomes obtainable from IEU.

From 77fc29e20d2d12178abef0215845e61e12ba60a5 Mon Sep 17 00:00:00 2001
From: exagorazo <68946473+exagorazo@users.noreply.github.com>
Date: Tue, 28 Oct 2025 11:39:57 +0900
Subject: [PATCH 18/34] Update Session4_Mendelian_Randomization.Md

---
 Session4_Mendelian_Randomization.Md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Session4_Mendelian_Randomization.Md b/Session4_Mendelian_Randomization.Md
index d75dfc7..0416a1c 100644
--- a/Session4_Mendelian_Randomization.Md
+++ b/Session4_Mendelian_Randomization.Md
@@ -40,6 +40,7 @@ The [IEU Open GWAS project](https://opengwas.io/datasets/) contains complete GWA
 
 To use the API provided by IEU, you must log in with your school email, apply for a JSON Web Token (JWT), and then register the generated token. \
 All necessary steps for this process can be found in the [API Tutorial](https://api.opengwas.io/).
+(also see [authentication](https://mrcieu.github.io/ieugwasr/articles/guide.html#authentication) how to register JWT in R)
 
 From the package, available_outcomes() will list up outcomes obtainable from IEU.
 ```

From a64d7d4c5168da988adca601e77faeefb9d5b50b Mon Sep 17 00:00:00 2001
From: exagorazo <68946473+exagorazo@users.noreply.github.com>
Date: Tue, 28 Oct 2025 12:58:10 +0900
Subject: [PATCH 19/34] Update Session4_Mendelian_Randomization.Md

---
 Session4_Mendelian_Randomization.Md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Session4_Mendelian_Randomization.Md b/Session4_Mendelian_Randomization.Md
index 0416a1c..f11fc8c 100644
--- a/Session4_Mendelian_Randomization.Md
+++ b/Session4_Mendelian_Randomization.Md
@@ -40,7 +40,7 @@ The [IEU Open GWAS project](https://opengwas.io/datasets/) contains complete GWA
 
 To use the API provided by IEU, you must log in with your school email, apply for a JSON Web Token (JWT), and then register the generated token. \
 All necessary steps for this process can be found in the [API Tutorial](https://api.opengwas.io/).
-(also see [authentication](https://mrcieu.github.io/ieugwasr/articles/guide.html#authentication) how to register JWT in R)
+(also see [authentication](https://mrcieu.github.io/ieugwasr/articles/guide.html#authentication) for how to register JWT in R)
 
 From the package, available_outcomes() will list up outcomes obtainable from IEU.
 ```

From 84d32e0b2804bcca625e830f6de98845781cebab Mon Sep 17 00:00:00 2001
From: sungmoonie <munkim97@gmail.com>
Date: Mon, 24 Nov 2025 12:21:35 +0900
Subject: [PATCH 20/34] Update Session6_Medical_Note_LLM.md

---
 Session6_Medical_Note_LLM.md | 63 ++++++++++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 Session6_Medical_Note_LLM.md

diff --git a/Session6_Medical_Note_LLM.md b/Session6_Medical_Note_LLM.md
new file mode 100644
index 0000000..a56add9
--- /dev/null
+++ b/Session6_Medical_Note_LLM.md
@@ -0,0 +1,63 @@
+# Practice Session #6: Clinical Data Analysis Using the OpenAI API (Nov 25, 2025)
+
+In this session, we will learn how to perform **clinical data analysis using the OpenAI API**.  
+
+## Dataset Description
+
+This tutorial uses **three types of data**:
+
+- **Shorthand Clinical Notes**  
+  Compact notes containing abbreviations and short phrases commonly used in real-world clinical settings.
+
+- **Narrative (MIMIC-style) Clinical Notes**  
+  Long-form descriptive notes similar to those from the MIMIC-III/IV dataset (e.g., discharge summaries, progress notes).
+
+- **InBody Scan Images**  
+  Body composition analysis images (e.g., weight, skeletal muscle mass, body fat).  
+  These are used for multimodal image–text analysis examples.
+
+> **📌 Note**
+> This hands-on session uses:
+> - **Shorthand clinical notes**  
+> - **InBody images**
+>
+> The **narrative-style clinical notes are not used** in this session.
+
+## Environment Setup
+
+This tutorial assumes:
+
+- You are running the notebook **locally**.
+- **Python is already installed** (Python 3.9+ recommended).
+- A virtual environment is recommended but optional.
+- You already have a **personal OpenAI API key** available for use.
+
+## Required Python Packages
+
+The tutorial uses the following imports:
+
+```python
+import pandas as pd
+from openai import OpenAI
+import requests
+import os
+import base64
+import re
+from dotenv import load_dotenv
+```
+
+#### Install Required Packages
+```
+pip install pandas openai python-dotenv
+```
+
+## Environment Variables
+create a **.env** file in the project directory:
+```
+API_KEY=your_api_key_here
+```
+Load it in your notebook or script:
+```
+load_dotenv()
+api_key = os.getenv("API_KEY")
+```
\ No newline at end of file

From aa1e164b3ee773e21f7269d67efaa3685cf59536 Mon Sep 17 00:00:00 2001
From: sungmoonie <munkim97@gmail.com>
Date: Mon, 24 Nov 2025 12:22:55 +0900
Subject: [PATCH 21/34] Update session in README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 58826c6..2ace7fa 100644
--- a/README.md
+++ b/README.md
@@ -25,9 +25,9 @@ Processes and codes for various practice sessions will be shared here. Please no
 **Date:** November 18, 2025  
 **TA:** Junhyeong Lee ([email](mailto:lrainsoul@snu.ac.kr))
 
-### [Session 6 - Medical Note and LLM] 
+### [Session 6 - Medical Note and LLM](Session6_Medical_Note_LLM.Md)
 **Date:** November 25, 2025   
-**TA:** Wonyoung Jang ([email](mailto:jwy4888@snu.ac.kr))
+**TA:** Soungmun Kim ([email](mailto:soungmun@snu.ac.kr))
 
 ### [Session 7 - Medical Image Data Analysis (CNN)]  (HW)
 **Date:** December 2, 2025   

From aa8c6f50ab1b576520cb3f66728edf7e6e9f7e8f Mon Sep 17 00:00:00 2001
From: sungmoonie <munkim97@gmail.com>
Date: Mon, 24 Nov 2025 12:23:48 +0900
Subject: [PATCH 22/34] Update session in README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 2ace7fa..f377089 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,7 @@ Processes and codes for various practice sessions will be shared here. Please no
 **Date:** November 18, 2025  
 **TA:** Junhyeong Lee ([email](mailto:lrainsoul@snu.ac.kr))
 
-### [Session 6 - Medical Note and LLM](Session6_Medical_Note_LLM.Md)
+### [Session 6 - Medical Note and LLM](Session6_Medical_Note_LLM.md)
 **Date:** November 25, 2025   
 **TA:** Soungmun Kim ([email](mailto:soungmun@snu.ac.kr))
 

From 0008440249c9de7fc1565ef12baa10282fa6763a Mon Sep 17 00:00:00 2001
From: sungmoonie <munkim97@gmail.com>
Date: Mon, 24 Nov 2025 12:33:26 +0900
Subject: [PATCH 23/34] Add Session6 Folder

---
 Session6/Tutorial.ipynb           | 1195 +++++++++++++++++++++++++++++
 Session6/inbody_sample.jpg        |  Bin 0 -> 363251 bytes
 Session6/note1_ShorthandStyle.txt |   19 +
 Session6/note2_MIMICstyle.txt     |   93 +++
 4 files changed, 1307 insertions(+)
 create mode 100644 Session6/Tutorial.ipynb
 create mode 100644 Session6/inbody_sample.jpg
 create mode 100644 Session6/note1_ShorthandStyle.txt
 create mode 100644 Session6/note2_MIMICstyle.txt

diff --git a/Session6/Tutorial.ipynb b/Session6/Tutorial.ipynb
new file mode 100644
index 0000000..ce83add
--- /dev/null
+++ b/Session6/Tutorial.ipynb
@@ -0,0 +1,1195 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a2d9de7a-1c7e-4c97-b629-a24adceb6985",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a2d9de7a-1c7e-4c97-b629-a24adceb6985",
+    "outputId": "411add88-26a6-476d-d756-cdba637947f6"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from openai import OpenAI\n",
+    "import requests\n",
+    "import os\n",
+    "import base64\n",
+    "import re\n",
+    "from dotenv import load_dotenv # env 가져오기\n",
+    "\n",
+    "#######################################\n",
+    "## Loaded env (for API_Key)\n",
+    "load_dotenv() \n",
+    "## Loaded API KEY\n",
+    "api_key = os.getenv(\"API_KEY\")\n",
+    "\n",
+    "## Data download from github and save to PATH\n",
+    "# Path 지정\n",
+    "PATH = \"[FILE_PATH]\"\n",
+    "os.makedirs(PATH, exist_ok=True)\n",
+    "\n",
+    "## Setting OpenAI API\n",
+    "# pip install \"openai==1.66.0\"\n",
+    "client = OpenAI(api_key=api_key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ed03d64-963e-45e8-844e-43825ca93fb3",
+   "metadata": {
+    "id": "9ed03d64-963e-45e8-844e-43825ca93fb3"
+   },
+   "source": [
+    "## Check Clinical Note Examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1a538d9-96e3-48a4-bac3-b99d63c33275",
+   "metadata": {
+    "id": "e1a538d9-96e3-48a4-bac3-b99d63c33275"
+   },
+   "source": [
+    "**1) Clinical Note 불러 오는 방법 안내**\n",
+    "\n",
+    "파일에서 Clinical Note 불러오기\n",
+    "\n",
+    "- 텍스트(.txt) 파일에 작성된 노트를 불러오는 방식입니다.\n",
+    "- 불러올 파일 경로를 정확히 지정해야 합니다.\n",
+    "\n",
+    "\n",
+    "**2) 예시로 사용되는 note1과 note2는 서로 다른 내용입니다:**\n",
+    "- note1: shorthand 스타일의 간단한 요약형 노트\n",
+    "- note2: narrative 스타일의 서술형 노트로, 보다 상세한 문맥과 진단 경과가 포함되어 있습니다\n",
+    "\n",
+    "이 두 노트를 각각 사용하여 prompt 차이, 언어 차이, 노트 형식 차이에 따른 분석 결과 차이를 실험해볼 수 있습니다."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d494203d-f4e9-40c3-942f-0d147fedae7c",
+   "metadata": {
+    "id": "d494203d-f4e9-40c3-942f-0d147fedae7c"
+   },
+   "source": [
+    "### 1) Shorthand version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c73ab559-3f44-4124-9b92-759bcff0a241",
+   "metadata": {
+    "id": "c73ab559-3f44-4124-9b92-759bcff0a241"
+   },
+   "outputs": [],
+   "source": [
+    "## (1) Load note from .txt file\n",
+    "note1_path = os.path.join(PATH, \"note1_ShorthandStyle.txt\")\n",
+    "with open(note1_path, \"r\", encoding=\"utf-8\") as f:\n",
+    "    note1 = f.read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "73c7480b-58e5-44b8-84e2-0489f8b628fe",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "73c7480b-58e5-44b8-84e2-0489f8b628fe",
+    "outputId": "3b577675-5474-4863-c423-8b8f63598e4c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#. T2DM  \n",
+      "   Dx since 2010, recent HbA1c 9.1  \n",
+      "   MTF → 설사, 복부팽만, 식욕저하, 무기력감  \n",
+      "   Lactic acidosis (Lac 3.9) 의심 → 약 중단, 수액 치료  \n",
+      "   MTF-induced lactic acidosis 의심 → 약 중단 후 회복, basal insulin 예정\n",
+      "\n",
+      "   Levemir 22 --- 10 units, 저녁 인슐린 잘 안 맞음\n",
+      "\n",
+      "\n",
+      "#. HTN  \n",
+      "   BP on admission 92/58\n",
+      "   Losartan 유지 \n",
+      "\n",
+      "#. CKD stage 3  \n",
+      "   Baseline Cr 1.6 → 입원 시 Cr 2.1  \n",
+      "   수액 이후 신기능 호전, 투석 필요 없음  \n",
+      "\n",
+      "#. Dyslipidemia  \n",
+      "   Atorvastatin  \n"
+     ]
+    }
+   ],
+   "source": [
+    "## check loaded note\n",
+    "print(note1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d16770e-8775-4b11-bd95-b377c87be693",
+   "metadata": {
+    "id": "1d16770e-8775-4b11-bd95-b377c87be693"
+   },
+   "source": [
+    "### 2) Narrative version (MIMIC-style)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bd6bd01a-048a-4d9e-8a37-941a678eade7",
+   "metadata": {
+    "id": "bd6bd01a-048a-4d9e-8a37-941a678eade7"
+   },
+   "outputs": [],
+   "source": [
+    "## (1) Load note from .txt file\n",
+    "with open(f\"{PATH}note2_MIMICstyle.txt\", \"r\", encoding=\"utf-8\") as f:\n",
+    "    note2 = f.read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "85c38baa-30e5-4f54-8f55-fe4d57852156",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "85c38baa-30e5-4f54-8f55-fe4d57852156",
+    "outputId": "b5003f68-af23-40f5-a63b-c04ed2214070"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "History of Present Illness:  \n",
+      "78-year-old female with a history of type 2 diabetes, hypertension, and CKD stage 3 who presented with generalized weakness, poor oral intake, and nausea.\n",
+      "\n",
+      "Two weeks prior to admission, she was started on metformin after a primary care visit revealed HbA1c of 9.1. Since initiation, the patient has developed persistent loose stools and intermittent abdominal cramping. No reported fever or vomiting. Her family noted a gradual decline in appetite and energy over the pa\n"
+     ]
+    }
+   ],
+   "source": [
+    "## check loaded note\n",
+    "print(note2[:500])  # 앞 500자만 출력"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96b0be7a",
+   "metadata": {
+    "id": "96b0be7a"
+   },
+   "source": [
+    "**[Important]**\n",
+    "\n",
+    "본 실습에서는 **note1 (shorthand version)** 을 사용합니다.  \n",
+    "Narrative version을 사용하려면 아래 코드를 수정하세요."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2c3d0868",
+   "metadata": {
+    "id": "2c3d0868"
+   },
+   "outputs": [],
+   "source": [
+    "# mimic version인 note2 사용\n",
+    "# note = note2\n",
+    "\n",
+    "# shorthand version인 note1 사용\n",
+    "note = note1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a57d06f8",
+   "metadata": {},
+   "source": [
+    "# 1. Clinical Note Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cb52fc8-ebdd-47b4-b98f-4a25a46c2ac6",
+   "metadata": {
+    "id": "7cb52fc8-ebdd-47b4-b98f-4a25a46c2ac6"
+   },
+   "source": [
+    "## [Task 1] Drug Name Extraction and drug code identification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc67584c-7864-4428-a9d3-8908a7bf8d96",
+   "metadata": {
+    "id": "bc67584c-7864-4428-a9d3-8908a7bf8d96"
+   },
+   "source": [
+    "이 Task에서는 임상노트로부터 언급된 약물명을 추출하는 작업을 수행합니다.\n",
+    "\n",
+    "- GPT에게 임상노트를 입력하고, 텍스트에서 **약물명** 추출 및 약물에 상응하는 **ATC CODE**를 추출하게 합니다.\n",
+    "- 약물은 **일반명** 또는 **약어**(예: MFM = metformin)로 표현될 수 있습니다.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25159641-9085-4f58-9012-7a375f93c037",
+   "metadata": {
+    "id": "25159641-9085-4f58-9012-7a375f93c037"
+   },
+   "source": [
+    "### (1) Drug Entity Recognition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bc99df3",
+   "metadata": {},
+   "source": [
+    " Zero-shot prompting 예시"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6eeea664",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_drug_name = f\"\"\"\n",
+    "\n",
+    "Task: Extract all drug names mentioned in the following clinical note.\n",
+    "- Drug names may be written as abbreviations.\n",
+    "- Return the list of drug names only, without any additional explanation.\n",
+    "\n",
+    "Clinical Note:\n",
+    "{note}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5321c3c2-d636-452e-a8ea-1937970c6ae1",
+   "metadata": {
+    "id": "5321c3c2-d636-452e-a8ea-1937970c6ae1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MTF\n",
+      "insulin\n",
+      "Levemir\n",
+      "Losartan\n",
+      "Atorvastatin\n"
+     ]
+    }
+   ],
+   "source": [
+    "model=\"gpt-5\" # select model\n",
+    "\n",
+    "response = client.responses.create(\n",
+    "    model=model,\n",
+    "    instructions=\"You are a clinical text extraction assistant.\", # system role\n",
+    "    input=prompt_drug_name\n",
+    ")\n",
+    "\n",
+    "print(response.output_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d5c50fb",
+   "metadata": {
+    "id": "0d5c50fb"
+   },
+   "source": [
+    "### (2) Drug Entity + Description + ATC Code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48dc88af",
+   "metadata": {},
+   "source": [
+    "few-shots prompting 예시"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e8340062",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_wATC = f\"\"\"\n",
+    "\n",
+    "## Task\n",
+    "Given a clinical note, extract:\n",
+    "1. Drug names clearly or implicitly mentioned (including abbreviations).\n",
+    "2. A short description of each drug (1 line).\n",
+    "3. The ATC code corresponding to the drug (ATC Level 5).\n",
+    "\n",
+    "Output must be in CSV format with the columns:\n",
+    "Drug, Description, ATC_Code\n",
+    "\n",
+    "## Examples:\n",
+    "Clinical Note 1:\n",
+    "\"ASA 투여 후 환자 두드러기 호소. PRN으로 항히스타민제 처방.\"\n",
+    "\n",
+    "Output (CSV):\n",
+    "Drug,Description,ATC_Code\n",
+    "ASA,Aspirin (NSAID used for pain/fever, antiplatelet),B01AC06\n",
+    "항히스타민제,Antihistamine for allergic symptoms,R06AE07\n",
+    "\n",
+    "Clinical Note 2:\n",
+    "\"ASA 투여 후 환자 두드러기 호소. PRN으로 항히스타민제 처방.\"\n",
+    "\n",
+    "Output (CSV):\n",
+    "Drug,Description,ATC_Code\n",
+    "ASA,Aspirin (NSAID used for pain/fever, antiplatelet),B01AC06\n",
+    "항히스타민제,Antihistamine for allergic symptoms,R06AE07\n",
+    "\n",
+    "Now process the user input below.\n",
+    "Clinical Note:\n",
+    "{note}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "62baac55",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "62baac55",
+    "outputId": "53d8c363-e3df-4a6f-eeee-78448613c051"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Drug,Description,ATC_Code\n",
+      "MTF,Metformin (biguanide for T2DM; GI upset; lactic acidosis risk),A10BA02\n",
+      "Levemir,Insulin detemir (long-acting basal insulin for diabetes),A10AE05\n",
+      "Losartan,ARB antihypertensive (HTN; renal protection in CKD),C09CA01\n",
+      "Atorvastatin,Statin (HMG-CoA reductase inhibitor) for dyslipidemia; lowers LDL,C10AA05\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = client.responses.create(\n",
+    "    model=model,\n",
+    "    instructions=\"You are a clinical text extraction assistant specializing in drug detection from clinical notes.\", # system role\n",
+    "    input=prompt_wATC\n",
+    ")\n",
+    "\n",
+    "print(response.output_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95c38cfc",
+   "metadata": {
+    "id": "95c38cfc"
+   },
+   "source": [
+    "### (3) Usage check\n",
+    "사용한 토큰을 아래의 코드를 이용해서 체크할 수 있습니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "41539529",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "41539529",
+    "outputId": "044ece10-fa2e-484a-bde0-233f8e532f5d"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ResponseUsage(input_tokens=460, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=1972, output_tokens_details=OutputTokensDetails(reasoning_tokens=1856), total_tokens=2432)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(response.usage)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8624cc74-ab5b-4e05-a54b-54d2816548ff",
+   "metadata": {
+    "id": "8624cc74-ab5b-4e05-a54b-54d2816548ff"
+   },
+   "source": [
+    "## [Task 2] Adverse Drug Reaction Detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ba6e0b2-0ef9-4f9a-a951-b9069a9d73d2",
+   "metadata": {
+    "id": "8ba6e0b2-0ef9-4f9a-a951-b9069a9d73d2"
+   },
+   "source": [
+    "이 Task에서는 임상노트에 언급된 **약물 부작용(Adverse Drug Reactions, ADR)** 을 탐지하는 작업을 수행합니다.\n",
+    "\n",
+    "- GPT에게 임상노트를 입력하고, 특정 약물 투여 이후 발생한 부작용을 **약물-증상 쌍(drug → symptom)** 형태로 추출하게 합니다.\n",
+    "- 약물명은 약어로 표현될 수 있으며, 부작용은 명시적 또는 암시적으로 표현되어 있을 수 있습니다.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lDJjZ2LvnIXy",
+   "metadata": {
+    "id": "lDJjZ2LvnIXy"
+   },
+   "source": [
+    "### (1) ADR Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a93ce4c1-cc14-4ba5-9db1-bc79904ebc8c",
+   "metadata": {
+    "id": "a93ce4c1-cc14-4ba5-9db1-bc79904ebc8c"
+   },
+   "outputs": [],
+   "source": [
+    "## Prompt\n",
+    "prompt_adr = f\"\"\"\n",
+    "\n",
+    "Task: Extract all possible adverse drug reactions (ADRs) mentioned in the clinical note.\n",
+    "- Return results in JSON format, with each item containing:\n",
+    "  - \"drug\": drug name\n",
+    "  - \"symptom\": symptom or adverse effect\n",
+    "  - \"evidence\": supporting sentence or phrase from the note\n",
+    "- No extra explanation.\n",
+    "\n",
+    "Clinical Note:\n",
+    "{note}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "2837aace-ac7a-4040-bb1d-5125ef34d440",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "2837aace-ac7a-4040-bb1d-5125ef34d440",
+    "outputId": "176d2606-0f05-4598-b80f-abfbff7bcfee"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "  {\n",
+      "    \"drug\": \"Metformin (MTF)\",\n",
+      "    \"symptom\": \"diarrhea\",\n",
+      "    \"evidence\": \"MTF → 설사, 복부팽만, 식욕저하, 무기력감\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"drug\": \"Metformin (MTF)\",\n",
+      "    \"symptom\": \"abdominal bloating\",\n",
+      "    \"evidence\": \"MTF → 설사, 복부팽만, 식욕저하, 무기력감\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"drug\": \"Metformin (MTF)\",\n",
+      "    \"symptom\": \"decreased appetite\",\n",
+      "    \"evidence\": \"MTF → 설사, 복부팽만, 식욕저하, 무기력감\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"drug\": \"Metformin (MTF)\",\n",
+      "    \"symptom\": \"lethargy\",\n",
+      "    \"evidence\": \"MTF → 설사, 복부팽만, 식욕저하, 무기력감\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"drug\": \"Metformin (MTF)\",\n",
+      "    \"symptom\": \"lactic acidosis\",\n",
+      "    \"evidence\": \"MTF-induced lactic acidosis 의심 → 약 중단 후 회복, basal insulin 예정\"\n",
+      "  }\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "response = client.responses.create(\n",
+    "    model=model,\n",
+    "    instructions=\"You are a clinical text extraction assistant specializing in identifying adverse drug reactions (ADRs).\",\n",
+    "    input=prompt_adr\n",
+    ")\n",
+    "\n",
+    "print(response.output_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "FaUFHivt_0Aq",
+   "metadata": {
+    "id": "FaUFHivt_0Aq"
+   },
+   "source": [
+    "### (2) Verification Agent\n",
+    "\n",
+    "위의 LLM이 생성한 답변이 맞는지 검증하기 위한 Agent를 생성해서 답변을 검증합니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "937bd8e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "verifier_prompt = \"\"\"\n",
+    "If you disagree, provide counterarguments.\n",
+    "Provide agree and comment for each drug and side effect pairs.\n",
+    "Respond in JSON:\n",
+    "{\"agree\": true/false, \"comments\": \"...\"}.\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "extractor_output = response.output_text\n",
+    "verifier_input=f\"{verifier_prompt}\\n\\nExtractorAgent output:\\n{extractor_output}\\n\\nMedical note:\\n{note}\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "aYimxtMa3fhf",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aYimxtMa3fhf",
+    "outputId": "780e21e7-afcd-4c35-9113-84a81c297718"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "  {\n",
+      "    \"drug\": \"Metformin (MTF)\",\n",
+      "    \"symptom\": \"diarrhea\",\n",
+      "    \"agree\": true,\n",
+      "    \"comments\": \"Agree. The clinical note explicitly lists '설사' and diarrhea is a well‑known, common gastrointestinal adverse effect of metformin.\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"drug\": \"Metformin (MTF)\",\n",
+      "    \"symptom\": \"abdominal bloating\",\n",
+      "    \"agree\": true,\n",
+      "    \"comments\": \"Agree. The note documents '복부팽만'. Bloating and other GI complaints are commonly reported with metformin.\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"drug\": \"Metformin (MTF)\",\n",
+      "    \"symptom\": \"decreased appetite\",\n",
+      "    \"agree\": true,\n",
+      "    \"comments\": \"Agree. The note includes '식욕저하'. Metformin can cause anorexia/reduced appetite as part of its GI side‑effect profile.\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"drug\": \"Metformin (MTF)\",\n",
+      "    \"symptom\": \"lethargy\",\n",
+      "    \"agree\": true,\n",
+      "    \"comments\": \"Agree, with caveat. The note lists '무기력감'. Lethargy can be reported with metformin but is nonspecific and may reflect underlying illness or metabolic disturbance (e.g., evolving lactic acidosis, volume depletion, uremia) rather than a direct isolated drug effect.\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"drug\": \"Metformin (MTF)\",\n",
+      "    \"symptom\": \"lactic acidosis\",\n",
+      "    \"agree\": true,\n",
+      "    \"comments\": \"Agree, with nuance. The record documents suspicion of metformin‑associated lactic acidosis (lactate 3.9 mmol/L) and metformin was stopped with clinical improvement, supporting a causal link. However, lactate 3.9 mmol/L is modest and formal diagnosis of MALA usually includes higher lactate and acidemia; concomitant CKD and rising creatinine (baseline 1.6 → 2.1) increase the plausibility of metformin accumulation precipitating lactic acidosis.\"\n",
+      "  }\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model=\"gpt-5-mini\" # change model\n",
+    "\n",
+    "verifier_response = client.responses.create(\n",
+    "    model=model,\n",
+    "    instructions=\"You are VerifierAgent. Your job is to check ExtractorAgent's identified side effects.\",\n",
+    "    input=verifier_input\n",
+    ")\n",
+    "\n",
+    "print(verifier_response.output_text)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83b0cbea-fd9b-456b-90d5-fafeb7e0c557",
+   "metadata": {
+    "id": "83b0cbea-fd9b-456b-90d5-fafeb7e0c557"
+   },
+   "source": [
+    "## [Task 3] Clinical Note Summarization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9db9cd76-8c2e-401a-a235-0b00299294ba",
+   "metadata": {
+    "id": "9db9cd76-8c2e-401a-a235-0b00299294ba"
+   },
+   "source": [
+    "이 Task에서는 임상노트에 포함된 핵심 정보를 GPT를 통해 한글로 **요약(summarization)** 하는 작업을 수행합니다.\n",
+    "- 임상노트에는 환자의 병력, 진단, 투약, 검사 결과, 치료 계획 등의 정보가 포함되어 있으며, 종종 매우 길고 복잡합니다.\n",
+    "- 본 Task의 목적은 다음 두 가지 유형의 요약을 생성하는 것입니다:\n",
+    "    - 환자/보호자용 요약\n",
+    "    - 의료진용 요약\n",
+    "- 출력은 한국어 요약문으로 생성됩니다.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ab920d78-61e5-4882-94df-7e0a6cd0c4d2",
+   "metadata": {
+    "id": "ab920d78-61e5-4882-94df-7e0a6cd0c4d2"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_family = f\"\"\"\n",
+    "Task: Summarize the following clinical note in simple language for the patient’s family.\n",
+    "- Use non-technical, plain Korean language.\n",
+    "- The summary should explain what happened, what was diagnosed, how it was treated, and what to expect.\n",
+    "- Avoid complex terminology and abbreviations.\n",
+    "\n",
+    "Output language: Korean\n",
+    "\n",
+    "Clinical Note:\n",
+    "{note}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e9157de3-6c04-4831-9bcc-9b78e883536f",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "e9157de3-6c04-4831-9bcc-9b78e883536f",
+    "outputId": "f42be947-7b0f-45b0-d976-42de7884fc13"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "가족분들을 위한 요약\n",
+      "\n",
+      "무엇이 있었는지\n",
+      "- 당뇨병으로 약을 드시던 중, 설사·배가 더부룩함·식욕저하·심한 피로가 생겨 입원하셨습니다.\n",
+      "- 오실 때 혈압이 낮았고, 콩팥(신장) 기능 수치도 평소보다 나빠져 있었습니다.\n",
+      "\n",
+      "무슨 문제로 보였는지(진단)\n",
+      "- 당뇨병 조절이 최근 잘 되지 않았습니다(장기 혈당 지표가 높게 나왔습니다).\n",
+      "- 당뇨약(메트포르민 성분) 때문에 몸에 산성 물질이 쌓이는 부작용이 생긴 것으로 의심되었습니다.\n",
+      "- 콩팥 기능은 원래 약한 편인데, 입원 시 일시적으로 더 나빠졌던 상태였습니다.\n",
+      "- 혈압이 낮게 측정되었고, 콜레스테롤(혈중 지방) 문제로 약을 복용 중이십니다.\n",
+      "\n",
+      "어떻게 치료했는지\n",
+      "- 문제를 일으킨 것으로 의심되는 당뇨약(메트포르민)을 즉시 중단했습니다.\n",
+      "- 수액(정맥 주사로 수분 보충) 치료를 했고, 몸 상태와 콩팥 수치가 좋아졌습니다. 투석은 필요하지 않았습니다.\n",
+      "- 앞으로는 당뇨 조절을 위해 인슐린 주사로 바꾸어 치료할 계획입니다. 저녁 주사를 자주 놓치셔서, 하루 한 번 맞는(오래 가는) 인슐린으로 조정하는 방향을 고려하고 있습니다.\n",
+      "- 혈압약은 계속 복용 중이며, 콜레스테롤 낮추는 약도 이어서 복용합니다.\n",
+      "\n",
+      "앞으로의 계획과 기대\n",
+      "- 메트포르민 성분의 당뇨약은 다시 사용하지 않는 것이 안전합니다.\n",
+      "- 인슐린 주사 방법, 용량, 주사 시간에 대해 교육을 드리고, 가급적 하루 한 번으로 단순하게 맞을 수 있도록 조정하겠습니다.\n",
+      "- 집에서도 혈당을 자주 확인해 주세요. 외래에서 혈당과 콩팥 기능을 정기적으로 추적 검사하겠습니다.\n",
+      "- 수분을 충분히 드시고, 설사·식욕저하가 지속되거나 극심한 피로, 숨이 가쁨, 복통, 어지러움/실신 느낌이 있으면 바로 병원에 연락해 주세요.\n",
+      "- 혈압이 너무 낮게 느껴지면(심한 어지러움, 쓰러질 것 같음 등) 알려주세요. 필요 시 혈압약을 조정하겠습니다.\n",
+      "- 식사 조절과 가벼운 운동을 병행하면 혈당·혈압·콜레스테롤 관리에 도움이 됩니다.\n",
+      "\n",
+      "전반적으로, 약을 중단하고 수액 치료 후 상태가 호전되었고, 콩팥도 안정되어 투석은 필요하지 않았습니다. 인슐린으로 치료를 바꾸어 당뇨 조절을 개선하는 것이 목표입니다. 정기적인 추적과 생활관리로 충분히 잘 관리 가능할 것으로 예상합니다.\n"
+     ]
+    }
+   ],
+   "source": [
+    "## Family-Friendly Summary\n",
+    "model = \"gpt-5\" # select model\n",
+    "\n",
+    "response = client.responses.create(\n",
+    "    model=model,\n",
+    "    instructions=\"You are a clinical text summarization assistant.\",\n",
+    "    input=prompt_family\n",
+    ")\n",
+    "\n",
+    "print(response.output_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9f97075a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_doctor = f\"\"\"\n",
+    "Task: Summarize the following clinical note for a clinician.\n",
+    "- Use concise, professional clinical language.\n",
+    "- Include key diagnostic findings, relevant history, differential considerations, treatments given, response to treatment, and recommended next steps.\n",
+    "- Maintain clinical terminology and abbreviations (e.g., BP, HR, WNL, SOB, ASA, MRI).\n",
+    "- Do not simplify medical terms.\n",
+    "- Keep the summary focused and structured.\n",
+    "\n",
+    "Output language: Korean (clinician-level medical terminology)\n",
+    "\n",
+    "Clinical Note:\n",
+    "{note}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "c1f9ded9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "임상요약\n",
+      "\n",
+      "진단/배경\n",
+      "- T2DM (2010 진단), 최근 HbA1c 9.1%로 혈당조절 불량.\n",
+      "- HTN.\n",
+      "- CKD stage 3 (baseline Cr 1.6 mg/dL).\n",
+      "- Dyslipidemia.\n",
+      "\n",
+      "주요 소견\n",
+      "- Metformin 복용 후 설사, 복부팽만, 식욕저하, 무기력감 발생.\n",
+      "- 입원 시 Lac 3.9 mmol/L로 lactic acidosis 의심, BP 92/58 mmHg.\n",
+      "- 입원 시 Cr 2.1 mg/dL로 AKI on CKD 소견. 수액 이후 신기능 호전, 투석 불필요.\n",
+      "- 인슐린(Levemir) 저녁 투여 순응도 저조. 기존 22 U 사용 이력 있으나 최근 10 U 수준으로 감소 추정.\n",
+      "\n",
+      "치료 및 경과\n",
+      "- Metformin 중단, 수액치료 시행 → 증상 호전 및 대사성 이상 회복(MTF-induced lactic acidosis 의심).\n",
+      "- Basal insulin 전환/강화 계획. 저녁 인슐린 비순응 있음.\n",
+      "- Losartan 유지(저혈압 상태에서 주의 깊게 관찰).\n",
+      "- Atorvastatin 유지.\n",
+      "\n",
+      "해석/감별\n",
+      "- Lactic acidosis: CKD stage 3 배경에서의 MTF-associated lactic acidosis(MALA) 가능성 높음. GI 증상 및 MTF 중단 후 호전으로 약제 연관성 지지.\n",
+      "- AKI on CKD는 저혈압/탈수 가능성 및 MTF 관련 대사 이상 동반 상황에서 악화된 것으로 추정. 수액 후 가역적 호전.\n",
+      "\n",
+      "권고/다음 단계\n",
+      "- 당뇨병 관리:\n",
+      "  - Metformin 재투여 금지(과거 MALA 의심 + CKD3).\n",
+      "  - Basal insulin 시작/조정: 예) detemir 10 U QHS 시작 후 FBG 목표 80–130 mg/dL 기준 2–3일마다 2 U씩 증량(저혈당 시 감량). 저녁 순응도 문제 시 1회 투여가 용이한 장시간형(glargine U300 또는 degludec) 고려.\n",
+      "  - CKD3 및 ASCVD/신장보호 관점에서 SGLT2i(eGFR 허용 시) 또는 GLP-1 RA 병용 검토(저혈압/탈수 위험 평가 후 시행).\n",
+      "  - SMBG 교육 및 저혈당 대비.\n",
+      "- 신장/혈압:\n",
+      "  - BMP로 Cr/eGFR, K+ 추적; 수액 후 안정화 확인까지 ARB 유지 여부 재평가(증상성 저혈압 시 감량/보류 고려).\n",
+      "  - Nephrotoxin 회피, 용량 신기능에 맞게 조절.\n",
+      "  - 요알부민/크레이틴 비율(ACR) 평가하여 RAASi 최적화.\n",
+      "- 지질:\n",
+      "  - Atorvastatin 지속; 필요 시 목표 LDL-C에 따라 강도 조절.\n",
+      "- 모니터링/추적:\n",
+      "  - 퇴원 후 1–2주 내 외래 추적: BP, 체중, 입출량, SMBG 기록 확인.\n",
+      "  - 1–2주 내 BMP(Cr/eGFR, K+), 필요 시 lactate 재확인.\n",
+      "  - 3개월 내 HbA1c 재측정 및 인슐린/비인슐린 병용요법 재평가.\n"
+     ]
+    }
+   ],
+   "source": [
+    "## Clinician Summary\n",
+    "model = \"gpt-5\" # select model\n",
+    "\n",
+    "response = client.responses.create(\n",
+    "    model=model,\n",
+    "    instructions=\"You are a clinical text summarization assistant.\",\n",
+    "    input=prompt_doctor\n",
+    ")\n",
+    "\n",
+    "print(response.output_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5114224c",
+   "metadata": {},
+   "source": [
+    "# Extracting InBody report images into structured Excel data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7817d2f",
+   "metadata": {},
+   "source": [
+    "이 Task에서는 **비정형 데이터인 Inbody Report 이미지**를 분석하여, 필요한 측정값을 **구조화된 Excel 데이터 형태**로 **자동 정리**하는 작업을 수행합니다.\n",
+    "- 인바디 리포트는 체성분(체중, 체지방률, 골격근량 등), 기초대사량, 신체 균형 지표 등 다양한 정보를 이미지 형태로 포함하고 있어, 직접 수기로 정리하기 어렵고 시간이 많이 소요됩니다.\n",
+    "- 본 튜토리얼에서는 GPT 모델을 활용하여 인바디 이미지에서 **핵심 수치·측정값을 자동으로 추출**하고, 이를 엑셀(Excel) 형태의 구조화된 표 (tabular data)로 변환하는 방법을 다룹니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "365205ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Loaded image\n",
+    "image_path = f\"{PATH}inbody_sample.jpg\"\n",
+    "\n",
+    "## encoding image\n",
+    "with open(image_path, \"rb\") as f:\n",
+    "    img_bytes = f.read()\n",
+    "    img_base64 = base64.b64encode(img_bytes).decode(\"utf-8\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "e93776ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_inbody = f\"\"\"\n",
+    "Your task:\n",
+    "- Carefully read the InBody report image.\n",
+    "- Extract ONLY the required values listed in the template below.\n",
+    "- Extract all measurement values together with their units (e.g., kg, %, kcal) exactly as shown in the image.\n",
+    "- For \"Total Body Water\", \"Protein\", \"Minerals\", and \"Body Fat Mass\", extract the number shown under the \"Values\" column of the table.\n",
+    "- For \"Soft Lean Mass\", \"Fat Free Mass\", and \"Weight\", extract the main numeric value shown in the middle of each bar graph section (do NOT use the normal range markers).\n",
+    "- For \"Segmental Lean Analysis\", each body part shows two numeric values in kg; extract ONLY the first (top) value, which represents the actual measured lean mass.\n",
+    "- Do NOT extract the second value (ideal/standard comparison).\n",
+    "- Output MUST strictly follow the English template structure provided.\n",
+    "- If a value is not visible, leave it blank (\"\").\n",
+    "- Do NOT hallucinate or infer values.\n",
+    "- Use only numbers and units that appear in the image.\n",
+    "\n",
+    "Output Format:\n",
+    "## Basic Information\n",
+    "1. ID:\n",
+    "2. Height:\n",
+    "3. Age:\n",
+    "4. Gender:\n",
+    "5. Test Date/Time:\n",
+    "\n",
+    "## Body Composition Analysis\n",
+    "1. Total Body Water Values:\n",
+    "2. Protein Values:\n",
+    "3. Minerals Values:\n",
+    "4. Body Fat Mass Values:\n",
+    "5. Soft Lean Mass:\n",
+    "6. Fat Free Mass:\n",
+    "7. Weight:\n",
+    "\n",
+    "## Muscle-Fat Analysis\n",
+    "1. Weight:\n",
+    "2. Skeletal Muscle Mass (SMM):\n",
+    "3. Body Fat Mass:\n",
+    "\n",
+    "## Obesity Analysis\n",
+    "1. BMI:\n",
+    "2. Percent Body Fat (PBF):\n",
+    "\n",
+    "## Segmental Lean Analysis (kg)\n",
+    "1. Right Arm:\n",
+    "2. Left Arm:\n",
+    "3. Trunk:\n",
+    "4. Right Leg:\n",
+    "5. Left Leg:\n",
+    "\n",
+    "Return ONLY the above template filled with extracted values.\n",
+    "Do not add comments, explanations, or interpretation.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "47b9deda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "## Basic Information\n",
+      "1. ID: Jane Doe\n",
+      "2. Height: 163 cm\n",
+      "3. Age: 41\n",
+      "4. Gender: Female\n",
+      "5. Test Date/Time: 2017.03.08, 16:47\n",
+      "\n",
+      "## Body Composition Analysis\n",
+      "1. Total Body Water Values: 35.5 L\n",
+      "2. Protein Values: 9.2 kg\n",
+      "3. Minerals Values: 3.28 kg\n",
+      "4. Body Fat Mass Values: 18.1 kg\n",
+      "5. Soft Lean Mass: 45.6 kg\n",
+      "6. Fat Free Mass: 48.3 kg\n",
+      "7. Weight: 66.4 kg\n",
+      "\n",
+      "## Muscle-Fat Analysis\n",
+      "1. Weight: 66.4 kg\n",
+      "2. Skeletal Muscle Mass (SMM): 26.7 kg\n",
+      "3. Body Fat Mass: 18.1 kg\n",
+      "\n",
+      "## Obesity Analysis\n",
+      "1. BMI: 25.0 kg/m²\n",
+      "2. Percent Body Fat (PBF): 27.2 %\n",
+      "\n",
+      "## Segmental Lean Analysis (kg)\n",
+      "1. Right Arm: 2.56 kg\n",
+      "2. Left Arm: 2.60 kg\n",
+      "3. Trunk: 20.9 kg\n",
+      "4. Right Leg: 7.59 kg\n",
+      "5. Left Leg: 7.59 kg\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = client.responses.create(\n",
+    "    model=\"gpt-5\",\n",
+    "    instructions=\"You are an AI assistant that extracts structured measurement values from an InBody body composition analysis report image.\",\n",
+    "    input=[\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [\n",
+    "                {\"type\": \"input_text\", \"text\": prompt_inbody},\n",
+    "                {\n",
+    "                    \"type\": \"input_image\",\n",
+    "                    \"image_url\": f\"data:image/jpeg;base64,{img_base64}\"\n",
+    "                }\n",
+    "            ]\n",
+    "        }\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "print(response.output_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "7227cba1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_inbody_text_to_row(text):\n",
+    "    \"\"\"\n",
+    "    InBody 텍스트 블록을 받아서 {'항목명': 값} 딕셔너리로 파싱\n",
+    "    \"\"\"\n",
+    "    row_data = {}\n",
+    "\n",
+    "    lines = text.strip().split(\"\\n\") # text를 줄 단위로 나누기\n",
+    "\n",
+    "    for line in lines:\n",
+    "        line = line.strip()\n",
+    "\n",
+    "        # section header는 건너뛰기\n",
+    "        if line.startswith(\"##\"):\n",
+    "            continue\n",
+    "\n",
+    "        # key:value 패턴 매칭\n",
+    "        \"\"\"\n",
+    "        (?:\\d+\\.\\s*)? = 1. 같은 번로가 있어도 무시 (있던 없던 처리 가능)\n",
+    "        (.+?) = : 앞에 오는 문자열을 key로 (공백, 괄호 포함 가능)\n",
+    "        \\s*(.*) = : 뒤에 오는 내용을 value로\n",
+    "        \"\"\"\n",
+    "        match = re.match(r\"(?:\\d+\\.\\s*)?(.+?):\\s*(.*)\", line)\n",
+    "\n",
+    "        if match:\n",
+    "            key = match.group(1).strip()\n",
+    "            value = match.group(2).strip()\n",
+    "\n",
+    "            # 빈 값만 아닌 경우 저장\n",
+    "            row_data[key] = value\n",
+    "\n",
+    "    return row_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "b84c9033",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Test Date/Time</th>\n",
+       "      <th>Total Body Water Values</th>\n",
+       "      <th>Protein Values</th>\n",
+       "      <th>Minerals Values</th>\n",
+       "      <th>Body Fat Mass Values</th>\n",
+       "      <th>Soft Lean Mass</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Skeletal Muscle Mass (SMM)</th>\n",
+       "      <th>Body Fat Mass</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>Percent Body Fat (PBF)</th>\n",
+       "      <th>Right Arm</th>\n",
+       "      <th>Left Arm</th>\n",
+       "      <th>Trunk</th>\n",
+       "      <th>Right Leg</th>\n",
+       "      <th>Left Leg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Jane Doe</td>\n",
+       "      <td>163 cm</td>\n",
+       "      <td>41</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>2017.03.08, 16:47</td>\n",
+       "      <td>35.5 L</td>\n",
+       "      <td>9.2 kg</td>\n",
+       "      <td>3.28 kg</td>\n",
+       "      <td>18.1 kg</td>\n",
+       "      <td>45.6 kg</td>\n",
+       "      <td>...</td>\n",
+       "      <td>66.4 kg</td>\n",
+       "      <td>26.7 kg</td>\n",
+       "      <td>18.1 kg</td>\n",
+       "      <td>25.0 kg/m²</td>\n",
+       "      <td>27.2 %</td>\n",
+       "      <td>2.56 kg</td>\n",
+       "      <td>2.60 kg</td>\n",
+       "      <td>20.9 kg</td>\n",
+       "      <td>7.59 kg</td>\n",
+       "      <td>7.59 kg</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         ID  Height Age  Gender     Test Date/Time Total Body Water Values  \\\n",
+       "0  Jane Doe  163 cm  41  Female  2017.03.08, 16:47                  35.5 L   \n",
+       "\n",
+       "  Protein Values Minerals Values Body Fat Mass Values Soft Lean Mass  ...  \\\n",
+       "0         9.2 kg         3.28 kg              18.1 kg        45.6 kg  ...   \n",
+       "\n",
+       "    Weight Skeletal Muscle Mass (SMM) Body Fat Mass         BMI  \\\n",
+       "0  66.4 kg                    26.7 kg       18.1 kg  25.0 kg/m²   \n",
+       "\n",
+       "  Percent Body Fat (PBF) Right Arm Left Arm    Trunk Right Leg Left Leg  \n",
+       "0                 27.2 %   2.56 kg  2.60 kg  20.9 kg   7.59 kg  7.59 kg  \n",
+       "\n",
+       "[1 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parsed_row = parse_inbody_text_to_row(response.output_text)\n",
+    "df = pd.DataFrame([parsed_row])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "4f80ba1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "excel_path = f\"{PATH}inbody_sample.csv\"\n",
+    "df.to_csv(excel_path, index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Session6/inbody_sample.jpg b/Session6/inbody_sample.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7d9283d837a11da36029f0ca59251d21ac9690ab
GIT binary patch
literal 363251
zcmeFZ1yEegx-PtMf+P^!-F0wxch>;Hbq1F}!Vm}v?!hI&3GOfu+zBK|AV7ei!QFz*
zo$Rx}v%h`n>{HkNd;hBYbxl#d*6Mz{*SlVQx*Pa6_zFO(7~t#x0II6&04DH{0^bAh
z6?|=7p8-e!D&n>U0KivJIGlWZJj8f-Al}^8cJ4Oz+_vsuo&akP9uPM#4<I2O;9+g+
zYVSj9WAEq;k)%It>!hc3wv(hc7S!a`^pLlAa#jiSveysPGO!JFwH39amzJWH2oMVZ
zdw}hItZ4(lZV+#=07?445f?+$f8XYzr~O+KA6H3w+25nmnrQ0K%Da2n(+YC)bJ_BO
zK(s<a+#mr#At4YaEgvt4kB1lW6XF61hzSab@d?xZz0e~@^Rjag(^F9T`&fu8N&3IP
zl)t|}w?99(yO$#mNK{nxHw`{ME<_71?`IGn>i{l@H^Uzk6zsiiy_`LKoZTU`zbRVV
zxcmA@(j#X2Z?6FM_;<4ZDX;$2P*d~2ZVCqfX29Rbdi&_v|6{)YDPeDeXCC%EdiLJ#
zzFxNWh~^BxJ9~)9d)Zt2xO*A6ySx3tMI9%1A9rsjcMn>5eIZ&_V`qq+yT3Qv-`Z<x
zim5`peXJq2_Noe!^au*v&dzpXa*6`Hg0jN0@<M_j5J*u(R#a3;5hN(8Bmm+S73LTD
zqpyOytuNRf;`2veyMOeR`<H!xivrjKF|vZam$RR}osyS3nD%e4Eav>L=OXei>HWR0
z-M^lT=)dgCgP07@?+^Q@5B<k2g!KHb{=3*BF8*Ec?I8$h_d<v@`~Z0PH(>?rx)ADv
z1YZDN0Z9K`kbhqy|Hp#zyC9?ft)Qc!p`rhy{JlLYGBPR}DhleKAN`}ze^rBb0r;4x
zH7Ku8knjOyd?XZnBzPavLxheap`swD|GWNUprD~+BB4G&#=`zR=-<oVA0r|E9vcJm
z0UiLLp&%h+V4@K`M8(HICHUJD038GWF(w^|-iCmmP}Z8yi-Cyvfvy~*0LhW(%UZoj
zecR*|Cd02rsWZl;Sc%>8Q`3U>Z<&Q=O~}X%Skg@2PbAg#c-Je~Df%`lISBi+hOUPf
zH#Li_<gbSL6#2bY4mf_*pd#A4sd`fA$Zm#@m^(5G3I;khI@)i>VE{<@$aIfUK)R^1
z)`{=+2+$CNP7o3ut<du&P3jw<6O(q!O&Ma49D6bFW7^aSgnBc|3kv1;B-h*e*u7$c
z&jUCpNC@Vj-~-aY-K`EQAr>0)OPP0nRsNr40u8zd$-0gXf5Ic%xX|=AR?l=P;XXnd
z$%fp|Bk%ksgQVGCE~xI^e!u{;)Sr}6{dN2QX`ZZ8C%kCNqfvWQ<AY4sgChInLyFJ4
z@_#lUG|WOnI;Oc|TU`M%=wXjuNGT|EH<8~$ZC=%$bDt3QZeSu)n#Jz|UkmFq2|o(3
zKFk408^eLrA_9~txfi5)B*UdCo00-76OqTyGYS8y{l$a7<lz6RE;N{VONAb@9{Che
z1!aECpCK_y0}<nGqy%M#D~{oGM<1C0GF?xp1oP_{4d#Kh6)dc?KZg}zRX^qlgN6G~
zN<ok>`XhA;KhW7fM35Hr{~!&JfAj6#uqJiaJq5AjaT4uq-532+1>^Z4I8a)0+UTvO
zJD5oy{B`h!EKg36!KsAye9K`e(`h5hr(<cCwT^db<6k5`6I@h@?>u<E|JTjG81k1e
z{QqAOCjL-ZvltSFFp+3$<=FWqh*yWKR3hP!?3ckW@_He9-zw@sVI++XXx*x#_(+qL
zGZT6@OhQqX<Reun|72VWw|v-IUR0vv7-CGa)=<+u=`Mi-M$og;q{Pyq|8uzw-i5b;
zZp{-)k=vaw(uQhZvs9IgunSlXS`?n+ua!S96jBrLrG5VNmtnZoKP6olip!x;La12<
zn3M*ia6*P=1%36HupIOzhl$C^;GK?#gaI|^dhO08qyFo*oi%nE_5x|s&lgDtWdWko
zbzy9dWsEUFN)UX0xxTTU^b$}ug_D*o@H!LloZWyDTrE|j*{3-x)(dO&&D7V47Sk-i
zf&9EFON?XYj*c{{;G)DLa6Q%c1p>j0$SPOJ;;`b)^zkn6DYwQ=IhAK`47?`pV$(OY
zHII*58!f7&Y2a39>tC!p%;ZoUXYV(Ihr~8QA?D%QYaxwuHC|0o<a=F<FEqwm;pP3Q
z9S+RoZO@`zxyMedv~V<XH|uLba!<e+Rn9Sd3lLIB9>?tU9sgCgY}YZgNOo1Z%6LXI
z`_AIHt-Zacsh9;uG|B^cJxk^lP3B)DQxn-mRa%Fc@lBRC+umSXOjZg;qM3tXHGI!{
zyl9Ed;!8a4vR}<4tl<vzyN`ea8WPgST(_F-zSm9uppvw<ESuK)nbwN7dcq=6zaK|!
zH=_#|_o|m__w?;h8jcg`JdyG7JjG?xJ2-IG_=5)A|AI?;g`@F~nd&NH1EsT1yiCNw
z5P0%#lKfCspTJUA-{7(>G@<u9K3xaz*Z9G9D=ydzk{1p<a3I<eMLW5=Z}xT`+7qvS
z#<Vkb-nbfXqHh``*bgga{VY_WqB*u!5;t;Q7>%4AQO<&Tr3dj%(p1F6rq8$6rOH1l
zwa~U|Jfi8#eC|G6Y@xEgo*R(%tM|I-j%rOBcMNvy|DwlAAbOPzB<{6HI_XHRp~;+`
zMcrb&O*8mgH4yyNdDgsZu>6SmKo}PNeK+#yp84woZ~|SWp0b_N<ij<^hRE#DpM0RY
zL~Nh%EqS{@Fv=Sp@+)Y>b8Y=sD{cECk(qtA^)yU0Q=Ns-&=xD2<A9@kzTN$)EN)nn
zhT_*K{h#JO`cz|_=c0kr{$c0gcAQf#4ng9$3!s+Gv6vQX;sN|qw=_e?Y9qHYi%g@H
zOu7g(4b*pXDKfs(%26HKEAf3|x8Tc#a^gO7d=Z7ipBceFuV?R=j+T3~g<dzK1Xs?w
zEc<9b6B?tZXJ$(s72CO7?X-gf68|6I6`1ByN4A5~CvDP~W=px#Z8n^P3_~j7?@O+I
zeMuwx>nA#po?FdSL88z9$(n4n9X(o&AHl-GzEAFlHfP;gZyThfDI%2n(Ww8nG;~gB
zyYupmH*~D_?d|0;9JmRzG(D5QvxftOou?w7@7kmfTGN7K{NVs@&>(a<+UnQKx4yS1
z_x*5S^Dh3;Z7Cc$$QHi;1P7LRrl^9?@6A~WX;L0dM%}G`uf8`LqO{r<0z<{_-?>e~
zfv<32A6nhH3cbnpPgb5P?Uj_zdIO36sI;4*_iG8`!9pwF-pkL`V3CXrf4&IT7%!v!
zmZMlTC7$_YJK}({E`@xshJ<9-+Ld&73xwefY~PiBJGyMNF5v<W4Dsxc#9S#yJ00Cq
z^jQ>@_`R|K;k(UvFB1*0B+*NJ*1W7a=eurAU0mQ*GrtNZ!TL_zB$d|o-pdWAq8pv5
z&lckmf!97uookatDV7*_<|``w`MJk5b{jYM3|Hna54f|y!yp2XV4mgJ?dJ{XE9pu&
z5LwXI;P!R4&2sddtH~`3{7aV~U!|xPB(ee$cQzY%H1zpH+-@r;9589xN)oDngDI8x
z3IC$}2trTcSx;MN@WBx{ir}JVr$^~tJTN}B>dkC6AK13vS5{b&ZoD77uR01eg9EPv
zMg?mlCbJ4|XKu@(ce;<ZWhh_F-|xYJbZ)2Lk`~spJV^%!E_~FW%aYait#`r7$IbUR
za3GUrxpU154kRnzkC*H{Gr#8TyqNl^d^g(#2gb+CPdh_c?ku!V28-^z?~UNVk0a<!
z8XQ=7G{*$hfkvwR@5kIwIr2L9jt6gRKHtqWmo##Ji>h4?O&jF0P-tw>?RfB8fbH8z
z<^}!?^ih%IBHil#l9hc4oeZeHIE7Y7KGw4!M1X<PT%CmoLuhwX3<s8|M&W?afbr?_
z4#s~b!<)=Me?eKFF6*aW&0*kK`E!eY{N`@#I9LY*lc)&<CUIvD6t2*7r-!^WppDd^
z`tpPPN{Xa!$2q{V^4eIlS@B|x+VNa^AQ!vG6Ar|-P*cQCH5;o1fYla!jF^*sc~2U8
z9?cBzQZ^@~LwbYSTh^J)P)J;~#N;#;rr5eQ&8QXY>jiZbPz>=Nm(mT-e^J2LFuh8S
zYj!C5b~sqzeJP=Q20a=tTo67o(@k|HjBGAWCOXl=dH%xjzNVLSQ|M~$RF}$!^QPue
z0-Dsz6Ao%Pu+}kikP!%Nq<MY6QE*1r5>$G9Dg3>gq@6Fr<t|}~g|PH2a#85Tl~G9w
z`T#HTZr^w!>LT-c(F&chSi~c|V)A{AuSbXdx0bQTOWMyIIkk&B*B<o;BYZ>rJ=!Xz
z<kDaXr7*sBvhUkbOBL8yGQ~E&#BJ>T9Yc1q)24**&dJCOPJ)K1DKYm+d*w5(M7Fo~
zv^~RKA*JaNQ7%XZfGBQ{JdhF~<GOn<JlkxnE^n;m3ZC#8gabvlyZ3xomd$8`0cjY0
z0~Qu+Q%zexBfmx#--EBDB}iSLQ*AXrYi6vZq{{Vj;A~<lj`tkyp3{pX6S6q|0{M3E
zRh5Adr?l`Z--DJjRt&FBqMoZf{Pz$Gii0NRXS{<@{X3>LXy`3RU!$#g{7S=lBRv~|
zZ!N#tr%8@s(>zJzikwcVtGV3iARBwlO@6~u`{DEg4wv!ILu}N01iX~t0n^0nt|=v~
zq(wT#051^Y0ti+y^k3zed%3t+2pwefzVGdU14scZDCLyO4|=w?ca%o+hsG$j#cllv
zySG?MbqDs3Z+@Acn0EX~jO<K`w-okw;AmF=^qJd#i~CbwLZE%fFl=v>GUhSitEj5r
zny5s24ulcI7=P2Gu=9E3ZgGnLs5dXo9-57?Z0gVT4tp=>y39$OjabZFPx42dMHGdM
zIDBSm@#}0HY|rR+<IbB$4}`KGKJ4bAV-gPk$OQ*f3#%SXNA15Y$E%BKc3C$~mx*Jf
zqC!JM{fe*2M^LEqhHXecc=T9!)pUiepdhaB*EV(MF*I8`KW}0|A{erEE9W30DLRTz
zpgIj><0o2g;vh;NaBwCdczpZrQ{Y!go2ex34a>uqnF(IUx~2vz8xr5Yq59%)nGP0y
zg(<H5V7CJfjZe!v@s4M;Z<>9kzQ6%1C&aX*ah(^4&AXi@g%?b>Ge7;>P!gZX*d>U5
zJ}=vJW!h~x&xy^L9n%w!$NL74eQwT3QSOFG6t!Rv*Bpv>JRx`p**<SByJNW(e9`I{
zIx*<mkSEa3EM|QjdoFmrQWbk#VgXrfkF<`=VfaDZ!Wn%+gqr@|r@=u-BFxcq3khFN
z6&6@Um0hH2AhY_QnC+439m-MjYEVmgTP*aF!S15HwsWfWUh}>Qs(98;pe3Q*%duDQ
z%U$PCU&3E!<4D%zh~o+p7;t@E9=LE9K&DT=G1}&bJHE+K$yB>=&efimp*2^3xucO!
zuY7dm^ij3fhrsSd;S&)tB?Ds=BB>aH1>>K=47a9bC1n!QxB{cE7S+~9@pqTk2hZWa
z#OEzxjJ_kkfa+Lt;eiT=smY2y`)r%tVm5wZa71LGx!O!aJgUL@yX#&<ogJFzPtGuO
zRy|RF=HLMezUv90dLcmol2zPo_rP7{BLA`UmsB{QCXIVHDwl|eH5p>6J0D(}j*s}p
z@e?0q2s3><WL(gw1}}MGJYQ%OAjqiOK<nD1i(r2ks*5WV;9Di39PMc~WB0WKQlINu
zdoICnL=qMd-0mqsf?HBljt|QGHY$))`+By7x=6w*weGyUvoi1HiQ(5Mei>^O(RO=(
zGwrW&=Hq)zIig_djUES9KjRzZIcuSJGSVtY4RW)Kw;a7YE(?{A*eUQY8yj03*ZK?T
zw@?cS^ROT1?c!E_K9w^oZM$oF3kAZ%SvJfz!V9MbFW5$6{dLdV-P1yv3u>7zW5!y;
z=+>OD8`ZTzYeD<FC85zoT7Hk!iIQ&frQ&#_wiARj*jXmx!a<=eSN^M(9Lkm~mN!*R
z%xMbezt-HZI!adIz}9SNK>Vv6)0Iqev%pL9EUg=Q`g%ebD1NccZhX6%;qnGskjS%F
z;XChRjZNB;0*W!Y(Lq*2bC}aMJ@?!QR)Ru_rol8yLU$J}EuGaICyYBHeU9hXT=o0l
zx0lX43JbpVAdt9??Sse@X;$iwP%}$|LDAf~MW}CkxtDD#r_(oX&N2UQ@i<@SCns@7
zoUZV^BKDEN<Q!8~GWaO0WmvT63UZw}!Q8j<WhLd5(vSR1n<<J}()_DO;D99@kdZFD
zWg+NVUR~lUfK<XdS=1I*Yg1V$wk4Xi@Wu0=aYL3%!#gfBq#rM4O}H!O@Xg4F4LyA1
zypkn#_116uy=}S(UP_BN-@xPa1FC|#tZW$^y~5JhMFs^{#z(I_#WKD7JV_RPEW&H~
z+uY7ps8yG4d2U~}b+>2<eSUD1OQWu}Fa5UMx2aFlK}z#&?990HGQT@lb#VEG`)TVx
z<L-Y>i~nbqvoMXN;K1Bk{4eXgn@V?W7mHdV+fFo^I5SwJt5ZSy<EzffC!>Bm4NiYv
zE)?UDK)EV72@OMr1BJ&{`<sZEEReAf#)g<%usKu}4%`nnYr%nbj;wodO!d#s=|>l5
z?e$x4t_Sbg5TUMT-ue_AfLTggBf=@0hu48nb90)z0lrS*ZbaY}YjrmYwY|}fhyFT5
zL0rQ+va%Gf1&I|4yxi?GxXnWsBQvw>cxxmY?CkOA2V*@B?8AH)`L}_tq)i1hsRS4$
zrfmv_d^*PB6@k)dOV&YpMSqJ=ClefC4Y&jIV0@XJgn^s&%{LS@`Ss>9wXPxuvN-T1
z;uk_@&m*C)@<O4L6t$Ju95XoFT;hSui<0}Ve(<Ol)?y|iOT0C_T|>*OQ0OWmN>P%e
z6>XUAv3Nh-LoYqpNxrq<NO5BGwu$Al>ACcl@hy1myb%sOw4Z@N4$iBa=Et1Doayl_
zBYDgNT%F=?zh8fNp&cb+sF=d(XAaRX6^p>9NCh!QRuy!~;b9?t2dXR)!NWp})m?CU
zARH*U=nLjK{@}g>2liSIgttC7x;1Tzz8e*m)ki;_L_=YfOP7%(Y<Q!ofD!h()AR~U
z`l=ZY<Xy;L3EvjMYzNNhg@PoYw`0m%kFN&;PMPQHg_Pv#xLhYETF9uS9KN@jH2Y2T
zs1d5!ltv9_!?q-lDQP8WX$5aAEU*Q+hMz0+ov7b#bOlrs^z1BD_qtd(6xEx+=F-zo
zdw)ecwxny2d<?b;?<E)w|M=$eWnOH58iOIsP-O;r#V4D;VbKMWu+E_H3G2;kx&}%V
ztFD$&@Z>5amaS)DGgl>>=3M>gqVX0Qwq-SFesbQxnWOHk;a<nDC@M5gnMIMfThYSE
z?QSX+oRT0GV*h^6kV23*Zq#RHu&MmLB44_)U7KtAd=NdufZ#clXpw)=%IX@r|7I2X
zK3foG6@KN)wVq=oeLbHTFG<fZbO#G;1Z_me`9E9)#fC$AW%yf~Uo`MIZ_wjw@=K>?
zgjh4N(eBFuSmjk4!=G$P6#{&40RZK?Ib_S!5u+rD&73Qb^OG&lQT(}lUrQXzF$S4G
zzR#l2?v6RauP<zJH3=&y$f+(%Q-7Ni_SO8c?-mY`e=>8@61<wguUe>Y5Fz4<cid}S
zN+yG^=(kgB_}Cy6L*oZJYe;yYguc?~mFs>72btr<4GsU1bfhOGPxR6Jcfl$9yZuCy
zoLXT84Xvy69$UMCM_telbT?berb1f>3^Rr#Sa!X<I$Jx)tm4s$E?Z$Gjba>>#fO-?
z-BtsLeDl*FO^Md*9uyHc>~iNc2a~MP@7cCh=W<8JwY&nB_@wTaRWV&ZyBKkwErlee
z8N2AHaqA?(u2~l8-!b74Vb(VCVq&^h2CRPTj4oPWzI}dj$28yUagQOqb(__2<+3ma
zDtV(wcP9b9@k5?`72O0jQOSZVzOu`<6ClL4Gvjw!q<{HXw%%b~Q(%e~Jwz`B;JDdO
zRjegmAdL792i{6(6O>h!iNR(yXBP!CGAceDW@KBU-zSbs)G`axgbyX9?y;wB<fU6P
zfP+eh3QbYXRT$`#XBfTTu{!J$hIkiL4W)-mWhS6XQ(XO&8(yLR6){Gl$dX3>0}jYT
z2do~?)(;-6RKSAD&#JO2s|Y};HqISF3j2khyxKSKNZoIomJ057e?Wga8Xmy`0NPe|
zZ*ta&NQOo}-*OYc0WU;4Z%rJW(6MHPB0A0=B>s@rd@c5eRmR)fWX3Zku`@*m_PRmj
z5W@Goi*z28Huh5<wwPc-j?Y(9JwcW3&UI3nH_)-;{gbcf&^7<HL&y)79+9l_%3kH6
zs)dGPXBxR+?IiH`97!|F)*ye>!1hyOsnN%kMP37Y7h5cC-x=!)CZEJG8SN!ZplyJ&
z6Aov9Co(enPi>HZK)PzHkbBl<<xtp|>90V~6Psr?e#)7FMZe&{&YLr3<38!fPM#8s
z`fpaN;DDKpl>N+_@ACD2jPSDAet<J97-{&v7JLH!9(-?OWVAAJSw4EmYc&e6)~oc2
z2!sy`wZ3WtN<)j;nS{c{!;16JzZkE#3x`gx1*9V)IiH#B%?19Qp9MFEYczdNcvh-W
z+aj92MGLq^`|Pb}m8Fi_re<x&m=j3EZ_8Vo5?ArpRdV{;_BJ-}Es%eAbTRu96Q+<^
z^K?<DQimB9vB#W_UeF_9L(gEf*x&=6s^hH3wqc`oqd9>CeIj=d4$FI%h>SLUy=~a`
zGQVKPw*A+MwSR(F{|U1BbMpJ&>nvrlelFigAW~CKemLM;xxKI6L|@_mqu_YdSeFY-
z=pSlDa$cSR>A#5mGc<*v_%j4b7_6i7)aopk<F0*q_mRTiz#*h}w@tyxmk(JMg|$ci
zEwlEVJ%s~<$M?B;sZiTHYdAn8{dasQE&iJ}9QYR2kw)|IqDh+O#=t(<_EjDnSl>Z_
zL*_R$^Ag^0;IqghIM9edfG&mk&upPrsY~MV_r00hCysN*(+EHbk;re{Rrf(h^6qC5
zxz(ZeUD91ad~j!3XYc2pylr|Y0-SR7fCJY}=CC*SSul=YBx~si&#e55VT$MS^41m(
z9FMNSffIxaYU)$&e1!2KZTjw=C;#Xz^lp04IIX|)#vd0BR8Kvsy~2hAqqnYb;Jowx
zNh69MkLkl-;#qV20jK^NP2n${p7^V!753>h-PLnV4zo`b5VY(Q=uC1D7??0xgl1z8
z&pPVj(`2mNi}rgoX=ak>F}v2W&`5Yo+h0{*+yBTFu68F_4V2t0YTAhWTm~V^DB^;B
z%XV;RsFt(s_~;W{PZ{Z|VcJZ5ddfw|lEFsB>Z*r69U5q7z?!nIq(}ZBY#LQf>_MVQ
z@!@Q>`LQugncJLSF#(KTY?b}$7UN9#s2RHw(vkLnz_;FHEVyXGG6>QE&XdppL5+Tv
zFR&jjW`hGFZXnLNc+CgIpHGQ3Jfb3%8)9YkI1dEt>j^O{k{f#SWgkC?(G^zlKpnBg
zkVZZNYw;Vg(QNnKmaP{bA`q+SsXMLvLJ#PhTdA7^7)umvE|!Diq<DMIXOJmT<kL!B
zVoaZ8+#PMav5^`yDY9dRs-Y0FE9LOj+I@2<8r=F_{IxIEw-}SLoH{gun7|l3MKmqF
z2wFRtK|J;lraS31X_OB4i%QmvCYv<<r0fng<&0bXdr>%`cV!-6<#F8HpP9PIUnHza
zzXfv@nsso3IoOgfnkfy&n(!lV7Uac2%I?)Cs_l#^%u-D%KH#=MmxpuNd#u^3@0wi>
z{KyqYoCr0<A0X$DiA3qWK?^nyyHy=<G9?f6zfGSbj4a8uvA?TFKoNyBZzQxM+hW#4
zoN`<{eEMhSET2U~BB~`hB+65*&N~A8POBtaJ&jbB_|)Y3+Y3J9`Y6cC%zufJbf|IT
z9&`Ls6GP>Ljn|{7&;HI3lV1L16g|pR)UD>AtEpIE+HJ<dE5+x>H~~Cojz_a^j)gp*
z^WfrFmIy{>opVRzsz$(W@@t0$Dzrec)H&B7A1&tiR>z6&uUmy?-!>c@3C9I?u#l}k
z3{B&l4ij99`jv#0QY;a~eDGDj!3kA7E~YH<g6P}La@cbMJ>P<;=_peg;aq2}h_k(K
z_xf;v9$NOCI}geKrAKrBty0C{Gu3K;Hw`V*^a=-<4Skl){HMuU3$F97>-Lklt)QWG
z%5o&I#t`KAZMCES*LbLW;rn^{_P0L%Rb1OdNl1IqjUHtq(^|CH^r#>l7Bn^Ow^v!+
zz9JUSrkhyWV6Fr(M{;j(X!Iq`1a!w3drgY0sK><$f52&uc+qYj#ucS_G03T*2q_*d
zH!18JCvv|&eBpR8&1i>0^)pw(9&cD=GtO~8h);DpS>>G^b_T}9?i+5xFQRG{@8hgj
zK(AbL^H#^DzKt%qE#vb~X;{fe+`kGY{5qYZeq|B~b5-{hw2_z~=~GUkPna3|`SEGi
z@?kQOyUZb3hCR=&-`50z2BM}VDROd2me(p<7yV?i%1ZRG4Nd)os1N$t;h-p8R?;qM
z={^a^$kluX%U>{tq(uC$cz4n(+S{K)ub3`f0vujBFF)6VjQLK1%qE$4tI}iWok=x8
z-!4witflu00z(jyZapTIFCsLsFy58^?T~5C28(WK?hSu~tG+kt2>koaj($+(yxZ8J
zu>bH)=Q64$>B7mfctFN`4MHP!)0VLG3_HMws;MeTQ<fDIoPd)aH{6GD{V4K?*i($e
z$*OK^D_0eo+=+hAI(y{)c=DK<=BM*wj9+CochmD)lg(^DL~v`zLGDkwJli1z(|5=j
zi?5pFU|wMiT=)83ouA9uKd0l75NW7gB-5q;1}RMTq1gR{$^tR}@tg&4M&wEFec6?J
zg!Azep**63Cz+{=$0h@53K~YD%-QkZNlIxv^Tww9z6!tYc;8)Rwxk>8oW8!qQV`K5
z5;i3-fctLxbs~Y^hM%%L-dI-`x>{$&J?>H5ntW%6lx2xwdye??<JRBEbRtds$DgaY
z`zr_4k9t1!9kdx=mgMn#ik-?#S6`00i@rVjue_ks(!KGfd>!X=!p{`T$?2QwjE6z7
zAfjHc1r^1rtC}>bp6CY#6sUbZ1wPY;1d+0`3L2JtTLLDs{H?o8tB*{GRcd%N1`{DK
zz8{m67YjcV4Z>x`_Pn{&SA<nzK6rrSi4Ue@Z^_7JBSw8}Qn=l>`GsJDFSD51Pvr$7
zm(e(m_G*WaS@GLwM1aSMmOkSz`hTpMdy`#x$w{PJ_(_akMXk}xC|-<_vUsHh>7`r_
zbs|BcHM3FT!D(lePK*5ugD)3LXV&dAdP~;>5xGlm9y|dF1=%bla)W*yP=xMBCN+W=
zJdR&SWNevT2!9aSXRDk@z<MtxD=4%dm`2sf{W!6`g2Lb7(9wA#rd&Am*wQ{=7-*=%
z9$(VaeZb)veYV8DEsWeiu%5-a@j)|}by2R#)S9qs(`!_ZC+DJyVlZ%j05|+|`no@K
z1d6TR&S+o(3&4pBFK|>7x7kFRAH}Jm65elgoXRQpA5JgN$IeGH!Nw1B;o>^jkNt7%
z$C%M&nN*v%6h^g}_K>`V+hpJa6RH#H(1faMF~_)~1tEDyz@i`?K!jvV1nY__L7vDI
z%%d{BIjQ?RSB)JEC3$G<?gkm8KD#tLjQ2bX3W6MeWX@@L`POb`F*J_<CqAfY>eAuq
zhP9%+h!fTcZiC>5ej!?k*c{=mMJe&;rev|Z4$V2-qTtA*hdjQ}xh4e)r}naxDkIMk
z))fjZV#)*E@W@tcJB`qCw+dr)+LsDg)|}>#GpoK2XVM+Gn-K5w@aM`&PPF+Qq#m5L
zyx|wHccznS6aA*%4LeF^WpGWRGsqk@!+27x8R|ksG?Jb*3?@wplx`av6Ak=9NfGe<
zaE!ytB0YGnwTet0pD}#&=TSwD_!E7Z$YNPtLk(~eAr|=-fYhy<F`w=f{@)edg%W-4
zya-skH*vh*HJ_SKb5k@q>{eb&t;#o>XFWvi;Mq0iTa4Z(|8v~<F#0oa&wBgrU03I}
zgz?R@&Kc@RHtr~d>X738it_Z+cc=W!V(W^*&=6sAU}LtNa6I*(QCBbJ1MBxB+-zhA
z5j>zr2MG_DjcVETm=8=j<ccoqpnt(ruyW7Sf0Qijk=eYhu|MoPGn2IqG7_5gf3f^+
zzAlx6lkAP@96=JB`k3h$I6`4HS6l`QQ_PoIiFVO285zBMTo&YmixJv7h*Chj9?dl>
zkrL%2DSXd(Owow26cJx@%xx0=g6b8tzC~8rh<<4OmadLt^YgRaaVq@)>5iJ}JKn^E
zT3Y&i5|0o{LR4cT$pY^jUQw9^W_;X$fL${Q=G5$2ErhjN#HW74gLk|n9^J&b+Sn)v
zj;v6;T=Y>mjL68=dl5*(MzHja0BwEv)d6RG__35s3%-p?UkQ(^7q#JvIiv5xEH7B_
zSWieIPFY_eiW9Om-b4Iqp=z{(GSTX~g_2x)E8CD;`Bh4;oMr6l@)CPjb$8ges%wQl
zW6Z&fnYW8;fY!pyVv%T=$Jtn!Ls-&0@u$jqtW_yiYF4av%o%k)F)o;Q3QmcF-hR5E
zP=R_27g-Zm7FT*~qDtX2@`+p3RYZv3>U>Pzv}2_)t9YrfsF_jBT(70JXM<R~d$^tV
zHOSw+E6er3-uu!W_rmW5>A55{`_}dl0j|ISVmMH7jr-+Ni}hu68~P#I<G3Qcy2^KW
zSIf3+2TL@2d)J^H?Qu)c<wqzYoP*A2dDJ8{t(%Le2hww+6N_?(O3@R+G|k@S=8I!C
zBLr7&M?w+6I$y9eCv0MS^s>4-xbn2DjNf;v$-$L>Bg2A2ONH6i_v0LdjKrJZ2KF>#
z*ncPLu*iXn;>GCOI0yli;Y_^fxb;CqT;qCY?-_UIqJ6!T%DJ>`!Bu^~&wjMPRgNn-
zHjzB7&qtwj@SqHKCO<RjDc<~4WSChkbZ?zuZ}%C|r08>k!VjMPAPuT+q?w`)s@}dC
zf&J=Vw$re@=ytOkhlQ)kXH9;6J60e>ejCW9rk7HL`15*LP3H@M5&|H9Ot?M$a4-3)
zoJF@T#&lR&N@mQmCODB;)-H~mN>ERym~G_g4?MgtE*c8`EVhAxX{J6_cAqRr`vwRw
zi8LqtbqwV{RPC9!mwtFnP@11=*u)Hq5lh!#Ag8Bi7_^l3J6d*hoQT&y16_>{(6dom
zcEW-F&RzCopT)0}kT!)&r^R1FRj=|lYB?7?2y|4$60~Guzbr8HmwM<gewUt`JF}fm
zzUuAYv~i4@t$^N{-`5M)@-|WS$JtS@QEJA5M(6A>XLJ|eppZN)Pf6y@5%|tV{k1iP
zPQw}x1vBIkuJ6Q(K*OfvAJQMzC*d8ty2|xi`fY?9J9f*~E5>NzADOdM+6F(j|CoA`
zRlDXk6ogG;URUwXCfcQ6Q7B!tV2E@ujzG=7j_QPkuPToAgIc28F=4mWCpdtAPrrJ0
zm31a?P?QL&qi>`4+kbPTe=U7Sk1(~}S5`!;4dP=#`3~L|J_{xtTk(lc>R-8~r*D=U
z-RVh{Q=GnVD|g?}D0lnR3#oPZ^w{fXUZ$pjy1xjdl(Gh&MF-3ALB)H4z!fPqXQUwv
zv>2I3IalsS=eI_ME-uQ~M&p7ToAErHt9Qod(btW=S0otz54x90Jg*bgZP+Bg`yH%D
zM*M0TpBqSt48B_shD@N63&^5Zn&lhqX9=^ZWI89j)5GT@1hDi%oB&FI=q`?{m@@v?
z@{3cSO_(cx9CyES(!a)>0Vj#w^CU^fM^6t%Jx0aBWJqrAD-jU!B#5FM{kW89&h(<O
z`?8pUS|;unI)xGT_aU!%9=t-1#?fYk^rm~Io@T^9!>0bx>t(XQ55W{KH7#QUG5a&?
z_GNGM3~Y&~CP3y(Cv@{rVW@HL%sM6?vRn(0XR#Q>{XI3;_KQvS(I;CpS1*)5oP)cF
ziHb_}=}&%=da?MBsDouWcsZ56G|;fpzsOBXZ1BvQEW+Kn822w0IESpbb^2M~*bJRN
zv-oyO+XMr>`=s{7*e+Kv1XJbpP)njcRtyEn=1CG;S|gP~?|Zfgbb<+Q@K1~5%krel
z(0bmB{n@w6PFe@EZA}+-<`XtIZ$nQM+AW3j^wwLRg4v859E>7xo)nEROXO=mbmaO%
zZ}FDkgS`qr!=7lX&<RCT)A#BX3ZAEF-!Q(`gzuS@54XSQ2}=4=OEvGM<GIF0nSuE=
zCdMr=oIO9ZJkgp(MKA3GZ^o<Touj@cIv&l9%jEuMiH*^aQz@Ni(y%?ZHl_?v<f}X-
zv9-=Tb0=}x@|17dOAjqY@Wd3^x?#%d&iEReB-#u<HF2!zgGtYN4%!$Cn5rqiX0D%b
zW|iiq4ABwJW&74U4;I@et8_UW>EMKS18u<GIF2kYh1-CG+0^p`sZ(}Hc~(v3PyYd>
z4V=*oyUS3@c)Uep@EYWGTC{6^uZhkR(+}J26al0Wr^(LzoQpMSPFaC3ZPq<gYxM#$
z`<LNBAmy>YZI&bRA=}HuKAj`oq}QYoL%Wt=TfT%euh}zG^l{PAF))eKZY^*n5%@&X
z8jQ30j6~!r9Ujyz`UTY<tWQj03;Hae%;SD<^3bJ^EL9JdDA8Ew1ycI)@bIYd$Qk#w
z3FZE^u>L8i_+JEnTp@l#d{=1{>$hb{Zk2&rYZA7e>zL-Et#9d*##i234K>BkO(^z+
z^A}MN(LQFj1+qelsgX`SGp9<5{EBK#o8~gz3ytC{e^666k_~QoWTx5G8yk%>AAGX#
zQCN)WvnF`rz<^u*x%2X6a~;yujX>1_9B4bRLM%xtxE^)ph{V_AW?WaFKiZx`ET&|d
zPdFY^!hryqAevKWi%zTWolyd}+kG#V0v&hqO0+TVo!yo4jvpPO9NON4IhuEG(vFq#
z5JcKOYuxFdZ6mPLF|Mttd%C+HFZQ5gMSgcsuY1<1(-Qp4XWMxnf^#HjeieNrhxSRg
zKl&K|tD$PS`oOB6JlJPd>QIvZTzje{3=Rl17ieE076mSA5t~ZhE}Y+uUKMDMXDcuM
zEDkQa_Bn!Xrk**&0lfPosSD}N$+qCmRw$wKuc?Te_Os7lrL}S&-A*pWO0Uu^VJr>a
z&KtYWUh=HQN{e~Y_#<Am<c}9Exs|`^y7>kNvefpk?>BCBOMYrG9Gp%`8{CNnNS~?)
z3c!H~=wF=pzrzWv|3oGtgrGB1qP~mq?z<(^8F5wSJF4AOLrcj=91^J>A5@EzTq@~T
zyLG$A<rwu+21FtN00TgK#&fGGY>|8Ln0b36eXxY%@Mw8rpNoog<?Ok?-Wcngn-#v>
zGp<+{uLDi+M`+tLpIewva7B4Wfl}$)HlxbiC(gy0cC3Vm5X}tGOL>Pl0!j`Te~>0S
z#wU!UMrcI`5d!;M=;6+Q9TGJ;_>KLRn(DhB;63R-#NY4_1*-o`c>hx4zYN;{A=AF_
z4f=P>uJeawH-!GlIiUZ;IfVb=9RA1L3;I&;AMWMfDf~<K^6!)QA9OG7)_*u!x&PeJ
z{>jql|4%sDIE=HG%{6$4jT6om?N%#)ca_q=U1j+{>=pjC$3U9??oTQcl8S7x)U&rM
zcKsH7cv$J1;zaAD@Ai!xhzL}ASaWn~Bco8ddyx9AK7N_b+H3k1rO~G;@%f}W$|LpY
zR;xI?K9jR!*T#R+fuqT9vSPJOg6U7O)W6=V^1m0L!B@YzS1fjG0=*=w6EHa7%j_^t
zlv;LTh!|E;VxyNqar1HHeBU?!8kQnJ%}b@&V#q#o+uR}0A>GbL6z016dNyau$?iEg
z=Er1mi^P{n)dOYzP(edSrtoBbg}EF6Wh*n^gG$~}6J3E_rLXm555{NT8_svZ&do4_
zFXTc-kDFq!D$FSH9V;G}d}ZD3gXb}OXvN|l;|AJ%v5ik8X}(vUPMBlqpH~YJTlNV}
zn$KcqpKk`4E--6m+NyVmEk=7RxEFdPF9b3_-4U4wg}HrDA$?y5P|%Ve?Fg1NMH=9M
zkJ3qe({4WDCW&)az406Rw&csAX6^c%!~U%6rq+qVCe3+nD**5lq<L7VOkZ4h`MQT=
zy~M<`Nw1D%MAN|VL#nJ*!9tK8Vrxr<pYP-7!ubepzaR8vdMBYWEV!%;em9#3ci2vA
zk$dk`JuM}&he#(mn2B-K!^<}-=hPb6hd5*2>$zo)8R|StHfGl3^<kopX){Zq-8M1b
zUZJQv`{>zz{?pmi6l-cYjakCoVQ7T0e730Bz=I_~&xiHQMFjFy8P!d!Y=Urrdrn5v
zC{$~4S$v*ic-7pN*A&fyl;T|#8Zi%!yoj3*C2M4&=R{-#RtO=^Q`QJ{7!XgzjJog}
z%f`$xzgNf@&gDese9bh@>I=<P*1t&bc-5w|`i`<PE=J{th6MQzQg4x&wZVo$Mp)n#
zr(zE85^0cYM}~V6PWqB@xua_S{4`@lsE1Kzd=?c!hDTPqNS(5L0;WxRZ)5RLJ`wF=
z)+Y#Jf6_A7an4HG9*5VR*Gd>;rKu^Zp8q_Pmcbf%b|^&tjg1E(K5u9gfqqK94t|-e
z$IB(`2Nq<+#*Y_Tl}0m$@jkS<GdK=*C@Q})4M||6NO)(>9$im_J+DU_IWaqe<nl#>
zPRCp3-Hb@=+A6*96a8mTlBR8K^4)%>x$W6EBt?HHaj@{gccc!Ddhpg`eVyJZL3>1}
zi~Myvu}!k9Q{Ac2(dZH}qmuEWkDIwNLsc7TTkbII1*b3aw$MX69i_84xus||LBWVG
z!zig$In5j|>6LZF<kFvhvc9Zbg#F;!33##?$EY``8$E8RMWH54n8EAz!%Q@Rugx4w
zP|<@h#R9}<r=Mht)WRyoAY8jx<?tEie$~DZw`@9NkY(bU|Lg&wB6D0`lr?!|bk*Fa
zGecH^Z>$%w^7#uhp_28&Yh7u$c|LX=p*QvW*z7#qBQ`;5KfGD93aii<$OQumTFxdF
zqA<@~vq$pvvUS-VK%W4Vcj`q;!YF0_-!8DoAid9?*|iX-X-PMyt2BG8+JLUz4egnz
z4IaMZ{OYA#!Dv!Y1I___@sO~KRXW$*%09^x_9qKF+dauPCs$Q|!90mc{@6vkSpI4M
z!{ovkx@TsuhvLy;&ZSMYd~fA~Mh{UkJoFb-$BM!@Z<C!YNs4M?O|;c4N4g}m{I;{y
zaFei<QYSNc>@|&5(s$2p%;~`d>nDYB_38}I)E^qHNA{Cp?F<+h9cdXD77oydv<wS5
zRCNnl=691ZF|3ODE61}@MSIFb6M+bja54gA!%#c?PZv`sYpSDnXjtacR*lFCqB$P9
zNI^dQ<Sfke<f5Ww<|Q%;ZT*D2LMQe-8F&x_=;E|VI4*>d^Y)+(7!eAYwD1q8DO1mD
z*q6jYYUm7`xUmbyu~m}DJr<CcfPOY4Tm>zZ2687MT3bKJMa`J;NCHLQ!bBU)GT>PS
z<>Hq~Ki|Sa{+6r^M=-5DokP@;ghk@i(D}R%vs-4@j~r5FdanoX4)R_|AfN2^_%+r)
zm$!JCebAaEw3PPBBjbzHHGW36n9J4c34?5`0aZYu59MVFlF*QI<+~J6(~*ffNNucp
zb+5pF*=uG;X45ph=@$z-eoIBcQ@;c}AJdwO#ALL|c@-^W9iet24C`F=XIyXjm<7T^
z6c4-iUt;*QWn26xOqxs{9ZziMQNlIc`jmv%XJM$&Tc<erC9GGrG~&>=-HgP~vC;9;
zv7Z;Mt4SUEhHhMP<ipz}941l?6#-qQw}V@RYI<_zQyH>!dPpcPPiGoc2&UtHQPeKf
zy?0w8n<6&5bag1yb3vAKE+tDEh~=zyw6Kk`V~hKys33F0Z1RpiL-SpT8g{|mzOYMl
z?f&-!I3Q@kRZ-rgxTn}G;@|hU-Iv^6l{r);1<hKVPUfx@HCJACTgc_XJhK^hAh!_Y
zvFfW&oTI4rHcYECn!S#iPnZqOTiswvAVO6+N*wtVg<{r_kSzfc^8bN@A#_h};DBSC
z^u}xGLE}fsGQU2<xLd+tQI{R2%KW$^_0{`EZ?1106ZwBeI=0&X<DsRkq@=APgafLq
zx4M0@_8AV4wAI4_CouW)!LO;eA6h^AYb<l6@D*#<R61eT*m*k;4PO*>Tv(9yst67K
z^lO;%wcrfhaP!70Vwbatn}Kb2>#-#2=d#y$OD?t(#vNzmPd0)gdR7TmDuUwnTkD@_
z1wJ72OZHBhoMeiPk$Gci(&W@E`?#!5k8(h&1UVIV_&>VF$`<$gZ^G4Z;3Fo_-J7Oz
zf4A4Ru5xMTuJh`UHa5cNB;kZhIWr0`{OOV&(Gc@xawX2>%6hh-l~!TB5{=4jGkarP
z^-_=9dAadY_!Dodr^qj-?Z{pH*XO4<!=R2_D{fUR+#>sp1L8;)$9EG<w`WO{A;a=i
zS|dI(JCS3JU#55`pT|D(h3vod%H4ew8^K*s@V42u&*a`{BB$kH&kt)!5;vu}4Qq&8
z_2aSw1rkf~Vw;M<e1n{fr&jNg_oG^-14At4P9Ae+61Dqa@yO56u02aDXI>8{FdrEb
z6A=Yn_>#y?uhSL{)(_9By(P%~>4$q6CH$iHI9TzOHLj=a(KENVCe6d0Zg(HFNpvfc
z*zpBoGM?b;P$Lc|CIp}-GdQ{+<VLm<B7M~ziAY#o5%M`7G4gL8kPNYExggoO)N$0|
z6?+-ao!!Vj|9!y2NFm0fj}F68Q00Q7Opc3izpFyEKr{vP_(^C+>KrbPE_tF?!@*Vi
z;n1LP97-#h_3-zP>6dN2&%~~qCtEafrtn4lNxMHy``FjCtMIUoWzZUU%p2A%#w6ac
zsJtg~$yb`z04t3?1&^PiOAWW=Gw;P`q}JDTShjiD`N<`9JZX*c$sXpmQ7*?tFrCO;
zO@wL0M#b-~Vyk=!2&bl0Gkepx3Tf|o=FsL#*`FJE5r&_os$2GSW}H&LSI*gfP!C=)
z<<1e}izy~djP!w#rx+@-#7y!<hJ>Vt&>|y?&1!0Ar|v0T8WAtKO4@yQ_pmgX5k}TD
zb5|jwU^D$C6>KJEDcSCmEG=w)5#;d6EW7GSC;e*uS&|`N0+u9OL&cs^^B{yE&kjV=
z9dlko#}e24B&XYGdVe&yT9AnKS`|~}<njD#bj=4Ok2f&ma=awJDty8$mZNw&63x5N
zKGpWxICbfvmH4*=;*GAEnaqBYoHqN81TE>$yJoB#Dy>cEPli5Z*(bU_7xEfYlrIq5
zN+<>aLZh@Y5B;^4WYs5mM9Li7!n1{phMQ4ebM*`N@Ops@s(a?fNmT^%_>miQN7xt8
zwPXq0US8{pIZAaQr!t2*`b#ZF9d!!_ZI@@#FUC2n`7JO|@oSN5PS-S~#>46?`X#za
z;wM!{DwD9w-mBC`=@_Vq;f2Vs+G&xU{#t=u!hsli=naG4%>Eqb_nReGG7C|)xdD8?
zOpR23mqVJkI=5CUPRtbg{c;$)gU)t(m$({Hy?o?@0b3`k`A-59e&Si1#HRU%^+Vg@
zp<Rhfzh+MN4q0aF3!hRndh!M(GJlwhoiAK5H5wPN)8?8>fdJlRD-1?<PdygixhqX}
zbyP$IImec`C|frf^*2W!i1&+UGG5{+PC!0^+-*jOlsE*j-LX~t*-(@Z#`VaIHc-_t
z$z&550)^8W5T}?^cTG{KY)jkn_xqmJa_0H2ZJW;21`2uFE?(Tc9vY>pPtnSMScQ6}
zmivZHL0(N}mNPt5xN0cbe<1LgyBRS=u4TRUE*U>&Z!Ue9&1r%8%iFy4;8n4W+?SZ^
z+$`}2OoXaB;JP`~uCgj@ibAGp+IX<!Xa5)*`|qO)i42Pr151~L(IuCj#hVP^tW%PH
z)3UKj&i#7pXLQ8-D5Wd4GDTEE=Gnt9an&NNyzjo%EeUnjwjXFT+r6Lc4WOE#wB=`e
z-qNb`yshyERvA}B;T%Zw6)3rRs3!57&xW*I1_3MHPcb`497!EF1w@|TBF)>t>`8_)
zpXwqSB&qPp<2P|GJ>QzKu$Y<IWSxcxsmOS#<)SHZ08c?X*8{c1O9qR_QZ>pT&3uj2
z<DeFWH3=6E7#*6alHbM}X{{yjCPBL-QwCV;qAR^*Ww?4VRY%~|DnQd~&My9<lPkO(
zeJ;DuQ-G=wY>FS3M({xLC^>?J>hW_M02!IWu)GQ%{1t}o_j)wGo~fQjQY7kfTvwqf
zRlI$w!5*{cG_%<ue*-_supk8|^L4nG`vQgCEgP#=f(j$qV@cA*X0ngH0W^eb515ZD
z)+<Iqg%V+yj#|t7kROE$ruD+{T~)-g1ljs3J4hNfz{BC^cuCAV|G`<BmT;3|TM9a7
zrFzjXzJYk*KXaZ{X>iL(;l&H_{Z7RPqz?eBm`4#?grrk3<<avD^czbmHCe)MQaBmw
z4wxAtl&ZC`ACR?*W&_MEVfF$V^Q3;7-NRHsuE78{(MY6S^aDA*2+o@+77+DQ#h0Hl
zFwb={7;0X|)B=RmDv=?AD_AHJWifV!cCY-DYnH1S^M{q`$899_Tlj7ETiDSUUJ^tp
zu80s~*oi2Gm53%xxQI-QRV|Spg$<Fs1b;2b&{PtZEda&5eesj|qgeM(8ZPQgs+`gc
z-EA~Yp~pHh^xz-ORW-a2(qXdKye1o7wyb^xrx$OdxgPuX+0&mht4J6a`T&_?*@Gxf
z^d58};k>U?OkT~qCC3dzRF+se-FnPGkBH(@VX>@z3}!8gPa*aGJmJCNOqgXa`SE@t
zp`0iHPRW?&LAX1dl2gro!{R;PKgMmx)f}JY%0@I4FwuYe*n$^rQ<${B@-@*bRK092
znJ?r)WDL_oRSkN*NE`Mn{UJZ(#p?WyigR()dB4$vIL(AJKDF%OBI>H>6UyxM$8Q<D
z`ebBKLL*vWZCnn*I2QRAo6U2J$zUQVwwqYsoG3{IEiwn^*TiYGx0<}H`=sG`+}Q$D
zsC-1PHpCMhF8klDPze6asyf$6va{(Ioz?oxXj4Tyrx_qbG)MmEwJ8ntutZE;T*uzv
z34xyW`}gT}DK;O*mlR)5XBiFSdy^Sv0xmE+BtGkv5M8HFExuR`nSYj||Hbv5|G3!~
z3m2RT2UdSlL$5wA=H19U&A<Ww@$3qR@5}N2>ebI8$3Cwxdxb7^EpWa~YvCekS!3jn
zx2MjqBolQMmS0e9f9zQm(`_&>#eeW=Uk5d-^urrE+}E3PMdqGLYB-Q1m@Q|tS@2<|
ztCwVC(`ssZqO)V~=y$#-#+Rr+88AiA`Mk8#@E*$HnwORhhP>iE(s{P{X!q6mi&3*C
zEqQb9<%=qgp}LLGx>G5Y{$DoJwN`CnJB{qB%OryF1jtsLiwb>%foLd%{rE_%;pD?S
z>RBSP^O#~TFmAI}g(`|xh)s?lfe2EB7iB)JO*)1S-VeM2_OpL%ti}cRLNB^MLhrU@
zp?i{z=52P2V3nDBhrUZo<@RIT+C;aTXqL6~RQ_kk1FwR^%2<^qT|~7cPAb2MewrS~
znb3-f+RK*72Mc_0#nqiN=dfKrc>q!~78k{dz#rCBF~cjgxEb115AB&d>n3CT!C)>z
zyhDVR6J=+z0n!J>AQP%FNa%kQ+7rFFZR2!PP6rLB44Dj<(LC`mIxT!6Wj8VT#!Y$u
z93aa0kgufO&`gkTi$1=TkEB>&yA}R7<jU1N{+`zcdTX{WeUZ=CeAD5xq1KAQw$`pU
z`RzqwHKw>^<33bwbwTFT6<MCe^9hfBeE1tXEdut9$(@%fav@uQZI&G20FEGDWBbU}
zvt0Ag-b43t%2t@yOps6a+KS)w<%1Pd)|Zyj9FNn-2^<tDnRRu#Pzf*r%y=|>wlA!N
zNUJ~IP49oJ3UV1=)F*o)?AwdsDgz2LZW@0AS*Uf0S7qE5xHjfaQRM?u{?OYe6~qVD
z$d^jSQ<`mD8~D+ghWNDT_BadT4xjl|#4>Jjf60nX_Q<kf%~bNp$H}hnD%P)%i|i9X
z;zfr=7!ffb{@sXte@So{SzEQMFpXT9msPWGt=&SIm|{}>toKE2MO1w_R<w`C_D*Rb
zhQTIsLYrB74?rG^u6c~9<LdYPJ^=C&aef*z?|x{padIpvsVwiZZ86^0XTQila_s5`
zkNCh)&-{I$6oaj(X6-`p_7?(q(pdAHc}3IiE_@X&8PO4D?)LeJa^@k5z8|;dG?0<y
z46*5uIM@p1BJU%uPAAO(2R0!~C8+cJC&->SNdZxD>)HiM>$?2xWmsMjQQWg}kUo)X
zSIPzBD)qX`@^~KYA~&1SU^B%!_bQT|V*7|Cs&{3RFg%TXZ!*3}HA~%BD6hluUVER1
z9B8(2?Zk;JVFYr5BHa5|@LCBPs>_<({u^y?9TsP^?2Dp7g1ftGa3{D!kl-3z2WODr
z1b27$1PD4<aCf)C-Q7d-ok`Z(E8p5{pXZ$W+<X6<>9?!9->T|<tGcUyWgLZ|z?KA*
zSknmX=0RK9RKwVsl8p)t)wMUsmHqmG<9l=!pQnC&^L$P<{^z!1!x>i7=?J{dQyMML
zmL#QcH#*p}bTRHiDe75AzCg7>d@ViSq_d?c<bl-Jez$44xJ3)$iEqtuOpA+*sn$-c
zPT+!_r7OdOMuLWs&EGJkr#Dy-_;8!%Y&zfazV89uH70TF^{`=ux3S97xXerIRTpVH
zjL}XFe+e4OfrR1YyG_7o;K0E#D6v*JLWdfd7j46FvNl&stEk>TRn1g7ZoJ=tagY;p
zuU!s(pHKvj?!q~fwmT1$!`z!Hs=;oT!OQ8u9#j)}MY^}Jzm-&9q#s|cFn&A71i;M<
zj!6}SH@R2gkHO;_P+k-Y-OLQCv^bosW61P;t18qWWfxsf$2gApK+{1p<Pj<#*1(S7
z0YA)T-swm)UOWqzRr$<)o|*Ih6C{o`ok>0214W2#s*3_9cDyR3_Uy`e4X-iFwXU_q
zzU;Wqa0I+-2qrjsbwCB6${C?ZSf-~4&lc1&1z9U0XwX6Tic_M(@v*nlVFsj*#V2_w
zj727{?GMBa>khX)RM6?GBWpI)4?QvVD^X;<Pj!SlGXcLLz<OsdhHM%UC1n!;I$FK)
z_DM9ISex{GQO;z`@<RdG46*pKtV@q-LAA0<uN)Opf6Y_c44L!&8lakG$=lQs{<qLs
z%CpU{ji1|%&_^Db?ii!&_w29~h7&v@#I!8gvltSwSmC62Nx5|7wqOGS!z++Y!x5pT
zO|5UD+|S1O5(CEehQXuCW}}(JY`UrfHKMwzNC{eu`{CB~9^`V}EmKs?{p@zl)@bk&
z1PAe^nR{=bn<N_D!3m*scg_c={_$68Muu8;*&;S20<F^HHp$)^qHE8{Tk3ZMsOy+g
zT?PtGYBfc%Gn_%;148dN#Z*ztv(lmqn(?mf>m8QkK|bH?Pw=p2*GV#NplAx@n}*jl
z<LqoK7wTwmyjYh(%4=QSRJLg5q)6||B;u1TyT9swhTp(9-#<`Y$URp(V-cAFewDvv
zBpMSFz`g2K(so`c@t9{sU{MLLe%_a_Et)VR80HZid~=+DinZ+3JUd^N7ZKz@^1;zW
zk!>i4Tsvi_ZS6UKSYNI-v31?}-go@f+V%7VN#zkX2Pn5x8=1yLSr(45fpMPG{maw}
z;<u6OZ*lF#qPLHo=n}f=jfR^##%Xx_s<Eui2ME@=%xQxs>qvCte?b+Q=TR?9<khDf
z$P~df^$!WWl~A{gRQs|R_PNZ*#|qw&IFz#XtSIB`$MyqN+ojQ)PtI@g#yjXTlZFV{
z?!toP67wVQBB_aIXv6u<8eD3ymc9|{r{H2mVdDaLN-VH;H~4s0n?<dN&4pb_iOJhV
z{klEeMiGnv>tt)u{?-HCZ<EE)`YUHxR+^k{qv+6xIwGx)B(NJ?hPi@X3^yL4n_a72
z)USK_pH*fj_<oeMqj65mJiuDHEaloVlS(dEZ)OeP){+PU@y<ndnoBZCs+54|Xcgff
z1hh)@eBwonzylkPugO;~zTVZ4`frQ$rUQK4ySY*WoeBLXIoeqStic&YVQ3N4gEB?7
z@`3;_nBv>YVFrm}GMy1Twtg-pz`J<e{nf1aFAgi4tfd;Fwf3^@%5jVsC7J!RLYxbb
ztFC}!IPr!8m1Rvs@eV!<io+HX=&<C%5*npR=U}}z`+wg|Qk7_V_&G`9KMDyuU$gV}
zQ9ld+O!~<1;xa#{s4q>rnb!Zqc3;2Nw@CVL$3WuKUYq3o<jFV$_&?|)|AKPld+wd4
zrJYuQpsnZ~D5*O-#7(&3gwS2^*-W@XPWj<`t;55^AtAr|KQ;ti+IdE^#+P|J9&_tR
zFRyq%HWhJv`gYtm;Nak}cNOiXB^zPIPU&k@n;;Wq5<QAIY$fX%F(Zv@Oc$o0b`-0Q
zahIm|0VA;N;rcFh`3o?5aPnr=J<EN|_leK9FUI`3olM{x;C<EVmrSIgUIlp_XD#LW
znZkm=o~{)UX$^{;TGGHFwDJWVMc}|o+tpIb0UJOAK9BXJT8Hl}qH96sIwMeDW-vZ7
zEUgd8oOj`PmP4oX3WS;9MFTS-0^}VHW-ge<B9um~cd>}6y9YbZgYqR&M4Ny;Lj-a3
zR>V{g5Y)!~>KEZXZ3it=n6+DV?AOoC7ChXosK%J|rV|`(t_1m}#Tg;HEUv97-8)8S
zgnYm;=V$&m=zK954e^5@%ZnDgDJxhPLf8rsdTJ-bBWWoqZdIxCViEyP-}LZ+>@p}-
zxO{OAl_Y~D%9trvyzkDBulCn0PuBIcUD|V+jXzrv?`N^W_`%Gkc>$kBkO8_kZH+=R
zbQVM6;6quC5SNe-D+1z^a!mZ_H1X<(k@MO5#Fx+0!0xoRVyBdYw$h`dM<HOeVm4be
zFdHCqc~z5X=wTpo;ckFW0}DZxP;Li}6zy%#XbJk!oK8eMo3Z4F4l`mlKKq;2RTDNi
zZBga7o+as8?&88Bnqi=rLRBUrU_g@!NHKm(47hyn>hZ7LK|263<UShe$s^?Ihs5is
zzo43ZY(XAFax3%MesqcpC*LB&sgSFl0=G(#;}P+T`Hi09%nLtkLX*a$%0_<Opq_~7
zasga5k7Av}rkuZyT^Kof9t0(~pZz%d62Xt*==8PCf|}iHZ*Mjg;WLXVSgLj$<?M4v
zKaMU{A|qWuQTf7o%y{-X+QVpD-9@+G0gPF;cACSTukJFh6|iT?-n@wZY~gWyj&<yL
zHCKuzk1yWLNjN=D9zhP5EJg}jnOg(9+#-5_L&`3%{`^u4vYkcjgYvHPpad(;XmiZ<
zF3b^Yxh{v_&Y25H@a5Yu+GzqzkkcqVced9~4zK3>6h<^z+V}Y2C}Qj9b!}?Lu;}v*
zU57G&V?knqU7O0bQ`@B(BSsU)a1L+td&i_!pAR3jCUt39OVCa8T`Alk@u-sF9+#*c
z@%l<{d}3m%`O;#Az5<@$z*Ikv>k6x7%j;L*<m&es1XU}bqn(#vnJ5gUWK|BQBP<&v
z{x@5V+w5P;*mn7zfOrNOux?d{yE|_nfz<dY`j5y)WSometDfDVQXn#g;r=g;E5V=2
z09L5oS!_8ih)JeY=?noap7f)QwTIqZ>1)9kx{IHKq-9jhNaW5N+Xc6K<SG(osDPdV
zMib3+?us+?4HPDfAyDp9Qv9}D!|cj$^`ZqJpajBoLrA5diVIJiR4Uuz(mgNXNV;<9
zNE2d@-8mEp6g{l|{L{>Q)(5zL^X$)UZ2NeYHxD^cZ4L5g%4?U^biK<|3>PTYp#oU@
zO%C7zts{@EEKTg#IBB^Eo>>ZHv*lPSKWL1enR*!wk&K74x*N6&9fj>NeyCF}nY;SO
z{+L<ELDW%az6P9LkmeHxJ`~0JY?LGC@{i{HQp<5cdQ_EVjZ)Pk#O7kEc8gBB+xS}j
zhK(nG)(;2wu_~$S)HuepK6W*j_#{nOuWxnMFUadsKAx=k1(9l>O)FtCF(audp~W+l
z=z?%)bATzM8F?Ne548Y2uOnCzpBRwE*n->3n1PbU0rKwav4Lc@S84!UIMaT8>sV@a
z&kPz&X+X?+B?9zk)5MMM<X+0Vb?0uaG+z`!@ZhC9w2KV-Hax@r#np(Z)EjD<p5df@
zB;FvWOEyM?QIKC>r4Tdi&K%q)s&X|Kj))TCq&$SD53Lu!_6xm#BK|h@_gECY4?H5Z
zV)j1tb}efxk9ua?QHGuA_#|T-=12#zV>(lcTJXy|N}@c+lBIj1FyXd}0af%1%Xqi;
zV+m@V@A1~JfA-g$hk|mEd)G_8Gh=VtRZ5Qnqc`+DEP6e}HX;c}=mlRnN$bgm#N&7g
z7HS+qi6PIO)(8sJ>TD<h;eU_~WN;_awy#`pgmoXgnmBnT!bY?I1;t0ViL;lY&dDjw
zPo4||^J;6M$e`|y+BaP%!@zA69$Xu3R$cW_H^+<GW+YQAWzZR@8x+UB$d)FDoL`v|
zXFp64R7xfh5CP?>0Aso=N!AD|w1<wQGBe`lNpiU84j?%=EWguuz)iF-a3Gy7CXSa<
zw`QV=h-pw|m(f7HhMLp&T2ms&=c7G&bC=#8ND%tf-uWKG0+ZRPp6l^`88Nkgm(8Gb
zDOM-_JTW*i8bMm#ZC=Q=&?vqH0|UDbKTpeUv~u+8L+;>mDlrjQfM{r=?lGv1ebw8F
zrRrqg_LLxMHK|r<cTG^Oa>QFtZjTVx-J2FnDjI*l*nltG2byY6SxtM@Uy1H7kRsyC
zoVbVW@Os<8=HPp*#0LvEw(0oSBDC){R#5z=Ld!2TZF!$EETL%+Y#h<s);7<we@flC
z?;8H7YxSwoXV2whosGG!4_mwyT71AZU!rUH)ch<i6hH0o$yrON5#xe)LMnV6-~PZa
z!K{SZk_h)jDVR`(UQ=t=hosW3n2DkSTrH+XP0%J^nGtTXsGbgoAP!xWbo0cM$Z1Ko
zD!W*cc$u)lGrVAQ=M`(akobuqs(BIGc11+E&4~grX!?+$s3$hKAPM#t{D5OB(@NFk
zy1DP_L&V_s$iY=SE!DLO)`8^%6hl9KjFK>Sl%Gu7(V(2ccFnefqfQ6GY*4nofRnSE
zyM`N6<))MhMjJs5tG?q$143hrovH&D)FgJd3+<1T&Wv19mks**zVP{Y>P)0X`kJtu
z1WDCvJYzPvu~*J+j7*T#2l@^6o9nRo)1RVt3rL-KZ`=4B*I!XQEL^0^91T5?U1BWs
z*oih%TXK#iMT}-3j>cNhOujk0pEG5^Iiiv&6GL9ZA&wt7U%K4@x_qR)K>B8MX@~F*
zYv9nVHT8|_Kt>_Q;Ww=VkJxrNHT_l(&B$j@3zN{PYcXU?{aP7_we)Y67y}W+H6fKr
zI3@5XFdZ)9@IJjpmo7AaS86E6K<e&+Ol(VR&hzFnHn3M5{CD)0-%%+4U+;RMF!o0w
zpHTGfmT3APp|BQFT5w5~QSjoquv4!$b4DQhNR%@Yq~nNfLcy3|kcvqxBGY320JWZ<
zE;vMeG(C|78sOTR31`=*5i*m4Jz_crkhj#1#e#b$q<G@wKi{xrh($()OwhAKHzLPq
zshM|vE}Oq`V(7Lx>*wRk(qf}+fHSRogy5xFjuFx!)|&^W2J@E&AtfUwT1*u)SgS%6
zWa=UdMV%`wf4iUVIMDw>G0RY{>0)Q@d{+GaoEmp}1826FMGW=|fr-!rAIcX8^KFRW
z)HtI>6Lv4lmD*QDCD~}bEm-i#3Vp9bK2?Nmw8Ey75sSli!z-9+m3XkVn+RbesbUW|
zQ|PHf4<FtDpOW-V43Kq=k8FM6rfx{gh?iHOe%nH)S`s?EP2d&A_@cB#I8<P`69(BC
z%1lCC?3sRx6=9U^_P7o#(*En9F;(391J0Pw@J_-J0-Eq6){C5Dq~&wsVz2?lrq|7$
zhf<@pzUN16`|97SP|}i|REURKh{^4};r%x}`e@o$$z_^b-i=+?p%@5S)qTAUoGLd&
zAKBR|ct0!!37`3`NvN;5UR!||Zs);HQg86;7RQRbJ{L??)&=a1YOxlns$#22CWxe-
z&eBB$in+~+NlD4fnK>^xVo8}tWcTAnpUR!qJNMB-JDCq~C?)28?1m*d<OH%vA@7-E
zbB1Ekxx<xUVnU(H<TnfgegvBW6k{)A`gHbECV?m!)w;!$OF@&Uyhw1%sbs<8+<9W+
z-h#Hf3Tiqkw9qi~vY3#Q*Gm%9%SR3LoiF-CN0A({L`zpFMFD5T85&KNu6%N#c!$Ub
zI0=|b@2tU)%_yV{S*`nRaO#RjlU!|8)f}H^NL@Wjq)UtXbWHuGdUSKDxa5~rOM;X*
ziNo;nis(dJ9J!3>50qj`g3&Xhdwe=;h3@k!_l{5>hQrc+BySLHyrX3u3PT>(uY$Gs
zf>IgTAdoXy3_~F^5|PMORNyJ|$>I$-b?uu~m!#f8HjO689y~qN+XcRqqnS^liy3+!
zZ$DoLPh~@sfecTH2&EnX1!W^;bclnDVzS`d>N8CIG6o0eV@+YXErHf-K-@DdzLWWZ
zAU_ozIg||cA^u1NbyTii6jeD~Uz0kGa-pcT)}{-75Oz3&Ti7#SzP2y@uvYw&PP2lf
zG@s@yDOs-GeY9XkHQy8w5o*e;UdIv3zzcbKRO4}?FLOjo19ykzC59PmxNld(N+0H^
z-M+G%aMXO^aC8QfD}Y>V1$v4>llG(JuW&u8y*EwFz^H|u#Cz6Gm+dsC6a8ym+SPEy
zfi~045mJvh{b7&77HM(SE*Ito7})&KY@7_z+bcF!St4Q-Y?7s|aGa*(y2+>*oS}uw
z-pYh{b%v?!fCfBExovSVjl+8{AwefK+h~@xi3ioeujxO*wGWOxTE%1x%{l7?6THaF
z;ec$Ew%bo_M>@GN`LC^Fu`rdjk-`~VT$Dbr!y}K@ExP$G5Hx^vxYl|Rc;gz(OG_QJ
z5Lc#3BzV?Fq!XK0Yo!S7DT)h7Dz!@~pqgQ`aaUl8)iQO)JhpvOr20AoW7fEUyiRpZ
zyY4xRc%QNygU|{KjWe}4MMeyREws*zvJD^?auqbi(4ZUC!;we%9ok-<vvP3?^*43%
zr*wEbBI>)N3?#!rj{Z5Rje4YDAeLqh^5W{I!?%3dVY}Ml0$l}s_&i5LbO-I5X!w&h
zrmff2EAMf2*(P2H+f800sjG2T?2<DXCIzIWRA&Yt>J?&->1P9y+NJx+fmY*7Utig`
z78bsSCUmigl7QLrTaLif>?X`V)(vjd5W_V%fEzJkn+|cClPT;QrA%4N1KPBy57yFt
zwWRko-_g;~=l?ln1j98RKW!!d>bPH)<5gEAil%&}lv)KcV!1j7nmA0FxR$)%R<vXa
z$qL%F<(scAu<bsAiiN$dUQ)G*!YqkWoHla(ge$LK%hE9RmFTi21t12X1#vTt6gv=`
znr=3o56dwuKG5c7%~jcy$>W?;_ej%B`LU{)kVR`NQ^jwSP#K|7VqkIL6TCwgP94#R
zb9M-N*5?<mZK`K}m}b!ynI|Qfo<-c}gQ^Vt?&EQm`z6w*ra`^b6$e%lQS~!g)|#Tn
z=hR+Vf>i=$ys;(a8k#yzy!H?wTK&{*$w0zOu$=M%RX~J~(V}vFHz@x|`ZJ?f;APf#
z)8V?cZF{%IqL>=Fm$<m9z%UaON~`VMTp2-I)&f;Hun6Etk|pyf84a00xq2!*g6WL%
zlk@A3;T`&tggWKB56FZ)565cuqf8dD!wmf-QB$(TM-#(vD>)9;@k2NjNS`>9^Yun+
zu!Fvi;T|4PNo=3sF+I3*Uhiq`nGwv>i!)M1faONw_mrUGjHn8doObh(zldX%yF!Jl
zF%5C-^A0|y&fA!oy=Ge;T(CnzLekP|%pXU*M-oW!JJynS%WR^uNj^6c(RG)CO)ePw
zu!UKmncLsv2_(R~qT}yRf9|D|%Fx>B9JTKK8i&+^0C%|P&w{F|*x39&JE^j1dW-~y
z3WsFpBYXJe4KRkoh_9~p1bDh4)1ojPYukr1BV3s(5ErkM$z!RFj9c$CO`~82W5IxU
ztXI8|$~(9~l5@pnzU(nx#-7Ah+u?$f(lOp9y<e&{UhFwMTpoeQMiOPoP|QRk)uWDp
zis=+r_vP?&-EJq<nZAeMeS6^!#X0y}6y31=2<IoRn?CONk5VJ`A-Y7GQy{8PtYW5$
zuh2~n(wYv6GE21u>L8X!2Cxef{)Gj0RD@i4(TaI)b7H)6oe>X7n@8nHwx<e&0hpMW
z`9qL3^S>KD|6>UR4cGra&~W4aiH5uWe?Y?}>3Dyr+-NA0n}Eeq>0_WTEv<&?C#OaW
zCeAfZ8L{cgP=YS-9JI>Ys2cyuPh4og+oH^m%@`ceZdPB@bTQYhsnKE)%M?0mB<YvT
z|9Y@McK`crCh`Z@(Q7YdehqANXwP1|caH0ylb!H|JL!`bLqgwN;z#o~+^-#{Lm6DZ
z<Z2jU5%hK$y;+$B11kc`Q$|@_p$Nv^4slf7V6KuY2}D43dddi0zpJX?^!=`S@KCu&
z0zb(<yvMUe87&OIgIXm5@-Bt?k1kA@s9i=$_86nV;&KP1Q0*45;XVKdy5LPVFF#qv
zG9(lL6gz=~Yumi#k}P~H?f6&0#;FneIB3rn;qxC#IINRwLPQxg4BzQC+3^vCI+ft1
z*mN~<0g@TzxYFHRhwztD3M3@eqD1Nl-GGP>hj&~mYWK1M?d$%2)jA}22Wg}+N{h+9
zNly4t!8sch0sp<E6s8cLr7{vkIW~t}xS4Sa^GHRDhNex9$_Tp&lKOh<ol(Z{ox01&
z@olFz3)*Yf(b$1fyAeo)(d+*1iHo@|=Od|sA+#zUObjxZt5?}Sf4m_#N*Z+_{!|@M
zj=}%Bzjva9!k5)$6jORcgJ+ud6X!8>bpRb2nYMRwPthj8%O&s@FB9iGE#&Rcg}y04
zEeba~99p&~ou&n}8wj7@y@55j-U1b5y1c7X!}wiCL9G1SC`)EFGNe78eokBXZD>An
z_HPOw)Rg{r#MkfYj9{*uZrbL$P+dnu*F$ZFMTgygm&}_|No$_Vo6o;5;W9*2#X5#x
zR9jv&()KHs!NvJTpSI_cd5KYq26GiV(E;vMf}Qd5(`KxJMmE`GCpbMhYrE|dp-)4t
zLYWQZOy_?+ASC!5@PiSia*?G1jY5MP;}Bbw2j(ih81w$;aA>v8f3~Orb^A-p?YboX
zMg9i5mG;S?G#e@KzjS;io~g=ZBuP1`fm9#U2m_4^+Wmg*xy3~Nw$Rv4xir(w$3S{#
zX>q^O1RA3Co_bF40}yG1F8*RDM9tp)QnL?`0YlVmt{6G2#X>Bm4`oVP3Z>xj^#Fn@
ztco!=(_Wwwq^e5c?0=gctGRf&(7zOX4?^4pbC9-s+4xV5uLgRjBZop-D<`%okC+w=
zg>*qEhALpxZ4*%XpA!Kxw&Qr)SxHX=_@@H&%nJ=D)^Qex08E<^{^V8o?ac$=5jU<c
zZR(+Zw$h@F^hCzv>(ap5((G!dxj;FP@YhpYRsjWT07PuAZFf#EtJgcV6`9v@b^j>2
z?Sd#L+CbrAvNQD5>vc$D0p<AIHBy21naA!E8O^WzpmH-^9-kzY;5mPhmN)D%{+;yS
z^m-Qhnop|ZmtJEdqU9S=%;s&BP<WuaC<Gxhys~T1CdGivCvo!33(ZlOh-xn<@YJ{$
z{W3eWXR{Y%QGx#NB_^+0X?q<dd8W$#QRm082~*4{^E8_imtpZDmBl7oCL#m%SxbQu
zJoP`+{XaC;4x+K1zorv}Z9J1~#NLEuG~a3GewS#5N8l>DC?W%t8y4R;d5b^^mClBx
zoW4&lj)cs`>lS5CL=6OJ$P#Mouu%$qB;3iIya<U_al()G_@zfCWWOu%|5qi)jn9}^
zOn7^#Sk+XPt);7#*kbGiIh?WW5JBXZ{U<rBn_}o9V;PLapBACr6n`(i9AN?N9~#S=
zE;OJui4%1t1C=b-A2f}i&=Esa`|sC>C6dJDAX<l}0a;deeyzaUd%&P-)z`VkNh5YW
zk_4Ww2Z;520C)dX<C=e}vCSDo6*VRHQUpU7qZj|Vt}^zCfOyl9#oZK<5>ZQAiIqKH
z-$;;<w3@)T><yY}@a&PB|5MBUhefd7E>TkxOy|l2Q@M$)A%^N9r2t#T92a6EhZE&G
z8Yf$k>5{w*nNu#FEryVZ=cJ!&;K9RXq;{{|WyFRC^ect>?<MKz4b?VgIauj>*Yiuf
z0Y<C}<UAXtu%FnxOfys&Gp})%P{rB}eoZ0tr2l)F(~2=52L9GEb$Ba2no30hN))gK
z{wt*b6I2f|CFmOFUkjns|I>xgrDg577L!VN3=6QKtAUyw`AQx>y+uwwp8$<5E6oTN
zt)MViTZO|Cf?D|dz-d+$f^z?V`mPfV|9md#6FqPq?4M&E?7@-Oml7705RrEyt-;ST
zd=iuF-KCyO1AAsa7uHesbW*Z=0a3@<MeF^##bCQ2kFvm?FV7lI3(A+Stcz^}7oY*F
z1~b-*3}W0iK1K0Wa~C(m;&b^g&PhyFMtp)Y?Dk+-YbVm299YBrTL|`iMFkP?gLYip
zHrw<J1k#jyec?pX*B~0q4)`2%al$l|6}$c!r)lU7r{@pZ!_GjpG3R1<LjD804yLMz
z@}ve_bz3Oc)6lMDUu`F?-Hz{;W;C;821X+6n4Rr)quX@skz0C$?@HRQGLo=|j3UkM
z1(1i`y(#LKG+7&HvIrLewsRAtZ>UT_j5Cnk4qUO;atTC&ewf(CoAc>THv4+nnOJHS
zz%Vv$O%IWOht~J6`t8f-8#`ISxa%#*D{W?HpX*5#L<A=>Rip+O!h`9Db8r)2aV6<o
zAgA~+6sl9Q%Uze)74B>i;U#vLVZnXv3fvA)*w}6=9G5v&O_DsO_}CAIl^ZlmimMAK
z`VorT8xoooxspu)qA%@HCQeLzN&!xooDkQT(nw`TJ%Kw@WsLXs+xl#oZWAC946?*{
z>Z~UXjCH2j2V|$4ACQ6X6V7+WB+F%KJ?8r6D|8|>VJfX;R6}GXp-2W8Y>7^kMTo0A
zX&h4Kw^{2|+^zQecy3->_#WJ-u*FWOHpP-R6##~`@#zHmT=!G8G+%52olR|+3+RWN
zOh5OHAY+^In8uefq_6sj#!|~FjJCC8m1bGx4Yy5PJ3X&Frvhv7`cyaw#%6Opq(3I6
z$4}hT)&q2qEl#S(20&HQA8K1mS?xYu5i<1|lm-E!>r~R<i9yof*vXly0&!(Tx|Pjp
z?A|rE357AK$=-s)SURvn>@@_3`%O)68vM-VIxK=&1&_%^dwon5QmGnQ691=WOI>ac
z%zX?-GTgcexq=yvNLt27HCO^}RN0_ZYg<FyCOWgBg)-bSOZ+uVA&+apOXA*5pUmw>
zRv{p}nZRi3bu8hi7BZbpQrNqdAj3D6%QUO$gV*L9_hP(FVzSCmcG^xWseNl49GIh0
zps1OQ@TkK2deZ7_k6V3EOLj<zW41HZ^-P*op?y(&Uaa6iGGiiI7h}p)gn<JAGEzEJ
z&j*+Q2dDRKA@e0J<BW#UgQP`zmXQltUO-NtD30wY-YpATLoehZ5P+w@ZVQ{0Y7JgP
z`FPKESbC(e3o&J6T^?Fh0XdsjzM`3#*V+(*fu0c;){CJ37{!_mGQ<=$4%0uEWZz!W
zZ)}WU_0zq2h>TmVrES`KrUkChgb1%MY2doUuLdfaCz9z7!MFq@!%4`*+yJa2(l?NR
znC6D5mQ;G)LO0?&FJ`INe#WVNY)krtxw53&k6#ppv+HJtF{(m&8WO@k2UtHyd#pJG
zdgDCFD7E6CpWhQ#cYy7yIds3cJ(;<7U+_1L*36aSt1}7#z3BZ5xK;$7d^){&`L7q6
zoj&zOjz=nf3`th*3wjV63Q3eZOJ(GvTj0mwkS#sD_Za-Lm;k{=y*{#RWgFMC?=6EQ
z36jp%a<ESwuZMsx5G{IKd{TF>&;Gi%&M9uU+lJ;j#bLXexNetZ50WN`43pO7JY^H1
z<}42!VneFbm&Q7?g~9DPC;INucPq5qceG{zk|BA?L(|NiU4I80UD*OTD8A)3{am@%
zlH_!~9s(-U5q7%bZ4=wnFI}4na;BGX#tK9G^ya8QO0r~?x2l%Qy&jWf!o#_hPf6cl
znoIad@UXei{=2EmX^tlE*QPnemSrIP+_ub_D((V@oQAELeY_jPt3p92rx>TY<yKQ_
zyJ_Dmd*#_WqNi5T8&#p1&BxQO=_q}#X*%+rwWg<3>}a!r*9>&>8c270O@ho}+*@lk
z+Jrr8B^61IXI}hdoIDNNKaxg?Gm^D#vRc-(;aFSjEl-u5CXKLOgPN!ctah@ipQPY6
z3#~EnWmotS5uqhRq~M_7#KZ*Ny{@p=a!3!~2dwkS97Q>_(pg!)88Ua(qK;|HT5`Om
zjh(ql%z5pe(y=Yyg0C*8b~H)^#cYiZ&!gZ3pq`0PQiBOcM3tMKOCOgn;S=<c$yPC#
zHq7;?+8Mu2XHDa4tgV;I<sRhqyU@;Qbm(5cW`9QzeATR)>5}P)li=+ZUZF^$o{VS$
zi?)b>fl#9#l%1-alF>?!PD{p0juFwRzNR3h%YnPxR8^Kdwv<WRR_5Y`HDIz#{DJvQ
z(`dL!sQ{%eAdxdO!)d;*l1Uv?Y&X3Jo~;xE`D!5MW>~2G!eLb=+aZ0~m=k|YXrv+P
z9f?pMz9nii63~j5|NM~nd`p4rLqUsrOc630CNAw10z&{V2InViYeoWhsEeYWu|;7O
zyah&b6cV1tp-7;s{lc*scW>I{SeEYIsUf4M&{yi3Hj%r-kk8>oU}a+(py^TKE0?EQ
zb$EBqsw51~UA9t1^|L^aU?Y+F+i{NIc4s{0WhygHIez!m5bw?Vbw1I$QwCBXC0W~k
zyPvQ85>qR>8~-kis8Y7&1$JDD!Izav2{Lj@vf>VK-@&3e-0u&tFDPO%AJOw;h<Boo
zBX*v+rqf<teA+imRF9SjCr{twgAYu9BND^7d_ugVy<evn4WQh*#yplkYgh4ww}QX)
zLi|4d)?wTASXFH{ZIgQLWpU=kJ0E(}1@l<>3^E+3-=Fbpe$<_tL#A{AQ~|{AQ$N8+
zQLZOBAr(-bBq82OK91yhhX1Jd`*V*5C5#3!5t?ImZcg~@abwAp^C-!^8P8fyTu<;E
zXyyJG2FLJd8h#QYlMa()z6fya8bh+x<PO_6$8x7oJ~?@m?7ocMjq8eSARUl(zSyxB
z;$81qyOFOO48niaJVXln*H5No;b0>P(H9M{tJ=YoI)%G%i1*h0I=g7VA5w&YiHfxN
zxKKw2NO^EWzE1`~!srL|WlQP7;@fo`v|mza76HXfRtGqpv>@JfpS1&gp~Zgv+~E1m
z0g;K5__$8&`_=iDTb$T`)z2ppJeLr0@^K{26Z<3Apk{<b|J|7_4qwpQfolJDhJOg`
zJ49fHP=8D(sIagh*$VciD}h^F4K&Fr#I$cnXDx>}Cz!<Wl<4U3aq7kp37weymYm`*
z$^DJn$m7AOBq718kO0Hml9nCaPLo3QN8u1RxZmQ_iT;}%_BXM@TXyehOhx#t8Bx2w
zKbTTuaGBbrUcFzvI+#KveFtHJFDPm<A43p=OX&375kF7dFy5ykjFRHP##3xn6ziiw
zD>4ncCPZH6S^Wod`I~`r*O(_^V9)mFzX=Lq1$vLz1@mEY95_6g|D+=TfN0Ooe%ryp
zl+z#fLTxR&V4ehnJy)Nhd#kUle{=d+rQ7h*r{eImr2HHIWma5r8994wJz(f>BB~1P
zt=^?xL}<EdxrGfggp38<VaN6GO?x@{Fyq#;k^l?8*h*Z-@&j*Z%TdXg&;LJ0uXxW+
zd|Z2nU8a$*<&U5Mguo%iPuK|1keNec!~R8Kx#c2einBk;LhokLcg=$%CN3bLak=GV
z>j<>~*_DGh4Ac7|DgEpo$YIastYHBZIWTlX*}C<pShnJ(U2C{4taa41p8K{}iiHI?
zg}Uy12MCFusJ`z@^oZpT4ctIJX(1E{icGScZV)GvW|@#vBSiSHypDkbh=^)(i$%6p
zuJgI$InCmsRyQ@Zv|Iuo?C*XY7bSY^R5)X{Tn>+ZNf-`$%TBjXYj!@_j%}v_y9IwH
zRNEv!Nc_|`zL=}vGCxiA9;6pP@jg$+t^ZXl%57u-W{W27m=jj^r>!bmXF(13m6Ygu
z3n~G-)>yrdWKH^R6#_--^}@*;#><VQyC}qY1m}D0WET@op{nhi_6yGnmJBDAcns%7
zv|G6zsx)<2+b)H!S@r2?=>(ceXv8L};N_!|nzD8M3VbOU1@-pkYk}j-mD(b|Slz7p
zg@&H2lgrfS3+NC>dZCRE!nKhz3ZlfGLYx{<7ii;ZIGQZtX87sM{fG!)`HK(MZgK^|
z!{wRq_jfl9JZI;CI!M%AG5GNIPP7cS*Z2(YSU1!$37D92REqj_eG$H8o!Fy2>k}oZ
zY>&Jl?=~$c?fn|D`MPwc12b7<8?injL%SC?f1s>+5EVQrcMUs2AJDtDf`7)3EDaR9
z)wV|5P=x$*@)PE(qbI?OuWwYi`@HaxED64VqFEX^W}4>QJATaB$M=D_@Kkix<nhO7
zrQI+oqYE#RV2JIEpcrEoqh^}U<U7DcEaM(O#~W`HC%f$o0m-`VAj>AkdZ6e;Q{@G0
zSUr($m@je4c%a)l3MhU}OR%pWX~n@%snOz1l9qGz=BfZ7b@UB$zR*|0A)0{r9*6ul
z1O;bXcc;VVS{Z;Z*TTZu=oRa>hVt3>Qqmm7TVX2vFN#7+#Hp;Pn-3n&Zp-)<OOr(2
zovD4=%6qtY_sWq#4zp5ZjzeK-_&^I@5*TR#9`FiG97B&@$E?K)&>}q|>jabpcW-yL
zP-jVJx+bU%;4d6w{N&OPvU#pHGu}Flz~l+NUb|pUL-AyULDBGv*iA9rI_}yq-_@;+
z7-7gV|1`A%ZCBm#ElIE*Uz#nvlp_|6gb&rXwt&!*ZaH&{UrWtZpZ9chNz%8SHkPA*
z)_wP5;0d%J-rVhbm%$jTW!j}Ltuxp*feEHE70`EF!Pu($!)I~w^&9OmBFZ9)=iyCj
z{5M<Q4GKpax$oRgBeKfWy#$^tzUmeMvv-#Vzfg^7O|;c1_qN|CSt|*-_SGAL=}W>q
z8RhbSN_`Ttt~GhZ7}i*sp(GI)PTg%}h(@jA)Akt?PpiWZV9Yh!cj=w54wld8WTne$
z+Nl#hoj*q%_rSYZy1y7G@K+pBFw*%svd+8GO0^+cU+sb6gkf9dJ20LDZN1#*5~GxX
zEzmtAs3gr=m_1C4SySeb40dOX?pt8S%}GM5?>W}5!q?SXSZb{e>60?U9-n;=>uf@@
zCDLYedxe=EN5L#Y-<fd}(+0K8I%}1fx_yo)wKhm`7@py34MQiF&y)<Sfx`n`Ba??+
zM%1KYU8>-)=f%F|ZYA?L%5%YGzq@Y_u<RG`;ka;3Dm!h%ducS|WZRl~bXjUEGh3~<
zjOqI^azXUf?J<W+d8`tFfneJg3)`MpT1<`%K(77nodj#Esm;fC87JEf?mYZakX%O0
zWgJEdlf7eKA%|qCaS42q&4c8|c+q(0LPg^`%OB~A$ho<eEtW*%RL}&tiZv7%*rZ}d
zECVVfpbXLIImFy+YAp4)Yq@-{IO2>?RmdeZM6+j1S?WyfNSgZv9?m5m(Ge_PYvIpl
z&lsca>Qs<OXE+XjEM*dx!!QLa4GnsI8=Y4O4OKReBq>B{Qc#1nsW-fPjG}Y5aWlhI
z@|fP=YIaaGlW7{W$od>Q@-29Rc~x6_Fs4=sn{a3V8y^OkGwGfBN<xpY)v2=ZnvHek
zkPt@!=r}7c>A(WdB$r)zh#Zf4<;IA~H?gh+ynf2_kEoi()7Mks;?7mh;kxWEYHL`^
zP;?)^bzRFlbU$zfJY_7<UVpl91Sf{~NgKR?nDevTx))`Fg0|c|5;=zRDTwuOz@k5y
z)yEBA12V!p-jsNea|O?IN`|+tC@p*NglB4R8I=}npJHaPz5rX{++y*DIg(P*e%e{Q
zKBE?sOR;nksifJV+1+&<kyEG3go@}xq=h2QfnBbuF<<+*AMJKTaDDE{&F>&rufjQQ
zrd}U%x;|P@#ypXRC!3kj1hg6EEorm!?k%jFnZ7SXYLCk>Pj8_H_f6!<v)x(6DP^G0
zSY|2_mr@{Hmtt$V@wjcTSVd&inGKJ(Cd{&B%fyx}j<ckP&();mIXLh45VOWT_$CZz
z_%FEH?6xuOH+^B<)@oW>TIA^^I%?CVkGA2So6KN?qy(C`yqk4bhvrMhfW@}Xry?^{
zp<|hyX&sKpveR>&y%)(eVDnYBbBqXYO|Ieb@LI_B`E&t^TXm8jwes{7s<S=w!_cvy
z7zhArMCX4wX)r6KjTZ~3$YmJss-Y;yfiq~Lblbv;F;up*-)1h8i?$+7NngHP%KXT)
z=u{7Av^=FoZHPG8m_$@K9PpVM{qRB$8_%k^NVm$1_7@}@a1iiD-$k>bZ$8bDLq#}E
zq_1CV^9=tL6q6K!Gy+qxL~Nw}ZRmw%b+WM=ST3U8!Zd+v7&LWbTvQ|I0sg2wOO$03
z4JN54N4mbAd<@31(1-Pv#rAw=`}v5{d98fsWucGHAoY!!*w*2#AeW5`7<&RkzOI|!
zih_}DX}g?P6|xZmgLCwB=U&w&k*2{d1<75rw8k;~PI12X(AW`MpE=PI639Kgo1W`0
zvleF<OM(nB!z6#1Qcv&N|KIo&zf9D>`4k<gX`(GhiN${`P&ep*8K#FY|L{&W`VOYF
zoygEB$-T<X+8}>a|9BKo5W`5eg0Jc7=@uqT``1rQh=~NRfk0UX@eRnqz8nBw*V(`O
z28U2&2oRrV9Xxn2<y;0WfOtoDzy9{u&Z5rN?(#L}Lt!pB7bIG1IiEs-4hyl79UUP4
zM&;i;3aEb=rQr}$zc_M}?7(VBaYMn$!M_pWhMeL2F>|Z`<{&`7`InmkuO1d}Hzd0e
z=>xD8enyr;D~pda5C!-Dn}z(id~LByy_E$9Ld@ke5|J?wCA(kPt3~eB=Y#J(e=O9y
zsXx9&{vW}>e?WI_-KD<h`vKws_`+lVF=G+X+92JLfd>x$PFW4ToA<=sPW2t&A7cPP
zrBwan*R}r5i};&tnBoP{T9ACEAk2yrt!-BSL(ue3u|b^5segEE-#~Gb`Cpa<J`Bl;
z^zDRiDCp<%WZffE7$d~T!Tjca4Z|Oo@BP0K30Hb!vip4Q1K4IiBh#Ykk%ag_x)gqf
zhOK}Y>F7;YUbla9+nDRn-D-Df$j;a?n?F)HyBspa3sy^r0NsAMWkG-3JGehg_su6$
z15uKHd|CXM{fwRxoC>kKJ4Ed|{CpAqXI_yAUeLuE-Wa_VEk4Cv5AaB95Bc`&=rExX
zm!=CcNB%>!(DiQ~$bb111V-=CcatAz-|T+fg0&U>Zne5XbWobcar!%vjk3hTw5%uX
zmA_s~>Z!5y^xu)X+>AUCu4MP%;q2(YAE#k_&lx1gSFIsORHJqH2m7ZyAZ{#h?~j;{
zBZm=Q$4ZD^KULd!-9dQ%NBL)D$}cq9lh#%#PnTtQqlmu(fD=0tQDK!lhvIM}P6A4q
zDsU^e9bMWpm!0UbRF-r$vV&LtxVd;>K$NSNB_H=!5o?X?yel3QK@9ot*E?-BFR-1)
zX4muII%;ArJ9*cZV)G*I^Qy4iLZKfCLrF0OjXPCf#W<7<eYaLu%J$07CS?cw9NKD0
z-(uz6=TA8KM>HG3Ao=?l03oR*0)R(Pw(G06AI*QVwZ3(MTu_j<cFeNTSw?#r(OQSK
zT(}&Xi2ixtvVE9h*s`dGSI*XEsYp?XI#ElIk4y-;UXEyV01xc!Dm|eZ?nK-V%Tm0e
z(f_zX<T?36-sih&nb+HGTLL$Vw1>Sxpz$MuU$dVs2sD57?QIs|RQ6rpcg2M$K}ifr
zlwPzEOC{JzHrO6mghvFyn!V;f!^S><G{nPzMjT3tNCSN{8hhS(iT2|y>!9Q3owxsZ
z|7)@a#;N7Mps-a}lN^4&G++343C+J{)rU-)=oc{1RM3z9?Mkp9G36x>dY3~!xp4FO
z5hTYIx#<@_qFBh=+q{kXJ)V%urbpfdSXZx>uWv?%(IndOI;-bO*}`g&_YJ25Ci3u0
zOaS4>rvU^=1^loQm+-)aOUAiUyZaCI6?>3$9heWAqn69n8xbu_8h*nc>)$%}RK*mU
zL^&+gr8To9N1nr*$);e#IwaV~4l#nZYz2K_jfEmF8D_(?!IspPe?bWyL&O7^&xlTt
zYqA1daB-tv(I~+G6q9v%*F<iPyZ;wdcS~c$rQDf2`@UiusXqqswj93M6Ex|t=ptOu
zsy774J@}#GT<7egpw?T=wWSlotP5S25EBC=GrVKThExR}dAJQ0M%<YY&f$DBSO6?R
zdAek92$XN+*rQ0wDBt#cXE^(479H-!eC;af-i6dTJgdgG%cp^HO@(dprmETap<;^#
zSnT>sB*ggiE@3@Xxyvlh{mHN-AB~-r1P`S==F!=qLCKHuFcEL#F*j*=rx3F7b|5Ia
z!NAYOkF?*aYhosEXxCypBOurtL#F}W(BxeoFv!+HKru+Z+KcxOG#;%TT#y>+N4`Hj
z>x3jk17uy|KxL3dP(4L1Hp*J48|Sa`($7!bBOPO?3g-EjFuRzTn26=!J^Q5myIbZS
zesvQ4G6*!Qf?Vn^)~s{XZ*O|<gj(KiwRGkA^*)YYK&?{`2`Z^*d9j<c$|ohxb-=Tp
zciosiXF=HSXtwpG_S)tXa54ewAnW*ah<~qw^XFOp*cUf8{;UorulBiO-KhlY7K8pa
zz*C}irDR|qTp?^3I$kRxaH|=Wp(E3~Hj7Lyj}6zWxf6FjNAUPaNk8N)r#-zV+vDjP
zZ@d$UoG?sBT0ce?i*21f+Yp8n77G(f9v;V9$RyrAS=L$#p=YZ)X_JHR-RR&j8A!+<
z+t6N``|^6Sf~xOAGo?BsKP0FtN<mp9ROMWPBk2I){q#9BSK%o#s^t4vG&u^uqYn=x
z<rTtA6SGGtCin8l?a|}U#s_ndLcPM-?x7iW+sWGf+?Np4-Y~UK877<9{)9A7BZ|ci
zIr*@q9u^pLWzcA77*mp}L`P+tyoQ8T9ks$6#HnT54vK79fZ5{nn!dv7?~4k~3@Q~7
zx`|{G@}3Kcbcx1=YNO`Ux;?aS&9^wsJu*UI!%=X}y1rzL=GrN55WbCQWXx*owcLpC
z4O!ozUfm*i!_PmnC3?5RkYMuElT+Y%zvppBb8FLMuBKYX4@4GR;1MQP7}=jI5$F&(
zv-wFi-;v>VqFcJQG)cEtr|6sYEwk3rHw@e)GDwn~&4^C1V>9cHtJbPesA^Ra=}w$o
z&^<t;3F`W?+iAr&(7zGUNR=`Rt|$F2fH$arH_7F)m({M-{kf@<u&8u`#sE`lP7X{@
zIb>-<%b_F=4ZQ`uSv9!zxZC-8$I>6ykU%-Xn7!}9K>uP)!pxEoF8!QxsN-8&oiYaJ
z>ok?XV5|IH5|~$uM#+k`wcUe_tEFiH(kn@2d5Mi>S__^v{n|E%17CEpbxW9AnGK_8
z=R4Lv)R}Cu>4Q85wN6nN0&zfld&ceZ2(&mO6}%{xBrv8fBWhDp?Ud!(hUC4^f@#W-
zq>bs|uz?$A@+kfsW~v{`Bu#5|cA$#c@Qp!+HH^du;aBJTX@Mb{h)25oNBFkIk)`jf
zL*66=gva(!nOVcu8DUQG+ipC%Dhx-l%@<FMiSlxH5;)_7`M^c`X7-C#>OKBCDf@C#
z0FBLi=>Q;I@oO*V6z3_@9XI`E-P(6cZn%h<N9l^Iu<&j#wU+j^6>&*PB_saC$9t1&
zGI}$OIp1EZK9BCVh+j&g_o%XUcpA4rK($~lwElu(`n3?p#7zQ!B)h%H-lr*F#&x6c
zhEm`g3$ynT`FHuuy;L{?yhXWg*8YOBSiGXin^ffC$LmsFAtf-_xg1vROGFDnccg<~
zFhKZvv>Y6qwo$|V_$tKz&ciUx>H@HG|I=>U>My8*rd6;%(67(oN6D<d@x3JG=BeKc
z`;DM~z+%~TNvDknIq@KQY|VFuXN}{x4XutzKN&teF-AyzJl|=nF1jH5VLXz0e~|z3
z;J*y{I8AUuQp48c;TD7gl53;ir|+KVNw{m#CmB*$TDnY9dsCHbMY`oXiJ_M#7s3cS
zg`s-hcA|rQAHbOzki~qJ`P(gj9ApFK$mZBQE)84AL*0^(Vg38X!K$;P^cvs(@jF(1
z7x5lvj(RRE6?Jj<Rhq38MmlE`!So|KS12EN%!-0dJ)+-&&uODxSj2wJiktz7mWJwE
zrCR#Rjrm%E0?iWAc@a!?QVbdKq6<olU|pLjkt@qF9eGcYL#X;W!eJpd!Kk|^jq*l$
zwQ_cEKRS{hC@W~WeZ9W3m&YIYzI;R2BeR-ze_X>VM<`1Z6O)>e=$6RLQpeOxWs|Lg
zR?2CU-bL1h>NI^Rf~r`z{8=|<>du)w;xUKW^vsZ?R%qW>u2{qty9)~T(-uE6&Elkr
zP%C8Vc{rJU(i3pZ4NJOdgy6N2zQty#|6!KFMWtNdc$%}O+EO-W-da^iS(5#wHKI@x
zUuTcf><)vH2lE2nW&VYytPJ3D$4K@rW>NMkw5e*D`}VWP@%fD?t%#Wb8{Tr`g}lRB
z*5C>#pj~Dm$CYKbDla3kFu2R!`NQbpd(tEySVe!dqcyi>KnWmHi;<)QlDV?~d0`oU
ztwa&9%#&>cN{B+JlJG=%cg2n<MDn&%h2K+ZW0Umx=t)n=b!zWv{h}UXQ+4~=+;q{R
z2YRZ=H}#&*#y-5j)a>bdu|qWZt<c83^X!$VYh0DWUaz(Y$S#x!bWWvgmSsmnb7`^0
zjpIub9G8Y?sxolkSRWNgFJJBu0CkEV+1T|H8>F|~DcsMNzHFGpVOZNT+1E*O?DU*q
z1;o~OXmhlsvZ+t9qEA3Us4m?{&3h36zXYElTz{JMd1U@(G&!vGqxc7T%;wKsTi<A3
z#yd`8#f9&QHCd5l)AbJ@Vw<aNK;IJau-Lq2Vo6zOv0>y&F(F_TaHLTDCq#L{x2H{x
zomvSb4UcPX{^f!zX#=^rA&^r`s-rv3rj7=d$78DHT!)t9%3ec{m-#LG;gHcO=~IhV
z4Kn9zWE8ESC=98d+Q&%ftIyFtai^k6mlp=;s7$ly&JsXr2@7vYuHHu#h4ma}L|YMn
z4&IILnmTth8^m!Sy%oT1!?8QmXQiPzx1pOo|3t?q;<grS*20(h1h3|fm?7J02BmG+
ziD1(|?XXgh?J-nrHX15CLF7NjYFCxDBSXgu=E;<{%mQ^#i#X{L=@D%<n?9IF`yyG<
zEbomotb0u-nrAIC*4qMK-57m>*A|d7Ht#KAdm$~umr|+bsz+VAVEI{KXK24h#K8er
z9_M!ftRL~;4I4-6NGk}h6$%uaH-9PSj*0*c=U>#9BeAij2#ev0DQ(p`GsK2&k`Xoi
zd|hX0%HtgCkYhi2hpk1$mO~JplgH$Itl!yGxQ`X+&)4QJ_9h&?n9ZlvA0GJGII-%x
z{PSBPe-|Fn0ny~=6of@xS7aF_pf~82bB(PdLGT_<blz^Q*bfPGMqemm^+2t0YIgLl
z;zCJ(Iw{hO#`3Vx1}|I`N9_4Dvf?bl9s5<QgY)7hAX1rpuHj<bUggn1g%p@1TfNwO
z^>k;^Xu5Q=+Vgqtg1K;&Ng+zj0!q%c(3zdRlz*sis2qVTNgRqFUkpv9HT-&#S=u-V
z*hWvWrxaD^4lU_LY#a^M1^cpcfXX#2JG5;T+*qwX)L!I(p4j;K=zqNEm$94ApRsvn
zmt4or0G8aX@)IS0ZrmvKVnCzw5Ijk)qyP8P=SK!_c-4aBYr4pAhbLm=F!9B>=^D$7
z!O3psZwLlu37!h@n1@xd6th38fEC%INQRexLB$A1^{6sDF`I=$0Ovm&9$W3-KKE{>
zY@k2R4z<(5#3eTI2fur6Trc%=eD}qLAm<Rei!)bxTB@7Ni`h6Fu8{oJZka*jEnl(o
zrNTwni_NYPR-o*#K8bkQ54{UWdD#LvT=k>>K(6!_1J1{kTF7^2zWR>q#@qJ;gdq4A
z0ZfD>gg~;o^VY6Y>191n=b4V)JN*ow$y}8zdf0AS?<dlTX;$pYJqq{a_z^jDgyE$>
zWY}G53|^37yD$EX0UqUL%?LcwdJz2&4`xr$gS0=C{dymGtT4#?gc<rDLYVvXO|*8H
zLudw(phbf!AW$km1h=No1cOZIpQG61U>;X^i|mE3?kTFUc<=sw<u?ytvD#2`AS~F;
zvKCIM9fTyi&}y55jO#AIRQp4Hsm4%yk^=&ym&doqi{8IGxy_~bPM8(oER}{W&Sw%o
zta63`kx&Tp{`2uALCTWNKj>an=>53(!|aqL`=jJ*b{eqwZdI%(`hPw6w-MC*Z3K}K
zrfKp>QZ63istUe!XC?NR=m=xrPxp-3zFD}<Z_;IHjeF>Lr`uCn27CX4(r&w*q=oKR
zL9jDO@^^`85LH>-fjCp2*Pg8s1GdXx8>{6zTVz(q=>g6VJ()lM!8I)Athe*KSn}U$
z$3~u^jmxs)xP1wVSv;J0IxN&o#lW^LECI+I1;z{r!7=&<AkQ}!?f%&?So$9ieuH0+
z1imEZ?v|3Il2ACvZrFd%k~FxT*%$eZHfmG;f00Bj#{BlCPB6_{5dMy>)ovGeb0LB)
zJ<_1lnY@y{=`GhcLm*TJn{o5Id3-Q=)_PUsFtT3FIraB3$)2}kX~B_@eKIL{Hd)AX
zVimQPAII56qi^*vBMxJ!mc)k8^LGCib#ECJN7uCpH<Dn1;1b;3-6goYOR&b>g9dkp
z;1Jvi4&6wCyITW|y9Njlf@J!>-{+q9nfIGnGxO(LYyR}A)u(pVUi(zlK4;5y-D2Tj
z^hmz7V*c41`4add|8xA>EKbb2z~HBd(*g{pX{i^}L2|0(ECK1=3@^ErE$p|*UTg%0
zuB7<hrJd^~aIf$&wljErb$RNFJ=f)7`&NYhaPvi}*TM{gOpHwZrPY1UA>$|I_iu2D
zEN$#QRsMSI#%<k~baPzmAM?Dn<ld@g%iFlVo%hw<JAR|S>b`PXR)kSf2>>JnTw*ny
z$o%$&#(VxQPyHQ)7nqSRs!-OUHk)4%VBUzS<*hW*ky~wHMxMHx##yhC?pQ0VXJx3#
z6kwIhLZf||=(+5gsz~%bq+EcYM7hY6ys*j|)|nx*5HIKlL)w|fy%|5Tv$va&Et_|?
z8&2&QTs+*5T?$BU8J5}6y|gM_G9s$>>(ouff3KYx!7uxvB`-a~HFgMBouZc^N_G#B
zFMz8`LW_0GT_c~A5sA~@dIF8h&-sYO?YTb`q&9grEi3$^4xf3XqZ=BNk`^mR+9%|^
z-5)tV$c%g?vytA|51FNxg<ey6DOU1xQ<HwhR7Ju7)Y(qenmIX$io7`I8UIQ=@VqS7
zzt;5edrGf$vi$)7L;;YHUIkVhZgIRG`Jod5oE!*x<v~vPSiDAx@S<tj5qw(<d$x~0
zvchPQ7yba|kAYdMh8zCYmLrGQy4>S!Vw2!WyJ0hjCJ}dTCS{rIB5{SQ!A|M3?<4K!
z?{L+cD=;#~W7>{-j)z#Y+gj@E6zV6u=jxwmmsBzbEqP=X>c^HC4R3Ta_8lD*-Y76q
zJBAss(3Ryo$tw&oV;l>+3Zkv6sL=r<KMUaTS<4>^BH{?eQVCIh30EfPv(#P5Ih^Df
zCS7qn{I#jO*20$S2E#iP2q;+4&4Az@)bUXvR}qUm=pH1=oWt7e%FnUdn(?`{s$_>c
zTsJ4IZ~XQ(eAl%E;W|4|i-=Q&?j4q^2xQZhm*~5)`<O*K=917q8*D1e&<S_M15=DS
z>ReaG>-9QfHH>0TJKw0$eY4bYNc*%lq_)|t(E1s76(ync={MUx-!&sc7SpEAn-3W2
z(gF^d8K9*(F>RF~HJX6qS|~}DyIS^|?m1cwH=lCQ-DqJpkp*PG^m;fywRV#b%BH`2
zthM8@rh4(1dC)HUipIWp2!rNs0?Z4=Dndy$nNT)L$K%Q&{2fB##Sr2V*XjuSzH$T0
z8hPuvd1H>UW4y}~{s4}qlv_cTtbDQtF*)v&jhs{A9Nw1gP7X?RrWgy+;T^1biK|!5
ztOG*05(6E1G<`a+S(O_1W^7jggRmmI93+69>4tb1=<&SWQKcpO4}gu+jHSmX>go<+
z{W~pU396rTW6qOA{oyTbR7t6o^3v9z2Zy&tw$vKCS<WVRx^}pWONVn6iJ*a=1D1{q
zB$Zj6iL$upOQI$2m#sLja?ZFKFQE7JviBL^ZnNcy%}flekg6T|p;^GXkmY(V%c**e
z;~uU2p)0*agqx*uOmU>sSi2qf740`R8C|Ah$~Mp_Q!+m~9tUNE6yr&KYq!8s88weN
z@tyg>GuZyH$~(eAke=x5{K3f2W;$|AWPfZ<N%;ex`)Sc&({=Wps-*60G!3K)Xo!~+
zN$q`<UR9bcy^0$F0H6a96rbtgoOq872Mowe8isva=Y7x!RH#5{dl2-m=_x~L$cEVb
zb&Juo7lMLwB5j47ISAj3VIP*V+<xT^K5&$((Gc70gCW2ANsL6F7pi;c2cdu(r^PlP
zOI$G(XFstek1i`HBWKX=U_UVKmd82HM)Q>JI8RJ(6y&Z#_vme;P^$WS($$o;_OrE7
zgN2i=%?f2W_%UMCxvyCfK!wvmYDr+*rBQr1+euWzWYNw!A_~YFqe1v;jylwU7C6g4
z^-&AGwbTr^L6q}!bt8(n!fG&cDj!D6J+3@o!COJgZO+s0$kVje()S4c-X$#DKt8Bs
zMAY_iFEhG>-eQ*|KPQ<31y5H6+97WYHwBTxDS?B-oDq$E1m!>kvO!KD3&-d_IzJo7
zYjMGnFB?DnEKV&f9?LD9qx)IG{5M*!PubRdzK{HH-bO%di;9c<SZl$YGp*flzE4}X
z0Ljqo=HPbT$1grR)Pd+EYp|t<WHEkq{iPuyZ8u?300au_tI&J%)bR!6T+cPknYf^A
zO0PlpqjLgA>ss4#U1Or_SD~E7%(C4;y{rrtFMmV632z3<Z71a8@gT)bRhqe|bmD81
zxbPU7bz!M3EK$r@DRY35wF(a=Zy*^eFL>M;<U9nVcVbKx<a62&Xu#@?x~PY7+4oUd
zq}O>&s0s%;aa=1|QE?4%t6_YjYTb9B^`Q)r!^nmUns#igcZ&grM`3)nEvFd>24dSa
zC|jiUljb~p{_(N<=Nx3bkZ(%Ary!;w<@RUSdpXGNzELiCw2kkth&G6E|NEFD2RGxX
zU{x5rlAt2K_oj~^NnPZ6cLIG|PGb|pP!EA4h8DlhN~~HQ!Y)5vcx>qRukWS@tU*D>
zG8VzXE@QR2oNFp6igS(c+0kI``4}28t3TRAH>N_@*wicJq1mxm<5!i)gubyYwGy0w
zlM@A!pNzfhq*(OfD74R^x_}io+Yl9&ASP}T6xDoSmw+{v)c0zA%6GBLd#K_ZH<ZQt
z11R?^XB2jL8?}>pB!@yow?6AbXE?>3WG}emK+MSMzd<8DNO!zNo@Tef{&G^rya-$0
zb=SPu!dUMQcEzt+#K(P}=OAcJ*MkHHKX*psb-68kg##7do6V=%j&pRFQrZ^DI?B&&
zrLlHFX4v;;G}IC0t0YvlqEY1bkL~OseM`VIV0v^JH9rq9>p8d3#>h-gFTPZ!Lly!f
zq@zbJo6n^8oWc^;Wxb?3i_&l02Pc~BaI~9q@65e8G%tSQZmM9mF=9(|pP$@HS#U1t
z7gg~nQkQ>|jl)4utXy`y&1dh^=yl}<oHD#N@pQk@Ds+DMEKa;IpHI{->p1={qf{$%
z&0DLW<E$?)1#NLN-F3a^>VEzYK<X-Y?>yeiO@-QL9ot@c$QV1Jc3VVIHYvq(M*x?3
zU`%>!J;ahcmE=pU?T^*j{xv)>aWNy7LiA)3tv7$e;E$U@6HcdQB^b@9KFDH%BaiQ_
zl*ZQPu~Auen^Ewni!RPjyTlsPfU}LLGGnN<L8ztK&l?4k;I(NW%nMq18<rK*%D=)P
zM3@ZhQnrcnm}Fjsz!=6a$IWsRA!~B=quX>m2S?5Cyo0<AYL%TQ)$+s5l`(Itg7sI%
z{l^P_f6|?7H(K-6G*$S{df#a3WVkImrG82^a@CC+Z0r65Ockf1c|2i3N4M1gtdIDe
z$70|G1Rky7H1msz`R%R#u)X?dDB^gsmPy5upN2d6IW<eSD`h;)hF$QCk&)TXAhY{4
z#`-)@sBAT%cx=aX)jMp<jli;--;(FFkgr{R%Bm36nIuIMNvaYvN*zHhxBvKbK;>R$
z*6YDRagY=2%^fZ~=Cl{Jw8Jal2Z9PaH#yBuD(>eju@o3nHFL)=Tb*a3`7}YlwZST4
z4fWx|rFG3sH94igZF&5e&i<3BusS{OBfsAQ<sTZOWr(~ZX)Ta&lB@02&nrY!9UKap
zosawXrbM%c1L?Y^#adjmohy8>3g0;84EgrA5<1s)`u(t5`t9UMWtLfTN(9Z*95J-f
zw0J$O3c(((RNx<UmSa0dK(q{FNE}TO)S`kS$)e%DtqTl(oqMhN@qA6Nu}_d~NYtPF
z?Qq?Ef|8A5*3ig!B=@*8PUK{oqE314kKok;SlI5$ZpiEr@2#23w?6;~_z4E`4P~DS
zqW%L2+I;R7f26G7y(33q*m~S@%LEQQ;FRHNHF5E<&TMUegO6jxzaXIOELV<X1o!do
z@3sg-iGM!oPI&e&Z$GJd3>efvK=j3?C#Hh!-h?CT-;;E^&kc;o;n-capFC}cCvkbY
zJCi5vTSqXc;%7Zb`<&(z0<8T7a;@I>eZWEw#wz*lrp0Hb5ZB7=wV04*4IH~zBQ~48
zeUb}{^WtfihLmNFyTv>8vY4sH)u^2529nN^S(WXwAsb@=lgJDcOuksuI0z{`2{uib
zSu(LWc@DzoQGz7&t5EU_#I1}&h}G+tOa_NC9boCCjkR2qi-TU_g7m#fLjn6?1R;oX
zf;jY{6WF*h$kF~bw=HXb_29U(PWgR2v8tM^bFu&RT`M82Ktvj`L!HB9cD)5NFP=;&
z>)7A}M3#zHeV-D9fH{nXkgSZ(%G}z@=y+n)2F%E9miz6s*2XN#*iZ+IOmqO(-b*{i
zxljLYfhel5#`|c34eff%zFUt$^kb2T#RoAQ<B+KKiKSp%2&Ie#yp9>qe6@no@v1hW
z5w^{32DFGmw94k{#g>H9LA{^sHVHPpOHFc49rGL%9<WThS{qV+1Y@1rK`X35+(0F^
zh3M!qyMhzPp)}kIn)B5gp<el?+)q0@#Y*=|4zZFBYuhV{;^qbpUlRI6^SVTIp6Im>
zANF5zVOz|#_EEIok@7-ju7##qqKyyGlgTwwgWHDYm3$OU-1qHy-d<SC$*N@U_Jws2
zJ2(0Xc{brP_r_a6F`vzxH+p`?D53$k5sjSCQiKhp6Mvb)Z{4>syha3}CnjagIgD1e
z^wqT85Kr!^EOPJ}ZqQQ|SXd18&6taS9EPB@1u{v;*LcRJvg&$Ia%#)WzIi27y7%LB
zurkhp$6oCGrFDY1OV_%IgL#6tyX_*Io^cghuZJprBk^0HZ97>1eNkE`_gaJM@d?`m
zw0h9^Hz7jF_Ob>5N6&!c=U`VHk}4Uu<3nzg+c1+my1I6h1_BA#ifJ@mdH{zY9PmC|
zz_#D8q3)uQO>E;m;G&bmZ@H7ecLhA9JCYKiP<(|BKVUH=<%SXk{Fqahd9;UvD6g&7
z;v}&~0#7?0VI(|H+olcD{w;$tN2gM>$`LSw<SG19E?s!^_s+dN^fQySvAJ-(Px%<S
zPoNXVso$H5@LI16wD)BAZcS<{ua-I9=44Z&$&7S<2PvVTRK8`XU{u-5Z&3Zf?k{y5
z+Tv`BmPn_F`W)%xYT(XLE0wB}J2cwmxSd5JZ;e`vnjta63&F$!l<=5or+kQ8x~lH6
zN>6J^1k2yzQR?aN{5;0Uk@~<HYw}(LFeBXZeu_m{f#~kw!`YsOX3Md{9Ev73d^x<6
z6Eda_+syIzN{1m_lCMES8rAG2rY*@<TZkhf7GLn>x8P~9sA6Jboe&WPkP^c>|Gh@w
z9}t;X>j;YEl*Q~s(XU<{gzI7iZvndJq&Tsrdh7J3LAFm9w_XxEpEK@JesAW83wKKh
zl<j+21hq${2l;#~JtI`r!Tv}oT4IUW_a>kytB>r-=6g|uN3)&x5bysvu?g~=I&X$N
z57xbKB4GCfZ)dkyah2xBQ|vnO=cs2jKJay-bOCvaQ5Cz-z`-J3Dm_{fReM)=ft_FL
zUzG}K2%}7JGr1aAR}U>I@Q2323D;|Ac5U)edFj^3+kIDcJ7zE_P@QiPf{Cuq^)rrT
zMXx^%*ZFr~uxqITJr6k7!uEW7(JE^2hf0+Pl+SW_hi}-SHkVBIPgLR;?|ui}@9+=H
z4gD%O*#>j}?4kbmLI2I@O;U2Ai+bW_aitx%htJN<_Za~}SXGaa1^*%DUlVyYJ@+f`
z#rm=KdEfL8AYAUbU(NrDc(-I%bg|#t<~Ts^zzj6Kp>z35I(VJuz89-UNQ@h5%nuCu
zJP`E1mQoexI$sL9Zb<6m6?dKAc#{2f^lRG76`K9q45x6S*Q>CrX7fih3}DgjGKf0B
zGr$Ja;XLuo9{n_aZ}2z<!+S<f`mahR{f}bK^nZ)_n!?0lSj;CETuH(1N*`(Ae5AVb
zTME}S-;zvt_qugaqIPPj$XYY1jlwa4bKKBjd1IWVTnp!{J_~QrytffQt^x*AIJt3f
zk_IyV0JuaX@5X*_@6MizcP<{@FSYacPu;$l&20Lp4IJK34Z!su3ZI5SKF(hRir&;s
z&R!LM{>cxWzar1ZC*QqExcxpr+FH2&+yBrgcl%Ine3ksx^9g1P5%IgRhhNaviuE5A
zJzkwXWL{6bU#92DN^Yq7cmDv0%tZeHyjva%aPOzu`6VBM-v>fDPsF)cBt*nKdV0F;
z?!_8<T47=y*Dq1x${+JytOSD3^KWlY)jDrQZ~vu-_{njS#zTrZ48qc9dGv1;{~3a*
zNLIA=#o}kS;>uo1mlXaF3%$7^0u>=w5u@^IK15X(9tQiPwSbi6usCpGfc-(-*V>>>
zVGS5j7u^_CsdG1NVko$I?e%lpNUvGr<7d;PiFbbhKVca!@%=>Nm-?(*a>!*^(3+K5
z!^-n`#Q}QY($4^!{eu71kkt#(b}vj_t)#QN@4Ze<?8|3C`|~%idro0l%Ij(Lo{Pem
z;PCY*f;E+c)o)OUA|qNN!;iG8rc?{nxmig#4Nxf7$1`FTL<E)h>SS=3^*+Cr>gI7g
zjd%`VxtBkHw?_~he5j}N`S6au_+NUrj&xu8G(KjVh;ul=!auWDopRd+<f+au6$)W0
z6#l0Qz0acL?_LbH+hMaRK(5z@K7YSP^&B*QVWJj%deL68t2Qpe_Xx6F_?5D*hOR1A
zNFtKaJMJPgR`#)7=&b?Xw=Un<GdzS!+j(3qL<o*mkqHdiO4<v6W|MSk6!g)^Al5NI
zpRRp7kyuL&lnu(LU%7U+=!Haq8`knl#eXY`U&#18Ee^QLbmN@*_R{>wy@bu`bCy3i
zuyf3ed`jaFz@71-CI;#vH&mGFD|-KPxbkH}6dDAzX5VG_1L(vP=eZ)TJH7p%4_aR7
zVSzS!4IZe&HiMNvCY|*`m(Qm(UbvDTQ?zc>U}G`=Cdmp7THmMq1DN~ku1qiXJN!no
zVokw_Km1`z!RGES;UUep&$Q(CR22sW^j_WMME|;SUQ*3lA1Co3cNlTd@!xs{^STk&
z9TggghyJZ~m1jq{nZ7WE^cr97UYoQkKW>}pa$5q)bF6(ncs|qIQkZiW520$lR{)TX
z3Q%XTt1lLT%`Bz^#A{jF$fz-oezf~+@rkwH%sNvUszhbMnP^adK>3{R-x3HO7avOL
zb*t(9TPK`Tzg{}m|FC0v?q8#3Ln)hX)j_s9K1Q=w4SO3nfzPnlttrhrsol@v*PQSL
zuQ-pJa2@QEHQGh@_}g;f@R5<<_R%l1H-WlmSL;R&2ebi8Q~t+AVRk5mmYi=GzVIYI
zCa6u4sKOp%-lyC?m)>$wxjT)E*}F@YKY(|#&qEali7*@CoQl)^Yg+$JovMeDzb7?F
zWt)E!_O=FU=e7WXUaSk=0ySX@(O>;_)0o?ey{W&oHFQbR0iHi(YKk3#E1KpsIlOO3
zya$r6-73ot9<4hmO2YR<nEIIFyb<wK>LUfRBv9dBq4ftb?Rb{3rZ~|7y;UJOmp1WF
zczq5~f=&J<Dr%I36x>9Wnf~BLHBXlwk<BL$ynFa=@w{K%OP*?V`!AcW!nZ<}Cxt5&
zApT8!=NEz4$4_cg0j8%y^h<?n8%RMF%7I#sxa~5JJCm@qLpfE?mex%n0cZ;LM6GQg
zz)XCrC*Y#xe44p8pwkcMXH)67!iY<|UAAjuso}mjZ>`wugUljN!HKIP1p`<mm5@MW
z=6~VXsfWc~n=Je)mNp8v$WX_NADtKCN(l32Zw(5RZ}qmG#s!x4#)!0hbLX7m?aWtt
za`IyYidJG7zEZ?mwMR>jFtK<<<9?}%x5Ox>;yv6B#&xhAGGXS~5iAaLkt6!W9=XFd
zT^Cd~kEIOc3&iz$j`c4Iyn>a2?PfJ};GH}j4qi0F&ZNJ)|Fl^(Pp<tkC_3BotJ&vj
zLT?>4$YbZ;U{<X4ALE3bz#3xI4l$fw1Y;k={;PHLiV~=26M*p^p6f(l#Cc#yB?aAz
zd~vq4H1fM21mXUz4>6#2okxGxIE$1~49MuRA0Ft^X81riN1pGCr_~u^IXeXB2l0Sf
z7v9i$&D)91x?MyYlDMANx65>5{eq^C?apsCUd_jB(bukdo2sOpRt@RdQAD)N&f`0j
zwp1r5GN+`xVa{e%Ma2HGWa5}7axS`|<m!KlIdExdeE|W}nVZ`k6l&zh@BEB!c3Ml7
zVNPQ!Nj1WkL8@T(k@fRLbeA>ggQxTPYFTM<<Uyzcj5#*(aUaMXo^<g6Jwrf)WvANN
zopnyDT&jX&2GmPQ2vi9*7VM1H<r!U$4venDJTQ<kdZXghsx}1FgpddUPZN$1P+rRo
z_2hE9=`^J<7+35lx8_n0^zxhV-rIQE;ddYRhDl=nrx;LuCs;}*zX{EFZfe2CO~}Wq
zGCl0o8VM9W@ZFjbN_07WK;2q}GwX)~lKGSSLC1Eijo??Z4V}6su#%T0xO6V36t{sv
z(cXhvaUQXBk-Qv>&<5N9&}my>i@G+hW2_wKS_~HJyj-zqv5^nA4NG^rPuPYKPXx$^
zuwVV9s>}C^7p%xus6U56w&@|QE&;e8snqX7-Cr1HgGl$ml@TuVzwZ7x?pV`9?5}d}
zj?WJTFR6j=?we*b5Wu>A>y&~p#IZ@#_HdP!_W_?ta&f-evyfo%H5e6INy5j5%|(7=
zvWLd8Ircxidb!T#`*EA@Ox|_z%g>1Zq8ogwGH>iWGh{V;SGWBZb-}%qM+f3upC+s@
z0;SXV(a9Cgz{?nq#E1c3l#oT8aLR8tIS=s<tc+1v;I9Bb+X%7;JX$>5`n!ZV$W8UM
zvXXc&?zfb32dfd+bGoS%t+Xf0D)5fM7sr;<F+@A1rU+;E*xq{X=k@}ApLrz2g}6)J
ze+Av&bQU${HEDQER<M!C@2HNXg0PyhNY_7z+i}fYj(BhhkX}m*YqFSgU>D1*^v}K3
zOr+}N%q6|47be^2Vp-P--pusx(nw&>QxT6~DNoh-5z7mrWqAY!{q8wLO`G9%7uE2@
zl)p$`#L_Mom$uO~UAv2i-c{iJ0T3!ZPn2Y@a{H|2={=dV-*bKXfABa|GKy!kBqt}3
zVZnMJlwC7gL&pfNEujg8)M`#3aw_LUeS0aC|1&)Vg`B3UdzHE}z{5ipY(Pj|<nFa0
z<J=+K!_U`(L~pXEdm0Ab^|1Z7Ls(0v2`l6F(E^4NhDJyC-E<D#v<jQ$%H?;t-OQYb
z|2=af@{(Gl4jUY~N5>Y1*cVeaA)2~kwYQ_JMO5m407jGbPep%icc2RXJ@kB)wJ8F|
zet6ycXE#cdwVPkn;aw~CbWak7#KiehIh4-5&x&V!&$bq8G^@AAF5bLi#GzF7$Q!~0
z0ALOM)Zg%qwkwEyMWp!r3~LhGGoX)b(~J%lXM;&DrwPc5bok+|W$(J(b*o5e7K}Be
z4>&N(jYH_>C2)|MO?$4v^m9K<D!e)-K4j&N41v9$!}QMb#A1Z(#<-lT?U~lc=}?Px
z<|)HRV@4<}&H?r$l;3qbwgxU`_1Jv-?o2>0mtgmZ6zA0|AOARb#gC?vAXfEUTkGfp
zMwxUD+<HxJk56f2nAnM5zH7Sl)frjOhjBGs#_?0H#j`!aAGbFM8|pby*AttsvKz24
zGI;7RITPngA*V=BXuSrdf1?mWwo_h9{gt(PA=FuSp!Y_m{Mv%Ib<$N%Br7b;Kog<D
z$ufSH{{mB5`i<ud_fJVl4EUJw20~4nd@6{i4SSprBt0&oqh4QL!LFl|s50w;9;LV@
zGX<r%r6gS51CY&E)j^qo`9tvo)^4ftbqbC!KG=+D*9bB|Y$Wxo%~_6sx6<KYQ;KMn
z?aeh2KV!2<4j?<qse`Rb3lAa8)^oSqk*{`OXbCtc%O`1os4s4Ht6$MiOqa{6r9Qdt
zslX0dW_&}HQ<O6x_PvEP4cBu<D!X%~j;~hFYu818mNuu8>`t~ufj9g^C`x5>luIF-
z>_k?xV~lF?cztyQC9+g>65$bm>dpGOOtZjyq+!S!@xGGkdAHlmTH<bKV|^8vZWsHT
zT9c=OgVRmc=n#6Af-JZDIWQ4Y9TN-XK4;LN<Vz@e6L*Rhm`12%#i8;Y$y+oO<P-zM
zS=t|VLR`mPH!L$~Zc9v60A8qThtdk!V{TbQyCUmhFgaM8)?+i)$gUil3Eqyq<rB4C
zVMAkcZphfj`v!UIM|{0DjKkXKl<;Ak>ieEZe`ahpb2TTp>IgACB}qu3GG<OF$&V^d
zGo-eXr0v{(=vr6Ia^v0ueX<F7@invU-WQert*rIh8rFe->Um=WOGdkktY6Bw!KsEj
zUMU1ixW5u|ykJ{17)y_?LD|<%Z2R-x+W|Dv+phRsZ|c(3G(n#r5wX2KK-dXdd}tT$
z@r$nhi-8gYvyiK0v5XdV*v}~SzSn$^3E}sI`5j%ex8A#PH9pW&E@z4jOAJF+pQdh(
z)A*jq+pL}=@FDA21|mPAzrz<F%+E@L@bKnc%?C<C_KOsb7PYD&@+qi_b<lXx-pT`Y
z1A-T~!Qhcp@{6l<0?;c&-ZcK0ZvuwiBdyJ^T?ex24@TX^Y=*t>WIBo|O{YM|*mBJS
zyeZxgJ$ui%ausaGPB}*333cq*^>M$;*B=<NI?v8?IK}Wnb5jVT6>8hv28<HOgqdWG
zCk;EZd?$Hz5l85;G5eg7vF0V-sYffg&Ap-BT^W}Vv!Tah7-b}cz(^{aebAREb!0s8
zm4_(j)ua9<0)vBP=8CD}O}b1kJNs0RBKja}==%?GMA9kU#f@IGD6WE08eRkn*ou_c
z50F)&_bu9GuR@CHC_{L$b0ww6C5oE(0Y4M<UZj&?RA^~M-LN8z|A2IClJS4phw)#)
z(jK{%N9>;e06nr~{sDBsI7ZxqZ_+?$(0^dBkqxvTl#-+?9|FDLt{z%fp)qV2s^F2j
zWq&bot`-_%br6@&C}+?}VaNtm;8s2VxvrK0?2=*Fy|oUO^!m%0{M{vsnavH4hEn68
zoi&j6s2tXulaAFb?#5S>n({*-W<u0z)D+FP43Uf>h=?DO?){<DC>IrR6t3l$9WV6n
zc7@%1*r{17%Sb!#-!CJ1p3he_VUU77x2?5tNUaxP;B#<%A%IueDloo;|5>mMhS!Zn
z(<wG0|3Is9F-!V>H#diObu#*hB;O~+s2<}oVX_YH??vU-#jo|%e>;4`&P1T3==+fu
zA+<kU#Prc-67~POm8YNSGa#vR-4%&ky#Yqys5?s^wU;@A#SmO~upFe0?HeTHT-v>f
zldG*OpT{N=yK#NzS9Y-Evcdo-)JxT%?acXCd@OvUDtW>HOS)D0KoCQw1rrmK0~s0l
zb&=3=5V2zy88xv;!?g7yC(Z}!+L6Kidsc7{U58Jfm&;y!e-iSBkSr@WKnZ~!4Ld%?
zKoC~lmnskbpy~IXz%tCy>r|w*pMaruz<pBOLCMHQB*j6>I)?c(m$ebMu$8oV6ZPF;
zC-LgUf<_NxWNKW%Pk5r62vRYVY1uhm`1Jbt7^AGw3`&jzHW=kv;su>W3hu8pPC@Ux
z^ZDAf4g1G-mJFI_Yit6Ng7=-f*ctH_KGb5{21ssFC7$b4uho?qop6y%cmZ|pe(S|_
z>}AYE8tARLE@*~JJ-Ocx(Nf3a<5POC@zJ^)9fNbeG;K%{NAgVOv_%QO8u6x*i)mr8
zu%O}i8GBA_V>qFD00vD>E;i8j`!jlcc1MLUZH|utg<pcXcA`ZRQkuy)yn8!YqaS@}
zSIwE7xou^SNnh8Wuk@c6(eo{R$J>j|NthHi7{x8)$oYs9bX~I_LpptLJ;`P=p<=b3
z?X#{9k8Mvf_BC!&-G?Q>QtCCRTz*brm@Y?hz^Wf?1Zqi&T=Kj=%=M$b)2K{q)?>ry
zY+C$8;BDHm=UVe%vU$Zii{o11RMh3c|M~g%EDGbNgbdf>3MqSzge-pLHxJs;Sy^S<
zBq|nNQwwp$^dF}k4@H_C<);%`3oeB1Eq;K2*SRT~g~G2Lpe}}x`fbG)e_?kgKN^z~
zx-BLA9P9I#5EQj)0Q`h%e)H&$!6OWg_))HUAG+ym1gg#Z^S1i{;(g}r{JHzqA)^Wr
zGWAB5{w9gaaz=`hVRXoen_&F;n2_D~=8nDWjcw22u{J7Z`R5Er*`u3IG{V*U!3?(#
zwO_{+Z#D^-%xcTZ-Zk0auR^-GoW|Y1=J`IzmE%V1S5YxBpayc(6iZ1($HaO5h!s2;
zv`ZXGtSkXO>$-8L2v|@mcxJi~x$Ony2I`}eIJ9JZaotlYaGzrInvtZI8sQa^nc>61
z{wx8hf46!50R0IRKI8YAC#Z$72GM-XG#XTber-b~;wNVfTu(_XKVM^HtejWs1&&(h
zc6*QU)XVf8@#{2&>qw8#QpQ?jNhnglVXiM(IR|#kfv$S)48&H#+NsGbABSeXYu-6@
z_*8cs<uPE#_O`PjO_ZjpXofm)c|$x$K44;EA!z8&5qGTIlX#25{L-D?pnfpn3evap
zD=~ze_Rs|{JdB21m3P~6$sTGeJ?4-%^R2So_P3-d^om-a)$!!9g+YB;vlEVIQ5lUo
zxvxU3d;;1y+t|_^@ZhiqxAkfIKJpgkeip*fK>o_aIVGu@fH>ibn^a%K6#rw~5haP$
z@9dPh+sN_G$eUKg#La~^<HCru&n;eGlx_k08h^m4^YzHi?dYqSJ9k0Xuca6mmF3Zx
zO29*RU7^<6hVYt#KY*sX36B*+#aDj-qD+H%^uQo1`N83QW42t=nD2RXDCDIOfvh48
z(xQd9J<?&9^+}AE_58>V!W5}epX9`3?-XTE4R@*7Xi}+=Yyy-@I2in*77`#N+O_v4
zk@-a$7QFI_|I4F@^xU>u8ik&(C;1L90htG~iCxGl_;aY5aQ3$gCk*i8BAf*YZS5i(
zlLos|a#LpH>c7Is$rzYIoRFj6HMz+WKU$pi23pqmFQG(n;q~HChPFEgCaZvkek>(`
zMQztPUpps2#0e<QPc_oRGJV6{(Fb?sD72?SKUko=l6==TO+8PXiLJ!5Rh8?Nz6g!9
zb}whqWn>(|bXGVuH|S_os<2sAH^3JEz{abrnjNd72v1AOQ#mdzYPM8h1ifK)LFl|t
z^-8$r(PjL4CXx~FaUNkO1xdx>yPfLZ#$MX&qCDRtmU~kxbQcSKcb>*b;Mj=EWtXS?
zy3VBg#7h<xjf#9q(Ch(Q{CPtahCx=;th%m_4~8FGpZ|JyX1+%>kgDi4hE5#UiDdir
z!vemm=Ll1$`#ujA9axdUBDsi;0}v5;IjVn3&WM};7;K;`l@_HTAYK2U%USMgB}1WK
zr+DT0N`s<ar7pqSJFbCH!*;TVW0vrYcH3?}>9Dx&v=~~D)@p`mGEItl+gyQFul$<R
zNL=QXHn*2FDixwSz<+@O2IY(|-w?dsd%YTb;~4nr;vXmZDs}Td^bg>Az3^%8;xFxC
zjuPg$L-DoqD##Qxu795(?A|!E=3;&S&yY)am_324>I2<>MqHk&#~djCk^Lk&h{Qwk
zx_cGF_Fs#Q|G4H~x0FuZd!(e^%isq~d%aC`wq17M`@h`9sbBe@7xFL{<QNY(Zid1@
zrg_o+_vB|-wFL#<a_U$Zp8_RHijtD`LXaf4i<L?#qS)8#hetk}F_^l{In+}pr27RO
zc#Fw*DH7!V%+cI7p(?3<Iq)Rg#Uy7S=Q0Ic^RmU1Q-$E6qg?2j7t8Ld3y83ZQ_ENO
zHK?mK4*#@c`kc<!%^0$Jb!981R}r=ED<YRR7PtIiaE_VZeY7O<LK(?5^f-A>eGme{
zfo+S-(L?6VJ}@slRND^@vGv-U3WiR#MF-7QGiIx3A!Kl_VMm{+YkwmV0Z>;E&gnK)
z0^cYn*>KZEy0p)EzE%!ZX~=;<=1re5h4GmXG%F{oBP=S}Mylx~5mhx~6g@)}h3Nns
zA;_-Bl7zrFf=<9V)ZJl^IUnA3G#`Ry;|50?>BNAmFW+Lo7T_Wob8a8ov@gaWd#g?g
z%5M}|HM*Oeyy59%bV5QxVw7?Cle<*8R8xW}$fWNW`6;y|VnR-f=rltJR_b&aCVi@_
zzsK&tgX!W>jPX`btCCll)FDh?D&MjogrE<>8Q=y?<Fx6BER)XRc|!42Kd~0+V0+qD
zA~e^}qKq<Jg}5TU!f*QRhet$Bc0wh^8+KGtncuuJ99>@Hm8NBy!mA;u{62j(v52zh
z-cGZj6ZsB%!emIX(T4urb7ziNQnGv;MUxyH<Gc<nszqKT+>9b+QG~v9MQjIK={ErY
z_0kN&9~jN_l^N#=tRr;&hMgGlb&LyKbIDE+w)ip9F7+Eh+8e1ZyFq>$5PrLrRtUEj
zKtjGqj7#dh!#5%HfMrNz_WIXMI?Wz_+txbt_3O~ApvA;bqnQV`!znXIJj}zo=?PVD
zLdIDcMjx#xsjL%g!e~OOEi-U&7{3w7FILz4XPc8$gj_rpf4oz5KW(izQ?>*JF^On1
z`}!2b?-J$?0+*rNgmx9uwmT|HAqJI<a5|ir_H?%E7z{-U>HDCFvX6!<aU2qv=nnAv
zDI0Pe#&<qt8r9w(9NadSS)RLDShP&Cm+hMkv<Yy>ifu@hvm<x`Yn-$KMs5$fymjA*
z*n)<8came(8*6LF0~fT~yV6kRyu%~<**;BK5R|~D%gekm$3pCv`b1_scJT+m>%02c
zhd%9i`EF`+N_e<`|4EoXVRzf<bQRSuQ#3Zrv9AAJT-L7&EUdBbRNsr;om5jMr1H1%
z<dJ_I+|UKwTW(}~z(WN3DGUu33+$RuP_OA}E`DRx?r8R=G=JvLH*622%H?;J@qGYB
zN@;w__6!9yeUQq~Ooa^Qo3A{@Cf@t;PV_a7OY=g735_092HW0OAlFN|F!~F4>+cX2
zA-d*Sw!G<7DK1)+CI0xic*dQdG5C-~?<t1z<&sEN4+NNzASgfWpr^9Ja}PW<r*$={
z-+E^_|JsL=2`e>af`sKfO|TNTke+}h6-_{J9?2;kg0;Xi!^3JuAa+U!e>2vLuBQrv
zPZ>p(misQ2?F+t`c&Js%D3DH7rKyi2`!%6wJk}<hc_>7`PiGMg2&wHHA+Ei6%%F8w
zK9C&zK_J~@I;`PaNG&fW+*C#H(kCyL5tF2@;aVt*PNu*DQlxFFQvHA_Rz^1xW0N(F
zv%4;iSO-q5E5Cd4Z#SoA*cuo7`0$u4LpSI}&+u%cMplp9u0o;HS0O+rg^Avz8G>4B
zNGWOc{$ZH;4}ePU4*)zmV|s)j`OtLRa4BcLu9a}Ev?K0|j8EL*5*VKTwP^P$Qo{>f
zm?=`NvD6txKBvDXGr>K_8?WQL5~(1<Rnx)Uo&MPXNb$Ztzb;{O9>**hg1vGZ(;oI+
zc*@*}3wt~+2kI}r3#?~Dc5qV&(bykMx?ptU9;r#LQMoHKWE<pkM=lP{VG0$(hU_^`
z*O}4mE1`J-@rQ^CJ*>@)z8{`>fKHVYXT1!`AU4S^$TjYY*8G(zGVdy78*mi}XwX~m
z)ML}MU=GnKcI>=URlb9w$@GdiY2A04cxBW%5YJNdtIgLNpITN(jplt)mK!!rY>E%f
zc3vDm-{U|59JFp8qi-uvo8uMR#!uN2+E=N}oA>NbZG}&3<dXhH>TZ(L-k&6gx=&zk
z312CA1MLss|Fg?=QkPKf?KDaHyp5?Y5Fmzm&0FIz0_)mdba4lt`huixdeRJYO2k^d
z;c|;8ag|>^3@BEfBVXfeqobOl9@G~6<5l59-)^`tDQ$JH`GifjJ@+14-I4YYm}*6|
zM!B4&fj_=_Y6c?AMw82-@*qBlh=`Ez==-*IvS!=5WS?w>ZmLmT=6BW9UItlaPlP3N
zXJn^cJi2pz>mWo}ET!75%`zC`8yEOR{pBk#(Na~8k)YIVE<@rSzc-HT!|!tblu2P2
zI{dx;29l5*fAskBb1sEZsDuH6wp9WVFC)xGHy=&1N?A&PD59L$@N`r~u@c$?>A+v5
zTQ|ALd--Qyg5|X+oKLgb3&8GoED7rd8(J+aeDNvmtR2H#T6}#>@akCOOtB|L)k>il
z`IXJZr9tt!{Kzq#(PPh~vm^gnLHwsF7%~w9wyA4uWXV>9vMK;@KRVuPN47foAxZDQ
zANSALH2V3MU5&~{NGkpQvU5<hsn499Qpo;$omTrx)z3!}hk<nIDG}Bm<XKV9v0Sxm
zhci6@QN2WurtxAXDxy{YRIL1;HOJ5*7-)w{{tp4&EI$=2y($Wamx^4IDm;?dpubC2
z9x<#9CH@uC-4osTt7fGH?!D!=Yi2JxSZnEI>D+5xx9|@X(^KIoOz(1w&!n=59W-{2
zQQ^unHlyl!e1ENX04>Z$-&*WF>-GwpR-1u@HpTf0#WkLi@7UB99d2669!qK`E2bBM
zX+-1FZVjK#8H0{ax$SOl$^Vg3gb7Wo20Y8QMK!ty)tKd%u|r9%Js*B2?fvVI(!>Aq
z$6sdvoy3Al9w^U@3#*q8?Ue52406>9D_`XO3FuE+A67M=XP1EUhbO!(|GKZ3>Cq`O
zOp!@PszEZ}FD^9{s}hC`6s3K3wb3|X4#~06gmQ7Jdc*Z3vW^|yA}o+X)bfqc-A4zM
zC(~Z2&=0ZVSgf0bXscYY_S|yobXTjH1pNp+eD$$vH~3M26UiZGyoiN478zDi9ppm1
z?Q7xS>GOP%w|~-kE&bKUKLF1o(t`hfH6)vkm({*tf`)t5PycpyaeTnq2X*3}_T*QY
z+!2z5i*YSqhP5i4vMCmZ$gr@s-=S$98Jnia1ZG4mn42AmehTC{-i4KA$5*lkz8bz;
z!ui!54D!VIPp$tK_wghkU(;9HxqUgF5f=TQ%1OH2Tbql=Ek9OBeCU%QBDmb_0Uk|d
zw*gaCh)Dqy6eT4w@${aD4h}7L1Mlph%OOJTq}xub?-7+kH3)C(?e^;8{L05->u-No
z)*m*vNn$e;hvs_631~}z87XpV7-$u^^S(6v0Tei2?7A596S1v*@xJT6OXSIoaIoEA
znxf@6U}#RzP+eCAT-Q+S@N8WnDe!T786_#oyiwJ`kM|d{Qdw{(J8vt{oroJ-f9xLb
zX>HDEXD7z<sV++Bxy`t7F-GP1WPPZe!>LO|e@bc<`w2@~X2etY0~NC_F!Ico&7WrE
zP~FbQSnG3Myy(es+}hMduni~}8<I^4oN3Z6G+bkD+p~g78<&5>WvVJ~RGCYED^;sT
zyVE@n-5_x08grUS1KsZh#d3M`e&-ZBoStH_#nn?-pr#1${Gx_G#Y#_F6~c$z$)w~N
zdNi6P?PB~@r0s$u^FqIu(<nE6mQk6^lF@4Iin+VLN^4M-D%EA&6?#lM0DgT&j7iKg
zYi$T0D1T(W&}<T)M(kn`#x;<X!*W{e*%Xw5Kl_>>u-#)#G5*UgI#0Tvg8O^v==vJO
ziG404lsU$D6N&PfG+akH@93t%{}Kd!i*?<0>+|aI6z<l)V0cowZgPFEJ#6Gx6*M1#
z1B%<=!>OM}!LsA+$Fq#UjJsi-gZ^b!9gg?2{3QUNsa9M07=N3gklckJHL3ST1>&+E
z;lC<q&U)kGP`^3eaFK1JKlTSOgl<`C);ffLM&KyI$+iO2(x@Ysz0+Rj#~+u!i_jH}
z{yH9E+Tr42-EXoIwCm+DeaSSm(psFqj?Fi9Jm>R9WuL1P6PwH7-NlSf>F5Gg)GD<n
z30-Oh<eXSOeXxY@TsEk-Hi0(d{tYztK7X9c4|EWipb-nc<~j9YwaQl>1bN}HjTz9_
zWp@xEA3A1Ooy(+BE*jueT7=};nzVMOTNO1Kz5FnHF-rRb$jgT<D*oBUNsXnYcl=aL
z`n;@8_VCcUwDO(gKf<SX0YTRG$uz{uV3z!AWwR!FQuH1A4b*+_MQh&VfBb=DoEblD
z2@s+ev6)P>W9}%dyPVu{X%KV8z-fm$SX^(F*NqeMxkv#d$CVSAOCdB9wUr91ou=pC
ziPuZ%w#tpRAa2^aRR=yL3lcDW1Ya;G+7-9+iAH!z{w$WFqo`9|!a6zEGvTAv>8sQC
zr1R8!lepbPUra}jvie-<%-eN=yNTxIC|t-NV3CK<20C|_lY$gM!Mv=C7KygQ-X{||
zN3pLH4ZbA9m+gf|Fs3eYV@mIOwd9!!ufa+-9%alV*Iy<OgYR<QB^Gu(6DTSS_YI9q
zD2X}|<6#fl2?`QKA<jBO5htq0nZC5ZCx=sH^@#xB>PSq3FY&skjJ^lepPmN37HFIA
zWOvZQYRFNGzfeFyrZJSM&)zfS@~(EI)76xWqn)KrZ&8k8)8SM7BweJrhL=P82GS)u
z?sOQ>3Ob-mY$M;F=mkOzH1-ycR~KX2&;8{lSxv;eCu8gK1Py+s-|(r-GmU<d7-zF2
zE<)60F^VwkjqSE1z__{KfJT7_;~eA;h?fyJ1jp$l_bTc$<J+{GoTtMMGka|lRhsHa
z<k!!kEzb8=Ri%)o`uNaL#&$vA7@q_7Wp^Z(_vs4v$BL<P$5VD;&PXSOH^<@OrD1l*
z5l<UNcG8rPZVkPvQOvVgM7r2obw*wsT*N73TZk|M1w>GDCbGA2au(eWvJ7uuh2{FK
zR%eZcby=q*y)CX!N8L3vx;8rmBej$h<CUN?KxTEbgWB2Q<GZ7{^Wy<EY5u$<qD(bv
z69gNE49|#p6q<KJ_T}IEooMH%!bug~VH;^UciZ!lyShS7&#)ymqm(uN1PmQ)*j^Ax
zMS6iEtDG{Hf|dg#6rs{p-TS`>CH{^@P>Euf-@7K3{W>^E@M`0blh`}$iiNuR1UNg{
zC;w+6*-joItsdt1zeBG&TT1_XP5^Dxa*T{Uu=yau^8d%IvR9CdY3aJ<?u{KaT348C
zydV;L20vWX?tN%C2^-|^m_tMndmP)T!J32NW$T5_8`Rf95xo?%$1`wPj4~E&I+7H~
zAt4UX2JJ~RzSJ=$R5}y*Hi9S<Y+fd+ttn-T&<|MzwyJbkGiVhUFlf}wq)4@Fg=RZy
zlJnYd&p7Q(j-Wv)rr4uu2@Tp59ZpWw_vzm(vPz0VPH-t9#p$T85VZrKaWSbSoB$IA
z=42qe1obv?Ye2P}CT$IWlS%?rnidyz<m8@Fbup4Y9wRPgIEVx^)(V~EcK<lXQd=S*
zKYW7?zg27bj^SZDrwE(@M-xjgkn(!8x;hj<hJs2*wuP>%sfwQ-pO+Pv6Om%c{+1K&
zP(IssP|Kvbd<sWJn}pwlnk1H{7>IzmkHr`wAtC*`)6I;pvHN6eCnuk^q9NxI2C<fX
z5!W=_|6uy_YhB5P&*GN^8|OH-Y2|8?xw+XZ=lo&W^cZB4zT-X`c|b{vu<>7u^nOt0
z!>MuS_~LgYpbJSotQeJa`2t+yM=Ygjke(LjeAV`$4stzBOSm~^WWly67VCkpNwe?<
z<r9pBuF^6$Yjq{bkW6-Zi3CG9rD==5H559vu4wpcLr%b1|GH1~(uLn5*UC~p4{vYE
z&W<p(bbGCn!eU?w8M%l?Y=r4O_R2@@D68}1T7|gsI^q)}2E#PM3v6A;sK+?X_U$0q
z0cX=hg(+K@E+Yf@r>Y(*VKkiv=YXQr>j(tw6j%sQ2KGgFGrOjVdhD|P&ln&Q0<>ts
zans(!vdi=sq7kOE?^r|XzckUgn|k~vSCF`zC{-QD84#{zj=L1AD>4qJ%osO=1yI)J
zR3gTDt_r{LKnwQwkEA?{_~h-vSAs(9KKHp}u_L{uV(F9AT6TxwBwI*UC0&lnq60}*
zHtxtmKDiXzn^@W!!70M2Bk<SBeXvtS%t!%SeiPO}=2JwOzhD~~SBxS0xMr<Za}wSf
z#i*ycvsH9)anozg2wv3Jl2TIIblkmUEBZSdDU-_sc$+q>jyo4dQ$sc`McnSu&9=~q
zv`oz(&8%ZG7_k*huP89P84^Qbd>>d9%RNU#`$O0IKEkp#bP#;D@ov*~0^Yiy_k&#S
zoZroj+a7I!*ly?2sEY&PCqvkAff~#axQkkJQZW{;#i~#nWMgo5V`g+qV>*V=P&P3Q
ztw1JLbSJJKPGZTp{ZAJ)7b;F@VR0ZgS=Jj%v50DkMkuDlfeseyTpw2(elL)vlGqC|
zY}<$N@)YQTBFv3xnU8$>add=lVm5K#A{jLqf27&4zfugoK9iA`EH^xbT_$Yr$)0K)
z@AC2Ui;>!kQ_|lt_nRJ3eg8reg9;zzS)@=`rwGQ7CYm8wP}Odw4eVKOgAJo;!LcgG
zmlegyA{0ueiSvjiR6NF=dPfNO<{(ltc<~l632k)l?ZL_FzoWaGgK>T?g{@eif9w14
zxiA}SOnIz6lJ)kTLhh&4eM<d|kz$KB;k1%Ao!3^NO9B^JXsue7PhF(0LV;|jvw{q}
zqdx%!Y#Q0FWQv^XC$YGlSyHxgvnoq})eFf{nY1GcBxWe~DSr30Sp25HAxvz@JR%MZ
zKil8s7i6FD9#vQ?aF^ZhC?^fmCZ^Ca8&eW+wV=wzR&z1u!s1H*f^E&Nfr>`kdkM>A
z?01Skj6z}0u4KbXoNun4@W`~#U^2hTc>ES{bBz(WlM%`{&=Ri|K{U$WzM%79CSMGh
zqnEhUbXZCJqN=T@_u-NDOC*0xtIsEq+9~D`kj<455y96V53N#PrPd3*$o9@Qehaq!
z0ZhGJE#0QYK^nBxbwH6es%;e1ro&f(6-p0+Gq%=BMD`mC{9TN|==y8Aaqc47pFe)l
z)~+pi$I$G~@C8d!4~_7U$+lfVC!JGEc?b?mcXF0whJn&rVa#nWgPgs-2v!mVHfa2}
z^^@)2b&Qv(7SAc-a9z@m-;G^XYs;JdS$i>1I32rMdEex=9VS4_fcZv_+}<>>UDUc`
z%p5Zrw??8q))h7mChieN2jgsX2nm9foMiDp7j(VtdZX9NWR|o3*e}d&y)n6I<8ld}
z(0-wCfJ@h?2KzQ4O2V?JOrtQXlPWz<p<i3`ylSLH!D_P2f~LNVVW~W$I`~${S%toX
z?hxM@wUdT|hP`50WJ6_y(Gb2pn{a%cJ@HCDOT##Z38--ducD>C&rmZP8>;DahEKb!
zYkxEJ7VEM&ssZ0W=$<$cNVf)7buP#}KUQS++TK;Bdm{UqMx<k*HClmbh04oii1(hg
zJo4z5XNgXHi@$Z3SCyNU$cIZp3E3KodHU(W6}{C#A-j~;(uZqG-+UPi_pEY5JbtHS
z+;783sHH-7m7k&|3YsTu(zs=}j26vn1uaJRjI%RbpX$Y$o%BCzC@a^D(!4@U*IAzk
zx71EibFkg9mhjwxd&in<o?yLho>J1@5A6>G^Anrg`xB~cvYSAgdf4L)-+V^E_o05Y
zKMG5Blxt?$UBtiU8_fnWxu93rsz_id@TzJUGt4C{@Y7BECvE!XiY1B!X4QkiUQeGA
zn<G9YS(%TVP+fL5<<*;oD%4bE6PDLoj&x5`Vs4M(E<{m#g%`&xYEWx=X?dk}zBU}_
zX$Tzm(T~#qjQ_qL;zGRVSmwIMPS~nYeWeFB+PL(<to;h_5UP~l=NRkx)IjZ62FDjZ
znV^?(&ob(J@}dgqJDKj^89BZCKC*IsY2zWnJR+UDG2gE07?mlx5b8|Y*@KnsNm|A!
zz_dMsy0aetE+<B}-pDA{CFqje5dZnisLk;FJqaYM@66xLY^t<4k6Z5b`rWJD`DR1A
zF{O4NH+k-l<@0VDt`q{;Kld0kDnZ1}ATs3Xm0=FD?QRQ=(4j<*5~B}y69ZvH1ROUB
z)Mr3e!?o!h(o&rrJypt+L}K|CuG$T_#L>BEQ3IY6<*lD%CI2vu{y$!(5wRgNTN+HW
zcU_KQHhTH$qk!#@>07M5^85WwoZWb6f=@ug#u}N_jtJk*_+T2}B-jsfjCbDeBhuj_
z5E;`6$^s=YbJL9^WFX#6eYwS)@eR*sjVUt#Jp#{~4gLUL_gN`#c3HQ2nq^&0T}NaU
zej3?_Hi|g!yoS2)(vmWNC@@z}Ms!?>OVPc^iyg(|3X@QQqx{xqj$;7QZ&zK_&C57l
ziFR$fHn?V@N3-lu4kLA2!C!j!l~rb|Tolf+PbOgyD3%h0nsGXGJx$i1S7x;yKdT<~
zS`lwlwJFwt733h>FxTcF(i8mSmz3o<$foZr8V;$w(RhPhZ#_2bfm4f)HSY}@l4jv?
z%mLLg2AOO*euEB*U*Br9bF*Sf;XUtUD3rt7CZ_wd<9^l+zi}HLWq0ZRbmvt+KlWp2
zu4z_sP@nc;KRJ4XH<JrmY)KCBd4{CA4eoX^Tq5S@OlhZ~GW|F1-ZChzZ|xUs+zIZD
zySpdB9fG^NHr_bFrEzzHJ0!Rh+#$F(?k)+0kmNs|Bkwu)opY=1ovFDWhA*t5c0YUV
zqI&H|e)8kVt#@*g(Y=JV68M^PnMppvK+CzxlbR(Ky~GJWk_qi72sMvQU_Daw4WXLu
zib5yn>+_P_w|zp{ZH>mr58QE^P52sqq0E(^KDwvVYX^(vYoKQA<}qjNQKwbDVmQ?D
zDotgb@KTYLE?ag#erU|3-Bw!63x>W>ru?X#%aul#CVN-$z_*o<R_<QJHA<v49p~BA
zghaD{c^qbtljnSk;I0DSc@a46^Lyp1-7iDWKLCd1-5^F0CZ7$rC=o_boIj1s>SYa{
z>V@sTjz9nEX#}BmE>)?1^g1Ol4RD95VSlMSJ1SE#omjqSwWHGU;L;N@l)jm9u#9R&
zyACYgP(N9})q>-WDgp4z)#$&}c0^D696ty}&bw}j{Mh|ud_V#I%}FwS)y;`Dl{z8n
zMEvRmi5Pq?CrNLt)Mqlz{!lE-i(s9`YVIURU<6VY3!ofH<jDDT#!bN=G!bWb;GBQ;
zE96MTAz(me1?8d5VYx5Ge$J%C(~BJ_;47mhq3&;qHeamooDplcrq4#S=DWXf39b+x
z(baw<+A^2srdc-2$xx~v(>CU@J5ZHDkFSC_R2{8$I-eQ1ylFQx?>jN&lR&|U-tM(@
zq#=MVE=L=IPtgBn91F!VZ2Z3g4F2mK2wNb$`&tG2^{2VND)f)$enl3?@BNEyW#4!!
zzim8{zW_i>)$O~lORO`MTN@L-@00CZD_#|J<vErpm1zB-T@DvW<z|OUi}2E70O@d6
z>1HaQR=%g`zrvduRJ302E?&qY!X06^yfG732xOE!CdR${Xk*glPH=qRvJ@5%%$ij<
z@E9p-uB>8YWAE~=ku@^vCHmUF-2K6&LXcX1d(94bQ1&tB!hg0iD>}7@Q{Y}%#g7RK
z=Qm5~K;ZR?FbRbW`UIg*VqUu&HML2q5y@-Bo_pYdlX6b}8{F|6c#^tti^Aq@!?O^P
zH*;TU7i@Q9^cB?ML(^Gg0HgcsEVKGUazW;#UpNn9dvMWgHlM@r7BRoYlySa%0XDHk
zJ;>%wa;t+bmv?YI3h1@17URm=lsHwIwIS{bvsyg07E#yl^KV8xuN`c1ZaR6bPHrlP
za7>!-!;f=@hdAFE+6$#8rKGtLZEX@akV*3;Nb)ZxD`@EpcraP_w@VRq3YC3{;kuxl
zXi9l*>hJgjItFKc)9)rV!%6^Rq5pu}HVP!Vn{(dt5MjU2CrUU}drjS;UJGGl!Eu_;
zOo@2C<ou}tiWCp2pKe0b9_J*Z2(t`{#fEE$!t$d0MX}QTKMZCN8G1%KlevIfLZL10
zd_(d!%9Us);A6gI*8VN^4l0SyPMDuK8JQXBeQRttaj^;nR>R`R%?ge9aE}Q;y&)Rz
zj)HbVc$Ri*q8Th-A8;RCxf>_Z)m{85&%(mB5YXk_4?e>%Jmwfbx(6a?matOiPNLf?
z;$^F4j1i4r$JC1R1zgv<?_O;3wn0II-+WZr`hvN7k2qf-Z$@)layJ-S4N5_iA6fVD
zjyTg4gk#MicDz^-m^+%qt(upCwJ7z5^V^lYAZSma+lZU57L&R17w_@!KlxWo0%kHU
z$gza;jaxR`hz49D@%i&>c0=E75Ydb;BWj+`zqLP8bfJ7SVCq?JsUPuRGqOnd?(2no
zZD-Q~KBr;Ym$;%&@OW+(LT^X7n9tFUvEV{YM=r&iv?su6DmxOs|67xeiYm{fZY}YD
z1*rL7)0+RVki=6?yNpOm)!x3sU3w$ZS^6hv=)b^{bN|9n7X90D@V?lqr82ryU4&gk
zdgP`paIV4M!IN?<82v3p%MynRflrtj@weI>(8y!LzkkC~WKTlg69lWP%cbVguqNl3
z6ov-X7rqun^6mbr=q2BH|LfZ_@v(3xnV$Xz!rF82^SSZ^#pGJu2kYe^F@=|S?_1wC
zWtf*$5ilv`rm$+mSNz8jp4up}zZWp|ZSk3ezn0U2!zFo~(&KH<M--|ioNNOk1DDiw
z*%l7BW(5hA%w+kN2Xlg2gw%8rFB1TO6-bRd7CCva_fh#o*GY%rk{owtE#t@WWJ|so
zvm6;N`Hb(1T_zn}Xvh@?t+i}FtvPo{1<E>5KCE0sWl@Rebebv3!g%m?)UfX;!%-{I
zIrf$hDI7sq<7Hvho?hKrRhkqjA_Z`xLWh4C-Cbn3pVneo!JyCQBYR_;Xabv*yuP3o
zJhvYe?Qncab{T3%tgZg{@&&{>&cXPN<SHR<x3Xk7RBH8wbd`k}$6|>h6eOg^GI|-W
zh?ZKC73agsWm&)W1T7ZqE8WwKE;eDj5HAQWEg`o^rx6m3_d1INAp<TII3BSa`Zg#J
z-$2sKL@hI=<8X31L@$YgVNN)BufskRT9;W}bz)U6BEXW!fQz1E(QtpRS>H6QI_Kct
zV!up~C*Xuv3ZBVJFH_~kDTDDE&;dv+5&!@~2z=vc;A&+!%n_q``GLyh;HWgLL@0dg
z0?#Pt{i{s_Cf@CJHIKomRiq>I1%r{E?w4)ZSv?)0kK_&ZpG?ZgD_n?`twq%>b5N|S
z0OUDk-FZ(2IeOgN-)DD+9)l&`7G**&Y@x|~+)d?iz3ZtLW3!a{DBAU+`sxMjB%KMJ
zX(G51Mz>YWHSR1N1viRZ>NED!Ldj#Smg^+Q^Jgj0O8-=Yw6WR5n%(Nf4*jZMf-LkO
z<QSMoI(REK;CLmAs}hiQU^qASkzD;Ty0~lY^l{?rS9#Ep;egtp2bh5QR<mGF?lU^C
zTT=plxF5imaxF!N_q>zW9v6?)D+XP+KBF`Md2@K%plN3Oy9W~uMxa&#=cushT2`CN
zG1_lUC-C4X>eft|m9Ch=Gl`d;oS=&UE-eR<Z;e_EdI%dH6Y|{kuv4sfdEX`tk~Asb
zwmaM?Tv50saP&=M0d!(C#shN;B#(#ANe`QIVc%99rS~Id?#=&C8a@9+bHngm6NEPx
zdH?7qG%6p&@q&*Tpe*ncTYL2*?9U9VG-~M)8mlPQ7PMQt*7;>|A!q7m=!`O>TBUdy
zHlY%ecrme{qwZlkx_RH3=-gaUMHuKr6$}AQ=kym@mRF;T0mMW@+}zYPNKk3S7?6qR
zwG)y}4MilK6Bn@abF?}=VlEwJb$Czk^Sej}tx#jm`H3CTG1{C@VWO~J6e$9W9y!D`
zYDgM%88YbrZtN(UNn{vG2nJ=EGaB3pL84;ENdeTA9?~CXAdiRUi#S@(E)O2BEz4@~
z(i6<OsAw>wqQb`fMqNX#AKMTJy=2Q7NcJ9xn^H~=8Gkl5$X+mhX_LTgw%)QYDzR95
zi-wJ?azYc%{mR_;l>HOst@~d9jtN)~K<!i@_xJYkOp^=N?v3r7X1j_mLWb!$ABhg$
z)hgl23L)C68}~RxQ4R!(QjKe;#Hu|nVhrieRn^vwlLOk`^tzfFG*j*&w`o0Q$v)W(
znAs+o>^_h5h#%E6xJQmhytiNh8qHA1XVOf^>T$`_*Qib>qsQh*%6XjlGl$*S0lV^;
z<<L|LhXkIO#5@#P@Vd^zl$otLvd1*?_<Hkn_rG5mhX^3WwZT&}6sxdqxarW~rxQ{`
z>=2i*q&2hqHCSW>c5yeb0QL!nW&!cWVnfCOon!9|&rOLaSn+%oco(iVKC@-Hqi>-~
zZ{Av~E<VR1TBgxGIh3R*gxhGmRq&+HVsVJnz1s4r(DGAZ@PV-@M(msBnwmb%2|x0B
zBOPZ3P|Pq)VZ8}o(J-~h=B*_p%n{p@kg4Pu;UgGiBx1u4FbX}a8a<{^+9asF{gHW1
z=^fWG*I^LZ|6@C$0P@yeiIYEUdGgCB_ZV~RVlq4}Jr2)oiUZbaDYJv=L`z?9<;T56
ztE3F0CU**!wXb?RCa$kSXX~Mjcu2MLkHjX#ij}T!R<?65K7Am{n7pc4Nw^i-?SDz!
z0B0N(9NCpxaw(VhR%4w0^teZdKt$}*+R;-0hx9Wjj2;c8E!K-XS}e<WjP{L~uU6RJ
z+w^F3HK>2l_heH(n8-IuD%?^j)tBj4C}qAIsw!J6c&@ZOLW~hq(7U&Xw^g4cP;o<i
zR57w5u<~`Ceu8I9t&6^UV)XTdcxv_TobWQt?XzO-xr#V@kOr?qQW8T$Xen(T#G6qM
zrUXH(8G|8=w#E1+{phq9pI7w!dk1ItM(3odj@dqYk%u!&9Fn8UrhoO~*dD@dn>a0D
zHyi1ZL{wUSM&hTB0A`QC5kdGgh~uc^j82k;;<eDOn;lL98qhtyYP$p&9L?I>Cas>r
z8#kVf&nnO$;B((GVxsQJyAn8$m`1__C22HiW+heB5nK&q8(yfs*pl=(`&~+c#z*vD
z!FXV2?S&;m`IjasbST(i4h=}2B;QvjSr}N?Wfe<UJ9ruFnLD&|{7A~zFJzspjY;u#
zI^3{kQ3i-~b;im2l}2aBm>2N4aFNm<cRexfI5s#qvG?y0omM30sjc7=Q;Slovsu^#
z+q1B2rrOyyh|78sIa9};1Zu%=TCFTNeK<93xY2lQXsDQH7Gp9~Q5(G!5y!1W5){3b
zSo4^xvKdRtvi(fDv+APxOtQeVeIC8(%PLkq!RMRJHtOvzio$(E7PH3|lPs}0!Z17`
z4K4KSYHim|UOCp_endc<`ITaJW_6g~$eYH14}#Y5T-FBP9m(JdPV$VcC?bBJbm$n*
zkWgWb9F4DIc-x=q7%G0PKTealNCAy{mRLA;XI_-u<|?*wy7GO-6Kl0LAeYm_pD`gS
z|JePt?=EZk=crDarM&d;I3E4<rd(BO1F+lRpkkOBkitzf>q*f|86AtWWOD4&yxn$)
zST7D*u=DnD&+9sIo_gy+HN$nh90Z}zmqx5}_J&$1mDKUq1`G(O<+V9)=U3u9ahGVg
z>(X#EeoG%2kp7zbee$uJ+C{dQ$7Cmi%$djBIGlZOQu7VVUjRqjnLKb4HW&Rkwz`Q{
z={$Q$eVXe$YY7i;w@(F3mP(%NP&@@8S;)r)%XxoV3@L@P%w=M%U3^k$=5Q)%5RKZw
z$!L*D93PB>AY4<X@MXcWqPAJmD^nttw-R%v*Z8Wuiy^XSg|+Qc>}*@^eOy{doS)m*
zPj*fz*KvkBY;B9Tb=HgiX&%NQHA+Ql2_ET~TFx;}jBW~1g;rGh(B(vft;dF2-K}4n
zoG&6aamx6IM0Vy3S#l_T6~apBSX(Ba(Q{`O&C8z<*MN-6dYoFSgC(IWEMl^?21YRa
zroXOW;rT<}to5ZI_@L}c-+{QDQ!IVmoj$zdSr2CfBuy_BQ+77VAc~?xzl5l!#SUSG
zTcF9MzP~x~ls5$@zUg`L%Wve2hb#p1Q80L_6g1&yVFFh{5)3$N#No1=UWZoK70p=M
zzU##B(+w)6+5=4$@H17J$2-^O<L&IP3lc2L+<Ty<J+`z~f~O`js&ZSfWTc0SO`Y_4
zY$geYfYE6NqEC*pHfmDFSHoU}T{3BmI%hDa9A32<8}~5_0|uK4H%j3zZo2pF2)qU0
z9JE4haK42?9#Ub4@7Qk6EvX>pR+UFh>L{)1BDmJSQ~#2yE=uYs-_K)9FupY{+T16t
z-5Hg1Q&2K(o$0pOsDppjvG59#)(%B?ppWQanjh{&f#2hw#PDKJr_f@fqcZ4T_0i=Y
z<2LG2n?4L2oSp7${K;+HST$M;mwbs?9<<T)&K7%GDYeTIEy{JXBo$g9RpF&}2*FV)
zv2nrgy}2y&(HRn3Y7fgMfB(91Wa41cR#qmj;~~=|yUm<K&;z7^QL)b3*R_$cF@hvY
z-+xR)$W0KH#)7k)op8ZNbh|lg9?xUZaD%)t7BZ9C*=qm9!;5#|Zo!@wS5NGY`HIIu
zh2IB>Ib*9R!#c8d#Ha&Fa9!A$xyDdqeah(6nq&KEZc4Wt2C2~deKr5ccwJv82GPYr
za`ww_PvNx{WiR1CpFv}>{b+5u4>u{*73G+tql1@Nk`l=czA`f?=zKr3*As-1z)-^V
z>+I5qm>#tB>X<B(R~LoGa2@Y9&Yg!n-)diW{1app57O<3Rlu|m=QXD@&g!nDJvk&q
zzw<zS7#Pq67M*&`O(Wgp1)ap+H)GU)4M&#rY40jx_^{nPV=yW$WWBB&r0h3P(5iqg
zNtjE}iz6z58Ouo77k#m8GlTe!V;zQsYr_V2n_oF!o;6hLH)PUxp<D+4<F(ch5<GSk
z^ljz#JH<KN3suI`7o(xwhZ?nkNE_2Hr}2+tstfjGgX@bs@xKzkc`j~bIa_7_jmg|l
z>||fsgZKS&z3~*7tHu7Gn2SG<d{HiOgzu&IVPX8~zrHC)NRM+3n!=dOtS}<;`&X0o
zOIe-!p0%Hu58v%Q+jPOaJk-234Crn@9_c=1HO_E}!qlK8UhVqn_@}*ZKGDoh9lhq2
zs|lx0^L_4;utIkfkf|k6tvZF*f`K0^eauo!zWX1@$Hm8#>BNGKl&_X{R+xWZe$<<U
z0$q{VcV1JGuDfuxYrWSmeQEl@=H;E<90(J`v1#dps{XP}x}FTIpI_0Od6>T|$Xs~w
z7WxKx+s$&{E^nYL1hbO*SHMbGJe&XRa3cR9+{vH){RwAl^ye0bWE+ORf6fNHxs!i#
zd82!J!Mon)|3=WkqMRt=PCLZwTu!=)S>1&>nO{M1pvA&~u3NBp3oDVznk^bvNVQ{V
zad4#bw(w(upw|=CM%B9mq~FJSUxMUSn;qno-+Ih$wUG&Dc<C*~wSi0QJUTTL8+F}0
zMnbCG6yX_>1xt`vl!pNXz*!g+P7Cd)`RA=kly~m10qbvWvuhiK%8uX5QtwIrko*_7
z-aA^njVFG|kz6$2P-7`^roHO66oAlr(oTf8;wpp{PZtkYR}yJP&CfCd5h7&dVH#q8
zK2-eo4_Uk2+pvWR*u@tc3CbbM>SSF_#^6Ni3Xk%%W#a%4)PVAF(Y=ykeA~qMZxUR1
zR~aqaaXH&0S!6(nn4221wc%)4r-JAjvM-($R;chHHU7TVD|6Nw&xUhZtup~KCPdoh
zWqQAzFv8yGRXE!*83>NI(JVLym1h`O;>2cYk*KIMliPO<xH#qWEItS!3(dX>%<CHk
zH;}3jTI;Aj1FkX^tv5;e$|{PG)Ug_?LeN46;UwqY+zNLxmRuP}C*aFeb?fM039Cko
zWWVJtSBGY$H03L|Vj=@@Xfa3t!-~=byla>#ASiF*Uj4+Tu7_e+;`npAb7+PRW&hzc
z6|N<2(IqQ%-Jej4J2Xbr#hw;UGTUpB%xR6zdwu`P@$O>ehRdp3a=eyn%L+Iu>Ud_#
zA@cU-&5h66&72$S)I||b;yxF7c|Zm5^V0`6;og*8pztYt-DuQ&ynPJ8K7e!i0Pf!6
zhGv)BJ;}4P9_2OrXXHAoa1G8bQN0-HjNcE}uZWwsiw?@O7q@x3&Ke4;O<()AUIvf-
z;HNTSZ(AvVh}pTS255F&US2d5Bqce8zq5)qJwh2%=4-Cvj^A8ozKk33+Q2=E>W%RQ
z2uG_?nKQqMGzdOmz7h}&-^h6Uuic6oyuT&65&%3c^hi;BeELA*^Eww>)F1&xj;BP7
zX%1<l$NY_oz@=3`&FJgdUjUt~)znF0<pqEvXU3|Pok=}h_U*+$B~iZU6SF9{t?}%&
zq(?3b+ewoPE&?R-*R@4OG_S*}zW{>aH2*wwB&|c9!jqms?|ysuNS5*i#H<ltf^LOh
z=mb;Knwo6YKTKyo?uBKh#ixX&^wb~HTr-Zcd#+eE#+)`7(_3B9dl`E%GNtik2;`Wq
zetUl~C3-XR<ON(5cA$8(K)pgRJSqELh+?01BFGB2S#ebvI6wn!4dg5sCBaPn0?@$+
zP#kjSrPUfUyygg6)2mP(KR?9V@1(!Z_oJ}aUmGErt#=S_u{q2HFKX4vyBwL;$n9zp
z(sgj7R%y>8#_H+@;zqp!GX?)7UOO`_&q7-Ujm}>!q_~|DU%}>Y8#1&F{sLbOc<ZbP
zAOz9OaO0A2+CcYf7Z;Tyu_PurQ{G!nPWJvUz6Y`;^Q9>+$yV-j`;YkbTfztcL3&mW
zSvkw>u(%>=^pM=RlBxy*#qT$m%4?o@K5r{4k~?pvP8PRjpIfAn6Rw5qHyR&H5j8;?
z`j>dl5n=-94BF=;6R|4pG>;M)^2FfF%KWz5K4B?^>#J^M-!h-faUbb<S~W9edE+Fm
z2z7ht>xTH*YbJ#%hVw=H+*qd;mXJ(_4zt=Z8L9IR=?9ZFlx7{khlkqMlrUkDA2;kD
zje%dtKN#X+Rf#tFO!Bm(2s^sszWKgN-d58(Ww+szCs)V$V!6|$-<0OSKT$9^)4i19
zJ8<BK%$Z7;zQm#fIx117cf%@SmdY+cW=6HP@T=GyYA+PeZh9CB5c$p1wd9H-h8El*
z*<&1<*oX~H3vsJqsb64C0c+v{ivhc2m_+ix>!u31PA-;?c$EuAqvUDDfC<%3%a(9!
zEV6qtfy;ePJ>jp%i5YG)C?Ewk44Vv@8ZV{%3JvuhwTtcgk&wv&%ifhGq80y0vFtEO
zv<X!Gkvd{FNEKTP*6akIOha22MQjEV=4)JZ4c&f)Ht6x;*a}k3x&N@Yxo~<?*vR0h
zJHM8$6ph*!iU@)@y$N=o1Z`*fcg;1d=|?yRa3$0e@xYFRZVoRtsxY6Rj39rxPtXSr
zBDDJaFz4OP!8qh;PQj06^2ua^38Au;QaRWsMY05wDJc@7wEHLIty`%t2^Oi&0j@+_
z*)uEfreH2Xk>rQ~I1c+mpKA(Z_QI-b6Q5c<4SNC#B^K%>TrW3y2b)YuDQbLMn)WA$
z0o~=sShp_U4?v?-23I=!wa4(P;}Ksxjx3YZ%T30%%@C6o$FR`2))ojY;=O~tCe73h
z_-dm4{fM5fbk+b7Kk}R5Ol$9jd&*S^4@BVwlcC&Yg=cO?MUxYZq0~*X5<z|19UdmT
zsSU0esjJ<qL+Wq_i;g_Ezp@l3bhUc6T^V&6^|>^*4xsk~@6psdwB2aaAlBfKDbyR0
zs8(wV<Mj5UTPA}@>K8l$mwM;DItTT>me0p-?LVLJ;o)j3D6D&?D)D+_UNtr7N)8)6
zP2}KLc%VDhnS^eLc)`WtL7+Px%-I*TQxQ#W)6V`xy{j>GVqlI<Yf0bgp?0}%VYXpv
zlXmp}U}hpk4op^3f-VzT`bR~b6qB7GQ%w-pMd-E|G|D32hO#=I*SaN?+SG4lC&GG^
zbU1LbwyKI#&IQMD-l~q)gI-2cB&1BGh;@{GO5VLYSM#p54~;$FhNU}G!>;R4C2~}M
zjma*JP1%WY7;i1zijgw|DYGl@EvQmKKB#VrLIIuRSk%I{J}J5N$))Sgd6pz_2v=nv
z1061&{qZ%zNHrD;6>>*}BWF%Ua4bZHD1tKD9&H{7x4zCRU5s;?c%2tEYxGJZr;!`;
zD93x;Sbt`c51X!Fq&En>#bF2X)gIqlNSMA?19K2Jq)>a+ypa`%5cPssR&|uquYqc!
z_&vM7e?Vb@Sd-Mp((~|*;nd1>E2-R+ZuN`B8E94P-e@aMDisy0c&8&t>{?wd#Og^c
zHnuB`WnNXfY`6={ZML`j_7ZtJG&<HPDmoZ@s4&n{u*zarCr&CZinPa2jB&FU6RDS0
zQEWt!5_bLGUvVPo$wGnLGL#cD(4l~b%#zPW#{}G3R3gfUT)JEs!(u8*T!1s8UeoCA
zG>H4w{Q!(&CwAeykNzzK@!zIyHM(LCXr+j34?pOT2VSkywtt{BI`%p}M<Z`DARqWH
zKohT>4my*GfrsS57stJAe+u48#Wp2*tav-Xy{KF8#U(<LV@I&fpl({VuLY?=L$ziY
z*EgLd-F6M;?wgC2hE?_rox#k78~f<dFUPQs{ZiUVWH_t<HV8zY*#vXCM34==39)@f
z9KT|)GH8bRC5%)NTOiL6>kx#*vRY=h+Kx20TlvMY|AI;;G6ls}unVSiybuD1{pYVa
z*3#7t#irv`8Y3<b79CR)(m~`L1H7bs_~s>uHH&r|-Blw{{r&SM;t>{89``4#v;>CT
zqm8ON>uX}nYp2FEKE|)dzICPPn~I7~ZKWA8aT&uWw07`K7+s^#0p~{CAk#aNUh%>0
zEzmWu?w$@rb4t$0X?&d9%S%r?$|Ih4;T^GaQv^4k=u`n6BWo2SErVy651Ng2A<t1x
z_Y@me?KsAI&Z;2;qdPSAF!st$BQbIyaC^BsmN=9`ZcLS6#12WOc###E!^V49m>k3E
z=O?HWcJMHrxKIM&h_D%&!KKTif>kY8`&N02eIr$bI?!_Jb=!JfQK#U9C^ri_S+)v!
zs0=f$h^GS$!;#J_aEv__#nx`ainF}T$wf~S3@a-R^0vng?w;aUMe!UxQUAB4g`fJ)
z1k3Ij0i`mw!d<}K2VtlGP2z4@<xPz-Eu~~I-rYC!I}HX9{`X)VP}%285twj0;%}-<
zn6|9v@QDs#>RXH?c9^zoqJ!V^A8pz92$OYhWOgo}mr^3P<?#8Ej<PpcE#Tfxtab_P
zL~U}R#N7L7JbpMu@%iRkyE^`yJ#a79rIjo!)^)aBRrDfm7-{5SEZsYB&mt36lKv`b
zKqft~O@B1T?9yE`Yo>{mR<S$Uxvr#3!CG524}1+JAtx9#KUD`qAb{I+2))G|0ppSr
z`$A9rsIIOkHqX!L?qRF_l3W%V2I@nwrX(VS$-w5_uqIA9KFLtOIvg$z0Jjz|RvEWL
z5<0%TsgvTdJgt;SE(|gkb!7BN9@gMXm%-EmoWmm&MWgC*vK{+v_n|ZOoH>6BTwP`6
z7g|9{Z=S^)5Sld~7P0f`5g#I6$MY#2>)ZhT-0-2O<;U*PsamRU5HpVt-1YJY7*~39
zE~!T<0(iVdYK>%3Jgr#*d0zPzPRF=r95#j#v)t{KjkBY<)f31|WAd?7G>Qp}3rTJ$
zNh6Q31Ej-Z#Y_X&4Hkm~ZF<y9`a3fkD7ICuo+Q$?^GH<9U;GWHP3kVt+@htaCYsYb
zLHjj|I@pZyfozPPVc6gWV4<objHNU9wAkxY-*I)c4>HnXv#MOf6&vF;emv^=y{Sie
zxG>3UeL<$s5=8y;R~@d#Mh&lVfE8XFZ6bDw>qa)MYK#8|IVDU}eY1!q1FHp4$w_1~
zb6jL3FodwQ(?(dxyE?>c4ly_2O@CAa@74wIlYKKH4P{PV$C`c)0b!wW{}F~udB@5Q
z>yAZ-Dy5T7>UcIkca7Fn?08KCw+;J~T@y(&wP4GBteZf7Qir8$N}y!EWshab6rXBc
z$N*Y5k9NGu7|4y6^*&UJ1e=;By0X)jUX?zVXwnf^A(IqOCZelcxil11s&LC&9?cf2
zU7yaUL-Dn@T(@*xtvb(-4EubuXzi6H#o)w<;uL>!YcGB}f5z~YERa4ABZ{T8{M~Wt
zWta29sei6xWpfJ?LJc}|t{t--$pgHUm2#Wh_mBj>+Dx6YW)U=&*hnG`OVyd&y<iNA
z<naRat4Zb(dUX}K{gPfZt+UKcw5k7AIsUx}lsLP7+sgf66yw))(Z8xybx{~6OPlEq
zrU~H542032YVf}gyzflMhSj7x|EfuA{&P+GZ;{piZ;@&8#%aq2?^*aH<?kcC!kjZj
zchNHPWJGK=x?QkZVmknc{EN{h67c^LA((J|8F>GXLITF%Wd7TjSQ3LZn3z(>{Qjrp
z-Iv)v=Mc(UlcUUwzW4trd3~&9sY~G89s^%oh|KOAMNQ_7ptm-Yn`GMjN@(uw**|Hy
z76Uy-pelMnWrdQHHo;K-etaZEEt9Uq-0DyA@WmJeM-D{V-}JWq7&m&@Pu5u{)664Q
z%(KRx2)6tWsGs|+Kksp!%snY7;*1w0^z=B)S;~MzJs5{&>LZK7j;3RL>c6>~rs#iu
zZK@lT6W_2-o<K2rmnhbB_2luY9EHVL__rlP&PH1jbpvjq^FmILwDpmYf@!?wk*!N)
zT<Y*sO(Dp6ea|TPvFS3;$A420W(>+z_a1d(L*wyPCHCc$>a~iO2lbp*rn!?!!zz+d
zk)8*W>dIp9G#Z=7py~u8#_~yu<$YSfk#nRIhf~vb35Kt49G;BXWF-mv2kXi#R4FAz
zL0#EYExKU(kGU#Gw(R+3M{)(|@8ZLlTWUl{f7hvWClqmu>0g${=g(X}hYJM{H%2*-
z(@tb8IAgr^>Tx<lH=%2;NFNRwCugFrvgMjroKWaQM~yV-{ATx~hRNAzsXan=Dt=q^
z^++tp7PQUnme=u4g0D?=hL8K!DFX>R-m`_5$K{dbDyzBKIyU`Gq95X**m(+47=C8s
zHWD^n_<Y165gpq{cN2J?ko;peOdRsbp_UFYvg0Z(aeJvA_JBV}`AV`!T;{S>WRuFW
z6><ewh1|r}6A%5~dDwu&;H3uN==t$=e^<lI&!J|I<njWY#_8(cEa>RT$0}4>E(aA;
z&Z4IO$e{xo${c8mc=*?TR74S~eK9>Adi1=A>0F-T<4yq6>x}A52;VUDGf`vBS=->o
zxF=1{xM0;p80)?(<uy;4u{HT}zA&Su>1u7$T!xgv<8c^wf7x-prIz0;?U(zbtAVXx
z3!^sJ#83a3!LU{sIU#Mic5Rh`a8CP3Lz|Ga)Uz}t$ng1u+JX`6cT>O0;atfO{t>#(
z)m=E=WL;ueUUGfugps&u49Y9#Zf?iNnrkBo!PSW*$R!jk!$+_zur9YS6ZUWOyf!f7
z<DklNJEXb)b;0M?=W!3OA&-^iW3l(5Dc0QULcP4Nkg~!mdTmJ9U({(*#+sqUtwNy~
z%g46~``%9kTAW{fgY2K?@y_+$N8JjJ`uEUo9FRyqPPFHsK~LN^-5|y5)7_D4BAqXI
zv{ocVNJs!J1d{S{CuYB~n$e?9Cfy0aubGt1G`zQypQgGy#)ph!*840vF6;E1g+0<x
z{gh>GDw4H`OAaT*CF(O&Clhp3uw#K_4c6&9%e5ZEt<41n?bxDY#M6`yenP^N)^UX0
zQ6GV_X3w5`0_6Ud0=mj$#+bQnZoWr`<8&)!%NGLc?6Jx+(?c6SY8sB#ETXb>UXpGI
zKPleL6qD0%4@`XV+5G;(97tQr24-whLw8tZPS%H;TEKyH1fQyzgE>+oEj~4FCK#h$
z4|Fpk=Dbfh*)|96(ePoE9~ugaTi)Ha%cS4C&z~}{OVbv$`|3vE<C{mq;lN>GNP$M{
z9a>-O8i-EBwOG2aeMT^HvAnw~PHHII+`Pelwa^BNPvI@Ca1`LTisB@Yrg5(5!XO+#
zLc*ghV=HA&o=$65+PXg+C2a>HcD3Wu_yj;3hDLHkRqZ`wH+{C{TIx00IBK`odA}5j
z8ygsTrs6I*vLew=BEYB1T*_3Z+PKZ+h1x1V3EE&zB{XBTnpe9UKaMJdHZ^F|gUEw5
z4_dCB_1xsTvJyQ?uxU}d1KoVj;D>rtQOY#(%E=KrZZ-O2gQ%8wz+67@ae2{r0xh<#
z(`GqTMuOI~a|)Y_9kXh>8p^>-pe&H0FnRba?ed<^yv(AZ%)TCSGLJfkz8cv#Q)B(e
zpoP-BcY6*_d|2kr%8;g!h933{+nWY)G1h%cyuSeLWFxb(%@1|Cv+^x|Gu@#euRPXV
z2s$$DVXehI<KYv-bC1lnvS-jR<zQzs5%27N+%PeD6uK5!vv0roo^ePHg3K)SC)^1&
zdIE)(P(UsCN}W(rv#&0okDTCO6McA6w`|WHY?*1e(#q>ZI@nKz$A#o;NVLM?;p71Z
z!uZ<l#SKSF%j((+#a-4J3KJb75W}`rFOG$hCySs0GbZAM7hCPgSW^w&M-{WYtx}@x
z#g?n|(n6qwhL&UfCp&-mF=kSiLX8+!X2q`h#S#Ksiq4kg)ccyKBpH5~lntl49!$%o
z#Pi#>0Zfs!P)M}p@2{os&I8Y?e_O&7NnKq2l@ee5A54jZKf>DmdA~l0rgC?Wd)rY>
ziu6=>s195{jdtH|+U(wut{(-<d_o%V?(GsX9G*2(%4H+Jd5ckibRH#4->ux`F%lPN
zSCzz8>ebWNyC41DWMr|?o0hhD<^lvp-u@yyU?5z?aon*@j%Oe@1kPWMn&g!Axt<#z
z>=t6MU_7?4Dqc%WW=^Jn<VfN$X9G&x7dUghM9fM-Vt41(f`UJ~w-1@w(BcCEh~QlX
zOjj%gy;sFt#3kdE$r~fLx<`?09Wn@0*<SIs=`=&NSbtPW6T`X{4=mt>lq7n+nmEFn
zUTn<?J+`0kJ>N-et>8U#4);0d6(aBW!LQ*%X29RtN-$0OQce5>$Ect6n4Du}J*s3v
z&Z6cfx@@eJXxYf!zbbn^F>VX7+p_$Ob+R2ug!EYKm2!UXHmE~ybSN5msTQns$K{Ui
znPOS<BrQf=zn7<(c2mcGhv-z^KE1~&y5WwC4;m}>HuVVAg_b^4@_8S}IZ=UHgErfi
zH$4tJjJkcto}Zlzh|%ADVqI;zYB(w;xV#X0IL9ItLlvBrsje$wq0go$r?nbwRw&e)
zk8kFq$ky^13b5t~+T|pV_8)iR$aMeg(oM$Yo2_jLgz`M$>vwJvDH9G=Q&}@Rj3X5B
zO>N_`2pddJrOXawxGdtlU`dVauwh+mRVq8FIINm!&A-!eY-SpCt5QTC!XQn#_hJ+v
zCOa3@qKNb2Yx1Yw!=BdXC(UTchu^}-*~Muv{Cu_g;jzo%eQ(rdU<;(z{v*cO8^94I
zUN$CKth`-x1d1d!_9+aO<5+m_ZUFz&`2e@F>za4_74A-(*7ui!ZG}$5Pv$F%5`12y
zN`oRHsTy+VWuLOIHwt7IffDt@Ufj8D`oZL5or~Vi4Bd~{V60-$@r4xzs7#sRSh0&!
zYv654H;3<<{!}n~V<jX5F?%x97$`5*8JUBs`PB(KPs+Agn=q>G>i0?RvGC=GShrE+
z@%3=BX`;Pt@WN;4wr2HKC`FHVDHV!y;Y@oh5DT^Rp~Ep{yZ#fRx;E?VwlLVSnfn41
z-KjGW#dlSnr*Czdk6m?Lmx;fv32n2DV;*BLji=n4l-#bM1(+7-ACgQY+nA+@2!(t_
zZ?Z;=n3TGxeWizB`isO(3ocw8F9t56gkf(F4hQEMo`A^aKN9Z#g~R$6+)~oI{nzg2
zpY2S40Yt?9O{g<616J6we)0>Z{(f@u=ZFda&~QKHz4P`<JsR>7&_L{nWJ4ieKq%%z
zpi{z&PWp;mqW_@oWj6X^vg*%t&+OhyZtZI0;V!Rr3Hwd{HDwi2(0GZhMvc3*S82%y
z?a)qb(a=v?sES5@+sZegRDGhWZ@wagOr=cp0*<F!%Cuh88wS2xG=tiy3o_&3Be4=^
zh1QFs#PM;MJ}8}zU}poh2&0ySCv#AI>X&{wKd&N@(ee@Q9hToW9Phbtdp$xg!H@V&
zhsQq``Wtd6?v#wD4<W@09xtEUD8LaiSh}>_@~gT(#4a|RY&Y$boVy2k=i(=c887{Q
zY&Jt9D76aPI8=1p4Zi;p{#s5ywV<d7UBwgzGXajyO2(S<Yw33@38L_`+9$W$$yj<U
zv+E0)n~tx-T@g}O+H=<9#zpcA$G{-bycX(Jla@(|v&+)x>jOQv9L(Cjh+p~D1>A_S
zxM=F#Yc~wmNA1bQ9$O-;YaK#c*{#;a__3Dxi);c0@y-jIUk#e8z8~Y=(Ga_Jhz)PP
zX3x>m5TQrEt%nwMWCzIFLsx!%bCoyIZ9Y)!S0<E}@8e}_CxjzR2UVtcrJ`ra;&FRG
z$nK(%!{rPZ{@Z__?0xy4F)ggNL6qlop{MVuDK4GILO95aODFuJP@4bCCljVndXg|(
z5BFu&&!%hl+IQ>x-<B{oGP#kGuc$0a>QL$*h0<BF|80fR%7WA-74=i%rI9_Hd-U57
zM*>AJnQ@PyR<T`$(&en2ujgmE^0d|>{$0M5GF{Ky$3dbwWjYGu=Y5GL0dw9<p(9PE
zlx=0xH8G^v5|5QuC3173+$X<8Q-m>Dzn+ei1rOF#A7QmF5MWeBR^&IY)<zu#{sq82
zyl-V>0_VR%Jmxu+<7_R34k60tQM4=UdIb(q_td?a=#r%J==Shd{MbeblvesW|8V@$
zUNdmL7w*!QkS%GY4C2URS~)D$y^1L!NaSFU9ciidR`=650hY;ki2E-j_a1FJo^Iqd
zTt8WMiA_o4uD5NLZoAwQzr))s;jty7e@0GRTo;T$L}T2=Q|V$<OMYR%Dqfbla;kK$
zpU*1;t)mIixexD6@rr^>wVOS7IdDYnG$uVUuMy*^(VauJQq9v+aR8{Y0GT3kxfM-3
z?+QPIDBk{TPt5L4v;8e^7ya4mRYcQWLnO+0z0<^|Sb6{>HzUN>qc{d)u06@f9V#K?
zm~{>XuPb{SFcT0lPuU_9$psrS3ZR~>Dle6T2+6L}6QT4_!4Ytc1SnpQ09Tc-25D0S
zRVrHrkueim3jzLIy`2E%rC!USpE&=x%F{jWpdp14YvQm?)-mul?K#s4DtMJ4NfDZ^
z<ViK4hp?g`0WgW_<ue){9p0^N#&Z#qqVN%8U{H?IQc|MKxbtYlGD4emXBJfn$b}?n
zlVi(rP?7!sdI7K>^oKhtzS)}r6TWH#>&^8qhuvD<E=Sk-oXbO7BQ7x6Fu!;EVCRLi
z72P8{Sh^UdN{@)OK=DOO0bNx@dI0({|AnWuG>s_pnyWnu3SiF<NYU!L>(*CZAompm
zkM%N&AD}USCWl51V|OksD932XzAhZFaASOCb1Av1$=T}S9bX=a*ty|5R^A5bes5Lu
zz#H$Sy)@E7qr7ZBF+%!mv9&UN(B1bE@57w$Q~JbiT|6M~@LE>rH9pvdkVfUGFA}1`
zb`6B^nF_jmfT>b}y}QwmSDNk~xuVST%fuOG+}0mAv(GNWe4pa+{RhyV*jk<4MNzcR
znvhU^SV)O6hP#YbT_#{~j5nmZ&_+6%16pkY2KS!2t<nix7Ws00+9db^TgEl&SRC1*
za>P;i3`x9mPYzP3so?UkOM8)HJ*2)lf=}1`^_SQr*mPrmQx%-VQ>U3)-%I4Ue$dgH
zSLw{Ls7Q}W|3gw54n1#K5}&NIl3^&bXI7x^WBtzU@TE)DUjTkpv#WNzKKJDvTS-OA
z_vBa!q(EG97SKqm3p=(Ep2@>f-g75XeuL~*1p_IdR`=U=C0vGkre{#JD|t%5G)Mgy
z_s>kz&c}3(?w`qVaf3TqA6RSPWaZO1s5SXYNd!j(ucpm8=g>;jM*jkE{XBsD1xTTP
z3q4?-{IBNBzusK`IKBQHYyT7B5NXF8tk!;@*u~d5d~6?ppLxB^`0g=G{Hgl5fHVAX
zYW9b>L6^pdjZI+e%D}SLWoZAXHUJUD;tgPKbhnO;5q3OsvzNy^2QDUZIV-2`sd`v#
zCAf4q-xzrTUt1fXj!lrF)Ma>5?v;j>!Q?|6;RvuwP}9AcV^D2pj4GD9!PTTz73=Uq
zDzn-eHZo<*1W2-(q7aHfxIwFI6}mxMI!V+2_&9$*mEW;j2OiME@6F4%VVBbYB*ZxZ
zl<905yrS~FlzLDu(cNG1!p~-E^|tBQ*5V(#OuYwQNZjxH3RD(QU>E4U<3H<*q6qw0
zsl^}9#tI3Jc?V$cOlTTQ_5cv?#J}{?^y#R+F5e_s5xdp+epFkP*#;3C;EMaPJl$^j
zA_w^=E%J|=cF9BkTgY2xW;(@pJK7yG;$c4-{+O<nC~dL&1{?g3U2*0+-~98ECiy+H
z$$A);A=lK~_E%WcPUum_x#}q8ThWNdsRU%s6kGX)Qpy~eQQArRk|HZd@nlv@S_%r$
zRS+e(C(v4Ep;?aUIM4U}nWdS5=wAS(UXe;4qao<oV^<RY=1x#-koCncR@(xoosXSU
z)q`sH(^}Sw+UJD|2k43q82sm=&;K7@)cfzZ^ABn$vUJ($8~*+}3butZGx3xaAJ`u&
z>@0;6l0MMIOfcBxhs!D+KgOUl;U&&iB6Mls*_&B886JG}n&}vtAKY{{i&&k`6F+~s
zadbUT`Rv8k|HH{x)aB~*cy3NtdMzeX!m=qo0kuV4ra~l<UXk`Ki-Jihb|kYsh2(&w
z*F)`1Z_ei$xnFZde#=kW(>u#_!KdnfqS^i}8Z)JL&{MO}H-()pf>)w_`(JIH{@jF_
zBjv0eVyZLklKPyM)A>qtM%3lFsp@W3D$dRANaB4<^<<nEOty%YItN{1uACX*y_)|1
zj6nT|kU*8x=iSZ3rNAE7pKm)Q10HH&M!nVl+yTskbLpR3_zUolCxY$a{Zs2d_we~;
zBkxC<D-<22!0gwR>Mu>9>qvyzkG~%)epiz}Nrj&1{RIfndas@EQY`WRzu-ZV^m|0e
zY!&DH1_s0pBVD5^V=Q{UT@zR6^6Fi&+HkwLMLlzW?>S2p!P0P+u?&6j&u(IW(r`LN
zH_vZ<uu32B_Ic~aT(XPga$288LRsvPmT&CSXa}^_)XWC>a8G$n*}UdVl?U>b6DvCb
ze&H1V9AlHu1A-3Q>?JOdvMmVTjB8dM0YaER1DjEkWzf|Db*)4zltI^x7uM4}jo*ia
zU29X1TrLn;Kp7c6JIYuaE0~ItgX2+M3rQ^sMkzg0J{1AS`^l%pYw2`_b6Pb=uM?P|
zEAn)6MrNz{-Is;e&(VHUH`q6_(L5D&kdREr8)_q-hB#lOg-$Yw=Efrqr?A7A%)q_L
z)1p$TKH8Zj^<EJz#`*r~vl;vEeivawZu&pu_Iul1cf_b{teds5ZyrZ4i3`%_TVLFb
z+u1!*Z^w<U77I*)i{1v*HRqhXvM{-guuWu9S#@hmJ*tKmaJG{f+PIVj$8{67^sET-
z@1c1T^!^r-*L@bQ;*JX6IP=u@!Zmk6s0y?O^ogdSI;m?*C+ZPcjyZS^LC(S%QTKw#
z*Tj7{0nq(Q5oxyCuuJjgbQdC=$Nch*I9z|<3Z+_cKiLKDkdm~+(&-5ufGopoCM2HM
zLffPZ^h4x?!#}Ty@!6;BkZ$?{WchQ)Yh<P+!4aAQTzgoZ!tgywP$`Oqq{1vUq+;xg
zLSlPpig^;qY|eYXC0O_F=E@vS7hJH71x!qDytF8sOwV*8lMb@A_LPLyB8Ob1k!E)i
zvyOKq1yMtgWSdnhl}O52l#rKjppT0wG_H~trwL6@7TcrO!(vUYXh}~Q(8uYPh8Fk2
z2i+|v{#@DZ1%|}9&SRX=ZmI%7tZ6X}YVR};Jrr3wMkL-Sn(W2VBu$_69Zoi%UxOY|
zKe9M-zy}A0BpS|<gb5zC)^sQ2sy!eXYL4}a1k=}wC{7dIpm`}=&rr2s1T2!6(Imlk
z#&6X&<ZnHLZa=;=AfS&vq3S@ITUOn5F}5P5h)=5ppIQKg>?e33I!js-qNfT~A%LoE
z5*+-P*n~mQ1<wG_^p9(|cyXPc&&Rc&of8({CRH@oe4i;uB2rEiB9W?s(oEdD&o+Sj
z6Z&p1V#&yzO{%<>nA@2{!LiV$pML=^vY<mg-`R|4$M;pwnkeBr+-FKN4%@TGz+p^{
zmc?A2IMl@MOafe}d;4Rw<r?6-nf#hi8A+)OsT^*4{ck<dER$Nv{T>uWmiax@)HGG7
zuY%+IPXC=P_$TX#IhrYp$jZ=uy+u<5vjz(LV-56=MjS%i=!tH~-{;gQ#0j%S6o0Fx
z0Q+~HOC+Y+f^F%yUXWJ^l(6CS|H)3A+r59yr)T^>h*~*PKkP+?Un*88N+A5e!tZ}~
zbD)_xk}rYV+WTng5V)4izZ#WO#(#Y_Hc&YXr?Nl+oCQiXIHWR+7M$mldC+X{S2$2;
z@kfPjms?xLnPsv$wPu9uKeWvC9U9cqQQ2_NPB%@pON5k6l~S-FgjeKXBJ)600Vo<k
zmO|aM_LSh1)Wm4>dtH+k=Bke4V?P=O+G-q0L@lh+nx@I@G8%Nas_a1k+?XzBx>852
zWY;2I3@WC_AUx$5z3)Xdp+NKOH#f7_%>oZ*=0(d1H|?oGT@5VplQCA&>twH2bebB=
zhHMvHET<@y7jDCwjTBi+CPXzfi_ZP%&=u)v&Eb}Yv2}>n>gq_l{fDgB50cvb+gx7A
zl&Q@+AXhUgJ4a5%%2N0<y5TY4FB|+RO33>fi+iN04JEP?=;7MSCeo*x$wzUHxt}PF
zlfGoX#j!)p^WSUPzyHl2G2y;4vXY{anj{4h>4B(hS4i?mT0?fxEtq3vq}6fZN>u28
zD^D3gJ^tMzLaC!)?$WMyo>H@+XVcE<c@6?)$gFr<IqZ$`^LVs7l^p#@h^Qq+>Wsb2
zSI+4wf;BPf1{Cjw_dg#hzIyVV{FhnEzol&dA+7vhnqMXhd%!ec4qzHEA9;ISg6m3t
z%y1uzHBvWqW%E%VINeQOGO9QZ7$EhG31HB;LUq3v#hpHQ;U!R}*F39Y&8Jr&;HV&E
zUUnsBI()OJ53;#H{xQ7nNQJf0G3BP5<i1I}?&&ZLb!c>DuXB?d=OA|jXIEb4wk5K8
zuZcJ^xMgQ|TQ6Y}#O0z{{!U7J%-->L0-wx&-y}7D-bSBNn@WO$C*Ok%J_#{I3nvT_
z2lTcwTvxYHOo+Q}RSQd2=eDULuxcwwsv`;a?z?^|tmZg=>KSFw3&6VN>9d*v_XW3m
zMzOWq@+`1g#(?M48;Yq66YG&GW}gpO4!22tE=cdxbRan?EP#qLr1oL8QM>LBHR#I7
z*7d`@Ve4Xesmk8$A98p{o}KoceF&`wRC^}d!j%>CP=(j*7}&HejJQD(9$#lox@avH
zSP?2F7=Ntb)V|XAtt1_=`QFb@b&m&YwxIlTjB5gw$CfTay8)vn$517D?r=t5R+sJ?
zBu)|p5ka0Fk6Qa$!!)kQOZ<kwwc&>HV`n_@e6HsS!6)qi#1c?5G@0q>C(vS8pGO;*
zz~Mbnv^#Gb*f@<Tb;yEgnlGvrFN4@^j5w2Eph9`z`6;;Wd;LIhGya9>T^v-W?K+_u
znmHnuI0xRw75*sRZY@8oUnY{aAxeFUO^?gWs)b5{mPgi;67`G9Xmz*QKM!6@co#!N
z^;k%|^%Kqd?xpkbW?Vvc^L2#~Ia}npvhh8L!C3JT%i5T|T2I3U)6zZ%Uj@71V$tO0
z31!0Q8>}|(egoJh=h@`Eic@BVQEvk(+Qen@rJW%wa10r`(D=)TNKfpUgI1;LCDs%D
z0#YPCoPm0hxN3o@q@yqDSYa#geC=nxc(}I*jC9Xm*9Gm|{}$_oxiA*43a;!r2x0ir
zvACPOqlBM<X)TR()XsmE<4#6}IjJ{R_tM>Lp4%Gv_msyx*klFnW|G2>Lqj>18Weu*
zyAL@>0(e{s>gh<m`ej8jl2pqsow5=!V?HiYS7W19nS^E6p!OxWZzp{X0&QKN--m~8
zGrbaOY)F9Cv2(a^q&9~8In-hW5T=1I*y1OM-=g8V@OR`>bJ8!`57`4wWPxGi>g<kh
zWt{ZeG4^?MjC1@Yg_U@k<(*x^hElRrAoQv_xOCrrrU(c`WHv}O7ERGbl%fZQpN_Iv
z-=DAULlJiO1Ma6X-hOks1BKqYzkarv{P3god%MJU;liIyNAG?a{xm8Iq<9x>;8FRz
zKA2<f4K~wf_D-$80J@)vPN`7vcQTEu{{qyS#d*C7kG+}V)cy;w{BfiU<^Sb9V4Uiq
zzs34I(uFy?85RJ)488&7C0=NF|FG=t6hl!;b`J4!c<v2{cfNI+WKQgb3Nv#%{2j)H
zMfo=vmvPbmGLCCwLu+7xSv->>W&dgOjoa#L{>~(;&q!2>hK@x4PbyZX+n+U1#rJD}
zxF={o-?F!Bnx4eY*09mTz#RszegPB<54A#<1b|;=8AjDYFK@i*Eqs#CXWwR8`DwZ3
z9X>>L-@d?RR+R6=?x9i5k81r`JhqmF@iSwIJ(uLmr8*uQr!CO-_g~7+@CF5}$n4&Y
z;*eefZH(Eqhc#&?C4j#E$JxW5l{o7_72|6gmt%d{54=Q=cftVYOAVw<cTS4FhPrm2
z&87#1u}mzOsadb(<Gi*8TiIcMmcy&2j?3iJ7#Bd5NrFk>0k*Eak$yLs%2;KF`1M7v
z_eq9{yrsMwPy96oG0<ezWBRko>4gx9&wOJWWJbkX1(a%#H}6|iMJt}>e5Siu8=hE^
zz;mzl_?3Ur2lGQ3_x5#Lo0W^pAwNt&77NLB#Dsj<<e}Y~Va_MPx(v$YxRH7Ppc(d{
zKNzZksEJomoM;7yFO2Q{e^K|AVR0@?qv#+3LV{a@yF0-pI0TpA?hqK<EhM<RyGxMZ
z4hinT0u0085L|-=$ht4dT6?X%zkQx_&i!+r`)j1TtGi3Ox~r<Ysu+*U?G$G#)p#Ws
zY&uxo$J@(~yAw*kWh^oqNa@s^F5(B?zza8ryjf_ZNqsZr$OI0G$STJL@=&<2S~PCj
zy^btNWDrmVU-sbl3qmZBS}l^#8rP>x>YD9s0|-KgVoPz~J9+7y@f}uurfsm3V>9TE
z+dXPTm4xP5J;{RO3<I6WJ7hGwN2w)k)nww2EgIGdL4}S+o(qXt_ay8z(Wp1)7frTh
zuy`zS*y@kHj&7n>e%7uzp>&Bx{fhLi*wKukz6og(t?R7X|BD0l*@ZD|eB2`<cbT!T
zb^GX3U4{Wq=QRFZ19wT#{-cGo1Ksw6=~~_=(nlGs3j5i@a&FkKpEOCFgiE-O>vK1p
zePJP$T+(?<q21)am;MfSxhB~FCfGk=*;>}O*wsRw_@srB)y8unwX7m35$Y_!BDo+_
zVUSod7KRz|-ooCWJ1s1iX&%3C3_2WIc-a&IU2faxXs!tCWA>=3SF|q7DKLvOVLm5o
zWPusWu7|ZYG3#)9VFwkqzq_BfXHC*B&@AYel8v9y4|sh+V5S^0$Uj}2ULMVVlJSH7
zyTn&327apGHK+oK+eVddbfVppN6>dzoK4ZK@vNRiLjCiqxRGe2v*FKVQqtx8WZtyC
z0X*p6?yK8WNv<+4h0SW7?6k}Uej(GPX?9<Rmb5k4$#b4|v!+7MG4-peZfRM*7v`JU
zS&WkIXF5A`i5W&k7g7*cmG|-V<)!l^amdkDE(Ug*-Zn}&IQg&pQ1L<_EOQ4394s^L
zIuL@V;&LzB5A7<g*oXA$<mhmSFLf#y`k3!jWmWK*Sei1=!rzrk>b(Fj@24XOG<@`M
z?-v|dNbMavrfbjiSz)oD4{Lh7X#MaoJ@FBx)H%Tj<+9Ui*P(E7ouxf_<~v1E3KS$9
zly*mLrV99zl~bRRkuK)%oJjXvH_`)69=${Tr7RA@4R&cM2~n@TA=qUZF?lc?GZmFQ
z6^<~qY!3F;osz<z!OvX|RCKidEsNiOcvk-NobWq<3`lKr$0}6udFwiGBRBik11Y~^
zR{?*7$U`POH}m5pGaE|BSs;_$DUiudBbAjd{%gM%jgY_*R){WlILnFg*|to>K&j*9
zqk(7BDDY*d(LP2pGq~ug*tYo*LKMj%kr2UM{hJ|SgUVATl(ONfokJVql}s~BqOvtx
z%P)<_YI3_VJKpAFLGL}x5A^v+#4yPt)<I@;(shS#YCbgkGe>rU`=d45oA+VUrsvt3
z-qOb~&Y3~^hFrU4ma$CxA-#C{@lTjL@oh@zWpyM4dj@gy){HQ;;H6Q&yWm{j;E@!A
zKR^U_{p%gk2wW-6w3#7d=KAB1IlpgAu}5x2xH>(>1{wqo1ndjDNqNkh)AXsY`=q(t
z8oYJD26JE6kO`cj>deKfR?lwc2vt9ZQ6J`ITJT0kInv3(ln`jk-6(^wvqG@b3TO>%
zMlT1Oq)}MqN9F>Ea{Q{w!9lZu(p`>8zAA$Ba|QxMyMWr+OJLY@34M<sw$j9EO*Fmq
zz8&mEdyc;~iW{^^TxiUW)vLvj{91!SN$pv?6gx`NCnUI4Ahw2{Z5A+lzAOGObbU{i
z%9DU?sG%~o6Y}nKynh7s(6Y}}>0eREe;xWBf|Egi$0;KbB%@Uo3z1L;4x0==yxjW@
z&1FVD?edrHwdDWg7A1G$%*4>13AyW&J;x)jM?mgAYy%EN32Q;;PF0zpkZs3ngT)75
z8OquHQt4|IQn8yX5j-y0ia45zAHphrgS|t%_`{yQjES5T<S1vtKpkUrvDP~gJW9t`
zCcGbU>SPX>E!<u&9)d!02<oU#A9=UfYX!z;QG9!f{9(|w-S<lqM_<sovN82xP`ZKo
z)zd;tyApLy=T`#_KJ<0W6SWEy(|KwY1~TUTbDgTCxC^H~QfFINA6DGF1f8PmDa^mL
z+C!G=TOS85dB9m%AUWZOy`tk{d_~#npHLhF-kj$TGYkt*0l~pZ#n&grQ2v-t_JaKZ
zavy9NxnCxrGzhAfXGu3-JG@SVvNc7pfPGA<DNNLz*G{n1MGUYwWBS?>9TXFZ6m7sA
z<^Dwql2SILDOQ4QIe0k(X)9Qr&D)te+VPhA+6)q7qaS_yi0WJj8G=3#j4u;MzVNJg
zf;$pMFM&c7Du$$;mQQ%4X_cbA)!wRf?A}=Ar=!x;0kzem){Ap*grF8J^^~#B!QQ}<
zbP^bA6elYspBJQmRS@074Tb`V23jfkAJ4a0(xs3vME76WW~iPQ+qG)XAG4N6E+tIj
zxNsa3j!ukA=wobZw^`oc5wSsy2}!7^X?7wM-`mCYAY?Vg_vzQlcz1XxN8#$_#}+3C
zlk;=!>m2qv)?nXjG0M@Sn{Uh|3a}C9e@mqivxu<3X86t!Xr7^va>mxM(p@gNwuBRN
z@>+k);{Avnn$s{1gD{(`{3}J-HbKiWcFT-9?CKo0$)-08VR<>rJ?5;zXp)j{STcf*
zw7XW0ntq*jFI$VqpCOv2+I1J<jAv%8!gLU*Uu$}6uA3-wry`CpJw_#=2((+wQZS?P
z;Z+zj4MA($e(^nHw`G(raYp>Ob>KR!5v$#vJcxNA8_)M(j-6V7c0XBjw<3+EE<+yo
z1GPA_S{9a_fX9rI^zv#8ioC`hO+C1uBe1#Z){%mItq_NiWs&<$f_1a~dr1(3Y=${}
z5<(b!tCOlc!^C$-7r+Gw4hEvnJy#yNEj5cAx5t`i_NbY@q%mqguFn}63T&o-&VZv}
z;B+=wqe|KTh_E-k&S{wX<6~7t#PGbw;vZc58?^n;1M1J0@0IPD;?GpVw?`iZV#Yb%
zoXN8BS}w95#+k<l4;4gAN1{@`4#1}Dq@5%%U&l(9Br4G-`aJ5;-Kl;z9$r@*lsw+9
z#F%5b_${@$2)y$xy82*jDPHrsS9v5+O1p9amk%?7z5<ocIn#a;!j;jM^tsF3U^s1)
zCXyu1$)eb%0YAjsW?yYjXhU?BLEZM_-ts$F)}S6r#~p9%#2|c*P}3-(zL5%8%(}4o
z<Q#aMe!|gXtS?emd0S~)7h4z^|K~HN;%cNf)n;fp7*JvAGx~JSCzM?Z=<xyHC_0DL
ziD2%M16wDagF-{PP2iKpmYrrqB+a*!+tcTP(1P`d2!qE+p+QU^r1r0iF;L>l(xQwk
z;230#trHvVVei!$b(%ds{2a)RYF*DYybaYyt1lw?LVl?V*{r1Dhyu4e%67(!eFzj*
zpGGb(bBk9TSzXO*b<e~t5BIkJ$5VQRO#e#f`oFPIpjV(AGLtllUP-sv5oLc~(=0G|
zbd(vnZB=UE-XzMN_`oa21vN6O6<ByZ66(Mg9tBV6jI+9iwGQ~|NEy*^V5QC|(M#tO
zr#k74mq+{2qjhfx2o|cf@hb3%C5!GZ_Fbi9T9_I?56Lgh3l8q(7RXz$+lLIl1Mx>I
zj;64{l+Jp6qRQJ0Leml>yV5p@5(XQ&JjKZl%!Br(KQv@eKere$%YL6A>P!QD(q#Tg
zatm$}ae`aygtYvAsrKp51Fe|-%loQ!*j4*db0%Glyy<gpbJknc!&)l43Fz5A^<mui
znn%+ZVFxhsqSLlFu2yTPtZpG>U&k3-bn$1ww@i_Q%uUZ4IQ%vz)F#F4b>JFJ2%!@N
z`K;Wu@rdAX$_B1(-t;`O5|u%fpDMff=?Ptw7hXEgQe3~R9`%0uMCbh3P`aLDiCT!+
zH7L5<V^gp|$TG8fS(R6|D0^qBO^QNIl}e^rhg*(XvP`bT3ru>{M192H=YHSrQWB{b
ze$aPu%#RIGtB$h$kh7Z9sugPAiYQmWF_?lMPTYY+X-p16D8{xdZwvdqqxm03CPa0{
z$6h@WTA}2ZQ?u<zt82VSc@(<g@zZ=%TYgKW`h<HDF0EfHr{8@hV>;RrSe;h`hzi&c
ziLuVQj@7HsypX=RR47ifS5{5^AQK|r{KgaI8<+Hn^v<fcESh=Ipf+mb*U7If{QvaX
zfl#ZZ{gXdH7pPM-_8hSlIYvg*hPyXeLK?s&9>vkKKR_Q-+x)G(%PQkCHSW4^hkQzJ
z60Uxo{}hKFO4B^Qd^vudWAffef|bQgoDoTLlP_C>IZL+^8#e&tiW3z$M=q#)Viy*R
zjGn!`NEnZo?#0(hR1+2^-DYSo26jIQneRGW#zcD?+WvKAk;T(5RCV~3R~iug){f||
zkoV?jX`LP=>?h#wdQ{q=+tpjPabr<>p8?A%I7Du%np_1M`h4}d#aVE8(czOecd(9w
z+;%uYK7Z}gscaLyf1n$Z8jF-RcX%Qx0dqgbw7nBv2H^PM7bYZ}a|Aq&w*QPppCvqR
z?}Yo5@?|${t^5!_2k1n8G*H3T-7!glbg!8&&}i-GLhzvvM>sF^p*uoYN1<IP(2WSj
z+FsYxn_K++yE>AW{Z)&C(vtwJ(?39D9=pE){h(muekzVbXi&l#ia2CAZ||7q3POK=
zbgyjPa7lEN4vrUehR%c!bq#o=LnGbvKs+wsG^jLWCSYds61bGrRNSm5>&<6RxNkrH
zLP0FWAST1~AhbOCBiuBU*q7`gvD;7~;Rm-41)Db1ce><#ug8*()%IL&qEtr8Ft)~d
zbj<XA!IM<u2xim63K|epMZx8b%d?(g8m(yyt56P9eZ9E85n>#A*SW#Rp~^poS4v(n
zu5$L)LS_ieMO2$4MxexRgR*~^CpkVhS=jx_R7XY+h1&whyzEiB=aieh)^W?j`0OsR
zMv~LCuz~UOM)3PEjo2vqwZv*W#R-vFbraw9+kHCkbn1$+<bBc+f6Z^o=tky1B3s_z
zgfxMs;dw#L5dFi9WC{I}1u3rT{Lh@$2KCPt!OtXhs4Lna0;1Cgxkzk7!U)<|3dvt>
z&1}JLI$0UWMOnJMJl&K4*iC+m1>0)YA>TwhjO2gyR>p4uac}I0;3NoA0izWm`!T!>
z%<PAVmZt=9Nk8Q2)AS!)q4IW)7#PUsiJb}w8fX72IR72`fBE;r!Hv%DQ&S@RO?D0t
z;^!GX64;ABKt%xZ5^8dTQ+;2q11Q0LDx$ftPV#b}&Oe>H{G{$U^iIoM1pZwpfM|)*
zd-LNN>SPgogOKijRQ?M}wPNxzrYjHYkTJP<`5cs38iWbJ&)PFjs0Nw<QXFv*`bmZT
zM$atF^$$?xL&v2(qg`aD265M@NMuZQPUyJ+!zd)YzSeOk{I}{eKNCE56XaTXG(DA{
z5SX)X!oS<9ilMT}dkA_2NNV)*3G2noYCENEeIwAbuI<(EfjV`v3owDehY4VZ2Lcli
zs{tke{QWSRx4>X}RN1|N!Eiqe=3$gFOdTNd7}g<gdNGwQ01a^L828lM(VVZe$eyrX
zzw8J2h(1lIs<sap+?>({=PUI!ixuSS>LFj?y?21PfkwZ{=*!#=JT`;}!3ZiaK5H7p
zwEY95BRD%uqa5rJ@HoxMUSdidHRx<^5CxQm{4>Etx=2suTF2w_1NT0b{0#5|`TWm1
zkBZHNJd0FQ8jSY`p(E=6ochug8qQZLoTIff4|Dp>{l8>PA%Xu|%d1Y}4u4ZI8J(K^
zd($9FNX8$aX(h+kBO(&a?IgJXl5eCJFaRW<yq#0J*H3u;wC=$DEik7oB4c1qgP$J;
z37}FxMB8290Xri+LteIKv4Ox)9#H?Z`b175gmuV=j;};@7aYSrqw>?)<$XH!hfQEa
zkYDpn0tW!&Bt!nqCkX$g-v^x8%w-xqf?$Y|2tdtR{{XE#u-b<M=lV&C*b{@eOSw`>
zsu?{}%z&8O38f3#VG^pWf!*kt$8LHm^x-W+tbopbsebVWf<ioSwrq51e3YBLT>W0W
zVNg@)#^<d$MsM%=;}b{V`GF7gm0|Uy--ww~vD>uz`QPGiZZg|YNf<_&LEqC_#~q`f
zK3*Il<c7vse}tFDqHL!H!L6<m;-Jscn)PYXWOKx`jFraFo!+-;L^~R()W^r<rv<v=
zIIkMM%G;G5dM{g{fzgy_7bn{LiTnlq2-v4?zH>DVhOaT|0C;P2Uu{Dp<~^6iB4dIz
zh^i(9tt-kIK2}ImG${x?D?sQ$5DVBKDsf(KJ+E|apLCrV9W7s+TO?0-b?Hvqz{0O8
zs*sMt=u@HSa!4$>RK*Q}4?yhvN5Is7C~)9Zi*+8AngY494#}!U$GL?wsEopz;*Z|y
zoVsQAP@Xu}4A|yPxso~f40|cqDGC&HbpQS?7Q7`!CRy63WzD1Q>}9lOcON(1x?4Qv
zLvl@j=RBj**r2-n*w<*T+M=Cc@x?#;B_YUxyHmXp6C;%K|54mWD)#nCWSLe>IFn;+
z=%`FN{j~@kXZu;euODoUr~JuM=AprZEM#83UtV;KjNd*2ks+kSsyWzhoHL%qh<bPF
z3ctE9jUB51i6FC(7A?;YZmVdBdRc50A4|1Fj%lUqv>O*8^e6$<I%{IbK8*ZbPvdV;
z^h`WtjJYfnR{|X%!#7!zm9UR0a3}oM7qpjMFF7mih~qp}w~FU|zeJ8zAd;4*#OjO8
z_X8bSix}jxkol!9UJZ{`u6$Hg@XpZ{b{M<v7}5LaEVk!mebIX;qy%L4!+a=kyj3Ph
zmlLg+^lcZ>6}e@@^6J~xw2yj(J6OI|_zQS(ZNO&W?Ag?Q=%Q!V$X9Y-lpD^osgv<T
zpZ;yNIL7@^^_18yghkJ#kwh%9(fG6KKrovh#&e%<+nV4ga$jQaBcK=LcN^zSt^s@(
zOAX$~X{R?yMd-Ms?ZB8x=Lfc5r$wMvhmN*y6_M`q{?$<ZUv-n)M1KMC81uXvQ3Gwv
zJ5TL*nGODGHW3PYayE^I!}i=)3GCARrY!8*x4}EXM;RyGzy<~7Uq9qTe}BIP3gkq~
zTF%BtW!@DKI(v3x=#TWr$))`wE?oma+_pcfKEU`_Goy#^m8%koqcY1jPN^MA77Xw)
z9o=Z8rQ0H@>uPa-QgCJvK*`_K1km%$3yoVq;F9a)z>mY;!gPp60r}Hb;cVt2Fc%m(
z$QS*A2>@OxCU->m^uQ4<kpQN6rQvHshq<;2kEMi5h3zdY$192uR-LEz6c2^`EnJJp
zRkwJz3YRXkPJ#aT$S;H%ypPs=l`LL`kpz&r)B}u`%;)}gv9&SN3`_KH;=#yKgq2xH
zTl`AHA37A<Ykgn_pdABzt;`Vj7R_-#pS!0Ju1_Xz5vt^Go|@?cy7-g7etj3=KC{>=
zT+3Yahy;MRD^WmFfc_@eAlsSg+V^Xc8}nY0-m4Eh+6FgR{fmiW$MWL9%3gwN^!M?6
z&JxPjxt>fwsJ>H6!P+;Ln+5rB+$_Cw%p=opDvtRH3I|K%t_GNUal2{3xD&=WZ~>xx
zqT+Qg`EC#O_%e2S^)8R8+I>?*8Vl`(C-T{u(HbN0qpdH$4W`_$2E|Br%tl0V;HuGd
zFqbb3Rx%*B%2gC`WD3|(x<xTyZ}=9#;Pw#{9L$n-j^_AXoadl@34Bks#S)(e#wns}
zk*gr`NItg5`s68$(y+@<f``o`OtLhVT5*#*7bX7+%6Iz|y2C@u>cRuFQY{}1;TX)x
z$VM7!w{$7sGAJxY{L0{Cq(mu9CyQ%&Bt3C-l-v{jxuecQe#eI4<Ci{ySeDDkI%{Jh
z^!}Y6v=gM=Ds4ETBpM`=h@N}_qZa=otmE%BO451bQd~YM&Zoh1w8nMGH~yE`>X#%0
zE?zH<O#YTKA0wi4(O+@{;GdT>@X1KvT|&+lE#R~Q86dCN-L_Ncf1lnKj=972=l?%U
z7W&dpZ5@NWJGg(AX;3n`MWz?v-Xvc#K)!B7fBi^Zyv`aasFUc~NQv#}vdS>Dc!;y?
z`fKHTL@Vi%bT%FyY&Up=Os&xJzCM3Qd}ORHGAR-1Uf>gqsxnF)c@*<~K{4xOqB)|c
zBvVOB@?xUAzT3sYbhGfaq%S`{^ez8-W+7&2_G%5^?o%b6-u*KBGagTI1bq0N8eIH}
z2C6&(X**|B0|Hq(IC@;gJdJkgTZdugr11{LRGX`&FGIz)V;d+|Svnmw4L<W&`X6AL
z6>@D6&x;FaHn0SCX-nlKr49K5KGar+;C>QC4sa#Rk`DgiB7b?~Cxh0nQ)Lh4uXHHt
z(S=*1`*T6p<TniXQ<AUmMr0${#$Zvk(KFEp?dw}FXa-X(746I}bL^6w6R&P1$$1X#
z7Fb~jZ(6-CPzcdG#H#GaiH^eCIKrS&-o)IVnapia`yTrqp5lQ5VFvzJJ}{LNuL>8Y
zV1vErvyj)dC;h2(ac4q3lkToK#%iV(MdnHIdRS5dl=O8Rc7~N|U}7Mk(=OCiyxqnc
z<_7@}SXoh}G<9aUf9pnxeVI=N6`V3@FI)IFR(ItcbynD*GI*YV4_+T&RXEanzTr&8
z`~zk;R8YY{yt#|dI*E+bIQ^4AjY-#}`PoGaWMCI2r?q9*V<Y0N`caY;HxSI?t$NwT
z#@TVK-{zReq4tHC>Ny<=c_sCZgnY?lzlxq9BRG}IqDj>KHR2LxkLntT-B$iEEhw_U
zT~YgR*nd&eY@bo!TW(~Cmp`nyo`u>VAfPi*wJ<ciL4G(4ZY+_8$88ePm9<@(9=&oV
zJ$$r|!%SThYDu{}U>Uz<F`Nl#ro5`p!>3-!6jQ^<<y!?J+I}^WV^vQHsJ4j6p6Vp%
z&{2)$AdNI>yc#>0@w|BqXCFT&g<a-86_vwQ-;@VR!<posm4t7!OrD>RcOOk@YHV{K
zzZ$1}{;1xu&miT#)fIXiANQ`+j7T3d_6HvJE~G<@04-%FMT+}zQ$_$dG3hDc4mrYS
z?MzF^C=0~Z0L#@b`L~j64?5*fN{QFV%;1<4?L4#RX|(E*KJBf0YIcTb0udCg{F-E6
z7$Pb)|0YBJ-O#%W+$b<2pX)65u6y(x6ntKH7(=M9m<n@Cncz8;Cp?=4R6xLG4lifx
z#GnVO%t#xs)aGxn`QG_PjJN-#Tpz?<4<XiIRHhokcEg39Pvw`+efV3={XaD;1U~)O
zs*K!t+M4|F@5Le@?=Rh)OlMrN<2TvOLVjj3uXYtaUabk%pdiBlwm3$%>n==n@FHY@
z6m-q^ZL1*qiU2yxuO@sf@3&H2w)Qb9E*`7)HZT9Jif;kcX#o0rr2zeF8<m8Yg%LO^
z|EE5EMB?R7D6Bko&(@a*x+ZS$PV_M%OBeln$MZfvG;Ri1V!j;ur!{l7?k1G{sS$zP
zo38)ZHu25>R<M9r0$Ve)*hDKpXGi!bd3TKTXKKC<_^on*eOGlEu9xDC^_6gqkXI7%
zuicP`V;3D~QOqZdR>;}Z)pVBN%R~Le{&p#N40QmxWVY+)OnHB9n~Vt3MSm&YwFg;v
zU;dy!i;o-m+`Y=q=^6fOrN9j%T0>ot=bfdV9V6(&eS(}z4V^!T1%+l@m~xL%>Hf7L
z()zuKAEyHwC2X|vV>(|NvN}3+S(`(g<3~>2Z>sC#9GiuhJDZ&)`j>bPKR@eW{{D2s
zHM`Dxd|F0qMh2{pC7B{+K4D6qK0?DGDJm*zOGz=P_wnd!=*p<vy5h2<Jxfw<m~gis
zO-vO%iq!H)l}%M?*14$IH*T3}yGcFqeX^7wO2PnD%UuihYby-soZSF_^QjU~;9xTf
zqs}chDJn|?*Qx<7v=W1I@{RPUYdEDikzHVe+B=y55Pek1e8ZtFDOz%^1yu7sA1wX7
zki<dv04h`KFvIBa#1)N@HbQb2@AV+hu!K5F*h$!=q_)w`e#h1Q&qBb~g-?R7eu(+k
za~J>PumGG9$m+25ivnSV0|q`N8=KMY^`-)UV^1^SZ^ig~2lBw{NkFtC*=Eue1IOma
zRrno|TPqj@_5b#o7%kx_B|Hs1>XVSEi%FsP9aMQ}*aj37{D%8Bv#E;=V?Pw#mf*#m
z@8`Z$ur;Wsf6rK`jDNf}*!TkLAj2B*<ydNH#lw~y9!JfmD({9084h>tq$^P;y8!pg
z)akVLX^?r^fN*r;a1*u5rII>Ff(`)ztRo?uUY#mG!Z}NhT*#!%nj0FDKF&o{I>p9I
zfW@QcqY31Q#h|$|uRX4Gaz7SsneCOacT%Eio1MVFm^ilEDTiMx3`gOMQ<0;4MBoog
z%m=|yv=IdH1;k=N)*D&cnCD}&-AUh`eD7U75zvCT-MzRQwd?}dlDx=$k@r+D?zmEG
zFQ=}?&dqBKl8S@2j`6Ic=t;1OdTnQd4zg@bz7$uHa~3y(Z7C=uc|~{BVSO7~P@B9p
z`L$g+AhI$&eEe3=i!HwSeJf{aWj&GU!AJM=6f;}em#Ew=Mj&nz3IUquC_ByQ64Dc7
zjI~hSMSi)AK?PK5_>dggnVF_>rsL?wn#|cNSk(vKeI1L10bNaxBbcF*tM!WShlUh%
zmavrgVb3i4=yNJ3+7mQ~!5S?e>yL6g^hP~0`pQqOiY&hm({D7;=Ltxb=m(g<C!4ul
z(vNfDwxwwDUnvS}^9_a@BrIe=_10Od9D6O?r`?ZZnmju2vfUP*3w}L)W9SvS79+>y
z^Q~uKJ6^_QpYI!Le_Kpc{Oi|_g~8CgR!L12_X%}e*BLQVeUTyq`{KC)?a}@InaJrj
zm?DvV)=X{z`)6m^H_l_0w9Rc7V(uA`2R`~~Gfj5<Y4D&>Jtd2SyINydW5FugGxx9|
z_@4BPc7b9Ix4unPG1j8TeLFTfXS6&}+zjWlu`dUkw!!Oe7qo7a?gktjT*JdyL|4kp
zhgO->7M1x49^A|EPs7_`kU+h@jUxPTAJ^gDA^cZTMXHS9yv%AJP%Y)n5#7JT&Aymx
zJ@MAmsj}27IPikC-1{7_HBzY!H&OCi(oK$Bv4#vse@tO*se!ijYvI<x-b|+qqT+Lm
zJQf?O74u+)$tQcnjBNG1igdXQ8jF_<!i1+Kr?fk5)>~H4!^wOxPf~U5);y5hY{ZdB
zt&WzXZ&jLJDcJ1rD6ty0b!FW!J4S%FWwotu;Pq(3nRU%v=SxSp3qPd#nKd1qyQe5a
zS~6~g<Rl&(7qN;$0mwlauUE%Ddq&Q{Zjg55hi|6?2J(QBduPh_IDynx*o$fo;>=c|
z@T;$spQS!FLYzlnsfZ2V=bTzq<7^yd1>H5!BTFnbzKJ)>k`$|d*h;}#WDf(QIi7~!
zc)3eT`U=Gw1q}XhaUB~QpZO{BT6G_{x#pk~Z17x$sMjz?|EScJyOpCDX3s#V-L-;L
zp0!+qr)Wcal*A=L8$knk<D_UQmsB#PK5vBH2Bl@`O6uZ>F|>x9e29&N_!rmv#lPI0
zz`-rSIcxP@)=p9Es6J1nxo<n#P9MZDfBpepp2Y#q7NW&bC3UH=l)${Do*iX?H)ZfB
zxPIpog_Jna=T$g{F$6wCe3q|WqwBTcdXWfky$wES&D6|SU}w~}6MjC|Y5hY`XtSM-
z^hX~BEpJppQ>KZ~(f|U<$~;t~bTgyD|NCwY{v_rrnk3a>!teB+!jGfq;1obg1{1-}
z2BjQ!zGVYcW>HQaP|F6z@1t&%R?n~HUUWG3-+CMW0n&{TZlkl(*KL_1dbJe{hB+Jh
zzQs;jpTP4W^>Sgp-gmV$O?wvNoR1?iCF>GO4{P{d)vPYP19fvZ*}OJB-#aBRD6Rp2
zEp8}Mn9Bsr^|*sf@y@hITC5v4kNO9FV4h?gt7(-s3^4`S9k@D%w2g~F4Cy661`;(%
zsTYalC`loVACN-e!@Le>>ebkqQQDv%4Bu=_UKmvQM=xq^2zcmW$yP6#D6<V5IFR^R
z-a{RHeb@IpozZsRQ*zAWW&4>wearfN`m%B1QcR-8Nl7Yg^E~hs4hnUKJwhSkg<|WJ
zcJVH;aE5p7iy{Nvjb{q>(X)UBaD$uM)xP<~7(_NaEz8tn%=zYSl)Ct)!*%?4pR%@=
z`)B)T#^8|W=~E*IrI&(kcw&Emva?5fdWXvB%K}4(8g|r3(@H_`@l<h8B$lRe`ewQP
z4Le1vLNYGieOtvJb2bwkJkv~FjbRSe8|fy@<Dtbz7K*--3R0Wtg1TGv&fe9Aa?ojo
zMDXHjh}ei9SA*Vjm7Qd5)TR)~>qlx?y=xoaosATDoSo0RazmU*wzHio6&ZxGzHWVQ
zX6P};)4#1Md$Z|@7>b3CbAl01N_e9CgV9PMM+*iPn7oW|=}I&Z-Ax?1tt!<eol^3c
zwnqM4wvwiv{XHY`qlT+^#6;)pB)7Xg9Ay?l`8D!TEE<qyhJl3B%gC)^Ut*DA;)kLg
zdOC31Z_zFP5ciuOZGbb*s83a{-Z?4&9@utHB+^+M!4wgOQ=>vJ9}jMZwCXgzmJHgu
zt;&6EWL?U&%MF2};P>CR`bzLiae~z8*kE=)3T~zNb6@3Ev_5VU*?U8}B@-Z9Iyt?z
zD(Q*5j$tAuE30JG2Dy*|CLxk2ij9+c)a0k?SCI9kXz~%~gWfF%^ls+E95nTVX1QHw
zg$Hs$ad*q4Ww!WAlrf)q?k~TT-vo<<{+uxYp(x1!u%rQ4KNCgaapE6fwS<)l?!LfN
zO6MW+DbKT>y)#!29bxGqmGl%$$}2*oqXNcQ=p)7d2k2EE+~c7uJ(05IDW$j5)*x|b
zZuc)0yjZy3?rDKH!?icb<7?V7LZX%P!g1HOKKufa9LZBz_U<3eY%3m7@Wg$EihRSu
zy~+Qf2ntUe0?1;pM47cxG#hVZoQ<ID#*Ic?-a{=C_|*wnVyRNEL50%%4A^nZ-Sq&K
z=0*ScYZ4`0DDJW;aLY3w!o^b{1NBq70McT+&8I`Lc@t$5QqY-@FX5?*>VLLL1?bq#
zUdp@sf{Zd<fD<56h{%+(ardjdgJ(epBe&%>-F%n0GoN{p)d%&#ixqVL#!PYiK*GVf
znJNrx0D0V*O;3N$fsmA%UMq?|M~EqZ7l06#3p&K@;0Dr@3|9cIcQH7Vx8JJISFV1<
z{yaVqU@l#x_>Wy4MqpMr<{qPSNjfSW{{;CX5IP4I2?+kZQ0l1X9`1l5Y=*d^m?U0F
z5WEm@3(pc(2D)>qlB(0Nj~Gu|*IBev%{ll`>8OaoS5Sw@9<~EFofRPZ2WSNI1CN{r
z6Y}2GKI--_r!i%?DW*BK@a#eXn~YjoC>F+(09Q#{_Yr}ApWuIymS}{L(}M>wc(uS@
zS)mr3FGSvkMDpwmRUNML`b!%_(#xr8cQEV1K<&!=*g+Np$^W<ZXHm(*Rlve>Jo5Lk
z3wZ32lz_OwZ|huW8ZZ_fkob8%1`O4_ji)!sUXto57$IN7!b~z~m^I@fWfR0HI*)eS
zrT03`%lO62#YV!C_L^0SRNfPYKLr_2iZaz;lz!m)nWS)B!p4SIUTF8WiUVs{rx5dP
z;+1})oDTAhAZqKAh{sR7S6|Pl8rRRUJeq8@-gRzaj3`QL*D?=f4`l`}WP^QVko(+O
zcXHK3n;q+oQY{7jUwco@JiG59)K1iH?1(w6I_)1)_24z9qIjy^pH*5>)^ZTgLMs)p
z8}}(6gi(g;HVfaT6ibjPqPO3bXe5zj>&Q<#HfNC|*Dt`hH_&tR2s3Ekv-rv^8tgXE
z7}-Yj(E~BT8HHVg;Ax__IHOwCWZ74)I3;*;<9S0Ytop(MrG;*En+s}NM-H6lm4o&i
z^AS5Iw=u64&KA}+eLTYT7*`Y3R&#)NxWinv5@ayR_}VmjgYi^X63OAW`N}{lAa)Q)
z(@Y{FvcA80ewalW>$75gDX$KPpi`(0k6uVYPhX_8g!{nh_0;E0J0$Rmc!{isr8e<%
zC|Yn}zN4Eu!&teOB|9iAEclJ9XWQ8Ho%@<diO&&I{PW5iL5?T_J{iGivCZbz_lyqi
z*9Ym0nZeX-mNj`pH&UsuUZ?15K9aDokaJ@AiiYlpg@6(e)0&OWXgAEWBD<YZwa!Q1
z-4R(i-c`GAq17b~Q>PJSd1I7Lg#v4u`Gg=GkeME#Q?Q;MwEO;WY{Nv9A>b*<5pxlQ
zi6&lO+T+AC4sP1XrT&pQyU`+lSUeBm_-TES()9|aInkH_!Jm-*d~)eyuJpO{<D4n=
zvl57SQmB2VS+yx14nbfc2r^@cPMGTGD>-4)w0A={+ve->*v=yIp!BA2E(V<Dpmmr_
zVI#NrxvjsO$$Y3Kv9YKisaeHwxc4JD>l$7dxv|P8Q4n8Pm^C4aHKAL(d}JFfEn8YH
zTlv7aArd6K>5wlC62%osc)YYvDw5-6wk+5QnPB73Pir^ObH=obx&J(<&b+$Am=+$S
zWaf18Oo5_@szz9eAiRx=yIGOPgf&Co2-(6JX08RMB_N^!&oAk@q;2=MI1xpfiq+ph
zQ>L3X7s}p2>FskArw=C`1!(h=IF^V4&}p$m+G1M2PR@sq`!#Esn}}F8w8Y?>+STnd
zM4QqI-Bn6GRc%$n#L}$8Uq%sM3~jk*j%tp7;q;t4ae>m`Nvu{0QE>?O#iIxnp6tb?
zFF5+MZC$T+Tw+a2bRn`ChtWVh&a-ou)Mz$ojC@DE0Ug#?3_|ZAI|F5}Lh1PkWCao)
zJ+)6apW~0wz!+f&l0@VtjCa5Znm*t$;p66YbkR<)-vSSCoRt?wHq-Ows1T`pgpAwI
zDHtE69AXBG(T!0Bcbi#my%6IhLG%$YRYS3RBaWVt)JC`X@dk#Sd&lf=DRk0DB-vXP
zPoBT!aJU@V?k|||uI-d2o47(UH7qH_^sroouM9qbm+>gk@qKw2k}NhOdCupkH!B{#
zdfH6~*K;;62K<R9{4R666)!!U<fKe%Wv><8i{)de+=)ik9NY|)IH)f^sdK!tVIWY5
zahr~EW}{Mb71J_tU2Ne`k&H;{R}_QN!?oCEXZT71X=V4kXjatqg4<jSc;-h*8>4-2
zTxBp=!=(yO+7eRx61{x-uPX;j6Wqr?8`Qa;imE&<7@t22E2C;iZ;>Sz;x^^WVsA&c
zv3iNDGcfW+h+1CU<<SBMl>=m+ho^b%Zl@uCQKrkBgeldQzP;2Qm$Sev)j%SARzu=(
zj0qNd*gVs<pKU3T{Z1-agf6s+0X)~|Ws=M{!Z$n8%5E-5B*BM+>$^VW$<2$Qvr;L8
z`~k>~Is<L+asS?veip-X*1dr~u%@MQOF5eTeWYu&3eui;2_uDdvLA8}X;NqA^b`5$
zs<COkmO`>4<<T)a!2QP4W0N+(6HFhrL##m;`ZcT)>LwKF@VuBywhfZY%sz9B@vfsJ
z{R6IJd3Bdh^fy1jZUeuShh;UD%-eCr#zBFRf0+*cxs|v+Bm%L-m6rbIh$~WkNBFIO
z&hfe+fh+0vwXW$e#iWf~03?~B8SeEMJU5Jbr>x^r^##6|ei(8mpF>0au1uxK$u+LG
z(dm4I>wi?}xc*h4>guM)*zA3Q{(N}bGFt0S-h9v6<Y?&Lv?qywKMZHj$m?M|)3JIV
zzj+FwTcmKfSW;qQ<D!bZ4L^bt>?{q9s6P<6kL-&25$omn2M8ECeawkAm3nNiCFPoI
zex7pQ`P;P@F~~->9+HXc`Y#vtzURL+Nv@4*unkYEj5QhVZFbJmJ)1hS)3nPhES*lS
zomZ5=#N$9QgF|!=0?8;M=#DS3Rw~j$KPZk55*2b}ur=jqUsRy04lH$Yl0<(cP_AQc
z&wQy+TDR|RzheZRI)u)kMp`hxq2o!%9>6>p#uXW=+kmG<xTW(ht+NH{2)D~LJ18*3
z01&!QL(LqERPIkL#(!8CS<D^4CVlK<?U&P#={M|+u6t`+)ad=M>$dMXbN)egbd`bL
z=AY~bCSNH7Y$!)FzmNFPHd`JAT^^zXOi|IKHc*0nuqjCFH<bR;SH~%2B7e@Kr2^n9
zvUU%#!$skCZxi`786xl>phc4CQmNA8NVc>;Kz1Jv+F+S&1W;Fi@jd|uQ|FfhB+;MZ
z1F!m11$UD!4eZBZ$W1$!hr6M0NlwjFcP0xk)&E%-Y_`SxhT4t{C51g)j~MMzD)m{m
zDh5RGLO4;c{;%$3Ib(P>-$n@%y`w@tj%4+iVZ3DpW)S$l?>pc>TQd9_HO6nemyT#p
z$}S*KYWJ8n@hg!!S$v{&ljIXnLKledc{6wfVAuTtyJ}Ahqg%-Usalo&e7pXiQbj<&
z0WNx&`{$Bwa~<Ewe@8x0YP{1p<jxD<7UtET%DbCv7W_cB9B3i5xdg8Zx3)%fRs{0^
zT+qTFgFrbNfLt)?y35D+L1g%YQKpCZ?XMB2(y9)8p0bTQFrHGy0t6eN;Xef%=g&^q
zt;@ph;~$tqIQ19y+5qAi|JBz1k))*1`Y-zbjlG^c3ufENW{e<tA{A#Rc3_Ag6q>x<
zX&2Ake`3mfgl|EB#hE0EkdgAGxsHo}y7E8R?{SjTM_1iV!DlaCOZpG}C;wd&=SN?n
zlA*Zsv`ep^N3t1r|H=Kz_LBhjqYs~@a3AcYuAvVs68IP!@zt$EcwLJA07;kD?p<2t
z`*lkI{Le+95`no#TY*F|i-6hBnrDp39uRO4)8RJ4q;D9O#qO8&;9c+-1MKV5MMUb$
zO4o8P&6SAbW)|s|Q8(PbT5!h`ZgliV)=fDiZZKU!x+VvjN3(2v+nWq%cTphQ3JbDA
z5sq!*=09996GcaGOGw`F4DA?W!byr5A9uF7tJZTONex042igwwb$1J&?CnM9nAMqY
z{dgaVRIERgR88Po25(Ir0{O}xCJq;s59$e@{ydaosT@F*F_10pOv9iRhY^HhP@m$=
zF@bACK}6oaD9dkoTCw|_8_JrFC%a0+FKSKY%XW9M>;uF*ssi@Y`%tN*Tgktky7=XH
zMPhO`oNFwIm4C@O9zLenkdX$pEhG6%`S@K7MU97|;Dsq9p$gpQk?QE5+?b8}TE0hZ
zc-%SmbO^#_m*n@3W3Wqim(T~2nQ`H`oT@mRA1loFQ?vk_XXj#<r#f#S`wkSIy7A7|
z$&QP2fJs9J54q&o6f|`Xtz-)l<$)X|S*mhJX-wgcYJ#<fg`LFb)7W!ZUAbNNaHZ!x
zHDCbe)Ng1!1PtK*wi(=uL!U|h_Wv-6I_(3F{m+NK_7=aRB?2F!Gk{mpFwW$BlG4t!
z9pPNb{OXSmg8whCe*8+H6(;olCjo&<4GFg9+ApV&>Io1*;IRewFAou8#4yH_7+sOG
zn;aXj<cGKDg^d7*(UKJ4&WPV?uDpAjz!xil>Hp=`ejTCzr_dLTKa_jliu?hh_O`fT
z>(Ax<!rb9>?7?(V(?=LvCo+zpdMO<)p?c}_on9rs?3PMAr0^gbH}B00;((a^(;c2G
z7q)NPGJk+tG6C<wlv+*tdM@Y6dGFq}%$KgWJFV;iNJEaum|6mH-{hE>J{F`+81d*c
zCs1~{_Ctv9$locLKAv<u#nm7uey>iTmL;J=BGIaUQJra*9$kxl*sDYEM$hgbJ#07W
zD?vmaU36jrA@8|wlD|dBM;4D!`qm*J?HWJ69Nf(A3oTLE_mp78&!2>-44JOmt0FC(
zq7u2b!+fe%tGb12gZfXgno-AkEBux<1P;+hegyfTB%DVdg@1Z;5){qd5w96X8n(Gh
zz}oX}DjdWUx`0mDby93=*S99yOL(B{Zl>MFxuH|Emg<n4+6tQr9^hF_@%`wP&g?+&
zl3$Q`L`_U4mWFzZut}AE9ZPBkPBAwb$lh$MN=o8J%5xnB)jy4~iBVv{GLrAORxnaX
z4AD-{P&iEAgbr^@HOCNY_BI(PETt;g>b%#-#@__DK#-Z}Pp#)=d7TTCKzZpni1Uv`
zUqi6a5ES!QFRV{Hw;<ps2As_*Ll%F8_Ga#LyvlxtrmTC<8_YbE{gOPIKR}x|+y|cJ
z7@Y_U`Lc2?vIuJ;3)jalx3Ao`Hr_7g@)jSQ>;N{#l~AgoaZkS-(CQ7|4|;!RGZ#;G
z>EjfClTXj_Wf66^&Wx;~3yrD<rlsUt&#YYJdQge{P_kz{FMd#^<=Mxf66pf5!v0(_
z-SUPD_amA|)>TZ~>3&@Zy$5rXZEt_1mpceeH=OrzW(Bs<G(BfLu^%}J?3)wt*ra8w
z&G=HxMEe06A-x=tq>(=s$FG4XXZ6W~;5C#Q3IeT8^6z+N&y?aQ%&XaF$KzJ^mSU~p
zUnR;HjN0P&+j3_}-V~Blni4pt9+PNm-hWQ2iZr|#;%ugtG$`7NyrPUqDTKIlq4Imb
z^M<FD=tuvw-{4mbe}-(B>e=D@p{mIeYD(MAGi`<;7@hOvB`x$gtZlDPiq;=&t8;E#
zuV|k#UX}M_zu5(?$|a3MEj<OD<QYUWQYgssf^Qd3$wM|Nj6UHTM3DoFGeDID1x~NN
z@28<&W!jC77kh@*8uOl~f>fekbKhOoR8YTZ!?lB4ivN~fKI@nnmFHOSh6U_QUGtzw
zx3)gz2dt{9oQ7}8{rx>HD}goL8+eU?B7|^P2zJ<frdEH+zJ1eB@zg6QPKb3lWaPre
z^M#&Yz3+C@UOxlRMoO*Rh61TmEPjXKxZnIYif+9wl%Mtlu_!0oJFvH5=C+$oMmJbL
zjTUdSsNbI2{{a&6ritGn?SEE;%kT(QOgzpSm)@H6lM1~WDsEt8L@;^2tfP?rYdN|P
z?xcN<PC**??u(2o%14=1v&6n8n<O$#mXHWp5(_D5Jvz?4=U`MMk~q-3xhp0ade!{O
z2Ob@MQ_6OxruG{$33+9?@n^HMfGKxO`UPZj%i@amh`jMD#BR7C1#fF;Kv=r2pS>mA
ziV9t)2-e9j2^ywN$0v^kg_ZWMJ#v~kCDhZ~cov?^LYov*TBy}y>c&vYr^$0B*>5Xs
zE3^I8Dd%fU8&(=+xoo`&PlmMdby$#HVgr;%za}b;mvdDktQ`8TX+&&36o}wXu62bd
z-gVRxb=&5)u?`c=+eseuBnn<?rG({D>Ks%urEgU(ZdtepMhHaH>a*LoJ#KbS*efbD
z(1H&iQ&B)vFjOkcL)qazn4J4!Zxf-hzV7Kc(`6T4#j{qY&GYU+%Zl&;m8AWsVtGEe
z3`p+|Q~><``XEHE{i{T!@ZF5qdOq75v(N)GVe~McbTZrT-Pqk|9usfn>$eBv9e>B&
z0j}p{Jz1X8j_i{p|Dh~?iqZz<4!nW;iV<7t4lwL!wFgJ@s#=YSVN1|^u-{qmV9D`0
z>xK9_7zGOpdm$1}g5TZiqhoGnbn|qP43Pykqbh`*I559_^G)zRMiNV;(rgpNDl^vW
zaP-RUB4YJvxV@Ep<L&cwB+Z(vr0*f<_FFNp5NW9n`RgUSg(8AEpn3TbYpVEG984Np
z-Z-raQvFUA4m!zKwi7jIQJf*yV2!?1VpTl>yR|-a?vE^))uzn&R3W33MYCKfr6TW<
z3?7L}sO_s}W{S19Kjv!>^|Iu`nK-gRbU799N*&_1FKE}qi!gl`ZyAA-YFq*zGAyBj
zPD)Bh-DLXDJ0?^gx*2rlwCR(w;`kHD(c`h&8C&KT_}v45_YJnfGz%b%Un9Fc1)DsI
zZ9(FC9D`_as;zA@mgqm$;c9Ex^C-?G3@C?8?ZwcE`P|!L3|Q+^pQOFk@j&93aKU(%
zwvB~FdL0c;CPcU9%d2?L$ZvqZ6ZaapsVarr8#TZmPz<_We?hV5i)%t*LP;@$i7hoi
zL5hTaEFQ1Ecq1q<pJVaWt?btZ;r}xaxedeZUMi;7qdQjCd_w{1s-9n=BL7<hCN2mF
zniWDx^Ccroe?^GXZBH<ClLH%6aGIrh{EglDI9rZ-U7D30N6|ci(h@;V$XI=kniL)-
z%tI7pd*n$*00i<VmEeVKrhk|0=Dv;%nw@JJnu(aUN}-xzKesx^Uv@cri*~JUeZO2Y
z<+Blbs4TTyy5%?AcWJh(xBrp@{Ou*($x1L!ENt)3(!!-IWlNFduKItrE$!m-pZ#vI
zC;b1%hJN29f;jGYk?(C9{EyXFh+n?(X1$=kro$P=`d)R>r0zC8?Q)pvZK!N7$jYB+
z6Q%ePJeb~a(Q+^%<oVv*K2>@|=ksU-#ag)NI~ZmSa+qgH@EkP7-2kWVNSSNr+9Bu^
z-^k4jTu%hPXirSrGh9qhXgU&1#D~s5Wh`$_eV@^BNE@q5V!0zI8Kc_*B#4hHK*xEV
zXBrUxR2`Z66Ze<pj+eF-eo8r;>+Ql$`?~kIY|eMRZ$sU!V2eKUp@(kD<$mLRm);w5
zRe0De1}~DshpvsQH*(*P8*+Es)uSruR+3Y~;}j6tFvbOhI({POW<3=lG&Qwyc=01;
z1$n)|7Jq~IbYGD(UFmz0A;+-&IJ6+e!o8u(hymGEdt8x<c33GHnNwQMLn8>Ck`hD?
zPf1nMhUQuIb&aw%!wS_*pDO>TJ!GT0XtRl<8h60VpUGTPv?ISC&em_0i!$q=@;1c2
zhKS|0KA{bEAvtVBCKo&V5sC`hyY-EEL&C3jGEY2?m4;#r6|<uBdYqc3QwAW}wWC1;
zUx#kQPc$4{VML8>k>?2V)^jtF1%uC~z@&%01EgDz*8@n}ViXYBF)(+)Zdk-ucXTu}
zx^pe_mO|0DyhQqTO-aS<B{<qW9S5izeq65o{gNX_e9To8#pYWOyx?!<%^+Q!*ZA&4
zkCpH^<DM?%iR0!yLI>4I=aTe5xm*pJsNT}0Z_l1AbM8di*D>I54UaY_hkDa0caQO2
zdszCxs+TX;ZVlA;oUV9rAp5a8JNdv)oqc5umU-o!^Sem+H(|y(7NDBk8)&YuR(TXL
z=xK<4O)=R@iZyjyi(S3BXz#bFldsyv=-0uH>m4%U`?L#LRvCWET94NBr7~*W=GKT$
z+egkfJ-oZ!Rx^4fPU~1--dWVt98VB5#wPeF2a|<*j;9N3%q4(twfGn3KFEu&w6Qms
z(ci`NGRX|_3~AqyQgIM|?d6H2?k*;TLzSV$V*T>Cp_<@2_mSVt_TC(WoYL^1%h%#H
zQVNT77QmTI2X3tNnt5h!@C~=s-lst9@&Kp$$R|JfOp|NglGk%iz+E^3Yuu7D!ElGK
z*;SG#u3b-Db&{KY2XE^#27y_RU`&nScA;-#xYq|t%o}c*Su$!_ukSo2*{4R!0iEq7
zQgpx}dKq0!z-tfZA#9A+GY9cvdb@E>e`uThig&uy6~P^+RD1p24V7lb9Kiq)m3!KA
z0twu<G$UU=>_I!BWc9@NWuNK3{cv=0(cH>%<+#%~w~bIw`V!UC11m?$SN-du&%g;?
zIc94rACHPcG{mroYQ!ATkrPTXK#q3NdM!styOl6LSY|4iq1}b&+93>zJN!&9)y}TU
z<5)KK%<`OVQ%@ctdS1Irdf&~L&xy!mqwndS#sSk1L9D@J)=502daVMa`GBWt;i7r)
z2m|B(@RaG>Nu{hq8GUvu7>Y~0;R^FK?Y6;`^*}rd-%(1Dzk|mgAZXN!mt>S1_irzA
zx{^GPpFFeN%T)&wEFW>z&XYDRnB0PkKZLU~d%TQi_XY1W#iuXFU+!!^A(5Jm1V7u2
z3@zTVe}qs-d6cN&M&ib<^<`a1FSK2kHS*&qN1glp(2u2;flfRp=jPBD>+IUPL?Q@X
zLD225)xrT-!hf$8u`W)+IV~4C5je-^Xg^6H-5%w~hGdOjMGBXel&z|TtqOvHCT7du
zkH&A?mAy~ytIID)x7U4u#Ydf{31U_agf~4Qk*<JOQdhdK8#5oHW(-gdP<ED-b7Tiy
zAtL=d@0b6t#loBZU$Ru%>FGcJ`p2I`YyGa04ZKWgyV7;=rM=;*24itASM|uAu6~}h
z5f|Jvfnt^)=UB1%Na<Zn^sPl2oMeo7GI^NNwIn}c3HoY4u<(BGWn1a2PiFYR#bA}z
zl(^qdlkVPj-lL0uE2;dhlKVEz;RKjx=fBU>H)sA8C5aT<0jJ~)eYQl-T|Ed@1-OMT
z?Gpgv+x-h$9S_*DVH|0<Jmk~J*MZ%(F3ru%_hpOI?zS`Q;D@*GjED=_p(>pzQG?(x
zNuClLb8pS9U+aD2u?b(ni(UA><4A0oWgbZhhP}x0HfUgLzaW@D2tP|9)VX{W^+IF1
zZN4+Ld>T8#d`C2~h*6qSn$sMDhAM^$x}-kUr(ub>gQY2#m#tp!!MN};KW3TD2kx-R
zr;f!<?!nsI1T-Hb*-?Gdhq-fXiWT|9xlZNUJs;0|#T8&v2Qp!!R;>T5wb-INww+c(
z`=atD)?~xEzm?LGJ-nyIZlLV)o4psLH8R7N-1s}0K91b5kV-QwB|&`mJlY)xLD>;p
zQHC2lA0!E4nkrq_7jEjWb;rVmh?M54_wcnJWgay@&p3=R5`gUF%P4Ox6Jh-Pd9c#n
zSo52kIDXTz{(nsvh3-EI^B)YTrnY1kpIOo*)>BTut14ED;SaIELq`-S5C?q$rG1hd
z10%G+3yJ+ZH8uX1bQZ>?TiGVM6RZa`|8F%b)7p~rMc@02)RU{5ke`ZImiI8i-*QuS
z#rCkw>!v}$Z6>xSfx+*FXl5ecD%*7>ni<524CcB4769%`o4>dtOAFwRH}Bp&NLj4U
z%zxp&*WGVDt$lL807X0h-u<*4>7iH1w+I_S@Fxj<%mhTvC|cuh#@P+}#r%mJ@s2?m
z;aQ+f?64$HL_`J=reEBixcW<a9h3;E3oWgiwbdxgXcHKd<Ph>B#1N43RN*EiqL|JS
zn?Bx;$8%tzul?%^O4W=1b_XSCPU<6tNkroR!`oX%#j&;P+Ks!rL*wo)!QEYx;NG}P
z2=49{Xb6O0!KIPlPH=a3f)fbI>8!Q)yVtwVIo}xj`*9fkgPNmj)|8@Z^340W?m09(
zVqLma9$cnS6c}>suWz}DI0mDb-uJ^}e8Mb|T2c$cH^Sv7bjD3o`@*XhYM=Zm1DoS3
z9SQ(OGEa<5#J(H>L4G2S2NWjYJY_finI5!UX+gj@s~Q2D9FrUZ&`yW4<KZMxkr~l`
z1H^p_e*?Pb-aWiCpSG5m{zG{AOa6dUu%GF4%K1_1Hsn=?hIY8v>(NRWNXVxXc$MyE
zYKgBH4s1C}nPnSBE8p8iY*J31`0>j=B2Jf6=I>L1+o#IzeNho$b|@UHjL_AFbyY1<
z;-Me&748Hm;QK5I!=C)Fz$Sco#8$^k3bnSd!0I52eT&HVBMcd5Zp!bKU#OtMm4Oz=
zX@{+&zrU)$@yd#>O$iC25a0fwHZ(%|7h^`;2bx19UYjsp{SSHVf5jh}pbkH;Mwl<O
zEl141B1{F{e}mK$d{g7FT)OO0*v-t1s0Jr~SK0a1<iWnwH4Xk}^8QiBcdVVHQB>pw
zRsKv_ekO;|pA69KlUn4fY5G}w|8JfRCo1Jgf%)QQo;m%l%)Y_LQ29`+*td_fe6h!L
zq}3OZg*O*~pCi5sDcUxAlDON9?MC*~!~j{LpRYd7SIraipGmU%)*Wl=XlaUN4IgB7
znsTxpUi~b5x0KW>A?{x>`Xjp)aOvYowXj>EGinEfTO+OS*oLf8jLI?9jbn{HcEA2h
zmyF4>lA6kGa8vc++JE!RF^S-YvJ30w8?WHtn{Q@2uP$T$2wRMQ)PDKm!k^6JEi|Cc
z@MWNy%5U#jrE8;!-7#2`n1VJuf0r;-U6e0j%nrCOKYb!(Mu(_`nIVmzXAOe`?zfmz
zn-<JzzZ}mToOJJHPNW(p%*#)T<%pb;lQPLQ)~1~9;wYDQHMzH+6SPV0qM%bOuQSd7
zV)rMT`eNuGmCTODw-(J$9A5tHGv^tIHn(SUB?3u}x;?H)EDm#}x)(E)oVU~xIGhOV
zhMv*IMJJ@Bx_Xqg7SO?$YPd-95E4h8kPW?F9yC6(^y@cv8?KCJC*QSAT~b?7;g0O2
z=d@eU#1pSrt*@o0Ix>&nA2q|Z<yi%U1`*DLnzwsOhYi0oMEq`m=Qy*qJ`(8Pdokc>
z@i?|}l8#qRS)>ss7U#LDRcWd`!f#Ki2hGr;L)A0+q&IrB13v4a8|Z;pW^}IB?LBuh
z?T#SjkRVp^=#}`57%n5ClnT?JQP2&KuwjS-iL5WyejRwm7S-cAV($xi*}^eMo8dd@
zk!ZPED~29QVY{~Ba!-R8V4d>;vZZ*@$V7I&{1=+OhaQ&=85m?XZ(0fkhsG(l***Z9
zr5f2EQd1cNw=XQ)AA*wXos^ky^5iWx%L4ap(c>{yt)gX3oX>IOuUnEl#}@-_0&E^6
zWF2h?59%)z4d<=go-}ad+&6*;8aX5T)das$eL`(yUtCrXPM303tN}!auA|z3Vg<UQ
zznH1l$m{Q|LPRJTw%Vr>bnkE3ZuV)3+x+UqVKwM*tm#TT;h#KXaoWq6y>QFirofc4
z#f8^*%Z!N+nJwHDxu<OFkl5X<)^&)mxe=o}Pr<rf__JQ|eyMY0gtGWZDlgqPE;%z#
z8&y47bu9w2a@tuZv=qY8rO3hVLSd!x(mk!&!Xrx!Ox@DuR-^cmHv)ym0Y!utE1#0n
zYxD86qf@LE>4oD*-+-yrcCM{2W6liG$_AUOAsKxgwKmIHBp{zj5{6OA?Kbe%9vvA@
zg8_r*G43z`_&z=bz88@vL>oob(C^vz(mLC%#(B(l1(g>gp(ogkY5iXO;zig;lQA)=
z;X3cZIYW=cd*{cjcWXq6U~HMqS15V&tEQ{`TU+a~h}RW{S;{^GDK;AwTyBIJ+YUqP
z%<^KVd4y76j?vH#g#9u!r@Rm$>G=A(t)hj%^L*PcP5a@`RummVg*UArp^h9sG1nhP
zijBoiqRlrTZrAZ__LcY2E2EyHs1R9<REF3x183ZRv76rK$Bji<8>Xy=Y84JV0#iuO
zYb}zR*J5Wii~7fh_4#~i<%C;g8_iF{qN(C*poQ}iBgC@>{97gYr%JuHX3FU(pTv^9
zTnzF}BHz_+z>u;88Y<%PV8vVVj1-VUREl?eUsQ9%=O;g8Z*-HP8tKw)hA5Uq)#5}Q
z;s_w)+#s_B>29pg_}I~;{4SKhGaMs8-0pV4Id=cNbXZlkR*`%ucrVwLugrd-bf7vP
zEtQJPdFw-+7kQFpE`JD@p3&BH8s%M*_hF&vX4AK0Af;sUQrxl4+T2*65O%=u*>?={
z#CJja=pA1@?;?5HpnCN|s(xATppP|pKF;$^ww`cvqBA~=9Glid#6$Q7p@KwlRKp$Q
z?Mf+sRk#k)0=|!PVs`uT3Sq*Sdy6&yF=VkS)vEgIHeH^0IHPJTy<tuND6FngJS&!z
z-LM#q)E?q>!0!G}r&ZwktRWaMv>wK9c0G>QL~8orDi$Bw@4ComN%S$&Zou%t!<jP*
zrIMjth6LXozY;A$N6UE4cx7TyFtM}2Y&-ew#ZjKRgmA$Jth6eyiIum4>P}fr)q7_e
z93c*hcHp<ETVg^j&-Xy$QwnTZ(O1e<HqeKTtl$UEHdFi98kHH~?w4vJ?6EKBL>x-e
zN@tGplXS-X?w&ivlX1&}Lu%$~;r^&)B}4K`u%EPqFbHW5&<egkWVpC-x>iK7Ws+Fv
zvk<{@4UFZ>t3|AwYz`WPMR88hdh~K>6>FsEJL$vjBZ%l~aykpO!+ta#z5NXU>>2pI
z!Cqb*PYch=O#@ws?6KfPMY7;dC#}n=@DR_cno`H)c36F8SPI_eMUfq;Duyp79`EJe
z_zmz0$nzcdpa3oXVAvYS>THuJ7;rMvJk8)sHLWP17uA*?nTdicElz_2T4gYi_spQ$
z=n+`;O8X6_7<f{?YM`k-Ry>Bx@T@ru?q^1;(dBWR(<#fTr)b)eCueES+9jmWG_J5P
z146A!2+TNH4%ac@)?r1EeZCD1SK`rb9eWk87~K{3l_%V`&#g0ktF}0+_aw7tg8jMM
zm_-z#4zri}rK<4HTUzoTNji{c%sA%z|A>u-3U^!=6kFjRD|I7jTXU2~CQci`(hf9}
zzSqJ0(a(=bO*&`9lg6&l$y>eYd0S1JLFPasiF<Fii)AOQpgC=8nil9vjgJ&930=Bi
z_pe3B{`I2}F8gk_nl|?D<&lR}OF+HJGe!+-vF<I;2`)}bKOZ(Tpi*eI18CK^o9<Xx
zdM)Va=$Iz=*J?@*cjV-kWPprzbO+9|kZTl5D^fE5ryL5rj|<NqKbV|hi5`1F`9CwK
zK{MZ~?~w{_+ig-tW=>@q8%}-u|2~Ycwy?L;AS-hMA&P|}v2Moycz=Cje-4#&y8TZi
zosNgPjkB-c2SMo^b_UB(s}j+`E_nacpF;wW>qI1%thqb6Cn}y#HHjOQcdu`#vVWv5
zxGXUtZqinqB-}~_z1E`IJjZ@Vz8<%iYF4sv+UOaD7^Nm1gDJIqL0pDitAI9k5cum=
z+IHIyVOvkD%umL3icmWg+LO=ODLGy7GOShgW7fLF_X@OMQA<_)#q|VQo0X|4aXt_D
zla9H}+%f!g?3D0tq%Bp8*~uey5o#PJyHx2eXxY2aX}7ALyMRSS1KZM*9V}4f634QA
zALP~o#vEzRc{dgM8+OYt=X;x@*a_B*aOe22QjRv91-hj)lqr4{x-yBpV$0hz*Y99M
zYQRiSx8tc0bZx*Kx0y(j{6rvw`5C33pTc&qP+m08W@#NqT)R%mCTNEj)1&tu5?4t`
zn^NcfxHw4~0@<?I%tetUHjFOlkNfmNEVhzG%e6kjWhLaJCTe!RB-lKs%%<Zsbt&dB
zy0F<kmbdwuq{NS6O7BA_yPf{0lC<-H>(wxe7olZ?%)rGws^KuL3~toQOyweZu&bbr
zCLxzIfhG+Ouc6J0D0_3@x{Fg#;UyM@S5_=)tX{N|Z~kuJSf<GCx~(r-<k1KE?hhOt
zwf3vT`SoxVuzTa>OUBtN%qOu7-sk#emSsMi`6ux1`|6a@(RAv<_Zk@&2^7sk^xUVJ
z%Sx94%)HsTpv&(L?B)Bsq`tVvK}eo#;Z@3`yN%amdDU^8TxHvabh)wdQ^{N(XS$iS
z@rPy3qf{UFZ-mt&*eBjZ*w0S1HFwqKH;Cp`A+Khh9bx#H2%4QoT{0%<urP~d)`5=m
z_<!ayB@5uu?!O(F)?uUzDf{Zn-}m}t`Os&ofun}3QwXAy_Zx7b0?BmP;3T&#IR5Sp
z?A9Kc)^RW<dg-Ggqt*vqy(F{e$Jw%CzmRs4G^>?`6)nEM11?)GeyDn$$4Ye`y7!jZ
zBy)ehpUDtmQCe9n=m9A!2=iLcdajMt1$s?uY8&A)seuzmMiDXK0d{px6(!wKpOJ7k
znAb5{qSi`CH*fUWWUMY2YS}-<M0w9<4-a88!}T((_ol=Swb*z^tl-PRf|(<Gl#1=y
zTAp8v8+0#jdL{J{zgy>1t$*h@^!efG`J(R866?7v(#T}pfBr=eub%KZ3UUET;bwIi
zM{oKJffQo&Me?BW`ViV`9f%Bxa9}RTdGC1Ma+Q9LIj#2q#Md8l-9<fp{8BKIObzrY
zUfaYotG!g1yiON0t?opk?(9uHp`~lQM!vV!O=cMoHsB2*kldm+Uo<AM4*Ip2q<4Au
zux|N%km^AV{eNqd;cm;uow9957Cd*`sfSok)<Vl3JH*-3%*3MwD>ouMHnj@MlWp_H
z5XHS1-{!$mn94C1aw2|;Ri#&94M;NR;%_lBdOQ^bO}|~?9%XCvs<nO<9-AO}p|3R-
z1|CV0j6u-L5Q)M~7f1xseu_?Tq^lF;>HhZ3{qj^Myc?PJHvj}>Fa-w(WW6&cr+<{9
zdiLT`(#I_Hz4Oo*R#-oNBU&}Cyc5OaGp9Exy?Q}z;%JI{6hGc)TNE?dqr@ysH!vGY
z914k0X5>y6au{RH(eB%5m|u*ej$9lRPV+kRa7LS_i(LP*A=%G;j`|+8GI4|wW9AqW
zB^^DeKo-DH2`E!iZqWGe4Z{4Dmu2_rvAFk5I1Uu%3*{GC;^d^wKcr|jUg?@GLii6U
z`~;jMoW^kAiwf(5!#ym=c$!GK@$n^(1*L^1ajdk@k}FV#)lfbu#YBV`q~0L%t=Dkk
zSsIcl**-v@t_-Gbp<MbLc0G{tU(Qyx96CvMUWVM}1h_q|RmY>{Y7ncCk1zzfa`eD{
zy+PRxnn=N%0pI1js{6BVeld)dE5xA#-f<;ihG0Rlz13rby*Rm2Hd`mzn%7m*(VxJI
zMX8enN(2aaanYGA_^bj3;rL|RLiK6Mk*)|SR%r)u)OUDbAGPG;0Nzq$FiQ?DF}ij>
zstmwRJ`!~73bO7}yi_-N<-=yq=xhY(j}#lmYf<~?Qt<H(itr4F<gQ21EHfV>eonm?
zUKV>}H->-cn$ynzb&#Jw3j8N}@DaeQOp0Ip6#xVM$eTjVZDeFdMIwV33CMu=YpHw_
z9p~35Y?Vk+HWy+S%S^?pR)o9;r$sE-lD(<1cxGwY@LYau+)-sV=c~Dv_?AW{sZ#|$
zo-01^>G;N%;JrQa(yO2G9!<tM`jh!J?+J{o;&nc`rw`(A0j3>NKSdxUzFuy=Kp<lm
zcj=k<JOU}Jn$NNeOQTEMLui<r;Tg$SRzS|y(S-}yqeycg$H$o?yd;KQbhCmqyMicN
z%q-=oSQAZqOt`A?TP7GhD(LKFTCB3`h-@?mO*L_ELxGHoTHtxEX;gF@{m}+n{ZtQO
zdlOy`21D++tk-&B21wou+%uOYCc-=;<a$aaSuAYtF)DO44}>Mr&2i8i#Viv_F56T~
z`zV*r+&%XUr6#Ow+}LmneTR$YKH8j^CXAlz0*JwYoH^X193f$ZDZPA6Cfc|>Obga|
ztF_mLwd7`%AAMSeQ8+!M244x==JGg`E;yBW&TFB-8}rJwgZyXcq_NQvm?aSmL=DI+
zA-K{fne}XB91|H`Wl3%uGu~A?#D{peEEIb9?^qgSaYS1fM25IC3{~Cmh@ni=q6sFh
zs`m+X3x>`4Mmg#|_4Q_j=c_&!=VI^i2W`0ZR@f9U2(`jvF*SLjKj-MG_Npnqg+mMl
z@DcL_G+b=OY9y=poB>mLyxobfmGI9MaWe+G<|b5EP~5`+2(&YVOfjT+y0Oe@(vdx$
z?Ap~3kjX+gzFf6^kBssCL^pZ5ECvMBB$bB9j!n%FrteIKLzg1QgO4hWU8Egi2lElf
zuSin{Q{YKKT%$|jKs@dFkD!bSY2~`pz~r{e%T7_Curp=@@~R8zyXTMam)O5Wel_~{
z_r$>6M&6fS8AqiAIqff-oR$aAk+%uyp~@Jy_~d3oY^;o2BdJ4DP}nu=EOuyah=l48
zCJsBH?!)NUkyWDk3I12E#*UUbCf=wg4WL#f8$nX_O9E0_TLBlBs&6gA`fj!()cw(P
zmMmN9o06`vBMd-#c8l>gD;p;JiaZvnmGY*Vp?i#71h#b>ZOz$~NSZeTO7*VVOtHsM
z^}5zJ@KXVQsz~(b+(=ri-rs<4W{2IEcT_K%OP)p=jnaRXwB`ohl1)zO2ujO5I3qF5
z8|*c)B05XUitzA&V7p#~%lSAP?P|HshVyAR&+)gC3v*iiiVRLc<qkw~nOgt>{(F?7
zD8i{l!+MguWT|n>{81)L3YlzGj}KZnI25w73Q<~%5EJXGe`Plf^$eY9`dcTmU&>Wg
z29uB6f^gp&aK?%S6}u5+!cbm}B+@^AB{*z%<VZo^0H^;64NBpRo%;={sK;%V4As?=
zJl@qT$Ri(7xs2YKxdves7{i_acwJe8RebXaX%#wSrc^SAn&vq=j?>KN{^A0LzM`bh
z=O60&{-@v<tKR@9`Gv}(b$924V0Rmpt{O~9MMGKIj=323t>$MD&Gt3UiOPA0dXSb8
z^|srv!p0l^2~G)U=9?^>ltyxvHi*{u=Tk=sQpYX4r7#_4LwH!Nr*NOyqjTE9OYMiD
zYyK2Alqg*CdaJtqE*V=ZIrI+doTftR7WXBo@8{ot2s$7!stJVq4jJESZRH)Jdvmk4
zcqXjY=J^jafjXq@RlZs(e&AX<!<0+A3BU-&9@o($NYgx@M9FfdN4FrC(E%i3sD5ca
zH6!jne=B_BajL+$Y~aKoVV!h_7JHxDyazf=yI)m0YKzQl(5Z`!4I*u?j%a5V;9^J}
z&ye#BDgF{95aP5-o^X7ss?KV2mY23{$U-3~$m*zk)}=kF-R6L+z8E7$X9_oBSe6`R
z2}f*DGr%yyV}}x@Ts&^h9qud3m*OPt=!WMb2z-mN$OTYb=Ghx0d~)|axk?@;RJo1l
zAxO&3^vpXZ`jnptP~{*8>S4;9W2<UX6rwiTcD6ebXuP9U-wJf7!Y9|Jv-k5M=y9Lc
zZuWY1OuheN+|l`h5aL6_GoIp>L*JLR7!RDt9&UEpN1%Y{P7rXz$RCsNr7iIw=-#lY
zFB<J|Kx4uCH%_wNzxIV$rpA33B%7{v<1G;`y@|r%Q#kWKd(;fF2rw`wbh@o@)GQ|R
zv^PYnHRK4wi)N%@REI)klkR-b{9PiY#hB(o=j7WoPgOmH2<qxJFTi%+EihMkY)rL-
zS+OI%V>~XW?bruiLz=+gTAQ^^^W)z8bZm|g0Krgntl=g`;CiLOfyP4Z3F0Qv0`Do>
zhKcV_`cxnLrYaw8fr8Y0echaP!_lx$RY7+55eUd}0&d;1peZuA*>fz+kC>P5ANcNo
zz$EAvbUXVg?ayYA)T2`HJNYK2<4tM*4aQqz@wa~Nm-Xf6<2`L-4Bi_ELqbj09$6S?
zx#h`?Y<6fiN>=aD{rAA@23pD#aLDh+lhf6uiDiq866_?q*9q=YewheT|D@}FRbrg(
z*cJ8+?y7$^X0t~Xo^^QZs7!Io!Sx#emN}(L%!*4k;lEJyG#oCv(*EfF#=@M%A^iY0
zWY;1jF=A`Z6Rh4ypRo5%&SwE9yCvqEA82{pB)6{ly3oGeUA$}5d%JViKaON%=-L_A
zH!aZj7|0=8fRE2iAS}5OLflg{Urag`(cRAaZ++~%rTX_KYF-w%n8XCfXZ|wza;cQq
z;PsN(BA?a7P7EoCPY?Pa?#BuAm@wA)!Y&72xWs)Fjnfad(XrP)$|jbv`-(tDX4tLI
zz_eC-M!NBQodEH<8u<43;dRX~O4Y=7UmOE2M+MF|hlpP71c`&uqwX<W0)EJ^Lmqby
ze`=IBN~JI^NtC{vIGY#<N-4g1?q<8Bx46&LC5`_G;$=u{<W!N~6-n&B2ivenufSM@
zB%_0p`!SN4vIo*?5oOI^!b~=UwOT;GgrMX^ypv~H60Cn~g@#=i&3}I92i~7HsQ*Gw
z$XxG(re}9FQ;ms!+00_U45REfcRo<yHDfQ@JDhjlVRMShT|K+nb8~6}CPGw4<(D*%
z(@NklH>P0J#<U;XX2yXD?HkX?t3C|B0YYi4wyk0jypX)k@yiZY3{mq+kF4qE>ANA2
zDmnQRlRNY&6J5s0N+1hg<~|pbrNH2%W8Rui`ED7~VxvFFt}^rv|Ka_Iu1hN2Ms@O`
zHik(LFVel(uD2JWh30#r^UiYPwI6GY$9h{OPf^l6&+E6cKY6OtlIFy|y~D^A9k-^K
zIdh)f2St7l*c-T*jW26S>~QC!=&!D3u;+W9u}_#Vg35iwaWAE4Fp(ceBv}t{@KFQI
zohFQaJs<bfbY^zQ7T<Y_fqsk@S5$p`eq6G5{&gtX2cB2qLUX{dm+xDV*!N@4j4)ao
z6n;j9MtbkG)bq1+WU0V2|5^EeZ94t8Un0qo(!GXNHfaYzKYq+Z?YAF=yw<|~FY~T`
z+!fOp7hd~()qJLY2k&+N$K`Re$q1TOd%d7=7)NV)IwT0HK5ZRpt>=DO^fwhfq5_%-
z<TD|6wr&@ntqC2k+J)aSI?xrZ)Z+}3aO1LU#R3smm_t|tzkInAPP$}U?@~#)xr#$s
zK41ItOLpCHmP9eKR)qQBK4Lfd;G)fYA_B;CPNvoL*fZ9LT{0D`j*ESq$CiVJr&uzr
zHsR!0W=Z$de}g8>yei$h!RoJ%m+kJ(&Cto(I_5}Wc<*rv=4NJaF=1Mfmxmb^G&~|t
z#8IupbEd}?jvp_Jhq0O*To)Y^49MUi_}Wg^!vL=5IbN;!{DpM1e(k+3D_T%0H3D!z
zQUH(X){3M3gWW+4dn}r|F0yo@RHv+>bh<hXM#a5WlA@|J%~yV|df_2SOzqL*Vt-(%
z<U(n2Y_SaW>j=Db$ucIH6D|Bbow@dYWCz&CC=8iiDUL&B)Cg(gYb!JQgrE&n96>o(
zn<3=H_<h#0Nd!E5TpcIkXcC;Mub>2(BkG{AWD!>5diLdfgAt#4S-B|5%6+k69YC12
zAQ3Dr#uua&PF1mj!eoWCx?%v(G1uK}v{K-GS1mqijvl&?v0VBf^P0FJo`T{RVPx%g
z7CElOjb5ei=eJQh`d>pc2dWB$wPsg8U-@rahSm`IZWm7$J%?#&?u=>SdDEVV{2H&F
zX=m?WOZQTX29Kwtq7`1yM8JX3aP-OETDEwHFafxC$mDoaKby&@HKxtM=UI#Q8=z}o
zqTOx;we@$FH^RNns8wiKLG70LI_79;CBlj3_*NmxMP?Q^`4o{ljzvxpl-DuH0xxU8
zQzg)^yxtD`>6oga(URI)L2`Lj_!hv>gW6xpN`>2>t^T&P1`HFfiojBJ%TXQ(!Z1w2
z7Ne2T+L2@T_1Dtg(I#c2>iEUym*Mo&^1D`77?c~MV?by`qV6zfH94K_zceaN<aS*{
zAf>mPX$yjw3BpTC$w%pFyLu{U2=7<L>{5N(Wh+Q)h$L@ebYE3zfuSU&{NW`w(h=an
z?kd-16nf<#l1Nj4kDR123o99u2#<>z6BEnauI=1IcFA6FC2)q;<>zYx8Qma!X+t)B
zpH^e41slJnm*-OT<2a7lwFafA*cdw@6Aq`sz+HYg_A>*_zJ_QoyR)i*V6!dI!e;xD
zHB+DRHG4<CX`b~`fgt;W!&fa=nT*It1m(5SkjM~VeJ+5g1pZ8QRF#9V-%UZaA1(7t
zbr1%i8CyTcPkZe^`UQK7C|nTP4V>li5dp-ksK>w*4cs%FT@(qY4Xv^{)GO<WHDF)F
zPblHZvl@^b!3;%Q628Tj=o!vQP3~NN6cJIk5zaO<NcMIQ9TPjIjdz7N$>2zroGw#U
zwMIp?qgy^k>RH;;XE-(%#J+D#$>ez-w0@jERsed>6|)Qwjj9YaZ^E~9!mf!;@uKj?
z)zHGbjnqLcGtt(JpvIK!#H63qA*Z<$ymX;Vf%ydprpD}a`RFNi9iXh+FDyf|(T_*%
zDY=hqrpySs18DL`VXrV3WN4p`tSX61D$Ni`p850AufVvdgU$<=Fd+gd;*`T8w25I4
zm5wF2_*#_ui}JLit5aJP7ArhoQ*a+;J0~spSuNS#MyztjE<@Z5S7c!PWk}%IL=e9z
zdK_s&Z!U_3=Djc<yLRo{_Ckid#Qbs7a9inOe|rRf+FPv{=DLy@ypS#DbnWexW7SXA
zQZ*$~xGJv0ir3myK4kIhH&@qC_su#AyeP9_D2>f|*W*;u^|TEcnvK%|3Ic2|y5pRN
zt@^G#L9=m?pxHR7+3tp}@0<Q)<2e7%jaXjpRHB0>caxyB+N5x%)aGd4z;OT6BhlBK
z5>Q@kRMfCS1!wb>@SU7hSi}$wlvlgR*YWg+SBqJj4CU22@7^5KlC`c338^J3D*Dy&
zzzopi%3cVBD};j)H2G#A-*33<ws(OH77c3Mb+LZ+%g<1`EJC+)t-522g3ieL^2&+W
zveOGxKN`B(Jj=gYQXvWvHB-oA|In4u^~L0V#RlT(#rc;rwprc7RP6#<@Tvj-b0^YY
zMb-@uGcA9Lbf9N`x;Q)cqI7Bwe$s#Haapy`J)fTY%RpPe<W&%B(A$>t#tHAElmD%L
zpQ3+1(P3io;+A5)pr&LZbJ6|S#O&2q!k6YU#}<EFPv{kRtGq`^I)`&pZfsIajagoY
zOz6rAcw(qEdSnan0cKj=h*VeOMZY(%5^eL}2-yb8n)s9%m^)dn6?+X+KG+@HrG!iK
zoy9OY8n3Y^Hu1JGDBy;>krJ=q%Qy}0yM_2tX}x<i%=#H;!&z-Eaj9vvbyHU(;HcPV
zLEd{+Vjn|Hkb&sBx9o%eGH!C)sO?&^m*1e7dqt+E41|S~^8M$j5`@tF8Q^l<si6jB
z4l5!CbU`@&hQ5k%{RB<Cvmn7sQflqHVHj6ec<c76hKx9(>+$WxYoG$g{F1h7Ime0v
zz9$zT3OfV?VRzf&%{6;=S5x?Nr{q7cU`|5jx8$T@JNl;Of_<Dzzl!H<RdJLg+;}Tz
zv&)%$PK04rg-H}dQKk-?n0q1uK%TZB>F(`s*?YtF;)wF#A05c1@=LCPXyMBTpHEb1
zxW54@fdh9{RL~ZR6?*fDuKG(TU>^`(TvUX8?V(-To|uKGi{BQ{z!ADiwhJT}KIKX7
z9oK*XH2<IdemC4~@w9+<afgWAeDmd#RrF8A8jf*<?tQkX9D;bPx3M^4qDW_!6r$%a
z{8N#AIdn>27*{E(CBXn7Z2acG_dWBkiFy0$QuE(Nm(cF)%-#s)v6(oGt2M|;@gts4
zG;b8AWc&EaFoRmjBtAxCSpT;QFH8!gaCiXctG~MH1nnwxGLU-vyZ21EgZ>Q2`|ov}
zKcKT@+#9q})X;NiY3j6?9N8}%u`6(ci_OY{zCBXXT7F@%`4OBJK}ENp8@?F5;-H0%
zO^P=3f2-!CYrM9^NGv5F@&=b}iC^oq2|~x*jH?*xg;8%bjWhKTX9~_3)h*}aTEA0Z
zM=<%gI_*9QJ#Zj+1l6q3K=pHJP?~y3<g_YWQDh^<Wkk%sh7;}|LnCFIKRMXEOhdyP
z&5I_G$Us}9rEApp!N9#91H43E%!n?bH`YH=lZ)6M9Rf)B`;Oww?o9Ik)6xI8cL72^
zZ(U^I{ZJ341xf$*n3#=OBGpEe;lyao<_7^GUFyi((~X(XtAEZ!@1@$|xs&S(_kmP{
zOB{XvT(qn#q&=fKn{Iom%u5PB-NUU+hg>;3F+}7+s2?^ou*C+$ReDGbUAK`cU-KLx
zE+41_QG}O8MZ+P;qLGyp2$M;I{%L&&$g9JRitN!}rtAs^{yc$7qXNlAE~LeMCLVwO
zaYTm#kN46ghClRB1cCoDQq$KlLPdl|okG5J-z@rFKEFV77Jcsjh8=>pdoeWi#xjs&
zkr`OnDjs8DhN=-e%zPXx-}j^_s&CNK{y==0hZjoMQRlvgi+(j-z3Rfkb#g5EnSdGQ
z>X(|$(N{b98zXJ^@r{T1Zx-@zR7{eMWSBOmvzor-&9o@nSF+i=cO)q7x(T87D=YC~
zCfm`>!tm|e;b#v(EKytrKX9sJr5KlB$YyZ%%KvC?)cg?;;H|3I`SgQ(TVn?D!$;FK
zHu<Xv>=_O9er&OCkKl0<fLNfl#T2{j(m=1d@K<5I4EmYYK)gDu?@=@qm7!b0?#E}9
zh*HMD4Pl95CY*v-bc4;4K}Ikv)b^G^->3|lnBlDQLW}rdt75a>;8Wb+^UCuLE5pyw
zM3E05n~V4XE-TjXsxkg_&~Oxu>ROrTM>yS7xq-aWI2+3J-DT0&4n6fYY>`8@Hp(kn
z^#Wmfq&58`a#a%q#Mnk6Y6N-F)D}px#i;JS3{1rEuuJ{*PwUifU&#aY7^u~jCh0V-
zPRz9JN_IUl!hN5BSWGe<mOqPXbXd|I{@!qA0t&x<rlB2C^a=#=+Mg#4{fEOEg%1r>
z{nnE3ts17UW{-9esrjny7Vcl~`y;6OjS8xMQ#Kzl??s%0s^8ig`Tzf@--M8Ki7?Pb
zkTh$F6LYobrwI@DuZ{2MF+1|Vl`SfN^+bdoLJ<YqMiREO_PGe2$pV$v#@`#LmSb+{
zcdwfmj`XlW!3y-{x**CN37=Ve+<3}6RUJoC26##ML%`7-^KXEG)j_QR#^`t7B*u*W
zE}qS%3+;~Llop?W;iNBjL#gXK*;U=h_Z*t%rFk6tb*7Uhs`$pz%%QP-LSb3d+Ua?W
zTmWL$X(9ugq0|>t`K6i_TElMRV3E`w;XFPna#J-*3-Tz6q~1QFiOCByC551jlFS;I
zHf8C&hIWDBWQE~;ejG}a4LPzxLW`r80EwZ(I#$%%actjQ{ju!EI6<g|ce{bfLv+nN
zpOtf2ixPM;2S;_Cg)QB%H>et8npl#L60MLiW%3uL1?y|$;C@J3^WC)Q_Rf#00t{=F
zr3CZZa($`dgSFCqYxE5X9?2TXW4dG*btRq<BB}4PUfZZ9UKSPAA5-nw8>@w(c~d}K
zL-68y^2V91Ij5g-^rklYT#C63J8%kW9xhu1H4i7Xe%~`oqY7&Y!v~m>k5gJ5?p7Qg
z>gV!fW_!suwF>ZnyrZT5Y)2g`3&TC9XRWG@9RZkAHJ@Vnn!&Aj>V++%hA3=4*CDgz
z=2MwO@QU`2ZMa1ICdoT-pXlF!E{0dn-F1!!U429w?OSc<stCy!jx0wV=PIM5-26^=
zRX|Bfxu2t_r8A_&Wo^!<#j#4mUG8z#1fsmL)<XGUSkh_pPphqa@epXtwd~TTP}?@R
ztqELCx~X5OsL1qQP2SaT!<sGBvxK<CHKs<Xvdr+1CWBWeriTt$r@_@@KD+0HLS7k5
zpwpbJ&$<J*h6LSAY;u!rcBC?N2WcBuL$n;#PEvj?@uq<UCM`V}*8xpYz^veBl6>&w
zUALsG=6oII{dP|9-Eg7{#3Fd#(zSPa>DN@+YgFNIOFlM7u(bSLVryM$u%q3SGU2I8
zFt047XHqoUc~wR9XKLdXnQ8xcd`fozLEa0yx@*-9?nW9*9#!&8EV7N2KG4!C&p2J-
zV4qdakb9aNM?4X28uvR#S(af-RCdV$EkPh^)f!@xlQ`TuFU|N~V1TDmSNB*}5%qQa
zd8N4xkP%mU9lL5lr6?7Fnv^U4O|h9?4(bFBP#Kj>(yW*(o+ENvD}#tasHoe9N6up^
zuOY`3M-{8+4CO0W3-@#oKrN!_>*www+|eQBc3GO`)#<Sxpux80Mu_o&%z3EJC1uap
zHZO~4qB$<n(@2<b<g3v~qzG1|&SJiW;|;cL+=>>HQo$_OvjHC)Q@=o!wPgynu3pUR
znE2*vZS<Yxl&d#}nNB~)fT<)U3{f{u<4{pY`uz-iPy2yrI-xMw3}df^E%?O6L$vu|
zWjS?fO3);GUbE%kf>3#c+wN!|w{(BbAFBl4J=a21I>nTMan{zU{REa$uq5Zkb;UPJ
z^tGUM9)h?Z$T|Fj%HH6@xTA1@Ucp_!a=g3e>WC4xe}4>cdFI<_ZUXYa2U}nzxB}U~
zdh2%8n{4H9_>qY8bxXihR$hLX$YAT}IzutfJfk|<kOo0Jyc&M59#}~nwp6<eP)l8;
z2B76B{FLCa5qvn(94y*$c;$J^2cIjPI1tlLkQvMOIkAWybYzzksfx0a$FSUc)V+Mp
zf}^P<UdIwdogzDi#adf%!O<FUD9CJnX=$ghJIAZDD)2aLPGLn)4;t@V`5|{@)D)|g
zx33*Tv@p=^l5#e!_gZbll;C|T%Y-iJI#+<Dxyqy|p?HqCM?Xfl`^1Xd%pyKY$UY%w
zeuJE?Q~zh=Y!L!=3p?6cBn$-sBdJQVudmxAM!Fxo#jCw!dUjW9?%SLL<0u1y#^*w}
zHp$-*(U~;rfxeg1dMBRmjh9$y60DOeO0(rij#L>&Wo3^A<bTcBc3HkMN~H{5Qt>79
z{w10lZfW1)n7O)UP$r0$JyB{=Oq<s#rfYpn{o`BilvAakI1NS42<=+cPK!X_#)tWx
zPX`BK(hUoSlM9$C!&iiB(nbt(DLv?ByFYzgOhB7$_I$tg>;d;S&iXZrUUVa$wPBBK
zs<jnga`4haz~xNobr@D_H~vmTDHkv6MWDPo&&_oKy*d&%bHQY-<Wqz%%>+@94Qt9_
zob$cBvX49M$x1N<$mc06R<cAK>-sfO^#sIBVpba;L|kBOOh{T8>#NhyDBil{Te066
z)8aXadf3TXAd~&VoNb|4YYtXR@^h#38dV0Dlnw9>q-iniEFtjF8%H~Vj*L!e_XFE<
z*`Mpr#0LlAtqMk#<G2%Ssv}=&Bs<R^A$}e_4q;&wXLPIz7Fg<^%eY8-ZCQPo$_!P-
zF-tfqxBS@(!tGbef$ZsssN&3%G5X7t(PKEgWtP|&WmEWHf#lNol-Qe)WfwCiQ=#pu
zzDm%2+u6edaAD5W)XB+l?0R2=evi%9yZNr>M8N!}><R~+gyXF4x_XV<VqZa>Fi<;G
zS$~J-XkpLKcz;ZQQZglX#Qwln#Rb^Vau!%0XP7yVZ?QAdU~82K2R|L|Ul0j$kbkAC
z#q@Fc2_gIfSN9;s(_-y}C-NaGv%82(-|WLcgX_Y9<oluehRnvjpZ5DJ&F&N68h@F=
zlwHiJp!9f@bUoPHJm6blNkxmk$*PuQ-k|PeMjrO=yqZdjnU*gX%;Z%&?u0{PRyaHq
zmsoK0%vjXbWd;c`AQ}0wiB1wh5NV15Mmo>ic>*ocIa$F=ujhmP;YXIfj+znd{PawD
zDTYNYO}Q`XgN+%%qog}57->qd;r#(tQ~;p#M{s+YcCxZ&{N$qO0BAem;lme=#Cjb+
z>=>f{WM^i0u$>~mo&+|p0V=pnr6L4uksZl&7830MpkZK1VP>Q-QoZVovp3YbcAsT)
zCMKN1pl3u+S!c_$8qL9Mv6XA5r+^EIVUQgr#gAZ?1hApYd1B49QDk;-DD)b=vikX!
z1u^4O%6Ct!{Wf-y%@W4RCugH}VVn=frMSGFaMO{slOhang?v#SBnPJ)QAhq5V8A0p
z{00<4zP;N#fxZpD9MylYi-2cLxxf?h#8%UthG-#Bp(ykj#FtW8A;Z-UJ<eBJ;t(!c
zLD4N3=97<y{&_R*I0rBrpn)D&^qu$xciht<l#S1wKC|Qlo@uk|3zxP`4AkmzB?4eF
z6H^i0c2+bz^4fuK)jso0r_5b1=H9F^l`$B*poHywo)*_g?dI6g>9=y{OPoT(1F1NE
zLw$%ic3)h-_oC(S$2GQLS|@_7clj%4trw=BrUner7U&%i1>~V#P`gAM$qQRDsrL~K
zlr)rM0=CP&N}n_ef8k}=%Bt?4ht6tpx2wm!mh+6xDzu@DbxrJm`W46REmWXGt#SCU
zEMwC{YaFJSY3ZBUhXnC<N;P?$Qn)BiYqnK}>dkz|L0f@^`)+wB)v2I$!f4<!{Ael@
z0}3!lC{{Z%1m>C**!iD1C1Rf$Nl;(_&PAQvVb0ostGsvw$(bfx5e3Po$`Sj8sI!rq
zt~}!jWD2;GTKaNhs)j$a#4Gvtp~<b)k4?2QJ7Rr3MHM@Z53+pI(GGH`mH}5EnpUAA
z9_g~ene%wZzZ{n4=TuDOD7LZ&UK|cg>ViuC)?xn5)RCI@+oBqvO2@~k`f&N#ROZ5m
zJkU(KBalWmqfw?0Bi!>yshoA-a4}dD`}+^%JG%P~MhU1DqHF6O?ImaKLDmPO?zcrS
z^Uxh<weybPAUbn{%+B0bk~Ds{+cZrV(a#LE3K-3=v2gaTJCI}71$|rEPRTZGzqURT
z>;>ouGJ5YNoL)IkDlS1jFR4rUXwc5pk$z_JuE56!I>z=-4Z+~$OUWR6jqul+#GjfF
zPV)Cz6}SEd=x==A$;e)wV>1z=zq-4<0j^dkD-hR!n!oWFxBl=TC&CuW&Po-@Mlz>n
zIzP(>$Z(g7)iey4jC@}{F}ud9JoRfkRW~!yXJLW;B|~aHhKQ+1PkSG~!bxG8DIsk=
z<N1UiPPgo59HoqKEzNJM9E85wZ({0gd0aVLJ<g>!5JJt{RL6_EK}op1A-~m8x5K_h
zy<6HsuF%U9XZQj$i9)PG`4)eoIIbKSvDnPH*@Aw5VJ@zMyRFSsH;Ve5E)qmhk#B3O
z80ks;M7mqJ1@Y~$0RPAKOdBQ0gP+#<Vu$I4Lso(s((IHH!kS@Rrm+O`4oj-idJ6@<
z=h6@pDFuV9WP~Oj3(~IFD&y!Aj+5#{N45^s3qEpVqj|rhr8=x{yrrt{1z$y6c8C^_
z<u`zmAFYLI*dltQS#`NSfH=Od?cqUm?@8<Jdt)>L0-%DLJAkojlNIGrYtj?UHlb?&
zt+^{!9P`d-baKQzI&zlL6@q3$Yqb_+4)LUIZu;oFXHtkbCFJY?%rP&?O`%0jT(M@p
z!{&FN{|#{Y_H5=^^5P<RW=;~eNOW?smgc*@>l%C`d^{;K^2v(Wq{76DO#L<oU`dm7
zRx{k<9NJQvd;NsxE@f6U8xGsSSwX0JX5PIae+LzhnHX&DJu?I|Y6Vp0WL!N-c2&f?
zFymqQxk%mv<=rPjyLVP2;*!@qQv~TmnU4%ioSo%jUr!B65%}{WljoT*GONRC(%G-|
zb$2A3IR0v`?H;*T{izOqFrfPP`ZX_V3>=>ny2=@AOd6a!)Aq?lO%swepf-E-k<*iJ
znN=#Pz60hI+WAB)b0{93q<&Sf*#>U-?%Z!Aobm6!HPaSQTg4`1kaNed+Ih{^^ZeFz
zVb%=6pMEtTVxQkHkel8M3nv3<i~SAoBb!@%aenhI?JVc9K&HL**R^0p?bAxvZvf*m
z@0CcDmxsi_Mz@O|p=~$&nQfwAzMt#sEbUXl@M!*|<ebnlsrqk3^JA-e^>nbnwZnZV
zxL$Hj7ND6{&K+OYeeQAR|IUyD%XV62_sXH&#{lFpfpp*Q-vPBCB>GjcNfoCTkQdwY
zQd3y_>J;HO;Iq%r&b(+|EL4SqaH_l}zvtp65<Xebgr>eJJ}dD~o)aG;3cuE5=XI;U
zp{fC{#pUP$X!O-gh5+%}TdTOg-?cscRrQInPxsFJ>w}$|cTKbNhc9ot@6;cE8kc*x
z8NT}O1*$@jcR{lrqNn=2q}K10s9v|>XXh@-j2o~@%(^l=n`XVIy)UK7%@DU>Cn2I>
z3Dpe)AKgFc;nG|Qj5`|+6uKbLc3f*=e}As-F`vIAq49pwKA$`tdlDyI2itqH%Y`G3
zJ*aQ8v=VSL52q@n=wVutSAc8unMbFQzZvtwUW~P8%5<KqP!Zc!pV-(sBgX-uh^ndh
z#;y5=jbU!+>)Rw}#V*8T9E}pKg<EA-ln8w_&FVr#TEE`nHa{&Zc}Gx?zj<eJOmH+)
zCFfMh(KK)xLE`pY*9}+yb1fqu#)=46>FadU&j2mFkZ3ysV*BPxKaXI>)yeU#iT!Os
z9X@hGL<M%WG0;=f2ISL2eM4DP3yB#2AatXLXg?}TSXJ9OAS)csp;8A&0X$Fg_mlcB
zr4j}JdhmxF^PgqVUEz#>><a%;hhajJlgw^{Ur995v1&jeaHwzUk-jUuvHK9{n6Rps
zt}mCn(@Mk68ti@2_R&bn#Cu${GIT31RTI}~B90Dof3+MnFC?Zk)kb(+3;>{&`uIKI
z(fH!Zv)QPVOX4a!7D1(b>C08AylQ;&s`91|QG6(l{w|qXdeA&$vyXZ~kreMPKTxu4
z1uQ#jc$}N(M}`n_lTn_xw8;=+DGFNi?WPeQ$l)1kADv45q7$!a^IX?~{E*6yiAg-d
z?;9E|q{WYI#0%4e!>q*BXzsRfxAE>56a}~Y8vspg2roYYZnFJMajUh$#{f5htR=ho
zocWl8mBL}e^z@As&{*2b2?&g2*y_UuEy-njX|MVntJH_(M3$ffhLxag3sbS&f^7DK
z$hUKhrvt4O-?@6D_IdQU%Veag-G#{6)e!F<0vs&OvsSDaz0l%iLZ#q>zYm931!xU$
zJ20X|E<HSVO@NX|beZtU)62L+N}2KpDNwZ|!L&6Y0GQeaDWJ*Yy}aZ|YsPAV6%g?#
zs#Yj~3`m}o2%@}m&&v?tdOO2k@3c%Ue?EgLZG9gA28sd2uc|Q5|6@^riX3a$ZAGVG
zPV?Z_D!YOe9_(u1NDJ?o`2QldrSUM&LGvJ*C;=tJNMM7NSN>L#6`lKbJV7DXe~HYx
zR{Ia<2~shM$itzKYtxyVQ^7NQfJm1#)GV(h>04<$%Qc3HLS#6Gy6Ya<n|m3ks;vI+
zC{|PVd!Ly4H}awl^lXt5E-j2j#x*~-;<kvcG?n^trLMUuQhpeY$mI`ep2t>3eod}o
zFx5W};>+#Gaxf7!SX>J8%P`uA->%ii{=DJ5Z$*?L|28}jm(@hGj%(+5MxaejH;*E=
zB71~@UQ&c#U`_&Trj{lfSCyy{rNR|EnA0q+|NY%Nb_aFf{Zz+!oEcpyf#FDVf%43a
zBN7X`sCemchiG4c7E_Fr_Is6bx=%4mT^sV4W!M`M7(e#eMHdMCgvJ98DNV5U_DmVT
zZo{N%$oPexDc?pbOQrgJKx=2`Z1omeFx0pW>Usq#enwV>A{+9)Eo{~kqK;#$*E~wY
zk_|4@HV2XwZ9E4~KX`j;FeOzzBWTOOKq*N~1Zoyf$;kjB6mrV~MmAAm`U_uM5{HRl
z`fkH-HQxslffE)K{Dl6z(TTd2ds&9ZRWY%arJ+O1LOFcHCfK&k9dqXB+yb}v{Q<+G
z9M?@tgRu>l(bq=TljLIWnEgi8OQ?^EaS`DP!orhHVTR~r2NXT2iLWipD{C5D`Yavl
zO1*vqNYLma<<gU~vh@9YxNAz{EuHE_>|&LwU~{dHk;@ff=>Rl+GU?Ip+u<KF?G0Gl
zvL4AKHk&rr>ap3R`5v5xN_neI@?Ub4;OJzt{N;Dd)#wf2M_HkH*fNpJA?lV;bTmo(
zarOXO;;HW%$no<oTeFr@=cXsd9jDz#Z;fyoTlgb3+zCsFZ%X@~UcE!9Za<!qMTXU|
zoU%@+bE1;hWm$egXOF;7`m$@x#b$7Jk4f`>D|WK$bVUZkxv_u}pGk`pPY8@+4l)80
zl6#w*{>+N#cMtIc&dHW4m0LfKH%702K99O^6LhK)(%4-DFV%%kB{!0*G3m(eo{FI#
zHkZ2%0zx}WLOO{U)wvhC950qD6-(4!wT%S)2E3^>_?Zv+=}{s7{HXfl+pPvC^xqa^
zA5^kS6E1}2F9R{LnA0W*v+oV>tv{eSQhX<GaTlfcv`aCpI1&W}rEoWUM>dd7x1qoD
zNPYO#tzY{?*yr(3JHhAszZED`{1m-<Q@kA@KQg44_-o<w*Pd%m1neR8QHG;*=6lJF
z9L(Ia<C=7^&-YZ1&7-UhqY~kRz~0SZR+|kOw*9yy`Vv4>$ni*aD%9np!YF6hY#6po
zmI4|pi&&X8gEM1EsUDB{4fyvS$z5*!I;6jR39f!~?S6SRnJ)V1gn0S}T;dDbFTC2Y
z^XVFB@fN73g1GN&x|p}yl!S^U_>ntDAwtOCt7eO>0&p=G@<bvbQkICE^D(i4Vb5Kb
z<%st^kBF}ly^bz_6gnlog6zFJ1-<!7^V6g9oO~=5>*B|Ur`4?Q(3JaKkpKnmRFapt
z6L$X0J|NG}qcQs9yO5?>RD`std-Ysp4w)vWC(~Le0&P7ZL1o*qsi)|`>#7<(`Rgml
z0f=*Qt2@N<@zD4_(UOj8{rc5i3+ER0FX7o0;rL{%qaRfNxD1a~<TeO$0;X8Cbu-=U
z8-D!`F>m1G2Z$i}7wEhwlW#=mazZpXMdHZoQTn_<BJxCpBi{dYiiF^14#YK9;3hDx
z!S^vve!WT1x5uE%MEfbl4KJm}1arT!x{cKxJ=)I;nAOezmYi{sQ-5Dy@jiA=V2w<t
zELC~C5(;gA-_LNL+*_T@^31$<;y~|j!}E>_n%yq1CQ(wcSZNifpl{>Hzv|C-LXA?*
z^c8epChE0l^wnfe7h%Dkcbv=@#OyaR@%+jCUhqxs;E8x6_}paYu9XMhYR)2MA*@Yh
zzW)xAG?S>M-RUb|_kf##<GIf5ltX+31bmj#;>exQ35Fe7bdtO3$TS@0MvQFdxiHtN
zZXs_!&;L9#-k9CoTpcc@U&$dU_hY#u7ia-Wp-JY(XU2!-ZL2oB{}Z+Lhhz4iXaIPw
zEwPds>DNCfd40Z3{_(!-`D^PXa5L#!H45U~Lt2-T%vsD&oW&K!P14=qKMYzzn+NYW
zF}dXV?yIUAi#jNym8XN-i4I_<Zd+oS*3g+i-)yyj0<41C$TcqsDypf}JA2E1xuTSD
zA7M2nU_JB}3rIdUTd13S5%LH?cZ2Yin)X{RIKS2N9b@cltU3<YOpUhz>!1t^M2ULp
zX{`#vF1NyEzOcLSz7tgLYr%cE|CN*a5Y=|B;^IHCyR=S7&_;78#Qe6!IS%t%y@_(0
zB<7&Busx&xX~b|!5C8ktQp@+UJKBRF4YCf&ArnEw>o0XSsk{t1kD<|HodLkzQA2N*
z$W<TU_RQfn*KwoUln|UG0F`IiA9<Cb`-mowi@A|6tXlnzHJ>L}2oy($;;4U)t>+E?
zdmul;h9$#rg#eJF6GLFM<&~)HOtnQwhzDj>-&uQ8;CSB4xL7Id0Tx)v!=S<1z7es;
zXt&5uTv)#WzD4iqDhl;u2wB!6Be7h$mH7FlCb7me0}4r+uosswQOC#Q*8e4Nmn@3j
z4?7D_qW`nBm?&?4CB)j4hco?`h;ZwELr|jeua%=I<?7(!6z0Fy8%4d>Mepd!+nA&1
zqmaq89#$m|rWVEd+LA205vUT49SECaMSD2op616}ES}|K|LfL(4FlhNeHHtPiHX+e
z)uwK1)Y^s6V|#?}qjU_b-CqhAj<BYko6~M4<iEr*&n|c$boCX5Ox<xFvL-xKzCr!i
zGy&g*QM?#-1plloK)Yhfb`3X`1|U2fE;^jkvYZg8a4Gz_3YKyR!Fzk>%)P_ZX*JS@
zb%pJ+$p>pO;Wxl-v4s8SCYI>o!R)&D>yk#J-vCDrs#@%qA=Q?fK(Pa7@!9zkQMrQq
zn(f`i%|9p1e|+BEKIFB(D6G}`M?G_6?*#iMZT_Q{&Hr;H_1jNZ-4`O(ki7px4gZ&l
zXR<DnqVgxo+x<Hg>>9e}*IvCAd>PXD(`w;74bMCO#A(|rc(c9@%%!Lcmj9|+M|em@
zl5$^_mUv9iZD}4ybP<I`tpM(|5?QIpm5*Z<4w3a>k^x%UtC5(%`nGG-hZ@7Aq1GXC
zAzQdWzWBm|)|wpR7I$}4<Av3Wxe|d)&mL5iCf_iN=o%n(GUofQFk3<LQiq*euNVw{
zzMnWgi53kyi~TR+-ZH9=CTI}8K#&kLI0SbH8e9Sd*WeBb?k*SC5ZnoNf#41a?!ldl
zySuv+2+t0Wzp~%i_nhzS{_C0PuBxuCp04hyt_sRvo~=_Zb9hT<950(_Im^ysmlDJK
z<x`3;N@W{-Z$4V28a{#8eYPk<BRV{S{NK&(fadNeZs`4q#1DL_k5RiY+|7uagXrMk
ziIi`<$AJWum1y9W(gT?bv(Pg!Pua#f1E(4Ta<8F)-TYt03VmU#x^5rgk<^4yGefM(
z69~97Zu}ln3|2aeT6d5A|3>pfW!M{6XK1nz!6E^d5sH!LXTB9M{+M~V&rK44jf8?$
zgv`4Xwch?DvEkc-w>hhRdxc|$FY7Wc9vczZJDUZBMxN*i*%|R{^O5_0vNpML2^PLs
zJ|&HR&425}{fl_i=*=7=f1AvlgY3GO;+D?*w)(7TmFK##)RI_MUcR^nEYIaLEbMiZ
zWF$*|Qx+#ZIGW#^3-B*L>Oy@bozVqPVD8vETZ2`6$Clv!#xrvX7Qt#5PN}JN_}u=v
z6MymIjb{G)*OU&pH;ES>HbyiQ29w5Fy`-?|S>n`vjvzd<kNJ~8F4pRfq7MKNFk2$=
zM8iYl6K|uuZkiFn1b=cTFj{cum+#>q+RbmEB$W?iO9*eC$2c`FLN`#NXuFxFs!SK!
z8}+)+-i(#Itk25eh;j60Q@o44lj7Uu7Z!waqNLIM$?GT1{KM;ihLq%i7XqMV3zsD~
z^K4|AEYW=*rkOjw4XH&7U)VNAGa;w>(D}MZxc;(ztT+dbF8S;b$&zp%`(LB@Bm-an
z%^3Jb?Sqk6Hg?AW!$XNihu8xtx-h(<;kkhT3B@j&`cpV_S7)?e@N`7If+Ovn3R&T7
zl*Xop5Lt>P=mk46jRsw;VXQE-0LDBWsmMf=i0fZN=<+m#0swzD2hkph<$>|Qht9=R
zwdCPWt4-5(qpP?$y<316|8l`j%MkDMnmkrhc=i-7N9trCJAIT4Ej1EB_AhDMe&WD?
zFDSob0R-g*f7<{cD2D%?pqLSEv|-4pgs>ar=hPbBaAKe&jkp62+h)PRKm?-SXT^Wu
z$UmY2h@?nF64{PJvuc$OGMg};!W{JwOV3^MF8rHDeqk}h)f@eI3pUnMi#&i@;B4mZ
zP3Ph(Sdw)4-$0THgn*Mi$#s&#A>jGyytP1z^R7)d38WL5yRaP3kRR70F77u7Q~E68
zBS{4EGbyxhNNLZ<Xo@D{If$$b7Q^+qT<Tnyc2~~BsOb~X&1G<HS$FGMq?RO<yR3(z
z;`t=#{om05`C=(Pj&@3*{Et<G7$AZV*=v@E%l{PqT{wmQ#ymqlOHx^~ioulSyNf_p
zt7^9Po}r9D`I9EXAa%kX9vjOwh@g-j)oAIDVi?@Zir{}PQK7j>070>{=Nla!8s`ZR
zAS-GV-CJhx6#1Et9Pd@aVGxl8IZU}odO!Ti1m;;h=|fxya0xqIt2AQj(`(g_Al7Kh
zS<%2;ayV-r-d+{-enM3}hJUSa0Pg>HYbq<2EQKNn6B`c%Lkt%L@75a-%A8!I%O|e{
z_?8|9ZF{pf%B}O@4($3;M5SYjmZI5B&;!mW5XP;Cu(y{J`Z2BS`e>E8OURk1iJR`&
z9`QbaLOj7Rg)e>>J7Gge%U}%m>VcXSt=VXitz@d>C@Hv$SKtV%OK0c!4szU6EAH<i
z&*CU8De&c!g%CXN%Y<;Zjacs9NOx+IsirJpRn*GdtO%H*>d|MS6B%=}ryp`$DzvPb
zHe5w!)q|$I7LTKP<|>csW=z`dI3_jwDxxe)dM&?D$+%#@qze6nER2Pg3bN8jAEq!v
z)38Z6wEhGqn^L^3OnfOqtHbm1RX*+j85)v<oD2fYOMFB#dC_or%W1yQ2=f4Y+bcIu
zw*qvc^_dy3Y<cp~w=dtgZXSwqrWR%?Q$*_b_tuDb=iqZZ8=@>gOI4@XO^@8S(6e1C
z?YJDlFK(-K3|eqlqN7Fqj2589q`9dppw1+TnkpcUqi7jd!2Vg}CxbNx3Ym&u)R&@M
z_qf+7ogO0=C$zzK5kYqA3;KH;c{@&qT<*gpf;4u>sfiO;nDRMmj?T!TXb$!w&*bb?
zjD^KAvBR^gna3L`xv9<5TcSu~wKL3k(}$f`Nlwiv&kxvM=xWbScrc<#GR@DX(opOs
za4u1UMZ}E}K1E-88Nc=!N*`#`;0V=AQMY{+Zj>AuM5f0Xj|1w*XXI_*ZX8b7g5(KE
zLn-=mwzRm_J*0D$<Df^oU$n1hEaLUzYAKUY<0iLJ15(71<;y^F`k1KG5#p7@+>R|}
z5i#jC-g9xGL-RNCwH9s`u$W>I#tE?JI3k9zs_JrWRBF`7D*5(oXv)diFrA@aW3KEI
zCsguSjaYWd&j@`qfutzMkKNx=e;#ss%7S^9d6>_t>pb;W%zShUafvtX8tSIccRLvn
zF20M(N_Gqv&MP^C6fNuCfkH<nVA}c<o>kNo@E05DZIwNoz}TBu>E8x9W(Xk6K>M!O
zIcjRx{7bEMdl+DOaY$kY_{{|ISThS>zhpz5Ps9Sfh0(8hgRWl8s_5R{x-GPfeC-`V
zvQ~JIxc<40KzI17_VhvCJSKE8Q#hkvUu{A*an^pACt&WU0%>+}F~^=v7ABUzs?=1~
ztCH!>Yh05mX6xb~@<Bh=Qv0+MC%pzshE=IB(^?4?K9nruaqx-IQjW347?GnO;UkAQ
zTf|~W1^C5e?>h>@=TIX?Eu5}1owC@hkuJ_+tzkBMcsBFbloqHj7AuYAK^&}1uF<B|
z#ctrxx#}tJ1D@;o8`fEA;Yu>}1#&8c7AD(C>?LkNtv43Z)1VEmTCNjAS-kY&){w0k
ztHhAnL@nmNsj=?M<b1VPIGvv`aE9pWlsLMu=5|A*ZBQ}g4GC1g$b}X9C&+a%>{%bh
zh&=;sEzl1ZI(*I~KlB@QD-rU$DG#9zgt7W;kn;6Ye|%1aBQ|u%Ox#}>?C&eY$m06C
zy3x#4D;#Mrd%*(dQVz%Jdr0Z?j7Dxe`w|waIiBMS6JPB+<N?#4yFm`t^LKAGtJn(*
zm%FO*b%!H%E2{{p_SeiPzUd0`WtU`5mV>q<WIfD7c38B}@yeO{aMM(4por8$7H=bs
zXw3vUtg~>G8rp(PD3ZTO4A2SqX}!UDXH_j4v#OFH+Px5scv7fyv>0%GE!RMf<M%x$
zUrT(5VMNZG$Z8{Ch3nEf4liDriI<uYhi|cT%JkWsJ#*M*7_uqh9u~UhjkbStSsJaK
z=n9HiHkC>PGr0B=e*B=ezk7lCxTqk}Su8MW{Klda31^N=72f1oK3Rf-0Ss#o_4jIt
zFuM!uWsQ91TBaAcJp*{EOWm%FFRi{3N;CIe2cS_<K^R?2q19asaSF+>^F3TRt}n>w
zm>uLW_mDNsx6VQ@f+oNwFVa7gE`NU8>6E1ZCZ)%w-01qgoDB)3Fv4bD3x|2TKfm&Z
zvmLF3D{?5*C?~awcSsOjc(^WzWEhB7qUtJCVRTZ^FQb3>dbdX-A{7=vzN=U+hALo_
z72A(msa@eCVBvQQ$bqnDUPOBiexTRWipSyZal2vSb-_g9e%W9<m8O>9?3w>wO)cpA
zRF;XNNYQp8NWoCvwFFivZ&yWH4HvGK(}q`@Gl&)K_IiDGsb&HZvaJ}>pLmew%TLab
zCZpJrsDshayhWS!?mQV=(Px0c8#SJJml&S}-!DdnCa$I^*~icG>eT@H>ndBm<v1hv
zg$`e^j;qg{z%r+=iSCTgy9N@81?I6>BJ}JOf1fevylWhaR<BfTq%rO-&C+dyS49*g
z6Opq%51(Yu4dMaOzFu1LYOq*2)6lE2(qUp5tbtCJZo&dysjr8ZUCbMm1}9ZrxU8;N
z_{xpgv5=mR%qP%+GO#2oI6jh2Wu8V;uX#V3=$X;Z5l+{$VB5X;b=7V9z3t}UO68r%
zG=C!n6E!|H)o}lXwKH}B1eu@a?YB=<YRu!34r=4y7t=>eQFbg%7CY(Vv%i_5=Dz!w
z#M$j6gL7!o&-_EAIrk%#&9pz&O;w$hjew#5r8sIrP?ZuKL1rxePzzShYlR2SV;zz+
zt~U}h4zV3GK3vFnjl+<R46DkL?aIOOZbStYV`f)ACyJB=z^0IgWsXh0_5ua|06&5$
zY*>juRpA)$8grfIm|cpdB_OA!PCUNjwaQpaV;p>aBqHmmmVLJSlF1oQzJVZxrmwgJ
zOGBn}La&$U#qscWb~xFNsn$bttydxakNnl_#s%pnv%S<tS`HU_e2*?E7)N+-2*7-o
z+k4_7x;!{A_|Qe@)a|56spslE9`T*OEx-D#207W))tHq)j0P@dDv-Xwz@?*47Bd@t
zlTulWlb}X5N_p?uJ~s|>sudlz)K;C<U`2P+h;FoDVsfX}kFMAnwqKumsmZMr3Um0^
z{HYx7!^an*qyU>r_8do@!3w8*oJ_ig@-PX0l!~nQCZm&NDSvspe(f@9WtQD`d~AHA
zdZOAVrr|@<{+<pNZmRY9usAm~{ea=wE{3VfZ6L+VJE8&74*ag%h2yrWN|$fsCLczb
zm_3z0e558b7FQVB#A2mOW==JunZ?+GOF%7z8Bjr_%fq19u+$z^mv4Asq4U~n0nV;B
zj-4sFxN@68`m-NHy<_&^>#SfEVl@R5Gy|+x9R`>f(Mh(2g_|B!V(@(-?!s$pLKBQi
zl&eRH=kpZNy00wpfP8XQ&W@CCYvS3h(_zAtLt5DyC=jkct!)r?RC)l);eSb?I+o2@
zdn;{R9?GVPLQLORDZyP!mMhGhzB($psUeFQ`rxt4NU_uzA0R)f){)ckJX4A7hcF0f
zGWJWw_hXGle625WhQvk}FhY0!)xM85h82BpXn1Nc<~S0v!@zR!FJVOYgXd>{NvT4%
zv>-3I7NbY|dnhXX?<t|`#NO%HlUe5=Mot1=8D$`@L_!{&xpD#r2nc0>$nmdpygv@~
zDBRWeQyxP7sXn1x_MqnYA)S70sKqIa9p^V`u8Wl{L!Wyw{L{pWgUof%-Hsw!o3!$Y
z9j5;M3rt(hVS?s>1_=g~Oa!biN{UR+FTyMmGEs!(fcyTf1i=`ueAMIx^A_-@`A?N@
zw@L~V{|^q1q~8MvN1W#`PCeg>XahM5R2(0I52^3U&3u4cR0)f%PLCZ5Yj^2Cvn$EG
zb=^?~y97HQ`aQ3n5*w+w3y~MZa@#*V{-Oe$?T|R5G2iEm?o9YC{RT4AzO-L3Sv@0p
z&v+het<}_qJ6K_OmcV@Nq>-KzaZ(Q%UT$WZ8FNriaQ62H!+n%WNwa`5lEvHG#&RVp
zwq^Zfqw`bWNU^e=%cFttdX<)tX`aU^xr>+TvoEI{A7OFnzl&TV97O<!jil%u+uZX~
z=}NJ&7x*MN4u>+%wO<z@8`i^!q1jm9?LzB}h!}%ho$5%P8I33q4EtxVcQ4kC%H|vv
zQ*02TRW(H<c|TGfcVVzgaE&EI+D@6~kq4@_{RAP@lu%f1ePfeKj~A*yk0R|)e7kXr
zOt|lzdj7)dmz68~ds1Dcs_?1`_-3m0!kFn_$)5xkx)Pj?iM})?fl_px5mmQf?FSSz
z(~lH#<uBNzew9k1Z4hM%PTmXzB=+)Z$<=sh)~~Dm1XI~`RG#nATkC)hi*U}cDTBol
z6hy1B{TO0Ijelb2(a9QGFns_I)*6|<O%NvzKK~WUX)mkKQw|(BL`0f#HZ>4uk-EH)
zn06^}S`RUIr_HNFN`OowQ%zk2a+zl-?7<d3<LVg;P}YVVB_C{Vg{oCB$?XyT>_o&J
zKC8TunXbM{m<yXeIwIhOL(||M#5S?+hf{EGmYOOqB$jH(!{P%>1@hv^hQ+FCwvBU3
zrv)9~R0Xx=ZIhb~VluanLU{_^n)YB9k{GAK#_v?fayh`(_GzfAmBT3vVVl@X&Tw<8
zPU8ejqAEllPAsHY{qjHTX2%=!X1T)}&Z4k-uBl|uPa+qX1$s#n3~htiLc13?zc!3;
zKS$+?Pm<zOC%ldlL*DB2!5()e%b$(yz)4F|rr;$BnW|-mG|+tA*w#KOI`p~sl6;v|
z7HBKhzEO5W$<EJj@ufRj@BO?}h+&8h`~ffi&YM!I_*w&1HEFa^2oAD{tu*#D_KUGE
zSds2`jX+3N1v&2b8I1a^p@$<S41*2U5lSRM<K371gT0i-r45*N()roUl;kJ6fsT6L
z9gc}g;g#THqf>-&#8wfc<#W40vYAfPVOk2#V~vfoj*{7QQ{G$N9!;?xKW++_g}}|%
z3mlBT+%1`Q)aXXS9I*y-RA;|Z=KNljG50Oe#&C_fa-bVqb^N^E8|pT|_zmPSGUf0H
z*F3VC;Jbl?*N_N~0n%<)O|^z^lYcz!gKot53K}e=kO-6>xlM(!CCL%J2&39zv&Dcd
zG<B4I-rpmNl;HL**yT0W{j^U>^eNM}@3WlWK=_4VZqntdD_viIyEnJgq_^Fo4GnW<
zKh;P|W`^f0Y<t}UdE7Q572&iUR$*OGo-ya_;UOZ89-DkIjvjZpvN22x^;uu|@y6|v
zdC1OvK$Dvf#l~%Glq!OMYa=5TC*0nTPFSnKFCUF{?HFkF+4uKu!2j6|wC#;2YJC4q
z4YdI6PZ^SO;1ngH^g%G15*(s^{)3c}6d|pp>eXEBJX4ZuW^w%@;w&A_VrQ3t0HN~B
zBi@kGg3+thW0h|$7i2d=ZrZ=5%UhtsyjdgHYqn~S=qW-Wz^jtQMYF(r`#O5`n;*uH
z<W@J@>ChfG<5Q9b!O>%>l6h063|$}}rBxnS*HjxP3q@TPDkdD$k8RSN6|}fuKZkBf
zVM|s70Qt=Jt6nGJH&CGI`*qw_m*N|4g4M@Grh(S>3)-_69~vi*ZQry5RlfNCRRhdT
zu-4*PdSVpL{iBlB?ocLuI1RPIxyY=~LlsIR8(y8t;b9v|wb%&rKF32rF~okK(B4Y3
zaW1k1OI0O>omr)(?rJfI<lTpF1nXWkAGf%<w*?sKIw{WI%|_Mfb_!ki&!wE9BoDqu
zkO~iw*hGpEYn;}JjO1tlHx5OBe<d%mzq|cfRb^azhMNxkR7(F&gw{Z}o)qd(&Y}_0
zqV>&Md(~!k%f<4=^|}CVu-NBFL<;R`U4|8OP4Ao73jOtkMH4S+Pl4VPQf%8Z@aFxy
z1H%C>5iwGoQ<g0VR^?!|*JPKHgrDe9t_?C}RaP~tgw6ciJ%Y$)S-8o76tfxoOZ7CT
zo8kAF$l<S{I5>*JnJ`5WKnC_~g=wAtm}>BEI~QyKzCFSou{*J7b+rAW0PWhwQ>>p`
zUiqz-k$dj%KE6KHR77&m2>2%<q{is&ofpqfPiKf~OXy330F5B;TEej-`GCfb*KQM~
zGu&Eh!;EGe{!_CpQgUxVKq7pE>I}k&US@%WgIt@6^)4S;V7mzYqsv0Mua*G(t&#1o
zvCOK06Y|E_V=`JyYd1dj^rtl6^40Iq^pE`<rLpJjt@0byLUCR1W}LWs;69&f!WbbU
zxk9F&tv%+Na7kX#YiYAPbLTEer+O3VZE$;7CVPr8-&+U$de;Y1&LfT<ZgLrB8GaAr
zgTK(~Y)ScbxXz{ZfYz@S#b%|E108r8`TGfGxyc18bQ|CH;WO9C5+Qor$%n?V`noS|
zlm(#QK(EV|cOGLie?A<5bssC;UYzzU-RB7Z=cl@VzKQhi=rt}k`Zy*dZg>D49najk
zC|h#+*}uxtNd!KQg!@lV<^TGINSf|*;5)4XdIZQOI{Lk?y_+xZUo89of&>DWFWJL=
z0Qzk2JM)AHLB-5Wjy7f+Vfp(T+}A(M+KX0DsK45G^*8Und>Ih#YN{}!@-e_3j>t@S
zl5+$3xiS#_NfvwAK8_^VYN=;!%l`E1Ea8#y$Eer=JEmjbxxjiKuunB2eRCZUMa2|t
z?QZEO%pYG`0x#|_@%2?<E@j-rnPTP)2DW^Is;(tvcJh<h4#BHAXM^(ZrpF}RsLy=0
z8xjvcn2-I-hNtMP?}m6Lxds2sinfy>2^8J1Xpcy$_ze$8`Zz`#yqP$PN#|R(4fyN6
zbn_9n&23r1f&f;vG-iRY<@0OzyzQqDrZ9Z1RNvjPz+OH!fWvOAvkhq<ue;>wZ=eT%
zy<6`;{B=kz^oxPJ##5ox#6}OY(GG@50oT;^5k!#30|zgC7I?62k}u6Bk{X^GxTIbY
z8J-M>BkZFz`?(6eXh7~q`3ZjamrM-U$TcyA*o7OyGgiX$6K49y8NEL_3J+e;7;cV;
z=P0>3i!>}<8t;j-a?N6>PMFIepgPaUTehp_0A7;mK%A@i>EA#`x^AsNs?HC`?ipxy
zEk6b8OQvOi*q-wb>hpJR3K%e-@7BGJmSC#a+=2SjrTLn{yn;PWqw>z<bC~29<@lC*
zU%Nj}H_dYFzKVYEHQ5&-%hz>05`3P(KC_#%X5C`1lk}|Trr{k^cU^cHiux4Npe3z=
zM51>Uo;ACp&zHO2JUygry7IL2DzrY3Ikwca_-~-ai#F03A=XFKcaKMTc&+jG8E$Yt
zceY8}3qLGXXRQp@k^UsTMdQ6Ecvd$wxT6%_l|duUl&lxaE=k{pD4Eqpp-t=k`hfK@
zbQx=?!H(W!uk4gPO|D0^Z^E5Io0)k&1Q(h%EZrEd*JPk}H@O?npGD6VK{E%M6mv5N
zk4LpO?Dz?l`xrbB2K`!)VezHC5jaJ>8W|D_RBUas5N%CZY4Y8zG$>nm<0>?adwku%
zwVu*@ZY9kglv+m-GSyaZycOD46yqcM`IEB&49o^-0}WXMWvS#1whhZQ%sTKZASLs~
z>h;+z|MgIBEc6{6_cxFx-~)4;%6}uyf5%Qbix}=(DhOCB3^rPwRZlkiQKqM^h&P(l
z(W!;2N5Cby?0Shou{2G|uKYHm@7DbqkSmFp{oqBkSLE5|C%I5;QJpaqs#6Std_R&f
zJ$?sFQ)e9D<FJ;86!b&5?$&9N-$0KvQNP9%9_4}of>sXDeSuI!l0JI{zBG~r{Bg%l
zcE>7_$JTbX`tf+3jHx&3*V+m;#i%&aYMQJc>99lSvc&w<;aSm1vdMK%ew1JwZKa{d
zd`myP(HOB;zP94$2H*Q!2YvNVe6zwkKzL>GvRj-W9d}zCtxd|YP>}~xfR!9Ldgu@R
zoUO>YR}VxQb6e_{8=hyM4jTM1mD9@960;fS%3bq~(<&$*xlJrBmmQhnj7Sw9^F|>S
z(o?VW%}nQGai|lG{OYL>cp2S=>8Q@Lq5MI4d38kn*Gbsf)*w3RYiJsEj?;G_4q<Mm
zi@sFhqgK3`8Q->8A4Q{|BE2%IMCi+TT~91d7{j?!Zqn2qSG0K(R0cP3T(zue%Owu9
z9H;U)WNXr#c@Ucms`I4tq*xUp`M>`Zf4PVPu8$a<0q&Ce(n>c{(V&|V=<j|S{WI=3
zjVETk+v=YJE%782?r2!%uuh4({z@kJCoIro9zwJoOUvgIT(NNIa%Fn2j}Eel`q9~|
zDADlhW9b*lu*#QmV~T7SH=a^?YepnLl=i*T@oYov=t2`<F^wafl<XKZBt6pTeyH4%
z%WS)G(|?<CU{KB>xh2PL8ucg2@|36V<VKMELZu|P+lM290h=!9VMxp#7a5j~jj3)2
z)m#7zNXNX1`q+R`?NH8MJJVRcchy(i5pE3PMam3CD?TW8!r@So%XYzLlTyUOhYiQn
zB|Jh;<Mv(3a}P&weMod|{x!VAzvP&OeP!x8<fGg|t?KXO&|)6YCsE}>vt=6j#oq7;
z2S@$w09|ReNwV#(Rr}9N6DWM8ttH9A=fwJQdh=N#A6Pc`qW-xr<Cf5Pt|DSE^HPKE
zUVa>1eo9mp;2tgmOF;%BQapG96Ffo0$bY@CIOa}<(Zv%$Hp)&f)U+%W7H<Q!>tYU2
zet`kaNpzTC2r~~<oph!>+`=)hw#Mb?cUAaUQMMQxRq(RD^gb_ZNvuDXdI4~WP%B9s
z)^3!BU*NS~gU7I6G9D;q#MmoTPRpmmOjo4iM7=(}rDcE$YZmIAkA;4VHBPLtojLnP
z*daHtzSrjB+|xVQD9$xN5O6vetAquj6H8KpQ4J|J#El#NK726?78FnxB7QI3=;2Aj
zNGHmf5fKxrtUZ;Xw9Z;5AWfyi^`?HN;A0_81zg~}(6-EMLkEd=C1fN|s+~i@;NwzR
zW8;!@it)yavUN{HEK#X14$7ulT<e=dqwI9WV!a<hnJ!|Va8Ob|8YsQP?hcWC>;=zd
z^WIU4K%YszmN%yIipwe4K+D=71(GF0Lw3{B!Ld5!XeW`-GML{bboKalasFRLAUq46
ztc;rnCtO@-f`I<L85H^d-ook6BfY7<FTZm4!-mT6hPqJa3$8Sw@@g=GUpB<P$6D#_
zbR%rQ8YE_QZgoQ8En%%5SHu3Ej_<Ic5IoYkZ=%M{a}?X!Hw`rpSj%NMH%>hF#A*AX
zhQ??gVVb`k!NX%881W3KBnz!)*a&7H8_gbiA<#*=KD9$OG&rwFhkjd)y82+=lzh$q
z$_tya|55A1A{ATX?C|!?>3fd?UohLDtJBy2g6hu?#W|shynRXlCY0Ebj#4uM;dRop
z_guLLOGBQ-VIpBKHvt9i+wx2N&MhN;$cJ{peM!tGi16Ld@ueu;KQ!lYNe=Wl`387i
zdENv5KUHbWbW-AMmjjPm^p)h}J>b>=&&!kJZNaEv7cn+=28PA_=5MPEB>|@A6>0CN
z*M9AtOw2QH3x@T2P?GX}c8#AsSbwzMb&c5rok?s$%oImYx6z2LxDVvIX^rwyaThov
zOA{()v>F8+fi-tP#ViaD^6-46T&-r_->AIG+A5CaQRx~n053cqc%5fMSNO?fytu5-
zI@T9B+M_Z61+8PUoYMN_PQ>J_R@7-;dTfRR`?&nR({~rX{`^;kd2DpJd<P`ay0O0J
z(Uk%i%`2-4zkrAZ7qiSQF{Gb>%1W4Mbow^{j4#zWC(t_evA{Lps=#QmKU@G_h|A-%
z6Xn_jZzcj4n(Qr<Sl`{$judAkeI_|l3;xNnJW_<H#?D;8uNDu;pM6i>{$lu#2B^d?
z!P1ZHERWLHq5*j656_nY+E>SBG;Q{P9{v6@{0s0lA07uUyfIF#{Y9K3D8MSrgqYmT
z?|0Z8LH|40_T~qreDdr7zp~zcCw#_&$9Rj?0(7ZAr~LaKM?XLbwS3HK(2g%GxX52z
zeEm3gN;RmT`!*qnJ}>~Usp<jgEtGXpNzUjvbN&y?VL*$DElXbk&M;aC8<m%9-1fEJ
zA|Xsj=^u~$&X`QL84bn<<CUNOf7jH^lX*K8-WXy)XB_?@j3KW;r~d{!K<JFN(h^#|
zMR$vTP?8|@9Pcqxm>U}iLUfDugVPEC{=dWdLp=rH`Xmy<deHIVZy;FkUNE`1xZ!cO
zIe8pALcn5J^a%@rU*m6}uJoVPE6>GQxb-}>y{L@QzFCFgZFAmBE7z92y8E#Y$ae%V
zsGkCC>GZd!zn;;0e5uabJ}7k+eqvaM2tbUJ6~HfbdgbUU2|d7seH!n(pN*inh|&Df
z1SGVsV6JWh2+i$<_f-EM+<E>C;10A5_;jT`ulV1-XS5K7&S=oe#rmGc^Z-4udE&h$
zU7`74A(DR})ymJ}&`GYW7d;3(h!4`xA}rXikmK_APh6Iwd8%&l8|WptczWDVQtzE{
z=kM~j(b@ju;vK04y^aigRAXdCxm4?&<Qxf-KJ$BENJ{1Na0F&toaILV@{brQCb;}x
zbm>I`G`g5pD9KgK{52E2V^@Q=pZK{yJr2yMiruRx0inC6Y<~d-M0LD2;RUfvR8IpX
znPYS%8O)3Be&Fp?m}+*W_>$gB#9Qe;A{ib&)77AL(EInd@oAhzA=&>e`~#dC^Zz>2
z7%e2>ja)H6YjG@Vn3&ALugL=g71lC)=YZ*OOprd0MCbd-YR9@<gzQ{VKpWp@3|ykb
zMzLu~B<>p0(3^*v1Cf^#D90T2Ba9}+v37KHSr)f#ngcS~zeFw6BjDdmZZ&@2W{3Iq
zRD<aHkB#_~cEm$Mo_U_*&9qSmPUurwC^nwq_H@H<%AOo_w@%!GxDFR_Z4+v9t<{v#
zb+4}G5*2Q3?0&XvRT2kbnJU`C)b8Y3TT%jdXEnc)r>ZY5O^#lOB(8hAz44rvXQpGR
zl(R4E<&D$PGdxg;sEDFiAxBd4Tf+armV)h}jGtGgk(BcD<uwCuL+07R^jg$SK`ZN9
zcZUdT6#lph_@;(sY51ds?49~-TUA?kS@qR$eSHc9n(hV&l+OZtb+5Zt=|VYWU_T%&
z)3%c4Fu6qT!iQs6;Wdj^6i@4+y8H##Fa8`u9a7MOt5FJXVbX=ZK}yA@LAHsWSZAq2
zfL8<6N{U3`wR$<fjNfw{(ZsS3xja3C>`~FiYva*r9LDi#%Vle&&J*-<MfH-O*7YQE
z(wZhoz#bFUvUOCpR$y({1O7OL6>z-6=4Zo@Dn<Sxu-g^jHEHv<RVms$l`*Bg1GOoq
z&<|t1Qk+j%H1rz9a;`fQYQ%nMdRrgOF|wkeFSF<b{&m4j!!tss{~jj+Kcr;kSsDJm
zwDeV%YD`^@ZQVh47DdJ6F52#Hih(>F{+FUOlFuGBhdMv{mj=9ltza?Pe5F5ONpdPH
zu^c8Vk#wj%I*Mn8Brb`&TE$Wm5u4v(>!!fP6vz#PqA91Lzz{TAuI#g;gA{!}MmG)X
zHQGho;e{-?x}$Ed)n)cz5q(>fh@FAgt+Hm$KTV-n&>GAV50{;DyO#7a!obw(ieVvi
z<WlN?uV*#63>{eqyySOpTm}+1Q8tMqKYOd6&C*hGCYf)==6vH_2%C(RnQ(&y&{okS
zERVRN>mWHt^1ss~!^@|cG&Mu#pCe>d^zj1EbS%u;6*Rv+|90b3a~7P+t=}C}{~lFu
zPFvlTd;%rmWo@lNZ2b}ToUk;vn=^5T4ilOSu9#nmNh}TrI|OHZF648|Nl~-atCG++
zJ*RUXW*%}J>HYiN!2|bEq8l11XsGxR0d9j^OAljrHU^fn6H4!6!`=y3&gVxsh^@j4
z4@WD8Xe{S%F0<(!uxr2A;t%L()}##aWvA(ONJxtDf)0uQaIY!zw4HJcYT^THAXhRK
z$kK_+Nr=;0U@yR$7FS5_(=qT;NE=#_Q*M4C{($d-j{h+Bw5Jf;JZKJEvIl$F+L)s$
zH1(CuAcA|gNB>nca93>9R_Pooe3kerYaMVVHrM!{`Vv3+Jt-&ju*K+tayKw{aaPgz
zs2bl*^|CD5{*2q}C$U5H*K=xnA>L~zVTag{Er8|3n=k^8qhkFXd3n>BuzGF{&fzdA
zux+Er<;q`*kZ02~as^fbW;B0e>w-atzZW#-BU!(6C2^g<bHS*!>WHfADA;pR#A;D#
z3uaGQ%`Qewg2Pjf>Rc>Aq{}~!1#DWf-%63i%{SidL$@<y>$&7dwv`v^{5T!@Zit3+
z+w=Py76%$uGygN364$|i(EYJl&vfA}?4|KSQa0s-1K1c&hs|F-C;nJg{oLPeP2Ttu
ze%lPJXqw3<cBdTLRvUqb<^0fhAGxDTxf;!#B6q5!RASO@8t{7Qb5z@W2zsRg=B8@n
z&$e8NlKtrVrKqrdf!OWSTb$&kS8DwLsFt3KAOq58?#%<sZZgj-TjKx{aXxurZeP%6
zATw}U*S{VWhkU=$L<;fS>-h)P9jPNfG0vnrC3IWerZH?YQsZ2I!@c8sbb!FJiJbs7
zDeRO_#E%X4wPY{M>lZ`$ZghoA8uLQ+OMbAc0@Pd?3&Cx<%SzX4mA@FX4x@TCDERz{
z=7jJla`oxciyt)>?RaouP9@WaN-~fUEnB|;M8W%cVeU8qL?N2DCW1^a%4XzapPqrp
z7K35KU4(oXc&4mK*xvC`S$!wdhH7$08s`83ZQs!Y1ddIt1gPO^J`4bSf^B<?bkP6^
z09^Hl0lzoy)BCStckFMmA=%<)Od2>d80Ndna?RBAEddA_@J_WB4B1S&j=zBMrgNj&
zc4EwTlYxB_Ejzxr-~d}QVEi+gG>BUrn)kyyD}m?SWd`Oc2i{MFe58)q`moUs9D(oQ
zTC{4-iw*zXtS;lFUyZ}ylRRXtLQ<X>v<lVOx{q8@RYa3+NE?WED{oTkk|7%#y^m$x
z^sm1Y7>IQ&yw)J{Id`9Y=>HMCu3pulh)~W;vf)0N49N5y(4|*!8n2=qnr5<D?GqM{
zO{AOE-l%m&I-4IlTiO#N3vqqlTLm9*L2={=>j5F(QT})eh^G3*e}MzCY^4Nxn1dJq
zNO<1NG`vcUu8LKTq*r1A3@VT`q6Wy8wo<ylko%ViyU_qs!+nhorzA2Lm>Ug8@)x2{
zeZnk$lm!5dVPe8;pPe)4d4E(#2)|?k5%Nx!t76h&&wp;gO+9DCg;E2KR=(87+-ZzR
zw}4D($eku2{riQA05<SUB^bHtZb0lR3F%x_3*Tpi6aR_OKD%V8VTtCaGzljrs)QPK
zrIm_I^=~<5<Xz2_D7j;CXkJtGF1&CD_(Vz^>mcWcvUHl(xf=alA7GTL!j$b$p)2=0
ziW4h8L_ID!!rNDB6kAICY7cew%Px(#l*8Lk4CfcW(ph}Zw+hUdMPQ6Z%8Z90w8G9)
zQsQ?*@T$+A2}@-JcEB8Ja2j9!ZA?e~$7lffhyHO<H&E#!aZ~e!8>DP&A)fNv*38#x
zf{h`6yZJhoF}UYoowE~CA(Q9eccQZFCa<B?xr=w)^h7qCU&PjpRoM+S8zXD!V1$ts
z>17m0^H?xI_U5m2RVAmM$urCu3B|B-I)8jKfsdjH1VMZt?<1V5Q*<7l@?|>aPB;I3
zHUJY8m<@w~2x!BtM)L!I4aMI(ngMi_=J!d)T3Q?sR81SjG(+xeR2&R5ceBAd(T<$z
z6sA`Z`9HYmAuT+uMm8!5-1LBpF4PE*0&aQ~OuOTF#38iqyVuOeD)q&8o-?{oAK#`8
zRQqdD*w>#kdJg^yb+3Wszz9EI1**n705@bVEGR|2jdX_eh>C5O_wOno**f#Pjf5M6
z$_gUv%n_bRWt${)x4U#o6rZJ=9a{+Gi#$Z=lJn`FORlUnzp}C%KY^g9tUuVy6Y`eb
zO>lgYZ)rbI+*|m*kTTsfpoMHkWu(P}!*a30K9ZMR?rfZ%Lqs#T$zP*tI$F$pSRgkP
zFV>IEB}Lw==SEkhqtVnz`Fc7bOl9Q2exnJBia%Am2g&|YI!)8OKnWG|v^~-B&1(6z
z<t*G`yyzdEdxIA>66x^DxJ2PI&q*l8U00aJV6%Cs0z@T+l>$V=JCY?mz!sRdNZc|c
zHl*X~9yan7k%;Hu{;rY2w0|PJsL3q0R@!bZ)QqY0J@>~h-K6S#z21ow3fMW}p%fkq
zEIZ6GlM$Md0(f!252rZtCApkWY#S1cKP$Wc){V>98$1KtA#wK|Fa)o4*;P)5YlB<d
zkT=J!WT3Xk?k=>vDQdLVVH1ALvwtZ$FHzHiWK470PU5Xd$5Bv|#QSuLoEhszcpQ&#
z?<$&DIr__d)G)oZx-PjDHtdvW0e4>7Sjz6#jfSs7Q@h@$dJ7ymH*i;f=`y_vS&3mV
zDGjnWXt+0Jt)&rm!PaqI)K;TCJ9_mlrKzgaRZ=xitwT5khv(8{ZXQP+U9B1W!_opr
z#8eO0NYB|y@9<YoFZI1zqvv@P^#-SlbFwU6qT+f)-whvfBJE0CkCt)!5o?$BD5{Y<
z8$FgehV@~?SYs4bv~048Ib@R1<zk&-Fk$cPR<n~n7<V(JC(qcTn|=*ZwywNQUBOqt
zo{R2GuhCpTe=Aa>wIpgiq+_$(;58S>E3RGYvQXo^R{4IMUmRBsx&*7o09R8K$0kPx
zYs1)EVywf_Vejee;?(Wx8{Ii{@l(Z9wqtJPx&sLQ;$l?iF>pN@#onuYfS(ySIDjTE
zma7K}6>j?oE;a2*#qUUIb0)Uh>xH*Iy>RxrO&y7I^;ltcyUz&=r`^`Wj(+Xs=1l9=
z{^J=9`QXs$*LDTTA9m6xhBr)N@TIwIgx^R>(Ba2$v?ZitvYj86Go!|wF-BihJHSRK
zNd}G-EUQy6Dtu6F(SGo<*ay3tnttiXsCOpgUh4%5MmH^OS!ipdZ6==SdpUg6VffNy
zATMizlb9-_AiPgd<r4x$Nj0h#B(KH*G0%mUG`}@G$Tp?7;hU@LDupmLQuyduY%iQ~
zYOtK>{?H1(ax;BR@31m0Uy*cE9{jz$5MGa<DE&J@E`fA+e~ooz*)(0@WE%nuv6L+<
z9FoR2(SUGTWvEmr#O9m&OmN6X{_XhWL`q2GZRlPP4Uv|~2?WNYdFcCVHd|o>kB>wC
zsL{5b6l!kHD-GU)RGRf$$)O+8v1-+nU?>WBWem|q?KR?6{09?F4U@!1BvX#Z&yZR<
z#H&*0LXta^T$!O?TdvCDnHT*{G^c~6nDgJZ&{iCEGm#vV;TcgQv@d*72a{C!JI<Bl
zB^L(L%H+}&7m1-!5yuF$`a>va_7*S&np;sc-ghzm*sJYJ$sKZZlsFNqX?1P6Jhakz
zVLO6<?#aAWlEbx#XQ=+ER?`!al=<{yo+`tU?pqfsl<tl&W{4?LN?%z1%LIjFbC|vL
zTcZ5?d<v#;Lq?ei{B!rM%{2ju1?R)CCFi9#GUK$*Z^Z-!Yw|vC*4aQljh>J8q=#B@
zu%2)2F4f>kQPkHuXN*v*{*;dowr?Z<z-*m6fkJ(4-B{ao5_I}$-wV*NZ@lzvv_;?h
z)5f#J>U6T6-GoE)$kb-HzV?-!_)P@&Y^x@Rq3OmC@}0{Z%ztu$*;o_8qp7ksfLr}X
zL${4Vq}AJ0KL~8vaI%nsfBuI#;HhbwnzJy@GP>BeAJEK$VIi)(iciRrf7i8A_2cC`
zp{Ua(ULk_W2*3LM!1fwY`K2S^&BUfUqp1q-C>+q;(sWaIY^Rj|HbbW9Ss*<Z5TdA4
z);|dy$|HN0K^m)5!~*Wx9*<!tmshZ(DhxK3R3OdE>+UbrdRFvL-Li3z^x7RxW896G
zf(bo{>OdfDN!oYTiCzjP4NLDURpEW4(Dkc%Wq6Y4q{%WM4aI%y%LG4IHxj`9`D=wV
zMMfAfcv5^*wq-lJtIkC(pvkq^OeA$A)hEqof<B>w5kLpX@;Qkf0#H4zLXGh^UOp!D
zaDKEf0%7f&@=IrTPb-w>=DGDl*z%=H(`9!<4TP&W2CEHN&S;Ns9|rcMgmbsJ&l$n|
z&1c)nG0$k#0Nw^i00Si*g433<V@mw887+Yr2xw!uJ#7;q4vSR;H0@kEbFXO>#z=n;
zoO+X-JPjijt+L?DCHrVn-~nDae(^{&OJ^m_-U=!SQB@}A`wDPga0L(YghJ&BMbr9)
zg%l|fiCklL-Dleqx8?z5MDxuX_GM|s8*_8Q3wda%$`<lJV{PU9$N1?T0#qwM3$(<R
z>E5C0z9;j6a|^u*Xwa+ClKkls^UD$uz30SU(MhD&)&JYTZ9Uy9FE3I;e-S9)(9k<@
zO$YN-`b)w2vw#$(nc626EmnA6-&yF^yB+CL%lhwKq2+&&Ik@<8fCcSKXARZ4e;(_*
zVx!5TDb=^!j-=uM4kQ1gOQvrLRO_DsLD&6weg?VK@5a@J3J6iv`h|-AvI7!&`u(kW
z%x71MtvPI}-y~H*S-7fv+Mg5;>cBoo(*~e-=-w196;CeLp+XJ$iw1Y~=ZjTbxB7;H
zv^4uA0t##0RMF_aC?6I&Pm(1!NvE|T(1qX&z9PkPjmmfH-rT6?ce%W8=)9JJA8e{N
zlizfp4)u53>NQxH*7dxtOOd#&eAO?I*q|6pT)j{e;fdXiRK>4F!H_kyH6rm5L@)Qs
z`;w<jka0(OU}yp<J@)im6xwC4-8e0gnA;`pki5Wg{&Q=(o?E0($$f)-t6BZL-QorK
zxY>KY?0|1wUAo9n8>dn@p^(S2{YHLHJQhg;LcRCAFl;ZxoNJhQZX%qe9M81;vemDt
zC0&+Yx{8ULBFCelT);8^0IOc#r>)-XJ5wyR!P2N!N?=fyEw1B<u?Z<3h0ROX9G8%D
zwSmHTQIKWw^Gh=Sd(Q!u5yDtDy!SM-1xYC#0u<K$d`?(gU0pNAGaD(U{B5?JE@p_h
ziiKVlwhCIG1H8?)tVC$-vD7<;ws3rTmYI3O@sL^!8x+X;5z#_i2lfk#9oYOFIvvgm
zuxhPpxn>4OT7EEg+D_)KVPWU?VBv2_ZAHH&n%2p&Zw_^%ZjC1(svHe7YZ*UPp04_A
zc-Ikgun;w4KS%Iv7}}sFKl%!^BR8LyxvR!G$&^8EQ&sTkA$lJAi08d@WGXb7$6L5-
zb_jkEUwVk%=+YTOnaN$<6F%3#(CKSf6DtNye)Hw^n?0l%+xYYBrEp0!H4qPezF0Bx
zN97=WB4ry$jiJy)YS;N)eE4|nz0GK&MG<y#L1ISOr65WDDzlWA_5OU9d}h`$-GQRb
z0rm`L64Ew;Jd8C|Z6MHPQZX_N^id9(n5GX~PEV};;&mg<&y`Bk%AYq2&N_($>XZon
zC+PLa4H~G1_a9ynGLo1ydph(k#%dgXTHFk<)~zCQ4I*+bLt#i6hcKiq5xzsl&R?4l
z`6<91vN;JUFI^Bwi12Mcza4pzF3)dM8SAAmKa6dBQ+wcE`7lze08U8gDU@nI-eL?Y
zAWv=l@@#QHnmYkbx{jwSBzK{}N90)q0uMIo048XL&r2<FPa#cXM`J9|BaI|)zM_Pq
zW8TBPmU58Aq4byay5QvZ1-YE)a4UoG{58*brUnV+N{l)__DY#Papt`M8FAva-t_eV
z_3+n7>B1DBS$ChY$lm_q{c*odQ)it%tgTN}Vw;hXriWcy6D7U0m4CvIUFEIckT}r|
zDKyGzuIMU;)Si&>wrKp6seMk<FOx^{$;~rzMj|`F1hl_bnQZNc{XkQ;vaYV@eKYQ(
zR_!!?S|)2fQCa&CUNVTU#<+e@iEVmxsc)wga$LuLy{Q(kKGgp`a?E**y1cJJ+>j#C
zCooruEm0g|a!DUr{1M_J+m<Efwr{CdkhSyyf4X8b&FtExY1hJH|BU!C?OfTbBABIv
z^^5&t<%x=WKBSkEDb#8aCEBAd)z_Rq*3z>*p`W<6R#R8WB#cpg;JbK-;oiE0>M*uk
zKK6AR8rDKKM^m#Y>q5VLb%u}V(hT_5H73vDR{6kM{~*tdBT3|y<I3Q1h2b$2oD;&5
zz+`;fDY?k<YEY6dDk;@x>XPbGXVXBhJh=)-mu4@;PuW=^+PoWM^C$BezvtdmZH2aN
z8U;NY&(*oW<6dihBvB=lvGsXMhY|XjM@o3<%`_TMuR2HWV)_EDnKoNub>tXj%|u^$
zd%S7!4;^z=uK8^-1%3(E)6<Y>RIhZ|^@Htw<tmnjoPxN%%9>o2K?QZkcP?+mZR<UM
zuyEz%tV(YVZmOR`mrn2?BC67ZDo^UiZy=MQB7Q_>>8*5kYRM(hE&(rEV8xsJx9(y6
zuOq>~ioM&D=>a9#?%)h*TRhX_i)d)+LM1%q-)lt8_kYw5VZ}eS4j&uoS%yZM@>c+=
z{`gOC*?1Z_2cX5dRHqMsj^O!&maB>EpR(US#em|?$sLkVecX^6pTs1thP}9dwP^(X
zvsnCD{0(F<lv%!-fgY#n-Sc&TmMh?MFH4b5(X~&~`Y0#^5m05g!M)p~$O_;3nI<6-
zfNK@J3RcW0A9Xz;Y2XJy(J1#Y{!`JgJ&A^v`#n2-Qe&Ea2yq2QM!`Pm98dmOroRFz
z`Zwq9aOw%^mv+hk^`yo`>JOFV>9fnfl#Ed%g#zG+6&?S!c|MNOUvPMckJ^oxj?e0G
z3R<dDt@w|wpa=R#ZG>U}djJE9q&EqJ#p+tIi8;4(pqaV<jIG~4#lQ`t{-nOLHAvGk
z3wvO-qi2d8)wfpBKz)mbnvt8Je-`nYKM#66-wLq@h}6KDgty2T{)!O-o!vga2>>!u
zeMpMd8NSpawQo7Q`P4-5jUP&6=VhnXH7wAHyI_HsFI}r2*{G=#dy+W^+mUJ;7gZml
zDgF|stG(J_l#zMfbt-dq!F_1LUwvu9{^9CNrS}ZjqX9e&<sUvFU?F%zk=uOC-Q`Na
zZqu;Cjoi#$t~tU}pJW+mGRY_L68rOH5Z##R?EC_Fx!H5UEq(DOq3XPryIUTzav!Qk
z)VCq2Q?XnpnRav<EkZqA$-)c97z>kp4@XN@&mYa2t3)26h!ssXv^TDUPI=(4oH5@^
zaBSqd1Z<IRf?D*O3OB;5_=Zf0dTeS&*jQXOt&gd&>fn(IEE*-KaX=$ccVbg6-+e+>
zof7E8W7TVFuRfoYh1DfoRiB85Ivn%%&#sUj-#6i&&aw9na=UauBO8bq!&DMtgDY~h
zzQ??;?Wd<{?A21Ch!v?F57H7x0_ny|c@Wp*UN)_qz#WLK)Z(5vn3KB7I-waH{D4+r
zr^cy4%X^M%^O%Xu$1vOR!`1X<DfZN8vDJjxE%O7+Q;fs(BcRh=@6bZqD}U6WUV58C
zoyyPoyVoJ2PD4Rh{$R%n`?3{EOUe0xPlVAKAJBDJ)nHq_w&wVZ6YMDz<xrB3Fp4fs
z$|zVBgfeJuuBjISy+1UiOB+Cc1HIXbZiMyh-SXbAN?X6aMD4W8;;3oDhyds0*TXMP
z#uZwJ?Kso<?PeF_D`dsfXyz$Sr5sc(t9V!laKkrb9BFzl{!m$@@br=%9pQf6`HZRe
zI`W3sXfdWES3o7OFPDWZ2v-7@jDf&-O0^#&pBj&vlK9B0+S>|pWGc9KI_+L*lje=?
z|Mh2P`O&HAckHF^*Xn`-uLllys3VvRU(DyJBnC3lP-!k<8VzUrIZyNUIYKMm^P9eA
z(ZMUlqSRhU74A6x)}-p{bQLMl*ixq!kkVApu@X{kQ({1WP`TDhLjJNOfG)FM?Cfw3
zqX%0RgLkOd6ItD~@6!jI)@#M(9Ph7cLpW=2)Pd(wBr8^KOw-)?-HBowz1NG8?Is)B
zw*G^hed-wwk%}bwO&{%JQmDE7xN1d2dn(7Ne+t|hRtG-P4W#<syzqQvQR*J)B+k3e
zVeo-n1_IGeqJQR>J(5s;fNuekJ{FRqxF*d<6x}}6RD6qn#8N|`a*BKNpr7f*Hvhqr
zy<nB;<QB*-V^sZP(&T-c&dM2(45in?yKvyEUtR0^72I0X_`T8>As9Q~?tR4n-w~Pr
z`U??)k<AiYl8VVv4_Ezs?wrqX7PFMQxrx{UnT?8sUOWyq&F9lTCFW+reOPRKjtb70
z*mIevZxMXK5kBTKR$P?XzuR}~`G0V8RsXY@G5=Q)NbjJ>M8CE>32Y%0&peO5axiA3
z>h@Nw1Vr~TalZIJ3W1AKtPA^g2gpU?{jOQpSz1RdgEUS@SS;(Y4x)D_{P!-@M(MA!
z%en0&$WsVh;BOw+M*O_yH_*-nJ*7z9`A`ZYs!sCOHCO$YT0I3>Av2n9#)b!Z@>Fr`
zCUYKRZFt5C29u3Q2b~|R+Ia+-tjZ&$$|!gn+`pzji1%9V>R9i|)0kRSy7MlZKYHGe
z?ZOgB>q|Ws)eJ!+pL{z7KDhMRT5A$?I`Gk0dH7&gEcibAq_c(I`Q;X;=(|ujpc*BN
zpXg`CS=NcgW^S``Z?kPQmbxjD0@AMtmtF=5UnY^M(JL?nM9W*xG<-7tfc0VMl^Fm^
zK}hh|?kx&{5H?iwPz({{pwQ<EymxF%R5kOq`k}r1`e)9h&!0r;fu3cOnES000}<rH
znl5ixQ0ET5YMnejA<JW3F>-IShm7|7&yNWoDLb;(o))T35-TLXGIW{41Yx2&|B~^9
zd_KGyDf4A@fH+-A7O7mUE=Rw=SJsJBqO-t+>Ehdeg{k-R&q{#L>F>x99*D;6EfT(<
zqR=o`V7&4rh3f5lCI$ToC;dm^6$6%!;2sh@tO-^KOs$_hsvS{qV552LHT|*vQ!lD6
z5D6Ccqm{F`cbBu?U`Qvp%Eh+7BN}o&2`aQd?eZj7SxjRAxMp=17;$u^vu~|`*fhP<
z^z*dYu>9bVI=R~r60jL9Y?y_yyIg54TC0n;%!!2*vpgwj?3u~AzR}DWTuEV5XEQeV
zwyvqO*(!9hareis#P?J-1+ibrn-O%ZYSnL)6yaYH4EQB@DY<^(@k<vytU#5s;SmGE
zcO5UGE;${%_c{|S2fFh4j3A;Bq%XM%F&FG4=q*IN#Sxsz0obg4zY}`oqO-4ebhwp9
zTadYG^i7$ToQ-v!DIkhKBEUpzU@to*>Z73ntbYV71xSE+V7MUIT*rEeYwgv=f!+IV
zx2EH8ibGS2N<q2IJ2*GhIem1MW(YGb=kW4mg6$FntF)w7me)3fgG}P?UFbYfw8;7>
zk8?R&hkZ>g5UH)yh@Vf-d#y)#4;oa3S;=c9^RYXa%WN{qaQ4D+uRexr(#ol;60V%j
z-$g9D?b*)|i+-5$u{QgQNJ=#KtNM{}2hTV1Ua|YeZ?y2-3Pl&7GxcdrV21^N=PCnY
zLa52OwaFqED~}{J6G&HLX_GlB45vUs443isMW>!t$Q;Llz~XUZ26>N`6r>`#yzk11
zEpA8d)YUAOsh!4@iuix=_Lk9g<x01x*^Zf+nVBJGhL|DS5HoHwL*f`>X2zHqVrFJ$
z_BJy!#CH1aPWL&d&-d;d@7_Q6-x_OaNL5m)q$O3&*<ggr0FFpu=nkOO`0-9sk{CKB
zQgWp#`*QnK!F2ySAg1G~Rfl_SgtS;}t^YtWY@9DGNX1xnciEI*HY8-jaCq_UuWt7F
ztD7B#`Yptv5jJG^QKw(Nam9#}U9rB2to%6=dY9RB4GRvX*}KGRiL8nTsDw7hLc=St
z#O{sV3*i6&3Nhmc`UrL6R{R`cD4fHcc2Nb8Kx(9K-Kn>yD9#S+LWF%A8nyk6@agL#
zUoNR|(UyCN-RA*ucr>&tqtBgFEuPkI8!h*p#82S3BeJdq(hBu?T-QzelL3!L^N<nw
z&IV?^!5JM1v*_USFr*xY(6bVl_rp2jsyQye69c!E6-ah3hb>PbH&X@RvyR=MpM&A|
zu<OTTgG%6i<4VCc&YhiWwJ(_IWFW>{zmaO)|H0j6(PmW!sAx#Yvh$8|KOHT!G!a1p
zzktzR0gfXa0=mS5r_$<5nB+nF8ar&0YvhpURU>9@K1-mrj>sB=-<3ngXT>4&IaN;9
zAcgDceyD)zU~yexgh-OvKq1#S+JHw|Y>SjLW0~;uJx;F-7KRb?&vlmTNkNku*SXi4
z6Tgm)CEuev($=`v3mN779@v5uFQ62)6yy|=lkQM3JyzjTTvdj_gpF*lqf3yK>r|z~
zb*CUNh~p24A`zw0u?@7(NI%b;fV3&QCDu$G5ku1s_MZ6PfW(wN0XIw}Jng-HKaMg`
zhhy`gj8V5v_1%0$18P?~ft9?omVSI~R-V<NHb%J8^J$e#cFxiszBIU>eF3wmUAt1d
z^$>;#K3^jCq&><P{_-YG$)f1jS)Jv6%AI{{!w2Q%2<wl&_h<R1Vc#@j8b8WK`hEl;
zYlV3qOL<d+DXCBLV0fn!m3wkf(}R6CsQO$@KQf2*vA9pJ&iR};ZgBL2`X+AOIb3Ci
zSLCzCaxSq_pT&>k8Zf^~`5;@OTlZYPzc+HXdhkA6oa*$<Uef9;o@ojnFD6m;%Fw_r
zjjk|-j#q_1draYG^Z_$^f}P!_@CVznO^ujIDvlG1<G5xqL@Kb22dbcXd6d1>{iBH5
zbH9a%ln5su0_4BiV!i(_3om)yaUsov_96?)Q6udm=qGgand<oPmo$G*6F(x)icx!L
zLrYRQx@zOdD@kSOuPNTyXER{;T=6)CwR+Mt{{*gvEQl}ee$H}(BYQC2XKcB+joSyj
zo{8M{KeSff3%u@d;X|Mm$!tN=!y8BLka&vhZ~k`?$aWdiX&5#xIDhKLNK5q7>X)&u
z|CR*avwbWV!hy>w4JP;z#u7a=2EMr?0p)|+(Vw07x46)rkwZBMh88Fg3Z&bAH&lKx
zK>~Kbzh%jvPp~_k6E&DF*S8pP?<jYmqDEIfs78-83k>;2C&vN^Gfn20R3}C@UV(rh
z#dr{IZ5Kr`+aKfN-mBQ2y}x?(z$>-)y#_WETEs;k^~C76NP3Q`xJ|V2alX}O=xo?9
zS`7^`N^5b*<syQlmQ%~_f^>^$heN}Zpo>vl=eC+{&nGZKlKp(6KW<PBT7%+GYbtqV
zOD*3!O_|$8B+(VhP3x!gz|)4Cz~D4S)vp)h5+bID-nh;4*H`*<g;X-&+)P!DZLAwG
zWuQ@eXL+n_3d)dB@7c?qVGBx_h2C%kNSK@x2hhOcr>L$n1R9yaFINJ#Uxm-YpAQ!e
zc3w4=X-~ZJ=a0dwP)t097g4+*wh*#qE!8jBkB=5H;5E|vFW*;!vcpA=0AD1t7rpEQ
z_KtzSU^o7Iz}|Q9w~y7PiEzFjL8Vg?LjGh$Rg{0pYNh#T_-8$qe;f0#14bHzm<XpR
zwR_+?N2a&`m#_cBOjU{cEyU2#lww9mavqT_Npr}y!0Z1%4f)R!5~0>!MC#EqetiUk
zg78mx|HB0L_`=6?)UoEc&SH0RC4US$;=7A|+0dW<Z<+c3)=G9SdiqbTRMD>f=ZyIO
zEm1mjh?e?FrWBQf`ez-Hu4q^9|9e)I-U$s@sL{tdaM`7)#DazK)TgL!Z+q1eS;=i;
z)Bid3*G@o0%EGi_pmnsUbY2TPNp<zOG*TRc1R~w>)F^R<`}4_2h1UA-Lz3TT`<2v0
zs=Rta^F{y6>zGr%@iQRmyqxm8Xo#&dS*q0l<nKG1SGX_Ex)Fma0<#y;ulqG`9P$cO
z+HI4_*X~AgTs7J8#8jDB0KR>=@%MW6#iE_GpEo}qM6|KmMYMNb+I!fJCM)$7QP~Rm
z`;g(FzPak<;s@Ss3Py*>=Y>bi;pJj|l~d!LM!r>`U=o_|3a+d$NMIkoxNSQ(v$OTR
zxVm(UP?_mslY3Nq&`Ld)$gDU|oo87bk34Qc&;+!`iO+_Ss~0H0D|zQKHnOP;U-rct
zDTdC{hso`2-MMfenoH%Pkrl%-{)~AS-V&fO$s**H)J4FPZfA`PBpDM9Is;KTaN`qI
zr6C|vn$p);ts8w~GhvC*v_J<&@m1BamfteAq-=ii$Tar9y?yaJlVC~X8|1ajjjLw9
zTkmFd4;K)ZENSk*6Qi3}&>Sn38uU{1G+h{paHPHCY@6M!D9lV%S$|TQ@70;)@nFF9
z(e`UdLQLucj*r$*Gpe9b&ZL{Dr}4o^p_<}jqU1w_b=F9I=zuT7f_I=vxj_X6OcE{H
z_jp^ZDQ88rOZZfQMgSF@FaWrnkvtDcJSX+iidtnD0FGJrH*Bc^b|Nb&DI-}~3h#6<
zZIo?rRFE_3HBq*Hswl#D_hF=6x3)BVc`rpAO9eSLhYtb?kr_<ABqt~B$Nj-Z4a8&b
z#(~@z-ZFiiWm$_qaNf*}JX1MQ3Y7d>?7r>t-ls2$hu1*bj;@b_W;K`sLmLd41P_#Z
z%SyKN?`W}7rBQMhgEqPFFU8J9WC0D5(w|CvKJYlmTT%o*nzuYko+sb!w<cud4YnG}
z55JFW33|w#*41!h%qxv>Rih8ZDqwh!2=QWwA@?q2mp%>jkm*PFViGkuEq5ySBC>Tq
z-*HOZMHqV^s4-sak)y4<`lc@8y0^-$6qA5UL+P&ymH4$pb&55b>R#$%;2SH-@+H#8
z{RvLKs<RdMmnpM$`?MlXPLGBCxIE0@mJ6ik-B1_$=h@2X5BXf;cCeUuR>%<K)EOIv
zFJy#~Eq}-5{OA5k9AgYi?;RO+$;s1SH&^a;CW3VTx%3qK&+n)Ie;xyoMuTT(vq_`G
z_I~W6;dQRuO0*mog3~FU=wF@ZR2%BmHs1LfEmoPg;jME26$<oMlO%ZAkgsab;^KrO
z{^@$?5FA0O`VfV?WaQNl9!K#<5pG_Fdv3jPV*K#wg7B2BhyJ*uH*dqI!IU8w&3&eU
z-Iw49DJ|tze<q)_VVh~iUg)7(Lp12<KZCc(?W+?=M~{T#8c5({QhK(u#?|(VD`9D{
z>N&`>Xf$3#sa0`!P)Wa{TIn2rL75us8X-;ltaxqa8zEeG<O!<&sr7Sd2gOQY-Y+g^
z*XXVUj2KubmSgjN_yrq9c7+fYD$t>c>ko)};$SdbYLgB))1cUhum6{M*Fng_)%WjT
zo>>*Q2aO_{afU-SzMgp;RRqkWDXg`gM}%prY||5kC9amx6wQn-j4GxEFNw`}XMe49
zS*YWwvwzC3iZ0OeH4;w1nqJkc<z;9nG#C~);7r(WuQMZ+Oj6bzSPjzjbq%;&kUJ2V
zKSvC)#te}mM1bq;))~(x(|#(P!BDi|U998yz;)`Q9hIWA#wAc4QH-cgX>%cM8DZTU
zM!3~tsZ4E~d?UlT$EiZhhCJz%E;ra=1BcEIB}@NIJq|~2o8ttl_(as@PMO$PQ(H%5
zTq%mtlx)$^na~*LK4ON<l`Sf+xy;{YPHl)q(gVo%Q)oz0mTV18j(6VY3UWE9cGpp!
zwWJSd#aDnRc4f+1$1UQQolGk<O{Nqu$x>PLOionhQg&X$Nk0!u%Ste8b&wR$VQpxw
z<YmV_%O9-VV*`Y5wd@e&)!|lQx?g96_LXfG>wlZqP*aYMeSy2UM)C2qN44v;Z{?M)
z!D>pto7{O??i{WiDIRUL;FVf*?5XmG|ABMqyCeu-vze3V*z{Jk`YOI}00s<RZmH%Y
z__f~AN%HHy3sp9roa*ejryIJ@C^z-9Js|&UQ`pyj{L+E?YZ_X-^a<v@Y5<Q^%*s9x
z=X^ysEjgT#{M|YJ<S;{`tSUeDPpgm5!VV#mRYg{Njn+a{6-tU{SEMxd(aCy)7O$p$
zEf%zbYwU;KzJd=wI}F!a1aAk`%qOl0q#Fj{aqp$8bmiLIIavTst5#(ob-RiQadLi0
zF%Wr}zbDh-(6>|b(AMQv=W$>pt%XI`cFSGy0Jo+3S&DL8Hfx2{Ye{J6*j#Nhhc3&N
z-6CH!v=@<(quv8aDNXDJQR{e&{4}@4&S#Y!4~W=CdlIZ%`^<0}if}lX2HpT-p4m&p
zR$wP>g6n?4GbPj2zH{9mbHv@jN`@WSGmk`$52ZMyMPYdl3uMp)mwRq{MDFSkl`EJY
zKd`~>#u*+vIbzJYDY3#Fm(=J{icuw;z|5H`5mOlWt+^VdkD1U3E7?8Q-#5RkuGFzS
zD*XXb%)wJVnNdDAF`7=(RN3=xL{G-rcWR=hiBrF0JYz+>iKw_RDz4UiJ09NN9Mpl;
z3LinOCAVD42f>bLHeJNyd46?nV-O@YQA3<za+IxLtbCu+dWkz7)@X8^8d2nBiCl+2
zKE7)(A2mhx5+KUjrT!YE*5v~}9J~T~!@F$vas~bYAvU5jyeoP`^;&lvvJPgnxwd@0
zK1voZl;tCY|A9?>{3{9i19+yZg6XZg#0S5g!NLoEDHNLxjDlW9gt-1v_;a2{2#}DB
zG7)9UjI_8HTMJslQ!Ec)HLioD2DI5fQ_Hov<OP&wMxYPdNTN|o`n&E{qZVZsws!2t
zrPs*s6?FU*OxHOYOnOvqv9O>`>UA%y_Xw)f$!z~FWJ=EbDROqZpFu<kHAhjVFB=1g
z^b80oZW3;r(F~O)86?JYBO78Q7rMqXv6og3x>OckQtF>&;C(h>hyjn)ozlK}dT*m?
z<lRV@EhIc72h4Fr-r+gMjl8f2wZ`*s1tJ~xz$eWT0&tja(LB6rXJ2Wy{a(cs?Rf^%
zlH`V<-?}Wbl+>VbLgabz`_YCm<_+7;7R2LbI70~&7O$q@ChS<wJo>?iLQSgx`7*%*
zi8y63e+URlVZzX*>1}S43I4{!bklv`Dz4BhWk6M>N!<rZ!PyQpA#mrRSp;T5FXvd1
z4t@Tb<3sZEPv#bGG?RZldmItM*NAr?=UjoE{zI!kZ4dsGc2zR~93G7txcBsm_{ws7
zQ}!#P_||mzGOM<4_Go7M<OhNy8Xi-cUdF2BVy&feL=LA7E$wHg8I>bDtdJn+fR=K(
zrmPKUpEI?zt485|d?WX<li4Xep9(+o^%gGj^@qJcYb!8{cl(|)6A50_g_ODl3XA!i
zv}R^W?g9tI2zr|gLrHk=G1)1@ItV~%{}?~cvN`}jSo@ahxUtzbp0TNj=Q+9VVO!MD
z3L1a)tt;7|j`1N39~Kng)nLyQt5;C?wYf;rQWy48wff#ZJSMjGygcCIyBi;#2pOsF
zi_&nRYuT*9orXIb3Tj&b-{%DLd9}K@vy;S#XF*myz}Dj*5Iua!YCWEVJ%(qk1{Nz3
zjqC8EYmeL}4|hrVM0m6}g1Zq30d^NGqr>B1=)=vp&XUY<|I~0=IxKqxA1?2~ls@%Y
zc6HBO0eK&n&z@O})i6Y31ng0Do2#lGV`tGN5zy~4euRD(8*PGE!3oJf_IE=s$ED8r
zvPck!p7A|sR0j#!b?>?LbU>RNgTY!8H1yGfUALN5{?U#y$rfS&5LZ`nw=Tz+VM?Eh
zUH-0rfY}1*@%?A-quq~{h7as}YlaJ%U%WutH_GR<O~SXdQREKKW3UmW#pjj%5x5@C
z7*JE7U}acpNQ((v*LO)8$-qRyn94fn`aZqZ*K~7s0Va6iE;w6E30Zv=%~(?lGxM6`
zENR8$4czK_gMRN?Sj)Lc4{LIMs1!+r4AK&rVcz6uW5%)i2LWDrIiB;-iWS2!v(Y?1
zDi4N&GT9Y&Ach3ws4yG^i9}@E{GV~k;z*Zx*>)^TSB4rJNOUdU8w34~wqv8dabW?~
z#5gs=fw=UkBLUG_6(;0gVY=Zo_aV_C=aJACDzvg)lCPR;w|MDCglg)h5GDr(H<EMe
zlD|g}Sj%&0>y~`rl-UaMa~9%qQl>qnEO4=uu+L(nl!P4PP%cGtUSCMNWhH8-sWWe|
zph&2xvgo*~51QnJj{>%pD)PGRk$({9OLTqi<Go0-a*Ym{)W+6PR=+EP3P#Ect-~-Q
zdDXj=w=zDvb-jplS;h1;abe!H`;k=aIXD71lu@9i1(|SZr1Y1u21t82Nb|t)u*OEF
zkR<jisA6GA9Xef#VwgJ+2?bqxE@#*;MfWXU$b}3e4l~wX?%50F$t>DJ>ALRvE0i>w
zeU~s{%}wUa5u=c5&r_pb0|PT7f|f>?XV*s-gBpIOW3t9I0NTifT_c49uCb8Vm0m<D
z`5(`<tgg=c_wDz-Nvlx?WBTVA|Bdhu-fU9pY2t7Pg?+6{fXq%68-Sel{LoAMwG!Y4
zuXLVt(+S?|l@J-gM<k6`9tP#Ok3JhY3vO$oR0R%<9)Ndxs4K?D^;W0B202@#tVWz^
zgE(M-wZ@?90oZT@{1CZMBw0SD>~jXO702g_jU4U2F+_C!_jaGZeijyWNc=+k8$#q7
zN5D5l5Q+v3!K;1sT&`qKTE&QBO5Mm#coO(gtRl9w>^0^6&2#Q^{H-mY43t0O*B?Tx
z(8C#PXrdW9%m{PTYY(9ANeZFt=?2z%Kw0i8m6x=vVo_LBw<)<iZWUr$5Ok0N1SO%Z
zVK$n&i3Kn%hI-taOsF4Bk;f(VA(_&TK4xnZ=d;XJ^_E-RBWuUo+gn>_VyBK<;d0&4
z+R1yAYn?p@#9#0LQRm~$&9$~;d62a`^H1KNF@e5X&qTdlUl*s=TRk2hE`F+ed^Cu7
zAJv)v&kD1C*O#@JVltKQQ1Zk#U=-KR^h9F!Hdg%Qi~VIG-T(E5P&xjs(aGaK<t|7G
zw<C7mI9841_{<u1{$ny#SetM)&@8WVfgHySTdf>M#VipOx`?Xw(=KE+A68{)3Q>{M
zx#Cif)$w5wz$xr)`7QRzJVJ!E!+8u}w}Q^Ddxfu0Q5#pp2UX-|C%&<viL<PV1nHsM
zC9S{J-ym@mLX6%q^j%2bxpzl-tNC3=h-8g}6sx_Xj)`iha8`HZqFUKhXhMY#c3Na0
zFnYR1!OI_m(tyEzqTfK$kiEF!<e};~VcFFO_fA5v;mjiDjAIy2dZ`T?rSV`l(HzGq
zDhUR|-ZV_`hWFi$I{bG|`1AA7fZ*w$_$PKDjtswBHG3c-9q2$$4D|2<ql|stCh=@|
z;){Bz)>-3e^KczG6C{c7CVvGLeCcxxdH(Z*#_GL~&fgy>V!-UdipH54r|I{g-VIaK
zmAa9Tx<+a$sdUPFmqv^7Bx2<v&Cu47H12UH2!U<|11T7W^K?*otJwnzD!AD}yI1>y
z;O`OpYy{_P!17`$K+UCIcT@x-#DR3b^M@?XNUX_J16MIR^rQY>YLrUX?SCsRz}%FF
zDQ167DSEk1^-G27^Xb1O6ZtLj-j}`~F~3FrV7qW`Fh}>}=z--Zh$4e!Z3$;N`oi}E
zMO#u|q8~$4eZ`uA4<k)_^=4bV#fEr<Y5cZbZWd*a`O;A+um%dey$UF}{+JL1;=UPk
zcl74qi2%lMb2fm(PZ-ui^};(qB0@c5Xo5`**n<x%W{EaLpI9|MalQ$}FK{_`w+Tb{
zb@R-v-M@H%&&-EF?V>9ycc$~hUEHZg^=(amK*X1PXw`gp`wRkcor-|^-m<>Sh^XYh
zEwWe@v6<5l*pCnq5iiKzaI#&yBGQBT)Nye2%o7tC<Rq~3O!0dmAP7VMs(j>PgXrw+
zC&!6#^rLTp=Of`y3WC<kDHNZPs_nCa&pb^j6vmj}V%_yDWEJ!kS`F$6$mwWj#PCp$
z{jD!i{%U@u&+pIXm6G|~+c3?~=IsO?b&}$m43h=7jY*<d78XBk9VeNP-%}SVf%9Z6
z8M=!hFzEe{b51`vES-bq=-NEL)YTmgm3qC-8VmjSw|3d0Mok_{Kd0Qmh)0%|GcoF8
zj`kqqk80t{1_~GDK*@qK<)q|hj>^`N8chR<f^Z9i@iO_Zi2~h)kR(dR5U7McpS$cY
z(y03WuAWR<506pr>WOm-WhHSC=#;Vc>)8#`MB*X!wD28$G6pi~Kw~p>K3C{5yh4&R
z#_DuBq(8j)z`9|@gvDw3eu~^yy~%owdHszv7BL*5v6~DHmn{g;t{;2KSng_`V~6h%
z6`hBe1skfhHbE~*z=Oqs3?Z1<O)gAPC{nG{;GtCf+m?b)Z$0AB9xpIRAA8yzK;Axv
z)ixJtA|bNiS<GCkYyTeSjM;8rp{-vjxc_cu@%Eh%A_468W=ad)^A+lf8Kv4_r2u0F
z2q{D;K`ZQs4#Ncyrw?k3$wsJg<ADfs_g<QZ2kkk+O0r0=XCiA+qcCNZAg)tnrb5Z{
zh-Gv<yw$nZj3z40GR(6WG1Xr-&U7XSHhv0n*)5kb4s!-Pp05S@)akUp1f{<ThwH&k
z>jtag#OVnHVn|I&LWY|}2M{7SqWn{(5ErmYe?Xx0itcUjU3C2QDZ4L!xhW5S{u|@h
zus9D4CZCHnJjUJ)?F&CV3X`6^ZTtdc>W|81nwbp4GhEnWrwEXU6uqDRJyXH%NfNc*
zu84C{L1&=P<Dv@<p2|}ZaOG0E;z&h!4P#G$kgA35&2=CxZI}n5`l{e3E%{E2XAAzf
ze@ptyK_zwZg<<UY1brC+_q5GlJ^jIW<_s_ce{f0li|DTp|1|{cZA197AJJ2&6O-23
z%uGWOT=#So+y{cJ(k(pQ)&|`Zs`+E7W$n>9<-ya)#Xm8R68fK>B3SoT#*9cWva01A
zbiUSk70s{=Kx4*t&1`m0kP!I15{{^R7ZKqc8rGcwph3yq`j2Z80xQh=3fkj-JV3wv
z65+YoI{&ZH4FQj?%X8cC9}pqNWH$~(kA~Sx<*RK)AB?BLsvuPTs!3Z_rjVOF0|o1i
zWc(r=wZvdD7w1zm6-<w`WX^DAcpsPOI+HP1J)NRq0}Dxi0>Sp%xlud*>8L5Pv5;%j
zy?iuNR4(aS)}-Ts1I>zX_(nw|(y*AqLhR|sU!m#M$mwZTupqv*&i%`i|7kLwV0r(S
z4l_hT0}dBe;I4oE0V*^s(#E5fGd}f!rp@B0NR2aswY83#0znl;Y7iX)mE)g7E+O)<
z-<D1^I`=H2+FhtV#iwDPx|eYzz`0iYr&{eG^P1*#HilO72#?*g7CpYdIroxn*gsdK
zNy@7+f+jBH+^Vb%^9{f(sr7ejf=_+TS`nR*;SV}th1@QbRlUKyJ>4idU=Q?P%6R>M
zDf3O#d57PJlii{kY0QI39X}$jX1@x6i4pNhP1&1!z(d0t9T^BOwxo9P&&7lyTxz%L
zy;)c}Bk2yPOs}%THkXN;!=0Ndq>{8^UjnDRapVK>QKA}DiJAbkfhUahU+cNw@8z6}
z9+`9SBpfJUX8`EXkByO*s7w3$!#JBqQ%&=z>|L`1cb)|%eOf3G-v9sYU=D9R;{VsP
z5z~wmf2fjjr7`=*b>e!f6uJ-n<Z6^z`XE-Sf=T1D%Zxvs*qoi)oZ&^{zijVM&*GX$
zpHM_;T_J%1jK>bl6@#{dK!h-aK;dU1%8uRn!YqX9eVI<`!P_85@K)ZioOKS_Cl7rD
zPa@k5MyVP-FAH*M#B1eD0&|1XYy<VxaF0H%7dslwPExB2p3)~aa&?|1cZ8uvjxd(7
zu@>NQ;Bd&9g*vXTPQ$ZEPqDFEmWz530VwoVpcywCVdgXOcN`Q+1=_1mS6=5{9h?;>
zhCjrCHYPY_U`iCjG9-V<({l8RDO1s_t+AAA_<StzupRdqH}iU%HjX@5A53+}6XE~b
zb)l&6v#CH=VcqLJsO8+fiFh<jhTRdg`z;*WJjYC1@6`+WNjfisLA46U8>#JKw<QA8
z><pMnoEw&V_%4+-<2%F0C^qB>&k|jRQ^u8sbo$lnP9fjS{yH}xH%i$Nmpw`%41y}|
zzM6^hG8gg6$(vi6N90tC{M#Dl6tP9tXmOJlO%9B{Ay2!#L?I4^CxIB93{)bsB3$LT
z_^}*~PP54|LK|E$D7*Bvkb8<4DI$Z|u#V60oSmP|D+H@aMWNB8{bu8=)j=rxB>Zdb
zo=I#=eA1UMWB!9mSx|Vx#JCVRv#Kp`^g^CzSeZYPh7zt8-`hkTJ6%S%xySDzy}wQ|
zL(aY=Npi;4V>u0=<nf#*<A@wVOty<ZsTYqom)~{M$HrBO_&C((;>uR7qoUpH!~~$k
zydP4oq95b(Tux>+^W8&E^h>$XcY6lj4a&zQGP#!y1+b07Vqx!=NozuXtT^z#M71{U
zAISDYIH)U*7Mla+;i$QXwPA;|YS^?6H>yb~6iuTaDiv|KVlc@1Qz0e&&Qt-jNf5R3
zY&5yAm-}bx!_}`F{iu8$aCLX|lsoopfpbxf(dWX!IH#wgApW{2Y1hH{$pnaJYlVcl
zpxEW^clN={61a8kI*9b+F=Q(Fy-1%%6m~*0Za_q1MMG7{Wg($_F3ig6t;0fVvfbBt
z8+9&mZWTDyeGZk6y`Os;Ki}ixlt-R3<q5ep3w?&Val&#2==KN|ROVMCKm06}Y6^6s
zHC7w%DN>LoE;|a6`k2}cVIEk(pB9u9b#1oLl*0AvW9#>U5k+%`Py{6Yn!M5|B1-^!
zzX~VKNRm8@_U(NA-1hQ(A-tFv);lSw1eQ-zJ_&ow`5nQ59aSQe9Qd<$1KPek<z`aD
zYt<G6B{Da+NcwUUIaw3f5eKz3To-BR{^_>dfPClp9coTXW`kk>N3;Qf*2zhiN1Ed(
z)d2tF8zz~vDn$#6O+T|C(9+u4^0ex$g5e{#@C}*~t7MtyLxB=v8hnvlX%XmSlafe$
zNzcF1g92!hu^ygL5Sx#lR_#KMx5*;>Z7XX61nJgp)<Z-beT)xXz-eXgH<R}SP6OA_
zgur=aWDaA_#P^%r!WTdWXr$4+FH)8bu>QQdHnbRXn~^U)x^s%D2fv}ji*jwP^4l?=
zx+?X@+}Y}_{&}|}$R-T^lz$Ttz@7MZuV~<UcER8LGuSG=Ax3s=pRAOw0o76voEsG2
z+o|?5{zdh`r7V0nz`J>$Y(w%~@<4I&HwPUZl_XgyUjynF!FcwuA=5h9Bjat3AU70#
zQBB*KJ{8X2{I<_VHO|?Np$&GC6ExnJZaDaTUID!uUPY(vS&F-^<WQH|9PZJTfvvI5
z&H)xostcIt*wbQLgtoJ6S<UCHji5N?3;}mGPQcKitto{=Zp6DJXRH>bmvoD3_Y3<1
zn!Q9KcNIQbZz1wQNXZuVA224C!!6>?v54>Rlw-+JXkTi=Um0aG)mMs!hjQ!RR|^*>
zT<W)k?m95Xd^V_=HLQMEN{sZI;2?j4$ATNaMOJ2RRmo)3XvZDFA<gV&MEnG^x&nc?
zkTUs8mc_?}C0+LN^HBZ59Cy1kmz)N7r2S3XdHWaZ^^}QC+DMj4t6qWTs&`tWE=h2a
zGlHk3GX)wYPEfI~6o|C!N3f6>5CtF>02Y^=O!9lbS~dctU!-!;*Lw&2?YMQ8xtvI;
z->OjSY)wFI27=bh`((7`7H6y$y)_T7X^q|8uu!Q>A~sEW;Cb^0M8Mq(@6Pg}<GYrQ
z%1`0VCT4{zp!d!(2=T?Z6*jC`YR@2?_lZM^A3g<x8PNN>VN3Bw&02uzliDs0T)2}4
z-_#7%^$^^2bup)-_47R9iW5vriRF37Ba&Yj=}NovV(&6O+MOF-1LxXO;QB5uy#QXw
zjScr!@^s)Gcw>*{mRSPB91cK0D!hnJUOi4~5(0>tHmVW3nm7Or5$yGpQEocZ3sC;3
zU^Mt;%3w$IGA!oT<=MyQsj4M|Gd_)k$%NSy<)V!_TNn9@(s*{LIy}lrjbjl8k<J}P
z5yP;$&S(Mi{1o^>r3@2sItqjMugcQ%k}0I8ErS63?jAhiZEHLZqHe>Lt*2H)zhUqp
z*HhTY4|spU{RSP@$iXXX`qqv_g}UhJG(;$=MePL_5xha|x)(g6-BVg^V?ea}WX%px
zk15!Zo#Z9-0fAd@yZb<(F<Fo@=Y&!)24UVf69qnFt^30H=2yq97RY|3@xBKu3Ew1*
z;nXulIH{b1gTq9{2+csyg>#TqO-d=yE40RT1YJ~&Aw~k0qJN=IhRxl+q21u1@%&d(
zG*gS^b!C*fF5jYr6M3Cmtd~6qfAlWB^?bSUVaJ-bBB5OE)TDtv50=v|aTR7V7afEB
zo)UZ&d;OjIXS=iHak(l-880<T17wDS?OOt-BERLzm=maZ=Op?IW!hFD8f7etMXR=W
zsg`&jc(lb=c=W^??AbO}p6rm4yM*BYJ%psI_4%{_4B?=+j_C)psU4!#3loAhmM!A7
z2|$A$flEFmrd$g{Fl81b`Q3@8!nVxoOrlUbGS0S(am82on2K??Shkd_4BV*V<jGQ&
zVoqv38bSt61U~27wI)^nmUsp+O-x9L<8xr*kNH=D)`_u*;;Rc?YqQm&kw#Xikw%e?
zpv0q!_JZ?G&tAh&90uMW%&QI7RMacCDI#eB){QtC^4yxr*rO5?<0)>S^&3=*(Pxvh
zHo_HT3QP3F(4hDRL)QJbK}-SM*N(ZA@AA5X5THIY)>%pzSn%MsqH}00<G{{oKg=ZO
zUFJqPTj&g^xkoeAUQevNf4E@8oE3naRmKen_QBx5;b8#MAK-d5E0;`)%dkYZxlo7c
z)7Omtpj%mF=E)$t2ux*`_uVD18Um#L93N}M88GkQ)QK#3iSv7ZOerx{cm8l|o*!jD
zcvm?I;-h1_M@|bGxq}fX*Kh-fw9=BcU!&B-jrjl?Y2tsr4Rbsy0mGs;%6>5tRG3Ft
z=z+4@3Mq5N-g14kbkcEZAPRL6fhuqqVXlo!A30}%7mgy&85Zm<cA-eHTzV~}o9zWR
z+;ba4Jm{IQ`_>@Jk+RR9lDL*1vt#bcd6fG!NY>na6kH7`5||ZrYpM<fPR6=#JV*Ac
zL)3lIu8HFOzh!x&-$IAN4GIMpAQv`DVn155G_0~T#%E>K0PZ^YQ;&;vxTaB7uLpX<
z9rqi|K{UZY{*8~`J6<S2#=Pq|nyh9TOVxlV1MTt1a=H3~@Db>6hI86DDJ*i-VW~8g
zdD+yAHT$@DmklLt^^Llk0PVPnl_#HYdA&o`n2T=-$z~0;^~67F8jM=SN3`AJRO8`o
ziXy03zngJ>V<?$dMDKi5*`T}tJ@gJlS7v^2?W`@VZo+Wno9MrGXOP*%5O}{98smJI
zTw5mS0vueg2`jrgMvRfOOi$IO&~nk?T44Ij)6b2~EZKsy50aK@jCNtZ{Hh}>j!Er-
z2v@XULXAZf5^2NUuIhR0cEQxHp!Y(5uwR8tm%ZzNlni&t66Rf-)EHp&L)xOvsh+tl
zU#OiB+Dh&<iV!7HK1_U=8<JC=pCQEw5RpqUY#);jQrVJ;mgsKQHMP~i*5x9=_10P`
zT%n1$q!;RxuwY0@(^~y_q8uJ+K((rY-H0KOs2gB{Nn?B>>d|$kmQ>|dUp_4VEtV6w
zO-CZ#3`F4sM`6n6H(&|?1hu{^Z5C51yi+l2l{ojeJI6B+6E>-RGi>YA<>aO6X>}Ud
zcU^hz-R{_~t#>(Y^U{v2(rX8@h%)g2JMKCN9&h&Rzi0K}dM1wrYj7ZI&|9LMy14QV
zj)G2^3p?zh?OVvN&tPCCR!`|gQi775e_54@4kjCXD@-<<4>HlBn)R_OrL`2{$3BFC
zAo}_+E|=7EghC+hGiEBA7J{(Jza0sXU$B?bjq}8-y~xq{464YWt6EMsP7r`iM+zb$
zAy+L)g-{|Aucai{P4?fOnBj{ZBETH$I9%RYEA>%-@k!QGViG)}nKsLc@>;?D#V*v{
zFn7+pLg&csT2T{}x7}-xBV?E)CQz<pfD#1a@(>OVzJb$jcEC**qHigoT?`_6k>9z<
zUWeLv9DX!OAwIE{;efkXQh1OmR>IZ=<q(IKm>^nkD;8B=<g;*Yy1*E$+>+=`FG?xd
zb&ObU+Or{iifb`k-I_RQES!Ppm8;h4oqX`OIdJ2_wkaba41?@<?++GN?{8>a06}~5
z<F-w#$;p3I9j|)}^Z2OYVJj4TJ7D<1z~LY>*PAQm<EFipYP198yL67Wz=G0=5drZU
zY{LdVsm2$#3O$cqex>Z2n?^ey*aNwdy-ri7Dn8HrCxBPJkNRRGWCEX!bZ<abp{<k7
z;fjqI!#aL`{U?qIFdW+|A;o|=BI~>JmVGkA>G$&@3Ag1{d%l@iH|zQLJ?E0&Co98H
z=T_j@xH0A?>2^^>^8Bu=_|CO+E{VDZ*+I!@UIZB%)5Geg7c0PJ5B+(PGEhRwT*VD_
z+YvricTWDEwA%T#%8VusrrjMKJ3=B)MFl`J+~Depi|IRcuR+RrX$<fc$YHyB!O1I;
z5*qR~F|Ey5-f>8BK*aZ%G;LY7#jYg*9g;))gQ;y&&KPs8j_0_twLrUKf<puzmdxt2
zKqGxr=CB7#6X~jn8mJ4&F((0oyCyn*NX0atNtQ-_rj?6PQ@S;@ET<qpHR{>JE;eoA
z;V$_@ZRN-~W_`lSlYD8jx@df0SpBvG-lLQxlpIO#^*fFXdV=X#1UO}7S9-`G^AylT
zry*xVH-3z$UhJc`u4Oyo75ft#+3qF}e`ZVJ1#g*%DdvK#dK4A97TiQ8>%G=S9NchD
zYdAxmX{mGBa>f=o*+^u<-BJeCLpq`6M&5~K8+b_sCPRxOXjtY*j8PGEdKcn^8MRp#
zC$C-3?dsQ|lI9;lT_#0?nR#%PO1RomU{`uJ7q(OFnW~dQ92Ng-Cse_lmSZMNS=8OL
zffQ#%&A!K3P<2N+p4_Cw7z!UnB0kEg=q00UpBZRoj<0kbp1W0Dp|Z)t71?UfkISjV
za-Yuay1!n>B~Sb@5Op=(0on8&x~4noClVek%t;oJZKsLn_`GXS4CCw#P$(pBB9`yI
z>^w3O`YCunF+{OunA`OSvkPNvDzX@Mf4j_5KrmJz`k$M9$eb8X*Kdl(*23?p0_qMO
zQjjp}EqjpzQ<r6eNb)OwO1m>}whCZUz?);MV=?9=2DpaOX>s;afav%(y)He3G+51u
zy-3UrYwa)aOOY(5<KF4Ekg-pVN${jK(U8y(vzi>tMo~TOYccR<RHX$;<-uc)n*`Oc
zx@~H0+~BI<O)a~)mn$G%^*^HO#`=U_DkkqH1p(22qVhxtXqugk`Q<R)_aBU!#2+&B
zw8hcpWarC=7NG>DY$!}N)^!X8UEHbHrLQ$d<1sVDRfQousGi0<Z~57CM=6apQ;hC5
za6&yR#|lJC#3ECv(I=X6e&bGSCDpwqKpjl#p!#XJX(cl@R81cu;f4t)ijnV0p_6e{
z(Ey_JO65WKLQ6!R{D-_fl%_k|F@An<sfow{;=R?|)o;|kt?$YEWL0Q3#20Q>sPCTb
z)qZY0<W3%{{`1*5O$q5J^C7^PB$*fr@)zz@Tgjg+;Gc#*BZ?B&&$9rrJpzpI?#t8c
z2OgrZ{|$zP2n#QL=>G-#v#P30{P)gM$51cWC&i(8rU9v><`3J?TCLy%r5}enpH;D*
zKI?($3Z}5Kz;p$#tirWmx&nd1L+wB53htM0%F*KZT!s$SWpKPQv}e}qh?0$`H~)Z8
zUHVL9*G@_`vhP`HYJb^WkXYY~77@QDxx#17dbp_k>>*bTOqQ3o4c1_;5F#kH>&tfn
zwz<~Y#^z`$AeN921li8u_64uDChk3{ajm7=x-ykh)3GmdssY-_ECDfeYtCp{f}CiU
z4z$Vrk^KdqVoz}Tu(I9!>8gS#(}}<SpcZIIW<2CG!q|_I)^vh_JJnUWYar3RN~xfk
zbC8<6$P3p1!(y7|RhpY!>XeH>x2b*;+Y%9EidCr@tu@Dl3xQIQrEuMJHLY_vvzOA=
z8FGpK$oZ``zmP=qtRs%rkD~vrp^OcH@J<7LdYE5nhpOA47BUECAM(d`yf**(VYH!O
zY>Xc#z*{+JJds{T#nnvDMdp1<jG87StUmUDfi9lOS1EC6@Wm?vA%a@fg`+FEM@au1
z&^X#Qt6i#K78y0EiIGr_vLaogjkY{zvlyx>mwVdZm_$izR{%olc>VZND8hnE27Uy8
z2jmHg8sS4tShLm{!d@d);Mld+eYnJC(8T05=)<J0@h?JG8YD?@LUigyVcJ>CGtYVc
z;-){T(>7;OE+{rf(O!nEmDGQsOS0b4wPJdv8(NC*_|Dxb@MsrsVF(Tt9#oL>U!g3j
zNiRU<a7&21^k!aYLb6P#cIZpX*aa@K`J@na3$)~&15CCZzHd8Ur;gRRt9ME8KBNs}
z#xvIFYiYliu-D#lYmD&^5Q~K}gMy-&*VF9!Na+ZA!PGuqD-{Cm-)6)+32~kxWTIK_
z(ubA{GUYnzEc$l5F?!LOI|JjHD6>uw&Vbl*^xtN`LEqr6$?!DP-HqSPG-ZUEJVy#d
z+c6<->$Nr^(LYB7Y|F<7l)KMj%~l^~*VvT!BZhJt${kVrL0bjU9}ylAyX~Yq$fCKq
zIes>LbNJk@{L=&aKKhc)pU*F0K-uZk!##K_UB|Coo-eCbk?f<yo{xN1{_qX#3hNYn
zosq}`*!hOHucOUo-d7;}`t?b)d;ZsvX1U)u-P<mhY)NR*Hk^%!5^km0v3A^^89J)g
z<yP^d9Al0gsF9}2c@%?-_8c7s@j^{iDi4ZA2@%Q^;oY|aYe}O&AUXt2bzi)|iTcnQ
zFS+WK9=zW-z-{=ocYpa>`Zista<DLocURD1<8VNkS}pKZ9KKeLTUXwa7H&mNJ$8+f
z6FTQem>9cEC6T6MA1sOGqFZQF^aeEc$0bxO!<9jT+S8%ro!N(QBq1RL`K9Yh<YUbZ
zO34ZaUi!=d3Nhu9uVVa+3sf@bF^|O}wqy?MWLq|0Tp6{h4(49wvKwNWQjxBBL|d^-
z*H>2!M!(2IFKT2x=JYS;y`}e-edIGM-%&2au%TUX!?MJpAeS2LvgeOQ&6h~rfo!Oy
z%JDy<Ftz17?w2+M9}C-OKO6brV?D*2UPpHrOjP4qimnxR>TC?c{-REOH3h4lw)YDp
za?|<1&UQ^bv`ZqCp>&hTjHdtVAw{`3<IoIv#+Wb;j94{{rM5<6lv;4_IJN>5Q{`oQ
ziF1mr`k6v1(D2E@khqH}*gbLt93&dOwfgM6oqaOek&hV^Tyn9oOn7m99DP~t?&?n<
zi*&sJa*cqC?EC$z2v~x`)9+<XpDV^X)9PxR*MY$#S2noaYrAHgo>LA}q_Gd33#)r(
zymz<iK14!-HbA#|?(I@DnZe5RwiOq*MaQ)x`>uG$hU+B7cIrX9)Zv$&bvFC3LnnI<
zx^%i(FGw~A7V-iKwf$HZ>|O%w)g<*YaYu21IQIr;x3FHdOknAk-4A-88ea#4P{2CN
zW8km{PI)WoRcbkvkv+vHG#nd6k;qyHCA)j85^7Q*|KQK(bi+-~lERFcKUCE@O{kEu
zF0Rz!$4F~XRy?%Z<MkeJcu$|%JTt0h4WtuHom6C$^>{N0(T3yZVCN!a_C3$3$0^mD
zIawhfH8BKW?8>?CKbk8Za$GP4vmwU_(YS(UE~{Q`ex78gj&&wEm>c1-WDqev_uO+{
zWOIF$c1vbIXUvUs!oWh9VT&{jn2G;S4EMiI8-Kr{aGM%)+D}GvwJyrKy>|Y+#eDNf
z22?%&^0SlU%{dv_q#vA>+AeLEdfC-?SgnNH;BzAyW4SDB<5e;xK%omHT8poFnXE~0
zj8l1;`KD2}>keaK3KfmTTzT>~Gj3R-#@b<SUeX4i8v3)@qrQPd1uiw?Te~x^L}U6s
z^OqDBWG#WH*@R?;kMLx><%qkEk2DgKy(DFgjf^&laq3Fdu~hen{`BuEKpz*aWS_wV
zC!_YNvsanp$-ij3DDBTs;yI#XO!n7CDSn9B`x3NC=iHn~n0V#$UM||bkaU_H*QisM
z7vLSQa;v@1Vvmwq+TI8H1vks6e=*jp&5f#TwyoXTFo^z;k(DUy?ac6Tl246T-O1fF
zRUfXucW3_Wm7P5Gg$Rn0C#|JEn!U)|vbC%HW~+%q$n~~yf9_%8n(eDu(RRl3X07SV
zcl`AlrDCFHH*8ti=(a2olf3XGtcrB`vOIE^%((O)NIz#c>8On!P;UhITmc$XVyl61
zPN45Cw-Eue+Rl`i7vS7FnROlH`N-6@s_ck0XEi)|ZpWOasJLZ01A1vW?`Fy~l#{(N
z{K0839Vn9bWXt7?j+d}B4@Y9WLEa=aiw~2#A{nmIFh*ZK&Q|{6AZBUNiUF>m0)aSr
zk{U(?AC<mPGpTo@qbv8sQd?n3k;c%Em)d$ALeZp#B_}t|-jW&z7m3Cc`PH*{rebCk
z8e$!VqVV!Nh^^rmIkQqeX+TB`@3}w>fu--))XVWNM3KPAd}xnJY13p*HHRqMY9}+k
zB`i<r_QlqT$bFg7h{JC-rj1Mui<CBHu&`(ZY2MW`3V#OHVOrRIxEitHY50tJGs$>X
z7dr7%{+osG5Oc9aM34^{@`9_fXy}~C@`Gj20Qu?2HZzL=N$??X=SWd~a`4O}l(r9v
z@uI0#|JH(OSUjyJU2eF|^?^~mKB5j%!5mPb8QMA1uSxHZ-Aj1e8hyF6c=RCB7HLQ9
z^NGUPsI7OSh4QNYlchda=wRbpueEi_Q-(-v&)G0##!i#ec{mD&+*n1P*t^&Q%<&}~
zr@`jMgQ!WOz5}N5$ncAwZou7j#{}uG8dMe^uVp>wi4zeaGLnif<O`J`sLG)H@!eSc
z6&hqcqvp8wA3JG<H;S?KpSI&Y8%LWeht~*SzR+Uaz9oADy#{ZY*Lu2?ArX#uOL3pE
z2;8e@ly)R3-4Zy;?R%rV?!N%=)>yVJ4iB%fPw9(95!oNk21Ws^q%xCXZO&4`cLj>W
z<p~60?B4l9OnLAIMA70BVG8brxZ%vXc9pXaC;4grn&<&k$?xMa-)o7@TnCC>ILAyP
zo?G<0s(fx6V_AZC89dbXKRj)(1p`tG+q*&h=MfGs38k`7n1nKL#CU|1&9Ckd#RYtu
z@bG_fn?OQ?Ng~B<-x=^HQh+8HC}4Z+=Hy;_3+*-9&Ao~$PBdgc`9yL3T42Ezs#d|V
zdOzv$yT<^VV4dq0J>=87eQh#I`@@*YQsCN%37@T{O6(_;S?4o3)3DJvWFWNKHc3_`
z(_Ej*YP+zjQomZcY3l=*nzTwsAm_Psc&$yz^Xth0cINY*0WUQ3vpC%8Pl?+?DfoO<
zY?8d_5newH-dkaT>Y~X)9~TeBk2BeUsZA^+x(AIfA(&1xV-b%52?IN;JxNl;A_k+O
z<e4s<d{ml^YEX%Rb!BF=Qfr;>G7cDG#M#<p_%mJaUz0)@3xd)wh==8xytqNV2d+Uc
z=D}KP!g2?w;fKBw{?S3O5D1Vn#Wl9}PjVUaxlNx2(K$J0uTvl73FsS47NcFa*T({>
ztt99(<r0a7HR0<p%n_Bvyy*B*b4+a}?4M3)-QJUJ?MS*4JJ27mEj3E_))mi=eqb&&
zyMTIUShC}_WG#giOHP@K*Yce{En($ovS@}^DlAN^DWlT%^Wm&et?A-fO0>*un|Qzt
z^MT6r93MldPR&t4AYCc;tm#a%ROKv(Hf_i;ma=kH94CI+S|?`Ht7s4RbCIf~SY+(h
zML6T@xs3%CVar6kSDwzfUC6xF^J><aFMWO1r0)#NL!;ygxP;L1lNy6irdJ-|+|2*c
zJL$7~oL^nQe%1&)L5=U~iM%W_i2S>WZcg4vX5o?<H%cx9>b~QS<(`N>Ngdv9%h&Rj
ziwglhx<jHz?u&td8=fJjJ^p*+i`OJeK-~wBOhfHC?YjVng8VFIGfc-B*HJazU=HOR
zok_pYRvm|NG$*zbt=13E{B@nXYA%!agNja-BL~{z{ey>Xl?u&3(p8Gu8{MC?`p5NV
zAl{1A*wKD`g?I!?sabMoIf<_d5D<`(+?AWDq&r6Q#jf9PoGkqYbF)3Z|IZhlBh9=!
zrNrTMEf&XNC29W0B}y)qkm2Nl{{BI{ITVt@m+pIyc<b@@3KABUE|m-W2Q-^ZbWBWi
zF+e(00|5g`#<mcoa&(uHvJn2%L-w#pN=mAd=?Nha*>4pz{#HSCN@(hTzF6{P8E#^0
z*iZYgCX{F2V?Q}q!Z@TXu8W&53!F4b{l>`(=r78JQ80$YOy!4>2m&#8-I{{xes+L!
z8hZH2)^S`Eu|;{6*S?f*puJ*!ed|%0RefFI1OBS^6unY`e}v_dm$mM^0CaH6-?}%G
zAva1WmvF>OcT~ek@Qan_?Uhui*FdQH*SXF^WvH$(C=aTlTf|3KAB?m)-23!^ucI7A
zn;Fs-$TDcpyB03tf4%GewN+$I|Jg31@ec@{cCsfr=$C-Bf_%O@r)b_GQs*b(p+=FA
z!|agBU*GzFM4bC&6rQ~m|7emdJZg101KQTr1NnzEM`wzEyhJEV9RLE^;a=JfJ7msA
zDsGggA{YFkiNxMBJ0Dk)*)t!A64quh6S>a9AWjHKoKrJGPMR{;B=V%O=&seb__yy|
zGKXc}{PHp!Z=+>H)<8>)u*E1WJ27t$gw>4UX|wBYcCGV#_D~|Kcs8HR&{7d2$<I9a
zX2JUhgezRU+z!Fx$`Gf>1izY3M@LpW(sg4>OJZ0@W?6kd)}3|}8fF_MdgSPOUO%!V
zr;QUwP`GP8r(5rGKz}!XS#KZzD2tpA7BRpIz<bJvnAN)zPfqE93ZkcUTW?k!)axC>
z;4DiWVh;_Sp;Zt5eP;8I6M}!CNFo@x+^ip!t)ogoa9@7UGB7!k?V116e><STM@^Nx
zZc#I-x|y80M>^EPqbToSMFiP~H&k<6)`eVUtMU?MH8%Noa$@3QTPVc`^*l|!b%{rE
z_eaia0!s04{kk|K)SM4CJf((f!cARe&>>L?d2kyApSxI?txXJ8IA7BwR%_0L^zKhR
zraCF}GVq%h%Q}3|HD{Hjr7)zi@D<d<LX;t3Jn2W-Hn6z2*XbZ_6wQm`T`sDaMi7{R
z=BtfoNh^-yABtAy?&AELwo~Q7t`#MXb?c-kc@!b`q{JD$h#i;Gi@*?n0{`*-GRF!X
zf#Unb#?4{TK*ykQ-yo?>-%Q0rjQAPMtDA|m_n+Pp8q3JH>k{SiEb0Bfr6YuW19%(a
zE(+IOjDPi3;%z!RC0zaDX^=1laUdUQW~yb_<({ifF-a{AgX&_JC@Z!FNl5u?VH6F(
z=ehjGWN8)GiwJ=L&II@x3PgsWC_!+VHfCSpocKPRGRYVbujR8zEY_pH<f@lZCBbXy
zOsXG;h%++Bs1V)<cc>oQOTB`UgOYbbKUMOtvmMzL&YezRhT7b#ZOq(AO5Rw}DC&|A
zb0iity<a|o7eg>WluNrbF@R1`Sx3VFQ7W3Mn}Wc8;5*qRQh>1w@&$Kujppj?BTb!)
zX$wz6l0jxtx#JkXS^Cp+xD<7Q^m<=OIUNHn#6C1_u7W{jEb{>RXPS*B&+<Bv<tGic
z3r~J=qFbVMFX`a!BC+5WlG^B|EO8Fic&9iF|8yqSgI&8c1sw}W(nL9PNUBiW*j3z?
z<TbY}+Q-ibZBa<QSh(zCql&BvMm%hIG>pGUsMo4wL-h6I`kgb(1Yzm%%*6Vt5*28i
zS;Nbi<87()D#u|BsK=p9-i5=_6GKReNx{JIT&UsvyEo`58BC9<s7V-eFLoUvJf;Rl
z5f|!0kFr#LZ$(~M-P|h3-2^Y0oAk-fTIMStf7`X=m)b-1AME2LKp?}`F*YAG`pRFs
z5mRN><^LnxqSP?+ICLy*aoB!jLw0m=L-d{PJjDXmrTv$hO(>d&zVX8)8)-3v>aj=D
z&Vu}HG_LR3u}sv*K%GZGC!CMIDL8wS47JYn6MbOAGoi3YZ(NX<%;D`Z2Q*rnnGxlN
zu<GZhZyeE3)T;X!Xwe!`-u$*E7KUW#Y2g|^0O0w44a%A$=Kn+7TSmndWNo9369NQx
zx8UvscMVQ(cWAt^puyeU-CY}Zhv4q+0fGfWzRt`$Yi91cKi+SxyVm{JrM*wp-lytR
z?fpEn&F=ogk6cS`Np{)w4x3J~W^fYz{T1eBtGAK%m_`zPjb&E+H>f$`Ez5!4<IeYG
zq7H%~HQ_()1uQ-6jGgpRv+*Nsfk>ATo@50>=xY*D>pd!_MBLJ<0d6P^YB<yV8JQNf
z->#*D)xapG<(*2*N<Wo6>@}D$H)qLdG5q)IqpU`v{8`re{HVWQ*k9g)KyCDjEz8gu
zNi}q_GaRJ$Mbe=vB-sYnZyMTR8zY_{bIjk?#(eY*bE*zQIwo4jj?-gX8Vt3xj!#pN
zA01LjJX2q6sr!Zw7oX3}gRg<x%wO+V9+u?K&R*}?`?fZ_KYyC|-JI|X+#{$^=icg&
zIKvKM!ZGWvshv?{<lt|0ND7ELpd!|%Ejqm$dH0Pg=3Pzsw7y7w-iaxqtzPqGRIJv!
zuhS(Qqs8ydqzzl&99^7d4r=T8sH0#xc;9!Qhghwa=7S<{WN}#U6bMtT2kw*Dz=DN%
z17)!;$K8z>6XU?^fV~2Xh8)o;oCC9o5ftrqdrxy&HgbG+C#v7)4HA>x0-DGfTq>3h
zTF~6UG<*7d7*UKtjc9SMfu$C)$UgTsm5FQi`*|)VE{75J&f9`&Xl5y?)5MlEzEmki
z-lG|K5;)&z@C3GbK*cZ_JuGIJ9ciz5^20ToUy?$=(~MZ@f2FG3+pnkQ?Wu2J=#cBv
z%*}4h1_*uMLK<q3o1MC2{K&>GGLWZ-x04ec3kEHTI)X<NEZ7m_#u?wmapj|(Nec9(
zd@}gZ&|qLN-q4Ooq5VTFY6I>vDHp;<L&i31e?T<OnuMb=Rcr>i=s*%=hjVQoj{unS
z0cclQ{3jSxUii}5AS|I2W*>%auw8!OoBMQ38h-qn^uo3xaUS&`I39{%?bV$ybV|Kn
zj)NX-WRY^O(MS%LT!34&AKHWm-3m^wgDzC?gVJ(DO<>rW4>2MRF_U_~gnWl;^&qO@
zmEVQoM3uUDF~vR2QNIxBF}|zK{PcXB%k4Q+_rdPY&-&6Qb(0uZe4;=tnfWW+sv+<W
z_gP|;H0me)U~MKQhN-uK)J;cC)Y>o-o90x?C!d?dqHOw(V=GpZ*(_JPyAfbkyYnfh
ziYRPMMFd_ExkNvd7#gzRY{%8-9yY<ssM{qQmC4DDvGf*IEg>V5$RowP(y&fM85sfD
z3QT*FWCUi4MeUEHMG+w?6iFp?k!*CrgoN%<T`Zn`rWfDRkEt#`n%O(G>fDV-A;D_=
ztg`u##ze_J%X5L_4to!q!@*viqyiTviEK6rkW*p)3a>5t2S9wpavL5ej9;cfV9r}b
z9DMoEDkRduF+tn@jx97+Zo7kh^6nTFaqJ?Y$N?9MiRMOGw#X$EDXlDAO_rm~i4KVZ
zHUzxZfY(TI4T;+u-GYCM>(@kmm+tP>$MW^yp^P^}$(d?&gU#i$K|#M{kge1;L6=Ha
zV31w=c*a=3AZ$sgsd2r8FXB|od1WEdmS?{vy^u1wSweazdlThDjKWk_f~-J9R$8=|
zf7P^PN0Ow398z9|T%1Bi5Nm=-xbP19_=|g5x6<EfSfCGg*x9_s)6>kZ{<`hif~tBC
zilmk7VjG|SLQ|MJxwi^i)W{c#Yzno$e9~fLIYEJn%S#oPt)z&gfuo(Od3_5?(U`bu
zC~@(N&Sys!ti+mp#4)qy?3-+otdLnTZY65BW$qZ0R=Zp>Nl;7{NZQP!4et~4$4OYx
z{Ybi}5NWlZx|r#Hcv`7>5h~^dubd7;Nx{JFETX|djKG7zK#>PHaZM&ySLmhYlQTkt
z)<}ZP!G9>x^2FBdrX|tFBTBC4LDeCOpxpq8jVz1o9Zbl$-A9tT5mw(hNl<OEB%eho
zqp60vmq|r>gnvM<L_NkJ-j*&{8!u;jES-Vvh`RcG?FaZXP@w_ba63VcH?HY)HCScp
z9_&LPLp!XGRWYMml!;m3=$tPBp&h1f5r#_91S4;mK)B)3VQw;_7H9s`?j<X=oIzrn
znuF2|qQaBf0oBWqqiYnm)^MQ1vF3bId6BUVw&AZ^I8w@obd&|DUhV!N^eChBOm(8P
z;U-J!rbdz6euR6K#~;lXo%Oe&u*y;Jy$*Stj<q2;Rb%dRlZTW2ya_RBymG=V^s#p8
zp^9z9V&MRVkAD6Ww0`@YLy%h_ynl(M|L>QRWTjFKaw_<6FgOu2=5G44fx(Z7=!>-S
z6x<YTPlED1c38o3=Xw{K)tBLDq|^Oxk{pf)r+KGyWAWf&wg@Avq<IX1&gnzzi;wtm
z9O7B=_H5ceJK1VKl_i>dv-nZ7nHL0Zl!$da1mj}DX#pd@RmJ1VjH4CvqQJKQ!2QR|
z=Ma<3m*aaS@+h|j>CjSE^rhFPb_@Sjq~lb=*@(fakm6j88A(fYtkpC(v)lQI5>bao
z0&PLyM-@p_RfzXL#P|z6^~k*s8Qr5VvJ!JBu8e$(r`*>b6$jGE7oG6D^<M^eV>$;?
zMq5)AZdI-qqBYcW;>(aq6fjAYi;6y(33OWUq^kQ0jN%u#ZD#CJl_WZO9?JY+XU6qi
zEQ<0xIMPmjwJk2#DA{}w)u=d<R8~}ru=uW^!u0xIY8TaDr-?C$TwU5)J=V$Hnmmc)
zR*h@5(e8Vg;77$ahj9d4qhC)<@vskZV%FmOMI4CE@h*ypAVLGX|1<DKz{~s@$1|6X
z(q!kHe10-TJ93$Bgq);Guj-snHi%S4wi);wX}Uh6Q?;9MR+MqMsIW#<C|Oj(Xlrl$
zwbdSF*>qET_CQB<dMBqPr<1*iDv@VWlN~jEbYNOhTG*l;ncyc7jXWq&&Ho*;+B<Ft
zFa<;{gduA2roxV}FNjn$%4?DGQ<vA`y*#hQV4#F%mhUgcS^p8XK}{d3-SuQ?HAkcf
zJP}U7cK~!))DozTxEC~HT<{A375Xjc6>6d;X(jd>*l_&hW?Kh9lz;4U?i!H0Hcl+1
z6m(WrmLhTprR;`=67&S4LV|P%TJ+ra0IvI)<$kFV89OoS+Hu^B!~`4A`y2B_5J}dD
zwHH%5Wc5%|zo&1i!s_M4Q9K~XIwf<Bk=V2`CT@7=yl4_ja@T#0Kf_??y+<2zq2(wv
z>fzuyL4Ob)U3c}}h51q=HDPEAH0pA9y|Q_n_=&^aQE?x?XdDlYo0<)4mHfO&K`?Q;
zyuHrBe&jK&q3%Jkz3z&t8K{f1Kiu}P8t3k!%Z8iWgM`-jZq+Ie!xC?x7ejDD>I8oc
z*46k#qPzkw#;HCpb5`8!^(p8|x`&5zdXn<m?nB&8D^H`{msL1R`>M1(KZ~Vq=8}4f
zI31VUOErtj&cgYm`f!>CB_gtl3JR~qHr7w|?eHq;Bc2wHE6!&fjRqFETQQSkiRQq2
z!%zL=Iy_!W3>-A|5%DH^GpsQ#I4NVQ2=uPyNfq=ic=f{d@tAIr#xR7O`nuJJV^x$a
zUDa*d)O$tt-r<w;S-cYf38}TX03(X{T>hYtt|Xg|vXo0?6FylDrFbyhdx=UXpD$0f
z9H#5JjIxK`5Rk3?<GNwN$U4<g=!)x1u{tpOej9_^`J$#X{iv9T{1CT?IK4|rNibi~
z28Dd_d9~C(zfC`2!%C;u|9(zjlB&bI=HJfjR?6p&%6RM+h-xa%WnAUdqbYJ)HvrvF
z4x+1{#fuYF_bIa>!SP}#!J{>qxwk52Mj9Inp)@G{{9stpKh(LZQ)Bm(9$KwsV=>1A
z(?2=6>LjAV#=SwYC`Onf2rWRp3RudXuBVaQ6vo$1^W`DjVsE>>oZ(3-?f~+)6>NV+
z|Kc#LW3U$Db@*B_AuI`pCYi4*U3T0^Jl%(-0w!jQ*5^|4Sn35&b#mX}XFP`CHORHL
zI37_dO0L(=t!ch`B?8Sw!WdgEHGbaDQ$z#_jZH^N4f9&TLMOCw&Uj_7xGafp6Aa97
zOBPz15$T()fQnl|CZGJTKE*vc)`LrmNFJKBB;C;65R<^{^+;t1BDW|JlB%>nnd7+Z
zcBQ~xo$|mL2QA0V37t51<O^-~Cb(9&^+v~r#P2Fv%!<jKVG5a8l^_nMxe$iHX4AFI
zq5`^zGQ5a%NgSkBr4QaVd`bU06`=6cV!(d&hbg6o?qU-uM*q(HxtA!frezrMlz8q<
zYcu8v8-NPohdWpI7bt$M?I8X^4<MqPKAww1aD~vvnRdg8zyx`2{8EL-PS#Ri3c5Af
zb%_%V`)P>|VYXu1TO%0G&>Q~00ER6py{^!sAQAP&xVcBVQK1GxycINnMLidMi`z-C
zZ=@*@A`_RvV-4&C;rFqOt({#l)}D)*4@dwX(5H%l1I3M-=Y{xS1B=BfKgMBnwpX&J
z-#4|=C@lsb&<}2VxV;On8WTB!i^G|l_J6?T$J{LaOlTL_MdgbMxRu|BK;T5=P)qx-
z(hOQP<kyjWSwllKrVR&47pG|9xVcrg`dA;?NG5W@5Js^H9OD{X<1!}KcDZ=&?RFUO
zaj&}l3g2cHg;mX>%VnC)(twjQ2oO|WB_l#mbRqoV2Bv8Yy0{Z$*?7S+TxK@k5__=x
z&`}g39K1b39yGq(T+<nRB$+XZ?o*aN4LM4|<{;*vfQ}1CMIbq`uB61j?aWw~kdWdk
z@+2Ai;eUGZLrbT##tL#qpbLs6x_ZCuy}4UI-aY~~Ems$jKB}#`&_ZT<u)nu1#$dj|
zT>Tzi$B2~QA~UR+sCQIGfx4oid`(qCO@rn%DXTuMO3=wZWm>mYTF>-`JLkQ?YB~>@
z(rN>~=1%?bt-7rYGBL0(?-`qrTR-UH0)ml-JOpvONOu5s#@qC*h45q&($Mp;C>(P9
ztooci?s`Pgbo(lV*W$f+o?grY!MQ1A)I<I<yon1}_lx_+5(j}nY-&Kr4S|y}EPsJd
z7yqZtumrL(D-AUiI<Zunt-d}_sn{fVRW~xKC#Pf3ZyS^J_x4Kj-L~TU>sT-m=uvaA
z94NbKSKU;dig7G*dqd(S3r9miB;k2Q4M>c*&_XsQN|z}|XAic?k1-b2&s*iPUjQBO
z7-nY9#HANlQrGLyMUM_&rZ$-7DQsMm^tN^tG9_Xt-!a2st&bSMQuhCZ$CYnZR3ECx
z(tyFJ?%CNdz#@oGdo=Y+m`TM7>tae9biwXIu+~rMdbH(f-yxNh<Y`;yGyX{6$y=PN
zIJzOorx2NhnlV~JVUB^VqFm2NFYcl}M%QU|cx35K{?1%_1#~|x8wOnD5G0POvFaF~
zWuKQyC0FrfpTy=&jJM0z&#O<QU|OJMlC0ush)N=HK6KFP<W_3W15&fU2T2Ewx8f+;
zL}k4*B;oT8)R|6Y3~nnoPlt8=X_DfEX-sQUrJmrpQ)1_vsLX|{!X$Rp!@thMKH>7r
z6ji0FjppKg&drWEsnmS^!<V;>DxAtQwLoTDy6K}4mV)o%;fvBte1S$AmV{x3{rf8j
z4DfPvdep6SANHk_R0=mCC9HMv3?70$IjBNGmJ-c{zR*#HYJoCoI(`x<X<Y(<F`#kj
zGhNHzd@`V=_BA(#QqQPT_LbUydahn?BpE!%T9dC|r?DP6E&EdG)0ZMAJAeSxbZsi2
zYv=eKm<*lDuDE#&a&uH1^H$>R?qas(0!o^$Gqds5#oS4@A_#bVmg4ZJ6ZT%;M%SHk
zRd-d*6E8~OCsPY7RZnE9EZ|!9+z{}w)U#7?H#;b^1J0@L0Gpq515tjSGXw&{+8EZY
z_t4#$itce{)cN)EP6Wwg!j%I2)zo(-Lkd{hw}kpcJ5lw=JSXt>&k<<XI9iqrJIsyO
zbuJfz!PHRmdmC!Exct#cccQqQ_AM1b3iVN+SEsc?&;&J0&UVsSCEf`8czs>i4Ko*a
zZRZxIv>FMUzg}boF?CtW^4Z$<YS)(y@M(If<XzS^s<3CWsGkzK9+T0C!X}3Ewqz0?
zGFqk@VS($VB<tCI>XIVjkv2ST4TP)%^b6D%G|F?N+ML4}#i~TvxLf>@p~?HP%**j<
z1aPjs6E4riiLT|tsi|6B#?V^3#YbBBs@D_FS}pBQml_nbfHzgOHmzCh@R7qkl*1Dx
zy9t$K@2E`iMHw}T5t$}gzC?<>hG?<tRO+R^*@SG-8`yA9;GlP>>&9Muym1yq3Z6De
z#vNacsN_trMVry`C@3qNl!0TfOmE>eB%lnv@yU$AOw-xbT#I5?)q*}ru7kkWn;Xao
z!WXM3i$u0N76eJ48YvXO!Vp@juqO%6=6x4DaHsI@jQ(+S(&n6GX2&7#<Du(}IqQn!
zwn`i%TU4zZsQAtTKCSUUD~us@7Ktk3oZiLiDLk2K9-`8bQ$RR6>X7OzF2Xblrjsx4
zRey|ZQt4L!1G;+R!nu=|Q4mKT@shuRM#s3NU`zP(i4xW4zmOyiBQ<jfl9S7c#RV^$
z1w*H*Ec<iL$Ho-0fIqNX`siNB5_ckVrKXp@cW@wzK(2lH%rd~$S+EFntHo_FqRZak
z$k(=h-_e@au9qyK*Q%hvKxJBFt9eI5mpym<@m8|cOa5?(iag_^X?^GJmN-&^8j}-*
z@*VW00F#pC%93tWi57B-+Tj1Qn3hH=BTW&L!}Te2e>O$Xecis6Q0cgdL`HG25nO%T
zvR<xhzE(w*QS#hSt?oqfj1EWGu22!4p#H5%+#iou?Av6f>7uo@c4J!pPa&=3l!(D+
z(P@zu!mYTeTp4j5)t}!w6Qi088$EH7c&zS_RBn+T%=_jkiP=j+z_Xo~X(QgXQ)}Ao
z(<W`VYVXr(-hFh$S&9hc)PCophX!(Ge9hO-v5@H>Q(Ysg)D#othj*+a?mwg_OHWVP
zQ9&nh9kRbuV$B{=3Yo?#E;6B(OA&2N*LRF4|G+`Y8G(`V8dyE&v8pzb6iTNds|vU*
zsF25sz^Z4W88E2H>({1-q19Pc!uL+uY@Na<runsq<`s_}U=8bS-dF_0iil3y&;=c_
z!hP19;l@G;CFf^j+*qmJaJLYGPG~1(`3koanMtrdsHrPa2+c`x$sqcB?rq!b=WhrO
z7ch>z=9tRyM_xEKImRs5f^IUOMpsM1CghQ@cH~;kczER&LsLT4E=PulbZaTgXNgIk
z8if+IDhThB^U#;}lwe=_`Z!6I6LnM_d9;2xMmb8$w#1-37mc(R=8A{uiIumbf$Fs&
zJPaxNMQ{;|bvaJ&>+LzsnM~|8wCiUq;Ja_2BAKblii$R_N;Ma2#~h}VVYFrA8t?Mx
zb8j=k`v=J`Ag#yzfON(48WTQ6(pS|6H6(6Pc&Dj3=<K-?wg_|BwOo;?$r2lK(f2jJ
zy0MIP-i3;gE1Jk;eRs-(f849P&)l7xh&Cf=enzkwo)dYcxxu`ScIvSK!;XEq*NZ#;
zfN|gQAz6DB(X8~#Nv-blsetFiBf9<33~}rHH);|t4e08|I(jVmn*3t3f)e4;797py
zHAB4Vo0jWyZqe5uwf6<H9^W3`^ZkwZrwIgR5=%^iYQI!%Dnl()pbe2=MB``|qsO*_
zt4p;Ms^iwYsJ;7<poPxTThR46glzHYj})^^@7JM%#mG)QEUk;2P!iLz0k9Gw-|6+1
z1zXK^8{OUZ9mRA&#f><fha)a}F>$7(Bx4O0+7Q=TecA+Cig+X%{s=rIRsXOsIK&{&
z5SKUfEt(A<ccZLYBl8X$`Y+$v8*yL@WjS>ODP)<Mh$A)Bu{2LoDf}e?uxe~d0BSY~
ziGIVpmLUa6r3S_*sw~$@&kg2<*u7NQeu_%-1-nC54a%$n*A$7<Eby6Hk{mxGqOz<0
zCTPKD48c+;C*0a?V>SVk`yRaOI(mODQ&_JgJE6H}#2?<wndzuLgn~$qX^2S6{y$r2
zalRSeHD09Ys1D|ohC5WC?$LX^FH6ZSn#YM*ZCBddH>~yOhoId8K*2;i46$S~g-n44
zj{1Qj2i|*hEyMSg5M<9m!<fC*v2`B!kE0D9xpZr|WGx7BF7>ipphkD3CdX57Sk1U-
z<<bOAUSM>p%pNRjS}-P}H<CkNWMlA5G+Dk;Euk~Zr!Sc4kjMh?36&?F!NYyqn_yB>
zlrm?WD;SzizR@d?c}`X%omo;&UuKunW|AAdMXx@`eDy0{P~;y#P}--j6SQOE6@9l>
zI5<+G7rN52GG!~(IBXjuRpK-WC4Ag&d~)#2^|T2cP12nI`gp)XC#wGmp^vD5kBuTg
za7prY)cVUG;c~yF4Qvz0;o)yW5Y|6TIsewa?^5<3KOy`P|GbDHkw=yJ%OAmO`+vzF
z@&D0hf*AA~FZZ;)k^`dfv!ec!eZ%t`b;GAx&g!Yd)Vj;DkIPDe0p`G(w__hmFZdz_
zW75`Eb7GD(J_5n?75*J#NduT<4Uq{%>H~j`dx@Rf&0<VmC58s=QlxeEvG$n3rC0Ou
zsQNT|^g?S6-){|!1s_z|BYJF1+UdlbsB)I(OA6>)PhHt;sMp->(tmbLK1PJA4o)s}
z#afB!%rH)Rb!%WzCz%1)DJsvy3wrN6U4rCH>Ak-qn|Daff>i^WFn(;Tt91-h$mn0~
zV%SNwno%YdkGXcVWbsAd3|XWsOwADDT#jeA@5#2XgZe{2yIhNs?J;TZM$`iBhSmhS
ziF%sd1Gku!9MJWbtwigs-B;Vbu)9W7+~zN8SWz9UmW2#7W?kYsXO`?1^qXWi46%KS
zGvzQ+Ov<r&Thw?=mV@8>R?p09Hsc{*N=lpWWnqUV8kIbY4a{>x%t{)C&g6K<n1-za
zCkHL`SsBSfcK*=bq4DO4tCICl{JzZwV@aOW9WhdFe#|4<eEb|`OPg7XMnben{iIE%
z_0xG|&PmHQ6&*g8DY2Y@s}GVR96`H)IE`xY90LVwVb$5><Y(p?wX5gNn(ygj64Nbr
zSR)LBRnyMf_S~2Kjxd7w@0@g1)EXGqDa@0#ll0eqL&ufUVUb5R0SaU)Qc5Oe7Sp1t
z!#A$c*H;K;Mmz$S9dCXLhFTHeHO%P}m%n7gS>6Ks*pssJ&x?~BTY8~D)BFH<e<H5S
z;v_E*+dG@(@mCkGt1df(y6EZOmYI{gUF$q?Swl~=Xbo5D)mBfs#<xb#4?@iXx-mIA
zC@<0KCy`}HndGGa1eC9*KAQp*rnpJ@zeI)FRQ>?&7r;I6c7{V<G1-gN5nGnaL+4UW
z*YemQeB0c{ou6W^R)&u=81;w~E<$or>zKwE;w7<EH}z!d4>oz(%K`pWP8d)!aI4T#
zSwP#`$3P#)o@LduSNwWE?31p=(*MqI*Q{~OEqZtU)xd%rA@fx4$f_yRRUhkNGfFkW
zLOq!GUEm6>zg^gmbBU|VjdY&E`@3HYOejY@5zTBZ&-3;-i}l&SbFV%JTsX5&HdzdG
z&+xMnb~o~FeakY{2?~h$Nvv65JOv8=uxJ76dRRY?kIV0WyD``HW#Zl#L~iQ*cSfk2
z?bqL5NWi(;4?f%HAV$Gdp4xT?A_t!X^zaU*<eh4qAdi*!>nvy~$Np`roq#zC72@Fc
zH5nfOVHzPU^I;LK9WUySzu)TB{01R_WIFf5$+bAvF25_h-~Ir;l=tk}T3#{Pwl{Og
z{&$u@rNUeqTWM9T&+`k5hqkrdGv}4q$iPKSr2a_8<<A#}``U97Y{}m8L9`0QhBW7t
zaetGLt?%%&awgMfn-jQlq;HE5jugKt_3az3#z&l57aNPj|B~ddzj4}rj}GeZJ8kHp
z(8Mhy$kJ22k55RpGK;e+DuL@2C{IJf$ff`=NfTE_BS^K@m5eO@^*E`*8$MMLJe6Tm
zeJU?btnl$G*ZXuRkHvSe%8o+?2S%{!#O;rsVXGw3;^{6lbZDBegP(xWc$)*RQKaEN
zq{K3&pL$4Go6R_qe|Hn$W&AWIc_SodZ&wF3pv*_`>DAd>R*BJY6q1QjFynY-x};Nv
zCsiwq+i&3Fw~nk=Ycp#!QC{Fr4n7!q5Vm{|vq|zaAvH`;sxkToi=RwE!#YJ37e&+S
zh~DlYt(YIYu6!8Y0U~qiPGOp8QyOn_>Iwzd;B>TtTpYEhGV7TZIh=%AlYg4wiWG5v
z)757%cLDPU=3&3YD}<h|3zADZ7IkqIQvb^@1>)EFFNeebooDgT$|c^K3=tPkSngNb
z=ogKhrB`Qs4m7WUTavr9n2pr*?mTy%!SyCdWN(dB^5`TW)4{$Grm%$gS-L}7hq7K~
zI2E!v${>KxH9FdC*ppt`S#aBV>$(p2^4VyJyXB5J8azm7DuX_l^a}rgU%+<z-I`1d
zU;0!G9PM~=dS=2gYZ^u&PDDS=IBw`+*Y_dGJ-=}(hyAh-zFS87L3<1Yw=9%@vl*1)
z)0D@IGygy|iN;`}AhnkG`5i7L@(*3%2;iBz>Z!7#a{{^X*t68hH*ig5A<WU&bXOzm
zl_+7Ot(OrC!W!R2R@0+rT(55>?YCK+oIm@~j2WZUopGVm*w5=UxYS+YzUVLLI6T=o
z5Z%2d`gqmUO`Rtlm~k-Lm*?13r6y;Kap$D#xZ|$1=SRhL@KNoM6@SFEw;j&AnLCap
zFUF9Rl#B1{lXJlyinaKC(h3?+ZF0L0za}gt0d6`S*$VY;llt1plHIO{r^Z;Cn?2*b
z_hH!M(wx>Q#OIX(&9O&Mr>k9#HsAs%%2g`S0&c4JI~SK`b9CDpLvDRaaXL1KT3ABe
z$SHA2opiN)f8g4eJvd*qj&4@4D;;0BQchp!Q&!LjD~T1gL_q6gP!MU((5<bEFocjz
zuTKxxZ5G0h@})L0orid-n;ui&DXFzOq+7<vdVAFtlQX51c+J+2JAVtz+b>WNE8;@c
zJh_T@t#rR`w?T<cWT`staLd_dSzqryeA&|YJz2Rjg7a|gaHQSTVW!lPp2B3A8ZU`p
z%UJGzsNJr?rt6PWbm3jEGWic^3l!AtZS*Z|nvKnp`-TEiz3E5$ZGyoqn!Tn1Ir^^n
zzz9_-5?4GP04A^VSk$lDIiRAt-c%oz|9Dm~rsg<d-^$hPw>F;c%#JGLVuG0^;x5?J
zixD>TptUCZv`Kw*8%e!X7k4Ec794F3?X!w-)U+D4HmitJs`4b*A&?7s^?a-AWcY?}
zC85n-ou!@O#o;FCVu&x$LJ<x<I<Y;)zr_ww^I^X+o_(CtcaH9&JS)E`f$9WPU2bPD
z!_5Lz6n*;R;r34Va%DA?bhmZY5L(QXLF)SijvRZ-q)n@s{nhhwL~D$J(te~iS6(d5
zoZbl^IV3_Uq8d3b?T|dUF_2^f<68B8%ja^L_mNhfnrfLr%Jik3E>lhw=_=Yi;h<#y
zV`)OmE!EEeVo6E*BvYukV5>}51zM;G8oK~RgGZ&p_<tGn|BoKT5Im9~@llq3BI5(>
zy#l$lkMl*h6ISmqe-p>}xIsLK)dp@W-KeiTvaaqq`XtSgcJ<&i+O%WwT;!+A-R=X#
z?Y`zFfB7088aZ;T#v0Q=RKRv=5^P3e;99C>MzJFFmArBIBfJbBhI>oXa{FfO=MSzD
zQwGdDO6LW%;lp06a$-@$OH|CI3WdOFD`+y4G?7p1y%g|hBtN3P8dtk^A8a4x<Fcb!
z-){6nFg*?4J3C6NCR*A)wzetH8=4etTT<mr`JTEQKlPGFph3=u4iB-IR;Q4mw(cBI
zWV$y`FP5)TM`xsn04Q*7S^St309%ktC3mJ7is@JMRTS}vDo78tjfP;&2c_zhV~m(J
z5d$hG&Uzx5iYn~EvhrCfbv1G*O$2BvvBZRUPBO%_Uj0HmDXHR)>3vnuV2!a*?=Tdi
zeA~zh0O~!+T|WW!yWdc!Xi3*uV>$3{wRV%GzCHbSMQ59b__MI!(8b%Mdxhk~?YKAB
zOVRuEQb3U6em_6pm$2X?e{;^%IYipTIncM#mC6xOVi-t^NB;DSt-nv`teadOl=mfb
zP~vEn7Ov+Lf2<<_nm@7J;_sg@u9F(F#`6#;9pD?F=saksoPKur9A=LQz~W>ydbQad
zG{m$gKihQ$MxL`yG7)x<hP69)PUf3u6@E3mtg&*zSZ_;Gu8wc6E;GgUX;Zz(ZIY-U
z15gk`t1PRaeL$7<;cx66=N)vNtvW_LAsvI@(6(cZk?G||#Zc4fC?RI8!vUI)pCr5k
z9w^eOiQrX+#oz9;c6Z)vXC8%rO0vtfBNs+VD-ujQ#ss{k=0euD1l}i=VSd1a_7tq^
zhl9>PfQ<;V_?i9?_FdEoz8bLg2_y^e58wonJJKJ*0fojP+lvu1=5O!$2KB&sC+(F^
z<?^F2%EM=Zub2PBt%2W_B)iN(`)2|gBR<0ExYIP)5~f-qXqXK&w6GllvxM6E@u&UJ
zv5ik&A8svnAm_G!dmcTsb5>zf$R32Jy-0&!Y4bo>`F&C%`yu$N)4;(M3DkGLL>>f)
zU~xqCF}!X{yF0`P&m4|Eqb0KR`@5z@@ZCx~|IQ+n8ecgmATz7M2p3m@YGFo2@q;|M
z%-@Qkh#0#loZR_#8$=|%Z$H%-!wiLVI_j<kEm`Yaf?K@|KtcM0OV`#&-xzMG0>~ym
ztZdR2Q8<m$)26HN8@arb^GWo#Pbz`ermz~g&|_)$<r-Keg<4HJ+6y1TcQwqV(WS5J
z$sw_wuAeHPLi#0pL)6WuWwOY9#PYdu7Jd`}h^cC@IFwuNPkT@Cd4Ij&M`$M%Gm#4Y
z+wMq1wMnfM{jflCun*wApOR#@4C3CDs7>+H--s;JE|VUs1YVYdYXljXefF~X@EkD4
zn~1*gFm!A~^&{n=vFKK>{0nlVTZW)YrEG8!AyvUBzXk`bdhg3eR=|nzVv3faZsU(J
z6US%UN`EfWgTs-{KY&^FG1{-}U$TUB&UR3wpZS~~{eEh&%b=Rp%MVvC5XA}KIq&b0
zkpDg_ktO<DPoHbCpCI(sdH;dyMNO+CerA9=yzj8425mt&F_m<qBAv?V?_YR&R|avt
z0rkEb4ylh(=7uofaZ6}`f3ktbDIu9;&}0;(Cf*BU<o~E^3#t17BF-ayI+YMUqtXW^
zH^-3t*Q~a;Lp7=xNY3ZN%}?aDe<gXNhsg2D_9YJvUGx0=4h?N|yVDPU8)))}kDZ+V
zFWvn^7=+KRKW_X!G7Gn>|Dy_O29q0VVV*Ta62E_nCx9!KSqyGbpiqr1OeIAOCCN4E
z_&G*~pD;+wsdKgqv3)+L-{U2+fXNiH(}HEKzh*ZPdj%P&{oQ}oA(j@I+xVxzzIYe<
zNWhe{Rkk31p%^_cM1R2P%i>60FuM%ASv`Ux4!BKP<V{J>&$&E7go8Sa->P}BpC17-
zrZ35VP0_6G=W;2yi3IRJb)=UEup=92PJvx<+`XBz6S4k2v0FL!>@p46&J{>y8d@tV
zM#>qGqaoDq{&s01;;~`k2KP41qLTdA^etU15QYcgY}wAg!EHm3;I<bs$HT{nTwKH-
zn#^leB@h()p?~gV3I3Cb1PR%fX1+O(il#WPy3!X%b>H~|5T7>1K9kqC9u$P_)j$__
zp2a;PP9)(Wy_z>l`!E0BzaFoMGNaFu;?lz?0D+^uLpUJKw<0%QjoV7wc?8@yEy#-%
zBVb`yYrMKvfynS#@60kVFv51;Q=|FR*DqUP|NVH$(p(N|ytwn1AB2(HQO^U~^J}Sg
zrYqNd()%ej{~J^<u0+-pZA?J8L|F})$xUdSRuFB%>HFq(9e$_YaOryx?^pWsHn_dl
z9@n<B2;p-`a*<3^cCMe{Y}aWvFO6&dNPb{f6Bq@D=F#(UQwb8}?6>^mNj+>3YHcGH
z(k4pyyzOrrsJ;Eh%2b#NDF|TfbTOHR=vu$y;zYnBeZMeSL`*lXGsJh64m9*EbmmA#
zhbJMt=om|1Bt`ZC_u!>?1W9W74*-H{F$dGaEI`oi$X{gq2cSQ&s~X3^18S>K)T<FX
zR`&27QWp8jd}wjftAb$bETYd#O6BnIRR~+2ER_n>6OZn?<EGW|2apKq2M~)(5($xZ
z{B+=N21Fj4OH=0WaN~Q>CrJE{Zfv{=$=YCwz;NO!K~iuQYDF002lu{Xb%=wBbY!gL
zRVgtrm$dI#^AzyUB+Q}f?jOJy#Lt_)8B|yE9d06E@jGs4$M{FUZ6yTdJDj-sple~j
zdj7Wqe{r0A!Y4g17B^l}hSCpjpZQy5NTF|h0$<8Uf{d0AaI@t^D%X}VNwpa6cL@9{
zc79YSNFp#3S0RA`&N~32a1od`f?R(-jYwld1P{nny;=;Mi14yYWidHSr{OrH7wA0F
z^Y(=zs4bR5pN0JZ<q!88B-pZHd1a?-y&ZLz%rMeYv<UV6z8{e4?da40qt@A}jL0?a
zZTFc|ZyCa`D>pirZ;&3(PJ6BXCO;q*4JvCoUmEE<A~1xEn-jyd_bM@KxU|}oMK(V}
zXgzx4UH<@%Tq>){i6V!deb1a0vHX@Gx=@7rSAX1icVr6RT0{HG*|4xRUF(R}`1Lrg
z!rKH6KCAp_d$Pr`*H*)gy#2kfUqSm;GxH%>pAK=yfMxtqJ_a_j)VmilwtSHCu7w>=
znF8W#7|1X|$l*2!`1Axv)0>`NJ_+IaCIv_sur!z2IF(8cm2wOhlR<bMh_m(Dh*f!B
zgdmR-2Wq>pIUP|q{;k;U1{skTf#wzhh<?%K4|>0;0MQTKD{-XgOa8NwYY{{T|DzAU
z-;_S@uFOE~uWmPpu4xK1gSop;oyKik2)hL-Iw{AiR`DvrKIwtnoOj%0wlAI+=i*{S
z_x8*nlM(tkah$}B4j^qCD_L1eY!wR8s+}L@GS~rYn=qo63}==|fXAOFO@F7xgf@O}
zf7ZLa_gSCU@78k>EQ0=@X~+aaloO{W>qGK<=pVpG$aqwsE}am17x#vB@FKn%Nq_6V
zo{OXVe>Ngv7Q_YrN24bQt<bxxQWCU~!hf0qNa51RuIwR|;M-Ij+5@*@->#Du1E?bW
zABwWWsSXg>!bJ$)s%Z*xbdP*CbmK+A2A~M!K=QP~toXmH$`{N|6!I76AdX$aj@+0#
z`vk`V8pl@;zKvhqz#y-BcUxU|`^(3$2~QY2zKQQkTjvi|=F=6*)l|um&K*TW8!D)X
zbX95g?^M|U$oh(9rz#zGRT^ACzwRacZ7qzARTl!DZpIc(c(L@sRjIK_6;lU2&Eg3F
z$_&$c$B{Ddf!!h40fVm>hfNLvfDRK7p{m%F!JVyrB4P6Bt!h$7SwF4z(?em9+<d-J
zCIcv*_hX&CxVXI)r+JNT_q9u8YkbFJXal1~tAyForFQ!RY*!b(>^IlkN$HXL1ji#}
zozX6V5PjqTP8HQ69}zbNh)4KTBcYm<=xE9t?@0Ow>WTD)C{&K7Vh6(bDqOLSZirij
zCxR7OMkIx(BV8VuD$yZIH5VEx0aj}KllO9dM~+r1WOFe?*m6A^RKIn%+-bnm-@eba
z!nj|hoeqg0$)a+`h1DNnSBn1?mKGL<CdZFA%qAnAR`SyB%_uvnL7ftyWWsnCy$&;i
zHr6IHJul%3T><KKQZ6FSJACK5TZm;VbS7}H`}?}pVI*_zi&S>Q&9Fj3MtCcMxkyZ8
zBK;37tgePro_E{{@j!T$1^N;R#P*=Ea0!tTbUKN3rnI>1;4q4Dc?C%+ngBKSGnqD7
zj5us%3>gJ_B7cBO7#8>@7GT3EDSd~|CT)_GRpjcN01Sw-Cap=JacuvlTHsIX91($<
z1;<>tm3L_EQ8I_Trzbm-&j1+`>0!nObH{cJ0Jc&D4fpD}2#K=4qq`MnsxR>$09(x-
zdYk>!OO#8Ce%1^hEt?Qe1CLK|Y<8wMJIJO>7P`)l1EqBi8=Ve{l4#EuusR<St!uB1
zbC$UW|I;_)+vW;S`(}VVpVgoa&(_kMwN?2iYqB`j;7)a#!=Ym{c#^Ya%feIDd0Pj6
z#U}g2(-oN}Qh~(uh*(=ZSp1JHSrifWzZCg}vd)O|a+dNNo5-v2ZS5i84{wYky5m=e
z;Ef4^>ihf7-=*$b9uND$l7V$7tJ+SNX2pv1&$3vW3L28D-n<2Y#jNK|Ch&pO6HM8Z
z5A^u;m|nBuT?$$js<fimVE}l{A^>Fz0H!9tuNWQmNN}HZ34YL%gYTf@h@OF956}FV
z<#}{ELFPiuArGJ2nn;=<+Xb&zwMg~dU_Vqb4teyx{!#TsY2wDdw+oR4O?~h{oOS^V
z^ORUga4OaG*M39^X(jBlF3v`i6L&>08SFTjfT=jWm$fhgS?^pGO#!JCO?_rL#nd=G
znFf-h!dS#0ZyYi_aXb`>5E3<U2zz{kl*gA-9F{4fA2)MwW)(IOsEBZwd39*RE6RCB
z=yXkJ3%s;)yZ<^42oS}R1AmD<K+<AJLi#xbo!Z{-3phl81J2F~487KZv*08A;j?Gn
zf9tPF3PQc{W4J1XD#(K>b?N{69wZ{(gZQQ?!AL@AijRZ)cGc5*jkUGiP^>=Fm?yKj
znh3xQT@RaHM7uOvgNC_cUa@@nY<0qI|CivUp_Uux5WxC-q*-cvbnepIS6N&z(DDPe
z`yo+SW-0`T!<fbOb^2aK8=br6Q_st&bkrXJyTQH1??8XNjaZhKccD?QZ}(J2&mXAP
zzBH^e&3jFe@H1ZP^s&k|FzX9=lauWbu*MoqRmfHCP6Q{@VJfnR2YGn-OEnjl@+dM5
zgWAu|*3FwcET*2Ww5whrHw-)y!7(eTpQ!f{96mb9ucs>LKHbM#9Gb`3?Q*3`5Vk3*
z6*&iFmgS4cK~5KX$#pxVPNL=;$A@##?U&70U7e|2Vb_fh?;j~P=YOLZ+&lM}g2V9E
z5+~mJfv4@i&pHd^Fr7d3T+m$Q=5AF0XbwF6Rungdg}X$x>!XlL51SwPI{`G$$pR2h
zyZ1zs7mDo!Rf8AvylExn0Yt*SrBdk%4cXAtn1mv0{1I3|s2q4AkQ-m<;*`lLCi$AM
zi19N~)}J~oFFa`Pp1XDn@{jQMi3O(|c)XQ>5YRv7xhD}9_zv6yiCK944kJ+HKF`HC
zf?T_7g9hx{d0#I-DEs|O_uZqd++CZuV)n7y%R+6Y*I0aO)=-)^83a+{>)3`}%|$Ug
zdfJ;1KaI*MHBqufST-1yphbfsD-@JQSi~mMAwt3LxYX2Ud))wRHM1X@F^ry{2Q&4&
z>#!h2IX#6#F0hY5O7nLK2#T-|_@+#DLSBmJ?h~<#F6*^zhUrPl^1}M~dFoozKhS{^
zq5&2pr_WtyNY${L0>UgcweS1NpFAb5ArMNem1nQbH_@q%W`tXAh=g+AV|0LC+A}gH
zMVn}}nPO-mY}3XEalue*QDn*x^ww&t)2W{=n+?a6mG&u9kBSud^3rqyqM0SNbIz;N
z<Chy`PW(1B&T)f$sLE(;3Mx=gsH|1POS{Snz^Lw%hoh_96&A0i3J2o%+}Fh6sg9Z*
zN^tPOkoa|2m^7ILa}))qd9fr2(K)@G!cOFmBc~C8{#EL+FBLwMmw9jI3#Qr#_9XSZ
zRg2;!?Qk6nXzGw<yew7pN=wmz<WitvJE-l)t479><$T6SJVTb^VQ0@p-{40Ggm6|z
zfoIjKk(dRcDeS01w>+i6()#-Nb`&K~DX<V8K{+y8x=3P_#$F8)SKUmULxPEETbw00
zZ+0d<6<#>wj%j8imGpWVQS1i0r91I9QHuY(d3G#75^QM#%sx(q@R1OL{W65T8iAu&
zF*LVZh7U$px!GIQ0fT?5PZH1Hbu19k{-eOq?Dgtic7*|q2?~B~$x-Me$o%G?eRgQE
zsDjSJ0?}}T)93873;*zTU&9BC$Ip}3%CL$Pw-79>RIsn5S761SeWRz}V+JwAGMMjO
z=BZ^QHhLg7>|J`~UYUpZANGy_ZR-5mb3uKTo4%D-)gE2`wdZAu`Lrt#vKy48*Ogj-
z9Jvr0dkej(G=c_7r~VGwoWFZn0Q}ps@+zFT2R^gB-3dcBz!DHp_@85{P&}0Xu)^ox
zXD9z@c)gG}iK{QfJRt>BFL0*eVD22l-)E(emfW@6^%jfCWdH8CrVLB5DKzy#p!B)^
zTG>&YaP--4wJY&XV3A4-ipSFO@M)87p!_X)t4g;?g)H-LX(-s&!3zupyCn@7Pr>;+
z?*-_0ow2FE(;(0NSjGSqy}I`7_qdlm|2i&shD7ym3s6R+xfL>eFuuyo+{$~xBrIe}
z{U0^_*5Jfrp*wMU_5CXINU?87?+?I3g*S7;({J?~*w-EMZ~rlS7%1gs_3jgEFJ!(X
zq-meOg|uxp8$u>tcY%`k4?w5niVx$Zo#$x?5=L@}sad(v-bRQ&uXi%Z+o4yrL~f1f
zVI3VZQ3S7I7NvwvZ`!vxQ5}D4;PvInn~QrhWtv92w_|Dht*Y#UF4mRS_@pyRHFJ#i
zC^u)8Hjbs|!6<JMp`22a11rECs1TOzg9NRJu27zFc<k7Mog=p0dcA7D`55vx1nXlB
zKUz=NKDNFrcCEWQzIJ4jJX)Mw*VJdvF_ShyvoLyb8#-oR2C(rm)bwR{>M+3AVpw7e
zbrez_s`C#0wZ1U(luCT?C0*XxLL5oIPIIx^NtD%|HvvdUcExn3Lawo(TNEqhe2F%Q
z*kQ@Sv1LE%h;kbBTj5zu^*+jzGPkv1z>~q7aWGQ?OJMoYLLoFEg96@8nZMwaMhU->
z{VUD-XC)ydzD;4e=eEt^kuHsqoT{`>RNG70DUbC7d=EoyNAC7p?CY7{{!GeNhilHa
z)r1PY^|n~()miV>geVh<TT1z0OJ>EgEkM(Q>z2B$5FW*<W%sOQxDKYNfCeJNYB>W2
z_Wt#!J9?qE^nNA2_*f<9Uz4p8MM0(yYQg9@H?t=tQ&{}s{_%o<BU`KjSY{@$IS=iI
zF3h3e>=l-|>ZYHNK!bCM<Z%0EicY3wmUI>;V`H5qrVeUMTy!TPyF|<I>2V4pNpN@?
zERxtIPFu@oB%rgv<VTu608b+HIA?^s+`>aRVhfD7h#YcVGdvz#SZ~f*`trg#r4g(W
zN)yz%Q>dUpT6@4O&E@*aot~dPk3-n;Z_AGI>(8Cl$<Dt@>PrMRt}rHBnNPK59}D5(
zr1o1gnD7!pg1|(bir7e}t8&g%a1i<gYVyc~tzn+9Ui_ootvjyPQIG*>`1$Oi+i{`h
z5ZCj@{4$yS6EdvbSF(Cr8#7IOjIHLq*Zz8JsY862+!A3c-69QrWED&-Z?#{dnuAI6
zq*@AlCk|*!-y7UV9h}X)*WD~sr&$ENsClJ}omdc?1f-`4z%tUK6|lr8B^l9pbJ^9Z
z;EQ!FZlHB~ZP)kfYwZ-!7K2k%hp9tf1hH3d9C%ZnKx)}Q<l6pR(Yh_{K8q(l-0mqx
zn}_a~#*5Ibgn|fx)aI1y7;niL_oa8wvHcN`@w<thCARLvyDlZWZuZU01ZP+wYLhCp
zut|q^JW*AAh@ol|1Y|>UOb9z>8C7ne$Z^&n+my_>l#7?1Yjj7`sqq^R6UJffOnGRB
z-7wRgt4I?QVwvO`Li>JDr+kV-Seewrq8XBA^@6&%RlgYCo(cie{5uEIgNe0g@x?T5
znzNV51AN8rKI+=J@6F|fRF}H#2Di5qZVGvu?MBywswnl7&qiYCkUE_M%M}`oHuiR4
zgJ|%YG&eU>49V8L*EmLW!DHO@4%=$G$COoiy5a1K@xYd`mgE=fiDJZnYo2)wT#X^5
z!5A;?05vUX?1*%QcWJ|1>}#3F$Oq?3qg(d&Lyo@o5VE#EfQmch^`a&w2GCCHQE?$<
zgGr>>IS7spr#g#3bezh!d_8fTSLEnq+__3b(BdPJrsDpONw>q0E{nwOYM%|BZ-eWR
z8y#gPgpx;siOn^mMmdvC&l%HRRmIJ~$r8&6CVXJ41|8finLj<WOIoiptyMi|F{tZ!
zW5CTra6!=a<$UF(Yu|k(6G4AH$F%$@F*&K=VWOkG;~69`q@kflAwAQ_cB&8Hg-gQB
z8s^0p6(qU>*FBg{;dk5yM?CqRx%TWY5^X#e^7ty(8}9r~!Thrf0x(zn2S8FrVl}m$
z#HjT|%<^<Eawi3R%{S@kJ@p%@K*->5KYWk<h9LhYsQhU*vxoLe?GJv#o3ntUsLf}w
zA-{mRKY&<u+%Ga0MO%sut<?KQ6XLiI&2>R7MW!3M>eKIj0++!Dql&21m^J~;05lX>
zkUt}Pfdj9sr+uzF9_OL=#|P*RZ9K{%C!Q3@>e`6l3R#z}Q2)Jn=MnvL|F9`6&?Opt
zC`u|(+N@>8{l(eSaM$j4+}!^yowJ<pui`-xQNQ<*Wm+-e434p(6Vm0hPeLErAv6R3
zNU${D@Fh!{g$9o>k$Qfe;p`lTtfT-^n3Pym2kZSiI?B(ji$g6!cEdO&3?0{{Pir>U
zqZepHsS7etpNZW#Eg7h>@?R^BS!p$G3RII0`I7iV0ueCM1izwIFB$SkXvRmm*m-=C
zmfF;%RI*6q2>Jt9Y(D0g+I64V9LDr`RZB;2Zop9+%50@esOR<!u*<l@3ULz}NB&Hu
z0*u$-r_qpxML<m2u{cA@W>Y{QEhQ2PDO&f<wr>Fz4L&m*-8me54c}{vrtSHxcBqfD
z#C%jGJ#sW$6>{Fr(-yXw^D_EW$fbSD>i&Sy#GJ_m2YgIyRYm;;vSXH>tvN0zoVTlr
zCw4Vj<chAlfTJ0t9|jO<1EbD{S|JYLACV;axPgBFIJG^7?U@%~I=6bZsL8uYE#J!*
ze5POc53`Mr*JJU^z78$*aP^TUBko++>DEWLqoaicvy<mdx)Y2TWU4}t7+`+D8Vp|e
zB^3?QbgsMG8fcE@G<`}=4M@nk+CHz<)v~F(2aKekWU{c;F>NL8n03o`Ghfwf?8wf#
z^Y}E^#O!eI$vyG73T?MjHjTq}-oP89TxQeT3mU^3=Z2=w!;+LlHOInqTWA?=cxf0J
z>y8ioIN%+U{rH%on{%dLsy&kW;F+>v+{JCxej{B8T<fR}ov5E`Y(3DF>@>F|S)VS!
zZ9OQWtZr-7l5o*-AVjL552H03r{`rwE8z~|je$f5F!gAf2-(vSg7$fpND9TH7dk#m
zG3RD;7|6{dMhvsPs8(&{+&FrME<T3blK3RLae2pxH8f21g<29n$cFqNbCJgGuN-Lr
z_y)>Fx-%q0S0M!ip(0_Gd}J_HvRmw42dSELCjZXN24-9J$Sb|vWDHI!t+|@^WS6_6
z{El<ZIS^=-*?vZSXe%m!(88HV8BTvn0%@^OAVQ9V2xMK8$g}7-HBTcL=xJ2gP~^xB
zI$Wz84KC`j#tely27Id@e=I4^bG#LE-+oklY^>gnCc1{gjC}M5AJ90##m62pAwHE`
zRz=&CotbbU7?T~!V#0mDHAeVNH}ANrN)<|bMq`2%>G_08vVJ@DFvOhM`Q&UIEQAdS
zGK<LMkW8JjkF=hOvL7LMKJEGXJGMAA`@VEo5v<XkJpsfdr8+sq;^x=5f=4QYfO&jN
zGOiUWQVS~m?dy|pZ@p>2{n0n~((UnzP29&%<83b7Z|!x|1^^%`ZSfRA@P~Yulta$L
zFKq4xeHU&7dY<{&2#@FAz4&GCe`NKA8mHgqqne+HZxQ2RG^tjR?b|Ed*g&g88mEsR
zw@(bsMzz>;ekU=OHPD+L*ttdAKlCd;^S$j0jX^EuzCblY$xQa=b##MHf}XF2t6Dg_
zRna~&*9^AnE8UOO!A>TuA?az|HO@3Y!duczBA>bA7>Z<ftxWT3ZRvE=D(#3d{IhZ<
zI4`@OTA0$o4Epgzn;&pWsvoR3UANKg4omn-bM3jG9YwEP9*m{kueAx7oD~moUJ%Si
z-M>`-;vp=z#*;8Jmx&BpsDY^{xQ6U6T}_ba^8pgQDkLx;{45*0<_6vu*akDqNauKD
zE1m05ShP&45zJ>)CvU<-?g+xK3%c3c*pu(R>S&=Jr_`9Z6MA>y9>{r)e@AS09t=z{
zv(Z3$R&1K3lc6A^fF{E}+UKmk;*gL~@{Hm!x&J}X`XVI@wxAi!a^J~*9BV${3HX+}
z*-fws26`Oh=vFrTRGoA}UAZJ}R~*n~!rgLD4WW;od~Udok(e}v%AkU0?(Y;bfPb3*
z1JJC;+coHl7y2x)xX15ONjxbrjh*3UpLRGhIrqxZsejIOHa)Gm{=8Z#{OKwaiah2h
z)#y;Fp(-hq6_z0<xxII2Rk1xIQw@>U6>AL&YWMNnacKnGZcuaLsEZ|c;KuzkoU6j5
zp_y84=Hih?AD;`Ih~*>;rlY06xYFuacePd4jc3PbOf&;2v5@Nqv9_!BJ9I^gR>d^e
zV44v1JY)4^zM!9b|A)G_jEbvi+D4J!fgpoRaCi40gS)%C`=B8NcXxM}V1o=6+}(!3
z-8~ROlKXH!@3-FX`FYOI^JlN_)q7R1-Ca{X)m3#}ybT2+m2B~ROaK_Cd-wBbL$iSL
z$cBdZ8d2l3l8?$>3)@hy?lt(<CNA?=%Y7zpQ3~Xv@rN~yp2v9W_yH<+DmNitYJD#4
z>94#C@|t*+CS{a~YqEs#dshi8mFd~p6+%LFQ@109D6xH4PBeU$7q;z3<+Hqp_ZF>I
zX5S#26-j@L^-n(S!zyphjoFIKHVx><I}R!M96mWyIB<`Hz2kgP5t#DKWi(IefI2wj
zaJ-LBGHJuJ1$Pe(gZ`)Yx6fv7Xs+#PpXe_wA(oxL1q8hONSlNbd0V`KNk02+Cktds
zrq2i<1K7R`VDd3oRt_hW#k^~^lNWQ$m2b>XoiWO@uVdHDp~ICcZ6f^OTIj#DT&~Gv
zD8n*kCik;1i4fE%zr0w#>Xg}v$2Fno{dXECUEIINmF%qhJ`>BVx9oE2A2`pc#2bTE
z6;B^LH2Ehy2mb6rdvak0gYNohUvzqt2pO?&@T(oZA8@LI?PEvoh~!FUx9QJTTIrTD
zy^lSP8+m36d=V!6h5qcNasKsnOWu3n7i*)O@!;>Oq?r3YQ?fLkA<~Es8&i|)PKE1I
z?3O2OB{h!(qLs$rCB--x5sduup5hTre^6T33+Om55HlRP!!ew(1RZ=t51^<N{>Xll
znbkKlKzCD=V@AJ~{Hkb20m)1<rdS(2)*U!l^IW(H+xWRTi4gX0v(IYmSi2y!uH;$j
zcUu^6lBUfhF#LFpg}=_AN#)!F{AoN3S)_Zz1gFl3p_ycE%f>SHjtwyWkoXpn#plKX
z__q8th;4W`uw#Q5LT;f#gD-jEy$hs&$1N@fDrMsBulH3HP2oiZ!p2+FC;KHDW?KQ1
zI`5RbZHz;$hqM6iHLx+Ijb)CA_;z~48QVzo-E<+vRr3>Bo3;XNk!!wWx3gyB6D8O>
zxRk=mx{{EJSUKuMXzH34p#(YWcDg)|5bpQL<g~<`zRT<!R9jZ<YzqeJ38Y;7J)@M8
z{PNmxZu72mMtXWU7!DkJ_cHiWH!*KB&awT`<U74Zp|mL%%Z!Y10)^}o*1$@PHP%0H
zl^Tmutrl{UNZ8B~aM)Z=3X=_6g0#{oV@4yE#yQTv^f3I578Wj)P2647ivmeP->xR#
zaH6!DV4<p6)b(jz$X%1WQMl+L4E(dD@N=T|8#kjT&Tb5zJD@Xm#?gj)7G&7?{d3^T
zdM2iRV8Jj<e=Naza`!B|fUWbi3I>InFD3aJ;!vm%srk&Ze>}6<<B{5Y0g5N7mAlT$
zOk$QN&WpU<3Y|nBcy~%Bs6q#~;Y>EdcxnMx>0z(MVSu^O>-pD}gm02N)g6cHgV`|t
zW?gj&AWM-qBG|Ipt4M;`mJ7fbjS!(Mr)ZZN??iIL5Wx*$wQVR%aS_0Mu^csgZEc_{
z<ay|>&*P2vr_8O_)X~Bj0-TUMmHXh)Ghq4&I^+e+wEQyjzuMSfFViy`HqV?jd`#Hv
zFg^cD9aAk$Rz)>#SNv98!i!CTO~Kh9BTzxNj(ObSW>V!O?PpBTs*8!(A*6WrmD~QY
zwqE;}R@iqY{%~(h?`6L_{`4$)fr_oHH;T>Z-^f%8M4t*W-*O24lIpfjY8sHwyp_MV
zN3++TM|>yuwQ}O0$m~0;a`qvaq;K=)X5Um`o&e&XW^vQnH<mN~zym0f<b|gBGRX?r
zxRJ+m5I)?x+M-!)K9$D<$&OlsLAqG-$NNbS<+v`HG*Ou3ajPdA@}!N$SAbzogm5lt
z9`E#0WqRN@G8Wt!jOz&#7?hBIihq2(g*#4xs&-1QT&kvKvog1WD+Nm*Ao2FKBu2bM
zd~t;yl~V5;_Yn@`P~$m*u!MUW1zd?<TEPjuNM7CzMWuVz@tY0ox<_1zmPEB*$1Ov$
zG`gO14Dxk>V-H$5^PJCDX>7uOMd{XuWXD@fHm;C($q>tpXiJub#qfr;VQ{#Y`H^eV
zgeEd*F+N$7f(v0;_1lL=@Ohm&o0%4MHL_^c%LbwJAS`(wMJ9C08X1)aemH3-6{&V6
z>|)|H9PbI4NWD>M>aB-1W4V`eo+_#hnhkR73GY`KZYBZsbGRtl3a*4=fcFWWf}!FV
zU(u~g>-+oNf@!}#ehO;rXYH_~N}6yq5+Jk=CQ+_G$}Ocgj;5?m-k8D(k0a!>`i4ih
zI==pr9wr!-b(OqmusEZ*!8RR7EvRlrUpo!P;STWvg>Te)w_zE`Td)^vEEJ-%eZe5W
zZL*-*yUw#c41Bvr5j8Tp*I=&ROidsN`o4HpeMjTXQo25-RE9_{bszm^GAQ#F<JDiM
zEXtcMI7ol_I5p4(NKG4OUS9a#Im!s~io6E!9U7S6@8->tvkk5fOIzun)8j_RyoYD&
zw$+W_U!PdNzxh^tpV1n$!K1$o5=~1XoFcR*VFNkFz$MB*0&&-I4$rB{M9G5i7d2ei
z?T7v<{dTd!>M+~s>~mYX5KL<4L8ez}^_fe*YsPW+fk#T>B6_D~W&Yutg_$H2XK&t5
zq@9UQM<wU>C9hh2N^n4#15pC4vp+|`tIhY^Cbb6j_#&v3>PR}wYV<fkKVOdFU;AwG
zApVIPOiQY@t@=~(leH>Y^3n#wHu^#EiVa_K@j=)>aG7D6zHOUHzJH$0ogs35og%e2
zE%lIOcJ=1{Mzm_b7$5X9o7jy#E8LeH+T0A?m}+ff7eFO@No84$<vIVzJ5A?Qm3akT
zY)R_UP}FpEDePh-CV9;eI5;?aIQZg!(c>`xH=V}HSM0zZ%4#%e>Z{UaKPr-}%2b~)
z|56`--mQ<t2v)IEER|+<rwT8J-ylbk*e(8<&~tf~gE%~Fk~v*o>m=e_Zte9{*U20Y
zpHoE7SY9)W?25}aqQ0c*U329CHjz5q@Wel?PsdP*?$AnZ$P>+uaNeVV*vD<>HP;?B
zt1{EdymA-dB&s7`=GmE~%nK&vM`Zf2PcmDws~q+Hrmo7B>r}AO@8(ABX0daq^diQW
zAw;WwCkP)N$<#J40c2lBhr3g^dZt@*f;LCxL9XRVHSzhPw-n+s4nS;*rJ<C$mV1ja
z^vIl1a8A|p9xf)}h7;10I^pAv>v0d`87W<;AN18VLrfU>3shUHQhJ44v8G-;jtKn+
zPAV)HLkrqzKQ_PMsf80y+bG~T23xo>rcJ%sq=wqnQBL4vY1Nmyz!+;{C2-zNTA@0p
z0}(DHilZx{F2l3fD4#t#r<%Z@c%9nwa25rBY_<jLKJ^K3x1s#+F|Cdz=F3LWDb|!Q
zT|9VS{0|&$RhL2%T@)Ol<TFhCdR*^~>LSUiAL(PFp(6n}R^&TJ+^?1C4$D{B*#D{y
z2rGRU_!Rqzk&#A0ZAZH^Y@B1y!w3CtfzB1@_Qn1O-A9z+<2R;@|LHvSF>qJcC*O7+
zmECaU9B1Vi^qUtW?s_hqt6T*I+t5lAm#QK0M}^rHMh<wv?E`Nth|R9BLCbR*GTt)Q
zDV|%J-sK7l1DKV?$mH)>`Z1~+yw_yud*(h8*OgcUofb0&t8-CUUCo>Y3LP+fEp=;5
z;zu%8ZP0a=(}2~rp)Q96?y*(wF}Ugz8GOKDCZGfy9FS$j7k)(TdMj+qEyi7UHOnY*
zwc}{Y&eJImd28JYZ;Q0=KpwIRq6eMZkc`)>XQ;BPpdq&Dz>o(ZKKRrKsqE-lIhZB5
z*nUwfWfdc^8p0iyCHbK|l&t7|L6){EHPs7C`ZuR@Y&`LsE63(DiAk1mdZheV!`;kQ
z(wv>zo&ig)<wJOyrvvibQf83DPcRY^X+|+^s>G%@Vm|P<2;Y+FEc4IkDWOs)LrtqZ
zH21xBKan>^r!HG@7u(Z9!-I2|00F|fJCqb$)Wn<9{;>AtRtwV#lJVNM7!3i&LbjHW
z>Gx?`IExVgMM{<T=1bvD6QT^nikg?UJrLikz=OnHC;dDY3N@|9C$p6k8q4-k4vnol
z=YUDz!*u=kcx(ORo4Jm*t5(f61nOpRl>H$bXGr!t;5!N3+f8ee+$_Dl*|br2JXb|%
z76@R58F)=__9Kh(>>635#|yxvi<gpQt2NlcNTwylIAV{mz=?CK2}Pf4@yOl-i3H<2
z!G4m7>vTFF-?K#<i#L_CuKArIxZWm2(9q-E^E*&yzq4$4w^SAYk{ByJoBC$P)Y?8k
z#%C9P!1AA&3%3S<HVx@p@+BPZzk073TDW;;I=nK!2q15-WC#2u;nT^Aw*}(akI1|q
zR%R^KJ<H&BDv=0>n@2Ta>){|Kr9S=Xuhe;saB1tDb04HskN*!`XGMp%tSR_+B}N#e
z)DjPTaIFtwBNcNaiKTUo55U5)l}>5KrNpiQfZ*VG(X-r*jYkdQs=<Cyh|b4x4KdK$
z0Z3EtFt_L>+xHY&_cxd9eX|s|<+<g;Lu3SE<rgp3quNNN2u=!#C{(y-W`pl{pgork
zV35#^8TPJQ?v76@SFiuKLRS+iDxYuPa?PJ{301xwv7Di1uZno8&){+=;-tdclb3Yi
z;h|PmR?Pd=k@S4RCm8kXre?r$@Lt^iDb%RYK{QrKwU*H4OBdH-Y6a4beD7wVq>Srn
zy70h*$LW2Ahppkg*9FafvpUHL`5Q6PA@2}F+d)P60(LJt_pKzq0Y+(u8YV_|o>^?y
z1a;7#RI*vgJhInL$MU<13WB%FM3s*r!{{~Gr;|)?r0g1Rw~*3?zQ($E_urr~S7Zc&
z(8R6mrl*eMEC&;hj)t}&T%%_v#x|F7cE9YWh8+f?P<zTa1Yw05ZRCl{ujtxf@EZp*
z`~ZpNA1wL{&uJ5`E{NYOE_4RZS@p*;^~u&K?s0xk$<Dzzr28Bf_pFOZK!u?NNxp(v
z;>UMv#D;IC2v^aUQE=Ljh<$o$Cf>-hggr~!)9#H~==MNzTL(SRRg1xXcX{SB{6g=n
zK8TY0tl&F&eoNT;4{v1sUdl&^VNq0K$Cihd!i&E1m|e<L|8Dc>H0W|{@_Gz-?S)5&
zFF@#KVT-4p`BMKghQFCL7GfVp>n+QL1Yfy8g6@>xi|Q}-PTEeAL^TVaOJSyewr}bC
zYCE{CD$h4%CA!_0?yD)L9}5q{5Qh`_0$T@{x<|vZ<%dby05)xfnD^QBrij@n8dx`&
z?2G$Vt_}|sJ#t7y!_X*R8mA3Javmq*L{1lI2v_F2Zo@gr<0+(cF47vxRf|&RC0|_D
z+)!H2)X$9eB@&T%aCy$??;3Lh3_EvfT$X|~(+)kB*XE<+cpF<-bB`15M0#RYFVeSS
zpRDjneVP(Z)?pRG1pBy*^i}36+LGNN!vl$nr1t4}oRyT+h2^(E2|wtN2f-Sv+9#2f
zFQkK8^4%`EY&9@Q#oX+0ubtz*kWwbW7GIHb<0yG)lA(0@!XR>762Rawh9CJvNEq<Q
zJ`n9N$a~$H4a?1_HEI|%;ZkmoF83#@$jj}Z8>3-#LO33I`dab5n`6dJu6kWZGDk-x
z&*L=vEtMie8i5HprV@ct>J1&Du`23L&joC^cjUQrXNo?T<I`;F@PigbWXE;g#)POV
zcm-P|MM$p9XP90*>_+vE=d&Ijw6Mvjsjt&?)2D_sa0@0^zQnQ7!|Z~R<})(+25aqm
z{#qAvIj2|uyoLcw?3lmtg?L0ryzh1NOh*I&6GM29d}S&NHUj_qC1^*-)<A#?_n%VA
zH$e!$jhlXQ1Q(v~2l6ZjUPN4t8MY=|`??BG5p{*mV{<7YVW0i<UVoeGxi+va`cmPh
zkFbN>1z~zM@uI7U*Eq+~$Y0b2w|t*yhA~UIp<T@jc09TUXdhn*bUf9#t|yak;BWP1
z#xLoeIUL}_BXaBRES&xku#rTzXZ3v0w47Mn!7-ebk;SefFog2-8U^oaERc|Z{DisP
z<ibL~>H~<0v{^Q^7ft+TRL?p)B0H0Xz&b);32l-dy}??Inc|-@slN-RIGrPu!A<AC
z0*mTweGFvknPo+DU<yPyoKP;l^e!V-l0TTLl{$<|jhAfOEeM-{%P>LX%hZ@}q|8Zs
zhnk{M1^E%k-S@Qj>ao~o2J5g0-1teie&DC%Ae&;~o)Za8$cem~EZ#dhOKi%?&YBb5
z-2u1g9E&Ru3<D;h3+iwI8j?87f|#z8a&hi|I?uFve0@g+Y$*nb%JZg;zQtJ(L9J=n
z<$rSF1k^1*nrpR)=ETZI6P2T=grA@&z^ATY^H&5Sci1Wg=|v^&B&G)!o~~kx^&A%5
zD8(HXnYy_em1s?@yKH6B2kgBuY-Dx727mc%G@oVdQA_59;!CsFeJ+d`(s|z<r{;V9
zWMen-GkDXxGG8Fvy-_4?nt$siuR*!U!B*5*S;Yf(O=HaYfe&*inSoG24nt9~`dhtn
zIK(R^%3hMX$V8bEZI=(iowmOz`u8`D<#k>(O~almETCm8%j0H!GxOSvO{SyMV!Kas
zIT_4>Jh!Xqf!{W}&kKX1Fj5~;r$AFV9dpHq3%@r&Gl~fIUfp9WzEQcnpK;9lxI<L>
zg&cF#XymedlL>vKdkB<yD4F-f$|2$)TMHzvb*0&Cu{{&O$Ukd@zLO_sdk*ffes21p
zRz%JgwlxI5zqREC5r2r;W}eM;b3XV!rbd|>a`_H4y$a%K$Li9iEenyENQy1nbh#wr
z)vyjPt98WvcTu&rdp@~RsBC>KOF3_cxCTaEq->Y@r+?gBa$_ev<t}WUjCJa9uggG<
zrWjNokl@Y*ytg11DI3$;`n8qgwj)elZJXI~`VU+{i}P=f^t*J^N0H&K&}kJ5t!|UP
z-1hf(odXJmDp?j9BIL9a;L4CD`J5jd@F2O!!kg90iJJoQt`fqi)8&BLpP%lGdMQFB
z*A!j_lA_f9fkXM_eu^))^R`mYt4WR&GnTzO0`A`-5j~rvgeQb_%_xAa^=|5yei7FC
zZq%&{Rl$I>NslY@yn1gn!6u+)1}78&zMUNCL-zKS#wR0w{<$-^VJ96t!}k`T2E?8|
z@oz~%xk5&vTg?sF2_4^ul_`NGjH_&I=dQSE-b{3`H9}j34JOLeYP4h-O*8Y=%ckCc
z6$etWx%PqRjPG+V8E-)=ecouAZhkyf5r4e1mr)W;k}V4x8*LlLF|IC@yiNaoH)eoT
z<wC2w^R&_Uznatc{O_+Q{fdmoX29xVb~%09b@GeHCba0|iQsE0I*iwd{=V~?dLSyn
z{^{eK8vK$#j8$c&dk!IRggX|UUS2V|@(I&*O8jZ>!uW2n&7v3f4NNjYu=4t$h;^-I
z@CfmUP~$lC)R-9(r+EY}kfr`fn=~(%p_HSCnS2_Lio=@>&q?U)_RR^T8)Ouf&oick
z-Se#Tjs5ZqhecXI9r@QSqqRcrsBU9}tF?5x5<j5vU9H^I8~JK(izjP>H#v}^+LNwC
z(D1R2y*ue`O(x}HZ|`337CXhZ<gmmf^}giuizRf$|N2uD-?`@k)HJAHImx8CU*y1N
zzE2DQ#TUt?q-<nM7`NdiPbRBW+im??d1?8dMQMX<MiH7aZl>IJJ&;I>cG`U0zGqwq
zYif)OxL15-bAVi4rRL8!pViTqhfsSFOX6NKHZS9`b6<bZ<m!ex&fzXVFkEBH$$q%G
zHW#tKD!wLfmyjZ#gha+CWTbv0q@fw_3d>d0#W}CycFYf%Abe2Kl7CMNILyMkLPIm@
zT^k0`og|UG+1)EC-$aJ3%W9KFaOQ2NqQ#QP(-+!<I!9Y10yP<-R%1=q`ZJb_y1sN8
z(LHx&T{)Q+^BH*D>a;RI6z#Jf&U#^F@ADAoO0F}F;J{x$&v1XjXvmUO_5y6Zq*0R9
z0cTTG&~{B(psH$3a!A^cLr%DxQpY>JunssSEf==de~;XEu`hzFmT=qK`O`nIbl6wV
zx0{##Gz)Z(jSH^V2^k|MxeR${W&ME=z?PaQh%=}eoiU+qn?fKMk|F&yQIj4S0T;H{
zqpadNg?B*4`u8`lwU{YI0~NwWud2+vN=e_e#4QR+tK;v0vr95HOM~YQ3|QpbA2Noz
zLdV%w)-jR!?eh5MO@$@`h`+tMiDZTj(k5aU36x9DdK`IlLaErPvB!sI=x5^aBgNBR
zp!>+u55Iu6CEV)5AH;wu`qN$<WJ{EAL=CJDGKk%P=P!8cVpoGtd%rLR&lc|&E~MXX
zjW=HAe<iB^>zzW5-`v7C=Blcmg4^`q@q8hwsy4MA{Lw$P;7RK`Zc-X(XRfUA%+1B$
z<)YpzK~C4h-E;1BFN8&x@os8**DO2T6H9gKSis2{A0-iLaTeWsuT<yFW|unObuHxl
zj`{9u3@W{2*R#e{P}}c<W<gk~Uo83u+teuc=#O?5Dg`ZQ(Ql9d!Ux4DzW`+Oi~ypQ
zGe4OA^(=%N>5O$7VYa!U>Nx@`d<x)JE~6o-{qfq4o&pd|0bmOaqic{OhP<=qyJz?;
zwnb0B>+;AxRTCaDCqB9wE;eMhouZ<XlIk5t^c4Ap$&JT3WmC`{n05wWBsxZjLJiTV
zZ%L`2*%$aS{>%g6pg0i~2{vUVlW+U%0L>DmEzd<C>S7VH&NLsnzev6`Cc^bZkN_l7
zYF*}q#>j%&&+Peo%D5JvMRD~m-|kT#V_UVoqZK-%6MQH}rBn!{g0p9gIy0S9W50qF
zedIOBl>xG962&7qrcd4(d+t`Jucb&o9vme{H`m097dFrp<i?cisKb@k>c>5F0H#dX
zr`2RfaO#>`b|zov`MR%n(tODRlW&yOK5zMAcBQZq*sE-Ux<N-{j4kdrI7sVi`11NS
zw2?v+5=S%%y&*b$DV(9zwNhEEJt^a`C798Xl>ys1=aY;bF<nxV(P>_vJ~8t~N=i$U
zh%WBB1=-*Ep0ZWPwD2Np0zLh{D4oW{K;{NKX2)7N%*+O$rWyLl`=m(M%_%Pf*7VVk
zbIaddTN`*D^%WoGL~N|D_d_)rYzk!L+4Y7Q721?XwVLa%;5DGCvSZvfgGE>%>^}23
zj2%5!&2PS`T^xD2e+IJ;2!)Hj9W*e=<kL)b1n?5cK@9n!>#_kb*K@|HOx_6uH)Ydo
z0!i&y%LT@L(hG5zh928pbW`B0hR6w@)wEeMeo9QV!9^C~>`mwKLVJ_1Gy~dV{x{z^
zP=(h5poSbCWE0(4j25pD9UP{?l%~KtTN{@j^HsUKSe<JaQKc0gUFuxeQ5X*88@wE!
zBI-waH@3(-q{+$IzD9${4(XrVo7GZAw}gQ6BXeI2K9$9zuQ92o@;-UhQs|*3B}{E=
zk|nZ9H*<Mhqn?{t%;F;*Xq5o4S0Ojp7CS#Q*R>UmKkHAJ2culFr%I`5Es(~T{)Yr3
z0I9DE#w4Mv1sWABvuT}qMa<&D-XNY4`t8G)cid2LcH+Jg_JJ7HyK>X$@Y_i)8+-i0
z!YRNY9^+X-(DQ3<&OsPCl7V-ORzYoNmLJ<C4ObzzteSuR5(b9bfwER&%kb8a&G%W6
zwn}y<sUzLs-Aws6$nZuouC8L5GJx=j25p*O%_jUb6MMR=C9&8+Pv1-CsWtS|tm3N=
zgDtd+LXmn$YP78s`YETC%~(kYc|1jvp!H04BrT;N+qq1Jf_zLCSt1rOa=%*F;r%@d
zm0i%)7juDUQ`>^bE}iXw00jE?s|X%dBZQ2WDtlKybX?dN$k!`380+H|4DqeAC_|`A
z;}{KS(K*XC&I*rwk4+}^#c8N*)5|U0L?YH@<qhuTIpybX8lIfr>TduU<_jgp0{&u{
zlz&=A;qe4a#FDAtYLbVCT<oJ0^@GRd@0ULKlE!zxT0O`@44TRDl1!%L8b3H4c(F)$
zTy)vLu|5a6+Q9waNZET1<`4IZf$7=z`tI=FA3wKVUq<|YXXwh&jwf-a?7R<C-<34w
zB=<q5?Y8Xd3IcaO%IpHPj{h)sFJTj+9No5^$GxHl?NsPYve0(@FiT(C4i!aBJ63vZ
zWX^gkelz}<GU+fF6IH|T=}ad(W<#{)M{%vyhG6tN&E29j?9*t?59&Kf))h@le@S1S
zUuYJ8XYJ4B*kzgutX-Hz^L@0oPyhE(S$3J!EV25v`jtj<_N`6PM&>9!KL0l4Cs-{6
zPwqDs9jt@Aave%@n+E!_xw8K0Pql8JZ_MwhFEefxkD@GpruwIRO9Z3BKhb_!XjH%W
z(7$$2UEfbJ){@a*?^gprJr`HJjZX@Os(`d*?5ZnkZRaJtxs)k|F}+s1EvG8^ht*18
zku40G>ji1v0b1p<JRR3a-1$VjQfwOrqgX>BX$a)favjN;DYng8$~u%}=$hETQdjb1
zOzFfPfja@C%&UHCTmNI~kJ4%p;NMS|FCI8Ecs#G<UuN#feMlh*{aIda-#pwqAQgmh
zfEB0Hjd$*sGo%^krMtEG67PO+p>$JizWW(}ndkAsJJ#NsLNi3|5m)Cn*$AjKS>K;B
z-Br>(kb<Rz7I+XlH;=mCFM3|B18&SjGSf4iLnpZnC25y%E9YfQ2(aPk!#8l?8~N{^
zVLW*F5Z=+P1+vYbB5IKapV?a+oQ?2!lx=No1s?ZfnvCT>SQ$F1Om0W`^Pcagb?ok2
z8idZ6yfnbPGreHn*WvNFRVAe&2NJspq1aFM9$Ahi^6^Y~JSM-F{n|Sjms)wZ5^SlN
z!1?X9m{>UTeqph_O_(3Q{8bFq8T|BA<Q(K%^blBkf7gGdmbdui7xbmUZ7Ai?&R50n
zEn#@RX+h^@Vtt2ghch88(>G`wRi1h!QZIADr9o6lxOrMaQ#m<_9xqydRJ|tDO)F7{
zIwtI^7M?0OTrdaus98mmi@3FsQBOv6Q*Qg`xevQ-KRN^JB3^2o9~Rpl7UQSrTr146
zR3R(L-D9|h?4Hh8B+Vt%)C5^VCuT+-xT-RHST7}c?aDW?Rz+I*z=INAHm~_RHq~n;
z;>IZf<<)H9wCl#r@K4>MK=0|p4qhWb?16m2Pg&8wvRog!jL{C8S1NpLR?M3&BRG79
z%h1T**-b+e3jex<r%v`ilvxM?cqPz{nBWo@LZnj>T(kVz7BA|-U|5#()eMfRsxONu
z!3NCcdGWQ?Lh<*;@PVl_0h7ye{nu6#V{oEbMnQ^4r0&e>A#Fj|Is|A$3@F{9J+nTh
zcw~tk<t)|2k>CV?!h7)Rf)szhxPnviSZACPSjdu3YWDc8^XmI-J&&U+IAhA!h^x4b
zTA%saU7V(fC)JeYP2KU*9x{C<9Jf*s(Yq-4Tg#6(I%zx=A>P1{Q{-^(TntBb&Z-vk
zCjP3}EuRh`5I90lPv2=<ejgxg+VBw>47=*K<5iwkR@NeG6*&%YIP{Mf+x}#CcxF~|
z66{LXAK2%9z2)yT^jFmCZIchtm|w{3{sO;be&&1)`uLY^=OhW(f{D0}o@o2^5~W#k
zHvO$rd?4|J@O7m6Oc%{r+mJsYEZ&*4QtEqDzl|qgxt1HHP-B;Vq1^G5A2|Va!CNA(
zVKL^rv?JB`VpNZyi{mR5qx!hYbui7G-eL>u(n8A%m?Q5Pw1yf}2{_8M@NwP3Rswqv
zVdU8Awy{mWWGt3}2by=GW$zV+@r7O$WCqBoG$=RL7o;4j&uNU?dSpF;TSU_`LdR`e
zk<Xvq&^vnaJA$%vns_xKcSBe*`LE!wG*@X2{y1m`QM$MCTqdP5D!lB3DzHk{<%f`h
z!5N6c`x+!}fP|d&J`zsDSd6YM_a6RvQdG@s5CiIq{;a9$D$yH3W+JNipN<kXqe9v3
z&_XYd65N{gPbr>r&2SJI9S!4{a+Xd!dIVL4VF|f)^rurtm8awE3V#-3VG1l7#VwJ0
z7+)nRQ<4(ayjbE79}h<Ld9hgJub1i`#ewh=^I{>zCyH3_1W{Cl@tPU;;c+*;s1a0T
z2aDifjHD)(Chl;kn0`NRawzm4hj#W_|JJ_+^lqe>+u8EX5Fxg75ZV#}Z?*-H>=xD$
z`7z>_xMz_4OO{G*xV~ge@^Vai`8`Cmq42nUM^8)hn)<@hZ3^(xR&Ft+$Hpec;^)Ls
zc*A@AmRrPIsK}K!%Rp*X9q3EII}l?{EVPM2o?3V)e{?wNp1SDqoe`HY2cN)MzfwX3
zoo3$N5}<4q5}n|tc%`CLr6q1?#-+?8pX-18#t5{y2<9zQ<DE(28*R^S2ueA80DDUN
zh$?xGnP~9;__o2mKdU%qwvMKI&g?pdFq2V0#hT{MUAYW+cr71LX+oeDzSW!U7;bmT
z&a1*;A@rJ?jDD0;Iyc!f6ad1Utv=Il+sC(x=bTFDH_na-=-%Zmbhy|b^h;gFQ5<G`
zv*M`Op>k^#wSE*06iCu@>@voup{|*>K#aa0?forV+0Y|iV0*cytecX^#S?aNn`@|d
zC)|wVYGip9yPBd99aE?)^Vt<*z6DKa&mjHF{_*=kwcHDK_A0xq;STF&>knK>{pYp!
z;f4-eJUW`{IwA#W(L~XqQ5)zdW&4D%3W2%Fm~IC{;37>f!^iM$CqLVQUklbjxkmWq
z$|JV^^tPN;95UL*u44%<N-Fd*<0WFjV+LK`Yt#=KjoQ|GM!d$YDpTySHdwQDK866&
zY<Y9eeAN<VBm{I(In4{OCNz&J@e|9;)MVl15_nR#V@b2q;@*9TLRGbmG+n?dX^j8R
z9u<Ca=m~_2>=A%v0G=Z#qrWs(qpnSm<(8OTyDC@ER*ns2q8E;whfA00?fDcvTd_dw
zK&Wf!1PMNgi@oh!0-wdz`MgE_HS?SlRXk3kPW@&-9LeeCqn|BT%^uwA{Qk9RwsbVH
z_9^3t_riM*n155D)6Ik^-k!+t#&%bVgL3kLUNfC{oq-5msrvaMwe3ql4Rl(%r|}|h
ztSP~BRc`bNBrvfK`bm!uwz(m$?(hwZ|H6U($0&Hm`ImyNtXT3}AWpLDI~~5fHk2}E
zU|CcxYIJSlzIV(;VNdjT;-i2#b#rUtRe3~GzDc1#*0|C94gP25^DAFKX>;a7>R_+4
z0shBo%|<Asl8$TI!?oR2!zU@B<;_wFiXUz*5!-dLAnu-jdiVz}tEV1zd}NnwTtqb>
z`qhWjjW0Xob5NTF#fFNWgOsS?OO5tAsk730Nc+h7$GSL>Q6r1+xJ`Vo>k-4%-P3h4
zc3$VqVKf>{sEx-zzpRJ*KE;E-ykf?ycBnYy3wkc5H96y-e8>0{<Ak|dIxg=$xz2VX
zMgYl9pBUtm?{!qKN|mFu{HC7F9OUzzn-S=@u&fE}`Nkzib{g6a458PsS=BD5k`5om
zMrY-_;kI3U0W*-T6X;THt1wk`#Ed*k&9QHK`ANyXk;RW^4qvu*q<S`E8W!iiFUwR1
zo+EcVM^<9P*U4ph@Y~)#^(lQxslRmV+1flG-;N4=Uk)QrNNvNGlrc{6y@<1YY+)y^
z^N9U$e3V~16NY*cD=ujsWE0V19zxFJC96Aovu(5lar~J(2HRGpvDol8qmVZCcq<GG
zhd6;ACSZ*dGW583{IISlwkg1%gB?OD4HJ|^Sfv>ol&eswOb~r>uqs;7wtop$>IEk1
zv>mWQ3<ulYNG%VNI?R-qD~%%yM2Cb>lNF}4s7H}}Kae-p;0mQ06JjMRTX12<khY3$
z3KY__#qsnf6vZ`x6-+rfTF8^U`mz#x_l*sJ$*m{Y?l{aN((<J<*NNAFl~{beWFDL7
z6q@+#a@tRN)iyQt3;x#1WA=9PtWmzMGLdYX5T?u$SE8S97_HGPacrIarMu*cd@3<H
zd|vJy8eKHnJUEitvoQNSD-HHPO=7)%u&fiZf8ctM`NsvBwu4*g>9}nAA*%{K?&bIs
z?D3U+U9L_UV3~-F$Io#C>eCwvyuoR9lt|hY+IaBVN+k^<U8E(^N7t7wJp{OITx1~i
z?;`y+DpzDyk%HHE?Hb{tD^<4Fy;%C00~@EInksdm7SJbPZiBYOBt9KFJ(o8&&v;Ei
zN1|tYT;?-q(!-yD225H_i<wVjYsuEd;$zU$^~Lb#g1+`uruxMbB-nPu+}xNS0LP(P
z8k=7#<A8m2{nEehcS2~X{P~N;MYr%RtE<>!-+oS4uChp`G$m#-G-DZq?8f?IulN0}
zj$RF;X-tQWF~E1qT8}QzanR_d9m7F?-Hq{t1^H!0=p&w$)A66L5Q}U2<`d8lf-K{(
zy)RC(4Ikel^NJNWlL#jRWAWA1>ouW-=$SkO;F(YQo=k(s{!#0ydpKL6?3Vl6n!?nM
zT!Xh78jEYoS`Lh^^Rl#ADsB=cthIsi%(;&A@2JN%s1!239&Y_;`8+5eD84-Q3?l2R
z|Gew&6$EY{uq@5Z%mmJ2vLe&qu7WmQ?;e;|J-BUzqg+RZTu`a1&jF5XYHL4k`>3}2
zJbxC98Kj=JyEbgQz%<#Nhzxn2rRb95yVyHiH@b=VIU>3y<8v7}6e{OQ!%R@5WJepV
zrwh`rrfK?)eMaBlnYcACw}(iZtkkx3TQpMZhPz^iGOd@w_uS+KB2EE-?PB!mwUA*v
zY-wxc+6A_ghATHQC^x;l$tL+;ZgIIGd7$Pr3TTsu5f?^uAN;EjP(;AQP*+F2h-9H0
zS6~_XiUco76P-zGq%9fy*?eu7L;%14^LpCac^1FY6vu6!R7Pf4f(rwt!!bdozO=ag
zL=2&Qy^~T48$pCZYn0lw3@UK8Z@k{Pah3N+j35dz!jHVD&KuUi<ma$vt2-|yLe6P3
znC{j^{Ihqe3z01AJR!%5q82Oropk>DREG@9+RI3S7*_gG=@0$gf#0Q-Es<dmGv;lm
z8cG(8VfH!mg9`=&kAre^4-`V+%j$TEiO;cdMaV{qz%EaxVOs~PJi<?RQD}F{*zq&i
zcZwo6um2Qe5dL0Aa0xVZEh)_x@A1(Yw&po&CgAhw{bg)#3|sBy77FkRN#=q*C0J{A
zl6(Jg3|CpfSig=~ZMs|J%zru%gNylx1e0=W7FR>2i3S270$ymAni39<<Q7ib7Sw=S
zmrYBH=m3rgFo)v?;v9S;inVHR+!{YC@Om-TQf)(w0KDudHs+`cpWOFU7TBYpGd=WW
zC>Q(8l5;Rs8Nv_72HjL>F%EjxR+35u<rY(2;~ZA13-l8wg`<W116TGxKt6u|0rJU>
zXedp*RoegC?!~s=w@~`uV49~rw;%SCCecw>)t*<_u|H7uqu%-ar@;OHZNkjT3s8DI
zZ{z3h$uimJt?z_?y*rv;F)#&@@{R7BOyx5jUhVh45~^_HmGP9(qmauBhqy<4PC`0x
zUg$nsZsh8Q>=gW#9yoV`IXf(&6|}0%3HP_2_%+>%pxkCMJpKCfq3==X7v0Mj_q&ge
ze;7ag_gLVKbWy3=|1SqTDVhrQ+n)=<ek$O-einN!nOk7!6pVppok^Pucx<)`u0Ic7
z=EFoJjK&Y*Lbo0Dq1Dfd#8am~NIMuSV=FlUs^-f>1Mp*};vyq$ZY|U-5ep)8SG%Hw
zrc=kZ*)T4!kcDq27a*gG^&I)U&;3uU=u{cux7KHG3<mqqniT61-C5J}68`ZLWfP@A
z4R|bg^n;tvm=6o=&AY~`r)Lp)^SU(OZ=1jVRk?Hi+m@gfczRP9@l1%<Ev#c`h?yrS
zdBNHrisLymz`ivUcn(9Xtd)ajiZxF?%#P$3$F31)DG^k0I5hL{;EI<srppO@1D?y(
zq_cz0`L@(ydhkPVL`X{~U6vI1IEf)+A)#ks5$IG+>B}N3#~m?y)NPGiswPFv7t6by
z7d1S}^gSu@o(~NkS@?UnbvoI0WXREN`N^G%(k81hOCmYilW~8uqd?{we9<N;$^*1m
zyKt*b2Yw2JPAvlW&Tqv`thayp>FJrkc4=8|KiLZlc0ylgQD$J>9OcXe&v{v0tX|Co
z31qc*xt~LHF883T`lG-B6@_8MDkO#dPDL&9_C~(OIoUNetAjd&%E{m>COrM2To7l>
zqN0g2O^%9f4;t-4c-%{+?c?GK<KulTZR<(^un3@xPK+B3m-zKc0-@XCXA-9K>Q*b+
z?>vgnYUl?X(Q&2pX*^&dQ1cf_qF|j{4wU8~m{}8z$>*E0z5ue`u@R6}#awayCl;cQ
zxBJl7vj1#B*k+KPt+2tUKn%Enbdg}MV48@$T>Fdtm8zHkSkA7(z8mfKU6S{CI!hAX
zet;g7fA~OjP7Q7Mw@hw|&jKJZ*sicBPvz+OA>_we+#dSO+F$>srklTn?JFEnFK%&{
zKZb;2M};!c*JbCTCjF>|F&l2@X!#Tx{(-a3u&&e>FsKu#5k@o34o!8%ERlE>j>gIj
z!SpI3+`WeIEhR9ufLsFDZdJu$o<<+(+hL?unKdpAL%mi;v1i?3ysnqWrq|gH9+QQN
z=cBVJsw#1s+;)s3tyzlY3mfv4?~v4)lqumGyOI1E;%;BSY@;cFKUZn-!*i@&V!F5D
z427<P9hzT29?|8>uDPxj)1@6z@oCNUxYOC#_v?2|nc%pSj+OB4nwJ;3M!E<qHd+tQ
z(v#rk@X0Hp?sF1*5)fp#lQ^!lUpQmXpWmO~Q%ElSXShm-7O9g$z30{amAZ|q8$8KO
z9)XZA9N6t+qTcXjx5)M&OciZtFHNUL`yq36y_5W#k!iAE$N(9>-J7)fRgc`u0*Dw{
z{8;$Uc%Tf;@zI~HG`^oo;>lgUHL0MP`<E}EbQ7*l!tNzg@xxIz<?_7h4)^OrvL~Cx
zvo({l71WaD4V=RyVpO_4H{!j!76{WTjKxf8$rowgS9Kg_wgppEepD;-GU(^eo4Pqb
zKs_r?8pO=o{!85SrN7g4O}o$RsE?<WoD(Mc$&$C7^T(8>nhh)Z;Bo3ieB-FH)N8vo
z%yOFz#;1!|!R(HiVGQX0@BuUEFU?3B`)b_2ZH_I!>d()rZ~QcQ-t-gkUcjrv^1(Nh
zNR|(iqbR=x!NI4?&wYt6r)nN|CfR3VNER>p1OLE95zK9o1n$TMJyIO(L%0@^Vu!!{
zW;J(93o`oT?Qrv%{9!&YE@q3IBe{hpT=^{{7+X2}G9M$zNQzPz?(~zj-{Z%)(8A}1
z9Wjh+3I<<e@!tOk-l6{q{$eQmp8>RMah6tccCmLBFM0$0Q3P+9D<vmyb4tI>xxe2b
zrzqcgjR77_6jkZZeGXFPoD0=J*3>h}f8iy6JK)ZmO}orWwc$jm9h#7fMe&D>68GHl
z*Ob@hKIkSzd31XySu)KjoEjPScWF2T@JWlB|Ii$lg7>X%xO1xYjZ(0x7I7qgiPv(;
z9-!91lmM1}|E_S*X;6M}53&&Cq97dwnvgeij<P^q>*nJrt)}vxgHEqbaAs+?NX}c4
z=3go#bh#L?Ugea#RpLpKW64HtP~mhxH-*>F3cmP*7WoCp-j2d}&dO_*jDnWL!v^4y
zwb^9flpnrci<(ZdUyYlSIYhC(BwhWLE}2PMi^va-w@Idttpw*SbA;{~@?Eg5(ltAF
zPSmqo+KLknhphE$2pk^{-<|W>KX8I8|7XWMM9RuBNJK{v30ZeF7(>v29V6|-qS8(I
z|Fc7Vd;7jVLq(GM!MdCR6Mbw&t0`5zkG^wKfl0BKV+#MOKp)Zz;Y|Cld*1&@6#I`+
zy5|@5@Wwl;N0*8T$u0Z|mf++3s`#-_)RLyy_w2v6zDZL56!Gl{k%MCQu{EvzAK9-_
zF<WaW#r0p9f07>V5X@c3FKz!4IVlkv8NN<To0t8R5rlE+a5mEZ{H6x;|3^QJtnRL_
z{8f=Iayq6dXYiRE#>eD(>?l{h<BP@zRP*AZ+X<p!*(g$)?=D5fc6x}@m3?k@R`Ezw
zS<<WedYkZB<iU){CL1=xNWZ)_wTfZ8femIArDykYF)FC0A~wcsK$GscYSi{S#PgsX
zSsXyo(crxQ6S5E2WLg#NE?^=sy=AZ^AXjRt@(J?}gEfvV&Vf|i`RabCcI}M<B~Upi
z$U5dKb>p~WxlPeaG=w*XYE`g6Km2GqxtE0p^|loK^77srdbm=Az032Qz1j}tZT#hI
zE6Otr={sl2Oo!oDo?m?ix<W5-!g5Bl-Z{njpc%`!G&tyEkpCu$u%Xz)CuQC|W6X)}
ztEr{i(0*?Y#~zBHzG$*f_73LLYG}fj&p^y}yMqJ1aE(by0VHJ0&It>FeE~a~3mBc0
zmTZ~Z1sjddkn)Sk5q3MSIYQ^7K@p|cP-$}6n7nfONK8Q#N%FvRzd1A?9_2m0cm}sY
zW8NT?gjRn2qzpW~c;}*9#bii-z=*jO_1;F6m52?d8+UtIjG&V#aH1^^RJNXs*XOu9
zsu4OxHlxiMv#<<$CsOF5DiG4`WrfGX6X6efyv(Yc!@6U!WQ%Xb!68}%IUgP#2K8*+
ztkVA#x8z$SkZL#~H+C(mXErhsxNob={LGt==$b-XfRUCzOchcg_%#-ejy?LIlPEj8
z5wP<LLA$-d$<EHT%4>av`SYu?@EvvU)MHS=>e)mFGJC>l^pv+n0j3gQ??1YzAYG-&
zUdL4SkBnP5>&mlO9&mdHdktqlvk+~dSM_Q|=m+wRIB_I@@%BkFN3s@ERP{$wl>Kck
zY7$oO7cabTgIw*`4z19FubayL6E}M)`j}(#F3d4-Vh2s`GG-{1BoO%e-PrXF;<JTo
z6rAvF@+z;NL~^vme>3yar0kOyeTw8k=eQit2Wq0mZ^#QN_Pqlyz*tcv?sHT>X~{b~
zgD0>!n2NQI8i9MQ?P`=R@oUFkDl?{`0Aq9p0GARDyQEDRPThL>jbA0~>wfVlhtcfX
z7{AZbm+d7smkaT!5MQs&hotZ=3|Urnkto#<-?WG%Vk&7$B$P!Z5NIk;g11xrxezhf
zXt2-WAImjy*>{VwE8Qn#QmG^DceQXBYm@WLU#Y3u5R0ST=S}TNkPsE$W>OFs>uRRa
z6J#IjzgxLVy@8d!0sM7w#<L3+J?efYhJe<y{a)-@LOSs4YdO~9OqQesWcHNL_|jD*
zE(doHS#^+L{G+ErnLoOTjQwJd+T#+p1=_D`>h6E|l4M5;J$|8<5>1W6=uD(YHnGn7
zpa*I}C(=cV1P=IUBCd=Q#np+dWVl<)>GuUrH!E0dvLU9iPN!F@W;<`&Zf2PLs&XOa
z9DG1ol$ZF8#OGJ5`raD)q)kSPgo~bTI60H}EvXKVmo0^g#K_IU+(jmmJnif<55(!@
zHLPHj*}<BA7{Ih$A$)}wGQlk1*BTmHQ&pmaP#H^#LvKr(`+2ZKAtSH5am;VVf6Skn
z+fn#0ZNZd}ctRJEu`5Mi@M192^!YR$Nmz^=J8}6LKAvsuWbJ%)fpqrp3`d(U%9aaZ
zW=}Sh&?_fSZCYwESn8>7OTM4f@N@f|Gv)~O{*lh~?$AV=ir-zD#z6bLeBz*`C2E}q
z;^D3P0!e#c!a-tv!{$vE)Qx1;rpL>@KWt1-EY6jA0VOf?tSBA~CCA^EAI`v0IIKnY
z9(I?}Tq>6bjT^F3y=%5T>zLMDHMIvHUn-o!Vt1Wa5m{`A3Da7UI@ylFWaUi-hCAFG
zA4?&`Nj#NyEU^j8E3}g-!=aYLQ^Xz4pL8)VEMQq=n9uRs2D8M-_HA}3v{UV-LUqvS
zny6Nf#FefEe}eGv>{d;n1~(AY9Ia<woM$a#v!La}q4S7w`l_JtDn(!mLp^@KmymUx
zxyUvUeemkZgoJk1OhyFf@dF&^`BDAq{RUCVQhFs$u5KL+^;b{x)=6Y3^0vvtqucOp
zPLm*OmIuQrk-?L$W}mu)r#RnfT&45-0b5v?^Jn?p#_+^(Z^Tp<rlZP3kxWyrrT3H#
z7O6>W$4^gOY#7^@@>E5}&5JA({1KuylQ|_;M>!^m1g3n^#S@dRGSbEFf+Ud+t~#x*
z($J?vq$)SCPC8~2`s!A=ZTCf&uricVb|#G{O0mAB6u1P3SM5c%X^TTa!o&FMEsU<<
zx%gjU^nWoe{x^1R|6lbhWC7QPV(x)vq&ffMS^QN{^GtgqG=gIfy8(aRN^T5r@S+$C
zM*l?Cw#?=N@C`L1{85+VI`Wk8zren44HTJ^kS~B>b#>X)qP`jrlw`BP{d4?-QpHW0
zz59i8Mez?Y(~ZB2|I<;r3R1PvFreD=n3CHz<C)>$ZVmn}fiiGZw)`KzT!EGTf!t`!
zJ$bYBCsmd20aAdV!^W-0E7ixfI^I49<wd{@)&G5bzeM7ZKau!jif)@mAxBk(;_kg$
z>7XE={q0jm^z-Mz&M(FIW6>{3W=MYzghtKgO!hzB#3cQ_xioxf`u1I*e>-?>3j@)#
ztMVY8Z}u-P7u0zQt#$O0Y?J$RtT$DW4bnmBNv-Zi0g1wJ{#fFiI_3PaY#n!4*FMqL
zZ|;hnHH$5#w0_sgW|KQd_}5s+=?u<mjm%ZuDpmWT+=9a}w?dq!lG!_2i$(8UV*%w!
zmiatuEPB#0AS3UI|IAhP^^xDc2<xgxqn-g<{>%&$^N|B++M<~4Ohl92Qe`PS&h>zq
zIyEay3t#R}s%Qn`Q`F2@84{+Ei|*Cb!&h#V93<<A)QSRf=iSXOsdt0t(*zd76>*@}
zY$r!~r5H=9$x#!jjcXAn+R0k^#L=M$iJ_JfZ;kFp>*9O4Rg_+~GJGDN>(&6P*G$``
zl+nu;<B==-iGs4Wa!aUb*0eUY<vuJm7I-}qFN}y-HPOXooS|*R9II5H0<p?C)%`Gu
zo1?j!m)v<3{VIIj#sQu7@iqE(uc+KC^T_hU@UfmwCm~8>5)1oX7FFU3O_sB~C48)K
zj&i!3$;O!T%2Il6XHXgF#DxD1cQV5YyVP8PJp&j6_Q1qwGiUFygddVT`~&MB#27Jn
zk-Gf;u)2wb3(4s6JoN0qqpK*+$n10LAx41a(QBwdr)yiEu+{={-C&i=A=0$1{qN5C
z2s*-iMPXvO#W!ACU{;VA<nC&Ja;}Tg!d69mtUR@?A#foz!FoA1TtkJ(Bw<Ov(%B?p
zvP7Pl{+;$=c9|t(^ce3U6Pa2}3%znoOT|5FM4W?I7YwTIn$7L`+o04YK3O!)9okLH
zq{yIt%$Z&DH9TDXl_?u2dO99BAa4knT{kfAjGBEG8~IA@ktvqw;8(JME`)nKIzwZ9
z?f3LlH)2UWC&v69C&*6Dy~ze-U6x5DEJaNk{rwb#J5e>YzJdPYfox=}v2>;c+V9Zx
zfi2o_AhP~KwrI8cQ&8k6DX<WSk3Rw!(SA~8s-(yLniJkAstqJ9d;mn^bvOrpP58?3
zh0gaFMvATA-+Y10I9L*3;=$ihRK$3vSzY^nIg>IJGgZe%8y63_h))P?#>6yQg?An?
zg$$%&TD5tGJ#_&_6@FJNF0zXov>}Cw|8-RP3X@i1-Fi1h!9J0rt1P*Hj5O&D&92k%
zoLXKduGAwV;~OHe!6X;M-L@5-A~fF*E0^z<^1MA2VMZ?5B;kg9z(=mWq3fk_+1N2m
zWGD0Tn1kXGZ0&g&>n&T2FDEC^mDz3*rp9Ypw<fNh!N9Bc70U43loChe=KPa(tkT+?
z$G;PCMU-U7#M4YiGObdkd%*!VtrtGq2IDR?E~T6?ovbXTl6#52&XKZYE^MjOa4Wmf
zMVZHQ43{^4c#D0l9?HeU*yahe%LeOl>wn;Ue9SYl9BC<#*u+q%%=JZoLAatzN0t2+
zk=RuMmvSM@G}kgB=Zd9OzLGn%fIbp!32St#RBpKeY$$ttZGcb&5bzHscA3tFL4l3s
zcMYv6nK2%?`m(@F2jEPs)a_JC@M>%f^8~rXvO)Xl6)~e|_H^Lw=9SiXX2Qr<npNK@
zlSrE`!VV5k_65}VAT2X5997yFhfY^QC0@(2S0J}knLEv4@C06_CCMc0^G-*OZ(bA2
zG4!)VnO#d{q~cw(jb<iqY%8{t@tR9xUJi0(T+)M6U+xn3sIv|sBW+8tzA8F)cy^gH
za`b}BnLF~Wz@G~j>zVCVIH-VFzgt_3=@Mk8M-}R4i+6dXj-TC{801-CE*#}q?Z_Nv
zI}_fETbrMgZP+31wGG^Z`+s<QtEjk|Xlt|~NYLOC+}(pqa0wD1xFxtZ(6|MHL*uRq
z!5tcHXdt+|L({lJL+}tHm+w3G$Un}@z4zh1?XIy$t=ctKRj*oW)|~fF0Ewt?jaf;d
z&hFr4^nYnk@&1YOXn8rG>+LT63qti<A3LRv+4~os)onj~U@-8X!RM#{3_jh~YC+w6
zfsL;}YOX=XDVHZ}z->Ob<9_Rzb5<4;S6ZlteYo47Ys^y!b&5oZcUb8e3b{W@D`>Qg
z%y(v;3U@=&O8F{zHn!-_5BWNUk71F;$yDa2saurX)@<%f$#$$f^QIWUDdC#nSdfIB
z&1RLiQk;@rMaC8l>R`p^t=k2~LU5+R1`)94XS>tEpoV^X9cGNOFqi+MCJz2qrUB<l
zj$a;CF1%g;iL9-~STEJZ>p2dkd<AJ>>p1Z1w<!IMV*XbRRt@Ow4#usCtd1de-~*?F
zY^JFCy$jq@+X0bghR>@yC+fp8?<r}n-qKWz{7x9N69iN$F)7=vDOr5hN&O^O{dg{u
z61P4cXzXXqHq(?0Yu<e4E|j8zR?zTqY$eTW@KLy#Hlg2kj<J$jOcnGgJc7Ly`O)lc
zzVG#@<niubfMH!wS7Xamzc<Y(w>mr^aA}{Xxu7zIJDO5(z%p(j$(qu2SX{}`mQB}}
zdC}gP*;+)5JXUW`VvdpXc9_U*H%DiB@(euX1bk>E-N5m~H5Vp0pD+<40S}%`)jPE`
zo}*DI3+1(MFuW+v9?)uERHf!cb22#Kh@Y&S&8oqcIhgR_deoJ;@Q=fbx|(}H9bzQA
z&Oxhle)*%%;8#$%za40bA|uXKP%@){o9|J8DwMOLjQw-`VBM8WOV{Qnj3)&ZEz9uX
zO8AT~&wM(wsi~8EZyS`#0vm$dxA%m7Y#j%`l&gxeTLh8nCihq>?(4adGncA|e;l`4
zfLilVC84@;4xNOs(c&ZT2m~4vP2@+pfTC}X^cW7ApVydhuFK~h0&U4OCN`oKrBMP@
zr}V%1Bpe;wM{Yc0D#o4KN$gFfs<JY>k0$DPIl#Dm9k^r9E-D50X8J7UZjg%06NO!Z
zIPZVTxwWQR1t)V4GNf+mtiE8nP!dhF@+{LGxJ#%H-Qu%hye#r*z-i}Dk2|v3?{BHY
zu?v<%{Yn(8sy87Y{XFJ*Vzc;5(QJ>!&D^wUc*XmN_Z0hgBX*bunN;unbB-PjuE6bh
z@t39ZE@lSYwFOQsW>08zAtGLHmoT7@0sFJCWEhiyXpzP|>_F}l1hn)W%O>A86PX>G
zSDK%x0y$vW7!wKM+<b1T2rb1zahxt&uq=TT$8w}*uyHiWYFHN?#Z<*k+F7_1j0$GD
zx4g6gTVdkw9^u@<8*`d`T6$k|cT^}K#D{jjv@;<jhweaCUtftkisEUL;m*y(k%SuT
ze44Ve%NBgD31>S<8@x<94-^5OjZ<c>baFpdqx;{TwdSvCQ{*lUw1a3t$2{MtsC%)V
zM<q_u+GaiDy)lVX%ds0mji2`Zqohp66?WXpqsUZXE=rBpykX^?QA@BB4EM3|W@Fw;
zV6x7m>5C1kE8wlvl_QENP>3NZWR%c$GQXxtZj`z4^xI(*-<NvpVDbYW^<G)%C5tlv
zmYNO)M&TPieTU0*g#;7Hf6aQr%e`vdCfic~_eOL<>r_F9lDfxnjlPw`0-3j`T@f`j
zT@?8WViRg19T6c+=*}W8g;GA%={g*%*?n9n8rCGGJY;O7lTqiQod@Pa#Lyi!&v<*`
zj!e_wkBXMRtfj8BWBT?4hXeHbyf$K0_-Gx`9)p_bl`08!E{(F)8&*!Z)ba^Fa<0?y
z5=}L;AOpcZc_-FHBEx=N;A)dID7>+K)mq)|ZYWESdB6V|0DKf_pcQ0DLV%<PFClV3
z)S=dT#+`vDm)*SCzh<3w9nF{GZ5TiwS<ZYGYH}@IRV}jr0tn?5o-{}J1*`epC%ZnY
zu(=c+8YP&C66l!w4CSPzV;qQXaJ`xO2D--8^jcf7_><Rh6+BIJ^3YzV-cQrg*u>1F
zoXGG3kMrvn5mp_e{g@<;WA1t<9j3U%u`nIhgWYpy8;(zH1}&J(2;c;a%WqkDGRQ_A
zS8}d*xJg+C;o(VmnRb?xV=5qUq>5c+POhf#MUGg9H@6KvZAsT<O-&i*%s~6Q%FXDt
z%~w(-FMWFU)QYO?>~e;&4^Je(1Ozm)4r4r-bd5w(&Had>UEc1MU$b>fE-H%ujqIKI
z@6(Cwf8WFp#~BO%mHK&~RVtGjV2-)z@(5!vyO}$X{+A=$vBG4H{Q|9<@6=Eb0Y<+3
zFV2Lg2Ym1};1Bz+!omcW*TK3wPX)xt!3C~i#SExQ|I+~^J&2At$NWL|-yd0PKk~<l
z8Ws9>Gm|>I?Q<<`|KY}d@#%@6PqgLoAwAVd^FX34ai757{=0(K#Sr#IziU=_(&~#T
zo}^RM4KiEj0C`dVop|uaF*{GAF<?EV!)vTtD>bPar@FM958p>iteV&yk4aUZYj2Az
z<*}ZUj}!O9HS_aKm)$OUv7Rw_!HH`GWh}c(+S0#4Y!hs(B^`4?4#URdqC%GvDtSj)
zuC!@kC<O(&oLd@H?10cvH!1JV>#t_kwp?@Z)2B4fT&dhRnPZKDuKN){<mP#2n6vI?
zxLxT4k;vkTp9)_F<COz*xMK2$t(|u@s8XTxr2_9NH>@p`%cF8NGzOh=8}1$sCM8G2
z2JdRY9#j;ay{&(YS}z7yQLPH6=zRxtE+>8jko_i0NaN5U1{YPm!ykQPx$vIYTby!X
zcHGs@qn0q!dQ<*a!+lR1x0nI{xu|a=FODgteRX7?EU&SdFWleugGSz0eo_&n9PFJK
zEdhR-O%Ih4!NlfOd+z*9Y?CPO$~ALm^eN83%Es0NK_m23Y<y@dhxJli<hsq$2pW}S
zd<5Yl!Wp!k5t!JT9@8bjYOB<&loe52yI`y_?VlL1rZ_X1KBXPRJt+H7ekt>Psi|82
zKB^H!bdTOWO5)nTZnZ*BH&Ky4)<tFiy8@lD1k((F>B@NJQ?SR^q$$!rlPK*cxh_RV
z+kCA)oN#ePbF;KRInPtb%v)WIeTdjJv7`=Iyz(f|w7EKrC`gwS<<-zjk!lL-wA%N4
zkcljGJ<>}uJ1^+AZ&P0B6CL>V+UYL<ue)G4&{m2so?_31oX^ST(PuO>E4Aw-3z|O^
zo}E=}nke1OTspD(XdJQ8-(WTSNp)|uKp&-7kN+q#LBlFtZF`5&&N(u~R>XfJE&x_n
zU3D?k;C6xew#4RZJ@qF`C5=()Q}L9DmW#Z^wbp%VF>MX+g<xW$#nDXum?}2nn3z1B
z{aTBQ=|^)fM#(eLk0ZY;gA=bGTdaa=J2gww@1uI5ODDVr2`SP|A&3ToIRL^X#QKH{
z%t47Uj_Tx+INItZ5?;7G9sEYUJf-dPt;arG-mY&HHV*U%G9f;rRvwdG7hTNXCN4X!
zI?Ez*Rd;1BEZmnzjg8gP=0D38{;FooGtqLgGAO!N`-t92d+HOp>)BI+A@T4MPvlrd
z^()2RTSB6Ppo!htR4wV;bNo@X-mQ|jYV0xwT=d-1x6|dI*QW^`-yY<fKc3EV@V=HQ
z82JmR6FJ%3$DVzxmrsooH1N80JTPqSuZis3I3=fo8{4Ku2vSPm<s^m=u0Z|CZXnCV
zvcc4s4z1;&-VydiPsZe`ltDoUMwaykA;W4g%4=8=_vD_aWuwjBK1_I$k}dTsxq0OZ
zjaN#H5~b;<(cM*k!K#c-5FpR4$QI?5Dyv9o?wH1}pT10gpz!LyfOIaE^Dd&)E#H&%
zq@TOM`ajyWbR9Pw>{f@J!N!zpzbKu<#en@OZd_3t7SkSR^GpL!H}*x<(d?%1yx@X9
z+zkmW8Sg8sb{)t%W84^_Ft!ds(N6B=h7JOUbPaj-$RxTwa-_&ir@?VZwH=W+%xCkN
zkAeQ-CckEUxo#Z_+B;!yJf%^f@Y9eYb>yB{%>M{_gg%MM;<EfOM1Sl~PyV%k@A0RB
zLj8Lk=6x^+Bbq15$!{n#g&98ZjFi<`{48T;o6&+tliGU1OnqA1Ly}spZkRq<OTA#;
z>YJJ#T++}L%KSCdQH^sPqIuB{Bxy5SgTe*2E5xE3l`=!%7iFnW!(d|dsHiwlws3uH
z>A=vLTr0xnq)+ooRpRG#OfG$5+7Z3ZLJa@xvE=K6@*!?Yz<TFF2+)2+b{m^e8GcFx
zlrdjkL#5V79);WqNnE&1jK%!s!?Uu&bT0y0T9i^|=aSmm6C^{U^dnm)WWdF|m*%-f
z{Er+vwfL774$ouqoRsWT5fv%|0fVXsiXR?c&4a@BG)AiQoB2yCI_#}IYAc@dUOP*K
zE3KQ9rdDe$_&=xHkcUd3niLdbm|}&hj2Ccb`*O0G*Nm63Dw>o;TXBegNL%?lugA6~
zDNQJoVoJ<udEns%u0RVABt4v^p4W}Yj_M@Lk@A4Qk!f{h<B)Nvd6sS=qEi-$AFIm_
z;x@-Kr%E=VI@k^B&$&+>Xp7e(a*pto*O5v}tAl0Ur#lA>%Rp?H&Y9VkhwYk7(;Hm&
z84|s0Mg@zUavJ=ZQ0^RT^?S)IPGDy%q8RVq5?EzJ)eq!lbc|rA+jDzL4AJQ>vn|I*
zjT1Pe^ar$u=Hw9ij;}M~`j~8bRTQDD^=}B(SdYsPX!CMt@6b}@=uy)n*a0M^99{tS
zCzc@+TZ3@SE`gTV6m_DEYZoO1&(^}n5@)qA><aR!6e0pa79A8(<w|OmF(~jW2}c~W
z*mvi`nW~qO>xaIoSdvbzkzto=)h6y)T*k4nr-^d&ha)`H`{3AnBBJy3T#8%EoPtm_
z?0p*>Lb9IZ63x`fxz*_uMNc{6O{fC7&0q<_!l!3sWL!x}0h(>~Q(63{n%wxqA4=u7
zyXMXAv;7<dnCxOe5O;1N21mc_420XR7ZHMPjZ+zUXl0~odVQHJ2blDFKUT9|dgfxC
z{?QKqJ@>*DxH%q(@MkLFGgJ~Rt2(52naO#(pz>@`u2K~!3X>3-YXxPR&oVf1xp%gQ
zZRB<G86t@-w3+2?JJ_Y6W~8iwUwQYKvy)M(x5UCrG61}hRquzNhPzV<pWACdx`*YC
zyoCkNomr?#pkua;jr;h-bouOyXv*E3F3YY~zLO<)Z!FYo(Xu4wu!zw%E|=c>LJ-)3
zFGIJgZh(GJxIjJIslNz3jDO#8N85%}PZHQOv-P$cuHM8)w8TcY@~99%qK#(+<h&Ow
z-WcrIO#;|ablJ4pTL*~>Ar(<Nu#L;h#|dDU)df=mkIFgew=+_pQH?(*66UL_(SpRI
zGfgZ(E0c|DO?9XiCI^t*#jyd1juo?Iv*-Qs&g)2hp~Jow=RYRzoO-lshq7li0=-qa
zY}e0uN2WGL!;jk?$oQC+qMxyr)FqIJn^dHZlz3>z=GF}G{D}55f8d49`)Qab`vxN*
zk$(ZPJUH@NE2fltTu~<I3$<$}M#XfZEPR%-Vh#c>*6)(N6kpbTO*RK;;CjE}=D>n^
zo*^=Yf)kRnCjFK+hbf<t9h|1&jMI*!cyj^Y2FNsG`Ts-&J;Zya3F)2Yw2xHDdgT{&
zC=(Lt@WzRZ_(kvP5L6D=?iVjbw_LVoEQ7@VjSw$#E*~-=^lP85eEW2sH0}1HKF`X0
zH>A3&!kdCu8Rh&r8*W>_-OjF|ny2VlCu^zqvdpW}T?#ya7Yufv*n&%U7FEZK8kb;9
zXJ10gG|;%Xh!yqVi=!|H_4TCv*-=9mrExqj0Z*GJJ(Gd>XZ|=l41SVJJGNxoJtgU#
zyM~E!yLE&D>*Cs>3hicN<U%#$i`5<RucQYlT}`s2OIcpK?5aCb4K3%V*o#wsSDcvO
z7N^L0wRCJM{UP*>y~24-m4+%*UqArOE`2jX)MC&NRTs1c!Sa!Wo>xooPQZ9gr|jgN
z<n_g>qi#FqgMP1vjk8w|?hE1;opM#UdPOqGR+i*otL3(G#%Qw(P=B&ASyk@uf)rf7
zEE&v5ai!!U`yQh<-m2nF&O7PwfHCOT>ocWIs_hO6tEf=Is++}q2Y8OrFm#Rslk)zP
zsPjt3JhwZ|-?htI($|){SQM^lKh7uV%)-88bsdEGNeIHv04-vwRirxQAD}u}JDJV(
zZTy6aI&|y;pP#myC47Xp@k}PW^~q0~X?+S8|MitllGy*P3KIqHo4jT2bNi!0k2Nm&
zx^g>Cd3t)Yxnt<9{rL~%=pOaDapge6h(xZut@GDeL^0#t_OP#WI|-SfJhQQJjUo1p
ztW-87F|+P7Z#~7qsqOM=I-ghcr96geHfrNqj=v<X?vTSGC1Lovy`3E!4e{oNs+`-(
zp@B=XB+oe6w5tHrJIw5nm7k3#Xm=<AFchdKS$yqtxT2}y=V#48j=Y0y&`&#rkKdp)
zHPleD0XbXwlCq&JB@_*fgg<)-bYvKtlL35e-=IU}nlE$<nf!WU3umY1!6Em0tE$!*
z+w5vTpM->c6mH5ui)~F19yk^`kXb8L?a8iS0bnza^Nzq;cGipi8IpO01>>c+A{!I(
z{c9XU-e+1aCjgJ<myi~0_UjFf;%Hd`kP+KBYjT$!RDgtbwMG*@4&)v*PE}~DE>FxV
z)5z#s^QGG}VfVE_z#bNv^OPmHY6zU07A(mGQez$<ai*eW<>*CIJkIWs&rxDu&~$hD
zV*+c7k8ZD92?}P)W#r~cLBKrVVtq0TY<VnLsn@JVZr(gB^QNpRYM!Pb!i!}&Lym%!
z_|$4bq34T65ytlW!50k~<q<p%{<R0Zrzm1B{tT#h%0r+%rYNx={+V(ud+#Ee<Yto{
zhTdq@q`2oQ?W>(^O<CtL>&~$n>8_pzbUMdayK2ZJUS&E%UEcXLT7^@Gu-<-UA&(su
zMH7nIX>Tfl$5p=>6msDdH!KyHU@wcsrmreWh&GhI@SAkquQ~EZ9Ms_qlbU9goF5a_
zxV#o?{l>yrekCgw0i{h{b0)L;N^KeliK`MupBKknRa%rtx99Bl+7utU39(;o6NYyt
znh#@6<bAVUXY%+(j38SB?W_Cw9ZTNH?ARS}D*bdcX_$q8>0!IV;&Y^Wvmlgo;<wg<
zyhx)q_6=1VocITM+6(zr<teG9SKsQ#`A>)1y!KCh>WQ1qjhG8i)ur_I1P2A;p43%C
z@iVAT7LaG6_Mr`;lG8I#r?pW-8^mOYiE-nVJ)flS`!<rkr2%9%?7G!tHv`JNUXw3e
zmg^uSyW7f<URlOT?E`k%3H7ZVc?gD|CNZ9G`r1hf(%w>)R<gNBY8!g;mR3fct3j2<
z@)zq_8`^jVaC7N7(n~>GV<Py;KS^+&<4?ivONL!-SZW`5c1%ntMY(Mrr1EU~*0k_9
z*3zM#F3bffY$V0bntL5QcH8!)OI}4E_J&k-1vKEcLXpPB#1PS#4Ih-W>P0cU?XPZ~
zu<XkNqfuvN>(%qVJyR>>?6%vpktXFuzjo52Nsm^g)-j%Dxre()b+Y4l;~4uNyE1ML
z{$Sp@(HypbO9;q-y7iz=qt?y3dsEWY21~E8BmkEnF}4vqha&y%mt@AA&ev4U)ja0C
zLAlF$gHlpA>h>=4afi(WLG<}f-PXlFUB?NHk&&)e{!3M>StLPZs?4}9X1z>h7*>ql
zpdmt0?i3-Nvx%Qd!s&X|aSHHG3Kb4ZDXFv}K@Z^EVCzp{v0X0I>ayb#(F}fgtJa*7
z7n#OdhGGVTmI5HX2LlB=UTfLne_uk*`u`;<VKb~|qmtGWwmXKWP+=JVibg(`HybL-
z{mQ;4MWu>QG{;<U1n1NKccGIwX&wL0v37gEs?<KlZ(U+9UEP^ckm?gVmEKTn7PIvv
z|L-N1C*bJ=y_kYx%r1SnGu6TgrnX8EJ^4OK%gJ`JbMKDSXTx;OuJ_zl5E1d_+KIB3
zZ<&|)Ch6l8n<4^{Yz{X5{?USOcg%+TXSzz$BFy4&n0epmzv38pwGe)M{{7IOEw*OK
z3L@}^{%jD2uhd6wTbkiv6J^+b!9o3)+8AGZ8dF=LW=~MVH^*;0@ba#6VHD*O*C&_p
zdv(@V0mWfOxDs|+d8RWJ;?=E{RhBM)Vwq_N*<M>heWJ(Ubc#JQkL0NIrdy{ZkBwT3
z+-})mXFc0JlOuUjUN@uFJ_(sdRU2mj)<AEoc>#xQcf{gy&4jXm-f38yNLDW;Io*c4
zze{67>&8XH3hPxXbx&DNSx}FCbW%F-{)sby*0h1RxS<Xv;gw&9W80k1GX2hiH||{q
z>2yHt_x)?<{d>xnjz~nFVArg;AGuJOF)M4VKQkmVp38{Y9)D4f$%{c%A@5x}nn(<)
zdH%fzSZ7c}1EeU}PSam<2;se4Ip3Yc%=oe)ven894!h;)#==p_{Hbm0UMO%qS0L)5
zOZ|44y!iOl{@IDM$Tt7yS@j}ENU|-@NZM$s>6j`NOC{dJfW?oiD;=W#@T6CGVew9~
zD|m;mBn^SrAB%#Ck6JrPUu|oqByz)RRTG_V+eTzm@Bc8=iYYQo#D3i2HWZsfb~97S
zn>8Ao%pP^vA$GIsieGj)$THRz&zURDsohkux4{`&%_KP09I6X4xF?i(GmX7h<axQI
z;e4YuO(gHC>pb>PEnCLwq83s*=Nzs?Ue)HQXsE&k&QqKZnMIx(gl(6*p;N^$(lE&#
zl~I{9p6<SQ=hJ}YYL&allXUP_K5SNHmasLFF!B0Bt9k<tifa|)y7nqbq>O5s=4b5v
zfuLav{I0_>IC#wbSXw?WJ77SxVMD>M^U%)?2P;^jbBnu$V%HeTN?4G}N4Hv#-LoOT
zl4)8>n-901caVAsFq{mAd9yMmM||rrJefMT3I5*y7tjrUcS#1`e4{Y>rRehHa_rh&
zXjw*KV^Fm9PO4QPjcCx!r$a5?yWDNq)Wc4@yiuRf$V91(GaC&s=K7qZPy%paOB>=<
zznJ%+y!qmL(c&+qubXE7s{twan>fTE4>N&IbmVa>?yP_nNuYbwq6l-$$2Tv$p%BJX
z?^oz<&$fLr3%vEQUC&7o>{?C{ZPq#(U0&#xZzqmz=kYSh_%(k|^cH)SX_bre)h9#N
z754l-^cggj#OPp8^gqY%t3|I8y6Lu+s(*kLx}pEkkn~>-?OfY0W|MA%p1|tqwNl@#
z_wY{E%|)E14CCatptTvR+2)O&v=zLmBG%T~5^Q$!3Q^&O_v~raLg7{+4aJ_Hk%%vU
z0f|rKrDV?a!Mx%O(|065k9+t(jbC4rIc}QSM$@h6v=46rL;gra{4y>N_Q<5PZ9g$Z
z$gD(vvt$Bx?)ezMoL8RwB9!CzF29zbAQGy|Ghymgs3fUJ1Yji~SRuy9jd?D48s9MZ
z`pIHa_xfMJFK?_LO<F&9x&K$+FV5K4cQ4vr{{_&<f8?I;hK^&4s#FYns&$IzeE&6g
z`Z;^mI2R5M>t;^N`=mMV<L*nXw1iMj)Nj>FWm?-xLC5u)pdp(90AeTvyssrq+OTRw
zkx_2FcVwqC?Krgioz^Vz^sdd92LdDHK|8AOtJIWn+LbuF&VJU@n=Yv|ei!Q&IR$FC
zIgve0GIgzhtl#X^HIxUu*CdIYH{F})N~QnYEDT}t8y(wtu@Eqm1y0YyCAOFK8~2D|
zBN=VdPg8o8hU6*<Q_)^@w}LGI=gNXxuhKW}(x4$&^7UaLyHgPZg<(O^i#>~heI+I<
z>&0X&qY62tG1=Y*HvF)}G34bKN0kl-v9{v*TX^#aaMi--s-#~YQc@oO`^1lMDr-m6
zH$7O4LuV^7W@F`I9iUn5#FT)WWlMNYW9?mDs}n*%JfRsqV%+O!^uHu3|9v2fEp}<5
zzmonD1L{E&)8}*dE`-ha@8&|D6h3X-24h7h8qd_hoIOp5k2i?8r%;yrmPws=QH|r&
z>7vz^vYh>=I4Pn20!|b@W+{HmcwuEIA>$Z}No-&Id(S)tZlHR{5(DBO5YvXc8nlv)
zy*uICH5yL?cN!M!++2V2R&_;tS5z9eXbzuxomVOK6jf6zm66R@C>u*Vr=~8j6!>@q
zyp|ksqqMae|K=NS+=&i$cxjD~sDdatN}h`&2AY5GIf%6z`=nHH_x=hb&aNU<c|-46
zz|TNr>>(6M`I%q&ZotTs-wtQ~`JPK!N>iC{&Pdy5F37tu4+(<lJrfg=jyG*NxiNMj
zA|?co;1<Sq;+V2}MI~H%InpWw)BstISmoJ!3tS@>OT*uv_#X5%szGYJggST8>R|no
zy@<_>(@(Wd50-<~u4E=OLjgj@QbM#09YrE?^%;~Mx&&qI)%6z{TlPz#?j`2ZZFGzs
z8ZNTuxHNpvWSRlOs>Ro4L2*z25Ai>rptfmWuPlqNL%ozxAN79R0JiI#2J8iI+uRP!
z+`=sc@(s?$MDXA#wGU*YqOU;QF3YWHSg$^F`!XAWt4jA4v^faHHFT?6t*}%vc_@7N
zH^6xN6f4Uvz!8|Pg)_am-x9_|H=PnBFFC})yp1J~zL;W1Rb1i-8t4SmsYq5lRD>yW
z;A1kN^-H?uK%T9fHkxM|4o&UUupUcxm%f}DtXLyunIoMsFa~uH2{Fco=9d}0LA4;D
zVb?WeHGWN{rUrM1tYQp@2>ACC?KCqQtMltG)cDUCgJcrqL@{*cJy};3!YWZ>SQw(s
zv0t$d3fO@fuLdC>5try`ZH17bx19p|BUk4b3D_!`ihB`g{c{y*Z5$aXSqVCd8QO#V
zrC$>jhjl}<ZDt+}$=9_<%nbghHk9++YrscE@}sg7y(X2XHn^x4K@xJk^McNjvPY~R
zM56V|7!);7N!V+;HHLYB&jZz_WE)KpzQn^ROTQaCt5H>DJg8re(2u5Mp=aqdxYy{C
zD7|(qC8V;&+p48{jdIX=YBga>sr(vy_W^!xAhVF_fMny8mCp@!_~bLIKZ59wD%AWW
z0|R^|A)+V(Sk3NzRJxrsUbPtQh2Ep>1}!Fnv>tLQ@m4%acY|pM(2?SNGQ+(VbGi-g
z_szeWIH(*uKczFipdbl5GbNQn%Xo$bZ3<kVR_`)6yVu*ppd1!8gBcsG#{SZGmY;^V
znF-kw84xDXx%Uo@PWMBcZug9YUcZUsD$q(0ssLCK3_l~r8a>c#Vrq<Ri`r0YS71Oa
zk!)zTS!zsvFF7DvPj1^OGbfUz!r_;+7ga~iAv7W*(8EO|`V{eeTJl%$OySSMq+-Fy
z48F8_T!}u-xg6~NrVk+W>E})4BW3YQ6X(J4T+Q#fS49$0yphoZ0@`{98t<+`HH%!a
z8PC!>)vKwPh{;W$d_s2Bm0TQ8_icDT^GoN35nf(=d7Ws-n+bEnF?CeRj5n1p#CAYm
z=rdQ)oh_9dx=2Y+X)@??&13VE!m+Z<FJ-X4aQZ1iJKXLjsVqL2+P4)AOAU9p`f1DN
zCLld9>91{LEu+S3L_n<hLD(q9geT#_=8Wr*g^v9_xw3o<@-*t)Vm`#VwI#XLFwW9t
zYk4x=pFWS7E`{~NZ9l_huk0vpR)4|ATY^Ny`kgZkUrp6*)EFhLj{@<gn+G^v$U(A|
zM?AU}=Jnxxv5xyM0QK^{I6pZbt@2y3A&n7459tR@8fsL>3^|Q~k#Uc*Klh!~1$Tb|
ze5UoRyS#am=|CAv+-8s_1G6R!S)7s9OjW1MljT#=l)-|YTZeY|ELxF`Mn}U@q|04V
zPf#t#ijYMkM@es<Kfjbe)Rm;1D9Zuh7cINIf*sGP?F>}gV3|xvi3Qi1k>@mNE@Y&x
z{RL#RKFw>m-;ewHFT+AYrFHPWr8OPfVzQYE)LayA{w(bU2%f=)dZ06M>a@M?Zs_ih
z1>&01vgY)IRdpN@($+>zBYBznHq2MiF3pi@i3TJQ-8ni-p$}*5??Wg0o(uM{ZSB7H
z4F>z~n7#K|dr17Awn4>Y-nu0EWrE$5?D_!TRlcxz#S!fTld*_6wK60`h#JaNh1Aty
zqAEj^v(l{yqzRbWud9}P`8pf%BnF}TeZ;S9ri-GXkybd{>dNW5#nw#~`n-gaaH6mO
zATK~opIpjcHD!h<FBw$D=<?q7w?o7Cll602;AB4I*4g97-3}nRuf)=x^PNV`k38Cd
z17z~JM!_6D6@Pa!2|T}xV_8#COD{S@fh!jI(~WB5wjW=BOmG6P=FlIclWNU^n<h3<
zfc5=d9^1;G>>G`tG|OWa$fKytfnF)6y6`a>QC$ZIZtVu*TGA9gxZ>KV|9L{)UR^jQ
z#ATAu-JFtju62+3N8Nf)YEZ-rejfa1W9TW`XxI#;L{_m&F>x%01j)&~34XNM&s+s#
zekkwKeA<2xuJ*-GS?2fZ#Qiw~SY*70K9F`cbv>*5aO_8UTZmGnbm(!#k?H$sokGF_
zBiI2WEJfwu=^fTp3M~Uer<u>JF#v}Fe`dfx_hBO&>3B<1gn3tnAe&)H!#w7SEzLR`
zSV8ZX>TF@;Yp=BbM>U&<{4>fgod-F&mv5X8^_u4`J_QT^1vnV4KH+X9gSycIQhyCM
znp}*D(R9Cmw@7{zG*;9h8Bo6%IPuZ?K>Z*7c&ZP~CjrI5FTSWzXdc$vB`$vuxBo5@
zva(zp3FpI%aft}&KU;3YL%8J`$-MoTkra_37M$DOP0EG&qDdD5tEr)unPjxyX3><q
z^$g-Pz^Ah-qv{yzq4s9Y>lkanei+zwcjLd??xZJ&d1NXg5Y#Sdf~YXUd3SQAywFhF
zR0%tWnlQJKVqbIt*|<i&bjAVzRsN8Sy{TJi{_V~1Kq7;Cw_X2z6fYDho-M}x9tS_K
zv&j)&>VEc4E{#L+U1+?W0T(P)>4{QoL+Uw41sC)7?3gX0O5fPFy?Zq_7XJN5NVxm&
z_Y_x$gA}G%*5i{hlU^o2R#^ovgE`2b-{NcKLzs<)DB_5u;=*+)!dQsPP%ZGd62Coq
z`Ob87%=55sID1SR;rnlV@#_qS)%|?;gPiK=U}F7prKV23kA-(>skbTS_}HW`+Kx88
z55R`69qUEQd8st>)4}bLCm8tcP_pA2-j%S~Oe`kz&(>f^U#STyxWbZvrzzo?z-<dq
zP4Latw-oe!t1au|17^kATaL)nr7<XR5_ey4@l}MA(oDtU|JhOWoyku`$ds@5N2)z1
zyHBgOC$QVLHosY4+9u~xpQCn7{TZswCk-C#55&X@?)Zf--REE7eq#%0k#-U_P#ZB+
z$~&cPM>;F-b+eS$np#Ru(=!?!EhnvuS@l%3Ei(6ZaFX(K28GVce|OZFg?{)8Sam-2
z{tFOCEdFpysmy)$KSv`Zo7S*d9ljK6W0MCw`u5w(kmuB4FlS{P&SfEb8rE@~-42YZ
zfL$pcE$PL9psn8cG#t5TX%7(rgMR__w!NEw^er;xJKH>kh(21<LPAbg-9M6AG+(%`
z+ooMVzXksVSd52%uez<=FolkD53Q_&Lt>z2F+=vj9II+nsZ;V~3qj7g7h*#rZ0>TZ
z6*pz?(zA2Gm^^oB`wkW9{)|oqdxn;oM!pAxr{sUSvM{&xPfh+;=jDOGCx#3cU;Nvc
zwt&h0f``L(QMoy1-cW>SHZr*!MchjiaDq}6-mB})vu>N!dQz3j!Pz**n#!L1<Yy7s
z@=quwYl{9t005(i$&AWRtZ4K_<<4!AX7$EKt807w5^jPS&Ms-t1Iw!wELtp|PsdD)
zOpO4xzI8e6D=Unu<HrebKxSZ{{Dpmi5G_w<-BC^4rZ2ZCN;|idnT=jcyHC#GvBW8Z
z$vDlKR9tM%C78ld_$3rr<Ps>^#4+1^Oh5=>bqhTXYVRaFHRRUP9EG;w?6CRgu0yYE
zA#NFRVdLCIUoDa3&X5kCcI7EzCiHV=qYfrd#_iF0C3LN#W0Fgr0IZH*8<y?9u04ii
z-;<HZGo)lxZDr6f+A2{SlX}5`q;MJh4iu9?$kbGUp<2X3f1*tYcp^h9aEL_xpEvZM
z3S7Z^U&y~{Kj9Ny`I`QzaQ(YwVLlsp{W6SHW;5d?VCyHHM8~-b8~keR#@yj)_c49a
zQLeZ2ulv%C`Tf;H#P7IWpGC)|8~)X(nQ1EM1y@`{%X}vlwY{wkwOY`wMhJ(?;5uOH
zUUxjzQJC7f8wig#OQ2cBD%xi1($tLnDUb3~fPKH(SIRLO5iTq>Aufck2Arn>*_$Q7
zy7@+R#m)0;tD4M&8GJ89I}P5s7iV4U8F#x2?0YxoHpGYtPE(Y;LwP6ROoH&&u@FSy
zNZ2Db?{!DSz`p9`1Zx!5G(s|RI5Y<wQcJdyHEAf`t!Zt>W+&5qyqz4D1!`sLmDQk}
z)8e9RjIf<b6ZMdL_1nOGnEcVGXj9r|V7o<{pTTPQfbqw}w}ckHwRPJ;U0>X|S_xsQ
zU`;AJehcLdh@H=x#A^t{JlLR)I$XyfR#ahogJEM<<5{QO36W%_P2NPC-A$H{t8<oJ
z9k<EaVmHt3{@Vzq9x1DhD=dR5a}U>au!Pe9D}u%B=$qm?J}7?H0Jgtr$b0oL=E-5^
zLrYJy?s0H)zqqmUeK5(k*5|4<D2TKm@jxKIzl3KsR;+vJkluRut-dquF^JVz&e+=Z
zF1cuM!2{*wW}^_4kQHcSO3c*_ZBjUM&ILW|G?a|e4uiO1JP3xcz9$r#U-oVSAsj`c
zQ=u6q{C8g`RQj6{azM>25{;RgV#4zTkKJj#Enqot7iKUxanHN&=7Q6>`oZr2x&Hp@
zj-$vXGc_%dG_S6)k#*I0i$+#J78gT6mQz6Js_J@a?bh7lC}0?r_z00~YaKp0sQyN|
zp0xV9qmZ__b&viE{hN;s$HC+()`IcCZlG~l4?&^3xr(XsiXH$z?IVc|^8W26KIbE}
zku)ztP%<!AQszQ9Z7#CgZQQc9g#{&K$fB$%wMCiiC_MzlOLgRRyaw}}W<#@sZ((Cc
z{&Oq4B!w2L(EB+{adI)&RS@pFa1&u_Z3P&lwGXlW#IAnI41|NqUk=F#F=dCXc6#Tz
z=U+N~<ML%pvNoS7yC@UOi4%B^T=;Ei-<q%1vnKv=dO};E)%*+i!nx|Pb-g)?;(C81
zC&$v7&cD;eKq(<R>^N1eUKJt>drql1g<fh2vF};kIqHPG>Nj6+OBfJA+kJI-!0pEk
zw&FgY9D(4wq_}y!;XT;;%&4uaD$AZ1`9a_Kst12+J12?rsGM1c`)9ZNQs8hq>n^M*
zlfIqX`Ln%ejar<bvTSZ%>j4q0ktI(w&1}P$2s7q$s*hKxrniV++<6{-RV07P6L$DR
zf&4?F@`MfhFNCcd9ZLpegIb-()KG>fa=0#LV+S1JK6s;XJILtCo}Us_<y0!DnMUA;
zR)k8eX9v|OZxV5?%->q#@|$4uF#*Aj4#Am2O$3b~rl*<<ywWo8?Hsc`SxdekSL>DI
zt_m}wsj`F<Dh8jjwO6IVH*36cih1v24Ucrx<v{Cg&D~|F8^xn1!T3I98SjDh>X-`L
zm&8lY#^ua9O4TfR#kX;&pxW|Gtw?`fy+pH5p2s$?(>i!qh?Zr@AOA04V(DC^QxIFE
zpt;yz3c|r$Z9fAZA2<1N+ZPV{v?(1S^47d6rbervH$SaR-DYx5L(`uGQu?i9qM#tA
zG<zLdPPn_ul}qKf{>|pFdjHq{I^uv=O0(<3_<$7D-as?W;IL^{^R$WBR!<kL|6PGX
z#GCPXBjh$eb(5*<je&drl}o+SW^2=D6|;tF7VZ+0y1dbHiXt}+LuI>a(ikW+D@lCL
zSTI`DOnSO=>V0278Bcp6kSgH5K~PN)Xx-w0*Fxv8o;=QbY?^o<4Wa2yVg!<tm&ODO
zDoAS*5VPu1<272vXpeK@%Oy>=8eCra8`5kqG%mJyZnK*O9VMKzLsf9!D^8o~st}hm
zo0Dqlm8msay#S=kD#S&)7wZd2F&)KYcGanFq@inME{#b}@1Bl{lCFvN)z}Oq<m`;g
z<6WNPco^7ejJ)k9oGH<NrWb=MDNmmRrZP4kwLEvUv#CZ%{RO<r6t@_KAtydL+Dgr5
z0|D+=wj_+KiOpQqQCq+!0tZvaT;QCXwpu?Z%$Z=MK>lTh3Pm{GPbkn?g~ilat7V|k
z&yyu{=1Uv%HTAXlqiS{^vfV{p#ytF{Zm5v}cO7fJ&32}MsAl!!7%({;q65sa`3}u<
zcL{g5ca}J_E67}vWFPxrLzX@2scEu-wa4udQ@V$NhT|auSNi2r!^AwRs5qcQASlUd
zq_|6_Nn-<~aWYBur^B?1e|@<A_M_U~x`&Xr?Px5Q%u1IX^0B_D&3(PiI%;-S3vQOG
z&h>IVLUyF0sIs_9El0q3U%fR^t%s@vi13md-qR5LeU2OVB+!ZsGy%Qj_*M$SEa+gd
znssQ}4_fiZLfg!#){oGVC+QtKihpI>c4t14acFO17>1W_6}jxv(5_<x2|ZLyS}QrK
zq}T#r2%wmw$%=Uu@#naH6H{}XJ>rX(lB6B|aa*^f{QQ{CZQR@B=If12u)~YC{f^oI
z?Vofqp~2sH7M<s!XC7`SEcDiH-UM1CX=YNJzI;S}f%F^Ar~ju+)&FzW9fo_!L>b<%
z7BU1yZ`K&LuLw`n3z2@EKQG2mY3UefOS<6$hl`}C_~g_P!TZ-{bJwS$XC$w_i$A|*
zz1w&#lZFNvPt@7d<xZTSM9CBwE7ywSeHN<dhhkNQGCV{IkB-S*+}U*-C*&WL=F9to
z{fE!`@pG3sqVu=l!=HI61VO-i(O;y$EQar;C1L(QZSHaoj>6|2zHQa-+`Vo;M!=`%
z*_x+C6=}03{{qlM$RWR{wazy_=17<MchKK-yZzw$(Uo)_;OldJGl~?NoW(4?4(WYA
zcoyP04{Q5U_SW$;Fy_Av7yM&4P~+N5AxZt<NsQzhc;(Mwxv$%6jnDs%#r*#qOJ4hv
zjgBW9dn%CMlUgU65o3><>j;7=fxVmfjEQhx^B7_bFgqYzc_dGp_fzGpV$^&z*c=1u
z@zR6rm)l#cYN24ZJC5Pujr+-qthu**?=!v33qA_96mIhv_~gJ-+L4l0mI@UmgH7C9
zCo}_%MU0$hpL}EiueQI-6dm)}nDGnfdZy(~+Oj(s8Qqqav9cr&yXH1KA%wWM$Fp#Z
z(KMM;HNvc)$_yN`iYl^&Jx}CEb-TenCZ1HY8{x@FoJ7^MJ1+)E(Zz0&VP*=B<vN(e
z6_ZPZ{L-PYT}hjFC0h~uJ=#~+Uz8T#mY4*Bt&^T$=nixkhV{2G6AFI;2S$GZmoLL9
zq1)5;9B3Jcu7U?=+M_(>)z&oVWP(wAd|H8Arze58Z3!=Rb)zH}Tjk_BHtu`EhK?)r
z46+B;V?J@`A<8B{+^<yj_^&?3OHY864|Ag2xsjNqN<P(9EP`y>TH5nEG_s1!7vWsX
zKL_miYCQe|c6gp`joto%P=ZgXLw<ne>R|mHV<JjlgmL0T^S@+FV<gyv)}ZG|L-IEB
zbqT?<l!CM~U~SAG?rJfynD)5qCv2;zBBoKIKG#;Bw)+}f+j+YS{S+YF%JG|16cm}d
z>w891+HB=j`&qbAUriTDqM)0tpiNN3_w7GE{k_psygOimk4b6K5pqeG-3=Lj(#UKk
zWErX%=c*@yH^A4-#_K~iKr>TJ=6vxer)BQnubotJC9O@=Dvo_pT}>JHDpC}fF=STN
z3$r7OMVq$`zJ1kc=Qcm9htDD0x9uw4u30CP7}FS><X~bE&P#IG#CxUFCvRaals$<N
z<FUtQ{T~ys6qt0Ly#(B|@l6}&W?B)^IX#><*<DCcB8g$s*VmU$)J=}yP}Hy_0N@iA
zE>(g*-^XlF&9s@W!mMNHef`tNMEBOgXL|89W^tbU6@y}4VXZsP3?wyyS#NSi;a-wT
ztkoon*w%WzkXaF>UTIP)arDk5-JHmr5AijM18*ph<A$zgtonKFF56V}$5p8%VieKz
zF%(6psPwOF-G2ynh14ip{T!zI!#Lx`%xxOS&;i`{%^FKdFbG=XVH3o=++mi?s?)I<
zZP%(z)Fw4_ZzodyOt>KMMw|FKI@*V-tEJ9vSzgtRo!zDng}{{xD(^FRyv2xynkw}6
zW!a6HiQFs{^q_*(#xSP9t{c^>x&6Gz9M?AB2>}~Fc$@h8mvZ6Ze@+<pKOW=eYhxyG
z&a~RS;mpzK%iWkz1ea2vDJ#w(Qc~e8TaSi}aZ<z-dE=rl$VL*s06Y&>SUK5!a~ju1
zmb@wbn4^Am)nN&l4-8Nsm0&&<Gp-~t#Wl2VDexJbPgFuTCnXd}<<E1eOe{rF+@ko`
zQ=|XGQ>S|~PJdeokuQlotsJs&^lqk-nh~5<t+#<VYv_{@XAVy(=?NEyQH&U>a<%HV
zG4ZBkPM*X@R9Srmk}&!YYYUjNN2`jypr5bZ=mNoz1Nq<&!<(s3g0w4x{f!NNxk^~C
zOc?CzzGl<diV(ZMECB+Z<yjD<%HY<&Km9L1ees`53G&ZULL>Gw$II@!8-E+DlKNtG
ze}!6~-!N6IgxHP#+#}rI&l}g-7&TR&71O=Q6%E0kUkFGUPG%|6`4nYv9PFWNO0~M#
zMxF7Mn_tu===rW8c(5B4HEc|Y_0A;9rF%3K@v`upV}Dt?@*A<STfmb+CV`;LfA$Ts
zaAncrvB2K*{XkRvRG3ikbyNwxi4+t_WgI2uE4Vl_-DFLV_Mx_;2SKFynLbxWV^F_8
z)#<AQZ|p!$Ms%sJBMn7aOvScg6(h_h-RHEJGcLQF9=i&aS&3gDLYRO<w&N0ji4I&a
zO>mv09L>j&h0Uv)=Zpw4^3zZdOl7#^^=Lll^s?2Il|_5oHk)3=N-B~rqJ+BER4AVg
zGCyO~dy`#Z!!xPzK`BnY@ECy7Y%QcCHzVS+tOo#u#uC0LHkmJ@548c$gwab-SD1E0
zm87sc)&QabU8?J5uCwQq?q;0jt{NG=1|ssXMb$c0XtM-A4P~4mkD(wVOE||JzVe}B
zAI38;r+nF-Le?V7#hW)FO<{D{<kN5P0#N){k#)&$S3lwBE6koJxs~AO$?^`XO1Rv7
z)CzBOc5)bJudw0jiI5mJs{KTP7e+Eg%Z){%jH}rwFa9L1ZYk2I786s!S)g4tw2|e|
zh$Gg0-mW+M%q!tN6NcW>LdrgVvPr+Fm;?6$zY9etS9M+Cb5w><YKa*lmSqpDPS@Zr
z@AX728efJdX^~$agI!b&{*e|Tkr|=ur3o$yzN`~P8t53)2&y!wJ?7}GG->JVDl%=T
z5i&lD)OPX37Lij5h1qE`!Sn{$6C;W9WdRxzVKNdjENKoaWw^UcIVI1Z*kDr#<rpyu
ziPZ$tOZi?9pkspmBe{V0e<vAU_IHZVbY7g6`v!eU*f9B%4@73t?y-^~PMTUh(&BBA
zIGIyvU<);{8cTK+mf446a&)H#)R-_kjW=K8-|6s(jlv`M<3G7isb`)KKgT;Lh&bN~
zs?U$97WFxQ*DYwaZJmta?Am7J#$dNVVpO)O?kb1ZmAVN2N~sah++=moV2gxbzktup
zk`1Q0Zs^!JbF#O~h<V1F!RzzhyiyA95AT7{FhAr*V)W^+W)GUHHsgC6@LAsbRzJy7
zmrd||E>r0(`C!Vp+ce|EK3O`Oxz}31f{D%B<Wre)JR`jjcU-moj2-=1p`2>OqLrOG
zQFp!6wlqW6f@yqov{g;Qd97*YY9`Z24puvtet%B&A16fqC(<E-XRv2`EF)*6>@JjO
zi;6q8_`31uSApSn-#aopRwmC6?a3L}DV)y64Z1Q2#lp#SvqQK!Qezn~s;2aeuVb%d
z%$o)+u<hwn@KVH8CE}+o`fscFmwz9-HzBvaOFzW!*ypTW&krPIx3cZ!6~L9}zUy9I
zUt$KVvgB1%Tl$kF^U70~kqq?cpv-}Zm5UB1XFM%}ol0){C)9uzLKfM@Z1}4;O+Qxd
z=CgCRhb~>=uXPEF>NN}viKg?}?M56v$B;x5-3)P?E9$*B6fsw-%{-TR7@S>}{^YfB
zu`<RUk@hJ)e$=0KX+#vqcD*c|TxZH^(j}#d4KG~~e8Pzo5{nu%8`;^swRaPJ=D4=h
zK`Hq>%stJ(b(p!$J|Eqzs}X6A*fMgFmcy8eTl97hOcQgk#YI@GhmEm%7bnBPqD_Is
zi~F5;;DBBACBaj~!<O+6c^njT1M{^$3p;!#mW~H8yA3hh8D*z-aB$lZF@G$6Dlb*V
z2d-p&&YTSOlf8-WNJ38_BJW$ZS5G_4CCXRmhj9cmue$tReAjMqM#mSz`IPGtiVbC*
z4%hi3QvNPNLHS=wjb*+yeq*aCjh92Oo62Y#(=wZDva9X1-Uu@luJ%XaYCAPf&gv^^
z<=RWgTcr;X0tiqDU&afxe|ThI8d3EQ0tz`Ilcundb^~7ju1u(OTf%fLN`KuR(=ul3
zW&6#uX!DUL?F`pl&m;cM3c68_^EZ~GmVFot!FSW`&BD`~yw)rKFKIXbKOO+2a;4R}
z+LESA;yJHpUJip)shhr&c;ET=TO^XZbW(pD$?UTFRc&QwYP9@WDoYo*!Mz>{wI37l
znt94LwHS4&7x~0tJ1C_H$igKk#NIGc3!h92L9T(Zt&Ut#sv|y8ED&H&+Q%_6qUk0p
zz1%NpM;S)CO40xy9oabcuM^fNZ*<cYHRu7<uO_)%n}jsa-jN<kdFDu+MCLr{r5fD0
zxYW)&tqWWf?OW+)ag{RRF!x?PuRy3^2}zV_^fq%~k<44lW3`Ykag4RCdc({W++M_h
z3u0mJ2o8+*x-&QHnai@>J&kKL>T5q`!po0(^pAX6f67=)P1?ET&XxQ6n61awYZ2@K
z?vz`Irv}X&H}MIpMT0oY@};NkAh`-}KL}TotIoL~y2^V?`#qYYoSh?E;st>M!>e|?
zFlw3(+{xr~_L19ObUDQ|#aR!KnvA}F#5;cOY<CyhCZAG%-}%`Z&xyCX^8Q_RjMug8
zj;%S+V&2C^RiiwQi2Zs{@?;6n%TB+IYNDB9kKBrP6oAy>`V21Cl-3tCXOs{2jaN=b
z?)}h*1b*FTKO4tE|2W(r^`?%rpyhBov4U@f6*=mAx8F;R4~RwWnbTtFV=?>y`(R$1
zdRpiY1y@_o_rW#ayzR#r+~qs0yQI4xcnX1i?}UTy7ZoHQfZIVA?_uT-5iaWI8;x}4
zDQ<ZqqOjfd5k*aAha6_gfj40C33E~BL(;Vl9lb~`Gznn>_qaGlPlHJo@D>8zSoV5a
zR~-8`WmuP=5RyXxSr)D<2?H$?Po#iW?}Z{+tY`#DighA|8+=?w)X2lCy|F4&NCwJ@
zdA=93V1&EH!Z6gP+Rn+lAnRwiAn4tCyDJCAGHz$Qwa+2co}1F{de~}+(U@{>ICD~8
zAS!TH*oP~$q7t1k=4I;%?_)mjFoj=2Fn06QXyDq1siDCJh1)R6BGbG!Dxv2~nGoY0
zE3>J(#={Rubt|uZRYJCo%;i+|(oQ}3@KMA?8_77V&ki2<yXD-JQ3Reph}lGi-p|Dx
zOYB~dlTll;_PUKqR!ZrZ+?4Uee&@HajuTgr!66E5ZX3B0`-bNFB?6J?Qk~Fs6IoB?
zY2j~hGTKOXn|?VFq4B;%$c=eqH1E)(;RH&^*;5^fyU0_HsVCdvU>QGAe!^6TtoB9H
z2zX_4F7A4^fRbZEDTT3_#l2Bbo@fHa*rkFxq|J>P5cN#)F2g^HCLwi$$#K`~LeWTz
zm05nu)Qiz%M-xKLt+B9!lqBeEd_Z!$gK|%-l8`t(io=Ej@a`BI_IsdpLs3TAGHI(0
z_tQsZ1apfAcsZ5-QnScKl5<22W<ULPOW&}ZN^$CjECTJ*2laPnUeQ8A21~PqtO0|U
z?e*CE+;6!^QC=<m1wcw%n|FaFKaJ+w-B)Gp>_>0Zk&G$;g?tGicg!##1GiKj46=}^
zy#Wd&>fd-wR^5~0p!u+;{+ig-pTJ~eikWAW+fiJSFWJ?gRlU5>--f%jVRDmlti<-^
zb81s=Te(-K-dA0y{p2#@h$X~;X}fLyw<u)({{O|>TL;Cpb^D@?1qtpR+#$HTyK8WF
zcL)S`cW5BEyEV|bdvI%9g9iu>*_XY)d(PSKRlTZPuj;*jx_Zu9J?C20v(^|j#!mwH
za`Ian-MnF_h<miPt-iS0fVILRfyT1z@$j^Eo8p}02`K{NDQ3{(Bh*|GE(3#Uf1v;1
zUHe#FarjC75;%VRF5k61_j?mOcew(25guhM{prQ+<2fIWpMR}u*yh+;!`qP1W;m@&
za7B8g;CnG6fgh)0yXBJV#zKG}zmb)w8E10=c83y$J$1-BAdhpw_{>$8s1tZC=JpP;
zXnogBcRPD6_vp!k-j1iKT_PrZ=SkrThMtr{GA=15S;fz6HdE+|B!@%b9Y~=3SedK!
zwnib>cH*J~;x%tmRdX09MN4VE%N-mZvdOuO1A?uvK<V9m!nn`agvQ-Hr)SRCd7YP=
z-i}`$!aRWit2ATmh$M2nKhFjRI%-pJS%)O|sHx(rWf)5sXta=`@Fho-*yk!TidTU#
z$B&F1B~wk0c?gWL<htI(VCp*Gvk>*!_|b>>W}i?S-wq;aq3MCvzAlb11CpQ3xO4<f
zh}i=u8j1|lKTHR^)gn1beVxfW;U-fqU&{l`!_K4h&ZFUC#%b=XXLc>A^7m`P^qVbx
zM^xueo6s#nY??eIGx$H+nUyIw#p`p*2$Wx{WVsk#B<g^v*?^Pk6WDd>EFay(rH0n)
zVQ}@brrMg~GeP^wUxdh7!G0;?JCSY;)YaHBHvQUXRd>q8aPZL&EPDde#&PVDEqB#m
zxJuvc46V74z2b9lhJzzGa4bJ0H!MgUmo96Iu^_+^wkQ^d5^+RKroPo{n#^G%qb_^5
z<E7f^B$;*iDV6q0>Rc@{74&5dH#Symf5q7n!rgttyusm(NHQ#g)4UY&NUt0s-%iK$
zv2p<)hYpv`W2uW%JY<^hit`Z=upWtj@<Q*ws|~kfhtNxV6lWfhE!i{9qp#qS3DdeJ
z#s+IB==b}4otf-$nlg**WPZHGW=)3WU8hMBoqS1CHe1nzNrF}SqBXNsqbI4u1h+{W
zH0f39Lv`Iz^aI<f?^lyl5<^H(EAIY$NMAsFf_CS-g4=_ob8}~e_~?^4{W{-ze`<q^
zDodGJd#0P0Mcabd_gv(brq3EqO$Zf`iK4>jhDKfutZ+Q0278@lh4z3eI}88Q#jSF5
zCB4T8l=Sh`I_(t84=!;Q`CbUe>>r847%bh6$0;}Yo~RdXIxOjTP%a+qwh9Gm4L6&)
z5;GSX(=6h*{Mp7j5N>7D=d#krqCYk<!ca^0%#6*^(5)7}W9uZv%TYl|i3X(GBgZR-
zV+Xq{ZWw1h&ZEc6{|L)$h0_Hj^WMm){=&M7ckQ*S$;M;ZixVT9ux7S+^^Gl}%J=Qz
z*P(cKdf_&7L8%LdJJ{qRUtEZ^D55NPYf{S@o)}S<ZpaQ#HfO_NnC3$m*KO5sS)25X
zLZayWSYn<mIimp;V-^(@bV`g(@(c$ZEcUA^x+o`>p%7D!0x71dAoxA8;9`q=FQotg
zi;^~c^g%ls*rLO%j@ZK_qM|;cqT->X;a$uE$!~*rPMf&ruX&|Z=pX|J%H^5NI3N0S
zRa>$cqzbPyMC1=Ym!N@SD@t@~xSU&wUe*iKwN?VdCZJ4#aX0<*i+7R<bmCHAQQsyv
zJqJ~A21h7^i6SQS1!4!keZSNs{fgl?qo}f&pLJsVj5Li%g>!84toVSF-0=!Ebc|Vf
zvuH(P6EZSR{6&;I3q$r5PPj&&^%dfaw8v!cI40~+nS?!q3V3YDc$-sG4439kc36pK
zDeF&uc(@N*Vv9+E1N5P5ax%4)=9FLP{zyxUiBp6iP)N`<P1Y2og(-&7WsR@~y@PLm
zsd|{OAQ08zvK+J??Xf8M*=AFbmlqHhU8lC4ewep!^Vm40s<J{wXG10v>M!V;HE+;y
ztn{cCTwd>}J&T^M<}3xdw}twx?IkzSqsK7xKM_?FfZW8Z)Y#<#!~juAPV_=)sei*;
zCEfb(#br{GUfMUT79gK;an`4!e&+IDioeU+ITL;V@~<2i9u@reNrH?V&zkzGilk_4
zqz$GFhM`ov*Tday?DKOBM*oAm;rggAMY5AQ{APEg-xyFV^{*zk*qraqr{Pa&*cdfg
zK?)QU>RC{5cl2SR@MDa96wugm9^v9{-D3-tpFtuVCrc|XNYOWMV+&Pu_HE_w<3>Ho
z)X7K-+bbnW(c`C-U!F_AdnB?#F4_)4PwD&P<M)Ytwa~3Hvex)HY;}_6gi8FuXRwTr
zU%O+|FP}u(^xY3<zVH5!x|k1rijDccpj(H-{lH>V#m|;Am^cLV*>YM1hV`7y?3{9_
z$sP%o$^jvf2np%Sla<)6i)}PNJKIes=5X;f=r;v=kB%7E^|CjL9XI#!x@$(YyMGO(
z5I}tl4+h(3YOKKAPAF3)=OGmh7jO!=ze5L2=wUb<<_(Y4DSobjfYyUEzneXBh3rj9
zZb5}M3#8lJ3-k_#mLel79~YfyUFC<1k@`R!E-4<x8K<4M$(U<O64@rJeClgtbYD)&
zu{$jfmwVczeAiu$Nv^zF243C@ql%F4m=6N+A^PRFCvK)Si-|vN9aUsTC<|dxFpbhA
z%HUvgyM;>4nYRV<erC7jZSw&AS@ZkwaOR-0n`|#?X8oVf?_#QL2ggB1r#6SkE|=#q
zfu6<qWmVJ;W4`uYL-SB{_J@GNQW_y9>!_?a`pnwRh|T+-4Ob;B`r^w|7?(9FdgmxW
zGpv@48A8^PdN9L+=v>oTRn$;j@shtnGF=jc3|=@Ky_7a7Pd2OU6Hoo}L-GD=@V$LE
zs}F@Q(m1o=I5=-r&*^Y{sH@N7dd4EX`7=|nH9k=m$&7AYGN(%&u+2HeZTQ729>Xgr
z7#3-@0NO=Ql;VQ-9gkMaJ*1J0#I*ro`jS0A)yvEj#E~%~*j8(Aw4UEvTJ&j#FRpFC
zSa-aoq_Jc_eZ?hhf7z62#<V(xtUYVIsuG(c_aNt0Na<lNBlX9NV18sVq`+)h*NKo~
z#O~43@ct~}cZX##OIRYUQ}#Gf(XLMlkzEY^XIj=>$$6|SSq&)3kp&yyYux!!W!%%#
zmF#_*SB~ux+D|<hHy7Rd8CuB#U{OP_^|((z7-PqFtRhL*^1aBy*O;dNgmaJNq?#W|
z`q&=}FLl_)>IyNvvG$H9usrX>{$ZV~PfBaL8-LcmsI$So{va}XgFAlhn6c++OWlyH
ztWc^ijiE3H4O7{IHb*J!U0Ff~Ev8tCi9fW)nwS(D@b8sopWsWsm*G9J4R0Hmd><kF
zMD7;Z*{g6ce=U_gk~Pj7Do3WLg=q|TfzwcBl&jDvY;Fg;q6G8h6M5z#5cs=E-%>Ga
z8_Rsd>z(sJo<yTI;#{IL0O!)$qen}Q+_ZD#iW4l+3_G+_h!h}`rb;3nIVQ7}5twCs
zFp3x88uAU_=4<HukA;t~S$kJ?tSb8`Xi2#b4)3B0^0r4d&5$c{Q(X*Qi=PvOmjRe<
zcpM?Q4fF6Z^w8Y4lX+-l+K1fz@F|oP!Jg<6JeFlBmAA+BXII?gJNGyv-#qO>W3EHD
zSO3gB*Kxd5>m1<cf4zJ2c7V{T+;3PvXqS(%S!I;;*5k-J#Msv@x1wi@Pbe9<0X^Bq
z(rtT1#F8bKu@970X>(>Cw^Zc~i!vkaEl_A2*h~Z8mkBu1BmWDqyf4z(TAyU*d%JS?
z0JM11A$VIVA5%2?;k4Ce?;7TM0~v0$8ReqKt=OK%NA@X_F>D;8g2gYzwJo>)`CN?f
zHP1eNu?}TWGXJplN0ajFYSm&`@AZ`7ZRf@QS8yDN`4`NL{5Fpw<!`4jKbokDLUUc2
zcE3F(OTm<SDK@OMsWhl~b(_;G{WP{qJ7dpdaF2xnVyaV%`!d-U+!oH)StEnY_*!bg
zaRmPuX0MdEPovbx(>J#kUS6?OfLYm|L05yaY6oXSHZ0xMix-yaMDNX=T&puI8o4ct
zEik?^BL*w)pOcVV0}G9c6Y0i_5;Nq&HDA!QijHa>I=POMDx(%EMZ1tjMgPB}$KQZT
zxFQEpf-(yA@0(FK$`nw`nUO&fF(DV%_F_jgVu;-NpxVt6mR6~cfIsy$6{DogWMs&(
z?zqn*%C!9)L@7EFs3|nNvr?Fq8+3E+Pd%QXB~q|vx4uBVKHgbYQCtr@u!D>l829rc
zf5_-CL#V0V+>MMTk&1E|mOd;GpT-O?2o^g0IspmTtIq=_`3t~v(Zr!u9{qXoYLXGh
z>cyIN4Z~R%id;{iW4E?i`5-zaHAiKJ?K3lo9c8nut~zE!c)~<7MLyF(5z;OuaX1y(
zwft)ZR@39`V>1_JOru|lO<6mo^Bz1J*_WzpO_+$e&u~t((-|ze;p%+6qNr>%I!aRt
zCnd_ZTSDKM0uJ0Qy65b8dFX^t+hW2~tw<^#U#Q|dJFyTmqfnp3cCZjs!S3$w_M=<`
z64rcz_3weJaKw}M>*sPf&yC<kML%OVj$KX8!^)XwPyIIJ=Vf#bT0fecz$+1Ia@L?5
ztlh;C?hp~u4&|jlHz7N4x7=~BZXI6IK<uSsSH`cbZI5Uv8Xl<a7zQ;TdVQ(8v5IQn
zbu_7pkram+B&#yEW%>mrbP+^Z6ND3oc~Y;P&$?E65ko}jmxk>qrCA`YI4^?^>Qw!8
z^j~X7zG1_~?14Gk&1mkjQlkPPcq%g&13RYtvbhM+E#zsM8*6$sZ4e^AkvB8Qk1bte
zLdMKSntf0}^yy*Vwhp^+`$d*QUfu;4qQCMwl?6$gthy-xY{M>F*Ww>!h*?z#c*ZWu
zrWp&Pj0><ZYL4Hp6~O4u>n16Tp~ipCW^vY4dT>n;X`>;QH@NRn>aJ~B;}Dzl0J6*7
z>W;|PP~?Ouu@jG%F3`_IxygKaJzkzWL)(0wX>`xf8GD5ANjX!Vf^xSR#)sxI%z5b`
z42M>i)GcD_U&r^F*cCeo*qL4v4BW!7e}-e8JET&{|8Vu27qV{G@w~WTdFeUpV0j|a
z@yPY>Dr1=TH73@(w{MSL+laJ-XmFqDyHP0XwbWp(J}u$KC%Z&zvC<SEL{GqoGsQG)
ze=%~|zClBMo(73egBKxBEbs0K{&-is%LlDBKGl}$BL<nxjCO&;Hf&dL>N0tgElZT;
zNK6N6GKG9`T4@+uj>)?HjLv6gSlg2xi|mE2sP`!yDKc(x&asF&<jlnMj^x%k+DDwe
zo2c?&tFn1*AzMx3@>K$eN#z8zIvrwGRh7EFg+pe=R#kZL%za=-BsELFf%DY33rpuo
ztMxGO-72-UIPZmW?1pD?3Cz47Alo|0t{;5KmE!6^`mD3jm48n_FmE-UbUL`{OhR?U
zm(mcMrb2iK`$BhL^b@KDS`CROU?aA+`;2-UM0fmE$#$J+Ud0HXD8NSW)^7hi=N1)m
z-wGsb&9mQNM`vFhJ{u3}q#59vgw^o#wU7;tqmZ0&kIG|9A&q=4Y&u9d%P*=X@>M)b
z`S7Qu#z4vAC@bSs1{CK?fHbZ%QR;A?w(Md{tI%uHF?ON~r>QoszD5!f9k+?onRKnc
z_@m3Oo%Y)XrPnr#JCX197v^L)j;$&gs`UrBUVviP<!Sq8Mz%%dI5785VWla9C}}F~
zQ9eIQO;vbH@t;nQHNHf(VA>jmWvb_;I|paEY*5C#ao4A<A72%UJ?$Sp;tq#IE7j=J
zE0h8ilKs;Ql4pe+Sgj8zG=aaV6&$(H=JUi2EzA*$7?;(0xTVLmHxoUNAd!5RlRZ5b
zkO@*GFK2AAeTe2zqk}+%9p4eVAA6lsHg2?+2c%Qm78={DE2+k@n8Kj3)ogI9DSqD@
zcH<aj^)n5D3WRTTNy2jTPkqHUr%bk0hamwSm_mGAR(_*uqQcS%H<eayEVoqhowZZ4
z2&<ILp6C7-payMOq_gK*W&P@7p7n*$7VBA;rJdnZCe|KUfWkbJC2x3u+OK^g(X<qQ
zL%SF>KSNwLjyo)g1f8uRxnox4&S#dzl-YI<Jk$05@RY1yCO$a8YrK3Mpqou#xW)8>
zq(d*OpR8?w9899m9o}c<c$v+?P*<PHZx3dmR`jF?D5@VO=(Lm823)W(AMh}@x^*2c
zcsPGdHmN75aZV;_Y9`n}3r6&*#8Lku?6#Pc%R!^gl%$3U{%{$N^(ie|p(3eVU@v9!
zQ#d2=1}&?1>5E8wn#H4fM=#k^?3-5c<C^IIcLTB9br5m*$lPT1NA8nmY^VJKO2!Iv
zILp{34UfvFI(bDPVM1;(R4dfZLPRKf8lTu}&`0gExsz6`+~=s%K#Vz)d5}YKi6_=g
z&w=%FL~bkJwsKv;|7BP}BJTVCYA4a!`RCOxx5`>mV?reE!w-u@f&8@)*)ia8%41d&
z{V#xc$65>6obVsn@K#UE<vAH2>g#V`Ix^0UZRY+Rdp(SN;ICy4;b84)Ursa8U-Cvj
z;i=YtLQtc2ME`Y1AtdKPQjhojU64;=@PKkL6XKKmT+}2su%GlU$Y-Bp6!5noAAInb
zLM0DIS6~3G&44rOgP=E6@T5ox|3e_}N+41C&i=gwpx)4!vx4{1cH^F2Z5lHY4-bx^
zj$v7bivgRuxTA1Od7NO`8(vQ*EIuf{7YrfJgQpo^ScXkYP3c@r@Y@_bBIK2gFE&kc
zf>*Io<@n_S%RW{>m8#bdQDM^0aUI=Vj$z9;ONz}O<aMXt$o_B{@Xb7<{dvBddGr!U
z^_YO3%y+rpiKnzBq;<2rS88((I3t^l>SBxfyux79>7}5vhQI0bBZ9P7Ah6pw8~0?A
z*BR+Re-**M>3EdB^Lt>3g%s_KI$fK(608QjtmJnjp;GoVZqZ1<=MU+l#&00Uj}%;G
zlvd0vTRba7@!m9BKwl;j4^!~oL%MZ<=*CfQP+&jURau5~O`1X!7!rLSJ@+dSeI){7
z_Z;>8WvpHC+nYZ7_v;pw8EtS&wZPc-l8@1@EeD#|bys{ix6$^fYrS*Y!e4kyYU^VC
zSoUqAJ2hp6C}ic6Vr+;6Oav&%3*yGKLcaMDA^ru>o+^KJ#JWxQZz_LCZuoxZF!60<
zPJ~bSyt;$rSq9%oSuD&>A9V10Daj|5YEk<OtwovUp(3&)0%d`t%L15T>;~-wagW$D
z(CG1xhPOk~z5FovpymWVj=Gp1E;G&#Or0)MA{)8NH;chC3pg2p(jeC+e>-rX{jW#R
zx}5mFB|Y$MshR#S0FmVDp+Hlm46@&wE3Wm}FWo&3z=4lUbFD=lHO(JoHpx_kRZvq5
zR8+QCq(fX-VY!Dyhhn%o-u>F%)mZ%7`JczpuNU?m$8sH`_v>bVPraIx+_w7XyYs!Z
z`G9XIzkS1hze4#qcWTnH-hf7+qo}^ps@>_%DLm8`^D<d(D3qi$S#U~{jw)ip3N^Jf
zTk*9P#-eO@MHJ6{#>&hXVjbJEGr)6SJbW)MaBTNLd}LkoXiUpG^;kDNk?fkVTYOak
zTfQzO!iTK~%|HWCk|+bHu{u03#tqZN3pSGMHoy1%`~hcF8}2K@qIV4fn<&T$PY6R6
zW=w}}&^0Sk1*{2HbF^>~iE6Qy6YsTuO^S_Dg$iKNxPYhCRB8|?Hy`EDeK?q_ljZ9;
z?<Ydg(5b!>mGc@#04DD<cc8Q%q+QB0Rok?LU0RGYp{b~72DK^@iY&hNW_oSrqsJ?G
zJb9C_O(R7U^J1<QS?f)NT}oVde1NcQaV5E%V%5o%xl=qWK5vp9bj5dNw@{0)DyDR4
zGe$<dYIC;kANp}l)2T-{62y((2E28wD~FfU3wU<?c}nP11wx!IIIwvbF04$+@<ws%
znHHV4Qeb<Z|2whyJ$KJ>-;%pV1FP<nh**C9%Fx&KDxoU#gobTS48otlqc=a}ppcXn
z5=`@OJRzKy!^Tr<I#1Wa-Ylv>pJ?-_LbBa0aW8X}b;p*{{@4sKnY>s4W!905@R)ZX
zo4g<iec20*_pz*b;sWv}wJu$tq(Nd_ia8UC#cpWWkSGjA=~|7h>!^m??O^5%6cWz6
z9GwW%JG61f;%)IarDS82#+t@kq{!4n<)CTch09jy73zYZBrR~4iXHSa4g$X0_KoOg
z3~6X^B!ouD)<Vd#r_6IJ%kszYY$otWNZBwj_Av;NmRlR0*0`6zR$tJQGnPti#W>p>
z{oQadd8|JX_jfw#vD<92(ql@&s?%eUONgfdU_wHk1tqR4$TcGM$7E{%?*5+D9k{vO
zZ7Ka1c7waT%kXdP2C*+?g%9GxNc(2%m)7pY>J@k9Cm}oyzc_e1%kq19dvbeaVIyUB
zr5wgA)Mm{?@=wx&xW{Z>;V=yJ7jA8yB=*cw3q<(o*QcPnZwCe<u`bLDoqvRcnFj2H
z^sylgG|3T0$n$=2_2%gjfdis~JErUYEjwa!zu6&H078OtdkLPKJIM{bCV%e(kX_>g
zhI4d-FqJXx+N!xP5vd%6(bJvZWf$=$pTFrO&t({t+r`KjfwYX8aMloeHJu%o;|s*O
zQ>5yLw-Ur9f@5?=sB4h!0IcspOO&<kt2JJto9{K9o*Ce(JdhzofK{?t94#k<l%!A{
zf-Tz38l;xpA>6Vd;K3a}D)$v`UFFMO9QunFBPzSi)%Rv=Q+XbxlJJIVo_b=ki{daf
z20O15I@ix1RuE1V=OZpeG@Yw+Mn|&k(IG^2de`?K+)eS&(u#1C+DSgs4B(1&l_=WC
zfin$nwUrE8l{DAe3^_%IQKf@R@i@!Qi`%zO3Cf%q415)k`;0XBnlYRDeWlKU*{lTm
z+FZZnS!4Lnzb0dxU-scDSs+PGxWGW?1le#Xe4!SS|5JU^2R(V2XgN;9Z4eN&%i{)O
z8$05s2ARj;l@Ls4st^<&0DE+iFDL-mu>gST59akDaOZ9=9!IJZv6cmQ%h4^#O!!Hv
z#hf?Uh2FaXa!P{KS*nQFnH16{G*)sGOj!}MDF&t@SXTsK`Ua^1W!)m`F*z`Dy)DuE
z3xt}0N9psmZP&Kt1+aSEPLxVlR7Wv`+eBr=gW%y1b6N$6p3Axo+avx01WoWlJkMC>
zdmIJQ-_~rWWxDg&%r+*-?pk6n;+E63nzdQflXP)RVk~VO3o#T_p`ed46dazLy@{J2
z?0X+X+8UTNl`pczZ+`!I8D=hHVY4J>T#({DiQBoQ@klW#p;Uv#)UAk=hH5N`LYU^N
z@}AOfHmP}okH6?@O(<d+a}Y}=Z<=AX)5>S%#2I!@R~Mg<uEjrm7a^Xl&P4z?=D~~6
zMWo4gs+FMb01NcFg>gn^edOWN<H2oeo;j@39T4gzY1Ct%Q@OKB&2U>dY=cu~mV*Ic
zC<%*h5SAza`Y5l$ZuCy{8RXjXB2YH8X*Jhoy^kooPAbo@ybr@>?;$NGp&}!bc1y#k
zjYcrpsW>$Y4MI&Dp|E7Vc>h~Wei+rQ;t32nF?($Dl%20?EbX6OraOA=OnrS#VZKaf
zQRi@qvvA0pbdj$bOODSU<?@KuAWc_ODAp_|3D@9qTy-y<$^(C^1o2rlTi2xIpFHFn
zq^HNEWM!$}zw>=TjU)tSczGe#E`<av%`S@Jia)+oIVREj1OX7SyoT;l-o5~%4p#jy
z>ZCXSET-FkwW0-%Y&P4CDX}F{?_2=ARO)cunGOlN6STCa7AOklEK=BH2{S{YbM%RV
zSxNCXzvK6r5yP11KSBsakuHlIAs*Q5R(k7YE{BjvWgM5?h44k*JgH^XQ(_-Q)14y4
z6+4R}Y-}u#HLVppU>LUBA_9wk(jitE5+KH`P}KA9fL>1m=Z6G11li&S6r!%o-sexY
z8KoAlubyyot*@=4a4jg=FwXv6vVoOh`eZ>RV@kes-9BC3Cc?|unjFGU```8b<)DB1
zM4=F$jpu*iVct>jXa{Kz)T{>$yD)abFV-9DgvVu>Z0FuP#n+Qc;{Pvm)hC2I>RGQY
zqX6$iC^WRBM0I1wJ@?a?arlo3;=(sib+*2D?`3{MAL1~-RDb7B-*wW=y|HYDlPxfv
zmlDV&2a?wd*ChulPN7No{Q&-4E8<p+GKq0fSuxZ2^}q|29=%U{+pqWF<%`X4Sp6B(
zw{zF|&aJ{v7T;pO-L7lD&tm-lJ{kD;?h+4<x}E>MeWS19w|E*Be1+22;w9^gd$isC
zl2~GD5z{orZ@jF!P`{vSGX*%Y`DLK!DpIT$Y=X$GoN{efu37Zy<$uVGoGa-*o_f|>
z`Ln-ql#}i;mxLnxnYyd?mP^N@GP{E>F|4S_7A^8n)_6a^lCS^?F0i-pP+F6SpPw*i
zeALAx|4INnoDGw7vZ{;eM-=E8K5k}0cDrB<u@47w?#Z0<6qe)&>OoK*fLZ7c`%OOA
z1W<8!tot%ekEP}%7#ituVw%#yA=FEm(XZLvUpOvA?M1RA-X0Pr1-2F<0LWvEx9|nQ
z_QA7-8hX<C7^`_dN`piwJ9vKS{>B}P8@Jqu*G`HZ&SsaZw&iI+;{B-QvgVF`H>AHP
zSST+nHpqp!w~*RLL5KqPW0!E_HQ`{p-+WdWqxQ$xrP3>|5IlKu?JPR_;2JM1uc-L4
z2pn7$7TX$+Ca2VokFo3qC0wJpTYfaMZB>1!M1rz)=mt>YNxSnHqC9i}A0N>VAt%4$
zodRXtoOc?3enMD2KJcdb^N}<Zt=BtpxV>#D`Q0kVJ^2Fsg~TgLR<XThlTI?>Zr|s=
zfXXSS3ZY_m-U0=QAK{e$L$R!GOtac}!`DxHsr3Gzro+E2RO`uqLTvc<Za?(MTFXfO
z3kbh4<SQ%kva0jOuPs$2!KpX*nJi1z>@|OFbDF`2ms81}7sKs@`!iKSv}Az^)Q7n<
z`pOm*B=Wi<07Gp3fCK*Zo?z+V=xf&B3&?m1_RLY>yi02tE3tnV$D;1sDFxgE0PApK
z)p6IPWR7M=_b~5OEQcDyD<E2zS&F)=4BmBS#SgAd^t?!ySP!wz+QaO!6RM#yNmzb1
zvLbUDnj%BpQ7{<tgIQ<;qmd){QhZYx)aDAf!4#8ezKL@hxmBsN%``-(=m^<Qb2=-6
zBA2b8&`hO3#9?2`%Bx(8ABJ8(?qc!Ikp7i=)TNcEryqC}_w^42>)Zug;n#7(onyH~
zWtg`8BEX5}A(2|e#C9ZJgNrarW5YeqP)8BN$f(zu|CcqBQ;hv^4V%TF9MR@*R9ekv
zc)BAJ8L*_>H)NYYC3n1?+wb2CS{i=Lx+RuV>aOzn4xZUQySh7$J9R-hbxsorD{&x(
z0VKx+6ra;1Ik`gzD*%Q)_>e#U_0z9xpx2zE&;7?DBBGDx&QDxZ#-THTfguYh-wQ53
z#EL{VG`$?f9~$>pb$aa&1o6jxJ>t6ICzj_gIxLEgIw^><gC;MS{eN0gY}p^UU%Unm
zyd^opJzs58+i#tA>O`lILsu>wLNdjeEydVDLW?>-Bw!{T(H0>+f@h-2E+rf8XI>FS
zqG&`ak^!&<N!XOVa}#U?z9YZu4AFGN{__3tV^;WI&(NxP{8J#H=<^X1anEpZv^IKF
zU1|HTK5KPn;7Fs4IAWw$E?9|`gcFLph>S1_Ze=&R_fvnH*(<60?QgUQ?stnnhv7%T
z<Kv&Kh_x4u>0qzXRrh29RF>;>vt@DNO<$xALx$KB=;0<l4ai@BulyKtWTaTpDoxOc
zx$<AQk(5l_@s~y=tH?U1Ghqz@NeAnEC{2a1p7f!HutGTvvA$P6X8astmfkTXURL66
z`nMwr4hiX26d!RpHL#y0CB;O!`^uXt@HN<!Sv?2I#1N6nO}kt8rE~#~)p76=6#XuX
z#n{jv){+%riwJ}J)e#&E8a2=gdkZEsh;_F@;8ud#Hiszt<ggOL5gIXeVl{aM$|QL=
z$dKSPuc9pR7|oi%{Suv&;#v2<pETChCbh#cH0Odu$w+o#M={nIOgxdva^&%YmmVd8
z&ij6vUnR*-`|IM(`Dd1_p7&i@sNJf_!mpBWb9;5F(3Z;*agwAOvkvJ=!o}~<%9(@M
zVx3i@VTW8UD#NN^O_GKo7OpUWXhkt4Q4A%`IG2i%+o%-14;Sl(wiQ3&i#Q$$MxrCa
z5>@@A3}q-JAE{CS5LP>~N}pK+EM(Z-<%9u4=V_nU=d%vte|qoU(~qyut4WS3z5)kn
z!eih62PCec_ihdOA6)nU{vr+>ae9dbRSgi(dQbgurgk@XD0QfH*l-<LV;B#<*L1o|
z8KHm1b68R0LCF9taYac)BB+_!yhi$!^vcLT_PzWCI3%v`uF)V$#ki~uMCWnVny|pl
zJZM~ym&QF4Fl7g$m;vijB4s5-sk*X=`<B%6WbE(ku_Mo&nRpD3n8qCm3!2N7U0)}?
zrh#drYEo##2PNbPsu*l>6aWBJMLaQqP`gCAvuH^nYEJY(tKtDKqT&ZwEU0(t4HOEm
z4QDy$uFxN+*Ho7cUWLGO=kRObFDu+jo_3c(fz}P5lHsP$aC&#nV;X$$sHs|RL2b0s
z8X=}Fu<MvZgElUkpfpEfM@Pt7F|DMPVm7Icsk=oMk*9!KNmA9*#55dH06kv{=TE|l
z6qw@F-w|Y$<5=fGnd%ER7sQR3hBZqAN=ED;sNzxtv$Bekxu0~1#1aw?h(Ra?zw8Gk
ztw4FR%VRB0vE#oDC%GpUL0bmOg*B@y&Fq&8Rv`(f>ns#pc&PbcO{_I3SPk|VV$o74
z3?#HZdH=oV^T{)vPr7>D#{SO<`~r!gFgt(|onwnf<_mvQ$DpU!qKn<DKMOlgqZ77s
zXi_p|8HdFWSJoTT&I-z_UHH-)E>m?`AG|;6`VVlO3pQ(R@hJ5Z$qkJaZ7%T0g#a1J
znk>~gg3|h+p|O3lssBC^QHP=DVTsh_53c`}(*ZH&cs)15{C4iQc!XR21>ZpZn~3la
z+__-jrr;Y@c?W$YbtD3j+5<%RXm&-2^S_vlcDQXZ0{x$3pO>aUW-rn)|2lf1MYnw-
zF(PzylzJlDn(dJrm%NXi;XaSz(Tp~c@6F#zmeAI{+p}Ie1fQ>Aekm``Sl>0ZWqA$U
zFdj0e-4%Y+Y&WO^PM;7xY4&*Dfz2fiz$u|0B;#amKXwv&etfm>MG-I}6|tavzB|gC
zrq2=2F8QAiclogar`*)U^YYnbZ_i^#h`J7EF|UGe*yIpV&D}=mBBd_KAte%q&Koti
zEXESBI7$K&k<_-v*`Lwvway0wUEwM<b2F!TT}<Shm!(*+&A6lHimFG&J6Wgr5`@J^
zk#eN8>&lMu+x-!G70Yk>Ixfx-Ggg(YoKnf~9}gDHEO;>#SjOq0*U|dW7(0!LB-hN_
z6`#bHJ@dZ+;1}E;`*YTd3n%@a*?w=W{=`dg%zUp#00E3Cy<;&_RSFOCIZ(lGBht-w
z)%Iwt)t`}Jje42URgE1XtGK@)gAfG9)ftz&`s`*RP);eaMbmcivNrr;(-0jMXGAKG
zDnlA3G;@e>5VhNQrD2%TG)ftc8k3vS!eHstUa4)Ke$p*1kH+0?0v<ag23#SP#vkpI
zEwmd>zx&(B`<9CEQzNuMAwu_2&ly;J@1|-h+QJnnN(rRT`qC~_po-1qAOH!siUm<u
z`a!VpsF(*oTJKkLTiWn)yI!a_I_b%P(!T!r{E3GuVGI&q`|~dV#jWEte?V!%yXzlz
zro@~d<-2q3wazk5mz8c4d+A_wk(uMm(cHJl<!%CVU+Lwcl5Y|O_7?W(_8D&E-aj`^
zD@izPq_P{c3)n^#vBjj|=Epr~Y9k9@$}DWZ=EWY;9Mo$+N%Pav8ghphXAt6YR&M;7
z<4|53orEe>wI{9YtBm!;u}Q|zl8h@v5ak}jyr|7BqH8~Sh6D3Rt@}}W1%5Y-?}j&b
zwvMzm65_QH6l#B{+ajf7YuMCL0=gUuXfSUiEo)_?!A{5U;VR|(wl@kwbY&l6<YYnA
z-L`<PHH)9`bi80|yP9ZCI7Sa=n%%Xkv+4GmhphG+PAel@DDS}$F^ed##%WdmV<Oa<
z_QTb*WO|C#Eyv7UpD8f)2_}W5m^<AePMV<APiJb3hy#hu=+#WS460mW=Dl2)bmlex
z^ptr;!cGQh>=jSa&2zT|*?gdQvH=b4g0(~EDKx3nq}6NF()kBH@a1`cZLL7(@&`4+
zu6ZP(mfBLB<SQ%JHB7bh+{*k0HdHH1L+Fadn(P=`rX~gK7{Kxgnj*-u=h7e#{D-Xf
z@{S1HUN?ymHkDQ_3Dty2Q6)+*U$9zaB1L-=Y*3bk5G-E+Kn+$4I}xiDSTgcu?;CkH
zZHYZ{{HX_Rweih)Q$a<Bp33s#X>tY=)v&}D>7o(ct4Up-+NFR_bFz&r>$}M?lOU)Y
zG}>Y6ta#KK+HJ+-Y{E7Dx#nz?I!E}2*ix?$qP->kCoWK&rcsq{-GWV@XzYm#|2#{~
zuNu@u`Ng4HwTs%s&ulJwUSW5G=>CCzPOGM$d0-2Ok;nHlqL~g0gOyd07>I}7Z7fg;
z<Jfah6t`%}56%OU9-Nt*J@i%aZ=QNC?FGomo)H&^f~Qspj6nwGL&%#q9~i9?96e^L
zJyLKsqSC_j$mnAlD<ZRfdcVe%<?TXQNggq3`#~JLC_mZ_XGPiCft@o{JFepV@+PYB
zeoX;e-F&hp*Um!|xoAS-iBSg!nY5La_gc9Jx16ntfjMX*%_H{HKDUe(XKpk&Jz^~+
zJG^U(zNYK<T`hz6&6wU4+P7=jzhbn`R<5UIe;Kf>rMl5+k5x9$R~3qeM}RI}yL&Vy
zcp|P;u~z&wc)<?(R{ZWCD`Dpe=9rfnx=@bJ=x|b|i6(d)N>@*8sHUJX2Gq#0XceK|
zwd*3AxbW!&jker<rp_$;jx8<?lH&{at`nX|;v@Kl?Lnlx&AY!2K`G>pKu`ai?VhaO
zP8H54>g1C-T6;AvEJ=MV>!P}3^fjefP-ySXrNOlJ(wtXohaRN#>mTtsDd30d)kMhl
z%2L|^#D^J^Vz0Qu2R9+C9@#ToRm%;Zs9lUI+CvDXVo=*V>RqN3)tR$JF+$Xmn#5I%
z_0~gb*XI61D)R#4(GLySgXKi8o{0d$lVqW~xKfwKzC0euD)m^*)wKrFAtjGbUWI*>
z<>#eW;hIJ^|G4Ce^<ATl6of02IREdERR>NU|A(XVbvyA#ZS;r5htwW9iSy`JjFolT
zZNmM)C0|hQpND4s_QTCB2TxGypnm24YijI`;kCyh)3|+m2ZU67HfiL-tO&v*y$6Cq
zO($wReQ&IyR8XkCGkR-U9ow~Hceix|))nMOymQW9%6?9)EDM3Kn;LqkTa-|!z2s)L
z^hj}WozNCO)<&_3yWx)Cj+&(LQzzJIkmyuJhgB|3oQgg#lTqCa?$<`TyZ+M8)v=xt
z%w|IVf~l?8{Mwk>XgPBEL<vl$q#Eb@sFaN%6{Ado-OWFus6mm3O*6D)dDY=2d#Te!
zdWX0CIl0-Y;o>ff>BVo%$LoXOdIrvg`+CN{^k#^e0o-E9937i3awXu$vr$xcuA1rr
z(6tzUNZ>`Od8A!=eH#D%Y)pS<v!nB5GvcY0Cm_M98oMr*esEu5CYq)kKRHbx$}22K
zR(a>tOG{Qf?KCSoXU=M=nDlx5*QF#8B190T+q2uzf=<!Xe`L#B60SYWTtS0z`kBMb
zuzqI!(yP;T-tAC8yi79THxS;%YuJXQz)Y!L^RAf12Ai-oghQ~+Wn6!(-Q)n@yoMwr
zb93(lJ!c9IHH{fXp(P97s50_9#hBto*BnSDMlaD^Qx#HBldQ@#Abgi*>`R2}B+#X-
zIP`G7%+p)Lh@p@JeH84Be`uBmHwQ<Q&1Z#44~0uUC4nEtP_kmnk<<qx1+w?fqbj<{
zB}+|Tt!jdo<Ea%RSHr?Dg41F(<VwXt_mOajVCga;@0T{xzntz4IJ9>f2e(3i$BSb>
zZdh*kbkL2n%}zW9sCCEAwC$P3T65fHxXHm>QL#ig+#_<BJmjK%ZoklF%{A~rre7bT
z>WISIn4%`=#4H3Fo31lnIMLbN=!EgDnnN}@E%6yPnw0h}^m}oguyBwzC70A#qQ#@5
zrE~lJW?5;3N4dY=-+>Mb!;jEXo4Db+>pIe72DYfz;#edg>fO0BI(nLOnV2}YuwfJU
zQN##^Lx&h_mCf$0Z_T7tyMF=ht~M7Q?>^@hS){MaEZ?1uMOs6V)At-fC7OE-<$G;-
zoZ7O}+@(0@kB}^wBXaUfc@uY1nCw6l5BH%8Qw(jk4MeK$t)zu17z2g2si>(*OGya(
zOV+KmJXiSCrKqA{7f1PK003e$PWFk|?*`wh)`012o!jN!5tli2UNidDyh->m=mQc-
zebgwh$nS|Ly8=ban}cxe*~MAZnZVZ#EY}_z=@@=TELJJSY-4$u0S@*cbvH5Vn_6I!
zOpvB(_&%#D(6Wh-h?{rx3Fw`xYA+^drFZJK7xb~EJ}OAE9?o&hzyivQIh`ZPMRvcu
zf-VbP5<Y!rj)L%YG!Sc|TYSzm4pW8J0K97;P-uRpEboD3Np`>5R^m*cw{jD|N0OJS
zxksK?-5@;)EM>5@uL4W4=(ynp{Cv5Y+N@K+*R`2GKsi!zZ)$U}pD~}Rr8L%4EYa-D
zl2vSr$yu^;E{>ZDN;QWrTT)$9j9|rdl+*`5FSxe-N>HLDNN}ol8c!OYVV7#9x*Jd~
zTQRI=vS=6W2^MR|X$cFhDVnR1x8>J0u84sUkvF#JwVvd4U0S{(GeTm>TYFsuW1LQ%
z5P=PWskC-f61;Z6S+NFQ1{n-82E;T}sJb!xQi6Y8fd47g`qHC;<3}8Y8lU<NYgQAY
z|C^(DSMWa>evr#q2f|(dMVC{+2fTEcCu*T_MLs58(e=chwFmBqj+4?Uf09SKSGNV#
z{}Wxr@hI3P3Tg;yc!1pC-~rCg&b>{_DdPO0e-MAsHG#4<{3o~0ODKMW-gn-6BOZi)
z`f7RDA}+uBB~-=j>-=|iBUJwB7dQaF!T0wI8i2~`xqsfw)~JIlur<8#*D5fqND5+k
z*e^8saOjAO3`=GNsd?=y5L{9G9(8Lb`$x|sj}M1C+;(fux);9>-rwm=cXVo#g52Y?
ztv1&%T?hHRiw2~%7ClZh*~8q%`}fDPQ-2O7(Lk+yIm3ueGt8kx7_n_^(>?zBuHWYl
zxxZH)US_WGU+^ZFKDa2-Pet9%qZ<*!wpm7-0n?PiBf%W<yV8`BrM<h~#oMtQ9k{+L
z{oYu4&7tt#MIB}P%gdiNxr_+<EzfWIq4|AWe8B!4YN8Z-+YR5Sc2bD@Q`-g3wk3KI
z6HAnPxwuI!`+?t|tF>fit@zUj@YJx{xj5Nnip^N1dkR#W$!0Tpfaon5+RA*#c1Az0
zxr#N3ZOq3vMb{!)!f+Zq<Bm5#fVZ!7VOU7O;Z$FGz73CuHcEK*8CfAGMwMYeVLb1k
zF+ZTIK7QYE!)mQ+jsIwTm6mFkZ|!oV_yg|JcNgiNRJW?nC6w|P2)5cPn;mu`5&D~Q
zHcAFq_IY!>Pp0SdJ*!dmf-30*4U?c3lzldm=m3#v{B`!VgptduGj<s=$Sq4@8MgEl
zRg2&*(RO8TPGYjQGS|(#$*{$O(}uhodQg#LGZxn<Fuk&geQKD`HgY(0$ytY(YZ4~r
zqD+TD83I#X_m<zBEleL(tuL+q(0*_pVI|r3L8cipvs$7V22h7SNBA%k9PgZc{8nAe
zwrvVtTz<VhYudnbB{}9v?gI`P^Gtn^w*DrzsW957&)!tqpZkvfj9c8fD3VcBG?DdW
z>5TO1LaTSmof&d}JN}^=^%vk#W8!--)^>|p-O-V4z@Oxms#`)u>3$==k95-V3TxW;
zVkRM8U%^3wN(*o#ZvE{zL}i7dI*A!#YKk(BY^+H;8o@EJ@d8=K58ngSMbZ^3A2Ebp
zr0_c0|J+_x$=sBui$K%%+7xbVT#Wun^{%o~d%+*^6``-X5!-Xfp|Yp(D6rJxr4e0c
z2S8QEmlgL*<}qfT9sf=d()QW9?_#^yPHu{`Tr)FN$F-azP6x7gs3yPr&Er*sTr8+j
zi*W(73f%DJ9l4;;(Frk1-Esf3Yvq4-EqdVjv=wkC`$vntd`I%(0$Tu>qD=8d@mX8j
zW6T8~Ol17TZuPrE!>>frzgk?c5LNabwUiWph6ICZ0@h(%;wR1Gx(#8LC>)|!?vS=d
zkl(R4I$)=}-UI~zfQF>LiQZ_fbaYH|eu9X!CvLZ1J@U}MXc_J8mEZqxq7Wnn8vO+b
zb^G3`dFjN69D}ud++jH~aGn;`c_pJ*<L{GcIM!JEa1!gqAU7(JZk8^dHi6KYAqu2|
z;*MBfuqZ?K*1<6^Jj>e<<*m10<@NmIIN|;6xvG15SmomThRoIa!z)qRTUFlh`#66?
z^4m%Ge{MkeXs^;5+i%KSWm@Hcl1ZTGIPj8NdNbNX=T{;G!0fT9n&BWOs-*}JlM@JT
zL?z$BmfRnwTXt$Pw;Ng#aAqB(5g1iZ!G95!x?CPvjZOwTy_Q{m;NR6N*1fl5%7#ko
zBS|I<4$|@}jA1~M`)!M4rBSBx5sitZ_LZjs#73h%_OPhv#MR@?vB80UrS{#`-{$^U
zdbhJ)!oUqLyrH32X`zfcOJ;47Y@wM+O7>#xcMOyCjugsp!{*yB_w!5UasS?ZOZHIa
zt#jgTxWLbBUC5mn@a9CW`qz!9%(0FG4<D^v$DS6O&<p`|s#<1^?DY6$s*=bM#8h{K
z6~3;r6aQ9c7>_=#HWtB!(2KY{0l*OU`E<VG5NBYA-tRq20oFWIb|N#HpF?1Yx)!<h
zH9K<Af_)1ZWxx)J#D1dp8zJg@9*xS0;yL?w|BtUg)wKW(AD*;@_+)f@mk<s*WPy_m
zdA#MjEJ-7(a?z)z%*fEy0<fxYKd>HLPknU~3F0#MHp;*i7CzPKx$S_~|0LHLk&*Is
zVz0I4%uI;h3W?$n_X0c5=T39XF+5!fy6S^0YEDEEYc<0L7T1B}*#pv$9{*MQ02ZU+
zvc+_!o3V?-Iw0<%_w#4l&h~Ygnv_~3Yg9Aj-%-)XQHViciYyyWu)WmvrPSoy+!hE~
zBIqG!9AqUi&}Lw<j0)oEx|s7uX=J)tOm(Fcj@vx^NSJ-mL0c=@SeFT>2QutDMe9J#
z%3T~8+CJUhLguNH@c}lIZ25kf9%d@4WyB=Hv63@aJg6ppQfAj$I=GhwVXnYLOq0-(
z<IDawjEQ2X)?raoX|7XKr(T{<SI)1V{PQ;9AAWiA&Ejmp?N6_hf|b<Lw2wig;??)M
z+~3VEhJ7VDazW=*>cO(&Nd*A7cP~ISmi6uAe}wH%W)i9b65le9(vDrO-}!gMC%(om
zffJ-_%HC)s@A&Tf70g&#aZNvxPv28361qZMZmQEETDV*7gxkIDPC`?7Fhd6fYxp{3
zraa43p_~Q{8Ky~84zyQkJ`7nBmYA!<3Wb>XRq5`I2o6DnObSzR(d}@zp$;!dcSGJ*
z22(mCsJ^yevJ^AU>|O06mfLD8r{YRv3Az`!NJsXmzM@KhLGd~T+cZt3o-F9JLdp9r
z8jN|n-}!7MfV{dWIUKLuL@i#gO=QH4;|}#Dx{~6wBFx91EWz1urkinY^UA^)FC2^O
z_+QGZq_I(p%y1CFxTAs_>_`WL&Hg>x4`BOS<PH5D#VY{;vmbrzdNpZ+(~o}vXg`}w
zm4MUd0zRE!-O?3Rl@?7Sp`^+7(eOnB)8~h`>T(rAK_LpvyP_aA7EW7t!ngR%ePT#*
zQX7OZc(Y({GAGR>&NPQ?!$Oa0=nR8<x2kq%&z!&<QF1e6!T!0^@x`9>3-6O)A?w9Q
z1@HJR?*Mt(>nQXya_}33(z79z-Q--fN-<wlO^=KOoz4+^6l3-y&))ASVU<Q{;#oSb
zZy$DjnJ7~p;t>`Xz9rn2X=(WIdWWe7wJ09sDl#dJBJaZBqk81>MIq8mrrK+;>{3IK
zNnx?*rN8(&AT9V2!=<<&=dDm%3>+emx0Tn?X-{u-i)AlT#KN%iFJV?W#Ua7-p#q=<
z^E}WoIIS&O>+6-wox<JR_r<tI8P|VY&#S4GMtSmAw<Tb94EL4!axTenVvE+wKNMgu
zVz45G)Az*_rQA8|r$RdS-!?9hL7u%~qk1C;KhSkTxoz08n7T*yE|cl!U8V=fLgrHN
zfRPRc6mV)>7>;$bWd&9C++1dDG?)Ivdj4C6dKr$zj7Qsu6dNpM8`RM1KahC0kY)wb
z^d+gtUm+eM?$9w#IEDqF5B{>rs}zW2@*NEZU;Ju$0>@WxW<lQ7O|@||cTC@tn>Www
z#-XeG8NCk9;#R1#f%x)E?uD2%ADEQMBuJp47RiVup$U$Ym;MZW(}KUlANcg8>kT5&
z1@^n9J+-+roN5A@TY57NI<J0FJqFd5)e&{G5wL5Ey*9<GqC&>W^X1xgC5{$ZA8foc
zojr@*6auRe<akdi^2UBOBkD%}oRAiFSliW<8uUb}Vx)0`ykjeZ$b`pKuUNLu`0wbR
zt4KRs?zZ+<)dtJ$^Cr_59eyi%zI)Lfv)*rUAe?dvOoE#HMV1gx=Nd>mQ<}Z`hPd~S
zD_i9DvkpkHCoqMz`CYSBss{lfpZAD(>Cixm#T!LRH*0Z~;pI2DR-5H?Bb&3oTy;^;
z+F5KBTz`MLB3AUhSxaf_$Gu8TwUHJNrkZM-OVW1`{aEND9j)Ct>LlhSxdGP;Ie77O
zh_t^dbiE3QFF^tPOui6eXr*SV^tUps|E{UFCli<BLgkiH5v>E8_A?9%d&Z8%Bijjg
z7a0lz?2!H=wvI@Bg-(8r0M%=naWV&bH}iHfcK$RD?-nhqoR;j=cWxCQgTWzHb-V`n
zduWJ_=Ae<hW3wbe807L6Ou-8#GXIM9ODbxvB?Ub-GFchp5QO7W_jrmUqtM@AeD12~
z0$o>iM&D9F<`Ip@ezEXNa`_5>_#&3aZB(V^`8K6<oi)g*SvM=}6OsUi8uN;td2)wh
zbf$0-z;a2mPdz9|wbO2`>OCN-O0h>MfW`n#SU-QS18tQt7B>W8N}bkZWRDakUPA9x
z?<d^BcZ;YNUG?oEmS_&;0==Sq^EbWk#dJ$lO8S|r#k^3&i6hz~to}8A4x7;lyFfHA
zj-6Q<`C~JK5!e5yz5k!!Pi*N3CrStr-oerU*E^(Z*Ewn@OwW8Nr;;v_Q{UF@G}!!4
z$@-O_y@!sLaFS_1CQ^aAy7O&)Ti*dF02P?A>P2AO*FZ-)1;nn&VxLR7@kx=$!RLO%
zN81r2R1pg<q`l2@oJ|eBHa{a_Jw1}0bKKPhDgwN!q-rTno0Am8Ryr550;*p-w4V_Y
z(cHSd^>OMReq7N|0JBEKHm;BlQq31;DE*{)N0#~9XXp>hg=o>6Cw&JHcw9d+wD|ob
zS<s5v=q5)NEL@3{KtKkXnO1Wht;kcl)nLIp87wy=0E2mBK?u#8H<nyfnQYUJtnenA
zn8ZG^?Jx_c^<g3uWxBLRCnH{q#%?v(1rsc~vB6!r@w>LhwMXk?buw|5=UE44WJWUe
zWfj@n6&`jr&`416EikR}81>$zZALS>hO_VCQ9VYLx2kXX9Ma|Sf%Iz&uPC^)4gtdJ
z{M6>S%xGawk&$5un1&ov#0E=oZ3{1stgca7kkn9C!70h{s4b|-JAE}*+*x3Uxah=Z
zCMHr+Jm7G5eJ`Zi?WCAqtYY4jfs}l;K$gPs2u^6v<)l}`J<TnUp+})O0IM*~Tc@`G
zn=FW|8AsL#7^b|cA==N)DV-*nE@E;{GuNH==n3dN^wg>At7;rBNjub}=!E-2hWe!W
zxNGrWR46bzA87Bs9XlL%IlgpRT@kfg<fhaqM#s3iTdZbzr$kslKIsAGuvmT<)k|g(
zL!gF;`E_Ojd^$DXO?}Xo(B70mEU%wH!fvto-u3Q(>WsCtX0EGWiaibI$`1$zWb|vc
zlqJOY^yq$;p%hc-(G=~t<A9}=FvVc|yfkFP$VHJ<kqAmH{!EEzirn#UfPV#UJWUWB
z`@UrbR(w14?C{=s>$pwhn7On1Q&X?$u7kjD-}&Llag$s3e-ZbVacy+}z9?R-xJ!!$
zrzAKOiUgP94#iyq1gA)G4erH@x3~l-P+UuK2~ylCPATn8pZ`AlJp11B;@(&H-DJ&V
z*36o$eAfKFR^8(M4zoiWCPRgfEVpP70l_FzveEXo>3u$PU56#@<-2v_xn7T7?drtC
z499nRMFsItcD9Bl=fzGDa7Y`yoSvaBzBhpfDODd66O#bH^34Ce&&vnCM~y!yvqzA#
z-UrV;cDwxJx{+%i|Bu`O1w#8n0X3&3PHV&=pL6VZM118xPGhccFAXy_YA}W13LAoF
zQRwkysY)urGBLu+(oZK^F&BRK+<N`yib4uJn+tpz6+Q2BU7y~gBc|@^p6@vn_>@1{
zb!ijcCiHpzz3pv&x!;<3hk|Q*J37UT+IROvO#4Y&Ze~{&DuutHgrO@6Hqx8gD44`g
zcr-L{gI_CZ(e|OuygcJG`h&vn()*bqp#2XD`IlcO{F9fMIzBH}1;16cy1-A*@apP&
z1vF&<412(+=rRsvCML-qZRGdl+Q)nl{qEeXaqcGY0;_|&l{3&%n-b=Uu`A|l;o&!w
z+%DBbKc$|vTQUqMmBBFMquDE!#nSoh#rP+0ikE9*Rb1J^8U}jJSCCX0p?jXyxJ2Nf
z>PSW@9ha{nSKV<=e8!LmC>F2DEW39nCB~G$cnQ2xvlBCJj!rjRevn^HQ70TUYkJ>6
z_M34~-}Z{iaRPD6*s15{`_f!hkj>6SSgqv*PF@?u&70g^7QYJsk5=d)q=mqAg5;x>
zJqhZ*aG|1yb;|F?>wcuCUw^Gk+QkiS^$R7yx+T@3{Nlz)*)T~(`JpRnAY<J(#@~3-
zbd{dG`?em1@j!YSf(nX7dnd|yMR3=X)S#9bxD~L%XI<&^oY_RnwJvKn*e=o6$zQdS
zr8Z+yU5KM3a@rJ|9hSjx->+EI<SUD+iltr3!Y$YOl+<<QM?52gxfN|+?-w~1Fin%~
z923J*wKXY+VvZ6Zi|B7Wb|DHK8Kv^SfMX<7{Wr;Z_dQAyJeeMEXg+gmF4UJMT&EFm
za&cQ{GYB8BTsdW?wyp6@It>IHK7>70i4;;^=tzqWK9#t#Bt=dl`3C4ETY~P1I134<
zEWq3V91&x=_{AF;9q-d=YOyJ?)~KqM8~>yHbm~XYNBTgcnAH~<U3$>znU|s5kJJ^K
z-bJBHh{19l#ZBG*jRq;1j0lS;Gk9G4L`L1Y-ISparzejaUI?==B>S_6Tt`_mEiop+
zp{rq1?j|eSX>+>9g+5-yYWRmyK!bUX1T`)$ZbX&tpmFP@VfI^)8?ifL{$-uxxJcjp
zz{NMH%cosje2vE0w!8r)MQ}A_tRwj9$fOD;M{rlHSIK~oNC@XFoQR-58N2n$KnO#R
zJ>c8T_s>&?=!E>9^_?X}YCUNZ_GUPgiZGH;ikTo|%+D&etSt&bPeHFM&8}%l{!f_m
z%u!%0!rmv))#b0#pgMfGb|FCW6{v-UR-*&tHv$YEejc(Kj25&}C+wnQ<-RM-mpfs}
zvmS#Ho{=q%v28NgHExH+Su<Dfif@daiq^dUu8MpU!Z8a9b0-@>3n4==#6X(*Dtx%k
z9^r{R0hv7Bt}y|rjNet{{evDq;GlZ)&dbQJw+`;Sb&+3vT8NN?XUo4--sumpne#=o
zWqWIIf<;5>okx4y67SDiTU#&ij0?U5S-x2;8)%yUA&ouOoF*P_N|7e9`qj$BGy~Dr
z9v{Y!5;j5=R)K^2DXpT-)he#G1g-aXqqAn9#rRO}B`vc>92d=w2;6@eDx)-2>%OdC
zX-Q}KP#K4v)x1{Wluq5GrPRP)!Rn<;!kC5@+OJkqt}_3jtw|+D-tFRgIIhjBEb+RY
z`(<>|P3=~xF|31Cc}Aq>JGtO+XEBZo=fJ1%C{`sx4q5Ill%(K6-8c@#A(!>R5dpPd
z0lRjb(RE?{m?Va*xJ+1#qvn4J0jTrw@HAXZtk0I6v!Vg4dm^+JaFWF=z|CudvGE#Q
z>ec+^JXO`>dKw%ZPKWvgHvvR_JS%YCXC`xmxD789?DP55dL*FBH9XG~%Is+`)D288
zbSf8l;kyTN)>UIoVSWb}S7L8W*Hn`xO9PI}<e{kMgjU+m!3;~RFHqeExR<Ux!fwXI
z@5iKUoR!S%+q@}8`bLR~lLIG8BAvw5DhX^N>M0e+j<2K|#Df@Bhs38=(Yvs$8_#~F
zziTZEa@)?#Si78Xrx061r|SLS3RZ$1TbviPyR!Q%U_^uTIN;tnb#S?K)jbdcN4aa7
zvZIw}aGu%Cas5*qhc{4Q-F;l(3`G?IUEwb>H7(l4SC;{HCwh<>H)&!unb2?z^pNw=
zj8BX3JM(N6&~O_lebZsnp*nV_3pml&_cPOaREYO*Nc%&J>z!s6?4`e>mln?R20BGq
zQCS;vMyX{~OX{<|+V<A#W<=)s&D=w^Ov<U~I;EGM;*h9cm*goAtxAJHyh@btKtqme
zZhGGdKHMrt0~Op0tttiZ7PZJynkqA(rIarOPE4(fx{hRA!&_eZ|9}YEPuE^MRa8|~
zm@_$)XR~4q0^=D_P-vphF>#es;QOc0uvn(<8}X*%4o+%TEu)9#SWBvsqFBrL3T$j_
zdq$a*;}+y)=vNH4*&#23ccq`7JHFxMW1RW6<LV@lz3wVqN*GQ#SBDY<u<2=ITF0R|
zDp`iri#(5&Kw(e204?uC?IGyIM-J%y3b}Y<HcPm#WgF$eps3sd4R4jLeX%9I2nt=X
zHa0dvShV`(y4F<vvc*`U3aK6C-2(4Vve<jaXF~J_v=);xFgrp=fwrGY+izkM%|1($
zC(ukUyU2kjw+{dix`8;y;d=G!`BU`~;Yp5^f%f-grY#ZiL!4mYksq)Timm2S({9UU
zC=nxojhxXRip3QFzI<7l5r`^ZN{UI6;L#CeZ}33hof$W3+<K-Nx5HE`yUsCy_>tje
z?@f~49%Sm+sYG?Od7rWvH+XU6rH;xPZ|x6C9?dGHm2VI}T^n}S*PVtK6LAbH{$zVj
zx9b{2!nV7euT=O#BkmwVL&FS~J?&Lx`kaycD_>GNyN;zr{$IK}cA`Pfu;M+dg8m@A
z?BFW_r+>)mY^K|KN8#`-PbVpe_nMq*n|QadP{{w@496Rd`1Y;fS%TE|rybh3b?5sc
zldEsL5{bXaf<+xFjF#EYFuGqH>4Ok)Cp3Ra@&AieM-VrK@|*ovVPS&xosHH8G19Uf
zW$Tdq`xer&o#p&j$}$=&;dSRS(z5+E;c)G2Cj7gnW9$fN#1PDQ&1_1ba~X+xJWWDJ
z?#hL4T{>Qr)^pXywJP#)$-kxq;(Lf{GA1(6B<37bhQZwzz^a`l-i<&q3rXwj%?DDp
zFGJfD_oLUh_2(sfKKiExQONcOUAAk?Zb<%dAp1W}&#bcu@5*0?#|`sbrkxd@o*^Wj
zWCJ8;M;@i!@+#L9`l5V-5B5EGmEJdNz11+@<M`X;jcoGx+XVXWroTtM9O1tye|?A-
zxFbG%r}6hx1cCo)!bPh8#`NKzGuVpIU-jubscn9p(16utx_Mgkez^^&wC8k<nn+13
zFMM8_nS!asl;Q?+mlC0q1A{wI)foW~PDWq6-~c1_Sxxcw+$#tHXq;F}*lTV*p2(B?
zP;kt$u&h_0L#q#752HsDcwWnA+0Cg$7v-FT4UhrEV1w#gYULKR()t2x>sH6`x_f*d
zs4W*bJ78MljW6do#Gr?*O53p^b|79&B{hGRd9Q{KY7#{xL{c4zIScfT)}`X-m;BW^
zR!&Mj+1FfK`DeH3le9hCs`;>EyM^W<+;?O)pA@}wYM)cut`MW_i+N@0MDk~FB@al3
z4=<xEkwo7WA4=ws|33MROo*Mw6CnJ9lGFQptJWt*J^=OyMd1$$x^?zq_7dGeCUa)Y
zCzm)>7DJ&(68f%>u5v30nZk-+6-o`M8Y&7sP{$c)utK#(<uM;u9uY{P_A~pR^Mtqb
zX6Hr!vq8Ks@z`yD_w<^YsZU8_gP${2p-brpsb<ZykqB1YLX`a}Qw$aL^AhIaw<SBF
zARZ(ocN+))EOTppUH@JF_I)_uYhNs$QkUsrB@3qjDGs(xKQ5El!4R_R=l`>(|3lX?
zTtq*^KLyhc^JP<@i|#mx8UQYyYFP1pV=&HZ0DfeeU%}7-|4e5q2YLykh6v6IqWVOT
zu-&j*AP3|8OKNu!`9sb}|7?@DmXXxI>aEWz<brpr*Ro$9YkYZ?L6?7x{c&|pe1?m+
zB}juRWhv&frLm23><-t@udIcSy3_IK3@|{^Zm3R+aR|;&MN85sjU^zwL%S=L8~7Y~
zikENj4aE<M-?UfVmg~etKWZ&j6Imb8vV(W%!g<&%O%m$#fDP<}>6o*Q?i!U<9Kswu
z#f)DHj||~j^)fupHxD8Y4Az}C2^;lYg%=muA#2ryJ4(=}&*U1&ENW`9oy<s`!csnF
z0lZP;naY$vS*qUPhz<6bxQTB|169;fp3c>N)?M2!?7nGqR%%DW4ZZLwb!&J30lpBY
z_@_yco8hK8b;|b&ua)9x$YZ0Kilywz$?>xo#N}cXh2-hlYU2-*TRVo}r5__-Sbq{+
zGGEJzd}cOb*<#f8>z?q1gEHqKf$q44`ULxj3ZdjQ5N`-eqMSC9gL{e{F35NWgCqU6
zZotrkXEhl6V;(I`g6WMvMuE}B;c;{R@(bqYMm_cp<h!PT#-ZWjMkV=?X4TrzQO<lB
zCTSk`GP6j{u)izrfB(bb32Zn{NTUAb+B`ogJ9}zCnS(sU0eO`Bf0U1@wHI98MTj5m
z%?l7wQL!_oR@39VU!zDuvec6GnO4?kYXULa{@0!4gO63Srd#zw7Y(^R8+l0PBP2`k
zxK8j(HZuW&;?&hoa-ienEW7iK0OAO<(8^k!Q%F(*WXBnzL1cg-NJKN*-{k#PcTF{A
zv|Z>lHM9S4;0@(7@EEL>Hs(0`R4!ox#_jjuu_|k9;lOgzGQRk^;~A%tr4WC!A&xPg
z*Q5=lpqseumazI%OP$2JSYM;taS-;IZ>P5_cq?Ev96)$-6k5k0HrV*Wr0^*we<4EL
z1-^@=+UHqi1=C6{<A~(X;RCDutlIohr&w?8<I-WX0hG-81PRK`s(U!R^exT`U^i;5
z&2kDt%<y<*=R7ST&au^VV2|^fHADx<gs=-ZNkB_K1jL#p>Za=ORtpsWe(guO0buTW
zJEYU2DC0tCyU9-`Me1{$gccwTjw=ZL{-BG9O91X9KaSabJU*gfn)Clrupm=a|Kntt
zy~@~J+nH)OI#Xjoov#G{5cy1Ix|Tg&-aH)<pEG`8DDLgB^Xuvlil50_&thru;MlF0
zaHm(eKeUd%cG&^xHX&pin7SqJ<QPT2L2xK+lFv%QPJ?!R-i(W2O&5eU+k%>FoD^FH
z2UDY-X0bF>(%YfUeM&yc!!3vaETL=8sukKZ0aChl*lQ1Lt#(%|X~%F439RdxvxYM>
z1Cd<AADInP24N(HRUnUHu~^4pQ~TMI=&Cv~QfirUTmWN{u<Q4LneAVq&X&mNIezm+
zq6Ux>g#05{w97-RplpZPTGv+D`c$Qd38hx5dYLtEKSGgsIE>(%nwLIP$cZdbd$AA?
zltN(KJ@;&tnwinkdV9m%wPf9O+ni~SG{N@D<7utE5+b9j--S=1=lo{bV^}tQno=<m
z6Mt5hakbJYyVnkOEp5@Y|3kMPz1tdnX;*9Mi0e$;%W|=uavXosj?ENMkdR27MXgti
zEBy&0yKR5Gm{%W@j3Wdt*7u@(5dCramG;85N5^MExA%j%FM>P)`H%Fkbmqn=ZDqz!
zAHBp9kxKTK^omfoxM`rvv(Emei)fsgJjQh5AfX+#W{g1;p<#kvWfbwFj61^V&)4!R
zq@P;3dC!DZlydzIJGWID-{?1lq-Mksdy$dN6(HpjUOirX*n~upY2ruu`hQSL><-_h
z2Hq&$oVF@|WTCIPUoHw-$6VI5ijAeUPTkO$9VGsWE_F96ew119VtVFU&Dg^yPeABw
zLDpb3)TPWG;W80u77NX~vSqSMl_ID)7*7sglpl2|XXOhO0z*~JNqidCxn6k&`oX(*
z^yT;9RmR*dT%z6La2IE9H#1U_I-}MAuL8rvy&%2=(N+Y`dzGr9gil4WnoAsBC@B27
zG^1Q^V}1JFNa4KVPVud8I)9WuuRkH@EnZ!sIE>f1P3X?mfu*si1Zr~TRa1G>5MTLo
z!tHGe$XN;4smUhMK8*pNc6Gl~2X}!ew7=xmmc0s8l9d`+h1#lh?5vhJ1M^T^sCrX>
zDY=y+M532Co<t~)RoPu|X5m+Z)y>|iQNKGq9Yw&Fe)~Vx16|d+KsJ9+_PEdp8Cr2f
ze6uR{1x~00=)ER}i*n=iNeIn^RneWFcRD@+lJjMyo9hLi!g~#9Cyac^yV6Ww_7ERy
z2SDXVM?0R7Ow!4kvKDadlNrInQnd8x;2hyfl-Ap7q)m+4VC?KMgOaaCAmo22F<rf2
zGVDS*@ngJLRD7`gf|C$)@&8_PHa+?8RjK{IHj(DxraT;*`?CZp1R6gAPTmV}{eLw}
ze2-scf+o4=(L)y`(`n+RWDG8No?<KoF8bY?i?sMq+Po+4NVyGEchNLta&Nof3K(13
z&CLB9Q;Ko^GPX;pZD?rso0dWP{J{zfRdz{~^SCG72QnQXMh|)g%JK1$X`64Y8LyZv
z6MJ9hHUG`>>%+rekN%J^dv^JI1?9mjN{^;*-j^F7>uXcmuUtxh*L|Ft;YOA&_bT7F
ze0!^}zju&<f7y9CJGJ8Qs=E5}Y7EP7&F|3er0M<K-2G3R@Rw&V|NFF{-U6Qj|Lg}B
zwrlU;;A{&|MCaw|)QaD$I&$Qp2}6F+`<J);H*2qtB9Ob8A?jr-JMXuYsGsP|D2WAM
z_eqUdT{%^GW@@mz?^|IzeKBc^vtpj>MetX~cbr27TyB&3tKqR5BVl~mz<4@wcG|&{
zXxq6$L!p3aIV@sQXJaD#&BH1xX$e)U=;-K#z}tBPemW>sF9q48+AzOyKb!4JLPm0%
z^-0`E&WZeRp;{9Oug5Ll?4j7=X$+&hQC>)x^#&>tDOOn(U(ZFG3aiS50S|=Woa8oF
zls0A8p5KSpBvQ_|l$W&BmOC>eHBoIW8cn$&F-?gPxGZ+TWnV^NX)Je6X%hq=f3ri%
z5X?W#8Hi>0#`Ti&9z6U!{)+QmLsQ1-qLJ%BD}?+cpP3cH+$h|tnb--*3&l-=B)i9U
z1vcUgaWvZJKIBNWFPIE4s<&f+ai0Le;IU_jyfz=uB2cN%+IiE*1lnAWWnAZRy`J`6
ziSxylwrSZAg_4r*mUwK&{;Xkczao2$1|OL>W31MgRX5u4$5y|%U&YphgZ`|qL&msH
z)-3;^uxF$HD2On6(KRp>?VRg>HK;*7#cvt^ffi%%T=16J%ck{Hz3MVX#Q1$~yZj4Q
zn)J(IE_I##rmtgp*fd1=RT7Mrtj$!Di(l>?=XqA_O{mu=wzHC3S*T1X>(pXlR?KAA
zz6TU9wya&TCN&JnSExGZLZ0xB3<FgAfzip5|Gp@4@8_GHeq;mNdHGWr%q3Pi>_26s
zZxlakNn;Q*R8);|mVNyvn`aRC4h2P!hTMU>+ZiFGrmEu6YkNFHZ9S^g-cgsmm#J>7
zE|3ghU2#^xAG;im1+oT7kg!_6P!DMr_k;m~?O42q&WS7*?;34BGrBJ>P@JQz(Kt;X
zv{hG~jJj(%KckML25SV614Txb(Hg0RCR0{ag{F1+qfo<Q>Nx{3m;UuE2i;x<!~jpK
zUj?k`XBdSwpF|JX0(SDPUq(WT!{lcBbiTEdY0Bn?l&XP{aAc^WHkA~HZmBwig5Zr{
z8MJ?|bATh}WCFp{9+ycjjmS2<>YKS2+j^%!FQ(LJh?!{TMjKXv&SyZOO2<-FNzR|H
zA*f1QYq98?#JU7+k!s}a9N}?T(@?7u50+NBWp37#pG@Jb^qMeTRTPod3T@IPkJcp>
z!6DGfLV|V~zy9?zoaKy%IJ;tgnW)(lCHOs-%$N*fq2OrD{rMJ_Y5418h-HnFCk4B;
zEnOsZ!OILLjlu+;-2*CX(y$1InrwFLWJ;t536HFd5_hWgE&bvqF5|azazRscfO&($
z-66Vk{bBPgTd=^4WqKO1(4jJ^Dw@*PvX_ijH93xD5}9*KT4*tx1#+MJ%h@B#NTN-C
zBFi6zjPj0^=hQY&2n+seuFM)0t^61~ZJR%Db;kS5xC_uvSa4NEoZmd)Z2>+*?1!<q
zhLt)s0i=_v*~+O_S8_s8COb&4;z*93A}p)GO1RUt#fjl+W~a(n$M@kzB0D!m@vOxh
z6zdn%Q~|bt{gW5D6!bRfnpC^_jpp>cni#4TkavwF2&vu-eaioJ5m(CXclZtPRKK`4
z5B>j4;DxbnH|U}t{(*I6X2Uy)l`vqcYD}uW+3yiHnRP3^H8q1W1aIC&Wxi($-x1XM
zW&vY>AJ?BdW66Pwf)Re}IowFmA{>QuR@&%77<eq8eT(Y2fEx@|bGfZ7;7&}ZdGan5
ze!lb`A2`T92^)|z7aC$Ti&s^#{~wd(|1pD2gLZh97A*5zQZpK>?{DCPgXXsw&8~s1
zeAO}bg%KUR?BxdvBVC{v4%}_66S)_52f7@n8X^%COxo&P0q`}utS$y;yeV-d#l_fS
zhY(mNC=a62?8(SaHm?r!xpYYl?ENz4{gb`j)vB~8StPAbnj>CWlm8dylIck2z;8{^
zlPw9{CM*A!+MW5?0n4|pXLh6QOnq)BUo#~+0pLzUIOL@B0SK)E)$KgXiWPS}5KyLP
zR7}C}3JTTH(xnE;iJGO4v@+YbBxs-D0uaz)02h1lC^peHJJk`l4M9<xh^htgU*nhg
zX6)92m2Cc0UXySi81qZ<WNWaw7*q29F{cP)l!l<)#qcYmvMBEjG1AUV2Sl-3&c`&s
zsNofYvJA|wDBPzNcd9}``X@(6P1ici5J`dL_N9sF33PZ6vRaC10`n-cA!ennyh8i8
z_-(x8+AZbE8`ZRoriZ2aEG9<uR0Qz@Tp88b#3a3G4^Nmqi!IPmI|S7k%r!_g`3`@w
zzFO)fjKJF~!|FomJib{_WIOJOQfJ)1RcQV8M6Hn0s+3;H{}r3`l3=rdO_~mfd_#ub
zsz$FT9;+AEj*tQ)mKh7M#7GAtBIe7rVCe_zkgHedSh=YR7-HtV$uz055A8rAcHx{(
zj(L8vr4*kg5^3-R@oBMM;2z$hqJa5GQ)}kPo5_O%a!q}hQ&a1ZFMn%ON#lRD73|?`
zfvJ?oF?UbDv#sGGwH1!@H}C~B+B!xXrNPsow8n&HKg7+AIL}a0xpVY(wh5)p5Uryz
zOr6%`mYX>hZv@90X9kcBQ!FRtgpgs#@)(mV-B4VJbeyDxed5LI?)${wA5Jk;fLL}s
zYJQJ6pE3dYFzyb)&KpTsH6g7@?sXM(+aL#aNS2~36^`83hmq6c{3)bw`;@OvW}-A7
zMPE~bSMR&LjjYkMBBGu*S7#V<=pkM#LdDtv=rWA5m}ltbcGZ%L?I%$uB{p?V8kwIA
zwCa}dw?vxXcPF^suV9^0h|<=47qkS_<zfNxk*ZZy6lG1(zwgUW$T6gCZSLYjVHf#v
zj{KMLw$!N(c5gBbYB1%vvbUWB&$4J3aH#ae4Mz0p=(*WANI?fI>M$u2A|B#3=#(ci
zl#F#lmDWmbWvT&sK6Pr*@_tRFO&%aA`7MIlPhX{cJj!%*NbDq835_Zbm)=4$`pOae
zo#dCdD^kLkZNj_n8_JlCjVP`joRX+OzNt~@&=q~MgiJ=~PibOLXAG)Vfl<~=OWzpP
zwTZQfLYOpoP3(v3C-34V=}r*~o~epl{36oI%N52-h6Ua!k?-AnS6U@{X?RR3>9TVw
zLKbISW|8=rh#*rLP<1<DShY>JgQRMth?c373!tTq1JYD%JD_B<MfhMcMv&=y<=RDp
zql7|iP?=+7-{HClkO*o<{>p77nH|hbID)yXYLcd^LFD+~a4HV;F;ffMjZjeRx9pg~
z1LZ46hLpT&$&>`uwb@gUgRG22vXee_d9a%FbIbMPZ;jgL9akK@N43|t0+#QbKCG;`
z4o@`AkScv?^A29Fgsni@+{Y(r=`IR!OYxd?8AZKOL<vlxvB*+>=}ge$*qaXLfy7(*
z$1eZtUK)PeZ+}plW_5LS_=p8kR8#t_=mz;vl~F5_P&7&_Yd-^ac;+-a9yU&!!uBhy
z_2f)K9n$(m!BYLAYP@J@Xf)V_*czp99^r-6xt}Di*={ay=E1KuaY&!%+6Tu9`GmwB
zIH(N*QO+V!)rY1?d19mFZS!_yp~$g*qyOo{j8tU|@*W}u)wt6SA7xUqfb-V<qM;>2
zce$KafyIL_v0tohRSu6dC1a{LIK);Bib1qo@;f4Ui)mt1QTsH%rdi^*X7|0Q;mQP*
z1(4$jeH$jM2ag?49q1)?u_k3+qMfTTA%Jq1t)oiV0<Pg+1U_6iiXrR(-m((Xx83qj
zVn_IVGo*jNcJf=L#4s%#a(cE+DqF?{nx?FuPCB(dvHAjp7URKW3wM?sS<vVim1Gik
zZo8kIQVlvDg?2X>8wVkhtC9B016yTtNiIx-%d{io)Hbw3H4_dgy-|58R4L{D&^3*U
z7%<j5WA2-8lfHdF6Ps90oM1}ekGV*{s1QedYhs^0ansejpyBBw*5%SoSjjWlHpl%*
z#cVBitzwG<GSv?(eMW@8jgZ9D|KHxH3JRbK8o=<vHyZ5w%-iJO>9JyXv%-Vlw3{ZS
zyf_XW82PE@y($T=fIN<$RlOhy9p5-~vh0wP)qNa4eo}v{ZpM@H2xtuM1>;7sttN>O
z3m=D0Ze0leb)WBfZ<9baZ3tY^HcRVq2^M<C>hBx$*M6RP$iU42@wKa&KJ8Rufw#>B
z{{aw4Rh@2i>PxvRw@~mJb0+7BP4y2|x>V_}c_xe>vAPp4vv&)gaVh5c2=(#%o7nd;
z{m36(5PI1UgRa@<eu2HPb8TZ&9R5hquEja1SsjX%sLLKRXW5cSi$bW3St~bbx_V(1
zxl8TtHWpT!_ugfxVWBH_zOTJLFhkBJYyN!_H%CT%8*yt!wBpYg3KJ7*HE`^qrs@Dm
zVi-fdiqrT)BSLwrBh3yve{JA;HrXKl*|=3zr*`kT1>Z1EhwXK2?X#8?mF5<imm{K@
z3b^4w=4K(K1|2fh4FLKfP6wBwrI%~;gg9lVZl2Xe{$swg5cFX<N<`?^!FpJ+-6S?e
zT0wJo7;Qhfl%ZTJ-4w<i9i2qbg(UM?cZ`D7e;JY?vz|R$$4@~Kqk8yt;1&|yfXGn`
z?h!+PwXFJAt|rdDA~||59Sp(O;cjX&$=SSR4q@w-XLw<tz|SRjdwg3?-@Q<$e@!?}
z@CEKG4iBd<hafr*>9kc%*eZL3{G=GfupP%6KX_RIIpVhFB5iinFCUgo+P=y1tj6<3
z-ROqH%`CsPxZDxLT&!1BK=4!SCz%%Wa`yL6u%wc;ahMiq(45FEOjhKd#gz=)N{AWh
zM(@<x@bYw(EZok;#A*&fqD`rBL5tAF2R%zMg^8Ne8z1e#M$OSU)|?T61o?RlGm+O0
zXxznMHg#!W-=3|2do470aL!dYt@M(-gxE*D*>(PVeEW-+7G6*iYN4aiB}!{VCi{;n
zc~GR<6ZH*pQzo2o7RHcbD~G0+-n%X0=QlHYF!+sb^Ww1VjOpf`ZzCSh+mXkFHxk#B
z&du>9kdwUpRM6vUpjc)?v$ryFkz(*-0^DQ3ge}%x4y9k*kWlbOUyg`K442*P1VcZe
z)h=<!4`K&k^&3}HAZpDr(DTfgfXNd<1jnd-*NS~&P&jW}RQj13gOl-=Mq09PZdyVb
zJ?+^eu!_%kIs`v<*3QN7R)=lsQBSf#4-ESJ+S5Y6@wYqK+D#_c(Wkv86<IPiWLZ{b
ztg2vYOi6<)lO1I67T>~MPlzAjpk&*G17HAMi+5ujPbR3=SU@1C%%;hAbTLNM=)y!q
zDutH--sl`VBnih!Ln#CpzXG!hu=zdht&!Mm(0FqjgaG2kI1B3)d$b%;T_FIEisBvX
zR=gy*fZ(j-<j&QqSXW{lZG(6Fvufn@Oxv?6w&#<)+GIu-^{-F$DHDdg)>5gl&f4c4
zb4kQgbNox1LmSD|D~(&E!oWnYop&*!O&co;vvye~v=BsvmvtxC-UgXz&b1$SlIy0z
zA9kudkZ-)wjYN9hoi;z9*0e4x&tov<pSfjrnq+;KdEW%k8Fk6W=yT-F__~oNa756*
zX6E0u4Wi$Kxv_k7nmi61Y4R|EJGe<1#fM@&#TY5pk~ikTEsvyc4wVySaA#B<{~$(?
z7Y9-t>Q2(wRZYsS8b72|Q^rm1Cw>}G)GS@zAfDC7h3Qlu@p<<<+p&pF0`Rd&vQelm
z8WH%$J!dNt2Km~T%&i|sD04z2Dg+}%vs3;4gu`)h^;JU2r{ACkk35sS_!VO@O!m~t
zu`oHW#~#O$)>FmR7#3ZVI?pOdxlD4g1zH*|3JN$?n%2hI`3sjZF|<D|b-3k+Az)&J
zquo-AYS+USaqbf!--nA5CBNy*#8mhr6yJh?p8eD1TT#~_$O@D|_cM!IURN$e?!af5
zU1NY^priSKl@>arx=dJGK5F8F;jBbGG-$#q;5Y9+SL54!f=lk2K24`rY#b5R8f%jZ
zltWg<GqH9%)lCFny)YlyQaPZtRSxRA*$&Vr(2nNG^jgDz7Lsk@n5}rFOUY3Ii`J%m
zH(s>()_~h%cdr)j!TWK?z4oq8c_^%fyE8A)wK_LamSK2uNU9;;!_nHftV;RA{>l)+
z=nozIIIo{-Pg}B8i+2<=R?|OplSwqDx`>DU9HbPY>vmgp;-Ij$ly_X~$Z5`M02sGn
zzP#u;8mcMEYu4!Ew;7ver)T0s!53m$VVgU7VrOFE&YWsi8Zi;vvu#rAu%e;qL6nkI
z%hmKE*OXmk_j{pqtBms~+!q{Q*rjRrL_@iFPz0xazxB!CbSCq9;oPsrjzozGeYqJA
zvy0XcM$ctvek-zcultK5SESMIgR6)G+1h}Z?ZzSC#;tUJ(n6IWR$*zG9h(9WeTL~O
z>t+t`o5_zIUI?0SUD8PC!(D2}jvals4ArenTFCa`w4&}Sd#P3&P2Apw-7uD??oOr{
zV|&<|5}!uHoa;m0%&!Eh0ttr<36A@0jny49Klr1WLS0I0xQw57sjmevO)CH)7!Ho5
zaQ%Ibrj$cE^_&3?Ty0Hn5-1uX=M~1PPtAkVq~)RdMrcC4i~R^ituYD3N@RA&o)t67
z$rp!j!)=|W0N&RPY*Nu=O5cd(bXg{hrNQjnmjMzmiQ=ry72*wseF+O+-vnu&Z{d2L
zLO;%qU)Cr1yd9k9r+c5<;GGWH!J=qtn#y9%G8QTvXiiCkrL&k!e*;~RjvERn(qeN~
zm6Os7d_yvm@JsN|h#l-ER*z4%e|SlZc&zxWy`tS+Z&J>+ww>IFuAJ%NjEmwBR+pnx
zq|b>E+kokDgy<|jts;G@ZF7WC(CD0h!z-~R?3>rGotwI>3VJ$C?l9HdvBisR=xf(V
zj4WVdcu|vjX(VZ~MT>w#7PQ!H@L@eyM!dH#YH**^^z226%JiA5Wa6(-?QTXT^|zwB
z`sP*+>ZQCzx0aEefXB2-HK^}Uc`28cxT^t<guK5|vqqWQ47@T&Y$|ZP@q-f}THv&l
zpwkB+HZl#ad9xHrRyGpbi{bA2EJ)1RWX(BABgRN4EUY?Bj=j^dy#&O~sltgI&Fy`*
zhlxxh;C!aNTKDrrVU#j|VB4u~n?UDfYrE-nrzxY~x>7Z?_r1{y%ETg3EP%fAJF5ot
zogdD5WW)<h2;8yxGT@h?pUtsnV(kijoCA)rkAI$EIxDf)CSZp}FgM)=ugVXSZ$*ah
zWmQb65fjBBi$YWg;KP)QafsOIxG`_I0N0&U5YR;oQ^r%)Bn)1bwpf-~SniyPtKB^H
zi|tG+&8b~sX;je0dehi$5W>OaQeZfYB}f`UBuRDs)-E@Io>O>Gw0>!O$41!PpI~cy
zQG%j3-ZMVo!KZW*ydx*2R_eK_OT5IG$#<Lv4@|B$F=jiQMhJsWL+X@TUff{6Ub%M_
z;%67_vVK0)nHAYio4?Ax!YY>=N^VBn6riX6T?iT*I_C@P$IvECIf(J^`Ug`bB*%(B
zO}84~JkIUblkCveN&I-SyVIKZiB%8X3vz>)d+Hh%TUc?ST4P-;tV#*SP*1UTF6t?T
zBu-amcyvWlDgce<D|)x|D1!|f*cRH{=T3iaW;Em2tMT@Zd+bscv{*CmVb4zFSENS9
zgAt#?zjA%b9*`kntrV8uxaE2}n0f7K6%w!2CyODP7Qe=Mp04p+bd;yA+{em$r#9o5
zoUb803v}^<j^$jAfs_H9(4-w&0jDW;zHE2B@{0nj8;=^PZq#?{cGnp*QBchJaU%|x
zcygb886|dPW4f<sUKW^LQ+5yQiqWczw<qsYVY&pT5_@SS)wqZYO})io{b=mJ(ZEwp
z@Me<HRbIW^_NGL$U$0UBq%?N>2U>H=^e?M)8tz=}Vn5yS4RVeQafmjqn0t2>w?sDu
zV*=nF8aUgP>wYGlBb*!g<<wM7y3=u;)hrR<0`ouRRp!`IP?Rf8nOvqTY1s586+une
zjP!Rleg%JX==r<4@qN@i;g90Dwbc=}R|=kUtPpv_Pcz~zYVdzBx`guend)oFv1d)4
zHAsx^Tf3r-Hxi>WMPhVMyOQa{(#qKUJWJ4jib_^E*+OWxBM><rc@v+xmt9#G*E4_S
zla|L3Ll3<iSBDr|)UyaiLZp1(VX$|dOy>SUneMul9O({VxrK5Jm??^DUN#3w4zn)n
zB0;+6)|nz?g21Q{bY(U_==CAqm5+oLblYZ!@G0DSOYs<q(K(pb4*vX{cA~jIF83M`
zLT{Y5t73D-Yy(Xt$%6jH=q#hpc&*+d>aIoqpy)15efbr2H!XMO>QCE#x<Om*O8vxr
zC7^Drwe2U(fm)TP>e|GlnpfdOe_Z(nuG;W$UM}Zo-OE%gxDg@Jdn>mtd-<E(t>*Zx
zSPMq&UR(nSv@G<9ZuFt&wbhu}aePE>Myu;x^>@kn_M);v*LjrG=v!eLJnD{*xpm+H
z?X==5hw?I$iM)Q_555M~ZeA3IZ8L_PPCoG};=zbZR~D<)F_rbv!H9O3!K!OJd*Y+=
z^G49fOK5#`>|}f_f;~D*St+AN*;NVF2>wzNkoLnX&6RoB_(oz~=pi#NGv+|M<%5T8
zG#k`t%_hhFDm7uZRJG?&fY?z_DR#e;G=_xKuwv@8MxslS+s+1&`*CUaT}p>*b@t4I
zmYQ#jutsh9hesCI_y>6Cm-o!>$|FiDrUY5fTjEk%rO9=aiWNxo;AZU+e??tPej2i^
z!;VYSQ7u{E0TC2JoI5Eo#@ZG31G)-7HwgSFINHqTn^VQVKiNOw3euaao^0KZ*b4%b
z9#iS*VT64xn5M)OiubeQ!wI^ZTE4QlILgmeX#v*!<AJKL$>T!i6ZVGkx;oo+;3nZ;
zv@*;ma(f|f$$~XB<WoXxs-z2(+E?A5JlFV0Nl&7;$!55%H&ev$wsv<RScAkj7^z6<
zS|`m#I%ehZzKmVFxbZ97op)PgM-}_b^ij(d_~X7QzXYfAi%q8wL;ueEFVB#L2iz<7
znfT6Uj1AA<(;ClB1uyC)a*S(;{9edi<EODxa+=)!#OufMOxC33a%s=8g%NXkk_rT~
zpVL`z+ytfdix4c*ZK#Re`s(M8`c`D;Iz_y)q8%18oqz+(8$hQrE-??f16{msa2Kv8
zJd-8FWEV77>KuA0CGI!W2J-~DfT(oo;jpSSC^1Jo*0j*Ly7>Ik6t$H#*m+lf^#c?>
ze?7Pu#U#>LyVv5sQn8~NfLl@!5|d%M`FWRK4iAvnQw3{`#G}(TP{!O(L}4y!(=+6M
z<{)@&39uBJr+w97dvgCC+vb;xu$R>xCG==w`ou)n*ObSGin^cWv5hcPN4gDl&PyLd
zQy^4FtpRgJ^J|A*ZG)wgcwTcePX{2^vD28(cyZ(Sr1N^r`V@hg;e0%ulC?(UIkv8;
z_cNMdnY9s}=r}V8rU6#Xj(VpoaeBf80NCbgS&r4lPH@ncRo1UW?kWl|1gc#)_>RfU
zsBo^<w^ye34}1wuJdMn<4{yon_ik(Y{!ukAF-c4JYuXA;F&hz+Rn|<V!*?bq1gSY6
z;QjQu6-lgN&oTka`<6g%TgXZWnKQVD9M$sTW%VxjP3Z#A6*$kovni#9P+|~k49z&B
zmBIaJzVnndPQ3`=s*&`6+HpR}3G1|RTWRhzC-ME{wpX%C#^h`=YIsUFy3!cJ<kj+A
z{)8P;g<d!6KoxWfi?bEAORwlY1P210hFw)+%^|vQm~(EqSns>An0Hrz)pyQu8gWi;
zoHkpF{liWE!}8<`gsMK=3u@*X6q+FR97nQ<Np*e0g^pBG^6M^KM0sCpeXNF%T3u=w
z_{&h2j)??NeP?ATkvxc*>4z2!RB>?Bs@!I>p|Py9D->E6Iu#vj82$lC*r~PoF8QmV
zNa?*n;!oP)GqRzel`S-e-Tjv1DJce*2%BW~fvpD-%nfnYbzE8%uV1YDA_C`;-A<>*
zm5L?$Z#(oMPyV1x(6awV7x@jdUkIX2Ni=?>R5~ts<*Lqj^lX?S+Z^aplrY&RG-9yr
z9-y}r+p3c$lJN&cR_=X{fJqe%L6k^H*48MA{6`S)?*r43-(Joocle*L;^fXsJRZNn
zQ-AF*DV$TkJ)npRqRnqRn-*+ad}VnTWKS*OZvBHzCiK{*=fnP__A%jI|1;DXbx!(Q
ze(~{F#WND=7i(Ih?Eb?S^o7Vk?HQj7ic*{8j)dY7AmC7{eqxduwT&9kvscUzW;0uc
zx<tUh7BHUp_m1A?|Gl3Vk4)16`>Sug>bC!&hzV)_!g}*lu4r9;`waeSf9&<?S#C{D
z|L2-F%x^}PHd(Nmwz+q5Sk5Mi3BQNC^sKu{<r8%u$d1rzvOmG<2&0DU)hi37KH%Ei
zz4}oZ;8gy3qU<fLQ)|%pX4>z*?6*gi0l`nX?pE}>cR|k=wRxZa?#OK2`1U!WS^U(B
zOSDNsC8ebXpb{l{+xjkRTbx=PDC}rFN@RfRgMokMtlFK%(PDuuEo-NB&d%H<=A6BD
zNp0}zhHUH)imA%Vxx<^mWuMhSedL*6JbJ%y>qYkMc)L0%`+#dJo_lDv{0HS$;NzBy
z`a{;};N{y2fE~#7um$~fu=wtbp~)O^W|xSQQ`M$Bw+VSKKF~Dn1y;#)4S(UM${BmK
zEUccRCX+WorUH9hGOzwUdh_)E3}4F@a`<?)o)LE1J^#i^@KadA?ppn4ZkdFE{=8Wi
zi*PgCClR32R8s4OOxFxDu$0Gp5+_E<!5)wkPzvhe?TPFaKv~fIUP8I9-=gK34@!Xf
z_6hn$)f!YC<f&*ErzEDU@ly+pRBeJ&7}J4LWuQp3ki>E#0vgfgR<z|MLc*;)>cn#H
z`Zn8B0O-~>u$!61p!6HyIETNwc*9>Wu0nT>mF*S#ekD32w-_BYX=Jl_l(IwE5$Uc_
z&(AuP#!cJp#%j7UiRJpq>c{M=we>91jn?>v$Lc$inGgFduG)hu3F{(qvUDMKi7A?x
zD2D*cIj++;@Xoc9HkY}2f0O=BNU+ohrI7QOKC`#JX~P2j3h%Dzksg<*8hN^XPaMlg
zM-@P}Duq=8nUu`J0dQOqWBeCAuu&bWCiN*s&~jwqe^CS9TNYkA2^snb0k1<X5I@kS
zyUy|)gQm~&mtF|9oj#Kf)??D08*?;+J~Z_AxB8yQPmN*xD2`2+Okjn-QnpOnV6>=M
zlILj~Gj4@UOuKiZjAp$Dscv&ApqbKsc{92dO-YGwEXqNf-~TH;`J4R9L*mtYiuU+i
zd3!QMnE<(%JV}}$&vYy?fP35n<2qs2bovAdX<NHJfzN_>-bdeN+j&$p)#)DJlG3JM
zsa2=`WUEbiNtu&#qfiML7(FPDku3yEmBl2a%w4q-cYFM1nauj^HRj1Re%hD_n#WzI
zMYH0rf69jAL2=aRCQuF`hY=qzW49}eCt*4daSk=JO|zV6*oTGbOzNmmLdIsh=6{?_
zaCH;>GM?`aX+Mc06_nk=*Nj^-t4|?<q~O<3#oUslDt}QCvc9jvQ(VIn0N26PWN)lE
zwY)e1#juz>_A9Bsik&o0h<CTre`xC+rwVNUFo(L{KhHHLd0JsgE&lXhup4wvCVR|3
z-Yvv60C%Lr95T1=yPbveP(I&$ex2`O2EvtJ!Ru+s3EZrOKW8Y$E^Hnjktjn!8Pq}P
zNNi4zu4elZ&}(J#fJf|Ux!@y!Xcl#3h7PTg@M&whU__;+M|x5<q|_kmlr?5DZE{zT
zLVY!SOw1X;k{~IWEjLK5j5*acUS|5|;Tx!T40;M%34Yjs8hdVr%vOA~Ng_A|HeMa4
z`9oDEOI3;iCj@0@HlEYicl;G`tgG{4*(@Qjs|c!MFmpLQ@r;vwwAWO<rV?ttN*gxB
z?~tL~0*sYrhym{aV~XB2siTPfE+g99xM4^7RIoGOIj6dq2w!g|7ABq=**$Br4cK`A
zB^wNM!lc=1nCR+klwH>v1*4BO7*#o1#gsWY!uGw2;|V2g@m5Hs6<#FrQ7El({z#`(
z*J$vVsi2x+8i}>m+!}(k3e@nEU6H3MVc=F~nrep-OUB1>!YrxqU4Le|zV^%tx=rC(
zjAY3h!oOg~_BoDzcOU<2M92XHirdD-nyPuRZH+<R%bO=VdKIh{X=2~8MSsLyJ;k!#
zCV6YWPGWl;B#cLP`3yk<tEMreO?f5*eR|SyfH}5@u__pm%#q50+u+FWU1gbKqx0P=
zy&+{vKQSJb>_qn4;|o9;E<?j^9R9&hH4<ptN|{K*fXmL<U<T77)!d;fstBdZk4Zh|
z&U7*Iamj`ZjmfV4P#!l9kg+}0S89(<ot+#Q(Q;2Yo)ZQikdtyI%*_cZ%Z8+(OZ?^k
z(h>Toi{Tqxb#r%mxG0<X;hwhCs7O8~@(}bkBg`2b5>OpPBRe(g)FsnK1%{xgn$LpK
z6T|DeWj`4q5|4%TyNcItEgUNYKR3>&*eZb;wG0wwGfhJ?FlVfFbvQ74qj63Ma*e)f
z);H*hk4z`odQ9Bfzo|ziy*a{C8n+FH3&RU05t|x@NLMv2x(P&Sy|M)=NP+Q-R$ehv
z^yv87inmE(n)2P5Um)h&*8KK`28+DK+lVgi+{%pdc(rk5T!mo~4n(P63s{Un+=l-h
zE@M-Jx#c(QIO;Y(;YMKnxA^CuTc3TPmYm2b)s$D)bwpgKA&A15Qp%kVSVJPe%JC;J
zJ7*QMnEs_gBW<-_H;^z-b26{Qi)9YJ!iLDh4XOU&^8MGG{D;ez@sGmfA1>d2ZT>fx
z?*(fTyKmRcR1W!+8n4yjS+BxYMovU+apJkk_utK)Jd54a#s4N=?{}UPeoq?3#h!m1
zsmqJ{+_P<iK(YwQxnrp<eoZ*SQ^<sr-x^QU{X*))f5jq{0=n6RDr#UNCVu2yJI9wQ
z6MqeJ(R`oDnU$_uZCyIBAPsXngcG;vIvela{FbWwCCr61&5A@dtLk<{a1fIU%^FO|
z4XHGcqiWi*hpCe+^TGl9a~xw8j$#C9^t0d8$cAH;U@R#NEGbGMWMv%4?3*&A6%dL5
zN|{nRQm+}dK#zk*<l_kLmm8>L_^AG<xO>20GyBv_tPh6ECd}@uR`J!8L(7Oh+gZWS
z*iZlN=He|Esg)=qJ`+p$EuC31j`(x`Mn6dP(iPAI*Fo?dQ%n-hS6!VDsjByeURLS~
zLE@D*RxbV(@%6K2G6TXw<U@qOmjj5HQ)l=5up9qd%bmA;MjQfZ<c3Oh;F-iYQz>)A
zY7VcW*2iUH%ftn9#sne3K)GrD(X}ak(y@63RM+3P3~i}CUTys+WRM&8QR;`tE>s!d
zcxdLFCbW_F-6^KJK^0(m$H}C*JfK6RB|-e)gSc92Cj8Oe9X@;ZYz%@d_L|%&zOX0P
zNXrt;!Naetf6U&)xXQ~h-?Q!@ea+T*jJ*)cG5XU!V3YDH5ovsT^W<lz%iWazcFC*Z
zg@upzdHqbjXJkt$Fx*9<mG-i=T>8+|0wbKTR$9o_0vh^s5x@s0Rf^;|#5rr{>laDI
z9!cW2if0!u%->cIuE$>oD+V(ChOKX#_54B6PxwrIaG_vRAp6M7FYc(H<=CfIl$TtW
z#_^-2OjVWElE&mqziB|)Ju}aU{`vtrXt?#^rAtgrr3rNMI^D@aSyECAGq11w5hB;7
zL(yhj@kFy;(aNP<+wmnU`#HuvGtQmdBlG9suF&5fX@mLa?itVHek#0$1)x8g3H`GB
zFhWv5(ft_Y22sUx9*a4y=!8HU<nsfE7k}w+mc{w|ev7hdGvO&(ODD;-4)0;Ei<p(Z
z0LL7>ulty^F*uE^U04SpY6IW(U(GBS-h-On&KW-Uak#*LttR39K9x&$J7@R-{yN!m
zyf)zT{9UQMjKox8t?fna@$CGkRMmNC?AZMUSu7wp)_!LRd!|!3jfl69j>#pNY#V6N
zo&6Juu><syepWM|o=8f^9DJy2ys)wVgHqKXc{;OA)kgcbQ(zNPro;K4ZjP<FrWTaf
zapgOQ?nbA5venWOm*s)IHOXL}kQj7g_Y=~}mKDn7%uw~z*G`RJ4D&kU$kl^CI_1uL
zNI<qrTOaCcSCm)Xo;(YLbW+!PQrnfgw@GpqB#jQgmnp>{I*`IUCg_-V{lHYll1tuN
zb8VC2$-v0gR-XsAuKs}A${D?$@J&s}sG>3j+^0#zS1xnnP}%&9X3Qj7#Rtw$w2Dl&
z7Iw8gPi_MWJbN_s6V0`?*v`EeV_Ihz6N(G>3e_`REh2X>#%VQ2^Kk#f6iuuq%}?Q%
zOWI+^-Tvw3;P`x0|2uT&giN@)T24G>lh=-dOSJvK@5pk=muon0YRrC6{ZkT9b<akI
zL4z|wahrza1RPvDZYmzC)G3eMS<4M|+M?ZNPcE3G8WGJnKD)9pH&Uw4$V@EAZZlPw
zWRe>vc`AKaiJdQulZCrwU3_w;Za?y`b)yD5_e^NDz#Ves7=`k-C9_k|+|2PmlDhvF
zni9*i6wAltSE!xf2V`35@<)l*e#B>vJ;c7Sw`UTh+rH;1kIUkviP$~-={s`{T48?S
zNHtVy0O&_b!kqM?oS5fQNU5gTs5oabt_x&Ujd5p`%Jz3eOrK_U02F?}{n}-~YBiwq
zl)%@{(`Hy3M%m);<Acwsog`*l&%tqTzfv>NGH<x;duTn&N6lH)$Cr4R*pR`|<!Smc
z#LeRU(Bj2Y`Z>xhrYT#VcKe{C%hj=Y%_L2){Sq#6qDC~{1{J!VC2b-y5HDeiqpMp0
zH|r}a|CUzx0EI0YoabTmRe<UTFJ`2$aw%85ZLxDAg;Eux23>CiK;kmrLib6nq0$jW
z2mT7BkcHc_t@ybCt!j2R*SLzmaht4f+b?ma#cZYl536<GU`u}w{E&Mto{0|<&z-6v
z%VT^d6L<`3yt|-CdEhW7mUwVx8LrU~P@=f&!)M7u*)-tJk=Gfh`@tt(Zl}vX%KTK!
zc0mF9D`Iy=ZM7*d_tU{5_Z$-!8Aqs*xU?Nz!E8=q@#IwG?EzMz4O+Mi(s&`S0mAzm
z8uT5H4LahM%9gRjd1l&<XLithVr2}9#%WWZ6WF4N<+uyA<pR(fOjl>G-BG{cw#o(J
zk`is!%pvpX@KbKC5K|E}#%Ir$HLbz;r#39S9K%AR-F)mge2kOUqn~0&Vc=;HX9^<;
zhpiPiyyJ-H+egIg<!{Db(v@xzOGQnkl*!t*I~-dW5fP$zIX6F8C0R=jN4^jtI4V;P
z(DcwLmZbosT;7}`R06pijkeb^u-oyN_Qqohcp8?@+ffzCdD&Hc%(v+BUB7S%{ho_W
zinI~uVGMYw$=_;Ug+J#tYeL+^aAY*+N6QxH36*NQopH18>O^{X#1*+Qt?+#krooj^
z)2@y_iAi0eWN+2drMMveW}T91rsRWZa!6-{LjQl!_SV61JWaOfh?$w0naN^iCXXbG
znb~5pm>F$ZWHC!4W@cuvn9*Xe@A&t{?tb^hz8iNV-uthnCpxP-E33OY`{X&T>RPke
z#tQ<%eUD{Z+o&H^oQ>}FiCi;VCoez|Q_bO@hUMFh<kB5(k_;4qC>Z8L2Td8cx`^bT
zwI-woz9>r#>@V~de2gie2?{iEsvQ0Ucp=zE54>Ype&<xLzUAX$`nFJbMwk~i%;EC2
z>-~b|sM#XBv&nh9A)<An=xorD;3Pz$9r9p7s=Q=L8<rfFW4O>!e0@ei0+WZzb=meM
z@!i}RPZ}h|@FGUEX^@oIFmX`ia8$c2uO1L;g;3c*6YJO0Di)I%A_<?^oBu>LsO~$m
zajftXFKnT@zyFEnoqY0YFGo0_q2y+y-%vsdOI?yUGF34%1qB{<6$QW#70F+ox9@1K
z(_JRJ!~=tnWJdk&4@Oe3IWh4d*>0u#qPgS9(M1`sbHSxq!;V)Vq^VbGoJ%2^qRIT+
zYo>vq7!^!vJ^p>$5=yK0yIiRvxahJns^hm&*e1^wtI@p;1_Pa|QN!SuU(WKR4E4XJ
zkxyO|=$&!WFr&hh@`DgcCmW)dJL0KnuZe~XAE+yhG(j#)M)%hHu?)@@M4p>Db3BUm
zrdyQCi9Q?!Q5JF&@p$q{lk*N(=9&xcSRBkq*z3B3&9A<G5uMbYsx4KWM^%}X;?PPY
z32c#Nx)1L$<`YzGp6wuqM=zzSj^8%=e9DD$Tp=B5rCf|sR9~>9n##WFuz@vkS(xk^
zzvg|L&LVfYv@o-Gv`<&D?{M#b)h6)MPuk7d4;1L*NmmX-IWT6fv2&KWT8feU&fco+
zF0Ibj9OF?j%r|7&Q5NR-W_yt?)R>x`Ez2-6DBt**g_+YO54qwwZHe2y97I9Z9w4<h
zr_4V`iGpkh2+~WD{f=!8-r`dVnA%ZL?Wd7^!uLSKf19mdvX_<GKkJCzbpFc=VU79^
zD+HX^(BaI#HBCONwSCm=pDiU$C;q+*R)Db9{T~WI$g6H^H7%=5?ZqUN>O?W=-T8H?
zza;yr9lBM3<sazA56H^cnoz9-ODiMkD&p1O_eB;&%sWp1vcp0@6BpjoUc!my$>SgN
zHGYJz=MKf=q{d*U)zUfdQ(y4FAdPeIc;J5KS(l)-lZ}aOqol0UVL3tqUO7rWffovA
z^<8_Q?$EVWLxZp=#=jjeX5C)A$vQuuRvq}<xAeN6_?zPoV4_SBbnj>BE5tQ_L+aer
z^{_7*8~BS+<Kq1j!PlvYyEoZf7N^cuv^}!7vDCkI|35OTlJiacD{Jq3JCCf2X>QiJ
zSp=J$TeIFs`Nmkj8wZBcZt@X%W*dC!5Gn=we-2!p;`l=YoqBz<ZN1C18kbkyEt*_C
z6Rq*gXnAt1k|wrfEHrlohUeJ5pZO;%OP>JhsQ66@7S{r~Cog;5^XSOxhOS)b_74<w
zt8+Uf-uKTq<B?J0v2TJ#UvbaecJN!RT(`@&T1~<QhrBQ5<$#+-9>W5zhMXKKMdk^1
z>j^??4K~)3=*7x~%@;jB{>rm<^*BHM!AOf~-<|-a+N+&~?K>~=m^A@B0V8n!xA4Y@
z&M{QztK*`e6I)ggdld}lZ;Y|v*}c8+z8Klu{heiFNi~QC0B3^m6~r@Ve|Lmt;Or*U
zzZ(-H=={kIrdnba8h*Uzc_*1M!&Q-*(w)q)F`CV+!RC(da0tg!gsrKG3eu;hCZxQ;
zUV^^48AAjsHR2<hpm>K_a|ZtR#+Sh(#j+y4&vX{yXyMG8jC3h#WNEwP$Z2_E+Ets1
zx6!v`KSng(?G@*MEPpEzDM1BuSjGp@L@GiEbinP8qn`Z%Y)*i!NVy$#{uHIv^SVBD
zdJ)P8YaF4Crjwxk{#^pD-fKfP@})dt^3fTK@`%0>9N*UETxxWUyUL0S%a9>7vL9n>
zN{A*Gmm!dV9f7}y^5$L;;|$7~2B$24j-&o2o(z<ANBcjFUvOIgOSOAgkL&>c%<_jr
z+SKuj;wV1_Pe}IY%*b%Vx(Qx*rZv>YF-vktQ|06wO~5fE=7{pqAHZlj>6<{#@AzZ(
zq(-*y-JwjVm%a`0>MWhdk>*LQMxWm(rHyjUAkBWxH$AANWX#;<kuIZ7HDWYAT<A<L
zT1=Yx7P@Q>Un+UI8Y!3q<NLlkZ|*a_elaU8n6EM2FfArxqXd`K63m6+NJ&X4m&W1N
z4>)|lF=HYTj;`7q^kNBWxN&2c&{~_0a}VX$GQBEYvB@dt2{SV=kU6Q(WQ^+*XN@g(
zb?+}?=mA*kXkNtFn;V!oayN1eL2RxKH0j}wae=Cc>J@~uQ2hm^Az)iJIT5BP%Hk;+
zO?EIa3b_6cPK*8cZ{cYls`ww_$zSuY@T^OSCxZL<;Sj8SNJ$uknh%lk{I4l}6a4Ei
zQr|78TZ@Q5)PJTI$**($>-U19gALO+i9+wv`=ShGqC;~PQ}V1dc65{kM8ZhCQ1$#m
zh|fnc;i$NuOIQc2n{uNZ*jMTBCPs*(TBTypf2<CdpyWdZp@vY@c!pg3chU4Fm<{fl
z!?&ICNJx;OvLl>v;8M-Yt?y%za^5d?I+!C%Jqcblv8SitTInKqx9hW7sC@WiNCMHX
zprGYYf}IP|*S5cd7x=YxO_ste^yKk(E!m-d-2V6pZa>Efe}A11W34_ziix50EZp?n
zdy2TopV7K#UjOxsrWE@mTA{l1JPJ!Lw-7R#JiFd8QZM{^G|O#}n`>ZBd^O$HioTh@
zG7B3kQAKM}&^l@i_9qkqA|<gP9FF4xaN^<pf|h}$T}nT+pH_RC_ZsIIVy=j?AQ8B$
zrUrz-#(ubif!2hIQb5e1sz$rzPi)sIPKG;<HcJIT-2g--dWPwVW)m^!84Hsk8d2o3
zWoBT^k^nDUZ;{X#EHxI<xk5DDzkWjO%SGB!I7Ej3kk3bh12EQJM?=FwX!C)tt2HVd
zK0@2G%4y?L@6FT0$D|jj{mfiHfDi!Yxuv!n!>WamXCi)2l@t8xJSwq*a-0{7I#{xr
zbQdjI0MpKnuL<Liw;;d9qv~X4lrUz0fhH@y#s!u?D^V+Iw!d*iA}%er8P0d3MJ2`I
z$^>f>AexcO9AO5B0glviBoNZ1B`WYT`eMlSg*+E67kV6*DiAUpwdg26D9)A?VX2Ay
zP@h5l!5Wk;**{N=4*x8sI(D5?y*DGUaeCGY6%qhU4QLTAb$ec{qgeALA3O}9By|zR
zx4FQ&igW3<tq^R#@YMAq3A+qnXgYCSPX_sS+f~*fKJOb4WqN9&t%ESu1(BCq@Js4?
zxG6H^;KFvQW!N(6hN#Cpkr%6SnO{jChE7sbekBQ@RLHtmKR5oor#lUu%i@k*7zrr?
zs$mcC2K7)>A=WVyBtE)->%UHJ@7Ty3HK4Krma&J>@^aB$Ct<`9Vptwi&wkfX&W5(7
zQRlSB7NlSpBZ7s%oMm0tB}UnE>@i%bZ1nH7H&lyr6mkrp(WqQJgB!t^=W|FWkJ6Gi
z*Jc<oCV?)1BzBkn6&*qxw|NHLf6(gph!f#b8DRg--1@n)dJI=s;E{m@c2bLRu2_s1
zUnP`ULm=!Wi+i3TOhE#GTNWRwM$g`OWdFnKSBwXYnUOiYkXABVbf}B=I(!)%5I$|Z
zkJ{K?HijLGI%!JXKwThG6Os-y%;W20R2|3@l&NZX-O73HLSLA`v!)lY^tHoES2|g&
z{qZ}76Gy<$I6_*}=qYRl4Tkt^Uqi++^n*z4_$^&H#mlE*pUjO=%wLcSX8ZMF0pgA+
zg$0eAlVb-sDVAC=s~P;n@H+Vk);JO0k6(<W5x(@}94X?lj(+mI?><@qPUDR?gpcr&
z{2a7i^)9B)ET+S)ztCPN=k-}MR)?7tm}3u#x_~TW&Vs>PeWk){aP!zlsPwlJ$qcls
z!t+!G1<S~Ml%U~m(h+tX8y#ORL%;&S_Q0*rAZoxLW6Kyisq`A6vx8V_SO|n23^0@r
zt4>;^lbuKR=om08t3UEf4J%7&D;;p4s)H#6q2UyeCjVi*(G(0*0t^da(dU*0<%$86
z%pequNRK0f0A8{op+7H)N{|pM3bKGS^ZcCQ^!8ZdB!#CRl8@DQ2+`tKW<Gk}xNp~n
z2CT60j?dfyh5Xs%tEYyhQJ30m`Ul1Dbd^=g(3#Q?4Q1ZTxDj2WCeriOwGg37Q#1h%
zE^7WoV2vV`=(0+QQg<cyFl2!H1U0xELpH30J}AOjQUS3MOw1EPo&ou}Pf?S|Xv4uB
z@Lc%V*REejg7u%vrFAi%pYEVl_AGQQLMTt{-`e8g7VK;VZfgi(o_fyka8E#y%R(><
z&_U?`L<iyI$lNp5G$a3$^&pBARKBh{V1fUy6he;c`@&;6+quP|zhIkXA{NiVYuTg5
zr<L)E7k@6f@}!*8u6QHg`3McX81oKc2l*^*a@>vt4-qnI6Es?*!1*#P`d%l;godFz
zh04;qXj>#~sFmGWN!^O@bvC%j+t2Eu1o$UXLI!3Jt}Jbu-`DWVrRAHm!XjwrEZH5f
zd&rIVAbt|wOCD;N*<I`nC0!7A2b?s}GUw#24=12%CCgKf(Wy&o@dqnNq(V#e2?Cts
za26tRzqAx)j{|RS-0D;{PFBYx`K9MQ<QrWbit6D6kcp(&ZJ|o?DK-e}h>}fBR;;MV
z$Wc4C-gB<a@8ym{CN-F5S`@{!VLXwA5V6$O=&vI@3nqk8|IQJ{h;NS`{@Tyj<`TbO
zG_<$7jTc#*J^9)DF-DKyXd3LlN{z9`_c@Uvj1d9cu%j{$>0zhlEHf2#Y&JPQg(GNx
zUfCr#<8}^fXy9;ujy1QPoY+|fMle@J7mT70t)a6P7}LXt&xRV>xpn5&Pd)m#JDlAB
zeXbk#lQTe$_*A3P-E!r$v`Pm6%+lyVWvMi!9%^+OL~v~@eguP(I$&pD6Ucafoun+)
z8r%J~eIRffi|u`TrRf)$VX#xMg`qwijOs^Ep~@J%&#DIOI*aWtW=J7BAnNbfIay|a
z!vmoTTMe4=@M{MPofPfzXhg)c?o^5(D2YD6X4lLFZ~Uz;x74r7GYT8=_2mwpWwzuJ
zM*?fOQcRyIT1pL=&p)QtSWkppI<m}{%}F>mqo&EtSPNt@IPZKS)jl_LR~p@5f|Z|`
zcHs0mDh-svQagLT1la|tC8s+Hn^mD9P_kpHm({~nYNj!mmmnSz)LUr9uT~da#3ohP
zjE#6<$4si18g?UgBYk1td!?z`7x36`Z>UMdkn!f!9JiZBHMny|!oTT^#J3+hMfB7N
za##eG-r*L)z$N6|uD|YeP1Y+ou5{@d$|DnB8<X^mPRsTU2{_o9-x>0W*W0FVxKY2z
z-?5i821nks?b})&7^3FsXt&#pZ7I(aZd`D5_Q*HhD&WqwO6<5quUo<#ZWxTNor-W2
z_Gv3-Sse5z)hK!H`#I+E_C+CslY&SD>C>|$Ue=`th3Uub&b=1vdD!2$p<~NwIi!m9
z$qi{zuo7jh0s9;tsF;z?YTgMRtH6^1nW5f~nmL10=Etje>Q96h87ax!>gi5dH8iZl
z&(K7SYb2&0>JkVJYf~iiAr>kTliL6~JDsnI>|J_#`hbj&1aVc(&C-_D9ClwxCOccP
zVpx(askl)WTZ!9KR*1ecgwoCDDj1WL{664>QfEDMj3Y38Y@~Wko^APQdMOk>lb9Z1
z7^9dzkNYLr+ykB*iBsq>A(657LdiWY|2oJPKu)R#lhlcXY+)w90iwN4A$8d-gyb4)
zesg<f6Od&g@a*$rpujqmXiopopK0t<aj&UuL(?3a4Yvl3#o}0dC+dj{T4#oeAvCX%
z>${c!%XN-!*_9t!$F)hza0q~yO6$Qw<{1*YR$7N@<G}-m9AQcvS*qAFtyesbpO$gH
zB}~Ux3*91qgz}EHa-L$ZO_zrn>!Ao(A8gq>(BksY{biMd?c82ZULd2+JrIpQLZFq(
z{Gqs;;AlKumRouCg6}-MQFlaRMu}O>D`_@Q{}t%P@TJqUHd)&e1t>GN;p$RGM}+QT
zI$GhqxP51n)#qU6BHP;hY^ewZ3uaUtGoXpI*I(2RkIec>sj^_+0+q<<fZz=kx>9=B
z;e2y#Q_@J7)j`vmw6VQndD2kgMl|k7HFrFII532|!mT{-#Vb5w$vR*d%9$-itzjF3
zCFrSBR&9!}p!r!Mo#se{59C$u5CAMAF+4VRfNibb26{bb{m@;<Eoq(%6dT!BRiV7p
zFOwam&8uxFK~{9-|5Tz4vmAdcJ#LOwog&+6a=zf|CY~?QwYU|T#l$aXB0t`2L3D<q
z|L|Ttwv#TwiMg)VFz;~b#S4<f-l0_fTqow7T|H9wr8n^e*%D09h)Yw`o+ss`vrV;2
zM-Y|nmC%AdtY))a)M@d9Y#pXqK9U(XnoqM)0KeE#@>AS?y52ipZckpmj;N)egwMk4
zIgAV<8j)8*?c=w2lMH{X_Q#aArG%|&MM~3_(rE?MxPj=*<aK78ucxYHhRs+jK<h`O
zA8}({eX*E^tXYq#5C-=xnf69U7+F>E3X@~|^#?P?4YZ>ZOvzh{tuY_Zkftfh@9lUP
z_?Yrj)q1N?so`(aGO6il)!+~WC=lFcd}0d?QX)C3jbo@q6dTQ);ER$t5o*(L+Q=C;
z>b?CDv&*idlTK_Mg%de=Rs_au#Iq)%d<l3bTvH7T2orfoYujK$D2BBRkJ;^B>CF5z
zQNQ%3x}qe+{Uq*!Uz+ziZ-Do-6~In+Mwxe`+-bAaEO7k(Ahvd%Iy5g48Dw$bJu@**
zE~^|zYT0vF6a5_qLrSq3F{JH03c5q+<H--yMhr{A=_e|-bv~4fC^sYOF<ghD`vHe#
z&%O3|Qb$gXaqeI*YdO(4?*rIV%EaIW$_NMsZ&|UO@6vj?_n}r*NQD#<miC$)#w!;S
zmv^1Jm-G?nHwYMYq*?m%+(sk~8aQoV^pwiR0GYvW7!diLW=dtTT*3u?e*pc@V8rtS
zb$NLqp3k0*_wz^uo!D;nfP@T7f5|DqUyW7G$W(8*@K9kgX!i$tivFzX^4H=k$nP~N
zY(k;c)hQz(;dTd|3$H2}5mMt*FCOx3GX8e%rlT_@9$Khd&KOz%XgK3s0Kn~92j?i@
zuC*KI@>jR#2>}t;=J`1h$<hhI;h4lxn)F<pjQ;&zd&Pr2^o80d3+<eNR<$p@*=bZK
z1KhS$aA^dRR7bg;*^jwRMXsF_p-j1}t{hYB!R__@0$x!F(MQ`W^%xJ5Odd_0$fNVg
zgB=RH3SuOJ?gbO51tDcQG{8a+KFV5_a|etwxcU{P{fK47bv)7y2JuhxkJP*2;V!{d
zxX9EWF<s8)PAgLoa3i$Y=#3qvR48SRQ%fAv+m)`o5Zqomy+~`>X$>uBm}#HN2e&DH
zDTZ9t4lOh*CRiq8DjRt&aE*H7o^E2OR~mdU7ZXr$Lq9D@nku8xcagR`d>bdS-IMp*
z>#)4>e1C!IXW`Q_>I?Lj?b&i}5Gc?gJapsN#bE#P&Aqsd<6@t9l7}+Pp1xC>Tjg4%
z%rvd{C9N<f8wN5|3`CBwyS-J*Y2+_jvi(h}FG1;-QQ}N^-8n_WAW1cEFFcAuW=2vo
zB3w9Jp++L8W6KiBi%4M`t55{EM=j!JqmSj{SvBs#`GrHBvc}5t<2r_`qc0I<6e_&6
zq!6$`3^fz~j9M<b$^Aj*RKUvxtTNz$G*7>4|Cyn^c5G$&e%oP6Of){Wu^JcS{IV5K
z7n|9RdaI0c6Ru$LNUFVPiV+eJ6>GXUM3b*u)!A`ToZ_hbRxRYB^u>E~^<ko}_RSup
z^kK>8Wnow^hm+mcH+?20Bg=Kgocb!ljI{UH90B<KibEY_uUN=z?}HWxYU;XTxiY=p
z^GkE6O29~##=oz&P973!Eg;!rGCekC?(M^vl6QID5`W(0Qzjit^)V+8CZ(;TPjSTg
zd{5=$Gk)HoZyjjmpJHKdbAoc?m5szXH6EYlV9$^+KZY94FcUPjK8jz;TCjuF*b>Ce
z?oNXp22JG-N{jL13rLH&7v3OxnnXKMh)JJ3A3B`)R`d`?E2OiuDdh5_p~n7T)4XI3
zlO6{*#l%@9KhzWIIz4?3Hd02|grx9Uvy@1@q!$6h@xFR#lB}3~_Yj2M&L_JeA6MRS
z#_ecTHz$V_2Ckvt?9>K4sQ|U%!I*I(*q!+x?@}fG&Y9H7z+$o7+-Mh}@0a);`ec=I
zgBzoKFsK{Y?35l*J7T%t$pHxf=STWfkS}Gk8{3I68&VQZ8%SltA$5;Q08VeQ(l5qE
zet%^HU9Nib`i+*Q04DOIxG_n=XAN2+v+CYKW*@3fp;Zr;h>AI1VU7ltjft=K{Mj-W
z))U82darTjD!@4Sv{dI~@isg9dSe>BpA08u6NK<t;k8yWOJ}Q2AC<dprexQaiPNt$
zVZ{lK-B+r|D6NNTv20unC$uReYn<x+HljD6_42jN=H0WV#4!YvWhp0-e3*|s-`89g
z&x`Aj7@VAz48C;c)j_`FJ&doL7^HY(0IT5(VvJRWJiH!YKH`|)*{*aFrIgcGwjLil
zwzm7<<9P!0%h$TU$r^e+xQ(4S9$1P-!%U;au5GTOVlHNLha@!K35u+?u5=|t!{#T~
zz_RBFN`FdbZ9Pt?c=Wb5xQIDe0X}U$2*nQDN;ENf-9}`(*MnfXN!$e#{C8tBOBIva
z=8l-x5FsO&>Nq2`4z)bZ_@s$0Wd349=u^3j1F0Ghc9hqZ8#T}i0D|9wK9qSgsdEZi
zaeZRC{ITgAFl6L4U!h+Nl~t4WsCn5UYQ`pXvM@>R#&g@&=Kn*9@R{>hSDw-wdu}ED
z3xx=bes4k{3A}3%-gfM3C;3}>wl5e}pZh;S)qm?MbBSnO-T}jEK2lfp1vHF-*;(Hs
z1lw<ezV+XZd(e=wVda4l^>0ki+F(eHo8r`8NDb5zslH>;<}_KJgr4%D15no5>|1&O
zRKSmVo^9}p*tcCGirx4F{l4*`_3!(rA!qe@`*yDPqZ!bsLjLrV3<7X*hnA!qmU1`F
zy0ly=v|wIA){^DWR-+g?#EzFGrEQ{;IP`8%)Rr5^Wq+30Rcvf(Rh9eYELmoewCi5#
zH(uw)J2##J$~}kHj5%@m9ljy*>i1TXl5%p%bHs2O*PN0B>1KV0S>?L}CzYKgzf0k*
zd0!@6Njjdagl1-B=f$}EB~nZrJe#&4>JSa&y*&)EDvbm@CQ4Uc3Jq;pSZy^NsX{~M
zZ)vQ=(zCo$!$KSzKN!qjxz~>*rnb_<EEbLe4RUWu=Tg&*=cKPoZ;~BSY-c^j$_70&
zu}ACG*Z2Uo6gs0rcAY$xS>qAZ+|?u7SH6XMUbq^rzbS7d_WHcO{En+)#3j;l7@lSA
z<weamGda?2F{-mx=X64#-aysFMx}(ovWe{S$*Z2JZ#<|G^7K-k(x*JzlCi>4aUyea
zBCwRboJUFNqzH}yts;GHWzZ<usXy&4kcdhVJHVtXwJ&mYNY#Q6XaW~5+1U&Lvs1G>
zMXNfB$v+o<^->>c)j8TiTnK72w?97LOyp283LSM(!Lh$Zx0kJ>&Ps@LO_BY%CC(|<
z>^yRsh{YL?&F<ii2sk{WwX_+#J2{h*Qo4xDJQ0)I*~o(H2%x{)Mh4DTa^za_I=?e}
z5V+Ph4Jj<hT-i5G+U0eIe&~fJ;B8Mcs6AES)SFVrjzn@ODa@U0z&zm_Sm(c}dGez;
z)A{*y_;awCTf5yo@#D1e7T=|+Kc)qIS)nj*rz6oQz8PcERMsS6?#)N|5)<-hpG2uP
z6t|^leHD+LtH}zzRn^t&jAWv_m%Cd~#g#wHKFGI`52hUy6XD<n^h$E&W*Mi!4o*!O
zeh8EsuWzb58f}aaBg|>E6?~$pfjS5N(6b$WtaSSWPL$Ns?KCdm0cF~&-ODlp<r{eR
zghDpHMBRTf0yTWz(6`!`#dKLtt(USsJ!+<`hI2l}VRpWT(HksMnUzEc8WJ?6Rp!8;
zkoA~~{sZVV`}P-mpCpP>(p|7dvMlg^M*Iix+j=AF$${q`cjmHnbL497$8<swe!b=*
zLt=Kx#jdi-{$t}zh=tk#eXf{`i<nrx;7!Ev_f5BkiQyNORS=%VMp^96yDnbmJy!hf
zhl3a#Us)s96TU154BAs+1f3I_xUY#(?NrWsUl_*#5J7+=RJHiCJfc4UpJ7ojBQGCi
z0FiQ_bfZwU+v^gzUg~YO73+G<?u7;1#gDvIQF$(2Czzb`TxpKe{;s+Ok2Orw-CJKT
zMIq&H#*v9BwDTS=6Q7mmvJ$BYD-EI7t|RTEavD!?Ds27$n!&th$7D}v?Y_My)Cs%F
zUYDOY+{o60Q~v-;J=ULOsh(0)j&dgg9bq*tFG)OqV~qT!o^L(k;<D3Src0{kXU<?h
z17~Onz<^zei}-k>pMGwN%uS;Z&>Y8^OCglf@Tp_*VG5e@W3(ti5Ln478G@E3qW_zD
z-PR+6;fJ3VqNo369_p-TEO0^a;*BclP5y`5R91Rr#M$iF5--x&YE<=4kl$T8pAiJE
z&PQG5WZTiOh3{r*9K^W8dM6L22?|2=->muJtm0d@Er=Qj3a3@C^sH{caU!#?+HX^H
z++2IH>DIY(uj(^g^GRQXYBY=YVKdv!6Yl|5mL+m(>1(<dYf-dJtQEx(B=hAa2w5UH
z57C!At6$@+sq`35sH#B!{)@>g>F>T@8DGXq!~%VQ=(el)GLXw1tm8TYz^Pw>1WrKO
zB${F__`<1l!+B~k129W%Rx0+Gx?qTjeM9Aa(5-)eo0{W&uq(7-x0}pbEv_W6kb%v$
z<yY<pfy^HiWHf9JlYPqMjqSXaw(Sp7!F|SMi?rnc6ihn84%cyAUbSYBs)!=7Kt|=c
zy2+(Sic8uLIODC$Y6c*jH$Ky;1OkFggDy?ql9Bx!X5E3xRwA3V!QdifiJJdR1u(8=
z=C`oUg;8%^dUvvY++Edo!s62yF?wowGfpw{$V7ChcEFxJlnM%eIfsyb9Qwbr>N3xV
z7E(V+39m)K2R5fyB29`P;!1MI4p*&v`FdU`5t|)v>pp0;oZ~-J2GzT`)%clNw+>=4
z^qa`a^$Sq?SW{}@>MA3;o!TX-E;26;{yOd1Y{nyRK#Jea9G>^g^>gS^ca2%+5O^M0
zH>n@^WRGiG)s-WGjZ&tP7~~u@>$EnvQHACN#8a&fAK20*Z5VLQvhsM9r2d`)a`xj4
z_qF&ccv@ez#3&;tBSD=9mVr~OBh8d<Z%mV?$}I(9p%{*Sa@&?as<(XfJgOX8tS7u;
zFfn`6_AvJijx136<h-}8ZC5Wd+(9|(Y?>NRG?i{+BO#1}?w)Ok?v6lJ&$^8=zL5bh
zYbKC)d|PSJoX&3s*4v<U2_3NJWA@6TOldZ*(&0oiMU?td6#HD&WRaJ6lB7Z6ENvQc
zT%n#*QguyA@w8xpnEUROu3LqA6gTx(=6ibSS=;C5rT50mN@A7xVrUhznGL1$fp^rk
zOPou)ZGH5J8a){v3~ZCR;oNnz_82;W>m9fP&LB}?{n9{EX5*3AzmvJCn7QM<yyLxo
zNWH@#JBd?8<)mzrk3UU0i6mc{oxzP)P5A2p1~?He9#3PlUUm7ytHHaG$dHrWdfn9x
zCG4d1BE=%jc^2*q#h7$&eLM~}@^>Y3A8Bz8dd7TwPC@W)2<L694#9~jgPgB%!Nk*W
ztn$TEhE+koz{;<~#Z)I<JjZI3&w<uc3U>D$dgNGRA&)hLLY%R;PZG#J%X`p@LjvQ}
zBl8^v0RcVQA)Cu`k+Wd6LgzR&OrAQ09Q$7yDiGox{}FA)a8td!FUlczL54vh!POyx
zk#^^{*pP`~5RlkK5bU{P`4FX<9vD9_BNI^I7XRk=Ba7pT!V=Qdgg1<T-X!$yz<AcP
z%gP;{b<~4_As-w)tWg8n$61!)+=h<S{?)V@+;b}hL!BKQ7=E5$lPaRhw}7F}xjg?v
zsPmlW<02~t8hJN@_sbXW(qs_An<yhl<I@B2DVRP#5N(`gA)|HItvq^^?8|UEm_C1)
z`(LmS(cpvhQ{|1D=Y?eOrc6kAuT@r*d2b9X9!PB0jWTxa@=8&`vEFj|3(dKy-PBnG
zKc+29K5SknTW~p4LL4vXhXU%HPYPzGcF<na97J=|&hp1Qi--^p)?y~sy8!537mj5E
zDI>grn>yW<psm?hmKAs4gniju##Cg<M3L9`0VNW8qO|^Dov>5GE`3D<f8+Z0<q_t}
z80oy1L-gkHnAJ)<?t|1giWClfQmM$$P+s~1ap<sD01YZCpcvCJTQez)tv&vVwOYD{
zj-L#<r9D?U`_RRM&93*W+^wvEA*t9^?0^m)t)8^FmU1)Qfn5y>1Jc%}g3c$**KX6r
z!g>w|B$>4#K1bEi&uFgueqK0uBp>qI8N#Nt17svD78pb4tXqgn_`)C|c}zbHfBw)=
zhjO{xx}b6ea=(AMeg62=W?^5Vx631w$j&<ti$1Z%pM;$f7i7JNL(K2KKUV#7r_s%q
zn1^yCyosm5M1fDRB9YicjxZzJEnYmR%c3rTycvH3lvfiCw8%+gpuIFg&>K|^gLbuF
zX>y&_WyFr%H!96SYj(EPISI-ep--=F%am;XnybQ9Zy7_&wZ(bZIcl{A0-m7}S|FSu
z)m1^Ur+g8zI9u(VFR{;z4aTP8zva=YR_7enIzz0AtkW?)G8mj6Gb~mzvpRqB;|Sk!
zbV}R%m6jIAx!`VFTlR@pO<6G1;Ah$1W|Sb^&5<5_COwNmNdcuZJ;Z}&5CFE-1q;Y{
z(XXGOlaq8Xe}=iS>}kK(VZ<>-P#hQKSDd_Sr4?*My60zXeMPn^oagwJlj8J4SL_Cn
zPk1gl&aVQRd+?LJS#z=K-HP%L0QO3BaIBTu9FMH($Kr+c2MaP+Cmb@|2Zim}wk-0h
zb}wSBgiPZt4W1FOq!$44!muPC5I+Oa$PPFKj8c*PURBir-RH)<5q10l;4vgizFG)>
z++K2JyLSBbW>6xJqkcd1(!5cb;?9>9BRF2Jr9lq#O0+eD$NpZr4u&~%k5XQ_y=({@
z@j8q;f5c<Gsk_l7so=j{Z=qZSIsVFOIAkhaTz4a3^4n(zYj4s=DUl00!$+WS%GLb=
z*#D>izD!B~M%fCCNgbtlum4)*bS8lJ#P|6(t0r}9{m6Z_Pxr0#G>N4?uwQ1%pnl)k
z`?y}rFcL9;v3!~sP2ms*yWa%&hz}vukQ+rxj3HT}p6!BdBO>(8hvO=;&tTid&Tw%(
zx%TK)@sa$kjr})uYuD}~SQi-XA;VGf+1=z#-pzloY}2r%k_{uE_I-+$z(4~dD=(*6
zCs%;(5@#I6C5YFuC=|bd*_c~Es)w~GQB@)+{s+XoIkW^=C-}>^e>c3;o)g{sUi_LE
zV2B@x3GUoxV?zfGJf6_o)BdOA!`B$iww6j>Hk6}E#SUcb;_*l}Xf}E|eJ2ii5%IzY
z<CI!yIuiI~@e0YD(Mt6NaCy<Zp-1JW{m>jum?1&i5eU@W5A1khfN%Pqb`7SJPLsI*
z74qA~@%Ly}EtN7Mhks-gB+?JxG%*8W{66!6f-C@FcwDPDUsI&%p*fzb>6hWOu@o7J
zmAz1aCCWzh;OQ}Ip)cn_NRa8=iDv;*668c<Yqo-C7zQSXRJDeHE~k8XlO2;=or;1g
z_rB6j*e{$KyN<3$wNcN35oK|lIp;a~3E%~*qN_(cog*>*Y`<oUb4c8xzA<GfPOs_#
zyw<M0UG){;)M-F1L+qD8DcM~D>$2yTiWgK|N)HdVR$86gdY${Kz_A~Aw6l7aP8d`J
zh3l!|f^jOahFS~iI#Eak3F7JfE4@7{VLD@>+yQJ{9xj_*S_5_L<6KG_=9hxRC9w3Z
zwpn3u%Z$Q|G#HGQ^zcE+WsKnO?f+KJj>LTg^}p|Ta4eANVjCFIqs)cu=g!s@+g+R#
zWkr&rldO(lZNbt6YZ@ebaOUEB+OaXI;zn_<u38L1P~ti?&W<(h_Y>s`qkH`3zO<-;
z?FPQqJYNSvHfHinqzx6HQ>=%T=In#AS-NH16|^-V0o1Or`7{+Er_0$emUY70VT6^q
zOD07su@{y^pt)?lm*(hkt#u*vo@KG4RsHnWCi(hdd1_sQ0l9DvIT#3Z3>^{=NBwq(
zdvinY+lSg>HYCeuc&!?YHb=!02?b>&2M<Frj?es;Hu^OBvMq+v>pWcX!94LE3Sw$U
z?2%dF(m{M$U>y&Sr3@ivtoSTFo2~fl&Nf~7^W2z-ALiL*+%S3L!U0`<G=|C{5ItY8
z@WpsI!lcGcWFrH3F#g%#0RMf1W6}0=4~^&>5R;@-u+C$Qqif__*cPQ9u^AW$U-`BE
z-(3Em3N`=M2%$<BNyZe>7Mr~Z6}xmzm_od;9B^0p&*!;L(0@JV0sqy6#Y;n}D*ix1
z89Hh*O3LQnaD?JEubBBDbglk$Q1ac*y08QDsk_Qx+>@=*`ad=vf8qXuY#*SW3U5O~
zxKrQn6~iP)*!qmXRZNWFd`lqO|1;lm>79F8P%pdVa*+O*Q8&pZ2qw%EL#qZq>C4z}
zyx&gc;%||Y@Oh#+)>KE_e`o<}Z|X524ZmXGK!kcpsEDBCqxfcDFFTSR57poi{P38?
zmhMU_?0tZJ+d+|h=5l!yy)*Q2Tkrh&VE3q)*ph@XL(Vq4AJxK=lMo4zl6|acfzTCh
zUtX=*WEG~8Oqk$1g103bt%zwI67BTUx?<V#<#{c-iz7v={ntM^31Ah$f>SGy&wu~N
z5y<D2=e2mmKrT}Z67n4bGb#vjwdE-YTJ|^7mG+g{=kv$Q4AP_mN<y*i;8I({bzMBL
zY>e%xSNfH3_WPWVOyo%s%8Ow9mpSlRuiK_4c<UYDp(`+?G@21xjO6xW4*ia4@rU$?
zWi=nQ-C%?cQ{PNi;8!q-y;U*Wau1hUrzKDF7&Arq!bKaWXlkcZHM>wu-*0g(!{)|e
zwyDOeecN|GG)o;@cR(g)gbIW_#uUT<kYTMelC7*$q1fgOSsvtpt98VgM-g-|ajM^3
zyQKV4NfW^y_CozZ2EN%|=`#vWQX*I-LY~P;;4ZDnh4G*#KMLMTWh#~fa7j$2t;3HF
z7{l++|64&tJ0{g`ZL_^)*~Tu@isY6zz~!ksCJ-oSlBE>oy^oX>`jDbx3}%u|HX(Bm
z>n)hoTII8i%q*8Kb;iu-74X17C%hRnxy#&saQ7*XQ0Ds>*e8f5SDDu*TCrVmo0Mn(
zjC!fB!J8o45Nehm>oS2NvSruT^Gj8yf)8gYqyhq{H2@(fK-w%RNoz4442%Vv+i>Cc
zJks)-)5br5F!%S<(T#v%vVd>&q76S5wR=Rf*Ds*mA%5wo`~j$TtEMs*o?hMS@@nJJ
zJGQ;^sA-iK<V%}hLZOTGP?SbGqcy0evOe<ee66$_+i!6^%keA|uuDG8^K#z6v>h<7
zCmBDQ!gfD#!ys4s!Wmr-esJeQ)BpgQ2T0^EZNl0XSP-C(evp#(_#YNip5RvYevA>L
zRs2}gA`H50eu9P*N8>TSx_e!CmZWd+BE7fXYft;KZFzaX0Ocx`)~7^+9x=*DK<>V3
zX%~cHS}wjW?UoYOA=%rXKYxHZBJ2K5H`1;eDr^55ZAB?&xIf=R__V2&0aeyLP&Y|x
zkQXWog5d!g7K`#4tyV;~Q|Zx(0dSdgdV?b7Og#0yN7RIq?Lzoe=Qgo@$Z@(wiHj*(
zkPlvk+rndj&dGot7mWg+xKTwsw%nRnWus3GPCb&L%$-CIqB?|}aYF+)c#~>zm+&m;
z&;w#7pl|=vnoag?BI@z)8p_Dj6;yL*eyP7byZF;}|Lqu6lsiE;5w;tU@&Tgs3@Isa
z_Ez`>-1W)_seUhF6a)rGQ$^PE`W;J}56`r{c_5!^Z9_!)@AC*>W%=3BWRfJs&{0tt
zC)T+dqp8j&2n~R%#+7eF3i?Qzm?j;;F$ejEYZ)22q-Fl{ddcMXZDAqUax#+Ov{fj4
z^k#m4xj#H)19cM?w(TL;re#m<XzaynO~F{^ycaP~;<YE*(6V-hCr)Z5s&r<QcfD?c
zs>3LyaYDowCv*wgkf>IQ4Cn6(j7iX}`nFCA{6y$_>n&ocvU{~r)Ft-gL*0C7?)ba6
zai)jpEDb#zAq`Z8Kx&Mzc=0e>l-M2Vo>82!^5BgY90`lmoF$Z{TJ5@^OJ*DvXG5%I
zNMQA4{+XMS+HcH>_VteRCfxL9qo$<RpzEHjPkeS(viVZ8YpTU~cELf!IM}TEv@e~A
zPZo9kM$(sru!V1_ZC!g_r{<}y_M_$*eJW2V3UFxq#ft+UBwr_$lG9wSC(X`ECa<cq
z{{VO#zq0VWDk~*xfzSVK`t<kC$>8AyrY7FCHBS>Euy>i4FnXgX4Wx0bKKdPAB2Oj>
zW=5Qm7W&B>)BFi+w2zpg=86vwhEwkOzp;?`e|aovx}PKwppN;85Wm1Rp&&yS9uNs1
zvEq2oDYv(iKKvZB7sm7V5;nkC=%}(}N$|0s`$lgcK8-3?T=H<r!CKS}D<k-!7Ul=i
zu-3FJj8dKL#E;IvnLf_+(a^szxL-%QL|pL&WKnx8jp61_OASUE_Oxa&9&n9&?$~}E
z6Vdv~)D|PnPLhlo+*B3;21NA%n3I|OI9CZHC?98J#eOSneh3^`7$ql2A12|ZxfF~K
z2D`cCpV7!J_tT{06BTxGbA;uKL~njDDf#|7IoyXmN_7MtD2V`G%c&sTt;XrzZ`u-h
zz(lhsY*^>hUz>&oe{E7+kDJ+44_=jYkby5J;VC<d+EpNWbQNQzvxm1CT4u4$eR%}~
z-x1L~$%^CdB(ww=toAqOaboWzOD76E09PKzesJ`+mea{U?VY*6Uz7W<9YW1`O|>Y$
zNL{CecgXmN{`it&JfEvsOK!7P$Hlg;MZYKseQK`%CV4~=m%SO@c~OGn1hTFwt`aW*
z6M`R4tg>wtpM7%q-Ik!=J#Lbt<5KBs(iHqvXy^M8QVYx<KsT#Y2q?oLVpm^QP-S<F
zZ;gUAT9H><vUnyJrgxdY#1J07gtofAKFhItj@{{6zx{pIa_56Kl$!-p_1o~{=4?hi
z<oQjb^00A>Y4mj>Xdk6Ua82&^M6Tc@rdV#XOkDuk0~)1K!%H5?`w!q#@`}qASMx<B
zU(j-wIiHG6XP17=Xjc7fM7VrmafWzjR>GzC;XJZqE68|O`(svV8dY(vphNC}(uB?8
z!q)im?JoeZ<&}N4MF?%9h4ELPC#y@8O(N$2G^<=IzEKqA!MNfl(jl&I0g@85&Erk`
zzJiSOnjw^~si>~&B~wmd{*nOI+AHB_RvItqxRgydXS=9DP+9{5n^w;F=!cYWX<v?5
z>9$JxOr1bxaFN`Eg;bonRt2DxhSOassT&*T413#diM`+X!?2qL$?E0i)D=Ba1NxTU
zJpJW+vV<~2i5JQVYHV784KDrq9P<9W)H_sxJxTe1K#vOaDhv|tpi!A4up8BQnrOgD
zx2@|&sC!+HiXHs1$ZGRQX1E>pGAcQ&Gqcm56!3Ok8&1_6G=ud?DlCE{!c2^{gv+Bb
zx|{hkNaP49l=EeJRnK?9XN%gaWmxET(^KUrGtjdYIOa<jN;@~HNQNPRit=rUdXtO3
zPH6>42{8y=At`25U>TW7wgEWmbNtKm;2DW@&)^`WR_LBe-^|yEo>!FxI547do1qC`
zzv1S2DD=gQvq5j=$22zi=SiKueMDGAsVO*y_{%a>zNP6-!&SO&kACAz>3B=$>b>XY
zm(5!%nl1f+twzku7KbS72V|x43~oO+`lY<A8?ais6(vHVIJdM#vnKy2zASS-li6MH
zmCZQi{V$yh{2B%eMKZrw{kC%E>k<V5pvXS0cKkPA3_2%6R+jK}1<*(63oXjH7Yc!(
zpVcGqfXkf#x3li8@F`vu|5QG#dI|@_mB)tA%}+i=B`c$n<T_KApK&WqzpkDyH4!pV
z!sE<pJL^IQ!4r^^-!5PyB$E^c$}ys9eoonKLDr7^v^yGimV0SvA>x*@t3EPS9gw9r
zGG6r{0KZ7W)3~LGUL|B*$w(~;c}N}~8RgqSSl8}V{py2k*3Qf9-{D^tewn-2@%v7I
zak_sMjmzA7oN>|$%~Gya=S1f){R5(M!N<P5e*WVHOr;?|I;fyDWh^z2&0eVO`5%B$
zwZRs|(5a`)Nqa7k;1h=vm6JK<_{Wsb1TpY-K5oQvMO!wQ230#>xszj^=WWLMqU`VT
z-4$zzEHSh4M6?oGhpW@iw;Fu=FVDNM`*o0I`%u;sXjwkzrpF)`Ix^YE)bj<hm~42y
zcVRM&WR)ySB44akX|m@T&vLBc>0{tTv{`nZ*W(WeZ`-3Eu&3^S2o*y|--CeARhx^q
z`oXKZ{iW*7c>duO?*HdDD(rK8z?*~99_%-enh}*HrKP62b(GwRmbzwvw%+2LK{0QT
z5G|(+KUmB0M4WJ(Odf%ZG2i;BH{VM@_6Wwa!Bmh=)bQ`Ev~|y|Ld@-cOCL<V@|908
z=)am^#nAAjxv!{6;`_Dx33y$rzrtHjiYI*k$5>$+HnFbRm?4N6rqT#_b((n$$&3mR
z#D2_=lpB7ZwKF1FN|xt<bGJpjPjnmm{=7|;1rqTUuT>+jDG^B<t88dMgF_1~$%X{k
z3XrgR3-nh}H2seo3Hi8haijwC1$^iJQ@Zm^TEqa*zV*da9!9s>O;?tJSb2hYGog^P
z$N?}&{s(MduHaqhpne-Wb`bEH<04*GEAEvnu763ZqzA%jWOhAIkSbt!&z}<{2LRh8
zY5i+AfN!2b4kzc1gwdBZ7cEg!47>kOYv)WTEvFDdR_7&UGv599KdsPNEwXvE&KHxd
zycwa^gXADR1F40phJkmYcqdKPj7`TrmN~2&@DKSjf+8FXxg-vz$I*xk4}mn7AQH!1
z=*wI1B5JVS&-*s&TQ~Bo{+=-S;^0Y~sCH{~?kb`cXm%5jBd!jTU}{A7{5f=Cl8@+T
zR>m**S6mhQRiw<fZW#Cjg8z-IL|2Hn;y5HC`Oz%tw(ylK=5FmgA@~@E^R;d`+-c<%
zY!yZvn>XPSB<$@%x6KC!1dC$5xXd6<d3gVXYm7wj<Zn?OJYdSp(Y|((9DA*s244Zu
zNz*D+G^y@pywCRcr)}l@pSuxhk7kLt-IBZ4T{&B2LNL09a4J{2jsRb^;8tSEvM0f@
zBl~FC^S!y(e|!M8Fc%aseIVJ?g2wcZL9yNKvD2Gr;D+kZi-V=Hjw`+U_~}KDD^l-l
z1RR7&mR_@qHU{onw@2%80c)4)z>9@@5xMZU46wV@1I<vaz*9pU2|9SKo9e`n^7|G8
zp32N<#F#rGvr7=frIa@ySG@Kcn*QcqrGArzk~He4cH*;Hya(`Xq%9J|gHNmip9prV
zNR~z-?D-4XrlU{6@8Mm4&IWg5#+sRH+mjyOBn}dILKkeFGp1a|_)>@0i)rmU5vx}R
z#}AP&;HY^t`zLBP4*rJCKjC*Xt>v`x=2Y@`C?&_dgs)XYd5JI9Dx&nwg-c%i5_RUL
z<;E<}q~~rwq`;GrTT`M7F5dXx4rnCuFLqP?6Iw&x+|usGCTD}nT_g)_DnNvun#bfS
zA|Kll^fz`8CzGPff<XJeb^nC?rE6D~Ap<;|)tc<4qZB{)t;sLRyEP3snBxx_?Zh7a
z7u?U^N^~4!VbqQxV6)p@e%NS=uk4XvzF4`|IWB6@S=}0)xhy4|n@qkqcodPVOz(HD
z?EBS%J@U?*3V8=LYkkv`pd@Jf%`4?@EJFse6cIA!lSd2s1*5ePu^;@k&72p%s7{^z
zZn@O?Z^g}c*|CK40JeF%VhZp=pt&5TI*|SA`^F-)P|twau5*{2oCgtbOoLY>Ns2p3
z)+Z1C`DSaXx=B7beZ2<TGGevc@9V_({|Yd}ME>sw{wqh-Xn&o%<ha7MZlJ(yXix6}
zH3fG-%qt~;aBb6Qd*Hcf$I{>06y<tVXY15S<-EaYq*Hw{=iBhZlwQUv%magg7b~X2
zRU4i2&>0a<wgEI71s7JpgWIBhlr2(F$z}&E6E`J(&vBus+q&gkpKxqD#31sNW;UU6
z;Z@HYbri45jP%(&=%mb$ZXR4kh2fNDC-#wU)AZlJEg}Lfl0VpC?fLm4bUQvWA63DE
zkc3VTy0?Ij1Hz|yQ~L&8ci{u=--}r$n|<y$tQ;q~IAs&n*@Kldq>o4ZC0k5KKsVe*
z7ww}?B<)g{qMP?a=Xm~CrZrC8scluV4NjMI3c@-cF=nX_A!XG2s`kysqN{p-N(B{D
z8f!eJj6F{o6@1XmqH~*z_4=`8)K|}!JA}(CG}Y_|<4~62g_*g_5T;#4etFbFZhnYR
zuQy;IEKLUu7Y|UW3oERJo?0yhbCjsSd&GI|58}Pg`fopgYy9xA><XD4_UbGT1<vHs
zIjVAd%U4UH%0p&Hh2(S?gS3Nq7^2Fglz)9}dOj)>fqfWSeoxAMT-X8$O>V`VEBer0
zc9BXZCOY%K_!sx15s)BH3DD9R_FW+ACS)jG;-E+&OA?3+_zr(>IdcaJ5j(kc1U%k=
zq{k^fJ26=5FDXyjiNbc-CrC`#UKG+~B3h@psJpgf=oSfts-Ywza`BnSs(Qpt*>^5D
z`k!3JD4e#j>iRBSQSfwBt*!0B2IblpttlrPi3ru_N5*LN)*`@&b3Dmvq9C-@h&%@0
zlkMeRB|fIN&21%!n)$N%*he`WDka5K;WkH4EVi?l)o}x_ghtn=X{4h~r;FgP-L=F`
zVEBEw$28T;q$+$~@@ofg+T)*ITSv^_a;K>Lgl`TWH?rGSpg%c)E?edC5u~z)`Lv^}
z42JoPwiHL&lnw=uow0(OQ8lcFO8eLrl1v!_lr4~hJ6V0k^bxPNG*PcqeGNcdOwt*S
zEw$q|>1r%Q^<hCV)?+;V`tehqBLyUwas^rwkec{|SD0N=_cA8u3ARr9Hlqi~>4PN?
zzDw)vtA<mVxXjwI8O<Am0(JyMbDSK~*@f6*{N=EklBnURh_qfZJ@>NuEg)}Q0U*g0
zUx!6T_?8p$t51Y))jAp_KCog-!;oSdV`5xypd>w5-$Kx=FK?!iGl)nUGd$Jk*7{4~
zyR;`sb075s>x2HwRGW*Xf9tQVb6ub%Z_51MudSYQP#7d~k?|&PT6_i^^#!!IIB#hY
z3}!|#W7yQ1AIIrX9s6(nO<x|NDS>$=bCgw%!4u=A7T1Xs8?OwEs|@+})WlR*&jCTo
z6*&Yh@#a4I0vOuxd29JBthL`i>^S0o@z;1W$8_ReuMW{cnU8*G{bJAD$a`Lts??F#
z%0VP&jzi(klpbSh^|8_PQf%}`Z6>~oMkca;H=?k<2+uGh@vufuy_R4&R_;->DJ>Qn
zd}JhO2V*%tRwMw~1+@P#{F}k#^=t68SzI?=v*?W?+QuDPD6zw{F!*Cqv|hkbz90Uc
z)96LQn@0bmFx1asq5xI<z}<AR)?+eQ{`Fg5vp)dr+V^R(Cyt@*ly$PF;)qKhxZZf)
zx34RI0E5h;ZZjhPZ~YJ;3wJ{~kylv5%SG~@Rm?MQk<_z9x*fx;3#d^5l=H#hvq5pP
zF+|}mS(~|iJ_mLL1r@*!AK<gtU+UgmwcD<9w`I7$8y7Hj@4x-BhQt0)UGw*LdefDo
zA%l_D#wJDA=v+2kNJdLDBm6WSVOaO9J*{fvImGjiAiV7<&Hn{;*FWF0Nxr$&^#1`Y
zFiE8~O0AV0`~jHK-+asJA2fWHrKCN04qQ>(6y@p!0}YkSe}8)47oL9`^<B()Km?e%
z@_c&0@=~3?(vb2`;jD^_V3FmUl5d%I_{fp3wSSaqM}E?-uCFGICCEivEsaSatso}Q
zgVab)cuI~luc+{PIc~|-Iu10N6mt8-9e2*Sy7dXwu>OIXm}ZsH>X|3G`sSpVyqf=E
zULGUlwxH5(6+XRc1FN3WB4|KOYyd^lP}0z_-xi3)g)jTTV|H3KcD~J&GQe|!mTA1z
zqs1#^5I62r$%D$XJ-z#Fq9`W^OqiF`N|W6zXtm=0BC?f`YFJJS6%-PaSd5O2PQnQ6
z-}Y!N3xCdxyp{f}^P5R!y<X|9YF7Viq;X2lf#b6M(D1~hMd{LCH4R)U8|W-0*2GDW
zc>9-KCC{5vFhW^2>Vnb)D!4hCqERx~%L%4}UrO1OX|%yC?>9vuhJO41;_R)X;@X;h
zQAi+IaM$4Ou7ThlB)BEGyL)gNcWErR6ReTO-91?2?(UNPI{Q2K?tRZY<GnHNKRvp8
z%~i8zRjsPE=Blb6Ql|6i>aJNFUwm)vk}?8PXWL5MNE0!J54@QGAd9L=X#$u6mKRdZ
zlOPx8{i0kw-_48OK=IR#i-ffhl6!BG_HI9VHtM-7H$^x(vyfltY^9l%DQ6at=-qaj
zOyAU^TRkGxYBzC8#c!|H>Nrh}2ay%qoF-YxMrg$yIX0(c&C#-PqcpdRscqUEi9qnF
zgqhd_SMl-f1nPSpiD;$Wd?L3u!wnO|=6L>63<Y|sBc@~ct^tQg1C<UO&nXF6VV{8_
zojFI;q_BUo0q+6<BxG|}ao+CO+LXzj?6S7H*#`70%e){R7_{!JrKFEhqQP}zL(`zj
zfhK{mIiL=cECvV1l(Ouh=dPEZgf5*^)C9rT<KPgkt@ZUHpJ4%9IXlOg6ci+Cyo3br
zdgEY7VZ8xV)|K1Fhfv7VazvBEz?Me9GjN6QaAD%6z5y8xGG8Ur;<|XTE6^+Jju<&<
zMgk7B3~6}A7AKKQDd_`UBeG!f9r?d2lB>Rn2&<auusvtEGPd0Jz%=(i>tswR;Z(My
zJzRS9=L?116dlKI*FRr8s#j2whQ!a{!|haf4r$XW)6!CB6YR=?HOpMj@V_iOVsa1B
zvOmKruvAg<ju^N~YPyRTa@QjbHAgl_@?K%8v#2FU$6LFQ(m<M(XySFKU>xIj_z4ge
zi5k)~m1mYB0RzMOh$dbiT>ZyHX0LsIY1{>66U8KR4sgisyf===fnt5i3c|{(Vv=1r
z#+aXmP?6|Bfw#;eld}fWTzqmkSHL7oNfr6QdjmG~<Fpk5#&v9Zyej2|GIEi@7QqJm
zU1EaPmIkJM1~}EMg2Gan30$W0NuLE3C}MVeI|^of==Sa_uxpoyRtY^wmzt_S4&3wX
z4A9<>D9F-%6KiHlP?I!KQR?lYjLdgUIv8@qI8-$FL5k8Kj0@(jZe42Sl}Iga1g+v3
zh%z3{2uHG6LS)dgB&50|LRQYu$g6SP{RFVzOtgV=j>u4zx_kW5SZ}Qt7vqA{*Ys9D
z)FZgQoZZ@@nRq!smrpu-4W1(*8Ms<Icuf~7*}pGW?$%iZmU!*nOm8-9iq`qxny<z)
z{4>C#;w!Yy$_M%y{46(nEpJnTSgNh_q+|H;^(g75rYkK1$^j@xv5g40Nax+6)A%Js
zY4qwZn4f8_{ubU~a!PxnwAYaK*=wOWSZwE)&J&um+>Eun@@Amzn$hcCoGY}kvO>H5
z&9E9KAKU2f9>c@O?4yG>|M<X9U6C#RMt4~EpU2KsH1(Crd^>Ws6<Qs>W(jLd^n7XL
zT{n70uM7C`VtoKZyW<tGsI)37+AapmM+K#3e+cCgxdhvm`|8DZvIgmQM3Z;RVPfGM
zdl@KKtFzb7zouZ6hW_viZ1oTD4i3b27VbQuJ<H8qgC=%s@&gO3pw4-%+~f1L^|cM2
zosQbS2p@e{v@8C7a3@sqSbMJGC_y~*7_iiybS?CYS__s>Y|K00ekMyW_fX`hA1sAM
zuqFf51-k3Y+O6xGfBk?7a-f3FVNQ1V*nWgu#L@b$VD=)apWjXNXPkFX!g~j*v$&=3
zR(}WYK<Et51$W=ha)E1k57@*7Y-x9UXSs`P-obTFeCB$ihi6k~z&)kze*M2-JXHl^
zcH5_)**!i_oNI~k1OH2fxodgQJUFtR<Fuwb$roW!_A~@UEn-l`gx@A#-_QfLH@JE@
zs<H5@y@e{g7S2$#U$xpe-@W=JG?5J`X_}VdK6gaBZY+?_iXae?X_bChOot77H7AY8
zsWtDFuDzO*(6l<OgThEmCfZo7XxOtGMHoM<=yJ(!G6YIw987>;Dxr*wUhT@JWQ^?E
zr$7*6D6P-VUh@YDnV_2v`+8;9y5fdBEf0FIY`W>)jsFE>r{Co31C$``7@Ey%&+Ni<
z&ytV%w$>Pzpds16e3+zpNLy!tC6(f5-l;1&a9U|cue8DE-^32mXjc*yvC|_|V9=Su
z+evA+lY-Q7#^#?hd^KMhYi4r+#mTefG0eFlM=&j^;eVlA&L`HI|K!m+;hYBaYVn!$
z$hevu*9OUD^bfWWELqx3#91>|<e6H~aM8Hn)CVVzq<@}G566BZ>na6wd~hit$?Yr}
zw>szIbLbUoarwGM@+nhqE!E_ao&Z$ufm*}?rtg{*Xf{V);!kBJGLzYLbg4#4p-?qR
zP^XQQmu*517wY`}YL3PSXm>pQ9?hPmame8J+33_6ch$s5B?&JJPFgs%)`Z65#~I#9
zgxuXkRJ?ZSTm*Gu_*E!7{#PZ*(IEJB6kpWci~iMYr`s5ByOoD?q5gAByg6gzRe|@|
z4#vTTP5O+Mgb_Er6plJqs?;udE#$&<(xs%F5gJii8Gpg(;)G$k`K&$?hSqp)`;2-5
z>*$Duvcabc2FrL=WzLNQHvNki)!5ge3*?;cJIN!Fu@e!@25FS)lnDw6fHCh){jcc?
zDb*LLVSH9zwWAqT+XTak!M3^^jo~p4gYIC-uP;wPbYcD2_n9#|(o}h^c;(CmVXus)
z1g75VVCn0`=iwE?1j|abr(gY^%TG>mRquf`tiQTeKkE?myt>B^^HH8G7bcb>ebIlG
zQ5mG|dgEgO-LbZ7B&5f|^NFAvSkFRHnTBhXCLPj~X-2M1$CLOWYJ%TyZ(PpOZI_db
zjlnwr78X?TDvHT+=p@bYh*{m;ySQ633W@E-^SigUAkG<&1h|;#H{r}d2o*d{zDiXv
zh{@BkQ!nOkk5ZcCYgy9+4okh~kv=;^ay`TOG;jGQ`F6}(CRTcu5@LW+T(v(;$UlrX
zdDu!QDN4!x43TTIuBP}PN0_nFAjL`2WjY~V2`EA|P?j!U6uLHBng@f=Nh!F2l_G7O
zm9(cT156kwh&MBlO(sR>K<VWcJ=x+Z>d(v#?a{#S5S?xaiido0rX;6aDUbO=e#`nt
z;huZ<QxMRHgQ@a)(EPG{I_@*wA`0H~v_k&nX*JR!8KCMJHUA_C4ntIt0S89jg_M6x
z!X()6-~~XHiI_7t%E8wmIl@a?JvC$bt*O#3>S+^R{!4uG4&~zKBA8Xu1YA6EMva0J
z1P*}s+Yn^K_?}WqU7k>-7dzf`wf-^eyC1eVY~#=}=LIYo>M^d^@Q_RpD`a9u2%NfQ
z84Yw-G6dJ-NYxHW$$KahHNpNej7LdJpi(?Ha_Bj`jN+;v{kUcN%r&Z$>K7ba)ED!C
zP1H|8MqHfTu_eN}aHX0WP<|#BwrH(h`DKC}DLq+sk%>pr{8@7|(~PL${b^hxE3=e8
z#HJigcwN+Hq#2MPhcBf>mR;WSUB6LVog&%3++5I#L@@)dmuyXoYdu}CJE?=&myRb>
zd?hfYt-ADKDZP}ED<aeyZ7n@^_dT`7Q6VW2-x32ZR&JpNR+^=xI7h<TlUe9(;BqW{
zCj#m@f&jXO?xWr8;+z3bdUoT=s_KrL4RV<Gd*onskmC+IC@L}QeMJ5z!}49N3BALm
z2l3nXDvvwJ;XxK(oSkBAPPKHkx)|se*jqnQbBt;SNQf(S6{?2(2~y}ji<8O6F~Ugi
znxKYh?Hx4Af#v0V-BYKegzlqzS7|R1ZSVH!OOf4@7P>YS^|Vq*A6oF@uXxw6Ao_^>
zZ<vgS5s3h`d<T9xFS}e10`6TOy1O^fZp562{|@A0{|@B;I`Ah=m+Bz-0vY_Nt~Y7q
zFBlYyldzQkn?zmmE0hO)1MxOjIlU0ykGdjqGToN~DeK8_fB*2Si5x3uq84TquzVif
zgTj$$Lg7eYZhafPlbrAe_cV5i=#DutUIw_%N@OLbn@?d-O3({2GcAQEpuO!;<8DtM
zoIg552W%T$?1s^_l${NosD{(JFjeR?hk0^gQUsC8P+yegok_nF$C0hoJQOoa;Vx6v
zKXiu2E~LtXZD{kOea;`5Z_+B6Zkg=8HU+scD1yumN|fpKevuzOFZgVNhu)!w^VEa8
zhYg+4-BZ{tH;8ES2;ll9V*wY~=x{J_Fx=I@Ib?^^_GTDla=VHx+9t2BP1<XpPwiK&
zEew|Hv&+mm0BX<IKkDk)Bd5;L-6aON8BIA*N~w2uT-10lFxY6s_%DE#zHR8(g5IEU
z2(MQMLAyUqw_Zho8|dX|Ve$hWIzyei%5aMiynXvIVQifHnuEJlIx*R?lBs;=tQl3&
zvi=M};7z*n@N5#Vbp~(CJB^9<k*6m?cbzg1RDHI}M?UYT59mgQ&o`A@+jhy3`|Hnw
zX6OFc)~7c7Y*yywGLyS<@K=gpy5W;%TRFHSWrpCyKqUEk0?;@SQ=K5vEc?Z=QSL(L
zuGvnsh@X&A>1M&s-fl)sx$v~?wXjZW9j#`+&&kt>L#4#GPo`EwByw<jYG_Jsi8$%+
zNhnA~?U(S%-D|BSkVnYrKHG5uO?&K+MXkq$*rsMckA<3kY!mAhWfquKZmIKD1YQLR
zbQ>8%%(zD{UFaA7Zc*==CQPE_F9QVoKV`-n>TDGO>rR{J59NZbTnOLfMU>h{BO|ji
z7OLZlUvIGhAELmc?vl68lUz+GzE&3?<rIiL;5_p*0@|{Sgy=6Api8RpkWYw%`^ZH{
zb-r*vctN1=1noz`wEx~3{)t`)Go*Bfmn2B6wrgMMGo$c3j#*TM*&jd5+N3g!IY1qL
zm*{MmeMd$;M^dw*v`;RxuAp@Wv&xttk9KJy!M%i`sk_uId|pFk-eVKw`umxLYTA{2
z$OQAZB`8$FI}R*l27*FN8204PJs(9_h~2nt<TW2+4bolOpPudyop7p*x%ZqRs*Kwv
zs?VOs%<Ff?dx-S!D??ufee~Y#=M2?yvS{Pd?_Xv!2jfrEn2*2Zp_GKN&({;rNJ9Mq
ztHOKOcQJ1|SEy#2Uc4?O%!XeERN$WHp|siC4ck4pP2fb$3p<Y_JA&hEGfi;NLBRx^
zn^J8M^Swb1Rce7#SkA3pf_)o^D+%w2h85%az?LL(;;fdkq&2<$Tcdqml(DgdIv|#m
zg?t4luvX9_?bmyKRdcGi+X~<0q(`GFISk1J8FycoB9JFF;E)Hq>Cn$1yY{scNzNfy
zx3{^6c-$TNpI&1h`>vyH+27gRob|l&=SpM!wC`>+yx<}Xcf7l=Hp*<L@QCZvGTnWK
z<?g|KC(9qAY~Zd;5R3={6qht(AHH`(Z5+9fCs#X<+Pv8sSFFWMH3oWF?V1E_-Ik~;
z@cfF}vN5zi^I$)_dF+w#e4VB++U3o_TO8BM5pQHG8?^(vRw3nbWNAi=lqVJ26=E^F
zFJY)UQOLH82+`^DLSff8U7suVl7Ys{TDDpNX`51;52woa%1xTAz<jGZD{vYzbO#G<
zK%KC4WaZgMzb43mLBV;eU+>n?!)_9ZWEHTJ{#~i4PUj2GX|w@A+CHrnRvbyPknww7
zL|9IY<!rVO^ISA*9o2B-{uEek?StAYL$ThTZ*8cb-O#8&iI6@&Me!xDt+SO4Me}-i
zDoNFI>1~|__t8j9n5xd*Ph!<k0faMCVrG+*H|1|lpCqG-r_jS?6qI>{+T|tt?DltR
zcm&raBDQ$w6gp-y(leq3tT1B)Y`A{a;(u}=b~5rZ`qeLLu_Nb`zc6~dQ@EepxtTW;
zPm$m6|Ghq`DfTUo<09U>30zaxS;sGcudG9d2KJj(pmFW=2%uG+McQOsqCWH1S;s3O
z$OhlM%2>pJi&!mo8{D-!+R3i(>ELMv>|kPtr0PAFi@*CszrZ^_Fg(^v@y%8D>b(xd
zbY3FUoqq_9Dm<cbX-442Pc2r_usR6W;Vkp!sCK3@wpIY@)-CH?wutq9M8;0xHi-V#
zZs<`<zoGp`*z7zvF2RU)HU8NlIL=^HrW)O5+lA??({YRCZTG3Ky%_>B`v3ZyQMp@V
z>e0iuq5aMtmd*ENWE@Pc&VReo;JtCz!N&<gAOoky>60xC?5W`V`D$=YCdyfOf@^@@
zioaV(rr3I9cq!00GG1OtwRtstq;Tr1Z00T|)4N+Rm$v+nH3AbCJ}Lw4Kzb>VjEhgE
z(_;BNj&5_)(2_E#n8NV^JvC0v$Y#rhDG%`h8q5-(jj%JsFfSf0Z)xs2=s1Jwzh8J+
z`HZu2-baoV!IOIV6N!8tdu8j(gLxo<#y%gT4;f;x7Xp^Iw09k%oIyXr<7ZRj+9pr^
z)0;S<kE_LRH=L)Blumv9&0JwVyoFBYV6%Nq&%e+GWe>Rg0A1}fyh&V=#i-00z(?7;
zZKNNl`6bX74H|#jYvRVLOL4rE9xd-hYJ|O#Nie)~bVWQIp?np_cA>W{UCrFj<Se{a
z0JWPe^FAGUS>E=Z`Wl+KzCl-q%KU@P)-^pGSqW5n9~b)fR5a)x-?cTgKa`+NdXbsI
zQ~r;Gd~J3{M`e2X8qV=EJ}vQwo9m^fbxMR=WZIj#ylzTvFE~#_`)oa|j2!$+FS70>
zZ3fN&QPa;GVL8(uFSLm7DvYkA57Ijs%<c%Qw-eeRtF?#PvUxK!5Xr>|lZtBdi=bsG
zO%!^{g2R5mzSLVgat68Sbhz!*{2e8VqupNgYQ&ZsHhnN2D%;~&3=1mBZvvf>_N=j(
z`t$;FEeteVI70oHzN$~Hkt9SUhm3AYbw8x7v{`F3$xu4j^olpZ=h34gCaX>T*YP_S
z-sc+$jnbc~br#?gyh>fTn~2BxnI4`KhLRfP7+G*I-sK00yT)p_FS;D<eRr-WE>b$?
z>wK^@meUqLN0u8^WUYs|sKz^SYNkmYsB{+*)wx4(L%*0HJ~!rTeLZfpE=^?Bw>Q!p
zc-ykjPTJC3x6Rj72scN#6AzT?ZN4=(abk9qtMj5DdvyE2LBjTaw{;>GRB;!~ih`@c
z@Xno-;i$T&30uIdy0X3vcfo7CBd3|#S@9*by0R|G<(`!soC?G%Y)Fd4)g9TSHv=}{
zQFayLBa)UuN+lBsXAMzSlE^kH0U#Zdc+NfXA@o;nP2-JWwtN!8?Wji|*wwDxFO+$k
z(~45Q&O@g&Vv%`)^sQFKNXXQZO3)4WomCl(RdCm^1D&JH$k6(=P^<8&vj$)^-dJC^
zrWlpG)tc*ow|_w~KZG%_<c*AgyO%h(Y)cvE$1;87_9z>Zbdr)b(hS?4vs-Mi)V!X(
zGK33K{szrZc{9dIgoe&q7EHB~lIeQclW^3iv^j?*q~r=yhBNPF!Ie{u0(*12;H<lJ
z?A7*-&1>bpuW)<ChR1<j`<pX$h1_%bwEeD+&hqJYYMOn)-9(~FU1geq|Hv!O1X6Ma
zFaY3$TNQ3{b^uoGn+<C!mdY5O>F{&ZV#E@6>Jo6zWPBs{r>m1VY~LWvMeMiDe8pc3
z)5!4fVTU$OA3@dcG(+ahn%ux)&dZQy^K>#*ApQuYbEEqa2j6tKdm<x}va9?qg$j&3
zGPXe;#x>-FeMQG=)Yl!MFW_sFa6YL`dtqfJa_P-Jh=}rjk_VZ`Lh^>-kh65MuXGHK
z{q!69)e65l1W8xo{Kc>e;zU*wXw87Pt$|tMOh7vtM+UXuFFT=N`IY{`NaHo!=++rN
z9~mT}j(sEHl;#;{*SREv%TQduDf;r-WX>RSg3ywq*Y6<vO7Fh=%?}${qU|)(9cV0l
zG~qY~?BKKb=9Ti`vW*m#SW0a<Lr#Z`Mk<_GDdm;-wL|Mtk}D}qh238G94pV22K-mf
zqrqO_)C-x3GmN$*-*UC!1F3y8Jo>;v)1mK4?KqB15kS+S<ZQygEn~z?x)=9ZDxP5H
zVzu<fUHkA^R)Z8`mWr_2=nSOqyo!*!Moq^uK;l=olrPH}>K{xC6%oZVERu;*z`t2@
zdmmkVG#!=&z3j|i=3*~3m4?Ky^WXz4ms7|3s2bm@DnVrKhF37qU^IvYB(bnFaJxCj
zej(7buuL+6UzBk>bGN~Fu%t)MWWJr3S<A|EjSil~zYjXk66(+1R4$nzC!2tG@e!-0
z$~8y_LE6JO<%Hbh=DUXYE60><!54R!9?6t+1@}H4vCgzCU{)NO?6C@W?KHI{YwmRa
zE4j^uP&cBb23=`((j!ycR8_O&#hHv++2%<Xn>X)fbFC6FQ6?_hxNx_#&Gxod<)l+N
zoi9_bzYoB>D=#<OHrPn&)mp!VQB_BW3n->pYH&o4mF|-uq*!p=)<m&O4S(gRO-pmX
zW|2C7<I{R!xc4;=HZk0bzNkkIewp=02pMK?QqcH_W<F{92(5A7x!z>tePh~q^U?3t
zn$wj%<_aQKhI009+m)}6z+XDlq1$Z8?^>kF(M`Lhmg_aTHM#ICZ~%0;1}NXPrFHb2
zAP{cZoid?``#SB4wt2ET_Tp<hyNE})%nCwdgEY8jKHeVX96?o{9jddm=~&pF{4aM2
z&IYxTD%uI$pE1*W`~&=SvYrkrN-LE6SLfJ8T3JU-C)udDoC=(jS}CQM$VU3<!;r{-
zh5<?|0$Brdru!Z9wNgLSVd*ynuMTXpb}atgY}{<-pQzpNujqg#;=w}JEK(CQme+HY
z_ZJLxNsC+@O*jnxCuq0vv!VZ+cH_;#Lu{K)ZpfF@H1_`q)*lf1_O$#}{)ybBmGKwN
zy9MWL8GC6N(4?Qdw>`NmWQA)BgL`VGHhY&98+4iqK6;)dwY|oxtUm0$!(>XlH7T}V
zT_snrxJ-%|Efh=MlnuXyLCXx(stKN*Z#_CVjSGjO>>OL_*&9;&D?U0M*!Z$cAH4*+
z8MnlO^T#jD%k68Q2^mmYuIbJ63SEga1%iL+^GXybK2yJ;g;Cjnsj<EAHC@=55c0SG
zs5RNt$B$St@(BLuP9`<t|FnX4&^|wmUc(=u@VKM8waX@idAeXb+%8#&RU8rwalDiV
zD}rfQNA~zFVMxSa)z(m=qoC+6_L-j_@E_7(`7FjU-5<uuV#-*T);wH&I7y2-ZA=x!
z(J1nm^KH}X{Z()^1p$?<#dwrvcp4Isb6>XT1o{-eVPX<(dxT!9^8hW%mL~}}s-i*b
zIM+|<wJsmGf5c`Pf0jF;+_s2bsdKS-ONe1^EaY1MYqWN;5w!^!koC5dzzr4_raI?r
z>)K+J=bG!>5P?>4I?au4E}_@gZrO{-Y}6+)KJZ{(B3g1epiSs@bXEAXxp%hTV~_iG
z&0M%0b9l2-Mj~C^c&De>ps7%8W|cj-RI4S`iBclTwCp3}D+4~oCr(&I=LigBVsavQ
z_s;LztaU6v^Mv8x$&xL7_@;@XCP@P!3li1IOcAYKn7dym={umdnE8~aqZLY*-LKg6
zn5K#TQJl$GQid_AiaiPP?>>tYzy-n@_yOY}a#ajN+o%4-ij4rxhC%D6sB_JHykF2w
z{k&LaJ8Lr?(>;d+yvr-AM(<5r#B!83lGZA=45capf~bKE!Is=)L}M3O?v|}d+E?Do
zv7)_Yyqe1fO-Q$FPJAU5cSJW$bvSOl*1T<1KEAdOI^8@yE(JJF+9S0%a}<e!^4T-x
zj0og6fUKhrmU|rGTwRw*wh-QNTAQI?Z2_?OQkSlpl)3G-kU@|83TS%DimC@ohLg@z
zUG4j{kYIfU!Cm^&d;9)lkV;h!^(NxpQKbvXAvdKd`(-!`3=cmU=fkL5J;xs4(W9{l
zDygJQNIsnfE6Fr?ERjXHv9+i-*}yQh{}qZJOFarHuk%0U!+vIxlH_rtG!?=sdY4V?
zI@H=vPfJ9i{B<|WZL_S)iFZk%s;I+WpR}P(Jp`=I)1>+2auTg=WrsSf;B15+NJpZO
zOWQ9PI8<-y;ezWGhOSO&uPaMXfgqnSq=K5{2m@nP423b^Z<T83;k9+iq5EFBs$F>s
zi2{00t^gL&2GxHmk#84qNXT$_o{re)OJ7UKggbiJWzv?1lT&xvd?}BYrx8%3W=C>G
zPP4pp(N6DiKj~-Q;wLtzrDd`oZ)U#hpuhE5=1dJ~2b-Cs%Q3MY7#=s7ZZD0{ZWc6&
z;LHEOXY?^Acl}B&+rtp=DXSKTYJbc>92r^J*DoMqW!?m9+t*K-q}}7EXz%!NO(;QU
zbJkT%9MM?E^NaFapnr!-{|xbvfxcgtNcG42q@9WVA}qpTWfgp+<U;IsfyPG@#F~fI
zl}dKe49e1M+pgSOnb@}G85z~~hF^xXh$~S;V`_V54-!}UHU_6uqjl|hPMm9}0eyU~
zt1mi){7PS~57h|z`H?{5HKdMrhIHJkiWc_6G3Sy_D`c|uym%)NV)jbaI9^Z_qv<n|
zJf|JZ_-``C!)*^#bm81gAGHC;OG)HUZ?d8L-6jGWVjL@okE$Z}uAb$JYEF>)vB^e9
z)l?hYCi4~1!Ib#z*|Q|T&M=!vXEY$u#_oryw1{`*P2|$2ChgH8Y!Xi6>)JZTRuUD7
zZ(Ia0J#Wb05d7No1a2jfH15Qv-ZZbFDBGA0j5tpa*Dj`OO)dtG@MEoG1|c}pl1Eu&
zQIs{m4z5>YZnnN=9EJz@IKEbw-F`&Pb;7vGG`tKh&!afA$U1+Z^M}@dK8rZ|K*CPl
zK9{Y>j`NS$9hw;1&$HzUstnlw0&4}i1evNz9FQhKD>tO;J>FJf>NKG<^H@Y47;uA*
zCKFX<&+Eakp`R~nh~6;5gw?v}>kE7LAFkFYKJei#9mna7pZ~^g`t`BMg82BX%S}Hk
z!<<>(gVGssK+y>?-T&A9@yER_FsakA$Sc$-<@?E7tn0Z`976YAjF1B<?l>)?QeN=a
zOIn(%2lc%t{V%ec4{}xfZkvqB;w6~+FG}BhFroEqG3xev@&N))+y*P}&ygUpk+<)l
zWH1j!2m1a)#^isKnS_!FaQs6?tv=?C&q%O5J;Rt@tdDgl+H{}|Mk!!x;#Oq5?@I6B
zHs|-IkDvOHD(@4x7LmXok23hnh{`h|#{jWtx8G6i#)^i&g}(Q^uusV8pyX4=3fiT%
z^+BMYYu7sShc1p}`87A%_l4MsaX4c73C%%c5#QL06Jko=Wx_jwd)-d6>g_*jyVv(W
z_*6~ef77+iZbzHiUy&3t8jYC188PtTnqXi03ns{;?CjKkQlIQ@6KtS<^hM9}y_iWv
z>O=IVZ34fZ@HPWBEcE8qMY_<_zRmyq&#-6cFkdvkK8ePcrsz%CqWk*)B2@m|X;i;}
z(B5pn@a5_IWOIajXX<4Z15U@C7j#7z%C{oxHA~0AYb$&<<$hxOD^erB^`$O~)sB~P
zTmm+-k9_`hdJ693L*O!!LwfggElSVQJ^`%TUoc*?t52^v%_670?yV(rMHkNzpvIdm
zF6Fq-KEm6JvoBaV=L;vd-RJhijlVvYP~<w{(>*#6b|pleTV$Cu{sjYf{(lhI-h9S{
z0(DaHV{^!?O!5!fe7-O7&pG>tM3IEWi<mL!%{pdgLT$v?lS%%*qlkcZkzjb)i&f9p
zwLhHPK{<JN{~XaGI_fzA7i|RPgtX?r8Gh;iaQxq_=5d`-La~8aw)wHx(OYsn+&ee2
zpM1do2r~sW_Yx+dR{Sv<lgcH&dnv~$at;Hk7LOyn&DP83c1NG}@IM!r;y;8q{7$Z;
z|0dM&FQNRIX;}d(l;Q1rt_d`ixCfelD@7S4w-(XWd+pLU1x^Wi1?zwKqqWt4v?l)p
z%1Hj)58nYhOqZ-pC?n`HCC{^MJn0v!-Y-yrod2zwLjC86w%n^VuEgqs0*h>@Bk*oK
zd;Q1CP@t+w`mHzMtTE?md)@v=GwT0VExxER#=&g|l>p<B9kKA%#(&K8Uj)9q{|hEU
z5N`*neal#77oUuMj3C{Bw#S0c9>;gy8~079%SX9t87^m(R{>DH$hXGE<aj`(`hVD1
z5Yz;rVu#vjiW@1h@OePD(A>R$r$sfokK^hee<t&BizN_UY3ueaVjNU0f1m$vt&Bs3
zk@w@xW6UyF)lams>+Y%UnzkdKCaB`z9y-yNkCK0C$kv%_@ThWHXO+`_Wg}F4he3vA
z)AP2<i;}jvBZQH}dreow(FuQGPcwx=H|N5wNA;O90FL`E@;<0}z0pv`A)w>+Hr%5=
zxtqnKX7#NblOUCco+yjd@HvI4KEBO!nEY0YGmbC-bEI#l0+aT1d#WLmpr2DdeL!DQ
z5e5b~HGeLyO%&R5zb94sGWE8_)<B2Ki)EEVYQcY{`7l|w!TJje(4NeWDf@2a2Uw(q
z+~)faJyxkX4QRUpazf4OPn7+QvWQQ;%vKD<BY3M_Lh}adAf=lymH=E>D?Wby7HPu%
zZkqQ?PuHl6DY78cCz+_qP2M7bS;;8+ja^x}lrg%>G!L5;J&xP9QAQ>0CZ0$;qkCEn
z6Z=+T_h~!NpStoZHXeeUp^n|7$r#x5YI#41z9-?!NS4!@yf2lZ^&Vg52eEiGhYh!~
z*py4_Fr<Md{F~OK1$`_Ox1)b}=%#&9Fu$?_*2#7b4#-HwY9Zl7QMV#8Qe#x4(5!s?
z$z;suKKwp<IKOxVsV?=WNkNuOIs3L>|7ICUS3Yub4WKFWWu3?o0x!1Zhdl@9-b1Z>
z_7{xh1ZSgO7id^h+8`JT*L3K1#pL69sGI0A<#{-vnA@~rAmER-!WzPC)0m{9dQ!s4
zNQtIc`(B<<C%d;jWcI}X%<Ejw>;sq`)N$|>av)i&)dA30F3qt(8n>Ty-zim4=RU{C
zB-P~ONKU|PnFAsf6L3`W(?@2qWR69L`J!n$x!jjhm*JDvD7RB}c?ZLRsflhuy*Mo+
zns)uzI7`Rjtb4h5x<t%d8B#Q)r1mjy!n$D1=OB*WQ?{0RJyWsC1BBvz4V!2B-(lM-
zW2Iv)SNBA3u(|<5Ocm70uHc(5N}d^eamWQ4Brs5LQnWO+WK)aYW2(MkevsWlbDEdz
zNtwH8G5-V|f=DBxUQ=;$6ZP3VRs}nA0wj?~J3^)!-pQ%lc-2&c0$=eEmUhXT-~-)a
z0jnhxi5B-=#Mx;U7sxDlot9N;f;OhZ(f{WD9r9pOXl#l&CIJWoudqOXA(b}Wad-K^
z>rw+xiBD-{LmnylG;oyyg?-;K>o1sq{bw#-flg<<_oKW0KK&D0C=#q^zhRcicA}!<
zLY*yQe+>BV3ERw1=C&Hw(Yk+C!C(sM{h`A}P#0M_eo?1!e*CV5&px5GiiFEl&w9AT
za!bEGx<>oBhFYdqDxQW_0h?T;xxjAU7Bb8~H^dv~T4O4!fepjb5UTriWo)wT{X49d
z3ziB#%XNIA*UhW-uEx~8Y3ff?HXH{xx6_S`oGzFaN}a%}G=OVFg6c9kzJ=xPTWQOj
z#;x;dk|`NS9oE&B^XF}{E@*K%i8T<1txS3&y)G^>(K<SbU*iidZ~w(l=cwLdxM^-&
z$vuq2h&Q_A4B0mvH)L-oo_mGsIGw7LR?kNBr#esjbo+=*H5$lBlY3KTqo;MtSMoEG
z6$hNv3E3A+-;?X&*rq_Uig*S=Je&TaQR$iA%=JFor(N<sVyIq+5b&ywlyOs)pki_s
zQ%ck!d?~`*W_Oy#;b^?aOm_Meob;7=h%0Caq_1&{IHMhjxvWI9_=9|jk6KD&ma#~w
zTWmujJ<raSJscIu*lUnmZxL(Q#NR;0Lvn?9CD_I!Mb4^l+6*EHKH~@LazH{>+f7MR
z_+}2E)p8l)7?NEQmfYHaT5!FmIj_0h0D-XuzFF*OY6+RnU5=sU426Q$<k)G8O{1&@
z)&9?PB?;+IEh^PP+}byx2k=8|I|u5>RV_B&#0t1((W=3aWxg=^5kfmY&IC|Q;Mp&w
z9a}Cw7NWLhYRHNQPuq|5d=GD6ZHik+;TQRY?_4So@+vxlE5IR-Vnz%PeBUMZ%QS@{
zFBnB3ed?Y#;GklyDh&=iG5sFPtjl+hhSHa41agdnY&90n-aB!b)O4Tb6|)7%hfJR3
z@rZ*g?=z^I_Aw&P(0%*AUwP2~Ya0X#v=)%4zEU`t*c*tF_k5b`Vak+~sXth|9uzmo
z!h@nGMY@sJ|7yLbSL;_G^*1obe>$#wn)cfG(ElMB2eUFAW|Z=caXdVHm!h5I+aabN
zau|mc$ut%9&nai~2C8DdnQd#BS8oltWRc~rUYAG9*@&#KJ-q?u2^!SH<-bqNKkm#1
z0(^%(WrjkbfD~B8xpb>ztw{)hHKI{NP8uNhf>CHL5iGJE3T~u6`OhcK>+h6V`D8oW
zyR^#4uWXw&xgLoh5?{1_ujc672!EUNOQ=~8#jyT3G{y75)RJh5C(rsLw3S-vGnLr<
zpU+RCXMI!`-DD!`pK4e?{Jy3;9)yCnHSIV3KJf7seO-U$3xKfXq9aPQ<X!k?UiFD!
z5`1hx|H{b3J{V0nVsyuC3P5U&OK3A8_-I=bn9ua_6OTG?rXxWy2GMSf>p=D_u`$hh
z7>G`75p24Od-xgSVSYVFk5L>2?=lu8oBTvpjuYGV@^pPvwqCz9{tG<;vN*O{#Kp;B
zsnbHZdPM6=n%yHd*Q9Lt)a)h;SfoCDG|B`>sPeL2wNGb%$NQM5yB6c*1On#@MpN2j
z5L@A_2ngZrc$czj0P@~IA0xvF!W&UOQn-HO-45kAwXJ^tT(>m9T2-4_uax0o62;^b
zWDAL@YGOcVKcbzF9bliS<7B9}1@2Gm7MVfv>P}bX_&#w^+QDgJ=zAi<i1Gf=x7|Jm
z)w*h)%<lA1xw^FDd%9=e0L@c(BbTDb#;#{a;}S5}7IW5XPv0sI8CuIJwYSiav+S{-
zVrwMT^|#t4R!84AULl(5!;Zj6I5)7inHaXCv7Ra%;>|GN4hC>_*L3TZm|L~h>p(l5
z2^<B#jpmmL^bFrwXx#L(B<oU<Pgi-nDY&qKj=KIAOrBs38}aYC$96V{aA*fmhu(x8
zPsCFMbb@!CWkx^z?K*%>-`d<#ekv=(G-H~5NTj>Mu}h1rPNVvXq*#GZMLomg2DeM0
z>d&!fPf0FFvOC2CQxh|Ishg>LH|Dz-2`tF0#l6<7Y1wQe%V|P)RmLBm#+wwOwc0Nt
zOjTYrA>$otQisHD`T*?StlpVZf;L$q0t8tO-?wL@)8ATHmcFKmukzq?0wM?op~=o_
zM<l|Y0hn`V1<dBHX$Cx*-|!V(PCd5Tn3Q?U?XyznA;y$U(GYAd{$3Km3TRPYi%bU!
z&J+*myrMaD)tSDW#_01PTd|X~n66nZzm>xq#9~AhJH)|S-o)U|eAn)9saNJU57D!W
zvasXrQ{ETy#aaZ~c05K^1M?Yh<C3_rdCGgKS~~=sOv#QLoExVZ?-bsTnEMzrny-G!
z(6E5J(UxuUEa&<RPopUysrMb(aSFj!%yoISgQg6Bw{<Zt1#d!Vt5#7^-{GQNpq?jf
zY;1{7QazD2X>_`gWBYWx^4a;m&jq_qEzMN2>Zvi<QzxxZPkR@>2UJ<PjE4NHt#@Cx
zP@M}a$N@|08^&8$UP(CL*53?n^Q)LRnVPh8ze{7LI}?B2U#y|pwWb=lj!*Nc85>jG
zn|n+UIQJ=x2C@bb1!^7GXzl(uKs#T5;aXjNIVP>yxITJ#G5ReTkm$$$+(sR6Y4HN>
z8fsgki|`XyKkI<3AAtzrCl2b{MdWwiPvGB*-M#sq=L0J?B@&qT$D-i)`OCXmDt_jj
z+$Cu1BFpzjw&IM{O%m#3PfAVNm?oH^;zUy6NSO{r0+X`^#;AGc^WJtB`BwRySKUjF
znMPBM>r}r^*7jO0VV2m<_uVJ)WZ8hyO+tA`3kN+UO_Rels+LtEo3jU{y5&isbxMLd
zUfjE?QzS6x#?*oX+q*|OU?JVGl0F6y2jKUJ>%4HZ-lj$oYmb$3>(!N-rcWHI?)(bR
z(#Ne^&wBbW4(m@mw$u4%cy9E`d4=N_pacWz_ldRn2pnksV&4P#gBnW)j9mL`?U!%W
z33~WytGbrVnhf~9;S^(z>eD9iZ!9=&Vl1elz4{UQf{p%&h=?2gs|etaXb=PuYCjG9
zmyCk5-!A5D|94>_ES)<|rGYTZ==MM+5S_3pRZLjQ*6^gQ4-`hB9p?2iH{+r}G8y0D
zq!FLScy#%<8%z^VQpWbF8L!o9DN)%e>R;Z8ZI_dOh1sF&A~#M8`&lJ4^4xfi$<Ssn
z+=h1!9^O8IuCxbKOaeaj4UW}nm#uRTIlbf~dj&SIRmkdPeCohgk{rv-{EqmQ-I_u=
z{~Pj-gXXW{q%+dZ8c3X%K#(cv{?C*JaH?9oi38HNcC<7Qc6g(Sab8L@9+gJbu$U8L
z(s%7W$ATp|WjH0*(4S*f39tGKXmUTdS&rjaxAltMl(q}+qdSb9HHFkRf3nim^G<IV
zvM2uh)lPV`W8IqF%C9fWEPafVO{vVwgP8#^`JnM@8c=CYU<g?Y`UFC4y&SKKyMFyS
zF=IRLeaKRRR@AmKjeVdTY$;ur5Xoc12pOMfHKr<XBf2Sv`8Cn!)LC|g+}}9;9px_=
zq%NVaZ9dY@o<gV)UVJ+fLK9PB4muBlzEOTRChqguZ9&hAGugQTdvRXlxi8v#k$jvP
zof@m_td}-kEPIf;-msj6A$slaX1H=+TWNl(zYv^mH6c~UD^7=6zRbvRFkzKyy>TWT
ziHsCvY)Oav3#P1#l{s+l+ApiJKb2g66W6VMP`0(>Q~Pwp8M8_u_VH5xqMJrwjP$nZ
zj;o1z)r{gU10Lrd10ID-JINZhdyj?RJnJe%Zymi-hcJp!5bM`)LiEt?81)>{m{HdX
z%Oc?jys9QAM}#1y0f!qFj9qxBYDAXxuV~;=PyIPc;$icNZ;?oJ6iv%pk<ltk&tefS
z?SYdbLSg2oSGZ{J>gdg6b?pU~Ay>(hA=8moKk^7XR`J@764{WGsHZ-U6pryGdo$qC
zMfQ+p4$;849kCmM-Zr+RaapO5CMHV0Sgm{lJ?}ZBO-D=Su-`B%_I+L{UPU&~+2+w=
zOt)5!=nyir5ywaOq2a;KEb+6`h^iH%x8&b#i4zBHgD$78oR!gT23i(;HzDCR7$aV^
zr^|)kkV95(iK3x>&)$W9Ya?tU!-`x-(_C^1+o@?gZOaM^7&zU}QOcnyS5r-05xm3>
zcOY2mR+MDfCW*yh=(SUURgi4_GB>gr+vGdqIj}vh6E;Rm6gUbQ*|QP69RVGbFz-Q3
z;1NeC-+iOMSDKA<{_WIfBZFK*UT>-{xF-qtF`ZA#4=itghd=r##Ju&q|M3Gl6)reO
zzuDQGzzCm?mD8Cvx#>(noNv;E5k?wjjcng(dCE3_*YyDFOF4O_I3O5J#>hvXFP$@c
zz2y}Wscm9~ZGYNUd!0Q^nm|WG-hC|^{CqV^g@+;y;iIiUZpzTYrWa2}j!z3wexoJ{
zQYT|eT<vc0NHMNVM@bp$P2vswNu8_s)3PfJE+g5jkdj=}xwY_f@fRN=DaApYsF^ok
z>q0DCiJZWGp^jUXqaFG@1FKsx&UgM2o<7K%9B8Iwy~8?q1`UT9X;ZRj+l6+aGlD{T
zQnuzLTVG&MID=WUXSoW09|&D-xS~e_*Y!qr-X}Sl3|cFVy;m^D2RCY(Tcw#bK8vFf
zV>%sTf2!-pgn3ieTZ-4*#6~4$<}<Q1g7?L*Jq{wR-)tZYH2ijQ62WYfFGuL^u-IX%
z;3i%`uu8=(_*q?5Duv@0LuN5j@(A`j)Hps*_b|9Ff=P;%dsouQ2q#6J<X2xl_Z5{B
zjigvWY@E=P6PYECQK1AoHf7SB6z1D>9LePi<gLM`s4hZ0C?4zXdv)X0$aVY-=hx7L
z4+c8bDq~W!K3e3EwBRjLZ4|d2BzpHL@zqwA#o`D8v4JV~G2^~%4d#NN^UB6HUc8~n
zs$;=n!0OY>P4U!-`|2!r<1h=>)4Jz?^Kv|B)Io+r3OE=$q5&&&CU2TR+0TH7m4YR?
z(eFZwIQ%^Pfk0}PL<D@>++N_g>?QEN+ra><u1TrPQLjnIT%WHI+psT^Bgr5ME`h1m
ze??NCpRy`hP9%+zHhO@!x0#jY*_=zll%&4a>F(rHNbIXIDR<F+@mVt6DmBEm=r9s(
zo)AN@2=;TKrzzl@D$P)OHIC9%2zh4$rsq^^JNrpG05Kft0YOrd=b=|OGTC(^L%0U4
zgOI$?rIp=1r&%wLpqQhzS{XcI0}T)xM+})_!`RR`5btH+3-7f;D#az+Q&)?K=X?2m
z)Fn1rqJTpA-Tn4QQYm)y8*8gm*2Mv*zaL5rBpVx4*N}XT=Bv%X_{-e?ZUIS(p9A}%
z`uOP+k-Yy?Q6Jf-0F<awt=LE=%fIuj=pR1Pae}X6CI3INhyTu?`jb7ZCTsQa>4Ecd
zBDE?8+d>4SClm~HqCQzb`C*z~oG*g$#sUs!8CpN*gL~(TAJrs%T>Lci?eV;i1ovrP
z4^Q!%aE)MRJ+vi#OMmB}^y{F}kNf#JQHiUy&G~hQ&wT+F(Eji)Q30_r&#^gA)B6V^
zy;U7+xe>o7_4n`gp{7Jid`~KJTQE806RzjiE{ge@(=vHF!SY*>?@Z9=SSB~`9u4&D
z?N9YA;*tx=(tB}rSaJO`!>2P+yE`ao^qu#<XcK?a!@+v<;dvFAfAESR<n-_%?&NkN
zz~0H>a=O!Lc3oHa6`|R!2{L>6Vd*&Q_sZ7#c7gwPNk@a%6T0KELsQ9?=)acm+0XL&
zhU_2y&&IaS|ND5wbYdslNt>Ni_0Ky_$ElIa_c&I=q>N@MhtyR|+5=li!74j9lSmCB
zJx9WVR)&UL1Kjp?2?n)7e2U8V(7ZMg%egAMC1LQ^H#Rx@VGjurT|vX!zG+UDJ0A%o
zfg2Tlpcy<8D6uTLf0-drLS++n0nnE72$i3A!Noo1_{_m?2khKrU$zI?g?+2@7&8CR
zm-apv*O3a_x6V9{QJ@Sy)V$sDT^(=;t9g+Cjv_=-;Jw<>I|cv|q7$mzr5_<c$s11;
z{Sq@;twZOQMj)aRB3LMxC{TlvEi15hFM$n83w3BBpA*vqJ0o%`lF%!~PiekI7@V^i
zwIJ-#)0N!Dx&EAsP@&%Ya9qZx2))CdB&iSk=vFN_HJX6JV<^hG*@re_ciR^!?o8i@
z?<^PeIlAoqocs?w3bFSYU2-Lu;!PeAj`bmZCr2?^?e^}TM#KbteI{J9O-X4#+?fq+
znd7oiGcFP<Ld4lcGQ4D|8c$-f7*ER$BF5l5<8{;tDHeZyq>RAfuAC(9nMuxW+^s2^
zUq|0IKAPuMPfUi?Wn}@=_0O1xJQSpr=<4=Jlk5lNUV1+nZhn1-fCZqg*p(8elt|a$
zq``cP7-%&NgM4fec`-k37YX=19!r#i+P{v8uc#>H<#D=WA`0x=-~E8{8;j?0`S_2t
z!`VhxWcU<w|MkDmHq==ER>COvpr_q#foHEiXA3cUMu<}y?#TSMxfw!{Z5{Q}+sL;)
zD*OY@PGk&4eo3~P^<(F~o@5=_m%cLz6H^Xj3XHMAhZD$~XsE0MqC!SS*XZbzQxCHb
zMu5G;hl~*OBjWBpJrtcwFD=BMO<4xa=O$b>uKg)u$#GJygl$yuG9?r<R7rub%J7?W
zVv;ZtSr6+-eCFwyGe7}42d2i{@MehZFiw%N`&FO1v!0EyL;1{ZE9=#MG|m<jO|(bc
zZ}bspHX<(+ZJ9*BU)o^EBYnC`4xpjHmkd!4R%MSv^`eWtc&W9cP0+R59dN4hHXQX8
z>dQau0L>ppW&@2Gu<H^y#(I}FVvebHDqF%HYmlQG1Ftl03&^i_LmVsI_VEqo%F9XJ
z0Vu)Es@)Uc^lZf>U1x*9pQ9Yrv2ijD<Tt?<Y(IL|;kUPpjg3L`>F1E)7^mf<#?@zU
z?SsXKFr5Izq7TI1hD@XpX`H%_lBt0O!oEW|Tn4p=n*6y%n79gk>VfZ4D%!MyGAjGF
zd3GTLTGo0SZN8Fv*f<eD_krO7px|BoLTc<YTlS!+7Vn0daC9@0JFnxi@}%TmGP6%<
zCqF-@su}JIVMRZdAm9|KOTtE#K~nhcas|#da!J`BrHZmXL}U3c81ze~78APw4?6~f
z^a)X9p89LNQRoXf#S4#3TaPEV8&UR)m6Vc%>rUoNpMoL~f1HO6V5ZjtHB`WV6Lfv0
z!&9^}o8OX7A5kyPFiHcb{0Z~zO%#5xGV6K$b#olSG+37nFv~nJ;h8qK*@hAkgEX6|
z#jU(%6}o@zne|ez8PFr$DfHPF>agf`&!~XKpU1acuHr(WciTiSry`e7zv^$eCD3a6
zvPLJA@!>t<UoiLo%VPvzK^@5~Tm0&tqol;<2b2qRL~r?p;J;up{-*aKtvS{{!_WV}
z`I-NmMYne;@F)XCa0`j?ch7l%`Xx~|Cz4gp|9Ld)TaS$H1;x0~8`!0V8R_;3^o;F|
zk`8XD_x<}aYJqYmO8;PVkx(-+yG{gk*8f^d7()9Yl~MNJ!;xM@{x~^FP!&nLMNf0n
z>B@At$=1AJMRELRsqzAsYULO=!e*U10ElC>%Y@Q7;_~uhAy++a(*evg&GTT_e=u{g
z#-+THswnBp`%PPq>T*Ik%FOO(+*or$g^;wFLKs9!C^9xCyOJ6gm;Q#5Zg~++u1}#y
zn54j_<&uoZ+}zlPL#}t*d>wI{B_bmIkxP3oCsZ$(YzOiK;gq&+9>*Xn@g&uGxM5Dg
z6q#dKKq^61uKtUBq#9Er401Y7J}o8&=4P2i#KrtJCcpn`Tb1>L48EW7AwY@!%66wa
z<X>Lhrs<D>$O8VBP&4V8c^3~!LTGQeGI2tUNATX?PX9m3U=84lT;+G>B|szPx0mkZ
z$GIPX#>YpWh`qz4|86D!(W)q4<mAXl{1<AQ>xO>@r@aM_Z6Lzz__7j9<k>j$JiL&^
z-!c4gcmK@uKb-deKS#m849e-TZNi#d-~&qFVJSjkj9-o@Li@iCyF9ij`>S4bZvHQo
z|Hnf9Uye4n_mBU5%Fy@U_tMnK$HH`Wr_S=pj|8@Vqc%%Wf$34jq6fO*beAF`23}sM
zuL}wvo}p3w&dH5D5R@g&hqrbqXjnvC>4Kl*p^z--t?lhM0#>sv>ET3#fBjFMb+E<2
zo`w~LZowIXZ+_eot}w>zGTj>Tw)-!y_V$HJY>Knl6zBF5yUj`&KW$HZ_X-;?+iWhM
zoq}b2ragR|4#^lqAKF!sH2!(xPdJxbC5#HgJn$(7IMBjK$oodTUk!Cahi9Fr@x_;>
zXZk^b?Ny~sS7|7OeIFW3`;5Q((Wjd3S(%5#P~EXVbvlnW+$HP5l)H|IVZ+k*t$Oz~
ziI|uqQ3#I77CFRBTuthv_vjaJ20uI$*VjaxK@h*C*(Z4p4vA>E>84=uXwZx~%88To
zkFr*#K5~>9XFpx_cUljwOaZfKUw4obW_G!3`U`CN%*=`>ragi1^22=8;O-2`B7I(U
z_%5XxS4l#ZWSPo|cSg?w#4m(7A7%VO>NO71GhA}TIsC+iOD+sf6qFk#U2z%~%qu3`
zWP#{E>$(J~ja7i|*VIipbp{KD_;?Io4ViXa>tdb1caJN<lCozkHa8cT78yft*$tU1
z$XK>8OSoi0=**oBloJOO0VL&wX2u=umc*4^y5r|4tS^Ah9|SvK)^Tt)pZvEpv+(x9
zHL1Rpsl&4O<w?<5mEFdiYW#r0OL2f@Io%YTnL>5b%_?evt@-VMbEE7eEAMHlNxZz|
za1aIC!DDKM03iW|I&*dHo^Y-OgCWj%-zQ^&K*hRP<~VIUJ|9wleE|(UJ$$`+(}DvW
zU;zykpWy8gp%#@1;+$SLM<C;9bUp}!d7QVzzztW`)SH~ixw6H1lx2b#Qf9X~#**ye
z5<W=Sw)9NfwcX}=bl$SI3_2X;4GG#Lf3ixccM}vVh4YGV{r)`}o`)hZj6M*H%A0?%
zqn3##8Bg=G*B!4tVTaQ@oA;z__yF@==P#0TkyVIMD|n!s0bR$LI9S9M!-OjtfZ|C%
z54;gAyvi@<GgD>})8`r%H?vY5;(lqoGY*zmOui&uwK}`Ag7qm0CviF(zUUbD${%y4
z<YlsBxGFpFIE!$z*fU{r_Sp$Ox;nH|pgQ<aCn*Dv#xC;mc4M_Ak33jr%%D&M*kWJC
z|6U?~<KvJMS`g6}v*amj_eWrtA#wc{qHlqf&fV^wRK2p$fYw{RFsV_rp@GLv7BKY;
zDz<I^y0t-T8WIkiay|_PELl9XgufQ|8}ZF~(7Ef17cQ6O()bn4Glv_*Hn}OA*_24C
zYt-N;USDW0eVuQ9Az*eWf0fcY{|rq9<{-&HGDJ#ecmMw(?k(fuTDEUdG`PD%<L)lO
z8i(NS?(XjH?jC|CxHRr=!J!+sU;zSzkYwM^KL4}NdH3D>;eC4FYSmh+X058({i|6u
z#~7uzXj;K47@)L@_4_9|yU_r6y4)WRNm)t)VOIUEg{4B4AUJy*f@y#J6MgbpD-?7M
zRdJX}{E<fXR6htVZ*Y<sJcRgxfHUb{LImV?8E80L6bKdJ+TOwCnj;bKEfBE=*A>kt
zcN4%kx@hQsP<HT4pNTQF(47Dm1g^BUN-&IP0Smts@%9wh*|N-6tl6wcj@NN~PowyP
zC_4hCMYW-jJ%lf}hvUahP=5Dqj48g=h-;gxpm2Qn-7KH-5v|o_7X1#RYQ;RI|BlXM
z4;2Th#40jys%dr6h8+RQ3ErI~#qv+E7ymx)Dmssy`q`s<_G9N;KBtb1ND=veP&vBc
z{{e--&Tj<B2HqCj^<)#9YEvC&KQnV5f7*vTmw2@UZ=s&m<Z)m9t?8#Ky}m5(Pk$F?
z*pqu@X21N0T>jr&_ui)dNO$rlhzJhMuV3=80}y|5a8#IzKsn|msGu3VRr^p-4^%(q
z?~8$ru~Qv_OiCj1_K=eYv6Tl_!7F^2f8aCK$t(}geSRoN*`aW;WsL-TaVP#_GS;*|
z9wlt3D-@VCQSl>V{;R;1(PH<|f%6wkc+-7kj|Ua^<^JHV=@qN#R_>r*LIA7lu%amW
z&@it^x;@wi;r-;3Knz}OL7e2xn~n!F%UV{P)6L@UBeqZR;FT36&DB<Un#0<5GkFT5
z8g7T#2}p|SMTyKOC6c2!-+*N6IiD3p`W3E5Mno;O4k3O|#$d9ygqG_YW9!!fvq`cW
zu^Q?9WL&wfhevACkZ!l0pcK~7oe&3^LGKCvJ}DDnLk*#2sfOGk>2B>EI44fgu&>Fx
z5~?<#6A*vj(NFhp*aqV$&3spJ=RZ_s1~!sY%*@N-ei<2II^m~v6^IoosI&wEWxsJ@
zW7?1H&v~{LTgBgnD$G1%q`bL-C)U>@!SrafwxaFPC}s;vU}Wy1$zmeWqT3vj#DT&2
z92?DlQmT{Xk#zrSsOD<(8_xvR0A8!x&mc0o#$;)LKQY>;MMKqzt<)3B8U?46-pC|<
zAUVat3QDy*jJCGH_FALcKqt-vA=h|*7TQkN(ft9rv%qok&tfv)`gyP>p*6fe;|fkj
z8=0xIn;)+qE%g?n1}q*RLf5#SL1|sDir7$kWKO7geA>av$#GBfHe5;ZaiC3CO=b8%
zc51+CJ8=!rjIm2sX<mz6UJgSH!@p96$8&D`qHKh_IklTXTt5$LCP{*9P3DgGeQ&DV
z{z&h=tC4$2E1pSbTu{U>rO0LAgACS+v=$g~Bv+ClWH40j>p!qL{<U=XxIr7GFSlu6
z>bU)mNlB*9b_-lnPh^d&4A`3S-K|s`O4CwDKUp?tc~e_od9!^@_XdY&1n|qU6JywM
z9_%jKYPg^tKY8EtI-i9JmKBrWi%Se(JKaa1clM2JOgwf*;dNcrP4VnfkXCjCho%6F
zY%asf*X7FQE{(qTe7NSbs5e?Xd6n_AKgA-p5LNo2fkt>eyJ3l`?SQQ-kdruGbeVC)
z4@ap&F%6^8MJphWkflE%u#>H%!BrSvAp5$1Dk|d#`b;rxB18zMITINVYE^CVYW1DZ
z%;fcj2oBbm3^%UX6Kv5%CKp`M`BdY1R^=#I8#YGyDOKx_r&*RfxS$t!ZmZ9@?%5Ad
zaC&mLwTiViFJRJCpK+o`*%%$C#bvkC?1?D)ZN6yrUQctmZ!sblwbfIXV{2McW(8tc
zd;Fkqa{mhksr^??GXFb(&8yt#Lb`r|7^D5HQ9nku)8Vs@Zc4E{YX(DelII$ox1_8P
z>wR)ceH~@~O*%fF4sVUlq;s`Ky@hV&>F!6nL-t!ru%Iqh>%Lt%l*w={fidV^M^sz!
z(4xs#^QYt9<Fz)E4}{v}pQQNIrVTJsmm=ROOzSD&&v5Xmf<E4wq;{2*wxpniU%#4B
zorpP}{sl9sr(Kx5p6T9bt&_mWHll`2v%<>t?sNNR*E))(l_a0&V_8IwbQ3&`AK!4N
zUgtZ)k_tmV%Q>;Db$SsLiTBaZ#+Dp72{5cB`MgC!%+NL{OcKxOq;-B#j3ysRmZo0o
z0{X$Kdu*=~po3l7SC}(-V#YtLufO_x>~^|?xED|&KrT}ay$4f%Y^!6*MzU+4oRdUx
zd(%PYMIA92JoN)kp{=GO!d~yU@mS(CX1(I64cl`MT{rTsG4?4`yMm&d$mpWdT%12!
z*CwXW&C}XJ7C3a*kG9}QDf#pj$zAjqJf|vq?ZZE`9%$gf0!;K>{K`A!AubI+jXysX
z(%V#hpAFbm_BPyN`7yOwxG*8$laa`Ny-#EFnb*OG&KcCPwy*+rKdH0GXWF(H$t?;O
zbEH0!VT!;tvB9*&UVWe;lL=z;v$NPy`fcRPIFJnSALP8H;JLHwY*<POa+;k}Q!qqf
znsqkr<6cz{FUM}upvD&Qi8EowVLu<?d3w_9Y6dntD_a`mm868uST!twqebvE&IeO8
zcykn9Y$__UJtMD+D_c~26UZwqXcFpth_fPp<wK5&h2vXGO|mD`wQ%y=$mfq8h~wAM
zQBSt9Gg7P9op)!dHQ^F7YSSw%U|_K~A`=W8)-51Wq*fSfZK>Kb#F~pKgC2eJ(}~50
zN#fsv4(E(D?p*2BZIC2>6H;pf^*>!-ax~zQv?3Uv%I=3>Gp%t~edI1npY)rfMD6Nw
zo)mPh`W~;4Y{vF}Y_>|j86r0!FVdf#+CeuBlU)@Gx#!X(N)iQTrBtg6!7|InyPA@P
zr9LpwO^~<>QtW(f<nfh=8!VK03&r?sWg3cuzS_4Y(9wy+fBQv}Y^o6NNKUH3h+@F&
z-2_T<$!F|@(6`*Oj@%6#+^v<lDJS{+q7LSHqj<OC*2$_cZS^AK=%iZmX`gPv;YTu{
zvZ-_^Gu%=|P#s<`+mu)-jGcCs_ub{J;n@ok+b--^m4SUDax=3$8@y^7Y<Y0!w(1M%
z3CKxI@U~P43`kpaX=n$=tnn9QIyfjkU!5n7Von;9U@;GU<j4pHTW>FSI%n@T(Cf9U
z6Wu4ooF^)@E0nr<R&v0i0AaaF@xh$x;)t!Hoh}W-;0LdF+m5^jj=A?Qp^M<JR#K_B
z?QZT)DXv#1!6h?IfJlMaPxCNDqNf1tdy`&;(oBJ?M%AT^5d3ZT+$k4AR;EQUHxv+u
zD1?m4^0F7wMVd#xBZ%ob;JThLLC`3j+%(xlyH8}XmE-yAXOeJ7am`vcT-}u*cX*H{
zqI!!V_m%1Z0aOc@&44oZy0fF-&2$DRW2(7ud}z|&b^5`?tO;<WX*)viyFw4>B$>MN
zhG5r`jF?~xG3#(Mb92h|BeqIBxR|AK&%br3owup?>oje5!PvjP;!&ZuSJ-jwC$jO`
zo7Abqq}?;M?b8gG$`&jnM5^;%_n5lS5|PzJT;Z`c0!oRjG2}Q=JoLNrq|Uuu)G&a*
z6?h(vatr8+!;kR=-v}s<J&p$Pb{iAql-Qc#F$;F-wG+8sjCfq4fL#R7vk->0ax;qs
zLm`}0XAL(Dd&#5U56D;^+FV7u$gGte?s2D7GUtQrfyRkwz^2VC>bXTXd^mo04H;3%
zI34Z6_zYf>!Y;cdBidVmwci{Lud1?*t%S}6lLCs_L2`2j<#5`hyef5ck~nzCI6SaQ
zr19}zktlF*M5(+*Pu}0QA)y4Foo?tK19{VopeSQZ02B-i)n|O?FLHt(I#tZi<Zz95
zi|8@v*q68G=kDClClN5y^JaByC)YY3y{$K=4s@(~(U-5x>3Q^`k1|&VI6Z1FXlXx@
z{KBMsU0;n~z18r!osq2<M>1u6lh&zg4b*S5ldu17w}*<<I!I!zms40sG>YBCZ@10B
zz9A3IWOGIWV20CaLFH8LyXdO!LHA2AaWG2~pQ+oW(|W<6mk{qq@AIzdtlSENOo!!_
zUDmS*##hgP2DTpQowWQ;7k&RccFzcQ|JMPJD5Msw;=(O&->Wsn5e1U{j278ZK7k@a
zSN<I5`FX)X@S*3)<mDdO_~E*)Ne3(8p`t>~J9Y3PnZ;vvXzOx(H$w2OZI3&Y7ix}O
ztsuI*wg4bo)KZVo{Y$Is*Rq$H!oc);7V8^7#{TqHQXZs<5Z4_+{;1sazE@_oB|NdL
z%g-F5fr#)qw7gOnRXLj9yp3;*jlNt!<fi0y`l2^oSy{FP2e=fg>YR)6TUj*Kbw$y>
z@JJoDk>Jy7r$EuPv)xmK&Qc9FsNOljEymupny%4dmVsSeyB!SWttvUw(HxCA4&#=Q
z^$UN&ObY`pY&?{Xf=bl_<zzZl3_}Q-S8RlN2^y_c?AsF6%Pnyhs$}?hCrg`6&ZphZ
z{Pka>{GM5EKc~`roPJX)o!Vu}9b;Qio_^km%fH|;N=m+1cF2LWGE<TAsh#v<$&TUG
z?xGarbUX&SA=>U;wNVtkjlB_F8Jdon(WNNxR#cUjovp7?g8+{GNVr+H!P$mlH4*dE
za2y%7=USn=+m+toAH<B8wvFHSntbk~!~yS+*=-hMh4i+yHOiUJKRJFqF<r>C-+gd3
zm5~REr@9x700LyQRORhIY>RLksH@5(NY1GW@Uxh$E%&_<N%~F=UJoLbx#@R-s>ab;
z1sjm~o9a)fNdRtRnZcu?J#57M)vH#BVpVzQ`P3J<;+DP68zykP3ffI}K<<GCisS1B
zEkt>NZtj?Q+9-Bo$!kJ)12VcJpPHJh;J2dj9JVOnTv-|hi-Jaob|!jhxlOjZl$@)f
zH%oyMPOx|aSPQdnNOZsoU6DaKL^w$eLj!t>eY2mQib8ZtK0Wm$jMl7XX|fXX{!1ap
z-EgknC>NfSc}vj;!VwG-htl6dcJ#Nfpdb3P1Uwxj#xtjLR_Pm>cNwPK1;qL<466fX
z*@P|>3|?#pXwK&V(`2+2@=7aMN&B@nMJL-|kY=N}fJ;9{W1%x4n^!Wr<BMS5>i){0
zj5nQWmTj*31{~42?EQ3Cky~$WDfnoxmVF-)HJAf+5R_f!!$F^HH3J6TocA~qFY|t1
zgx_yWO5!w%z0OnTkY98oXnEO`P_5(!L}gGLD3L7BwMsMUS4v~XqSCut%(xx}*pysx
z<vs`_p<4!X`rSNufsf^>H`~ri%lgf2u3wx?IdB!tJ6(<#Nj(*+nCdyS)s4iQ6BTl#
z6E-SL<rpu?z^`x5^z=y}h{<W84`hnqJc893pB;vlEVfc3HR3=jCLa?4-y+dud@-v$
zcCiNST%I8S-KJ2d;fts1TA%FFE&7Mk6w8@+#_ZOvY&hOZkg*s#ogRL3ne7`7%_|oJ
zp%VcEQQJZ3T_QH=YHkzMDG+yilP(G50@Qwsk<td?=;3S(Z4{7bA%aj;p%B=zccJp7
zO>g9if!K9_yF|ZHT|!vtDg_)c#KS2xwIVQ8yQTwF#+jaQ|0FT|oyIBa`TtYCX!VEN
zdSj_$qOaY(G5+V7UH_uENQ;8fT?{h)hwlQ)RqJo;x%`|r_n<rn<xW)p51~aSSHmLY
zZkLYI;5#a5=qJ5UGUtJ?c;~nn(f(U--UG#hlk~zVltPu?rnPrHs88*b<D7jH^d?J*
zheu`Y;c#ARr*kSHojaZmp19KTqE;`+?YJ>?gIyK5Q$THOG^s0Qk5hO;>a0=K!Xlu5
z&lNrW6dW_Tf;QmfFWQ+u+Z`L^{<1Z@epe^Nl7g?@6hi~6A|R<2g5cxwS*XRgB;($;
zC!PNBl4W+CJ;yF7Nn$i-D)%P1pUwFDc#<&XEQRwH&)vMWS&Q457E9!ri_q%0h7LY;
zk@JugEDX9|7ET+HvQUt%oe!YY_vWbS@T1c`Yj9Byq3*{@Vvd)RPc2cgQ{4oVJ)IS}
zJ}m8-{q18W+`-Mmh`1jrB*#}($fQ;nI`T??iKw>Ba;;opTq4frje|i}WRWLeJ9@r_
z)(hb7_cKgF7tw(gRa@3xji2?d4D?1dQ4&dDm<m+aePs81;ho?EL(wnpPe+~1OIzr1
zmz3k3^I2I8*C0+71+ajxXh;S-vS{Z{ezc3nCp!Ry-tO+kn3sFJ$d4o54wZx_(f*o=
zrd12s(FefF;rsQ0x0CtA!_Mu<726F|#3VxB<Y?4=I@W#Ec(~KnbW<h@ejl8n4-Z`E
z?&BTbL9zrQgzuNJ%b+0HlXSh`CeK*dZe%TEBflf_5N(_F9Gf%9DoufO>f8~3A@w9V
zuLgdWmgBxat`7ky!G7j))Ou6?X0--p)dY{&#cNQ_rC7^^m?`tm9!d+<?_r}8M9)JB
ztBv8Xo>M#G*_X>c`CoTD(9DfrNoM>{#BJiMT&ysISq|2Hik<4BLj(B%H~WMiciyTU
z)8-F5I%oiimo;xQ-Q`g5Q?$H$b~6CoaP{iY<+2RO31!7Ne(Tw?4Ryz@iDVMXjdg3r
zE2Gt|OktX@XQ!t-N6x!>BSTw{1i)c~_DKdggDz2eP2h-J$I9Z>$H;UfIgE<<i-yBA
z60fFbmL`@dhxTXDI+TE}R?d=olu9T~-zBj^26BP7N;DH{8_ASHQZAwCZa1xg%FyC?
zcXUx)rb>gL@BE6q)GQS9j-pmrS8JJHNebQOU&h9BS^4TEs<pJ9?pNfWk%RK^R_?xU
zmV*aqP*^upxQ`Z2kN8724cMzTzNl8D8Kp_yG^UBWwru7_v4L+pTqMS6KSar}nDXi1
z`0BnVI>U|xg9e8cJ4;yK58afyjWHd4-SvcDsaIY$5(uwE@?LB!H>4x&TGVx-%`!c*
zgPmAz{kk7}bA~)+t6ibCJWruzRs9f=+R-b2lIx2Vv8=3YYOdtudZ|W7=~iC5<Gnjp
zDU@MCrBpo4J8G)y+Qy2CCq2b|c}Gu`k_R|~R$hm%sEL2fMGRvvTZ!~q1;xEEVmKP5
zIoA=eztTslcYY)FdIq?lkQVuO&W!drXZPUw5ubl*QoBn^*J$g=LM&#e?=4o=6*%%J
zSuYXrouA;=kXtN?$W>nKqK1)=)I<&xNRJ*u_1%2WaUKPy3W%<+h1-z9tm5lj^=FDN
zV%fhkDJqoVn(mx#X85G0XOO5Uk(q{9eFy7AL;h_!jilh%YgHD{cX+6zAu}9qnjDps
z5UT6CQ@v02+hO(BwT*&_+urzKd~osA<HSkj^i+k&vQ18!uKv8*1;YmG>AVREM@fSZ
zknjs#hJtkBfJ^`Kb7%)gk?mt_t2NwZ2L&_rg0F7iQHIH72l*UG_2*qRJs4qjV%C^5
zb!{%5qrr-!yxjXKHpYnlDh+F*l4f=i_Au$`N6fUKc>Bf4h1(=O;fP6cH3+UiV3>~k
z)OSVvRd-=6O>DG-nB{=Cc3vl2qa{=Qlq>O5-cI>G2?<G0&UI1+N!=1xogkGj1cUMu
z&#l4qTV0eLgJaz|UEppY>r@@3wc3j`EEsiS)xPI_*3qvWu2eSA>Hzm+?8184Hw#hj
z0$P))fSzmz(%}|S`)(Tw==7?@w2>D@bxQFzW29fGgvJ-5WS_}Ooq-pzr~r#6k=wUL
zdwD_(yjpCw3_CUD*-tOIBF^fWs+ElX%DTdgHHv_wF|wMZ3?1xE99_z1&tDs*gV29X
zip~n-DX>MrEMj%lrfiuAA$CTCV1^s!MWWhKv0&#W!I<ZXDB~Mw+<{jt5Z#fTlFa4G
zKI&xL%sv0fDa5H|Mp?N)@BZDBhHF(v!6Y{dV;XF-mkw1ouPsKPKs<>KB3(QSl?G34
z+~G?6#;cRhE}jFWI}Mw3PRAs*-e(QXOwD^XazKIgaJxoTwOOwt4h9y_g>H+D3UQ}_
z7F!}py{NZgLl-y-roHX3<x<7SS)k>>+||gEXVn#`FwXQkM8H1txh+SXCMy)Jm;fH;
zE_ETBWwB*Jpshu<U0H1^EqH=<!HogT%1J}d@oVP-iA6N<N!rk2=XsHwrM{9uXJk^M
zkjNx69F}OOW)pgVR+Kmg0vNp8-f+ePS)N2@GAe9c1wDP?4_qEb8d0^@;l`v`!YPj?
z0Q357bmA{?6;B?6u7VPZlMKF#K5050T-VQ1+*Q+=2p4ChyH{rp16mMYvWd*|BW<*i
zrSad-voiZ}f)5ypdatgX2AF!@_`Lt@!5iK8QI{PrCrwO$a#pt0&b=JM{Gch_2}EK;
zPMj{!r<REXSR&7DCh2Z;T?Lu$v^Futx)Z*yfLqw;G_Okf1kMf)^4{jLkKm`sUIyZ(
zM_j>Z5E>_?T%$KQv)Y?3-<rvkDrT>s%1$Ya4LCmWZ*%60Td-nZN1eK2o4^$(cIn4q
z@NGz8W2X^;{rsNQb31xylGqFMl`)m}a$0ClmD)z6DA(DTq-c@tl-X=C3R4D_eid|Q
z5MXLiNRxl5x5{m#?>qv1>;M*d-?c?$im%AK`td&V%sk=a+9wKpX<+B^A?Q*lLQvA@
z&9@vvR=?U!uRP7&{8`FD%9GHViaEZO?{Z|FWWE8jeM4x-rV&Or>Ov$P-k%zFx{>_q
z?KCiz-fMnY?RZqhsFd-9R$-}IAin*T?{OL@VAsESx!3iaui9vG_t`5<RKZ(w93N5%
zn#L2+8L@pgT?0(EtS(KcL7;}U*Rkw)Rs^q4_p-f?EKcC)j?KqrCTw3w31>J_Mr0cv
zxa;J^0}qIW4G=k|xm{b&Ddc|Dxyz0rqQkLlZUcxw&mm)7;8(J&8)b*fq%REUov+O2
zTMo4cZ_X<Ozy+&r6x3^o#;x>If5Ef}t4D%lgtX@u3Q{ewCm6dF!H|RevL3yOMjDQt
zl0K(^2FPR(v+Fz7)0k7^HvO#5*Yn8>p`C8i$XSLK9e1D#Fu8({iLe182>ys{DOwQJ
zMW(NE&8)+|vhBPx(VI?u{(WDGouF`ZwEj*P=e?)CfaSSCd3p~I3W<mMbhf0*2PGOQ
zbkP4q{2qA&p)NkViFTA-P%bfY;9XBn3ytBxSHBtRUm0()?J40x_L~K-b*pZ%20Q7%
zg;@R<v%fc|ajA+_L6%@WK49&5QrBm;d;GR3))3`@p5t^PIL;&E@>&*V=K^{JkR<84
zVUlCU3gi-@%Kund_i(^vhay!ZtF!4@aQvAWivWq4)mtVvBw9su(i5d%v{k+t?1P6(
zq}iOG<)-DFnLNb{;k968#PSCdLPP>%l>}HjR|ag$NecI~uW&w26|>0Z7y#8gr?X>R
zD-@1ZG$`ZDr;87h<;Vhmih5fb9~CriwLt`$Z(BFs_otKXOxS})6!dKa6+7+UZ_lWu
zkBp7%y%&a+CXQQ0cIPMJ;4{2szh(;(eqv76-RL_x=EWo{n(qYpGDJJeR_>c_UX=<2
zo@&|(Ba{$t9BDDAt7~yFd~%71;Hg!J2D{pz+xhPjA%3v8HX{7e+r-5L7Fg@y82%P*
zyPXJ-O1E7bX%O7jz{?hq&eOt|Nq&<cm#Yv6rRBS2%@i^6EjcQ*6jk<T0JXonrAyz$
zWS8q=-P+u-smn`o3$63UNjN?!>LQY`*`zMU49OmvWNxf`W_0rd#nN|riZA<|qKEo!
zrb-LY(v6L(jQ6I;_^{J$Gl=(9@rc2<i!_^HhofCNjL;6j%U02cL*R*&c)N&-K<q(;
z(vMR_l)|6Nn=fWV1pVN5JVT{nwMH_E+g8M`<;*d0fxJnPbQP!vbb5$5)Rj&fhO~vy
z;72OSjHsk5m91fv2BN7by-i5bL=yn^LW@Y2%V=>S41Sp&Drdm0PDBz4f76FgngV)y
ziJc=_7oU7C<uf8!JT2MTxiyF*spBxPp<~yfoa7B&H%22Pm&4;82ubCwi7%O<#)Pl*
z{3z#9ImZo}4AXbA<3CQ88<|ci(!xF6wTW_LkO(D&DnS%T2@zO+V1HGo*+q!v<-36q
z%>z8r!jep*XbD;^Yo46gHTq@FVhA>LbcOXl`)clfx;LrtHOc?XHfnr*0wK-~>bCtT
zciy=Y@2u}l8NJPD*{X@z`4nGx?AijA02X9O&PGKSU;AapT~yLlx~tJd&+cTNSF%zR
zg@Timq&}O$n5mWw6Q#+zMaUDvJj2N1NL)^@a;QFe*K9k{6Uw1xMF~b;;EmR4)jT$7
zo4_Q4U&pY?XK52=xY}UhC=gQ|K}`%otu`02<(4-@cAVNP@+nT-ehG50p$?oR-<+`q
z^-BF@A0-F(q1vjKdP@Z`<tBh6PgREUI|&d~g<%}oc9@Syj%NkA4Aft+sHO&T+wI1O
zWT&R&6@e@tZ9Ut7aV^SxmgnK9<?gT|l5b#Ed$T2FQ&W{^zh7d@k55~(CVx^`5OW_P
zsmCy#67y{kdiMMn#EQJWNM5fym(OwHmBY=VP~(G1Pa|vJ?%T{(XKwg((&MXS+(Z=F
zXWE+DW+KUOXrP#LoXH!@EYp#wczB!Kioi2|jvNNisuy99Ax6a6U4PWag-vBj4-2C^
zK!<$QxBRw$WOKZcrJcm@*b>C&9Q3{j4y|92V0;=6L5GW>Jo{%Mz#<;hw6&F#2}#a|
z?wgjMyy>D7iP2zW2d<d#0@V`7R8FNlVSDKl;W8t&B2)k}5i&@BsH?=<NB@huO8&oy
z4VTFOL2^p|htLYD4jA<_v-E^PESDU{1ZmIt6+~nFZRJ$_Z~YjDTElIoTZ945OFbTN
z(lzNnXW7sKX3`?eZ>FE+<tf%fat7G2OvshpFh!~&G5(J?P<23j)tURbAC<3`UO{Uc
z?x>6>FSa2XAuYlsUp5;v8DPz3d7ty+qTdy82L6#xuPw_sDe#fI$%2X;7_p3SYNC+I
zeer%*ANNJ}?eY@q+New3!W~A&NdRNyvkF8H0bRAn?c|D#CT;T=DbK2NrBnI6?ZUc}
ziGkp}X3oPBEWwuXv7%bCWHEgCMfnf*gf!au(2lGWh5^0#rXE+XPl{v-9yj+NTA$4m
zr{ktP*1>~>Am!l({`Xis{f#jr(UA_(fz8?yE0uW)bX@5}mR~e_7zET%eEZu{mhT6M
zL5YF=`;@2i*PhWSE8sbX`^_1-;lxhZx-s*OVk^qz<8&t0L`Rl`IY(vuaT;_=<oJsw
zwHsF6{*VvH<1MePd4i!}2WQ&eqJ9V=2&emo#NR1{Eb^7t2M;Ne2z7D6Zl>HPN4S?q
zL?sd~qzaaQL!-&a1(59MIKFYZ8_%vfa_rIOvfa$47dE!p^A6JxVWNQNJRuo>8<P8_
z7KNfKc#VHZYDFoWZP^D=Ekz9v_DQiVzcqi<&x60o_njtc_@mig9la;zm)Ui`;_>ll
zjyZm2UwNsS<gwPQ8bw<_{GZ9m-y&1N43&~d3Lup1h)S!vXeWUSk#|IR)4iIsgL<dH
zZfVUg-S@xhCz(1Nl25K2W<7DHCgc~bfOLJkC!dKtkwjw`Gy)OSOK4=#;oMjG=$$|X
z<N1fQ)^fy;on3w^7`{hnQQXHr%K}CqTW+Gp9$9`xJ8I(k-L@$ew2F8*RHVy(?{sl1
zBjjCl@bSoH`7x9j_&b@?*t)+s%C68_JG-#!@gy)45r&E4{snVIFrco-L3=75O!Qi6
zcl2(9{j&Tq;)eT@1!9Kix|ie9AVVf-xB$Y7%kH?|vXT0hD$zc^Qqrs@JoAIBf+Ggc
zo?-~Y>2y%)!v{Qqy!=6k&EsIGnRD5YvfvHBQrGVGX3A-pLeP3s(yb98?#jY?kuTm3
z=!iTGpqHyt-jl-Uee~*ikSna>?*ctL&(t1c#6hK2-$!kn$mZX9og3)7oxRTbj$-?3
zqD~i7^zN4D$KD`kr~E#B(KBg|Xpx@6yA`9yK8eD3>mvyRY4C9&h92Bzr#o^6P(j9C
z!ghb3REJRskMNsW^}9>saicjmykvItYX^b$y8&-dBdY~yhhfpg`l#Xt1^G!<$7|Gu
z$+L2|+zJah4SA*70f_zXxgf-CFK>tOEAISjiqc0fhhL{=M$act(?1c~LXR{*csV%<
zQvafc!lX^zAVs*AS5ZaR!mJXPndwR{Nvs?zj`h_}7=RZ*&ImVxdZ|tw{}wh5hKwm`
zOL9wSv8>Jo($k#V$>i4vyRUJO)wef}kAAv+0oA{^N`8hh5r*ymsp?;=E0lViyFC`8
zLKX<aLV9qddgd2xP+9rbux54%4{-`CIlNH`$ZxJCDKg~5Q8dLc1ddLA5)yY|MVQAc
zu$@XjR9F2`m}i3EO?l$A+6rLBFk)YY5K6unt&@S?e0h#k5*wLuoy`)A2f5I%YA3rg
zch@DctVX&1;+vB+5gK##OZ}{HNY5GpbcfMFP&36`rlJphwA*{02!0H;d@)Y(-M!B<
zj0iqB3xJ^@XMl9HyBXC?rWA`sbS^j`p_eba+cB(8>I0=#<u&NPE6ZHlcQ-ot1@603
ziuJY09p5|M%#)<IL=_+L-N=DsT@}z;d6apLHgH^xvago{sF^=dOS2SZBNnQmtGm}t
zrI@rk0fJWc?4T5yL3Lz~8{J(DvsflKmmu-m-ge8(MoPms1Dw|7cK5qRT^Lp|+&#3-
zqivRtwZ4(bfvh6xlmT=Y8keb8nhV@dzh9vVkB-&0LZ&NY#ASj80!0==EnPOv?tSKG
z2J+Qb#7#Sc?A2@gm8H|7lHEp98b0U@b~dJk+UEb2p!BbQ_xJxm^bsU4;1h2qYwT{T
zuuu}N%F-fxZ-BJrVXT#Nj~D*JvS)tv;7@kzJ+jFrFa^9$)E@VN$o?o_F(W38o3}qo
zbY*~*9%q9tZa$}PN=+#5>ogTGfzOvSJi*-FMykcH#V(Y(^6Cx3)U&3y#rb1QktQ&(
z?H4f8{6yyWy>4r-1G5;rO`>ozq{4lz^k8!vWi&dl0B^GHlYJ|t)hF&!aZOz#THmL}
z>#a*OiViQHYEhl{D_WI`fEs$uhJcE!6O0MJUJv9$&+XwXqBpu3K^~nQ@85AJL*a-o
zH`AlsHObSu{pY>4pw7~11Ldd5^i;8LtMdxBbu7T}RfV43Mn4q@#RMH>eJ-~9qp@1@
zlhi_{?h|{L`Xna%Zh5SXp|9aiW)R&M0Zm8Vw&X+SIknBP6AgHj8Eb?%VP2rRtNf$c
zlp)h$9;pBDg^xkCmwqw1wW8)D<GnRn%*lK#JuNA9$*hOCBA|fPR{O9@lEqX7)nj!k
zd~9y;#{jp<t!OUW&n$`vBg-O2eCz%*edB^gDx-Q#Y18{TH?{F3ftHWE%84#1Z*0s+
zHF^<Mt)bb=_9`RZ4SKU(o`L=MXsjz<0Zc}kmwop>U6)7!rCGy*1P=`T1^!!VwuTu<
z35JW!Z{?~!SZb*=RDwx*S))$tGQ8|-mMwwv26`U99$d*Xe|Ni@?3uCXc8k13kTDAi
zd2(!Kb(`1*JEYUnHbztLR<Se)GZ3yuz7O;1Cjwl{c#qp6an(EV@$P^;F-}T`Z!wTX
z+dKMfx~kjm<@hE9wF+kWVn;RcKx4Lj33!2nL|Q;?91nu@gKC+%@QVYA2|!|ey02OZ
zugo`dir*rsYWC2>1_YTtLPn=jcMjL#kJZ_+HU)PCvx$@{0F$<MrNh`D?5a#`bdw5&
zj|fAgvArJ4mL35;DNwFZ!nMm-zsTONUI}D7-f#6T=XcqI-{E-}#TS0>uc6#-P!oyD
z=F3bW86yy-fon5VFlr240W^36DbNik7JQ0?Zu|=G<(_y%$HsTsT7+9C=Y@X#z7ieY
z!NZ=X+4nGHD>pi33-cdkrpwxb8!$17@rrcLyGTAslRKacA4)MCXDVJB$+I=wdyDKQ
zWK0t5dh2~4Og6GGEjT|c>MATxvz2CHQ%OU;Y~^H}CGWmzz1v!#a&IrFQ&Hq?LtUVO
zJJyUJ_)bm<5h=6v`#Tz&MxV<(@aD?<=o!$68dNz<u}23p)2{30C$poWZ+4_34DoTA
zv^2#7UV<c_L0bmQHV${nIxEUsSfc<^crlx+Jui#dQ%Gl9e@ntwZ(yn!Z*3n<<2mc_
z&+Bo%^w~)36k1R`AVL<XlOyWMR4DK4y^gE07Bbb{De!31PqA3^sZ(l(uv7`eF7^)A
zd=m}cA%|@T=zCN3UV**lfJnK#`;^{VO72E#sN-C=nmZAp0(XWhlpF%LIZTL=`xlJR
z9^u-!otg>jXz!qU66mye=%c3srzJE^Ax+oU?<_e&17v_m+DrR^H5+SZOv#6cZWex?
zcv48-=GR7BUlyHLTc4`a37NoS7x%8Cx&u@H83CqK$PRIU;xrBm%14)6ajp6nE$l@^
zuL6N_Z4EYMQ)@r->FQlG@9TNH>$y^ho$pjI(0Q-tFBsBZ$LL+9+{b2bFj&85hI8Gw
zZK_8=f!AFgLsD(rW=7V;X*s{emp^#K^&rb+TeN0^Go{s40y2s+WO!}W6)};P5!I&Z
z%HOvjhz|UWynX4pwY~BwMdegY+}0J<PA!q$m_#<l_PD{VK<?dC;P_dy0%kVbj+a-i
zK&Yx`Za>`Tj~^Lvb+dSjk*se>xRng77!LqN?Q`aE%~IULWN1tY$-gC~bi?7kjaDS9
z%0&MRj0^2@ZoQzudog2MY{GI@^9$^aSbm!WU0^Rgjh*?`BFRGZW_d3|?Tc?76*swx
z8w(<rO1WxxB}I{)vz0q;Cqjk}ntkc#oc<R5URz~%!S{<aBYJEoa2<b2`S*F=pZ|X(
z;8}PWeyF;-Tx-}sod;IWuE!mekM?T}uI@4xmL~0Lp49eTz#~*XMNC3kTrs38VG$~y
zLe3$K43$smd4IP4M?S?TRF$%NSMKiRTRySQDXBopae7~e7|sUhZUXdm$7cWNNj`^4
zukJxf+e`9xiAkuzfP9r)v#JmgS5tuPw=ex)Fq^Ew<;}+ObAjJEpT%ZvKrvj851D?k
zr2`%ee0>Z#vpCn0j+YTh>E713pV%U6${hTcbr7N{B;lPT4^{@8uGUI_I|p@!>Ge^4
zXM}%^jIl8Y*%9>dwxn2qA8E!cq;oCU=rB*ER|OF8eTL<QU8-i5e2+;D1A~c5kV7?}
z=DYai8H(0H*bj48Z!9aKye?b2Mf#00^n1U;z|&;Q&y^_x>cZH}i6Rk64nyJLuhimD
z8yXYWLA@d-b{wBD@jXvQYD`As2pe%0Di#1%#b>;*c*G+j@oC5@s55z|lOVgtpkXMt
zoBP{tCf#@cn?=xa5eRxDfkwSzNQHQ?S)lMZa}u3x(}Elojn9dq4YbZrKj!j7sDzhp
zO&7;*A9%dpYb**~lZBRl|4&)+oAk=-MptO7%c~BOOEv<&<;bZ3AwYa$sD`x_D(SsN
zfuP)3fGUDntc{&&0fy|&5~cD?H*w5%k)AlqW?#8vWL$ZUGrb_s78IhNRqDD4l5ouX
zHpgV?kugF8XfkSd3RFw-7Cwuo<_q=j3-hhq17x!WL0CsuL6HpZC2fw|Xd}WvLCp`A
z^@6Qm)TES4=Euh6>RA#m6P7f9>pW8|jHGN}or0(1CyG1UlN~qVJrZ~^W0Fw}JlMUS
zH+K)@#`#7l(TOou_$du&Fw+Sj?be&*R+n+DMmoIWF(ASUNr`BghqFzV6;Gu>{g(mH
z>&e_ZJ`SRS{<Bz80o7#w9;V-c1onemW56z{*8sgEYD^$4mLtHHS)l+m9l2NYi30os
zGL|QxF{QMdCm=N=TqsQs0Zu(9Sm6cV^`X1VNljF4lZr4G`8sFvd{E_s29>%7GQ<<=
zatS<TOfmnglKx)>WQVPgrHAyqf%yqnj~6HtJTtk-jWm=)$&IYc2$!Oka^Gi~$qDcQ
zR$l!p-ffBp$gv|!@OBTRdsBdT!#}IX{)!@wVF5fLYc+%$AGkqA^Mw?IiIR|Ud+$Cy
zcCq3hFWV7*{(50e0no%j#23opNoZir={goxlP->Bp7M;C^0cR>rh%e!JYuuuu+TD{
zXH#DXTB^lbN+9`b(PBAg(B(@F`f{o2;j%mu3qY3eB5_y)Tj3Pf`F^N3_I(xQJjp02
z)ctHf1lyq0Ay4IHrLZe7Z4yr_Lg{1!;PaC$_V>-Vrlu7o-08S7K-&1~N?PPf2sFuX
zfRD+HB_1_jI~_L`IP<bI64SaVw}}u;rt#Qed9k=j%jtdtJmCVey|cYm5ytV#PvEG`
z$An;@&YTca^LcSo*Nbt=9He`o;7P98C4JlEyPS!UMRV<CpvIvURn@9k7lUV=9~B=N
z$x<+sATUn<gIm`7HWa+3fB5Ufi*>Gkt5XDwMy+}XKCshCE`T3;XY}6ILE-oK>CNi5
zv4=?NjIHWABJS|o{G(b}&snE@2^WU(>@0c_TTcwUz<CRJA{TlERO}rFxOhhm*d-Y=
z+hmY&$k+0-*r>lZ{Z=xAi;cZr=+y&Hm`;@jG};|ed)zBDXtH@+b^uzn^|5g0(%1+S
z6fiKd_WXKCx9ZOvdt0OUyZJ=y2aS2rG)+4_zdnRCIg35?(Qhgaa`h)=b~U5KAL!^b
zO~$iDHY-_cQ@bFij|&y%SBZnm-b%W?jcuA+b2idbEyMDh-{n#?(reMr)TmrPndEd4
zQ$4UUo$7=~i}yi?$voqjies|RLWkRhjI-|f@07vm6FbqG5k5)elbJD|O_8wDxfuCc
z1A?!(dC+q~xZP^qa0!BVH29U0`-f6O-pYxV`2sAazgx{Y8l)!*d-nmitUt{Vvz)}e
z6X@urxj*$~@DkPtDzq2@$Q<+EFNM?8#x6N3@%(^Uvix|~*!|K)pPk)B@@{y0c2Qj8
zzUi~)1o4+_BJbWfY#t2EDhWJd$yod`|3L07<O#AslPy;^0}neU_Jgn90|f1q>{Pxd
zo0rWfMz+k}n2Aob^kVXd({)pJ%n^z8@3D6{=ij`q5^~7p_Kp=#p7q!QjAo2`d89BL
zcxs{eKbBLrg?eY1UsR8%p)Xq(mv|E3Wk|8aa!XbMXk@K*=RdnZ+?!lQ<w8l%BLXX@
z+6%(HlP2mZTEBF777)4mATt8JF*9Q}G|6k2!_nbjh;!BFzQ-X;^YnNLXinxA?q=pH
zp-P0&%}q-4n0y(sqFZC+Q8Qw&Q9<DDl-#}wPs2uDGFuyDi{_Lg20M*xC<BysINTIw
z(Qmh-N8^3f4BPzJlFb{oG3SY}G8BPBkp*g=_^R#Zu&5jKXZBm;3H)1LLpz~ixpWAy
z4B+z3#j;u;foc|j#goME4S<o0E+1v=J=aA!2BJPQn#E2-LQ~%<iTtuxtdmcB@_B^q
zJqtB>DCdX6xhF@JPo_Ge<hF<{&8TL6B~7F<(iRHNrh-qfE3d<K+5+mewRvj7X!9}c
z22$r;&ttie&u%nSjRqnp5nk>CQH7G(ccm@<A+ryIa@jM-(Z?Z^X+XK`PnR~t`Fhf<
z1`(-J4IQDf>Y~G(Nk7)+x-4Ji5}~`{{|324RXz;8O?=D5L~HPNPrEyA_eSVDG(qRP
zG?Y9cE^bP`jw7n$@Zl1gqT?SN4V9#U!e5~&Izq;tIL~?WUdoGqQgmRx3M0p52zK7R
zJO;?Ued{=Ce8H<^LF9;2p{)U9Mg5W<US#4Mlht_`h7`YjvD;9XKPqTI<dWFpKDMq$
zyPH+aOg#D&>X$4q7!$O5`yq&8yr@ElT+y0*|BZuid8hVYFoO?$Hh@U@*UPJH7Pa<U
z)5H0t8sfs4<JU~L?1JRCc4z*hPVc^N^y>|jT<jY3lD}bS?V&s_K2VsTYJ2Fj==jMB
z`4A$y0DbNMey2W`t`)NaW}XoN5wBQg5o(cn?Cp{1qvCqWCA3ZaQ9J|VGEX)48jF9m
z`QfJ%q+w6(+YK8S)`LG&oUn;eSjjnU>jlE`?(4_Ul5e-7NrEzwhfYj^;)V;D_5b@V
z>4soMYYN+OUd3USRG7Gx(4go_df3lb|9(}vfz8IT#efk#^2l~zIrGBESnF6|>gtP0
z<t)sz14QM3XVt-oc#I>a%}T!Nl`+u0o}TIiI6gl8o9K46O3Hb2`TX}Zq?jc^{zb#%
z&ND3L^-+mMu=NPDO|w+(8!p8Zl#zEPDQ$SQ{O#RfXbk?W?PP$s^{tvFh~c-kaq!+j
z=r2^n|3}-Cf499<b?kh89hp{q`wm^^W!d8yW^m-!5B!jn0<w1Zhmwn-Pk7K;56{7v
zbB%BP1;je!_^p2Bu;eI;KWn?c!)t}1Jx9h+A+=e_RIjLgJ8o#Dg7yMRN6iVTkbH8s
z_bya~6zBNL{N3rnnAcS%%K22xJVnt(&AR_QS|^IiBGh_>+>n{8_6@G)SV8hz<<UM2
zp2x4eTV0o8>dJzv&7}*ztbSpZtGN5hqr(%Z*#;Z<t(J-v2gX28oh_^r;N<uWbgb0N
zg9P|QROxQjl!|DeUF_~L8~K)*n6%|#tz((1_OMjVln3pMpRdnfFe$H(3%k0BR<;wd
z>zRHZ;B2_USU5Pix1i0eUm#-?k&;Q`LaTvPXTWjrpW2mh0cU#`7)40ozn+!&``I<7
z+KxP8t<<@aiw|}RKLVlilb;a(7tF-(>DU<jiTM<aCTMpM63w9#VQkg*lXI^^(s^@f
z8#-S53N_H4k57oc{WIuq=2K3fpX1J;g#`V`BZ{&=lg83yGl4IO#uy0WYe@y2io19s
zjbG2VOO<&QEg-{yg>RoLH~v&#IX&%dRY<aJLgcqZKgZA#hvg6v5xY421w-~+tNu+u
zFyl$>+kwgi)T<^Z$PB|Ketp5$7SAI_aHe4lbo8AN{tG6Dh>Dm(9qaqvCKHqo)}W;?
zt<~=XsvOr}FwkM)+W!1o5IVes&Th0<B^M(Z$!}+pl9C15|BT}*Qy>{sh8WuS8Y`?n
zeaC2edldSpSctF9U+`}eXWWB-Taf%?0jAN&$Hu<q|Jd5vG81AES$i7vJ926^?kf*p
zDzG}gF%izEP^SGc-3&d*X5o;4{xAnaGc~l9x));Vex3!oF!fA@LBDQ{g0k#xFxaWs
zgx`=k_untoW<v*riiq*M*`Nt-#Q)*<^e+QR|1rSvw}BV}xVhx>qM`-An&sW%SSlqB
z32KCPti4e8rOJZ;YcqfM^S?|=r)9KNb)rgm^$V%c{5<b|4X64u<GvU+a&V~RbhoON
zp!1;+rta^ZDk7n;&$T>SMAG$S%tb&Le&I3MU=AFvVp;huQVp3nlPRd?ncI=qIUTDu
zpZR6@`&WR+aq7Ub#P<!dr!<5R>+dVyd!N%F5yh5d-#gQ0e}8O1BhuFZH5lA5vKy_0
zq)eBn{a7EU!E&DAw_W2gc1Etl0!;2M65ur=;+6<qTJNXc+p<H}E&UkjQ(H=1+CwZf
z{cPp4$Y)`;j~vogK9wC5<)q}m<N2{!bg9iZ4IR>e^s94;QjV8Jk1D0%QZWHmb7Pb{
zt=|c1f+3S$O?wXzHk;&+DoU|zlfs>Z=N;=H>l-KEiwQig^GOJT>w$IA93+`i2!|=h
zZ$4&3`V$9`C<$kpQ_D+$xfuLq{xQIK$#)$%uj_iU(TelJoKr;p`=r+x*f$}l8Yvj0
zU~A(?!}8d6QLXEp<-O+Zdnv3Qqkgg}oPT1E`NPqU{kUXHm{QR1>O-QRe})lLTYREd
z!ac=>uQ3tR*io)7{eyTK8>o!@olzSrg7c-20f|j&aiC(C&X!@x2!o5l2Ue?RfiaSa
zl|4*`tLdjsX^&;AtAUZzX(DZfRwZd&Ln^Q1bngS**+E<2@$PnJjY@p4b}X+Qb!H+Q
zBNwnBF<5w;KL1$*BA_G~6zl^Vb5EBm2${HDD6sskTCCN=Oz%;0k#pl5TJI1k?PFrH
z*c@a}468(a(H;;ZF4<RQ(8<yDBmtq^hiIPV#EpU`4ARQTn2(Ycg7CMV_ueNNp_w-{
z#WenkRWIu%F=VZLvM3Hr|I}Cr=dmK0=jfdjh0LnSl++xom7f;5#g{bP6Yx_tDU&mg
zxP}x7$96Nx@;UH+angL)=kB@M!Q}gb_pPCrG|RrLMV#Y{Fp};0s{>}8t>wpcbe7xQ
z(XpN!b8bziiSqVj{YE{&mRGe5+paM_D(W{o9F9#SR@I;mavQHd8`^|Ui3SR2p}s!E
z0ZB3X`hGmql19_jjZsGevX}4z%p7gr3kMbqBS4WDreaBiaZGG1@h<zxE^^<8*AX`o
z7oNp+Ta0=Ak)6CAj|ophhtDUT2tf(5dsD+wDq@-G$%=i#q`U?Qk|}!Jns2JPHStjD
ziYZLS%l3$6yoyLE??!r3jJjV(!CyUn#e$U_lqTEy?#D-IDBMNl6puZvpvfE|pfe{x
z#at$Jxig9NLb~RaO|qPM1JCb08A3ObctNNBF2`x1MLJXUo$2E`e+7^Yd6bh_WVaPg
ziobu}0-ER3%Sjc3fv9@DuXj<c4Zf5OYUHQ+7%1nHL1xc3&lj-c?U4Qh^<LYPEclM!
z=Z$z$miMoA)Tf{c&EvRC$MGEoJ@{BBwrnaFY@h*-WE?DYv1F%;XQji>vyhov3%l)Q
zVMhNXBJ9aybYnJK`m3vYcEbsUomL6o7LI3zHoC)tS;gr&Ss|Udho{tKMa#TQY3$}N
zW9Cn{8aH>B8uMqNL>XehgG;{5Ie{xMq>b%K-pWy4R<taHIc*ew6JlwB^=28=vDfZi
zVVy~9jmyRI-4q{{C9y)Th7>YGhG~aJ#s}V*|1iiv+tYUXRTm<iTQZ@4#%B1S&b#z9
zqmf%-)J|Wm_faFs&Z)fWd114GH~>*0zxX1O`Gc;l1;eQvNgDNyvZDoC1!C5TU$ULU
zA=)Ulb+C`yRJp&f<N(cF{{?b2s~*nchbEu$G`>Tegf&<m#dHl(J(S1{fJZ$LtTYEn
zgn-xAyZSDYuSM@!0ec@m2KD}IzahX1X8UrN9SFJQgKZWf;!@+_SZR-{#<an6t}oY}
ziZWN_s$OY^e9y>BdFj28852w^qi$VynaHzALbyaWAVPd4OvKEOAd1Ki6?ly<YR^)b
zuB=~}8?9a)icBb>R{zwYu<UnObnYr6>b<Gia%<j2KTFstThMlJylc3Whkm?{8MsZf
zm>{5&2{TyP3RvSOf+vm-VqjQijnWg0bn1J5)MwoJ^}7_?hTF`W%XOEwUTxX;xpkE(
z?%h*aO4jGBCo#h=y*_RqScZ||YOlWV2p2N=;A#WA6P$Ffs=N$hhRpPTKIg-_$!701
z^KHGW+^BeHaZ8|Xi7`D>VwVe3yP0zT>bz=~@yT--$`_rP{@T53`+0||m|%N8xt9?W
zln#}(+?hCA4?o!O#JP;x@!$Oc`%l#I4GN&gf&%F2xsE>_z&k<#^t;=n{~rPLPK<v5
z^sgn8R7C0@sgO13U9i8tAV1PC`SzKH_?^IFVWmUWFI^73z9dc~p`}Vo+`lwC1Uwc!
zp~6ri(Llwrb|%kO0>Ufy?PX<TRj>O(!w8fj!mN$~@JmHePSATStb&*Z0VqMsuC!hR
zXY0+IOyS5^o*GzmZZ1c!ZyJwUjI;T+j%h~{`k?_+gCh*7vnS4%chH7H45(Zd)Rr;>
zsZr5%)KDUZRNGq1o!Yu;WBa$q#`2l-WZ-P!D$ViHsi{L`q;{~EBp++Hg{ghSq62RW
zq5G=ke-{s)AdC``4U+d)FUVg-v0U~TC7?+XK&~$V$WSFr=r^)$B48r){Lh5}74x}k
znsm8{5N?t6Pnqd`N;3)zf^B5@T<c|ua!)Pi>#yHs558V_)-h1LqoD+y{xl771c$5d
zE2rlfi^)N)S_dNs@Z4)`IQypzkC}#8;pnLuK*#goy3dsgwK3H=0XRAm4q!l%wjy$h
z=Epk+kQtL+=ps%3Yf`)Zgp%t0el#5aiXZ>I+eKBT>5JMNHQRp-khm16>R5Oy(dIgZ
zpGx6J^C!|wS!{~<k5?eV|4&%@^2o8tDuU%LbWA6qQfzyRmzHDPxLpsRq=HV|z|Pp&
zhV_58p3DCeNlNLspOmGizh+=>I9)h+T@os?NdNQB`jE`%F?8>G$G`Xo%-^9XXXl;p
zH-cTHs401f!>cRw0-x{XJM!aO!Eb|<;jsJA`TXM;Auttd_t4^)8&#Q<_Q8|`^zpc-
zJr$!e{Z>WIKfx+gAQkdfqVqqFwdC;u_TSaeS<^o_-nz$mt}NXt?UHD*UCW`yf`@u1
z)R7)8>k%=vLZ!tQT^1=rU?NuUaW3QHA)FG`iTjjic2mlGO7>)C*Tz4_K|nYpE$GUN
z$^{xemt%{RLU$8i-H6I0RsJbTX;<9%_C<SNC%U`%Wt=T({@al_CBh~D@f)Ox#Vk%5
zDs;#W)QOPn4{mIFP=dY*3G~#Qt+U?8!2h$pyEJ#dg!<R;^-tFjgc!%TZVFE&Dr+?n
zd>^Ph#{^WKLt{V-V&|tq=5CN%YPaw`4&V5NV8im$^;MF`*r-^(-g{My{QpJVTZYB8
zZ0(|rTX1)RJHegc?iw_>)3{4;cXxLU5Zv9}X&QHzkc1GD)5+R<t-bGa&U3%>=iYxk
zdzOqERkLKgquz16hz6=`cdNk^mW3v^WZ>Rp7j<cTRm40LrTr?`t;yr9YP;fz3VRlR
z<Q<CY#2A+Go^vGMGYO4xks-Rx`T{1L5Y%eq@D>;N#B1g937da(BTP}(S?m~v-;#A>
z3X4+e(78b0bpp3@GO=7YXM1r%0}vEVqsxl%y1!~#IQoyL-ru$fz@#n_KGr%ut7Ji3
zC+&elxs=lz9!a~Zs4N5&<`iw`1$M>A;_Gbzr%vm_Bp8j{ei9P5*C@b;p1h)Ni;1Kw
zL`8=@=e(&Hf`M@`#zya|r#&AI5M98#eFod3Xu5S?l9_IvAL=7jzstF9YZP3#TeH=D
z-B3jB@lspMlIa(93xw50`fSz}t!L97<5^p|rS!@#N1#fnXA~3NMe978G&!zx*%L}N
zS3$&CZ4RWvX$`rFPX=tLXh(w#U0yljiL>!59r4JFnqunF!QzoPu-r?kg)v%S5L`nt
z_?X67E7329;;yzd?O<IU0l5893_g~c^b{bq-6B>kM{y*cWuEdHQ6jaB9?CXDSHyyL
zA~)4_v<Y(&Rl{%K`F8}k;wmx8bSX_pxG*#gsA$v3z`%179I0q-CUHIIml^caA8`X@
z+3A{}mUR=gCZ%lKl)c&%C~4myBUjU&DhrgG5r_g51;W+-D4YZpu9^ppjApuPeSB2W
zXl~ql)&0P2QY)VyR^47JkEwjKf{~s!Npet-A+?k>j6oEdKOG(7yp|dPabDxeU;nXN
zziKZd`Oo?>|L;kKxIpwDs};m~EePVgc34i5PD0@z194u<k@&xNUQ=R@5(P+(vjX5r
zVZz|Nh9$X*6)G*G@1Ix%ihoWQaGtF!e&oBXMq9t+I*%q{s+jriz$sdrV!@NK94E0A
z^2{z`-c9Nt*NH1h#58`$wJSTYMt50o_9=?&!}t099G3QD2fw3Km)X6N;`dTr?8$1D
zHE~0DiPh^MOMWzJ)e&s$2y;okP`lG_T00jY%-g~DOrvIybFF9261ot?3;xRR=*6gB
zccdL0OnV0Y`8v0|wkn5qj8T?9%05Z2IIj0xDk?)w$0l=fqB10}43V-x7BO{4L7S<p
z>iHLd>GYu2`LtF<J9J4$zJQW;r%Xh4C?h?2%U5TWzI4jKGbMKeqtfJ6S$9g$MqP%D
zp#GdgDhE8X|LAQwS>Q}AGdvt|YN5GYQIM9>L<8whVs?F|`{J#wZJFLV{5rOZ@y;KO
zeN%VtpAP(AcI$<Ycr7M)yKxJwB@SL$^l%C(*v;18xf<AYByiBWGbce|O7YZpnP|X@
z_B*j5#R}LbGzagMC}^`}L(H_^eIpM)6k_rm2zx0xK<-o@(oVzXdeY8JZ>Un<;2v0S
z+)j*`@IHM&HIVCY>8KBGgMuFU45+rCDz40)h_=Zc6&YO+<%x!Qu!jUakmq*D%C!;N
zx~Zue0|V<ebtEM%>bieSKl?GaB)#D|@#bWCV}lqk+g97EG;!=2Qa)XJRL~S9SU&u2
z3|Dsi9=b~MXue-b(GXU=ao|k9ssT^uaPHFc4vq4PmCXIEPky%e8!)=ZD;dx0u2|Nt
z?aioO4m6|DnT9D4^Bl9*by;co2Fg-xT)%tlOH-VnC~ahMkrYdwgoFd)<AGPr)oLfp
zAWODJ3K~$SAaJ)f55@9Z-I~5`zJd3b-BDKXIqmJIdbL4<`;DM(tgSAOZ#f|9X)rL|
z{?nNFK+<AtJbbxo+=`I*z(6i}89lwRO7T4GV-25dZ+3<fCzzdN2Q}8#d8vZeMi=+`
zs&*}7k6`~PXD9u8_Qbo{@!@yh$FgIlh#TVXTbIE%w0A-4)QCN*cQ*^xG4opDD5!ae
zG|Qt)s?brR8~yrtGi%eM`b`s_QHH!DozCQp)9c>}wizCS%$d$sk2y8G%BMmbc^k7J
zF$!bGrn_9}2i136Ft17TaEU0Qh-y!ZHJ7k6UkDsBcPZf@q<sFZC(*NZlyo`dQF-Wi
zz1P>4>ygW~4jXSi3Oz<!<ai8DWrb^Cn0?KB@T@yT54{J-DE2Vdd^zi}9lmF0(X=;D
zVMp5y-%!(l`trq{wLjCHjMy9ugqT};H%DI??tf3R*i5mvfMAY%Paf3FF?|Qm0TSL(
zF=*ouc54*3R2F>v{rd!b;7@n`sx`^tH{}$BJi7y2)07sn74l9S8;ZpUWO^iUrsJJf
z+>aI9BBW@W+YIcyGfCJEsSU!Oz~V>TJ_xJV5%F#%VwXHB0}k`X1v6V(9=oTT_}28s
zcwbtGmj&{;mT^cSgDwe}a$gFLGisbZHXl0M@MuY>k?SyX=XSts_@WLBl8vQhn!=7V
zo9Id_TGH<~Twn75pV7T(X+(-=We|S0V0U<Q!n4$tatHTbmVIDBlaVKwp59f*f)6JP
z4XJ|F+~60!8v+H?<sGh4PL7fz_Zn^)u3Dcf=c@ZoL>)amXtiXBn1&Av-W%Z?=`_B^
z?PlLTRA~~LIkHfc%cB~Wnf`nd?tpcr-X<`~oD~`!KYnf+n97B%lMF`ci->G;$~lvF
zx$%AEi)@a{>Sd2%dY3PMElaTEpVM^I>6BOEp{pIs9Dnb{qrzosQJmMQ%Y)JgJQsa!
zDQ2Rmvyx)`&Y*1JK5*i*fee{m_bYt}<?G^9G2I$krwEPvn!dvVZtq6sqe?>+vB}$a
zK)LZn-{QUwXO=iPOGi0(FrK~be6xzd#7A#neKu*8xdA6IN=@@THHX;pO(4OuSijiS
zu$O!5$Q{0tePn(H8Tm*<r`xeZWb0EVNGLX94Gs_Vz?Pq-mIIX%(NiSND91@{#nL5l
zsQ$jWQX}&a4pES9;{Z)yE=1+~^5IAydBRVjIc7y}L7&!3^Lz5@b4))Lx`g69OJX08
zm-nK+aJotGJx?X$oGBJibyU<xBXihAvfoE+7ZZD}A!HEzK25juskK|ko%V$w$A+-P
z1HwjJ{+uBqZOr`jf~vRf2Vd&UNi+)xb-3%)TMid9#q<t7Zk@hVF1A=nBgXPug%iD(
zl;S=q2EibiFQ51F(qbDo<@r<J$Z20p9E+h{@#ajUBioR<e7*M$Hly-xu9%;x+z*3u
zd=^I~$9<l)vg)&2(q+5!t1<xEh#BFu`eZG5NM@X^x(b6!#U4$|_S8vR8p7g>{jSwA
z_gU!2816prY+9Smv|r6W6LFvUz4DEK?u7o$wuk#bw*j+ooP{#lnYM6`yCob>z#XN+
z+iVV7He$z`CSfRIUjCV-dMVg*1et|YZuRyvCrozx>A9(_ikuI5r>l2Nb)DZLqptUv
z<_t#$degPqU6uMGKPuBU-#i@7Y*Kwq9K&kk8cf{PUKFBlO~-^AH<Z_~4}yi&6fl32
z*kzkO24`<G^F4)Kp(*2A+E-iY{Io{?x*39dm7zKx=bmHDz*$01*u&fpCYx&`9TDnN
zd>uUKU;#(Bb{99|WSY)u!usftmDp)OAg!8s<`IBF5`k>Qegjz6%t=@!G~?@kSLKm*
zhso|>R*+M6Dru+d9A|fuJmkI8T+9(f(C%UE+<4T9mS)%+i(Wswh8RKWcb54eQY*Pc
zTK}|JbL7n;Fn^#c;<;pN8HTp<o{<<8VKU-!;-NQ3ah|cmzOcSMNH4yW4Y4*89h2`z
zrDjx^8eX-_m_>!A>c!HrKTo~2s9x0+X?mWTM1xA!=S|B%yh)cQ7B3A3i7C3V@!bm>
zUeW3<3)q7qz2_B-J>56^lqMDW+rI!uUtF<~OIb9J;-{1=i`FWpzdP7*QT*34r>KV(
zf@AVh7-e{mfZtB}5*PARtE$_4oy=BL1cUj|v45IZ6|ZQ?h8E~n&t1>`?8n%%gKUSW
zZ3P97o)fk4#nQ>kZvlyF#!=I=gQd}WL_RG2md9h#zGc47|J>zq;x1|x190E-eOi$*
zky|5?m<1BJ(f#b<XO7+sB8(P4RMS!r6@pR0+p|wI0936*7b06E0)fM)UjU^lUhx{z
z#^E@5IJ+u+5`-x_us|`9x6zA&xf*=63yeht^j{U)=@vn^=-7}+D5Je)E|@`IvdKoE
zwypeZknRuc@qsjJIj2wy{@e`}8B&k|iPBNZb+C`kk?;15B^|Chm6l%yO@;nv^QS(g
zy5ms9mG?9gHmfg$B96byfwirVa?wLb|IYE3dipn%gDUO0mr3cVj#8GCu=b$Ne3xgS
zlZa{#J6HsEO6dM<_Ask^6_S_g{*?(mezG#LJ3XJ^=WM~p_vMSgpbbF_L4mRjy9Rq0
zr`r5%`%IA-T}ZzRYWX$LROpe|(arwpE6eKhhSwE(Fl)b`<vRJ{eeKFk*<Ija`~7xZ
z5hP7>g<!u8m-C|gd~A62VyUw#Jo;3_Jl&9CpKF-p=rCvI{9}}8+hb9AZ){_K(~iNn
z!_G`E{=mG%a-kj6mF*Fe`i|Z`7Q!XUoEwCVrK3RP{}$DoEvs#;QFr|-O&jh7**N{4
zVv-}H)|xynv!~gCe&=pfz%EjgR;m3|_K_(oY9Xm~6~-XdLZXr|`=fH3(>CR2xOrVt
zPj~QgAad%+5jL6_&fXx@pxQ3EX-VFkaplCnk5_*%U@isdt&xwl#8Y0cBJ2)ffu!fW
z$w;3}it5gy_<7VglG$6ygdRfeXn`bSEy~-Gah??Jk&=4!C1JQ&_tU1`d*Elplj-*X
zX5l=|u`fVJ<PW;}O1*6rf@_{8cSjqstTuw977RyOU7BAeL-k0#sHwJi=^VuFP1j+2
zu#>UcI8iGeV-Isuu#RRH*}8Q3d3qhVPssfYTOkZNoIF<_p9F1?K*5X*Al`|$E+dun
z9mJ%ZS2z)>ksWH3Eqa{xRN7rL=9d}WczT0TR+&eI8S>{NP9F9q4#yXL>MrWTA<gyr
zp2gA9Hj`?Y&Dd<^b4$2Xd~&!%1bR@Rj0>2D{zq+jP56^`8=yghSEkG;Rw6Tc+9>+A
zqj;gtF%2NJw`BTq#0whyIOV3S2;-GTi8?M|th}luEgEehJ&AQ8l!S`fjaPkdg{{n(
z<P1`hq^}c5Oz4lrnn&einxf*Ju4t~-0l?Gg5><{7hFqvLj&QhE9z+SOb)VONbpv9t
zT4(tO3JUE`!n0UnJ6h5ur%2==y(m)77M;m^J@=x~Ccm5V)n;)7+lI8mPZtt#=1)6H
z<<>AQnm)$skdK5EGOa;A_d%B?1ePKZXIKD$AiPvF*fD?;9Tvjy8>T>qbroy%FCRjM
z|3P54KSUh>5wQT6-$CtvI}z@oedNJhDx%G8z%MWygMN=bQL>!!+bp9F=N1w+_L|(=
zZ;h+_`^-XM;Xg(gT@2~_cf!4Q?ZL-GF8D<0sigZc=2kRjs!CH$#WUv|uvA7_E~OHL
z+NCfA!@N|g5FWHQ9(yeZ3L3&pZyua;dHpK{T)CpeoV%j++3Nkl*RL{?Sp6dB+{fXQ
zGK1FyFc*?$){JwwK;z>ulCxbE6zmRht4YG8Q{(fBYM*`mh7IwCrTx2%-rY{wneVtw
z+g!%-^L`f4zNtAO8J5+ZKX8%j9BzN+?%igc8MTsv_ON+O!UV3^P}b7wu@@IIQbvet
zag(OusRlJ`-(YqTR!nA289CAoji-BQC9&J9OvE%xx;}Zze4B#q^4G)b8L>a~bH>cP
z(bvE_v~MNs(Z^!Xl^+~52T`2HnUj<j7cYa61h`^J;u4othU%~G_@i3ST4_(Cb#lg!
zs&*IHd8C%`W;Uwk&aE;?gAlsO?)iGI7GhnfNmkGBL(Zh1;4c&4oq3t8-i*gcQIVCx
ziLEYYEgB#vK9S8<l9ZmwtDO)vSCnyOJ~=n1vmYHnBDCv&F456rvL8O1-8dRrI|as_
zS%CPl777|2`LwZD%hq6zk$qk-#C`0tf>n*iwvNUbAw{iHR)fLWBq)XkaPPN1*JXCn
z5o9bZH-YvYI+?IhorZlIw=-Q9ETS&EU)*S-i<dmY+6U!X8AgV1CS2<~GC2x(qTHl7
z%ApChWz;z0!`|3uSgq8Jfc!VETRFX*@_ih=KJhVqy1KsVb&=uH=C8R^Aewki(rdd~
zNxF^+xX)_Rp7F|SC7-gP&33VslWM}z0N&MP)Wb<qcQHBHTOofQ=J2CTtR!}?6A^G|
zr8GO9G0WC?KUCLwQ6H5#gOjf}?8jhKF_brc70Tfdm*l|Iu~NN5W-P_)`ss?2Q*KGJ
zNO5Bm)~Rz+fO4|)^>);kFM$Z=g{F~Fp=x7_8$8q6ZI%rJhI_3iF<05@Ta6^!t=JYm
zJ$tp4d8;OR7IJM#U6tD!Iu7wNYF7d>=gP7b)OUmuN&OBO6`PFyh!~;>5!Rycore4T
z^?ipwzUxe%ScOS-P_hmwTbj+Tpl*FtESPiFpEt`beWpFAHS@knqf4f)J=1shYBq2>
z@&Rx8MxMz@JEY0+(#b-p(a8!@(~i?pn!Ig@I-pXv@7+{dFqg7xE;d5L4$7151zk8a
z``gHC3pJC}t&DetzgycHwH7j5W3bTEpP;jEox%IWfx5!kCuNXkX*(n+5On-Vf+0l*
zZJx%^FFC@1CaATyc!yFKV~3k#ry}z5!d6zdmu|?bZiBzr>#&1ljvs#XrRCAz3=|LC
zW4Q7&OxnH26KF*u1GS1}Lv!%QynDAXTTr1)Gk7=bcGMVYBG64O;LdzdzMfgmiH#k<
zIX%nX<L)N_s*kG2n^u(KQKw|U?n=*D2<g*dsbGn5`NEzy(MD8hF}+Rr%_@`2JSMUx
za0*i^tlCQ8ta`p*m7dyZliqF$69eOnjE>#EZONj;Z`O4WZze;RS7ow`mli9zjF&#B
zhf^|Lx38f?e`;}>`ouG*6XZ9O<5nR4mVL#eO?mnhnRUAJm>n%iw3Hr7G&>yZ(iWfN
zcCU}~q}zSn=H+HswQb1B-Nt;m@sr6?cq;&TB5!PsIqaFrZE9~?@+*o-SgCHCWYQ}#
z+(GH207(*nNPzaWpvZ;~P8h{fF-&BB3&dFArl_7(-aH41Afm*E7Gp_k+3e6^hMM1T
zXo81d(+WRe(M941RL}KVa=+Mixobwe7S0Df(t%stp1QN5ce^C%AvbW<p%J0lJwgF?
zWk2b7l~xi8LZ90BZN4e$<M~xVB14k-UxjYBd3@1dF-_T*t7!!10ef>CIhnr@cCwN&
zTFOw)B+JR5xY?xG!>UwUVmn#W07x6y5Jo(Ctzs=M4!()}oV~V8dM_?ArJqxiaro6a
zBWL1bWE?o*)YMT2SG1qVU`|cQM%Kh6m*frCLbsk8s39p=88xN~MUP#dKny;PjAgA=
z8Cg7P*n2tL;N;3-hXB=iWuBq|ODtB?1GpJ9uj{+dkGGH($E3L~rw7+v`llCxF%e8x
z#YfFh_E$8aBU;?(yLX!L9Ig5l>|rfOkTZ4VE-hP^I_v1UyU&x_MV}5Up9Z18#{*zS
zYi?0nl8kq&tA{VG;yQaiFglL7IU)4j^`ba8?ePXBbWtq){Gc-c8bQU!koPz<>WSZZ
zYtJ)OH-*KLj>nPuiI>AZp}nrnPsyS~x&wJHP{j*H9C>n9Tzgu_{f#V4#k^e8J3#?v
za@J_d=wnzMNqI^d$%rFuw{K6aREW9FoFpr5E4SmFz)>?UNfluM`Wa(S*)|s8y}H^*
zpn?48YfF<>FEsGjuzgl$TiXV@Y!LF{teA<D6iUDI?)qUAthf8@HHVeF+x1cQTD~F6
zt|_;f_C^`Yl(Hqhu&F+1&{5xdhjTsu7ofSESG}7R54GO6F)CYXO9|X!`7Z3hFf!Uv
z6+yo82ug9Eo)pHdjJ{Tsto2+uJ$6`8@jYgfhvPSw4#NsyeYYVyJCEX3Tvt`!7qy-U
zz<qb9m-e!#(t+#6@#c4+GO1?@#Qnno@>oXU^v7ec>llKO$i>2Wid{#LghouH#De#o
z?`!h9v_uv5p16FHeIZVMhBk;ues5_L)Kwsm+?gy%)ISfuQJWlfYLP=3kUtBnzv;9`
zH03{g0x$(^FL{bg72R*JrWBAdbZ`Ag{Tmxg4O(|1Lx0WYU2)Z|>`zZ`Gj)r39$<GC
zAgtDp-w#Rk2b3wJa8ZB!`zk_VwbODOKO~wt`c-e{Zzx7k3WF`^aN#eU2bc(W4+GhY
z<bb?>d{+mc^s7MR1yVsCAX|}Ir+fP+#ODPShtDG&k2yTmk^fZEsgA+mt@I=NFBFFX
zApQu!^7i(5W9F*ZVLn1#d-#R+d@c{9{ROZ+S#^m0jpQ6ITttApy1#=d5J^=a8vPCH
zoXNsYEkhIrf&SPa&C@J8B!Zwu`}v^m1jwUo&_C}2DcnUMuA2UBF>_@Ue)dls_n5ac
zS6w3~iy@y1eY1e#MlWcn^)?-k3EMbm|MZw!&|ug_clK{Lbn*gu(IghpuP8<HMYYi(
z6Qusd{&)2{4%#Y(=mgHr+TwqEbpHJ#kP9O%rE8XXtS$8A&TbUUa?C0iu6^Z7%Hq0p
zpIApjN80Ys{u<R`Qn4p0+Ud?Srm`uPsMsvJ`kZR9>XU9NNb;}Cg^-P^r222vI5F}3
z+m_`C1T<b<A0VZQFoY=O4^q5B@sH4FvVS2`PYn=}U$HTM;QDX3)K#}5R<re+0hxpJ
z@*+Fyl5VVTeRT#?9&Dei|MOa-0GklZx03tdVD6NcM}<iL*@FZ~NUwxY`sCKgKc1+k
zf<|m2UH5^1BZH4p=Kr}FeTD;||2;Tl6PjSX$vI<S1T#z-s+}2j4+}oB2li7?6lHda
zDwB`^9Ng+RF|j#y5@MG-*P~kSz6)i5KqoOOCp1G}m3xH2Ir-#v%R8qo7)XKV*lY7>
z!A2;_d;1s|7%vak7emU|GsAa2IWK6!WC~YT5B)?4_mH<=-uj&-gC|Ig%6|dGPVe74
zI_3lB-==0j3?H5z!1uq)6DI#BJ;ZzeOyq?_zx_*{($7Ga&;9O57pJfQd1!o^p<#i*
zh?@dWJxD&`8jTNypZ(d|+UD>IW)&i?3j(rOwtWAp<hxjTO4}UZoP1ItB4Oj@;YNd|
zE5R005%i8M*P_Q0DuOjv2P+xWmk)l=%h#7%zS<DkB$7dAiSpF>z0ov03zBU#gGA^}
zL~?gaUCl0kz32MKoS799N6W#HF_aEY((-xTQO|K74b>!&tXZ;EulL;<Z<R7Z1*y(|
zP2CDM4ft*qb%;GcqIC1wzAVDM#I{3Mq5cHJsd(P0Ik3qX8>&f_dO%~|%bh*}Bh1TH
z;cOxO2bwNac|=b;g6dB#!?zxg<2-|CyUPT9s=V)3v4#VVg1h1C!ds_^jeHSg%jAfD
za3Ybl-8vVk#FLiW%SO2ky&viZRC#z$h%(0Fd1t`o5?88zsWWc+R_%Ar?Y$*g>aBi!
zrFo&09I;=3cu46q_~b!T6kZ-TK+LuG^7Z*|t-c;-l@{{3OuUKA{MsHBVLiyJwKOO)
z{*rYADN6+YGH6Z1XsGi1?PN{G^$e$+Eab0*?4EWRwLb#eWXqGoc)Uet{IW+X^KY5j
zM)wuiH&K|IG6v>O=pr-6I)QPLSRS2Hn9rOK)M!MO$uW?YBn(ads~Dkxxf@8)61y(r
zr0(2Tg8~Wn)o0JmiubDDh?@`$m3+}kDj{x+^#xsAb2Km}AnHL{j*Rxm@L-e9`aCb;
zq=z}5a?=>sSQ(3o*CbmNJ>Pj@^-Iy^fk{xOlVbFUPsjD%^z9H$lXLqzg@^p8hUn>Q
zRX#-UbeS2Wt=ENrl`VX3l^idFrb{(Q*E8%P+6~deFcARZX3kaKGC5uZf%$i{CB**K
zPjfWrK~q!_8vo9p&vuai$T=a^Nrkx)Uu;IoSa!?U882obxu!G^P3bmnvJ7n%66yu1
zmQ^CcKIuUCS}2hXb;a!t0VCkYorX}V^DV!*^Oe!&S;a&3*|!~_&EEn-6@K{LcH!?<
zkO6R%^q|uQu=#P`>DASDHy4m~SiEhZU`eDU9b9%xUqO^T5mCk~9Z0KXi~;p9=c&y2
zN7;YnvL^rCiNEs{K`QfGx%&OR{?_zPx%cmZ^jkmhL4WDzEL)!c8)=gyGO=fG7;17v
znT`qot(bz`()aTW4c>;Ij3rl8id)i6pjNHRMJaVBFbD&S))hXGQ)C>{7y}qIT@sQm
zP|C=k(w2l$U2l2K;j@Xp?EV?Lkl4i}fBqCSKUD?we5Q|zCQCCE0K{iU`e)r}aQ)H7
zKgl5@{f~eoL_}Xu5mX@~-3wvk<q7g<8`rRhUV`}o`%PSUOu1$tFsBf}lcac1_;j*C
zWR)C~(SMav2oJs~QlzE#(Q>;*JgfZuEdd}|4<N%0m5ckTUm)V|;YQK~qlD_~gjV!#
zu_2>9fTae)^dQXm`|A=)b>Z`vn3n*4#lQq%#jUuVQeB>%<O@KQ%uy9=X~=;o9J;p7
zx*2RT&(qJ1$rqV0RtJiw_{TlLURJx(H)st?;fs{mVSLdp-44+~z>0T4UXN8Han}ub
zj|bxTdJo@v5BMvJd!KurFYI-AmuU&OJvuRr+It^BSTBbo59UVh9CUtzE^Wj(<!p&-
zE6x3Qg-s6xchVXJfiq8!>W+A8+Ij7|-`HI=N-j4qd;-bJ8@NVKWUnzcpiOeq0$<Hf
z&y(t4A(x$giMQ(-ja%D-S<UP6czJh$!8-d3FuY28=gl*q_mU3wj@udp9z=-YqeYs>
zix&}fjxl*0xtM-2C8;7nqh6mMw7j6jjy3uP&@VAs>@b}i792KV?nr0HtzdUEc-J}A
zNeP|=h_`9!M7b%YJ2^8l(ju|CjXOyW0-T_BvH2}vb{q7LFWT}ieCr*tyN#5Gx+;|%
z6>RelKRLHbcT)wae|F3;ty1-SBnV**wo>c@z6!QU9EHvlmE>mL;4YSaOuYGY;N-th
zKXY2dUSqJl4}H*jb=pNfX9AnD*M7V{RQgdNox>QL(Z3xBox+$W+g6pd5D%nV3=f`L
z<;}0JyWaoG2C-MWG|VW_8Ln$9@V~>x+m;C#PjcKZCNCd(GEcz8#qt`$(OltVgl7zf
zCQ60W48o~ZaAQY%)-SZW$bM*cN`H7K$Y@A#m%XTyiyPxAIl*VV&@n)>!zewQx@ns<
z4W~B1EnAKO^#(DFaEgVvQ`lWF|I@Y=?4B<S%kFeW42N)C;x%(aH(Z~ebT-EzcMQR#
z6xR_kB$kCd71ic&slj!UBB0m>xu_DVvdJiuYxbSZ%bCSNI=}RYZ@{4)$rI0D^QJtP
zwzTP%N(m@0-dakr_!6d!-Xj=LF-TI6QNrZ~%6kFoFPOdT@}7-+lUdoF^D=Na{}~<F
z{$Qz~z&xxs7lqGj9ky*%=WwQ=JF&*;F#|>r4?ZV(!LTWcS6`@XVra?OZ!ii!UrX1J
z22W!1C|_r$`?^p2cRM7cK9DYsvJc19TpHNeWs_P~mFuwYuJ2b@c59YHXM$*6dz@yG
z1s$=xv@s0gLpWlOuFh|11v;9j1r_3_BDz>3!=uNgWeH>l)mW3}aR~XbA}J3Ckr)au
zGu=p$(ha9V0+ePQ6n99w45zxU(Szq)ZgfY+_PrJ2*$JDwlNhZMOqpkQU&#QY8{y!O
z7qmnVv2Z-5DyNvbQ19B*(`M&*jpMf2P$7=NLe((c9IJb=!h=$nq6W3=X7amQ_(&9L
znx_D_QbpKfdK~VebiJJm$;a@IkB-+5m>JpoCmoZ&06iSc0o<eOKD4rTz;ZHvsjZMl
zu}$_A#_|*%%!^tx+^T*>Sc)i*{(gOyYJ--qpTR&0Pd}9Ik;kmY*4JbDL+>sfnQ(t9
z=Eg_KNpwUIRwVV5ozJ9jrqh>K@=-V?YKRmPQ;9odFMvdT0di94LXx(1mCl{i-cZHF
z+8=T>*7MCr2=&`iVdzlyq3kk9?ytj?K?4e`tiq$xRkfubbNAjoU%2I%LC=~w9I@0L
zea+#y;#`~M?IIMgvg>1Mt(#h^WWOrI_Gq)hk!g;uLK`d-*$xKKO!a&sfE*N;A3S8c
zZ+}v3Goc<%mnHa^bYBwF?#3mh8Ea3a)ihVnG>4&413M;@$LNvNRYfDPGDYcA)5jP7
z#jH$^gGutyKQZaBROFhM@Y9#E6F84%Q-S|4d@F6}i6%>l5FyT$0Ov>KJLd%&2Q2q|
zDPU>t+jH-hom3a<+aJ+jIp1D7p32%JJ_B#uo}<eLg_+-%x1hbqq>M_p%nlxGs9^hY
zOUSWOPAICCHp*+-;Gsz+Ac1Zo_)?=1l}+Zm0jii}yLN`(jNVfV34NFe&nxOFzrNNt
z(TO}<Z-zvHxL;_u5woLwFWe67WYLCDWMrZ^GtSBubV7-|I~yA?${soyjr+FOY^&7R
z<Q|i(*@M+&6(wNr?I%3?H7Z$bQPPT+NK^4892APlST6Gk+j;^i5?a8)7N)!zXz7r-
zc{yM%D00}%R)dUZFbABrwI1AV6yMksowg)GIlUrb22<mosnX|+L$Fer4KieLrDjas
z^&;29DYEGfdt&jEJY~j%Nvv7p+ILHZM`8hIITF1tQJP|K*s`+0rv6&&qF#ZLUW}SC
zmd<gBUE|w4wMr8}-9;T(4Z-?rv4a{*u&!yySd~n<)O%)dZ?P+FkS3d~KaV^uJ>t1B
zlxAdBbdxeGWDX4yt-+$yuo8M_kcS%~eumrhYJP%_7-J(exo);rP}|XhI9rR>$~Y%{
zT(+<{lGI5Rxd_236(=jaMETtE@UgT?m*-<+!_kgMqZPq|%<MpnC)(=i5`FFqwq+G-
zQ~5j}vN%#iJ2dy}Y-$4rHe9DE36=hdkJ%wPfe+nW!|D_5wpTT&vdx|>8M0%PQIKo5
z!~Bw>{Q`lJI;?cZTIaj_*_jWexfVYjx2b-9%lrkn{yEx^{fxG1I^*>fcC+@nJA!#H
zo0FHAiFGMB%Dy|a`|>h7IMtqnL|>e7@8X1({*pRN#ym_Q0*hKAw=A-Tw2wEuxlt?3
z!6-|d^1Sh=K_RXs#qt1wzA3Ha=%j$q*bD)m-0^Lu6sTo9Xaz&cl{T41U@DI~S@RG_
z;mcvoS-Un?lgBbR|7l;mBnEh(&aYD^iDnu^LPqnWMC?tF=)eN2)4YTl)G{Da0KA=n
z1<sk-0hQ}7Vl`#GCotpND)Ec4+-0{XlJ6Fjc0L!u6;-03VbB(!p$Ni)aMT4V!xb+G
z$-Ct+3kY*R6hn9xXV*|M-ToXlQ8eFFvf!P;NO&)N$@ZHAIye6ePsP{!%@%0q+IGTM
zmxvZCdr*pba4So*7<~2zvcaolO_~1qLJwd#E`OoGKklTog}S$9{REi?f>#augxM+)
zWR5?zDuFl8wAWrx=~+{MzggpBgr)8;1u#C<jY-3dv@DP-1t`HS{yD6(*oP_auzv!;
zT`{<sfiW)6E*d5ZJO2bBgMG7`U41dZC~}5*a8nSIVptoCdF$f7Ed^{B?B(V7L@lV<
z`Nn;MU1pXME&L99Zyy=5j|%V*_U^5}+WXo+>ziqy->vVG%=36y0$%s*&Wm)KX*{^=
z?pd&&Lm7j2I_eC!-@2Pqu2O@kq!7UA7UFkrIxT0020fZP!T`S?dWKA1Ued96as)PV
z@Z1UZ3EP%>6I7*g#6{WY@=E%!ARgjTg>`T5iD#bSa$vrh2TAX-T2IZ!u(Q>mds`cj
zpKCVnejNM;;n3@%644hN-f`}=6?@G8`O;iLxmS8*$l=D=!O`%Z)-;+{Q&dz4qlou_
z`7Po#e$UJB(8@z7$u(a7Me}xpulDn+vF>WD*5W$05$d`sKFcJJrjK)I-z8b4wt{aI
zFH0$jWnkjhsE35O;|gj}K9IPZLWo-uJmdD7UO7CTFR`9^(((_o-C-yU7$9w>G~l;f
zTj`RKok(+_$q`a^2$jl7x#41B!XU}R=m?p9nOfE8@Nnqqk@GM*`IbQp?$Br0zzd*m
zQAxnfjZaNCMA4SsekjqZ$8K%S?osRVV8of73?}87-B72<M8u2%#;#;!x38^abi;vj
z9Zv-Fu8IA9s@ciJwrZGmw?H&098A8cZJ$Oq90U@Y^j{Ng`s}$4o6)37ZPsgS+)MG<
zYFq}FmtQ@ozg+?KN<ZNJyyPoKcWHeNoxb*NhkpmofH3oU+nF_+`{qB4R==B{Hp6Y%
z7IeAimZM@|+yi}lL(9Zc_<57iW#+CY(?ochQ$%YfpuwyA`TWtpvmOAxa3+cCSpA%Z
z7N9%@e}7F~%L%G~Z-+^Hq#@m|I~c>|(T)_~bdl+Wv{*@d^(l4cyU_X)-Fr_~r)>!y
z<-r|gUh8HmVQu}c9Sp{hbhbJYJBqW<)48sBEwX#HArZ_1kVtZgV5A}IO((+Te97F5
zYUs`Fl2xV)u}R{sjjVh@gu~|uydP*-b3HT;>w+^eOazS?94@geXVT|gN;TzC#el;y
ztSj=8UH0&({3J|d!ibe2ryQ@Fch9jOx+DeKqkUHp*WCS=ePAhPmuWY}r`g3pp-dI<
zT1{2a_LIn!u&AOXi->`dc%W8qBcmJq_JZ2<AK)LnA4r#6h(dX5QV`6T1HK}^8M;Ja
zbK1l!RRf;pj(DpeXK;o{ir7}OW8T$6;l4tOFH~b6UN*$YcA-5I{3_unYuD^)xaR5*
z=VY$9l;Gm_*+?dN)SDParF4{s6se|l-^7)2(ssFBw>)}8Krzv6w;Dq%CpfUw1k+7}
z(BsysM#VQq!FNi2WFj-d>p2>F;xTW+v3N;n^D8`6_*g|4SMuWYwWs>j#)Ic{Xmb+@
zK~ErO>Y`@J8nt@RxLnWammm81j&;2awK=HD2X^0b>UU>mKKhl-#65(qSP#5k_zExD
zqm0REPrR9&@(NRn=QalpfmK!*dW4TwjU&f`(_sPQp%1~Qy=`GE258c;tv%F47y}34
z+mn7ztIMAR@-n9=clH~WjIHrz4>QvJ9_hhQhAS{5smwrVijiYM3nqdDmm#xo*k~F(
z&+6snGhFxSPj1;4_UcgK-#1)y%qgB}iJ%1Oq*Xae4JmJ&ZE+}>;eQ}GN=mbxKA{SF
z6PWe}fC)Xaa%$35aD9{eH7g^p<Bo^UEdJeQe$Qo{-}}0|=~T^7X%Q=`*i4zL)@0)@
zkA{>OU&?ICQZGVIdZJQ5{%lxQg@lE$W^V)O*~c$;f|*=1-`KorNF64w?Bh^*lgsK~
ziH?y5w{co6jB~f>ZGLgdP+DF!t{>R!bx)F<)@GAyb+yf!-tjm2p88pZ_NVqQz_SGT
zvjvHYWRvHS<1fG`_FsTjRbi}&owUB6^J70ZL_aIg1^hc1%mQ=Xlm0Kj)vFIzsp$Xb
zEdNn5yqUx37G_+A{9cDYolEfxC0ZBJt4<F4KsR4Rf_@+wsdNZ@I%~tBUv|m$0b0=k
zc{cavOyp^9o@bJNJ=#s&OdZt3nm2Ce?)A5=&Hwlbv901O$b0#4!z2%-3K>0H&HUfS
zw6vpzwlSAi&m84I3B<`Y6^8DH8k5YB)oVX`j@wZ;<foe1y=AKRg0gPEI-S{lqKpUz
zT5)9U7FRo@L+NkDhiK;rQ2>RLZ@oy|S$jV+z(_rj9eDO9MGL_n96d1V^T`f6nrq7C
zdR0b`2c^A1kcTF^CEfruyhm#FOT2~HK6uE}ET6lN%ow^i>Ge_=xVNBml6{+l<8rO9
zESflDKHr#06yH}9u`)GZmbEk?BM?Nw0h!O&=e&XBj}E6LWxT-S9LtGeb<ddE5Oa{O
z8Mcw`$M`+?ueRp*?o)0(iMFmZR@AIG>EaC1#uND8dD(+RNx3mfC0pKQ<?AU4O%y-*
zb~6B96~{}a&D?9F*oSp3xDLh8FnnpiX-hB`;y$c3<AK(3SkP9*<O;SA#HELX1V=S+
zZs5%8a4KU;C<{N(w=h^ryJRxIX|Anji`i*<432emlQWpHDq(3d`?kbse5~Y&B>zUv
zlmHt;O>=FXFK8H-J-pOR#J$sH__9u5qa3froa*7rQ3jc|{u=@Dt!|fcsy0?6PkhcK
zw(RTR>^aLP%`ijkcMY>@YGWz7(23QV6O^Jft?UVcyoUF*lsT`zBa*%T0&u%ovnd&v
z+%HZu4)7rd`Hr=X61Tn`^Kid&J*+kHzJWK~)z^!ZYfCrOCEoD#eC4P3th&hP;#~~X
z40CgyA5()#oJf<*phGqc|2iMl)NEKlTYxTa)9MI{-`y@|re?i5WtrA+bbO1vznz_8
zl{(74rLbjz1Up4MN0^OPjKRno8mtT!O!d&3zK!N`icAvYzjSDzD{$p>U^%SgZ(Rev
z<m9J(uhmoH9-NOzg^NLjgc@`oh?DO_3`?U)V$Jv>TP_5(F$2F>lw*Gj4V{UAYo`Cw
z_x8$8{=@1?xrm<WXMDmB!k&;z^1ZZgpZgl^`oF92w%s>U(s>>|elYURKj~zS+q}|O
zU>>OP*Z=w|uY8sGwUVSLPa953hiQQ{8;NHU;#8Y?he0{5rg9*$)&RGXKA(bk<4Qxy
z4M|-)?o%I7Yr^4USe1z*c0wcCEqXlz6RXs6O3X+MiKCOjnvg`Mmaa&_$C5Rzlq6kF
zL3)B$w1Q!XRUO&(J`9~er#X6{bBwdY*8s6C-$(OU=al0%Cmu&>X(85eJLNsU(b<mr
zGBe5y4jMg5Y22lS(*buelQ$Te*g=YlQhK0Hsgj;>!N)=>d-HC#kSlA+Hq>~>4~DG)
z;_dYn34%&5J_=RNc}qXcZ0KGKvIv|>jPt&s$hM(p#a2BiG3Is~A-kOnjFdU`kE2n=
z+*?xJgs>O78vZ8YgAQIoy^-qh{b2DPZEehFS5!m>7jE<SzEdv`@U%+p@p(R;o343+
zZnCg*{x{$QSB?;jMF9Ik@l@50oPE)9C^ys)5PbD}@5>^E-W(IZL!vIW@C)!b<x=_A
zu6ISSa$q`;yaY4a2~dGIm-H5`=NzT(566gJ=Fe}6@!Rt#si7zhA=hs0^nbDhiJ`UI
z_Q@Xl5;v~&XTl;2y{>APNE#W^YduE~X?Z{88+Bp;vzwDQU8i(9cV#}CgD)juOHD=L
zYF%h8U{5zx80e(KZN7}@aF)p&gZ5+S+p7CTQTIdK7z6N~>NFN^eBogK_!dV(p7rbb
zX5i-A5`lvWxD_!rB=vJW8Zz=<02&Mk+)_w1?<!jOp+qc3u-k-SyY=T=r|LcDt<~<4
zJ)9^FOxfu|@IX_5Yt67mnts?voRU32ro;ddnxh@<`~;r=T*A!-km$DYMiyVY2V`r&
zvg9@>&3{h^yh|CKOdVFkO;#P@S0qA2g)6`}jkO51f5&VVVUO~bz^uju7o)8<2Ti!v
zz)ZZhYKxVg(=wR>XR^9#S#{Ha<d{8{(gYJz^DiKe1Qj02^hn4*#qV>ya3@s*{V5X6
zu<RVDu%xnnJ-UYCsKmxC(o$K+9uhEdj|-PSk<Y#w_DvvSG3+)??-76F58##HoYWpk
z;kmLAhqNG}g|s4#h9li;kZQ1o$vzY9$isEmq&-h;_N<#`q{FFG@rbT6qJ)Q{W@-2p
zagScBG`m@R^xnqZOc1m+U+6Tgb!m3viByO*db*S@m!NkJebIuJ`M~JFGCgAURr-Y5
z-_{Uh#B8mm(GL{Hwx|78<!=ZI1<;YcNhEUD01MCZs(<QlISR~^!+LK`ZhOfi*!HD=
zC7%6+FhJ%BSz!u2YMvaOg)ZE0m1d7Mgm6rBwct>!RY>&f#ZSky&|sQxMolV$ak)-#
zy3B~i0+8R5eEJ{J<0S{xR*MxTbZj{xkgGP}lS26MRBG=TAE{A4EI-(_P?jXoMI36M
zD}e9#(`=xdB=?*TwZCROV?EgMN<#-?_d}A>9*~pgXq#CNv2mQvzD;&{QoH>HxPKn_
zmsKQSTqf7g{k-Owe{Wetm{ASy=#nkIxvqM1c0x6t)|5cGrIS+2`0=ydD2ILM{-RiH
zsB3yXaeaoYF~fMF6Jp>wGzFs$d|j`@=qssk=VR1XBfa+R*INempB`%JowssM799^Q
z$byZp#l@xNhAPnTH8Cj&PWWyC0BZo04hu?s=UHtTV{B@r>f8Ck*4Btf)1T^Usdjix
z8WmD<6<5iWwq1g5L9l4MM*!Scl9R9uv6RO2Q10`EfUs&B4G-|nO4mul(VK04?maiw
znyF|nQ{B&VxqK2$?QRzn_LY0)Ldh`%a1s?&uT1D8wOxh`uRu&8c8vlIyq!dCV$}0u
zh$EO%IP!3>5!G?Z7?6no5W%1fz9oDCw-Y|uKm7L;ej+kyLWD~Em|_}t>>7WV^?(QT
zO`MBqi|Aq*b*LroAcQN%V^B*oKGzEI_Sj`)*ZtwcN>sYriEO2cU>!8YP(w@UEZ$oo
z1k)RS<(mdtp9<JYy`er1W%KsV9GaM6aqbRl+X@jM8|cn^cY!j3sY|m816{#rQ!N>7
z!e|W8Kc;o^DUim?GR(_-DcpT%BJ%UEhbrv)eeeeSC`b~s4%@(sL4px|XnyfM0Oel!
z3Yk^T8=n52BdgXkW*aG9SB1v&$gCe?>-~GWGnOntK&FObfT!slkZC7H;)43NjBoqf
zo?t_fF&7ga(so=LD$`M+*ar8nN!V(*2#$*mwb;9CbVH((6zLH?MGV3;bFT`SnjkYS
zvt$Vs7aZy<v?LI@)z;aWn8zQ(1)_<Iw~oXD(@heosPD=C;hMe~tRG39W0HQ5F=U@O
zxk4f9QRHTP%A@$ciH;O?pKcrZ$YaG^p^|~Jx=c<gHrrCQvaEb#xR4T#wHT`Mtk9M_
z>D&I68N&^3Z$T|SmK8OJh(+2Ap0b!|)+{XbQSjK9hwC@rV3(gw_-)GSA7d$Bnihks
zS0tyeyuCK?W9Z$9gRAna5KBHI#5G=qe+M<})%*fbZLhVIxEZwR|7c@fMLcyF>2N8n
zHC1)M^a}}QH$v(xmC7kamYVkn6xifAfrP!yrQtPPJ9@Iev9mB^G9JzhpL}lI*}#+q
z1-TD|It-Z3nVpWCB}pzBTqUL`KO4mNhmrbnSSbbrQx%ovyQ_xVzTD$|xy|%-_1^`O
zbfBhi;5m6y6*Lg!{M2*VX*bqCP}n?cgCUtp7FhRp<Vwzncy~j!D26-FgiV25xgnl8
zdJ94=1-}hYC<l&hEsjmbp}4&V^&iA$%c2aGQ>t^`w@*F<w`4aQm+DbR#W3O0duJ$P
z^UFqCR~svokSWQvfJTg(zv+)|jPdq7MAKcy$X|n@seJU)F1_jU8;CwNm!DR^kfc1@
zVx%Ai8Q??-T07&j!f|0po--#B(P|$nv2A*m3|#x*g1*FoUG!dHiZ2<Jx?(VMOvIf|
zO)m-~oUElsN7a)5S{cYJzHb8+gP_{$#bU=YIHxeT<;L;w2)gc>$THD^ApE=sM}1!A
z_N?ac7+T4LGQ8KzO6Idk%5;oTQKGxHGd1z&Pvq2Q;62{ht&7(=r2Hd-U1o|_o+g6V
zQxVGR)}wgWGy`a1dDQ(02F*%dU-8;QUQ5c;*;iZedj!66nVsw|+6J(3^Kw0&^0=Tw
z$YK!!8pl1>x1s@0Yc0&_SPEz;Xf!<Bmy}~>A*C?(_bt_pt>mSZu?`8wY$z!d0~>g?
zQjcE6F~0S;@x~j33|JJB$+8V(v{_o3$dcG>^h5pP2raBh(aryMmHzz|VNPM|B4M7U
zk$V!jqZ9mCsBaxb{LiD3I^gi4I~wfd=?8RR0Q_wz<4Fw)I}$&$Q9&CFnlPE<#qT40
z5N|BcV88U-ExDUg5?@`A@uIo=px2Rp`QCcrkDc#d3z?gd7s8#)5ge=?-4J62;J+3R
z$ZC;3k4gWb?szRg70W9CVsY=;!OnpYq~)>-MQ`7K@h&_5O|EM1MJ8u_Pka6C$ENIP
zy)<b{GlxfbtcM~UjBL$9P&dVVN%J80PIzYmKCy!-yUEW4bWoS?MZ?cHpRQS(bHATA
z`RFncSKyw<wO;_~yc4cR(j6hPzY_^U(g@aj36qhxd3;@a|3c)MyB&JnvHRI801VnO
z{P5Xk+f_yVtKY4U&|BcuO?}^Yt{eV}%Il~9PN4>V7&yIs>1ylk`~^5&>ORgd={gBn
z*n9SL^oLAH3l-io!ZMz7XyV)dt|z5xMp#b3?{@ZXTDG|(`TeXj+9&fWA6mQ55v1}Y
zf`UR~23|22z_FUprLt!ePxYaR1$?b1e)IRMB$|d_om)E%rNG+Vp*7*xsv!{}{2E@1
z*I}=dIh-K$k|mV{qo_rwTu(RES5GNk=#xOwA8<K8ljzCJw_)ycB0fM__La&bWbp?6
zDBs)Z*KIh%3OZKWeaq19d5h=xU9zxMZtw;gFlI$#a$rXrN&JM)MFhwR^>QIcA1&;7
zN_qQJ!|CfT*V^YO(FfXF^3*NCZz#Mkl&V4#-3Uj#Xg{H6egR}t(K#HUo|K0A0-HZ9
z(!fmT^ap4--RyGhe2x;ciwxUFoYBTMuN4wQ;w^)YOW+U!+&MulbI`f*xm`P}8vaj;
zVulp;-va294Bo(<(D%Peix`HDxgwwU&Vxx-;uFDmB(-J@_`C7H0RP$gzW-<Iz5h2V
z3y_NAhO|)swMknr7nf)bjmhD(^)T!(F_AThY5`kynS%mwb~$hG<A0!n|Bzci_(tq^
zU!a(ncm_XA4&0_iwEC?l2Iq&&SW_+5Roee}{Z%*{rQq+b`X31k>EGW?L^0oDy_E`2
zXLr#iQym~^PYp6tf}j5^GtW&DNkR0?h5z>7J+$<nJ@g;q{lE0<`$ZbK##Gi@%I^jU
zG$#9jd<qgi=v;zEVj`p>7(*(i!6*MUs{VI+{g<f!j73NTLwX1?bJUZ}YgffbTre3r
zUms?UY%E@GRYwvCNY1;}ef}1iJF<-IO6f#@8>KWNX78jze=@-+(1E#TB%=1M<F^Iq
z8^6E}Wt#!{Wk*`k>&>u*kb1e8FOmmE-IC7(29BGx`At;QRx$Rm62QKeT7r;aiAf_*
zG>KHE2v#H3bIbF*#16WGcDFl=Re^YO8hjlIa&bVgIdr)?4h%)>m7veOxK>#kiEO$C
zFAAva0&h&C@akS6YdQFo!Ynf~CIgvtuuKtIqt+ew4RSQeC<^m2XA@AW0Uz6`_!0bU
zmEGTw|0TnQwU#~dG(5q+lc~H!at4I+CdL(G&>6I5U50^pN4owdo2T2q($2_a5>toM
zcTwatv7jAnh@{A!pibId<cN=TKIhRS!{{OUu?KV+&gt)ijciKl4PN&2YgZc??;UNd
z!x~gY+WZ#UdeK2&AiZ9CkNMqN;+4jz_}Y|yY>R*oiF_h0B|}uS>FT@hzG=Y47z4M7
zUO~5&a+*(jm-a5;(wE63)8e|5GkGyCAks9xH5Ir`&T>p-VNt*)>J=mqqO^_D)!kL=
z>QQrj>v*rc{BYwD`%$+<yPD(#G4npuR8!p&ax`N(_!G!NB3G%9*idqFfpF$u^qF`}
z`D?8=`Ys-G-45Rr>X$kx4MexnXU10$8t-2jmYN%K`pit=$eH6Q?rW60L@BC4`~?f6
za)D!AAP|}(f$ZJ|vi3kHT?uPW{(5GD)PXlYy$<GVF}WN{akZyHlltolQTJFA#mZ9k
zNSrVLi!RP6`p_EgGjXa_V_q6t5+z)AEE^<r$Yd{NQt_!79&UVQRJti!#$`3JM$ux;
z0+N8l6>^#U>;G8IB9EeKLlcrU-Th3Kh0GqLyL1~#u~X{rncN4!cO5N-e+@a(9pW6(
ze}0PnwRECO7rMN2iHO;;e=7RFCT#d-p}vdxRWKfKaE`lwP%OU*L8Oy6p=r<`yEktp
zp;gew>y|UR_FZkGC$PiDlJvKY=D4~yXCO|TbU}w&=Srg@Z+Vj6fW|vH6Ap9pHDxk}
zw%&R%^`T51Mua3heN2!^P)6n2BYIqTpfXEX-mh%37QTsjqz}XSvs?Z@aFkC&+^_gu
z_V4B<SohqMEz&o5%(E-lqod7Cb(xTD0FgoRjLfd5B*fB~2bd6q6v_A;lOt-@G_f<t
zBc1}PG7<n^EWAL5j9wQYj4+|B5x($BBSVdKK@uSpK@3%59b?{Fq)*kFCArXq2{lbX
zm<9v$CVc+>AJ|WI*Vwvs;k(#F&EDn>${G>=Ap#uS>L1e#7!X^k7PzYl&9_{@T_sm>
zu4?$(HUk~J0t?Sf%}4OU5X{m}sFF<<nVoYu|Hse+S6e9JW8m>5Vy7Va5_G10<d<jt
z7xuU!08bzn$nc%>m3|;&0f#W=XWsFD8++;!5#MI6C>7!}*ry!(&lVs|9Q*@4=BuqG
zYx*xz^$R~hj>kFwMjqd8xH5@Lw4jtA7^L7|MvX~$MSTs1@XP25oBt8=ukGnShJb(Q
zW?^NRmCULAl1pm-f3S210wks2`T=}n<4hxx8KN_3N4{xm>PBzKtT;N?oCuq``&{|Y
z6^3D^k2UL`aFc=MHP2adDC}q$<XLPSr3;#3o!tp89p_40WFI(cl@SLUG^ca;?l|Pj
zN!cSN;d4bD&5Mlc@lHo}4}RLe<#~6cP(WrWq?G0&G%Mak!9ja3FRD+xvj_UIv<hC$
z?JoJE$ijcYz5l<6d+Vq;wtj7}f#4F{A<($Hli)7F9TI5Vy^#cWcPAmZyF-GzI}J1t
zpm8UG5FqF4oO52eYv$hd-kG&#{;Q^T?b^M&sM_`X9yyRp<wZw8%I`Fvjqzh-#0RV>
zR;%hpM`dNjG3K&77zJYI@F8L0rMbQ-12?YVyzA7NSeBiY@B_!yJea9JeygW=NfdUN
z_8~}!vrnxbBUb~mVzQs3x#qdcZ>si@q=s$yf*^@oP%UcO9sGWQ$wu-#HNM);0Gt37
zb*zT1L|HL@HOcRcq0HyKUapjbt9ug_L8LwkI;N_=*}1^Oi3!8w%#vS~DBx;AoQdUD
zsrG|&|I(>VdFQ2+K(|iUGk4Z`3<xS3kpe5TD=mpAJb@HR^E_(#+rj+xmCh6)M4{Yk
z4$dfg9ve~poGp1P;=RKb@}7`;9AtZAf-C*)nLiG%h!9`OP-8KL)H)q?$1-azec1=Z
zSeiNBOf7aOmEdS$42W-GV~A12BHt-Fee-z44a_|h2fU_a`056;IKN)@NuEp0mfcc@
zRBR1kJrbmA;CR>DV=kqs;;bsm6zdMXU9C~OYMMImh-YV;dLv+W<XF2$7RFangb>i4
zAW!bw9Y68bY<5Z&R5-uWTvlA;5Z>icF~R3PI#4%Dx<)$Ym?inlA)<SEq$sk9vqmIW
zJFe$L*I?il!HcJ@)s~wrT{M?ZL12T4Tk;jb+hs&wzuty|&8E=dMwsb|0&gm+{aiz#
zxp6+7XnS6z4rHiYlZ2(bJh{BoYnq8&WdhLF*4#?^v*ojqmV<)jM8pfPu2>)J!(CpY
zT?UlRBO4nI1a#!Lqh@%nLSk(^<w_=p__AoTeKgPp0v&(RiP93!91xeTkkBNt^Y`__
zHE_$vM9a%(YQxj8;RT;*Joy@vH4knA590iR7v$#x&nOZtQ{vnpdBw>97$Qr(6dsL&
zNQ{c6E6T2M$qttRFeSJ1?YbBlAM_*GdB8)>c>7GwlG8Ef2f<s&K$){iB^VFayYZMA
zRj`!4(1tt96F(=nG|_~i)f;r%9-)iPPbKW#&fNbeT#R&OCuJ87IdyP!D9itNQr85>
zP~d3mp9ltVrvEFi=oQnNNU>KVL26!q!7jhSXPZgaE&$CN{#MA!>Y-A}-!O?%)4>Q*
z!P$KYm5+Zu?@w0^)AHXqOlhFaq_(l&sc*^j=7$mK_dak)n({Y{jj4^xl*bs>d4`$h
z{T&`P<_UM5l@2-i^I3?y9&sF#;OV#rzqQqd4)h0DIdks4Ky|E3zvKJR)s8d`56lD?
zO0~pT@OYxA9ytKr01|*i+E>)LI0*Aie7e4$x}R5PP<X$8#O^vb#nhUGP9wRIsa?37
zwjOnNjTs!^-Q~b}uWX9*ok!#t4hVw;j#l13BCU(utSqWPTV~+OhESFCllarGa$;(F
zAa>~Ix_6u~`9!$O(Dl_BZmW*z<7@d3b9*^>@^(R~P8Q#y&v4GQP)xq}__fzt!mU_U
zH@!C_^5j?AT#ERj!#k}d-`2KD(V+_G%+|ZJP>Xnv0As{h!U+LKAtS1~s#w6W47K$4
zcAIq@OaTz#75WPiP`|0rQwO$>6jccRe1oE_iM(PxZ>rEUS&zf^h!iSOYsco$G{sL?
z+G(;8Na(;Z`@rg^75%ORnDB#-m@&m38wHKa`v`OMH?0F6Zg;?|mz8?*!k?(vBuK}E
zmoL;Pzk*qs`F4c42~2|lxJ7zLlV?tZZ8h3%nEJv4&NE;aBy@s2`g0%0fSnCxa+n!Q
z&6qHhXt#jcl;dkgsQr_x37}@>oi4zak(nYSIz?~!L)#mD?b8A5U<wzmXO3lJ8PK=Z
zdOzzkm7Ny4jg+|X{e4e6lPnXguk@X!bKQkqCEW_h@I+(_fsPrPL?<#EoGv~vuxjk4
z;EHd4O1CNKqkL4qYeQK(gwcxHc+8Y3h8;g8>NFZ2vK;QJJ$CAJZJW)QZc>k0-Q+fP
zc-_58(7WEZ?u$y_&mRah(2KK%2&Os_A2f`1wlNeJLKLP7AaC*ES!>~=8jJm6v^D6B
z=AQ{I|5|$d%WD14$0E>BhloEcDbxl9;hi}WA*$3@2{d<<HWGI@z{duAKY0-D@)nMX
zx8xsEHGa|GnD)`TMa`cqvGMuG$8*3<M_TPYshO@Utl1`kxmE*WKJgczQPW(D?maB@
zX9}eKf9CnlgFgYJVPp=$j3Vte-y`~p!%0rxiTG9hO{d9s25O#tkCPT)0Vg5sp<+f$
zbC=rYRSk`vlGii+rHSCVtSEcB8-{~r=Qw0s*D^Y&x?d{VWunAKrQmS0n7qlN+jLzu
zB0TIb)L+|MxL3-;)?X<^!q;k7AE^wC6Y0{6++BB2GITd{J0^3WEG&j4YyGMN41~9a
z;Ebqt;sz~jf((`=f@UU33nzHGR7@o}s<n=Pt3tKqhocpJvk-1jVp70)FN)@m;mvNx
zDk~k`j*V_T{O#Bq*tX6r(U4~_$ix{BNqBZpBu+<NlADl$HtuKYLGd-$ys*DVRXk?v
zc*5Y3>6POdin)hE9j&VXK1URpj6ho<5#51exlF5#7vnRX(8dNV=PaG&7QyxUc$Ho9
zpCX3)0u$?BR<(qby$9LT(*}S<x0hrnhL>Nw5!^-vMQ<GQKg~;MqMqf-MCTP`5z&X0
z&;r!WIe>X_TUHCOI0E_)0V7cjnH~uAEwk6eiolPJagKN948dz>M99!s8JZ}<(+$E>
zk)Se7x>8MopdiBxkn#b3_U(WTiH(U{ElGa!`DD-nRgGIC?SforHcF)H&>GBLR@FO(
zQj@0gLje`b>urQd9cB5v)ASeuKX#2@R4mbBHpIzhovAn4WD)1;O&Q56Y_U2C(U2sA
zTy--VO*H^_HL{4{s3sm_lN_<<{wr$F83)r_=m}->=Q>8Es6k7T7|k7eov<_P__^9_
zIW4A^xjaEt+H!;-mD&OtGz*JFu@67ns>Rlpf``FR6YuJqU7z~iOZ4;V(Y0q(wK%+2
zCEvFGrsIoqzbjIud&!7bYh3*dC%AL}2**oP9jrS5KJ$BD>0kgWahN(W7u-L~9XHCI
zfRd?^2)!S_>>z%%=ip)b2Kl+U4K1i<G}jzd3B#18h9$IHf(%$w3ReoPQ({OlJZPBI
zRAE^;EuyWKWkRnubF@p2QT9j!w*?o&8cZZyI7H9Y_ea6aU6LZ2UT&rf*ZR8sO>Z>>
z``Sk*tI-hd<rL90zhaEwIkCG$_6XMa*5ABSs~CPWW4a@85uNz!;?(GVraV?C5sJUQ
zY4&wQpZ=G{CLP)5q4CfD_C;aNsq1z`C*QE<d`SSI0trn8j-O`LWL}IFdnrpYv(`e&
zJLAQ+yx9<b_6W&aeXcokbs_RF$<1v`HJccByNTbwewNdLW0$8ICtln#J*}BKjL&6h
z6jI`|anuS#qe4tOn**@xS;y*E$_T+rmtW4$emMBlY|?r-<1}*@JxpMZZFl$wTYdci
zH+;hr;^Kk(`w$*%k)Fq#p%VpFrrW58^|g9nWA3CXl*1{XMC-ykAePsCA2c_X%}2||
zR=U&riw$*)#b@wbT9s&lN(dCK3Bk5d!f$viXox@%T2ywn^?b-%Htdw(mNZG!Hy~Rt
za^T;5$aXjGW@E=Lx&wt?0QHEITcZU1-=xRo!`BCj1pox*<l)r^$`m`Pzgd?iq<QA%
zgD^x&i}mPns=mYM@^>+qCBN^KEtxJe4%ae8XwA(+fu+WwPZ)pyecH)41N64Q<GyCN
zPoWe33Ii{b{N0-|twbK9cML$oJ)!r37P7GNME=)78Zr*;8iXF_nY8E0fLVpmu*aV#
zj&Bn~g*A{(sYFhKgh%rGzAjfFO0^__>OFylF?KZWK_BCg%wGY%lEcd-0fA5UA8PXt
z!8MzFyq51ooQ$=JKf{h2^PooPlidTv7}Qtqld?BBAo(OrEKwVVKoG|6D3ZI^a^n%4
zYE_Z)(s+GfX7BU4vkx2RELwNPF+bJ7)%X{WM8*9cxv<bcLCTP~gk)#gfHTH94h%~1
z4~Ro^%Qw8e17fEJ4?`}2of$;XErR{LT!!Pw-t|Ij(ueUgyJsqfH{H(0PJ;^yD%^gX
zMlTD`^1Mr<D^p?*gHySK$m8``62AZ3|4!kOA8W<60kV=txiXmWiQlZRW%^u3W(9Es
zJIjpj=9;lC=BnSc8uRKxM{wO6#IhROsME{Q2o=rO5C|&p)7!GXzwzrY;(1YB=i!JP
z!F_8suzn6(75@WZcY*F}RxYP9KozolGLq5MPEu{cc{E2UnHIN!nL?8zFB&0S?i|&U
z)hXEZF#WUR$(F1~Wb=91Sqaw(=jxXA<jjwNub^~__1jQ?4CFn<GC&58?NH%!&WytX
z=Q%Fs@-y5BRulp>jHK94;w;Alo8TMIypbcrj3l#^SKT)*r_#>`04&w7mFEKtHQm~p
z))B>Xyv?aI*N$pP#Puhug74_QM}5t>p-5`(?`lvvpJVv^{w(){OI?6+inDEN%Z8Hn
zH(YN`{I^fxha2>r{8p-aQ**cwi0(^Ozf@1|7kUzvEB=lM-6%&1LSZBJEzNB-Ep?+R
z`kdmEyeq2WO|kh!S)MNuMux1Hgi#XKL<vSqzX@o-SVo_TNVX6^?08EkDjSek#4x)b
zU=>8GyYXl#m83DiN#e+y&;k!zN~!Xw2Oz)OyLZ&ko=lU9@A=6;@+E(~><Z-U&^{5P
zyAln)^!NT{>Rz_&t+3;Tw0(Q_*a>@GeVRCUjM{g$e(yE@(Gc<?dhvPadfW9$`m3f!
z34-z$sBJE=71sAQmH|u}ubw+^P1NfAoF08tFZloIG(DWze|?)!?$)pL+iSI9C12e(
zS*v?gbJvrZ!_pPIXw0Iyqhe!JmC79|Mj01<zHJxumt<<Z7zK#9YOpO30t!V@nI93x
zG2s8S3DFzORDub4-wP-~(+A5H05ArsT2a|cTU64Uj&H>X|GsxViMzV8VtDQYO?5+T
z5#Fj`*Q-Zs34Vci{*HwIMIMgQYj_@xta!*H!*9&0P<eyt^05-C6u9b9)x(VbD8b&Q
zWp)rf4NuYwAfYyN@%5*izo`;BB?SCJ%`mPI@)$YLlBe*+sa|zY2xNUlGA!}V;ZMBk
z_Zny^QtY$&Na4nt)k#YoM~Jr)8~xXlbYv2Cjab*{MZ>X4&n3%^_yldV;3S+V8IV?D
zX<{yUrn^dgE85_>zhq~#rVn`Tyl{`m=j`W*C-g+Hnws(SSd=FdImIGjOOcU)Ihzpg
z2m=L~q`S+%126n44;@Ar)v{peTZ)g@XsxucixH)q9OE)pkxyY$qAt)Oz!{=dDxD^Y
zfuPHzWs|;?-e-{E(0V^S7Oy=m>urc9r@)M{^E46pdb+-#)54=ubN;i))VNNW2o6vN
zU0%~;`tDhu!6%`*VkKK4x_hl8hL1>@1x;U~q8Wp_^qbGvnelLez=Eh^DIRV7w0$II
zaHfmN04~Ty9<MRcPNBV-;x>fINt}0r_YlfwB%-(PTv<k&gi?zYW1EG{KR;WMR>C5Q
zzN!8_{rqAzL~1a$EW_AqB~xh!Ka#I(8<UCV`(Es~MOsq;x`}W=!xCrC3r9&wl|E?!
z8$YQw4l%zu`>Xse;y8=vTfS327_D%u@;sRZk##h+DTld%5dHzpZCYiGSpaMC&Zs|h
z#v&bqzSz<5@l@pxK)v3*lu3JH*7wUd=e}ti)itrz?_#?t<xW!IiW1#-)%{YW6QvfS
zFZ*U+hFg}(#e}w@<!SSz@JRNOqXk8DBfSy0CHn(#fY${#gPXzmMIaq;RxrqQn%pU<
z)8s4;PPwMO5PFIP4^tB#H96l`dCLpRRpvOg`HU@;Xz7H|AbpAvMFU&~X&77)4Ry#P
z)_=VUAm|dZ9}+&^B2s&9ZJ@QhXX%b=D#QL&tJVI97bBM0O*9ghfU=*FJ1PM#;?KMv
zbp8Wy?=jF5Hv6&5HrBHAvK<R%-jMHl<!yz_(~~yaO@s3FdBB}5XJDTE#UFr7aj_q2
zm=CSN|K-5)7ysIc{?Pf~?tBeDG6aIx&55K|IixLW{*`<DCAWizbR_)@36IRy=U&``
zj$dvRJ-<vYFTV0KogbtA`_!Bl1pE*CT17TV5U|n=C-Pk{8MV0PN#<?Ms1(&CwxHvf
zAOG`i{`*s$6}0rbpa(5PL<2h(o|8$-wE-h2atH;u={4|h&!BS!o&Wu@|HIIKofWha
z_?Vy`q%TsQM0V1~DTZ?u&vrT?R-q+)Zd?6-F(WP0E|@`!;hAdeN0BiVI~#a>$Ws`i
zq*}s&!{?uKFtc2q|Kqaj|8-t)a1#Yjt-d~p3`GAV0oITZj^FwN!07|N%pth*Li!(e
zIw99&z^|IReH_Q5$goKFOnK^5=|cea0>U4FK(Kts(f^}MMGbx(!RuuC+-V!Jq&_A?
zF)-L36fe%v#A*Qd>p*xKHxa*v6jP*Vq)4Q6S^<!5kdSx;^*W)u@>9_#9KglLeADXH
z#L$m5*ArfS!_-2rn(5gzyxyLqf!-%epm)pu5dLj>ZYXET%zk!uh~mL3M4>3`-G(*b
zX*i+Y<`_*as?<(OQ4E_?#Hf^QjeJ|m9qdKLU(o%h>;|R&;Qd!?ZrD@M)hVZdM^b59
za2Mlgm!q+YKpdNXy?67}Mvpy-EoDA_b%6<C1d`V}GB<)M6N?3F!ot=9k?4l2&_!Y^
zFG07@&Y<39Y<-F)oRQuZ?&s57ur6SKv|yFb{+N$w5A0aadqxC|fTve6ico~xn+krv
zaWK|zDPf=YTkNJGg9=XWB016AzLM&6hKNb7p;T1I42dXoA`;3=N!3z}9>DJ2M-N@w
z87h2!p621z5VE^o22}htM(eGJ&nt2hnqO37RNT;Of)3DO5muB#O_ioib3xCg5%TQs
zAQ9M@y+fX$dTU&kA-~wHai+EKJWYgrSb9hZkD2*gYUfB$5{t$x>1#6sXCZP_)ysxD
zV-P(=FF>}@YW+2&?X0%{ruo1SErtukKwf+w+8idM2`uN7$?WE5B-6#KFkZb75a~RN
z<w^O4#@NV^Y%g8L16Ca#Z%806^J05}Ka}ffiYFsts`~<c#Ky8%HH1;HlvdRs=96kk
z&(K^1XGfLt9{_0Wz6+D1^WA$FA=ffY_dbrtjFEY6tkR6PRdex<ohMB0Eru0@hQKUQ
zEmlkQ4-w4{Z136GrYYzQmaV1R#K1)>urey&-uo$}7LU~uvcmg1TgP&QdHq6@E=z8E
zZF{7CB2ud~Y9E^y2K0jPlsT^spB<G_y-DaV=rEkg`-Q`f6t+xn_PU+XDN6{OHa|ON
zTF3DgxwlhF0NE_@tp@Ayi$Ww#Cu5{UkL$bhOUNB!c$&}0AmLauJ-tgU$yf%4BM1FD
zkhb+`jiNh-k^<WrvP=-+I^d%s3Qv?$#E2%scER$b=|@X<LBCBEj2bJxP^YfJ@HWbp
zWhGE2f5*A4tc_8O+b;1cMF=xHvbOHb#5U)r@xn=p_5%pkH?r~>_R>~6?pxn>SG{uO
z992sb<4Ry|HZDS3!ZUmw6GCmqG9l_tTk`0LF}O?>UVV8_PyenmxM&w8cmwul_y=5Q
z@C43)3k?q9r)v;qR(!4CLIa<T<A2+P3k@iYm3>6zP*Vm{ehUqz|F_v6b}aDh546an
zH(}lHrMq>MjKhAtNH?zFdcg65Cx1vp!ZMCkHm*dL<SY~WcPBV5xIj5(Ue-?`K`+b_
z$7>dIpFWC)l66oQE}>X-p){6=#D97b14BeZyUZ*wrCr&L_x`%mvggyjUV#^GkoY0%
z{fSgyRp3MCXAzNKRL?(d${(J5|KOMS{3jdouNt`C@!0a<hbPsZb3%{d0o+mJvjNw4
zxbOq4%_K2Ua6#wk^>gN>!qaizpM7M*yFm4&D5_ibJ(jr8!+e5^KcI_aGna>Aqfn}V
zrWwsY0DkpmGZB9P?!0(C9z}7kg%sXY-z)}hR+Ic}`vcHbk^1rLEI7GS^vUK!(Xa1s
zdzN~hpsIO)0C2)CI8I0YuWdj7Yh<?n8rdhYQF!B;51?qqgj$_>c^r=52Vl_}dG>4`
z03bqx(W%q<e0aJ2ig+H{^23+97XL8!GAiMtk$G6hZdq~dk5UGA9LK6?F$+t5X2L?4
zK~_gf3=matPE5SfC@KhNdDgDL{uSzyrOuyKM)qg;BWEWM#}U&Xre}}p{QgGHiL*4A
z`3%r&XYxNLK5yj8i|(2G3%JKrDd5Vtkd^fgN)QByOFPv31=}-oaMMVJMm!so4aVO<
zqZ=ucRulO9042becdnudt3wPG{yCl#w)}t7BtIa=QO;7zqVaG7b}0QknO0sW|IK;C
zRDnbK)`NvWbN7VR=N=nJc4eb~mkr=D$)C&z<5d^QIL{LTyx_X)S2zJ}6C5k3r0^h{
z*`zb6rPx}{_*>aW#w&vD-QQ*tmfw^9253WNg(jc~9OrP8$z9ITF3vz)P;Tc@#<=&;
zIPChl72UZ5#z8|kX*yYxaZ{(=5%XuI5k;AFS!FE@Yi{=J@*H+O&XfC!MMmVI?6s?w
zi)x3i8$(Or1tx~kb_=KB9fuJXQ6?Mn8amouF8e&udULUHn=fReQ>T^yC?{KvHr2uG
ztQLHczgnOA4$ou4xJl4kyz79j-nCyW$!AA!(2f1-tj1}Kiw4q>ZiE*bw!2`^jRB0Y
zpg~z%%v3Y>;g>JG2o7^+bZfob#d1BLu;GXkif_DWs#Dwe;s~7=46Ahi;-q>?x0P4s
z&GPL%bEa&|I^<O@&>+3h<N5w+AJ`Ce>b%<a(aGNQT7O(`nW6nW>xlma6Yd)CCi5h}
z4Mx}Ctma~B_<$XNH>L$Yb0gvmIl8E)Lmlxm_|4$!OUKiaF1Eze?oii?;FL%(&DEk@
zG6VOuItnDkOs<hhSJBcmlwS{t!mfjWr9+k=FIB+lJ6`4b@%BTPFV$M3AVH&L&8XeU
zAss~tmlyMqpi?S{yI)f~kF3S>)1*biDvKmQK(Npm`&ol<OOL;49|_#2Lco}$=G*yl
zSkpGxro)Y2HO_uQq@9Kn;{Rc+>yz`LR!7Wf%>Adclw?t&Ftj6q1l`y<r-BxP&iMPG
z3d`kN?AjGx4lD67$3jgTurg?RRS{f=uUf2oLp(DNJkxf4ON7u7S0-0GqFkB_B2y%!
z);>toe}ZM*B~DNb56^%mwEh4%I9l9euhuCjcBc3`LrLf5?*r&Wd(S9}kE!2N!1P|{
z(r@GBD1}tyHhZ!P36Rj+VzGQqFUbR^PTr25|C(0tiB%sBm0}Tb2%mErY0cBg^Q#*V
zc3S?*m@}@75nW(Karg`|TWTGT1x`|?(ZFSqRArjwC&*P(_#z5GeeT1YSc#2g-c@_K
zoZz{c$^n|Jh@pYF&@nlPNEJVIFadvUF~F-s&fg9zm6qf4Ya8u6_mJ_PExZcnX4Rll
zF~NG#ReHH<?ZI63BHc|oBf)4h#l=VI+g$734Ex$Ew+A8Tv*lX-d_HrLd7D|x0U<b7
ziL!#QR}+Cx@8041Fu@jXcF^!GDfzk*B{7a1)gvvyRo_97uF&KArXgIK{6R>x8CAj*
zmT?l=vFlO(Z=2lR<xWm4+ed`dDmaQ){%0SB9X{lkj5KV8B#v+hVE!lmv;{}ZNyoHB
zUf*#4dNE&P!g-UaU&#ds-1Devaifg>=ev280a1!}cp%(T<lzg8?^13Vf@In8NQr@`
z;=s`EO{Lr_e*Xo}Qnjm7-ec2`K&IF*hn0T!ZpwgB-)!5TgRV~Nu4}Ucj@Gf(_}_Ze
zV$a?sdKsPP8eP}~&rfQ{%eyg{(6BLgqNsk_z@@4IJd>H*68HGzDd)O*vbNb^Lg(?N
zP1f=8z{N=4^xVV?J<VB6ZAY-O@!(yty_)!sm|6`4zT&uyqFxk)H3$F?^*tu|xu^RO
zC_kgmr43{%f*(k2v>NO4P_X<(-#3zzVj0>5ec48)GBO(GuppA{+!;*|Mam-7lmVb?
z;R>e#>^AkEbxGS9ji`8dUbuqUJizHgLpJ>3e2Uuq5y5(#Db>KMvGy&hLL6S?2WtqL
zx8^%G=)7Z5>NAW+j&Kmw&S4NiiF=N}w5U^7$%-}rlmEh?^=P$2$;>-URI!fDWBsZ^
zI27#JYPdT{z_PRIZSGI6O(uz8ze-)m#<9(aN|V)#g6%~@s79KvlhH|D9TQz0Ggnz2
zQ-MZW$`XZwMA!$<H-8Se($9^$PkfC`u&F&heU*8$;FEChV+S6d()B?6#o?6OX_xPV
z`k<2qnK=}4&3FS&3-ezOermHC>y;8rj88noC`^Ez3!NIrwy_GcI<C>9>bkLpj;itH
zVd4hU-Q9IGCQ$VnW_qd3HDz31W={_r{qmq7{WaUH+mcIPsfLhtQZ>FEVQsSyUibuo
zR5t`^Xzb8rm3pjk!gGeZ_OA|Ys30}(VO9-iEAe!kVixLGFB9pDlqzIFyHE)whGcQX
zx>>*H#*J(l(6nR3igLAJJo0Fscsy$SEwi1TyJ)X2P7pMc)p;q|+D)Y!7W+~-rL`1d
z;yvkE{7$wa9M-_W8cQaK!?1=F!AjY$RgMCT<iaL)a(=AsV&~^vf#?XVbd2LI$JI9f
zCeTRPM-X7jwMN&!@_JtY?sAAn-HXKjn<JF(aG+R&S?(yjxH&@emumqWK$k*~z<*)e
zg+f}oW8sf9o2qe7__v_}P40|JP-f_q9omZ?I5G1tMiFu)SR*V8!U^eK+_n5L`i*J!
zmD*qZGjpiQ_W!ccIt0KtQs1p~EL{9ePGY9@nt_?Zy(w+?d;nqx2YNMm3Vn+w-HlLJ
zhq7P#qMfF~K-m^lJ)91CAqYW4=@r36yqiv7H&v`aN(0rLwG>P&5rczyhhwl+t(nRj
zU2!!+8|%e%*t>{1gu-)>QM<DOQaO;-GejFfw$Q-G^tiL<y7pdd*9HAEG&}1i4Yev2
zSDCGA`jqtc<}+tSt0B&gq!{7Q70|!XNC}Mn-JNENL;0g(B@Hs`neLkC8&d7`L&lez
zC#(k)7YIhnqHqtrR-urA%+1T&sT=R1?zCm%&5v#79P96M`Ac>beVI6HqFLxYSYIN(
zWv0(=?NXL!WFEUS*uuG-<h*1zlWV1=wd7l7>8TS&^@dQUCw~Bm^|LOez8js^ceMi6
z3N}qK6bcZK2|m$KS+&k{YdI<VUt3|tMzpzfHp+8mM~8M6a89U|Ib^*xB%B`BIt`0V
zlB3r#1mbD8B?;rnAoPX~)YBD&selOU{B6WAUzk=v^u?Ay`_NSvmmfi{)=@O~C|ubJ
zEYx#g_1vVHG&f){zHC1gO`f1a&>C8px%EX+DChUL$b-kr;#N~395p*T=j?ISj^aM+
znYPLmXsp5>bfeyt8qQ0ih~%X?@;tXb0))z1cX10QSMlt6wHg!_2ZJOnF#0Im2+>4Z
zMz<Sb7+Pzt9E(=FNl+T9j1wR+gpk}YLnt>7y7kUqZ*zid3hj~Yow@(c1K4`Q+j#ZI
z@U&BtR2|m%eAld{``9%5oPMQvg63K!vvlyR6txtNK}^eD*SZTy;9Cg;c}Y;l&gn##
zciik_t2i-1^V*&rani_;9?^=+dadCwlfZf^y5UH)(knGO&k(sz<Xm79hL?L;%~9J>
zdjs+LdYuar760|l5QKKo^NVTsotMoE0pViG@K_#Kq1=Y;bHQ_|M>o?Jx%Iw7Lep+T
zJ$DNt{tR89T+J5r`lREc8#BDlf7xub-G!ou(0JwLl2}|G5v=`I?ombIHC1mXYYgJt
z32Q~d>b5x&J)Fb0Vie8sYBSKscmzzfcLz@HXd)}9Pf*Bf|5PVNZ8Yv(Oz!4J%+Mzq
zJ^RI&-&Lp93$`<lQoZ7t!fEhr5Qx|*SvV9`EpR@&73fpl6<W*k&i}0?$mMmCn^j#m
zRcO~bX_aBQ)UI)*c9Dw+;C4Sf&PIZZx{rv!JGJ6jlpUTbLM2C<>bP!J#EPNVva}#n
z2-k9ZjOC5~urbf+3AHiDNOPmUmnX)x?7Qi`DAiF0Vq7}PL;88ues{delGmjs+N{aE
zJJWZllbNqBjdRX-vo2yIh>yOwoaz$2^qcCXhj!^&rArn*6xlBht6>u@BGXVQ4T{ss
zcG-(eUi+^biw&DE8?10ZQi)p)J8CiAwb94d(OB2ETIn1i`gU7v>V-!vM0$3vW#rI4
zEC%~e)WwU@$r`Av#LQ>T=e0LhqYtx&ccp<bQ_1b)jL^pFqmBt>R;XP5`7PDb5PN9r
zrF}?(Ap?~FiYu-Tc5~q#!WuIgx+O)enhT0aNq4El1R0}{An4Jb!%1KyTT_^k_+d+2
zQ9&H+wT_c3msLZ_KyZ$X=PHdb(45~{rm1%zfUmFiee*yi5Ze?yO)%*u9HHLV1Ut9h
z-d}U@5N)^7QtEKZ^^DtOrcm3XBArT;tB^VgPm>ZHFLjcV!gkYsZ@h2pmp^y8<FVSm
z76x~;xjMhph}&!oI9s}SVbPNfM#rkiBRy=4NOP92WY^%?=pqn8!Zl{qbue_b7R32(
zh_k%0_3r(S`=_0YlTdG!lb2*w!YERNabfD*A38OoE#!~N4+k~nU874q(5KD0qr+tF
z8F=2@Sxk!<X^WT`_rR*c^KaNq7aB^xsXFhg8Y$e`WX3*p;%o=gd9##eaXX$b0k8Ho
zblwyh$}SsfDwgqBZ?NXyWxJ$AQcS3<iY&8%_4Jxm!<W4F%IdLd<z}6SvX`U?clY8U
z!R-|=%3RCwtgyr6RndG_Yx4t!v7SxO2yHiiJ@XHOZM;hp0!Vfj@$utFg)WHSvIg3$
z4h6;WxhQLkUd=EOuQa#6TJFoVhcXa}<RM^N#c5IR=EBVGyV%#-_jkJ?mJBXb6HYaI
zb^8o)PWXQSUToHu>P57(XIAI7IW}2}f=FwO6cM&s`4j-1Cr+tvQ%UJR!|%z?qhyg1
z2FAl>fuMKk4yD5vLA^qrRMl~gPBlDq+;RLv9pH7|Cd+mT%7&e#zGXv(S=a1Ny(7d5
zdq!?Q9(iT77=xdRWAG=GfBOc2>gLKlq{VSZlu5hxH&*y-BHz>jjY_@i_v_&UrxzQ*
zOZ0;MPon)|H5T9BSO<3RprFEUuJeGwf=cWEN+-k!G_;g`HiRqXhC~wg3|Bu)xZO78
z2(#rr;1t{&{l5N2<ojB(28qM<)9OV9^ADNHgQa4OF;QdPr{#;_4=bcsr<suh)lgCl
zz?m!L4)mqCU%bfU{##aXm{}ETmBFm&3EQVh+WDx2ynwNIvkwH0_tekvAk_Mi#)Zy&
zbR3oteH^dOCQozrxC4RInhj+3qb4>dbQJKjm@E9)2DEMOa!NwBl;Au^QnjIapwXH0
z2Ws#~<EUZx1yVb;=n%e8G5OvW8aL{DmVIN8kMtYYQ<oBoM{EH(u^KQeV24reepDNY
zNKQ&q5T;#*C!$JMy6iqZ>3#npw}m4+yISi5xG1;6xyxhNvim8W3v<AS0=d6y?@gvx
zQ$p5sXF>}TkV-LRlEGB!n{;Y3G~N6<#0f_$bKk)m(rRX^W;7_Pl@?i6G&FSW@S0t?
zFbxBTwfDN=mxy^rW5AP^h}ltA6aD2mxcNNK->pTG+M7aJ41egBRxNlzyQNw4`<y!(
zqXyoYjfPUiSfg->!<&fbI&U&KvS10F6^1JjlF$n#b1aJwyx#ZuyH<vJ>?+1V)6~lF
z0N?vPBt_CYHC1dWX!B6EwKI`yWyrV)kD@zG3JQB1F^FGv#mZ(H%B35cT~gmnXZR{>
z_R{bL<61jZu2)jcTGmpfly{tPKocjZ-ie&3JrYoW9_5Pdwv=<U+jHA_6!>{RAxF+{
zGh~<Rp!Vv;-QWlua=PX4d81TkeB>hg1v{>_npy0%M0baKq53M0(tywS{is?E+Fo?y
zxoN+5e48uN0oC&cN{IVu{(C|lQAA2Cdtf!u;xYHH+F|19BZC=^of)w;cCc$N@!7qz
zs*FjJ{lr9(`H-pBY)VuaBNPSkWCn-mU5*1;r?u%@<}CS&iCC-me8XL>BfFF&1U7qV
zXH^c^N+L~9s4Zm&MW6yl(jTpx&aTJgFOL-O<xfiGv3ilg*0iJ1IIf6s%KS54@3&8%
zw%gKu(@N`Gzv~U_cQl+-QP0?<@w|Mp>09+yQIJb_A9@-COZIbn@CZH$j`z?J=^^ks
zX~<R<PU%;XXBEGcNoSeF62xvQE<|+l+(E+mB>(4P6~2y@2?@6?XMQWvefUGN?-X+d
zQR5y!^N*#hr!I;X^AI=UTLmiG_xZ|d5?cQ>=b{lYw?L3VNJDGg_K3tylvR`c$AZ>q
zT>JjW!wD7c&J}}v@vX{UqN3z42QH%nz<yZPv&m-~78?jyFZ&J?3ugc3+b@seJl~fc
zMqutxDFU3M+PpUXCx4%I3Cf8fa%WsWHyQ*W^$~tO^IMJhgRiAmcW(C!au8CO@5aw1
z8k^hB2+i0cDqSMnBoWE+V<1BPM^v<U_b&TF?Bh1SxXm8$X?|DpFPSmn{<R6gs~Wb_
z)4^I$_H>xhG1CDGcHX$WN<q%++_bYi`=BgM4H$EpEEbtTj=%us&rS|;GN<#d1joml
zyaCMfyvxQ`M&iS2Zw>but%MYd(35PRDxR?1?~2Wpb&=38)<jQJ={y)Vw%U?1k67&a
z`O+Q|Bv$+mzm?A+29xr(Z9YCVBD5p*gVHxz^2Q|RIZsXbqh$LfnNRb;B@M+=0)k?m
zoF;Me&P|qqJE}SXOF7YaP-1m-6)I)Fi1+=aY-0zksEpI&79!-5-(e)T!AHxJt|JAc
zk*3!IRJ1bc-j&y11t|3k2~=cXv#wT=)v;i+xoz5w$;uLS?3QLJDlk^Zt?S2Y0TXj+
zOtCZw62cH*wm`g6U1$!I4`p-&II_8k?l77?@a>q)YO42|D$Jr}w>B&{EH?9PZis*n
z<b=LAI_9lud~G@rhEp;Ei6)Bl&w59H!}#3ic3Wx%1x}|530K%lh~Z7d@@>ZAj{8>M
zWMZS@^=!Qs-|c{$Wy5(C4=eD9FgOxA*+}@DOYsc9d?OV=<su6BioMaO#nxj|k#s`Y
zhC|1)EP~H!$&x_ZOrsSHHx5J!iKIh*L%HoR!SlLruX=Gpc#{~v(U2+vWlW10?!i);
zS|gV-qlDN`8+vKCx>*kGoR4&yD{dH<iP&sr<5&2x1)hR{b{f4cp@||zaqR3RNv3<%
zW!~`OEuHxXXp&}n!*hE!>gJe?$4+e0kxz)2X%y@!<)WJXpIQ90iNkF`Vlv+1qRV{L
zX3O5HU5+PwSaJ<X{pvN@igHd5uMh=i_2v!Zn3d$l_(|I|tqD3vEZWjobXAX{rK9zF
z&8txQhT1<;?sn_tYJvO(CliAkvBum*loS^!81-V4=Pl?Bh!`V}FngW6a1aWW@w<6$
z$-eOU5huK!#(1NBp7EM_jiS=@I-{V;k^`6-!oPqfc%^1zDzK{Sg{7~aX|IpUTSBWY
zI~Y-@5#ADdXVnmN2zt8U#AJm*uh-h@`_2s}8b|$EY>bl5w_2hbN~G=aHSzgO2kMoy
zm4HPD7zGjQp{&uWh>feUAaFJAF)^zWLiFg5qS<|OC!-a+LT52kfxgqpf%U$^I&x7>
zLpzUYW}Az+z3uH1)is3HNjF2w+<J>sq44@wLPM(v8?p&ZjWyR&Mq2gL2A|c|6iLVO
z$7hOC)lf0>V0gOH5v8v>FXtHT!v=6IzHTmC?_poMXS+Whfm~QjH*%rNjc66vxSH$^
zZ@7gblpT|znLR35h(>{xm8@2>JB?kSWCs4hTb0#^B5Keh)PGH1H9$LeyzMkTlnbS#
z*N~#X3W_9B3>_LxA}dhxe85l<LSR+PNmKnqfGZq9+p)<t)JoMMzKiFg5O~35*XD5;
zB+}vB6#z?&2pIE%Divm*wXZJ9EA`I=D~xv$ndJQ>9Yzot+_C!p`HRG`zEXqJSXx`v
z=Qc}_Jye;&M!4-<OdV>&Bzli0Rn96T%m0b$r4o$>c`^?YiQ_(t>>3)H;kxrlM?Yi)
zTS%<ipQk(FqFTSFh~lL$;PV;N`i@SB5v+6mP<-!6PE4_Goe=T((9g7{7K6j;HQC#<
zt$mJn(J`mh7c8k9g*KQyj5cg9!@f7rD<rz4ntVxFYcsZOZypjZ<197hg5aQ6%4yLM
zNdl#_QmhTFkAED1+lk?twy?$`lf9$wpAjM~8<m)LIOU9G3=Y4jBQBj?Rul)}^Dn3`
z<LY4T9k*-BEouxSAPDGe-VP17d404Qx2l2FPC?Evd4D*(+JuF+bL4u(LfnJGl(n3l
zqbe9DV!cAkY!guuFyw-eby9dONEdGp{p%#Wx(1hiaNjZf0Wi0`y=w_hy8!jpxkFcY
zII5yk=$IIredTqftbnku6op!8AtCmR()1e`$Wv^~uZDfpzbB3A2e3}3v{_4~jp2UH
zx^4+|?Nd?{F<TTFicjZkLUAVNj(5xrClCjDAKJ}V*9Lv<KVepG%tN*X8Q6(ssG|yx
zT|7?)eF9aB)joDHoI#XI-5P4?(-NY6@|&n|=DrMM;!J|F8%#J$S5ylNlLeo(T1RQf
zUf1fe-2+h7m681{f|cnCTt5bhNSQjt;mwPcJl1tlU9tiw>HgwJsocNu7TeYV2rJ~n
zGbeD40@=<--eClxqW$JeVX?8+R@9*H4fP+;gyJ1e)4?58Kts*HDN^zOA5f$OAD^?f
zX*nMxP8Y|qUQ-z>xKBD8My6l;@0umxyM3hV+YzIi;1XOF+YFctPf3ZgKE82t@9Hoe
z8Gt&OyyRCZDyG6>MO`*c4aHZ>2@*$nHh-w7!KGY&_S1oja;Pa`*zxvZQ?9Xa>2o6S
zMNt~*5)2ck&IXb3QwCjg3T9E6CR5yit)#M)ZM&z;+Ov%eI~$JNUsRZ`(-(NI28OPw
znwW#HBRGT&=o}Sl2X|dD*OPfMUBz?DC`=LP;%KY{1qjec3mBQ%c$*s=E}@fcGNxTK
z##6E6f}&u0KOu^xerrtu9d93jeP?sFVomXEVq^nedA>r+y`8VUtZ$FJN8RYi37m3u
zYs5l@l`Xm>9bXYrLIpFZ)bdoTCCm9(O%iJMcT!0lu&SnGl&Thq4c`idrDjAR0YXB2
zRE>>B*z24b`5uuc&>f_uGAtGdK_w;5u(ftWSFzku<^*^ig2)QS=`*oNc?}#y&xEW~
zTTRs>w^yn|0TdKw5_Jls%+-N&#_u>o%_>Id<7bO$oyvI6COsEj`xqA8p-qjY?#b>#
zx?bv$iYSC~a(+Bq%B8bYW1}5PBSM)8{)Cs)qw`V~kaj*)9CG$&8im@P>N%A$V-!-`
zqBbm{anDe~wTKBLquWY8y`=$>_V@Mm`WBK?a{z_x=ZY*9W4)(l6-$72jM;!<T1fz$
z9o$Qlz$meB==Vdc)QgRU?Z0}&z`k_;=@FB%i+#w)xz}+)e(n(Xk?exPV?Azt_oSga
zB<UOjZM@!tb*#8ws<m%ob<adlZWP^*uN<lMLq4sVA&-ehS;Yj3OH0=@Oz&KDYnQfX
z)VBq5(@c;1hA*<C*!=7OFxxQONN`{{<D|=w;##4qo5pI#EUWDH50jWraFdv~w6QN*
zf#fPQ3=0q>*T3Cc;PuTCTEpsRc=-2_ACJlJ_EcZc$l7u){;r|UpB9rDq@qPp7Q59`
zPZu*b|5@OaENJc}3=auswO~{Q8Y5tdTfj|X$T3@flb@T@if@khSH^uyfYJU?-hQON
zrBd13v<0OxpIWs^Gig4B!nMgi6XAMLneWUZzblx;5ezhnQ_-@r!|qXeFN0Bz0D+&R
zz<<^@(aih4Y0jkUZviP^P(B`?Sr5LWd~*3+?dQJg_zqW<hgOl86j7W;|4`du>Khgq
zeySWhp@nOtYz6-S+(vMZdSi#%Y;_HfoqzQ}-un!VIxuPq$*^H@8y=KUYIaQTaqFMr
z%ahbj5(#0)(9O%koJC`oCJk@ZKpy+@N~XU_tYFp9#|cZt-8z1HhAZiekL3jcg4O1k
zkt8XUpk=o>O|savtNh39G{61VUSLpOWmZs$1BZsS!1oL*X7AO0tw(=5(}m1m`=n;~
ztvt{<*R|UtXNuupZr~I%yXrVu*@|_3)q2}%n1HgZAunAf6ITi%rKMj7`&D<u%@3Xj
zfm!#Z+j9|yU44%D)|}`H*B!c~qmPnnEJ15QNBu-(@$r=7*Q@!{KMmG_3X10^{uY$x
z7FN|>-Fq0C6nuJId8<5`c^%Ha^e-wQdUg4dw3J08aWPhMW0|Ta2srfY>e$>y^F76l
zHwH)k50u7TWpCCWH?qeAeJ3SwAn7G;QzZD39rY3BVGAb-ghsN;@<`6B7}=>~f@-qc
zhIp@9I?LE+GK5k$Ra{$JCzn~`y1C#MkSr2kOe0++1Zt0R@4S=g&oZd(GL>{6u=-5m
zVoMoxCJ3X6LSP~!1_jnE_}}R~BS|pxm+yRDE;&`%nmp^jUAJR%1EY6}f#~p;fP^QE
zT7;?=xHz<>C2)uUmppEIInRap=W^!et~Xhn8$)<)RUPU<8JO5}1luBA;0iZwlkY?L
zw<D#D=Y6u|!~LT~oYA{P#$reKvX!A}Ye|>{p;CFEF-+&V%r>FGO}wdxi2QxW*C}m*
zTkiIpQ1_N}A2DA)7T4BM0^B*=GtTyiLmDUp#$&NhZuVQLfkI|_Q;a2FD;hQRNz<-8
z=enTM{PtlL;Z0@6*8#NQ!V`Q(<4K<zMR8m#H%H}xW5!0`yEz7_2ufTa;)^#{o?>`$
zGB`$`-)yYijo<ykvJuG&;W?Wepxw-f!-imu(iv|oPZDhjVcVHTE8vc(Ax_CO*#AJF
zJygd)j0ys}6VeHJh=1GMdF;J2^OIi~P=UBTte)Qpxx;d42OX6sUT;A3-cY?>Tb`;<
z9VBJ<g5Z%2W~94T7C?Y)_N%gkYI8=qkratA+fxCDAq$hlz#+5TR%Pd}7O#K4e<u@y
zUp#gv<}j{6y9Sn?T6<`fv!r>=`j~=#dwX2{$*kN%SU%QMP{Cb3LR-|O#K+jV0sAJ}
zJ$%g4lHD!abpW{t^dilv<!zo(0-B+cUIJ7~Fxa}W&X}^G{PW?o>|~;>;^cT+wZ$#8
zk%=-waLOK((U#%SSZg8yn%XnC5aA1p$+cgiNU$3>Mcjp~;}H@*Q)#quMvI~G>{V}s
zSK^avJ+w%JO?Eq`purd7R^GesSnN#Ud2(vaGJA~z*(3}x`C<x;s><Jbzw!}P^P%nX
zr^Miz>X&dwXDZ<{=~LQ&aV~s#?tBBXrt}3{8yj;u+j%e$AcQrA&yeBhHyw4ia4M<P
z&SvMf0_n&$&<EvvV_>PgFKJ^s<B4?@IA&zCDor;SVbCoGOSJ<J_2<dK(hBT~Mu|+0
zvC0AyDD{_@4K6^AA*;`Wkn$-BR&_mh>F0_u0BA?AA3#&uRHx?4FWp|@(*7|296FwE
z*Z`P=&SIy%x^VE#!0izus=U-j%KPk9TIx_Wx#M*TYgxHYV+Ub1B6BA*O~p0+rChh4
zb<#7#^AKb2Q|t_<cWPa?MuFm~?%j<$_33wb0&Xe;FD+x!sNk7*nWD#$k{oNTO7(;$
zlqY(3OP$88o6A)B-K{t9^dvAQl$1iSe}@_CWm^59cGU84n#g&sHye2epAs4K-OMTx
zKjm%lPh>6BA*Fshdih=%-pD5%+{~6Xk1)0Arrh~?1usmF42o>;_Gilit>j~>WUYif
z>x@%bTyBz7tA%#RlC@NLNa@yx%+U$$9p@bK7-$J_KvVHv7fE3l8>wSlLYNJ%#-9`U
z`5U*SnxV)yLi+e8wz+ZDvG{MBq-HvTt!bB@G0zzy>3u%m&g2QqsQn{-D<{0BFyT^V
z=U0md$Lg24!awDSgujc9k_Cs|m(O<+$1%|6wL^4i7iBh1E+ZeLo0W`Ze-pRVqk2w-
z-?3hdixB(>Fob&^%@E}4=*d2EiZ^avQy=hmAOALPkkE(e!HpZJB}7wFhB<BT+--U3
zq(g=khedvBmwpHm4YHf3?0k$l8-U{J$0jsd1LI`ovOivWzUpv#aPs53_hYzu$h~_f
z`dZ#Puy6J!tnI9M26mX*(E}bR{$_3SNcFtdIAC4<W@3)XBj6%Z>2Pp{#6~9e^}E)m
z&N|<Jwlssc>{;RHaW3<}`~-c|^>yBli|-fC)=9=zo9;ybq1d12klPRJTYqw2zdZ)`
zU{EbRe4%10yph;G`vo72m-^jTna)2?`&Vab|D`j54aW_I)wADc@csJ?Gj4;Fv6$&X
zoSKT!L^;Kau{3Ur%H;0a8Uq`*YzbeA)zbXh)z}JuPIRW%Wz<Y`(NC-?@0QKB!Pb^f
zozn;H_)P_Q1y2ddqh2&IHrgQYb17=0<e|>Wglb;Csyvx@LHLCp8PJ~dG$DB;6!Zt+
z*+7qpS^%uTGq*P|#C)rkvB!N@6mEvp$9=Iv5ZGbM;T~i2ltJD&u>ZgqNf2F&utxpD
z?@Gh;-`h~ZU68i3z$ei?8_hUy_n=EvvG0Rh=SHYX-KakpsebOb+;Jck&pa*v-G;b5
zobS#`U*6zdkL8vt+2E|}?FxA!lGz0n@<Q<@(jx)Rlbg^(M({rdoBLk|`|IBZ3r|d#
zHQK27_;^5c&i;z7epkjj`U{Sd@Y4jep!1(!>36ID?Rb%Eu~nb;4dThnuTgW>4`teO
z5CDhKy`lpD+kRKhjW7)fXEy!bY#8`EOhvmt$S&u2R=GMrY&rA)qlx?1jD)NRmImIi
ze_3D`cMk&v^I+`Q%E}Z<001p;;|<U3KLC*%S>SG}LWke;{jbCRgNcOy>P8Tjk6rJE
z<LWP;+o{<0?>Izm99{WmgdF{6Kf}LIu-IJWR1t8HF=wZLFt+ena^XrQUy_WisFWm;
zvljZT7i5h}V7H)ESR&1<u<}xbnkylIQ}-|8-S5hZKefbPjBJbl8%8!EF=p+4cw>!!
z%5?`--1{y&N%4+@tzbeO4a>gHgnV{%dhNUibb63uS|MF&5F$Ud)7L)$2J6-9E;~Yc
zg1jSqD`uBMP6N*)x1G9U#!aJlYlQh!H(Y9?Z6|z3WjZZBtv6@}Yq&O;)@G~It2kM#
z3Bl!T+J2NT^BSy0#xr0`Bq$?v7P21N4%WxsQ^n1WjM1e}gGK7I0by~JdB!3-Sj@><
zS%BSPp6mqKa67A0wSXQFkp+wr?58|#$nLIV=~N~}2JDJ;S3PDTbESzDVxv-Au~8h7
zf=Fs=NJ>WHA$(F&xMP9~EwkQq(spgC{Q+20Rg&9tpuT+g1EA_$<24~ZW&Fj2`-h0J
z^8JS<_w*joerdInE)0aT;rr(NMh>Ge4@1E@I3wxHW`(+|DkWZdrRg(ceUiXx`-vfa
zQ&`juQ5%BgfkVJWOO1o~go}ru9*!yI7h)rKQAf^bgF|3boW<r^I)BbId2kX+#IqQf
zTrOjSyCA{K{nN;(df8ITW}fhmsiww)pzcsl`J8KcVj|}D!|(L@`SHEobOW7+mSc^R
z$SJm!=GT>NoDl>(l+nMI^0f9_IVO?~40GL7B9QZTGQJNc(bDuzua|aYzmhD|AaD(;
zsILIPW36x(eGcQD#>56JDP8j2yan7sKAL1*u#8Tuy9qtDh|dkqYw-~0m8?~zb8rrt
z(oP%92qEqE0K)?cS;*Sy8`+rcX^Gdn)@(XrBZYU4OqYNhj!RKaZ2F8mVcDbXC<}p(
zro&wXAmFca`7KSwJGIQlk3!yy4HKuV%vmwas|W7syiG(xgZ9D%UU`kAYtGbno5x0T
z%~v1jx}c?f19nw~R8##`EWU&k63?{a<Kp~|ytJicNTbm3ctm21qGd<A_MIa(Q7Qer
zNJw|KlA=DJzcck)$5~=MmNRL!8e*|GaogT5RwPj(BlYS-zQtUit1s>IzLiv^Jx%<K
zqBv|gLk@!EZ`jbg>6!LW?>G>!^qcU+D0g%i8F_%+2#gk7L(Qc*#szO$$6ktEa<i+I
zXs9*BY|F}Y#od*rrv`0$+lYd!v+OvQ_A}*wWQXKqmSS&!{PjdHOi#$9hosp#$$66y
zyY0OTm@CN%kJE;Pm$G!B2Y!1uy376;uzC}a7~~eS8Ghl@q^HoAi!{5)S>=qxrY~5J
zmMimmU{q>hD`FHT<>M)h!!%@*nR_l&a@`Zq>aAS5T|x!~1)FP>s($@&-iO&Z(vMT4
zH>&3|PEC~JOYW=n+Xy#1TF?jg35e=5AF3xAV|&UhOE{^KwjmeAI6H(=iB4QSbjVHB
z-lGt3@;>{Vu=Xu3?1rusCP!S!MzcnGzUYUlt|mqE2{LL!s}D3Cpcvkd-TykhT&n?X
z5C^-O8*352Ctat$v;vbmole+Ah6g*=4;mk~CCQ=fACtyZG&G+5AIjb`Dvotq+r}lh
zLvVKpZh_zq!5tE;@dg@bJb~cu(pZAKOXI=aorZ?s5G+U_1d{!A)>?a?^U4_Cd;V38
zviVfisD66RdtNsxb}R-FR_f=A%WmH98TGEKae{SJKWN5&Xq`Q4(qcSX=wxKs;{)K+
zE4@t2VX+hV2?DdGg4c~o1lc(i%XCGsyvVH?9H=k(Ojck7FM285a)Ad*gRKm#7s@Lo
z0ld!{xT@>{G@}#82e#P6uF+z|dAm$S(Pau1XyKf3Vsp`xVxq^o$tJspv2DJ6aDZ_D
zj?rFwUMp~ywZ+Cr{xB>#j)BKZQqXq*#r~OxYLNo6;?Izf;!|(wt6k`4-;6_^=r}@w
z+E6gBle|TPSfD8cs!_5tF6waZZTr05y{gzd4U~hQDOjeZmpJC=hBi!dk<=?}bf0Pp
zP#=kIl)<|?Sg9>00QFl=1K8jaYJ=%_4z+f+=KM%S6-+9eOeeM+-sv1?QQYcKbLCS5
zyHC-jNhRS~83w}dfJi@%{x+sk`Hw!cTK2zWn<4+T7=|+uxw24zpCY&4u%ATHyM7bd
zaZ)-JNc)513ACt#D7J242fbOTv+tsS3IEkL^JnUaSfHgSKz`DFkdu3^{_bb#@Dl&%
z@DlgdbC%ix8G2s2k|LUq6cEb?S(U+q;-xIukGzSZCWV2|y!8G-2<X?upSdF)F=zqd
z@f-Bcjm6qH`!Q82JaXyqn?`x_*0WoW&3epekiYJ)fH%VbD7Zsqi`12eKh6w&enfXq
zC!0j*3*VssK7w$WQjpfbC$pJZ$h4F*-zmER`;)^t(M79ZM%|5LwxmefOozI*Zq!k7
zNV`aX2wxOyJHN~@TJRB?{?Lk3IoEX5=of2*e5ujctrwB2SpR`=;bUOLArBR~OUe)u
zKcal^OP7%)__r(%jy$5rA*=AV#Ip@`bU@GPpBf_vug+VWfx+}$3~?nf)a-6~h7=LP
zzbRZerm|as7O<?hHVa7nzE&A-D&PNUphq+q3$ygAGGJ0dk>tb(2^53T5aq|e5Mc;;
zUPwQqAQLNbTzVUlc%PJH!0A6WM4@uH;$L)UIT`L%PbveC4*w`~zwE7o5N^FN1REG3
zhkxxSxx?8zRyiO$_Y&>jI6=~9xS&hw7GCpsR|Y6vAR2Q@ON?(^>hN=w!dJFDF_6$K
zZfj#ZGE#)0(vmvLOHw*-)((+#+I$<|5op?Q=G`}Es~cmH<s|0Kaq<Q4nShbl<}C)N
zR3)}6C;WMLmCt&zwqx97(ZYg?`yIS4qNLXGVn`G1J(0)Y8Kz#>hf@{s6CQK4yW{D}
zRJvL{X6o0`nrlPZ*rXJ4o4Aq4WF-Mz15M9kQbh&p0i~J#^GjoG_m;w|?>*?g8%jwI
zU7{>~9nUrSyAG%nsOc+xYGp(h516eAq`L>ah5(^yYvfK4V=Oim&QGBDxmzQHy14E;
zT;_`@0_?6tHSRup=-h`wk6pgBLNW<+YO}M`!E$;$#e`@XcN}-#;KRu~U(Su(Dmz?y
zkoisa4BK4$wCBqd?TIuS7GJbtpS|u)B?n6=(8+#ENu7f*hD-#R7(2oyT5$S1pftW|
z8q(cUburuC{8HqG<7dEhm!$^X#;x@C>_9=TskqF5^xIhba5>>Tmhaw%6kN3HlH`bF
zt7ybbDo?_qV5T&@V0QE1JgbWj+_wV%Fi6!IJE@!_TW3Pd@af#XNS@8JDY`99AT*6Z
zCl_}DBBe&g1~;hZ279~{P<du;As3muDR>W}OrwysW#Q`zQJmc$M<rh1Xd;Mb*HQ9i
zSS&FBM`bb(h!ES?t{3}i%nBXWc&3!rJn_=hT~b^L8WE14)2X5FwnudSl%B3-Dztt^
zG@tZwjMAzi42ou2C@0{w5Y3ex^VSH1ZktZ@*-zUnH@rz9lm~SuObIJdx#^{_z?9B&
za=^LRGgUdQPw-+=LT7loUz4E_GjySP;k>HRg(i*$u$KjgB;K2j7`gs_Wd`3|xPgLx
z)1L2fEnPUI;cO8fK`pWXnv@$mK=|$qjs8idvrSehQ;BA&w2<ha&lG9dWp>Z^Qf9ub
zcVD|f8K=4R50Q$JZW-<T8*PAZSxPCSv<;5S{)NtKnn>14g4iLrY!zuDiWI<?+t1DY
zj7^z19veP4@Rm&|?ut@?pn3})=Xt^m>XK4`gk)zY38l1S{7YAO#-|UC$pa43HI2%s
z)EyQ#;bJM^-PM7$W3jcw#741<2G7A5p&?en{0^@{Eenr&3^w*wi-lP25*%@lahBmu
zY$ELx#h-@gdZOpK^;UkX%oKd~=9#dMJ%#r+jraPYtOfX?sg2;k$`1Bb;sbChyWHkt
zM{4%j<O18WFbSuzg|^om#6ECkje5}TIL>BvaDPC%Ml6Zq4gW?*@X#(x0YE;jN$v!9
zdZuxb-tkSD7p9XYaz~$zt__E3vE?~@xPH+x1<(V0q?%5B4E*8tcxiC%ZAaZ9ay+nL
zO?eHE1MF9)r(m{clUuX9ND3aPDnU|SJXYX9nh3MEC2ZTNVP*qwNM`NMiu3A%An#>*
zjk1-+ui{2!Vw+(ix?%b?_$Du+plj^{X={S2?GmW2`p;M`EtjsgzWsP;y_I;FAxg>V
zV$Fr~U+@@^RDFiz8ZwKfrsjCQFgjqebDhsM;!W1D@A3|15bY=7)bPHr@~v#Txl?Mg
zb*-T}B5g}5NRzUt%6#o3z6kA$#Xz^-TMcvFF;Kdi!tOBFkuX0`;msiB8cU_5@_6T#
zJVRXvkQnyZVD%Wm{(P0cgX4tTtZdnP<73|mAk;K;Y>%T{SdS@^<~qrLYoLj)@)pZx
z`3A5Pe?XB6l-^}2Y9RZZ2O_Jr+szB5a;=yAN;KW$Ix*cF%q56vO@6+V6En-Dj8zvo
zsFzM6cuIQsg4jOWI-cKu)V_MsuLeqy!28q{w0r7HVxilf;E$=Bn6r^owojDm_ggL9
zJPL=SWM}LH7X#Bgg8;I9rvesH>+LgyZp}M66|W@>KJtb5CTaRPB4h8JDDBHj*DhV3
zFjb2@T6a*qirISAk&c>VIjK@d;Iik%foQ3CHO_gVXFG1)hy3aB{G+cO_y4e`q6QM)
zG?p#ugGNCJ!&9g*ju)ntkKy~&r9T0Joz7Dvpgnw4t7{oTU+C^N*RJ^7P(Jn3(4wdc
zXh3A1xf)uwio*Fc{w`zKv~v@k!MmJ6G{>XJ`m6p?FjpG36s5>TV}=VQ2C0e?!7`iP
zySi~~t2$z4im0SK#=|<v?Sa{ev_>MZHs)5Q4L;!ZmiA>hY!2ORh=!f{a8w(v!N^ic
z;=Hc&qOL3_8Ig<h!Q-BPo?@9qbFAr>eM0-Q|C_l+-<%9!VumN(eRp?MVyCsY<4n<E
z*u&}YX66Gd`ok`EY^kCys>k`+b!CAl-K-CH1UR*zp?3SrS&e<r6#5AjeR|X2lN1^G
zd74hpfRUi=)$DSHA`7mfIYXhkEln&<pF%R&hX~0xMY}YS?&}mVd=I;uHO`}J)~)#Y
zrg~{tN$9S7B?(lPy4^coN29}?+yi87a?E0rA*)j|FNcJukYN^AWKq{-lW`#XV|4{5
zM)2jKNQ^PvQB5#ywpA)W)s{cgnw|`XJ#3725HMA$1HK~32N4GF?eyeKS)CyILKOEX
zMLTaML@{2q@=L)^JDHi!r-CVKdcK^X;$%>M3ROHhF&9D4+@Q^WCd-gL*oVYy4v7ZE
zv4MH+tlmN|J1V2wvt1fXT$Rf40@(dD1u?$W=V(DqCpa74ab;K4tVKVT<X|c?=@8NS
zv1qD^>Jht$P3@Z%{gmD?KFiB;>uOUcGDt*XLSfMr5Kt6B#z!uhXOp0<3RO%`h<lZf
zaOJssw#~tvMRY^gi}Pz%h@q8r(tXlh?ImEMn}<zZ+hm&X+cL)5;h2d&JJNMR<0lW#
z)#>1DzrwBpw+UVG`=!$!w9-rO%?`}>@r~@xmyDFxx{nnn0OR3eKdY1>cgmfGS~GT6
zsZhu8_p&KUU9yHK$A;;!+9|hySErmKXP30=g7!0hct`Hzr_A(P(xZ16XJRy)X#I8Q
zrqRRGq?u3MOqWJUro{Qe%evkZDAwHe280|VZm^qTKdocCb6no9U$=N`^3OkuBIUxB
z{V((ydF&5%>P|x^z~4*0zntqvb9{Cx9Il(2&Prk+lAxzpRiZ>o#$@QsTT_BdChg&H
zcHVh+YJB)j2+~Dv)?3v^J@jhA?=S!$4DDI=wx~S^P`t`t?{NGzHzXMC0e0qlmySTb
zW%4^SIcSp}905O8YlyuG90g)`j#y2kmO;k!?=ExrCK0AgBpSUTSt-z^Y+WYO63+dg
zRhTw95(kysIuagAJiPVThYCSEM1T+}pBcFz6SUER-!U<t#T_xdeL#`?2a;I2Mrv<^
z_PQ`ug_`Bghf(C0`rq|N9kaKxrC|}A1a0y|Z7B5XNQyZG<Nt2Ylr0Fh*sy4fQANwY
zfCOWHSN_KWNT^R=PK)&Dc+4@2!3Px<QWUJR|MzuJJ}eDk5tGR4yHQ#)vGX@x*oFMt
z?T4W5MAmQ&ImZ!S5!FEd?)B(?yNl5U>CB1FiHcjf8GS?f%uhq$aS8LQ)+I73Nr4Cf
z!enuO!BEt`*YVHvzJ0q|WwmR0hK`xL3KXAA5Nl2B@Gt_7Es}7UX5|&Qw+dRE<vxM8
zXBvCxM8mZ=H4mLAAG$+REXWd+#JC!iyt*Z0C4lx?$|DKZ8o4ipwlE2ShPmv`B0XN+
zV_8#)!M#j^ZmTjQUxj64qOw~?W;<xmc*40UBHNdP4mr{01o^12(F~m#7gj2%%}F}%
ze*1P`Jm2i)XBV{w+4JuBs-}+X+}Lq(bmc+X;``Z&zc!+0LoxXFR;iSRNwVxZGl-+}
z1?M5HX}0dB`~`b%8$~}x^%Kr3Etdtr;CD02F=;H>ukBWw8_(Q+jNFXIVYA3BNoqak
zTEh3Ovna{2LtZs$w)F-wlDO+fXtD~RZFKMV6H{v`m?sfCqp%X;DRc{~uHBplNfk3*
zbOhMJPaDCSlMt+SZdAMO*=}X-PWJ7t#?2S*Zjzo!2-fvO#u*VYT8a``=p<U22LMY_
zcW1_aj*Q!A<Q6*vIzCSMn$lr<O;GqkzeTJ*dZ2PYaMEu~<fxtlJzK-P=@MJQ*}n~s
zO^0ALHPK3<=Dn>PMgX`$cbz&(T^-amC}QtY<gjdO51?jdev=H<kM~M&zzG_Wcu)P}
zWH2VsDZSj;<3u<=$@yf3YlIf}6QzoyEG%9%-m2=!OgAehaC)z1>{M!RvfP(uF-><`
zZBnYEjwYD*<?Y&eLUKf<01Z6-pc;DE_cEg|*BWqk@-TLR+mUK8{hTX@?L3bzs2*}3
z?Goj?+lK9VNuN}wsqr=4KJ&VH<G1y~4LH+PneWD<Xbv>;p2cYQm-xGU1Ih{`%ja4v
zt~qud>;_AU)&~JQ`8Q)bX99_N)vT{*L3TTdO?&m3I;pF~9x9%c#jE$EVJ2e_N~Gkq
z8ytX>itLoZW~SIkCTCiC$}%2}<)m~LFN_`P+Ba!gKJ)K37qU|YsGBakhp+bcSqT-1
zgAR7DIa!*%Qt3s<!mwYD=n^hOY?y$3KhE$#`~01)b9+rSGIzdv9Y}qoGtqY!Zj1a8
z)8VQN?#|8RI)k35w{$E>P9R0Icv0wZMC&9(zwyrNNn;TNUmLlWT)b_ladqhUE!qj<
zNxZ_h(Clq$s!1~+cge##)K*q-BOY!_2so&6zLP)BIq$1{xnW>Q$!Ar)*^_yV7OC3i
z6`KXG58e>>qEaiiw$f|%#Zfy1@dA}JvTD~m7OA|mEeG_99D}h+%xZ9z7PWj5QOtLm
zts1Bv>z4ZNY9|3!VoDjJg8_N8q}8W}yYcBh^b(LWDKJ1_eSTdqflfuEUCyo*C9mwj
znTAvY+`YZ^DhRi^YvyKs<a>5-;N}l0s;x9*-qs-4g3z<R=SG|T>a*f~LPmX*CT+nC
zm&LhG9w=OFMX_=C$#sidZ=IPj2CQknUbArzQx8kcw!tU;+>xWi#3Q#sX9$2~-junq
zruV+#=64LyNhIg#ZUR8CW4DbFr`U^IRylo%fXWOL%_&ytq3b)Pf}Gr8(}$YV&EIu6
zjnLGN%SZ}TNUk10^rv2MC#U&*Pkxvq(?|~#E^@}nU@eF)r{CpQnPC<&m)c`XfxhZ^
z;gHKW$@Z<2$E64sos=2VUUMJ&A_y`mKnF?VF6k~*EhH6q-N3+v!dMq353i-#CJ?_w
z4!yBl^qPQ9^Y$Np&jwM0k(-hj8J%YKpgcPXnT7VeYoi)$7lzf!P=y=sn)Cpyl$`0d
z*i9uNqajVf;D!6{u50i&>bqVaZ!6!RJT~;krWwhBrWCKntIRnBTCG)9fcf|^oWg;)
zfq50lT4+?7ah?d6>QuxAk1N>ec+W9wr?tie)cE~%?5b<zXQSKnSyOn#(66O1!0I5x
z1RruHsZC}mK9X9}X-QgRj!ey#D;&x)!784YrKo$RM9IPAFUCdo6e>I2!o7#&RwPF!
zbQN79ncxf`tngKT(A{uaiNcisD3vsBtXV<3&}#sO`4slgmCx*58%$t&*HvA^e7Y^4
z1zncva}0o*jzkJ&<OESHDH9_!L)_m9(PLqu{!Vas?QGc?zxNrPKGE#|31|8jvMX$i
zf5+43+0{HNkm%ck<9Aki%%MZ$cRGKs-1V{xR@5YaYd<Ky<cAz)K}<itEvyfnRH+ne
zy?RJbiLkP*Ho4GM$#h2(z6%_#SHY&7P5E*~n{tqK6z83b@nyI<vkT`E=jQ+we7cz?
zJ?piTjHLK}!uBS<k>kmk>4j*7kgO7@;>M`Bb&?hje(tL276V%CZ8XUR`!BLxf^w%W
z;EArk=HEi?JAtRYixc(cVpGk*={zc;LkVNoPUfNhiXX}P>B`_IA+?=!$@u7Wx?x4M
z<|YrqwtBpsUaNSUy^7R6K&kZlnvLGlR(_X>AX4Ghde?-mmYkR~sHgw-*hXp<4mv{D
zDdL00nf(cgMLY@r16fRyOX8@8&n|D9Es*S5jM?X<W%`cqi%I-zHz+%Go={7~ue5q|
z`w6DphO9iyc$nz14-v8yIN_(D%Q!O1JvUfo{K#{8De;=KiDM~9t8>d?Fz!Mr<>>rr
zIHxu`*NL*a8u2%M0f&MH?%bf00wp!j<0^^OoFjptBc_SueM}(icovjh-t3Yt%WBsZ
zQLolsDIeE)u;6DUt@ZM=RWb1CeJfPVwW&=vHXf&zN?E88SN~o2)Z{}%D8OE~CG2~5
zQ7*coKMMMYX+f0s-CEhphAjGv^GtT@dWW{i%Z_K5RMl?Q=yhi=cZziCFHE>Cm*(p=
zJd0>6Vq(8?6dDU!a$xr<Y-Hn@cfv*$sCHjKMkxx-<Mt*$%w}!z9oWVLHK`*6$z>C=
z2gRMw)Y#ma##R`~)0$98!=e;z^5O<i!WA6Rkb1Ew94t4e9a09Zj&qdP6Pj+xt7mjs
znQdl|Y+n8P0G)VtFcdUe|KVRqUQ^Ro1z`d1#HeLq*$_;Ezi8oatn2);JY1b(>NEup
zBBntzw&&}tbse=ZfuJ1C*ntI4Kk(8UEJ>vfiyo{)Jz()}r~)aqkjcJ2qPk)dS@yM9
z`shL<4ipSSl?I$Y-p~L<``ZG?o|-}AGY$KPbx{2@4v{d>Jlc7Sz6;NX*;KnZVs=|X
z;`|A=b;%ZUo8}K(n{sz2kNZ34TfSwRD7RgWdCDlGVi?DbL|N(T?qu?0KD?DwsyI2E
z8?ET3Xe*IP@8Xu|qO(VdbS8-mNeV^cS({$GtIzXIq_kf4u)p%R!CA!L>DWZ3eXm$d
z`u!}U9`xBoh$rqa(Ikw)FRxlqRlt<xS(-5lD1$^ySNV_)Fl|%w>Migwx>1>M_*;R^
zXm-^zwvpyt#Y2M=!6e1ndC61y%%-EY4MTlUx{jt7R&?|{Bz^L)%rbd}n-tRq%Nl{?
z=P#FcN6(YS>7M^>z1AwZIFllorMu?Y<Zhe%I!n&yuiP+>0e*pP<T_uUe`Rm@?wlmC
zBR$jKEE8VEMFeS7m|moGa<OBfc6?R);Ge$ga&Ih&6anz$VL7$pokF2Z;eXPq&i3Vb
zZ2r{DFuf(1pViUp$bSRH2ScwD{hb2${U*0x33i!M{yW_o<}i9_6dvVF&~6EeePyRj
zle`E+Sh4LPtl0kGNBm;~_6Urg`25Bip{?1R_Wse-aZL8+jgKx@ev}9=v(xYF+cR)G
zbhqOu8Gx<=vN1UJguy$4frA#$#V|+Ey6Yb6{aeM2T{SVS;%f$SI+AJQZ~G*hh<XSy
zMu0LrX%Q1Y4;IV2&_$eE8h=6}841MXkY_SSD<Fa=iI|&I{fKKmtFBv!ZnRZ-wrP}8
zE*HmvuH$;aso`eQx={x2nB%e}+Bo}~8IFAXAgpPvvr&9;3FEnsu?1)r*q#FDrW@fS
zpEI0m3@p3j<NVk(4P1XQv*e;3z{%Q4eKg5*aLVSsa`RV4CKTTn_D@kU<Qlp9dW~zs
zL=}aZoY*~fnb_o`Jf7(tlUgZP4V!J%LO0f5!<d~$y0>I5gS|EhGJ0(JMHu<SDn}}5
zeT%7h#Y1ajvRt!!BncobruJs6ElMP+mC=Naf|s(FsO_w2AFh>#aaSH`M3|;Ej2XxI
ztq&x8N*G~s8BK{IUO<_&K=&6hD9LXMp)~WT6ozS>lP&7%wx2!Ajv9Xe!GP;T==`9+
zKz>2)po*#nj_JZ^DJ`v?V7xo|*`$=Ac0!e=Su<xks9qV3DK^H3vTcND&-mL_J!Pt{
zs78~Pa%ryIsY6J?7;>OGv0BM-tRu}QlTX1xh&tU(IfHUQ=lZ2T=_->Fs@CDfW2c1Q
zMxA}@b)Musi-DlN&Q+L~w|yH)jvM8$Sb}Zhaot3UzNxS`F{PH0<M<#ekk-pa>bhU5
zrNFj(l8k^oVB^B3?zXhP&7X2|P*IwJV_2d(J#BoXVyXHQpCsTU7VzSeB~l7;rtM|z
zn&$;?{?@PfYuBq53~F}@(EecjQ*5o9hV)S9FUTqSv-2yXIFIjNBpEmET9!^oEJV}R
z5$D@8S08P-`jzjnr>abTC<Ap{X>7BjF~!dXHx8-xWRR-Xm2*Cm!$aC+gcmUeru;sy
zyyS{10fkX_zZAV}POIG~_mLT^6?kA}N+~d)E2|Q4Vlti-($zcv&<851D^M-x^rtL)
zDRb1phjo=1Zs*(xMjiXLZ~q)FaPzHB+a)7>4?s}^Ey3G^t21-JTRsGLtZL?ls_6(!
zc`IVEv2MZjzQBQBSbr}1Dub?4P0k(jjDz~NJSl=`&bU<%l~bUM7<@nM2GnXKc>(w>
zHfk(JQqL*?n;7EJIoD0Uj%k3u3tmB=U$Rw>znwUE<YPh#5ek7^92vhfBUfJkBy1AJ
zaveXjZm3A4G{#hs9m$BVOEZPNmp{8Y1Jb!%8||C`tqV{GcvA2<EQfdn{v<?{7Jk`^
ziF5}>uT)0sNS09)O%`kDvMa0eaRb?!pzNJB`ujZYc<vGnS9p8OSgrknLT<X@zDXMv
zMuF`SDBj2U;_WOfEU==n{xw3bSP1E$Wfh6w6x87jSTGgD@7unoC}|CVosJD_s#pj#
zahRPJvXGl;nOoxJN@*j7E1fVC%YGVXCL1T@)W$(4Hf6iLd_lrP?_1sdHG+*)0#fLg
zVl$ObhIFaJt+=X=g?p|z+*e*Hi`IF}_65&Tfj0m1y<JPQkZeb3+jTAUFg|Eyzrfu?
zy&1aNsh%p3A`=XX8*4XCv&=_<Fj|srQBMDq)tO*y;p7PzFW7wa3tR{|Z22I#(~}X?
z=tYx0x#BgK$g<0rKPlBv2j&`Mq%;)=@o(TR?*TK4irA0^`eZpI;DB8?6KHRuF|b`J
zUSt(OOoz#!pdn}RjUZb|LbxWQDcps-N=8C{qYAQku=}+6T9i_=BNr>WLYS>~yoi(t
zx8hfF+5rpQ<qMCjymyYP8pM`D|IUiuaDSo+xG<CU2z)~ikrn^G*%I*OuJ@H7qNW-4
zlv;4W_A-+O$r5wh^|6cbe`;pL`YKp&mw@4O1fr@rs+iIKR~<7wh@RyCENbp$CUy5Z
z<XG7wU}llMn26%%*6Qe?o-&y~L5FRNz(2Fhf6QZLn8y#&-&>VWagbK%RjQD=a2=M)
zXCMT5F`U%o@1=+kE*50HI>vm=htfe5j|BkFKQ<(T|3ccv;YZ9YmT%rz3+O1U-y_@b
z9($r+hBo(P(I<N<E5&Pbowu?Wc!bih#f4jm>WcBK0C&)nJ=R51Ov#=VIg^%ihV-E-
z#gy?V_-HNozTf;vsPv^4=6R#(nNV~<tNl<&@%l}>7sIV8-*#_uD(HMM>)EXxN4^rK
zT?CUoN^%4Tc21jobn6825T&bAKSy-#A3FFyCPL_SG`M)=8|x(g^WP$88~80;pYnzO
z-iBlTNi1(STzH+6#_e=;K>oi9r5<nJMns_tlcDgo(`XFWD{6WFbA+kv?*J1&(U&X9
zNWMNNMk{|eee!yRct&KS4I#_Ld7zPay3EsS{ijEh*EQtJZ(A3w6pzf2f>DRpNeHu~
zKlw5wKMuwy)B{@3!u{A)R_{9*rN5+0((Z^<yLjB*U;akYyQk^8k4*0oe+2%UzY!g&
zdu7M;a<^^5q4`Cl<DsicDyU6*OnlXEVY|MBxyB;-!=3(Gm!huoweBf;0Wa5<i0Fdm
z9Ak0<cMENHu4<7pyt&6vlQ#<<Fku^Pi#cM-*h}ROXdaB)OGz{UDlThJKuqpc%}eR#
zK*UCh4Q8R>As-JU9$A`s7t*2ve)xSL?KV&F=ouh;%lRm}1*)7mOg%|H;p5(J8P*Zy
zd9C?28?EaDGzccWjt}oo<J4LURcI6EVo{UV99T{8wE8XH+V(=&u-IF9i_e#TU`<B(
zzNOerKXWZP#?!a*J#MqwV`@-S6K*b|LDi*|kvQn~dHM)OsRt;Yu6DAj>T&k%C_c3~
zpmU>rKO@WWo1S1L%_=@K2}{K%uQGOy#7JUFje5dp1J$)9&3s=Hc87H~e;lu0hjlf<
z5!UspJnw2~d+72EHag-wAEk13n7@~2^Vv$h^%WpeWzoyGr{ya8AT7ua<VfT_<NG?@
zZ{GJs9HZ;d^s)02<!TB4GFgY0W)@DafGOI{!p58;Jtd@FSj1wdq$l9U-jr(_+sEd-
zc7kQpu`DRcI|ZQ%?x#G$5#kU}f_n;M`m6Eq7=NpQTalPRHZ{!aCU{G(T4K+bSgktk
zYy?$fMWu@JDr2#^3YnBPtU@m`h;kTwod+5=hNh(2g$&$6#u4y9el|d?jNy*D6f#RS
zJ*es_`qmCygu+Sdp)*9p5fTGAbW!%|Vs}5^b$toC_j!VY*V<Em*an$XxTu<3!xppR
z+K+UnS?pQ!$C&`%l6em@3~f=p>UQQx_4x+%GIp#F-{Eeq%It0wgS(b-w4btvf4DQ-
z(u}`1fqHulzBWxFZn@6L^|m#qIkG&hbP-+f4Jzd(o+oK%_U^P1^7!$Uv~uO}LV3Mq
zgbF~veDE279vBN!-LF};K6;w9mld%|i<Iqk?iLIxt+yAltj>s<nI=wib|MXrP-@KL
zjL_v<k9Ej61;5f&a+MNLGJEy50oM4X8QbV<D=4PFO4d71%pzP@<QW5lO*WFOvyhaT
zc_y#MmPT*|P8}xA%dRgFAbYCkj^(V7hq>Fqpa^K~T9^DbJxR7mk$H(B@Ad-&D<`S~
zXhuG}QQRN)x!vmJc-KdPE-H;RsztWF>ZZX_&z0E%Yt5c*M=gQyF2nZ59U8T9A_pV;
z&SDl6!UJ7{WHZHMrmhAua#r)Q+BUe%^k?R#-?tBPr%%a8qz+VZ+CQ6$Z(a&<xIn@m
z{h|cKgos7eWh8d2b6agm<5;#xT&s!J(@2WEmvz#(%MfLY<!RrGUwEjFuvOX}VatPt
z)-}IC>$s>3Y+9E?oad+cLEgD#o?NcGU*O^|lCZ-=AV?GWg3rlkcva1P^O~}1q*vZU
z-71g4{9++C6e1ztb1rHmHeR;3c1ucFip5rOsBF8^(@t?z6il3|gi_O1rV{rOucDQQ
zE_^=IBq?x<Xplbe*?e#a4B9=MZ?4NObRs>7n12;uNxq*k=6mEeC#NoUOlYXXIip0w
zmKu{j;K8b9(qrmJz<BXQB?^N`KZqsRUEN9g*ltQ>q-EZF^E8``XNs|jbC*wY?Hsha
z#o`%8%9JVb!FEVjK`fF3h`Sc`JrcI6b<;88*6bQ*^X|F}Xd<fHAPR3<Hcs5EJu<?f
zEYV_03^7;`h_}TeMKlmva<O)qDRgU}L2y{z<B#~{U}6g5S@!^ekgUcun_n*cOlvl2
zE5`Q2U?eKxy}DC5s`5l@LxdPm*L7KV?8_-hGdZ7qZu48$ln}uUnp%%yNE4W6*pGN&
z)7%%pZ*8N~^`@^>n7|HrniA$2OKK|248V)6{y;SUlR_JJU6AXenl@nMV%BxLUMy)#
zObnt~vOx^Ur5p1naX+F|*15S76IQ!WUYMI#BMQSj84$8*rVY>ct^~Kz)U4~r&fq(y
ziHmK;r~flyC_zjZ99fU(b3_)kSQr02H4qc*{Qs}6qR$22&2}}B{f|ZT_se_?*El~c
zEakhs5%kj_88c}$m41ZF{B}CpUn{}&F;fVV{7XIw$H&P-gl(`9!ZsM`2c7QUGX_J-
zj72*u=z#9IYuChLOQ!9Dc(U1kh_9$ptTjrRf$G8+Xapli$X7Oj?{}zLN`RX6=8Q>W
zWq#PeQeQ_u=}gl)l&B<Ck&H}N1I410Q~i1N2+=sgn;m&T<g;@Zf_S<q{b9x@a%q?f
z{2j_zw-G2|<J<OYs4w-K9%B+fT}3ksudQwYdHi{-HZgHkcH#$?7x<yWuU-BeI&0mW
zpB#jA^m@{W?O#ajH}nfi(sk$atEX?QoAZ7k&wR|R(fa-R@Ks4{pydXFqZ){pchraP
z{nTpXe*IqH7mGgZ;txo5&GkqI$*cUJn&f@jL(&OT!7BI<wsp`!;u~uID_)b_r;k(b
z)kyEUpa{I`9ks-42+hfZ^U(JGZ`+@$<*o2PeAR~ang3fL(vdU1uaG1bua{%~g*5&&
zdYkgvB_9#sYeeKh(1zc^P?i|BMM3ND8C+~C#hyuJZQkM@KO|U+O_p}qj*H8(aNN>N
zs^$f{whVG|bK2qdxWEasFWzY<t+hXJLrK#z<{j5RxKzP}1cVd~FwPcUyW8q=K0{$H
z^$0La^WLZN1+3{xh`I!DkhwxS%u~t2dp<ikIECG=1f^BD<XuoH+N;du7C!%;6D@9O
z7aOCEJb6H&Z=iZ_r=UUQBGI$aosogo^YC1)+LNMD;=Q8@?WV!|JbF?{{6K%9rM8fq
z3jR-)=&JGLLsUwXsxET8HrIUb?KaD-Zn6$mZ(D)4r8~I4F$G7t;!O3&*vZ(I?5-aL
zdlCcu%9UInK2@ySGF7luMhJx{S_|CMX7Q44+0X|_$=FxlyX7i~MuklCZH=@2WKs?e
zJI+3Q@Kkxd2Ok-d1h3!NEx9&fRe3K9=!=IG*=k^K>l1=oC*2PNi4Q_LXX)<nZKP@P
zitX1wGa9-6Y8skWCo`CWOn|Wca8XcDONkpMQNi6MuYOaUTm1{Ey(#H-U9m>u3D%T#
z5U}!dlH*@UTIm)9)76~sjBDL%{%lr2y>Xs+lW}T(%2WJHRHyIneV6~)mmB9L-KB)O
zQqE+gK??*0w168xnq!ZO+{Jy*+o?-9&9Y-$c!DPUg=kTCk0^&C>Hq)C|Bm9tE76+L
z2dmcJrioXJ_PFG}f!zlztc7b&TxRNbyAon9LYkj2G>RDU3N^khNS>cNzGN)Rhp}4w
z{m%#UpG$f^wA%Aee6PMXwWmL6im-NsLj5NMlV7CDW$>~gGZL<U6HqJDk<dL!Vag}c
zrYe0w$hH!~ptGvYuv_>)8{9uvs{Nj+`#YE8ZA+SOe2LQ$(dTF2WN%%L-t)di4g}QH
z)cYq9|DdWP`tf&U*v^t4s+Qv0uHSB^8rMB8!S?Cz%O)u4bB*+#>dm(QiZ~MUa5?=m
z_X>2yROP2@Vd&q(`S(SdH>JAmDci9-7$?BF-x8xiKTIkXIg+crRr9sOM`;#5krFgF
zF*ixN481hq#%TwxrnmP}oWJ?|EfD_SUXuUq@%*o^3?^#VUr~MjJ1QIvye+{r2-OH;
zc(JH7C#2}(&+UoGekzaiw%9xgXW3~ixSmve!yGI8aQVN6{O<pGtcb*~^B3G0R8YT`
zn%yH<H{76zA3iGLlA>BPUg9B<##jQ{Z`L6}ayrPVdYz-$-!I47PS16!P+IOp@3Q?$
z6yeF6)jwMMN9aQo1hjWl1($dKl|6j@C9+^%=9@9~T{U}61V+u}>+Je&ezI`;v$D3_
zmHU+&Fg{H;QO8D?#X)ozkj*1;B|*rzrk~vjwWnpKf+HKtqYTliNaufvdaBp?4LvH=
zH!`*1y!s;+>~X^G*HZ1Iz=zk=7S*$RUGnGY%7#;&NtuqkcI7n=DLu%h$SF#8G~Oe}
zOPYs8Ls1Wx4vM~mwqGB-Gnsz`1!AqTS7uPlX+2+4p0`>`3##m)qyAL4^v?9SAE0xS
zqROj{gXJA@Jgo|SR<23$86KM9sg2dk=ZDSBw>K@0ZZWbz(5ei9loVUE#%H(g!n=dU
zjrrBTdtz*;CdaW*+kmhrOJg#;M++K$ZSe2!eQfW*PO2T<_-wW@BH1m-lhHK78LkD(
zgI!&v^5t$4NMZ{ULM`h~i1?LL)nX!ai|jQiuqT4QHZ;b*70~Kh(UQ=lF~b{;5q{3k
zgx_-$`K>+4UxS>Tv3&sl-fc`*Y-uUYgk!yJ)i4bYpj6I~u_5%bd#{by^O8&#qmEFW
zw-Uy8-ehk$6DbhEl;G5H|Fk8M$+HEzbcvis7Knj%yG1c)#kcTjd|UB}uM=tl5oakO
zu>Q>gdavOu6jU2h@**IxAsO441hYMUkC`%F+~ngS2LJFzZNc_?_k$-$50~bA)5V#)
z`z9z8);MO)Whq8>y8;h0dS^eR0LJpJrDE!g<ExrZ778Epp=BrL0b;OyKJTuKKeI@2
z*fP3MXuXWH%%4ULgsJ6Ytt-#WbB#*@KGe}xq&lGbIOvQrTx&|n3Ioh)^FWz*;;Z7;
zhbuuq{B8jZ!wLJLT7l{|uY`KqFf-824Bw!o+7vHcn2~l-qS6|!WaL@lLRc?E9?Dq}
zUB*HDZHxXLkCOG}vd0@^{I6M0bzdK=@g3T>O)`Dpc$9$^jlMM3ZA<m;V<hVBOuBP!
z%Q||b_<B7+4;WsNPIOG>?j?J+dysBNo7)!3go}$r?^Wi-;(F$DnG@jokVaH;<|Cxv
z`2-`$RQ%ZJtmq@-TLB;BCGR;k@;&82b3N`U^JL<gP^OOb2p6U=(IKJ8N^?x*C`d8k
zh>kdfA9=R(JLN5HTXJ_<a`gS$V(KtTU|^s-JIDgydb`L&(ac_}{2JF6-4AeR$5Wo6
zqt6Y@at&c9`@mYP3#0=Jb%O5&&8JQ86=A9ycNg0F^oj@O?|ofT`M!kYeF*Q^knwcl
z_L$IQ(o-dtt%*0Z8fhkXtW(-3sV<Z?lwhM$K9^wk>zFF<He#8L99p&Z#K|=ruFde6
z5+3fM{Bh4>JI=nc87@3M>{FGVC+XN~YN&dv+U1atew^|m#!*XxitYX(_OYL`YGaHX
zRYUjj;N~~)NZJ%|<zkbiMEWMx&HTCM*>Le8O4<-n`>;2@r(~cVk{|&23vZ*T3)@}p
zz-3BT#8}VwXOT#~jW5KyfMc87L~d~WdZ_4mn)Ngsq#b#veG2SLr0b4SN@eBY3Rg0&
zO?^=%Sz+y}zzuH>oE#O}_KzYH%o%vF*=zCj7~L4MeY_;cX@B%NalZwQOf#_F9RPjI
zdBP@DCqEqMC@BN=31xA!A-baT?7&gP*QLFQ?y9|6hME+vVO^cNW$R||DuTxk99D}z
zd+x>0uq8LOX>2pSU!>lIX(<0nf?RF_^LXJ!cDh<I%l4}m+v2F;nyl=`MRVO>^EN^b
zZ0iZf@V!hNrj|J6L@e*@o+RUv<4vq&IawspoRBDVEZE9seQ&RPqwh>4TwM@j(%sVv
z2bP+hx!4KomD5<+dZ%W{Z(8x#A@RbRuBTgdI8k6yz;NI|Rq}<n1ZQq0??9!7@5WxB
zamNPR9?iWM@YK)zj*cH%=Tp*9S`X^uSP9q-<}<Kf_7(Hr9P-9nS9o@c#1PY0-6oSm
zqhXL8%bd^;UFf-QP3tYbFv(TtQ=x>-T}ebP#@XC48I6oh^g!g_4W`IF4HcFW1L9Z=
z-FY^nTfG%fqHjY!rF65wq`8Y((G<8|*|9u0VRlVD$MzpROz>V*^Pc&S0<<o~^R&b9
zd;dA&hXLuo6#WQ?27+t#!1U~E6Axr>`rzh^vk0$6jqE$AZ@NDY$_iZ^i@S+`e6F>a
z1oO8z{-<dIu^`F+67@&%^@k54X|$}Vbogi#bUFwnOSko)VwS1}wf^I575|yip%-_J
z8%<o8<9mM}CAy04+0vB~Lp3kw&ppy7^(b;TtUuq7d1YtlVq#ne;f@-_GZ!fx1@qMu
z$DrpW<Ofq#BcRsglgUC(Sha*6AM*2qBa#iU$8{nPNCQ7SU*UVGCYZ?h)xBEtkw+wX
zbC!LJ%2VQ1h_U$+ApR`=-79xn_?n>!ac`W{^BKY6*i;8zA&wJD3535nXPKUqq`6uJ
zQW902+45<e+jMj0M_jAwfQg$s?LJ5WXKh@-p_Jaz(*P(_mAt)C)oKvaOV;v7)mCdg
zcH3fQyK6d&ggXUBx`3{!pa_wr0VxxTjgS9%JhfTd4=VhzyjB6%G9eXVD=>Ok!lCb{
z&y9OmwzL!{AKe}Ac0Sl0il0a)Ir>Fx<qLJnDYWg2az~+)qCt#SsDEq;ik6Xen7?IH
zf9LJFayxU*3GGt9;%vKVvtJD6iC<GTpWRZHOiE~b*{I`qj!tU%LT;c$Yalv{n-Usq
zEg@=kAKp?1wU8fWdU!rI<ZELreeof+VU_Fwkh7m?Q;pRHQYt!l`=clHXH3uh3qn&Q
z&k5C5!9f?X7gH2%-^?p7{)LnWqT%gwheO;+<?T3Cy9_6zH1&kLy?lq!;t#5ous`vC
zDr0af;7k5ZIFwz|_`MheRGpzpMS10iTO40ojh){D!{2PMJrhY5*e}ikpHD_ynDgy=
z``~*ZA)$waAU!9=)TOZ%+|*QbO>CvHM}KGM;{3ZA*bPrB;7x3P%ERV2`4$qBn;Ppt
zvkthbHwq7nb5`tkA{0o3H#t+1a4K%H5w+!{Q0jZl<S+UTj<;o=iR&|L>fO&=Ljsus
zdI`7rS<RtmkE-B=Y#1DwZPPrq(7kITE!!w4Tno}E8h$pkVftCEDpxJuwEEI4Vby8(
z>@l~z5w9M!CVscy!>1uBgTLp!V(-E?-OOuVI&iA+Q~cN0q*Z0VDw+dD%2tU^c3^Hg
zq3&3md4DH@jk;+Je7MRInH2-IG>P@6wek{c)h^<?>ej^Jn~INgr-o9O92$DD$?n4F
z5WsI=W>1Xx?r$v(8Nbjw-A<$3Bfp6@8P8mqVw{g9+S{4ku{56FWL-$XoX9N8ya=ns
z+Z+sRJ~<<$rDe;GXoeJ48>&dbfP1Mo5hux5`Y>{@pvTYys4P&hxdO7thUz7lBY&3Y
z3OJ^+&xp5@9R(yV40*)KpFdME1b>TBJs4fR!Oi`6Z8-K?p4sa{JaklOg?Bap>gTrt
zew<9{{&kLZKEVwZ#VzJzF=9s3&QX3-qH`i@sKC`;EeE-v03T6HPWZW0&1Xk8d5o~b
zW9@$<buhmco)6M@w9ky_JU3lLNigHMFy?@wHi1|L8}>~pSR{cNcCGGGFew1tA=ERG
z26|cEwy;`5>H{V4m1?F2Q~H$VG|OQg#@exJe6sTy^OWOFUVClViIG8*#y%wc2P_~L
zyZ$KS?AuVUNtdqe43x&41yL*Q(WCkBk6q~48z6QGBTHYuEm~T(OhXZ*=5*QdXKB5-
z=Mb#=LnS~Mc!!ZUwgo?Hz9S`zId1VIvZe(OHZd2rBGdZpeVTAY<n=LlGDpkmgX;O`
z!G&|DL$tMrk&9NB<efV9e*y?2?*&Ug>lR%;Ry%Hqg!M_fV%nMOJ2O3Z8Xc8ON5}kY
zABqT+hu>H8y5jM1S}os^*XYIC=7g8Lm&6?fWB-C9L}hsZGsXhb<l71H^f>OdM#pN`
z#goF?#WEHfPZvUb{(SqO;30>*#-THd=vxb7Oac}i4nORx5G0RP?zU>y3Q|J+Zzj?f
z=LCt?1834D7J-m+i>27{M(!QI>-kJ~5xQYL$fR53*LGJ~6Ll#YE!iSD+D|+hrl2>~
z8rTQ>^>On5Lds|_^%y=$skfLDZ!A~btf_knb<!|hrRnmQKd+v@bBR>iGf>R=^lPE|
zHVQ>d3*{(dvnz5GL*J!4<~ncSaZxBJmJ`5Q1c~!fV04yfi9@L&<ie(^C_7hUTz0FM
zB+$5gd*&?!lH&N;^`cwfa&u$-9&FsO0(U3njS{ensQJxaj+Ir>>h{9Pc1&KC?0Uhm
z6{YJWL|-7PjLXhLaaq&B8}_rKktYC*<JcXQwP)*!Yn^l@FMyZcs<~n$MkSS=+LDng
z=c1+8X^MFrT>A!{HA9WIWhg=bUr2O#MjBjaIO>naIy|VsOb(~~KIzckb^>d@7Bycg
zpwbJ)uCjKaA;l}uO-IHPCM@Q4OxuKg>29ih^tF#aWlp~`QcdOrcH?-PiN-eKGn%qB
zemZYVGn4Qq9v$t{saKch(dXL7FB}R`2+65CsHXywAWWt_4j?^i^#&e-R9)Px$=A4A
zt*#q0sZ!)C=^}L#<l>fXVgP1VTk>Sr!jJ>*EbiMFE=U^^z)o><Kono$nW~9DzVeES
z&s|OJuMGxErN%&dU|;I&t*F^{5SkEsYJ!~(llGL4MTT8oQA~8XqJBwfC8VKUionbS
zmDt3@dE|Vw_38=5!}Fq469VrPr#zJmIMY=_>-6O#)3rwy9?PZ~Kx4!ylxLZsg1{#^
z)h|iD?Qi=}cCsPA5>pZxPH?NX1SeQO=ULtiXQo29Lw;;iw;q(QWN1?1nskJKCk&`R
zFjYs~cW$~}h257AR&VLyT3tF)*t&)WlNE7w0k@8$v{x8STLnv14HH5fm*Z^nG%ZV1
ztHNc<G!@m`bl_Wu6F&lZb~vB9A8NBQeLZNm3r1B28aSB^gC=bF0uJ65rVFJ>1&nA3
zFUt8W0Ym4QLKMy`^kesohRhoAYaanyp}`D!)ng_`jeM-c3e9Bw8%2srZ`~K_=g-ev
zn%g8@MipH~)ZxJGiH8h}EPrkPiIp^X?-@LHcVyI|h~P`TSi>d2P)|wB&Ll3n{}Tiu
zyTFbOVPF@~hE#M$f1Tfi?KQG6)lHhqY{m;J(s8tT;NYAup;5PHHJy{+GZxwIn~AF_
z8U%Gm3=q6|sYt|C4oo3d>I4{6;f|AlgSW3SFcWj)x}gzWLSa;d+|jpns@y=D<6hIj
za?th6ro2k4<>NY87H6j6K;)dp7|J=do@P_eenaSe#3-)7YH6%#MbiYik<QB|p4g`&
zPg~15Xp60O^o3cdo|XKzs|sq}9Fz=8j&aIRpNI!?A;7)a5cx*W(cokFu)9Eh7R^c9
zhWNIWnn+r1kWE=97axCmWCCZ}23x;5NUQ1t16Nore38a>-(77=H-kmsLU$r78@{g3
z)D#lxdn9b$H=!^n+Zf}$bZ%=?L=lwM7F~q$sRA;!qqGD*H;jX%0cS%lr?82Huzimn
z>qQ#^=Xr_qM=^nekmBO@fNL|Yfp78D{0u*Jecu*mz$?74$6G9MebDwH_U4m2r1uE}
z#xrDI-r~*K+wmKtW1G0Jva10Ort_{r6s)Qywe!5`zAoh@Bs6~}Ni3`!CaG1>qdtRO
zIrP^!sN#F~q28$f2!K)bHu0czHGajpg=7+FX#9+qe11%ji9hG8dZEaEOo{lM4_*Fn
zAO@k;Q18q6@g8Uwlg%B*=78}4`ke<Hjvam1-{qGL+ECOQC&L(fPL5Yi1d7f8#b|cm
zD&a~Jvp&B#>S+DgHFW^>7=6v_Ik`@WfeEJ8GdX+S0In@`lQ0~T0FrG`2A!}(bD*6w
z8Km*^=*uyh>Pi5<LYWW+f|ohF#@CD&+L$BwdP+p!l18LZ2Q7scEQJu?_WLaJTjHDE
zEK8&dyo2C_m0xJxm!)&~U$)G*EZyfcW`0_#t-Z*rsmPi8$lh@~AAL=NB=(sqDFRU~
zc<lrp5lMU^eJA1kmG%Dty0h{cQ7>S;mr9i3E3}d!EK-Lt?pzan)%bMo*ZuQy0)>vA
zk*=h7W9Vp^EES)ECOmjwW+TTc_>ab*0F#lgWNTyeXh8|a>!_nF7k`tzQp2)`-*30Y
zXAoQE?3SX39ABssUbZvmQ|Bu`)cPa01XyLlw$hnKMZ4UzvaP<L4NIQvV4d<CD$--i
z)2p$tnqVmA4eu-3+$Rk${FJWnq2u1lSo_Z9jozF5ku~z;aZgCV7y0e%&$~Qonl>Yr
zw_RbM$Zr>FjkBZlUc9*@GwU%7^PyoY!a&DQRTeE%6W}{O%1t3P-zd5;b+Hg%ISp&z
z0cT;*LCx0JuGRdbX7FeU=uTAT1yEE3<A??+N4cmR4=2-41a!a~WXv85sU4Z&P}WVk
zp2!<2;bF?<g!J7D`(VN7q!iHRRjOyKw4Q(?MYL+!A)}}Q%W-Y;+bWJvhN}ozuaN<6
zNu9s_F>toi+!S!*>edC4IF%}b^*kr%`;z9oPQ@+xlBqUJP0*bu7v$dqkt2;Z=*vYy
zvPle8R|ljw$JYjlM4qkX`Nv~Bn~54w(j?#Ax-8IQjOQJ`U`o>7(I;c+lZ|0RZAi7}
zathUFRDY)aBDWd;%jCpGMz0-T?hN_CN5FjiVizil6}XdctS^>rZiaHhSW&}}R5Lb4
zPJxA;a1Lz%>A8(-weI_rjI}!;^QckF^hf8{;_eilsy*2`!Mr|G>XQ<_4Dux>3uz0@
z1a`yLoX^m7XwKQt`TDZ#gqO?}bE{&^FKRpXG=%8(?%Ldnw&sR2OEN-j6j?=|K8<Wb
zxNS5xP}<eKXBg>HQqjFAJ7%W!%Cc#hQS8=R(u8Y`dk-Iz>|)HD0Jp<yD<I`ZdCStx
zqNEkYq`8mL)|kS}-qfm0$)CQLnCsO=lafeAmo%gsX~vSNSuEsS)h2{D5|s8QCg8Y}
zKXvp7bS~ZuM%nwYubGa7HF6)#$O)mU?h_$tKC?;?q|kb%gU-sP=UXZ|x=lksK+#40
zdtF^!g!+BDO%ulc<FYBCR`#|S>w4&gBenWcG?yOV)>LPlN4%t)S@}YAjd8i^kW2`6
zHnN<6Y|&diqxIpcdv(*+$R7`w+_G?}WU{eVzYA>TS}R-eWt>}dT0=>`1$q+rTEHPC
zi%HCn=SyvDqvkP!ZUZ$X4o%iGYE){)_06nLxbbLxNO#kGlhU9WiQ6l%xKZ|_gTl(P
zwwVPsXeGmS)MD6dqRh<AawlT=k-->4s}Ew6H>_3+*e%J36|?o8444}AYupTFs>Su;
z&2W#2|19CCk`iuR{K0-EIVFj*?O0E3)0Nuf4j-u$&p>uSGCDdoHH~xPq_b8l8u{hf
z^PLk7-ux)HQ9JOcLSn2Z#d;|#l}f4T$mbbxI{t4X|3YfhOO?1STf>)1#0>GK2aEk`
zN>4_tKkkKo*}J!Wzrm?SpzfB`D^7?1>c4ONdG+}`y(HdjuCw-yh}2qMIPi_S1M1t0
zjgaVvuL_Poz4KIFV27<Em$KN)q;Jp%dAy`~cjo?R&&hbA)p$RO+CO^n_r>Y{m#qw9
zT21t~iD**Y{C}RG>ToHrT7Mx<2TuyiN0JjSB6K5|<eK9viZnaMDkwcA{inWb@KxV0
zzwNsTJ%xo_+sj1ExF>->p+)`mB4E9L*!!<B(AWQU(t!VVkTS2WrBTJYkzct4#`-Iz
z;ydbm7?RxtFVQ+fNM#=b4QWYy^+K+l_=Pybm(}fre<8V+vUr{t{u#pe|MSdv%YVXo
z%KCdhn{i8{`m*}onE{l7I1wUGSn`|(Y|BxWE-fBYmp(~dbt0IvP3Fg!cisQ(#{6H-
ziir2um0Y0A)1Td#$$Np-20Yb$2KnKjk3|b8;C=9>W?>)`EV*#OBPJ{a1#w%@7hPS-
zOFUjtw~74=NnzseM~rx+e?K+Et(nEVPQ-}{kzV`%+WXF^rnYF^AS&euh+v}yL5ip-
zigZHnRjP=9M^KufBfSVHO$b$x-g^-c5b2^)l-_GVKzc6$0{B*<M`F3--uuS5Z@l-$
z`L{_{nQPAP`{vwX@3n9o=j}iI&2B+9j8#q}*myCXdICZ$G(xY{cMaO2;3#oq(BcR|
z^TMkW2${MBD_4mNj2G6^`bYO~)*1=u;)b~H{m&u(@$JvvixZL_twgw#0^kKx<tK(&
z(W}WH8Wm5&w|tsDwmkPp6sAaPcMiO?TpiGw7jkX-LW6Wk%khv1-vBMCv|AY!7JiZ(
z;wh|W@FXBMT}e=e*DC5x>u<Wo_EoiqpvyhTl8z8M@K(HY52`n}8bnX<axrW>cPU@n
z72wNJ+r(u}8>w4@h5ef@^i*k8CD<|!z!aW|YBqOq9K<4PP<1J8WM>0x8wY?@l8(Gl
zxiM#s;f7qUQk9d_+<#@7LCiEIfL%XLWBboE0|3Bany8UY!JQwIMqww#p!ugs;UZrl
zDLu<`Um+xaHuewwjAdw!44V*sh4AJsLbpuK2f+tBKP|tc3;hMVd<;wB8PN5p5wfok
z!EZAFJ6o~t_+cl;9lu+qS7qwdHwN4`pd#+PZJ@*JI93q#+N``l{balHF|n|>mKXf)
z#Rv=AjuQ|2%*F66ih}1V&8g#cAY;8&<a3^b;EYYYy1WU-UV@U9vURZQAlXD1is|aa
z%drc^m|r8|B>&A{Bd<mBQUtU4#rUR0YqGK09wt_C=e{K_|MzhSq|?jz)}*Y4g}xXs
z(MR-UqHapTO$4`RUan+{`J+u2#ZT+f*IznU7q-RT6hj)V!D2Nl0`@)oi}|mEe}gF2
z&c56ppuAzkgzA9Ry2h$3NT(r1=67OWOotzj@th!tP2@`^sA`{b;ftLt-y<lRePo!V
z!(x_ufgwTb$b#L3%ymBr$0BuM5B}Tp3mNdk#v|I*ZBAV24;-1ykJ_Vy7x@_#eUPI9
z`?k8P+&g3sy5?KOvl3eA7LGof58!7@fI#XIf9v1}rp!uyg^0`#U?yg-N0iZx7lMf?
z=<Hnc9jL*)CScw>_ySH1?Xie-@w=b(Tc=citb<)74K`ua(a@R1y+sDS607)Qs%$Pp
zgoFy2ej(@lEb0uRShZTkmeDO?6As)W>%&=J5pUVIdx83OeQZni$PWvGo~@ulP_)54
zfys+>>`HvG6Xxr5&6)jizJ#q%lOTV7M%_qT6oMOPqO4*=#G6M^uL|*p|IWv!BfaSq
z)fFp^oED5$EfPGDB5A+HcUh}%Y*OlVee`crb*BdRl2)yJS<C#zhCh7~u935{q}ZwP
zoX08S#;E?9#G)zAnu5=ck5U#J>U)RNjnkR)<(S|uYBQ6ts9<BpTlc8Ih%JA^Kh|xX
zvPa*o8ZW#-fv`lpq?xq~{wIW*1>?iX=)n5UbP}F3Yh_(a|M*UB;iG_4$D8)p;HBWs
z$B05#oZ(U)*`u4#Aq~(=R8GLJ=|AEpR#ZQtIrLn3`)=omz#>i$+Bn#1V*jPUQqP}1
z&zgx7oEbK1X*j+hmW~fK*oB>x?)m{<DMLg>e%YnRtF`GB4nwn*KY~Qs3U$!4(!!tg
zRI<l4TKZ8WFf6bEda<_VC&ScZUe#Cf?OZP;P&qBqj(DjEwmV8W@LV{#A;Q6Ryx^T9
z1M18gZ@1GA&;2Ff%<np0c-WD_8(sebXQ_&fh%#5OiR`q0-6EYK4uI5ko(tkf4?U8q
zyb1jFi6#G05!DZOOKXjf4Wej+7~3g@Mg&@HO@APqzNo$qoLWFHmBi{+;Sh5WqVx0i
z=!j%FMqQr@*w`0q8~}yi?+5P+Kw9ddRxIz1IlNgRIf!_2-^wDgV&3&|<x>GCx*G>y
z;DZGL>Tym#gD%mz;n<A_7)EAL$ay$mNWXRr7pUQ?h4nR(Z;A?U%%}G%>#vzwXLDr!
zLs_-QaWGnhWj~dTBpC`J6npyzf~1eFEYCXy0?u9qE84|b(NX)YM}qYR#_fQX@i;zJ
z4>LqOqMB~0M-^<4oLO1OZprzOV*ZU_NhBO7dXd!NbaC@#V0SSNk8)NPJMjDFUGNph
z=}47vEeAB;%rxm}nDYjt92z(Ps`}$N!D%X|koqo~%zS)ks`2oWhQ7+hVizo#lO*lY
zjo5B__G=E^c|dF^uyT6t?D!H>wj5&~t;={bjyz#MU*w1CMmjxnsBF$U;FsFj@sY<b
zJ;7I9F7{{8gt*p)GhDA9k<)lCb%zfPG~>WJ(f{Ccy+f@~qQ3x093G6K$eeJ*sT@#+
zpRH=VV6*(Lg;Bk~!SFDS5w@H|<RYTaHI5S#yHnzI(A>$pa#?Y8803fzkR#ZYcnKz5
zKZ=q)ClTyH@XvHDT?V=lk5RQ!m`Zc-O$oiIPUlQ{qF_xwPPG~xMR*WL3u9VxUR*=x
z5BRb%bVAojZA}eKNIMTLbA0aC)TPZ2IFRYgvuQ%45t7fP4kco_{KG@%ugkmAvIkrW
zJR<fBmmfs#^65}%(GV~HEHdg%olqO4%0`1_-$w+J0s>V~xN3#p!gu{vJ{9>D60s=9
zqAZ=b4+J+0^$+>Z|MCqElY(yR@9ULaiWR}I95)i`vYpU}Whkc5nyx4dj`2ac>J@-v
zovQEIRkRbjy4PqhON3$b2u=d@0AJJ<Y8|2vcB^ry0A2pk+ZxXLx<dMvhS`9;SZ_o)
zPSDi{IqTmhIE6HJ7UZYnlaEbzcb3o3;0QlRW&;#JTpbvV=*Fh3aTazoa`%y7qrpu3
z8DIvk?BdJ2^3zQe+F;3UheLLahroxK2!leK`B^vj-KHoflLH&Ysr2_!TpA7RxIBjh
zXcBR8P6ZCFWdQ_ua<dn+-~(A9PZL^od{xM!{tGkMJ`LzEcC;F&h9*?7Bk{>c2Ux6i
zsSf6n&b8cHzx}F^uYC+fcA5nT(ytli-6l4S4J@*LWd~>dz7k*!adLjePrhCEmv`%+
zXXt1gvur{k3UzVJyVMI^c#UET&baawa%jrBeEo+?$xSqKY}vfwEaW*!A~@cHvsm_H
zozUqFd^tv8HBJ<QAIG$(ATMP+;-?WyBe`+(Cx<tjjwn^;52Kc@VHjo+jRHF!m4QMo
zhFB#m%N;`S5-*kiqumafA*M#EBI70cnRfrAUTKF0rHh-lk2}9KUVP<%R6Gy{u@;eS
zy}~w6koRnpTXt|3_|D4am1?vu{8R_IX^w^d%V;~InDOK^4?fDQGg88Mi>iv#c|EqA
z<~}*^-Z`9_w;N4f`KWobsi$4ao@vGHcG>fJJ)?H)3#Z_pMwmSfZFKDkR-`769DQXt
zWYT+GpC;2ADf`;ve)!7$j{&+0ujI3`nlJRp5Ej!i2Y(WcnK{`OL(BkSpdx(Yu$2d`
z>L{0cVETaX9wK`bc3FS`npm$*ZZkdG?<_t>!KHIrr6N2P8jD!${k%u;6+&4$;vRWM
zD0C&UzG#;PnP1nsj+R?6OoGm|cAK9?2@0j#brY<cu2{g0E15R=n2+w8e?UF57jiD&
z5GozeGqn@HGF$o#9T(qOK3mneb0hNg=L}by($R-WY|Gm1aW!Ov?0bwmF6o;^?F0u6
z@xh301pBN@Eho;5@5Vh_G~Hzy$yHka3Q<~MNh13(Gp3y#*w~gb0?GRH>KvGw>ou6V
zJAPp^Vqh3&#K$`~w#q}lLj2^G!o<$lmX13`-+@-?%(|Jss_I$v5mK)(99rLxMXGPc
zPxss#_o3uYt;ScQ)ZDJ5g5fE`g`CS?A!at?2U^9UStHjM9o-a{vbM<D^WP7}@2coU
z5#NI!MLz5FG~JS`yd6KYA3vak(~w}g_w%CC{)H=5H;?_@gA7=NTexo9WeW{1Tq!R9
z&sWp6k2H449X{@wZQoipv&UbRm86%f?XP(6^cpbIm@$Ll)Vk3Y{okFakI_oME<E!q
z<Wco$(uSw&5hF<=JU}AA()Hf{Vdaf(l69v^>oI0(Z4zt?QIG7OSn5Q(q;8hli0tH!
z!=}>L7kBdh0-k*RNN7dIWi_lH1l#Vln{)a4uO?<MK-+WQH)T8);?rK{Q?C^iMLrt^
zyN>T~+s!~b7lexAaC|Obovi2YC4-61G8bl0NlObiBI|6`J)ust%(Ul*_6L_%rJ3Q4
zI}sS9&AWXLK~G*N+C@HFvH%0l{pQI37i@G!O!wVe2)eo?<FXtnwq1R82r{;}`n$vP
z3-+kybu#PLQmVF29&+PO=6|yc7M;0Q)q&Q2L%*l75^vqPW9OpDwjW@2`CJdZxr{5}
z`HVFD>2-55!l%iba+SY<qDlc1L_Q?|UH!te)-~7b)ph8}k@QVKZ8IEyi@654vXcVZ
zP)794XJQ@x$GMe}Ab_4JUm@?Awi{LT!D5h@wWtWC54f2M9soCc!Mr`^eIx4|x7f>D
z^dX<)lFcP8rkP2k<|DX@g*x|yUg5%G*&OhzwgVGw+Hfwn=#|)gD3X1{@K;DCR!df&
zCcwgH{*f9MlUo3jztJWGGhPibcD&H}uMqBW{;v?g1gUR|cAzRQxjQ%P%a|yyOv$-Y
z-%X%rG?(<IYh8|T1vUc8#`$c-ABX@HV`OpKdIS;=f^6OzS!^X(if}FZH9X30z{sOL
z(H(luhw<IG$DPUrfd1_Ksy}Mj;Yu;X9lC4&W!KQo&=d8sT6#a;crR!DfQI?SLXxGY
zng522Jl}=VONdJ%pKVUE&Y&lU2L2$E&SXG=Mh7|_<(Ayt!KminM%DV2wHZQ~IGQTV
zT&X&QR~@}lIUGlZWZ(NP<Oj-!8E#PnyOs-IAs{f{+5sI;{aMGm_NYGHRtP4Uxl&6m
zp0zyKp7<7y8S+bUCRmY6&bn02o~Gq@0M}s9HqZ%xzEhb9l^TZ`>XoSfZ!5VPR-I$H
zQ>uBzIWTK!&L3mn_&wFsubXmM%KGfvL`;PVHxW7tolP*(e;QwPr>P5MoAvz;M;wk}
zO`P}&!3fat547;DkuSX<+>XD8TL^^P$_=BEKKi63yXMxWAdbH?@gOg)g^5vbS5prS
z0M*41NAD-%%p;Sv{M|DmL0Te65pN<SsXSXluOFd=WQXf5?8khCY~ZFv=Raqhtl!Xv
z@zFpUG?oeVaeQCt14{nfW>fqh(o%tC2~d1jt3W0o&RE6Y&<9Cd4+Z`a{v#Q?c!ShA
z5^z$8Pi{?J<Jvtsgygc(VITyH3o35u1GXR&;rg-`!-#MBMkQLPd8-9Q28i>U28r$i
zRL~-TR5_<&MPLz#!0(Ve4Kl-bBfq7k;;)crf5=Po7~a?zWo~yMqq53Ej<36NdkWLf
zp(k5^n_2J39@Xw0e&|+8)wb+ItX>R((~y4W>=i(Vu|ACYp_)_;f%=X7S;>R<sE_Fs
z9^D1>q96ns!0!%y`}=U}w=l65VZ@l|Sy(NRp4?tl6IlJHVnzNycQ^UjzL~fSO#H2^
zQRBu4zzim92R31y{S^Yd?f6@)Nic~49Jd96P1+RA2iQ<o4@$3Wkh3wuhf^mB0V5yZ
zP~`7G#wl2(;Z3g_5)wkaQ6I~SG(fO_M(^l1JIPwGN^S#uUJVA#`3m`^`ofrbZ*&0T
zNpvH|lhk$fLu(O^Ic`cnmuXm;tO14Jdyq#|-7#4aE9JeLD3HN+F6`utyG|K)#%E<f
zfte*V)SyxlzS$|f?#S(}Y9s)&$`tAx2E|tbNT1apeOhm*eT9_k_<QQ@svu)RD6v8w
z|Mt$E8{hTkuO9_aI?8m#2=4azIeH?9Bt02uWc=74@Hmbx-YO5rmNcC7pnh(DV==SK
zq=B5-40jvGb)<4NsO7w}F&08p#k1UZ1kb)@xOWr?&`#(ctTP{tTYcqY%JR`JF-L76
z<Q5plK63N&?*9$CBAA*X1EW*Bqw;AO&VqL3Um?uD1sqdBTg(7E`Io#r+(5!P8m-gH
z`+IqzE?zB}f11@L7$h7BC8cJl`G=LDbguX+-A3bIbDjECKpdS6!X-JE<skWU)4N71
za5tjg$5h6M<g|_*;FpZA_qO0!@YGaqi{S6*#mLkyrqKTTl?b-VztOpWZAtalLLkf6
zHQ)g|IFfit#uXBKJ-gQY16uj&OnOY&t6wu+RwOpr$OElfQk}_P1uEbr<~bk!TdKn-
z={Ig~H}ORLhuluYh#9rM&dik-6%Pd$Xuy(%cmE$?k%5@*{@mSd77t1b$fZ$o^2?QH
z_)M7aj+60PF0Wu$RMbxN9c)vxnHX8K@Nis`b3NMruHw|Lp%b83HMXF`+={etFFtbN
z-N_Tf*2Sja4vbp%q5G3+>e~0aOvvxBc=Dr01X$Da_XfWcfcvEj3c&zBYxY%*b~C>Q
zP;<ZyfHKg=kH16DokcUbw|%;WjKB-lXg6DrDVA_+L_L;0s-ZhKmGK?rmWBNu9xd55
zJe7>8CbJJH_C3KL#po%u+g57TJ7Sku$&mmuLvK_AzAka=Z-pli{VNbvjG<HXXvW?6
zfF<;`@3MxILqOfB<6lF1uJZC0KT99xX5=fxtGpYeCQKc2;KnSTJ*tY^et8rmGx?o|
z<ox)eMxNo(9<I&sj(JTG`u@9Mk<aOM+kL;KRs9bH^Y=vmUzT8K`^)$52Qk`)(Q1tR
zV<qd?oUDFz6nS&cvuyKDvE37|)(H3<Q=rq$s6iErPq*pkTFG1Pwh-u?&ZdbaXF4L>
zmYe|&1+dLe9sLUN%o($fw)g3ZP8%4Lp7ccw>>|diw~xHrntEcgrJGLjAofj}tD4Sr
zj^}7|uv?5(XUk?0_V9$>`3>k)w6+B8XvF(HlP}r@F3A$#&rZPn;2_fOFbaJS4WlH_
zi9Zb9gHtu~*bOyJ*gj%^SXgYbovEE7G-6RQhA?6<vw6qXCfeZe#3VX6a+?8d%Nwtl
z8KRNZ7zxp6&sIj!B`3DNrDhtPd!3~aM(I8=;S`Wg%zV6S)_Olyqc3BWR9C8u%2sl8
zP&Iyi)*vK-rUGHbIU7)0k}fpU)ySl-%=^fHQ6q6t{dJ&Qq-Osu6EKROm60B3^o$Cg
z+46a{8}8@+OD|zw2f7L4ZfZ}sqFZNXh-@}DucP?-!!E&=bPwy@rnsg$m4^vQ1T3K_
zMny`6g|1Dq_1UuXgjNnvE6zD@(90at%Zt|;QFeBtZlUYT9^Z@eS=p*#8pewnj~8E#
z;m7M<vvl}7lS^6UT;qgGQ=4;sb7t-69$$)tu=kZ<6eH}l%bOTvx<;XlGVb51=fl-a
zto)h<o7x0>Xy~oyJ}6$fbc4BxSe#b?QFfawe7l%O0Gh7mlW}}<6*ghmOIE5Hy*}F`
z!?sNkk$k1>?AC+Lsm;GIp33sDT8JMC;9Kq#%bbs%>54w@!o$m_sXJ-HIN*JgBEDaU
zaN8u<D9t*e2^}1-vu0vl6{~qZoYZPt&+7VI6<pB{v&J&s#>rx%PZ;yT?^o9tl9NNz
z?G`mW=%;HKScJ%kwsv!+{j!2v^e!$+y4YbG-3d|S{W$5;(3-bh%!^b^lVO{fVhKkv
za<uD3tHuJ;k}mS`I@<0vbvYavqhN({%(I=0tUsbm5dk0*dlPIf_r-;;I7HUNYIU!n
zGwTZJKul{oaEuiT%N31hJ4wq6nV)uSWePo#rs!!#z;gO;c0PAYOyRTbW?;KO^zkDI
zT1ExGi8U<tb|aGruY)TH^=WUoq9JBMPkDJ=CyZdyiDQ^im>W;_^76tkgHW`IPm#SC
z9lf2R%@EknwKv_Gxm^tN-X8`|u^_m@2wa(L0ncm&UXW2HRg}!70{;(jCj~I<2JuPb
z<)T7|C(L&3ShcR1S{fBg`}ql5tqOt#a3382wKz7^zkoSUcyhuiJYCJ)3<Kuq2OK9A
zt!&`7CT%WFF1&_qye7+#^`RS={ru>E8cDs8Ni?Qh+9^2Q+7qr61;si?ETRD&oUSv(
z7c+9@vP_9Sx%R_Yd?l-5R8tfra)&@Y&$H81>zTwMnkuQGXFFq?v(~N{_lV;DEgz!Z
zNUF!UsNrIPwqA3M)wm>~%*%IlWWsc*n1`#Y)0gh}>OfCgyEranHLl5ao%=X}i8s_N
zof|_+JqD|X$0d@Lj*Sc}-K|zk>1QmojwoUi49|;wc>|Akz1LufangQPqL7tla<vMe
z*m)pkDIF|JET&2mShw^ICn0%?R^Z=;oU|vu0ZBYt!s<ERCsl`Q+g0-#TQ6Iu%%mHQ
zXo7|K1*vVsL=(+Z$yx4+dpT=Q4AeK|4A&JnU(6uswE?f8B!^Mw=~|~>XWv9p`XvlH
zo!qI>Qmpsd7gsKrY67o>e<;83R+fD4m8na%@eHS`HSG!VyYD`)H=h>I;&I+m=35v{
z(FyuwJOnnuT)S#mUc6Kxx7hG9W%c~~=6eeH50oZ8y?|Q12r;-#)ydCa{fb#by25c&
z@zIUYr+)Jkg(>2Gfxff@Mva|savv0i!t{KvlP>bEUh5GF__CLG&(*2CRrT=M$zj^h
zS+`GxiNw>e9OYmZeHJ6u%Oq2hs+>ebBpo_+U+QK$N9kBWi+Ra?scUBG(Pn)QgEku@
z5oevUnhW(AE^Ft_#%j>|XGmNVyRXgKCDM4zpOQVJn_LS|Mk0=raS~5LneF02OkX1F
zLZe-^e%Rn}_Wi$XCo?G=OP?DbdsXKdyY8XsSCs6maPpJfON-@pvjAvY@u?O<-{TLZ
z=>3@0Eu*M?qpyBhJ<&JY<zHWByU-b&dna&lA+X|$re6tdTB8%!m<Pga;@;@_Gkg~v
zE{M}!QoVX8OPB{wSXR_8B=~CJq-Dz%FU8S`5?18HB&*PeZx`e*@^Y#(@ZYFBG-Gp4
zuiCtyhA5Wz){*;sYR{q?JS6+t#E&K35-)X~iJpG*iFhJgiB;svbu(>ij#B|M*7DaB
z8eZ|Pni?_Zx4|FeWM!WpnenD|)NSl2nTg?gWFt-7S8Ph$%g(XkHQ1_I-BFpm$nP=8
z>2ap%aLC^beP`wG^i)UY4tnsWzN&x4?te6|Jvi#xsCcIuWk0t#+mmSGw~v~(puEz$
z*NQgjwYY7&?kM5I?|rHZW=V;Z)+#C>R5&FnA50#cU21irsq$g6N04l2?hzJa*8amG
zs+?iIfokX{q0L-F=LO5c{*JmM#}^!FFPE@p1hb>2OWu}9N=^w^@Zu3rKb870C@<u;
z?=yN!o|OCDDk?^8#(Tqdv+;F3w!tL)<PVhU%_=(ahiB@LW`WN<`#!2n1QNX)<}IOp
z!T2WP`MpV7;z;%f&7_f$j-o+LH*UdCE!(Ht$0R!4i>{^_dVb#%>hq+z=2Q_%B~Q+>
z%8l8dpt;+keVj51?{b%Jf?syRw0rg%bs}3p$L+8OOeF8k<|->oJ+Fob6NR4B(y<?F
zuCpF~Bd`{&<#1$GjkflvCL?pr9Ir$Uk%5m|tvIKZ!C%FN%F2GSJ#0^MVpyup<b12l
z8)>{9Zi`vJ<t$3Pgf6sMSsId*5SyT2yO?vz`TFy$eE!Q+Hm6TzWRWx^oFR=oomODd
zLoGt?Z#NZGX9h2$X@n2<!;kq3S_qFkix6pg;PV%Q`kABG+*8hVs?)y-9Ii`B8oqin
zQQ@V=q>#Si#DfAguC((T37+SrdpY$JZpz(y7DN8|!%fi)?a}jmT*K7jhx3ji@i{3n
z%34cc=z-PuWmLI2#qu0;r_2q+m_OP(iFAL3u#K3g>^X4sci;2xLBy4mj>F##2!HTX
zFknm-G4#tUw4uFjM)WvT3;rVbqA6F+>uTqV)*RZJCD!Ga2RDiNX=JoMvOu|rxD*^j
z$HPx(vD(~yF*{kfIiK01BPBJ1XHyeJDW1vv_5r!%s&?d5MP1C5?16W$bEzE<t(L?M
zEa(b_$()q#{vr@lg7;!Dq}y>@n8=uU36HVSA9Bd%!-Hgxv>Q=plL9BceA#ZkbQ_%9
zCqC3SV%98XkJq4ZPQHmt(6B&|Up@M~r{VF!%}@6f2GOy%YD#-0>O9VZ6a7L0kNKHg
zzL#TtZ5+N;(EYqalTyc;EXn7C;^D}wdOI44zVPC7(hrTIXQc<}1NwuB-s!Nf&s@J~
zOKcFL?8`ZDbe5^;FDrER(r7Y&&j8}%DRG_LAafe;E9cdihQgZ^*zMJhd-2N0(6tE~
z(jUq=D{)$ZvnEBJP-WWkI??`8jdtx&a7prC?eeM>C$mk4QMWnlOVCc253}n}(8{Ko
zG_?%DWv<GwtUL^R@Iv<1JNd`Yirm%f!h(3iLSD{{pfe_>UZWj9aET46o9LE(A#zeq
zin?Y>{y^sTy~Y<$6%0N5k-4yak-7B6{%UoDa0Mn2MW1Lknb<Xbv-dF!7S!`fmO7is
z24@X8D(1I*SPGvZiy)N&mh=OnqwY5fsInP2cuEOByd=CqLM%p{GC*;G>0ZVcIU9u;
z&vV121|tdug`V1;T$h9Y*3{{)g<m+tPWho8PG1`-eMdT2x`fNiOIxGi1wEf)pso7V
zH`b|1U%U*KEcd1j;HKd@sxh_!>>slR?ORSZ=i0Soy?y7Ha`_|8LS=?}BBi=Giwq;(
zGtVa#1-&LYeoxeGWqm2{w5_A<22ag=u#Y;`R7visVnt_VDavxDP)dRj6}sA**zZ$#
zpSX?kVeMPO42_)jhpJ)kMHtQ>X1!J1Vo2GAThqz^p&Yw{DaTeTXuj8HZwm|D;Y3UH
zkEJAG%1^IGBcIhP91$bfp)BN(E8)ob{?EtDa7sH5z0(`nFU;O=27ou&e%ucbNKCoA
zQMxKCOBT9SWm=Mz4Oy;$?X8!eoX}_|JH|!cnaCcKe>xr<s3ul7!PpfsXdknaR4DRx
zJP7PPX!`BF0C$4N4Pb}sl9la^dmJ?G_B39OU^7%`yc$G+R8&x=vJ}ypkmYXmu|aOc
z-;z2qA6DdAD~(?>!33&!)B34ypLYihvmkT?;Qa&4O9pKk59!olqPi$ep8>QwZynf6
zJL$+WFOjX}<_I?|=iwm&)ds1&ZD5@mA*~X4Ak?MQA57FHP!1D?p~>J#7R*EvS7#jv
zL7~5^3to*SI8T}$gt!A6d_UFW{<v$Ee9n6iy!Akwi~q4`f^q5P=J2ZZ9QQn>>bGXd
z_fs7WHCDpkMySwrF>)q!xVB2Z+`Lf!eyUCV_~eBKDJ0%Pg_(;?AFmZv&RkGLmG`sZ
zwY52}`Nzu^b7h3!ZEEoCL(*?gtd<6}CeVC;!Vwy;@`yOk-O(<_%X|32=Fm8{lk~wu
zkA45Nq>dL>#&oi4w%^?eeW`9kH*MJ<?finLd4vVZR;E>~<|P0Fj&>>aZOv;Sw0HJ$
z3_+Vy#0wu^-X`2Qzq!u>^T-<e$bIe{iUBq_V{APa>y>%XOb2ohT8Otd2<4TM72fre
zLj;};QX|G_Gw=r`=j0aQhR}`mYC3nkQ7dwAd|rjC3j{wKU?SRQL<r`QSr^n9<bw{{
zADr>WP5}9V104tmHfT2p%qQhs<|W)<-UMAhaBhuF^skbmlHvyA@?ixJy2I(o@PiO4
z=5XMQtRtkVuA+ji@K!n($hTPej9h`G_>Iy{88*lvWsRuy7Vhl4FPHcR1StIT+FZhM
zzBuswDBApVys-R_OOacN6I5WH&vAnl2$+X2ekORDl&oaMVf~=%G>psng##hD2^;>r
zHq9?ZW_=k<l2}{Z%nd8h3u8S9hBm&p4gek*eB64Pm;iIRoezx{Q4<?lEF(m(2*I1R
z<7J7ZuINmFBk?e6{_*tc_692fX*mEO-Qb6*?u0zNiTKqM%l-?m1%#-|_Lmc|FXG57
zR4BTbxu5F+UY1$dRdi9t31cRH(6q3xQ(Fwbp1f52otc*^*e{tjM_6E`-{Q@LiFd|<
z_eFGGl1bBqc@D`fKduTZ6)*+(7A6SAFt!vh)<NnU<Mxq&iI5-epT5Y2cT#nNY01_;
zAir2MUL}Y%><uj$Gs1e%x;|c3n3+~7R4Nbn+<HbDI1wxrFezq7S@Ry<tbT*E;DGLN
zZZfa<)9e{Zq8kEUj!RK14jZgI^tRx7Y!k#W;~-Kho(qu462QM84F6^k;hxWdyj0y-
zF8?ysW^urxe;x?l`saaQ=iY$UO#UNwwTlrIUX?p;@D^1>gjT5m@qw3nY7PkXBMv`5
zMT~C(LmLhaP)(&paD01OLqYUEe^}$NF)LM53KbzbwjN@+LA+ww&ICVU8z*6w5+gM0
zP>c+-I={T@H!R~+#ln@56U=kR;bi6H{F+>I4)Ue)A$vN|a2N@V)(o>cyet6Pl~)OI
zW#9>rF_fy9Eg_l{vL5r^Uu8qLn{j-(i0PvO`pEp#2NFjRteweiSO29O!t!_moP<8S
zvi<60bf^Rcov$*AuR_0BH5#Lrheuh0HM6*4VBB0lFex%uFWQc@`Zs4CW>KBcnDZ|G
zyk^AqiY3BuLIc%j=QQ!92W$+NrkLK4+e>Em{!2%*X9%`W-SCP-al$#ic!ttDg2>a+
zBI=0(<w6XlRdwY^7A}m+UbBTsn7wwYy2ADRAx%)MQm}R&-I9NEF4<$2&?~{(b=-Cp
zy6_`9fw$u%%$*7BK73J&^^7>b+ENjcyqNfz4o0zNijhn!+YvAfe-$@Z2?2`^FwwJo
z+}enkA`fdgq8UV6QyfocDn#>1zZVbK!lDP>e<--I*Wu)w(Z#Kyfj*j8J1{lrQb2P8
zt717=hZ9VQD`ozq4uvMEKuGdL<6G#Nm^)FE54u%O*t>!tR3a&&b%GAAg%2)8)UB+V
zC&E!lg2>d=_l<du2AM<xVAU$ROTNu{?g#NbxV+|3<Y5n!xrw6EV~rUAg=JN$S$hAD
zp1<%x$b}Etx=QG#4C#*p=SRjojgv8VgqE+rFDq`952ybT&f>O%D7RS_)F^#N2Ec*_
zuRt`g17Cu_Vxz3spDQQx062LWlrd@h5@u6&aRLK)L|xk_-;9kzPe-drSg@4!Z!_7`
zUZ~ASYjPEswF`UAI{OV<pqzE#Hs7#;iSsL*%agK7^j&->E4R|^DiB;A!QX~k+^YCz
zWXvU50D@VMq4cTQl6^%JX?x@1{fdyPJyH^fv@g9vBb21rFknL7E6mHc1Q0}vq>dI=
z#s+E=9Kw3!CQI3{1Cz^Eyj@f&nB*Xucz?Vk9#uMzZpa=xUEaE48oxw*NQtZ=w=1Ij
zv$bi$L40s8vqY_E9hmHIALrSgUNfbBa*o9ta7J!;g}3<SLAY^3B%)Qo$>YEzHl)ZS
z?iyB#LAW>}O+1JIoX6SqzAg@d)o`Mu@lfHUyd&r~yMnl(2tRTn^tpGicG>V0g3Dd>
z2SIQyJ<1u3CJ`ZOKrYGY$OvPcqlnKdVMsb$yW$W=bNmO75^yfj!f`iCgSC;cR_B-j
zvT)yzmJ`oW^+jNFFHg@<3{h%_SIS=D|9UyxzOr4+ayM`N@qq3&lgo~IXp^dcC{`_h
zFwOMt0Ri0)F2TOmPdYl{=mLZ%UQUu>&gvEYZ=Tf)<q^+g=_}=lBrFRP|G;tvuR|^A
zTn()GgceH@d2(KM)fg*<!mBM$rBu7D9`#o6S$>4eTVeXZ4wq(F_(-aO%c;&l<`MtO
z$<%hzG$nswQZlCcT3Lj9(YTlr`&Wp&hwvoJJHEY()v^s!?Svy_m$o=r!+2#@NZ;(&
zBU23oj|Ysdwt9;CiiO1=pUa<j+0l?{zs%D?+I9)`n2{+VGDhlxpHRVC=FR0(UgnVX
zrPstv%b}lXUBf|V>uE1@g6Gaul(IA_Ti!Zu`b!;!bUu>H4u-Xf;)zX4KDND^u)T|n
zGyICY*C{8pHdD`qLMSMFYVJb&w!1R6{}Sa6okUv6Vn?*!jc^SQ`b(6v9HxUhKPb|q
zRJoqFagqL%m(jy*yP;6{(=;W7=Zwy*hE&I+7!yh9Nu6mwS&!6#YtDDi)lby*rApPp
z32XY>F>SqBW1OL~ODf4huNcJ)KUPgdKfH0x8T(Noo{{9!)(DqIu09ikt*viPWHJ7b
zVi2f4;**lWrjyL_;Si0hyr7?&^QpFHB(bM4ZLNIkYk@s%yud;(=ki6Fk~&ou>_-<F
z5n|(9!j=4E5c1=bEW@2x8(}^lX7z2B%A^MARGgiKWPxjV1yiXKyEvY^Wck@VZF|>w
zv)8{7HFL9OfIPW4YpLwPK}&oi$-tiMs>$ew4l*mt*x-%!co{8_DWb#R1#OMrKSK+q
zQ)PDZK6lD$vPp+<@YLTNU3J+}a}G$P;=i4Tehx2~WT~%P&)d65&jos$lrL(P)Q81G
z$vVo{bKJjAYdA=IEZ%KK2L!(3d5l#$-&3t=qNXEP@qC9mmHN=#1ORjuFfA03a59J}
z=Z>CZE(SGYtF5nPkASs_q<5gZa=bB(bWlVP4}O`Iqw&D?V7ciu)`Wgb!V=d~=#R3H
zLsH55Dm{*{@vMt-dw+zDu~LG?9gUX*O);R^HXLF)gt!2s@0@X&)evhZZ14S8HAa72
z${6+qJ01*STz?@gyJ}QE)8b5wM5oA@Ql)IeHW*FE(QaV=p}owCanU*(rt2SI{)~hm
z9UxV146tDi4Rs9<UgwIje0+6gWI9AN*yftEB*huGd*fZ>vWR$bMtgyx(Y7RI40W)L
zgTO4FA7LcKOr$E#VtLgCs2MFXaYi1n_i|g39trGRuU;Q|J!1mS(|PjR`jU;zb!Oe0
zJi>4PrsLf1#;*A1-m^WzmkEcZ1^Gw&K2NgD`|ObcejZ7rg6OfML*OsFK!jZ`g4ON(
zrO2Nw1B+esWxfAblah`?QC^i&6eWf_zoPIDL=i6WGJa%(Wm9J85_Z6e%RHkD#_LNZ
zs%lLUVew~6gl*{%FJv1=FB7))uP&7_50c2oLkT*{yK>xblP4AddMcDqy@0{UQtdLe
zXdIzu3y;hoEeL%`J6wjb+)<-e<dHUbZ&=Q(nMOXq{AT*xI*gr4&6(uC0>VHNfb>4U
zrEN(J3ZZ1DLjX@;dlZ2uSQTTZ!zJ);PEtH87g>kdgO4TK0%&tJT|RR4t%i<|?PvnI
zpcdtPRIG!#G^pRCgz(0ISQt*&3IB$4vG3q#oCb?#dp8p|{n9-~yb(oU>e<+lj8Q}A
zCA3$<O%P=&mn*>Aj_0WBCgUx_hy_wGii^s`#8?W1#LHcTHU%B^&4VQ|#v-B~OU_w$
zgNr$M+~3oYv3w#{vjkd5_T1&IGV5ySXA)Qhkmm0!kv>eP@?ESgfS4IiA}uJTF@mX=
zYB!^GlH%2P=+!gXg1t$6d}rQ$=9O1<t*=k~5Kj5F4M_9S7DjIu=hpLVFEaiYfe94{
zLvqNg$ITwTcK6Ud%QMID(NMndV4&b;DRi;>nJZSIo3>-C7|NgfT?`Mk1(3x53`4YR
zXQ22Dt3;e{u-Yl+cHguFdI3IW8k#5#iK{?h4IHtWd*L6HJV~UY#)|mgnW5vOJDQGI
zUH?@zLvC}VoFHW1lDNM91Os@>ZT5|ejGnhS(kXnOyL^Q8J?(II7SL7lfBGN9xqYD9
zZm0+jGy?|3xRQnn03>k+xf)QycNX>)f>Aj{F`Q>IOiMKfWqe;k-~CLK2?Jy8#8#PF
z#`Yhv^Xr&+uNb9_B<zNgG1Fj&Y-YIF{Bj{VM~gt3);eQyoGYCz=_Z7gJ$Qp8B+Ry(
zK39t;b?^)+AEaO0^=fz5J+2Z*?wTw9JKfFguDE-^QPOgYG;;f|IA<L@L(wzte|34f
zth$HJ_rfVZWz1O#KYoe&pS%zun>z1kN>mHEfo5y7=Wo);8Hji&sqvTUabG*0;#?D^
z+~5Gb!*$Aw5VyoT1A0H%MWACfs~5cD{N@<%GFx|tlB#@GY=PJ0xnc;N^wX%_sAKn(
z7!^3k4<|(BrZ=-%Y)j;7!&UD4Wxik+C3L_2N&H6JVNKO3Hk9!TVQckI&~*hL)XsfZ
zt757LhENhs?vZF{cJ^Ah(`)(qI`w8$TK(+o?j$%7;NZKk#nDgBH|`)EAG+q{x2TqT
zax8aSyUY~qys73@Ts-z~w|`v%5cDrf{w2=;#iEdt-=enosp;{H#hbK(C-pag0;WGI
z3u89F;M8*U9-T<3K}aBlUaE{(q^<lTcg_>uw0B;;`_!0gANc9;gb@wR-y=CXH%v~9
zpWh@@aC&1wVsK{~H+c{=dKw*Oz05vv$ejU#pFv1+;&0zdcLmF<kqJ=fM7J-~J(UjW
zw*nrBdg2}<*7Ru)Ju#(?3%90ZJloc*yy9n7IjxIs36jZ8%j~CKm*V~R@?Ssv3xfYI
z$skkSQ~+)V((p$q6c-<>6n39xE*X%HJWNbQ@RBko?EbU9L&A_FZTNmkg@;2AdE51k
zCFJ71tjS6VWu~X{@F`_QLs>pk5$lA(3}9D6h36C3&aF5{5!@$xLFPZv#?R{9;H<~|
zxL@px*yn9d(kAFN=;_9UN6OnlTU849h2EdM->(|c{@eLqhyC9N5`unhh#iFB^Va%F
gzZY^MDwvm)f4V37pS)N5cdq|4_r(8AKY#82UzA^l+W-In

literal 0
HcmV?d00001

diff --git a/Session6/note1_ShorthandStyle.txt b/Session6/note1_ShorthandStyle.txt
new file mode 100644
index 0000000..4542b73
--- /dev/null
+++ b/Session6/note1_ShorthandStyle.txt
@@ -0,0 +1,19 @@
+#. T2DM  
+   Dx since 2010, recent HbA1c 9.1  
+   MTF → 설사, 복부팽만, 식욕저하, 무기력감  
+   Lactic acidosis (Lac 3.9) 의심 → 약 중단, 수액 치료  
+   MTF-induced lactic acidosis 의심 → 약 중단 후 회복, basal insulin 예정
+
+   Levemir 22 --- 10 units, 저녁 인슐린 잘 안 맞음
+
+
+#. HTN  
+   BP on admission 92/58
+   Losartan 유지 
+
+#. CKD stage 3  
+   Baseline Cr 1.6 → 입원 시 Cr 2.1  
+   수액 이후 신기능 호전, 투석 필요 없음  
+
+#. Dyslipidemia  
+   Atorvastatin  
diff --git a/Session6/note2_MIMICstyle.txt b/Session6/note2_MIMICstyle.txt
new file mode 100644
index 0000000..8624133
--- /dev/null
+++ b/Session6/note2_MIMICstyle.txt
@@ -0,0 +1,93 @@
+History of Present Illness:  
+78-year-old female with a history of type 2 diabetes, hypertension, and CKD stage 3 who presented with generalized weakness, poor oral intake, and nausea.
+
+Two weeks prior to admission, she was started on metformin after a primary care visit revealed HbA1c of 9.1. Since initiation, the patient has developed persistent loose stools and intermittent abdominal cramping. No reported fever or vomiting. Her family noted a gradual decline in appetite and energy over the past week.
+
+On arrival, vital signs showed hypotension (BP 88/56) and tachycardia (HR 102). Laboratory evaluation revealed elevated creatinine (2.1 from baseline 1.6) and lactic acid of 3.9. Impression was possible metformin-associated lactic acidosis. Metformin was held, and IV fluids were started. Endocrinology was consulted to guide alternative glycemic management. The patient is being monitored for clinical improvement.
+
+---
+
+Past Medical History:  
+1. Type 2 Diabetes Mellitus (HbA1c 9.1)  
+2. Chronic Kidney Disease, Stage 3 (baseline Cr ~1.6)  
+3. Hypertension  
+
+---
+
+Social History:  
+- Lives in assisted living  
+- Non-smoker, no alcohol use  
+- No recent travel or infectious exposures  
+
+Family History:  
+- Mother: type 2 diabetes  
+- Father: unknown  
+
+---
+
+Physical Exam on Admission:  
+Vitals: T 97.4, BP 88/56, HR 102, RR 20, SpO2 95% on RA  
+General: Alert but fatigued; oriented  
+HEENT: Dry mucous membranes; no scleral icterus  
+CV: Tachycardic but regular; no murmurs  
+Lungs: Clear to auscultation  
+Abdomen: Soft; mild lower abdominal tenderness  
+Extremities: No edema  
+Neuro: A&O x3, no focal deficits  
+Skin: No rash  
+
+---
+
+Pertinent Labs:  
+WBC 10.8, Hgb 11.2, Plt 243  
+Na 138, K 4.0, Cl 100, HCO3 15  
+BUN 38, Cr 2.1  
+Glucose 78  
+Lactate 3.9  
+ABG: pH 7.31, AG 19  
+UA: +glucose, no WBCs or nitrites  
+
+---
+
+Microbiology:  
+Urine culture: no growth  
+Blood culture: pending  
+
+Imaging:  
+CXR: No infiltrates or consolidation  
+Renal US: Normal echotexture, no hydronephrosis  
+
+---
+
+Hospital Course:  
+
+# Metformin-Associated Lactic Acidosis:  
+Patient presented with GI symptoms, hypotension, and elevated lactate in the setting of recent metformin initiation and CKD. Suspected MALA. Metformin was held. Initiated IV hydration and bicarbonate support. Mental status and vitals improved over 48 hours.
+
+# T2DM:  
+All antihyperglycemics were held initially. Glucose remained stable. Basal insulin will be initiated. Endocrinology is following.
+
+# CKD:  
+AKI on CKD with Cr 2.1. Improved with fluids. No indication for dialysis. Electrolytes monitored.
+
+# Hypotension:  
+Likely secondary to dehydration and acidosis. Responded to volume repletion.
+
+---
+
+Discharge Medications:  
+1. Basal insulin (Lantus) 6 units SC QHS  
+2. Losartan 50 mg PO daily  
+3. Atorvastatin 20 mg PO daily  
+4. Calcium carbonate 500 mg PO BID  
+5. Acetaminophen 650 mg PO TID PRN  
+6. Pantoprazole 40 mg PO daily  
+
+---
+
+Discharge Plan:  
+- Return to assisted living facility  
+- Strict avoidance of metformin in future  
+- Glucose monitoring BID with insulin titration  
+- Follow-up with nephrology and endocrinology in 1–2 weeks  
+- Code status: Full Code  
\ No newline at end of file

From 11f32211649f51958c0d2359c463131e0ea4ecc2 Mon Sep 17 00:00:00 2001
From: sungmoonie <munkim97@gmail.com>
Date: Mon, 24 Nov 2025 12:35:51 +0900
Subject: [PATCH 24/34] Update Session6_Medical_Note_LLM.md

---
 Session6_Medical_Note_LLM.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/Session6_Medical_Note_LLM.md b/Session6_Medical_Note_LLM.md
index a56add9..e8fd467 100644
--- a/Session6_Medical_Note_LLM.md
+++ b/Session6_Medical_Note_LLM.md
@@ -2,6 +2,9 @@
 
 In this session, we will learn how to perform **clinical data analysis using the OpenAI API**.  
 
+You can find **all Session 6 materials** in the folder below:
+[Session6 Folder](https://github.com/leelabsg/GCDA_tutorial_2025_Fall/tree/main/Session6)
+
 ## Dataset Description
 
 This tutorial uses **three types of data**:

From 7b81334b6e70d47b84a73bd2543551f5eea4ed05 Mon Sep 17 00:00:00 2001
From: sungmoonie <munkim97@gmail.com>
Date: Mon, 24 Nov 2025 12:36:30 +0900
Subject: [PATCH 25/34] Update Session6_Medical_Note_LLM.md

---
 Session6_Medical_Note_LLM.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Session6_Medical_Note_LLM.md b/Session6_Medical_Note_LLM.md
index e8fd467..2a60791 100644
--- a/Session6_Medical_Note_LLM.md
+++ b/Session6_Medical_Note_LLM.md
@@ -2,7 +2,7 @@
 
 In this session, we will learn how to perform **clinical data analysis using the OpenAI API**.  
 
-You can find **all Session 6 materials** in the folder below:
+You can find **all Session 6 materials** in the folder:
 [Session6 Folder](https://github.com/leelabsg/GCDA_tutorial_2025_Fall/tree/main/Session6)
 
 ## Dataset Description

From 6959d8e31399f8a2d69d187d604cdaf53b940c8a Mon Sep 17 00:00:00 2001
From: sungmoonie <munkim97@gmail.com>
Date: Mon, 24 Nov 2025 12:36:59 +0900
Subject: [PATCH 26/34] Update Session6_Medical_Note_LLM.md

---
 Session6_Medical_Note_LLM.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Session6_Medical_Note_LLM.md b/Session6_Medical_Note_LLM.md
index 2a60791..ac564c7 100644
--- a/Session6_Medical_Note_LLM.md
+++ b/Session6_Medical_Note_LLM.md
@@ -2,8 +2,8 @@
 
 In this session, we will learn how to perform **clinical data analysis using the OpenAI API**.  
 
-You can find **all Session 6 materials** in the folder:
-[Session6 Folder](https://github.com/leelabsg/GCDA_tutorial_2025_Fall/tree/main/Session6)
+You can find **all Session 6 materials** in the folder below:
+    [Session6 Folder](https://github.com/leelabsg/GCDA_tutorial_2025_Fall/tree/main/Session6)
 
 ## Dataset Description
 

From 6e9533f1497fc0a2f067388fe524d1acb2da9941 Mon Sep 17 00:00:00 2001
From: sungmoonie <munkim97@gmail.com>
Date: Mon, 24 Nov 2025 12:38:20 +0900
Subject: [PATCH 27/34] Update Session6_Medical_Note_LLM.md

---
 Session6_Medical_Note_LLM.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Session6_Medical_Note_LLM.md b/Session6_Medical_Note_LLM.md
index ac564c7..7d01f3b 100644
--- a/Session6_Medical_Note_LLM.md
+++ b/Session6_Medical_Note_LLM.md
@@ -2,8 +2,8 @@
 
 In this session, we will learn how to perform **clinical data analysis using the OpenAI API**.  
 
-You can find **all Session 6 materials** in the folder below:
-    [Session6 Folder](https://github.com/leelabsg/GCDA_tutorial_2025_Fall/tree/main/Session6)
+You can find **all Session 6 materials** in the folder below:  
+➡️ [Session6 Folder](https://github.com/leelabsg/GCDA_tutorial_2025_Fall/tree/main/Session6)
 
 ## Dataset Description
 

From dcb9a35c3cd1852c9c3fe2845dabaef4a4cdb39b Mon Sep 17 00:00:00 2001
From: Wonyoung Jang <43818471+Stomper10@users.noreply.github.com>
Date: Fri, 28 Nov 2025 18:43:53 +0900
Subject: [PATCH 28/34] Add files via upload

---
 .../CLS&SEG/classification.ipynb              |    1 +
 .../CLS&SEG/classification.py                 |  430 +++++
 .../CLS&SEG/cls_train.txt                     | 1360 ++++++++++++++
 .../CLS&SEG/seg_train.txt                     | 1612 +++++++++++++++++
 .../CLS&SEG/segmentation.ipynb                |    1 +
 .../CLS&SEG/segmentation.py                   |  371 ++++
 .../configs/config_maisi.json                 |  186 ++
 .../configs/config_maisi3d-rflow_brats.json   |  148 ++
 .../configs/config_maisi_diff_model.json      |   45 +
 .../config_maisi_vae_train_brats_stage1.json  |   38 +
 .../config_maisi_vae_train_brats_stage2       |   38 +
 .../configs/environment_maisi_diff_model.json |   12 +
 .../maisi_train_UNET_brats.py                 |  514 ++++++
 .../maisi_train_UNET_brats.sh                 |   60 +
 .../maisi_train_VAE_brats.py                  |  571 ++++++
 .../maisi_train_VAE_brats.sh                  |   59 +
 16 files changed, 5446 insertions(+)
 create mode 100644 Session7_Medical_Image_Data_Analysis/CLS&SEG/classification.ipynb
 create mode 100644 Session7_Medical_Image_Data_Analysis/CLS&SEG/classification.py
 create mode 100644 Session7_Medical_Image_Data_Analysis/CLS&SEG/cls_train.txt
 create mode 100644 Session7_Medical_Image_Data_Analysis/CLS&SEG/seg_train.txt
 create mode 100644 Session7_Medical_Image_Data_Analysis/CLS&SEG/segmentation.ipynb
 create mode 100644 Session7_Medical_Image_Data_Analysis/CLS&SEG/segmentation.py
 create mode 100644 Session7_Medical_Image_Data_Analysis/configs/config_maisi.json
 create mode 100644 Session7_Medical_Image_Data_Analysis/configs/config_maisi3d-rflow_brats.json
 create mode 100644 Session7_Medical_Image_Data_Analysis/configs/config_maisi_diff_model.json
 create mode 100644 Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage1.json
 create mode 100644 Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage2
 create mode 100644 Session7_Medical_Image_Data_Analysis/configs/environment_maisi_diff_model.json
 create mode 100644 Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.py
 create mode 100644 Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.sh
 create mode 100644 Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.py
 create mode 100644 Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.sh

diff --git a/Session7_Medical_Image_Data_Analysis/CLS&SEG/classification.ipynb b/Session7_Medical_Image_Data_Analysis/CLS&SEG/classification.ipynb
new file mode 100644
index 0000000..43c992b
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/CLS&SEG/classification.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","id":"ed5994d8","metadata":{"id":"ed5994d8"},"source":["# Medical Image Classification\n","In this practice, we will do chest X-ray classification for CheXpert data using a convolutional neural network.\n","\n","**Task**: Binary classification; if edema (부종) is present (1) or not (0)\n","\n","**Data**: CheXpert\n","\n","**Model**: Convolutional neural network\n","\n","**Framework**: Pytorch"]},{"cell_type":"markdown","id":"7f1271ad","metadata":{"id":"7f1271ad"},"source":["## About dataset: CheXpert\n","\n","* A large chest X-ray dataset and competition for automated chest X-ray interpretation.\n","* Total of 224,316 chest radiographs (from 65,240 patients) were provided by Stanford Hospital and clinics.\n","* Each radiograph was labeled for the presence of 14 observations as positive, negative, or uncertain.\n","* For more detail, visit here: https://stanfordmlgroup.github.io/competitions/chexpert/\n","\n","<img src=\"https://drive.google.com/uc?id=1Z2J6YVnOUrk36z-RTTp7JUPJvmrgb2aL\" width=400px>\n","<figcaption align = \"left\">image source: https://arxiv.org/abs/1901.07031</figcaption>"]},{"cell_type":"markdown","id":"86a9cd43","metadata":{"id":"86a9cd43"},"source":["## Data preprocessing\n","\n","For simple use, images and labels were preprocessed by TA.\n","* Replaced uncertain labels as positive labels. (https://arxiv.org/abs/1901.07031)\n","* Prepared a single image per patient.\n","* Prepared only frontal images.\n","* Prepared 10% of training data as validation data."]},{"cell_type":"markdown","source":["## Mount the Local Directory\n","\n","- **First, set proper runtime. (Python3 & GPU)**"],"metadata":{"id":"PIPrk2MES845"},"id":"PIPrk2MES845"},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"L8kDZwKPgUH_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1733716846487,"user_tz":-540,"elapsed":31371,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}},"outputId":"5f372348-2b38-4090-ec77-ddad851b260e"},"id":"L8kDZwKPgUH_","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["!pip install grad-cam"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ApJsqKN2s2eG","executionInfo":{"status":"ok","timestamp":1733716860819,"user_tz":-540,"elapsed":10207,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}},"outputId":"ca6afc97-4e04-4756-a128-dfd8c7f76494"},"id":"ApJsqKN2s2eG","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting grad-cam\n","  Downloading grad-cam-1.5.4.tar.gz (7.8 MB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/7.8 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/7.8 MB\u001b[0m \u001b[31m82.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m121.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m80.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from grad-cam) (1.26.4)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from grad-cam) (11.0.0)\n","Requirement already satisfied: torch>=1.7.1 in /usr/local/lib/python3.10/dist-packages (from grad-cam) (2.5.1+cu121)\n","Requirement already satisfied: torchvision>=0.8.2 in /usr/local/lib/python3.10/dist-packages (from grad-cam) (0.20.1+cu121)\n","Collecting ttach (from grad-cam)\n","  Downloading ttach-0.0.3-py3-none-any.whl.metadata (5.2 kB)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from grad-cam) (4.66.6)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (from grad-cam) (4.10.0.84)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from grad-cam) (3.8.0)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from grad-cam) (1.5.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.1->grad-cam) (3.16.1)\n","Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.1->grad-cam) (4.12.2)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.1->grad-cam) (3.4.2)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.1->grad-cam) (3.1.4)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.1->grad-cam) (2024.10.0)\n","Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.1->grad-cam) (1.13.1)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.7.1->grad-cam) (1.3.0)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->grad-cam) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->grad-cam) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->grad-cam) (4.55.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->grad-cam) (1.4.7)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->grad-cam) (24.2)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->grad-cam) (3.2.0)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->grad-cam) (2.8.2)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->grad-cam) (1.13.1)\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->grad-cam) (1.4.2)\n","Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->grad-cam) (3.5.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->grad-cam) (1.16.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.7.1->grad-cam) (3.0.2)\n","Downloading ttach-0.0.3-py3-none-any.whl (9.8 kB)\n","Building wheels for collected packages: grad-cam\n","  Building wheel for grad-cam (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for grad-cam: filename=grad_cam-1.5.4-py3-none-any.whl size=39588 sha256=3c7d3eb7081897d973fd28e93c72eeb9ae6135df3d6ebf7a026d24b138702dcf\n","  Stored in directory: /root/.cache/pip/wheels/50/b0/82/1f97b5348c7fe9f0ce0ba18497202cafa5dec4562bd5292680\n","Successfully built grad-cam\n","Installing collected packages: ttach, grad-cam\n","Successfully installed grad-cam-1.5.4 ttach-0.0.3\n"]}]},{"cell_type":"markdown","id":"6c9a483e","metadata":{"id":"6c9a483e"},"source":["## Import modules"]},{"cell_type":"code","execution_count":null,"id":"dfd16743","metadata":{"id":"dfd16743"},"outputs":[],"source":["import os\n","import csv\n","import time\n","import numpy as np\n","%matplotlib inline\n","import matplotlib.pyplot as plt\n","\n","from tqdm import tqdm\n","from PIL import Image\n","from pathlib import Path\n","import sklearn.metrics as metrics\n","from sklearn.metrics import roc_curve\n","\n","import torch\n","import torch.optim as optim\n","\n","from torch import nn\n","from torch.utils.data import Dataset, DataLoader\n","\n","import torchvision\n","from torchvision import transforms as T\n","\n","# pip install torchsummary\n","from torchsummary import summary\n","# pip install grad-cam\n","from pytorch_grad_cam import GradCAM\n","from pytorch_grad_cam.utils.image import show_cam_on_image\n","from pytorch_grad_cam.utils.model_targets import BinaryClassifierOutputTarget"]},{"cell_type":"markdown","id":"09d6b1b2","metadata":{"id":"09d6b1b2"},"source":["## Data paths and hyperparameters"]},{"cell_type":"code","execution_count":null,"id":"2de5ff83","metadata":{"id":"2de5ff83"},"outputs":[],"source":["IMG_PATH = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/DATA/\"\n","\n","# train: 58,079 (neg 43,741 / pos 14,338)\n","TRAIN_LABEL_PATH = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/DATA/CheXpert-v1.0-small/train_practice6.csv\"\n","\n","# valid: 6,453 (neg 4,857 / pos 1,596)\n","VALID_LABEL_PATH = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/DATA/CheXpert-v1.0-small/valid_practice6.csv\"\n","\n","# test: 200 (neg 158 / pos 42)\n","TEST_LABEL_PATH = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/DATA/CheXpert-v1.0-small/test_practice6.csv\"\n","\n","# hyperparameters\n","IMAGE_SIZE = 320\n","BATCH_SIZE = 64\n","EPOCHS = 10\n","RESULTS_FOLDER = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/results_cls/\"\n","\n","os.mkdir(RESULTS_FOLDER) if not os.path.exists(RESULTS_FOLDER) else None"]},{"cell_type":"markdown","id":"9035beca","metadata":{"id":"9035beca"},"source":["## Define custom dataset class"]},{"cell_type":"code","execution_count":null,"id":"4b3e755e","metadata":{"id":"4b3e755e"},"outputs":[],"source":["class CXR_Cls_Dataset(Dataset):\n","    def __init__(self, image_path, label_path, image_size):\n","        super(CXR_Cls_Dataset, self).__init__()\n","        self.image_path = image_path\n","        self.mask_path = label_path\n","\n","        image_names, labels = [], []\n","\n","        with open(label_path, \"r\") as f:\n","            csv_reader = csv.reader(f)\n","            next(csv_reader, None) # skip the header\n","            for line in csv_reader:\n","                image_names.append(line[0]) # image path from base\n","                labels.append(float(line[10])) # Edema index\n","\n","        self.image_names = image_names\n","        self.labels = labels\n","\n","        self.transform = T.Compose([\n","            T.Resize((image_size, image_size)),\n","            T.ToTensor()\n","        ])\n","\n","    def __len__(self):\n","        return len(self.image_names)\n","\n","    def __getitem__(self, index):\n","        image_name = self.image_names[index]\n","        label = self.labels[index]\n","\n","        image = Image.open(os.path.join(self.image_path, image_name)).convert(\"L\")\n","\n","        return self.transform(image), torch.tensor(label, dtype=torch.float32)"]},{"cell_type":"markdown","id":"969cda2e","metadata":{"id":"969cda2e"},"source":["## Define dataset and dataloader"]},{"cell_type":"code","execution_count":null,"id":"bff10e63","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bff10e63","executionInfo":{"status":"ok","timestamp":1733716975133,"user_tz":-540,"elapsed":419,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}},"outputId":"5bbf43b6-4c85-4272-d9fb-dd36b6724185"},"outputs":[{"output_type":"stream","name":"stdout","text":["Data Information\n","    Train data: 58079\n","    Valid data: 6453\n","    Test data: 200\n"]}],"source":["train_dataset = CXR_Cls_Dataset(IMG_PATH, TRAIN_LABEL_PATH, IMAGE_SIZE)\n","valid_dataset = CXR_Cls_Dataset(IMG_PATH, VALID_LABEL_PATH, IMAGE_SIZE)\n","test_dataset = CXR_Cls_Dataset(IMG_PATH, TEST_LABEL_PATH, IMAGE_SIZE)\n","\n","print(\"Data Information\")\n","print(\"    Train data:\", len(train_dataset))\n","print(\"    Valid data:\", len(valid_dataset))\n","print(\"    Test data:\", len(test_dataset))\n","\n","dataloader_train = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE,\n","                              shuffle=True, num_workers=2, pin_memory=True)\n","dataloader_valid = DataLoader(dataset=valid_dataset, batch_size=BATCH_SIZE,\n","                              shuffle=False, num_workers=2, pin_memory=True)\n","dataloader_test = DataLoader(dataset=test_dataset, batch_size=1,\n","                             shuffle=False, num_workers=2, pin_memory=True)"]},{"cell_type":"markdown","id":"4e0a5915","metadata":{"id":"4e0a5915"},"source":["## Data sanity check"]},{"cell_type":"code","execution_count":null,"id":"90a3945a","metadata":{"id":"90a3945a","colab":{"base_uri":"https://localhost:8080/","height":433},"executionInfo":{"status":"ok","timestamp":1733716977531,"user_tz":-540,"elapsed":1773,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}},"outputId":"76e300ca-432b-47b8-b8d0-981ae7de1c61"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 2500x1000 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["input, target = next(iter(dataloader_test)) # dataloader_valid # dataloader_test\n","plt.figure(figsize=(25, 10))\n","for i in range(1):\n","    plt.subplot(2, 3, 1+i)\n","    plt.imshow(input[i].reshape(IMAGE_SIZE, IMAGE_SIZE, 1), cmap=\"gray\")\n","    plt.gca().set_title(f\"Label: {int(target[i].numpy())}\")\n","    plt.show()\n","plt.close()"]},{"cell_type":"markdown","id":"24015f85","metadata":{"id":"24015f85"},"source":["# Define model class"]},{"cell_type":"code","execution_count":null,"id":"bb10e560","metadata":{"id":"bb10e560"},"outputs":[],"source":["# Fully customized CNN\n","class CNN(nn.Module):\n","    def __init__(self, in_channels):\n","        super(CNN, self).__init__()\n","        self.conv = nn.Sequential(\n","            nn.Conv2d(in_channels, 32, kernel_size=(3,3), stride=1, padding=\"same\"),\n","            nn.ReLU(),\n","            nn.BatchNorm2d(32),\n","            nn.MaxPool2d(kernel_size=(2,2), stride=2),\n","            nn.Conv2d(32, 64, kernel_size=(3,3), stride=1, padding=\"same\"),\n","            nn.ReLU(),\n","            nn.Dropout(0.1),\n","            nn.BatchNorm2d(64),\n","            nn.MaxPool2d(kernel_size=(2,2), stride=2),\n","\n","            nn.Conv2d(64, 64, kernel_size=(3,3), stride=1, padding=\"same\"),\n","            nn.ReLU(),\n","            nn.BatchNorm2d(64),\n","            nn.MaxPool2d(kernel_size=(2,2), stride=2),\n","            nn.Conv2d(64, 128, kernel_size=(3,3), stride=1, padding=\"same\"),\n","            nn.ReLU(),\n","            nn.Dropout(0.2),\n","            nn.BatchNorm2d(128),\n","            nn.MaxPool2d(kernel_size=(2,2), stride=2),\n","\n","            nn.Conv2d(128, 128, kernel_size=(3,3), stride=1, padding=\"same\"),\n","            nn.ReLU(),\n","            nn.BatchNorm2d(128),\n","            nn.MaxPool2d(kernel_size=(2,2), stride=2),\n","            nn.Conv2d(128, 256, kernel_size=(3,3), stride=1, padding=\"same\"),\n","            nn.ReLU(),\n","            nn.Dropout(0.2),\n","            nn.BatchNorm2d(256),\n","            nn.MaxPool2d(kernel_size=(2,2), stride=2),\n","        )\n","\n","        self.flatten = nn.Flatten()\n","\n","        self.fc = nn.Sequential(\n","            nn.Linear(6400, 128), # 256x5x5\n","            nn.Dropout(0.2),\n","            nn.Linear(128, 1),\n","            nn.Sigmoid()\n","        )\n","\n","    def forward(self, x):\n","        x = self.conv(x)\n","        x = self.flatten(x)\n","        x = self.fc(x)\n","\n","        return x"]},{"cell_type":"code","execution_count":null,"id":"3d5e82cf","metadata":{"id":"3d5e82cf"},"outputs":[],"source":["# Load ResNet50 and customize\n","class ResNet50(nn.Module):\n","    def __init__(self, out_features):\n","        super(ResNet50, self).__init__()\n","        self.resnet50 = torchvision.models.resnet50(weights=None)\n","        self.resnet50.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","        num_ftrs = self.resnet50.fc.in_features\n","        self.resnet50.fc = nn.Sequential(\n","            nn.Linear(num_ftrs, out_features),\n","            nn.Sigmoid()\n","        )\n","\n","    def forward(self, x):\n","        x = self.resnet50(x)\n","        return x"]},{"cell_type":"markdown","id":"f73a206f","metadata":{"id":"f73a206f"},"source":["<img src=\"https://drive.google.com/uc?id=1Ayfpa15eQfUlOna_AWi6pgYM4yvcyAHj\" width=1000px>\n","<figcaption align = \"left\">image source: https://blog.devgenius.io/resnet50-6b42934db431</figcaption>"]},{"cell_type":"markdown","id":"0807b187","metadata":{"id":"0807b187"},"source":["## Define model and optimizer"]},{"cell_type":"code","execution_count":null,"id":"72beb204","metadata":{"id":"72beb204","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1733716993026,"user_tz":-540,"elapsed":950,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}},"outputId":"6844ed51-2a17-4292-e910-3e04d0d0772e"},"outputs":[{"output_type":"stream","name":"stdout","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1         [-1, 64, 160, 160]           3,136\n","       BatchNorm2d-2         [-1, 64, 160, 160]             128\n","              ReLU-3         [-1, 64, 160, 160]               0\n","         MaxPool2d-4           [-1, 64, 80, 80]               0\n","            Conv2d-5           [-1, 64, 80, 80]           4,096\n","       BatchNorm2d-6           [-1, 64, 80, 80]             128\n","              ReLU-7           [-1, 64, 80, 80]               0\n","            Conv2d-8           [-1, 64, 80, 80]          36,864\n","       BatchNorm2d-9           [-1, 64, 80, 80]             128\n","             ReLU-10           [-1, 64, 80, 80]               0\n","           Conv2d-11          [-1, 256, 80, 80]          16,384\n","      BatchNorm2d-12          [-1, 256, 80, 80]             512\n","           Conv2d-13          [-1, 256, 80, 80]          16,384\n","      BatchNorm2d-14          [-1, 256, 80, 80]             512\n","             ReLU-15          [-1, 256, 80, 80]               0\n","       Bottleneck-16          [-1, 256, 80, 80]               0\n","           Conv2d-17           [-1, 64, 80, 80]          16,384\n","      BatchNorm2d-18           [-1, 64, 80, 80]             128\n","             ReLU-19           [-1, 64, 80, 80]               0\n","           Conv2d-20           [-1, 64, 80, 80]          36,864\n","      BatchNorm2d-21           [-1, 64, 80, 80]             128\n","             ReLU-22           [-1, 64, 80, 80]               0\n","           Conv2d-23          [-1, 256, 80, 80]          16,384\n","      BatchNorm2d-24          [-1, 256, 80, 80]             512\n","             ReLU-25          [-1, 256, 80, 80]               0\n","       Bottleneck-26          [-1, 256, 80, 80]               0\n","           Conv2d-27           [-1, 64, 80, 80]          16,384\n","      BatchNorm2d-28           [-1, 64, 80, 80]             128\n","             ReLU-29           [-1, 64, 80, 80]               0\n","           Conv2d-30           [-1, 64, 80, 80]          36,864\n","      BatchNorm2d-31           [-1, 64, 80, 80]             128\n","             ReLU-32           [-1, 64, 80, 80]               0\n","           Conv2d-33          [-1, 256, 80, 80]          16,384\n","      BatchNorm2d-34          [-1, 256, 80, 80]             512\n","             ReLU-35          [-1, 256, 80, 80]               0\n","       Bottleneck-36          [-1, 256, 80, 80]               0\n","           Conv2d-37          [-1, 128, 80, 80]          32,768\n","      BatchNorm2d-38          [-1, 128, 80, 80]             256\n","             ReLU-39          [-1, 128, 80, 80]               0\n","           Conv2d-40          [-1, 128, 40, 40]         147,456\n","      BatchNorm2d-41          [-1, 128, 40, 40]             256\n","             ReLU-42          [-1, 128, 40, 40]               0\n","           Conv2d-43          [-1, 512, 40, 40]          65,536\n","      BatchNorm2d-44          [-1, 512, 40, 40]           1,024\n","           Conv2d-45          [-1, 512, 40, 40]         131,072\n","      BatchNorm2d-46          [-1, 512, 40, 40]           1,024\n","             ReLU-47          [-1, 512, 40, 40]               0\n","       Bottleneck-48          [-1, 512, 40, 40]               0\n","           Conv2d-49          [-1, 128, 40, 40]          65,536\n","      BatchNorm2d-50          [-1, 128, 40, 40]             256\n","             ReLU-51          [-1, 128, 40, 40]               0\n","           Conv2d-52          [-1, 128, 40, 40]         147,456\n","      BatchNorm2d-53          [-1, 128, 40, 40]             256\n","             ReLU-54          [-1, 128, 40, 40]               0\n","           Conv2d-55          [-1, 512, 40, 40]          65,536\n","      BatchNorm2d-56          [-1, 512, 40, 40]           1,024\n","             ReLU-57          [-1, 512, 40, 40]               0\n","       Bottleneck-58          [-1, 512, 40, 40]               0\n","           Conv2d-59          [-1, 128, 40, 40]          65,536\n","      BatchNorm2d-60          [-1, 128, 40, 40]             256\n","             ReLU-61          [-1, 128, 40, 40]               0\n","           Conv2d-62          [-1, 128, 40, 40]         147,456\n","      BatchNorm2d-63          [-1, 128, 40, 40]             256\n","             ReLU-64          [-1, 128, 40, 40]               0\n","           Conv2d-65          [-1, 512, 40, 40]          65,536\n","      BatchNorm2d-66          [-1, 512, 40, 40]           1,024\n","             ReLU-67          [-1, 512, 40, 40]               0\n","       Bottleneck-68          [-1, 512, 40, 40]               0\n","           Conv2d-69          [-1, 128, 40, 40]          65,536\n","      BatchNorm2d-70          [-1, 128, 40, 40]             256\n","             ReLU-71          [-1, 128, 40, 40]               0\n","           Conv2d-72          [-1, 128, 40, 40]         147,456\n","      BatchNorm2d-73          [-1, 128, 40, 40]             256\n","             ReLU-74          [-1, 128, 40, 40]               0\n","           Conv2d-75          [-1, 512, 40, 40]          65,536\n","      BatchNorm2d-76          [-1, 512, 40, 40]           1,024\n","             ReLU-77          [-1, 512, 40, 40]               0\n","       Bottleneck-78          [-1, 512, 40, 40]               0\n","           Conv2d-79          [-1, 256, 40, 40]         131,072\n","      BatchNorm2d-80          [-1, 256, 40, 40]             512\n","             ReLU-81          [-1, 256, 40, 40]               0\n","           Conv2d-82          [-1, 256, 20, 20]         589,824\n","      BatchNorm2d-83          [-1, 256, 20, 20]             512\n","             ReLU-84          [-1, 256, 20, 20]               0\n","           Conv2d-85         [-1, 1024, 20, 20]         262,144\n","      BatchNorm2d-86         [-1, 1024, 20, 20]           2,048\n","           Conv2d-87         [-1, 1024, 20, 20]         524,288\n","      BatchNorm2d-88         [-1, 1024, 20, 20]           2,048\n","             ReLU-89         [-1, 1024, 20, 20]               0\n","       Bottleneck-90         [-1, 1024, 20, 20]               0\n","           Conv2d-91          [-1, 256, 20, 20]         262,144\n","      BatchNorm2d-92          [-1, 256, 20, 20]             512\n","             ReLU-93          [-1, 256, 20, 20]               0\n","           Conv2d-94          [-1, 256, 20, 20]         589,824\n","      BatchNorm2d-95          [-1, 256, 20, 20]             512\n","             ReLU-96          [-1, 256, 20, 20]               0\n","           Conv2d-97         [-1, 1024, 20, 20]         262,144\n","      BatchNorm2d-98         [-1, 1024, 20, 20]           2,048\n","             ReLU-99         [-1, 1024, 20, 20]               0\n","      Bottleneck-100         [-1, 1024, 20, 20]               0\n","          Conv2d-101          [-1, 256, 20, 20]         262,144\n","     BatchNorm2d-102          [-1, 256, 20, 20]             512\n","            ReLU-103          [-1, 256, 20, 20]               0\n","          Conv2d-104          [-1, 256, 20, 20]         589,824\n","     BatchNorm2d-105          [-1, 256, 20, 20]             512\n","            ReLU-106          [-1, 256, 20, 20]               0\n","          Conv2d-107         [-1, 1024, 20, 20]         262,144\n","     BatchNorm2d-108         [-1, 1024, 20, 20]           2,048\n","            ReLU-109         [-1, 1024, 20, 20]               0\n","      Bottleneck-110         [-1, 1024, 20, 20]               0\n","          Conv2d-111          [-1, 256, 20, 20]         262,144\n","     BatchNorm2d-112          [-1, 256, 20, 20]             512\n","            ReLU-113          [-1, 256, 20, 20]               0\n","          Conv2d-114          [-1, 256, 20, 20]         589,824\n","     BatchNorm2d-115          [-1, 256, 20, 20]             512\n","            ReLU-116          [-1, 256, 20, 20]               0\n","          Conv2d-117         [-1, 1024, 20, 20]         262,144\n","     BatchNorm2d-118         [-1, 1024, 20, 20]           2,048\n","            ReLU-119         [-1, 1024, 20, 20]               0\n","      Bottleneck-120         [-1, 1024, 20, 20]               0\n","          Conv2d-121          [-1, 256, 20, 20]         262,144\n","     BatchNorm2d-122          [-1, 256, 20, 20]             512\n","            ReLU-123          [-1, 256, 20, 20]               0\n","          Conv2d-124          [-1, 256, 20, 20]         589,824\n","     BatchNorm2d-125          [-1, 256, 20, 20]             512\n","            ReLU-126          [-1, 256, 20, 20]               0\n","          Conv2d-127         [-1, 1024, 20, 20]         262,144\n","     BatchNorm2d-128         [-1, 1024, 20, 20]           2,048\n","            ReLU-129         [-1, 1024, 20, 20]               0\n","      Bottleneck-130         [-1, 1024, 20, 20]               0\n","          Conv2d-131          [-1, 256, 20, 20]         262,144\n","     BatchNorm2d-132          [-1, 256, 20, 20]             512\n","            ReLU-133          [-1, 256, 20, 20]               0\n","          Conv2d-134          [-1, 256, 20, 20]         589,824\n","     BatchNorm2d-135          [-1, 256, 20, 20]             512\n","            ReLU-136          [-1, 256, 20, 20]               0\n","          Conv2d-137         [-1, 1024, 20, 20]         262,144\n","     BatchNorm2d-138         [-1, 1024, 20, 20]           2,048\n","            ReLU-139         [-1, 1024, 20, 20]               0\n","      Bottleneck-140         [-1, 1024, 20, 20]               0\n","          Conv2d-141          [-1, 512, 20, 20]         524,288\n","     BatchNorm2d-142          [-1, 512, 20, 20]           1,024\n","            ReLU-143          [-1, 512, 20, 20]               0\n","          Conv2d-144          [-1, 512, 10, 10]       2,359,296\n","     BatchNorm2d-145          [-1, 512, 10, 10]           1,024\n","            ReLU-146          [-1, 512, 10, 10]               0\n","          Conv2d-147         [-1, 2048, 10, 10]       1,048,576\n","     BatchNorm2d-148         [-1, 2048, 10, 10]           4,096\n","          Conv2d-149         [-1, 2048, 10, 10]       2,097,152\n","     BatchNorm2d-150         [-1, 2048, 10, 10]           4,096\n","            ReLU-151         [-1, 2048, 10, 10]               0\n","      Bottleneck-152         [-1, 2048, 10, 10]               0\n","          Conv2d-153          [-1, 512, 10, 10]       1,048,576\n","     BatchNorm2d-154          [-1, 512, 10, 10]           1,024\n","            ReLU-155          [-1, 512, 10, 10]               0\n","          Conv2d-156          [-1, 512, 10, 10]       2,359,296\n","     BatchNorm2d-157          [-1, 512, 10, 10]           1,024\n","            ReLU-158          [-1, 512, 10, 10]               0\n","          Conv2d-159         [-1, 2048, 10, 10]       1,048,576\n","     BatchNorm2d-160         [-1, 2048, 10, 10]           4,096\n","            ReLU-161         [-1, 2048, 10, 10]               0\n","      Bottleneck-162         [-1, 2048, 10, 10]               0\n","          Conv2d-163          [-1, 512, 10, 10]       1,048,576\n","     BatchNorm2d-164          [-1, 512, 10, 10]           1,024\n","            ReLU-165          [-1, 512, 10, 10]               0\n","          Conv2d-166          [-1, 512, 10, 10]       2,359,296\n","     BatchNorm2d-167          [-1, 512, 10, 10]           1,024\n","            ReLU-168          [-1, 512, 10, 10]               0\n","          Conv2d-169         [-1, 2048, 10, 10]       1,048,576\n","     BatchNorm2d-170         [-1, 2048, 10, 10]           4,096\n","            ReLU-171         [-1, 2048, 10, 10]               0\n","      Bottleneck-172         [-1, 2048, 10, 10]               0\n","AdaptiveAvgPool2d-173           [-1, 2048, 1, 1]               0\n","          Linear-174                    [-1, 1]           2,049\n","         Sigmoid-175                    [-1, 1]               0\n","          ResNet-176                    [-1, 1]               0\n","================================================================\n","Total params: 23,503,809\n","Trainable params: 23,503,809\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.39\n","Forward/backward pass size (MB): 584.78\n","Params size (MB): 89.66\n","Estimated Total Size (MB): 674.83\n","----------------------------------------------------------------\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n","  warnings.warn(\n"]}],"source":["#model = CNN(in_channels=1).cuda()\n","model = ResNet50(out_features=1).cuda()\n","#model = torch.nn.DataParallel(model).cuda() # use in multi-gpu environment\n","summary(model, input_size=(1, 320, 320), device=\"cuda\")\n","\n","optimizer = optim.Adam(model.parameters(), lr=1e-4)\n","scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=\"min\", factor=0.5, patience=3,\n","                                                 verbose=1, eps=1e-04, cooldown=2, min_lr=1e-6)"]},{"cell_type":"markdown","id":"91aa6323","metadata":{"id":"91aa6323"},"source":["## Define trainer"]},{"cell_type":"code","execution_count":null,"id":"e5f4e7c7","metadata":{"id":"e5f4e7c7"},"outputs":[],"source":["class CXR_Cls_Trainer():\n","    def __init__(\n","        self,\n","        model,\n","        results_folder,\n","        dataloader_train,\n","        dataloader_valid,\n","        dataloader_test,\n","        epochs,\n","        optimizer,\n","        scheduler,\n","    ):\n","        super(CXR_Cls_Trainer, self).__init__()\n","\n","        self.model = model\n","        self.dataloader_train = dataloader_train\n","        self.dataloader_valid = dataloader_valid\n","        self.dataloader_test = dataloader_test\n","        self.epoch = 0\n","        self.epochs = epochs\n","        self.optimizer = optimizer\n","        self.scheduler = scheduler\n","        self.loss_fn = nn.BCELoss()\n","\n","        self.results_folder = Path(results_folder)\n","        self.results_folder.mkdir(exist_ok=True)\n","\n","    def train(self):\n","        print(\"[ Start training ]\")\n","        self.model.train()\n","        valid_loss_min = 10000\n","        train_loss_list, valid_loss_list = [], []\n","        train_acc_list, valid_acc_list = [], []\n","\n","        while self.epoch < self.epochs:\n","            print(f\"\\nEpoch {self.epoch:3d}: training\")\n","            train_loss_sum, train_accurate_sum = 0, 0\n","            for batch_ID, (input, target) in enumerate(tqdm(self.dataloader_train)):\n","                input = input.cuda(non_blocking=True)\n","                target = target.reshape(-1, 1)\n","                target = target.cuda(non_blocking=True)\n","\n","                output = self.model(input)\n","\n","                loss = self.loss_fn(output, target)\n","                loss.backward()\n","                self.optimizer.step()\n","                self.optimizer.zero_grad()\n","\n","                train_loss_sum += loss.item()*input.size(0)\n","                if batch_ID % 20 == 19:\n","                    print(f\"\\n[Batch: {batch_ID:5d}] training loss = {loss.item():.3f}\", flush=True)\n","\n","                train_accurate_sum += (output.cpu().round() == target.cpu()).sum()\n","\n","            train_loss_avg = train_loss_sum / len(self.dataloader_train.dataset)\n","            train_acc_avg = train_accurate_sum / len(self.dataloader_train.dataset)\n","\n","            train_loss_list.append(train_loss_avg)\n","            train_acc_list.append(train_acc_avg)\n","\n","            # validation step\n","            print(f\"\\nEpoch {self.epoch:3d}: validation\")\n","            self.model.eval()\n","            with torch.no_grad():\n","                valid_loss_sum, valid_accurate_sum = 0, 0\n","                for _, (input, target) in enumerate(tqdm(self.dataloader_valid)):\n","                    input = input.cuda(non_blocking=True)\n","                    target = target.reshape(-1, 1)\n","                    target = target.cuda(non_blocking=True)\n","\n","                    output = self.model(input)\n","\n","                    loss = self.loss_fn(output, target)\n","\n","                    valid_loss_sum += loss.item()*input.size(0)\n","                    valid_accurate_sum += (output.cpu().round() == target.cpu()).sum()\n","\n","                valid_loss_avg = valid_loss_sum / len(self.dataloader_valid.dataset)\n","                valid_acc_avg = valid_accurate_sum / len(self.dataloader_valid.dataset)\n","\n","                valid_loss_list.append(valid_loss_avg)\n","                valid_acc_list.append(valid_acc_avg)\n","                self.scheduler.step(valid_loss_avg)\n","                print(f\"    Epoch {self.epoch:2d}: training loss = {train_loss_avg:.3f} / validation loss = {valid_loss_avg:.3f}\")\n","                print(f\"    Epoch {self.epoch:2d}: training acc = {train_acc_avg:.3f} / validation acc = {valid_acc_avg:.3f}\")\n","\n","                # save model if better validation loss\n","                if valid_loss_sum < valid_loss_min:\n","                    valid_loss_min = valid_loss_sum\n","                    self.save(self.epoch)\n","                    print(f\"    Saved model: cls-{self.epoch}.pth.tar\")\n","\n","            self.epoch += 1\n","        print(\"[ End training ]\")\n","        return [train_loss_list, valid_loss_list], [train_acc_list, valid_acc_list]\n","\n","    def test(self):\n","        print(\"[ Start test ]\")\n","        self.model.eval()\n","        with torch.no_grad():\n","            test_loss_sum, test_accurate_sum = 0, 0\n","            output_array, target_array = np.array([]), np.array([])\n","            for _, (input, target) in enumerate(tqdm(self.dataloader_test)):\n","                input = input.cuda(non_blocking=True)\n","                target = target.reshape(-1, 1)\n","                target = target.cuda(non_blocking=True)\n","\n","                output = self.model(input)\n","\n","                output_array = np.append(output_array, output.cpu().numpy())\n","                target_array = np.append(target_array, target.cpu().numpy())\n","\n","                loss = self.loss_fn(output, target)\n","\n","                test_loss_sum += loss.item()*input.size(0)\n","                test_accurate_sum += (output.cpu().round() == target.cpu()).sum()\n","\n","            test_loss_avg = test_loss_sum / len(self.dataloader_test.dataset)\n","            test_acc_avg = test_accurate_sum / len(self.dataloader_test.dataset)\n","            print(f\"    test loss = {test_loss_avg:.3f} / test acc = {test_acc_avg:.3f}\")\n","\n","            # Draw ROC curve\n","            fpr, tpr, _ = roc_curve(target_array, output_array)\n","            roc_auc = metrics.auc(fpr, tpr)\n","            plt.plot(fpr, tpr) # ROC curve = TPR vs FPR\n","            plt.title(f\"Receiver Operating Characteristics / AUC {roc_auc:.2f}\")\n","            plt.xlabel(\"False Positive Rate\")\n","            plt.ylabel(\"True Positive Rate\")\n","            plt.show()\n","            plt.savefig(os.path.join(self.results_folder, \"roc_plot.png\"))\n","            plt.close()\n","            print(f\"AUC: {roc_auc:.2f}\")\n","\n","        print(\"[ End test ]\")\n","        self.output_array = output_array\n","\n","    def save(self, milestone):\n","        files = os.listdir(self.results_folder)\n","        remove = [f for f in files if \".pth.tar\" in f]\n","        if len(remove) > 0:\n","            os.remove(os.path.join(self.results_folder, remove[0]))\n","        torch.save({\"epoch\": milestone,\n","                    \"state_dict\": self.model.state_dict(),\n","                    \"optimizer\" : self.optimizer.state_dict()},\n","                    f\"{self.results_folder}/cls-{milestone}.pth.tar\")\n","\n","    def load(self, milestone):\n","        checkpoint = torch.load(f\"{self.results_folder}/cls-{milestone}.pth.tar\")\n","        self.model.load_state_dict(checkpoint[\"state_dict\"])\n","        self.optimizer.load_state_dict(checkpoint[\"optimizer\"])\n","        self.epoch = checkpoint[\"epoch\"]\n","\n","    def GradCAM_func(self, num_images): # https://github.com/jacobgil/pytorch-grad-cam\n","        print(\"[ Start Grad-CAM ]\")\n","        self.model.eval()\n","        target_layers = [self.model.resnet50.layer4[-1]]\n","        cam = GradCAM(model=self.model, target_layers=target_layers)\n","        image_list = self.get_images(num_images)\n","\n","        for i, (input, target) in enumerate(self.dataloader_test):\n","            input = input.cuda(non_blocking=True)\n","            target = target.numpy()\n","\n","            targets = [BinaryClassifierOutputTarget(target)]\n","            grayscale_cam = cam(input_tensor=input, targets=targets)\n","            grayscale_cam = grayscale_cam[0, :]\n","            visualization = show_cam_on_image(image_list[i], grayscale_cam, use_rgb=True)\n","\n","            plt.imshow(visualization)\n","            plt.title(f\"Label: {int(target)} / Pred: {self.output_array[i]:.2f}\")\n","            plt.savefig(os.path.join(self.results_folder, f\"grad-cam-{i}.png\"))\n","            plt.close()\n","            if (i+1) == num_images: break\n","        print(\"[ End Grad-CAM ]\")\n","\n","    def get_images(self, num_images):\n","        image_list = []\n","        with open(TEST_LABEL_PATH, \"r\") as f:\n","            csv_reader = csv.reader(f)\n","            next(csv_reader, None) # skip the header\n","            for line in csv_reader:\n","                img = Image.open(os.path.join(IMG_PATH, line[0])).convert(\"L\")\n","                img = img.resize((320, 320))\n","                img = np.array(img)\n","                img_norm = (img-np.min(img))/(np.max(img)-np.min(img))\n","                img_norm = np.expand_dims(img_norm, axis=2)\n","                image_list.append(img_norm)\n","                if len(image_list) == num_images: break\n","\n","        return image_list"]},{"cell_type":"markdown","id":"ce0e560d","metadata":{"id":"ce0e560d"},"source":["## Train the model"]},{"cell_type":"code","execution_count":null,"id":"79693a17","metadata":{"id":"79693a17"},"outputs":[],"source":["# init the trainer\n","trainer = CXR_Cls_Trainer(\n","    model,\n","    RESULTS_FOLDER,\n","    dataloader_train,\n","    dataloader_valid,\n","    dataloader_test,\n","    EPOCHS,\n","    optimizer,\n","    scheduler\n",")"]},{"cell_type":"code","execution_count":null,"id":"53423037","metadata":{"id":"53423037","colab":{"base_uri":"https://localhost:8080/","height":435},"outputId":"236700c4-80b1-4017-a9ea-28342e8a2929","executionInfo":{"status":"error","timestamp":1733717058479,"user_tz":-540,"elapsed":55802,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[ Start training ]\n","\n","Epoch   0: training\n"]},{"output_type":"stream","name":"stderr","text":["  0%|          | 0/908 [00:55<?, ?it/s]\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-18-5abc74c564e9>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtrain_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mloss_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# training!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtrain_end\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"\\nElapsed time: {(train_end - train_start) / 60:.0f} minutes\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-16-ad555e78ee72>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     36\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"\\nEpoch {self.epoch:3d}: training\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m             \u001b[0mtrain_loss_sum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_accurate_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mbatch_ID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataloader_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m                 \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_blocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m                 \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1181\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1182\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1183\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    699\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             if (\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1447\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1448\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1449\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1450\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1400\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_alive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1402\u001b[0;31m                 \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1403\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1404\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1241\u001b[0m         \u001b[0;31m#   (bool: whether successfully get data, any: data if successful else None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1242\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1243\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1244\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1245\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.10/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.10/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["train_start = time.time()\n","loss_list, acc_list = trainer.train() # training!\n","train_end = time.time()\n","print(f\"\\nElapsed time: {(train_end - train_start) / 60:.0f} minutes\")"]},{"cell_type":"code","execution_count":null,"id":"26ecc3b1","metadata":{"id":"26ecc3b1"},"outputs":[],"source":["# Plot the training and validation loss and accuracy\n","fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (10, 5))\n","ax1.plot(range(1, EPOCHS+1), loss_list[0], label=\"Training\")\n","ax1.plot(range(1, EPOCHS+1), loss_list[1], label=\"Validation\")\n","ax1.set_title(\"Training and Validation Loss\")\n","ax1.legend()\n","ax2.plot(range(1, EPOCHS+1), acc_list[0], label=\"Training\")\n","ax2.plot(range(1, EPOCHS+1), acc_list[1], label=\"Validation\")\n","ax2.set_title(\"Training and Validation Accuracy\")\n","ax2.legend()\n","plt.show()\n","plt.savefig(os.path.join(RESULTS_FOLDER, \"loss_acc_plot.png\"))\n","plt.close()"]},{"cell_type":"markdown","id":"75e51a2e","metadata":{"id":"75e51a2e"},"source":["<img src=\"https://drive.google.com/uc?id=1qRKBGxrKcCX6REr1qpEaHUb6Ii_H98vE\" width=600px>"]},{"cell_type":"markdown","id":"f9780cfc","metadata":{"id":"f9780cfc"},"source":["# Test the model"]},{"cell_type":"code","execution_count":null,"id":"23e91642","metadata":{"id":"23e91642"},"outputs":[],"source":["trainer.load(8)\n","trainer.test() # test the model"]},{"cell_type":"markdown","id":"8a223391","metadata":{"id":"8a223391"},"source":["<img src=\"https://drive.google.com/uc?id=1qT33UBCW0cmU3pwcXkAmUrI0f9wkoU7c\" width=600px>"]},{"cell_type":"markdown","id":"46b28fae","metadata":{"id":"46b28fae"},"source":["## Explainable AI for classification model"]},{"cell_type":"code","execution_count":null,"id":"00c73c2d","metadata":{"id":"00c73c2d"},"outputs":[],"source":["trainer.GradCAM_func(num_images=16)"]},{"cell_type":"markdown","id":"857ad8cb","metadata":{"id":"857ad8cb"},"source":["<img src=\"https://drive.google.com/uc?id=1hTcEBmyALwTzZFoBFH5Y7CZlFRc1NuVF\" width=600px>"]},{"cell_type":"markdown","id":"a7c4309f","metadata":{"id":"a7c4309f"},"source":["<img src=\"https://drive.google.com/uc?id=1LutZ6AUT-5g5Pocfbo6DHBCb9ZyhmOFF\" width=600px>"]},{"cell_type":"markdown","id":"8d71fdfc","metadata":{"id":"8d71fdfc"},"source":["<img src=\"https://drive.google.com/uc?id=1dPfpMNSU2vaU9l-DU7tIeC_FpV7GprBP\" width=600px>"]},{"cell_type":"markdown","id":"3bc49e63","metadata":{"id":"3bc49e63"},"source":["<img src=\"https://drive.google.com/uc?id=1-JXoEaVtSijfiIMH-Vn1ELcaojJZBYSm\" width=600px>"]},{"cell_type":"code","execution_count":null,"id":"a568dbae","metadata":{"id":"a568dbae"},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":5}
\ No newline at end of file
diff --git a/Session7_Medical_Image_Data_Analysis/CLS&SEG/classification.py b/Session7_Medical_Image_Data_Analysis/CLS&SEG/classification.py
new file mode 100644
index 0000000..f543671
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/CLS&SEG/classification.py
@@ -0,0 +1,430 @@
+import os
+import csv
+import time
+import argparse
+import numpy as np
+import matplotlib.pyplot as plt
+
+from tqdm import tqdm
+from PIL import Image
+from pathlib import Path
+import sklearn.metrics as metrics
+from sklearn.metrics import roc_curve
+
+import torch
+import torch.optim as optim
+
+from torch import nn
+from torch.utils.data import Dataset, DataLoader
+
+import torchvision
+from torchvision import transforms as T
+
+# pip install torchsummary
+from torchsummary import summary
+# pip install grad-cam
+from pytorch_grad_cam import GradCAM 
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from pytorch_grad_cam.utils.model_targets import BinaryClassifierOutputTarget
+
+parser = argparse.ArgumentParser(formatter_class = argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument("mode", metavar="MODE", type=str, choices=["train", "test"],
+                    help="Type train or test for the script.")
+parser.add_argument("--milestone", "-m", type=int, required=False, default=0,
+                    help="Milestone model number for test mode.")
+args = parser.parse_args()
+print(f"Mode: {args.mode}")
+
+
+
+# Data paths and hyperparameters
+IMG_PATH = "Your dataset path where 'CheXpert-v1.0-small' is located"
+
+# train: 58,079 (neg 43,741 / pos 14,338)
+TRAIN_LABEL_PATH = "Your path to 'train_practice6.csv'"
+
+# valid: 6,453 (neg 4,857 / pos 1,596)
+VALID_LABEL_PATH = "Your path to 'valid_practice6.csv'"
+
+# test: 200 (neg 158 / pos 42)
+TEST_LABEL_PATH = "Your path to 'test_practice6.csv'"
+
+# hyperparameters
+IMAGE_SIZE = 320
+BATCH_SIZE = 64
+EPOCHS = 10
+RESULTS_FOLDER = "./results_cls/"
+os.mkdir(RESULTS_FOLDER) if not os.path.exists(RESULTS_FOLDER) else None
+
+
+
+# Define custom Dataset class
+class CXR_Cls_Dataset(Dataset):
+    def __init__(self, image_path, label_path, image_size):
+        super(CXR_Cls_Dataset, self).__init__()
+        self.image_path = image_path
+        self.mask_path = label_path
+        
+        image_names, labels = [], []
+
+        with open(label_path, "r") as f:
+            csv_reader = csv.reader(f)
+            next(csv_reader, None) # skip the header
+            for line in csv_reader:
+                image_names.append(line[0]) # image path from base
+                labels.append(float(line[10])) # Edema index
+
+        self.image_names = image_names
+        self.labels = labels
+
+        self.transform = T.Compose([
+            T.Resize((image_size, image_size)),
+            T.ToTensor()
+        ])
+
+    def __len__(self):
+        return len(self.image_names)
+
+    def __getitem__(self, index):
+        image_name = self.image_names[index]
+        label = self.labels[index]
+
+        image = Image.open(os.path.join(self.image_path, image_name)).convert("L")
+
+        return self.transform(image), torch.tensor(label, dtype=torch.float32)
+
+
+
+
+# Define dataset and dataloader
+train_dataset = CXR_Cls_Dataset(IMG_PATH, TRAIN_LABEL_PATH, IMAGE_SIZE)
+valid_dataset = CXR_Cls_Dataset(IMG_PATH, VALID_LABEL_PATH, IMAGE_SIZE)
+test_dataset = CXR_Cls_Dataset(IMG_PATH, TEST_LABEL_PATH, IMAGE_SIZE)
+
+print("Data Information")
+print("    Train data:", len(train_dataset))
+print("    Valid data:", len(valid_dataset))
+print("    Test data:", len(test_dataset))
+
+dataloader_train = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, 
+                              shuffle=True, num_workers=4, pin_memory=True)
+dataloader_valid = DataLoader(dataset=valid_dataset, batch_size=BATCH_SIZE, 
+                              shuffle=False, num_workers=4, pin_memory=True)
+dataloader_test = DataLoader(dataset=test_dataset, batch_size=1, 
+                             shuffle=False, num_workers=4, pin_memory=True)
+
+
+
+# Define CNN model class
+class CNN(nn.Module):
+    def __init__(self, in_channels):
+        super(CNN, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channels, 32, kernel_size=(3,3), stride=1, padding="same"),
+            nn.ReLU(),
+            nn.BatchNorm2d(32),
+            nn.MaxPool2d(kernel_size=(2,2), stride=2),
+            nn.Conv2d(32, 64, kernel_size=(3,3), stride=1, padding="same"),
+            nn.ReLU(),
+            nn.Dropout(0.1),
+            nn.BatchNorm2d(64),
+            nn.MaxPool2d(kernel_size=(2,2), stride=2),
+
+            nn.Conv2d(64, 64, kernel_size=(3,3), stride=1, padding="same"),
+            nn.ReLU(),
+            nn.BatchNorm2d(64),
+            nn.MaxPool2d(kernel_size=(2,2), stride=2),
+            nn.Conv2d(64, 128, kernel_size=(3,3), stride=1, padding="same"),
+            nn.ReLU(),
+            nn.Dropout(0.2),
+            nn.BatchNorm2d(128),
+            nn.MaxPool2d(kernel_size=(2,2), stride=2),
+
+            nn.Conv2d(128, 128, kernel_size=(3,3), stride=1, padding="same"),
+            nn.ReLU(),
+            nn.BatchNorm2d(128),
+            nn.MaxPool2d(kernel_size=(2,2), stride=2),
+            nn.Conv2d(128, 256, kernel_size=(3,3), stride=1, padding="same"),
+            nn.ReLU(),
+            nn.Dropout(0.2),
+            nn.BatchNorm2d(256),
+            nn.MaxPool2d(kernel_size=(2,2), stride=2),
+        )
+        
+        self.flatten = nn.Flatten()
+
+        self.fc = nn.Sequential(
+            nn.Linear(6400, 128), # 256x5x5
+            nn.Dropout(0.2),
+            nn.Linear(128, 1),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.flatten(x)
+        x = self.fc(x)
+
+        return x
+    
+# Define ResNet50 model class
+class ResNet50(nn.Module):
+    def __init__(self, out_features):
+        super(ResNet50, self).__init__()
+        self.resnet50 = torchvision.models.resnet50(weights=None)
+        self.resnet50.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
+        num_ftrs = self.resnet50.fc.in_features
+        self.resnet50.fc = nn.Sequential(
+            nn.Linear(num_ftrs, out_features),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        x = self.resnet50(x)
+        return x
+
+
+
+# Define model and optimizer
+#model = CNN(in_channels=1).cuda()
+model = ResNet50(out_features=1).cuda()
+#model = torch.nn.DataParallel(model).cuda() # use with multi-gpu environment
+summary(model, input_size=(1, 320, 320), device="cuda")
+
+optimizer = optim.Adam(model.parameters(), lr=1e-4)
+scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.5, patience=3,
+                                                 verbose=1, eps=1e-04, cooldown=2, min_lr=1e-6)
+
+
+
+# Define trainer
+class CXR_Cls_Trainer():
+    def __init__(
+        self, 
+        model, 
+        results_folder, 
+        dataloader_train, 
+        dataloader_valid, 
+        dataloader_test, 
+        epochs, 
+        optimizer, 
+        scheduler
+    ):
+        super(CXR_Cls_Trainer, self).__init__()
+
+        self.model = model
+        self.dataloader_train = dataloader_train
+        self.dataloader_valid = dataloader_valid
+        self.dataloader_test = dataloader_test
+        self.epoch = 0
+        self.epochs = epochs
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.loss_fn = nn.BCELoss()
+
+        self.results_folder = Path(results_folder)
+        self.results_folder.mkdir(exist_ok=True)
+
+    def train(self):
+        print("[ Start training ]")
+        self.model.train()
+        valid_loss_min = 10000
+        train_loss_list, valid_loss_list = [], []
+        train_acc_list, valid_acc_list = [], []
+        
+        while self.epoch < self.epochs:
+            print(f"\nEpoch {self.epoch:3d}: training")
+            train_loss_sum, train_accurate_sum = 0, 0
+            for batch_ID, (input, target) in enumerate(tqdm(self.dataloader_train)):
+                input = input.cuda(non_blocking=True)
+                target = target.reshape(-1, 1)
+                target = target.cuda(non_blocking=True)
+                
+                output = self.model(input)
+
+                loss = self.loss_fn(output, target)
+                loss.backward()
+                self.optimizer.step()
+                self.optimizer.zero_grad()
+
+                train_loss_sum += loss.item()*input.size(0)
+                if batch_ID % 20 == 19:
+                    print(f"\n[Batch: {batch_ID:5d}] training loss = {loss.item():.3f}", flush=True)
+
+                train_accurate_sum += (output.cpu().round() == target.cpu()).sum()
+
+            train_loss_avg = train_loss_sum / len(self.dataloader_train.dataset)
+            train_acc_avg = train_accurate_sum / len(self.dataloader_train.dataset)
+
+            train_loss_list.append(train_loss_avg)
+            train_acc_list.append(train_acc_avg)
+
+            # validation step
+            print(f"\nEpoch {self.epoch:3d}: validation")
+            self.model.eval()
+            with torch.no_grad():
+                valid_loss_sum, valid_accurate_sum = 0, 0
+                for _, (input, target) in enumerate(tqdm(self.dataloader_valid)):
+                    input = input.cuda(non_blocking=True)
+                    target = target.reshape(-1, 1)
+                    target = target.cuda(non_blocking=True)
+
+                    output = self.model(input)
+
+                    loss = self.loss_fn(output, target)
+
+                    valid_loss_sum += loss.item()*input.size(0)
+                    valid_accurate_sum += (output.cpu().round() == target.cpu()).sum()
+
+                valid_loss_avg = valid_loss_sum / len(self.dataloader_valid.dataset)
+                valid_acc_avg = valid_accurate_sum / len(self.dataloader_valid.dataset)
+
+                valid_loss_list.append(valid_loss_avg)
+                valid_acc_list.append(valid_acc_avg)
+                self.scheduler.step(valid_loss_avg)
+                print(f"    Epoch {self.epoch:2d}: training loss = {train_loss_avg:.3f} / validation loss = {valid_loss_avg:.3f}")
+                print(f"    Epoch {self.epoch:2d}: training acc = {train_acc_avg:.3f} / validation acc = {valid_acc_avg:.3f}")
+
+                # save model if better validation loss
+                if valid_loss_sum < valid_loss_min:
+                    valid_loss_min = valid_loss_sum
+                    self.save(self.epoch)
+                    print(f"    Saved model: cls-{self.epoch}.pth.tar")
+                
+            self.epoch += 1
+        print("[ End training ]")
+        return [train_loss_list, valid_loss_list], [train_acc_list, valid_acc_list]
+
+    def test(self):
+        print("[ Start test ]")
+        self.model.eval()
+        with torch.no_grad():
+            test_loss_sum, test_accurate_sum = 0, 0
+            output_array, target_array = np.array([]), np.array([])
+            for _, (input, target) in enumerate(tqdm(self.dataloader_test)):
+                input = input.cuda(non_blocking=True)
+                target = target.reshape(-1, 1)
+                target = target.cuda(non_blocking=True)
+                
+                output = self.model(input)
+
+                output_array = np.append(output_array, output.cpu().numpy())
+                target_array = np.append(target_array, target.cpu().numpy())
+
+                loss = self.loss_fn(output, target)
+                
+                test_loss_sum += loss.item()*input.size(0)
+                test_accurate_sum += (output.cpu().round() == target.cpu()).sum()
+
+            test_loss_avg = test_loss_sum / len(self.dataloader_test.dataset)
+            test_acc_avg = test_accurate_sum / len(self.dataloader_test.dataset)
+            print(f"    test loss = {test_loss_avg:.3f} / test acc = {test_acc_avg:.3f}")
+
+            # Draw ROC curve
+            fpr, tpr, _ = roc_curve(target_array, output_array)
+            roc_auc = metrics.auc(fpr, tpr)
+            plt.plot(fpr, tpr) # ROC curve = TPR vs FPR
+            plt.title(f"Receiver Operating Characteristics / AUC: {roc_auc:.2f}")
+            plt.xlabel("False Positive Rate")
+            plt.ylabel("True Positive Rate")
+            #plt.show()
+            plt.savefig(os.path.join(self.results_folder, "roc_plot.png"))
+            plt.close()
+            print(f"AUC: {roc_auc:.2f}")
+
+        print("[ End test ]")
+        self.output_array = output_array
+
+    def save(self, milestone):
+        files = os.listdir(self.results_folder)
+        remove = [f for f in files if ".pth.tar" in f]
+        if len(remove) > 0:
+            os.remove(os.path.join(self.results_folder, remove[0]))
+        torch.save({"epoch": milestone, 
+                    "state_dict": self.model.state_dict(), 
+                    "optimizer" : self.optimizer.state_dict()}, 
+                    f"{self.results_folder}/cls-{milestone}.pth.tar")
+
+    def load(self, milestone):
+        checkpoint = torch.load(f"{self.results_folder}/cls-{milestone}.pth.tar")
+        self.model.load_state_dict(checkpoint["state_dict"])
+        self.optimizer.load_state_dict(checkpoint["optimizer"])
+        self.epoch = checkpoint["epoch"]
+
+    def GradCAM_func(self, num_images): # https://github.com/jacobgil/pytorch-grad-cam
+        print("[ Start Grad-CAM ]")
+        self.model.eval()
+        target_layers = [self.model.resnet50.layer4[-1]]
+        cam = GradCAM(model=self.model, target_layers=target_layers)
+        image_list = self.get_images(num_images)
+
+        for i, (input, target) in enumerate(self.dataloader_test):
+            input = input.cuda(non_blocking=True)
+            target = target.numpy()
+
+            targets = [BinaryClassifierOutputTarget(target)]
+            grayscale_cam = cam(input_tensor=input, targets=targets)
+            grayscale_cam = grayscale_cam[0, :]
+            visualization = show_cam_on_image(image_list[i], grayscale_cam, use_rgb=True)
+
+            plt.imshow(visualization)
+            plt.title(f"Label: {int(target)} / Pred: {self.output_array[i]:.2f}")
+            plt.savefig(os.path.join(self.results_folder, f"grad-cam-{i}.png"))
+            plt.close()
+            if (i+1) == num_images: break
+        print("[ End Grad-CAM ]")
+
+    def get_images(self, num_images):
+        image_list = []
+        with open(TEST_LABEL_PATH, "r") as f:
+            csv_reader = csv.reader(f)
+            next(csv_reader, None) # skip the header
+            for line in csv_reader:
+                img = Image.open(os.path.join(IMG_PATH, line[0])).convert("L")
+                img = img.resize((320, 320))
+                img = np.array(img)
+                img_norm = (img-np.min(img))/(np.max(img)-np.min(img))
+                img_norm = np.expand_dims(img_norm, axis=2)
+                image_list.append(img_norm)
+                if len(image_list) == num_images: break
+
+        return image_list
+
+
+
+# Init the model
+trainer = CXR_Cls_Trainer(
+    model, 
+    RESULTS_FOLDER, 
+    dataloader_train, 
+    dataloader_valid, 
+    dataloader_test, 
+    EPOCHS, 
+    optimizer, 
+    scheduler
+)
+
+if args.mode == "train": # Train the model
+    train_start = time.time()
+    loss_list, acc_list = trainer.train() # training!
+    train_end = time.time()
+    print(f"\nElapsed time: {(train_end - train_start) / 60:.0f} minutes")
+
+    # Plot the training and validation loss and accuracy
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (10, 5))
+    ax1.plot(range(1, EPOCHS+1), loss_list[0], label="Training")
+    ax1.plot(range(1, EPOCHS+1), loss_list[1], label="Validation")
+    ax1.set_title("Training and Validation Loss")
+    ax1.legend()
+    ax2.plot(range(1, EPOCHS+1), acc_list[0], label="Training")
+    ax2.plot(range(1, EPOCHS+1), acc_list[1], label="Validation")
+    ax2.set_title("Training and Validation Accuracy")
+    ax2.legend()
+    #plt.show()
+    plt.savefig(os.path.join(RESULTS_FOLDER, "loss_acc_plot.png"))
+    plt.close()
+
+elif args.mode == "test": # Test the model
+    trainer.load(args.milestone)
+    trainer.test() # test the model
+    trainer.GradCAM_func(num_images=16)
diff --git a/Session7_Medical_Image_Data_Analysis/CLS&SEG/cls_train.txt b/Session7_Medical_Image_Data_Analysis/CLS&SEG/cls_train.txt
new file mode 100644
index 0000000..2b8b318
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/CLS&SEG/cls_train.txt
@@ -0,0 +1,1360 @@
+Data Information
+    Train data: 58079
+    Valid data: 6453
+    Test data: 200
+----------------------------------------------------------------
+        Layer (type)               Output Shape         Param #
+================================================================
+            Conv2d-1         [-1, 64, 160, 160]           3,136
+       BatchNorm2d-2         [-1, 64, 160, 160]             128
+              ReLU-3         [-1, 64, 160, 160]               0
+         MaxPool2d-4           [-1, 64, 80, 80]               0
+            Conv2d-5           [-1, 64, 80, 80]           4,096
+       BatchNorm2d-6           [-1, 64, 80, 80]             128
+              ReLU-7           [-1, 64, 80, 80]               0
+            Conv2d-8           [-1, 64, 80, 80]          36,864
+       BatchNorm2d-9           [-1, 64, 80, 80]             128
+             ReLU-10           [-1, 64, 80, 80]               0
+           Conv2d-11          [-1, 256, 80, 80]          16,384
+      BatchNorm2d-12          [-1, 256, 80, 80]             512
+           Conv2d-13          [-1, 256, 80, 80]          16,384
+      BatchNorm2d-14          [-1, 256, 80, 80]             512
+             ReLU-15          [-1, 256, 80, 80]               0
+       Bottleneck-16          [-1, 256, 80, 80]               0
+           Conv2d-17           [-1, 64, 80, 80]          16,384
+      BatchNorm2d-18           [-1, 64, 80, 80]             128
+             ReLU-19           [-1, 64, 80, 80]               0
+           Conv2d-20           [-1, 64, 80, 80]          36,864
+      BatchNorm2d-21           [-1, 64, 80, 80]             128
+             ReLU-22           [-1, 64, 80, 80]               0
+           Conv2d-23          [-1, 256, 80, 80]          16,384
+      BatchNorm2d-24          [-1, 256, 80, 80]             512
+             ReLU-25          [-1, 256, 80, 80]               0
+       Bottleneck-26          [-1, 256, 80, 80]               0
+           Conv2d-27           [-1, 64, 80, 80]          16,384
+      BatchNorm2d-28           [-1, 64, 80, 80]             128
+             ReLU-29           [-1, 64, 80, 80]               0
+           Conv2d-30           [-1, 64, 80, 80]          36,864
+      BatchNorm2d-31           [-1, 64, 80, 80]             128
+             ReLU-32           [-1, 64, 80, 80]               0
+           Conv2d-33          [-1, 256, 80, 80]          16,384
+      BatchNorm2d-34          [-1, 256, 80, 80]             512
+             ReLU-35          [-1, 256, 80, 80]               0
+       Bottleneck-36          [-1, 256, 80, 80]               0
+           Conv2d-37          [-1, 128, 80, 80]          32,768
+      BatchNorm2d-38          [-1, 128, 80, 80]             256
+             ReLU-39          [-1, 128, 80, 80]               0
+           Conv2d-40          [-1, 128, 40, 40]         147,456
+      BatchNorm2d-41          [-1, 128, 40, 40]             256
+             ReLU-42          [-1, 128, 40, 40]               0
+           Conv2d-43          [-1, 512, 40, 40]          65,536
+      BatchNorm2d-44          [-1, 512, 40, 40]           1,024
+           Conv2d-45          [-1, 512, 40, 40]         131,072
+      BatchNorm2d-46          [-1, 512, 40, 40]           1,024
+             ReLU-47          [-1, 512, 40, 40]               0
+       Bottleneck-48          [-1, 512, 40, 40]               0
+           Conv2d-49          [-1, 128, 40, 40]          65,536
+      BatchNorm2d-50          [-1, 128, 40, 40]             256
+             ReLU-51          [-1, 128, 40, 40]               0
+           Conv2d-52          [-1, 128, 40, 40]         147,456
+      BatchNorm2d-53          [-1, 128, 40, 40]             256
+             ReLU-54          [-1, 128, 40, 40]               0
+           Conv2d-55          [-1, 512, 40, 40]          65,536
+      BatchNorm2d-56          [-1, 512, 40, 40]           1,024
+             ReLU-57          [-1, 512, 40, 40]               0
+       Bottleneck-58          [-1, 512, 40, 40]               0
+           Conv2d-59          [-1, 128, 40, 40]          65,536
+      BatchNorm2d-60          [-1, 128, 40, 40]             256
+             ReLU-61          [-1, 128, 40, 40]               0
+           Conv2d-62          [-1, 128, 40, 40]         147,456
+      BatchNorm2d-63          [-1, 128, 40, 40]             256
+             ReLU-64          [-1, 128, 40, 40]               0
+           Conv2d-65          [-1, 512, 40, 40]          65,536
+      BatchNorm2d-66          [-1, 512, 40, 40]           1,024
+             ReLU-67          [-1, 512, 40, 40]               0
+       Bottleneck-68          [-1, 512, 40, 40]               0
+           Conv2d-69          [-1, 128, 40, 40]          65,536
+      BatchNorm2d-70          [-1, 128, 40, 40]             256
+             ReLU-71          [-1, 128, 40, 40]               0
+           Conv2d-72          [-1, 128, 40, 40]         147,456
+      BatchNorm2d-73          [-1, 128, 40, 40]             256
+             ReLU-74          [-1, 128, 40, 40]               0
+           Conv2d-75          [-1, 512, 40, 40]          65,536
+      BatchNorm2d-76          [-1, 512, 40, 40]           1,024
+             ReLU-77          [-1, 512, 40, 40]               0
+       Bottleneck-78          [-1, 512, 40, 40]               0
+           Conv2d-79          [-1, 256, 40, 40]         131,072
+      BatchNorm2d-80          [-1, 256, 40, 40]             512
+             ReLU-81          [-1, 256, 40, 40]               0
+           Conv2d-82          [-1, 256, 20, 20]         589,824
+      BatchNorm2d-83          [-1, 256, 20, 20]             512
+             ReLU-84          [-1, 256, 20, 20]               0
+           Conv2d-85         [-1, 1024, 20, 20]         262,144
+      BatchNorm2d-86         [-1, 1024, 20, 20]           2,048
+           Conv2d-87         [-1, 1024, 20, 20]         524,288
+      BatchNorm2d-88         [-1, 1024, 20, 20]           2,048
+             ReLU-89         [-1, 1024, 20, 20]               0
+       Bottleneck-90         [-1, 1024, 20, 20]               0
+           Conv2d-91          [-1, 256, 20, 20]         262,144
+      BatchNorm2d-92          [-1, 256, 20, 20]             512
+             ReLU-93          [-1, 256, 20, 20]               0
+           Conv2d-94          [-1, 256, 20, 20]         589,824
+      BatchNorm2d-95          [-1, 256, 20, 20]             512
+             ReLU-96          [-1, 256, 20, 20]               0
+           Conv2d-97         [-1, 1024, 20, 20]         262,144
+      BatchNorm2d-98         [-1, 1024, 20, 20]           2,048
+             ReLU-99         [-1, 1024, 20, 20]               0
+      Bottleneck-100         [-1, 1024, 20, 20]               0
+          Conv2d-101          [-1, 256, 20, 20]         262,144
+     BatchNorm2d-102          [-1, 256, 20, 20]             512
+            ReLU-103          [-1, 256, 20, 20]               0
+          Conv2d-104          [-1, 256, 20, 20]         589,824
+     BatchNorm2d-105          [-1, 256, 20, 20]             512
+            ReLU-106          [-1, 256, 20, 20]               0
+          Conv2d-107         [-1, 1024, 20, 20]         262,144
+     BatchNorm2d-108         [-1, 1024, 20, 20]           2,048
+            ReLU-109         [-1, 1024, 20, 20]               0
+      Bottleneck-110         [-1, 1024, 20, 20]               0
+          Conv2d-111          [-1, 256, 20, 20]         262,144
+     BatchNorm2d-112          [-1, 256, 20, 20]             512
+            ReLU-113          [-1, 256, 20, 20]               0
+          Conv2d-114          [-1, 256, 20, 20]         589,824
+     BatchNorm2d-115          [-1, 256, 20, 20]             512
+            ReLU-116          [-1, 256, 20, 20]               0
+          Conv2d-117         [-1, 1024, 20, 20]         262,144
+     BatchNorm2d-118         [-1, 1024, 20, 20]           2,048
+            ReLU-119         [-1, 1024, 20, 20]               0
+      Bottleneck-120         [-1, 1024, 20, 20]               0
+          Conv2d-121          [-1, 256, 20, 20]         262,144
+     BatchNorm2d-122          [-1, 256, 20, 20]             512
+            ReLU-123          [-1, 256, 20, 20]               0
+          Conv2d-124          [-1, 256, 20, 20]         589,824
+     BatchNorm2d-125          [-1, 256, 20, 20]             512
+            ReLU-126          [-1, 256, 20, 20]               0
+          Conv2d-127         [-1, 1024, 20, 20]         262,144
+     BatchNorm2d-128         [-1, 1024, 20, 20]           2,048
+            ReLU-129         [-1, 1024, 20, 20]               0
+      Bottleneck-130         [-1, 1024, 20, 20]               0
+          Conv2d-131          [-1, 256, 20, 20]         262,144
+     BatchNorm2d-132          [-1, 256, 20, 20]             512
+            ReLU-133          [-1, 256, 20, 20]               0
+          Conv2d-134          [-1, 256, 20, 20]         589,824
+     BatchNorm2d-135          [-1, 256, 20, 20]             512
+            ReLU-136          [-1, 256, 20, 20]               0
+          Conv2d-137         [-1, 1024, 20, 20]         262,144
+     BatchNorm2d-138         [-1, 1024, 20, 20]           2,048
+            ReLU-139         [-1, 1024, 20, 20]               0
+      Bottleneck-140         [-1, 1024, 20, 20]               0
+          Conv2d-141          [-1, 512, 20, 20]         524,288
+     BatchNorm2d-142          [-1, 512, 20, 20]           1,024
+            ReLU-143          [-1, 512, 20, 20]               0
+          Conv2d-144          [-1, 512, 10, 10]       2,359,296
+     BatchNorm2d-145          [-1, 512, 10, 10]           1,024
+            ReLU-146          [-1, 512, 10, 10]               0
+          Conv2d-147         [-1, 2048, 10, 10]       1,048,576
+     BatchNorm2d-148         [-1, 2048, 10, 10]           4,096
+          Conv2d-149         [-1, 2048, 10, 10]       2,097,152
+     BatchNorm2d-150         [-1, 2048, 10, 10]           4,096
+            ReLU-151         [-1, 2048, 10, 10]               0
+      Bottleneck-152         [-1, 2048, 10, 10]               0
+          Conv2d-153          [-1, 512, 10, 10]       1,048,576
+     BatchNorm2d-154          [-1, 512, 10, 10]           1,024
+            ReLU-155          [-1, 512, 10, 10]               0
+          Conv2d-156          [-1, 512, 10, 10]       2,359,296
+     BatchNorm2d-157          [-1, 512, 10, 10]           1,024
+            ReLU-158          [-1, 512, 10, 10]               0
+          Conv2d-159         [-1, 2048, 10, 10]       1,048,576
+     BatchNorm2d-160         [-1, 2048, 10, 10]           4,096
+            ReLU-161         [-1, 2048, 10, 10]               0
+      Bottleneck-162         [-1, 2048, 10, 10]               0
+          Conv2d-163          [-1, 512, 10, 10]       1,048,576
+     BatchNorm2d-164          [-1, 512, 10, 10]           1,024
+            ReLU-165          [-1, 512, 10, 10]               0
+          Conv2d-166          [-1, 512, 10, 10]       2,359,296
+     BatchNorm2d-167          [-1, 512, 10, 10]           1,024
+            ReLU-168          [-1, 512, 10, 10]               0
+          Conv2d-169         [-1, 2048, 10, 10]       1,048,576
+     BatchNorm2d-170         [-1, 2048, 10, 10]           4,096
+            ReLU-171         [-1, 2048, 10, 10]               0
+      Bottleneck-172         [-1, 2048, 10, 10]               0
+AdaptiveAvgPool2d-173           [-1, 2048, 1, 1]               0
+          Linear-174                    [-1, 1]           2,049
+         Sigmoid-175                    [-1, 1]               0
+          ResNet-176                    [-1, 1]               0
+        ResNet50-177                    [-1, 1]               0
+          Conv2d-178         [-1, 64, 160, 160]           3,136
+     BatchNorm2d-179         [-1, 64, 160, 160]             128
+            ReLU-180         [-1, 64, 160, 160]               0
+       MaxPool2d-181           [-1, 64, 80, 80]               0
+          Conv2d-182           [-1, 64, 80, 80]           4,096
+     BatchNorm2d-183           [-1, 64, 80, 80]             128
+            ReLU-184           [-1, 64, 80, 80]               0
+          Conv2d-185           [-1, 64, 80, 80]          36,864
+     BatchNorm2d-186           [-1, 64, 80, 80]             128
+            ReLU-187           [-1, 64, 80, 80]               0
+          Conv2d-188          [-1, 256, 80, 80]          16,384
+     BatchNorm2d-189          [-1, 256, 80, 80]             512
+          Conv2d-190          [-1, 256, 80, 80]          16,384
+     BatchNorm2d-191          [-1, 256, 80, 80]             512
+            ReLU-192          [-1, 256, 80, 80]               0
+      Bottleneck-193          [-1, 256, 80, 80]               0
+          Conv2d-194           [-1, 64, 80, 80]          16,384
+     BatchNorm2d-195           [-1, 64, 80, 80]             128
+            ReLU-196           [-1, 64, 80, 80]               0
+          Conv2d-197           [-1, 64, 80, 80]          36,864
+     BatchNorm2d-198           [-1, 64, 80, 80]             128
+            ReLU-199           [-1, 64, 80, 80]               0
+          Conv2d-200          [-1, 256, 80, 80]          16,384
+     BatchNorm2d-201          [-1, 256, 80, 80]             512
+            ReLU-202          [-1, 256, 80, 80]               0
+      Bottleneck-203          [-1, 256, 80, 80]               0
+          Conv2d-204           [-1, 64, 80, 80]          16,384
+     BatchNorm2d-205           [-1, 64, 80, 80]             128
+            ReLU-206           [-1, 64, 80, 80]               0
+          Conv2d-207           [-1, 64, 80, 80]          36,864
+     BatchNorm2d-208           [-1, 64, 80, 80]             128
+            ReLU-209           [-1, 64, 80, 80]               0
+          Conv2d-210          [-1, 256, 80, 80]          16,384
+     BatchNorm2d-211          [-1, 256, 80, 80]             512
+            ReLU-212          [-1, 256, 80, 80]               0
+      Bottleneck-213          [-1, 256, 80, 80]               0
+          Conv2d-214          [-1, 128, 80, 80]          32,768
+     BatchNorm2d-215          [-1, 128, 80, 80]             256
+            ReLU-216          [-1, 128, 80, 80]               0
+          Conv2d-217          [-1, 128, 40, 40]         147,456
+     BatchNorm2d-218          [-1, 128, 40, 40]             256
+            ReLU-219          [-1, 128, 40, 40]               0
+          Conv2d-220          [-1, 512, 40, 40]          65,536
+     BatchNorm2d-221          [-1, 512, 40, 40]           1,024
+          Conv2d-222          [-1, 512, 40, 40]         131,072
+     BatchNorm2d-223          [-1, 512, 40, 40]           1,024
+            ReLU-224          [-1, 512, 40, 40]               0
+      Bottleneck-225          [-1, 512, 40, 40]               0
+          Conv2d-226          [-1, 128, 40, 40]          65,536
+     BatchNorm2d-227          [-1, 128, 40, 40]             256
+            ReLU-228          [-1, 128, 40, 40]               0
+          Conv2d-229          [-1, 128, 40, 40]         147,456
+     BatchNorm2d-230          [-1, 128, 40, 40]             256
+            ReLU-231          [-1, 128, 40, 40]               0
+          Conv2d-232          [-1, 512, 40, 40]          65,536
+     BatchNorm2d-233          [-1, 512, 40, 40]           1,024
+            ReLU-234          [-1, 512, 40, 40]               0
+      Bottleneck-235          [-1, 512, 40, 40]               0
+          Conv2d-236          [-1, 128, 40, 40]          65,536
+     BatchNorm2d-237          [-1, 128, 40, 40]             256
+            ReLU-238          [-1, 128, 40, 40]               0
+          Conv2d-239          [-1, 128, 40, 40]         147,456
+     BatchNorm2d-240          [-1, 128, 40, 40]             256
+            ReLU-241          [-1, 128, 40, 40]               0
+          Conv2d-242          [-1, 512, 40, 40]          65,536
+     BatchNorm2d-243          [-1, 512, 40, 40]           1,024
+            ReLU-244          [-1, 512, 40, 40]               0
+      Bottleneck-245          [-1, 512, 40, 40]               0
+          Conv2d-246          [-1, 128, 40, 40]          65,536
+     BatchNorm2d-247          [-1, 128, 40, 40]             256
+            ReLU-248          [-1, 128, 40, 40]               0
+          Conv2d-249          [-1, 128, 40, 40]         147,456
+     BatchNorm2d-250          [-1, 128, 40, 40]             256
+            ReLU-251          [-1, 128, 40, 40]               0
+          Conv2d-252          [-1, 512, 40, 40]          65,536
+     BatchNorm2d-253          [-1, 512, 40, 40]           1,024
+            ReLU-254          [-1, 512, 40, 40]               0
+      Bottleneck-255          [-1, 512, 40, 40]               0
+          Conv2d-256          [-1, 256, 40, 40]         131,072
+     BatchNorm2d-257          [-1, 256, 40, 40]             512
+            ReLU-258          [-1, 256, 40, 40]               0
+          Conv2d-259          [-1, 256, 20, 20]         589,824
+     BatchNorm2d-260          [-1, 256, 20, 20]             512
+            ReLU-261          [-1, 256, 20, 20]               0
+          Conv2d-262         [-1, 1024, 20, 20]         262,144
+     BatchNorm2d-263         [-1, 1024, 20, 20]           2,048
+          Conv2d-264         [-1, 1024, 20, 20]         524,288
+     BatchNorm2d-265         [-1, 1024, 20, 20]           2,048
+            ReLU-266         [-1, 1024, 20, 20]               0
+      Bottleneck-267         [-1, 1024, 20, 20]               0
+          Conv2d-268          [-1, 256, 20, 20]         262,144
+     BatchNorm2d-269          [-1, 256, 20, 20]             512
+            ReLU-270          [-1, 256, 20, 20]               0
+          Conv2d-271          [-1, 256, 20, 20]         589,824
+     BatchNorm2d-272          [-1, 256, 20, 20]             512
+            ReLU-273          [-1, 256, 20, 20]               0
+          Conv2d-274         [-1, 1024, 20, 20]         262,144
+     BatchNorm2d-275         [-1, 1024, 20, 20]           2,048
+            ReLU-276         [-1, 1024, 20, 20]               0
+      Bottleneck-277         [-1, 1024, 20, 20]               0
+          Conv2d-278          [-1, 256, 20, 20]         262,144
+     BatchNorm2d-279          [-1, 256, 20, 20]             512
+            ReLU-280          [-1, 256, 20, 20]               0
+          Conv2d-281          [-1, 256, 20, 20]         589,824
+     BatchNorm2d-282          [-1, 256, 20, 20]             512
+            ReLU-283          [-1, 256, 20, 20]               0
+          Conv2d-284         [-1, 1024, 20, 20]         262,144
+     BatchNorm2d-285         [-1, 1024, 20, 20]           2,048
+            ReLU-286         [-1, 1024, 20, 20]               0
+      Bottleneck-287         [-1, 1024, 20, 20]               0
+          Conv2d-288          [-1, 256, 20, 20]         262,144
+     BatchNorm2d-289          [-1, 256, 20, 20]             512
+            ReLU-290          [-1, 256, 20, 20]               0
+          Conv2d-291          [-1, 256, 20, 20]         589,824
+     BatchNorm2d-292          [-1, 256, 20, 20]             512
+            ReLU-293          [-1, 256, 20, 20]               0
+          Conv2d-294         [-1, 1024, 20, 20]         262,144
+     BatchNorm2d-295         [-1, 1024, 20, 20]           2,048
+            ReLU-296         [-1, 1024, 20, 20]               0
+      Bottleneck-297         [-1, 1024, 20, 20]               0
+          Conv2d-298          [-1, 256, 20, 20]         262,144
+     BatchNorm2d-299          [-1, 256, 20, 20]             512
+            ReLU-300          [-1, 256, 20, 20]               0
+          Conv2d-301          [-1, 256, 20, 20]         589,824
+     BatchNorm2d-302          [-1, 256, 20, 20]             512
+            ReLU-303          [-1, 256, 20, 20]               0
+          Conv2d-304         [-1, 1024, 20, 20]         262,144
+     BatchNorm2d-305         [-1, 1024, 20, 20]           2,048
+            ReLU-306         [-1, 1024, 20, 20]               0
+      Bottleneck-307         [-1, 1024, 20, 20]               0
+          Conv2d-308          [-1, 256, 20, 20]         262,144
+     BatchNorm2d-309          [-1, 256, 20, 20]             512
+            ReLU-310          [-1, 256, 20, 20]               0
+          Conv2d-311          [-1, 256, 20, 20]         589,824
+     BatchNorm2d-312          [-1, 256, 20, 20]             512
+            ReLU-313          [-1, 256, 20, 20]               0
+          Conv2d-314         [-1, 1024, 20, 20]         262,144
+     BatchNorm2d-315         [-1, 1024, 20, 20]           2,048
+            ReLU-316         [-1, 1024, 20, 20]               0
+      Bottleneck-317         [-1, 1024, 20, 20]               0
+          Conv2d-318          [-1, 512, 20, 20]         524,288
+     BatchNorm2d-319          [-1, 512, 20, 20]           1,024
+            ReLU-320          [-1, 512, 20, 20]               0
+          Conv2d-321          [-1, 512, 10, 10]       2,359,296
+     BatchNorm2d-322          [-1, 512, 10, 10]           1,024
+            ReLU-323          [-1, 512, 10, 10]               0
+          Conv2d-324         [-1, 2048, 10, 10]       1,048,576
+     BatchNorm2d-325         [-1, 2048, 10, 10]           4,096
+          Conv2d-326         [-1, 2048, 10, 10]       2,097,152
+     BatchNorm2d-327         [-1, 2048, 10, 10]           4,096
+            ReLU-328         [-1, 2048, 10, 10]               0
+      Bottleneck-329         [-1, 2048, 10, 10]               0
+          Conv2d-330          [-1, 512, 10, 10]       1,048,576
+     BatchNorm2d-331          [-1, 512, 10, 10]           1,024
+            ReLU-332          [-1, 512, 10, 10]               0
+          Conv2d-333          [-1, 512, 10, 10]       2,359,296
+     BatchNorm2d-334          [-1, 512, 10, 10]           1,024
+            ReLU-335          [-1, 512, 10, 10]               0
+          Conv2d-336         [-1, 2048, 10, 10]       1,048,576
+     BatchNorm2d-337         [-1, 2048, 10, 10]           4,096
+            ReLU-338         [-1, 2048, 10, 10]               0
+      Bottleneck-339         [-1, 2048, 10, 10]               0
+          Conv2d-340          [-1, 512, 10, 10]       1,048,576
+     BatchNorm2d-341          [-1, 512, 10, 10]           1,024
+            ReLU-342          [-1, 512, 10, 10]               0
+          Conv2d-343          [-1, 512, 10, 10]       2,359,296
+     BatchNorm2d-344          [-1, 512, 10, 10]           1,024
+            ReLU-345          [-1, 512, 10, 10]               0
+          Conv2d-346         [-1, 2048, 10, 10]       1,048,576
+     BatchNorm2d-347         [-1, 2048, 10, 10]           4,096
+            ReLU-348         [-1, 2048, 10, 10]               0
+      Bottleneck-349         [-1, 2048, 10, 10]               0
+AdaptiveAvgPool2d-350           [-1, 2048, 1, 1]               0
+          Linear-351                    [-1, 1]           2,049
+         Sigmoid-352                    [-1, 1]               0
+          ResNet-353                    [-1, 1]               0
+        ResNet50-354                    [-1, 1]               0
+================================================================
+Total params: 47,007,618
+Trainable params: 47,007,618
+Non-trainable params: 0
+----------------------------------------------------------------
+Input size (MB): 0.39
+Forward/backward pass size (MB): 1169.56
+Params size (MB): 179.32
+Estimated Total Size (MB): 1349.27
+----------------------------------------------------------------
+[ Start training ]
+
+Epoch   1: training
+
  0%|          | 0/908 [00:00<?, ?it/s]
  0%|          | 1/908 [00:18<4:43:11, 18.73s/it]
  0%|          | 2/908 [00:18<1:58:24,  7.84s/it]
  0%|          | 3/908 [00:19<1:05:40,  4.35s/it]
  0%|          | 4/908 [00:19<40:56,  2.72s/it]  
  1%|          | 5/908 [00:19<27:14,  1.81s/it]
  1%|          | 6/908 [00:19<19:02,  1.27s/it]
  1%|          | 7/908 [00:19<13:48,  1.09it/s]
  1%|          | 8/908 [00:20<10:22,  1.45it/s]
  1%|          | 9/908 [00:20<08:05,  1.85it/s]
  1%|          | 10/908 [00:21<08:38,  1.73it/s]
  1%|          | 11/908 [00:21<07:22,  2.03it/s]
  1%|▏         | 12/908 [00:21<06:09,  2.42it/s]
  1%|▏         | 13/908 [00:21<05:18,  2.81it/s]
  2%|▏         | 14/908 [00:23<11:00,  1.35it/s]
  2%|▏         | 15/908 [00:24<11:27,  1.30it/s]
  2%|▏         | 16/908 [00:24<08:59,  1.65it/s]
  2%|▏         | 17/908 [00:24<08:21,  1.78it/s]
  2%|▏         | 18/908 [00:26<11:30,  1.29it/s]
  2%|▏         | 19/908 [00:26<10:56,  1.35it/s]
+[Batch:    20] training loss = 0.615
+
  2%|▏         | 20/908 [00:27<09:38,  1.53it/s]
  2%|▏         | 21/908 [00:27<08:43,  1.69it/s]
  2%|▏         | 22/908 [00:29<14:49,  1.00s/it]
  3%|▎         | 23/908 [00:29<11:20,  1.30it/s]
  3%|▎         | 24/908 [00:30<08:54,  1.65it/s]
  3%|▎         | 25/908 [00:30<09:08,  1.61it/s]
  3%|▎         | 26/908 [00:33<18:25,  1.25s/it]
  3%|▎         | 27/908 [00:33<13:50,  1.06it/s]
  3%|▎         | 28/908 [00:34<10:36,  1.38it/s]
  3%|▎         | 29/908 [00:34<08:18,  1.76it/s]
  3%|▎         | 30/908 [00:35<11:10,  1.31it/s]
  3%|▎         | 31/908 [00:35<08:45,  1.67it/s]
  4%|▎         | 32/908 [00:35<07:02,  2.07it/s]
  4%|▎         | 33/908 [00:36<05:51,  2.49it/s]
  4%|▎         | 34/908 [00:37<09:36,  1.52it/s]
  4%|▍         | 35/908 [00:37<07:36,  1.91it/s]
  4%|▍         | 36/908 [00:37<06:12,  2.34it/s]
  4%|▍         | 37/908 [00:37<05:15,  2.76it/s]
  4%|▍         | 38/908 [00:39<08:45,  1.66it/s]
  4%|▍         | 39/908 [00:39<06:59,  2.07it/s]
+[Batch:    40] training loss = 0.574
+
  4%|▍         | 40/908 [00:39<05:46,  2.50it/s]
  5%|▍         | 41/908 [00:39<05:43,  2.52it/s]
  5%|▍         | 42/908 [00:41<10:40,  1.35it/s]
  5%|▍         | 43/908 [00:41<08:22,  1.72it/s]
  5%|▍         | 44/908 [00:41<06:48,  2.12it/s]
  5%|▍         | 45/908 [00:42<05:39,  2.54it/s]
  5%|▌         | 46/908 [00:43<09:01,  1.59it/s]
  5%|▌         | 47/908 [00:43<07:11,  2.00it/s]
  5%|▌         | 48/908 [00:43<05:56,  2.42it/s]
  5%|▌         | 49/908 [00:43<05:03,  2.83it/s]
  6%|▌         | 50/908 [00:44<04:32,  3.15it/s]
  6%|▌         | 51/908 [00:44<04:03,  3.52it/s]
  6%|▌         | 52/908 [00:44<03:41,  3.86it/s]
  6%|▌         | 53/908 [00:44<03:28,  4.09it/s]
  6%|▌         | 54/908 [00:46<09:39,  1.47it/s]
  6%|▌         | 55/908 [00:46<07:36,  1.87it/s]
  6%|▌         | 56/908 [00:46<06:10,  2.30it/s]
  6%|▋         | 57/908 [00:47<05:09,  2.75it/s]
  6%|▋         | 58/908 [00:48<09:53,  1.43it/s]
  6%|▋         | 59/908 [00:48<08:32,  1.66it/s]
+[Batch:    60] training loss = 0.547
+
  7%|▋         | 60/908 [00:49<06:50,  2.06it/s]
  7%|▋         | 61/908 [00:49<05:50,  2.42it/s]
  7%|▋         | 62/908 [00:49<05:24,  2.61it/s]
  7%|▋         | 63/908 [00:50<08:15,  1.70it/s]
  7%|▋         | 64/908 [00:50<06:39,  2.11it/s]
  7%|▋         | 65/908 [00:51<05:31,  2.55it/s]
  7%|▋         | 66/908 [00:51<05:18,  2.64it/s]
  7%|▋         | 67/908 [00:51<05:42,  2.45it/s]
  7%|▋         | 68/908 [00:52<04:50,  2.89it/s]
  8%|▊         | 69/908 [00:52<04:15,  3.29it/s]
  8%|▊         | 70/908 [00:53<05:43,  2.44it/s]
  8%|▊         | 71/908 [00:54<10:29,  1.33it/s]
  8%|▊         | 72/908 [00:54<08:12,  1.70it/s]
  8%|▊         | 73/908 [00:55<06:36,  2.11it/s]
  8%|▊         | 74/908 [00:55<06:59,  1.99it/s]
  8%|▊         | 75/908 [00:57<11:27,  1.21it/s]
  8%|▊         | 76/908 [00:57<08:55,  1.55it/s]
  8%|▊         | 77/908 [00:57<07:06,  1.95it/s]
  9%|▊         | 78/908 [00:58<08:10,  1.69it/s]
  9%|▊         | 79/908 [01:00<13:26,  1.03it/s]
+[Batch:    80] training loss = 0.565
+
  9%|▉         | 80/908 [01:00<10:17,  1.34it/s]
  9%|▉         | 81/908 [01:00<08:07,  1.70it/s]
  9%|▉         | 82/908 [01:01<09:47,  1.41it/s]
  9%|▉         | 83/908 [01:03<14:45,  1.07s/it]
  9%|▉         | 84/908 [01:03<11:12,  1.23it/s]
  9%|▉         | 85/908 [01:03<08:40,  1.58it/s]
  9%|▉         | 86/908 [01:04<07:14,  1.89it/s]
 10%|▉         | 87/908 [01:06<12:14,  1.12it/s]
 10%|▉         | 88/908 [01:06<09:25,  1.45it/s]
 10%|▉         | 89/908 [01:06<07:59,  1.71it/s]
 10%|▉         | 90/908 [01:06<06:25,  2.12it/s]
 10%|█         | 91/908 [01:08<10:29,  1.30it/s]
 10%|█         | 92/908 [01:08<08:11,  1.66it/s]
 10%|█         | 93/908 [01:08<06:32,  2.08it/s]
 10%|█         | 94/908 [01:09<06:10,  2.20it/s]
 10%|█         | 95/908 [01:10<11:39,  1.16it/s]
 11%|█         | 96/908 [01:11<09:00,  1.50it/s]
 11%|█         | 97/908 [01:11<07:09,  1.89it/s]
 11%|█         | 98/908 [01:11<07:08,  1.89it/s]
 11%|█         | 99/908 [01:13<11:11,  1.21it/s]
+[Batch:   100] training loss = 0.454
+
 11%|█         | 100/908 [01:13<08:38,  1.56it/s]
 11%|█         | 101/908 [01:13<07:01,  1.92it/s]
 11%|█         | 102/908 [01:14<07:47,  1.72it/s]
 11%|█▏        | 103/908 [01:16<11:41,  1.15it/s]
 11%|█▏        | 104/908 [01:16<08:59,  1.49it/s]
 12%|█▏        | 105/908 [01:16<07:05,  1.89it/s]
 12%|█▏        | 106/908 [01:16<06:14,  2.14it/s]
 12%|█▏        | 107/908 [01:18<10:01,  1.33it/s]
 12%|█▏        | 108/908 [01:18<07:50,  1.70it/s]
 12%|█▏        | 109/908 [01:18<06:21,  2.10it/s]
 12%|█▏        | 110/908 [01:19<06:33,  2.03it/s]
 12%|█▏        | 111/908 [01:21<12:03,  1.10it/s]
 12%|█▏        | 112/908 [01:21<09:14,  1.44it/s]
 12%|█▏        | 113/908 [01:21<07:18,  1.81it/s]
 13%|█▎        | 114/908 [01:21<06:32,  2.02it/s]
 13%|█▎        | 115/908 [01:23<11:35,  1.14it/s]
 13%|█▎        | 116/908 [01:23<08:55,  1.48it/s]
 13%|█▎        | 117/908 [01:23<07:05,  1.86it/s]
 13%|█▎        | 118/908 [01:24<06:11,  2.13it/s]
 13%|█▎        | 119/908 [01:26<11:56,  1.10it/s]
+[Batch:   120] training loss = 0.619
+
 13%|█▎        | 120/908 [01:26<09:10,  1.43it/s]
 13%|█▎        | 121/908 [01:26<07:27,  1.76it/s]
 13%|█▎        | 122/908 [01:27<07:44,  1.69it/s]
 14%|█▎        | 123/908 [01:28<11:32,  1.13it/s]
 14%|█▎        | 124/908 [01:29<08:51,  1.48it/s]
 14%|█▍        | 125/908 [01:29<07:29,  1.74it/s]
 14%|█▍        | 126/908 [01:30<07:59,  1.63it/s]
 14%|█▍        | 127/908 [01:31<11:29,  1.13it/s]
 14%|█▍        | 128/908 [01:31<08:50,  1.47it/s]
 14%|█▍        | 129/908 [01:32<06:57,  1.87it/s]
 14%|█▍        | 130/908 [01:32<08:06,  1.60it/s]
 14%|█▍        | 131/908 [01:34<12:49,  1.01it/s]
 15%|█▍        | 132/908 [01:34<09:45,  1.33it/s]
 15%|█▍        | 133/908 [01:35<07:36,  1.70it/s]
 15%|█▍        | 134/908 [01:35<07:42,  1.67it/s]
 15%|█▍        | 135/908 [01:37<10:08,  1.27it/s]
 15%|█▍        | 136/908 [01:37<07:54,  1.63it/s]
 15%|█▌        | 137/908 [01:37<06:19,  2.03it/s]
 15%|█▌        | 138/908 [01:38<07:07,  1.80it/s]
 15%|█▌        | 139/908 [01:39<10:48,  1.19it/s]
+[Batch:   140] training loss = 0.506
+
 15%|█▌        | 140/908 [01:39<08:20,  1.54it/s]
 16%|█▌        | 141/908 [01:40<06:37,  1.93it/s]
 16%|█▌        | 142/908 [01:40<05:59,  2.13it/s]
 16%|█▌        | 143/908 [01:41<09:41,  1.32it/s]
 16%|█▌        | 144/908 [01:42<07:34,  1.68it/s]
 16%|█▌        | 145/908 [01:42<06:06,  2.08it/s]
 16%|█▌        | 146/908 [01:42<06:35,  1.93it/s]
 16%|█▌        | 147/908 [01:44<10:57,  1.16it/s]
 16%|█▋        | 148/908 [01:44<08:25,  1.50it/s]
 16%|█▋        | 149/908 [01:44<06:40,  1.89it/s]
 17%|█▋        | 150/908 [01:45<05:43,  2.20it/s]
 17%|█▋        | 151/908 [01:47<11:30,  1.10it/s]
 17%|█▋        | 152/908 [01:47<08:52,  1.42it/s]
 17%|█▋        | 153/908 [01:47<06:58,  1.80it/s]
 17%|█▋        | 154/908 [01:47<05:41,  2.21it/s]
 17%|█▋        | 155/908 [01:49<09:33,  1.31it/s]
 17%|█▋        | 156/908 [01:49<07:28,  1.68it/s]
 17%|█▋        | 157/908 [01:49<06:01,  2.07it/s]
 17%|█▋        | 158/908 [01:49<05:01,  2.49it/s]
 18%|█▊        | 159/908 [01:51<09:31,  1.31it/s]
+[Batch:   160] training loss = 0.465
+
 18%|█▊        | 160/908 [01:51<07:27,  1.67it/s]
 18%|█▊        | 161/908 [01:52<06:02,  2.06it/s]
 18%|█▊        | 162/908 [01:52<05:01,  2.48it/s]
 18%|█▊        | 163/908 [01:54<10:29,  1.18it/s]
 18%|█▊        | 164/908 [01:54<08:05,  1.53it/s]
 18%|█▊        | 165/908 [01:54<06:24,  1.93it/s]
 18%|█▊        | 166/908 [01:54<06:06,  2.03it/s]
 18%|█▊        | 167/908 [01:56<10:59,  1.12it/s]
 19%|█▊        | 168/908 [01:56<08:26,  1.46it/s]
 19%|█▊        | 169/908 [01:57<06:38,  1.85it/s]
 19%|█▊        | 170/908 [01:57<05:22,  2.29it/s]
 19%|█▉        | 171/908 [01:59<12:13,  1.00it/s]
 19%|█▉        | 172/908 [01:59<09:17,  1.32it/s]
 19%|█▉        | 173/908 [02:00<07:14,  1.69it/s]
 19%|█▉        | 174/908 [02:00<06:44,  1.81it/s]
 19%|█▉        | 175/908 [02:03<14:04,  1.15s/it]
 19%|█▉        | 176/908 [02:03<10:37,  1.15it/s]
 19%|█▉        | 177/908 [02:03<08:10,  1.49it/s]
 20%|█▉        | 178/908 [02:03<06:31,  1.86it/s]
 20%|█▉        | 179/908 [02:05<12:08,  1.00it/s]
+[Batch:   180] training loss = 0.685
+
 20%|█▉        | 180/908 [02:06<09:15,  1.31it/s]
 20%|█▉        | 181/908 [02:06<07:14,  1.67it/s]
 20%|██        | 182/908 [02:06<05:50,  2.07it/s]
 20%|██        | 183/908 [02:07<08:57,  1.35it/s]
 20%|██        | 184/908 [02:07<06:59,  1.73it/s]
 20%|██        | 185/908 [02:08<05:39,  2.13it/s]
 20%|██        | 186/908 [02:08<04:42,  2.56it/s]
 21%|██        | 187/908 [02:10<11:49,  1.02it/s]
 21%|██        | 188/908 [02:10<08:59,  1.34it/s]
 21%|██        | 189/908 [02:11<07:03,  1.70it/s]
 21%|██        | 190/908 [02:11<05:41,  2.10it/s]
 21%|██        | 191/908 [02:13<10:38,  1.12it/s]
 21%|██        | 192/908 [02:13<08:10,  1.46it/s]
 21%|██▏       | 193/908 [02:13<06:26,  1.85it/s]
 21%|██▏       | 194/908 [02:13<05:13,  2.28it/s]
 21%|██▏       | 195/908 [02:15<10:56,  1.09it/s]
 22%|██▏       | 196/908 [02:16<08:23,  1.41it/s]
 22%|██▏       | 197/908 [02:16<06:35,  1.80it/s]
 22%|██▏       | 198/908 [02:16<05:21,  2.21it/s]
 22%|██▏       | 199/908 [02:18<09:29,  1.25it/s]
+[Batch:   200] training loss = 0.568
+
 22%|██▏       | 200/908 [02:18<07:22,  1.60it/s]
 22%|██▏       | 201/908 [02:18<06:00,  1.96it/s]
 22%|██▏       | 202/908 [02:18<04:57,  2.37it/s]
 22%|██▏       | 203/908 [02:20<10:14,  1.15it/s]
 22%|██▏       | 204/908 [02:20<07:54,  1.48it/s]
 23%|██▎       | 205/908 [02:21<06:15,  1.87it/s]
 23%|██▎       | 206/908 [02:21<05:06,  2.29it/s]
 23%|██▎       | 207/908 [02:23<11:03,  1.06it/s]
 23%|██▎       | 208/908 [02:23<08:26,  1.38it/s]
 23%|██▎       | 209/908 [02:23<06:36,  1.76it/s]
 23%|██▎       | 210/908 [02:24<05:21,  2.17it/s]
 23%|██▎       | 211/908 [02:25<09:37,  1.21it/s]
 23%|██▎       | 212/908 [02:26<07:25,  1.56it/s]
 23%|██▎       | 213/908 [02:26<05:54,  1.96it/s]
 24%|██▎       | 214/908 [02:26<04:50,  2.39it/s]
 24%|██▎       | 215/908 [02:29<13:50,  1.20s/it]
 24%|██▍       | 216/908 [02:29<10:25,  1.11it/s]
 24%|██▍       | 217/908 [02:29<08:00,  1.44it/s]
 24%|██▍       | 218/908 [02:30<06:18,  1.82it/s]
 24%|██▍       | 219/908 [02:32<11:35,  1.01s/it]
+[Batch:   220] training loss = 0.518
+
 24%|██▍       | 220/908 [02:32<08:45,  1.31it/s]
 24%|██▍       | 221/908 [02:32<06:52,  1.66it/s]
 24%|██▍       | 222/908 [02:32<05:29,  2.08it/s]
 25%|██▍       | 223/908 [02:35<11:35,  1.02s/it]
 25%|██▍       | 224/908 [02:35<08:48,  1.30it/s]
 25%|██▍       | 225/908 [02:35<06:50,  1.66it/s]
 25%|██▍       | 226/908 [02:35<05:26,  2.09it/s]
 25%|██▌       | 227/908 [02:37<09:56,  1.14it/s]
 25%|██▌       | 228/908 [02:37<07:37,  1.49it/s]
 25%|██▌       | 229/908 [02:37<05:59,  1.89it/s]
 25%|██▌       | 230/908 [02:38<04:51,  2.33it/s]
 25%|██▌       | 231/908 [02:39<09:56,  1.14it/s]
 26%|██▌       | 232/908 [02:40<07:38,  1.48it/s]
 26%|██▌       | 233/908 [02:40<06:01,  1.87it/s]
 26%|██▌       | 234/908 [02:40<04:55,  2.28it/s]
 26%|██▌       | 235/908 [02:42<08:22,  1.34it/s]
 26%|██▌       | 236/908 [02:42<06:33,  1.71it/s]
 26%|██▌       | 237/908 [02:42<05:13,  2.14it/s]
 26%|██▌       | 238/908 [02:42<04:19,  2.58it/s]
 26%|██▋       | 239/908 [02:43<07:28,  1.49it/s]
+[Batch:   240] training loss = 0.562
+
 26%|██▋       | 240/908 [02:44<05:54,  1.89it/s]
 27%|██▋       | 241/908 [02:44<04:49,  2.30it/s]
 27%|██▋       | 242/908 [02:44<04:01,  2.76it/s]
 27%|██▋       | 243/908 [02:45<06:32,  1.69it/s]
 27%|██▋       | 244/908 [02:45<05:12,  2.12it/s]
 27%|██▋       | 245/908 [02:46<04:19,  2.56it/s]
 27%|██▋       | 246/908 [02:46<04:18,  2.56it/s]
 27%|██▋       | 247/908 [02:47<06:00,  1.83it/s]
 27%|██▋       | 248/908 [02:47<04:52,  2.26it/s]
 27%|██▋       | 249/908 [02:47<04:01,  2.73it/s]
 28%|██▊       | 250/908 [02:47<03:28,  3.16it/s]
 28%|██▊       | 251/908 [02:49<08:04,  1.36it/s]
 28%|██▊       | 252/908 [02:49<06:17,  1.74it/s]
 28%|██▊       | 253/908 [02:50<05:01,  2.17it/s]
 28%|██▊       | 254/908 [02:50<04:09,  2.62it/s]
 28%|██▊       | 255/908 [02:51<07:44,  1.40it/s]
 28%|██▊       | 256/908 [02:51<06:04,  1.79it/s]
 28%|██▊       | 257/908 [02:52<04:52,  2.23it/s]
 28%|██▊       | 258/908 [02:52<04:02,  2.69it/s]
 29%|██▊       | 259/908 [02:53<05:47,  1.87it/s]
+[Batch:   260] training loss = 0.451
+
 29%|██▊       | 260/908 [02:53<04:42,  2.30it/s]
 29%|██▊       | 261/908 [02:53<03:56,  2.73it/s]
 29%|██▉       | 262/908 [02:53<03:25,  3.14it/s]
 29%|██▉       | 263/908 [02:54<04:41,  2.29it/s]
 29%|██▉       | 264/908 [02:54<03:55,  2.74it/s]
 29%|██▉       | 265/908 [02:55<03:22,  3.17it/s]
 29%|██▉       | 266/908 [02:55<02:59,  3.58it/s]
 29%|██▉       | 267/908 [02:57<08:13,  1.30it/s]
 30%|██▉       | 268/908 [02:57<06:21,  1.68it/s]
 30%|██▉       | 269/908 [02:57<05:03,  2.10it/s]
 30%|██▉       | 270/908 [02:57<04:11,  2.54it/s]
 30%|██▉       | 271/908 [02:59<08:51,  1.20it/s]
 30%|██▉       | 272/908 [02:59<06:48,  1.56it/s]
 30%|███       | 273/908 [02:59<05:22,  1.97it/s]
 30%|███       | 274/908 [03:00<04:22,  2.41it/s]
 30%|███       | 275/908 [03:01<07:53,  1.34it/s]
 30%|███       | 276/908 [03:01<06:07,  1.72it/s]
 31%|███       | 277/908 [03:02<04:55,  2.14it/s]
 31%|███       | 278/908 [03:02<04:05,  2.57it/s]
 31%|███       | 279/908 [03:04<09:27,  1.11it/s]
+[Batch:   280] training loss = 0.520
+
 31%|███       | 280/908 [03:04<07:12,  1.45it/s]
 31%|███       | 281/908 [03:04<05:42,  1.83it/s]
 31%|███       | 282/908 [03:04<04:36,  2.26it/s]
 31%|███       | 283/908 [03:07<09:38,  1.08it/s]
 31%|███▏      | 284/908 [03:07<07:22,  1.41it/s]
 31%|███▏      | 285/908 [03:07<05:45,  1.80it/s]
 31%|███▏      | 286/908 [03:07<04:38,  2.23it/s]
 32%|███▏      | 287/908 [03:09<07:53,  1.31it/s]
 32%|███▏      | 288/908 [03:09<06:07,  1.69it/s]
 32%|███▏      | 289/908 [03:09<04:53,  2.11it/s]
 32%|███▏      | 290/908 [03:09<04:07,  2.50it/s]
 32%|███▏      | 291/908 [03:10<06:08,  1.67it/s]
 32%|███▏      | 292/908 [03:11<04:55,  2.08it/s]
 32%|███▏      | 293/908 [03:11<04:03,  2.53it/s]
 32%|███▏      | 294/908 [03:12<06:09,  1.66it/s]
 32%|███▏      | 295/908 [03:13<07:14,  1.41it/s]
 33%|███▎      | 296/908 [03:13<05:40,  1.80it/s]
 33%|███▎      | 297/908 [03:13<04:33,  2.23it/s]
 33%|███▎      | 298/908 [03:14<05:30,  1.85it/s]
 33%|███▎      | 299/908 [03:15<05:58,  1.70it/s]
+[Batch:   300] training loss = 0.439
+
 33%|███▎      | 300/908 [03:15<04:46,  2.12it/s]
 33%|███▎      | 301/908 [03:15<03:56,  2.57it/s]
 33%|███▎      | 302/908 [03:16<04:26,  2.27it/s]
 33%|███▎      | 303/908 [03:17<07:04,  1.43it/s]
 33%|███▎      | 304/908 [03:17<05:31,  1.82it/s]
 34%|███▎      | 305/908 [03:17<04:27,  2.26it/s]
 34%|███▎      | 306/908 [03:18<05:13,  1.92it/s]
 34%|███▍      | 307/908 [03:19<06:43,  1.49it/s]
 34%|███▍      | 308/908 [03:19<05:18,  1.88it/s]
 34%|███▍      | 309/908 [03:19<04:18,  2.32it/s]
 34%|███▍      | 310/908 [03:20<03:36,  2.77it/s]
 34%|███▍      | 311/908 [03:21<07:17,  1.36it/s]
 34%|███▍      | 312/908 [03:21<05:40,  1.75it/s]
 34%|███▍      | 313/908 [03:22<04:33,  2.18it/s]
 35%|███▍      | 314/908 [03:22<03:45,  2.63it/s]
 35%|███▍      | 315/908 [03:24<08:13,  1.20it/s]
 35%|███▍      | 316/908 [03:24<06:19,  1.56it/s]
 35%|███▍      | 317/908 [03:24<04:59,  1.97it/s]
 35%|███▌      | 318/908 [03:24<04:05,  2.40it/s]
 35%|███▌      | 319/908 [03:26<06:46,  1.45it/s]
+[Batch:   320] training loss = 0.442
+
 35%|███▌      | 320/908 [03:26<05:19,  1.84it/s]
 35%|███▌      | 321/908 [03:26<04:18,  2.27it/s]
 35%|███▌      | 322/908 [03:26<03:36,  2.71it/s]
 36%|███▌      | 323/908 [03:28<07:57,  1.22it/s]
 36%|███▌      | 324/908 [03:28<06:08,  1.58it/s]
 36%|███▌      | 325/908 [03:28<04:52,  2.00it/s]
 36%|███▌      | 326/908 [03:29<04:01,  2.41it/s]
 36%|███▌      | 327/908 [03:30<06:33,  1.48it/s]
 36%|███▌      | 328/908 [03:30<05:08,  1.88it/s]
 36%|███▌      | 329/908 [03:30<04:09,  2.32it/s]
 36%|███▋      | 330/908 [03:31<04:09,  2.32it/s]
 36%|███▋      | 331/908 [03:32<06:58,  1.38it/s]
 37%|███▋      | 332/908 [03:32<05:26,  1.76it/s]
 37%|███▋      | 333/908 [03:33<04:22,  2.19it/s]
 37%|███▋      | 334/908 [03:33<04:44,  2.02it/s]
 37%|███▋      | 335/908 [03:35<08:02,  1.19it/s]
 37%|███▋      | 336/908 [03:35<06:10,  1.54it/s]
 37%|███▋      | 337/908 [03:35<04:52,  1.95it/s]
 37%|███▋      | 338/908 [03:36<05:57,  1.59it/s]
 37%|███▋      | 339/908 [03:38<08:37,  1.10it/s]
+[Batch:   340] training loss = 0.606
+
 37%|███▋      | 340/908 [03:38<06:35,  1.44it/s]
 38%|███▊      | 341/908 [03:38<05:15,  1.80it/s]
 38%|███▊      | 342/908 [03:38<04:17,  2.20it/s]
 38%|███▊      | 343/908 [03:39<04:41,  2.01it/s]
 38%|███▊      | 344/908 [03:39<03:51,  2.44it/s]
 38%|███▊      | 345/908 [03:39<03:16,  2.86it/s]
 38%|███▊      | 346/908 [03:40<02:51,  3.28it/s]
 38%|███▊      | 347/908 [03:41<06:26,  1.45it/s]
 38%|███▊      | 348/908 [03:41<05:03,  1.85it/s]
 38%|███▊      | 349/908 [03:42<04:05,  2.28it/s]
 39%|███▊      | 350/908 [03:42<03:24,  2.72it/s]
 39%|███▊      | 351/908 [03:43<06:41,  1.39it/s]
 39%|███▉      | 352/908 [03:43<05:13,  1.77it/s]
 39%|███▉      | 353/908 [03:44<04:11,  2.21it/s]
 39%|███▉      | 354/908 [03:44<03:27,  2.67it/s]
 39%|███▉      | 355/908 [03:46<07:17,  1.26it/s]
 39%|███▉      | 356/908 [03:46<05:38,  1.63it/s]
 39%|███▉      | 357/908 [03:46<04:29,  2.04it/s]
 39%|███▉      | 358/908 [03:46<03:40,  2.50it/s]
 40%|███▉      | 359/908 [03:48<06:51,  1.34it/s]
+[Batch:   360] training loss = 0.523
+
 40%|███▉      | 360/908 [03:48<05:20,  1.71it/s]
 40%|███▉      | 361/908 [03:48<04:20,  2.10it/s]
 40%|███▉      | 362/908 [03:48<03:33,  2.56it/s]
 40%|███▉      | 363/908 [03:50<07:04,  1.28it/s]
 40%|████      | 364/908 [03:50<05:29,  1.65it/s]
 40%|████      | 365/908 [03:50<04:21,  2.08it/s]
 40%|████      | 366/908 [03:51<03:47,  2.39it/s]
 40%|████      | 367/908 [03:53<08:13,  1.10it/s]
 41%|████      | 368/908 [03:53<06:16,  1.43it/s]
 41%|████      | 369/908 [03:53<04:54,  1.83it/s]
 41%|████      | 370/908 [03:53<03:57,  2.27it/s]
 41%|████      | 371/908 [03:56<08:45,  1.02it/s]
 41%|████      | 372/908 [03:56<06:39,  1.34it/s]
 41%|████      | 373/908 [03:56<05:10,  1.72it/s]
 41%|████      | 374/908 [03:56<04:08,  2.15it/s]
 41%|████▏     | 375/908 [03:58<08:58,  1.01s/it]
 41%|████▏     | 376/908 [03:59<06:46,  1.31it/s]
 42%|████▏     | 377/908 [03:59<05:27,  1.62it/s]
 42%|████▏     | 378/908 [03:59<04:21,  2.03it/s]
 42%|████▏     | 379/908 [04:01<08:07,  1.08it/s]
+[Batch:   380] training loss = 0.436
+
 42%|████▏     | 380/908 [04:01<06:12,  1.42it/s]
 42%|████▏     | 381/908 [04:01<04:54,  1.79it/s]
 42%|████▏     | 382/908 [04:02<03:57,  2.22it/s]
 42%|████▏     | 383/908 [04:04<08:45,  1.00s/it]
 42%|████▏     | 384/908 [04:04<06:40,  1.31it/s]
 42%|████▏     | 385/908 [04:04<05:10,  1.68it/s]
 43%|████▎     | 386/908 [04:05<04:07,  2.11it/s]
 43%|████▎     | 387/908 [04:07<08:39,  1.00it/s]
 43%|████▎     | 388/908 [04:07<06:34,  1.32it/s]
 43%|████▎     | 389/908 [04:07<05:06,  1.69it/s]
 43%|████▎     | 390/908 [04:07<04:03,  2.12it/s]
 43%|████▎     | 391/908 [04:09<08:14,  1.04it/s]
 43%|████▎     | 392/908 [04:10<06:15,  1.37it/s]
 43%|████▎     | 393/908 [04:10<04:52,  1.76it/s]
 43%|████▎     | 394/908 [04:10<03:54,  2.19it/s]
 44%|████▎     | 395/908 [04:13<09:11,  1.07s/it]
 44%|████▎     | 396/908 [04:13<06:55,  1.23it/s]
 44%|████▎     | 397/908 [04:13<05:20,  1.59it/s]
 44%|████▍     | 398/908 [04:13<04:15,  1.99it/s]
 44%|████▍     | 399/908 [04:15<07:38,  1.11it/s]
+[Batch:   400] training loss = 0.515
+
 44%|████▍     | 400/908 [04:15<05:50,  1.45it/s]
 44%|████▍     | 401/908 [04:15<04:42,  1.80it/s]
 44%|████▍     | 402/908 [04:16<03:47,  2.23it/s]
 44%|████▍     | 403/908 [04:17<06:24,  1.31it/s]
 44%|████▍     | 404/908 [04:17<04:58,  1.69it/s]
 45%|████▍     | 405/908 [04:18<03:58,  2.11it/s]
 45%|████▍     | 406/908 [04:18<03:15,  2.57it/s]
 45%|████▍     | 407/908 [04:19<05:32,  1.51it/s]
 45%|████▍     | 408/908 [04:19<04:21,  1.91it/s]
 45%|████▌     | 409/908 [04:19<03:31,  2.35it/s]
 45%|████▌     | 410/908 [04:20<02:56,  2.83it/s]
 45%|████▌     | 411/908 [04:22<06:51,  1.21it/s]
 45%|████▌     | 412/908 [04:22<05:16,  1.57it/s]
 45%|████▌     | 413/908 [04:22<04:09,  1.98it/s]
 46%|████▌     | 414/908 [04:22<03:23,  2.43it/s]
 46%|████▌     | 415/908 [04:24<06:16,  1.31it/s]
 46%|████▌     | 416/908 [04:24<04:51,  1.69it/s]
 46%|████▌     | 417/908 [04:24<03:52,  2.11it/s]
 46%|████▌     | 418/908 [04:24<03:10,  2.57it/s]
 46%|████▌     | 419/908 [04:26<06:43,  1.21it/s]
+[Batch:   420] training loss = 0.470
+
 46%|████▋     | 420/908 [04:26<05:10,  1.57it/s]
 46%|████▋     | 421/908 [04:27<04:09,  1.95it/s]
 46%|████▋     | 422/908 [04:27<03:23,  2.39it/s]
 47%|████▋     | 423/908 [04:28<04:56,  1.63it/s]
 47%|████▋     | 424/908 [04:28<03:56,  2.04it/s]
 47%|████▋     | 425/908 [04:28<03:14,  2.48it/s]
 47%|████▋     | 426/908 [04:28<02:44,  2.93it/s]
 47%|████▋     | 427/908 [04:29<04:04,  1.97it/s]
 47%|████▋     | 428/908 [04:30<03:19,  2.41it/s]
 47%|████▋     | 429/908 [04:30<02:47,  2.86it/s]
 47%|████▋     | 430/908 [04:30<02:25,  3.29it/s]
 47%|████▋     | 431/908 [04:32<05:31,  1.44it/s]
 48%|████▊     | 432/908 [04:32<04:19,  1.83it/s]
 48%|████▊     | 433/908 [04:32<03:30,  2.26it/s]
 48%|████▊     | 434/908 [04:32<02:56,  2.69it/s]
 48%|████▊     | 435/908 [04:33<03:28,  2.27it/s]
 48%|████▊     | 436/908 [04:33<02:53,  2.72it/s]
 48%|████▊     | 437/908 [04:33<02:28,  3.17it/s]
 48%|████▊     | 438/908 [04:33<02:12,  3.54it/s]
 48%|████▊     | 439/908 [04:34<03:37,  2.15it/s]
+[Batch:   440] training loss = 0.592
+
 48%|████▊     | 440/908 [04:34<02:59,  2.60it/s]
 49%|████▊     | 441/908 [04:35<02:35,  3.01it/s]
 49%|████▊     | 442/908 [04:35<02:15,  3.43it/s]
 49%|████▉     | 443/908 [04:37<05:29,  1.41it/s]
 49%|████▉     | 444/908 [04:37<04:20,  1.78it/s]
 49%|████▉     | 445/908 [04:37<03:29,  2.21it/s]
 49%|████▉     | 446/908 [04:37<02:55,  2.63it/s]
 49%|████▉     | 447/908 [04:37<02:43,  2.82it/s]
 49%|████▉     | 448/908 [04:38<02:22,  3.23it/s]
 49%|████▉     | 449/908 [04:38<02:06,  3.64it/s]
 50%|████▉     | 450/908 [04:38<01:54,  3.99it/s]
 50%|████▉     | 451/908 [04:39<03:52,  1.96it/s]
 50%|████▉     | 452/908 [04:39<03:09,  2.40it/s]
 50%|████▉     | 453/908 [04:40<02:39,  2.86it/s]
 50%|█████     | 454/908 [04:40<02:17,  3.30it/s]
 50%|█████     | 455/908 [04:41<04:52,  1.55it/s]
 50%|█████     | 456/908 [04:41<03:51,  1.96it/s]
 50%|█████     | 457/908 [04:42<03:07,  2.41it/s]
 50%|█████     | 458/908 [04:42<02:37,  2.86it/s]
 51%|█████     | 459/908 [04:42<03:19,  2.25it/s]
+[Batch:   460] training loss = 0.570
+
 51%|█████     | 460/908 [04:43<02:46,  2.70it/s]
 51%|█████     | 461/908 [04:43<02:23,  3.12it/s]
 51%|█████     | 462/908 [04:43<02:06,  3.52it/s]
 51%|█████     | 463/908 [04:44<04:19,  1.72it/s]
 51%|█████     | 464/908 [04:44<03:27,  2.14it/s]
 51%|█████     | 465/908 [04:45<02:50,  2.60it/s]
 51%|█████▏    | 466/908 [04:45<02:24,  3.06it/s]
 51%|█████▏    | 467/908 [04:46<04:46,  1.54it/s]
 52%|█████▏    | 468/908 [04:46<03:46,  1.95it/s]
 52%|█████▏    | 469/908 [04:47<03:03,  2.39it/s]
 52%|█████▏    | 470/908 [04:47<02:33,  2.85it/s]
 52%|█████▏    | 471/908 [04:48<05:15,  1.39it/s]
 52%|█████▏    | 472/908 [04:49<04:07,  1.76it/s]
 52%|█████▏    | 473/908 [04:49<03:18,  2.19it/s]
 52%|█████▏    | 474/908 [04:49<02:44,  2.64it/s]
 52%|█████▏    | 475/908 [04:51<06:15,  1.15it/s]
 52%|█████▏    | 476/908 [04:51<04:47,  1.50it/s]
 53%|█████▎    | 477/908 [04:51<03:46,  1.91it/s]
 53%|█████▎    | 478/908 [04:52<03:03,  2.34it/s]
 53%|█████▎    | 479/908 [04:54<06:24,  1.12it/s]
+[Batch:   480] training loss = 0.381
+
 53%|█████▎    | 480/908 [04:54<04:53,  1.46it/s]
 53%|█████▎    | 481/908 [04:54<03:50,  1.85it/s]
 53%|█████▎    | 482/908 [04:54<03:07,  2.28it/s]
 53%|█████▎    | 483/908 [04:56<06:01,  1.18it/s]
 53%|█████▎    | 484/908 [04:56<04:38,  1.52it/s]
 53%|█████▎    | 485/908 [04:56<03:39,  1.93it/s]
 54%|█████▎    | 486/908 [04:57<02:57,  2.37it/s]
 54%|█████▎    | 487/908 [04:59<06:23,  1.10it/s]
 54%|█████▎    | 488/908 [04:59<04:53,  1.43it/s]
 54%|█████▍    | 489/908 [04:59<03:49,  1.83it/s]
 54%|█████▍    | 490/908 [04:59<03:04,  2.26it/s]
 54%|█████▍    | 491/908 [05:01<06:28,  1.07it/s]
 54%|█████▍    | 492/908 [05:02<04:55,  1.41it/s]
 54%|█████▍    | 493/908 [05:02<03:51,  1.79it/s]
 54%|█████▍    | 494/908 [05:02<03:07,  2.21it/s]
 55%|█████▍    | 495/908 [05:03<04:57,  1.39it/s]
 55%|█████▍    | 496/908 [05:04<03:51,  1.78it/s]
 55%|█████▍    | 497/908 [05:04<03:06,  2.21it/s]
 55%|█████▍    | 498/908 [05:04<02:34,  2.66it/s]
 55%|█████▍    | 499/908 [05:06<06:19,  1.08it/s]
+[Batch:   500] training loss = 0.526
+
 55%|█████▌    | 500/908 [05:06<04:48,  1.41it/s]
 55%|█████▌    | 501/908 [05:07<03:46,  1.80it/s]
 55%|█████▌    | 502/908 [05:07<03:01,  2.23it/s]
 55%|█████▌    | 503/908 [05:09<06:40,  1.01it/s]
 56%|█████▌    | 504/908 [05:09<05:03,  1.33it/s]
 56%|█████▌    | 505/908 [05:09<03:56,  1.71it/s]
 56%|█████▌    | 506/908 [05:10<03:08,  2.13it/s]
 56%|█████▌    | 507/908 [05:11<05:07,  1.30it/s]
 56%|█████▌    | 508/908 [05:11<03:58,  1.68it/s]
 56%|█████▌    | 509/908 [05:11<03:10,  2.10it/s]
 56%|█████▌    | 510/908 [05:12<02:35,  2.55it/s]
 56%|█████▋    | 511/908 [05:13<03:52,  1.71it/s]
 56%|█████▋    | 512/908 [05:13<03:05,  2.13it/s]
 56%|█████▋    | 513/908 [05:13<02:33,  2.58it/s]
 57%|█████▋    | 514/908 [05:13<02:10,  3.02it/s]
 57%|█████▋    | 515/908 [05:16<07:19,  1.12s/it]
 57%|█████▋    | 516/908 [05:16<05:29,  1.19it/s]
 57%|█████▋    | 517/908 [05:17<04:13,  1.54it/s]
 57%|█████▋    | 518/908 [05:17<03:19,  1.95it/s]
 57%|█████▋    | 519/908 [05:18<05:02,  1.29it/s]
+[Batch:   520] training loss = 0.437
+
 57%|█████▋    | 520/908 [05:18<03:53,  1.66it/s]
 57%|█████▋    | 521/908 [05:19<03:16,  1.97it/s]
 57%|█████▋    | 522/908 [05:19<02:39,  2.42it/s]
 58%|█████▊    | 523/908 [05:21<05:27,  1.18it/s]
 58%|█████▊    | 524/908 [05:21<04:11,  1.53it/s]
 58%|█████▊    | 525/908 [05:21<03:18,  1.93it/s]
 58%|█████▊    | 526/908 [05:21<02:41,  2.37it/s]
 58%|█████▊    | 527/908 [05:23<05:43,  1.11it/s]
 58%|█████▊    | 528/908 [05:24<04:23,  1.44it/s]
 58%|█████▊    | 529/908 [05:24<03:26,  1.84it/s]
 58%|█████▊    | 530/908 [05:24<02:45,  2.28it/s]
 58%|█████▊    | 531/908 [05:26<06:25,  1.02s/it]
 59%|█████▊    | 532/908 [05:27<04:51,  1.29it/s]
 59%|█████▊    | 533/908 [05:27<03:45,  1.66it/s]
 59%|█████▉    | 534/908 [05:27<03:00,  2.07it/s]
 59%|█████▉    | 535/908 [05:29<05:20,  1.16it/s]
 59%|█████▉    | 536/908 [05:29<04:05,  1.51it/s]
 59%|█████▉    | 537/908 [05:29<03:13,  1.92it/s]
 59%|█████▉    | 538/908 [05:29<02:36,  2.37it/s]
 59%|█████▉    | 539/908 [05:31<05:18,  1.16it/s]
+[Batch:   540] training loss = 0.413
+
 59%|█████▉    | 540/908 [05:31<04:03,  1.51it/s]
 60%|█████▉    | 541/908 [05:32<03:12,  1.90it/s]
 60%|█████▉    | 542/908 [05:32<02:37,  2.33it/s]
 60%|█████▉    | 543/908 [05:33<04:35,  1.32it/s]
 60%|█████▉    | 544/908 [05:33<03:33,  1.71it/s]
 60%|██████    | 545/908 [05:34<02:50,  2.13it/s]
 60%|██████    | 546/908 [05:34<02:21,  2.57it/s]
 60%|██████    | 547/908 [05:36<04:45,  1.27it/s]
 60%|██████    | 548/908 [05:36<03:40,  1.63it/s]
 60%|██████    | 549/908 [05:36<02:54,  2.06it/s]
 61%|██████    | 550/908 [05:36<02:22,  2.51it/s]
 61%|██████    | 551/908 [05:38<04:37,  1.29it/s]
 61%|██████    | 552/908 [05:38<03:34,  1.66it/s]
 61%|██████    | 553/908 [05:38<02:50,  2.08it/s]
 61%|██████    | 554/908 [05:38<02:19,  2.54it/s]
 61%|██████    | 555/908 [05:40<05:01,  1.17it/s]
 61%|██████    | 556/908 [05:41<03:51,  1.52it/s]
 61%|██████▏   | 557/908 [05:41<03:02,  1.93it/s]
 61%|██████▏   | 558/908 [05:41<02:27,  2.37it/s]
 62%|██████▏   | 559/908 [05:43<05:06,  1.14it/s]
+[Batch:   560] training loss = 0.376
+
 62%|██████▏   | 560/908 [05:43<03:54,  1.48it/s]
 62%|██████▏   | 561/908 [05:43<03:04,  1.88it/s]
 62%|██████▏   | 562/908 [05:43<02:29,  2.32it/s]
 62%|██████▏   | 563/908 [05:45<04:46,  1.20it/s]
 62%|██████▏   | 564/908 [05:45<03:41,  1.56it/s]
 62%|██████▏   | 565/908 [05:46<02:54,  1.97it/s]
 62%|██████▏   | 566/908 [05:46<02:22,  2.40it/s]
 62%|██████▏   | 567/908 [05:47<04:26,  1.28it/s]
 63%|██████▎   | 568/908 [05:48<03:26,  1.65it/s]
 63%|██████▎   | 569/908 [05:48<02:43,  2.07it/s]
 63%|██████▎   | 570/908 [05:48<02:14,  2.51it/s]
 63%|██████▎   | 571/908 [05:50<04:39,  1.20it/s]
 63%|██████▎   | 572/908 [05:50<03:34,  1.56it/s]
 63%|██████▎   | 573/908 [05:50<02:49,  1.97it/s]
 63%|██████▎   | 574/908 [05:50<02:19,  2.40it/s]
 63%|██████▎   | 575/908 [05:52<04:43,  1.17it/s]
 63%|██████▎   | 576/908 [05:53<03:37,  1.52it/s]
 64%|██████▎   | 577/908 [05:53<02:51,  1.93it/s]
 64%|██████▎   | 578/908 [05:53<02:19,  2.37it/s]
 64%|██████▍   | 579/908 [05:55<04:21,  1.26it/s]
+[Batch:   580] training loss = 0.568
+
 64%|██████▍   | 580/908 [05:55<03:22,  1.62it/s]
 64%|██████▍   | 581/908 [05:55<02:45,  1.98it/s]
 64%|██████▍   | 582/908 [05:55<02:15,  2.40it/s]
 64%|██████▍   | 583/908 [05:57<04:38,  1.17it/s]
 64%|██████▍   | 584/908 [05:57<03:34,  1.51it/s]
 64%|██████▍   | 585/908 [05:58<02:48,  1.92it/s]
 65%|██████▍   | 586/908 [05:58<02:17,  2.34it/s]
 65%|██████▍   | 587/908 [06:00<04:26,  1.21it/s]
 65%|██████▍   | 588/908 [06:00<03:24,  1.56it/s]
 65%|██████▍   | 589/908 [06:00<02:41,  1.97it/s]
 65%|██████▍   | 590/908 [06:00<02:11,  2.41it/s]
 65%|██████▌   | 591/908 [06:02<04:49,  1.09it/s]
 65%|██████▌   | 592/908 [06:02<03:40,  1.43it/s]
 65%|██████▌   | 593/908 [06:03<03:00,  1.74it/s]
 65%|██████▌   | 594/908 [06:03<02:24,  2.18it/s]
 66%|██████▌   | 595/908 [06:05<05:18,  1.02s/it]
 66%|██████▌   | 596/908 [06:05<04:01,  1.29it/s]
 66%|██████▌   | 597/908 [06:06<03:07,  1.66it/s]
 66%|██████▌   | 598/908 [06:06<02:29,  2.07it/s]
 66%|██████▌   | 599/908 [06:07<03:31,  1.46it/s]
+[Batch:   600] training loss = 0.460
+
 66%|██████▌   | 600/908 [06:07<02:46,  1.85it/s]
 66%|██████▌   | 601/908 [06:07<02:17,  2.23it/s]
 66%|██████▋   | 602/908 [06:08<01:54,  2.68it/s]
 66%|██████▋   | 603/908 [06:08<01:37,  3.13it/s]
 67%|██████▋   | 604/908 [06:08<01:25,  3.55it/s]
 67%|██████▋   | 605/908 [06:08<01:17,  3.92it/s]
 67%|██████▋   | 606/908 [06:08<01:11,  4.23it/s]
 67%|██████▋   | 607/908 [06:09<02:16,  2.21it/s]
 67%|██████▋   | 608/908 [06:10<01:52,  2.66it/s]
 67%|██████▋   | 609/908 [06:10<02:04,  2.41it/s]
 67%|██████▋   | 610/908 [06:10<01:45,  2.83it/s]
 67%|██████▋   | 611/908 [06:11<02:53,  1.71it/s]
 67%|██████▋   | 612/908 [06:12<02:19,  2.13it/s]
 68%|██████▊   | 613/908 [06:12<02:00,  2.45it/s]
 68%|██████▊   | 614/908 [06:12<02:21,  2.08it/s]
 68%|██████▊   | 615/908 [06:13<02:51,  1.71it/s]
 68%|██████▊   | 616/908 [06:14<02:17,  2.13it/s]
 68%|██████▊   | 617/908 [06:14<02:01,  2.39it/s]
 68%|██████▊   | 618/908 [06:14<02:13,  2.17it/s]
 68%|██████▊   | 619/908 [06:17<05:22,  1.11s/it]
+[Batch:   620] training loss = 0.408
+
 68%|██████▊   | 620/908 [06:17<04:01,  1.19it/s]
 68%|██████▊   | 621/908 [06:17<03:09,  1.52it/s]
 69%|██████▊   | 622/908 [06:18<02:30,  1.90it/s]
 69%|██████▊   | 623/908 [06:19<04:14,  1.12it/s]
 69%|██████▊   | 624/908 [06:20<03:15,  1.46it/s]
 69%|██████▉   | 625/908 [06:20<02:33,  1.85it/s]
 69%|██████▉   | 626/908 [06:20<02:05,  2.25it/s]
 69%|██████▉   | 627/908 [06:22<04:38,  1.01it/s]
 69%|██████▉   | 628/908 [06:22<03:31,  1.33it/s]
 69%|██████▉   | 629/908 [06:23<02:45,  1.69it/s]
 69%|██████▉   | 630/908 [06:23<02:27,  1.89it/s]
 69%|██████▉   | 631/908 [06:25<03:48,  1.21it/s]
 70%|██████▉   | 632/908 [06:25<02:56,  1.57it/s]
 70%|██████▉   | 633/908 [06:25<02:19,  1.98it/s]
 70%|██████▉   | 634/908 [06:25<01:54,  2.39it/s]
 70%|██████▉   | 635/908 [06:27<03:55,  1.16it/s]
 70%|███████   | 636/908 [06:27<03:00,  1.50it/s]
 70%|███████   | 637/908 [06:28<02:22,  1.91it/s]
 70%|███████   | 638/908 [06:28<02:39,  1.70it/s]
 70%|███████   | 639/908 [06:30<03:58,  1.13it/s]
+[Batch:   640] training loss = 0.482
+
 70%|███████   | 640/908 [06:30<03:02,  1.47it/s]
 71%|███████   | 641/908 [06:30<02:25,  1.83it/s]
 71%|███████   | 642/908 [06:31<02:09,  2.06it/s]
 71%|███████   | 643/908 [06:32<03:36,  1.22it/s]
 71%|███████   | 644/908 [06:32<02:46,  1.59it/s]
 71%|███████   | 645/908 [06:33<02:11,  2.00it/s]
 71%|███████   | 646/908 [06:33<01:56,  2.25it/s]
 71%|███████▏  | 647/908 [06:35<03:35,  1.21it/s]
 71%|███████▏  | 648/908 [06:35<02:45,  1.57it/s]
 71%|███████▏  | 649/908 [06:35<02:10,  1.98it/s]
 72%|███████▏  | 650/908 [06:35<01:46,  2.43it/s]
 72%|███████▏  | 651/908 [06:38<04:16,  1.00it/s]
 72%|███████▏  | 652/908 [06:38<03:13,  1.32it/s]
 72%|███████▏  | 653/908 [06:38<02:31,  1.68it/s]
 72%|███████▏  | 654/908 [06:38<02:00,  2.11it/s]
 72%|███████▏  | 655/908 [06:40<04:01,  1.05it/s]
 72%|███████▏  | 656/908 [06:40<03:03,  1.37it/s]
 72%|███████▏  | 657/908 [06:41<02:23,  1.75it/s]
 72%|███████▏  | 658/908 [06:41<01:55,  2.16it/s]
 73%|███████▎  | 659/908 [06:43<03:34,  1.16it/s]
+[Batch:   660] training loss = 0.424
+
 73%|███████▎  | 660/908 [06:43<02:43,  1.51it/s]
 73%|███████▎  | 661/908 [06:43<02:08,  1.92it/s]
 73%|███████▎  | 662/908 [06:43<01:46,  2.31it/s]
 73%|███████▎  | 663/908 [06:45<03:49,  1.07it/s]
 73%|███████▎  | 664/908 [06:46<02:54,  1.40it/s]
 73%|███████▎  | 665/908 [06:46<02:16,  1.77it/s]
 73%|███████▎  | 666/908 [06:46<01:50,  2.20it/s]
 73%|███████▎  | 667/908 [06:48<03:23,  1.19it/s]
 74%|███████▎  | 668/908 [06:48<02:35,  1.54it/s]
 74%|███████▎  | 669/908 [06:48<02:02,  1.96it/s]
 74%|███████▍  | 670/908 [06:48<01:38,  2.41it/s]
 74%|███████▍  | 671/908 [06:50<03:09,  1.25it/s]
 74%|███████▍  | 672/908 [06:50<02:25,  1.62it/s]
 74%|███████▍  | 673/908 [06:50<01:56,  2.02it/s]
 74%|███████▍  | 674/908 [06:51<01:35,  2.46it/s]
 74%|███████▍  | 675/908 [06:53<03:22,  1.15it/s]
 74%|███████▍  | 676/908 [06:53<02:34,  1.50it/s]
 75%|███████▍  | 677/908 [06:53<02:01,  1.90it/s]
 75%|███████▍  | 678/908 [06:53<01:39,  2.31it/s]
 75%|███████▍  | 679/908 [06:55<03:00,  1.27it/s]
+[Batch:   680] training loss = 0.430
+
 75%|███████▍  | 680/908 [06:55<02:19,  1.63it/s]
 75%|███████▌  | 681/908 [06:55<01:50,  2.05it/s]
 75%|███████▌  | 682/908 [06:55<01:30,  2.49it/s]
 75%|███████▌  | 683/908 [06:57<03:14,  1.16it/s]
 75%|███████▌  | 684/908 [06:58<02:28,  1.51it/s]
 75%|███████▌  | 685/908 [06:58<01:56,  1.91it/s]
 76%|███████▌  | 686/908 [06:58<01:35,  2.34it/s]
 76%|███████▌  | 687/908 [07:00<02:57,  1.25it/s]
 76%|███████▌  | 688/908 [07:00<02:16,  1.61it/s]
 76%|███████▌  | 689/908 [07:00<01:47,  2.03it/s]
 76%|███████▌  | 690/908 [07:00<01:30,  2.40it/s]
 76%|███████▌  | 691/908 [07:03<03:29,  1.04it/s]
 76%|███████▌  | 692/908 [07:03<02:38,  1.36it/s]
 76%|███████▋  | 693/908 [07:03<02:03,  1.74it/s]
 76%|███████▋  | 694/908 [07:03<01:38,  2.17it/s]
 77%|███████▋  | 695/908 [07:05<03:10,  1.12it/s]
 77%|███████▋  | 696/908 [07:05<02:24,  1.47it/s]
 77%|███████▋  | 697/908 [07:05<01:53,  1.87it/s]
 77%|███████▋  | 698/908 [07:06<01:31,  2.29it/s]
 77%|███████▋  | 699/908 [07:08<03:49,  1.10s/it]
+[Batch:   700] training loss = 0.432
+
 77%|███████▋  | 700/908 [07:08<02:52,  1.21it/s]
 77%|███████▋  | 701/908 [07:09<02:13,  1.55it/s]
 77%|███████▋  | 702/908 [07:09<01:45,  1.96it/s]
 77%|███████▋  | 703/908 [07:11<02:58,  1.15it/s]
 78%|███████▊  | 704/908 [07:11<02:16,  1.50it/s]
 78%|███████▊  | 705/908 [07:11<01:47,  1.89it/s]
 78%|███████▊  | 706/908 [07:11<01:26,  2.32it/s]
 78%|███████▊  | 707/908 [07:13<03:07,  1.07it/s]
 78%|███████▊  | 708/908 [07:13<02:22,  1.41it/s]
 78%|███████▊  | 709/908 [07:14<01:51,  1.79it/s]
 78%|███████▊  | 710/908 [07:14<01:29,  2.22it/s]
 78%|███████▊  | 711/908 [07:15<02:31,  1.30it/s]
 78%|███████▊  | 712/908 [07:16<01:57,  1.67it/s]
 79%|███████▊  | 713/908 [07:16<01:34,  2.07it/s]
 79%|███████▊  | 714/908 [07:16<01:17,  2.49it/s]
 79%|███████▊  | 715/908 [07:19<03:49,  1.19s/it]
 79%|███████▉  | 716/908 [07:19<02:51,  1.12it/s]
 79%|███████▉  | 717/908 [07:19<02:10,  1.46it/s]
 79%|███████▉  | 718/908 [07:20<01:43,  1.84it/s]
 79%|███████▉  | 719/908 [07:21<02:32,  1.24it/s]
+[Batch:   720] training loss = 0.477
+
 79%|███████▉  | 720/908 [07:21<01:57,  1.59it/s]
 79%|███████▉  | 721/908 [07:22<01:37,  1.93it/s]
 80%|███████▉  | 722/908 [07:22<01:19,  2.35it/s]
 80%|███████▉  | 723/908 [07:23<02:09,  1.43it/s]
 80%|███████▉  | 724/908 [07:23<01:41,  1.81it/s]
 80%|███████▉  | 725/908 [07:23<01:22,  2.22it/s]
 80%|███████▉  | 726/908 [07:24<01:09,  2.63it/s]
 80%|████████  | 727/908 [07:25<01:57,  1.54it/s]
 80%|████████  | 728/908 [07:25<01:33,  1.92it/s]
 80%|████████  | 729/908 [07:25<01:16,  2.35it/s]
 80%|████████  | 730/908 [07:26<01:04,  2.75it/s]
 81%|████████  | 731/908 [07:27<02:15,  1.30it/s]
 81%|████████  | 732/908 [07:28<01:45,  1.67it/s]
 81%|████████  | 733/908 [07:28<01:24,  2.08it/s]
 81%|████████  | 734/908 [07:28<01:08,  2.52it/s]
 81%|████████  | 735/908 [07:30<02:21,  1.23it/s]
 81%|████████  | 736/908 [07:30<01:48,  1.58it/s]
 81%|████████  | 737/908 [07:30<01:26,  1.98it/s]
 81%|████████▏ | 738/908 [07:30<01:10,  2.41it/s]
 81%|████████▏ | 739/908 [07:33<03:02,  1.08s/it]
+[Batch:   740] training loss = 0.422
+
 81%|████████▏ | 740/908 [07:33<02:17,  1.22it/s]
 82%|████████▏ | 741/908 [07:33<01:48,  1.54it/s]
 82%|████████▏ | 742/908 [07:34<01:25,  1.94it/s]
 82%|████████▏ | 743/908 [07:36<02:43,  1.01it/s]
 82%|████████▏ | 744/908 [07:36<02:03,  1.33it/s]
 82%|████████▏ | 745/908 [07:36<01:36,  1.70it/s]
 82%|████████▏ | 746/908 [07:36<01:17,  2.10it/s]
 82%|████████▏ | 747/908 [07:39<02:51,  1.06s/it]
 82%|████████▏ | 748/908 [07:39<02:09,  1.24it/s]
 82%|████████▏ | 749/908 [07:39<01:39,  1.60it/s]
 83%|████████▎ | 750/908 [07:39<01:19,  2.00it/s]
 83%|████████▎ | 751/908 [07:41<02:18,  1.13it/s]
 83%|████████▎ | 752/908 [07:41<01:46,  1.47it/s]
 83%|████████▎ | 753/908 [07:42<01:23,  1.86it/s]
 83%|████████▎ | 754/908 [07:42<01:07,  2.29it/s]
 83%|████████▎ | 755/908 [07:44<02:04,  1.22it/s]
 83%|████████▎ | 756/908 [07:44<01:36,  1.57it/s]
 83%|████████▎ | 757/908 [07:44<01:17,  1.96it/s]
 83%|████████▎ | 758/908 [07:44<01:02,  2.39it/s]
 84%|████████▎ | 759/908 [07:46<02:12,  1.13it/s]
+[Batch:   760] training loss = 0.383
+
 84%|████████▎ | 760/908 [07:46<01:41,  1.46it/s]
 84%|████████▍ | 761/908 [07:47<01:19,  1.84it/s]
 84%|████████▍ | 762/908 [07:47<01:04,  2.25it/s]
 84%|████████▍ | 763/908 [07:49<02:04,  1.16it/s]
 84%|████████▍ | 764/908 [07:49<01:35,  1.51it/s]
 84%|████████▍ | 765/908 [07:49<01:15,  1.89it/s]
 84%|████████▍ | 766/908 [07:49<01:01,  2.32it/s]
 84%|████████▍ | 767/908 [07:51<01:58,  1.19it/s]
 85%|████████▍ | 768/908 [07:51<01:31,  1.53it/s]
 85%|████████▍ | 769/908 [07:51<01:12,  1.92it/s]
 85%|████████▍ | 770/908 [07:52<00:58,  2.36it/s]
 85%|████████▍ | 771/908 [07:53<01:44,  1.31it/s]
 85%|████████▌ | 772/908 [07:53<01:20,  1.68it/s]
 85%|████████▌ | 773/908 [07:54<01:04,  2.08it/s]
 85%|████████▌ | 774/908 [07:54<00:56,  2.36it/s]
 85%|████████▌ | 775/908 [07:56<02:02,  1.08it/s]
 85%|████████▌ | 776/908 [07:56<01:33,  1.41it/s]
 86%|████████▌ | 777/908 [07:56<01:13,  1.79it/s]
 86%|████████▌ | 778/908 [07:57<00:58,  2.22it/s]
 86%|████████▌ | 779/908 [07:58<01:42,  1.26it/s]
+[Batch:   780] training loss = 0.459
+
 86%|████████▌ | 780/908 [07:58<01:18,  1.62it/s]
 86%|████████▌ | 781/908 [07:59<01:02,  2.03it/s]
 86%|████████▌ | 782/908 [07:59<00:51,  2.46it/s]
 86%|████████▌ | 783/908 [08:00<01:32,  1.35it/s]
 86%|████████▋ | 784/908 [08:01<01:11,  1.73it/s]
 86%|████████▋ | 785/908 [08:01<00:57,  2.14it/s]
 87%|████████▋ | 786/908 [08:01<00:47,  2.57it/s]
 87%|████████▋ | 787/908 [08:03<01:41,  1.20it/s]
 87%|████████▋ | 788/908 [08:03<01:17,  1.55it/s]
 87%|████████▋ | 789/908 [08:03<01:01,  1.95it/s]
 87%|████████▋ | 790/908 [08:03<00:49,  2.38it/s]
 87%|████████▋ | 791/908 [08:05<01:39,  1.18it/s]
 87%|████████▋ | 792/908 [08:06<01:16,  1.52it/s]
 87%|████████▋ | 793/908 [08:06<00:59,  1.92it/s]
 87%|████████▋ | 794/908 [08:06<00:48,  2.33it/s]
 88%|████████▊ | 795/908 [08:08<01:47,  1.05it/s]
 88%|████████▊ | 796/908 [08:08<01:21,  1.38it/s]
 88%|████████▊ | 797/908 [08:09<01:03,  1.75it/s]
 88%|████████▊ | 798/908 [08:09<00:50,  2.16it/s]
 88%|████████▊ | 799/908 [08:11<02:00,  1.11s/it]
+[Batch:   800] training loss = 0.476
+
 88%|████████▊ | 800/908 [08:12<01:30,  1.20it/s]
 88%|████████▊ | 801/908 [08:12<01:10,  1.51it/s]
 88%|████████▊ | 802/908 [08:12<00:55,  1.91it/s]
 88%|████████▊ | 803/908 [08:14<01:28,  1.19it/s]
 89%|████████▊ | 804/908 [08:14<01:08,  1.52it/s]
 89%|████████▊ | 805/908 [08:14<00:53,  1.92it/s]
 89%|████████▉ | 806/908 [08:14<00:43,  2.35it/s]
 89%|████████▉ | 807/908 [08:16<01:24,  1.20it/s]
 89%|████████▉ | 808/908 [08:16<01:04,  1.56it/s]
 89%|████████▉ | 809/908 [08:16<00:50,  1.95it/s]
 89%|████████▉ | 810/908 [08:17<00:41,  2.37it/s]
 89%|████████▉ | 811/908 [08:19<01:35,  1.01it/s]
 89%|████████▉ | 812/908 [08:19<01:12,  1.33it/s]
 90%|████████▉ | 813/908 [08:19<00:56,  1.69it/s]
 90%|████████▉ | 814/908 [08:20<00:44,  2.11it/s]
 90%|████████▉ | 815/908 [08:21<01:21,  1.15it/s]
 90%|████████▉ | 816/908 [08:22<01:02,  1.48it/s]
 90%|████████▉ | 817/908 [08:22<00:48,  1.87it/s]
 90%|█████████ | 818/908 [08:22<00:39,  2.30it/s]
 90%|█████████ | 819/908 [08:23<01:06,  1.33it/s]
+[Batch:   820] training loss = 0.440
+
 90%|█████████ | 820/908 [08:24<00:51,  1.70it/s]
 90%|█████████ | 821/908 [08:24<00:42,  2.04it/s]
 91%|█████████ | 822/908 [08:24<00:35,  2.45it/s]
 91%|█████████ | 823/908 [08:26<01:13,  1.16it/s]
 91%|█████████ | 824/908 [08:26<00:56,  1.50it/s]
 91%|█████████ | 825/908 [08:26<00:43,  1.89it/s]
 91%|█████████ | 826/908 [08:27<00:35,  2.32it/s]
 91%|█████████ | 827/908 [08:29<01:22,  1.02s/it]
 91%|█████████ | 828/908 [08:29<01:02,  1.29it/s]
 91%|█████████▏| 829/908 [08:29<00:47,  1.65it/s]
 91%|█████████▏| 830/908 [08:30<00:37,  2.06it/s]
 92%|█████████▏| 831/908 [08:32<01:16,  1.01it/s]
 92%|█████████▏| 832/908 [08:32<00:57,  1.32it/s]
 92%|█████████▏| 833/908 [08:32<00:44,  1.70it/s]
 92%|█████████▏| 834/908 [08:32<00:34,  2.11it/s]
 92%|█████████▏| 835/908 [08:35<01:13,  1.01s/it]
 92%|█████████▏| 836/908 [08:35<00:55,  1.30it/s]
 92%|█████████▏| 837/908 [08:35<00:42,  1.67it/s]
 92%|█████████▏| 838/908 [08:35<00:33,  2.08it/s]
 92%|█████████▏| 839/908 [08:37<01:02,  1.10it/s]
+[Batch:   840] training loss = 0.533
+
 93%|█████████▎| 840/908 [08:37<00:47,  1.43it/s]
 93%|█████████▎| 841/908 [08:38<00:37,  1.81it/s]
 93%|█████████▎| 842/908 [08:38<00:29,  2.23it/s]
 93%|█████████▎| 843/908 [08:40<00:52,  1.24it/s]
 93%|█████████▎| 844/908 [08:40<00:40,  1.60it/s]
 93%|█████████▎| 845/908 [08:40<00:31,  2.01it/s]
 93%|█████████▎| 846/908 [08:40<00:25,  2.44it/s]
 93%|█████████▎| 847/908 [08:42<00:56,  1.07it/s]
 93%|█████████▎| 848/908 [08:42<00:43,  1.40it/s]
 94%|█████████▎| 849/908 [08:43<00:33,  1.77it/s]
 94%|█████████▎| 850/908 [08:43<00:26,  2.19it/s]
 94%|█████████▎| 851/908 [08:45<00:45,  1.25it/s]
 94%|█████████▍| 852/908 [08:45<00:34,  1.61it/s]
 94%|█████████▍| 853/908 [08:45<00:27,  2.02it/s]
 94%|█████████▍| 854/908 [08:45<00:22,  2.45it/s]
 94%|█████████▍| 855/908 [08:47<00:40,  1.30it/s]
 94%|█████████▍| 856/908 [08:47<00:31,  1.66it/s]
 94%|█████████▍| 857/908 [08:47<00:24,  2.07it/s]
 94%|█████████▍| 858/908 [08:47<00:19,  2.50it/s]
 95%|█████████▍| 859/908 [08:49<00:39,  1.26it/s]
+[Batch:   860] training loss = 0.487
+
 95%|█████████▍| 860/908 [08:49<00:29,  1.61it/s]
 95%|█████████▍| 861/908 [08:50<00:24,  1.93it/s]
 95%|█████████▍| 862/908 [08:50<00:19,  2.36it/s]
 95%|█████████▌| 863/908 [08:51<00:28,  1.59it/s]
 95%|█████████▌| 864/908 [08:51<00:22,  1.98it/s]
 95%|█████████▌| 865/908 [08:51<00:17,  2.41it/s]
 95%|█████████▌| 866/908 [08:52<00:14,  2.82it/s]
 95%|█████████▌| 867/908 [08:52<00:13,  3.13it/s]
 96%|█████████▌| 868/908 [08:52<00:11,  3.47it/s]
 96%|█████████▌| 869/908 [08:52<00:10,  3.78it/s]
 96%|█████████▌| 870/908 [08:52<00:09,  4.05it/s]
 96%|█████████▌| 871/908 [08:54<00:27,  1.32it/s]
 96%|█████████▌| 872/908 [08:55<00:21,  1.69it/s]
 96%|█████████▌| 873/908 [08:55<00:16,  2.10it/s]
 96%|█████████▋| 874/908 [08:55<00:13,  2.54it/s]
 96%|█████████▋| 875/908 [08:55<00:13,  2.38it/s]
 96%|█████████▋| 876/908 [08:56<00:11,  2.79it/s]
 97%|█████████▋| 877/908 [08:56<00:09,  3.19it/s]
 97%|█████████▋| 878/908 [08:56<00:08,  3.52it/s]
 97%|█████████▋| 879/908 [08:57<00:13,  2.10it/s]
+[Batch:   880] training loss = 0.530
+
 97%|█████████▋| 880/908 [08:57<00:11,  2.53it/s]
 97%|█████████▋| 881/908 [08:57<00:09,  2.91it/s]
 97%|█████████▋| 882/908 [08:58<00:07,  3.31it/s]
 97%|█████████▋| 883/908 [08:59<00:17,  1.41it/s]
 97%|█████████▋| 884/908 [09:00<00:13,  1.78it/s]
 97%|█████████▋| 885/908 [09:00<00:10,  2.20it/s]
 98%|█████████▊| 886/908 [09:00<00:08,  2.61it/s]
 98%|█████████▊| 887/908 [09:00<00:07,  2.97it/s]
 98%|█████████▊| 888/908 [09:00<00:05,  3.38it/s]
 98%|█████████▊| 889/908 [09:01<00:05,  3.74it/s]
 98%|█████████▊| 890/908 [09:01<00:04,  4.00it/s]
 98%|█████████▊| 891/908 [09:02<00:08,  1.95it/s]
 98%|█████████▊| 892/908 [09:02<00:06,  2.39it/s]
 98%|█████████▊| 893/908 [09:02<00:05,  2.82it/s]
 98%|█████████▊| 894/908 [09:03<00:04,  3.23it/s]
 99%|█████████▊| 895/908 [09:05<00:11,  1.17it/s]
 99%|█████████▊| 896/908 [09:05<00:07,  1.52it/s]
 99%|█████████▉| 897/908 [09:05<00:05,  1.86it/s]
 99%|█████████▉| 898/908 [09:05<00:04,  2.29it/s]
 99%|█████████▉| 899/908 [09:07<00:08,  1.11it/s]
+[Batch:   900] training loss = 0.540
+
 99%|█████████▉| 900/908 [09:07<00:05,  1.45it/s]
 99%|█████████▉| 901/908 [09:08<00:04,  1.67it/s]
 99%|█████████▉| 902/908 [09:08<00:02,  2.08it/s]
 99%|█████████▉| 903/908 [09:09<00:03,  1.34it/s]
100%|█████████▉| 904/908 [09:10<00:02,  1.70it/s]
100%|█████████▉| 905/908 [09:10<00:01,  2.12it/s]
100%|█████████▉| 906/908 [09:10<00:00,  2.54it/s]
100%|█████████▉| 907/908 [09:11<00:00,  2.03it/s]
100%|██████████| 908/908 [09:11<00:00,  2.48it/s]
100%|██████████| 908/908 [09:11<00:00,  1.65it/s]
+
+Epoch   1: validation
+
  0%|          | 0/101 [00:00<?, ?it/s]
  1%|          | 1/101 [00:01<03:19,  1.99s/it]
  3%|▎         | 3/101 [00:02<00:56,  1.74it/s]
  5%|▍         | 5/101 [00:04<01:17,  1.23it/s]
  7%|▋         | 7/101 [00:04<00:47,  1.99it/s]
  9%|▉         | 9/101 [00:05<00:42,  2.19it/s]
 11%|█         | 11/101 [00:05<00:29,  3.09it/s]
 13%|█▎        | 13/101 [00:06<00:41,  2.12it/s]
 15%|█▍        | 15/101 [00:07<00:29,  2.88it/s]
 17%|█▋        | 17/101 [00:09<00:52,  1.60it/s]
 19%|█▉        | 19/101 [00:09<00:37,  2.19it/s]
 21%|██        | 21/101 [00:11<00:50,  1.58it/s]
 23%|██▎       | 23/101 [00:11<00:36,  2.16it/s]
 25%|██▍       | 25/101 [00:13<00:46,  1.63it/s]
 27%|██▋       | 27/101 [00:13<00:33,  2.22it/s]
 29%|██▊       | 29/101 [00:16<00:46,  1.54it/s]
 31%|███       | 31/101 [00:16<00:33,  2.09it/s]
 33%|███▎      | 33/101 [00:18<00:44,  1.52it/s]
 35%|███▍      | 35/101 [00:18<00:32,  2.06it/s]
 37%|███▋      | 37/101 [00:20<00:44,  1.44it/s]
 39%|███▊      | 39/101 [00:21<00:31,  1.95it/s]
 41%|████      | 41/101 [00:24<00:50,  1.19it/s]
 42%|████▏     | 42/101 [00:24<00:42,  1.38it/s]
 44%|████▎     | 44/101 [00:24<00:28,  1.97it/s]
 45%|████▍     | 45/101 [00:27<00:51,  1.09it/s]
 47%|████▋     | 47/101 [00:27<00:33,  1.61it/s]
 49%|████▊     | 49/101 [00:30<00:46,  1.11it/s]
 50%|█████     | 51/101 [00:30<00:31,  1.58it/s]
 52%|█████▏    | 53/101 [00:33<00:40,  1.19it/s]
 54%|█████▍    | 55/101 [00:33<00:27,  1.66it/s]
 56%|█████▋    | 57/101 [00:35<00:33,  1.33it/s]
 58%|█████▊    | 59/101 [00:35<00:23,  1.82it/s]
 60%|██████    | 61/101 [00:37<00:27,  1.46it/s]
 62%|██████▏   | 63/101 [00:37<00:19,  1.99it/s]
 64%|██████▍   | 65/101 [00:40<00:28,  1.27it/s]
 66%|██████▋   | 67/101 [00:40<00:19,  1.74it/s]
 68%|██████▊   | 69/101 [00:43<00:23,  1.35it/s]
 70%|███████   | 71/101 [00:43<00:16,  1.84it/s]
 72%|███████▏  | 73/101 [00:45<00:19,  1.45it/s]
 74%|███████▍  | 75/101 [00:45<00:13,  1.97it/s]
 76%|███████▌  | 77/101 [00:47<00:17,  1.37it/s]
 77%|███████▋  | 78/101 [00:48<00:14,  1.61it/s]
 79%|███████▉  | 80/101 [00:48<00:09,  2.27it/s]
 80%|████████  | 81/101 [00:50<00:14,  1.35it/s]
 81%|████████  | 82/101 [00:50<00:11,  1.64it/s]
 83%|████████▎ | 84/101 [00:50<00:06,  2.46it/s]
 84%|████████▍ | 85/101 [00:52<00:13,  1.22it/s]
 85%|████████▌ | 86/101 [00:53<00:10,  1.45it/s]
 87%|████████▋ | 88/101 [00:53<00:05,  2.25it/s]
 88%|████████▊ | 89/101 [00:56<00:12,  1.03s/it]
 89%|████████▉ | 90/101 [00:56<00:10,  1.09it/s]
 91%|█████████ | 92/101 [00:57<00:05,  1.74it/s]
 92%|█████████▏| 93/101 [00:59<00:07,  1.01it/s]
 93%|█████████▎| 94/101 [00:59<00:06,  1.16it/s]
 95%|█████████▌| 96/101 [01:00<00:02,  1.85it/s]
 96%|█████████▌| 97/101 [01:02<00:03,  1.11it/s]
 97%|█████████▋| 98/101 [01:02<00:02,  1.32it/s]
 99%|█████████▉| 100/101 [01:02<00:00,  2.09it/s]
100%|██████████| 101/101 [01:03<00:00,  1.74it/s]
100%|██████████| 101/101 [01:03<00:00,  1.59it/s]
+    Epoch  1: training loss = 0.494 / validation loss = 0.512
+    Epoch  1: training acc = 0.761 / validation acc = 0.734
+    Saved model: cls-1.pth.tar
+
+Epoch   2: training
+
  0%|          | 0/908 [00:00<?, ?it/s]
  0%|          | 1/908 [00:01<28:09,  1.86s/it]
  0%|          | 2/908 [00:02<13:40,  1.10it/s]
  0%|          | 3/908 [00:02<08:56,  1.69it/s]
  0%|          | 4/908 [00:02<06:31,  2.31it/s]
  1%|          | 5/908 [00:03<08:09,  1.84it/s]
  1%|          | 6/908 [00:03<06:20,  2.37it/s]
  1%|          | 7/908 [00:03<05:11,  2.89it/s]
  1%|          | 8/908 [00:03<04:28,  3.35it/s]
  1%|          | 9/908 [00:05<12:03,  1.24it/s]
  1%|          | 10/908 [00:05<09:15,  1.62it/s]
  1%|          | 11/908 [00:06<07:16,  2.05it/s]
  1%|▏         | 12/908 [00:06<05:55,  2.52it/s]
  1%|▏         | 13/908 [00:07<09:06,  1.64it/s]
  2%|▏         | 14/908 [00:07<08:41,  1.71it/s]
  2%|▏         | 15/908 [00:08<06:56,  2.14it/s]
  2%|▏         | 16/908 [00:08<05:41,  2.61it/s]
  2%|▏         | 17/908 [00:09<09:16,  1.60it/s]
  2%|▏         | 18/908 [00:09<08:10,  1.81it/s]
  2%|▏         | 19/908 [00:10<06:34,  2.25it/s]
+[Batch:    20] training loss = 0.512
+
  2%|▏         | 20/908 [00:10<05:26,  2.72it/s]
  2%|▏         | 21/908 [00:10<05:16,  2.80it/s]
  2%|▏         | 22/908 [00:10<04:39,  3.17it/s]
  3%|▎         | 23/908 [00:11<04:06,  3.59it/s]
  3%|▎         | 24/908 [00:11<03:43,  3.95it/s]
  3%|▎         | 25/908 [00:13<12:47,  1.15it/s]
  3%|▎         | 26/908 [00:13<10:11,  1.44it/s]
  3%|▎         | 27/908 [00:13<07:56,  1.85it/s]
  3%|▎         | 28/908 [00:14<06:24,  2.29it/s]
  3%|▎         | 29/908 [00:16<13:10,  1.11it/s]
  3%|▎         | 30/908 [00:16<12:03,  1.21it/s]
  3%|▎         | 31/908 [00:16<09:16,  1.58it/s]
  4%|▎         | 32/908 [00:17<07:18,  2.00it/s]
  4%|▎         | 33/908 [00:18<10:26,  1.40it/s]
  4%|▎         | 34/908 [00:18<09:51,  1.48it/s]
  4%|▍         | 35/908 [00:19<07:43,  1.88it/s]
  4%|▍         | 36/908 [00:19<06:13,  2.34it/s]
  4%|▍         | 37/908 [00:21<15:39,  1.08s/it]
  4%|▍         | 38/908 [00:22<12:34,  1.15it/s]
  4%|▍         | 39/908 [00:22<09:37,  1.51it/s]
+[Batch:    40] training loss = 0.515
+
  4%|▍         | 40/908 [00:22<07:33,  1.91it/s]
  5%|▍         | 41/908 [00:24<14:07,  1.02it/s]
  5%|▍         | 42/908 [00:25<11:19,  1.27it/s]
  5%|▍         | 43/908 [00:25<09:14,  1.56it/s]
  5%|▍         | 44/908 [00:25<07:19,  1.97it/s]
  5%|▍         | 45/908 [00:27<12:29,  1.15it/s]
  5%|▌         | 46/908 [00:27<10:54,  1.32it/s]
  5%|▌         | 47/908 [00:28<08:27,  1.70it/s]
  5%|▌         | 48/908 [00:28<06:44,  2.13it/s]
  5%|▌         | 49/908 [00:30<13:09,  1.09it/s]
  6%|▌         | 50/908 [00:30<10:43,  1.33it/s]
  6%|▌         | 51/908 [00:30<08:18,  1.72it/s]
  6%|▌         | 52/908 [00:30<06:36,  2.16it/s]
  6%|▌         | 53/908 [00:32<10:53,  1.31it/s]
  6%|▌         | 54/908 [00:32<09:28,  1.50it/s]
  6%|▌         | 55/908 [00:32<07:27,  1.91it/s]
  6%|▌         | 56/908 [00:33<06:02,  2.35it/s]
  6%|▋         | 57/908 [00:34<11:42,  1.21it/s]
  6%|▋         | 58/908 [00:35<10:20,  1.37it/s]
  6%|▋         | 59/908 [00:35<08:03,  1.76it/s]
+[Batch:    60] training loss = 0.556
+
  7%|▋         | 60/908 [00:35<06:28,  2.18it/s]
  7%|▋         | 61/908 [00:37<10:53,  1.30it/s]
  7%|▋         | 62/908 [00:37<09:47,  1.44it/s]
  7%|▋         | 63/908 [00:38<07:40,  1.84it/s]
  7%|▋         | 64/908 [00:38<06:09,  2.28it/s]
  7%|▋         | 65/908 [00:39<11:31,  1.22it/s]
  7%|▋         | 66/908 [00:40<10:10,  1.38it/s]
  7%|▋         | 67/908 [00:40<07:54,  1.77it/s]
  7%|▋         | 68/908 [00:40<06:19,  2.22it/s]
  8%|▊         | 69/908 [00:43<13:56,  1.00it/s]
  8%|▊         | 70/908 [00:43<11:09,  1.25it/s]
  8%|▊         | 71/908 [00:43<08:36,  1.62it/s]
  8%|▊         | 72/908 [00:43<06:48,  2.05it/s]
  8%|▊         | 73/908 [00:45<10:53,  1.28it/s]
  8%|▊         | 74/908 [00:46<11:04,  1.25it/s]
  8%|▊         | 75/908 [00:46<08:30,  1.63it/s]
  8%|▊         | 76/908 [00:46<06:44,  2.06it/s]
  8%|▊         | 77/908 [00:47<10:28,  1.32it/s]
  9%|▊         | 78/908 [00:48<10:36,  1.30it/s]
  9%|▊         | 79/908 [00:48<08:12,  1.68it/s]
+[Batch:    80] training loss = 0.585
+
  9%|▉         | 80/908 [00:49<07:17,  1.89it/s]
  9%|▉         | 81/908 [00:51<12:23,  1.11it/s]
  9%|▉         | 82/908 [00:51<12:43,  1.08it/s]
  9%|▉         | 83/908 [00:52<09:40,  1.42it/s]
  9%|▉         | 84/908 [00:52<07:32,  1.82it/s]
  9%|▉         | 85/908 [00:53<10:27,  1.31it/s]
  9%|▉         | 86/908 [00:54<09:48,  1.40it/s]
 10%|▉         | 87/908 [00:54<07:38,  1.79it/s]
 10%|▉         | 88/908 [00:54<06:07,  2.23it/s]
 10%|▉         | 89/908 [00:56<10:18,  1.32it/s]
 10%|▉         | 90/908 [00:56<09:08,  1.49it/s]
 10%|█         | 91/908 [00:56<07:38,  1.78it/s]
 10%|█         | 92/908 [00:57<06:06,  2.23it/s]
 10%|█         | 93/908 [00:58<09:38,  1.41it/s]
 10%|█         | 94/908 [00:58<09:09,  1.48it/s]
 10%|█         | 95/908 [00:59<07:10,  1.89it/s]
 11%|█         | 96/908 [00:59<05:47,  2.34it/s]
 11%|█         | 97/908 [01:00<09:02,  1.49it/s]
 11%|█         | 98/908 [01:01<09:35,  1.41it/s]
 11%|█         | 99/908 [01:01<07:31,  1.79it/s]
+[Batch:   100] training loss = 0.517
+
 11%|█         | 100/908 [01:01<06:03,  2.22it/s]
 11%|█         | 101/908 [01:03<12:36,  1.07it/s]
 11%|█         | 102/908 [01:04<09:52,  1.36it/s]
 11%|█▏        | 103/908 [01:04<07:42,  1.74it/s]
 11%|█▏        | 104/908 [01:04<06:07,  2.19it/s]
 12%|█▏        | 105/908 [01:06<12:07,  1.10it/s]
 12%|█▏        | 106/908 [01:06<09:56,  1.34it/s]
 12%|█▏        | 107/908 [01:07<07:43,  1.73it/s]
 12%|█▏        | 108/908 [01:07<06:09,  2.16it/s]
 12%|█▏        | 109/908 [01:09<11:47,  1.13it/s]
 12%|█▏        | 110/908 [01:09<09:04,  1.47it/s]
 12%|█▏        | 111/908 [01:09<07:06,  1.87it/s]
 12%|█▏        | 112/908 [01:09<05:42,  2.32it/s]
 12%|█▏        | 113/908 [01:11<11:31,  1.15it/s]
 13%|█▎        | 114/908 [01:11<08:52,  1.49it/s]
 13%|█▎        | 115/908 [01:11<06:57,  1.90it/s]
 13%|█▎        | 116/908 [01:12<05:35,  2.36it/s]
 13%|█▎        | 117/908 [01:13<10:12,  1.29it/s]
 13%|█▎        | 118/908 [01:14<08:15,  1.59it/s]
 13%|█▎        | 119/908 [01:14<06:30,  2.02it/s]
+[Batch:   120] training loss = 0.540
+
 13%|█▎        | 120/908 [01:14<05:49,  2.26it/s]
 13%|█▎        | 121/908 [01:17<17:05,  1.30s/it]
 13%|█▎        | 122/908 [01:18<12:44,  1.03it/s]
 14%|█▎        | 123/908 [01:18<09:40,  1.35it/s]
 14%|█▎        | 124/908 [01:18<07:29,  1.75it/s]
 14%|█▍        | 125/908 [01:22<20:11,  1.55s/it]
 14%|█▍        | 126/908 [01:22<14:51,  1.14s/it]
 14%|█▍        | 127/908 [01:22<11:33,  1.13it/s]
 14%|█▍        | 128/908 [01:22<08:50,  1.47it/s]
 14%|█▍        | 129/908 [01:24<12:21,  1.05it/s]
 14%|█▍        | 130/908 [01:24<09:25,  1.37it/s]
 14%|█▍        | 131/908 [01:24<07:20,  1.76it/s]
 15%|█▍        | 132/908 [01:25<05:51,  2.21it/s]
 15%|█▍        | 133/908 [01:26<10:21,  1.25it/s]
 15%|█▍        | 134/908 [01:26<08:04,  1.60it/s]
 15%|█▍        | 135/908 [01:27<06:22,  2.02it/s]
 15%|█▍        | 136/908 [01:27<05:11,  2.48it/s]
 15%|█▌        | 137/908 [01:28<09:37,  1.33it/s]
 15%|█▌        | 138/908 [01:29<07:27,  1.72it/s]
 15%|█▌        | 139/908 [01:29<05:56,  2.15it/s]
+[Batch:   140] training loss = 0.539
+
 15%|█▌        | 140/908 [01:29<04:53,  2.61it/s]
 16%|█▌        | 141/908 [01:31<10:05,  1.27it/s]
 16%|█▌        | 142/908 [01:31<07:49,  1.63it/s]
 16%|█▌        | 143/908 [01:31<06:11,  2.06it/s]
 16%|█▌        | 144/908 [01:31<05:03,  2.51it/s]
 16%|█▌        | 145/908 [01:33<10:01,  1.27it/s]
 16%|█▌        | 146/908 [01:33<08:01,  1.58it/s]
 16%|█▌        | 147/908 [01:33<06:18,  2.01it/s]
 16%|█▋        | 148/908 [01:34<05:09,  2.46it/s]
 16%|█▋        | 149/908 [01:36<12:46,  1.01s/it]
 17%|█▋        | 150/908 [01:36<09:41,  1.30it/s]
 17%|█▋        | 151/908 [01:36<07:30,  1.68it/s]
 17%|█▋        | 152/908 [01:37<05:57,  2.11it/s]
 17%|█▋        | 153/908 [01:38<11:11,  1.12it/s]
 17%|█▋        | 154/908 [01:39<08:35,  1.46it/s]
 17%|█▋        | 155/908 [01:39<06:42,  1.87it/s]
 17%|█▋        | 156/908 [01:39<05:26,  2.30it/s]
 17%|█▋        | 157/908 [01:41<09:27,  1.32it/s]
 17%|█▋        | 158/908 [01:41<07:19,  1.71it/s]
 18%|█▊        | 159/908 [01:41<05:50,  2.14it/s]
+[Batch:   160] training loss = 0.556
+
 18%|█▊        | 160/908 [01:41<04:47,  2.60it/s]
 18%|█▊        | 161/908 [01:43<09:36,  1.29it/s]
 18%|█▊        | 162/908 [01:43<07:28,  1.66it/s]
 18%|█▊        | 163/908 [01:43<05:55,  2.09it/s]
 18%|█▊        | 164/908 [01:43<04:50,  2.56it/s]
 18%|█▊        | 165/908 [01:45<09:55,  1.25it/s]
 18%|█▊        | 166/908 [01:45<07:42,  1.60it/s]
 18%|█▊        | 167/908 [01:46<06:06,  2.02it/s]
 19%|█▊        | 168/908 [01:46<05:00,  2.46it/s]
 19%|█▊        | 169/908 [01:48<11:15,  1.09it/s]
 19%|█▊        | 170/908 [01:48<08:36,  1.43it/s]
 19%|█▉        | 171/908 [01:48<06:44,  1.82it/s]
 19%|█▉        | 172/908 [01:48<05:24,  2.26it/s]
 19%|█▉        | 173/908 [01:50<10:52,  1.13it/s]
 19%|█▉        | 174/908 [01:51<08:16,  1.48it/s]
 19%|█▉        | 175/908 [01:51<06:30,  1.88it/s]
 19%|█▉        | 176/908 [01:51<05:15,  2.32it/s]
 19%|█▉        | 177/908 [01:53<10:09,  1.20it/s]
 20%|█▉        | 178/908 [01:53<07:50,  1.55it/s]
 20%|█▉        | 179/908 [01:53<06:11,  1.96it/s]
+[Batch:   180] training loss = 0.468
+
 20%|█▉        | 180/908 [01:53<05:01,  2.42it/s]
 20%|█▉        | 181/908 [01:55<11:06,  1.09it/s]
 20%|██        | 182/908 [01:56<08:27,  1.43it/s]
 20%|██        | 183/908 [01:56<06:37,  1.83it/s]
 20%|██        | 184/908 [01:56<05:19,  2.27it/s]
 20%|██        | 185/908 [01:57<08:07,  1.48it/s]
 20%|██        | 186/908 [01:57<06:25,  1.87it/s]
 21%|██        | 187/908 [01:58<05:16,  2.28it/s]
 21%|██        | 188/908 [01:58<04:23,  2.73it/s]
 21%|██        | 189/908 [01:58<03:51,  3.11it/s]
 21%|██        | 190/908 [01:58<03:22,  3.54it/s]
 21%|██        | 191/908 [01:58<03:02,  3.92it/s]
 21%|██        | 192/908 [01:59<02:48,  4.25it/s]
 21%|██▏       | 193/908 [02:00<07:53,  1.51it/s]
 21%|██▏       | 194/908 [02:00<06:12,  1.92it/s]
 21%|██▏       | 195/908 [02:01<05:00,  2.37it/s]
 22%|██▏       | 196/908 [02:01<04:11,  2.83it/s]
 22%|██▏       | 197/908 [02:01<03:39,  3.24it/s]
 22%|██▏       | 198/908 [02:01<03:35,  3.30it/s]
 22%|██▏       | 199/908 [02:02<03:09,  3.74it/s]
+[Batch:   200] training loss = 0.492
+
 22%|██▏       | 200/908 [02:02<02:52,  4.10it/s]
 22%|██▏       | 201/908 [02:03<06:20,  1.86it/s]
 22%|██▏       | 202/908 [02:03<05:06,  2.30it/s]
 22%|██▏       | 203/908 [02:03<04:14,  2.77it/s]
 22%|██▏       | 204/908 [02:03<03:37,  3.24it/s]
 23%|██▎       | 205/908 [02:05<06:21,  1.84it/s]
 23%|██▎       | 206/908 [02:05<06:01,  1.94it/s]
 23%|██▎       | 207/908 [02:05<04:52,  2.39it/s]
 23%|██▎       | 208/908 [02:05<04:04,  2.86it/s]
 23%|██▎       | 209/908 [02:06<03:31,  3.30it/s]
 23%|██▎       | 210/908 [02:06<04:06,  2.83it/s]
 23%|██▎       | 211/908 [02:06<03:32,  3.28it/s]
 23%|██▎       | 212/908 [02:06<03:08,  3.69it/s]
 23%|██▎       | 213/908 [02:08<06:57,  1.66it/s]
 24%|██▎       | 214/908 [02:08<05:31,  2.09it/s]
 24%|██▎       | 215/908 [02:08<04:31,  2.55it/s]
 24%|██▍       | 216/908 [02:08<03:49,  3.02it/s]
 24%|██▍       | 217/908 [02:10<07:14,  1.59it/s]
 24%|██▍       | 218/908 [02:10<06:18,  1.82it/s]
 24%|██▍       | 219/908 [02:10<05:03,  2.27it/s]
+[Batch:   220] training loss = 0.413
+
 24%|██▍       | 220/908 [02:10<04:12,  2.73it/s]
 24%|██▍       | 221/908 [02:11<03:56,  2.91it/s]
 24%|██▍       | 222/908 [02:11<04:33,  2.51it/s]
 25%|██▍       | 223/908 [02:11<03:50,  2.98it/s]
 25%|██▍       | 224/908 [02:12<03:19,  3.43it/s]
 25%|██▍       | 225/908 [02:13<06:10,  1.84it/s]
 25%|██▍       | 226/908 [02:14<06:54,  1.65it/s]
 25%|██▌       | 227/908 [02:14<05:27,  2.08it/s]
 25%|██▌       | 228/908 [02:14<04:28,  2.53it/s]
 25%|██▌       | 229/908 [02:15<07:17,  1.55it/s]
 25%|██▌       | 230/908 [02:16<07:44,  1.46it/s]
 25%|██▌       | 231/908 [02:16<06:03,  1.86it/s]
 26%|██▌       | 232/908 [02:16<04:53,  2.31it/s]
 26%|██▌       | 233/908 [02:18<09:28,  1.19it/s]
 26%|██▌       | 234/908 [02:19<09:57,  1.13it/s]
 26%|██▌       | 235/908 [02:19<07:35,  1.48it/s]
 26%|██▌       | 236/908 [02:19<05:56,  1.89it/s]
 26%|██▌       | 237/908 [02:22<12:04,  1.08s/it]
 26%|██▌       | 238/908 [02:23<11:45,  1.05s/it]
 26%|██▋       | 239/908 [02:23<08:51,  1.26it/s]
+[Batch:   240] training loss = 0.515
+
 26%|██▋       | 240/908 [02:23<06:48,  1.64it/s]
 27%|██▋       | 241/908 [02:25<11:12,  1.01s/it]
 27%|██▋       | 242/908 [02:26<09:29,  1.17it/s]
 27%|██▋       | 243/908 [02:26<07:15,  1.53it/s]
 27%|██▋       | 244/908 [02:26<05:43,  1.93it/s]
 27%|██▋       | 245/908 [02:28<10:27,  1.06it/s]
 27%|██▋       | 246/908 [02:29<09:48,  1.13it/s]
 27%|██▋       | 247/908 [02:29<07:28,  1.47it/s]
 27%|██▋       | 248/908 [02:29<05:51,  1.88it/s]
 27%|██▋       | 249/908 [02:31<11:24,  1.04s/it]
 28%|██▊       | 250/908 [02:32<09:55,  1.10it/s]
 28%|██▊       | 251/908 [02:32<07:32,  1.45it/s]
 28%|██▊       | 252/908 [02:32<05:53,  1.85it/s]
 28%|██▊       | 253/908 [02:34<09:39,  1.13it/s]
 28%|██▊       | 254/908 [02:35<09:31,  1.14it/s]
 28%|██▊       | 255/908 [02:35<07:16,  1.49it/s]
 28%|██▊       | 256/908 [02:35<05:42,  1.91it/s]
 28%|██▊       | 257/908 [02:37<08:44,  1.24it/s]
 28%|██▊       | 258/908 [02:37<08:01,  1.35it/s]
 29%|██▊       | 259/908 [02:37<06:17,  1.72it/s]
+[Batch:   260] training loss = 0.473
+
 29%|██▊       | 260/908 [02:38<05:02,  2.14it/s]
 29%|██▊       | 261/908 [02:38<04:37,  2.33it/s]
 29%|██▉       | 262/908 [02:39<05:01,  2.15it/s]
 29%|██▉       | 263/908 [02:39<04:07,  2.61it/s]
 29%|██▉       | 264/908 [02:39<03:29,  3.08it/s]
 29%|██▉       | 265/908 [02:40<07:20,  1.46it/s]
 29%|██▉       | 266/908 [02:41<06:15,  1.71it/s]
 29%|██▉       | 267/908 [02:41<04:58,  2.15it/s]
 30%|██▉       | 268/908 [02:41<04:05,  2.61it/s]
 30%|██▉       | 269/908 [02:43<08:38,  1.23it/s]
 30%|██▉       | 270/908 [02:43<06:47,  1.57it/s]
 30%|██▉       | 271/908 [02:43<05:20,  1.99it/s]
 30%|██▉       | 272/908 [02:44<04:21,  2.43it/s]
 30%|███       | 273/908 [02:46<10:06,  1.05it/s]
 30%|███       | 274/908 [02:46<07:53,  1.34it/s]
 30%|███       | 275/908 [02:46<06:08,  1.72it/s]
 30%|███       | 276/908 [02:47<04:54,  2.15it/s]
 31%|███       | 277/908 [02:48<09:24,  1.12it/s]
 31%|███       | 278/908 [02:49<07:30,  1.40it/s]
 31%|███       | 279/908 [02:49<05:50,  1.79it/s]
+[Batch:   280] training loss = 0.501
+
 31%|███       | 280/908 [02:49<04:41,  2.23it/s]
 31%|███       | 281/908 [02:52<12:07,  1.16s/it]
 31%|███       | 282/908 [02:52<09:03,  1.15it/s]
 31%|███       | 283/908 [02:52<06:56,  1.50it/s]
 31%|███▏      | 284/908 [02:52<05:26,  1.91it/s]
 31%|███▏      | 285/908 [02:54<08:05,  1.28it/s]
 31%|███▏      | 286/908 [02:54<06:29,  1.60it/s]
 32%|███▏      | 287/908 [02:54<05:09,  2.01it/s]
 32%|███▏      | 288/908 [02:55<04:11,  2.47it/s]
 32%|███▏      | 289/908 [02:57<09:42,  1.06it/s]
 32%|███▏      | 290/908 [02:57<08:00,  1.28it/s]
 32%|███▏      | 291/908 [02:57<06:10,  1.67it/s]
 32%|███▏      | 292/908 [02:57<04:53,  2.10it/s]
 32%|███▏      | 293/908 [02:59<08:13,  1.25it/s]
 32%|███▏      | 294/908 [02:59<06:53,  1.48it/s]
 32%|███▏      | 295/908 [03:00<05:23,  1.90it/s]
 33%|███▎      | 296/908 [03:00<04:20,  2.35it/s]
 33%|███▎      | 297/908 [03:01<07:36,  1.34it/s]
 33%|███▎      | 298/908 [03:02<06:29,  1.57it/s]
 33%|███▎      | 299/908 [03:02<05:07,  1.98it/s]
+[Batch:   300] training loss = 0.535
+
 33%|███▎      | 300/908 [03:02<04:13,  2.40it/s]
 33%|███▎      | 301/908 [03:02<03:35,  2.82it/s]
 33%|███▎      | 302/908 [03:03<03:32,  2.86it/s]
 33%|███▎      | 303/908 [03:03<03:06,  3.24it/s]
 33%|███▎      | 304/908 [03:03<02:44,  3.67it/s]
 34%|███▎      | 305/908 [03:05<07:02,  1.43it/s]
 34%|███▎      | 306/908 [03:05<05:30,  1.82it/s]
 34%|███▍      | 307/908 [03:05<04:25,  2.27it/s]
 34%|███▍      | 308/908 [03:05<03:39,  2.74it/s]
 34%|███▍      | 309/908 [03:07<07:17,  1.37it/s]
 34%|███▍      | 310/908 [03:07<05:40,  1.76it/s]
 34%|███▍      | 311/908 [03:07<04:32,  2.19it/s]
 34%|███▍      | 312/908 [03:07<03:43,  2.67it/s]
 34%|███▍      | 313/908 [03:08<04:06,  2.42it/s]
 35%|███▍      | 314/908 [03:08<03:30,  2.82it/s]
 35%|███▍      | 315/908 [03:09<03:44,  2.65it/s]
 35%|███▍      | 316/908 [03:09<03:10,  3.11it/s]
 35%|███▍      | 317/908 [03:10<04:39,  2.12it/s]
 35%|███▌      | 318/908 [03:10<03:47,  2.59it/s]
 35%|███▌      | 319/908 [03:10<03:12,  3.06it/s]
+[Batch:   320] training loss = 0.642
+
 35%|███▌      | 320/908 [03:10<02:48,  3.50it/s]
 35%|███▌      | 321/908 [03:12<07:11,  1.36it/s]
 35%|███▌      | 322/908 [03:12<05:35,  1.75it/s]
 36%|███▌      | 323/908 [03:12<04:28,  2.18it/s]
 36%|███▌      | 324/908 [03:13<03:41,  2.64it/s]
 36%|███▌      | 325/908 [03:13<04:52,  2.00it/s]
 36%|███▌      | 326/908 [03:14<03:57,  2.45it/s]
 36%|███▌      | 327/908 [03:14<03:30,  2.76it/s]
 36%|███▌      | 328/908 [03:14<02:59,  3.23it/s]
 36%|███▌      | 329/908 [03:15<03:49,  2.52it/s]
 36%|███▋      | 330/908 [03:15<03:13,  2.98it/s]
 36%|███▋      | 331/908 [03:15<02:47,  3.44it/s]
 37%|███▋      | 332/908 [03:15<02:30,  3.83it/s]
 37%|███▋      | 333/908 [03:17<06:56,  1.38it/s]
 37%|███▋      | 334/908 [03:17<05:24,  1.77it/s]
 37%|███▋      | 335/908 [03:17<04:21,  2.19it/s]
 37%|███▋      | 336/908 [03:18<03:35,  2.65it/s]
 37%|███▋      | 337/908 [03:19<08:03,  1.18it/s]
 37%|███▋      | 338/908 [03:20<06:09,  1.54it/s]
 37%|███▋      | 339/908 [03:20<04:51,  1.95it/s]
+[Batch:   340] training loss = 0.531
+
 37%|███▋      | 340/908 [03:20<03:56,  2.40it/s]
 38%|███▊      | 341/908 [03:23<10:14,  1.08s/it]
 38%|███▊      | 342/908 [03:23<08:31,  1.11it/s]
 38%|███▊      | 343/908 [03:24<07:44,  1.22it/s]
 38%|███▊      | 344/908 [03:24<05:57,  1.58it/s]
 38%|███▊      | 345/908 [03:26<08:54,  1.05it/s]
 38%|███▊      | 346/908 [03:26<06:45,  1.38it/s]
 38%|███▊      | 347/908 [03:26<05:19,  1.75it/s]
 38%|███▊      | 348/908 [03:26<04:18,  2.17it/s]
 38%|███▊      | 349/908 [03:28<07:22,  1.26it/s]
 39%|███▊      | 350/908 [03:28<05:44,  1.62it/s]
 39%|███▊      | 351/908 [03:28<04:35,  2.02it/s]
 39%|███▉      | 352/908 [03:28<03:45,  2.47it/s]
 39%|███▉      | 353/908 [03:30<07:14,  1.28it/s]
 39%|███▉      | 354/908 [03:30<05:45,  1.61it/s]
 39%|███▉      | 355/908 [03:31<04:45,  1.94it/s]
 39%|███▉      | 356/908 [03:31<03:51,  2.39it/s]
 39%|███▉      | 357/908 [03:32<07:13,  1.27it/s]
 39%|███▉      | 358/908 [03:33<06:43,  1.36it/s]
 40%|███▉      | 359/908 [03:33<05:13,  1.75it/s]
+[Batch:   360] training loss = 0.498
+
 40%|███▉      | 360/908 [03:33<04:10,  2.19it/s]
 40%|███▉      | 361/908 [03:35<06:25,  1.42it/s]
 40%|███▉      | 362/908 [03:35<05:34,  1.63it/s]
 40%|███▉      | 363/908 [03:35<04:24,  2.06it/s]
 40%|████      | 364/908 [03:36<03:35,  2.53it/s]
 40%|████      | 365/908 [03:37<06:18,  1.43it/s]
 40%|████      | 366/908 [03:37<05:04,  1.78it/s]
 40%|████      | 367/908 [03:37<04:05,  2.21it/s]
 41%|████      | 368/908 [03:38<03:22,  2.66it/s]
 41%|████      | 369/908 [03:39<06:26,  1.39it/s]
 41%|████      | 370/908 [03:40<06:01,  1.49it/s]
 41%|████      | 371/908 [03:40<04:43,  1.89it/s]
 41%|████      | 372/908 [03:40<03:48,  2.34it/s]
 41%|████      | 373/908 [03:42<06:59,  1.27it/s]
 41%|████      | 374/908 [03:42<06:28,  1.37it/s]
 41%|████▏     | 375/908 [03:42<05:02,  1.76it/s]
 41%|████▏     | 376/908 [03:43<04:02,  2.19it/s]
 42%|████▏     | 377/908 [03:44<06:10,  1.43it/s]
 42%|████▏     | 378/908 [03:45<06:08,  1.44it/s]
 42%|████▏     | 379/908 [03:45<04:47,  1.84it/s]
+[Batch:   380] training loss = 0.514
+
 42%|████▏     | 380/908 [03:45<03:50,  2.29it/s]
 42%|████▏     | 381/908 [03:47<07:07,  1.23it/s]
 42%|████▏     | 382/908 [03:47<05:52,  1.49it/s]
 42%|████▏     | 383/908 [03:47<04:22,  2.00it/s]
 42%|████▏     | 384/908 [03:47<03:22,  2.59it/s]
 42%|████▏     | 385/908 [03:49<06:50,  1.27it/s]
 43%|████▎     | 386/908 [03:49<06:09,  1.41it/s]
 43%|████▎     | 387/908 [03:50<04:50,  1.79it/s]
 43%|████▎     | 388/908 [03:50<03:54,  2.22it/s]
 43%|████▎     | 389/908 [03:51<06:16,  1.38it/s]
 43%|████▎     | 390/908 [03:52<05:31,  1.56it/s]
 43%|████▎     | 391/908 [03:52<04:22,  1.97it/s]
 43%|████▎     | 392/908 [03:52<03:33,  2.42it/s]
 43%|████▎     | 393/908 [03:53<05:33,  1.54it/s]
 43%|████▎     | 394/908 [03:54<04:33,  1.88it/s]
 44%|████▎     | 395/908 [03:54<03:41,  2.32it/s]
 44%|████▎     | 396/908 [03:54<03:04,  2.77it/s]
 44%|████▎     | 397/908 [03:55<05:06,  1.67it/s]
 44%|████▍     | 398/908 [03:56<05:12,  1.63it/s]
 44%|████▍     | 399/908 [03:56<04:08,  2.05it/s]
+[Batch:   400] training loss = 0.593
+
 44%|████▍     | 400/908 [03:56<03:23,  2.50it/s]
 44%|████▍     | 401/908 [03:58<06:54,  1.22it/s]
 44%|████▍     | 402/908 [03:58<05:53,  1.43it/s]
 44%|████▍     | 403/908 [03:59<04:37,  1.82it/s]
 44%|████▍     | 404/908 [03:59<03:43,  2.25it/s]
 45%|████▍     | 405/908 [04:00<05:10,  1.62it/s]
 45%|████▍     | 406/908 [04:00<05:17,  1.58it/s]
 45%|████▍     | 407/908 [04:01<04:11,  1.99it/s]
 45%|████▍     | 408/908 [04:01<03:26,  2.42it/s]
 45%|████▌     | 409/908 [04:02<05:31,  1.50it/s]
 45%|████▌     | 410/908 [04:03<05:28,  1.52it/s]
 45%|████▌     | 411/908 [04:03<04:18,  1.92it/s]
 45%|████▌     | 412/908 [04:03<03:30,  2.36it/s]
 45%|████▌     | 413/908 [04:04<05:30,  1.50it/s]
 46%|████▌     | 414/908 [04:06<06:48,  1.21it/s]
 46%|████▌     | 415/908 [04:06<05:16,  1.56it/s]
 46%|████▌     | 416/908 [04:06<04:10,  1.96it/s]
 46%|████▌     | 417/908 [04:08<06:52,  1.19it/s]
 46%|████▌     | 418/908 [04:08<05:21,  1.52it/s]
 46%|████▌     | 419/908 [04:08<04:14,  1.92it/s]
+[Batch:   420] training loss = 0.491
+
 46%|████▋     | 420/908 [04:08<03:26,  2.36it/s]
 46%|████▋     | 421/908 [04:09<04:23,  1.85it/s]
 46%|████▋     | 422/908 [04:10<04:57,  1.63it/s]
 47%|████▋     | 423/908 [04:10<03:57,  2.05it/s]
 47%|████▋     | 424/908 [04:10<03:14,  2.49it/s]
 47%|████▋     | 425/908 [04:12<05:33,  1.45it/s]
 47%|████▋     | 426/908 [04:12<05:56,  1.35it/s]
 47%|████▋     | 427/908 [04:13<04:38,  1.73it/s]
 47%|████▋     | 428/908 [04:13<03:42,  2.16it/s]
 47%|████▋     | 429/908 [04:15<06:54,  1.15it/s]
 47%|████▋     | 430/908 [04:15<06:08,  1.30it/s]
 47%|████▋     | 431/908 [04:15<04:45,  1.67it/s]
 48%|████▊     | 432/908 [04:16<03:48,  2.09it/s]
 48%|████▊     | 433/908 [04:17<06:46,  1.17it/s]
 48%|████▊     | 434/908 [04:18<07:08,  1.11it/s]
 48%|████▊     | 435/908 [04:19<05:28,  1.44it/s]
 48%|████▊     | 436/908 [04:19<04:18,  1.83it/s]
 48%|████▊     | 437/908 [04:21<08:22,  1.07s/it]
 48%|████▊     | 438/908 [04:22<07:48,  1.00it/s]
 48%|████▊     | 439/908 [04:22<05:54,  1.32it/s]
+[Batch:   440] training loss = 0.528
+
 48%|████▊     | 440/908 [04:22<04:35,  1.70it/s]
 49%|████▊     | 441/908 [04:24<06:45,  1.15it/s]
 49%|████▊     | 442/908 [04:24<05:37,  1.38it/s]
 49%|████▉     | 443/908 [04:24<04:24,  1.76it/s]
 49%|████▉     | 444/908 [04:25<03:32,  2.19it/s]
 49%|████▉     | 445/908 [04:26<06:00,  1.28it/s]
 49%|████▉     | 446/908 [04:26<05:00,  1.54it/s]
 49%|████▉     | 447/908 [04:27<03:56,  1.95it/s]
 49%|████▉     | 448/908 [04:27<03:13,  2.38it/s]
 49%|████▉     | 449/908 [04:28<05:58,  1.28it/s]
 50%|████▉     | 450/908 [04:29<04:59,  1.53it/s]
 50%|████▉     | 451/908 [04:29<03:56,  1.93it/s]
 50%|████▉     | 452/908 [04:29<03:12,  2.37it/s]
 50%|████▉     | 453/908 [04:32<07:45,  1.02s/it]
 50%|█████     | 454/908 [04:32<06:36,  1.14it/s]
 50%|█████     | 455/908 [04:32<05:04,  1.49it/s]
 50%|█████     | 456/908 [04:33<04:00,  1.88it/s]
 50%|█████     | 457/908 [04:34<06:47,  1.11it/s]
 50%|█████     | 458/908 [04:35<05:36,  1.34it/s]
 51%|█████     | 459/908 [04:35<04:21,  1.72it/s]
+[Batch:   460] training loss = 0.474
+
 51%|█████     | 460/908 [04:35<03:29,  2.14it/s]
 51%|█████     | 461/908 [04:37<06:41,  1.11it/s]
 51%|█████     | 462/908 [04:38<06:32,  1.14it/s]
 51%|█████     | 463/908 [04:38<05:00,  1.48it/s]
 51%|█████     | 464/908 [04:38<03:57,  1.87it/s]
 51%|█████     | 465/908 [04:40<06:15,  1.18it/s]
 51%|█████▏    | 466/908 [04:40<05:51,  1.26it/s]
 51%|█████▏    | 467/908 [04:41<04:31,  1.62it/s]
 52%|█████▏    | 468/908 [04:41<03:36,  2.03it/s]
 52%|█████▏    | 469/908 [04:42<06:01,  1.21it/s]
 52%|█████▏    | 470/908 [04:43<05:40,  1.28it/s]
 52%|█████▏    | 471/908 [04:43<04:24,  1.65it/s]
 52%|█████▏    | 472/908 [04:44<03:30,  2.07it/s]
 52%|█████▏    | 473/908 [04:45<05:51,  1.24it/s]
 52%|█████▏    | 474/908 [04:46<06:00,  1.20it/s]
 52%|█████▏    | 475/908 [04:46<04:37,  1.56it/s]
 52%|█████▏    | 476/908 [04:46<03:39,  1.97it/s]
 53%|█████▎    | 477/908 [04:48<05:09,  1.39it/s]
 53%|█████▎    | 478/908 [04:48<05:07,  1.40it/s]
 53%|█████▎    | 479/908 [04:49<04:00,  1.79it/s]
+[Batch:   480] training loss = 0.496
+
 53%|█████▎    | 480/908 [04:49<03:13,  2.21it/s]
 53%|█████▎    | 481/908 [04:50<05:55,  1.20it/s]
 53%|█████▎    | 482/908 [04:51<05:35,  1.27it/s]
 53%|█████▎    | 483/908 [04:51<04:19,  1.64it/s]
 53%|█████▎    | 484/908 [04:52<03:26,  2.05it/s]
 53%|█████▎    | 485/908 [04:53<05:23,  1.31it/s]
 54%|█████▎    | 486/908 [04:53<04:16,  1.64it/s]
 54%|█████▎    | 487/908 [04:53<03:24,  2.06it/s]
 54%|█████▎    | 488/908 [04:54<02:49,  2.48it/s]
 54%|█████▍    | 489/908 [04:55<04:26,  1.57it/s]
 54%|█████▍    | 490/908 [04:55<03:44,  1.86it/s]
 54%|█████▍    | 491/908 [04:55<03:01,  2.29it/s]
 54%|█████▍    | 492/908 [04:55<02:31,  2.75it/s]
 54%|█████▍    | 493/908 [04:57<05:13,  1.32it/s]
 54%|█████▍    | 494/908 [04:58<04:35,  1.50it/s]
 55%|█████▍    | 495/908 [04:58<03:37,  1.90it/s]
 55%|█████▍    | 496/908 [04:58<02:57,  2.32it/s]
 55%|█████▍    | 497/908 [04:59<04:27,  1.54it/s]
 55%|█████▍    | 498/908 [05:00<04:41,  1.46it/s]
 55%|█████▍    | 499/908 [05:00<03:42,  1.84it/s]
+[Batch:   500] training loss = 0.628
+
 55%|█████▌    | 500/908 [05:00<03:00,  2.27it/s]
 55%|█████▌    | 501/908 [05:01<04:19,  1.57it/s]
 55%|█████▌    | 502/908 [05:02<04:07,  1.64it/s]
 55%|█████▌    | 503/908 [05:02<03:17,  2.06it/s]
 56%|█████▌    | 504/908 [05:02<02:40,  2.51it/s]
 56%|█████▌    | 505/908 [05:04<06:05,  1.10it/s]
 56%|█████▌    | 506/908 [05:05<05:13,  1.28it/s]
 56%|█████▌    | 507/908 [05:05<04:03,  1.65it/s]
 56%|█████▌    | 508/908 [05:05<03:13,  2.06it/s]
 56%|█████▌    | 509/908 [05:07<05:31,  1.20it/s]
 56%|█████▌    | 510/908 [05:08<05:40,  1.17it/s]
 56%|█████▋    | 511/908 [05:08<04:21,  1.52it/s]
 56%|█████▋    | 512/908 [05:08<03:26,  1.92it/s]
 56%|█████▋    | 513/908 [05:10<05:07,  1.28it/s]
 57%|█████▋    | 514/908 [05:10<04:34,  1.44it/s]
 57%|█████▋    | 515/908 [05:10<03:36,  1.82it/s]
 57%|█████▋    | 516/908 [05:11<02:55,  2.23it/s]
 57%|█████▋    | 517/908 [05:12<04:43,  1.38it/s]
 57%|█████▋    | 518/908 [05:13<04:29,  1.45it/s]
 57%|█████▋    | 519/908 [05:13<03:30,  1.85it/s]
+[Batch:   520] training loss = 0.669
+
 57%|█████▋    | 520/908 [05:13<02:50,  2.28it/s]
 57%|█████▋    | 521/908 [05:14<04:04,  1.58it/s]
 57%|█████▋    | 522/908 [05:15<03:48,  1.69it/s]
 58%|█████▊    | 523/908 [05:15<03:01,  2.12it/s]
 58%|█████▊    | 524/908 [05:15<02:28,  2.58it/s]
 58%|█████▊    | 525/908 [05:17<04:52,  1.31it/s]
 58%|█████▊    | 526/908 [05:18<05:24,  1.18it/s]
 58%|█████▊    | 527/908 [05:18<04:09,  1.53it/s]
 58%|█████▊    | 528/908 [05:18<03:16,  1.94it/s]
 58%|█████▊    | 529/908 [05:18<03:04,  2.05it/s]
 58%|█████▊    | 530/908 [05:19<03:46,  1.67it/s]
 58%|█████▊    | 531/908 [05:20<03:00,  2.09it/s]
 59%|█████▊    | 532/908 [05:20<02:29,  2.52it/s]
 59%|█████▊    | 533/908 [05:21<03:13,  1.93it/s]
 59%|█████▉    | 534/908 [05:21<02:38,  2.37it/s]
 59%|█████▉    | 535/908 [05:21<02:12,  2.82it/s]
 59%|█████▉    | 536/908 [05:21<01:55,  3.23it/s]
 59%|█████▉    | 537/908 [05:22<02:37,  2.36it/s]
 59%|█████▉    | 538/908 [05:23<03:14,  1.91it/s]
 59%|█████▉    | 539/908 [05:23<02:36,  2.36it/s]
+[Batch:   540] training loss = 0.446
+
 59%|█████▉    | 540/908 [05:23<02:11,  2.80it/s]
 60%|█████▉    | 541/908 [05:23<01:57,  3.14it/s]
 60%|█████▉    | 542/908 [05:23<01:45,  3.46it/s]
 60%|█████▉    | 543/908 [05:24<01:35,  3.80it/s]
 60%|█████▉    | 544/908 [05:24<01:27,  4.14it/s]
 60%|██████    | 545/908 [05:25<03:48,  1.59it/s]
 60%|██████    | 546/908 [05:26<03:02,  1.99it/s]
 60%|██████    | 547/908 [05:26<02:28,  2.43it/s]
 60%|██████    | 548/908 [05:26<02:05,  2.88it/s]
 60%|██████    | 549/908 [05:26<01:50,  3.24it/s]
 61%|██████    | 550/908 [05:27<02:24,  2.47it/s]
 61%|██████    | 551/908 [05:27<02:02,  2.90it/s]
 61%|██████    | 552/908 [05:27<01:47,  3.31it/s]
 61%|██████    | 553/908 [05:28<03:24,  1.73it/s]
 61%|██████    | 554/908 [05:29<03:51,  1.53it/s]
 61%|██████    | 555/908 [05:29<03:03,  1.93it/s]
 61%|██████    | 556/908 [05:30<02:29,  2.36it/s]
 61%|██████▏   | 557/908 [05:31<03:17,  1.78it/s]
 61%|██████▏   | 558/908 [05:31<03:56,  1.48it/s]
 62%|██████▏   | 559/908 [05:32<03:05,  1.88it/s]
+[Batch:   560] training loss = 0.490
+
 62%|██████▏   | 560/908 [05:32<02:31,  2.30it/s]
 62%|██████▏   | 561/908 [05:33<04:02,  1.43it/s]
 62%|██████▏   | 562/908 [05:34<04:15,  1.35it/s]
 62%|██████▏   | 563/908 [05:34<03:18,  1.74it/s]
 62%|██████▏   | 564/908 [05:34<02:40,  2.14it/s]
 62%|██████▏   | 565/908 [05:36<03:54,  1.46it/s]
 62%|██████▏   | 566/908 [05:36<03:41,  1.54it/s]
 62%|██████▏   | 567/908 [05:36<02:54,  1.96it/s]
 63%|██████▎   | 568/908 [05:37<02:22,  2.38it/s]
 63%|██████▎   | 569/908 [05:38<03:44,  1.51it/s]
 63%|██████▎   | 570/908 [05:39<04:04,  1.38it/s]
 63%|██████▎   | 571/908 [05:39<03:09,  1.78it/s]
 63%|██████▎   | 572/908 [05:39<02:32,  2.20it/s]
 63%|██████▎   | 573/908 [05:40<04:05,  1.36it/s]
 63%|██████▎   | 574/908 [05:41<04:21,  1.28it/s]
 63%|██████▎   | 575/908 [05:42<03:22,  1.64it/s]
 63%|██████▎   | 576/908 [05:42<02:41,  2.05it/s]
 64%|██████▎   | 577/908 [05:43<03:31,  1.57it/s]
 64%|██████▎   | 578/908 [05:43<02:56,  1.87it/s]
 64%|██████▍   | 579/908 [05:43<02:22,  2.31it/s]
+[Batch:   580] training loss = 0.350
+
 64%|██████▍   | 580/908 [05:43<01:59,  2.74it/s]
 64%|██████▍   | 581/908 [05:45<03:06,  1.76it/s]
 64%|██████▍   | 582/908 [05:45<03:34,  1.52it/s]
 64%|██████▍   | 583/908 [05:46<02:49,  1.92it/s]
 64%|██████▍   | 584/908 [05:46<02:17,  2.36it/s]
 64%|██████▍   | 585/908 [05:47<03:34,  1.51it/s]
 65%|██████▍   | 586/908 [05:48<03:55,  1.37it/s]
 65%|██████▍   | 587/908 [05:48<03:03,  1.75it/s]
 65%|██████▍   | 588/908 [05:48<02:28,  2.16it/s]
 65%|██████▍   | 589/908 [05:50<04:08,  1.29it/s]
 65%|██████▍   | 590/908 [05:51<04:09,  1.28it/s]
 65%|██████▌   | 591/908 [05:51<03:13,  1.64it/s]
 65%|██████▌   | 592/908 [05:51<02:33,  2.05it/s]
 65%|██████▌   | 593/908 [05:52<03:48,  1.38it/s]
 65%|██████▌   | 594/908 [05:53<03:28,  1.50it/s]
 66%|██████▌   | 595/908 [05:53<02:44,  1.90it/s]
 66%|██████▌   | 596/908 [05:53<02:13,  2.34it/s]
 66%|██████▌   | 597/908 [05:54<03:16,  1.58it/s]
 66%|██████▌   | 598/908 [05:55<03:27,  1.49it/s]
 66%|██████▌   | 599/908 [05:55<02:43,  1.89it/s]
+[Batch:   600] training loss = 0.544
+
 66%|██████▌   | 600/908 [05:55<02:13,  2.31it/s]
 66%|██████▌   | 601/908 [05:56<02:52,  1.78it/s]
 66%|██████▋   | 602/908 [05:57<02:55,  1.74it/s]
 66%|██████▋   | 603/908 [05:57<02:20,  2.17it/s]
 67%|██████▋   | 604/908 [05:57<01:55,  2.62it/s]
 67%|██████▋   | 605/908 [05:59<03:17,  1.53it/s]
 67%|██████▋   | 606/908 [06:00<03:42,  1.36it/s]
 67%|██████▋   | 607/908 [06:00<02:52,  1.74it/s]
 67%|██████▋   | 608/908 [06:00<02:18,  2.17it/s]
 67%|██████▋   | 609/908 [06:01<03:01,  1.65it/s]
 67%|██████▋   | 610/908 [06:02<03:06,  1.60it/s]
 67%|██████▋   | 611/908 [06:02<02:28,  2.00it/s]
 67%|██████▋   | 612/908 [06:02<02:00,  2.45it/s]
 68%|██████▊   | 613/908 [06:03<03:14,  1.52it/s]
 68%|██████▊   | 614/908 [06:04<03:37,  1.35it/s]
 68%|██████▊   | 615/908 [06:04<02:49,  1.73it/s]
 68%|██████▊   | 616/908 [06:05<02:15,  2.15it/s]
 68%|██████▊   | 617/908 [06:06<03:19,  1.46it/s]
 68%|██████▊   | 618/908 [06:06<03:21,  1.44it/s]
 68%|██████▊   | 619/908 [06:07<02:38,  1.83it/s]
+[Batch:   620] training loss = 0.340
+
 68%|██████▊   | 620/908 [06:07<02:07,  2.26it/s]
 68%|██████▊   | 621/908 [06:09<04:31,  1.06it/s]
 69%|██████▊   | 622/908 [06:10<04:49,  1.01s/it]
 69%|██████▊   | 623/908 [06:10<03:38,  1.30it/s]
 69%|██████▊   | 624/908 [06:11<02:49,  1.68it/s]
 69%|██████▉   | 625/908 [06:12<03:53,  1.21it/s]
 69%|██████▉   | 626/908 [06:13<03:40,  1.28it/s]
 69%|██████▉   | 627/908 [06:13<02:50,  1.65it/s]
 69%|██████▉   | 628/908 [06:13<02:14,  2.08it/s]
 69%|██████▉   | 629/908 [06:14<03:23,  1.37it/s]
 69%|██████▉   | 630/908 [06:15<02:59,  1.55it/s]
 69%|██████▉   | 631/908 [06:15<02:21,  1.95it/s]
 70%|██████▉   | 632/908 [06:15<01:54,  2.41it/s]
 70%|██████▉   | 633/908 [06:17<03:33,  1.29it/s]
 70%|██████▉   | 634/908 [06:17<03:13,  1.42it/s]
 70%|██████▉   | 635/908 [06:17<02:30,  1.81it/s]
 70%|███████   | 636/908 [06:18<02:00,  2.26it/s]
 70%|███████   | 637/908 [06:19<03:03,  1.48it/s]
 70%|███████   | 638/908 [06:20<03:38,  1.24it/s]
 70%|███████   | 639/908 [06:20<02:48,  1.60it/s]
+[Batch:   640] training loss = 0.534
+
 70%|███████   | 640/908 [06:20<02:12,  2.02it/s]
 71%|███████   | 641/908 [06:22<03:17,  1.35it/s]
 71%|███████   | 642/908 [06:22<03:16,  1.35it/s]
 71%|███████   | 643/908 [06:23<02:32,  1.73it/s]
 71%|███████   | 644/908 [06:23<02:02,  2.16it/s]
 71%|███████   | 645/908 [06:23<01:40,  2.62it/s]
 71%|███████   | 646/908 [06:24<01:51,  2.34it/s]
 71%|███████▏  | 647/908 [06:24<01:33,  2.79it/s]
 71%|███████▏  | 648/908 [06:24<01:20,  3.25it/s]
 71%|███████▏  | 649/908 [06:25<02:01,  2.14it/s]
 72%|███████▏  | 650/908 [06:25<01:40,  2.56it/s]
 72%|███████▏  | 651/908 [06:25<01:24,  3.03it/s]
 72%|███████▏  | 652/908 [06:25<01:14,  3.45it/s]
 72%|███████▏  | 653/908 [06:26<01:11,  3.59it/s]
 72%|███████▏  | 654/908 [06:27<02:12,  1.92it/s]
 72%|███████▏  | 655/908 [06:27<01:46,  2.38it/s]
 72%|███████▏  | 656/908 [06:27<01:28,  2.83it/s]
 72%|███████▏  | 657/908 [06:28<01:34,  2.66it/s]
 72%|███████▏  | 658/908 [06:28<01:20,  3.11it/s]
 73%|███████▎  | 659/908 [06:28<01:10,  3.55it/s]
+[Batch:   660] training loss = 0.443
+
 73%|███████▎  | 660/908 [06:28<01:03,  3.92it/s]
 73%|███████▎  | 661/908 [06:29<02:12,  1.87it/s]
 73%|███████▎  | 662/908 [06:30<02:13,  1.84it/s]
 73%|███████▎  | 663/908 [06:30<01:47,  2.28it/s]
 73%|███████▎  | 664/908 [06:30<01:28,  2.75it/s]
 73%|███████▎  | 665/908 [06:30<01:16,  3.19it/s]
 73%|███████▎  | 666/908 [06:31<01:07,  3.60it/s]
 73%|███████▎  | 667/908 [06:31<01:00,  3.97it/s]
 74%|███████▎  | 668/908 [06:31<00:56,  4.25it/s]
 74%|███████▎  | 669/908 [06:32<01:37,  2.44it/s]
 74%|███████▍  | 670/908 [06:32<01:43,  2.31it/s]
 74%|███████▍  | 671/908 [06:33<01:25,  2.78it/s]
 74%|███████▍  | 672/908 [06:33<01:13,  3.23it/s]
 74%|███████▍  | 673/908 [06:34<02:12,  1.78it/s]
 74%|███████▍  | 674/908 [06:35<02:19,  1.68it/s]
 74%|███████▍  | 675/908 [06:35<01:50,  2.10it/s]
 74%|███████▍  | 676/908 [06:35<01:31,  2.54it/s]
 75%|███████▍  | 677/908 [06:36<02:28,  1.56it/s]
 75%|███████▍  | 678/908 [06:37<02:29,  1.53it/s]
 75%|███████▍  | 679/908 [06:37<01:57,  1.95it/s]
+[Batch:   680] training loss = 0.454
+
 75%|███████▍  | 680/908 [06:37<01:35,  2.40it/s]
 75%|███████▌  | 681/908 [06:38<02:21,  1.61it/s]
 75%|███████▌  | 682/908 [06:39<02:26,  1.54it/s]
 75%|███████▌  | 683/908 [06:39<01:55,  1.96it/s]
 75%|███████▌  | 684/908 [06:39<01:32,  2.42it/s]
 75%|███████▌  | 685/908 [06:41<02:43,  1.37it/s]
 76%|███████▌  | 686/908 [06:43<04:07,  1.11s/it]
 76%|███████▌  | 687/908 [06:43<03:05,  1.19it/s]
 76%|███████▌  | 688/908 [06:43<02:21,  1.56it/s]
 76%|███████▌  | 689/908 [06:45<03:16,  1.12it/s]
 76%|███████▌  | 690/908 [06:47<04:25,  1.22s/it]
 76%|███████▌  | 691/908 [06:47<03:17,  1.10it/s]
 76%|███████▌  | 692/908 [06:47<02:30,  1.44it/s]
 76%|███████▋  | 693/908 [06:49<03:57,  1.10s/it]
 76%|███████▋  | 694/908 [06:51<05:06,  1.43s/it]
 77%|███████▋  | 695/908 [06:52<03:45,  1.06s/it]
 77%|███████▋  | 696/908 [06:52<02:49,  1.25it/s]
 77%|███████▋  | 697/908 [06:55<05:06,  1.45s/it]
 77%|███████▋  | 698/908 [06:56<05:01,  1.44s/it]
 77%|███████▋  | 699/908 [06:56<03:41,  1.06s/it]
+[Batch:   700] training loss = 0.501
+
 77%|███████▋  | 700/908 [06:56<02:47,  1.25it/s]
 77%|███████▋  | 701/908 [06:58<03:59,  1.15s/it]
 77%|███████▋  | 702/908 [07:00<04:04,  1.19s/it]
 77%|███████▋  | 703/908 [07:00<03:01,  1.13it/s]
 78%|███████▊  | 704/908 [07:00<02:18,  1.48it/s]
 78%|███████▊  | 705/908 [07:02<03:43,  1.10s/it]
 78%|███████▊  | 706/908 [07:04<04:25,  1.31s/it]
 78%|███████▊  | 707/908 [07:04<03:15,  1.03it/s]
 78%|███████▊  | 708/908 [07:04<02:28,  1.35it/s]
 78%|███████▊  | 709/908 [07:07<04:21,  1.31s/it]
 78%|███████▊  | 710/908 [07:09<04:35,  1.39s/it]
 78%|███████▊  | 711/908 [07:09<03:23,  1.03s/it]
 78%|███████▊  | 712/908 [07:09<02:32,  1.28it/s]
 79%|███████▊  | 713/908 [07:13<06:01,  1.85s/it]
 79%|███████▊  | 714/908 [07:16<06:27,  2.00s/it]
 79%|███████▊  | 715/908 [07:16<04:40,  1.46s/it]
 79%|███████▉  | 716/908 [07:16<03:26,  1.08s/it]
 79%|███████▉  | 717/908 [07:19<05:08,  1.62s/it]
 79%|███████▉  | 718/908 [07:20<05:02,  1.59s/it]
 79%|███████▉  | 719/908 [07:21<03:41,  1.17s/it]
+[Batch:   720] training loss = 0.432
+
 79%|███████▉  | 720/908 [07:21<02:45,  1.14it/s]
 79%|███████▉  | 721/908 [07:23<03:54,  1.25s/it]
 80%|███████▉  | 722/908 [07:26<05:04,  1.64s/it]
 80%|███████▉  | 723/908 [07:26<03:42,  1.20s/it]
 80%|███████▉  | 724/908 [07:26<02:46,  1.11it/s]
 80%|███████▉  | 725/908 [07:28<03:39,  1.20s/it]
 80%|███████▉  | 726/908 [07:30<04:22,  1.44s/it]
 80%|████████  | 727/908 [07:30<03:13,  1.07s/it]
 80%|████████  | 728/908 [07:30<02:24,  1.24it/s]
 80%|████████  | 729/908 [07:33<03:53,  1.31s/it]
 80%|████████  | 730/908 [07:34<03:58,  1.34s/it]
 81%|████████  | 731/908 [07:34<02:56,  1.00it/s]
 81%|████████  | 732/908 [07:34<02:12,  1.33it/s]
 81%|████████  | 733/908 [07:38<04:24,  1.51s/it]
 81%|████████  | 734/908 [07:39<03:46,  1.30s/it]
 81%|████████  | 735/908 [07:39<02:47,  1.03it/s]
 81%|████████  | 736/908 [07:39<02:06,  1.36it/s]
 81%|████████  | 737/908 [07:41<03:37,  1.27s/it]
 81%|████████▏ | 738/908 [07:44<04:21,  1.54s/it]
 81%|████████▏ | 739/908 [07:44<03:11,  1.13s/it]
+[Batch:   740] training loss = 0.527
+
 81%|████████▏ | 740/908 [07:44<02:23,  1.17it/s]
 82%|████████▏ | 741/908 [07:46<03:38,  1.31s/it]
 82%|████████▏ | 742/908 [07:49<04:34,  1.65s/it]
 82%|████████▏ | 743/908 [07:49<03:20,  1.21s/it]
 82%|████████▏ | 744/908 [07:49<02:28,  1.10it/s]
 82%|████████▏ | 745/908 [07:51<03:24,  1.25s/it]
 82%|████████▏ | 746/908 [07:53<04:08,  1.53s/it]
 82%|████████▏ | 747/908 [07:54<03:02,  1.13s/it]
 82%|████████▏ | 748/908 [07:54<02:15,  1.18it/s]
 82%|████████▏ | 749/908 [07:56<03:13,  1.22s/it]
 83%|████████▎ | 750/908 [07:58<03:31,  1.34s/it]
 83%|████████▎ | 751/908 [07:58<02:35,  1.01it/s]
 83%|████████▎ | 752/908 [07:58<01:57,  1.33it/s]
 83%|████████▎ | 753/908 [08:00<02:49,  1.09s/it]
 83%|████████▎ | 754/908 [08:01<02:59,  1.17s/it]
 83%|████████▎ | 755/908 [08:01<02:13,  1.15it/s]
 83%|████████▎ | 756/908 [08:02<01:41,  1.50it/s]
 83%|████████▎ | 757/908 [08:04<02:53,  1.15s/it]
 83%|████████▎ | 758/908 [08:06<03:27,  1.38s/it]
 84%|████████▎ | 759/908 [08:06<02:32,  1.03s/it]
+[Batch:   760] training loss = 0.449
+
 84%|████████▎ | 760/908 [08:06<01:54,  1.29it/s]
 84%|████████▍ | 761/908 [08:08<02:50,  1.16s/it]
 84%|████████▍ | 762/908 [08:10<03:06,  1.28s/it]
 84%|████████▍ | 763/908 [08:10<02:18,  1.05it/s]
 84%|████████▍ | 764/908 [08:10<01:44,  1.38it/s]
 84%|████████▍ | 765/908 [08:13<03:17,  1.38s/it]
 84%|████████▍ | 766/908 [08:15<03:46,  1.60s/it]
 84%|████████▍ | 767/908 [08:15<02:46,  1.18s/it]
 85%|████████▍ | 768/908 [08:16<02:03,  1.13it/s]
 85%|████████▍ | 769/908 [08:18<02:50,  1.23s/it]
 85%|████████▍ | 770/908 [08:20<03:22,  1.47s/it]
 85%|████████▍ | 771/908 [08:20<02:28,  1.08s/it]
 85%|████████▌ | 772/908 [08:20<01:50,  1.23it/s]
 85%|████████▌ | 773/908 [08:21<02:06,  1.07it/s]
 85%|████████▌ | 774/908 [08:24<03:00,  1.35s/it]
 85%|████████▌ | 775/908 [08:24<02:13,  1.00s/it]
 85%|████████▌ | 776/908 [08:24<01:40,  1.32it/s]
 86%|████████▌ | 777/908 [08:26<02:13,  1.02s/it]
 86%|████████▌ | 778/908 [08:27<02:48,  1.29s/it]
 86%|████████▌ | 779/908 [08:28<02:04,  1.04it/s]
+[Batch:   780] training loss = 0.501
+
 86%|████████▌ | 780/908 [08:28<01:33,  1.36it/s]
 86%|████████▌ | 781/908 [08:30<02:08,  1.02s/it]
 86%|████████▌ | 782/908 [08:32<03:03,  1.46s/it]
 86%|████████▌ | 783/908 [08:32<02:15,  1.08s/it]
 86%|████████▋ | 784/908 [08:32<01:41,  1.22it/s]
 86%|████████▋ | 785/908 [08:35<02:42,  1.32s/it]
 87%|████████▋ | 786/908 [08:37<03:13,  1.59s/it]
 87%|████████▋ | 787/908 [08:37<02:21,  1.17s/it]
 87%|████████▋ | 788/908 [08:37<01:44,  1.14it/s]
 87%|████████▋ | 789/908 [08:39<02:20,  1.18s/it]
 87%|████████▋ | 790/908 [08:42<03:16,  1.67s/it]
 87%|████████▋ | 791/908 [08:42<02:23,  1.22s/it]
 87%|████████▋ | 792/908 [08:43<01:46,  1.09it/s]
 87%|████████▋ | 793/908 [08:44<01:46,  1.08it/s]
 87%|████████▋ | 794/908 [08:47<02:57,  1.55s/it]
 88%|████████▊ | 795/908 [08:47<02:09,  1.15s/it]
 88%|████████▊ | 796/908 [08:47<01:36,  1.16it/s]
 88%|████████▊ | 797/908 [08:48<01:58,  1.06s/it]
 88%|████████▊ | 798/908 [08:50<02:25,  1.32s/it]
 88%|████████▊ | 799/908 [08:51<01:47,  1.02it/s]
+[Batch:   800] training loss = 0.471
+
 88%|████████▊ | 800/908 [08:51<01:20,  1.33it/s]
 88%|████████▊ | 801/908 [08:52<01:43,  1.04it/s]
 88%|████████▊ | 802/908 [08:55<02:26,  1.38s/it]
 88%|████████▊ | 803/908 [08:55<01:47,  1.03s/it]
 89%|████████▊ | 804/908 [08:55<01:21,  1.28it/s]
 89%|████████▊ | 805/908 [08:57<02:10,  1.27s/it]
 89%|████████▉ | 806/908 [09:00<02:57,  1.74s/it]
 89%|████████▉ | 807/908 [09:00<02:09,  1.28s/it]
 89%|████████▉ | 808/908 [09:01<01:35,  1.05it/s]
 89%|████████▉ | 809/908 [09:02<01:57,  1.19s/it]
 89%|████████▉ | 810/908 [09:04<02:08,  1.31s/it]
 89%|████████▉ | 811/908 [09:04<01:34,  1.03it/s]
 89%|████████▉ | 812/908 [09:04<01:10,  1.35it/s]
 90%|████████▉ | 813/908 [09:07<01:59,  1.25s/it]
 90%|████████▉ | 814/908 [09:07<01:37,  1.04s/it]
 90%|████████▉ | 815/908 [09:08<01:12,  1.27it/s]
 90%|████████▉ | 816/908 [09:08<00:55,  1.65it/s]
 90%|████████▉ | 817/908 [09:10<01:30,  1.00it/s]
 90%|█████████ | 818/908 [09:11<01:45,  1.17s/it]
 90%|█████████ | 819/908 [09:11<01:17,  1.14it/s]
+[Batch:   820] training loss = 0.511
+
 90%|█████████ | 820/908 [09:12<01:08,  1.29it/s]
 90%|█████████ | 821/908 [09:15<02:16,  1.57s/it]
 91%|█████████ | 822/908 [09:16<02:00,  1.40s/it]
 91%|█████████ | 823/908 [09:17<01:27,  1.03s/it]
 91%|█████████ | 824/908 [09:17<01:05,  1.28it/s]
 91%|█████████ | 825/908 [09:19<01:52,  1.36s/it]
 91%|█████████ | 826/908 [09:21<01:56,  1.43s/it]
 91%|█████████ | 827/908 [09:21<01:25,  1.06s/it]
 91%|█████████ | 828/908 [09:21<01:03,  1.26it/s]
 91%|█████████▏| 829/908 [09:24<01:40,  1.28s/it]
 91%|█████████▏| 830/908 [09:27<02:20,  1.81s/it]
 92%|█████████▏| 831/908 [09:27<01:41,  1.32s/it]
 92%|█████████▏| 832/908 [09:27<01:14,  1.02it/s]
 92%|█████████▏| 833/908 [09:30<01:44,  1.40s/it]
 92%|█████████▏| 834/908 [09:32<02:04,  1.69s/it]
 92%|█████████▏| 835/908 [09:32<01:30,  1.24s/it]
 92%|█████████▏| 836/908 [09:32<01:06,  1.08it/s]
 92%|█████████▏| 837/908 [09:34<01:29,  1.27s/it]
 92%|█████████▏| 838/908 [09:37<01:57,  1.68s/it]
 92%|█████████▏| 839/908 [09:37<01:25,  1.24s/it]
+[Batch:   840] training loss = 0.512
+
 93%|█████████▎| 840/908 [09:37<01:02,  1.08it/s]
 93%|█████████▎| 841/908 [09:40<01:32,  1.37s/it]
 93%|█████████▎| 842/908 [09:42<01:53,  1.71s/it]
 93%|█████████▎| 843/908 [09:43<01:21,  1.26s/it]
 93%|█████████▎| 844/908 [09:43<01:00,  1.06it/s]
 93%|█████████▎| 845/908 [09:44<01:09,  1.11s/it]
 93%|█████████▎| 846/908 [09:47<01:31,  1.48s/it]
 93%|█████████▎| 847/908 [09:47<01:06,  1.09s/it]
 93%|█████████▎| 848/908 [09:47<00:49,  1.22it/s]
 94%|█████████▎| 849/908 [09:49<01:11,  1.22s/it]
 94%|█████████▎| 850/908 [09:51<01:24,  1.46s/it]
 94%|█████████▎| 851/908 [09:51<01:01,  1.08s/it]
 94%|█████████▍| 852/908 [09:52<00:45,  1.23it/s]
 94%|█████████▍| 853/908 [09:54<01:09,  1.26s/it]
 94%|█████████▍| 854/908 [09:57<01:36,  1.78s/it]
 94%|█████████▍| 855/908 [09:57<01:09,  1.31s/it]
 94%|█████████▍| 856/908 [09:57<00:50,  1.03it/s]
 94%|█████████▍| 857/908 [09:58<00:45,  1.11it/s]
 94%|█████████▍| 858/908 [10:00<01:07,  1.36s/it]
 95%|█████████▍| 859/908 [10:01<00:49,  1.01s/it]
+[Batch:   860] training loss = 0.462
+
 95%|█████████▍| 860/908 [10:01<00:36,  1.31it/s]
 95%|█████████▍| 861/908 [10:02<00:40,  1.15it/s]
 95%|█████████▍| 862/908 [10:04<01:01,  1.35s/it]
 95%|█████████▌| 863/908 [10:05<00:44,  1.00it/s]
 95%|█████████▌| 864/908 [10:05<00:33,  1.32it/s]
 95%|█████████▌| 865/908 [10:06<00:33,  1.27it/s]
 95%|█████████▌| 866/908 [10:09<01:06,  1.58s/it]
 95%|█████████▌| 867/908 [10:09<00:47,  1.16s/it]
 96%|█████████▌| 868/908 [10:09<00:34,  1.15it/s]
 96%|█████████▌| 869/908 [10:11<00:37,  1.03it/s]
 96%|█████████▌| 870/908 [10:13<00:57,  1.51s/it]
 96%|█████████▌| 871/908 [10:14<00:41,  1.12s/it]
 96%|█████████▌| 872/908 [10:14<00:30,  1.20it/s]
 96%|█████████▌| 873/908 [10:15<00:34,  1.01it/s]
 96%|█████████▋| 874/908 [10:18<00:51,  1.51s/it]
 96%|█████████▋| 875/908 [10:18<00:36,  1.11s/it]
 96%|█████████▋| 876/908 [10:18<00:26,  1.19it/s]
 97%|█████████▋| 877/908 [10:20<00:35,  1.16s/it]
 97%|█████████▋| 878/908 [10:23<00:49,  1.66s/it]
 97%|█████████▋| 879/908 [10:23<00:35,  1.22s/it]
+[Batch:   880] training loss = 0.424
+
 97%|█████████▋| 880/908 [10:23<00:25,  1.09it/s]
 97%|█████████▋| 881/908 [10:25<00:29,  1.07s/it]
 97%|█████████▋| 882/908 [10:28<00:40,  1.57s/it]
 97%|█████████▋| 883/908 [10:28<00:28,  1.16s/it]
 97%|█████████▋| 884/908 [10:28<00:20,  1.15it/s]
 97%|█████████▋| 885/908 [10:30<00:27,  1.19s/it]
 98%|█████████▊| 886/908 [10:33<00:36,  1.66s/it]
 98%|█████████▊| 887/908 [10:33<00:25,  1.22s/it]
 98%|█████████▊| 888/908 [10:33<00:18,  1.09it/s]
 98%|█████████▊| 889/908 [10:35<00:21,  1.11s/it]
 98%|█████████▊| 890/908 [10:37<00:28,  1.59s/it]
 98%|█████████▊| 891/908 [10:38<00:19,  1.17s/it]
 98%|█████████▊| 892/908 [10:38<00:14,  1.13it/s]
 98%|█████████▊| 893/908 [10:39<00:14,  1.06it/s]
 98%|█████████▊| 894/908 [10:42<00:21,  1.52s/it]
 99%|█████████▊| 895/908 [10:42<00:14,  1.13s/it]
 99%|█████████▊| 896/908 [10:42<00:10,  1.17it/s]
 99%|█████████▉| 897/908 [10:44<00:11,  1.07s/it]
 99%|█████████▉| 898/908 [10:46<00:15,  1.55s/it]
 99%|█████████▉| 899/908 [10:47<00:10,  1.14s/it]
+[Batch:   900] training loss = 0.507
+
 99%|█████████▉| 900/908 [10:47<00:06,  1.16it/s]
 99%|█████████▉| 901/908 [10:48<00:07,  1.01s/it]
 99%|█████████▉| 902/908 [10:51<00:09,  1.54s/it]
 99%|█████████▉| 903/908 [10:51<00:05,  1.14s/it]
100%|█████████▉| 904/908 [10:51<00:03,  1.16it/s]
100%|█████████▉| 905/908 [10:52<00:02,  1.04it/s]
100%|█████████▉| 906/908 [10:55<00:02,  1.37s/it]
100%|█████████▉| 907/908 [10:55<00:01,  1.02s/it]
100%|██████████| 908/908 [10:55<00:00,  1.32it/s]
100%|██████████| 908/908 [10:55<00:00,  1.38it/s]
+
+Epoch   2: validation
+
  0%|          | 0/101 [00:00<?, ?it/s]
  1%|          | 1/101 [00:04<07:30,  4.51s/it]
  3%|▎         | 3/101 [00:04<02:00,  1.23s/it]
  5%|▍         | 5/101 [00:07<01:56,  1.21s/it]
  7%|▋         | 7/101 [00:07<01:08,  1.37it/s]
  8%|▊         | 8/101 [00:07<00:57,  1.62it/s]
  9%|▉         | 9/101 [00:11<02:04,  1.35s/it]
 11%|█         | 11/101 [00:11<01:13,  1.22it/s]
 13%|█▎        | 13/101 [00:15<01:51,  1.26s/it]
 15%|█▍        | 15/101 [00:15<01:13,  1.17it/s]
 16%|█▌        | 16/101 [00:15<01:00,  1.41it/s]
 17%|█▋        | 17/101 [00:19<02:04,  1.49s/it]
 19%|█▉        | 19/101 [00:19<01:17,  1.06it/s]
 21%|██        | 21/101 [00:25<02:11,  1.64s/it]
 22%|██▏       | 22/101 [00:26<01:50,  1.40s/it]
 24%|██▍       | 24/101 [00:26<01:10,  1.09it/s]
 25%|██▍       | 25/101 [00:29<01:53,  1.49s/it]
 26%|██▌       | 26/101 [00:30<01:31,  1.22s/it]
 28%|██▊       | 28/101 [00:30<00:56,  1.30it/s]
 29%|██▊       | 29/101 [00:34<01:42,  1.42s/it]
 31%|███       | 31/101 [00:34<01:03,  1.11it/s]
 33%|███▎      | 33/101 [00:37<01:20,  1.18s/it]
 34%|███▎      | 34/101 [00:37<01:05,  1.02it/s]
 36%|███▌      | 36/101 [00:38<00:42,  1.53it/s]
 37%|███▋      | 37/101 [00:42<01:33,  1.46s/it]
 38%|███▊      | 38/101 [00:42<01:12,  1.15s/it]
 40%|███▉      | 40/101 [00:42<00:44,  1.38it/s]
 41%|████      | 41/101 [00:47<01:37,  1.62s/it]
 42%|████▏     | 42/101 [00:47<01:16,  1.30s/it]
 44%|████▎     | 44/101 [00:48<00:46,  1.24it/s]
 45%|████▍     | 45/101 [00:52<01:32,  1.66s/it]
 46%|████▌     | 46/101 [00:53<01:14,  1.35s/it]
 48%|████▊     | 48/101 [00:53<00:43,  1.20it/s]
 49%|████▊     | 49/101 [00:57<01:18,  1.51s/it]
 50%|████▉     | 50/101 [00:57<01:04,  1.26s/it]
 51%|█████▏    | 52/101 [00:57<00:38,  1.28it/s]
 52%|█████▏    | 53/101 [01:01<01:07,  1.40s/it]
 53%|█████▎    | 54/101 [01:01<00:55,  1.19s/it]
 55%|█████▌    | 56/101 [01:01<00:32,  1.37it/s]
 56%|█████▋    | 57/101 [01:05<00:57,  1.31s/it]
 57%|█████▋    | 58/101 [01:05<00:46,  1.09s/it]
 59%|█████▉    | 60/101 [01:05<00:27,  1.48it/s]
 60%|██████    | 61/101 [01:09<00:53,  1.34s/it]
 61%|██████▏   | 62/101 [01:09<00:44,  1.15s/it]
 63%|██████▎   | 64/101 [01:10<00:26,  1.41it/s]
 64%|██████▍   | 65/101 [01:14<00:54,  1.50s/it]
 65%|██████▌   | 66/101 [01:14<00:43,  1.23s/it]
 67%|██████▋   | 68/101 [01:14<00:25,  1.32it/s]
 68%|██████▊   | 69/101 [01:19<00:50,  1.59s/it]
 69%|██████▉   | 70/101 [01:19<00:39,  1.28s/it]
 71%|███████▏  | 72/101 [01:19<00:22,  1.27it/s]
 72%|███████▏  | 73/101 [01:25<00:51,  1.85s/it]
 73%|███████▎  | 74/101 [01:25<00:40,  1.48s/it]
 75%|███████▌  | 76/101 [01:25<00:22,  1.10it/s]
 76%|███████▌  | 77/101 [01:30<00:44,  1.86s/it]
 77%|███████▋  | 78/101 [01:31<00:33,  1.47s/it]
 79%|███████▉  | 80/101 [01:31<00:18,  1.11it/s]
 80%|████████  | 81/101 [01:34<00:28,  1.43s/it]
 81%|████████  | 82/101 [01:35<00:23,  1.23s/it]
 83%|████████▎ | 84/101 [01:35<00:12,  1.32it/s]
 84%|████████▍ | 85/101 [01:38<00:21,  1.37s/it]
 85%|████████▌ | 86/101 [01:39<00:17,  1.16s/it]
 87%|████████▋ | 88/101 [01:39<00:09,  1.40it/s]
 88%|████████▊ | 89/101 [01:43<00:17,  1.45s/it]
 89%|████████▉ | 90/101 [01:43<00:12,  1.13s/it]
 91%|█████████ | 92/101 [01:43<00:06,  1.44it/s]
 92%|█████████▏| 93/101 [01:47<00:11,  1.50s/it]
 94%|█████████▍| 95/101 [01:48<00:05,  1.06it/s]
 96%|█████████▌| 97/101 [01:52<00:05,  1.44s/it]
 97%|█████████▋| 98/101 [01:52<00:03,  1.19s/it]
 99%|█████████▉| 100/101 [01:53<00:00,  1.28it/s]
100%|██████████| 101/101 [01:55<00:00,  1.15s/it]
100%|██████████| 101/101 [01:55<00:00,  1.15s/it]
+    Epoch  2: training loss = 0.526 / validation loss = 0.500
+    Epoch  2: training acc = 0.752 / validation acc = 0.756
+    Saved model: cls-2.pth.tar
+
+Epoch   3: training
+
  0%|          | 0/908 [00:00<?, ?it/s]
  0%|          | 1/908 [00:05<1:18:19,  5.18s/it]
  0%|          | 2/908 [00:05<33:58,  2.25s/it]  
  0%|          | 3/908 [00:05<19:45,  1.31s/it]
  0%|          | 4/908 [00:05<13:05,  1.15it/s]
  1%|          | 5/908 [00:09<30:21,  2.02s/it]
  1%|          | 6/908 [00:10<20:58,  1.40s/it]
  1%|          | 7/908 [00:10<15:01,  1.00s/it]
  1%|          | 8/908 [00:10<11:06,  1.35it/s]
  1%|          | 9/908 [00:16<37:37,  2.51s/it]
  1%|          | 10/908 [00:16<26:52,  1.80s/it]
  1%|          | 11/908 [00:17<19:28,  1.30s/it]
  1%|▏         | 12/908 [00:17<14:24,  1.04it/s]
  1%|▏         | 13/908 [00:20<23:50,  1.60s/it]
  2%|▏         | 14/908 [00:20<17:28,  1.17s/it]
  2%|▏         | 15/908 [00:20<13:03,  1.14it/s]
  2%|▏         | 16/908 [00:20<09:59,  1.49it/s]
  2%|▏         | 17/908 [00:24<20:30,  1.38s/it]
  2%|▏         | 18/908 [00:24<15:13,  1.03s/it]
  2%|▏         | 19/908 [00:24<11:27,  1.29it/s]
+[Batch:    20] training loss = 0.426
+
  2%|▏         | 20/908 [00:24<08:50,  1.67it/s]
  2%|▏         | 21/908 [00:26<12:50,  1.15it/s]
  2%|▏         | 22/908 [00:26<09:51,  1.50it/s]
  3%|▎         | 23/908 [00:26<08:20,  1.77it/s]
  3%|▎         | 24/908 [00:26<06:42,  2.19it/s]
  3%|▎         | 25/908 [00:30<18:55,  1.29s/it]
  3%|▎         | 26/908 [00:30<14:04,  1.04it/s]
  3%|▎         | 27/908 [00:30<10:45,  1.37it/s]
  3%|▎         | 28/908 [00:30<08:22,  1.75it/s]
  3%|▎         | 29/908 [00:34<21:06,  1.44s/it]
  3%|▎         | 30/908 [00:34<15:35,  1.07s/it]
  3%|▎         | 31/908 [00:34<11:43,  1.25it/s]
  4%|▎         | 32/908 [00:34<09:01,  1.62it/s]
  4%|▎         | 33/908 [00:38<24:21,  1.67s/it]
  4%|▎         | 34/908 [00:38<17:54,  1.23s/it]
  4%|▍         | 35/908 [00:39<13:21,  1.09it/s]
  4%|▍         | 36/908 [00:39<10:09,  1.43it/s]
  4%|▍         | 37/908 [00:42<22:44,  1.57s/it]
  4%|▍         | 38/908 [00:43<16:43,  1.15s/it]
  4%|▍         | 39/908 [00:43<12:30,  1.16it/s]
+[Batch:    40] training loss = 0.478
+
  4%|▍         | 40/908 [00:43<09:35,  1.51it/s]
  5%|▍         | 41/908 [00:47<24:09,  1.67s/it]
  5%|▍         | 42/908 [00:47<17:42,  1.23s/it]
  5%|▍         | 43/908 [00:47<13:12,  1.09it/s]
  5%|▍         | 44/908 [00:48<10:04,  1.43it/s]
  5%|▍         | 45/908 [00:52<27:47,  1.93s/it]
  5%|▌         | 46/908 [00:53<20:15,  1.41s/it]
  5%|▌         | 47/908 [00:53<14:59,  1.04s/it]
  5%|▌         | 48/908 [00:53<11:18,  1.27it/s]
  5%|▌         | 49/908 [00:57<24:49,  1.73s/it]
  6%|▌         | 50/908 [00:57<18:11,  1.27s/it]
  6%|▌         | 51/908 [00:57<13:31,  1.06it/s]
  6%|▌         | 52/908 [00:58<10:15,  1.39it/s]
  6%|▌         | 53/908 [01:02<25:04,  1.76s/it]
  6%|▌         | 54/908 [01:02<18:21,  1.29s/it]
  6%|▌         | 55/908 [01:02<13:41,  1.04it/s]
  6%|▌         | 56/908 [01:02<10:23,  1.37it/s]
  6%|▋         | 57/908 [01:06<22:15,  1.57s/it]
  6%|▋         | 58/908 [01:06<16:23,  1.16s/it]
  6%|▋         | 59/908 [01:06<12:43,  1.11it/s]
+[Batch:    60] training loss = 0.462
+
  7%|▋         | 60/908 [01:07<09:43,  1.45it/s]
  7%|▋         | 61/908 [01:10<21:51,  1.55s/it]
  7%|▋         | 62/908 [01:10<16:07,  1.14s/it]
  7%|▋         | 63/908 [01:10<12:04,  1.17it/s]
  7%|▋         | 64/908 [01:11<09:16,  1.52it/s]
  7%|▋         | 65/908 [01:16<27:41,  1.97s/it]
  7%|▋         | 66/908 [01:16<20:10,  1.44s/it]
  7%|▋         | 67/908 [01:16<14:55,  1.07s/it]
  7%|▋         | 68/908 [01:16<11:16,  1.24it/s]
  8%|▊         | 69/908 [01:22<30:50,  2.21s/it]
  8%|▊         | 70/908 [01:22<22:23,  1.60s/it]
  8%|▊         | 71/908 [01:22<16:29,  1.18s/it]
  8%|▊         | 72/908 [01:22<12:18,  1.13it/s]
  8%|▊         | 73/908 [01:26<24:35,  1.77s/it]
  8%|▊         | 74/908 [01:26<17:58,  1.29s/it]
  8%|▊         | 75/908 [01:27<13:21,  1.04it/s]
  8%|▊         | 76/908 [01:27<10:08,  1.37it/s]
  8%|▊         | 77/908 [01:31<23:57,  1.73s/it]
  9%|▊         | 78/908 [01:31<17:33,  1.27s/it]
  9%|▊         | 79/908 [01:31<13:06,  1.05it/s]
+[Batch:    80] training loss = 0.589
+
  9%|▉         | 80/908 [01:31<10:01,  1.38it/s]
  9%|▉         | 81/908 [01:35<22:24,  1.63s/it]
  9%|▉         | 82/908 [01:35<16:26,  1.19s/it]
  9%|▉         | 83/908 [01:36<12:18,  1.12it/s]
  9%|▉         | 84/908 [01:36<09:23,  1.46it/s]
  9%|▉         | 85/908 [01:39<21:41,  1.58s/it]
  9%|▉         | 86/908 [01:40<15:57,  1.17s/it]
 10%|▉         | 87/908 [01:40<11:56,  1.15it/s]
 10%|▉         | 88/908 [01:40<09:06,  1.50it/s]
 10%|▉         | 89/908 [01:44<23:29,  1.72s/it]
 10%|▉         | 90/908 [01:44<17:12,  1.26s/it]
 10%|█         | 91/908 [01:45<12:48,  1.06it/s]
 10%|█         | 92/908 [01:45<09:43,  1.40it/s]
 10%|█         | 93/908 [01:47<14:43,  1.08s/it]
 10%|█         | 94/908 [01:47<11:04,  1.22it/s]
 10%|█         | 95/908 [01:47<08:32,  1.59it/s]
 11%|█         | 96/908 [01:47<06:47,  1.99it/s]
 11%|█         | 97/908 [01:51<18:50,  1.39s/it]
 11%|█         | 98/908 [01:51<13:56,  1.03s/it]
 11%|█         | 99/908 [01:51<10:33,  1.28it/s]
+[Batch:   100] training loss = 0.603
+
 11%|█         | 100/908 [01:51<08:08,  1.65it/s]
 11%|█         | 101/908 [01:55<18:58,  1.41s/it]
 11%|█         | 102/908 [01:55<14:04,  1.05s/it]
 11%|█▏        | 103/908 [01:55<10:37,  1.26it/s]
 11%|█▏        | 104/908 [01:55<08:12,  1.63it/s]
 12%|█▏        | 105/908 [01:58<15:29,  1.16s/it]
 12%|█▏        | 106/908 [01:58<11:35,  1.15it/s]
 12%|█▏        | 107/908 [01:58<08:54,  1.50it/s]
 12%|█▏        | 108/908 [01:58<07:01,  1.90it/s]
 12%|█▏        | 109/908 [02:02<20:45,  1.56s/it]
 12%|█▏        | 110/908 [02:02<15:16,  1.15s/it]
 12%|█▏        | 111/908 [02:03<11:49,  1.12it/s]
 12%|█▏        | 112/908 [02:03<09:01,  1.47it/s]
 12%|█▏        | 113/908 [02:07<24:04,  1.82s/it]
 13%|█▎        | 114/908 [02:07<17:36,  1.33s/it]
 13%|█▎        | 115/908 [02:08<13:06,  1.01it/s]
 13%|█▎        | 116/908 [02:08<09:58,  1.32it/s]
 13%|█▎        | 117/908 [02:17<44:07,  3.35s/it]
 13%|█▎        | 118/908 [02:17<31:34,  2.40s/it]
 13%|█▎        | 119/908 [02:18<22:49,  1.74s/it]
+[Batch:   120] training loss = 0.474
+
 13%|█▎        | 120/908 [02:18<16:41,  1.27s/it]
 13%|█▎        | 121/908 [02:22<29:44,  2.27s/it]
 13%|█▎        | 122/908 [02:23<21:32,  1.64s/it]
 14%|█▎        | 123/908 [02:23<15:48,  1.21s/it]
 14%|█▎        | 124/908 [02:23<11:48,  1.11it/s]
 14%|█▍        | 125/908 [02:27<23:23,  1.79s/it]
 14%|█▍        | 126/908 [02:27<17:07,  1.31s/it]
 14%|█▍        | 127/908 [02:27<12:44,  1.02it/s]
 14%|█▍        | 128/908 [02:27<09:38,  1.35it/s]
 14%|█▍        | 129/908 [02:31<20:38,  1.59s/it]
 14%|█▍        | 130/908 [02:31<15:10,  1.17s/it]
 14%|█▍        | 131/908 [02:31<11:24,  1.14it/s]
 15%|█▍        | 132/908 [02:32<08:42,  1.48it/s]
 15%|█▍        | 133/908 [02:36<21:25,  1.66s/it]
 15%|█▍        | 134/908 [02:36<15:42,  1.22s/it]
 15%|█▍        | 135/908 [02:36<11:43,  1.10it/s]
 15%|█▍        | 136/908 [02:36<08:55,  1.44it/s]
 15%|█▌        | 137/908 [02:40<21:21,  1.66s/it]
 15%|█▌        | 138/908 [02:40<15:40,  1.22s/it]
 15%|█▌        | 139/908 [02:40<11:40,  1.10it/s]
+[Batch:   140] training loss = 0.376
+
 15%|█▌        | 140/908 [02:41<08:54,  1.44it/s]
 16%|█▌        | 141/908 [02:44<17:30,  1.37s/it]
 16%|█▌        | 142/908 [02:44<12:56,  1.01s/it]
 16%|█▌        | 143/908 [02:44<11:32,  1.10it/s]
 16%|█▌        | 144/908 [02:45<08:49,  1.44it/s]
 16%|█▌        | 145/908 [02:49<22:11,  1.75s/it]
 16%|█▌        | 146/908 [02:49<16:14,  1.28s/it]
 16%|█▌        | 147/908 [02:50<13:25,  1.06s/it]
 16%|█▋        | 148/908 [02:50<10:05,  1.25it/s]
 16%|█▋        | 149/908 [02:54<23:43,  1.88s/it]
 17%|█▋        | 150/908 [02:54<17:21,  1.37s/it]
 17%|█▋        | 151/908 [02:55<12:50,  1.02s/it]
 17%|█▋        | 152/908 [02:55<09:40,  1.30it/s]
 17%|█▋        | 153/908 [02:59<21:58,  1.75s/it]
 17%|█▋        | 154/908 [02:59<16:05,  1.28s/it]
 17%|█▋        | 155/908 [02:59<12:12,  1.03it/s]
 17%|█▋        | 156/908 [02:59<09:15,  1.35it/s]
 17%|█▋        | 157/908 [03:04<22:56,  1.83s/it]
 17%|█▋        | 158/908 [03:04<16:44,  1.34s/it]
 18%|█▊        | 159/908 [03:04<12:26,  1.00it/s]
+[Batch:   160] training loss = 0.493
+
 18%|█▊        | 160/908 [03:04<09:24,  1.33it/s]
 18%|█▊        | 161/908 [03:07<18:24,  1.48s/it]
 18%|█▊        | 162/908 [03:08<13:35,  1.09s/it]
 18%|█▊        | 163/908 [03:08<10:16,  1.21it/s]
 18%|█▊        | 164/908 [03:08<07:53,  1.57it/s]
 18%|█▊        | 165/908 [03:12<19:57,  1.61s/it]
 18%|█▊        | 166/908 [03:12<14:40,  1.19s/it]
 18%|█▊        | 167/908 [03:12<10:59,  1.12it/s]
 19%|█▊        | 168/908 [03:13<08:23,  1.47it/s]
 19%|█▊        | 169/908 [03:17<23:08,  1.88s/it]
 19%|█▊        | 170/908 [03:17<16:53,  1.37s/it]
 19%|█▉        | 171/908 [03:18<14:09,  1.15s/it]
 19%|█▉        | 172/908 [03:18<10:36,  1.16it/s]
 19%|█▉        | 173/908 [03:23<24:36,  2.01s/it]
 19%|█▉        | 174/908 [03:23<17:54,  1.46s/it]
 19%|█▉        | 175/908 [03:23<13:17,  1.09s/it]
 19%|█▉        | 176/908 [03:24<10:00,  1.22it/s]
 19%|█▉        | 177/908 [03:27<21:11,  1.74s/it]
 20%|█▉        | 178/908 [03:28<15:31,  1.28s/it]
 20%|█▉        | 179/908 [03:28<11:32,  1.05it/s]
+[Batch:   180] training loss = 0.596
+
 20%|█▉        | 180/908 [03:28<08:45,  1.39it/s]
 20%|█▉        | 181/908 [03:31<18:29,  1.53s/it]
 20%|██        | 182/908 [03:32<13:37,  1.13s/it]
 20%|██        | 183/908 [03:32<10:13,  1.18it/s]
 20%|██        | 184/908 [03:32<07:50,  1.54it/s]
 20%|██        | 185/908 [03:35<16:05,  1.34s/it]
 20%|██        | 186/908 [03:35<11:56,  1.01it/s]
 21%|██        | 187/908 [03:35<09:00,  1.33it/s]
 21%|██        | 188/908 [03:35<06:57,  1.72it/s]
 21%|██        | 189/908 [03:38<15:04,  1.26s/it]
 21%|██        | 190/908 [03:38<11:12,  1.07it/s]
 21%|██        | 191/908 [03:39<08:30,  1.40it/s]
 21%|██        | 192/908 [03:39<06:38,  1.79it/s]
 21%|██▏       | 193/908 [03:42<15:53,  1.33s/it]
 21%|██▏       | 194/908 [03:42<11:52,  1.00it/s]
 21%|██▏       | 195/908 [03:43<09:38,  1.23it/s]
 22%|██▏       | 196/908 [03:43<07:27,  1.59it/s]
 22%|██▏       | 197/908 [03:47<19:05,  1.61s/it]
 22%|██▏       | 198/908 [03:47<14:04,  1.19s/it]
 22%|██▏       | 199/908 [03:48<12:29,  1.06s/it]
+[Batch:   200] training loss = 0.481
+
 22%|██▏       | 200/908 [03:48<09:28,  1.25it/s]
 22%|██▏       | 201/908 [03:51<17:11,  1.46s/it]
 22%|██▏       | 202/908 [03:51<12:45,  1.08s/it]
 22%|██▏       | 203/908 [03:51<09:39,  1.22it/s]
 22%|██▏       | 204/908 [03:52<07:30,  1.56it/s]
 23%|██▎       | 205/908 [03:55<17:30,  1.49s/it]
 23%|██▎       | 206/908 [03:55<12:57,  1.11s/it]
 23%|██▎       | 207/908 [03:55<09:47,  1.19it/s]
 23%|██▎       | 208/908 [03:56<07:32,  1.55it/s]
 23%|██▎       | 209/908 [03:59<17:10,  1.47s/it]
 23%|██▎       | 210/908 [03:59<12:43,  1.09s/it]
 23%|██▎       | 211/908 [03:59<09:36,  1.21it/s]
 23%|██▎       | 212/908 [04:00<07:26,  1.56it/s]
 23%|██▎       | 213/908 [04:03<16:35,  1.43s/it]
 24%|██▎       | 214/908 [04:03<12:17,  1.06s/it]
 24%|██▎       | 215/908 [04:04<11:29,  1.01it/s]
 24%|██▍       | 216/908 [04:04<08:44,  1.32it/s]
 24%|██▍       | 217/908 [04:07<15:08,  1.31s/it]
 24%|██▍       | 218/908 [04:07<11:18,  1.02it/s]
 24%|██▍       | 219/908 [04:07<09:33,  1.20it/s]
+[Batch:   220] training loss = 0.428
+
 24%|██▍       | 220/908 [04:08<07:24,  1.55it/s]
 24%|██▍       | 221/908 [04:10<14:43,  1.29s/it]
 24%|██▍       | 222/908 [04:11<10:59,  1.04it/s]
 25%|██▍       | 223/908 [04:11<10:05,  1.13it/s]
 25%|██▍       | 224/908 [04:12<07:43,  1.48it/s]
 25%|██▍       | 225/908 [04:14<15:08,  1.33s/it]
 25%|██▍       | 226/908 [04:15<11:15,  1.01it/s]
 25%|██▌       | 227/908 [04:16<11:25,  1.01s/it]
 25%|██▌       | 228/908 [04:16<08:39,  1.31it/s]
 25%|██▌       | 229/908 [04:20<21:19,  1.88s/it]
 25%|██▌       | 230/908 [04:21<15:35,  1.38s/it]
 25%|██▌       | 231/908 [04:23<19:31,  1.73s/it]
 26%|██▌       | 232/908 [04:23<14:20,  1.27s/it]
 26%|██▌       | 233/908 [04:26<18:43,  1.66s/it]
 26%|██▌       | 234/908 [04:26<13:45,  1.23s/it]
 26%|██▌       | 235/908 [04:27<12:31,  1.12s/it]
 26%|██▌       | 236/908 [04:27<09:25,  1.19it/s]
 26%|██▌       | 237/908 [04:30<16:04,  1.44s/it]
 26%|██▌       | 238/908 [04:30<11:54,  1.07s/it]
 26%|██▋       | 239/908 [04:32<12:56,  1.16s/it]
+[Batch:   240] training loss = 0.493
+
 26%|██▋       | 240/908 [04:32<09:44,  1.14it/s]
 27%|██▋       | 241/908 [04:34<15:04,  1.36s/it]
 27%|██▋       | 242/908 [04:34<11:11,  1.01s/it]
 27%|██▋       | 243/908 [04:36<11:56,  1.08s/it]
 27%|██▋       | 244/908 [04:36<09:02,  1.22it/s]
 27%|██▋       | 245/908 [04:39<15:16,  1.38s/it]
 27%|██▋       | 246/908 [04:39<11:20,  1.03s/it]
 27%|██▋       | 247/908 [04:40<12:53,  1.17s/it]
 27%|██▋       | 248/908 [04:40<09:40,  1.14it/s]
 27%|██▋       | 249/908 [04:44<17:42,  1.61s/it]
 28%|██▊       | 250/908 [04:44<13:03,  1.19s/it]
 28%|██▊       | 251/908 [04:46<14:32,  1.33s/it]
 28%|██▊       | 252/908 [04:46<10:50,  1.01it/s]
 28%|██▊       | 253/908 [04:49<17:49,  1.63s/it]
 28%|██▊       | 254/908 [04:49<13:07,  1.20s/it]
 28%|██▊       | 255/908 [04:51<14:30,  1.33s/it]
 28%|██▊       | 256/908 [04:51<10:46,  1.01it/s]
 28%|██▊       | 257/908 [04:53<12:45,  1.18s/it]
 28%|██▊       | 258/908 [04:53<09:34,  1.13it/s]
 29%|██▊       | 259/908 [04:55<12:53,  1.19s/it]
+[Batch:   260] training loss = 0.532
+
 29%|██▊       | 260/908 [04:55<09:38,  1.12it/s]
 29%|██▊       | 261/908 [04:57<14:40,  1.36s/it]
 29%|██▉       | 262/908 [04:58<10:54,  1.01s/it]
 29%|██▉       | 263/908 [05:00<15:45,  1.47s/it]
 29%|██▉       | 264/908 [05:00<11:38,  1.08s/it]
 29%|██▉       | 265/908 [05:02<13:24,  1.25s/it]
 29%|██▉       | 266/908 [05:02<10:00,  1.07it/s]
 29%|██▉       | 267/908 [05:04<12:45,  1.19s/it]
 30%|██▉       | 268/908 [05:04<09:33,  1.12it/s]
 30%|██▉       | 269/908 [05:07<14:14,  1.34s/it]
 30%|██▉       | 270/908 [05:07<10:37,  1.00it/s]
 30%|██▉       | 271/908 [05:08<12:29,  1.18s/it]
 30%|██▉       | 272/908 [05:09<09:21,  1.13it/s]
 30%|███       | 273/908 [05:11<13:16,  1.25s/it]
 30%|███       | 274/908 [05:11<09:54,  1.07it/s]
 30%|███       | 275/908 [05:13<13:11,  1.25s/it]
 30%|███       | 276/908 [05:13<09:53,  1.07it/s]
 31%|███       | 277/908 [05:15<14:06,  1.34s/it]
 31%|███       | 278/908 [05:16<10:31,  1.00s/it]
 31%|███       | 279/908 [05:18<13:35,  1.30s/it]
+[Batch:   280] training loss = 0.466
+
 31%|███       | 280/908 [05:18<10:30,  1.00s/it]
 31%|███       | 281/908 [05:23<22:44,  2.18s/it]
 31%|███       | 282/908 [05:23<16:31,  1.58s/it]
 31%|███       | 283/908 [05:25<18:04,  1.74s/it]
 31%|███▏      | 284/908 [05:25<13:15,  1.27s/it]
 31%|███▏      | 285/908 [05:28<17:49,  1.72s/it]
 31%|███▏      | 286/908 [05:28<13:04,  1.26s/it]
 32%|███▏      | 287/908 [05:30<14:02,  1.36s/it]
 32%|███▏      | 288/908 [05:30<10:25,  1.01s/it]
 32%|███▏      | 289/908 [05:33<16:20,  1.58s/it]
 32%|███▏      | 290/908 [05:33<12:01,  1.17s/it]
 32%|███▏      | 291/908 [05:35<13:43,  1.34s/it]
 32%|███▏      | 292/908 [05:35<10:12,  1.01it/s]
 32%|███▏      | 293/908 [05:38<16:10,  1.58s/it]
 32%|███▏      | 294/908 [05:38<11:54,  1.16s/it]
 32%|███▏      | 295/908 [05:40<15:25,  1.51s/it]
 33%|███▎      | 296/908 [05:41<11:24,  1.12s/it]
 33%|███▎      | 297/908 [05:42<12:44,  1.25s/it]
 33%|███▎      | 298/908 [05:42<09:30,  1.07it/s]
 33%|███▎      | 299/908 [05:44<12:10,  1.20s/it]
+[Batch:   300] training loss = 0.505
+
 33%|███▎      | 300/908 [05:44<09:08,  1.11it/s]
 33%|███▎      | 301/908 [05:48<16:14,  1.61s/it]
 33%|███▎      | 302/908 [05:48<11:57,  1.18s/it]
 33%|███▎      | 303/908 [05:50<15:32,  1.54s/it]
 33%|███▎      | 304/908 [05:50<11:27,  1.14s/it]
 34%|███▎      | 305/908 [05:53<16:59,  1.69s/it]
 34%|███▎      | 306/908 [05:54<12:27,  1.24s/it]
 34%|███▍      | 307/908 [05:56<14:42,  1.47s/it]
 34%|███▍      | 308/908 [05:56<10:53,  1.09s/it]
 34%|███▍      | 309/908 [05:58<13:07,  1.32s/it]
 34%|███▍      | 310/908 [05:58<09:45,  1.02it/s]
 34%|███▍      | 311/908 [06:00<13:10,  1.32s/it]
 34%|███▍      | 312/908 [06:00<09:46,  1.02it/s]
 34%|███▍      | 313/908 [06:02<13:04,  1.32s/it]
 35%|███▍      | 314/908 [06:02<09:42,  1.02it/s]
 35%|███▍      | 315/908 [06:04<12:19,  1.25s/it]
 35%|███▍      | 316/908 [06:05<09:10,  1.08it/s]
 35%|███▍      | 317/908 [06:05<08:54,  1.11it/s]
 35%|███▌      | 318/908 [06:06<06:47,  1.45it/s]
 35%|███▌      | 319/908 [06:07<09:31,  1.03it/s]
+[Batch:   320] training loss = 0.426
+
 35%|███▌      | 320/908 [06:07<07:14,  1.35it/s]
 35%|███▌      | 321/908 [06:10<11:26,  1.17s/it]
 35%|███▌      | 322/908 [06:10<08:33,  1.14it/s]
 36%|███▌      | 323/908 [06:12<11:06,  1.14s/it]
 36%|███▌      | 324/908 [06:12<08:19,  1.17it/s]
 36%|███▌      | 325/908 [06:14<12:43,  1.31s/it]
 36%|███▌      | 326/908 [06:14<09:26,  1.03it/s]
 36%|███▌      | 327/908 [06:16<10:11,  1.05s/it]
 36%|███▌      | 328/908 [06:16<07:40,  1.26it/s]
 36%|███▌      | 329/908 [06:19<13:37,  1.41s/it]
 36%|███▋      | 330/908 [06:19<10:03,  1.04s/it]
 36%|███▋      | 331/908 [06:20<11:50,  1.23s/it]
 37%|███▋      | 332/908 [06:21<08:50,  1.09it/s]
 37%|███▋      | 333/908 [06:25<20:09,  2.10s/it]
 37%|███▋      | 334/908 [06:26<14:38,  1.53s/it]
 37%|███▋      | 335/908 [06:27<13:46,  1.44s/it]
 37%|███▋      | 336/908 [06:27<10:10,  1.07s/it]
 37%|███▋      | 337/908 [06:30<16:36,  1.75s/it]
 37%|███▋      | 338/908 [06:31<12:11,  1.28s/it]
 37%|███▋      | 339/908 [06:31<10:33,  1.11s/it]
+[Batch:   340] training loss = 0.465
+
 37%|███▋      | 340/908 [06:32<07:56,  1.19it/s]
 38%|███▊      | 341/908 [06:35<14:19,  1.52s/it]
 38%|███▊      | 342/908 [06:35<10:32,  1.12s/it]
 38%|███▊      | 343/908 [06:36<09:43,  1.03s/it]
 38%|███▊      | 344/908 [06:36<07:20,  1.28it/s]
 38%|███▊      | 345/908 [06:39<13:53,  1.48s/it]
 38%|███▊      | 346/908 [06:39<10:15,  1.09s/it]
 38%|███▊      | 347/908 [06:40<09:41,  1.04s/it]
 38%|███▊      | 348/908 [06:40<07:18,  1.28it/s]
 38%|███▊      | 349/908 [06:43<12:14,  1.31s/it]
 39%|███▊      | 350/908 [06:43<09:07,  1.02it/s]
 39%|███▊      | 351/908 [06:44<09:45,  1.05s/it]
 39%|███▉      | 352/908 [06:44<07:21,  1.26it/s]
 39%|███▉      | 353/908 [06:47<13:21,  1.44s/it]
 39%|███▉      | 354/908 [06:48<09:51,  1.07s/it]
 39%|███▉      | 355/908 [06:49<10:18,  1.12s/it]
 39%|███▉      | 356/908 [06:49<07:43,  1.19it/s]
 39%|███▉      | 357/908 [06:52<14:36,  1.59s/it]
 39%|███▉      | 358/908 [06:53<10:44,  1.17s/it]
 40%|███▉      | 359/908 [06:54<11:25,  1.25s/it]
+[Batch:   360] training loss = 0.465
+
 40%|███▉      | 360/908 [06:54<08:30,  1.07it/s]
 40%|███▉      | 361/908 [06:57<14:32,  1.60s/it]
 40%|███▉      | 362/908 [06:57<10:41,  1.17s/it]
 40%|███▉      | 363/908 [06:58<09:56,  1.10s/it]
 40%|████      | 364/908 [06:59<07:27,  1.21it/s]
 40%|████      | 365/908 [07:02<13:41,  1.51s/it]
 40%|████      | 366/908 [07:02<10:04,  1.12s/it]
 40%|████      | 367/908 [07:03<09:36,  1.07s/it]
 41%|████      | 368/908 [07:03<07:14,  1.24it/s]
 41%|████      | 369/908 [07:06<12:36,  1.40s/it]
 41%|████      | 370/908 [07:06<09:18,  1.04s/it]
 41%|████      | 371/908 [07:07<09:05,  1.02s/it]
 41%|████      | 372/908 [07:07<06:52,  1.30it/s]
 41%|████      | 373/908 [07:10<12:44,  1.43s/it]
 41%|████      | 374/908 [07:10<09:25,  1.06s/it]
 41%|████▏     | 375/908 [07:11<09:21,  1.05s/it]
 41%|████▏     | 376/908 [07:12<07:03,  1.26it/s]
 42%|████▏     | 377/908 [07:14<11:14,  1.27s/it]
 42%|████▏     | 378/908 [07:14<08:22,  1.06it/s]
 42%|████▏     | 379/908 [07:14<06:20,  1.39it/s]
+[Batch:   380] training loss = 0.463
+
 42%|████▏     | 380/908 [07:15<04:56,  1.78it/s]
 42%|████▏     | 381/908 [07:18<11:44,  1.34s/it]
 42%|████▏     | 382/908 [07:18<08:41,  1.01it/s]
 42%|████▏     | 383/908 [07:18<06:36,  1.33it/s]
 42%|████▏     | 384/908 [07:18<05:08,  1.70it/s]
 42%|████▏     | 385/908 [07:22<14:01,  1.61s/it]
 43%|████▎     | 386/908 [07:22<10:18,  1.18s/it]
 43%|████▎     | 387/908 [07:23<07:41,  1.13it/s]
 43%|████▎     | 388/908 [07:23<05:54,  1.47it/s]
 43%|████▎     | 389/908 [07:28<17:01,  1.97s/it]
 43%|████▎     | 390/908 [07:28<12:24,  1.44s/it]
 43%|████▎     | 391/908 [07:29<10:30,  1.22s/it]
 43%|████▎     | 392/908 [07:29<07:49,  1.10it/s]
 43%|████▎     | 393/908 [07:34<18:00,  2.10s/it]
 43%|████▎     | 394/908 [07:34<13:04,  1.53s/it]
 44%|████▎     | 395/908 [07:34<10:01,  1.17s/it]
 44%|████▎     | 396/908 [07:35<07:28,  1.14it/s]
 44%|████▎     | 397/908 [07:38<15:04,  1.77s/it]
 44%|████▍     | 398/908 [07:39<11:01,  1.30s/it]
 44%|████▍     | 399/908 [07:39<09:21,  1.10s/it]
+[Batch:   400] training loss = 0.431
+
 44%|████▍     | 400/908 [07:39<07:01,  1.20it/s]
 44%|████▍     | 401/908 [07:43<13:52,  1.64s/it]
 44%|████▍     | 402/908 [07:43<10:11,  1.21s/it]
 44%|████▍     | 403/908 [07:44<10:06,  1.20s/it]
 44%|████▍     | 404/908 [07:45<07:32,  1.11it/s]
 45%|████▍     | 405/908 [07:48<14:58,  1.79s/it]
 45%|████▍     | 406/908 [07:49<10:56,  1.31s/it]
 45%|████▍     | 407/908 [07:49<09:31,  1.14s/it]
 45%|████▍     | 408/908 [07:50<07:07,  1.17it/s]
 45%|████▌     | 409/908 [07:53<14:54,  1.79s/it]
 45%|████▌     | 410/908 [07:54<10:53,  1.31s/it]
 45%|████▌     | 411/908 [07:55<10:16,  1.24s/it]
 45%|████▌     | 412/908 [07:55<07:39,  1.08it/s]
 45%|████▌     | 413/908 [07:58<14:06,  1.71s/it]
 46%|████▌     | 414/908 [07:59<10:19,  1.25s/it]
 46%|████▌     | 415/908 [07:59<08:48,  1.07s/it]
 46%|████▌     | 416/908 [08:00<06:36,  1.24it/s]
 46%|████▌     | 417/908 [08:02<11:13,  1.37s/it]
 46%|████▌     | 418/908 [08:02<08:19,  1.02s/it]
 46%|████▌     | 419/908 [08:03<07:43,  1.05it/s]
+[Batch:   420] training loss = 0.424
+
 46%|████▋     | 420/908 [08:03<05:52,  1.39it/s]
 46%|████▋     | 421/908 [08:07<12:26,  1.53s/it]
 46%|████▋     | 422/908 [08:07<09:11,  1.13s/it]
 47%|████▋     | 423/908 [08:08<08:09,  1.01s/it]
 47%|████▋     | 424/908 [08:08<06:09,  1.31it/s]
 47%|████▋     | 425/908 [08:11<12:15,  1.52s/it]
 47%|████▋     | 426/908 [08:11<09:02,  1.12s/it]
 47%|████▋     | 427/908 [08:12<08:47,  1.10s/it]
 47%|████▋     | 428/908 [08:13<06:36,  1.21it/s]
 47%|████▋     | 429/908 [08:16<12:05,  1.52s/it]
 47%|████▋     | 430/908 [08:16<08:54,  1.12s/it]
 47%|████▋     | 431/908 [08:16<07:31,  1.06it/s]
 48%|████▊     | 432/908 [08:17<05:42,  1.39it/s]
 48%|████▊     | 433/908 [08:20<12:37,  1.59s/it]
 48%|████▊     | 434/908 [08:20<09:15,  1.17s/it]
 48%|████▊     | 435/908 [08:21<08:00,  1.02s/it]
 48%|████▊     | 436/908 [08:21<06:02,  1.30it/s]
 48%|████▊     | 437/908 [08:25<11:49,  1.51s/it]
 48%|████▊     | 438/908 [08:25<08:42,  1.11s/it]
 48%|████▊     | 439/908 [08:26<09:04,  1.16s/it]
+[Batch:   440] training loss = 0.285
+
 48%|████▊     | 440/908 [08:26<06:46,  1.15it/s]
 49%|████▊     | 441/908 [08:30<13:12,  1.70s/it]
 49%|████▊     | 442/908 [08:30<09:39,  1.24s/it]
 49%|████▉     | 443/908 [08:31<08:09,  1.05s/it]
 49%|████▉     | 444/908 [08:31<06:08,  1.26it/s]
 49%|████▉     | 445/908 [08:34<11:52,  1.54s/it]
 49%|████▉     | 446/908 [08:34<08:44,  1.13s/it]
 49%|████▉     | 447/908 [08:36<09:22,  1.22s/it]
 49%|████▉     | 448/908 [08:36<06:59,  1.10it/s]
 49%|████▉     | 449/908 [08:40<14:25,  1.89s/it]
 50%|████▉     | 450/908 [08:40<10:30,  1.38s/it]
 50%|████▉     | 451/908 [08:41<08:45,  1.15s/it]
 50%|████▉     | 452/908 [08:41<06:33,  1.16it/s]
 50%|████▉     | 453/908 [08:45<13:48,  1.82s/it]
 50%|█████     | 454/908 [08:45<10:04,  1.33s/it]
 50%|█████     | 455/908 [08:46<07:29,  1.01it/s]
 50%|█████     | 456/908 [08:46<05:39,  1.33it/s]
 50%|█████     | 457/908 [08:49<11:18,  1.50s/it]
 50%|█████     | 458/908 [08:49<08:19,  1.11s/it]
 51%|█████     | 459/908 [08:49<06:16,  1.19it/s]
+[Batch:   460] training loss = 0.421
+
 51%|█████     | 460/908 [08:50<04:48,  1.55it/s]
 51%|█████     | 461/908 [08:54<12:23,  1.66s/it]
 51%|█████     | 462/908 [08:54<09:04,  1.22s/it]
 51%|█████     | 463/908 [08:54<06:45,  1.10it/s]
 51%|█████     | 464/908 [08:54<05:08,  1.44it/s]
 51%|█████     | 465/908 [08:57<10:26,  1.41s/it]
 51%|█████▏    | 466/908 [08:57<07:42,  1.05s/it]
 51%|█████▏    | 467/908 [08:58<06:17,  1.17it/s]
 52%|█████▏    | 468/908 [08:58<04:49,  1.52it/s]
 52%|█████▏    | 469/908 [09:01<10:37,  1.45s/it]
 52%|█████▏    | 470/908 [09:02<07:51,  1.08s/it]
 52%|█████▏    | 471/908 [09:02<05:53,  1.23it/s]
 52%|█████▏    | 472/908 [09:02<04:31,  1.60it/s]
 52%|█████▏    | 473/908 [09:06<12:30,  1.73s/it]
 52%|█████▏    | 474/908 [09:06<09:10,  1.27s/it]
 52%|█████▏    | 475/908 [09:07<06:49,  1.06it/s]
 52%|█████▏    | 476/908 [09:07<05:10,  1.39it/s]
 53%|█████▎    | 477/908 [09:10<11:25,  1.59s/it]
 53%|█████▎    | 478/908 [09:11<08:22,  1.17s/it]
 53%|█████▎    | 479/908 [09:11<06:16,  1.14it/s]
+[Batch:   480] training loss = 0.403
+
 53%|█████▎    | 480/908 [09:11<04:46,  1.49it/s]
 53%|█████▎    | 481/908 [09:14<09:32,  1.34s/it]
 53%|█████▎    | 482/908 [09:14<07:03,  1.00it/s]
 53%|█████▎    | 483/908 [09:14<05:22,  1.32it/s]
 53%|█████▎    | 484/908 [09:15<04:09,  1.70it/s]
 53%|█████▎    | 485/908 [09:18<10:31,  1.49s/it]
 54%|█████▎    | 486/908 [09:18<07:46,  1.11s/it]
 54%|█████▎    | 487/908 [09:19<05:52,  1.19it/s]
 54%|█████▎    | 488/908 [09:19<04:29,  1.56it/s]
 54%|█████▍    | 489/908 [09:23<11:53,  1.70s/it]
 54%|█████▍    | 490/908 [09:23<08:43,  1.25s/it]
 54%|█████▍    | 491/908 [09:24<07:26,  1.07s/it]
 54%|█████▍    | 492/908 [09:24<05:35,  1.24it/s]
 54%|█████▍    | 493/908 [09:29<15:10,  2.19s/it]
 54%|█████▍    | 494/908 [09:30<10:59,  1.59s/it]
 55%|█████▍    | 495/908 [09:30<08:36,  1.25s/it]
 55%|█████▍    | 496/908 [09:30<06:24,  1.07it/s]
 55%|█████▍    | 497/908 [09:35<13:24,  1.96s/it]
 55%|█████▍    | 498/908 [09:35<09:45,  1.43s/it]
 55%|█████▍    | 499/908 [09:35<07:55,  1.16s/it]
+[Batch:   500] training loss = 0.487
+
 55%|█████▌    | 500/908 [09:35<05:55,  1.15it/s]
 55%|█████▌    | 501/908 [09:39<12:09,  1.79s/it]
 55%|█████▌    | 502/908 [09:40<08:53,  1.31s/it]
 55%|█████▌    | 503/908 [09:40<07:22,  1.09s/it]
 56%|█████▌    | 504/908 [09:40<05:33,  1.21it/s]
 56%|█████▌    | 505/908 [09:44<11:34,  1.72s/it]
 56%|█████▌    | 506/908 [09:44<08:27,  1.26s/it]
 56%|█████▌    | 507/908 [09:45<07:37,  1.14s/it]
 56%|█████▌    | 508/908 [09:45<05:42,  1.17it/s]
 56%|█████▌    | 509/908 [09:48<09:53,  1.49s/it]
 56%|█████▌    | 510/908 [09:49<07:17,  1.10s/it]
 56%|█████▋    | 511/908 [09:49<06:17,  1.05it/s]
 56%|█████▋    | 512/908 [09:49<04:47,  1.38it/s]
 56%|█████▋    | 513/908 [09:53<09:58,  1.52s/it]
 57%|█████▋    | 514/908 [09:53<07:20,  1.12s/it]
 57%|█████▋    | 515/908 [09:54<06:36,  1.01s/it]
 57%|█████▋    | 516/908 [09:54<04:58,  1.31it/s]
 57%|█████▋    | 517/908 [09:57<09:59,  1.53s/it]
 57%|█████▋    | 518/908 [09:57<07:20,  1.13s/it]
 57%|█████▋    | 519/908 [09:58<06:25,  1.01it/s]
+[Batch:   520] training loss = 0.521
+
 57%|█████▋    | 520/908 [09:58<04:51,  1.33it/s]
 57%|█████▋    | 521/908 [10:01<08:57,  1.39s/it]
 57%|█████▋    | 522/908 [10:01<06:37,  1.03s/it]
 58%|█████▊    | 523/908 [10:02<06:15,  1.03it/s]
 58%|█████▊    | 524/908 [10:02<04:43,  1.35it/s]
 58%|█████▊    | 525/908 [10:06<10:47,  1.69s/it]
 58%|█████▊    | 526/908 [10:06<07:55,  1.24s/it]
 58%|█████▊    | 527/908 [10:07<06:28,  1.02s/it]
 58%|█████▊    | 528/908 [10:07<04:53,  1.29it/s]
 58%|█████▊    | 529/908 [10:10<08:54,  1.41s/it]
 58%|█████▊    | 530/908 [10:10<06:36,  1.05s/it]
 58%|█████▊    | 531/908 [10:11<05:41,  1.10it/s]
 59%|█████▊    | 532/908 [10:11<04:20,  1.44it/s]
 59%|█████▊    | 533/908 [10:14<09:04,  1.45s/it]
 59%|█████▉    | 534/908 [10:14<06:42,  1.08s/it]
 59%|█████▉    | 535/908 [10:15<06:29,  1.04s/it]
 59%|█████▉    | 536/908 [10:16<04:53,  1.27it/s]
 59%|█████▉    | 537/908 [10:18<08:00,  1.30s/it]
 59%|█████▉    | 538/908 [10:18<05:57,  1.03it/s]
 59%|█████▉    | 539/908 [10:18<04:31,  1.36it/s]
+[Batch:   540] training loss = 0.585
+
 59%|█████▉    | 540/908 [10:19<03:32,  1.73it/s]
 60%|█████▉    | 541/908 [10:22<08:37,  1.41s/it]
 60%|█████▉    | 542/908 [10:22<06:23,  1.05s/it]
 60%|█████▉    | 543/908 [10:22<04:50,  1.26it/s]
 60%|█████▉    | 544/908 [10:23<03:44,  1.62it/s]
 60%|██████    | 545/908 [10:26<08:31,  1.41s/it]
 60%|██████    | 546/908 [10:26<06:18,  1.05s/it]
 60%|██████    | 547/908 [10:26<04:45,  1.26it/s]
 60%|██████    | 548/908 [10:27<03:41,  1.63it/s]
 60%|██████    | 549/908 [10:31<10:21,  1.73s/it]
 61%|██████    | 550/908 [10:31<07:34,  1.27s/it]
 61%|██████    | 551/908 [10:32<06:20,  1.07s/it]
 61%|██████    | 552/908 [10:32<04:47,  1.24it/s]
 61%|██████    | 553/908 [10:36<11:02,  1.87s/it]
 61%|██████    | 554/908 [10:36<08:03,  1.37s/it]
 61%|██████    | 555/908 [10:37<06:28,  1.10s/it]
 61%|██████    | 556/908 [10:37<04:53,  1.20it/s]
 61%|██████▏   | 557/908 [10:41<10:38,  1.82s/it]
 61%|██████▏   | 558/908 [10:41<07:46,  1.33s/it]
 62%|██████▏   | 559/908 [10:42<05:47,  1.00it/s]
+[Batch:   560] training loss = 0.546
+
 62%|██████▏   | 560/908 [10:42<04:23,  1.32it/s]
 62%|██████▏   | 561/908 [10:46<10:41,  1.85s/it]
 62%|██████▏   | 562/908 [10:46<07:48,  1.35s/it]
 62%|██████▏   | 563/908 [10:47<06:11,  1.08s/it]
 62%|██████▏   | 564/908 [10:47<04:39,  1.23it/s]
 62%|██████▏   | 565/908 [10:51<10:38,  1.86s/it]
 62%|██████▏   | 566/908 [10:52<07:46,  1.36s/it]
 62%|██████▏   | 567/908 [10:52<05:45,  1.01s/it]
 63%|██████▎   | 568/908 [10:52<04:21,  1.30it/s]
 63%|██████▎   | 569/908 [10:56<09:44,  1.72s/it]
 63%|██████▎   | 570/908 [10:56<07:08,  1.27s/it]
 63%|██████▎   | 571/908 [10:56<05:22,  1.04it/s]
 63%|██████▎   | 572/908 [10:57<04:06,  1.36it/s]
 63%|██████▎   | 573/908 [11:00<09:07,  1.64s/it]
 63%|██████▎   | 574/908 [11:00<06:42,  1.21s/it]
 63%|██████▎   | 575/908 [11:01<05:05,  1.09it/s]
 63%|██████▎   | 576/908 [11:01<03:53,  1.42it/s]
 64%|██████▎   | 577/908 [11:04<07:56,  1.44s/it]
 64%|██████▎   | 578/908 [11:04<05:52,  1.07s/it]
 64%|██████▍   | 579/908 [11:05<04:28,  1.22it/s]
+[Batch:   580] training loss = 0.483
+
 64%|██████▍   | 580/908 [11:05<03:26,  1.59it/s]
 64%|██████▍   | 581/908 [11:08<08:21,  1.53s/it]
 64%|██████▍   | 582/908 [11:09<06:10,  1.14s/it]
 64%|██████▍   | 583/908 [11:09<04:38,  1.17it/s]
 64%|██████▍   | 584/908 [11:09<03:33,  1.52it/s]
 64%|██████▍   | 585/908 [11:13<08:23,  1.56s/it]
 65%|██████▍   | 586/908 [11:13<06:11,  1.15s/it]
 65%|██████▍   | 587/908 [11:13<04:38,  1.15it/s]
 65%|██████▍   | 588/908 [11:13<03:34,  1.49it/s]
 65%|██████▍   | 589/908 [11:17<08:32,  1.61s/it]
 65%|██████▍   | 590/908 [11:17<06:17,  1.19s/it]
 65%|██████▌   | 591/908 [11:18<05:09,  1.02it/s]
 65%|██████▌   | 592/908 [11:18<03:54,  1.35it/s]
 65%|██████▌   | 593/908 [11:21<07:49,  1.49s/it]
 65%|██████▌   | 594/908 [11:21<05:45,  1.10s/it]
 66%|██████▌   | 595/908 [11:22<05:08,  1.01it/s]
 66%|██████▌   | 596/908 [11:22<03:55,  1.33it/s]
 66%|██████▌   | 597/908 [11:26<08:22,  1.62s/it]
 66%|██████▌   | 598/908 [11:26<06:09,  1.19s/it]
 66%|██████▌   | 599/908 [11:26<04:43,  1.09it/s]
+[Batch:   600] training loss = 0.361
+
 66%|██████▌   | 600/908 [11:27<03:35,  1.43it/s]
 66%|██████▌   | 601/908 [11:32<10:34,  2.07s/it]
 66%|██████▋   | 602/908 [11:32<07:41,  1.51s/it]
 66%|██████▋   | 603/908 [11:33<06:46,  1.33s/it]
 67%|██████▋   | 604/908 [11:33<05:01,  1.01it/s]
 67%|██████▋   | 605/908 [11:37<09:37,  1.90s/it]
 67%|██████▋   | 606/908 [11:37<07:01,  1.40s/it]
 67%|██████▋   | 607/908 [11:38<05:29,  1.09s/it]
 67%|██████▋   | 608/908 [11:38<04:07,  1.21it/s]
 67%|██████▋   | 609/908 [11:42<08:42,  1.75s/it]
 67%|██████▋   | 610/908 [11:42<06:22,  1.28s/it]
 67%|██████▋   | 611/908 [11:43<05:28,  1.11s/it]
 67%|██████▋   | 612/908 [11:43<04:07,  1.20it/s]
 68%|██████▊   | 613/908 [11:47<08:05,  1.64s/it]
 68%|██████▊   | 614/908 [11:47<05:56,  1.21s/it]
 68%|██████▊   | 615/908 [11:47<04:28,  1.09it/s]
 68%|██████▊   | 616/908 [11:47<03:24,  1.43it/s]
 68%|██████▊   | 617/908 [11:51<08:05,  1.67s/it]
 68%|██████▊   | 618/908 [11:51<05:56,  1.23s/it]
 68%|██████▊   | 619/908 [11:52<05:22,  1.11s/it]
+[Batch:   620] training loss = 0.535
+
 68%|██████▊   | 620/908 [11:52<04:02,  1.19it/s]
 68%|██████▊   | 621/908 [11:56<08:43,  1.83s/it]
 69%|██████▊   | 622/908 [11:57<06:22,  1.34s/it]
 69%|██████▊   | 623/908 [11:57<05:03,  1.06s/it]
 69%|██████▊   | 624/908 [11:57<03:48,  1.24it/s]
 69%|██████▉   | 625/908 [12:01<07:44,  1.64s/it]
 69%|██████▉   | 626/908 [12:01<05:40,  1.21s/it]
 69%|██████▉   | 627/908 [12:02<04:59,  1.07s/it]
 69%|██████▉   | 628/908 [12:02<03:46,  1.24it/s]
 69%|██████▉   | 629/908 [12:06<08:18,  1.79s/it]
 69%|██████▉   | 630/908 [12:06<06:04,  1.31s/it]
 69%|██████▉   | 631/908 [12:07<04:45,  1.03s/it]
 70%|██████▉   | 632/908 [12:07<03:35,  1.28it/s]
 70%|██████▉   | 633/908 [12:11<07:39,  1.67s/it]
 70%|██████▉   | 634/908 [12:11<05:36,  1.23s/it]
 70%|██████▉   | 635/908 [12:11<04:48,  1.06s/it]
 70%|███████   | 636/908 [12:12<03:37,  1.25it/s]
 70%|███████   | 637/908 [12:16<08:36,  1.90s/it]
 70%|███████   | 638/908 [12:16<06:16,  1.39s/it]
 70%|███████   | 639/908 [12:17<04:38,  1.04s/it]
+[Batch:   640] training loss = 0.555
+
 70%|███████   | 640/908 [12:17<03:29,  1.28it/s]
 71%|███████   | 641/908 [12:20<07:24,  1.67s/it]
 71%|███████   | 642/908 [12:21<05:25,  1.23s/it]
 71%|███████   | 643/908 [12:21<04:02,  1.09it/s]
 71%|███████   | 644/908 [12:21<03:04,  1.43it/s]
 71%|███████   | 645/908 [12:24<06:35,  1.50s/it]
 71%|███████   | 646/908 [12:25<04:51,  1.11s/it]
 71%|███████▏  | 647/908 [12:25<03:38,  1.19it/s]
 71%|███████▏  | 648/908 [12:25<02:47,  1.55it/s]
 71%|███████▏  | 649/908 [12:28<06:12,  1.44s/it]
 72%|███████▏  | 650/908 [12:28<04:34,  1.06s/it]
 72%|███████▏  | 651/908 [12:29<03:27,  1.24it/s]
 72%|███████▏  | 652/908 [12:29<02:39,  1.61it/s]
 72%|███████▏  | 653/908 [12:35<09:30,  2.24s/it]
 72%|███████▏  | 654/908 [12:35<06:52,  1.62s/it]
 72%|███████▏  | 655/908 [12:35<05:17,  1.25s/it]
 72%|███████▏  | 656/908 [12:36<03:55,  1.07it/s]
 72%|███████▏  | 657/908 [12:40<08:17,  1.98s/it]
 72%|███████▏  | 658/908 [12:40<06:00,  1.44s/it]
 73%|███████▎  | 659/908 [12:41<05:03,  1.22s/it]
+[Batch:   660] training loss = 0.597
+
 73%|███████▎  | 660/908 [12:41<03:45,  1.10it/s]
 73%|███████▎  | 661/908 [12:45<07:16,  1.77s/it]
 73%|███████▎  | 662/908 [12:45<05:18,  1.30s/it]
 73%|███████▎  | 663/908 [12:45<04:07,  1.01s/it]
 73%|███████▎  | 664/908 [12:46<03:06,  1.31it/s]
 73%|███████▎  | 665/908 [12:50<07:14,  1.79s/it]
 73%|███████▎  | 666/908 [12:50<05:17,  1.31s/it]
 73%|███████▎  | 667/908 [12:50<04:08,  1.03s/it]
 74%|███████▎  | 668/908 [12:51<03:06,  1.29it/s]
 74%|███████▎  | 669/908 [12:55<07:07,  1.79s/it]
 74%|███████▍  | 670/908 [12:55<05:11,  1.31s/it]
 74%|███████▍  | 671/908 [12:56<04:35,  1.16s/it]
 74%|███████▍  | 672/908 [12:56<03:26,  1.15it/s]
 74%|███████▍  | 673/908 [13:00<07:16,  1.86s/it]
 74%|███████▍  | 674/908 [13:00<05:18,  1.36s/it]
 74%|███████▍  | 675/908 [13:00<03:55,  1.01s/it]
 74%|███████▍  | 676/908 [13:01<02:57,  1.31it/s]
 75%|███████▍  | 677/908 [13:05<06:28,  1.68s/it]
 75%|███████▍  | 678/908 [13:05<04:44,  1.24s/it]
 75%|███████▍  | 679/908 [13:05<03:33,  1.07it/s]
+[Batch:   680] training loss = 0.484
+
 75%|███████▍  | 680/908 [13:05<02:41,  1.41it/s]
 75%|███████▌  | 681/908 [13:09<05:57,  1.58s/it]
 75%|███████▌  | 682/908 [13:09<04:23,  1.16s/it]
 75%|███████▌  | 683/908 [13:09<03:23,  1.11it/s]
 75%|███████▌  | 684/908 [13:09<02:34,  1.45it/s]
 75%|███████▌  | 685/908 [13:13<06:14,  1.68s/it]
 76%|███████▌  | 686/908 [13:14<04:34,  1.24s/it]
 76%|███████▌  | 687/908 [13:14<03:37,  1.02it/s]
 76%|███████▌  | 688/908 [13:14<02:43,  1.34it/s]
 76%|███████▌  | 689/908 [13:18<06:33,  1.80s/it]
 76%|███████▌  | 690/908 [13:19<04:46,  1.32s/it]
 76%|███████▌  | 691/908 [13:19<03:32,  1.02it/s]
 76%|███████▌  | 692/908 [13:19<02:40,  1.35it/s]
 76%|███████▋  | 693/908 [13:23<05:43,  1.60s/it]
 76%|███████▋  | 694/908 [13:23<04:12,  1.18s/it]
 77%|███████▋  | 695/908 [13:23<03:07,  1.14it/s]
 77%|███████▋  | 696/908 [13:23<02:23,  1.48it/s]
 77%|███████▋  | 697/908 [13:27<05:34,  1.59s/it]
 77%|███████▋  | 698/908 [13:27<04:04,  1.17s/it]
 77%|███████▋  | 699/908 [13:27<03:03,  1.14it/s]
+[Batch:   700] training loss = 0.525
+
 77%|███████▋  | 700/908 [13:27<02:20,  1.49it/s]
 77%|███████▋  | 701/908 [13:32<06:18,  1.83s/it]
 77%|███████▋  | 702/908 [13:32<04:35,  1.34s/it]
 77%|███████▋  | 703/908 [13:32<03:23,  1.01it/s]
 78%|███████▊  | 704/908 [13:33<02:33,  1.33it/s]
 78%|███████▊  | 705/908 [13:39<07:58,  2.36s/it]
 78%|███████▊  | 706/908 [13:39<05:44,  1.71s/it]
 78%|███████▊  | 707/908 [13:39<04:11,  1.25s/it]
 78%|███████▊  | 708/908 [13:39<03:06,  1.07it/s]
 78%|███████▊  | 709/908 [13:44<06:49,  2.06s/it]
 78%|███████▊  | 710/908 [13:44<04:57,  1.50s/it]
 78%|███████▊  | 711/908 [13:44<03:39,  1.12s/it]
 78%|███████▊  | 712/908 [13:45<02:44,  1.19it/s]
 79%|███████▊  | 713/908 [13:49<06:15,  1.92s/it]
 79%|███████▊  | 714/908 [13:49<04:32,  1.41s/it]
 79%|███████▊  | 715/908 [13:49<03:20,  1.04s/it]
 79%|███████▉  | 716/908 [13:50<02:30,  1.27it/s]
 79%|███████▉  | 717/908 [13:53<05:09,  1.62s/it]
 79%|███████▉  | 718/908 [13:53<03:46,  1.19s/it]
 79%|███████▉  | 719/908 [13:54<02:48,  1.12it/s]
+[Batch:   720] training loss = 0.604
+
 79%|███████▉  | 720/908 [13:54<02:08,  1.47it/s]
 79%|███████▉  | 721/908 [13:57<04:51,  1.56s/it]
 80%|███████▉  | 722/908 [13:58<03:33,  1.15s/it]
 80%|███████▉  | 723/908 [13:58<02:39,  1.16it/s]
 80%|███████▉  | 724/908 [13:58<02:02,  1.50it/s]
 80%|███████▉  | 725/908 [14:02<05:00,  1.64s/it]
 80%|███████▉  | 726/908 [14:02<03:39,  1.21s/it]
 80%|████████  | 727/908 [14:02<02:43,  1.11it/s]
 80%|████████  | 728/908 [14:02<02:03,  1.46it/s]
 80%|████████  | 729/908 [14:06<04:46,  1.60s/it]
 80%|████████  | 730/908 [14:06<03:29,  1.18s/it]
 81%|████████  | 731/908 [14:06<02:35,  1.14it/s]
 81%|████████  | 732/908 [14:07<01:58,  1.48it/s]
 81%|████████  | 733/908 [14:12<05:45,  1.98s/it]
 81%|████████  | 734/908 [14:12<04:10,  1.44s/it]
 81%|████████  | 735/908 [14:12<03:04,  1.07s/it]
 81%|████████  | 736/908 [14:12<02:18,  1.24it/s]
 81%|████████  | 737/908 [14:17<05:19,  1.87s/it]
 81%|████████▏ | 738/908 [14:17<03:52,  1.37s/it]
 81%|████████▏ | 739/908 [14:17<02:52,  1.02s/it]
+[Batch:   740] training loss = 0.270
+
 81%|████████▏ | 740/908 [14:17<02:13,  1.26it/s]
 82%|████████▏ | 741/908 [14:21<04:55,  1.77s/it]
 82%|████████▏ | 742/908 [14:22<03:34,  1.29s/it]
 82%|████████▏ | 743/908 [14:22<02:38,  1.04it/s]
 82%|████████▏ | 744/908 [14:22<01:59,  1.37it/s]
 82%|████████▏ | 745/908 [14:25<03:57,  1.46s/it]
 82%|████████▏ | 746/908 [14:25<02:55,  1.08s/it]
 82%|████████▏ | 747/908 [14:26<02:12,  1.21it/s]
 82%|████████▏ | 748/908 [14:26<01:41,  1.57it/s]
 82%|████████▏ | 749/908 [14:30<04:34,  1.73s/it]
 83%|████████▎ | 750/908 [14:30<03:19,  1.27s/it]
 83%|████████▎ | 751/908 [14:30<02:28,  1.06it/s]
 83%|████████▎ | 752/908 [14:31<01:52,  1.39it/s]
 83%|████████▎ | 753/908 [14:35<04:48,  1.86s/it]
 83%|████████▎ | 754/908 [14:35<03:29,  1.36s/it]
 83%|████████▎ | 755/908 [14:35<02:35,  1.02s/it]
 83%|████████▎ | 756/908 [14:36<01:57,  1.30it/s]
 83%|████████▎ | 757/908 [14:41<05:10,  2.06s/it]
 83%|████████▎ | 758/908 [14:41<03:44,  1.50s/it]
 84%|████████▎ | 759/908 [14:41<02:44,  1.11s/it]
+[Batch:   760] training loss = 0.597
+
 84%|████████▎ | 760/908 [14:41<02:03,  1.20it/s]
 84%|████████▍ | 761/908 [14:45<04:26,  1.81s/it]
 84%|████████▍ | 762/908 [14:46<03:13,  1.33s/it]
 84%|████████▍ | 763/908 [14:46<02:23,  1.01it/s]
 84%|████████▍ | 764/908 [14:46<01:47,  1.34it/s]
 84%|████████▍ | 765/908 [14:50<03:54,  1.64s/it]
 84%|████████▍ | 766/908 [14:50<02:51,  1.21s/it]
 84%|████████▍ | 767/908 [14:50<02:06,  1.11it/s]
 85%|████████▍ | 768/908 [14:50<01:37,  1.44it/s]
 85%|████████▍ | 769/908 [14:54<04:00,  1.73s/it]
 85%|████████▍ | 770/908 [14:55<02:55,  1.27s/it]
 85%|████████▍ | 771/908 [14:55<02:10,  1.05it/s]
 85%|████████▌ | 772/908 [14:55<01:38,  1.38it/s]
 85%|████████▌ | 773/908 [14:59<03:35,  1.59s/it]
 85%|████████▌ | 774/908 [14:59<02:37,  1.18s/it]
 85%|████████▌ | 775/908 [14:59<01:57,  1.13it/s]
 85%|████████▌ | 776/908 [14:59<01:29,  1.48it/s]
 86%|████████▌ | 777/908 [15:04<03:58,  1.82s/it]
 86%|████████▌ | 778/908 [15:04<02:53,  1.33s/it]
 86%|████████▌ | 779/908 [15:04<02:07,  1.01it/s]
+[Batch:   780] training loss = 0.446
+
 86%|████████▌ | 780/908 [15:04<01:36,  1.33it/s]
 86%|████████▌ | 781/908 [15:09<03:54,  1.85s/it]
 86%|████████▌ | 782/908 [15:09<02:50,  1.35s/it]
 86%|████████▌ | 783/908 [15:09<02:05,  1.00s/it]
 86%|████████▋ | 784/908 [15:09<01:34,  1.32it/s]
 86%|████████▋ | 785/908 [15:13<03:15,  1.59s/it]
 87%|████████▋ | 786/908 [15:13<02:23,  1.17s/it]
 87%|████████▋ | 787/908 [15:13<01:46,  1.14it/s]
 87%|████████▋ | 788/908 [15:13<01:20,  1.49it/s]
 87%|████████▋ | 789/908 [15:17<03:03,  1.54s/it]
 87%|████████▋ | 790/908 [15:17<02:14,  1.14s/it]
 87%|████████▋ | 791/908 [15:17<01:39,  1.17it/s]
 87%|████████▋ | 792/908 [15:18<01:15,  1.53it/s]
 87%|████████▋ | 793/908 [15:21<02:36,  1.36s/it]
 87%|████████▋ | 794/908 [15:21<01:55,  1.01s/it]
 88%|████████▊ | 795/908 [15:21<01:26,  1.31it/s]
 88%|████████▊ | 796/908 [15:21<01:06,  1.69it/s]
 88%|████████▊ | 797/908 [15:24<02:31,  1.36s/it]
 88%|████████▊ | 798/908 [15:25<01:50,  1.01s/it]
 88%|████████▊ | 799/908 [15:25<01:23,  1.31it/s]
+[Batch:   800] training loss = 0.442
+
 88%|████████▊ | 800/908 [15:25<01:03,  1.69it/s]
 88%|████████▊ | 801/908 [15:29<02:52,  1.61s/it]
 88%|████████▊ | 802/908 [15:29<02:05,  1.18s/it]
 88%|████████▊ | 803/908 [15:29<01:33,  1.13it/s]
 89%|████████▊ | 804/908 [15:29<01:10,  1.47it/s]
 89%|████████▊ | 805/908 [15:33<02:45,  1.61s/it]
 89%|████████▉ | 806/908 [15:33<02:00,  1.18s/it]
 89%|████████▉ | 807/908 [15:34<01:29,  1.13it/s]
 89%|████████▉ | 808/908 [15:34<01:07,  1.48it/s]
 89%|████████▉ | 809/908 [15:40<03:51,  2.34s/it]
 89%|████████▉ | 810/908 [15:40<02:46,  1.70s/it]
 89%|████████▉ | 811/908 [15:40<02:01,  1.25s/it]
 89%|████████▉ | 812/908 [15:41<01:29,  1.07it/s]
 90%|████████▉ | 813/908 [15:45<02:58,  1.88s/it]
 90%|████████▉ | 814/908 [15:45<02:09,  1.38s/it]
 90%|████████▉ | 815/908 [15:45<01:35,  1.02s/it]
 90%|████████▉ | 816/908 [15:45<01:11,  1.29it/s]
 90%|████████▉ | 817/908 [15:49<02:18,  1.53s/it]
 90%|█████████ | 818/908 [15:49<01:41,  1.13s/it]
 90%|█████████ | 819/908 [15:49<01:15,  1.18it/s]
+[Batch:   820] training loss = 0.512
+
 90%|█████████ | 820/908 [15:49<00:57,  1.53it/s]
 90%|█████████ | 821/908 [15:53<02:18,  1.59s/it]
 91%|█████████ | 822/908 [15:53<01:40,  1.17s/it]
 91%|█████████ | 823/908 [15:53<01:17,  1.09it/s]
 91%|█████████ | 824/908 [15:54<00:58,  1.43it/s]
 91%|█████████ | 825/908 [15:55<01:22,  1.01it/s]
 91%|█████████ | 826/908 [15:56<01:01,  1.33it/s]
 91%|█████████ | 827/908 [15:56<00:54,  1.47it/s]
 91%|█████████ | 828/908 [15:56<00:42,  1.87it/s]
 91%|█████████▏| 829/908 [15:59<01:43,  1.31s/it]
 91%|█████████▏| 830/908 [16:00<01:16,  1.02it/s]
 92%|█████████▏| 831/908 [16:00<01:08,  1.13it/s]
 92%|█████████▏| 832/908 [16:00<00:51,  1.47it/s]
 92%|█████████▏| 833/908 [16:04<01:51,  1.49s/it]
 92%|█████████▏| 834/908 [16:04<01:21,  1.10s/it]
 92%|█████████▏| 835/908 [16:05<01:17,  1.06s/it]
 92%|█████████▏| 836/908 [16:05<00:57,  1.25it/s]
 92%|█████████▏| 837/908 [16:09<02:03,  1.74s/it]
 92%|█████████▏| 838/908 [16:09<01:29,  1.28s/it]
 92%|█████████▏| 839/908 [16:10<01:09,  1.01s/it]
+[Batch:   840] training loss = 0.542
+
 93%|█████████▎| 840/908 [16:10<00:51,  1.31it/s]
 93%|█████████▎| 841/908 [16:14<01:52,  1.68s/it]
 93%|█████████▎| 842/908 [16:14<01:21,  1.23s/it]
 93%|█████████▎| 843/908 [16:14<01:07,  1.04s/it]
 93%|█████████▎| 844/908 [16:15<00:50,  1.27it/s]
 93%|█████████▎| 845/908 [16:19<02:00,  1.91s/it]
 93%|█████████▎| 846/908 [16:19<01:26,  1.39s/it]
 93%|█████████▎| 847/908 [16:20<01:04,  1.05s/it]
 93%|█████████▎| 848/908 [16:20<00:47,  1.25it/s]
 94%|█████████▎| 849/908 [16:24<01:40,  1.70s/it]
 94%|█████████▎| 850/908 [16:24<01:12,  1.25s/it]
 94%|█████████▎| 851/908 [16:25<01:06,  1.16s/it]
 94%|█████████▍| 852/908 [16:25<00:48,  1.14it/s]
 94%|█████████▍| 853/908 [16:28<01:31,  1.66s/it]
 94%|█████████▍| 854/908 [16:29<01:05,  1.22s/it]
 94%|█████████▍| 855/908 [16:29<00:51,  1.03it/s]
 94%|█████████▍| 856/908 [16:29<00:38,  1.35it/s]
 94%|█████████▍| 857/908 [16:33<01:21,  1.59s/it]
 94%|█████████▍| 858/908 [16:33<00:58,  1.17s/it]
 95%|█████████▍| 859/908 [16:33<00:45,  1.08it/s]
+[Batch:   860] training loss = 0.528
+
 95%|█████████▍| 860/908 [16:34<00:33,  1.42it/s]
 95%|█████████▍| 861/908 [16:38<01:26,  1.83s/it]
 95%|█████████▍| 862/908 [16:38<01:01,  1.34s/it]
 95%|█████████▌| 863/908 [16:39<00:51,  1.15s/it]
 95%|█████████▌| 864/908 [16:39<00:38,  1.15it/s]
 95%|█████████▌| 865/908 [16:43<01:10,  1.65s/it]
 95%|█████████▌| 866/908 [16:43<00:50,  1.21s/it]
 95%|█████████▌| 867/908 [16:43<00:39,  1.05it/s]
 96%|█████████▌| 868/908 [16:43<00:29,  1.37it/s]
 96%|█████████▌| 869/908 [16:48<01:12,  1.85s/it]
 96%|█████████▌| 870/908 [16:48<00:51,  1.35s/it]
 96%|█████████▌| 871/908 [16:48<00:40,  1.08s/it]
 96%|█████████▌| 872/908 [16:49<00:29,  1.22it/s]
 96%|█████████▌| 873/908 [16:53<01:00,  1.74s/it]
 96%|█████████▋| 874/908 [16:53<00:43,  1.27s/it]
 96%|█████████▋| 875/908 [16:53<00:34,  1.05s/it]
 96%|█████████▋| 876/908 [16:54<00:25,  1.26it/s]
 97%|█████████▋| 877/908 [16:57<00:53,  1.71s/it]
 97%|█████████▋| 878/908 [16:58<00:37,  1.26s/it]
 97%|█████████▋| 879/908 [16:58<00:27,  1.06it/s]
+[Batch:   880] training loss = 0.506
+
 97%|█████████▋| 880/908 [16:58<00:20,  1.39it/s]
 97%|█████████▋| 881/908 [17:02<00:44,  1.63s/it]
 97%|█████████▋| 882/908 [17:02<00:31,  1.20s/it]
 97%|█████████▋| 883/908 [17:02<00:22,  1.10it/s]
 97%|█████████▋| 884/908 [17:02<00:16,  1.44it/s]
 97%|█████████▋| 885/908 [17:06<00:35,  1.54s/it]
 98%|█████████▊| 886/908 [17:06<00:25,  1.14s/it]
 98%|█████████▊| 887/908 [17:07<00:21,  1.02s/it]
 98%|█████████▊| 888/908 [17:07<00:15,  1.29it/s]
 98%|█████████▊| 889/908 [17:12<00:36,  1.93s/it]
 98%|█████████▊| 890/908 [17:12<00:25,  1.41s/it]
 98%|█████████▊| 891/908 [17:12<00:17,  1.05s/it]
 98%|█████████▊| 892/908 [17:12<00:12,  1.26it/s]
 98%|█████████▊| 893/908 [17:17<00:28,  1.89s/it]
 98%|█████████▊| 894/908 [17:17<00:19,  1.38s/it]
 99%|█████████▊| 895/908 [17:17<00:13,  1.04s/it]
 99%|█████████▊| 896/908 [17:17<00:09,  1.27it/s]
 99%|█████████▉| 897/908 [17:22<00:21,  1.98s/it]
 99%|█████████▉| 898/908 [17:22<00:14,  1.44s/it]
 99%|█████████▉| 899/908 [17:22<00:09,  1.07s/it]
+[Batch:   900] training loss = 0.495
+
 99%|█████████▉| 900/908 [17:23<00:06,  1.23it/s]
 99%|█████████▉| 901/908 [17:26<00:11,  1.70s/it]
 99%|█████████▉| 902/908 [17:27<00:07,  1.25s/it]
 99%|█████████▉| 903/908 [17:27<00:04,  1.06it/s]
100%|█████████▉| 904/908 [17:27<00:02,  1.39it/s]
100%|█████████▉| 905/908 [17:30<00:04,  1.50s/it]
100%|█████████▉| 906/908 [17:31<00:02,  1.11s/it]
100%|█████████▉| 907/908 [17:31<00:00,  1.18it/s]
100%|██████████| 908/908 [17:31<00:00,  1.57it/s]
100%|██████████| 908/908 [17:31<00:00,  1.16s/it]
+
+Epoch   3: validation
+
  0%|          | 0/101 [00:00<?, ?it/s]
  1%|          | 1/101 [00:04<08:17,  4.98s/it]
  3%|▎         | 3/101 [00:05<02:12,  1.35s/it]
  5%|▍         | 5/101 [00:11<03:30,  2.20s/it]
  7%|▋         | 7/101 [00:11<02:02,  1.30s/it]
  9%|▉         | 9/101 [00:15<02:29,  1.63s/it]
 11%|█         | 11/101 [00:15<01:40,  1.11s/it]
 13%|█▎        | 13/101 [00:19<02:01,  1.38s/it]
 15%|█▍        | 15/101 [00:20<01:24,  1.01it/s]
 17%|█▋        | 17/101 [00:24<01:54,  1.36s/it]
 19%|█▉        | 19/101 [00:24<01:18,  1.04it/s]
 21%|██        | 21/101 [00:27<01:34,  1.18s/it]
 23%|██▎       | 23/101 [00:28<01:06,  1.18it/s]
 25%|██▍       | 25/101 [00:31<01:26,  1.14s/it]
 27%|██▋       | 27/101 [00:31<01:00,  1.22it/s]
 29%|██▊       | 29/101 [00:36<01:27,  1.22s/it]
 30%|██▉       | 30/101 [00:36<01:12,  1.02s/it]
 32%|███▏      | 32/101 [00:36<00:49,  1.38it/s]
 33%|███▎      | 33/101 [00:39<01:23,  1.23s/it]
 34%|███▎      | 34/101 [00:40<01:15,  1.12s/it]
 36%|███▌      | 36/101 [00:40<00:47,  1.38it/s]
 37%|███▋      | 37/101 [00:46<01:53,  1.78s/it]
 38%|███▊      | 38/101 [00:47<01:37,  1.54s/it]
 40%|███▉      | 40/101 [00:47<00:58,  1.05it/s]
 41%|████      | 41/101 [00:52<01:48,  1.81s/it]
 42%|████▏     | 42/101 [00:52<01:25,  1.44s/it]
 44%|████▎     | 44/101 [00:52<00:50,  1.13it/s]
 45%|████▍     | 45/101 [00:56<01:32,  1.65s/it]
 46%|████▌     | 46/101 [00:57<01:13,  1.33s/it]
 48%|████▊     | 48/101 [00:57<00:43,  1.22it/s]
 49%|████▊     | 49/101 [01:01<01:20,  1.54s/it]
 50%|████▉     | 50/101 [01:01<01:04,  1.26s/it]
 51%|█████▏    | 52/101 [01:01<00:37,  1.29it/s]
 52%|█████▏    | 53/101 [01:06<01:13,  1.52s/it]
 53%|█████▎    | 54/101 [01:06<00:55,  1.18s/it]
 55%|█████▌    | 56/101 [01:06<00:32,  1.38it/s]
 56%|█████▋    | 57/101 [01:11<01:18,  1.79s/it]
 57%|█████▋    | 58/101 [01:12<01:01,  1.42s/it]
 59%|█████▉    | 60/101 [01:12<00:35,  1.15it/s]
 60%|██████    | 61/101 [01:16<01:06,  1.66s/it]
 61%|██████▏   | 62/101 [01:17<00:55,  1.41s/it]
 63%|██████▎   | 64/101 [01:17<00:31,  1.16it/s]
 64%|██████▍   | 65/101 [01:21<00:56,  1.58s/it]
 65%|██████▌   | 66/101 [01:21<00:45,  1.30s/it]
 67%|██████▋   | 68/101 [01:21<00:26,  1.25it/s]
 68%|██████▊   | 69/101 [01:25<00:48,  1.52s/it]
 69%|██████▉   | 70/101 [01:26<00:37,  1.21s/it]
 71%|███████▏  | 72/101 [01:26<00:21,  1.35it/s]
 72%|███████▏  | 73/101 [01:30<00:45,  1.62s/it]
 73%|███████▎  | 74/101 [01:31<00:35,  1.30s/it]
 75%|███████▌  | 76/101 [01:31<00:19,  1.25it/s]
 76%|███████▌  | 77/101 [01:36<00:39,  1.66s/it]
 77%|███████▋  | 78/101 [01:36<00:31,  1.39s/it]
 79%|███████▉  | 80/101 [01:36<00:18,  1.15it/s]
 80%|████████  | 81/101 [01:40<00:32,  1.62s/it]
 81%|████████  | 82/101 [01:41<00:24,  1.29s/it]
 83%|████████▎ | 84/101 [01:41<00:14,  1.14it/s]
 84%|████████▍ | 85/101 [01:46<00:27,  1.74s/it]
 86%|████████▌ | 87/101 [01:46<00:15,  1.08s/it]
 88%|████████▊ | 89/101 [01:51<00:17,  1.48s/it]
 89%|████████▉ | 90/101 [01:51<00:13,  1.21s/it]
 91%|█████████ | 92/101 [01:51<00:07,  1.26it/s]
 92%|█████████▏| 93/101 [01:56<00:13,  1.66s/it]
 93%|█████████▎| 94/101 [01:56<00:09,  1.31s/it]
 95%|█████████▌| 96/101 [01:56<00:04,  1.22it/s]
 96%|█████████▌| 97/101 [02:00<00:05,  1.49s/it]
 97%|█████████▋| 98/101 [02:00<00:03,  1.18s/it]
 99%|█████████▉| 100/101 [02:00<00:00,  1.36it/s]
100%|██████████| 101/101 [02:03<00:00,  1.13s/it]
100%|██████████| 101/101 [02:03<00:00,  1.22s/it]
+    Epoch  3: training loss = 0.487 / validation loss = 0.480
+    Epoch  3: training acc = 0.763 / validation acc = 0.770
+    Saved model: cls-3.pth.tar
+
+Epoch   4: training
+
  0%|          | 0/908 [00:00<?, ?it/s]
  0%|          | 1/908 [00:04<1:14:43,  4.94s/it]
  0%|          | 2/908 [00:05<32:25,  2.15s/it]  
  0%|          | 3/908 [00:05<18:58,  1.26s/it]
  0%|          | 4/908 [00:05<12:39,  1.19it/s]
  1%|          | 5/908 [00:09<26:57,  1.79s/it]
  1%|          | 6/908 [00:09<19:02,  1.27s/it]
  1%|          | 7/908 [00:09<13:45,  1.09it/s]
  1%|          | 8/908 [00:09<10:18,  1.46it/s]
  1%|          | 9/908 [00:13<25:36,  1.71s/it]
  1%|          | 10/908 [00:13<18:34,  1.24s/it]
  1%|          | 11/908 [00:14<13:49,  1.08it/s]
  1%|▏         | 12/908 [00:14<10:28,  1.42it/s]
  1%|▏         | 13/908 [00:18<25:47,  1.73s/it]
  2%|▏         | 14/908 [00:18<19:23,  1.30s/it]
  2%|▏         | 15/908 [00:18<14:22,  1.03it/s]
  2%|▏         | 16/908 [00:18<10:53,  1.36it/s]
  2%|▏         | 17/908 [00:23<27:24,  1.85s/it]
  2%|▏         | 18/908 [00:23<20:01,  1.35s/it]
  2%|▏         | 19/908 [00:23<14:49,  1.00s/it]
+[Batch:    20] training loss = 0.572
+
  2%|▏         | 20/908 [00:24<11:16,  1.31it/s]
  2%|▏         | 21/908 [00:27<22:26,  1.52s/it]
  2%|▏         | 22/908 [00:27<18:47,  1.27s/it]
  3%|▎         | 23/908 [00:28<13:59,  1.05it/s]
  3%|▎         | 24/908 [00:28<10:39,  1.38it/s]
  3%|▎         | 25/908 [00:32<25:18,  1.72s/it]
  3%|▎         | 26/908 [00:32<19:17,  1.31s/it]
  3%|▎         | 27/908 [00:32<14:22,  1.02it/s]
  3%|▎         | 28/908 [00:33<10:55,  1.34it/s]
  3%|▎         | 29/908 [00:35<20:00,  1.37s/it]
  3%|▎         | 30/908 [00:36<17:04,  1.17s/it]
  3%|▎         | 31/908 [00:36<12:48,  1.14it/s]
  4%|▎         | 32/908 [00:37<09:47,  1.49it/s]
  4%|▎         | 33/908 [00:41<24:19,  1.67s/it]
  4%|▎         | 34/908 [00:41<18:37,  1.28s/it]
  4%|▍         | 35/908 [00:41<13:53,  1.05it/s]
  4%|▍         | 36/908 [00:41<10:33,  1.38it/s]
  4%|▍         | 37/908 [00:45<23:57,  1.65s/it]
  4%|▍         | 38/908 [00:46<21:17,  1.47s/it]
  4%|▍         | 39/908 [00:46<15:40,  1.08s/it]
+[Batch:    40] training loss = 0.479
+
  4%|▍         | 40/908 [00:47<11:47,  1.23it/s]
  5%|▍         | 41/908 [00:51<27:07,  1.88s/it]
  5%|▍         | 42/908 [00:52<21:58,  1.52s/it]
  5%|▍         | 43/908 [00:52<16:11,  1.12s/it]
  5%|▍         | 44/908 [00:52<12:08,  1.19it/s]
  5%|▍         | 45/908 [00:55<22:44,  1.58s/it]
  5%|▌         | 46/908 [00:56<18:50,  1.31s/it]
  5%|▌         | 47/908 [00:56<14:00,  1.02it/s]
  5%|▌         | 48/908 [00:56<10:37,  1.35it/s]
  5%|▌         | 49/908 [00:59<20:30,  1.43s/it]
  6%|▌         | 50/908 [01:00<16:29,  1.15s/it]
  6%|▌         | 51/908 [01:00<12:21,  1.16it/s]
  6%|▌         | 52/908 [01:00<09:27,  1.51it/s]
  6%|▌         | 53/908 [01:04<21:54,  1.54s/it]
  6%|▌         | 54/908 [01:04<16:40,  1.17s/it]
  6%|▌         | 55/908 [01:04<12:30,  1.14it/s]
  6%|▌         | 56/908 [01:05<09:34,  1.48it/s]
  6%|▋         | 57/908 [01:08<19:43,  1.39s/it]
  6%|▋         | 58/908 [01:08<14:41,  1.04s/it]
  6%|▋         | 59/908 [01:08<11:04,  1.28it/s]
+[Batch:    60] training loss = 0.431
+
  7%|▋         | 60/908 [01:08<08:31,  1.66it/s]
  7%|▋         | 61/908 [01:12<21:30,  1.52s/it]
  7%|▋         | 62/908 [01:12<15:52,  1.13s/it]
  7%|▋         | 63/908 [01:12<11:54,  1.18it/s]
  7%|▋         | 64/908 [01:12<09:06,  1.54it/s]
  7%|▋         | 65/908 [01:16<20:50,  1.48s/it]
  7%|▋         | 66/908 [01:16<15:25,  1.10s/it]
  7%|▋         | 67/908 [01:16<11:37,  1.21it/s]
  7%|▋         | 68/908 [01:17<08:56,  1.56it/s]
  8%|▊         | 69/908 [01:20<22:23,  1.60s/it]
  8%|▊         | 70/908 [01:21<17:24,  1.25s/it]
  8%|▊         | 71/908 [01:21<12:58,  1.08it/s]
  8%|▊         | 72/908 [01:21<09:53,  1.41it/s]
  8%|▊         | 73/908 [01:25<24:51,  1.79s/it]
  8%|▊         | 74/908 [01:27<22:10,  1.60s/it]
  8%|▊         | 75/908 [01:27<16:18,  1.17s/it]
  8%|▊         | 76/908 [01:27<12:11,  1.14it/s]
  8%|▊         | 77/908 [01:29<18:28,  1.33s/it]
  9%|▊         | 78/908 [01:31<18:36,  1.35s/it]
  9%|▊         | 79/908 [01:31<13:47,  1.00it/s]
+[Batch:    80] training loss = 0.526
+
  9%|▉         | 80/908 [01:31<10:26,  1.32it/s]
  9%|▉         | 81/908 [01:33<15:45,  1.14s/it]
  9%|▉         | 82/908 [01:34<16:07,  1.17s/it]
  9%|▉         | 83/908 [01:35<12:04,  1.14it/s]
  9%|▉         | 84/908 [01:35<09:13,  1.49it/s]
  9%|▉         | 85/908 [01:38<17:51,  1.30s/it]
  9%|▉         | 86/908 [01:39<16:15,  1.19s/it]
 10%|▉         | 87/908 [01:39<12:09,  1.13it/s]
 10%|▉         | 88/908 [01:39<09:16,  1.47it/s]
 10%|▉         | 89/908 [01:40<12:58,  1.05it/s]
 10%|▉         | 90/908 [01:41<12:29,  1.09it/s]
 10%|█         | 91/908 [01:42<09:32,  1.43it/s]
 10%|█         | 92/908 [01:42<07:27,  1.82it/s]
 10%|█         | 93/908 [01:46<23:07,  1.70s/it]
 10%|█         | 94/908 [01:48<24:07,  1.78s/it]
 10%|█         | 95/908 [01:48<17:38,  1.30s/it]
 11%|█         | 96/908 [01:48<13:06,  1.03it/s]
 11%|█         | 97/908 [01:50<17:14,  1.28s/it]
 11%|█         | 98/908 [01:52<16:56,  1.26s/it]
 11%|█         | 99/908 [01:52<12:36,  1.07it/s]
+[Batch:   100] training loss = 0.465
+
 11%|█         | 100/908 [01:52<09:34,  1.41it/s]
 11%|█         | 101/908 [01:54<14:51,  1.10s/it]
 11%|█         | 102/908 [01:55<16:08,  1.20s/it]
 11%|█▏        | 103/908 [01:56<12:02,  1.11it/s]
 11%|█▏        | 104/908 [01:56<09:09,  1.46it/s]
 12%|█▏        | 105/908 [02:00<22:23,  1.67s/it]
 12%|█▏        | 106/908 [02:00<17:41,  1.32s/it]
 12%|█▏        | 107/908 [02:01<13:08,  1.02it/s]
 12%|█▏        | 108/908 [02:01<09:56,  1.34it/s]
 12%|█▏        | 109/908 [02:04<20:52,  1.57s/it]
 12%|█▏        | 110/908 [02:05<16:22,  1.23s/it]
 12%|█▏        | 111/908 [02:05<12:10,  1.09it/s]
 12%|█▏        | 112/908 [02:05<09:18,  1.43it/s]
 12%|█▏        | 113/908 [02:08<18:56,  1.43s/it]
 13%|█▎        | 114/908 [02:08<14:03,  1.06s/it]
 13%|█▎        | 115/908 [02:09<10:34,  1.25it/s]
 13%|█▎        | 116/908 [02:09<08:10,  1.62it/s]
 13%|█▎        | 117/908 [02:12<19:22,  1.47s/it]
 13%|█▎        | 118/908 [02:12<14:23,  1.09s/it]
 13%|█▎        | 119/908 [02:13<10:49,  1.22it/s]
+[Batch:   120] training loss = 0.499
+
 13%|█▎        | 120/908 [02:13<08:17,  1.58it/s]
 13%|█▎        | 121/908 [02:15<14:11,  1.08s/it]
 13%|█▎        | 122/908 [02:16<14:36,  1.11s/it]
 14%|█▎        | 123/908 [02:16<10:56,  1.20it/s]
 14%|█▎        | 124/908 [02:16<08:23,  1.56it/s]
 14%|█▍        | 125/908 [02:20<20:03,  1.54s/it]
 14%|█▍        | 126/908 [02:22<19:56,  1.53s/it]
 14%|█▍        | 127/908 [02:22<14:39,  1.13s/it]
 14%|█▍        | 128/908 [02:22<10:59,  1.18it/s]
 14%|█▍        | 129/908 [02:26<21:20,  1.64s/it]
 14%|█▍        | 130/908 [02:26<18:34,  1.43s/it]
 14%|█▍        | 131/908 [02:27<13:44,  1.06s/it]
 15%|█▍        | 132/908 [02:27<10:19,  1.25it/s]
 15%|█▍        | 133/908 [02:31<22:11,  1.72s/it]
 15%|█▍        | 134/908 [02:32<20:08,  1.56s/it]
 15%|█▍        | 135/908 [02:32<14:51,  1.15s/it]
 15%|█▍        | 136/908 [02:32<11:08,  1.15it/s]
 15%|█▌        | 137/908 [02:35<18:56,  1.47s/it]
 15%|█▌        | 138/908 [02:36<16:23,  1.28s/it]
 15%|█▌        | 139/908 [02:36<12:10,  1.05it/s]
+[Batch:   140] training loss = 0.404
+
 15%|█▌        | 140/908 [02:36<09:14,  1.38it/s]
 16%|█▌        | 141/908 [02:39<17:21,  1.36s/it]
 16%|█▌        | 142/908 [02:41<17:37,  1.38s/it]
 16%|█▌        | 143/908 [02:41<13:01,  1.02s/it]
 16%|█▌        | 144/908 [02:41<09:50,  1.29it/s]
 16%|█▌        | 145/908 [02:44<17:48,  1.40s/it]
 16%|█▌        | 146/908 [02:45<17:37,  1.39s/it]
 16%|█▌        | 147/908 [02:45<13:03,  1.03s/it]
 16%|█▋        | 148/908 [02:46<09:53,  1.28it/s]
 16%|█▋        | 149/908 [02:51<25:42,  2.03s/it]
 17%|█▋        | 150/908 [02:52<24:07,  1.91s/it]
 17%|█▋        | 151/908 [02:52<17:37,  1.40s/it]
 17%|█▋        | 152/908 [02:53<13:01,  1.03s/it]
 17%|█▋        | 153/908 [02:56<20:16,  1.61s/it]
 17%|█▋        | 154/908 [02:57<20:26,  1.63s/it]
 17%|█▋        | 155/908 [02:57<15:02,  1.20s/it]
 17%|█▋        | 156/908 [02:58<11:13,  1.12it/s]
 17%|█▋        | 157/908 [03:00<18:41,  1.49s/it]
 17%|█▋        | 158/908 [03:02<18:13,  1.46s/it]
 18%|█▊        | 159/908 [03:02<13:25,  1.08s/it]
+[Batch:   160] training loss = 0.461
+
 18%|█▊        | 160/908 [03:02<10:06,  1.23it/s]
 18%|█▊        | 161/908 [03:05<17:02,  1.37s/it]
 18%|█▊        | 162/908 [03:06<17:14,  1.39s/it]
 18%|█▊        | 163/908 [03:07<12:45,  1.03s/it]
 18%|█▊        | 164/908 [03:07<09:37,  1.29it/s]
 18%|█▊        | 165/908 [03:09<16:05,  1.30s/it]
 18%|█▊        | 166/908 [03:11<15:56,  1.29s/it]
 18%|█▊        | 167/908 [03:11<11:51,  1.04it/s]
 19%|█▊        | 168/908 [03:11<09:04,  1.36it/s]
 19%|█▊        | 169/908 [03:13<12:39,  1.03s/it]
 19%|█▊        | 170/908 [03:14<15:15,  1.24s/it]
 19%|█▉        | 171/908 [03:15<11:21,  1.08it/s]
 19%|█▉        | 172/908 [03:15<08:37,  1.42it/s]
 19%|█▉        | 173/908 [03:17<14:05,  1.15s/it]
 19%|█▉        | 174/908 [03:19<15:51,  1.30s/it]
 19%|█▉        | 175/908 [03:19<11:47,  1.04it/s]
 19%|█▉        | 176/908 [03:19<08:58,  1.36it/s]
 19%|█▉        | 177/908 [03:22<17:46,  1.46s/it]
 20%|█▉        | 178/908 [03:24<18:34,  1.53s/it]
 20%|█▉        | 179/908 [03:24<13:40,  1.13s/it]
+[Batch:   180] training loss = 0.523
+
 20%|█▉        | 180/908 [03:24<10:18,  1.18it/s]
 20%|█▉        | 181/908 [03:27<16:08,  1.33s/it]
 20%|██        | 182/908 [03:28<16:29,  1.36s/it]
 20%|██        | 183/908 [03:28<12:14,  1.01s/it]
 20%|██        | 184/908 [03:28<09:16,  1.30it/s]
 20%|██        | 185/908 [03:31<16:40,  1.38s/it]
 20%|██        | 186/908 [03:32<15:42,  1.31s/it]
 21%|██        | 187/908 [03:33<11:39,  1.03it/s]
 21%|██        | 188/908 [03:33<08:51,  1.35it/s]
 21%|██        | 189/908 [03:36<17:09,  1.43s/it]
 21%|██        | 190/908 [03:37<14:47,  1.24s/it]
 21%|██        | 191/908 [03:37<11:02,  1.08it/s]
 21%|██        | 192/908 [03:37<08:23,  1.42it/s]
 21%|██▏       | 193/908 [03:40<16:10,  1.36s/it]
 21%|██▏       | 194/908 [03:41<15:02,  1.26s/it]
 21%|██▏       | 195/908 [03:41<11:15,  1.06it/s]
 22%|██▏       | 196/908 [03:41<08:32,  1.39it/s]
 22%|██▏       | 197/908 [03:44<14:32,  1.23s/it]
 22%|██▏       | 198/908 [03:44<10:54,  1.09it/s]
 22%|██▏       | 199/908 [03:44<08:17,  1.43it/s]
+[Batch:   200] training loss = 0.373
+
 22%|██▏       | 200/908 [03:44<06:27,  1.83it/s]
 22%|██▏       | 201/908 [03:48<16:17,  1.38s/it]
 22%|██▏       | 202/908 [03:48<12:29,  1.06s/it]
 22%|██▏       | 203/908 [03:48<09:24,  1.25it/s]
 22%|██▏       | 204/908 [03:48<07:15,  1.62it/s]
 23%|██▎       | 205/908 [03:53<20:46,  1.77s/it]
 23%|██▎       | 206/908 [03:54<17:43,  1.52s/it]
 23%|██▎       | 207/908 [03:54<13:03,  1.12s/it]
 23%|██▎       | 208/908 [03:54<09:47,  1.19it/s]
 23%|██▎       | 209/908 [03:57<16:07,  1.38s/it]
 23%|██▎       | 210/908 [03:58<14:20,  1.23s/it]
 23%|██▎       | 211/908 [03:58<10:42,  1.08it/s]
 23%|██▎       | 212/908 [03:58<08:09,  1.42it/s]
 23%|██▎       | 213/908 [04:02<18:45,  1.62s/it]
 24%|██▎       | 214/908 [04:02<15:12,  1.31s/it]
 24%|██▎       | 215/908 [04:03<11:17,  1.02it/s]
 24%|██▍       | 216/908 [04:03<08:33,  1.35it/s]
 24%|██▍       | 217/908 [04:06<16:52,  1.47s/it]
 24%|██▍       | 218/908 [04:07<15:09,  1.32s/it]
 24%|██▍       | 219/908 [04:07<11:18,  1.01it/s]
+[Batch:   220] training loss = 0.438
+
 24%|██▍       | 220/908 [04:07<08:34,  1.34it/s]
 24%|██▍       | 221/908 [04:10<15:43,  1.37s/it]
 24%|██▍       | 222/908 [04:11<15:23,  1.35s/it]
 25%|██▍       | 223/908 [04:12<11:25,  1.00s/it]
 25%|██▍       | 224/908 [04:12<08:37,  1.32it/s]
 25%|██▍       | 225/908 [04:14<14:43,  1.29s/it]
 25%|██▍       | 226/908 [04:16<14:32,  1.28s/it]
 25%|██▌       | 227/908 [04:16<10:49,  1.05it/s]
 25%|██▌       | 228/908 [04:16<08:12,  1.38it/s]
 25%|██▌       | 229/908 [04:18<11:36,  1.03s/it]
 25%|██▌       | 230/908 [04:19<12:00,  1.06s/it]
 25%|██▌       | 231/908 [04:19<09:01,  1.25it/s]
 26%|██▌       | 232/908 [04:19<06:58,  1.61it/s]
 26%|██▌       | 233/908 [04:22<12:41,  1.13s/it]
 26%|██▌       | 234/908 [04:23<12:24,  1.11s/it]
 26%|██▌       | 235/908 [04:23<09:19,  1.20it/s]
 26%|██▌       | 236/908 [04:23<07:09,  1.57it/s]
 26%|██▌       | 237/908 [04:25<12:43,  1.14s/it]
 26%|██▌       | 238/908 [04:26<12:28,  1.12s/it]
 26%|██▋       | 239/908 [04:27<09:21,  1.19it/s]
+[Batch:   240] training loss = 0.516
+
 26%|██▋       | 240/908 [04:27<07:10,  1.55it/s]
 27%|██▋       | 241/908 [04:29<11:18,  1.02s/it]
 27%|██▋       | 242/908 [04:30<13:28,  1.21s/it]
 27%|██▋       | 243/908 [04:31<10:02,  1.10it/s]
 27%|██▋       | 244/908 [04:31<07:39,  1.45it/s]
 27%|██▋       | 245/908 [04:34<14:50,  1.34s/it]
 27%|██▋       | 246/908 [04:35<16:29,  1.49s/it]
 27%|██▋       | 247/908 [04:36<12:09,  1.10s/it]
 27%|██▋       | 248/908 [04:36<09:06,  1.21it/s]
 27%|██▋       | 249/908 [04:38<13:09,  1.20s/it]
 28%|██▊       | 250/908 [04:39<13:22,  1.22s/it]
 28%|██▊       | 251/908 [04:39<09:57,  1.10it/s]
 28%|██▊       | 252/908 [04:39<07:35,  1.44it/s]
 28%|██▊       | 253/908 [04:42<14:27,  1.32s/it]
 28%|██▊       | 254/908 [04:44<14:46,  1.36s/it]
 28%|██▊       | 255/908 [04:44<10:58,  1.01s/it]
 28%|██▊       | 256/908 [04:44<08:16,  1.31it/s]
 28%|██▊       | 257/908 [04:47<13:59,  1.29s/it]
 28%|██▊       | 258/908 [04:47<11:24,  1.05s/it]
 29%|██▊       | 259/908 [04:47<08:35,  1.26it/s]
+[Batch:   260] training loss = 0.500
+
 29%|██▊       | 260/908 [04:48<06:36,  1.63it/s]
 29%|██▊       | 261/908 [04:51<16:40,  1.55s/it]
 29%|██▉       | 262/908 [04:53<17:32,  1.63s/it]
 29%|██▉       | 263/908 [04:53<12:52,  1.20s/it]
 29%|██▉       | 264/908 [04:53<09:36,  1.12it/s]
 29%|██▉       | 265/908 [04:56<13:28,  1.26s/it]
 29%|██▉       | 266/908 [04:58<15:55,  1.49s/it]
 29%|██▉       | 267/908 [04:58<11:44,  1.10s/it]
 30%|██▉       | 268/908 [04:58<08:47,  1.21it/s]
 30%|██▉       | 269/908 [05:00<12:32,  1.18s/it]
 30%|██▉       | 270/908 [05:02<13:54,  1.31s/it]
 30%|██▉       | 271/908 [05:02<10:20,  1.03it/s]
 30%|██▉       | 272/908 [05:02<07:49,  1.35it/s]
 30%|███       | 273/908 [05:05<14:09,  1.34s/it]
 30%|███       | 274/908 [05:06<15:25,  1.46s/it]
 30%|███       | 275/908 [05:07<11:22,  1.08s/it]
 30%|███       | 276/908 [05:07<08:32,  1.23it/s]
 31%|███       | 277/908 [05:09<14:05,  1.34s/it]
 31%|███       | 278/908 [05:11<13:50,  1.32s/it]
 31%|███       | 279/908 [05:11<10:17,  1.02it/s]
+[Batch:   280] training loss = 0.421
+
 31%|███       | 280/908 [05:11<07:47,  1.34it/s]
 31%|███       | 281/908 [05:15<16:39,  1.59s/it]
 31%|███       | 282/908 [05:17<18:01,  1.73s/it]
 31%|███       | 283/908 [05:17<13:12,  1.27s/it]
 31%|███▏      | 284/908 [05:17<09:52,  1.05it/s]
 31%|███▏      | 285/908 [05:20<15:11,  1.46s/it]
 31%|███▏      | 286/908 [05:21<14:51,  1.43s/it]
 32%|███▏      | 287/908 [05:21<11:00,  1.06s/it]
 32%|███▏      | 288/908 [05:21<08:18,  1.24it/s]
 32%|███▏      | 289/908 [05:24<13:51,  1.34s/it]
 32%|███▏      | 290/908 [05:26<15:04,  1.46s/it]
 32%|███▏      | 291/908 [05:26<11:09,  1.09s/it]
 32%|███▏      | 292/908 [05:26<08:26,  1.22it/s]
 32%|███▏      | 293/908 [05:29<13:45,  1.34s/it]
 32%|███▏      | 294/908 [05:30<13:36,  1.33s/it]
 32%|███▏      | 295/908 [05:30<10:08,  1.01it/s]
 33%|███▎      | 296/908 [05:30<07:40,  1.33it/s]
 33%|███▎      | 297/908 [05:33<14:34,  1.43s/it]
 33%|███▎      | 298/908 [05:35<14:43,  1.45s/it]
 33%|███▎      | 299/908 [05:35<10:52,  1.07s/it]
+[Batch:   300] training loss = 0.465
+
 33%|███▎      | 300/908 [05:35<08:12,  1.23it/s]
 33%|███▎      | 301/908 [05:38<13:13,  1.31s/it]
 33%|███▎      | 302/908 [05:39<12:40,  1.26s/it]
 33%|███▎      | 303/908 [05:39<09:27,  1.07it/s]
 33%|███▎      | 304/908 [05:39<07:13,  1.39it/s]
 34%|███▎      | 305/908 [05:41<10:19,  1.03s/it]
 34%|███▎      | 306/908 [05:43<12:14,  1.22s/it]
 34%|███▍      | 307/908 [05:43<09:07,  1.10it/s]
 34%|███▍      | 308/908 [05:43<06:58,  1.44it/s]
 34%|███▍      | 309/908 [05:46<14:47,  1.48s/it]
 34%|███▍      | 310/908 [05:48<15:33,  1.56s/it]
 34%|███▍      | 311/908 [05:48<11:28,  1.15s/it]
 34%|███▍      | 312/908 [05:49<08:38,  1.15it/s]
 34%|███▍      | 313/908 [05:53<18:04,  1.82s/it]
 35%|███▍      | 314/908 [05:55<18:24,  1.86s/it]
 35%|███▍      | 315/908 [05:55<13:27,  1.36s/it]
 35%|███▍      | 316/908 [05:55<10:01,  1.02s/it]
 35%|███▍      | 317/908 [05:56<10:39,  1.08s/it]
 35%|███▌      | 318/908 [05:58<12:30,  1.27s/it]
 35%|███▌      | 319/908 [05:58<09:19,  1.05it/s]
+[Batch:   320] training loss = 0.533
+
 35%|███▌      | 320/908 [05:58<07:05,  1.38it/s]
 35%|███▌      | 321/908 [06:01<13:02,  1.33s/it]
 35%|███▌      | 322/908 [06:03<14:32,  1.49s/it]
 36%|███▌      | 323/908 [06:03<10:43,  1.10s/it]
 36%|███▌      | 324/908 [06:03<08:05,  1.20it/s]
 36%|███▌      | 325/908 [06:05<11:11,  1.15s/it]
 36%|███▌      | 326/908 [06:07<13:04,  1.35s/it]
 36%|███▌      | 327/908 [06:07<09:44,  1.01s/it]
 36%|███▌      | 328/908 [06:08<07:23,  1.31it/s]
 36%|███▌      | 329/908 [06:10<13:02,  1.35s/it]
 36%|███▋      | 330/908 [06:11<12:36,  1.31s/it]
 36%|███▋      | 331/908 [06:12<09:24,  1.02it/s]
 37%|███▋      | 332/908 [06:12<07:10,  1.34it/s]
 37%|███▋      | 333/908 [06:14<11:56,  1.25s/it]
 37%|███▋      | 334/908 [06:16<13:45,  1.44s/it]
 37%|███▋      | 335/908 [06:16<10:12,  1.07s/it]
 37%|███▋      | 336/908 [06:17<07:41,  1.24it/s]
 37%|███▋      | 337/908 [06:19<11:18,  1.19s/it]
 37%|███▋      | 338/908 [06:21<15:32,  1.64s/it]
 37%|███▋      | 339/908 [06:22<11:25,  1.21s/it]
+[Batch:   340] training loss = 0.544
+
 37%|███▋      | 340/908 [06:22<08:33,  1.11it/s]
 38%|███▊      | 341/908 [06:24<13:18,  1.41s/it]
 38%|███▊      | 342/908 [06:26<15:05,  1.60s/it]
 38%|███▊      | 343/908 [06:27<11:06,  1.18s/it]
 38%|███▊      | 344/908 [06:27<08:20,  1.13it/s]
 38%|███▊      | 345/908 [06:29<11:52,  1.27s/it]
 38%|███▊      | 346/908 [06:31<14:03,  1.50s/it]
 38%|███▊      | 347/908 [06:31<10:22,  1.11s/it]
 38%|███▊      | 348/908 [06:31<07:47,  1.20it/s]
 38%|███▊      | 349/908 [06:33<11:02,  1.19s/it]
 39%|███▊      | 350/908 [06:36<15:26,  1.66s/it]
 39%|███▊      | 351/908 [06:36<11:21,  1.22s/it]
 39%|███▉      | 352/908 [06:37<08:28,  1.09it/s]
 39%|███▉      | 353/908 [06:38<10:31,  1.14s/it]
 39%|███▉      | 354/908 [06:40<13:08,  1.42s/it]
 39%|███▉      | 355/908 [06:40<09:44,  1.06s/it]
 39%|███▉      | 356/908 [06:41<07:21,  1.25it/s]
 39%|███▉      | 357/908 [06:43<10:17,  1.12s/it]
 39%|███▉      | 358/908 [06:44<11:54,  1.30s/it]
 40%|███▉      | 359/908 [06:44<08:52,  1.03it/s]
+[Batch:   360] training loss = 0.443
+
 40%|███▉      | 360/908 [06:45<06:46,  1.35it/s]
 40%|███▉      | 361/908 [06:47<10:22,  1.14s/it]
 40%|███▉      | 362/908 [06:48<10:43,  1.18s/it]
 40%|███▉      | 363/908 [06:48<08:01,  1.13it/s]
 40%|████      | 364/908 [06:48<06:09,  1.47it/s]
 40%|████      | 365/908 [06:50<09:54,  1.09s/it]
 40%|████      | 366/908 [06:53<13:31,  1.50s/it]
 40%|████      | 367/908 [06:53<09:59,  1.11s/it]
 41%|████      | 368/908 [06:53<07:31,  1.20it/s]
 41%|████      | 369/908 [06:55<10:52,  1.21s/it]
 41%|████      | 370/908 [06:57<10:39,  1.19s/it]
 41%|████      | 371/908 [06:57<07:59,  1.12it/s]
 41%|████      | 372/908 [06:57<06:05,  1.47it/s]
 41%|████      | 373/908 [06:58<07:52,  1.13it/s]
 41%|████      | 374/908 [07:01<13:01,  1.46s/it]
 41%|████▏     | 375/908 [07:01<09:38,  1.08s/it]
 41%|████▏     | 376/908 [07:01<07:16,  1.22it/s]
 42%|████▏     | 377/908 [07:04<11:17,  1.28s/it]
 42%|████▏     | 378/908 [07:06<14:25,  1.63s/it]
 42%|████▏     | 379/908 [07:06<10:36,  1.20s/it]
+[Batch:   380] training loss = 0.594
+
 42%|████▏     | 380/908 [07:07<07:56,  1.11it/s]
 42%|████▏     | 381/908 [07:09<11:00,  1.25s/it]
 42%|████▏     | 382/908 [07:11<14:13,  1.62s/it]
 42%|████▏     | 383/908 [07:11<10:28,  1.20s/it]
 42%|████▏     | 384/908 [07:12<07:51,  1.11it/s]
 42%|████▏     | 385/908 [07:13<09:01,  1.03s/it]
 43%|████▎     | 386/908 [07:15<12:04,  1.39s/it]
 43%|████▎     | 387/908 [07:15<08:58,  1.03s/it]
 43%|████▎     | 388/908 [07:16<06:48,  1.27it/s]
 43%|████▎     | 389/908 [07:18<10:02,  1.16s/it]
 43%|████▎     | 390/908 [07:19<11:25,  1.32s/it]
 43%|████▎     | 391/908 [07:20<08:29,  1.01it/s]
 43%|████▎     | 392/908 [07:20<06:27,  1.33it/s]
 43%|████▎     | 393/908 [07:22<10:21,  1.21s/it]
 43%|████▎     | 394/908 [07:24<12:06,  1.41s/it]
 44%|████▎     | 395/908 [07:24<08:57,  1.05s/it]
 44%|████▎     | 396/908 [07:24<06:43,  1.27it/s]
 44%|████▎     | 397/908 [07:27<10:54,  1.28s/it]
 44%|████▍     | 398/908 [07:28<11:39,  1.37s/it]
 44%|████▍     | 399/908 [07:29<08:38,  1.02s/it]
+[Batch:   400] training loss = 0.396
+
 44%|████▍     | 400/908 [07:29<06:30,  1.30it/s]
 44%|████▍     | 401/908 [07:31<09:43,  1.15s/it]
 44%|████▍     | 402/908 [07:33<11:22,  1.35s/it]
 44%|████▍     | 403/908 [07:33<08:26,  1.00s/it]
 44%|████▍     | 404/908 [07:33<06:22,  1.32it/s]
 45%|████▍     | 405/908 [07:35<08:42,  1.04s/it]
 45%|████▍     | 406/908 [07:37<11:16,  1.35s/it]
 45%|████▍     | 407/908 [07:37<08:21,  1.00s/it]
 45%|████▍     | 408/908 [07:37<06:18,  1.32it/s]
 45%|████▌     | 409/908 [07:39<09:42,  1.17s/it]
 45%|████▌     | 410/908 [07:41<10:59,  1.32s/it]
 45%|████▌     | 411/908 [07:41<08:09,  1.01it/s]
 45%|████▌     | 412/908 [07:41<06:10,  1.34it/s]
 45%|████▌     | 413/908 [07:43<08:29,  1.03s/it]
 46%|████▌     | 414/908 [07:45<10:28,  1.27s/it]
 46%|████▌     | 415/908 [07:45<07:47,  1.05it/s]
 46%|████▌     | 416/908 [07:45<05:54,  1.39it/s]
 46%|████▌     | 417/908 [07:48<10:02,  1.23s/it]
 46%|████▌     | 418/908 [07:49<09:51,  1.21s/it]
 46%|████▌     | 419/908 [07:49<07:21,  1.11it/s]
+[Batch:   420] training loss = 0.360
+
 46%|████▋     | 420/908 [07:49<05:37,  1.44it/s]
 46%|████▋     | 421/908 [07:51<09:27,  1.17s/it]
 46%|████▋     | 422/908 [07:53<09:42,  1.20s/it]
 47%|████▋     | 423/908 [07:53<07:16,  1.11it/s]
 47%|████▋     | 424/908 [07:53<05:35,  1.44it/s]
 47%|████▋     | 425/908 [07:57<12:05,  1.50s/it]
 47%|████▋     | 426/908 [07:58<12:42,  1.58s/it]
 47%|████▋     | 427/908 [07:58<09:20,  1.16s/it]
 47%|████▋     | 428/908 [07:59<06:59,  1.15it/s]
 47%|████▋     | 429/908 [08:00<07:49,  1.02it/s]
 47%|████▋     | 430/908 [08:00<05:58,  1.33it/s]
 47%|████▋     | 431/908 [08:00<04:38,  1.72it/s]
 48%|████▊     | 432/908 [08:00<03:42,  2.14it/s]
 48%|████▊     | 433/908 [08:04<10:39,  1.35s/it]
 48%|████▊     | 434/908 [08:04<07:53,  1.00it/s]
 48%|████▊     | 435/908 [08:04<05:59,  1.32it/s]
 48%|████▊     | 436/908 [08:04<04:38,  1.69it/s]
 48%|████▊     | 437/908 [08:07<09:42,  1.24s/it]
 48%|████▊     | 438/908 [08:08<08:18,  1.06s/it]
 48%|████▊     | 439/908 [08:08<06:15,  1.25it/s]
+[Batch:   440] training loss = 0.533
+
 48%|████▊     | 440/908 [08:08<04:48,  1.62it/s]
 49%|████▊     | 441/908 [08:11<10:09,  1.31s/it]
 49%|████▊     | 442/908 [08:12<08:34,  1.10s/it]
 49%|████▉     | 443/908 [08:12<06:25,  1.21it/s]
 49%|████▉     | 444/908 [08:12<04:55,  1.57it/s]
 49%|████▉     | 445/908 [08:16<11:21,  1.47s/it]
 49%|████▉     | 446/908 [08:16<09:40,  1.26s/it]
 49%|████▉     | 447/908 [08:17<07:12,  1.07it/s]
 49%|████▉     | 448/908 [08:17<05:27,  1.40it/s]
 49%|████▉     | 449/908 [08:21<12:39,  1.66s/it]
 50%|████▉     | 450/908 [08:21<10:18,  1.35s/it]
 50%|████▉     | 451/908 [08:21<07:37,  1.00s/it]
 50%|████▉     | 452/908 [08:22<05:46,  1.32it/s]
 50%|████▉     | 453/908 [08:25<12:25,  1.64s/it]
 50%|█████     | 454/908 [08:26<09:36,  1.27s/it]
 50%|█████     | 455/908 [08:26<07:08,  1.06it/s]
 50%|█████     | 456/908 [08:26<05:25,  1.39it/s]
 50%|█████     | 457/908 [08:30<11:52,  1.58s/it]
 50%|█████     | 458/908 [08:30<09:47,  1.31s/it]
 51%|█████     | 459/908 [08:31<07:15,  1.03it/s]
+[Batch:   460] training loss = 0.522
+
 51%|█████     | 460/908 [08:31<05:30,  1.36it/s]
 51%|█████     | 461/908 [08:34<10:30,  1.41s/it]
 51%|█████     | 462/908 [08:34<09:00,  1.21s/it]
 51%|█████     | 463/908 [08:35<06:42,  1.10it/s]
 51%|█████     | 464/908 [08:35<05:07,  1.44it/s]
 51%|█████     | 465/908 [08:38<11:25,  1.55s/it]
 51%|█████▏    | 466/908 [08:39<09:51,  1.34s/it]
 51%|█████▏    | 467/908 [08:39<07:19,  1.00it/s]
 52%|█████▏    | 468/908 [08:40<05:32,  1.32it/s]
 52%|█████▏    | 469/908 [08:42<08:52,  1.21s/it]
 52%|█████▏    | 470/908 [08:42<07:21,  1.01s/it]
 52%|█████▏    | 471/908 [08:43<05:35,  1.30it/s]
 52%|█████▏    | 472/908 [08:43<04:19,  1.68it/s]
 52%|█████▏    | 473/908 [08:45<08:32,  1.18s/it]
 52%|█████▏    | 474/908 [08:46<07:26,  1.03s/it]
 52%|█████▏    | 475/908 [08:46<05:37,  1.28it/s]
 52%|█████▏    | 476/908 [08:46<04:22,  1.65it/s]
 53%|█████▎    | 477/908 [08:50<11:40,  1.63s/it]
 53%|█████▎    | 478/908 [08:51<09:33,  1.33s/it]
 53%|█████▎    | 479/908 [08:51<07:04,  1.01it/s]
+[Batch:   480] training loss = 0.513
+
 53%|█████▎    | 480/908 [08:51<05:20,  1.33it/s]
 53%|█████▎    | 481/908 [08:57<15:47,  2.22s/it]
 53%|█████▎    | 482/908 [08:58<11:54,  1.68s/it]
 53%|█████▎    | 483/908 [08:58<08:43,  1.23s/it]
 53%|█████▎    | 484/908 [08:58<06:29,  1.09it/s]
 53%|█████▎    | 485/908 [09:02<12:47,  1.81s/it]
 54%|█████▎    | 486/908 [09:02<10:14,  1.46s/it]
 54%|█████▎    | 487/908 [09:03<07:34,  1.08s/it]
 54%|█████▎    | 488/908 [09:03<05:41,  1.23it/s]
 54%|█████▍    | 489/908 [09:06<10:58,  1.57s/it]
 54%|█████▍    | 490/908 [09:07<08:31,  1.22s/it]
 54%|█████▍    | 491/908 [09:07<06:20,  1.10it/s]
 54%|█████▍    | 492/908 [09:07<04:49,  1.44it/s]
 54%|█████▍    | 493/908 [09:10<10:40,  1.54s/it]
 54%|█████▍    | 494/908 [09:11<09:00,  1.30s/it]
 55%|█████▍    | 495/908 [09:11<06:40,  1.03it/s]
 55%|█████▍    | 496/908 [09:12<05:03,  1.36it/s]
 55%|█████▍    | 497/908 [09:15<11:21,  1.66s/it]
 55%|█████▍    | 498/908 [09:16<08:22,  1.23s/it]
 55%|█████▍    | 499/908 [09:16<06:13,  1.09it/s]
+[Batch:   500] training loss = 0.344
+
 55%|█████▌    | 500/908 [09:16<04:43,  1.44it/s]
 55%|█████▌    | 501/908 [09:19<10:22,  1.53s/it]
 55%|█████▌    | 502/908 [09:20<07:37,  1.13s/it]
 55%|█████▌    | 503/908 [09:20<05:43,  1.18it/s]
 56%|█████▌    | 504/908 [09:20<04:23,  1.54it/s]
 56%|█████▌    | 505/908 [09:23<09:57,  1.48s/it]
 56%|█████▌    | 506/908 [09:24<07:22,  1.10s/it]
 56%|█████▌    | 507/908 [09:24<05:32,  1.21it/s]
 56%|█████▌    | 508/908 [09:24<04:15,  1.57it/s]
 56%|█████▌    | 509/908 [09:29<12:22,  1.86s/it]
 56%|█████▌    | 510/908 [09:30<10:03,  1.52s/it]
 56%|█████▋    | 511/908 [09:30<07:24,  1.12s/it]
 56%|█████▋    | 512/908 [09:30<05:32,  1.19it/s]
 56%|█████▋    | 513/908 [09:33<10:17,  1.56s/it]
 57%|█████▋    | 514/908 [09:34<07:59,  1.22s/it]
 57%|█████▋    | 515/908 [09:34<05:57,  1.10it/s]
 57%|█████▋    | 516/908 [09:34<04:32,  1.44it/s]
 57%|█████▋    | 517/908 [09:37<08:50,  1.36s/it]
 57%|█████▋    | 518/908 [09:37<07:11,  1.11s/it]
 57%|█████▋    | 519/908 [09:38<05:23,  1.20it/s]
+[Batch:   520] training loss = 0.471
+
 57%|█████▋    | 520/908 [09:38<04:08,  1.56it/s]
 57%|█████▋    | 521/908 [09:41<08:45,  1.36s/it]
 57%|█████▋    | 522/908 [09:41<06:47,  1.06s/it]
 58%|█████▊    | 523/908 [09:41<05:06,  1.26it/s]
 58%|█████▊    | 524/908 [09:42<03:55,  1.63it/s]
 58%|█████▊    | 525/908 [09:45<08:54,  1.39s/it]
 58%|█████▊    | 526/908 [09:45<06:58,  1.10s/it]
 58%|█████▊    | 527/908 [09:45<05:14,  1.21it/s]
 58%|█████▊    | 528/908 [09:46<04:00,  1.58it/s]
 58%|█████▊    | 529/908 [09:49<09:35,  1.52s/it]
 58%|█████▊    | 530/908 [09:49<07:04,  1.12s/it]
 58%|█████▊    | 531/908 [09:49<05:18,  1.19it/s]
 59%|█████▊    | 532/908 [09:50<04:03,  1.54it/s]
 59%|█████▊    | 533/908 [09:54<10:27,  1.67s/it]
 59%|█████▉    | 534/908 [09:54<07:56,  1.28s/it]
 59%|█████▉    | 535/908 [09:54<05:54,  1.05it/s]
 59%|█████▉    | 536/908 [09:54<04:28,  1.38it/s]
 59%|█████▉    | 537/908 [09:59<12:22,  2.00s/it]
 59%|█████▉    | 538/908 [10:00<09:01,  1.46s/it]
 59%|█████▉    | 539/908 [10:00<06:40,  1.08s/it]
+[Batch:   540] training loss = 0.465
+
 59%|█████▉    | 540/908 [10:00<04:59,  1.23it/s]
 60%|█████▉    | 541/908 [10:03<09:38,  1.58s/it]
 60%|█████▉    | 542/908 [10:04<07:27,  1.22s/it]
 60%|█████▉    | 543/908 [10:04<05:32,  1.10it/s]
 60%|█████▉    | 544/908 [10:04<04:13,  1.44it/s]
 60%|██████    | 545/908 [10:08<09:57,  1.64s/it]
 60%|██████    | 546/908 [10:08<07:46,  1.29s/it]
 60%|██████    | 547/908 [10:09<05:46,  1.04it/s]
 60%|██████    | 548/908 [10:09<04:22,  1.37it/s]
 60%|██████    | 549/908 [10:13<09:50,  1.65s/it]
 61%|██████    | 550/908 [10:13<07:36,  1.28s/it]
 61%|██████    | 551/908 [10:13<05:39,  1.05it/s]
 61%|██████    | 552/908 [10:13<04:17,  1.38it/s]
 61%|██████    | 553/908 [10:17<09:17,  1.57s/it]
 61%|██████    | 554/908 [10:17<06:52,  1.17s/it]
 61%|██████    | 555/908 [10:17<05:08,  1.15it/s]
 61%|██████    | 556/908 [10:18<03:54,  1.50it/s]
 61%|██████▏   | 557/908 [10:21<08:12,  1.40s/it]
 61%|██████▏   | 558/908 [10:21<06:14,  1.07s/it]
 62%|██████▏   | 559/908 [10:21<04:41,  1.24it/s]
+[Batch:   560] training loss = 0.422
+
 62%|██████▏   | 560/908 [10:21<03:36,  1.61it/s]
 62%|██████▏   | 561/908 [10:26<10:26,  1.81s/it]
 62%|██████▏   | 562/908 [10:26<07:39,  1.33s/it]
 62%|██████▏   | 563/908 [10:26<05:41,  1.01it/s]
 62%|██████▏   | 564/908 [10:27<04:18,  1.33it/s]
 62%|██████▏   | 565/908 [10:30<09:21,  1.64s/it]
 62%|██████▏   | 566/908 [10:31<07:00,  1.23s/it]
 62%|██████▏   | 567/908 [10:31<05:12,  1.09it/s]
 63%|██████▎   | 568/908 [10:31<03:58,  1.43it/s]
 63%|██████▎   | 569/908 [10:34<07:38,  1.35s/it]
 63%|██████▎   | 570/908 [10:34<06:17,  1.12s/it]
 63%|██████▎   | 571/908 [10:35<04:42,  1.19it/s]
 63%|██████▎   | 572/908 [10:35<03:35,  1.56it/s]
 63%|██████▎   | 573/908 [10:39<09:05,  1.63s/it]
 63%|██████▎   | 574/908 [10:40<07:54,  1.42s/it]
 63%|██████▎   | 575/908 [10:40<05:50,  1.05s/it]
 63%|██████▎   | 576/908 [10:40<04:23,  1.26it/s]
 64%|██████▎   | 577/908 [10:43<07:53,  1.43s/it]
 64%|██████▎   | 578/908 [10:43<06:22,  1.16s/it]
 64%|██████▍   | 579/908 [10:44<04:45,  1.15it/s]
+[Batch:   580] training loss = 0.470
+
 64%|██████▍   | 580/908 [10:44<03:37,  1.50it/s]
 64%|██████▍   | 581/908 [10:46<06:09,  1.13s/it]
 64%|██████▍   | 582/908 [10:47<05:29,  1.01s/it]
 64%|██████▍   | 583/908 [10:47<04:08,  1.31it/s]
 64%|██████▍   | 584/908 [10:47<03:12,  1.69it/s]
 64%|██████▍   | 585/908 [10:49<05:53,  1.09s/it]
 65%|██████▍   | 586/908 [10:50<05:04,  1.06it/s]
 65%|██████▍   | 587/908 [10:50<03:50,  1.39it/s]
 65%|██████▍   | 588/908 [10:50<02:59,  1.78it/s]
 65%|██████▍   | 589/908 [10:53<06:27,  1.22s/it]
 65%|██████▍   | 590/908 [10:54<06:07,  1.16s/it]
 65%|██████▌   | 591/908 [10:54<04:34,  1.16it/s]
 65%|██████▌   | 592/908 [10:55<03:29,  1.51it/s]
 65%|██████▌   | 593/908 [11:00<10:21,  1.97s/it]
 65%|██████▌   | 594/908 [11:01<09:46,  1.87s/it]
 66%|██████▌   | 595/908 [11:01<07:06,  1.36s/it]
 66%|██████▌   | 596/908 [11:02<05:15,  1.01s/it]
 66%|██████▌   | 597/908 [11:05<08:20,  1.61s/it]
 66%|██████▌   | 598/908 [11:05<07:00,  1.36s/it]
 66%|██████▌   | 599/908 [11:06<05:10,  1.01s/it]
+[Batch:   600] training loss = 0.288
+
 66%|██████▌   | 600/908 [11:06<03:54,  1.31it/s]
 66%|██████▌   | 601/908 [11:09<07:14,  1.42s/it]
 66%|██████▋   | 602/908 [11:09<05:42,  1.12s/it]
 66%|██████▋   | 603/908 [11:09<04:15,  1.19it/s]
 67%|██████▋   | 604/908 [11:09<03:16,  1.55it/s]
 67%|██████▋   | 605/908 [11:13<08:04,  1.60s/it]
 67%|██████▋   | 606/908 [11:14<06:08,  1.22s/it]
 67%|██████▋   | 607/908 [11:14<04:34,  1.10it/s]
 67%|██████▋   | 608/908 [11:14<03:28,  1.44it/s]
 67%|██████▋   | 609/908 [11:17<07:00,  1.41s/it]
 67%|██████▋   | 610/908 [11:18<05:35,  1.13s/it]
 67%|██████▋   | 611/908 [11:18<04:11,  1.18it/s]
 67%|██████▋   | 612/908 [11:18<03:12,  1.54it/s]
 68%|██████▊   | 613/908 [11:23<09:19,  1.90s/it]
 68%|██████▊   | 614/908 [11:23<07:23,  1.51s/it]
 68%|██████▊   | 615/908 [11:24<05:25,  1.11s/it]
 68%|██████▊   | 616/908 [11:24<04:03,  1.20it/s]
 68%|██████▊   | 617/908 [11:27<07:24,  1.53s/it]
 68%|██████▊   | 618/908 [11:27<05:46,  1.20s/it]
 68%|██████▊   | 619/908 [11:27<04:18,  1.12it/s]
+[Batch:   620] training loss = 0.496
+
 68%|██████▊   | 620/908 [11:28<03:17,  1.46it/s]
 68%|██████▊   | 621/908 [11:33<10:14,  2.14s/it]
 69%|██████▊   | 622/908 [11:34<08:02,  1.69s/it]
 69%|██████▊   | 623/908 [11:34<05:52,  1.24s/it]
 69%|██████▊   | 624/908 [11:34<04:22,  1.08it/s]
 69%|██████▉   | 625/908 [11:37<07:01,  1.49s/it]
 69%|██████▉   | 626/908 [11:38<05:46,  1.23s/it]
 69%|██████▉   | 627/908 [11:38<04:18,  1.09it/s]
 69%|██████▉   | 628/908 [11:38<03:17,  1.42it/s]
 69%|██████▉   | 629/908 [11:41<06:22,  1.37s/it]
 69%|██████▉   | 630/908 [11:41<04:44,  1.02s/it]
 69%|██████▉   | 631/908 [11:41<03:34,  1.29it/s]
 70%|██████▉   | 632/908 [11:42<02:46,  1.66it/s]
 70%|██████▉   | 633/908 [11:44<05:49,  1.27s/it]
 70%|██████▉   | 634/908 [11:45<04:34,  1.00s/it]
 70%|██████▉   | 635/908 [11:45<03:27,  1.32it/s]
 70%|███████   | 636/908 [11:45<02:41,  1.68it/s]
 70%|███████   | 637/908 [11:49<07:03,  1.56s/it]
 70%|███████   | 638/908 [11:49<05:14,  1.17s/it]
 70%|███████   | 639/908 [11:49<03:55,  1.14it/s]
+[Batch:   640] training loss = 0.411
+
 70%|███████   | 640/908 [11:50<03:00,  1.48it/s]
 71%|███████   | 641/908 [11:53<07:08,  1.60s/it]
 71%|███████   | 642/908 [11:54<05:16,  1.19s/it]
 71%|███████   | 643/908 [11:54<03:56,  1.12it/s]
 71%|███████   | 644/908 [11:54<03:00,  1.46it/s]
 71%|███████   | 645/908 [11:58<06:43,  1.53s/it]
 71%|███████   | 646/908 [11:58<05:08,  1.18s/it]
 71%|███████▏  | 647/908 [11:58<03:51,  1.13it/s]
 71%|███████▏  | 648/908 [11:58<02:56,  1.48it/s]
 71%|███████▏  | 649/908 [12:04<09:25,  2.18s/it]
 72%|███████▏  | 650/908 [12:04<06:49,  1.59s/it]
 72%|███████▏  | 651/908 [12:04<05:00,  1.17s/it]
 72%|███████▏  | 652/908 [12:05<03:45,  1.14it/s]
 72%|███████▏  | 653/908 [12:08<07:16,  1.71s/it]
 72%|███████▏  | 654/908 [12:08<05:21,  1.27s/it]
 72%|███████▏  | 655/908 [12:09<03:58,  1.06it/s]
 72%|███████▏  | 656/908 [12:09<03:01,  1.39it/s]
 72%|███████▏  | 657/908 [12:13<07:29,  1.79s/it]
 72%|███████▏  | 658/908 [12:13<05:28,  1.31s/it]
 73%|███████▎  | 659/908 [12:14<04:04,  1.02it/s]
+[Batch:   660] training loss = 0.437
+
 73%|███████▎  | 660/908 [12:14<03:05,  1.33it/s]
 73%|███████▎  | 661/908 [12:18<07:06,  1.73s/it]
 73%|███████▎  | 662/908 [12:18<05:11,  1.27s/it]
 73%|███████▎  | 663/908 [12:18<03:51,  1.06it/s]
 73%|███████▎  | 664/908 [12:18<02:56,  1.39it/s]
 73%|███████▎  | 665/908 [12:23<07:18,  1.80s/it]
 73%|███████▎  | 666/908 [12:23<05:19,  1.32s/it]
 73%|███████▎  | 667/908 [12:23<03:57,  1.02it/s]
 74%|███████▎  | 668/908 [12:23<02:59,  1.33it/s]
 74%|███████▎  | 669/908 [12:27<06:57,  1.75s/it]
 74%|███████▍  | 670/908 [12:28<05:05,  1.28s/it]
 74%|███████▍  | 671/908 [12:28<03:47,  1.04it/s]
 74%|███████▍  | 672/908 [12:28<02:52,  1.36it/s]
 74%|███████▍  | 673/908 [12:31<06:06,  1.56s/it]
 74%|███████▍  | 674/908 [12:32<04:40,  1.20s/it]
 74%|███████▍  | 675/908 [12:32<03:29,  1.11it/s]
 74%|███████▍  | 676/908 [12:32<02:40,  1.45it/s]
 75%|███████▍  | 677/908 [12:36<06:10,  1.60s/it]
 75%|███████▍  | 678/908 [12:36<04:31,  1.18s/it]
 75%|███████▍  | 679/908 [12:36<03:22,  1.13it/s]
+[Batch:   680] training loss = 0.446
+
 75%|███████▍  | 680/908 [12:37<02:35,  1.47it/s]
 75%|███████▌  | 681/908 [12:40<05:36,  1.48s/it]
 75%|███████▌  | 682/908 [12:40<04:07,  1.10s/it]
 75%|███████▌  | 683/908 [12:40<03:06,  1.21it/s]
 75%|███████▌  | 684/908 [12:41<02:23,  1.56it/s]
 75%|███████▌  | 685/908 [12:44<05:40,  1.53s/it]
 76%|███████▌  | 686/908 [12:44<04:10,  1.13s/it]
 76%|███████▌  | 687/908 [12:45<03:07,  1.18it/s]
 76%|███████▌  | 688/908 [12:45<02:24,  1.52it/s]
 76%|███████▌  | 689/908 [12:48<05:18,  1.45s/it]
 76%|███████▌  | 690/908 [12:48<03:55,  1.08s/it]
 76%|███████▌  | 691/908 [12:48<02:56,  1.23it/s]
 76%|███████▌  | 692/908 [12:49<02:16,  1.58it/s]
 76%|███████▋  | 693/908 [12:51<04:37,  1.29s/it]
 76%|███████▋  | 694/908 [12:52<03:52,  1.09s/it]
 77%|███████▋  | 695/908 [12:52<02:55,  1.22it/s]
 77%|███████▋  | 696/908 [12:52<02:14,  1.57it/s]
 77%|███████▋  | 697/908 [13:01<10:21,  2.95s/it]
 77%|███████▋  | 698/908 [13:03<09:47,  2.80s/it]
 77%|███████▋  | 699/908 [13:03<07:01,  2.02s/it]
+[Batch:   700] training loss = 0.392
+
 77%|███████▋  | 700/908 [13:04<05:29,  1.58s/it]
 77%|███████▋  | 701/908 [13:12<12:21,  3.58s/it]
 77%|███████▋  | 702/908 [13:13<09:22,  2.73s/it]
 77%|███████▋  | 703/908 [13:13<06:44,  1.97s/it]
 78%|███████▊  | 704/908 [13:13<04:53,  1.44s/it]
 78%|███████▊  | 705/908 [13:19<09:11,  2.72s/it]
 78%|███████▊  | 706/908 [13:19<06:46,  2.01s/it]
 78%|███████▊  | 707/908 [13:20<04:55,  1.47s/it]
 78%|███████▊  | 708/908 [13:20<03:37,  1.09s/it]
 78%|███████▊  | 709/908 [13:27<09:59,  3.01s/it]
 78%|███████▊  | 710/908 [13:28<07:28,  2.27s/it]
 78%|███████▊  | 711/908 [13:28<05:23,  1.64s/it]
 78%|███████▊  | 712/908 [13:28<03:57,  1.21s/it]
 79%|███████▊  | 713/908 [13:35<09:32,  2.93s/it]
 79%|███████▊  | 714/908 [13:36<07:00,  2.17s/it]
 79%|███████▊  | 715/908 [13:36<05:04,  1.58s/it]
 79%|███████▉  | 716/908 [13:36<03:43,  1.16s/it]
 79%|███████▉  | 717/908 [13:44<09:49,  3.09s/it]
 79%|███████▉  | 718/908 [13:44<07:02,  2.23s/it]
 79%|███████▉  | 719/908 [13:44<05:05,  1.62s/it]
+[Batch:   720] training loss = 0.413
+
 79%|███████▉  | 720/908 [13:44<03:43,  1.19s/it]
 79%|███████▉  | 721/908 [13:52<09:44,  3.13s/it]
 80%|███████▉  | 722/908 [13:52<06:58,  2.25s/it]
 80%|███████▉  | 723/908 [13:52<05:01,  1.63s/it]
 80%|███████▉  | 724/908 [13:52<03:41,  1.20s/it]
 80%|███████▉  | 725/908 [14:00<09:47,  3.21s/it]
 80%|███████▉  | 726/908 [14:01<07:19,  2.42s/it]
 80%|████████  | 727/908 [14:01<05:16,  1.75s/it]
 80%|████████  | 728/908 [14:01<03:50,  1.28s/it]
 80%|████████  | 729/908 [14:14<14:15,  4.78s/it]
 80%|████████  | 730/908 [14:14<10:06,  3.41s/it]
 81%|████████  | 731/908 [14:15<07:17,  2.47s/it]
 81%|████████  | 732/908 [14:15<05:14,  1.79s/it]
 81%|████████  | 733/908 [14:21<08:39,  2.97s/it]
 81%|████████  | 734/908 [14:21<06:12,  2.14s/it]
 81%|████████  | 735/908 [14:21<04:29,  1.56s/it]
 81%|████████  | 736/908 [14:21<03:17,  1.15s/it]
 81%|████████  | 737/908 [14:29<08:53,  3.12s/it]
 81%|████████▏ | 738/908 [14:29<06:21,  2.24s/it]
 81%|████████▏ | 739/908 [14:29<04:35,  1.63s/it]
+[Batch:   740] training loss = 0.482
+
 81%|████████▏ | 740/908 [14:30<03:21,  1.20s/it]
 82%|████████▏ | 741/908 [14:37<08:34,  3.08s/it]
 82%|████████▏ | 742/908 [14:37<06:08,  2.22s/it]
 82%|████████▏ | 743/908 [14:37<04:26,  1.62s/it]
 82%|████████▏ | 744/908 [14:38<03:14,  1.19s/it]
 82%|████████▏ | 745/908 [14:46<08:51,  3.26s/it]
 82%|████████▏ | 746/908 [14:46<06:30,  2.41s/it]
 82%|████████▏ | 747/908 [14:46<04:40,  1.74s/it]
 82%|████████▏ | 748/908 [14:47<03:24,  1.28s/it]
 82%|████████▏ | 749/908 [14:54<08:13,  3.11s/it]
 83%|████████▎ | 750/908 [14:54<05:57,  2.26s/it]
 83%|████████▎ | 751/908 [14:54<04:17,  1.64s/it]
 83%|████████▎ | 752/908 [14:55<03:08,  1.21s/it]
 83%|████████▎ | 753/908 [15:02<07:58,  3.09s/it]
 83%|████████▎ | 754/908 [15:02<05:46,  2.25s/it]
 83%|████████▎ | 755/908 [15:03<04:09,  1.63s/it]
 83%|████████▎ | 756/908 [15:03<03:02,  1.20s/it]
 83%|████████▎ | 757/908 [15:13<10:07,  4.02s/it]
 83%|████████▎ | 758/908 [15:14<07:24,  2.97s/it]
 84%|████████▎ | 759/908 [15:14<05:17,  2.13s/it]
+[Batch:   760] training loss = 0.435
+
 84%|████████▎ | 760/908 [15:14<03:49,  1.55s/it]
 84%|████████▍ | 761/908 [15:22<08:14,  3.36s/it]
 84%|████████▍ | 762/908 [15:22<05:52,  2.41s/it]
 84%|████████▍ | 763/908 [15:22<04:13,  1.75s/it]
 84%|████████▍ | 764/908 [15:22<03:04,  1.28s/it]
 84%|████████▍ | 765/908 [15:31<08:01,  3.37s/it]
 84%|████████▍ | 766/908 [15:31<05:46,  2.44s/it]
 84%|████████▍ | 767/908 [15:31<04:08,  1.77s/it]
 85%|████████▍ | 768/908 [15:31<03:00,  1.29s/it]
 85%|████████▍ | 769/908 [15:38<06:39,  2.87s/it]
 85%|████████▍ | 770/908 [15:38<04:46,  2.07s/it]
 85%|████████▍ | 771/908 [15:38<03:26,  1.51s/it]
 85%|████████▌ | 772/908 [15:38<02:31,  1.11s/it]
 85%|████████▌ | 773/908 [15:47<07:32,  3.35s/it]
 85%|████████▌ | 774/908 [15:47<05:23,  2.41s/it]
 85%|████████▌ | 775/908 [15:47<03:52,  1.75s/it]
 85%|████████▌ | 776/908 [15:48<02:49,  1.28s/it]
 86%|████████▌ | 777/908 [15:55<06:39,  3.05s/it]
 86%|████████▌ | 778/908 [15:55<04:53,  2.26s/it]
 86%|████████▌ | 779/908 [15:55<03:31,  1.64s/it]
+[Batch:   780] training loss = 0.420
+
 86%|████████▌ | 780/908 [15:56<02:34,  1.20s/it]
 86%|████████▌ | 781/908 [16:03<06:33,  3.10s/it]
 86%|████████▌ | 782/908 [16:03<04:40,  2.23s/it]
 86%|████████▌ | 783/908 [16:03<03:21,  1.61s/it]
 86%|████████▋ | 784/908 [16:04<02:27,  1.19s/it]
 86%|████████▋ | 785/908 [16:14<07:59,  3.90s/it]
 87%|████████▋ | 786/908 [16:14<05:40,  2.79s/it]
 87%|████████▋ | 787/908 [16:14<04:03,  2.01s/it]
 87%|████████▋ | 788/908 [16:14<02:55,  1.46s/it]
 87%|████████▋ | 789/908 [16:22<06:22,  3.21s/it]
 87%|████████▋ | 790/908 [16:22<04:32,  2.31s/it]
 87%|████████▋ | 791/908 [16:22<03:15,  1.67s/it]
 87%|████████▋ | 792/908 [16:22<02:22,  1.23s/it]
 87%|████████▋ | 793/908 [16:28<04:54,  2.56s/it]
 87%|████████▋ | 794/908 [16:28<03:30,  1.85s/it]
 88%|████████▊ | 795/908 [16:28<02:32,  1.35s/it]
 88%|████████▊ | 796/908 [16:29<01:52,  1.00s/it]
 88%|████████▊ | 797/908 [16:35<04:34,  2.48s/it]
 88%|████████▊ | 798/908 [16:35<03:16,  1.79s/it]
 88%|████████▊ | 799/908 [16:35<02:22,  1.31s/it]
+[Batch:   800] training loss = 0.362
+
 88%|████████▊ | 800/908 [16:35<01:45,  1.03it/s]
 88%|████████▊ | 801/908 [16:45<06:16,  3.52s/it]
 88%|████████▊ | 802/908 [16:45<04:27,  2.52s/it]
 88%|████████▊ | 803/908 [16:45<03:11,  1.82s/it]
 89%|████████▊ | 804/908 [16:45<02:18,  1.34s/it]
 89%|████████▊ | 805/908 [16:52<05:05,  2.97s/it]
 89%|████████▉ | 806/908 [16:52<03:37,  2.14s/it]
 89%|████████▉ | 807/908 [16:52<02:36,  1.55s/it]
 89%|████████▉ | 808/908 [16:52<01:54,  1.14s/it]
 89%|████████▉ | 809/908 [17:00<04:54,  2.98s/it]
 89%|████████▉ | 810/908 [17:00<03:30,  2.14s/it]
 89%|████████▉ | 811/908 [17:00<02:31,  1.56s/it]
 89%|████████▉ | 812/908 [17:00<01:50,  1.15s/it]
 90%|████████▉ | 813/908 [17:12<06:41,  4.23s/it]
 90%|████████▉ | 814/908 [17:12<04:43,  3.02s/it]
 90%|████████▉ | 815/908 [17:12<03:21,  2.17s/it]
 90%|████████▉ | 816/908 [17:12<02:25,  1.58s/it]
 90%|████████▉ | 817/908 [17:19<04:31,  2.98s/it]
 90%|█████████ | 818/908 [17:19<03:13,  2.14s/it]
 90%|█████████ | 819/908 [17:19<02:18,  1.56s/it]
+[Batch:   820] training loss = 0.426
+
 90%|█████████ | 820/908 [17:19<01:42,  1.16s/it]
 90%|█████████ | 821/908 [17:27<04:22,  3.02s/it]
 91%|█████████ | 822/908 [17:27<03:06,  2.17s/it]
 91%|█████████ | 823/908 [17:27<02:14,  1.58s/it]
 91%|█████████ | 824/908 [17:27<01:37,  1.16s/it]
 91%|█████████ | 825/908 [17:34<04:00,  2.90s/it]
 91%|█████████ | 826/908 [17:34<02:51,  2.09s/it]
 91%|█████████ | 827/908 [17:34<02:02,  1.52s/it]
 91%|█████████ | 828/908 [17:35<01:29,  1.12s/it]
 91%|█████████▏| 829/908 [17:43<04:11,  3.19s/it]
 91%|█████████▏| 830/908 [17:43<02:58,  2.29s/it]
 92%|█████████▏| 831/908 [17:43<02:07,  1.66s/it]
 92%|█████████▏| 832/908 [17:43<01:32,  1.22s/it]
 92%|█████████▏| 833/908 [17:51<03:59,  3.20s/it]
 92%|█████████▏| 834/908 [17:51<02:49,  2.30s/it]
 92%|█████████▏| 835/908 [17:51<02:01,  1.67s/it]
 92%|█████████▏| 836/908 [17:52<01:28,  1.23s/it]
 92%|█████████▏| 837/908 [17:59<03:33,  3.01s/it]
 92%|█████████▏| 838/908 [17:59<02:31,  2.17s/it]
 92%|█████████▏| 839/908 [17:59<01:48,  1.58s/it]
+[Batch:   840] training loss = 0.443
+
 93%|█████████▎| 840/908 [17:59<01:19,  1.16s/it]
 93%|█████████▎| 841/908 [18:08<03:48,  3.42s/it]
 93%|█████████▎| 842/908 [18:08<02:41,  2.45s/it]
 93%|█████████▎| 843/908 [18:08<01:55,  1.77s/it]
 93%|█████████▎| 844/908 [18:09<01:23,  1.30s/it]
 93%|█████████▎| 845/908 [18:19<04:06,  3.91s/it]
 93%|█████████▎| 846/908 [18:19<02:53,  2.80s/it]
 93%|█████████▎| 847/908 [18:19<02:03,  2.02s/it]
 93%|█████████▎| 848/908 [18:19<01:28,  1.48s/it]
 94%|█████████▎| 849/908 [18:27<03:20,  3.40s/it]
 94%|█████████▎| 850/908 [18:27<02:21,  2.44s/it]
 94%|█████████▎| 851/908 [18:28<01:40,  1.77s/it]
 94%|█████████▍| 852/908 [18:28<01:12,  1.30s/it]
 94%|█████████▍| 853/908 [18:35<02:47,  3.04s/it]
 94%|█████████▍| 854/908 [18:35<01:58,  2.19s/it]
 94%|█████████▍| 855/908 [18:35<01:24,  1.59s/it]
 94%|█████████▍| 856/908 [18:35<01:00,  1.17s/it]
 94%|█████████▍| 857/908 [18:42<02:20,  2.75s/it]
 94%|█████████▍| 858/908 [18:42<01:39,  1.98s/it]
 95%|█████████▍| 859/908 [18:42<01:10,  1.45s/it]
+[Batch:   860] training loss = 0.397
+
 95%|█████████▍| 860/908 [18:42<00:51,  1.07s/it]
 95%|█████████▍| 861/908 [18:51<02:36,  3.33s/it]
 95%|█████████▍| 862/908 [18:51<01:49,  2.39s/it]
 95%|█████████▌| 863/908 [18:51<01:18,  1.74s/it]
 95%|█████████▌| 864/908 [18:52<00:56,  1.28s/it]
 95%|█████████▌| 865/908 [18:59<02:13,  3.11s/it]
 95%|█████████▌| 866/908 [18:59<01:33,  2.23s/it]
 95%|█████████▌| 867/908 [18:59<01:06,  1.62s/it]
 96%|█████████▌| 868/908 [19:00<00:47,  1.20s/it]
 96%|█████████▌| 869/908 [19:07<02:03,  3.16s/it]
 96%|█████████▌| 870/908 [19:08<01:26,  2.28s/it]
 96%|█████████▌| 871/908 [19:08<01:01,  1.65s/it]
 96%|█████████▌| 872/908 [19:08<00:43,  1.22s/it]
 96%|█████████▌| 873/908 [19:19<02:20,  4.00s/it]
 96%|█████████▋| 874/908 [19:19<01:37,  2.86s/it]
 96%|█████████▋| 875/908 [19:19<01:08,  2.07s/it]
 96%|█████████▋| 876/908 [19:19<00:48,  1.51s/it]
 97%|█████████▋| 877/908 [19:27<01:43,  3.33s/it]
 97%|█████████▋| 878/908 [19:27<01:11,  2.39s/it]
 97%|█████████▋| 879/908 [19:27<00:50,  1.73s/it]
+[Batch:   880] training loss = 0.527
+
 97%|█████████▋| 880/908 [19:27<00:35,  1.27s/it]
 97%|█████████▋| 881/908 [19:35<01:30,  3.34s/it]
 97%|█████████▋| 882/908 [19:36<01:02,  2.40s/it]
 97%|█████████▋| 883/908 [19:36<00:43,  1.74s/it]
 97%|█████████▋| 884/908 [19:36<00:30,  1.28s/it]
 97%|█████████▋| 885/908 [19:43<01:04,  2.82s/it]
 98%|█████████▊| 886/908 [19:43<00:44,  2.04s/it]
 98%|█████████▊| 887/908 [19:43<00:31,  1.48s/it]
 98%|█████████▊| 888/908 [19:43<00:21,  1.10s/it]
 98%|█████████▊| 889/908 [19:51<01:01,  3.22s/it]
 98%|█████████▊| 890/908 [19:51<00:41,  2.31s/it]
 98%|█████████▊| 891/908 [19:52<00:28,  1.68s/it]
 98%|█████████▊| 892/908 [19:52<00:19,  1.23s/it]
 98%|█████████▊| 893/908 [20:01<00:51,  3.46s/it]
 98%|█████████▊| 894/908 [20:01<00:34,  2.48s/it]
 99%|█████████▊| 895/908 [20:01<00:23,  1.79s/it]
 99%|█████████▊| 896/908 [20:01<00:15,  1.31s/it]
 99%|█████████▉| 897/908 [20:08<00:33,  3.02s/it]
 99%|█████████▉| 898/908 [20:08<00:21,  2.17s/it]
 99%|█████████▉| 899/908 [20:08<00:14,  1.58s/it]
+[Batch:   900] training loss = 0.432
+
 99%|█████████▉| 900/908 [20:09<00:09,  1.16s/it]
 99%|█████████▉| 901/908 [20:19<00:27,  3.91s/it]
 99%|█████████▉| 902/908 [20:19<00:16,  2.80s/it]
 99%|█████████▉| 903/908 [20:19<00:10,  2.01s/it]
100%|█████████▉| 904/908 [20:20<00:05,  1.47s/it]
100%|█████████▉| 905/908 [20:25<00:08,  2.67s/it]
100%|█████████▉| 906/908 [20:25<00:03,  1.93s/it]
100%|█████████▉| 907/908 [20:25<00:01,  1.41s/it]
100%|██████████| 908/908 [20:26<00:00,  1.02s/it]
100%|██████████| 908/908 [20:26<00:00,  1.35s/it]
+
+Epoch   4: validation
+
  0%|          | 0/101 [00:00<?, ?it/s]
  1%|          | 1/101 [00:05<09:11,  5.52s/it]
  3%|▎         | 3/101 [00:05<02:32,  1.56s/it]
  5%|▍         | 5/101 [00:12<03:55,  2.45s/it]
  7%|▋         | 7/101 [00:13<02:26,  1.56s/it]
  9%|▉         | 9/101 [00:19<03:28,  2.26s/it]
 11%|█         | 11/101 [00:20<02:21,  1.57s/it]
 13%|█▎        | 13/101 [00:26<03:04,  2.10s/it]
 15%|█▍        | 15/101 [00:27<02:06,  1.48s/it]
 17%|█▋        | 17/101 [00:35<03:09,  2.26s/it]
 19%|█▉        | 19/101 [00:35<02:11,  1.60s/it]
 21%|██        | 21/101 [00:43<03:06,  2.33s/it]
 23%|██▎       | 23/101 [00:43<02:09,  1.66s/it]
 25%|██▍       | 25/101 [00:55<03:47,  2.99s/it]
 27%|██▋       | 27/101 [00:55<02:36,  2.11s/it]
 29%|██▊       | 29/101 [01:02<03:02,  2.54s/it]
 31%|███       | 31/101 [01:03<02:06,  1.80s/it]
 33%|███▎      | 33/101 [01:11<02:55,  2.58s/it]
 35%|███▍      | 35/101 [01:12<02:01,  1.84s/it]
 37%|███▋      | 37/101 [01:20<02:38,  2.48s/it]
 39%|███▊      | 39/101 [01:20<01:51,  1.80s/it]
 41%|████      | 41/101 [01:27<02:22,  2.37s/it]
 43%|████▎     | 43/101 [01:28<01:41,  1.74s/it]
 45%|████▍     | 45/101 [01:36<02:12,  2.37s/it]
 47%|████▋     | 47/101 [01:36<01:33,  1.73s/it]
 49%|████▊     | 49/101 [01:45<02:12,  2.55s/it]
 50%|█████     | 51/101 [01:45<01:31,  1.83s/it]
 52%|█████▏    | 53/101 [01:56<02:17,  2.87s/it]
 54%|█████▍    | 55/101 [01:57<01:38,  2.14s/it]
 56%|█████▋    | 57/101 [02:03<01:49,  2.49s/it]
 58%|█████▊    | 59/101 [02:04<01:15,  1.81s/it]
 60%|██████    | 61/101 [02:12<01:37,  2.43s/it]
 62%|██████▏   | 63/101 [02:12<01:05,  1.73s/it]
 64%|██████▍   | 65/101 [02:20<01:29,  2.49s/it]
 66%|██████▋   | 67/101 [02:21<01:01,  1.80s/it]
 68%|██████▊   | 69/101 [02:28<01:16,  2.39s/it]
 70%|███████   | 71/101 [02:29<00:51,  1.73s/it]
 72%|███████▏  | 73/101 [02:35<00:59,  2.14s/it]
 73%|███████▎  | 74/101 [02:35<00:48,  1.81s/it]
 74%|███████▍  | 75/101 [02:36<00:41,  1.58s/it]
 76%|███████▌  | 77/101 [02:44<00:58,  2.43s/it]
 78%|███████▊  | 79/101 [02:44<00:36,  1.66s/it]
 80%|████████  | 81/101 [02:54<00:55,  2.78s/it]
 82%|████████▏ | 83/101 [02:54<00:34,  1.94s/it]
 84%|████████▍ | 85/101 [03:01<00:39,  2.44s/it]
 85%|████████▌ | 86/101 [03:02<00:30,  2.07s/it]
 86%|████████▌ | 87/101 [03:02<00:23,  1.71s/it]
 88%|████████▊ | 89/101 [03:10<00:30,  2.53s/it]
 90%|█████████ | 91/101 [03:10<00:17,  1.72s/it]
 92%|█████████▏| 93/101 [03:19<00:20,  2.52s/it]
 94%|█████████▍| 95/101 [03:19<00:10,  1.73s/it]
 96%|█████████▌| 97/101 [03:26<00:09,  2.37s/it]
 97%|█████████▋| 98/101 [03:26<00:05,  1.96s/it]
 99%|█████████▉| 100/101 [03:27<00:01,  1.31s/it]
100%|██████████| 101/101 [03:30<00:00,  1.81s/it]
100%|██████████| 101/101 [03:31<00:00,  2.09s/it]
+    Epoch  4: training loss = 0.470 / validation loss = 0.456
+    Epoch  4: training acc = 0.773 / validation acc = 0.786
+    Saved model: cls-4.pth.tar
+
+Epoch   5: training
+
  0%|          | 0/908 [00:00<?, ?it/s]
  0%|          | 1/908 [00:09<2:18:50,  9.18s/it]
  0%|          | 2/908 [00:09<59:04,  3.91s/it]  
  0%|          | 3/908 [00:09<33:29,  2.22s/it]
  0%|          | 4/908 [00:09<21:29,  1.43s/it]
  1%|          | 5/908 [00:19<1:06:21,  4.41s/it]
  1%|          | 6/908 [00:19<44:49,  2.98s/it]  
  1%|          | 7/908 [00:19<31:04,  2.07s/it]
  1%|          | 8/908 [00:20<22:09,  1.48s/it]
  1%|          | 9/908 [00:27<47:33,  3.17s/it]
  1%|          | 10/908 [00:27<33:45,  2.26s/it]
  1%|          | 11/908 [00:27<24:22,  1.63s/it]
  1%|▏         | 12/908 [00:27<17:55,  1.20s/it]
  1%|▏         | 13/908 [00:35<48:09,  3.23s/it]
  2%|▏         | 14/908 [00:35<35:27,  2.38s/it]
  2%|▏         | 15/908 [00:36<25:40,  1.72s/it]
  2%|▏         | 16/908 [00:36<18:47,  1.26s/it]
  2%|▏         | 17/908 [00:44<47:14,  3.18s/it]
  2%|▏         | 18/908 [00:44<33:57,  2.29s/it]
  2%|▏         | 19/908 [00:44<24:37,  1.66s/it]
+[Batch:    20] training loss = 0.510
+
  2%|▏         | 20/908 [00:44<18:07,  1.22s/it]
  2%|▏         | 21/908 [00:51<44:08,  2.99s/it]
  2%|▏         | 22/908 [00:52<32:31,  2.20s/it]
  3%|▎         | 23/908 [00:52<23:40,  1.60s/it]
  3%|▎         | 24/908 [00:52<17:27,  1.18s/it]
  3%|▎         | 25/908 [00:59<42:50,  2.91s/it]
  3%|▎         | 26/908 [01:00<32:34,  2.22s/it]
  3%|▎         | 27/908 [01:00<23:39,  1.61s/it]
  3%|▎         | 28/908 [01:00<17:41,  1.21s/it]
  3%|▎         | 29/908 [01:05<35:43,  2.44s/it]
  3%|▎         | 30/908 [01:06<26:19,  1.80s/it]
  3%|▎         | 31/908 [01:06<19:17,  1.32s/it]
  4%|▎         | 32/908 [01:06<14:40,  1.00s/it]
  4%|▎         | 33/908 [01:14<45:02,  3.09s/it]
  4%|▎         | 34/908 [01:14<32:28,  2.23s/it]
  4%|▍         | 35/908 [01:14<23:34,  1.62s/it]
  4%|▍         | 36/908 [01:15<18:14,  1.26s/it]
  4%|▍         | 37/908 [01:24<54:01,  3.72s/it]
  4%|▍         | 38/908 [01:25<38:42,  2.67s/it]
  4%|▍         | 39/908 [01:25<27:58,  1.93s/it]
+[Batch:    40] training loss = 0.393
+
  4%|▍         | 40/908 [01:25<21:25,  1.48s/it]
  5%|▍         | 41/908 [01:32<43:25,  3.01s/it]
  5%|▍         | 42/908 [01:32<31:30,  2.18s/it]
  5%|▍         | 43/908 [01:32<22:53,  1.59s/it]
  5%|▍         | 44/908 [01:33<18:10,  1.26s/it]
  5%|▍         | 45/908 [01:40<45:30,  3.16s/it]
  5%|▌         | 46/908 [01:41<33:16,  2.32s/it]
  5%|▌         | 47/908 [01:41<24:07,  1.68s/it]
  5%|▌         | 48/908 [01:41<17:58,  1.25s/it]
  5%|▌         | 49/908 [01:48<41:42,  2.91s/it]
  6%|▌         | 50/908 [01:48<30:53,  2.16s/it]
  6%|▌         | 51/908 [01:49<22:27,  1.57s/it]
  6%|▌         | 52/908 [01:49<18:30,  1.30s/it]
  6%|▌         | 53/908 [01:56<40:42,  2.86s/it]
  6%|▌         | 54/908 [01:56<30:31,  2.15s/it]
  6%|▌         | 55/908 [01:56<22:12,  1.56s/it]
  6%|▌         | 56/908 [01:57<17:14,  1.21s/it]
  6%|▋         | 57/908 [02:04<44:08,  3.11s/it]
  6%|▋         | 58/908 [02:05<31:56,  2.25s/it]
  6%|▋         | 59/908 [02:05<23:06,  1.63s/it]
+[Batch:    60] training loss = 0.419
+
  7%|▋         | 60/908 [02:05<18:13,  1.29s/it]
  7%|▋         | 61/908 [02:13<44:06,  3.12s/it]
  7%|▋         | 62/908 [02:13<31:41,  2.25s/it]
  7%|▋         | 63/908 [02:13<22:59,  1.63s/it]
  7%|▋         | 64/908 [02:13<16:53,  1.20s/it]
  7%|▋         | 65/908 [02:23<55:02,  3.92s/it]
  7%|▋         | 66/908 [02:24<41:09,  2.93s/it]
  7%|▋         | 67/908 [02:24<29:35,  2.11s/it]
  7%|▋         | 68/908 [02:25<21:31,  1.54s/it]
  8%|▊         | 69/908 [02:31<41:29,  2.97s/it]
  8%|▊         | 70/908 [02:31<30:51,  2.21s/it]
  8%|▊         | 71/908 [02:31<22:23,  1.60s/it]
  8%|▊         | 72/908 [02:32<16:30,  1.18s/it]
  8%|▊         | 73/908 [02:39<43:33,  3.13s/it]
  8%|▊         | 74/908 [02:40<32:55,  2.37s/it]
  8%|▊         | 75/908 [02:40<23:50,  1.72s/it]
  8%|▊         | 76/908 [02:40<17:27,  1.26s/it]
  8%|▊         | 77/908 [02:48<44:29,  3.21s/it]
  9%|▊         | 78/908 [02:49<32:59,  2.39s/it]
  9%|▊         | 79/908 [02:49<23:53,  1.73s/it]
+[Batch:    80] training loss = 0.414
+
  9%|▉         | 80/908 [02:49<17:33,  1.27s/it]
  9%|▉         | 81/908 [02:56<41:08,  2.98s/it]
  9%|▉         | 82/908 [02:57<32:28,  2.36s/it]
  9%|▉         | 83/908 [02:57<23:28,  1.71s/it]
  9%|▉         | 84/908 [02:57<17:19,  1.26s/it]
  9%|▉         | 85/908 [03:05<43:32,  3.17s/it]
  9%|▉         | 86/908 [03:06<33:04,  2.41s/it]
 10%|▉         | 87/908 [03:06<23:53,  1.75s/it]
 10%|▉         | 88/908 [03:06<17:27,  1.28s/it]
 10%|▉         | 89/908 [03:13<43:17,  3.17s/it]
 10%|▉         | 90/908 [03:14<32:50,  2.41s/it]
 10%|█         | 91/908 [03:14<23:47,  1.75s/it]
 10%|█         | 92/908 [03:15<17:29,  1.29s/it]
 10%|█         | 93/908 [03:24<51:24,  3.78s/it]
 10%|█         | 94/908 [03:25<39:38,  2.92s/it]
 10%|█         | 95/908 [03:25<28:28,  2.10s/it]
 11%|█         | 96/908 [03:25<20:44,  1.53s/it]
 11%|█         | 97/908 [03:32<40:27,  2.99s/it]
 11%|█         | 98/908 [03:33<32:34,  2.41s/it]
 11%|█         | 99/908 [03:33<23:32,  1.75s/it]
+[Batch:   100] training loss = 0.465
+
 11%|█         | 100/908 [03:34<18:20,  1.36s/it]
 11%|█         | 101/908 [03:40<38:34,  2.87s/it]
 11%|█         | 102/908 [03:41<30:09,  2.24s/it]
 11%|█▏        | 103/908 [03:41<21:50,  1.63s/it]
 11%|█▏        | 104/908 [03:42<19:12,  1.43s/it]
 12%|█▏        | 105/908 [03:49<40:25,  3.02s/it]
 12%|█▏        | 106/908 [03:50<31:50,  2.38s/it]
 12%|█▏        | 107/908 [03:50<23:02,  1.73s/it]
 12%|█▏        | 108/908 [03:50<18:23,  1.38s/it]
 12%|█▏        | 109/908 [03:57<39:34,  2.97s/it]
 12%|█▏        | 110/908 [03:58<30:08,  2.27s/it]
 12%|█▏        | 111/908 [03:58<21:50,  1.64s/it]
 12%|█▏        | 112/908 [03:58<17:52,  1.35s/it]
 12%|█▏        | 113/908 [04:05<37:55,  2.86s/it]
 13%|█▎        | 114/908 [04:05<29:01,  2.19s/it]
 13%|█▎        | 115/908 [04:06<21:02,  1.59s/it]
 13%|█▎        | 116/908 [04:06<16:56,  1.28s/it]
 13%|█▎        | 117/908 [04:15<48:15,  3.66s/it]
 13%|█▎        | 118/908 [04:16<37:40,  2.86s/it]
 13%|█▎        | 119/908 [04:17<27:08,  2.06s/it]
+[Batch:   120] training loss = 0.415
+
 13%|█▎        | 120/908 [04:17<20:54,  1.59s/it]
 13%|█▎        | 121/908 [04:25<45:50,  3.50s/it]
 13%|█▎        | 122/908 [04:26<35:13,  2.69s/it]
 14%|█▎        | 123/908 [04:26<25:23,  1.94s/it]
 14%|█▎        | 124/908 [04:26<19:03,  1.46s/it]
 14%|█▍        | 125/908 [04:32<35:58,  2.76s/it]
 14%|█▍        | 126/908 [04:33<28:02,  2.15s/it]
 14%|█▍        | 127/908 [04:33<20:22,  1.56s/it]
 14%|█▍        | 128/908 [04:33<14:59,  1.15s/it]
 14%|█▍        | 129/908 [04:40<36:38,  2.82s/it]
 14%|█▍        | 130/908 [04:41<30:04,  2.32s/it]
 14%|█▍        | 131/908 [04:41<21:46,  1.68s/it]
 15%|█▍        | 132/908 [04:42<15:58,  1.23s/it]
 15%|█▍        | 133/908 [04:47<34:13,  2.65s/it]
 15%|█▍        | 134/908 [04:49<28:39,  2.22s/it]
 15%|█▍        | 135/908 [04:49<20:45,  1.61s/it]
 15%|█▍        | 136/908 [04:49<15:15,  1.19s/it]
 15%|█▌        | 137/908 [04:54<30:44,  2.39s/it]
 15%|█▌        | 138/908 [04:56<26:39,  2.08s/it]
 15%|█▌        | 139/908 [04:56<19:21,  1.51s/it]
+[Batch:   140] training loss = 0.380
+
 15%|█▌        | 140/908 [04:56<14:15,  1.11s/it]
 16%|█▌        | 141/908 [05:04<39:41,  3.10s/it]
 16%|█▌        | 142/908 [05:05<32:06,  2.52s/it]
 16%|█▌        | 143/908 [05:05<23:09,  1.82s/it]
 16%|█▌        | 144/908 [05:05<16:54,  1.33s/it]
 16%|█▌        | 145/908 [05:12<36:10,  2.85s/it]
 16%|█▌        | 146/908 [05:13<30:33,  2.41s/it]
 16%|█▌        | 147/908 [05:13<22:07,  1.74s/it]
 16%|█▋        | 148/908 [05:13<16:12,  1.28s/it]
 16%|█▋        | 149/908 [05:21<38:21,  3.03s/it]
 17%|█▋        | 150/908 [05:22<30:37,  2.42s/it]
 17%|█▋        | 151/908 [05:22<22:08,  1.75s/it]
 17%|█▋        | 152/908 [05:22<16:17,  1.29s/it]
 17%|█▋        | 153/908 [05:31<45:59,  3.65s/it]
 17%|█▋        | 154/908 [05:33<37:25,  2.98s/it]
 17%|█▋        | 155/908 [05:33<26:53,  2.14s/it]
 17%|█▋        | 156/908 [05:33<19:30,  1.56s/it]
 17%|█▋        | 157/908 [05:39<37:56,  3.03s/it]
 17%|█▋        | 158/908 [05:42<34:59,  2.80s/it]
 18%|█▊        | 159/908 [05:42<25:09,  2.02s/it]
+[Batch:   160] training loss = 0.373
+
 18%|█▊        | 160/908 [05:42<18:17,  1.47s/it]
 18%|█▊        | 161/908 [05:47<31:46,  2.55s/it]
 18%|█▊        | 162/908 [05:48<27:11,  2.19s/it]
 18%|█▊        | 163/908 [05:49<19:43,  1.59s/it]
 18%|█▊        | 164/908 [05:49<14:29,  1.17s/it]
 18%|█▊        | 165/908 [05:55<31:14,  2.52s/it]
 18%|█▊        | 166/908 [05:57<29:26,  2.38s/it]
 18%|█▊        | 167/908 [05:57<21:19,  1.73s/it]
 19%|█▊        | 168/908 [05:57<15:38,  1.27s/it]
 19%|█▊        | 169/908 [06:03<32:12,  2.62s/it]
 19%|█▊        | 170/908 [06:04<27:17,  2.22s/it]
 19%|█▉        | 171/908 [06:04<19:48,  1.61s/it]
 19%|█▉        | 172/908 [06:04<14:38,  1.19s/it]
 19%|█▉        | 173/908 [06:11<34:35,  2.82s/it]
 19%|█▉        | 174/908 [06:12<28:25,  2.32s/it]
 19%|█▉        | 175/908 [06:12<20:37,  1.69s/it]
 19%|█▉        | 176/908 [06:13<15:13,  1.25s/it]
 19%|█▉        | 177/908 [06:19<34:18,  2.82s/it]
 20%|█▉        | 178/908 [06:20<28:27,  2.34s/it]
 20%|█▉        | 179/908 [06:21<20:37,  1.70s/it]
+[Batch:   180] training loss = 0.446
+
 20%|█▉        | 180/908 [06:21<15:09,  1.25s/it]
 20%|█▉        | 181/908 [06:29<38:53,  3.21s/it]
 20%|██        | 182/908 [06:30<32:59,  2.73s/it]
 20%|██        | 183/908 [06:30<23:47,  1.97s/it]
 20%|██        | 184/908 [06:31<17:20,  1.44s/it]
 20%|██        | 185/908 [06:36<31:35,  2.62s/it]
 20%|██        | 186/908 [06:38<29:02,  2.41s/it]
 21%|██        | 187/908 [06:38<21:00,  1.75s/it]
 21%|██        | 188/908 [06:38<15:23,  1.28s/it]
 21%|██        | 189/908 [06:44<31:17,  2.61s/it]
 21%|██        | 190/908 [06:46<28:53,  2.41s/it]
 21%|██        | 191/908 [06:46<20:53,  1.75s/it]
 21%|██        | 192/908 [06:46<15:22,  1.29s/it]
 21%|██▏       | 193/908 [06:52<31:42,  2.66s/it]
 21%|██▏       | 194/908 [06:54<28:45,  2.42s/it]
 21%|██▏       | 195/908 [06:54<20:51,  1.76s/it]
 22%|██▏       | 196/908 [06:54<15:17,  1.29s/it]
 22%|██▏       | 197/908 [07:00<30:59,  2.62s/it]
 22%|██▏       | 198/908 [07:02<27:07,  2.29s/it]
 22%|██▏       | 199/908 [07:02<19:39,  1.66s/it]
+[Batch:   200] training loss = 0.417
+
 22%|██▏       | 200/908 [07:02<14:27,  1.22s/it]
 22%|██▏       | 201/908 [07:08<29:30,  2.50s/it]
 22%|██▏       | 202/908 [07:10<28:37,  2.43s/it]
 22%|██▏       | 203/908 [07:10<20:42,  1.76s/it]
 22%|██▏       | 204/908 [07:10<15:11,  1.29s/it]
 23%|██▎       | 205/908 [07:15<29:02,  2.48s/it]
 23%|██▎       | 206/908 [07:18<27:51,  2.38s/it]
 23%|██▎       | 207/908 [07:18<20:12,  1.73s/it]
 23%|██▎       | 208/908 [07:18<14:48,  1.27s/it]
 23%|██▎       | 209/908 [07:23<28:09,  2.42s/it]
 23%|██▎       | 210/908 [07:23<20:29,  1.76s/it]
 23%|██▎       | 211/908 [07:24<15:03,  1.30s/it]
 23%|██▎       | 212/908 [07:24<11:13,  1.03it/s]
 23%|██▎       | 213/908 [07:32<37:22,  3.23s/it]
 24%|██▎       | 214/908 [07:34<32:23,  2.80s/it]
 24%|██▎       | 215/908 [07:34<23:18,  2.02s/it]
 24%|██▍       | 216/908 [07:34<17:00,  1.47s/it]
 24%|██▍       | 217/908 [07:40<31:25,  2.73s/it]
 24%|██▍       | 218/908 [07:42<29:28,  2.56s/it]
 24%|██▍       | 219/908 [07:43<21:17,  1.85s/it]
+[Batch:   220] training loss = 0.466
+
 24%|██▍       | 220/908 [07:43<15:33,  1.36s/it]
 24%|██▍       | 221/908 [07:48<29:56,  2.61s/it]
 24%|██▍       | 222/908 [07:51<29:24,  2.57s/it]
 25%|██▍       | 223/908 [07:51<21:13,  1.86s/it]
 25%|██▍       | 224/908 [07:51<15:33,  1.36s/it]
 25%|██▍       | 225/908 [07:56<27:42,  2.43s/it]
 25%|██▍       | 226/908 [07:58<25:57,  2.28s/it]
 25%|██▌       | 227/908 [07:58<18:48,  1.66s/it]
 25%|██▌       | 228/908 [07:58<13:50,  1.22s/it]
 25%|██▌       | 229/908 [08:04<29:35,  2.62s/it]
 25%|██▌       | 230/908 [08:06<27:22,  2.42s/it]
 25%|██▌       | 231/908 [08:06<19:45,  1.75s/it]
 26%|██▌       | 232/908 [08:07<14:27,  1.28s/it]
 26%|██▌       | 233/908 [08:12<29:52,  2.66s/it]
 26%|██▌       | 234/908 [08:14<27:42,  2.47s/it]
 26%|██▌       | 235/908 [08:15<20:02,  1.79s/it]
 26%|██▌       | 236/908 [08:15<14:38,  1.31s/it]
 26%|██▌       | 237/908 [08:19<24:26,  2.19s/it]
 26%|██▌       | 238/908 [08:22<27:14,  2.44s/it]
 26%|██▋       | 239/908 [08:22<19:40,  1.76s/it]
+[Batch:   240] training loss = 0.517
+
 26%|██▋       | 240/908 [08:23<14:23,  1.29s/it]
 27%|██▋       | 241/908 [08:26<23:14,  2.09s/it]
 27%|██▋       | 242/908 [08:30<28:06,  2.53s/it]
 27%|██▋       | 243/908 [08:30<20:17,  1.83s/it]
 27%|██▋       | 244/908 [08:30<14:49,  1.34s/it]
 27%|██▋       | 245/908 [08:36<28:26,  2.57s/it]
 27%|██▋       | 246/908 [08:38<28:27,  2.58s/it]
 27%|██▋       | 247/908 [08:39<20:31,  1.86s/it]
 27%|██▋       | 248/908 [08:39<14:59,  1.36s/it]
 27%|██▋       | 249/908 [08:44<27:58,  2.55s/it]
 28%|██▊       | 250/908 [08:47<29:07,  2.66s/it]
 28%|██▊       | 251/908 [08:47<20:59,  1.92s/it]
 28%|██▊       | 252/908 [08:47<15:17,  1.40s/it]
 28%|██▊       | 253/908 [08:52<25:49,  2.37s/it]
 28%|██▊       | 254/908 [08:55<27:03,  2.48s/it]
 28%|██▊       | 255/908 [08:55<19:31,  1.79s/it]
 28%|██▊       | 256/908 [08:55<14:17,  1.32s/it]
 28%|██▊       | 257/908 [09:00<25:34,  2.36s/it]
 28%|██▊       | 258/908 [09:03<26:34,  2.45s/it]
 29%|██▊       | 259/908 [09:03<19:13,  1.78s/it]
+[Batch:   260] training loss = 0.648
+
 29%|██▊       | 260/908 [09:03<14:04,  1.30s/it]
 29%|██▊       | 261/908 [09:07<23:54,  2.22s/it]
 29%|██▉       | 262/908 [09:10<23:34,  2.19s/it]
 29%|██▉       | 263/908 [09:10<17:07,  1.59s/it]
 29%|██▉       | 264/908 [09:10<12:36,  1.17s/it]
 29%|██▉       | 265/908 [09:16<26:46,  2.50s/it]
 29%|██▉       | 266/908 [09:18<25:54,  2.42s/it]
 29%|██▉       | 267/908 [09:18<18:42,  1.75s/it]
 30%|██▉       | 268/908 [09:18<13:41,  1.28s/it]
 30%|██▉       | 269/908 [09:24<27:51,  2.62s/it]
 30%|██▉       | 270/908 [09:26<26:28,  2.49s/it]
 30%|██▉       | 271/908 [09:26<19:05,  1.80s/it]
 30%|██▉       | 272/908 [09:27<14:52,  1.40s/it]
 30%|███       | 273/908 [09:34<32:45,  3.10s/it]
 30%|███       | 274/908 [09:36<29:12,  2.76s/it]
 30%|███       | 275/908 [09:36<21:01,  1.99s/it]
 30%|███       | 276/908 [09:36<16:03,  1.53s/it]
 31%|███       | 277/908 [09:41<27:04,  2.58s/it]
 31%|███       | 278/908 [09:44<26:05,  2.49s/it]
 31%|███       | 279/908 [09:44<18:49,  1.80s/it]
+[Batch:   280] training loss = 0.401
+
 31%|███       | 280/908 [09:44<13:56,  1.33s/it]
 31%|███       | 281/908 [09:49<26:03,  2.49s/it]
 31%|███       | 282/908 [09:52<26:57,  2.58s/it]
 31%|███       | 283/908 [09:52<19:26,  1.87s/it]
 31%|███▏      | 284/908 [09:53<14:13,  1.37s/it]
 31%|███▏      | 285/908 [09:57<24:30,  2.36s/it]
 31%|███▏      | 286/908 [10:00<24:35,  2.37s/it]
 32%|███▏      | 287/908 [10:00<17:45,  1.72s/it]
 32%|███▏      | 288/908 [10:00<13:35,  1.32s/it]
 32%|███▏      | 289/908 [10:05<25:02,  2.43s/it]
 32%|███▏      | 290/908 [10:07<22:36,  2.20s/it]
 32%|███▏      | 291/908 [10:07<16:23,  1.59s/it]
 32%|███▏      | 292/908 [10:08<13:38,  1.33s/it]
 32%|███▏      | 293/908 [10:12<23:54,  2.33s/it]
 32%|███▏      | 294/908 [10:15<25:02,  2.45s/it]
 32%|███▏      | 295/908 [10:15<18:05,  1.77s/it]
 33%|███▎      | 296/908 [10:16<14:39,  1.44s/it]
 33%|███▎      | 297/908 [10:20<23:19,  2.29s/it]
 33%|███▎      | 298/908 [10:23<25:54,  2.55s/it]
 33%|███▎      | 299/908 [10:24<18:40,  1.84s/it]
+[Batch:   300] training loss = 0.439
+
 33%|███▎      | 300/908 [10:25<15:42,  1.55s/it]
 33%|███▎      | 301/908 [10:32<35:04,  3.47s/it]
 33%|███▎      | 302/908 [10:34<30:21,  3.01s/it]
 33%|███▎      | 303/908 [10:35<21:49,  2.16s/it]
 33%|███▎      | 304/908 [10:35<16:21,  1.63s/it]
 34%|███▎      | 305/908 [10:39<25:08,  2.50s/it]
 34%|███▎      | 306/908 [10:41<22:48,  2.27s/it]
 34%|███▍      | 307/908 [10:41<16:30,  1.65s/it]
 34%|███▍      | 308/908 [10:42<13:42,  1.37s/it]
 34%|███▍      | 309/908 [10:47<24:18,  2.43s/it]
 34%|███▍      | 310/908 [10:50<24:44,  2.48s/it]
 34%|███▍      | 311/908 [10:50<17:50,  1.79s/it]
 34%|███▍      | 312/908 [10:50<14:14,  1.43s/it]
 34%|███▍      | 313/908 [10:55<24:39,  2.49s/it]
 35%|███▍      | 314/908 [10:57<22:40,  2.29s/it]
 35%|███▍      | 315/908 [10:57<16:26,  1.66s/it]
 35%|███▍      | 316/908 [10:58<14:21,  1.46s/it]
 35%|███▍      | 317/908 [11:04<25:11,  2.56s/it]
 35%|███▌      | 318/908 [11:05<22:47,  2.32s/it]
 35%|███▌      | 319/908 [11:05<16:28,  1.68s/it]
+[Batch:   320] training loss = 0.487
+
 35%|███▌      | 320/908 [11:07<15:09,  1.55s/it]
 35%|███▌      | 321/908 [11:11<22:30,  2.30s/it]
 35%|███▌      | 322/908 [11:13<20:56,  2.14s/it]
 36%|███▌      | 323/908 [11:13<15:13,  1.56s/it]
 36%|███▌      | 324/908 [11:14<14:11,  1.46s/it]
 36%|███▌      | 325/908 [11:18<20:35,  2.12s/it]
 36%|███▌      | 326/908 [11:19<19:49,  2.04s/it]
 36%|███▌      | 327/908 [11:20<14:24,  1.49s/it]
 36%|███▌      | 328/908 [11:21<13:50,  1.43s/it]
 36%|███▌      | 329/908 [11:26<25:00,  2.59s/it]
 36%|███▋      | 330/908 [11:28<23:54,  2.48s/it]
 36%|███▋      | 331/908 [11:29<17:15,  1.79s/it]
 37%|███▋      | 332/908 [11:30<15:02,  1.57s/it]
 37%|███▋      | 333/908 [11:37<30:47,  3.21s/it]
 37%|███▋      | 334/908 [11:39<27:12,  2.84s/it]
 37%|███▋      | 335/908 [11:39<19:42,  2.06s/it]
 37%|███▋      | 336/908 [11:40<16:48,  1.76s/it]
 37%|███▋      | 337/908 [11:45<27:02,  2.84s/it]
 37%|███▋      | 338/908 [11:50<30:47,  3.24s/it]
 37%|███▋      | 339/908 [11:50<22:06,  2.33s/it]
+[Batch:   340] training loss = 0.458
+
 37%|███▋      | 340/908 [11:51<19:29,  2.06s/it]
 38%|███▊      | 341/908 [11:56<27:27,  2.91s/it]
 38%|███▊      | 342/908 [11:58<24:53,  2.64s/it]
 38%|███▊      | 343/908 [11:58<17:57,  1.91s/it]
 38%|███▊      | 344/908 [11:59<13:10,  1.40s/it]
 38%|███▊      | 345/908 [12:03<22:01,  2.35s/it]
 38%|███▊      | 346/908 [12:05<21:07,  2.26s/it]
 38%|███▊      | 347/908 [12:05<15:18,  1.64s/it]
 38%|███▊      | 348/908 [12:07<14:11,  1.52s/it]
 38%|███▊      | 349/908 [12:10<19:45,  2.12s/it]
 39%|███▊      | 350/908 [12:12<20:12,  2.17s/it]
 39%|███▊      | 351/908 [12:13<14:41,  1.58s/it]
 39%|███▉      | 352/908 [12:14<14:15,  1.54s/it]
 39%|███▉      | 353/908 [12:18<20:39,  2.23s/it]
 39%|███▉      | 354/908 [12:20<20:07,  2.18s/it]
 39%|███▉      | 355/908 [12:20<14:36,  1.58s/it]
 39%|███▉      | 356/908 [12:22<14:27,  1.57s/it]
 39%|███▉      | 357/908 [12:26<21:07,  2.30s/it]
 39%|███▉      | 358/908 [12:28<20:07,  2.19s/it]
 40%|███▉      | 359/908 [12:28<14:37,  1.60s/it]
+[Batch:   360] training loss = 0.422
+
 40%|███▉      | 360/908 [12:29<14:09,  1.55s/it]
 40%|███▉      | 361/908 [12:36<29:13,  3.21s/it]
 40%|███▉      | 362/908 [12:40<29:31,  3.24s/it]
 40%|███▉      | 363/908 [12:40<21:09,  2.33s/it]
 40%|████      | 364/908 [12:41<18:54,  2.09s/it]
 40%|████      | 365/908 [12:46<25:11,  2.78s/it]
 40%|████      | 366/908 [12:48<24:49,  2.75s/it]
 40%|████      | 367/908 [12:49<17:53,  1.98s/it]
 41%|████      | 368/908 [12:50<16:39,  1.85s/it]
 41%|████      | 369/908 [12:54<22:59,  2.56s/it]
 41%|████      | 370/908 [12:57<22:37,  2.52s/it]
 41%|████      | 371/908 [12:57<16:20,  1.83s/it]
 41%|████      | 372/908 [12:58<13:50,  1.55s/it]
 41%|████      | 373/908 [13:02<19:54,  2.23s/it]
 41%|████      | 374/908 [13:04<20:42,  2.33s/it]
 41%|████▏     | 375/908 [13:05<14:59,  1.69s/it]
 41%|████▏     | 376/908 [13:05<12:40,  1.43s/it]
 42%|████▏     | 377/908 [13:12<26:09,  2.96s/it]
 42%|████▏     | 378/908 [13:15<26:44,  3.03s/it]
 42%|████▏     | 379/908 [13:15<19:13,  2.18s/it]
+[Batch:   380] training loss = 0.480
+
 42%|████▏     | 380/908 [13:16<16:00,  1.82s/it]
 42%|████▏     | 381/908 [13:21<23:06,  2.63s/it]
 42%|████▏     | 382/908 [13:23<22:38,  2.58s/it]
 42%|████▏     | 383/908 [13:23<16:22,  1.87s/it]
 42%|████▏     | 384/908 [13:24<13:18,  1.52s/it]
 42%|████▏     | 385/908 [13:28<20:00,  2.29s/it]
 43%|████▎     | 386/908 [13:30<17:53,  2.06s/it]
 43%|████▎     | 387/908 [13:30<13:00,  1.50s/it]
 43%|████▎     | 388/908 [13:31<13:01,  1.50s/it]
 43%|████▎     | 389/908 [13:39<27:41,  3.20s/it]
 43%|████▎     | 390/908 [13:40<23:04,  2.67s/it]
 43%|████▎     | 391/908 [13:40<16:38,  1.93s/it]
 43%|████▎     | 392/908 [13:42<17:07,  1.99s/it]
 43%|████▎     | 393/908 [13:47<22:56,  2.67s/it]
 43%|████▎     | 394/908 [13:49<20:57,  2.45s/it]
 44%|████▎     | 395/908 [13:49<15:09,  1.77s/it]
 44%|████▎     | 396/908 [13:50<13:49,  1.62s/it]
 44%|████▎     | 397/908 [13:55<22:30,  2.64s/it]
 44%|████▍     | 398/908 [13:57<20:51,  2.45s/it]
 44%|████▍     | 399/908 [13:57<15:03,  1.78s/it]
+[Batch:   400] training loss = 0.445
+
 44%|████▍     | 400/908 [13:59<14:40,  1.73s/it]
 44%|████▍     | 401/908 [14:03<20:06,  2.38s/it]
 44%|████▍     | 402/908 [14:05<18:39,  2.21s/it]
 44%|████▍     | 403/908 [14:05<13:30,  1.60s/it]
 44%|████▍     | 404/908 [14:06<12:34,  1.50s/it]
 45%|████▍     | 405/908 [14:13<25:36,  3.06s/it]
 45%|████▍     | 406/908 [14:14<20:11,  2.41s/it]
 45%|████▍     | 407/908 [14:14<14:34,  1.75s/it]
 45%|████▍     | 408/908 [14:16<15:08,  1.82s/it]
 45%|████▌     | 409/908 [14:20<22:01,  2.65s/it]
 45%|████▌     | 410/908 [14:22<19:02,  2.30s/it]
 45%|████▌     | 411/908 [14:22<13:47,  1.66s/it]
 45%|████▌     | 412/908 [14:24<13:16,  1.61s/it]
 45%|████▌     | 413/908 [14:29<21:36,  2.62s/it]
 46%|████▌     | 414/908 [14:30<19:40,  2.39s/it]
 46%|████▌     | 415/908 [14:31<14:13,  1.73s/it]
 46%|████▌     | 416/908 [14:31<11:38,  1.42s/it]
 46%|████▌     | 417/908 [14:37<21:32,  2.63s/it]
 46%|████▌     | 418/908 [14:39<20:54,  2.56s/it]
 46%|████▌     | 419/908 [14:39<15:07,  1.86s/it]
+[Batch:   420] training loss = 0.459
+
 46%|████▋     | 420/908 [14:41<13:38,  1.68s/it]
 46%|████▋     | 421/908 [14:45<21:05,  2.60s/it]
 46%|████▋     | 422/908 [14:46<16:43,  2.06s/it]
 47%|████▋     | 423/908 [14:47<12:27,  1.54s/it]
 47%|████▋     | 424/908 [14:48<12:22,  1.53s/it]
 47%|████▋     | 425/908 [14:54<23:17,  2.89s/it]
 47%|████▋     | 426/908 [14:56<19:52,  2.47s/it]
 47%|████▋     | 427/908 [14:56<14:56,  1.86s/it]
 47%|████▋     | 428/908 [14:57<12:49,  1.60s/it]
 47%|████▋     | 429/908 [15:02<20:24,  2.56s/it]
 47%|████▋     | 430/908 [15:03<17:38,  2.21s/it]
 47%|████▋     | 431/908 [15:04<13:32,  1.70s/it]
 48%|████▊     | 432/908 [15:05<12:34,  1.59s/it]
 48%|████▊     | 433/908 [15:11<23:50,  3.01s/it]
 48%|████▊     | 434/908 [15:13<20:27,  2.59s/it]
 48%|████▊     | 435/908 [15:13<14:47,  1.88s/it]
 48%|████▊     | 436/908 [15:15<14:10,  1.80s/it]
 48%|████▊     | 437/908 [15:21<23:28,  2.99s/it]
 48%|████▊     | 438/908 [15:22<20:09,  2.57s/it]
 48%|████▊     | 439/908 [15:22<14:34,  1.86s/it]
+[Batch:   440] training loss = 0.481
+
 48%|████▊     | 440/908 [15:24<13:12,  1.69s/it]
 49%|████▊     | 441/908 [15:29<20:55,  2.69s/it]
 49%|████▊     | 442/908 [15:30<17:01,  2.19s/it]
 49%|████▉     | 443/908 [15:30<12:21,  1.59s/it]
 49%|████▉     | 444/908 [15:31<11:38,  1.51s/it]
 49%|████▉     | 445/908 [15:37<20:40,  2.68s/it]
 49%|████▉     | 446/908 [15:39<20:49,  2.70s/it]
 49%|████▉     | 447/908 [15:40<15:00,  1.95s/it]
 49%|████▉     | 448/908 [15:41<13:44,  1.79s/it]
 49%|████▉     | 449/908 [15:46<20:29,  2.68s/it]
 50%|████▉     | 450/908 [15:47<17:16,  2.26s/it]
 50%|████▉     | 451/908 [15:47<12:30,  1.64s/it]
 50%|████▉     | 452/908 [15:47<09:10,  1.21s/it]
 50%|████▉     | 453/908 [15:54<20:28,  2.70s/it]
 50%|█████     | 454/908 [15:55<18:07,  2.39s/it]
 50%|█████     | 455/908 [15:56<13:05,  1.73s/it]
 50%|█████     | 456/908 [15:56<11:17,  1.50s/it]
 50%|█████     | 457/908 [16:01<19:03,  2.53s/it]
 50%|█████     | 458/908 [16:03<16:54,  2.25s/it]
 51%|█████     | 459/908 [16:03<12:13,  1.63s/it]
+[Batch:   460] training loss = 0.379
+
 51%|█████     | 460/908 [16:04<09:44,  1.30s/it]
 51%|█████     | 461/908 [16:09<17:45,  2.38s/it]
 51%|█████     | 462/908 [16:10<15:45,  2.12s/it]
 51%|█████     | 463/908 [16:10<11:25,  1.54s/it]
 51%|█████     | 464/908 [16:11<08:45,  1.18s/it]
 51%|█████     | 465/908 [16:17<20:11,  2.74s/it]
 51%|█████▏    | 466/908 [16:19<18:07,  2.46s/it]
 51%|█████▏    | 467/908 [16:19<13:05,  1.78s/it]
 52%|█████▏    | 468/908 [16:20<11:47,  1.61s/it]
 52%|█████▏    | 469/908 [16:27<22:46,  3.11s/it]
 52%|█████▏    | 470/908 [16:29<19:41,  2.70s/it]
 52%|█████▏    | 471/908 [16:29<14:09,  1.94s/it]
 52%|█████▏    | 472/908 [16:29<11:10,  1.54s/it]
 52%|█████▏    | 473/908 [16:34<18:47,  2.59s/it]
 52%|█████▏    | 474/908 [16:37<17:38,  2.44s/it]
 52%|█████▏    | 475/908 [16:37<12:44,  1.77s/it]
 52%|█████▏    | 476/908 [16:38<11:46,  1.64s/it]
 53%|█████▎    | 477/908 [16:47<26:41,  3.71s/it]
 53%|█████▎    | 478/908 [16:49<22:53,  3.19s/it]
 53%|█████▎    | 479/908 [16:49<16:23,  2.29s/it]
+[Batch:   480] training loss = 0.461
+
 53%|█████▎    | 480/908 [16:50<13:42,  1.92s/it]
 53%|█████▎    | 481/908 [16:55<20:39,  2.90s/it]
 53%|█████▎    | 482/908 [16:57<18:07,  2.55s/it]
 53%|█████▎    | 483/908 [16:57<13:07,  1.85s/it]
 53%|█████▎    | 484/908 [16:58<11:10,  1.58s/it]
 53%|█████▎    | 485/908 [17:03<18:56,  2.69s/it]
 54%|█████▎    | 486/908 [17:05<17:34,  2.50s/it]
 54%|█████▎    | 487/908 [17:05<12:40,  1.81s/it]
 54%|█████▎    | 488/908 [17:06<10:38,  1.52s/it]
 54%|█████▍    | 489/908 [17:12<19:17,  2.76s/it]
 54%|█████▍    | 490/908 [17:13<15:48,  2.27s/it]
 54%|█████▍    | 491/908 [17:13<11:28,  1.65s/it]
 54%|█████▍    | 492/908 [17:15<10:41,  1.54s/it]
 54%|█████▍    | 493/908 [17:20<17:40,  2.56s/it]
 54%|█████▍    | 494/908 [17:21<16:04,  2.33s/it]
 55%|█████▍    | 495/908 [17:22<12:02,  1.75s/it]
 55%|█████▍    | 496/908 [17:23<10:45,  1.57s/it]
 55%|█████▍    | 497/908 [17:28<18:03,  2.64s/it]
 55%|█████▍    | 498/908 [17:30<16:32,  2.42s/it]
 55%|█████▍    | 499/908 [17:30<12:05,  1.77s/it]
+[Batch:   500] training loss = 0.557
+
 55%|█████▌    | 500/908 [17:31<09:56,  1.46s/it]
 55%|█████▌    | 501/908 [17:35<14:39,  2.16s/it]
 55%|█████▌    | 502/908 [17:36<13:12,  1.95s/it]
 55%|█████▌    | 503/908 [17:37<10:17,  1.53s/it]
 56%|█████▌    | 504/908 [17:37<08:05,  1.20s/it]
 56%|█████▌    | 505/908 [17:45<20:49,  3.10s/it]
 56%|█████▌    | 506/908 [17:47<18:52,  2.82s/it]
 56%|█████▌    | 507/908 [17:47<14:19,  2.14s/it]
 56%|█████▌    | 508/908 [17:48<11:23,  1.71s/it]
 56%|█████▌    | 509/908 [17:53<18:08,  2.73s/it]
 56%|█████▌    | 510/908 [17:55<15:30,  2.34s/it]
 56%|█████▋    | 511/908 [17:55<12:15,  1.85s/it]
 56%|█████▋    | 512/908 [17:56<09:43,  1.47s/it]
 56%|█████▋    | 513/908 [18:02<17:55,  2.72s/it]
 57%|█████▋    | 514/908 [18:04<16:29,  2.51s/it]
 57%|█████▋    | 515/908 [18:04<12:35,  1.92s/it]
 57%|█████▋    | 516/908 [18:04<09:23,  1.44s/it]
 57%|█████▋    | 517/908 [18:10<17:34,  2.70s/it]
 57%|█████▋    | 518/908 [18:12<15:43,  2.42s/it]
 57%|█████▋    | 519/908 [18:12<11:41,  1.80s/it]
+[Batch:   520] training loss = 0.519
+
 57%|█████▋    | 520/908 [18:13<09:21,  1.45s/it]
 57%|█████▋    | 521/908 [18:19<18:06,  2.81s/it]
 57%|█████▋    | 522/908 [18:20<15:41,  2.44s/it]
 58%|█████▊    | 523/908 [18:21<13:02,  2.03s/it]
 58%|█████▊    | 524/908 [18:22<10:17,  1.61s/it]
 58%|█████▊    | 525/908 [18:28<18:38,  2.92s/it]
 58%|█████▊    | 526/908 [18:29<15:27,  2.43s/it]
 58%|█████▊    | 527/908 [18:30<12:38,  1.99s/it]
 58%|█████▊    | 528/908 [18:31<09:15,  1.46s/it]
 58%|█████▊    | 529/908 [18:37<18:11,  2.88s/it]
 58%|█████▊    | 530/908 [18:38<15:54,  2.53s/it]
 58%|█████▊    | 531/908 [18:39<12:51,  2.05s/it]
 59%|█████▊    | 532/908 [18:40<09:24,  1.50s/it]
 59%|█████▊    | 533/908 [18:48<21:56,  3.51s/it]
 59%|█████▉    | 534/908 [18:49<17:28,  2.80s/it]
 59%|█████▉    | 535/908 [18:50<14:15,  2.29s/it]
 59%|█████▉    | 536/908 [18:50<10:19,  1.66s/it]
 59%|█████▉    | 537/908 [18:55<15:19,  2.48s/it]
 59%|█████▉    | 538/908 [18:56<13:54,  2.26s/it]
 59%|█████▉    | 539/908 [18:58<11:56,  1.94s/it]
+[Batch:   540] training loss = 0.401
+
 59%|█████▉    | 540/908 [18:58<08:42,  1.42s/it]
 60%|█████▉    | 541/908 [19:03<15:48,  2.59s/it]
 60%|█████▉    | 542/908 [19:04<13:05,  2.15s/it]
 60%|█████▉    | 543/908 [19:05<10:16,  1.69s/it]
 60%|█████▉    | 544/908 [19:05<07:34,  1.25s/it]
 60%|██████    | 545/908 [19:11<15:17,  2.53s/it]
 60%|██████    | 546/908 [19:11<12:01,  1.99s/it]
 60%|██████    | 547/908 [19:12<10:00,  1.66s/it]
 60%|██████    | 548/908 [19:12<07:23,  1.23s/it]
 60%|██████    | 549/908 [19:18<15:46,  2.64s/it]
 61%|██████    | 550/908 [19:19<12:09,  2.04s/it]
 61%|██████    | 551/908 [19:20<10:42,  1.80s/it]
 61%|██████    | 552/908 [19:21<08:11,  1.38s/it]
 61%|██████    | 553/908 [19:26<16:01,  2.71s/it]
 61%|██████    | 554/908 [19:28<13:34,  2.30s/it]
 61%|██████    | 555/908 [19:29<10:58,  1.87s/it]
 61%|██████    | 556/908 [19:29<08:08,  1.39s/it]
 61%|██████▏   | 557/908 [19:34<14:52,  2.54s/it]
 61%|██████▏   | 558/908 [19:36<12:50,  2.20s/it]
 62%|██████▏   | 559/908 [19:36<10:13,  1.76s/it]
+[Batch:   560] training loss = 0.483
+
 62%|██████▏   | 560/908 [19:37<07:46,  1.34s/it]
 62%|██████▏   | 561/908 [19:43<16:51,  2.91s/it]
 62%|██████▏   | 562/908 [19:46<16:22,  2.84s/it]
 62%|██████▏   | 563/908 [19:47<13:21,  2.32s/it]
 62%|██████▏   | 564/908 [19:47<09:41,  1.69s/it]
 62%|██████▏   | 565/908 [19:53<16:26,  2.88s/it]
 62%|██████▏   | 566/908 [19:54<13:40,  2.40s/it]
 62%|██████▏   | 567/908 [19:55<11:22,  2.00s/it]
 63%|██████▎   | 568/908 [19:56<08:30,  1.50s/it]
 63%|██████▎   | 569/908 [20:01<15:47,  2.80s/it]
 63%|██████▎   | 570/908 [20:03<13:46,  2.45s/it]
 63%|██████▎   | 571/908 [20:04<11:56,  2.13s/it]
 63%|██████▎   | 572/908 [20:05<08:40,  1.55s/it]
 63%|██████▎   | 573/908 [20:08<12:14,  2.19s/it]
 63%|██████▎   | 574/908 [20:10<11:28,  2.06s/it]
 63%|██████▎   | 575/908 [20:11<08:54,  1.60s/it]
 63%|██████▎   | 576/908 [20:11<06:32,  1.18s/it]
 64%|██████▎   | 577/908 [20:14<10:35,  1.92s/it]
 64%|██████▎   | 578/908 [20:15<08:53,  1.62s/it]
 64%|██████▍   | 579/908 [20:17<09:23,  1.71s/it]
+[Batch:   580] training loss = 0.391
+
 64%|██████▍   | 580/908 [20:17<06:54,  1.26s/it]
 64%|██████▍   | 581/908 [20:21<11:13,  2.06s/it]
 64%|██████▍   | 582/908 [20:22<09:05,  1.67s/it]
 64%|██████▍   | 583/908 [20:24<08:45,  1.62s/it]
 64%|██████▍   | 584/908 [20:24<06:26,  1.19s/it]
 64%|██████▍   | 585/908 [20:29<12:48,  2.38s/it]
 65%|██████▍   | 586/908 [20:30<10:28,  1.95s/it]
 65%|██████▍   | 587/908 [20:32<10:33,  1.97s/it]
 65%|██████▍   | 588/908 [20:32<07:39,  1.44s/it]
 65%|██████▍   | 589/908 [20:37<12:58,  2.44s/it]
 65%|██████▍   | 590/908 [20:38<10:02,  1.90s/it]
 65%|██████▌   | 591/908 [20:40<10:41,  2.02s/it]
 65%|██████▌   | 592/908 [20:40<07:45,  1.47s/it]
 65%|██████▌   | 593/908 [20:48<18:34,  3.54s/it]
 65%|██████▌   | 594/908 [20:49<14:07,  2.70s/it]
 66%|██████▌   | 595/908 [20:52<13:42,  2.63s/it]
 66%|██████▌   | 596/908 [20:52<09:51,  1.90s/it]
 66%|██████▌   | 597/908 [20:56<13:58,  2.70s/it]
 66%|██████▌   | 598/908 [20:57<10:58,  2.12s/it]
 66%|██████▌   | 599/908 [20:59<11:09,  2.17s/it]
+[Batch:   600] training loss = 0.446
+
 66%|██████▌   | 600/908 [21:00<08:04,  1.57s/it]
 66%|██████▌   | 601/908 [21:05<13:13,  2.58s/it]
 66%|██████▋   | 602/908 [21:05<09:41,  1.90s/it]
 66%|██████▋   | 603/908 [21:07<10:18,  2.03s/it]
 67%|██████▋   | 604/908 [21:07<07:29,  1.48s/it]
 67%|██████▋   | 605/908 [21:12<12:40,  2.51s/it]
 67%|██████▋   | 606/908 [21:13<09:09,  1.82s/it]
 67%|██████▋   | 607/908 [21:15<10:50,  2.16s/it]
 67%|██████▋   | 608/908 [21:16<07:50,  1.57s/it]
 67%|██████▋   | 609/908 [21:21<13:31,  2.71s/it]
 67%|██████▋   | 610/908 [21:21<09:55,  2.00s/it]
 67%|██████▋   | 611/908 [21:24<11:10,  2.26s/it]
 67%|██████▋   | 612/908 [21:24<08:04,  1.64s/it]
 68%|██████▊   | 613/908 [21:30<13:34,  2.76s/it]
 68%|██████▊   | 614/908 [21:30<09:51,  2.01s/it]
 68%|██████▊   | 615/908 [21:32<09:47,  2.00s/it]
 68%|██████▊   | 616/908 [21:32<07:05,  1.46s/it]
 68%|██████▊   | 617/908 [21:37<11:41,  2.41s/it]
 68%|██████▊   | 618/908 [21:37<08:53,  1.84s/it]
 68%|██████▊   | 619/908 [21:40<09:25,  1.96s/it]
+[Batch:   620] training loss = 0.381
+
 68%|██████▊   | 620/908 [21:40<06:51,  1.43s/it]
 68%|██████▊   | 621/908 [21:44<10:38,  2.22s/it]
 69%|██████▊   | 622/908 [21:45<08:20,  1.75s/it]
 69%|██████▊   | 623/908 [21:49<12:35,  2.65s/it]
 69%|██████▊   | 624/908 [21:50<09:04,  1.92s/it]
 69%|██████▉   | 625/908 [21:54<13:08,  2.79s/it]
 69%|██████▉   | 626/908 [21:55<10:23,  2.21s/it]
 69%|██████▉   | 627/908 [21:57<09:38,  2.06s/it]
 69%|██████▉   | 628/908 [21:57<07:13,  1.55s/it]
 69%|██████▉   | 629/908 [22:03<12:28,  2.68s/it]
 69%|██████▉   | 630/908 [22:03<09:35,  2.07s/it]
 69%|██████▉   | 631/908 [22:06<10:07,  2.19s/it]
 70%|██████▉   | 632/908 [22:06<07:21,  1.60s/it]
 70%|██████▉   | 633/908 [22:10<11:21,  2.48s/it]
 70%|██████▉   | 634/908 [22:11<08:54,  1.95s/it]
 70%|██████▉   | 635/908 [22:13<09:04,  2.00s/it]
 70%|███████   | 636/908 [22:13<06:36,  1.46s/it]
 70%|███████   | 637/908 [22:19<11:46,  2.61s/it]
 70%|███████   | 638/908 [22:20<09:35,  2.13s/it]
 70%|███████   | 639/908 [22:22<09:30,  2.12s/it]
+[Batch:   640] training loss = 0.536
+
 70%|███████   | 640/908 [22:22<06:54,  1.54s/it]
 71%|███████   | 641/908 [22:27<12:03,  2.71s/it]
 71%|███████   | 642/908 [22:28<09:25,  2.13s/it]
 71%|███████   | 643/908 [22:30<08:32,  1.93s/it]
 71%|███████   | 644/908 [22:30<06:06,  1.39s/it]
 71%|███████   | 645/908 [22:36<12:16,  2.80s/it]
 71%|███████   | 646/908 [22:36<08:52,  2.03s/it]
 71%|███████▏  | 647/908 [22:39<09:25,  2.17s/it]
 71%|███████▏  | 648/908 [22:39<06:49,  1.58s/it]
 71%|███████▏  | 649/908 [22:44<11:34,  2.68s/it]
 72%|███████▏  | 650/908 [22:45<09:16,  2.16s/it]
 72%|███████▏  | 651/908 [22:48<10:13,  2.39s/it]
 72%|███████▏  | 652/908 [22:48<07:22,  1.73s/it]
 72%|███████▏  | 653/908 [22:50<07:31,  1.77s/it]
 72%|███████▏  | 654/908 [22:52<07:10,  1.69s/it]
 72%|███████▏  | 655/908 [22:54<08:41,  2.06s/it]
 72%|███████▏  | 656/908 [22:55<06:18,  1.50s/it]
 72%|███████▏  | 657/908 [22:59<10:14,  2.45s/it]
 72%|███████▏  | 658/908 [23:01<08:40,  2.08s/it]
 73%|███████▎  | 659/908 [23:03<08:45,  2.11s/it]
+[Batch:   660] training loss = 0.526
+
 73%|███████▎  | 660/908 [23:03<06:20,  1.53s/it]
 73%|███████▎  | 661/908 [23:07<09:21,  2.27s/it]
 73%|███████▎  | 662/908 [23:08<08:06,  1.98s/it]
 73%|███████▎  | 663/908 [23:10<08:08,  1.99s/it]
 73%|███████▎  | 664/908 [23:10<05:54,  1.45s/it]
 73%|███████▎  | 665/908 [23:15<09:58,  2.46s/it]
 73%|███████▎  | 666/908 [23:17<08:51,  2.20s/it]
 73%|███████▎  | 667/908 [23:19<08:47,  2.19s/it]
 74%|███████▎  | 668/908 [23:19<06:21,  1.59s/it]
 74%|███████▎  | 669/908 [23:25<10:54,  2.74s/it]
 74%|███████▍  | 670/908 [23:26<09:26,  2.38s/it]
 74%|███████▍  | 671/908 [23:28<09:14,  2.34s/it]
 74%|███████▍  | 672/908 [23:29<06:40,  1.70s/it]
 74%|███████▍  | 673/908 [23:33<09:28,  2.42s/it]
 74%|███████▍  | 674/908 [23:34<08:25,  2.16s/it]
 74%|███████▍  | 675/908 [23:36<07:58,  2.05s/it]
 74%|███████▍  | 676/908 [23:36<05:46,  1.49s/it]
 75%|███████▍  | 677/908 [23:41<09:57,  2.59s/it]
 75%|███████▍  | 678/908 [23:43<08:53,  2.32s/it]
 75%|███████▍  | 679/908 [23:45<08:54,  2.33s/it]
+[Batch:   680] training loss = 0.601
+
 75%|███████▍  | 680/908 [23:46<06:25,  1.69s/it]
 75%|███████▌  | 681/908 [23:54<13:49,  3.66s/it]
 75%|███████▌  | 682/908 [23:56<11:28,  3.04s/it]
 75%|███████▌  | 683/908 [23:57<09:55,  2.65s/it]
 75%|███████▌  | 684/908 [23:57<07:07,  1.91s/it]
 75%|███████▌  | 685/908 [24:02<09:47,  2.64s/it]
 76%|███████▌  | 686/908 [24:03<08:39,  2.34s/it]
 76%|███████▌  | 687/908 [24:05<08:03,  2.19s/it]
 76%|███████▌  | 688/908 [24:05<05:49,  1.59s/it]
 76%|███████▌  | 689/908 [24:10<09:03,  2.48s/it]
 76%|███████▌  | 690/908 [24:11<07:49,  2.15s/it]
 76%|███████▌  | 691/908 [24:14<08:22,  2.32s/it]
 76%|███████▌  | 692/908 [24:14<06:02,  1.68s/it]
 76%|███████▋  | 693/908 [24:19<09:24,  2.62s/it]
 76%|███████▋  | 694/908 [24:21<08:53,  2.49s/it]
 77%|███████▋  | 695/908 [24:23<08:11,  2.31s/it]
 77%|███████▋  | 696/908 [24:23<05:54,  1.67s/it]
 77%|███████▋  | 697/908 [24:28<09:29,  2.70s/it]
 77%|███████▋  | 698/908 [24:30<07:49,  2.24s/it]
 77%|███████▋  | 699/908 [24:31<06:38,  1.91s/it]
+[Batch:   700] training loss = 0.513
+
 77%|███████▋  | 700/908 [24:31<04:49,  1.39s/it]
 77%|███████▋  | 701/908 [24:35<07:13,  2.09s/it]
 77%|███████▋  | 702/908 [24:36<06:41,  1.95s/it]
 77%|███████▋  | 703/908 [24:38<06:02,  1.77s/it]
 78%|███████▊  | 704/908 [24:38<04:24,  1.29s/it]
 78%|███████▊  | 705/908 [24:43<08:24,  2.49s/it]
 78%|███████▊  | 706/908 [24:45<07:38,  2.27s/it]
 78%|███████▊  | 707/908 [24:46<06:40,  1.99s/it]
 78%|███████▊  | 708/908 [24:46<04:50,  1.45s/it]
 78%|███████▊  | 709/908 [24:54<10:42,  3.23s/it]
 78%|███████▊  | 710/908 [24:55<09:01,  2.73s/it]
 78%|███████▊  | 711/908 [24:57<08:18,  2.53s/it]
 78%|███████▊  | 712/908 [24:58<05:58,  1.83s/it]
 79%|███████▊  | 713/908 [25:02<08:54,  2.74s/it]
 79%|███████▊  | 714/908 [25:04<07:23,  2.29s/it]
 79%|███████▊  | 715/908 [25:06<07:10,  2.23s/it]
 79%|███████▉  | 716/908 [25:06<05:10,  1.62s/it]
 79%|███████▉  | 717/908 [25:11<08:20,  2.62s/it]
 79%|███████▉  | 718/908 [25:12<07:12,  2.28s/it]
 79%|███████▉  | 719/908 [25:14<06:48,  2.16s/it]
+[Batch:   720] training loss = 0.521
+
 79%|███████▉  | 720/908 [25:14<04:55,  1.57s/it]
 79%|███████▉  | 721/908 [25:20<08:09,  2.62s/it]
 80%|███████▉  | 722/908 [25:21<06:57,  2.25s/it]
 80%|███████▉  | 723/908 [25:23<06:37,  2.15s/it]
 80%|███████▉  | 724/908 [25:23<04:46,  1.56s/it]
 80%|███████▉  | 725/908 [25:29<08:38,  2.83s/it]
 80%|███████▉  | 726/908 [25:30<07:14,  2.39s/it]
 80%|████████  | 727/908 [25:32<06:51,  2.27s/it]
 80%|████████  | 728/908 [25:32<04:56,  1.65s/it]
 80%|████████  | 729/908 [25:36<06:34,  2.20s/it]
 80%|████████  | 730/908 [25:38<06:02,  2.04s/it]
 81%|████████  | 731/908 [25:39<05:50,  1.98s/it]
 81%|████████  | 732/908 [25:40<04:14,  1.44s/it]
 81%|████████  | 733/908 [25:44<07:00,  2.41s/it]
 81%|████████  | 734/908 [25:46<06:18,  2.18s/it]
 81%|████████  | 735/908 [25:47<05:23,  1.87s/it]
 81%|████████  | 736/908 [25:47<03:54,  1.36s/it]
 81%|████████  | 737/908 [25:53<07:40,  2.69s/it]
 81%|████████▏ | 738/908 [25:54<06:22,  2.25s/it]
 81%|████████▏ | 739/908 [25:57<06:39,  2.37s/it]
+[Batch:   740] training loss = 0.321
+
 81%|████████▏ | 740/908 [25:57<04:47,  1.71s/it]
 82%|████████▏ | 741/908 [26:03<08:05,  2.90s/it]
 82%|████████▏ | 742/908 [26:04<06:29,  2.35s/it]
 82%|████████▏ | 743/908 [26:06<06:36,  2.40s/it]
 82%|████████▏ | 744/908 [26:06<04:45,  1.74s/it]
 82%|████████▏ | 745/908 [26:12<08:06,  2.99s/it]
 82%|████████▏ | 746/908 [26:13<06:18,  2.33s/it]
 82%|████████▏ | 747/908 [26:15<06:12,  2.32s/it]
 82%|████████▏ | 748/908 [26:16<04:28,  1.68s/it]
 82%|████████▏ | 749/908 [26:21<07:12,  2.72s/it]
 83%|████████▎ | 750/908 [26:21<05:20,  2.03s/it]
 83%|████████▎ | 751/908 [26:24<05:51,  2.24s/it]
 83%|████████▎ | 752/908 [26:24<04:13,  1.62s/it]
 83%|████████▎ | 753/908 [26:29<06:49,  2.64s/it]
 83%|████████▎ | 754/908 [26:30<05:20,  2.08s/it]
 83%|████████▎ | 755/908 [26:33<05:55,  2.33s/it]
 83%|████████▎ | 756/908 [26:33<04:16,  1.69s/it]
 83%|████████▎ | 757/908 [26:37<05:53,  2.34s/it]
 83%|████████▎ | 758/908 [26:37<04:23,  1.76s/it]
 84%|████████▎ | 759/908 [26:40<05:07,  2.06s/it]
+[Batch:   760] training loss = 0.501
+
 84%|████████▎ | 760/908 [26:40<03:42,  1.51s/it]
 84%|████████▍ | 761/908 [26:46<06:25,  2.62s/it]
 84%|████████▍ | 762/908 [26:46<04:38,  1.90s/it]
 84%|████████▍ | 763/908 [26:48<05:12,  2.15s/it]
 84%|████████▍ | 764/908 [26:49<03:45,  1.57s/it]
 84%|████████▍ | 765/908 [26:57<08:47,  3.69s/it]
 84%|████████▍ | 766/908 [26:58<06:22,  2.69s/it]
 84%|████████▍ | 767/908 [27:01<06:52,  2.92s/it]
 85%|████████▍ | 768/908 [27:01<04:55,  2.11s/it]
 85%|████████▍ | 769/908 [27:07<07:28,  3.23s/it]
 85%|████████▍ | 770/908 [27:07<05:21,  2.33s/it]
 85%|████████▍ | 771/908 [27:11<05:51,  2.57s/it]
 85%|████████▌ | 772/908 [27:11<04:12,  1.86s/it]
 85%|████████▌ | 773/908 [27:17<07:03,  3.14s/it]
 85%|████████▌ | 774/908 [27:17<05:03,  2.27s/it]
 85%|████████▌ | 775/908 [27:19<04:58,  2.25s/it]
 85%|████████▌ | 776/908 [27:20<03:35,  1.63s/it]
 86%|████████▌ | 777/908 [27:24<05:42,  2.62s/it]
 86%|████████▌ | 778/908 [27:25<04:32,  2.10s/it]
 86%|████████▌ | 779/908 [27:27<04:33,  2.12s/it]
+[Batch:   780] training loss = 0.307
+
 86%|████████▌ | 780/908 [27:28<03:17,  1.55s/it]
 86%|████████▌ | 781/908 [27:32<04:50,  2.29s/it]
 86%|████████▌ | 782/908 [27:33<03:58,  1.89s/it]
 86%|████████▌ | 783/908 [27:35<04:03,  1.95s/it]
 86%|████████▋ | 784/908 [27:35<02:56,  1.43s/it]
 86%|████████▋ | 785/908 [27:40<04:58,  2.42s/it]
 87%|████████▋ | 786/908 [27:40<03:49,  1.88s/it]
 87%|████████▋ | 787/908 [27:43<04:21,  2.16s/it]
 87%|████████▋ | 788/908 [27:43<03:08,  1.57s/it]
 87%|████████▋ | 789/908 [27:49<05:30,  2.77s/it]
 87%|████████▋ | 790/908 [27:49<04:05,  2.08s/it]
 87%|████████▋ | 791/908 [27:52<04:30,  2.31s/it]
 87%|████████▋ | 792/908 [27:52<03:15,  1.68s/it]
 87%|████████▋ | 793/908 [28:00<06:23,  3.33s/it]
 87%|████████▋ | 794/908 [28:00<04:37,  2.43s/it]
 88%|████████▊ | 795/908 [28:03<05:07,  2.72s/it]
 88%|████████▊ | 796/908 [28:04<03:40,  1.96s/it]
 88%|████████▊ | 797/908 [28:09<05:24,  2.92s/it]
 88%|████████▊ | 798/908 [28:09<04:01,  2.19s/it]
 88%|████████▊ | 799/908 [28:12<04:10,  2.30s/it]
+[Batch:   800] training loss = 0.574
+
 88%|████████▊ | 800/908 [28:12<02:59,  1.67s/it]
 88%|████████▊ | 801/908 [28:18<05:12,  2.92s/it]
 88%|████████▊ | 802/908 [28:18<03:44,  2.12s/it]
 88%|████████▊ | 803/908 [28:21<03:57,  2.26s/it]
 89%|████████▊ | 804/908 [28:21<02:50,  1.64s/it]
 89%|████████▊ | 805/908 [28:26<04:28,  2.61s/it]
 89%|████████▉ | 806/908 [28:26<03:26,  2.02s/it]
 89%|████████▉ | 807/908 [28:29<03:58,  2.36s/it]
 89%|████████▉ | 808/908 [28:30<02:51,  1.71s/it]
 89%|████████▉ | 809/908 [28:35<04:35,  2.78s/it]
 89%|████████▉ | 810/908 [28:35<03:23,  2.08s/it]
 89%|████████▉ | 811/908 [28:38<03:40,  2.27s/it]
 89%|████████▉ | 812/908 [28:38<02:38,  1.65s/it]
 90%|████████▉ | 813/908 [28:43<03:53,  2.46s/it]
 90%|████████▉ | 814/908 [28:43<03:00,  1.92s/it]
 90%|████████▉ | 815/908 [28:47<03:35,  2.32s/it]
 90%|████████▉ | 816/908 [28:47<02:34,  1.68s/it]
 90%|████████▉ | 817/908 [28:53<04:26,  2.92s/it]
 90%|█████████ | 818/908 [28:53<03:19,  2.22s/it]
 90%|█████████ | 819/908 [28:56<03:43,  2.51s/it]
+[Batch:   820] training loss = 0.406
+
 90%|█████████ | 820/908 [28:57<02:40,  1.82s/it]
 90%|█████████ | 821/908 [29:03<04:47,  3.31s/it]
 91%|█████████ | 822/908 [29:04<03:34,  2.49s/it]
 91%|█████████ | 823/908 [29:07<03:53,  2.75s/it]
 91%|█████████ | 824/908 [29:08<02:47,  1.99s/it]
 91%|█████████ | 825/908 [29:12<03:54,  2.82s/it]
 91%|█████████ | 826/908 [29:13<02:56,  2.15s/it]
 91%|█████████ | 827/908 [29:15<02:46,  2.06s/it]
 91%|█████████ | 828/908 [29:15<01:59,  1.50s/it]
 91%|█████████▏| 829/908 [29:19<02:57,  2.24s/it]
 91%|█████████▏| 830/908 [29:20<02:19,  1.78s/it]
 92%|█████████▏| 831/908 [29:22<02:39,  2.07s/it]
 92%|█████████▏| 832/908 [29:23<01:54,  1.51s/it]
 92%|█████████▏| 833/908 [29:27<03:10,  2.54s/it]
 92%|█████████▏| 834/908 [29:28<02:19,  1.88s/it]
 92%|█████████▏| 835/908 [29:31<02:41,  2.21s/it]
 92%|█████████▏| 836/908 [29:31<01:55,  1.61s/it]
 92%|█████████▏| 837/908 [29:37<03:23,  2.87s/it]
 92%|█████████▏| 838/908 [29:37<02:28,  2.12s/it]
 92%|█████████▏| 839/908 [29:39<02:29,  2.16s/it]
+[Batch:   840] training loss = 0.509
+
 93%|█████████▎| 840/908 [29:40<01:46,  1.57s/it]
 93%|█████████▎| 841/908 [29:45<02:53,  2.59s/it]
 93%|█████████▎| 842/908 [29:45<02:03,  1.88s/it]
 93%|█████████▎| 843/908 [29:48<02:26,  2.25s/it]
 93%|█████████▎| 844/908 [29:48<01:44,  1.63s/it]
 93%|█████████▎| 845/908 [29:54<03:06,  2.96s/it]
 93%|█████████▎| 846/908 [29:55<02:19,  2.25s/it]
 93%|█████████▎| 847/908 [29:58<02:43,  2.68s/it]
 93%|█████████▎| 848/908 [29:59<01:56,  1.93s/it]
 94%|█████████▎| 849/908 [30:05<03:18,  3.37s/it]
 94%|█████████▎| 850/908 [30:06<02:27,  2.54s/it]
 94%|█████████▎| 851/908 [30:09<02:31,  2.65s/it]
 94%|█████████▍| 852/908 [30:09<01:47,  1.91s/it]
 94%|█████████▍| 853/908 [30:14<02:27,  2.69s/it]
 94%|█████████▍| 854/908 [30:14<01:51,  2.06s/it]
 94%|█████████▍| 855/908 [30:17<01:55,  2.17s/it]
 94%|█████████▍| 856/908 [30:17<01:22,  1.58s/it]
 94%|█████████▍| 857/908 [30:22<02:14,  2.64s/it]
 94%|█████████▍| 858/908 [30:23<01:45,  2.11s/it]
 95%|█████████▍| 859/908 [30:25<01:38,  2.01s/it]
+[Batch:   860] training loss = 0.464
+
 95%|█████████▍| 860/908 [30:25<01:10,  1.47s/it]
 95%|█████████▍| 861/908 [30:30<02:08,  2.74s/it]
 95%|█████████▍| 862/908 [30:31<01:32,  2.01s/it]
 95%|█████████▌| 863/908 [30:33<01:39,  2.20s/it]
 95%|█████████▌| 864/908 [30:34<01:10,  1.60s/it]
 95%|█████████▌| 865/908 [30:39<01:57,  2.74s/it]
 95%|█████████▌| 866/908 [30:40<01:27,  2.07s/it]
 95%|█████████▌| 867/908 [30:42<01:28,  2.15s/it]
 96%|█████████▌| 868/908 [30:42<01:02,  1.56s/it]
 96%|█████████▌| 869/908 [30:48<01:49,  2.80s/it]
 96%|█████████▌| 870/908 [30:48<01:22,  2.18s/it]
 96%|█████████▌| 871/908 [30:51<01:21,  2.20s/it]
 96%|█████████▌| 872/908 [30:51<00:57,  1.59s/it]
 96%|█████████▌| 873/908 [30:56<01:37,  2.78s/it]
 96%|█████████▋| 874/908 [30:57<01:08,  2.01s/it]
 96%|█████████▋| 875/908 [31:02<01:39,  3.00s/it]
 96%|█████████▋| 876/908 [31:02<01:09,  2.16s/it]
 97%|█████████▋| 877/908 [31:08<01:37,  3.15s/it]
 97%|█████████▋| 878/908 [31:08<01:11,  2.37s/it]
 97%|█████████▋| 879/908 [31:10<01:04,  2.24s/it]
+[Batch:   880] training loss = 0.418
+
 97%|█████████▋| 880/908 [31:10<00:45,  1.62s/it]
 97%|█████████▋| 881/908 [31:16<01:17,  2.88s/it]
 97%|█████████▋| 882/908 [31:16<00:54,  2.10s/it]
 97%|█████████▋| 883/908 [31:18<00:51,  2.05s/it]
 97%|█████████▋| 884/908 [31:19<00:35,  1.49s/it]
 97%|█████████▋| 885/908 [31:24<01:02,  2.72s/it]
 98%|█████████▊| 886/908 [31:25<00:50,  2.27s/it]
 98%|█████████▊| 887/908 [31:26<00:38,  1.85s/it]
 98%|█████████▊| 888/908 [31:26<00:27,  1.35s/it]
 98%|█████████▊| 889/908 [31:32<00:48,  2.54s/it]
 98%|█████████▊| 890/908 [31:33<00:37,  2.08s/it]
 98%|█████████▊| 891/908 [31:34<00:30,  1.80s/it]
 98%|█████████▊| 892/908 [31:34<00:21,  1.32s/it]
 98%|█████████▊| 893/908 [31:40<00:39,  2.61s/it]
 98%|█████████▊| 894/908 [31:40<00:27,  1.98s/it]
 99%|█████████▊| 895/908 [31:41<00:22,  1.71s/it]
 99%|█████████▊| 896/908 [31:41<00:15,  1.26s/it]
 99%|█████████▉| 897/908 [31:47<00:28,  2.59s/it]
 99%|█████████▉| 898/908 [31:48<00:20,  2.06s/it]
 99%|█████████▉| 899/908 [31:49<00:16,  1.82s/it]
+[Batch:   900] training loss = 0.550
+
 99%|█████████▉| 900/908 [31:49<00:10,  1.34s/it]
 99%|█████████▉| 901/908 [31:55<00:17,  2.51s/it]
 99%|█████████▉| 902/908 [31:56<00:12,  2.03s/it]
 99%|█████████▉| 903/908 [31:56<00:08,  1.68s/it]
100%|█████████▉| 904/908 [31:57<00:04,  1.23s/it]
100%|█████████▉| 905/908 [32:04<00:09,  3.07s/it]
100%|█████████▉| 906/908 [32:05<00:04,  2.39s/it]
100%|█████████▉| 907/908 [32:06<00:01,  1.93s/it]
100%|██████████| 908/908 [32:06<00:00,  1.39s/it]
100%|██████████| 908/908 [32:06<00:00,  2.12s/it]
+
+Epoch   5: validation
+
  0%|          | 0/101 [00:00<?, ?it/s]
  1%|          | 1/101 [00:08<13:31,  8.12s/it]
  3%|▎         | 3/101 [00:08<03:32,  2.17s/it]
  4%|▍         | 4/101 [00:08<02:28,  1.54s/it]
  5%|▍         | 5/101 [00:16<05:35,  3.49s/it]
  7%|▋         | 7/101 [00:16<02:52,  1.84s/it]
  9%|▉         | 9/101 [00:25<04:26,  2.90s/it]
 10%|▉         | 10/101 [00:25<03:25,  2.26s/it]
 12%|█▏        | 12/101 [00:25<02:06,  1.42s/it]
 13%|█▎        | 13/101 [00:33<04:16,  2.91s/it]
 15%|█▍        | 15/101 [00:33<02:36,  1.82s/it]
 16%|█▌        | 16/101 [00:34<02:12,  1.56s/it]
 17%|█▋        | 17/101 [00:41<03:52,  2.77s/it]
 18%|█▊        | 18/101 [00:41<02:55,  2.11s/it]
 20%|█▉        | 20/101 [00:41<01:46,  1.32s/it]
 21%|██        | 21/101 [00:49<03:49,  2.87s/it]
 23%|██▎       | 23/101 [00:49<02:17,  1.76s/it]
 25%|██▍       | 25/101 [00:57<03:15,  2.57s/it]
 26%|██▌       | 26/101 [00:58<02:41,  2.15s/it]
 28%|██▊       | 28/101 [00:58<01:41,  1.38s/it]
 29%|██▊       | 29/101 [01:07<03:26,  2.87s/it]
 30%|██▉       | 30/101 [01:07<02:48,  2.37s/it]
 32%|███▏      | 32/101 [01:08<01:42,  1.48s/it]
 33%|███▎      | 33/101 [01:15<03:04,  2.72s/it]
 34%|███▎      | 34/101 [01:16<02:43,  2.43s/it]
 36%|███▌      | 36/101 [01:16<01:36,  1.48s/it]
 37%|███▋      | 37/101 [01:24<03:10,  2.97s/it]
 38%|███▊      | 38/101 [01:26<02:40,  2.55s/it]
 40%|███▉      | 40/101 [01:26<01:33,  1.54s/it]
 41%|████      | 41/101 [01:32<02:41,  2.70s/it]
 42%|████▏     | 42/101 [01:34<02:14,  2.28s/it]
 44%|████▎     | 44/101 [01:34<01:18,  1.38s/it]
 45%|████▍     | 45/101 [01:41<02:37,  2.82s/it]
 46%|████▌     | 46/101 [01:42<02:06,  2.31s/it]
 48%|████▊     | 48/101 [01:42<01:13,  1.39s/it]
 49%|████▊     | 49/101 [01:49<02:18,  2.67s/it]
 50%|████▉     | 50/101 [01:50<01:54,  2.24s/it]
 51%|█████▏    | 52/101 [01:50<01:06,  1.35s/it]
 52%|█████▏    | 53/101 [02:00<02:31,  3.16s/it]
 53%|█████▎    | 54/101 [02:00<01:57,  2.50s/it]
 55%|█████▌    | 56/101 [02:00<01:07,  1.50s/it]
 56%|█████▋    | 57/101 [02:07<02:00,  2.73s/it]
 57%|█████▋    | 58/101 [02:08<01:39,  2.31s/it]
 59%|█████▉    | 60/101 [02:08<00:56,  1.39s/it]
 60%|██████    | 61/101 [02:15<01:46,  2.66s/it]
 61%|██████▏   | 62/101 [02:17<01:30,  2.33s/it]
 63%|██████▎   | 64/101 [02:17<00:52,  1.42s/it]
 64%|██████▍   | 65/101 [02:25<01:48,  3.01s/it]
 65%|██████▌   | 66/101 [02:26<01:27,  2.50s/it]
 67%|██████▋   | 68/101 [02:26<00:49,  1.51s/it]
 68%|██████▊   | 69/101 [02:34<01:31,  2.85s/it]
 69%|██████▉   | 70/101 [02:35<01:13,  2.37s/it]
 71%|███████▏  | 72/101 [02:35<00:41,  1.43s/it]
 72%|███████▏  | 73/101 [02:41<01:12,  2.60s/it]
 73%|███████▎  | 74/101 [02:42<00:56,  2.10s/it]
 75%|███████▌  | 76/101 [02:42<00:31,  1.27s/it]
 76%|███████▌  | 77/101 [02:51<01:10,  2.95s/it]
 77%|███████▋  | 78/101 [02:51<00:53,  2.34s/it]
 79%|███████▉  | 80/101 [02:51<00:29,  1.41s/it]
 80%|████████  | 81/101 [03:01<01:06,  3.35s/it]
 81%|████████  | 82/101 [03:02<00:49,  2.60s/it]
 83%|████████▎ | 84/101 [03:02<00:26,  1.56s/it]
 84%|████████▍ | 85/101 [03:10<00:47,  2.96s/it]
 85%|████████▌ | 86/101 [03:10<00:34,  2.33s/it]
 87%|████████▋ | 88/101 [03:10<00:18,  1.40s/it]
 88%|████████▊ | 89/101 [03:19<00:36,  3.01s/it]
 89%|████████▉ | 90/101 [03:19<00:25,  2.32s/it]
 91%|█████████ | 92/101 [03:19<00:12,  1.40s/it]
 92%|█████████▏| 93/101 [03:27<00:22,  2.84s/it]
 93%|█████████▎| 94/101 [03:27<00:15,  2.17s/it]
 94%|█████████▍| 95/101 [03:27<00:09,  1.65s/it]
 95%|█████████▌| 96/101 [03:27<00:06,  1.25s/it]
 96%|█████████▌| 97/101 [03:35<00:12,  3.05s/it]
 98%|█████████▊| 99/101 [03:35<00:03,  1.75s/it]
100%|██████████| 101/101 [03:38<00:00,  1.69s/it]
100%|██████████| 101/101 [03:38<00:00,  2.17s/it]
+    Epoch  5: training loss = 0.461 / validation loss = 0.469
+    Epoch  5: training acc = 0.781 / validation acc = 0.780
+
+Epoch   6: training
+
  0%|          | 0/908 [00:00<?, ?it/s]
  0%|          | 1/908 [00:08<2:15:16,  8.95s/it]
  0%|          | 2/908 [00:09<59:54,  3.97s/it]  
  0%|          | 3/908 [00:09<33:49,  2.24s/it]
  0%|          | 4/908 [00:09<21:34,  1.43s/it]
  1%|          | 5/908 [00:18<1:01:27,  4.08s/it]
  1%|          | 6/908 [00:19<42:45,  2.84s/it]  
  1%|          | 7/908 [00:19<29:40,  1.98s/it]
  1%|          | 8/908 [00:19<21:06,  1.41s/it]
  1%|          | 9/908 [00:29<1:01:11,  4.08s/it]
  1%|          | 10/908 [00:30<45:40,  3.05s/it] 
  1%|          | 11/908 [00:30<32:31,  2.18s/it]
  1%|▏         | 12/908 [00:30<23:28,  1.57s/it]
  1%|▏         | 13/908 [00:37<49:43,  3.33s/it]
  2%|▏         | 14/908 [00:38<36:55,  2.48s/it]
  2%|▏         | 15/908 [00:38<26:36,  1.79s/it]
  2%|▏         | 16/908 [00:38<19:25,  1.31s/it]
  2%|▏         | 17/908 [00:45<45:30,  3.06s/it]
  2%|▏         | 18/908 [00:46<32:46,  2.21s/it]
  2%|▏         | 19/908 [00:46<23:46,  1.60s/it]
+[Batch:    20] training loss = 0.480
+
  2%|▏         | 20/908 [00:46<17:36,  1.19s/it]
  2%|▏         | 21/908 [00:53<42:36,  2.88s/it]
  2%|▏         | 22/908 [00:54<34:07,  2.31s/it]
  3%|▎         | 23/908 [00:54<24:42,  1.67s/it]
  3%|▎         | 24/908 [00:54<18:07,  1.23s/it]
  3%|▎         | 25/908 [01:00<38:18,  2.60s/it]
  3%|▎         | 26/908 [01:01<32:31,  2.21s/it]
  3%|▎         | 27/908 [01:02<23:35,  1.61s/it]
  3%|▎         | 28/908 [01:02<17:21,  1.18s/it]
  3%|▎         | 29/908 [01:09<42:13,  2.88s/it]
  3%|▎         | 30/908 [01:10<34:16,  2.34s/it]
  3%|▎         | 31/908 [01:10<24:48,  1.70s/it]
  4%|▎         | 32/908 [01:10<18:11,  1.25s/it]
  4%|▎         | 33/908 [01:17<41:55,  2.87s/it]
  4%|▎         | 34/908 [01:18<33:34,  2.30s/it]
  4%|▍         | 35/908 [01:18<24:22,  1.67s/it]
  4%|▍         | 36/908 [01:18<17:52,  1.23s/it]
  4%|▍         | 37/908 [01:29<59:18,  4.09s/it]
  4%|▍         | 38/908 [01:30<46:05,  3.18s/it]
  4%|▍         | 39/908 [01:30<33:04,  2.28s/it]
+[Batch:    40] training loss = 0.441
+
  4%|▍         | 40/908 [01:30<23:56,  1.66s/it]
  5%|▍         | 41/908 [01:36<41:38,  2.88s/it]
  5%|▍         | 42/908 [01:37<34:32,  2.39s/it]
  5%|▍         | 43/908 [01:37<24:58,  1.73s/it]
  5%|▍         | 44/908 [01:38<18:18,  1.27s/it]
  5%|▍         | 45/908 [01:43<34:17,  2.38s/it]
  5%|▌         | 46/908 [01:45<34:23,  2.39s/it]
  5%|▌         | 47/908 [01:45<24:50,  1.73s/it]
  5%|▌         | 48/908 [01:45<18:11,  1.27s/it]
  5%|▌         | 49/908 [01:53<43:39,  3.05s/it]
  6%|▌         | 50/908 [01:54<37:32,  2.63s/it]
  6%|▌         | 51/908 [01:54<27:03,  1.89s/it]
  6%|▌         | 52/908 [01:55<19:42,  1.38s/it]
  6%|▌         | 53/908 [02:01<39:52,  2.80s/it]
  6%|▌         | 54/908 [02:03<37:23,  2.63s/it]
  6%|▌         | 55/908 [02:03<26:56,  1.90s/it]
  6%|▌         | 56/908 [02:03<19:38,  1.38s/it]
  6%|▋         | 57/908 [02:09<38:08,  2.69s/it]
  6%|▋         | 58/908 [02:11<34:30,  2.44s/it]
  6%|▋         | 59/908 [02:11<24:55,  1.76s/it]
+[Batch:    60] training loss = 0.412
+
  7%|▋         | 60/908 [02:11<18:14,  1.29s/it]
  7%|▋         | 61/908 [02:18<41:14,  2.92s/it]
  7%|▋         | 62/908 [02:21<40:56,  2.90s/it]
  7%|▋         | 63/908 [02:21<29:24,  2.09s/it]
  7%|▋         | 64/908 [02:21<21:21,  1.52s/it]
  7%|▋         | 65/908 [02:30<49:52,  3.55s/it]
  7%|▋         | 66/908 [02:32<45:01,  3.21s/it]
  7%|▋         | 67/908 [02:32<32:18,  2.30s/it]
  7%|▋         | 68/908 [02:32<23:23,  1.67s/it]
  8%|▊         | 69/908 [02:38<39:34,  2.83s/it]
  8%|▊         | 70/908 [02:40<37:39,  2.70s/it]
  8%|▊         | 71/908 [02:41<27:07,  1.94s/it]
  8%|▊         | 72/908 [02:41<20:05,  1.44s/it]
  8%|▊         | 73/908 [02:46<37:34,  2.70s/it]
  8%|▊         | 74/908 [02:49<36:00,  2.59s/it]
  8%|▊         | 75/908 [02:49<25:59,  1.87s/it]
  8%|▊         | 76/908 [02:49<18:57,  1.37s/it]
  8%|▊         | 77/908 [02:54<35:13,  2.54s/it]
  9%|▊         | 78/908 [02:56<33:05,  2.39s/it]
  9%|▊         | 79/908 [02:57<23:58,  1.73s/it]
+[Batch:    80] training loss = 0.401
+
  9%|▉         | 80/908 [02:57<17:35,  1.27s/it]
  9%|▉         | 81/908 [03:02<34:18,  2.49s/it]
  9%|▉         | 82/908 [03:04<29:42,  2.16s/it]
  9%|▉         | 83/908 [03:04<21:34,  1.57s/it]
  9%|▉         | 84/908 [03:04<15:55,  1.16s/it]
  9%|▉         | 85/908 [03:10<34:55,  2.55s/it]
  9%|▉         | 86/908 [03:11<30:08,  2.20s/it]
 10%|▉         | 87/908 [03:11<21:53,  1.60s/it]
 10%|▉         | 88/908 [03:12<16:07,  1.18s/it]
 10%|▉         | 89/908 [03:17<34:16,  2.51s/it]
 10%|▉         | 90/908 [03:19<33:06,  2.43s/it]
 10%|█         | 91/908 [03:20<23:56,  1.76s/it]
 10%|█         | 92/908 [03:20<17:33,  1.29s/it]
 10%|█         | 93/908 [03:29<49:04,  3.61s/it]
 10%|█         | 94/908 [03:30<40:36,  2.99s/it]
 10%|█         | 95/908 [03:31<29:13,  2.16s/it]
 11%|█         | 96/908 [03:31<21:13,  1.57s/it]
 11%|█         | 97/908 [03:37<39:07,  2.90s/it]
 11%|█         | 98/908 [03:38<31:58,  2.37s/it]
 11%|█         | 99/908 [03:38<23:08,  1.72s/it]
+[Batch:   100] training loss = 0.439
+
 11%|█         | 100/908 [03:38<16:59,  1.26s/it]
 11%|█         | 101/908 [03:45<38:07,  2.83s/it]
 11%|█         | 102/908 [03:46<32:25,  2.41s/it]
 11%|█▏        | 103/908 [03:46<23:28,  1.75s/it]
 11%|█▏        | 104/908 [03:47<17:12,  1.28s/it]
 12%|█▏        | 105/908 [03:53<35:56,  2.69s/it]
 12%|█▏        | 106/908 [03:54<30:22,  2.27s/it]
 12%|█▏        | 107/908 [03:54<22:01,  1.65s/it]
 12%|█▏        | 108/908 [03:54<16:10,  1.21s/it]
 12%|█▏        | 109/908 [04:01<37:56,  2.85s/it]
 12%|█▏        | 110/908 [04:03<32:51,  2.47s/it]
 12%|█▏        | 111/908 [04:03<23:44,  1.79s/it]
 12%|█▏        | 112/908 [04:03<17:23,  1.31s/it]
 12%|█▏        | 113/908 [04:09<37:40,  2.84s/it]
 13%|█▎        | 114/908 [04:11<31:25,  2.37s/it]
 13%|█▎        | 115/908 [04:11<22:48,  1.73s/it]
 13%|█▎        | 116/908 [04:11<16:44,  1.27s/it]
 13%|█▎        | 117/908 [04:18<39:43,  3.01s/it]
 13%|█▎        | 118/908 [04:20<34:47,  2.64s/it]
 13%|█▎        | 119/908 [04:20<25:07,  1.91s/it]
+[Batch:   120] training loss = 0.459
+
 13%|█▎        | 120/908 [04:20<18:20,  1.40s/it]
 13%|█▎        | 121/908 [04:27<39:28,  3.01s/it]
 13%|█▎        | 122/908 [04:29<36:12,  2.76s/it]
 14%|█▎        | 123/908 [04:29<26:06,  2.00s/it]
 14%|█▎        | 124/908 [04:30<19:02,  1.46s/it]
 14%|█▍        | 125/908 [04:36<39:23,  3.02s/it]
 14%|█▍        | 126/908 [04:37<31:35,  2.42s/it]
 14%|█▍        | 127/908 [04:38<22:51,  1.76s/it]
 14%|█▍        | 128/908 [04:38<16:44,  1.29s/it]
 14%|█▍        | 129/908 [04:43<32:24,  2.50s/it]
 14%|█▍        | 130/908 [04:45<28:36,  2.21s/it]
 14%|█▍        | 131/908 [04:45<20:46,  1.60s/it]
 15%|█▍        | 132/908 [04:45<15:19,  1.18s/it]
 15%|█▍        | 133/908 [04:53<40:42,  3.15s/it]
 15%|█▍        | 134/908 [04:55<36:00,  2.79s/it]
 15%|█▍        | 135/908 [04:55<25:57,  2.01s/it]
 15%|█▍        | 136/908 [04:55<18:55,  1.47s/it]
 15%|█▌        | 137/908 [05:01<37:04,  2.88s/it]
 15%|█▌        | 138/908 [05:03<32:15,  2.51s/it]
 15%|█▌        | 139/908 [05:03<23:17,  1.82s/it]
+[Batch:   140] training loss = 0.410
+
 15%|█▌        | 140/908 [05:03<17:03,  1.33s/it]
 16%|█▌        | 141/908 [05:09<31:47,  2.49s/it]
 16%|█▌        | 142/908 [05:11<30:16,  2.37s/it]
 16%|█▌        | 143/908 [05:11<21:56,  1.72s/it]
 16%|█▌        | 144/908 [05:11<16:04,  1.26s/it]
 16%|█▌        | 145/908 [05:17<33:47,  2.66s/it]
 16%|█▌        | 146/908 [05:19<33:02,  2.60s/it]
 16%|█▌        | 147/908 [05:20<23:49,  1.88s/it]
 16%|█▋        | 148/908 [05:20<17:22,  1.37s/it]
 16%|█▋        | 149/908 [05:27<38:19,  3.03s/it]
 17%|█▋        | 150/908 [05:30<41:04,  3.25s/it]
 17%|█▋        | 151/908 [05:31<29:28,  2.34s/it]
 17%|█▋        | 152/908 [05:31<21:19,  1.69s/it]
 17%|█▋        | 153/908 [05:36<34:15,  2.72s/it]
 17%|█▋        | 154/908 [05:38<32:27,  2.58s/it]
 17%|█▋        | 155/908 [05:38<23:24,  1.87s/it]
 17%|█▋        | 156/908 [05:39<17:06,  1.36s/it]
 17%|█▋        | 157/908 [05:44<32:22,  2.59s/it]
 17%|█▋        | 158/908 [05:46<31:06,  2.49s/it]
 18%|█▊        | 159/908 [05:46<22:27,  1.80s/it]
+[Batch:   160] training loss = 0.524
+
 18%|█▊        | 160/908 [05:47<16:24,  1.32s/it]
 18%|█▊        | 161/908 [05:52<31:00,  2.49s/it]
 18%|█▊        | 162/908 [05:54<30:21,  2.44s/it]
 18%|█▊        | 163/908 [05:54<21:56,  1.77s/it]
 18%|█▊        | 164/908 [05:55<16:03,  1.29s/it]
 18%|█▊        | 165/908 [06:00<31:09,  2.52s/it]
 18%|█▊        | 166/908 [06:04<34:51,  2.82s/it]
 18%|█▊        | 167/908 [06:04<25:04,  2.03s/it]
 19%|█▊        | 168/908 [06:04<18:15,  1.48s/it]
 19%|█▊        | 169/908 [06:08<29:10,  2.37s/it]
 19%|█▊        | 170/908 [06:11<31:03,  2.52s/it]
 19%|█▉        | 171/908 [06:11<22:24,  1.82s/it]
 19%|█▉        | 172/908 [06:12<16:21,  1.33s/it]
 19%|█▉        | 173/908 [06:16<28:23,  2.32s/it]
 19%|█▉        | 174/908 [06:19<31:11,  2.55s/it]
 19%|█▉        | 175/908 [06:20<22:29,  1.84s/it]
 19%|█▉        | 176/908 [06:20<16:24,  1.35s/it]
 19%|█▉        | 177/908 [06:23<24:52,  2.04s/it]
 20%|█▉        | 178/908 [06:27<28:52,  2.37s/it]
 20%|█▉        | 179/908 [06:27<20:53,  1.72s/it]
+[Batch:   180] training loss = 0.462
+
 20%|█▉        | 180/908 [06:27<15:17,  1.26s/it]
 20%|█▉        | 181/908 [06:35<40:12,  3.32s/it]
 20%|██        | 182/908 [06:37<36:57,  3.05s/it]
 20%|██        | 183/908 [06:38<26:31,  2.20s/it]
 20%|██        | 184/908 [06:38<19:15,  1.60s/it]
 20%|██        | 185/908 [06:42<28:56,  2.40s/it]
 20%|██        | 186/908 [06:46<32:55,  2.74s/it]
 21%|██        | 187/908 [06:46<23:44,  1.98s/it]
 21%|██        | 188/908 [06:46<17:19,  1.44s/it]
 21%|██        | 189/908 [06:50<26:44,  2.23s/it]
 21%|██        | 190/908 [06:54<33:23,  2.79s/it]
 21%|██        | 191/908 [06:54<24:01,  2.01s/it]
 21%|██        | 192/908 [06:55<17:36,  1.48s/it]
 21%|██▏       | 193/908 [06:59<28:11,  2.37s/it]
 21%|██▏       | 194/908 [07:02<28:39,  2.41s/it]
 21%|██▏       | 195/908 [07:02<20:45,  1.75s/it]
 22%|██▏       | 196/908 [07:02<15:10,  1.28s/it]
 22%|██▏       | 197/908 [07:07<26:56,  2.27s/it]
 22%|██▏       | 198/908 [07:09<26:33,  2.24s/it]
 22%|██▏       | 199/908 [07:09<19:16,  1.63s/it]
+[Batch:   200] training loss = 0.473
+
 22%|██▏       | 200/908 [07:09<14:08,  1.20s/it]
 22%|██▏       | 201/908 [07:14<28:42,  2.44s/it]
 22%|██▏       | 202/908 [07:18<32:10,  2.73s/it]
 22%|██▏       | 203/908 [07:18<23:10,  1.97s/it]
 22%|██▏       | 204/908 [07:18<16:53,  1.44s/it]
 23%|██▎       | 205/908 [07:22<26:03,  2.22s/it]
 23%|██▎       | 206/908 [07:26<30:08,  2.58s/it]
 23%|██▎       | 207/908 [07:26<21:46,  1.86s/it]
 23%|██▎       | 208/908 [07:26<15:52,  1.36s/it]
 23%|██▎       | 209/908 [07:34<39:40,  3.41s/it]
 23%|██▎       | 210/908 [07:38<40:25,  3.47s/it]
 23%|██▎       | 211/908 [07:38<28:53,  2.49s/it]
 23%|██▎       | 212/908 [07:38<20:52,  1.80s/it]
 23%|██▎       | 213/908 [07:42<28:02,  2.42s/it]
 24%|██▎       | 214/908 [07:45<28:37,  2.47s/it]
 24%|██▎       | 215/908 [07:45<20:39,  1.79s/it]
 24%|██▍       | 216/908 [07:45<15:05,  1.31s/it]
 24%|██▍       | 217/908 [07:50<27:52,  2.42s/it]
 24%|██▍       | 218/908 [07:53<29:22,  2.55s/it]
 24%|██▍       | 219/908 [07:53<21:13,  1.85s/it]
+[Batch:   220] training loss = 0.500
+
 24%|██▍       | 220/908 [07:53<15:29,  1.35s/it]
 24%|██▍       | 221/908 [07:58<26:24,  2.31s/it]
 24%|██▍       | 222/908 [08:01<29:42,  2.60s/it]
 25%|██▍       | 223/908 [08:01<21:25,  1.88s/it]
 25%|██▍       | 224/908 [08:02<15:36,  1.37s/it]
 25%|██▍       | 225/908 [08:06<25:11,  2.21s/it]
 25%|██▍       | 226/908 [08:09<27:37,  2.43s/it]
 25%|██▌       | 227/908 [08:09<19:56,  1.76s/it]
 25%|██▌       | 228/908 [08:09<14:35,  1.29s/it]
 25%|██▌       | 229/908 [08:13<24:36,  2.17s/it]
 25%|██▌       | 230/908 [08:17<30:58,  2.74s/it]
 25%|██▌       | 231/908 [08:18<22:17,  1.98s/it]
 26%|██▌       | 232/908 [08:18<16:16,  1.44s/it]
 26%|██▌       | 233/908 [08:22<27:07,  2.41s/it]
 26%|██▌       | 234/908 [08:27<32:59,  2.94s/it]
 26%|██▌       | 235/908 [08:27<23:42,  2.11s/it]
 26%|██▌       | 236/908 [08:27<17:12,  1.54s/it]
 26%|██▌       | 237/908 [08:33<32:55,  2.94s/it]
 26%|██▌       | 238/908 [08:38<37:40,  3.37s/it]
 26%|██▋       | 239/908 [08:38<26:57,  2.42s/it]
+[Batch:   240] training loss = 0.409
+
 26%|██▋       | 240/908 [08:38<19:29,  1.75s/it]
 27%|██▋       | 241/908 [08:43<31:39,  2.85s/it]
 27%|██▋       | 242/908 [08:47<34:39,  3.12s/it]
 27%|██▋       | 243/908 [08:47<24:51,  2.24s/it]
 27%|██▋       | 244/908 [08:48<18:00,  1.63s/it]
 27%|██▋       | 245/908 [08:51<24:33,  2.22s/it]
 27%|██▋       | 246/908 [08:55<29:19,  2.66s/it]
 27%|██▋       | 247/908 [08:55<21:07,  1.92s/it]
 27%|██▋       | 248/908 [08:55<15:24,  1.40s/it]
 27%|██▋       | 249/908 [09:00<24:56,  2.27s/it]
 28%|██▊       | 250/908 [09:03<28:59,  2.64s/it]
 28%|██▊       | 251/908 [09:03<20:56,  1.91s/it]
 28%|██▊       | 252/908 [09:03<15:16,  1.40s/it]
 28%|██▊       | 253/908 [09:07<23:50,  2.18s/it]
 28%|██▊       | 254/908 [09:12<31:09,  2.86s/it]
 28%|██▊       | 255/908 [09:12<22:27,  2.06s/it]
 28%|██▊       | 256/908 [09:12<16:22,  1.51s/it]
 28%|██▊       | 257/908 [09:20<37:25,  3.45s/it]
 28%|██▊       | 258/908 [09:24<37:30,  3.46s/it]
 29%|██▊       | 259/908 [09:24<26:51,  2.48s/it]
+[Batch:   260] training loss = 0.433
+
 29%|██▊       | 260/908 [09:24<19:26,  1.80s/it]
 29%|██▊       | 261/908 [09:29<29:05,  2.70s/it]
 29%|██▉       | 262/908 [09:32<30:44,  2.86s/it]
 29%|██▉       | 263/908 [09:32<22:09,  2.06s/it]
 29%|██▉       | 264/908 [09:33<16:07,  1.50s/it]
 29%|██▉       | 265/908 [09:40<35:08,  3.28s/it]
 29%|██▉       | 266/908 [09:43<34:34,  3.23s/it]
 29%|██▉       | 267/908 [09:43<24:48,  2.32s/it]
 30%|██▉       | 268/908 [09:44<17:58,  1.68s/it]
 30%|██▉       | 269/908 [09:48<27:28,  2.58s/it]
 30%|██▉       | 270/908 [09:52<30:17,  2.85s/it]
 30%|██▉       | 271/908 [09:52<21:47,  2.05s/it]
 30%|██▉       | 272/908 [09:52<15:50,  1.49s/it]
 30%|███       | 273/908 [09:57<25:34,  2.42s/it]
 30%|███       | 274/908 [10:00<29:56,  2.83s/it]
 30%|███       | 275/908 [10:01<21:34,  2.04s/it]
 30%|███       | 276/908 [10:01<15:42,  1.49s/it]
 31%|███       | 277/908 [10:05<25:09,  2.39s/it]
 31%|███       | 278/908 [10:09<28:59,  2.76s/it]
 31%|███       | 279/908 [10:09<20:53,  1.99s/it]
+[Batch:   280] training loss = 0.546
+
 31%|███       | 280/908 [10:09<15:14,  1.46s/it]
 31%|███       | 281/908 [10:13<22:26,  2.15s/it]
 31%|███       | 282/908 [10:17<29:02,  2.78s/it]
 31%|███       | 283/908 [10:18<20:55,  2.01s/it]
 31%|███▏      | 284/908 [10:18<15:15,  1.47s/it]
 31%|███▏      | 285/908 [10:23<26:13,  2.53s/it]
 31%|███▏      | 286/908 [10:27<31:15,  3.02s/it]
 32%|███▏      | 287/908 [10:27<22:28,  2.17s/it]
 32%|███▏      | 288/908 [10:27<16:20,  1.58s/it]
 32%|███▏      | 289/908 [10:31<21:58,  2.13s/it]
 32%|███▏      | 290/908 [10:34<25:45,  2.50s/it]
 32%|███▏      | 291/908 [10:34<18:37,  1.81s/it]
 32%|███▏      | 292/908 [10:35<14:32,  1.42s/it]
 32%|███▏      | 293/908 [10:41<28:36,  2.79s/it]
 32%|███▏      | 294/908 [10:45<31:20,  3.06s/it]
 32%|███▏      | 295/908 [10:45<22:30,  2.20s/it]
 33%|███▎      | 296/908 [10:45<16:21,  1.60s/it]
 33%|███▎      | 297/908 [10:49<22:48,  2.24s/it]
 33%|███▎      | 298/908 [10:53<27:55,  2.75s/it]
 33%|███▎      | 299/908 [10:53<20:06,  1.98s/it]
+[Batch:   300] training loss = 0.506
+
 33%|███▎      | 300/908 [10:53<14:40,  1.45s/it]
 33%|███▎      | 301/908 [10:57<22:46,  2.25s/it]
 33%|███▎      | 302/908 [11:01<27:08,  2.69s/it]
 33%|███▎      | 303/908 [11:01<19:35,  1.94s/it]
 33%|███▎      | 304/908 [11:01<14:17,  1.42s/it]
 34%|███▎      | 305/908 [11:05<20:19,  2.02s/it]
 34%|███▎      | 306/908 [11:08<25:00,  2.49s/it]
 34%|███▍      | 307/908 [11:08<18:03,  1.80s/it]
 34%|███▍      | 308/908 [11:09<13:13,  1.32s/it]
 34%|███▍      | 309/908 [11:12<20:05,  2.01s/it]
 34%|███▍      | 310/908 [11:16<25:59,  2.61s/it]
 34%|███▍      | 311/908 [11:16<18:44,  1.88s/it]
 34%|███▍      | 312/908 [11:17<13:38,  1.37s/it]
 34%|███▍      | 313/908 [11:20<19:53,  2.01s/it]
 35%|███▍      | 314/908 [11:24<26:10,  2.64s/it]
 35%|███▍      | 315/908 [11:24<18:51,  1.91s/it]
 35%|███▍      | 316/908 [11:25<13:45,  1.39s/it]
 35%|███▍      | 317/908 [11:29<22:00,  2.23s/it]
 35%|███▌      | 318/908 [11:32<25:33,  2.60s/it]
 35%|███▌      | 319/908 [11:32<18:26,  1.88s/it]
+[Batch:   320] training loss = 0.466
+
 35%|███▌      | 320/908 [11:33<13:26,  1.37s/it]
 35%|███▌      | 321/908 [11:39<28:20,  2.90s/it]
 35%|███▌      | 322/908 [11:43<31:31,  3.23s/it]
 36%|███▌      | 323/908 [11:43<22:35,  2.32s/it]
 36%|███▌      | 324/908 [11:44<16:20,  1.68s/it]
 36%|███▌      | 325/908 [11:47<22:43,  2.34s/it]
 36%|███▌      | 326/908 [11:51<27:11,  2.80s/it]
 36%|███▌      | 327/908 [11:51<19:33,  2.02s/it]
 36%|███▌      | 328/908 [11:52<14:12,  1.47s/it]
 36%|███▌      | 329/908 [11:56<21:11,  2.20s/it]
 36%|███▋      | 330/908 [11:59<23:48,  2.47s/it]
 36%|███▋      | 331/908 [11:59<17:11,  1.79s/it]
 37%|███▋      | 332/908 [11:59<12:34,  1.31s/it]
 37%|███▋      | 333/908 [12:03<20:59,  2.19s/it]
 37%|███▋      | 334/908 [12:07<24:28,  2.56s/it]
 37%|███▋      | 335/908 [12:07<17:39,  1.85s/it]
 37%|███▋      | 336/908 [12:07<12:53,  1.35s/it]
 37%|███▋      | 337/908 [12:12<21:49,  2.29s/it]
 37%|███▋      | 338/908 [12:15<24:24,  2.57s/it]
 37%|███▋      | 339/908 [12:15<17:35,  1.85s/it]
+[Batch:   340] training loss = 0.385
+
 37%|███▋      | 340/908 [12:15<12:50,  1.36s/it]
 38%|███▊      | 341/908 [12:19<19:54,  2.11s/it]
 38%|███▊      | 342/908 [12:23<24:56,  2.64s/it]
 38%|███▊      | 343/908 [12:23<17:58,  1.91s/it]
 38%|███▊      | 344/908 [12:23<13:06,  1.39s/it]
 38%|███▊      | 345/908 [12:26<17:53,  1.91s/it]
 38%|███▊      | 346/908 [12:30<23:10,  2.47s/it]
 38%|███▊      | 347/908 [12:30<16:43,  1.79s/it]
 38%|███▊      | 348/908 [12:31<12:13,  1.31s/it]
 38%|███▊      | 349/908 [12:36<22:56,  2.46s/it]
 39%|███▊      | 350/908 [12:42<32:42,  3.52s/it]
 39%|███▊      | 351/908 [12:42<23:23,  2.52s/it]
 39%|███▉      | 352/908 [12:42<16:54,  1.82s/it]
 39%|███▉      | 353/908 [12:47<24:36,  2.66s/it]
 39%|███▉      | 354/908 [12:50<26:08,  2.83s/it]
 39%|███▉      | 355/908 [12:50<18:48,  2.04s/it]
 39%|███▉      | 356/908 [12:50<13:39,  1.48s/it]
 39%|███▉      | 357/908 [12:55<21:56,  2.39s/it]
 39%|███▉      | 358/908 [12:59<26:40,  2.91s/it]
 40%|███▉      | 359/908 [12:59<19:09,  2.09s/it]
+[Batch:   360] training loss = 0.464
+
 40%|███▉      | 360/908 [12:59<13:55,  1.52s/it]
 40%|███▉      | 361/908 [13:05<25:20,  2.78s/it]
 40%|███▉      | 362/908 [13:08<25:12,  2.77s/it]
 40%|███▉      | 363/908 [13:08<18:09,  2.00s/it]
 40%|████      | 364/908 [13:08<13:12,  1.46s/it]
 40%|████      | 365/908 [13:13<22:58,  2.54s/it]
 40%|████      | 366/908 [13:17<25:27,  2.82s/it]
 40%|████      | 367/908 [13:17<18:17,  2.03s/it]
 41%|████      | 368/908 [13:17<13:17,  1.48s/it]
 41%|████      | 369/908 [13:23<24:49,  2.76s/it]
 41%|████      | 370/908 [13:26<26:25,  2.95s/it]
 41%|████      | 371/908 [13:26<18:58,  2.12s/it]
 41%|████      | 372/908 [13:27<13:46,  1.54s/it]
 41%|████      | 373/908 [13:32<22:40,  2.54s/it]
 41%|████      | 374/908 [13:35<24:34,  2.76s/it]
 41%|████▏     | 375/908 [13:35<17:40,  1.99s/it]
 41%|████▏     | 376/908 [13:35<12:51,  1.45s/it]
 42%|████▏     | 377/908 [13:40<20:58,  2.37s/it]
 42%|████▏     | 378/908 [13:43<23:02,  2.61s/it]
 42%|████▏     | 379/908 [13:43<16:37,  1.89s/it]
+[Batch:   380] training loss = 0.428
+
 42%|████▏     | 380/908 [13:43<12:08,  1.38s/it]
 42%|████▏     | 381/908 [13:49<22:18,  2.54s/it]
 42%|████▏     | 382/908 [13:51<22:23,  2.55s/it]
 42%|████▏     | 383/908 [13:51<16:09,  1.85s/it]
 42%|████▏     | 384/908 [13:51<11:47,  1.35s/it]
 42%|████▏     | 385/908 [13:56<20:37,  2.37s/it]
 43%|████▎     | 386/908 [13:59<21:04,  2.42s/it]
 43%|████▎     | 387/908 [13:59<15:13,  1.75s/it]
 43%|████▎     | 388/908 [13:59<11:07,  1.28s/it]
 43%|████▎     | 389/908 [14:04<19:40,  2.27s/it]
 43%|████▎     | 390/908 [14:07<20:54,  2.42s/it]
 43%|████▎     | 391/908 [14:07<15:05,  1.75s/it]
 43%|████▎     | 392/908 [14:07<11:01,  1.28s/it]
 43%|████▎     | 393/908 [14:12<21:22,  2.49s/it]
 43%|████▎     | 394/908 [14:15<21:28,  2.51s/it]
 44%|████▎     | 395/908 [14:15<15:29,  1.81s/it]
 44%|████▎     | 396/908 [14:15<11:19,  1.33s/it]
 44%|████▎     | 397/908 [14:20<20:13,  2.37s/it]
 44%|████▍     | 398/908 [14:23<21:42,  2.55s/it]
 44%|████▍     | 399/908 [14:23<15:38,  1.84s/it]
+[Batch:   400] training loss = 0.301
+
 44%|████▍     | 400/908 [14:23<11:24,  1.35s/it]
 44%|████▍     | 401/908 [14:28<20:58,  2.48s/it]
 44%|████▍     | 402/908 [14:31<21:13,  2.52s/it]
 44%|████▍     | 403/908 [14:31<15:18,  1.82s/it]
 44%|████▍     | 404/908 [14:31<11:10,  1.33s/it]
 45%|████▍     | 405/908 [14:37<22:48,  2.72s/it]
 45%|████▍     | 406/908 [14:43<29:52,  3.57s/it]
 45%|████▍     | 407/908 [14:43<21:21,  2.56s/it]
 45%|████▍     | 408/908 [14:44<16:23,  1.97s/it]
 45%|████▌     | 409/908 [14:49<24:35,  2.96s/it]
 45%|████▌     | 410/908 [14:52<24:45,  2.98s/it]
 45%|████▌     | 411/908 [14:52<17:46,  2.15s/it]
 45%|████▌     | 412/908 [14:52<12:53,  1.56s/it]
 45%|████▌     | 413/908 [14:58<22:15,  2.70s/it]
 46%|████▌     | 414/908 [15:01<22:53,  2.78s/it]
 46%|████▌     | 415/908 [15:01<16:27,  2.00s/it]
 46%|████▌     | 416/908 [15:01<11:57,  1.46s/it]
 46%|████▌     | 417/908 [15:06<20:33,  2.51s/it]
 46%|████▌     | 418/908 [15:09<22:44,  2.79s/it]
 46%|████▌     | 419/908 [15:10<16:22,  2.01s/it]
+[Batch:   420] training loss = 0.386
+
 46%|████▋     | 420/908 [15:10<11:56,  1.47s/it]
 46%|████▋     | 421/908 [15:14<18:38,  2.30s/it]
 46%|████▋     | 422/908 [15:18<22:13,  2.74s/it]
 47%|████▋     | 423/908 [15:18<16:00,  1.98s/it]
 47%|████▋     | 424/908 [15:18<11:40,  1.45s/it]
 47%|████▋     | 425/908 [15:23<20:00,  2.48s/it]
 47%|████▋     | 426/908 [15:27<22:42,  2.83s/it]
 47%|████▋     | 427/908 [15:27<16:21,  2.04s/it]
 47%|████▋     | 428/908 [15:27<11:55,  1.49s/it]
 47%|████▋     | 429/908 [15:32<19:39,  2.46s/it]
 47%|████▋     | 430/908 [15:35<20:41,  2.60s/it]
 47%|████▋     | 431/908 [15:35<14:55,  1.88s/it]
 48%|████▊     | 432/908 [15:35<10:54,  1.37s/it]
 48%|████▊     | 433/908 [15:40<18:47,  2.37s/it]
 48%|████▊     | 434/908 [15:47<29:19,  3.71s/it]
 48%|████▊     | 435/908 [15:47<20:57,  2.66s/it]
 48%|████▊     | 436/908 [15:47<15:07,  1.92s/it]
 48%|████▊     | 437/908 [15:53<23:46,  3.03s/it]
 48%|████▊     | 438/908 [15:56<24:31,  3.13s/it]
 48%|████▊     | 439/908 [15:56<17:36,  2.25s/it]
+[Batch:   440] training loss = 0.385
+
 48%|████▊     | 440/908 [15:57<12:46,  1.64s/it]
 49%|████▊     | 441/908 [16:00<17:21,  2.23s/it]
 49%|████▊     | 442/908 [16:04<19:42,  2.54s/it]
 49%|████▉     | 443/908 [16:04<14:13,  1.83s/it]
 49%|████▉     | 444/908 [16:04<10:23,  1.34s/it]
 49%|████▉     | 445/908 [16:08<15:53,  2.06s/it]
 49%|████▉     | 446/908 [16:11<18:56,  2.46s/it]
 49%|████▉     | 447/908 [16:11<13:41,  1.78s/it]
 49%|████▉     | 448/908 [16:11<10:01,  1.31s/it]
 49%|████▉     | 449/908 [16:15<15:34,  2.04s/it]
 50%|████▉     | 450/908 [16:17<15:44,  2.06s/it]
 50%|████▉     | 451/908 [16:17<11:27,  1.50s/it]
 50%|████▉     | 452/908 [16:18<08:27,  1.11s/it]
 50%|████▉     | 453/908 [16:22<15:21,  2.03s/it]
 50%|█████     | 454/908 [16:25<18:46,  2.48s/it]
 50%|█████     | 455/908 [16:26<13:33,  1.80s/it]
 50%|█████     | 456/908 [16:26<09:55,  1.32s/it]
 50%|█████     | 457/908 [16:31<17:59,  2.39s/it]
 50%|█████     | 458/908 [16:34<20:14,  2.70s/it]
 51%|█████     | 459/908 [16:34<14:35,  1.95s/it]
+[Batch:   460] training loss = 0.407
+
 51%|█████     | 460/908 [16:34<10:37,  1.42s/it]
 51%|█████     | 461/908 [16:39<17:27,  2.34s/it]
 51%|█████     | 462/908 [16:43<20:30,  2.76s/it]
 51%|█████     | 463/908 [16:43<14:45,  1.99s/it]
 51%|█████     | 464/908 [16:43<10:45,  1.45s/it]
 51%|█████     | 465/908 [16:49<20:46,  2.81s/it]
 51%|█████▏    | 466/908 [16:54<25:21,  3.44s/it]
 51%|█████▏    | 467/908 [16:54<18:09,  2.47s/it]
 52%|█████▏    | 468/908 [16:54<13:06,  1.79s/it]
 52%|█████▏    | 469/908 [16:58<17:41,  2.42s/it]
 52%|█████▏    | 470/908 [17:02<20:36,  2.82s/it]
 52%|█████▏    | 471/908 [17:02<14:49,  2.03s/it]
 52%|█████▏    | 472/908 [17:02<10:46,  1.48s/it]
 52%|█████▏    | 473/908 [17:06<16:12,  2.24s/it]
 52%|█████▏    | 474/908 [17:10<19:21,  2.68s/it]
 52%|█████▏    | 475/908 [17:10<13:56,  1.93s/it]
 52%|█████▏    | 476/908 [17:11<10:10,  1.41s/it]
 53%|█████▎    | 477/908 [17:14<15:10,  2.11s/it]
 53%|█████▎    | 478/908 [17:18<18:17,  2.55s/it]
 53%|█████▎    | 479/908 [17:18<13:10,  1.84s/it]
+[Batch:   480] training loss = 0.399
+
 53%|█████▎    | 480/908 [17:18<09:36,  1.35s/it]
 53%|█████▎    | 481/908 [17:22<14:01,  1.97s/it]
 53%|█████▎    | 482/908 [17:26<19:15,  2.71s/it]
 53%|█████▎    | 483/908 [17:26<13:51,  1.96s/it]
 53%|█████▎    | 484/908 [17:26<10:04,  1.43s/it]
 53%|█████▎    | 485/908 [17:31<17:13,  2.44s/it]
 54%|█████▎    | 486/908 [17:36<22:49,  3.24s/it]
 54%|█████▎    | 487/908 [17:37<16:21,  2.33s/it]
 54%|█████▎    | 488/908 [17:37<11:49,  1.69s/it]
 54%|█████▍    | 489/908 [17:40<15:32,  2.23s/it]
 54%|█████▍    | 490/908 [17:44<19:05,  2.74s/it]
 54%|█████▍    | 491/908 [17:44<13:44,  1.98s/it]
 54%|█████▍    | 492/908 [17:45<09:59,  1.44s/it]
 54%|█████▍    | 493/908 [17:52<21:18,  3.08s/it]
 54%|█████▍    | 494/908 [17:57<25:12,  3.65s/it]
 55%|█████▍    | 495/908 [17:57<18:00,  2.62s/it]
 55%|█████▍    | 496/908 [17:57<12:57,  1.89s/it]
 55%|█████▍    | 497/908 [18:01<16:47,  2.45s/it]
 55%|█████▍    | 498/908 [18:05<19:53,  2.91s/it]
 55%|█████▍    | 499/908 [18:05<14:17,  2.10s/it]
+[Batch:   500] training loss = 0.507
+
 55%|█████▌    | 500/908 [18:05<10:23,  1.53s/it]
 55%|█████▌    | 501/908 [18:09<16:13,  2.39s/it]
 55%|█████▌    | 502/908 [18:13<18:41,  2.76s/it]
 55%|█████▌    | 503/908 [18:13<13:26,  1.99s/it]
 56%|█████▌    | 504/908 [18:13<09:45,  1.45s/it]
 56%|█████▌    | 505/908 [18:16<11:20,  1.69s/it]
 56%|█████▌    | 506/908 [18:21<18:10,  2.71s/it]
 56%|█████▌    | 507/908 [18:21<13:04,  1.96s/it]
 56%|█████▌    | 508/908 [18:21<09:30,  1.43s/it]
 56%|█████▌    | 509/908 [18:25<14:16,  2.15s/it]
 56%|█████▌    | 510/908 [18:28<16:04,  2.42s/it]
 56%|█████▋    | 511/908 [18:28<11:35,  1.75s/it]
 56%|█████▋    | 512/908 [18:28<08:28,  1.28s/it]
 56%|█████▋    | 513/908 [18:33<15:02,  2.28s/it]
 57%|█████▋    | 514/908 [18:37<18:08,  2.76s/it]
 57%|█████▋    | 515/908 [18:37<13:02,  1.99s/it]
 57%|█████▋    | 516/908 [18:37<09:28,  1.45s/it]
 57%|█████▋    | 517/908 [18:40<11:06,  1.70s/it]
 57%|█████▋    | 518/908 [18:44<16:09,  2.48s/it]
 57%|█████▋    | 519/908 [18:44<11:38,  1.80s/it]
+[Batch:   520] training loss = 0.499
+
 57%|█████▋    | 520/908 [18:44<08:46,  1.36s/it]
 57%|█████▋    | 521/908 [18:51<18:33,  2.88s/it]
 57%|█████▋    | 522/908 [18:55<21:00,  3.26s/it]
 58%|█████▊    | 523/908 [18:55<15:02,  2.34s/it]
 58%|█████▊    | 524/908 [18:55<10:51,  1.70s/it]
 58%|█████▊    | 525/908 [19:00<15:40,  2.45s/it]
 58%|█████▊    | 526/908 [19:05<20:23,  3.20s/it]
 58%|█████▊    | 527/908 [19:05<14:36,  2.30s/it]
 58%|█████▊    | 528/908 [19:05<10:33,  1.67s/it]
 58%|█████▊    | 529/908 [19:08<13:36,  2.16s/it]
 58%|█████▊    | 530/908 [19:12<17:11,  2.73s/it]
 58%|█████▊    | 531/908 [19:13<12:23,  1.97s/it]
 59%|█████▊    | 532/908 [19:13<09:03,  1.45s/it]
 59%|█████▊    | 533/908 [19:16<12:42,  2.03s/it]
 59%|█████▉    | 534/908 [19:20<16:54,  2.71s/it]
 59%|█████▉    | 535/908 [19:21<12:10,  1.96s/it]
 59%|█████▉    | 536/908 [19:21<08:50,  1.43s/it]
 59%|█████▉    | 537/908 [19:24<12:28,  2.02s/it]
 59%|█████▉    | 538/908 [19:29<17:38,  2.86s/it]
 59%|█████▉    | 539/908 [19:29<12:40,  2.06s/it]
+[Batch:   540] training loss = 0.374
+
 59%|█████▉    | 540/908 [19:29<09:11,  1.50s/it]
 60%|█████▉    | 541/908 [19:33<12:57,  2.12s/it]
 60%|█████▉    | 542/908 [19:38<17:44,  2.91s/it]
 60%|█████▉    | 543/908 [19:38<12:43,  2.09s/it]
 60%|█████▉    | 544/908 [19:38<09:14,  1.52s/it]
 60%|██████    | 545/908 [19:42<12:43,  2.10s/it]
 60%|██████    | 546/908 [19:45<15:08,  2.51s/it]
 60%|██████    | 547/908 [19:45<10:55,  1.81s/it]
 60%|██████    | 548/908 [19:45<07:57,  1.33s/it]
 60%|██████    | 549/908 [19:50<13:48,  2.31s/it]
 61%|██████    | 550/908 [19:54<17:23,  2.91s/it]
 61%|██████    | 551/908 [19:55<12:28,  2.10s/it]
 61%|██████    | 552/908 [19:55<09:02,  1.52s/it]
 61%|██████    | 553/908 [19:58<12:33,  2.12s/it]
 61%|██████    | 554/908 [20:02<15:47,  2.68s/it]
 61%|██████    | 555/908 [20:02<11:22,  1.93s/it]
 61%|██████    | 556/908 [20:03<08:15,  1.41s/it]
 61%|██████▏   | 557/908 [20:06<11:31,  1.97s/it]
 61%|██████▏   | 558/908 [20:09<14:05,  2.42s/it]
 62%|██████▏   | 559/908 [20:10<10:10,  1.75s/it]
+[Batch:   560] training loss = 0.520
+
 62%|██████▏   | 560/908 [20:10<07:25,  1.28s/it]
 62%|██████▏   | 561/908 [20:14<12:00,  2.08s/it]
 62%|██████▏   | 562/908 [20:17<14:39,  2.54s/it]
 62%|██████▏   | 563/908 [20:18<10:33,  1.84s/it]
 62%|██████▏   | 564/908 [20:18<07:42,  1.34s/it]
 62%|██████▏   | 565/908 [20:21<11:18,  1.98s/it]
 62%|██████▏   | 566/908 [20:25<13:58,  2.45s/it]
 62%|██████▏   | 567/908 [20:25<10:04,  1.77s/it]
 63%|██████▎   | 568/908 [20:25<07:21,  1.30s/it]
 63%|██████▎   | 569/908 [20:29<12:06,  2.14s/it]
 63%|██████▎   | 570/908 [20:34<15:47,  2.80s/it]
 63%|██████▎   | 571/908 [20:34<11:21,  2.02s/it]
 63%|██████▎   | 572/908 [20:34<08:15,  1.47s/it]
 63%|██████▎   | 573/908 [20:39<13:58,  2.50s/it]
 63%|██████▎   | 574/908 [20:42<15:41,  2.82s/it]
 63%|██████▎   | 575/908 [20:43<11:16,  2.03s/it]
 63%|██████▎   | 576/908 [20:43<08:11,  1.48s/it]
 64%|██████▎   | 577/908 [20:47<12:47,  2.32s/it]
 64%|██████▎   | 578/908 [20:53<18:06,  3.29s/it]
 64%|██████▍   | 579/908 [20:53<12:57,  2.36s/it]
+[Batch:   580] training loss = 0.410
+
 64%|██████▍   | 580/908 [20:53<09:21,  1.71s/it]
 64%|██████▍   | 581/908 [20:58<13:54,  2.55s/it]
 64%|██████▍   | 582/908 [21:02<16:29,  3.04s/it]
 64%|██████▍   | 583/908 [21:02<11:49,  2.18s/it]
 64%|██████▍   | 584/908 [21:02<08:34,  1.59s/it]
 64%|██████▍   | 585/908 [21:07<14:10,  2.63s/it]
 65%|██████▍   | 586/908 [21:11<15:35,  2.90s/it]
 65%|██████▍   | 587/908 [21:11<11:11,  2.09s/it]
 65%|██████▍   | 588/908 [21:11<08:06,  1.52s/it]
 65%|██████▍   | 589/908 [21:15<12:37,  2.37s/it]
 65%|██████▍   | 590/908 [21:20<15:44,  2.97s/it]
 65%|██████▌   | 591/908 [21:20<11:18,  2.14s/it]
 65%|██████▌   | 592/908 [21:20<08:12,  1.56s/it]
 65%|██████▌   | 593/908 [21:25<13:10,  2.51s/it]
 65%|██████▌   | 594/908 [21:29<14:58,  2.86s/it]
 66%|██████▌   | 595/908 [21:29<10:45,  2.06s/it]
 66%|██████▌   | 596/908 [21:29<07:49,  1.51s/it]
 66%|██████▌   | 597/908 [21:33<11:43,  2.26s/it]
 66%|██████▌   | 598/908 [21:37<13:32,  2.62s/it]
 66%|██████▌   | 599/908 [21:37<09:44,  1.89s/it]
+[Batch:   600] training loss = 0.429
+
 66%|██████▌   | 600/908 [21:37<07:06,  1.38s/it]
 66%|██████▌   | 601/908 [21:41<11:19,  2.21s/it]
 66%|██████▋   | 602/908 [21:44<12:49,  2.51s/it]
 66%|██████▋   | 603/908 [21:44<09:14,  1.82s/it]
 67%|██████▋   | 604/908 [21:45<06:45,  1.33s/it]
 67%|██████▋   | 605/908 [21:49<10:56,  2.17s/it]
 67%|██████▋   | 606/908 [21:54<15:46,  3.13s/it]
 67%|██████▋   | 607/908 [21:54<11:18,  2.25s/it]
 67%|██████▋   | 608/908 [21:55<08:10,  1.64s/it]
 67%|██████▋   | 609/908 [21:59<12:56,  2.60s/it]
 67%|██████▋   | 610/908 [22:03<13:39,  2.75s/it]
 67%|██████▋   | 611/908 [22:03<09:50,  1.99s/it]
 67%|██████▋   | 612/908 [22:03<07:10,  1.45s/it]
 68%|██████▊   | 613/908 [22:06<09:49,  2.00s/it]
 68%|██████▊   | 614/908 [22:09<10:41,  2.18s/it]
 68%|██████▊   | 615/908 [22:09<07:45,  1.59s/it]
 68%|██████▊   | 616/908 [22:09<05:41,  1.17s/it]
 68%|██████▊   | 617/908 [22:13<10:00,  2.06s/it]
 68%|██████▊   | 618/908 [22:17<11:52,  2.46s/it]
 68%|██████▊   | 619/908 [22:17<08:34,  1.78s/it]
+[Batch:   620] training loss = 0.339
+
 68%|██████▊   | 620/908 [22:17<06:16,  1.31s/it]
 68%|██████▊   | 621/908 [22:21<09:30,  1.99s/it]
 69%|██████▊   | 622/908 [22:24<11:25,  2.40s/it]
 69%|██████▊   | 623/908 [22:24<08:14,  1.74s/it]
 69%|██████▊   | 624/908 [22:24<06:02,  1.28s/it]
 69%|██████▉   | 625/908 [22:28<09:26,  2.00s/it]
 69%|██████▉   | 626/908 [22:32<11:45,  2.50s/it]
 69%|██████▉   | 627/908 [22:32<08:28,  1.81s/it]
 69%|██████▉   | 628/908 [22:32<06:11,  1.33s/it]
 69%|██████▉   | 629/908 [22:37<10:31,  2.26s/it]
 69%|██████▉   | 630/908 [22:41<13:24,  2.89s/it]
 69%|██████▉   | 631/908 [22:41<09:38,  2.09s/it]
 70%|██████▉   | 632/908 [22:41<07:00,  1.52s/it]
 70%|██████▉   | 633/908 [22:45<09:59,  2.18s/it]
 70%|██████▉   | 634/908 [22:48<11:01,  2.41s/it]
 70%|██████▉   | 635/908 [22:48<07:57,  1.75s/it]
 70%|███████   | 636/908 [22:49<05:49,  1.28s/it]
 70%|███████   | 637/908 [22:57<15:49,  3.50s/it]
 70%|███████   | 638/908 [23:01<16:14,  3.61s/it]
 70%|███████   | 639/908 [23:01<11:36,  2.59s/it]
+[Batch:   640] training loss = 0.485
+
 70%|███████   | 640/908 [23:01<08:21,  1.87s/it]
 71%|███████   | 641/908 [23:06<11:20,  2.55s/it]
 71%|███████   | 642/908 [23:09<12:58,  2.93s/it]
 71%|███████   | 643/908 [23:10<09:18,  2.11s/it]
 71%|███████   | 644/908 [23:10<06:45,  1.54s/it]
 71%|███████   | 645/908 [23:13<09:17,  2.12s/it]
 71%|███████   | 646/908 [23:17<11:12,  2.57s/it]
 71%|███████▏  | 647/908 [23:17<08:04,  1.86s/it]
 71%|███████▏  | 648/908 [23:17<05:54,  1.36s/it]
 71%|███████▏  | 649/908 [23:21<09:20,  2.16s/it]
 72%|███████▏  | 650/908 [23:25<10:36,  2.47s/it]
 72%|███████▏  | 651/908 [23:25<07:38,  1.78s/it]
 72%|███████▏  | 652/908 [23:25<05:33,  1.30s/it]
 72%|███████▏  | 653/908 [23:28<07:48,  1.84s/it]
 72%|███████▏  | 654/908 [23:33<12:08,  2.87s/it]
 72%|███████▏  | 655/908 [23:33<08:41,  2.06s/it]
 72%|███████▏  | 656/908 [23:34<06:17,  1.50s/it]
 72%|███████▏  | 657/908 [23:38<10:12,  2.44s/it]
 72%|███████▏  | 658/908 [23:43<12:29,  3.00s/it]
 73%|███████▎  | 659/908 [23:43<08:57,  2.16s/it]
+[Batch:   660] training loss = 0.397
+
 73%|███████▎  | 660/908 [23:43<06:29,  1.57s/it]
 73%|███████▎  | 661/908 [23:46<07:52,  1.91s/it]
 73%|███████▎  | 662/908 [23:49<10:07,  2.47s/it]
 73%|███████▎  | 663/908 [23:50<07:17,  1.78s/it]
 73%|███████▎  | 664/908 [23:50<05:19,  1.31s/it]
 73%|███████▎  | 665/908 [23:55<09:32,  2.36s/it]
 73%|███████▎  | 666/908 [23:58<10:57,  2.72s/it]
 73%|███████▎  | 667/908 [23:58<07:52,  1.96s/it]
 74%|███████▎  | 668/908 [23:59<05:42,  1.43s/it]
 74%|███████▎  | 669/908 [24:02<07:32,  1.89s/it]
 74%|███████▍  | 670/908 [24:05<09:36,  2.42s/it]
 74%|███████▍  | 671/908 [24:05<06:56,  1.76s/it]
 74%|███████▍  | 672/908 [24:06<05:03,  1.29s/it]
 74%|███████▍  | 673/908 [24:08<06:55,  1.77s/it]
 74%|███████▍  | 674/908 [24:12<09:28,  2.43s/it]
 74%|███████▍  | 675/908 [24:13<06:50,  1.76s/it]
 74%|███████▍  | 676/908 [24:13<04:59,  1.29s/it]
 75%|███████▍  | 677/908 [24:17<08:09,  2.12s/it]
 75%|███████▍  | 678/908 [24:21<10:29,  2.74s/it]
 75%|███████▍  | 679/908 [24:21<07:31,  1.97s/it]
+[Batch:   680] training loss = 0.385
+
 75%|███████▍  | 680/908 [24:21<05:27,  1.44s/it]
 75%|███████▌  | 681/908 [24:25<07:26,  1.97s/it]
 75%|███████▌  | 682/908 [24:28<09:33,  2.54s/it]
 75%|███████▌  | 683/908 [24:29<06:52,  1.83s/it]
 75%|███████▌  | 684/908 [24:29<05:00,  1.34s/it]
 75%|███████▌  | 685/908 [24:33<07:36,  2.05s/it]
 76%|███████▌  | 686/908 [24:36<09:39,  2.61s/it]
 76%|███████▌  | 687/908 [24:37<06:56,  1.88s/it]
 76%|███████▌  | 688/908 [24:37<05:02,  1.38s/it]
 76%|███████▌  | 689/908 [24:40<07:13,  1.98s/it]
 76%|███████▌  | 690/908 [24:44<09:30,  2.62s/it]
 76%|███████▌  | 691/908 [24:45<06:50,  1.89s/it]
 76%|███████▌  | 692/908 [24:45<04:58,  1.38s/it]
 76%|███████▋  | 693/908 [24:49<08:19,  2.32s/it]
 76%|███████▋  | 694/908 [24:54<10:24,  2.92s/it]
 77%|███████▋  | 695/908 [24:54<07:28,  2.11s/it]
 77%|███████▋  | 696/908 [24:54<05:24,  1.53s/it]
 77%|███████▋  | 697/908 [25:01<11:06,  3.16s/it]
 77%|███████▋  | 698/908 [25:05<12:26,  3.55s/it]
 77%|███████▋  | 699/908 [25:06<08:51,  2.54s/it]
+[Batch:   700] training loss = 0.480
+
 77%|███████▋  | 700/908 [25:06<06:22,  1.84s/it]
 77%|███████▋  | 701/908 [25:09<08:10,  2.37s/it]
 77%|███████▋  | 702/908 [25:13<09:50,  2.87s/it]
 77%|███████▋  | 703/908 [25:14<07:03,  2.06s/it]
 78%|███████▊  | 704/908 [25:14<05:07,  1.51s/it]
 78%|███████▊  | 705/908 [25:16<06:00,  1.78s/it]
 78%|███████▊  | 706/908 [25:20<08:14,  2.45s/it]
 78%|███████▊  | 707/908 [25:20<05:55,  1.77s/it]
 78%|███████▊  | 708/908 [25:21<04:19,  1.30s/it]
 78%|███████▊  | 709/908 [25:25<06:51,  2.07s/it]
 78%|███████▊  | 710/908 [25:28<08:16,  2.51s/it]
 78%|███████▊  | 711/908 [25:28<05:56,  1.81s/it]
 78%|███████▊  | 712/908 [25:28<04:19,  1.33s/it]
 79%|███████▊  | 713/908 [25:32<06:50,  2.11s/it]
 79%|███████▊  | 714/908 [25:37<09:31,  2.95s/it]
 79%|███████▊  | 715/908 [25:37<06:50,  2.13s/it]
 79%|███████▉  | 716/908 [25:38<04:56,  1.55s/it]
 79%|███████▉  | 717/908 [25:41<07:00,  2.20s/it]
 79%|███████▉  | 718/908 [25:46<09:11,  2.90s/it]
 79%|███████▉  | 719/908 [25:46<06:35,  2.09s/it]
+[Batch:   720] training loss = 0.604
+
 79%|███████▉  | 720/908 [25:46<04:46,  1.52s/it]
 79%|███████▉  | 721/908 [25:50<06:52,  2.21s/it]
 80%|███████▉  | 722/908 [25:54<08:16,  2.67s/it]
 80%|███████▉  | 723/908 [25:54<05:56,  1.92s/it]
 80%|███████▉  | 724/908 [25:54<04:18,  1.40s/it]
 80%|███████▉  | 725/908 [26:00<08:42,  2.85s/it]
 80%|███████▉  | 726/908 [26:05<09:49,  3.24s/it]
 80%|████████  | 727/908 [26:05<07:00,  2.32s/it]
 80%|████████  | 728/908 [26:05<05:03,  1.69s/it]
 80%|████████  | 729/908 [26:09<07:11,  2.41s/it]
 80%|████████  | 730/908 [26:13<08:44,  2.95s/it]
 81%|████████  | 731/908 [26:14<06:15,  2.12s/it]
 81%|████████  | 732/908 [26:14<04:31,  1.54s/it]
 81%|████████  | 733/908 [26:18<06:28,  2.22s/it]
 81%|████████  | 734/908 [26:23<08:57,  3.09s/it]
 81%|████████  | 735/908 [26:23<06:24,  2.22s/it]
 81%|████████  | 736/908 [26:23<04:37,  1.61s/it]
 81%|████████  | 737/908 [26:25<05:08,  1.80s/it]
 81%|████████▏ | 738/908 [26:29<06:48,  2.40s/it]
 81%|████████▏ | 739/908 [26:29<04:53,  1.74s/it]
+[Batch:   740] training loss = 0.422
+
 81%|████████▏ | 740/908 [26:29<03:34,  1.27s/it]
 82%|████████▏ | 741/908 [26:33<05:43,  2.06s/it]
 82%|████████▏ | 742/908 [26:39<08:23,  3.03s/it]
 82%|████████▏ | 743/908 [26:39<06:00,  2.18s/it]
 82%|████████▏ | 744/908 [26:39<04:20,  1.59s/it]
 82%|████████▏ | 745/908 [26:42<05:27,  2.01s/it]
 82%|████████▏ | 746/908 [26:47<07:37,  2.82s/it]
 82%|████████▏ | 747/908 [26:47<05:27,  2.03s/it]
 82%|████████▏ | 748/908 [26:47<03:56,  1.48s/it]
 82%|████████▏ | 749/908 [26:50<05:00,  1.89s/it]
 83%|████████▎ | 750/908 [26:55<07:35,  2.88s/it]
 83%|████████▎ | 751/908 [26:55<05:26,  2.08s/it]
 83%|████████▎ | 752/908 [26:56<03:55,  1.51s/it]
 83%|████████▎ | 753/908 [26:59<05:38,  2.18s/it]
 83%|████████▎ | 754/908 [27:06<09:23,  3.66s/it]
 83%|████████▎ | 755/908 [27:07<06:40,  2.62s/it]
 83%|████████▎ | 756/908 [27:07<04:47,  1.89s/it]
 83%|████████▎ | 757/908 [27:10<05:45,  2.28s/it]
 83%|████████▎ | 758/908 [27:14<07:11,  2.88s/it]
 84%|████████▎ | 759/908 [27:14<05:08,  2.07s/it]
+[Batch:   760] training loss = 0.535
+
 84%|████████▎ | 760/908 [27:15<03:43,  1.51s/it]
 84%|████████▍ | 761/908 [27:18<04:54,  2.00s/it]
 84%|████████▍ | 762/908 [27:23<07:06,  2.92s/it]
 84%|████████▍ | 763/908 [27:23<05:05,  2.11s/it]
 84%|████████▍ | 764/908 [27:23<03:40,  1.53s/it]
 84%|████████▍ | 765/908 [27:26<04:46,  2.00s/it]
 84%|████████▍ | 766/908 [27:31<06:27,  2.73s/it]
 84%|████████▍ | 767/908 [27:31<04:37,  1.97s/it]
 85%|████████▍ | 768/908 [27:31<03:21,  1.44s/it]
 85%|████████▍ | 769/908 [27:35<04:43,  2.04s/it]
 85%|████████▍ | 770/908 [27:40<07:14,  3.15s/it]
 85%|████████▍ | 771/908 [27:41<05:10,  2.27s/it]
 85%|████████▌ | 772/908 [27:41<03:43,  1.64s/it]
 85%|████████▌ | 773/908 [27:43<04:07,  1.84s/it]
 85%|████████▌ | 774/908 [27:48<06:23,  2.86s/it]
 85%|████████▌ | 775/908 [27:49<04:34,  2.06s/it]
 85%|████████▌ | 776/908 [27:49<03:18,  1.50s/it]
 86%|████████▌ | 777/908 [27:52<04:23,  2.01s/it]
 86%|████████▌ | 778/908 [27:57<06:38,  3.06s/it]
 86%|████████▌ | 779/908 [27:58<04:44,  2.20s/it]
+[Batch:   780] training loss = 0.328
+
 86%|████████▌ | 780/908 [27:58<03:25,  1.60s/it]
 86%|████████▌ | 781/908 [28:04<06:15,  2.96s/it]
 86%|████████▌ | 782/908 [28:09<07:31,  3.59s/it]
 86%|████████▌ | 783/908 [28:09<05:21,  2.57s/it]
 86%|████████▋ | 784/908 [28:09<03:50,  1.86s/it]
 86%|████████▋ | 785/908 [28:12<04:29,  2.19s/it]
 87%|████████▋ | 786/908 [28:17<05:51,  2.88s/it]
 87%|████████▋ | 787/908 [28:17<04:11,  2.07s/it]
 87%|████████▋ | 788/908 [28:17<03:01,  1.51s/it]
 87%|████████▋ | 789/908 [28:20<03:52,  1.95s/it]
 87%|████████▋ | 790/908 [28:25<05:41,  2.89s/it]
 87%|████████▋ | 791/908 [28:25<04:03,  2.08s/it]
 87%|████████▋ | 792/908 [28:26<02:55,  1.52s/it]
 87%|████████▋ | 793/908 [28:29<03:40,  1.92s/it]
 87%|████████▋ | 794/908 [28:34<05:36,  2.95s/it]
 88%|████████▊ | 795/908 [28:34<04:00,  2.13s/it]
 88%|████████▊ | 796/908 [28:34<02:53,  1.55s/it]
 88%|████████▊ | 797/908 [28:38<04:00,  2.17s/it]
 88%|████████▊ | 798/908 [28:43<05:37,  3.07s/it]
 88%|████████▊ | 799/908 [28:43<04:00,  2.21s/it]
+[Batch:   800] training loss = 0.428
+
 88%|████████▊ | 800/908 [28:44<02:53,  1.61s/it]
 88%|████████▊ | 801/908 [28:47<03:37,  2.03s/it]
 88%|████████▊ | 802/908 [28:49<04:00,  2.27s/it]
 88%|████████▊ | 803/908 [28:50<02:53,  1.65s/it]
 89%|████████▊ | 804/908 [28:50<02:06,  1.21s/it]
 89%|████████▊ | 805/908 [28:53<03:12,  1.87s/it]
 89%|████████▉ | 806/908 [28:58<04:26,  2.61s/it]
 89%|████████▉ | 807/908 [28:58<03:10,  1.89s/it]
 89%|████████▉ | 808/908 [28:58<02:18,  1.38s/it]
 89%|████████▉ | 809/908 [29:01<03:18,  2.01s/it]
 89%|████████▉ | 810/908 [29:06<04:45,  2.92s/it]
 89%|████████▉ | 811/908 [29:07<03:23,  2.10s/it]
 89%|████████▉ | 812/908 [29:07<02:27,  1.53s/it]
 90%|████████▉ | 813/908 [29:09<02:49,  1.78s/it]
 90%|████████▉ | 814/908 [29:14<04:17,  2.74s/it]
 90%|████████▉ | 815/908 [29:14<03:03,  1.98s/it]
 90%|████████▉ | 816/908 [29:15<02:12,  1.44s/it]
 90%|████████▉ | 817/908 [29:18<02:53,  1.91s/it]
 90%|█████████ | 818/908 [29:23<04:27,  2.97s/it]
 90%|█████████ | 819/908 [29:23<03:10,  2.14s/it]
+[Batch:   820] training loss = 0.500
+
 90%|█████████ | 820/908 [29:23<02:17,  1.56s/it]
 90%|█████████ | 821/908 [29:26<02:54,  2.00s/it]
 91%|█████████ | 822/908 [29:32<04:13,  2.95s/it]
 91%|█████████ | 823/908 [29:32<03:00,  2.12s/it]
 91%|█████████ | 824/908 [29:32<02:09,  1.55s/it]
 91%|█████████ | 825/908 [29:35<02:38,  1.91s/it]
 91%|█████████ | 826/908 [29:40<03:54,  2.86s/it]
 91%|█████████ | 827/908 [29:40<02:46,  2.06s/it]
 91%|█████████ | 828/908 [29:40<01:59,  1.50s/it]
 91%|█████████▏| 829/908 [29:43<02:31,  1.92s/it]
 91%|█████████▏| 830/908 [29:48<03:51,  2.97s/it]
 92%|█████████▏| 831/908 [29:49<02:44,  2.13s/it]
 92%|█████████▏| 832/908 [29:49<01:57,  1.55s/it]
 92%|█████████▏| 833/908 [29:52<02:36,  2.08s/it]
 92%|█████████▏| 834/908 [29:57<03:37,  2.94s/it]
 92%|█████████▏| 835/908 [29:57<02:34,  2.11s/it]
 92%|█████████▏| 836/908 [29:58<01:50,  1.54s/it]
 92%|█████████▏| 837/908 [30:00<02:16,  1.92s/it]
 92%|█████████▏| 838/908 [30:09<04:26,  3.81s/it]
 92%|█████████▏| 839/908 [30:09<03:07,  2.72s/it]
+[Batch:   840] training loss = 0.514
+
 93%|█████████▎| 840/908 [30:09<02:13,  1.96s/it]
 93%|█████████▎| 841/908 [30:11<02:08,  1.91s/it]
 93%|█████████▎| 842/908 [30:16<03:14,  2.95s/it]
 93%|█████████▎| 843/908 [30:16<02:18,  2.12s/it]
 93%|█████████▎| 844/908 [30:16<01:38,  1.54s/it]
 93%|█████████▎| 845/908 [30:19<01:53,  1.80s/it]
 93%|█████████▎| 846/908 [30:24<02:48,  2.72s/it]
 93%|█████████▎| 847/908 [30:24<01:59,  1.96s/it]
 93%|█████████▎| 848/908 [30:24<01:26,  1.44s/it]
 94%|█████████▎| 849/908 [30:26<01:27,  1.48s/it]
 94%|█████████▎| 850/908 [30:31<02:28,  2.57s/it]
 94%|█████████▎| 851/908 [30:31<01:45,  1.85s/it]
 94%|█████████▍| 852/908 [30:31<01:15,  1.35s/it]
 94%|█████████▍| 853/908 [30:34<01:38,  1.79s/it]
 94%|█████████▍| 854/908 [30:40<02:37,  2.92s/it]
 94%|█████████▍| 855/908 [30:40<01:51,  2.10s/it]
 94%|█████████▍| 856/908 [30:40<01:20,  1.56s/it]
 94%|█████████▍| 857/908 [30:42<01:26,  1.69s/it]
 94%|█████████▍| 858/908 [30:49<02:45,  3.31s/it]
 95%|█████████▍| 859/908 [30:49<01:56,  2.37s/it]
+[Batch:   860] training loss = 0.445
+
 95%|█████████▍| 860/908 [30:50<01:22,  1.72s/it]
 95%|█████████▍| 861/908 [30:52<01:25,  1.81s/it]
 95%|█████████▍| 862/908 [30:58<02:32,  3.32s/it]
 95%|█████████▌| 863/908 [30:59<01:47,  2.38s/it]
 95%|█████████▌| 864/908 [30:59<01:16,  1.73s/it]
 95%|█████████▌| 865/908 [31:00<01:13,  1.71s/it]
 95%|█████████▌| 866/908 [31:10<02:44,  3.91s/it]
 95%|█████████▌| 867/908 [31:10<01:54,  2.80s/it]
 96%|█████████▌| 868/908 [31:10<01:20,  2.01s/it]
 96%|█████████▌| 869/908 [31:12<01:14,  1.92s/it]
 96%|█████████▌| 870/908 [31:19<02:15,  3.56s/it]
 96%|█████████▌| 871/908 [31:19<01:34,  2.55s/it]
 96%|█████████▌| 872/908 [31:19<01:06,  1.84s/it]
 96%|█████████▌| 873/908 [31:21<01:02,  1.77s/it]
 96%|█████████▋| 874/908 [31:26<01:34,  2.77s/it]
 96%|█████████▋| 875/908 [31:26<01:05,  2.00s/it]
 96%|█████████▋| 876/908 [31:26<00:46,  1.46s/it]
 97%|█████████▋| 877/908 [31:28<00:49,  1.60s/it]
 97%|█████████▋| 878/908 [31:35<01:35,  3.19s/it]
 97%|█████████▋| 879/908 [31:35<01:06,  2.29s/it]
+[Batch:   880] training loss = 0.519
+
 97%|█████████▋| 880/908 [31:36<00:46,  1.66s/it]
 97%|█████████▋| 881/908 [31:37<00:41,  1.52s/it]
 97%|█████████▋| 882/908 [31:44<01:22,  3.16s/it]
 97%|█████████▋| 883/908 [31:44<00:56,  2.27s/it]
 97%|█████████▋| 884/908 [31:44<00:39,  1.64s/it]
 97%|█████████▋| 885/908 [31:46<00:38,  1.68s/it]
 98%|█████████▊| 886/908 [31:52<01:08,  3.11s/it]
 98%|█████████▊| 887/908 [31:53<00:46,  2.24s/it]
 98%|█████████▊| 888/908 [31:53<00:32,  1.62s/it]
 98%|█████████▊| 889/908 [31:53<00:23,  1.25s/it]
 98%|█████████▊| 890/908 [32:00<00:53,  2.99s/it]
 98%|█████████▊| 891/908 [32:00<00:36,  2.15s/it]
 98%|█████████▊| 892/908 [32:01<00:24,  1.56s/it]
 98%|█████████▊| 893/908 [32:01<00:17,  1.15s/it]
 98%|█████████▊| 894/908 [32:12<00:58,  4.19s/it]
 99%|█████████▊| 895/908 [32:12<00:38,  2.99s/it]
 99%|█████████▊| 896/908 [32:12<00:25,  2.15s/it]
 99%|█████████▉| 897/908 [32:13<00:18,  1.67s/it]
 99%|█████████▉| 898/908 [32:21<00:35,  3.53s/it]
 99%|█████████▉| 899/908 [32:21<00:22,  2.53s/it]
+[Batch:   900] training loss = 0.418
+
 99%|█████████▉| 900/908 [32:21<00:14,  1.83s/it]
 99%|█████████▉| 901/908 [32:22<00:09,  1.35s/it]
 99%|█████████▉| 902/908 [32:29<00:18,  3.15s/it]
 99%|█████████▉| 903/908 [32:29<00:11,  2.27s/it]
100%|█████████▉| 904/908 [32:29<00:06,  1.64s/it]
100%|█████████▉| 905/908 [32:29<00:03,  1.21s/it]
100%|█████████▉| 906/908 [32:31<00:02,  1.36s/it]
100%|█████████▉| 907/908 [32:31<00:01,  1.01s/it]
100%|██████████| 908/908 [32:31<00:00,  1.34it/s]
100%|██████████| 908/908 [32:32<00:00,  2.15s/it]
+
+Epoch   6: validation
+
  0%|          | 0/101 [00:00<?, ?it/s]
  1%|          | 1/101 [00:08<14:03,  8.44s/it]
  3%|▎         | 3/101 [00:08<03:40,  2.25s/it]
  5%|▍         | 5/101 [00:16<05:09,  3.23s/it]
  7%|▋         | 7/101 [00:17<02:58,  1.89s/it]
  9%|▉         | 9/101 [00:24<03:50,  2.51s/it]
 11%|█         | 11/101 [00:24<02:29,  1.66s/it]
 13%|█▎        | 13/101 [00:29<02:58,  2.03s/it]
 15%|█▍        | 15/101 [00:29<02:00,  1.40s/it]
 17%|█▋        | 17/101 [00:40<03:42,  2.65s/it]
 18%|█▊        | 18/101 [00:40<03:01,  2.19s/it]
 20%|█▉        | 20/101 [00:40<01:58,  1.46s/it]
 21%|██        | 21/101 [00:49<03:45,  2.81s/it]
 23%|██▎       | 23/101 [00:49<02:22,  1.83s/it]
 25%|██▍       | 25/101 [00:56<03:05,  2.44s/it]
 27%|██▋       | 27/101 [00:56<02:03,  1.66s/it]
 29%|██▊       | 29/101 [01:04<02:52,  2.40s/it]
 31%|███       | 31/101 [01:04<01:56,  1.67s/it]
 33%|███▎      | 33/101 [01:13<02:45,  2.44s/it]
 35%|███▍      | 35/101 [01:13<01:53,  1.72s/it]
 37%|███▋      | 37/101 [01:21<02:36,  2.45s/it]
 39%|███▊      | 39/101 [01:21<01:47,  1.73s/it]
 41%|████      | 41/101 [01:29<02:22,  2.37s/it]
 43%|████▎     | 43/101 [01:29<01:37,  1.68s/it]
 45%|████▍     | 45/101 [01:40<02:33,  2.75s/it]
 47%|████▋     | 47/101 [01:40<01:45,  1.95s/it]
 49%|████▊     | 49/101 [01:47<02:05,  2.42s/it]
 50%|████▉     | 50/101 [01:47<01:42,  2.02s/it]
 51%|█████▏    | 52/101 [01:47<01:06,  1.37s/it]
 52%|█████▏    | 53/101 [01:53<01:49,  2.29s/it]
 54%|█████▍    | 55/101 [01:53<01:09,  1.50s/it]
 56%|█████▋    | 57/101 [01:59<01:29,  2.02s/it]
 58%|█████▊    | 59/101 [02:00<00:58,  1.39s/it]
 60%|██████    | 61/101 [02:06<01:15,  1.89s/it]
 62%|██████▏   | 63/101 [02:06<00:50,  1.32s/it]
 64%|██████▍   | 65/101 [02:13<01:12,  2.01s/it]
 66%|██████▋   | 67/101 [02:13<00:48,  1.42s/it]
 68%|██████▊   | 69/101 [02:21<01:11,  2.23s/it]
 70%|███████   | 71/101 [02:21<00:47,  1.58s/it]
 71%|███████▏  | 72/101 [02:21<00:38,  1.33s/it]
 72%|███████▏  | 73/101 [02:29<01:13,  2.64s/it]
 74%|███████▍  | 75/101 [02:29<00:44,  1.70s/it]
 76%|███████▌  | 77/101 [02:36<00:54,  2.25s/it]
 78%|███████▊  | 79/101 [02:36<00:33,  1.54s/it]
 79%|███████▉  | 80/101 [02:37<00:29,  1.42s/it]
 80%|████████  | 81/101 [02:44<00:53,  2.68s/it]
 81%|████████  | 82/101 [02:45<00:39,  2.09s/it]
 83%|████████▎ | 84/101 [02:45<00:21,  1.29s/it]
 84%|████████▍ | 85/101 [02:51<00:39,  2.49s/it]
 86%|████████▌ | 87/101 [02:52<00:21,  1.55s/it]
 87%|████████▋ | 88/101 [02:52<00:17,  1.33s/it]
 88%|████████▊ | 89/101 [02:59<00:33,  2.75s/it]
 90%|█████████ | 91/101 [03:00<00:16,  1.67s/it]
 91%|█████████ | 92/101 [03:00<00:12,  1.37s/it]
 92%|█████████▏| 93/101 [03:06<00:20,  2.58s/it]
 94%|█████████▍| 95/101 [03:07<00:09,  1.56s/it]
 95%|█████████▌| 96/101 [03:07<00:06,  1.26s/it]
 96%|█████████▌| 97/101 [03:13<00:10,  2.53s/it]
 98%|█████████▊| 99/101 [03:13<00:03,  1.53s/it]
100%|██████████| 101/101 [03:17<00:00,  1.66s/it]
100%|██████████| 101/101 [03:17<00:00,  1.96s/it]
+    Epoch  6: training loss = 0.452 / validation loss = 0.457
+    Epoch  6: training acc = 0.785 / validation acc = 0.786
+
+Epoch   7: training
+
  0%|          | 0/908 [00:00<?, ?it/s]
  0%|          | 1/908 [00:07<1:59:25,  7.90s/it]
  0%|          | 2/908 [00:08<51:05,  3.38s/it]  
  0%|          | 3/908 [00:08<29:10,  1.93s/it]
  0%|          | 4/908 [00:08<18:48,  1.25s/it]
  1%|          | 5/908 [00:16<54:50,  3.64s/it]
  1%|          | 6/908 [00:16<38:15,  2.55s/it]
  1%|          | 7/908 [00:17<26:50,  1.79s/it]
  1%|          | 8/908 [00:17<19:13,  1.28s/it]
  1%|          | 9/908 [00:28<1:05:46,  4.39s/it]
  1%|          | 10/908 [00:28<47:45,  3.19s/it] 
  1%|          | 11/908 [00:29<34:00,  2.28s/it]
  1%|▏         | 12/908 [00:29<24:32,  1.64s/it]
  1%|▏         | 13/908 [00:36<47:26,  3.18s/it]
  2%|▏         | 14/908 [00:36<34:59,  2.35s/it]
  2%|▏         | 15/908 [00:36<25:18,  1.70s/it]
  2%|▏         | 16/908 [00:36<18:34,  1.25s/it]
  2%|▏         | 17/908 [00:43<40:59,  2.76s/it]
  2%|▏         | 18/908 [00:44<33:02,  2.23s/it]
  2%|▏         | 19/908 [00:44<23:58,  1.62s/it]
+[Batch:    20] training loss = 0.274
+
  2%|▏         | 20/908 [00:44<17:39,  1.19s/it]
  2%|▏         | 21/908 [01:06<1:49:11,  7.39s/it]
  2%|▏         | 22/908 [01:08<1:26:09,  5.84s/it]
  3%|▎         | 23/908 [01:08<1:01:07,  4.14s/it]
  3%|▎         | 24/908 [01:09<45:41,  3.10s/it]  
  3%|▎         | 25/908 [01:28<1:57:35,  7.99s/it]
  3%|▎         | 26/908 [01:29<1:23:09,  5.66s/it]
  3%|▎         | 27/908 [01:29<59:01,  4.02s/it]  
  3%|▎         | 28/908 [01:29<42:07,  2.87s/it]
  3%|▎         | 29/908 [01:36<58:41,  4.01s/it]
  3%|▎         | 30/908 [01:36<41:58,  2.87s/it]
  3%|▎         | 31/908 [01:36<30:13,  2.07s/it]
  4%|▎         | 32/908 [01:36<21:59,  1.51s/it]
  4%|▎         | 33/908 [01:42<40:26,  2.77s/it]
  4%|▎         | 34/908 [01:42<29:11,  2.00s/it]
  4%|▍         | 35/908 [01:42<21:17,  1.46s/it]
  4%|▍         | 36/908 [01:43<15:44,  1.08s/it]
  4%|▍         | 37/908 [01:50<43:41,  3.01s/it]
  4%|▍         | 38/908 [01:50<31:24,  2.17s/it]
  4%|▍         | 39/908 [01:51<22:49,  1.58s/it]
+[Batch:    40] training loss = 0.310
+
  4%|▍         | 40/908 [01:51<16:50,  1.16s/it]
  5%|▍         | 41/908 [01:57<39:33,  2.74s/it]
  5%|▍         | 42/908 [01:57<28:30,  1.98s/it]
  5%|▍         | 43/908 [01:58<20:45,  1.44s/it]
  5%|▍         | 44/908 [01:58<15:20,  1.07s/it]
  5%|▍         | 45/908 [02:04<38:01,  2.64s/it]
  5%|▌         | 46/908 [02:04<27:26,  1.91s/it]
  5%|▌         | 47/908 [02:04<20:00,  1.39s/it]
  5%|▌         | 48/908 [02:05<14:49,  1.03s/it]
  5%|▌         | 49/908 [02:10<33:18,  2.33s/it]
  6%|▌         | 50/908 [02:10<24:30,  1.71s/it]
  6%|▌         | 51/908 [02:10<17:57,  1.26s/it]
  6%|▌         | 52/908 [02:11<13:21,  1.07it/s]
  6%|▌         | 53/908 [02:18<39:03,  2.74s/it]
  6%|▌         | 54/908 [02:18<28:55,  2.03s/it]
  6%|▌         | 55/908 [02:18<21:05,  1.48s/it]
  6%|▌         | 56/908 [02:18<15:34,  1.10s/it]
  6%|▋         | 57/908 [02:26<43:04,  3.04s/it]
  6%|▋         | 58/908 [02:26<31:37,  2.23s/it]
  6%|▋         | 59/908 [02:26<22:56,  1.62s/it]
+[Batch:    60] training loss = 0.514
+
  7%|▋         | 60/908 [02:27<16:51,  1.19s/it]
  7%|▋         | 61/908 [02:33<39:15,  2.78s/it]
  7%|▋         | 62/908 [02:33<28:21,  2.01s/it]
  7%|▋         | 63/908 [02:34<20:37,  1.46s/it]
  7%|▋         | 64/908 [02:34<15:13,  1.08s/it]
  7%|▋         | 65/908 [02:40<38:44,  2.76s/it]
  7%|▋         | 66/908 [02:41<27:51,  1.99s/it]
  7%|▋         | 67/908 [02:41<20:17,  1.45s/it]
  7%|▋         | 68/908 [02:41<14:59,  1.07s/it]
  8%|▊         | 69/908 [02:48<39:36,  2.83s/it]
  8%|▊         | 70/908 [02:48<28:47,  2.06s/it]
  8%|▊         | 71/908 [02:48<20:55,  1.50s/it]
  8%|▊         | 72/908 [02:49<15:25,  1.11s/it]
  8%|▊         | 73/908 [02:56<40:13,  2.89s/it]
  8%|▊         | 74/908 [02:56<29:21,  2.11s/it]
  8%|▊         | 75/908 [02:56<21:23,  1.54s/it]
  8%|▊         | 76/908 [02:56<15:45,  1.14s/it]
  8%|▊         | 77/908 [03:03<38:03,  2.75s/it]
  9%|▊         | 78/908 [03:03<27:24,  1.98s/it]
  9%|▊         | 79/908 [03:03<19:57,  1.45s/it]
+[Batch:    80] training loss = 0.459
+
  9%|▉         | 80/908 [03:03<14:44,  1.07s/it]
  9%|▉         | 81/908 [03:08<29:00,  2.10s/it]
  9%|▉         | 82/908 [03:08<21:04,  1.53s/it]
  9%|▉         | 83/908 [03:08<15:32,  1.13s/it]
  9%|▉         | 84/908 [03:08<11:42,  1.17it/s]
  9%|▉         | 85/908 [03:16<37:10,  2.71s/it]
  9%|▉         | 86/908 [03:16<26:51,  1.96s/it]
 10%|▉         | 87/908 [03:16<19:39,  1.44s/it]
 10%|▉         | 88/908 [03:16<14:32,  1.06s/it]
 10%|▉         | 89/908 [03:24<41:17,  3.03s/it]
 10%|▉         | 90/908 [03:24<29:43,  2.18s/it]
 10%|█         | 91/908 [03:24<21:37,  1.59s/it]
 10%|█         | 92/908 [03:24<15:55,  1.17s/it]
 10%|█         | 93/908 [03:33<47:04,  3.47s/it]
 10%|█         | 94/908 [03:33<33:41,  2.48s/it]
 10%|█         | 95/908 [03:34<24:20,  1.80s/it]
 11%|█         | 96/908 [03:34<17:47,  1.31s/it]
 11%|█         | 97/908 [03:38<30:56,  2.29s/it]
 11%|█         | 98/908 [03:39<22:25,  1.66s/it]
 11%|█         | 99/908 [03:39<16:33,  1.23s/it]
+[Batch:   100] training loss = 0.469
+
 11%|█         | 100/908 [03:39<12:23,  1.09it/s]
 11%|█         | 101/908 [03:44<27:02,  2.01s/it]
 11%|█         | 102/908 [03:44<19:41,  1.47s/it]
 11%|█▏        | 103/908 [03:44<14:52,  1.11s/it]
 11%|█▏        | 104/908 [03:44<11:08,  1.20it/s]
 12%|█▏        | 105/908 [03:51<33:44,  2.52s/it]
 12%|█▏        | 106/908 [03:51<24:21,  1.82s/it]
 12%|█▏        | 107/908 [03:51<19:19,  1.45s/it]
 12%|█▏        | 108/908 [03:52<14:17,  1.07s/it]
 12%|█▏        | 109/908 [03:58<36:26,  2.74s/it]
 12%|█▏        | 110/908 [03:58<26:18,  1.98s/it]
 12%|█▏        | 111/908 [03:59<20:44,  1.56s/it]
 12%|█▏        | 112/908 [03:59<15:15,  1.15s/it]
 12%|█▏        | 113/908 [04:07<40:22,  3.05s/it]
 13%|█▎        | 114/908 [04:07<29:00,  2.19s/it]
 13%|█▎        | 115/908 [04:07<21:05,  1.60s/it]
 13%|█▎        | 116/908 [04:07<15:33,  1.18s/it]
 13%|█▎        | 117/908 [04:14<37:23,  2.84s/it]
 13%|█▎        | 118/908 [04:14<26:54,  2.04s/it]
 13%|█▎        | 119/908 [04:14<19:33,  1.49s/it]
+[Batch:   120] training loss = 0.459
+
 13%|█▎        | 120/908 [04:15<14:25,  1.10s/it]
 13%|█▎        | 121/908 [04:20<31:57,  2.44s/it]
 13%|█▎        | 122/908 [04:20<23:04,  1.76s/it]
 14%|█▎        | 123/908 [04:21<17:54,  1.37s/it]
 14%|█▎        | 124/908 [04:21<13:15,  1.02s/it]
 14%|█▍        | 125/908 [04:31<48:04,  3.68s/it]
 14%|█▍        | 126/908 [04:31<34:23,  2.64s/it]
 14%|█▍        | 127/908 [04:32<26:17,  2.02s/it]
 14%|█▍        | 128/908 [04:32<19:07,  1.47s/it]
 14%|█▍        | 129/908 [04:39<39:26,  3.04s/it]
 14%|█▍        | 130/908 [04:39<28:18,  2.18s/it]
 14%|█▍        | 131/908 [04:39<21:49,  1.68s/it]
 15%|█▍        | 132/908 [04:39<15:58,  1.23s/it]
 15%|█▍        | 133/908 [04:45<33:42,  2.61s/it]
 15%|█▍        | 134/908 [04:45<24:16,  1.88s/it]
 15%|█▍        | 135/908 [04:46<19:19,  1.50s/it]
 15%|█▍        | 136/908 [04:46<14:18,  1.11s/it]
 15%|█▌        | 137/908 [04:52<33:47,  2.63s/it]
 15%|█▌        | 138/908 [04:53<24:23,  1.90s/it]
 15%|█▌        | 139/908 [04:53<19:07,  1.49s/it]
+[Batch:   140] training loss = 0.400
+
 15%|█▌        | 140/908 [04:53<14:06,  1.10s/it]
 16%|█▌        | 141/908 [05:00<34:24,  2.69s/it]
 16%|█▌        | 142/908 [05:00<24:46,  1.94s/it]
 16%|█▌        | 143/908 [05:00<19:16,  1.51s/it]
 16%|█▌        | 144/908 [05:01<14:11,  1.12s/it]
 16%|█▌        | 145/908 [05:07<33:31,  2.64s/it]
 16%|█▌        | 146/908 [05:07<24:09,  1.90s/it]
 16%|█▌        | 147/908 [05:07<18:46,  1.48s/it]
 16%|█▋        | 148/908 [05:08<13:51,  1.09s/it]
 16%|█▋        | 149/908 [05:13<29:14,  2.31s/it]
 17%|█▋        | 150/908 [05:13<21:09,  1.67s/it]
 17%|█▋        | 151/908 [05:13<15:32,  1.23s/it]
 17%|█▋        | 152/908 [05:13<11:37,  1.08it/s]
 17%|█▋        | 153/908 [05:18<25:03,  1.99s/it]
 17%|█▋        | 154/908 [05:18<18:13,  1.45s/it]
 17%|█▋        | 155/908 [05:19<15:22,  1.23s/it]
 17%|█▋        | 156/908 [05:19<11:28,  1.09it/s]
 17%|█▋        | 157/908 [05:25<29:20,  2.34s/it]
 17%|█▋        | 158/908 [05:25<21:14,  1.70s/it]
 18%|█▊        | 159/908 [05:25<15:32,  1.24s/it]
+[Batch:   160] training loss = 0.476
+
 18%|█▊        | 160/908 [05:25<11:34,  1.08it/s]
 18%|█▊        | 161/908 [05:30<26:08,  2.10s/it]
 18%|█▊        | 162/908 [05:30<18:59,  1.53s/it]
 18%|█▊        | 163/908 [05:30<13:58,  1.13s/it]
 18%|█▊        | 164/908 [05:31<10:28,  1.18it/s]
 18%|█▊        | 165/908 [05:34<21:15,  1.72s/it]
 18%|█▊        | 166/908 [05:35<15:35,  1.26s/it]
 18%|█▊        | 167/908 [05:35<11:36,  1.06it/s]
 19%|█▊        | 168/908 [05:35<08:51,  1.39it/s]
 19%|█▊        | 169/908 [05:38<17:28,  1.42s/it]
 19%|█▊        | 170/908 [05:38<12:56,  1.05s/it]
 19%|█▉        | 171/908 [05:38<09:47,  1.26it/s]
 19%|█▉        | 172/908 [05:39<07:32,  1.63it/s]
 19%|█▉        | 173/908 [05:41<13:55,  1.14s/it]
 19%|█▉        | 174/908 [05:41<10:25,  1.17it/s]
 19%|█▉        | 175/908 [05:41<08:00,  1.53it/s]
 19%|█▉        | 176/908 [05:42<06:17,  1.94it/s]
 19%|█▉        | 177/908 [05:44<14:40,  1.20s/it]
 20%|█▉        | 178/908 [05:46<14:38,  1.20s/it]
 20%|█▉        | 179/908 [05:46<10:56,  1.11it/s]
+[Batch:   180] training loss = 0.507
+
 20%|█▉        | 180/908 [05:46<08:21,  1.45it/s]
 20%|█▉        | 181/908 [05:47<11:14,  1.08it/s]
 20%|██        | 182/908 [05:48<11:24,  1.06it/s]
 20%|██        | 183/908 [05:49<08:40,  1.39it/s]
 20%|██        | 184/908 [05:49<06:44,  1.79it/s]
 20%|██        | 185/908 [05:51<12:44,  1.06s/it]
 20%|██        | 186/908 [05:52<13:27,  1.12s/it]
 21%|██        | 187/908 [05:52<10:06,  1.19it/s]
 21%|██        | 188/908 [05:53<07:45,  1.55it/s]
 21%|██        | 189/908 [05:54<10:02,  1.19it/s]
 21%|██        | 190/908 [05:56<12:43,  1.06s/it]
 21%|██        | 191/908 [05:56<09:33,  1.25it/s]
 21%|██        | 192/908 [05:56<07:22,  1.62it/s]
 21%|██▏       | 193/908 [05:59<14:40,  1.23s/it]
 21%|██▏       | 194/908 [06:00<16:17,  1.37s/it]
 21%|██▏       | 195/908 [06:00<12:05,  1.02s/it]
 22%|██▏       | 196/908 [06:01<09:09,  1.30it/s]
 22%|██▏       | 197/908 [06:04<19:40,  1.66s/it]
 22%|██▏       | 198/908 [06:07<21:35,  1.82s/it]
 22%|██▏       | 199/908 [06:07<15:48,  1.34s/it]
+[Batch:   200] training loss = 0.409
+
 22%|██▏       | 200/908 [06:07<11:45,  1.00it/s]
 22%|██▏       | 201/908 [06:10<18:46,  1.59s/it]
 22%|██▏       | 202/908 [06:11<15:44,  1.34s/it]
 22%|██▏       | 203/908 [06:11<11:43,  1.00it/s]
 22%|██▏       | 204/908 [06:11<08:53,  1.32it/s]
 23%|██▎       | 205/908 [06:14<15:57,  1.36s/it]
 23%|██▎       | 206/908 [06:15<15:46,  1.35s/it]
 23%|██▎       | 207/908 [06:15<11:43,  1.00s/it]
 23%|██▎       | 208/908 [06:16<08:52,  1.31it/s]
 23%|██▎       | 209/908 [06:18<13:30,  1.16s/it]
 23%|██▎       | 210/908 [06:19<13:42,  1.18s/it]
 23%|██▎       | 211/908 [06:19<10:16,  1.13it/s]
 23%|██▎       | 212/908 [06:19<07:52,  1.47it/s]
 23%|██▎       | 213/908 [06:22<13:25,  1.16s/it]
 24%|██▎       | 214/908 [06:23<12:39,  1.09s/it]
 24%|██▎       | 215/908 [06:23<09:34,  1.21it/s]
 24%|██▍       | 216/908 [06:23<07:23,  1.56it/s]
 24%|██▍       | 217/908 [06:27<17:47,  1.54s/it]
 24%|██▍       | 218/908 [06:27<13:26,  1.17s/it]
 24%|██▍       | 219/908 [06:27<10:04,  1.14it/s]
+[Batch:   220] training loss = 0.467
+
 24%|██▍       | 220/908 [06:27<07:43,  1.48it/s]
 24%|██▍       | 221/908 [06:32<20:48,  1.82s/it]
 24%|██▍       | 222/908 [06:33<19:29,  1.71s/it]
 25%|██▍       | 223/908 [06:33<14:18,  1.25s/it]
 25%|██▍       | 224/908 [06:34<10:40,  1.07it/s]
 25%|██▍       | 225/908 [06:36<17:06,  1.50s/it]
 25%|██▍       | 226/908 [06:37<12:49,  1.13s/it]
 25%|██▌       | 227/908 [06:37<09:38,  1.18it/s]
 25%|██▌       | 228/908 [06:37<07:24,  1.53it/s]
 25%|██▌       | 229/908 [06:40<13:43,  1.21s/it]
 25%|██▌       | 230/908 [06:41<15:11,  1.34s/it]
 25%|██▌       | 231/908 [06:41<11:17,  1.00s/it]
 26%|██▌       | 232/908 [06:42<08:35,  1.31it/s]
 26%|██▌       | 233/908 [06:43<09:58,  1.13it/s]
 26%|██▌       | 234/908 [06:45<13:52,  1.24s/it]
 26%|██▌       | 235/908 [06:45<10:22,  1.08it/s]
 26%|██▌       | 236/908 [06:45<07:55,  1.41it/s]
 26%|██▌       | 237/908 [06:48<15:32,  1.39s/it]
 26%|██▌       | 238/908 [06:49<12:32,  1.12s/it]
 26%|██▋       | 239/908 [06:49<09:26,  1.18it/s]
+[Batch:   240] training loss = 0.452
+
 26%|██▋       | 240/908 [06:49<07:15,  1.54it/s]
 27%|██▋       | 241/908 [06:49<05:52,  1.89it/s]
 27%|██▋       | 242/908 [06:52<11:32,  1.04s/it]
 27%|██▋       | 243/908 [06:52<08:45,  1.26it/s]
 27%|██▋       | 244/908 [06:52<06:45,  1.64it/s]
 27%|██▋       | 245/908 [06:54<09:35,  1.15it/s]
 27%|██▋       | 246/908 [06:56<13:31,  1.23s/it]
 27%|██▋       | 247/908 [06:56<10:07,  1.09it/s]
 27%|██▋       | 248/908 [06:56<07:43,  1.42it/s]
 27%|██▋       | 249/908 [06:58<12:32,  1.14s/it]
 28%|██▊       | 250/908 [07:01<16:44,  1.53s/it]
 28%|██▊       | 251/908 [07:01<12:22,  1.13s/it]
 28%|██▊       | 252/908 [07:01<09:20,  1.17it/s]
 28%|██▊       | 253/908 [07:01<07:49,  1.40it/s]
 28%|██▊       | 254/908 [07:04<12:55,  1.19s/it]
 28%|██▊       | 255/908 [07:04<09:41,  1.12it/s]
 28%|██▊       | 256/908 [07:04<07:24,  1.47it/s]
 28%|██▊       | 257/908 [07:06<10:54,  1.01s/it]
 28%|██▊       | 258/908 [07:08<14:34,  1.35s/it]
 29%|██▊       | 259/908 [07:08<10:50,  1.00s/it]
+[Batch:   260] training loss = 0.359
+
 29%|██▊       | 260/908 [07:08<08:13,  1.31it/s]
 29%|██▊       | 261/908 [07:11<14:13,  1.32s/it]
 29%|██▉       | 262/908 [07:13<17:31,  1.63s/it]
 29%|██▉       | 263/908 [07:14<12:53,  1.20s/it]
 29%|██▉       | 264/908 [07:14<09:38,  1.11it/s]
 29%|██▉       | 265/908 [07:15<12:11,  1.14s/it]
 29%|██▉       | 266/908 [07:18<16:16,  1.52s/it]
 29%|██▉       | 267/908 [07:18<12:00,  1.12s/it]
 30%|██▉       | 268/908 [07:18<09:02,  1.18it/s]
 30%|██▉       | 269/908 [07:20<12:09,  1.14s/it]
 30%|██▉       | 270/908 [07:22<15:42,  1.48s/it]
 30%|██▉       | 271/908 [07:23<11:35,  1.09s/it]
 30%|██▉       | 272/908 [07:23<08:46,  1.21it/s]
 30%|███       | 273/908 [07:24<09:38,  1.10it/s]
 30%|███       | 274/908 [07:26<15:05,  1.43s/it]
 30%|███       | 275/908 [07:27<11:11,  1.06s/it]
 30%|███       | 276/908 [07:27<08:30,  1.24it/s]
 31%|███       | 277/908 [07:29<14:07,  1.34s/it]
 31%|███       | 278/908 [07:32<17:16,  1.64s/it]
 31%|███       | 279/908 [07:32<12:41,  1.21s/it]
+[Batch:   280] training loss = 0.457
+
 31%|███       | 280/908 [07:32<09:30,  1.10it/s]
 31%|███       | 281/908 [07:35<13:56,  1.33s/it]
 31%|███       | 282/908 [07:37<16:01,  1.54s/it]
 31%|███       | 283/908 [07:37<11:48,  1.13s/it]
 31%|███▏      | 284/908 [07:37<08:54,  1.17it/s]
 31%|███▏      | 285/908 [07:38<09:50,  1.06it/s]
 31%|███▏      | 286/908 [07:39<11:05,  1.07s/it]
 32%|███▏      | 287/908 [07:40<08:21,  1.24it/s]
 32%|███▏      | 288/908 [07:40<06:39,  1.55it/s]
 32%|███▏      | 289/908 [07:42<11:10,  1.08s/it]
 32%|███▏      | 290/908 [07:44<13:04,  1.27s/it]
 32%|███▏      | 291/908 [07:44<09:47,  1.05it/s]
 32%|███▏      | 292/908 [07:44<07:26,  1.38it/s]
 32%|███▏      | 293/908 [07:46<09:54,  1.03it/s]
 32%|███▏      | 294/908 [07:46<09:13,  1.11it/s]
 32%|███▏      | 295/908 [07:47<07:04,  1.44it/s]
 33%|███▎      | 296/908 [07:47<05:34,  1.83it/s]
 33%|███▎      | 297/908 [07:49<09:47,  1.04it/s]
 33%|███▎      | 298/908 [07:49<07:31,  1.35it/s]
 33%|███▎      | 299/908 [07:49<05:51,  1.73it/s]
+[Batch:   300] training loss = 0.552
+
 33%|███▎      | 300/908 [07:49<04:40,  2.17it/s]
 33%|███▎      | 301/908 [07:52<12:40,  1.25s/it]
 33%|███▎      | 302/908 [07:55<15:38,  1.55s/it]
 33%|███▎      | 303/908 [07:55<11:33,  1.15s/it]
 33%|███▎      | 304/908 [07:55<08:41,  1.16it/s]
 34%|███▎      | 305/908 [07:55<06:48,  1.47it/s]
 34%|███▎      | 306/908 [07:56<05:52,  1.71it/s]
 34%|███▍      | 307/908 [07:56<04:41,  2.13it/s]
 34%|███▍      | 308/908 [07:56<03:53,  2.57it/s]
 34%|███▍      | 309/908 [07:59<11:49,  1.18s/it]
 34%|███▍      | 310/908 [08:01<13:25,  1.35s/it]
 34%|███▍      | 311/908 [08:01<09:58,  1.00s/it]
 34%|███▍      | 312/908 [08:01<07:35,  1.31it/s]
 34%|███▍      | 313/908 [08:03<11:21,  1.15s/it]
 35%|███▍      | 314/908 [08:05<11:58,  1.21s/it]
 35%|███▍      | 315/908 [08:05<08:57,  1.10it/s]
 35%|███▍      | 316/908 [08:05<06:52,  1.43it/s]
 35%|███▍      | 317/908 [08:07<11:36,  1.18s/it]
 35%|███▌      | 318/908 [08:09<13:14,  1.35s/it]
 35%|███▌      | 319/908 [08:09<09:49,  1.00s/it]
+[Batch:   320] training loss = 0.407
+
 35%|███▌      | 320/908 [08:10<07:28,  1.31it/s]
 35%|███▌      | 321/908 [08:12<13:38,  1.39s/it]
 35%|███▌      | 322/908 [08:14<13:06,  1.34s/it]
 36%|███▌      | 323/908 [08:14<09:43,  1.00it/s]
 36%|███▌      | 324/908 [08:14<07:21,  1.32it/s]
 36%|███▌      | 325/908 [08:17<12:49,  1.32s/it]
 36%|███▌      | 326/908 [08:17<09:59,  1.03s/it]
 36%|███▌      | 327/908 [08:17<07:31,  1.29it/s]
 36%|███▌      | 328/908 [08:17<05:48,  1.66it/s]
 36%|███▌      | 329/908 [08:20<10:42,  1.11s/it]
 36%|███▋      | 330/908 [08:20<08:46,  1.10it/s]
 36%|███▋      | 331/908 [08:20<06:41,  1.44it/s]
 37%|███▋      | 332/908 [08:21<05:14,  1.83it/s]
 37%|███▋      | 333/908 [08:23<11:31,  1.20s/it]
 37%|███▋      | 334/908 [08:25<13:01,  1.36s/it]
 37%|███▋      | 335/908 [08:25<09:38,  1.01s/it]
 37%|███▋      | 336/908 [08:25<07:18,  1.31it/s]
 37%|███▋      | 337/908 [08:28<12:03,  1.27s/it]
 37%|███▋      | 338/908 [08:30<13:32,  1.42s/it]
 37%|███▋      | 339/908 [08:30<10:00,  1.06s/it]
+[Batch:   340] training loss = 0.379
+
 37%|███▋      | 340/908 [08:30<07:31,  1.26it/s]
 38%|███▊      | 341/908 [08:32<09:38,  1.02s/it]
 38%|███▊      | 342/908 [08:36<18:03,  1.91s/it]
 38%|███▊      | 343/908 [08:36<13:10,  1.40s/it]
 38%|███▊      | 344/908 [08:36<09:45,  1.04s/it]
 38%|███▊      | 345/908 [08:38<13:11,  1.41s/it]
 38%|███▊      | 346/908 [08:39<11:39,  1.25s/it]
 38%|███▊      | 347/908 [08:39<08:41,  1.08it/s]
 38%|███▊      | 348/908 [08:39<06:36,  1.41it/s]
 38%|███▊      | 349/908 [08:43<13:53,  1.49s/it]
 39%|███▊      | 350/908 [08:45<15:33,  1.67s/it]
 39%|███▊      | 351/908 [08:45<11:24,  1.23s/it]
 39%|███▉      | 352/908 [08:45<08:29,  1.09it/s]
 39%|███▉      | 353/908 [08:47<11:55,  1.29s/it]
 39%|███▉      | 354/908 [08:49<13:33,  1.47s/it]
 39%|███▉      | 355/908 [08:50<09:58,  1.08s/it]
 39%|███▉      | 356/908 [08:50<07:30,  1.23it/s]
 39%|███▉      | 357/908 [08:52<12:09,  1.32s/it]
 39%|███▉      | 358/908 [08:54<14:25,  1.57s/it]
 40%|███▉      | 359/908 [08:55<10:37,  1.16s/it]
+[Batch:   360] training loss = 0.409
+
 40%|███▉      | 360/908 [08:55<07:56,  1.15it/s]
 40%|███▉      | 361/908 [08:57<11:22,  1.25s/it]
 40%|███▉      | 362/908 [08:58<10:07,  1.11s/it]
 40%|███▉      | 363/908 [08:58<07:34,  1.20it/s]
 40%|████      | 364/908 [08:58<05:48,  1.56it/s]
 40%|████      | 365/908 [09:00<10:21,  1.14s/it]
 40%|████      | 366/908 [09:02<12:59,  1.44s/it]
 40%|████      | 367/908 [09:03<09:35,  1.06s/it]
 41%|████      | 368/908 [09:03<07:13,  1.25it/s]
 41%|████      | 369/908 [09:06<13:07,  1.46s/it]
 41%|████      | 370/908 [09:07<13:07,  1.46s/it]
 41%|████      | 371/908 [09:08<09:40,  1.08s/it]
 41%|████      | 372/908 [09:08<07:16,  1.23it/s]
 41%|████      | 373/908 [09:10<12:22,  1.39s/it]
 41%|████      | 374/908 [09:12<13:54,  1.56s/it]
 41%|████▏     | 375/908 [09:13<10:14,  1.15s/it]
 41%|████▏     | 376/908 [09:13<07:39,  1.16it/s]
 42%|████▏     | 377/908 [09:14<07:18,  1.21it/s]
 42%|████▏     | 378/908 [09:14<05:38,  1.57it/s]
 42%|████▏     | 379/908 [09:14<04:26,  1.98it/s]
+[Batch:   380] training loss = 0.720
+
 42%|████▏     | 380/908 [09:14<03:38,  2.42it/s]
 42%|████▏     | 381/908 [09:17<09:39,  1.10s/it]
 42%|████▏     | 382/908 [09:18<08:56,  1.02s/it]
 42%|████▏     | 383/908 [09:18<06:44,  1.30it/s]
 42%|████▏     | 384/908 [09:18<05:12,  1.68it/s]
 42%|████▏     | 385/908 [09:20<08:50,  1.01s/it]
 43%|████▎     | 386/908 [09:21<08:33,  1.02it/s]
 43%|████▎     | 387/908 [09:21<06:28,  1.34it/s]
 43%|████▎     | 388/908 [09:21<05:01,  1.72it/s]
 43%|████▎     | 389/908 [09:25<12:11,  1.41s/it]
 43%|████▎     | 390/908 [09:25<10:29,  1.22s/it]
 43%|████▎     | 391/908 [09:26<07:50,  1.10it/s]
 43%|████▎     | 392/908 [09:26<05:57,  1.44it/s]
 43%|████▎     | 393/908 [09:29<11:52,  1.38s/it]
 43%|████▎     | 394/908 [09:30<11:08,  1.30s/it]
 44%|████▎     | 395/908 [09:30<08:16,  1.03it/s]
 44%|████▎     | 396/908 [09:30<06:15,  1.36it/s]
 44%|████▎     | 397/908 [09:38<25:08,  2.95s/it]
 44%|████▍     | 398/908 [09:40<20:51,  2.45s/it]
 44%|████▍     | 399/908 [09:40<15:02,  1.77s/it]
+[Batch:   400] training loss = 0.372
+
 44%|████▍     | 400/908 [09:40<10:59,  1.30s/it]
 44%|████▍     | 401/908 [09:43<15:33,  1.84s/it]
 44%|████▍     | 402/908 [09:44<13:39,  1.62s/it]
 44%|████▍     | 403/908 [09:45<10:00,  1.19s/it]
 44%|████▍     | 404/908 [09:45<07:28,  1.12it/s]
 45%|████▍     | 405/908 [09:46<09:27,  1.13s/it]
 45%|████▍     | 406/908 [09:48<10:15,  1.23s/it]
 45%|████▍     | 407/908 [09:48<07:38,  1.09it/s]
 45%|████▍     | 408/908 [09:48<05:49,  1.43it/s]
 45%|████▌     | 409/908 [09:51<11:47,  1.42s/it]
 45%|████▌     | 410/908 [09:53<11:31,  1.39s/it]
 45%|████▌     | 411/908 [09:53<08:31,  1.03s/it]
 45%|████▌     | 412/908 [09:53<06:26,  1.28it/s]
 45%|████▌     | 413/908 [09:56<11:42,  1.42s/it]
 46%|████▌     | 414/908 [09:56<08:41,  1.06s/it]
 46%|████▌     | 415/908 [09:56<06:32,  1.26it/s]
 46%|████▌     | 416/908 [09:57<05:01,  1.63it/s]
 46%|████▌     | 417/908 [09:59<09:49,  1.20s/it]
 46%|████▌     | 418/908 [09:59<07:20,  1.11it/s]
 46%|████▌     | 419/908 [09:59<05:34,  1.46it/s]
+[Batch:   420] training loss = 0.579
+
 46%|████▋     | 420/908 [10:00<04:21,  1.87it/s]
 46%|████▋     | 421/908 [10:02<09:39,  1.19s/it]
 46%|████▋     | 422/908 [10:03<07:29,  1.08it/s]
 47%|████▋     | 423/908 [10:03<05:41,  1.42it/s]
 47%|████▋     | 424/908 [10:03<04:26,  1.81it/s]
 47%|████▋     | 425/908 [10:08<13:56,  1.73s/it]
 47%|████▋     | 426/908 [10:08<11:37,  1.45s/it]
 47%|████▋     | 427/908 [10:09<08:35,  1.07s/it]
 47%|████▋     | 428/908 [10:09<06:27,  1.24it/s]
 47%|████▋     | 429/908 [10:12<13:26,  1.68s/it]
 47%|████▋     | 430/908 [10:13<10:22,  1.30s/it]
 47%|████▋     | 431/908 [10:13<07:41,  1.03it/s]
 48%|████▊     | 432/908 [10:13<05:52,  1.35it/s]
 48%|████▊     | 433/908 [10:16<09:38,  1.22s/it]
 48%|████▊     | 434/908 [10:17<09:03,  1.15s/it]
 48%|████▊     | 435/908 [10:17<06:46,  1.16it/s]
 48%|████▊     | 436/908 [10:17<05:10,  1.52it/s]
 48%|████▊     | 437/908 [10:21<14:15,  1.82s/it]
 48%|████▊     | 438/908 [10:22<10:28,  1.34s/it]
 48%|████▊     | 439/908 [10:22<07:45,  1.01it/s]
+[Batch:   440] training loss = 0.374
+
 48%|████▊     | 440/908 [10:22<05:52,  1.33it/s]
 49%|████▊     | 441/908 [10:25<11:44,  1.51s/it]
 49%|████▊     | 442/908 [10:26<08:40,  1.12s/it]
 49%|████▉     | 443/908 [10:26<06:29,  1.19it/s]
 49%|████▉     | 444/908 [10:26<05:00,  1.54it/s]
 49%|████▉     | 445/908 [10:29<09:54,  1.28s/it]
 49%|████▉     | 446/908 [10:30<10:25,  1.35s/it]
 49%|████▉     | 447/908 [10:30<07:43,  1.01s/it]
 49%|████▉     | 448/908 [10:31<05:50,  1.31it/s]
 49%|████▉     | 449/908 [10:34<10:45,  1.41s/it]
 50%|████▉     | 450/908 [10:35<11:01,  1.44s/it]
 50%|████▉     | 451/908 [10:35<08:07,  1.07s/it]
 50%|████▉     | 452/908 [10:35<06:13,  1.22it/s]
 50%|████▉     | 453/908 [10:41<15:58,  2.11s/it]
 50%|█████     | 454/908 [10:42<13:49,  1.83s/it]
 50%|█████     | 455/908 [10:42<10:06,  1.34s/it]
 50%|█████     | 456/908 [10:42<07:29,  1.00it/s]
 50%|█████     | 457/908 [10:45<11:25,  1.52s/it]
 50%|█████     | 458/908 [10:46<11:17,  1.50s/it]
 51%|█████     | 459/908 [10:47<08:18,  1.11s/it]
+[Batch:   460] training loss = 0.431
+
 51%|█████     | 460/908 [10:47<06:15,  1.19it/s]
 51%|█████     | 461/908 [10:49<09:21,  1.26s/it]
 51%|█████     | 462/908 [10:51<10:01,  1.35s/it]
 51%|█████     | 463/908 [10:51<07:25,  1.00s/it]
 51%|█████     | 464/908 [10:51<05:36,  1.32it/s]
 51%|█████     | 465/908 [10:53<09:14,  1.25s/it]
 51%|█████▏    | 466/908 [10:55<10:08,  1.38s/it]
 51%|█████▏    | 467/908 [10:55<07:30,  1.02s/it]
 52%|█████▏    | 468/908 [10:55<05:39,  1.30it/s]
 52%|█████▏    | 469/908 [10:59<10:52,  1.49s/it]
 52%|█████▏    | 470/908 [10:59<09:16,  1.27s/it]
 52%|█████▏    | 471/908 [11:00<06:53,  1.06it/s]
 52%|█████▏    | 472/908 [11:00<05:13,  1.39it/s]
 52%|█████▏    | 473/908 [11:02<09:10,  1.26s/it]
 52%|█████▏    | 474/908 [11:04<10:16,  1.42s/it]
 52%|█████▏    | 475/908 [11:04<07:35,  1.05s/it]
 52%|█████▏    | 476/908 [11:04<05:42,  1.26it/s]
 53%|█████▎    | 477/908 [11:08<10:44,  1.50s/it]
 53%|█████▎    | 478/908 [11:09<09:42,  1.35s/it]
 53%|█████▎    | 479/908 [11:09<07:10,  1.00s/it]
+[Batch:   480] training loss = 0.511
+
 53%|█████▎    | 480/908 [11:09<05:24,  1.32it/s]
 53%|█████▎    | 481/908 [11:12<10:36,  1.49s/it]
 53%|█████▎    | 482/908 [11:14<11:54,  1.68s/it]
 53%|█████▎    | 483/908 [11:14<08:43,  1.23s/it]
 53%|█████▎    | 484/908 [11:15<06:29,  1.09it/s]
 53%|█████▎    | 485/908 [11:17<10:31,  1.49s/it]
 54%|█████▎    | 486/908 [11:19<09:59,  1.42s/it]
 54%|█████▎    | 487/908 [11:19<07:23,  1.05s/it]
 54%|█████▎    | 488/908 [11:19<05:33,  1.26it/s]
 54%|█████▍    | 489/908 [11:20<06:40,  1.05it/s]
 54%|█████▍    | 490/908 [11:22<07:47,  1.12s/it]
 54%|█████▍    | 491/908 [11:22<05:50,  1.19it/s]
 54%|█████▍    | 492/908 [11:22<04:28,  1.55it/s]
 54%|█████▍    | 493/908 [11:25<08:43,  1.26s/it]
 54%|█████▍    | 494/908 [11:26<08:42,  1.26s/it]
 55%|█████▍    | 495/908 [11:26<06:28,  1.06it/s]
 55%|█████▍    | 496/908 [11:27<04:54,  1.40it/s]
 55%|█████▍    | 497/908 [11:29<08:50,  1.29s/it]
 55%|█████▍    | 498/908 [11:31<09:17,  1.36s/it]
 55%|█████▍    | 499/908 [11:31<06:53,  1.01s/it]
+[Batch:   500] training loss = 0.362
+
 55%|█████▌    | 500/908 [11:31<05:11,  1.31it/s]
 55%|█████▌    | 501/908 [11:33<08:17,  1.22s/it]
 55%|█████▌    | 502/908 [11:35<09:28,  1.40s/it]
 55%|█████▌    | 503/908 [11:35<06:59,  1.03s/it]
 56%|█████▌    | 504/908 [11:36<05:15,  1.28it/s]
 56%|█████▌    | 505/908 [11:40<12:20,  1.84s/it]
 56%|█████▌    | 506/908 [11:41<11:24,  1.70s/it]
 56%|█████▌    | 507/908 [11:42<08:20,  1.25s/it]
 56%|█████▌    | 508/908 [11:42<06:12,  1.07it/s]
 56%|█████▌    | 509/908 [11:45<10:00,  1.50s/it]
 56%|█████▌    | 510/908 [11:46<09:44,  1.47s/it]
 56%|█████▋    | 511/908 [11:46<07:10,  1.08s/it]
 56%|█████▋    | 512/908 [11:46<05:24,  1.22it/s]
 56%|█████▋    | 513/908 [11:49<09:44,  1.48s/it]
 57%|█████▋    | 514/908 [11:51<09:39,  1.47s/it]
 57%|█████▋    | 515/908 [11:51<07:07,  1.09s/it]
 57%|█████▋    | 516/908 [11:51<05:21,  1.22it/s]
 57%|█████▋    | 517/908 [11:54<08:34,  1.32s/it]
 57%|█████▋    | 518/908 [11:55<09:08,  1.41s/it]
 57%|█████▋    | 519/908 [11:55<06:46,  1.04s/it]
+[Batch:   520] training loss = 0.526
+
 57%|█████▋    | 520/908 [11:56<05:06,  1.27it/s]
 57%|█████▋    | 521/908 [11:58<07:49,  1.21s/it]
 57%|█████▋    | 522/908 [11:59<08:15,  1.28s/it]
 58%|█████▊    | 523/908 [12:00<06:07,  1.05it/s]
 58%|█████▊    | 524/908 [12:00<04:38,  1.38it/s]
 58%|█████▊    | 525/908 [12:03<09:22,  1.47s/it]
 58%|█████▊    | 526/908 [12:05<10:00,  1.57s/it]
 58%|█████▊    | 527/908 [12:05<07:20,  1.16s/it]
 58%|█████▊    | 528/908 [12:05<05:29,  1.15it/s]
 58%|█████▊    | 529/908 [12:08<09:01,  1.43s/it]
 58%|█████▊    | 530/908 [12:10<09:53,  1.57s/it]
 58%|█████▊    | 531/908 [12:10<07:17,  1.16s/it]
 59%|█████▊    | 532/908 [12:10<05:27,  1.15it/s]
 59%|█████▊    | 533/908 [12:13<08:31,  1.36s/it]
 59%|█████▉    | 534/908 [12:15<10:47,  1.73s/it]
 59%|█████▉    | 535/908 [12:15<07:53,  1.27s/it]
 59%|█████▉    | 536/908 [12:16<05:51,  1.06it/s]
 59%|█████▉    | 537/908 [12:17<07:14,  1.17s/it]
 59%|█████▉    | 538/908 [12:20<09:24,  1.52s/it]
 59%|█████▉    | 539/908 [12:20<06:55,  1.12s/it]
+[Batch:   540] training loss = 0.450
+
 59%|█████▉    | 540/908 [12:20<05:10,  1.18it/s]
 60%|█████▉    | 541/908 [12:23<08:48,  1.44s/it]
 60%|█████▉    | 542/908 [12:26<11:26,  1.88s/it]
 60%|█████▉    | 543/908 [12:26<08:21,  1.38s/it]
 60%|█████▉    | 544/908 [12:26<06:11,  1.02s/it]
 60%|██████    | 545/908 [12:28<08:19,  1.37s/it]
 60%|██████    | 546/908 [12:30<09:06,  1.51s/it]
 60%|██████    | 547/908 [12:30<06:43,  1.12s/it]
 60%|██████    | 548/908 [12:31<05:04,  1.18it/s]
 60%|██████    | 549/908 [12:33<07:57,  1.33s/it]
 61%|██████    | 550/908 [12:34<06:45,  1.13s/it]
 61%|██████    | 551/908 [12:34<05:05,  1.17it/s]
 61%|██████    | 552/908 [12:34<03:55,  1.51it/s]
 61%|██████    | 553/908 [12:37<07:06,  1.20s/it]
 61%|██████    | 554/908 [12:43<16:01,  2.72s/it]
 61%|██████    | 555/908 [12:43<11:32,  1.96s/it]
 61%|██████    | 556/908 [12:43<08:24,  1.43s/it]
 61%|██████▏   | 557/908 [12:44<07:17,  1.25s/it]
 61%|██████▏   | 558/908 [12:47<10:39,  1.83s/it]
 62%|██████▏   | 559/908 [12:47<07:48,  1.34s/it]
+[Batch:   560] training loss = 0.420
+
 62%|██████▏   | 560/908 [12:48<05:47,  1.00it/s]
 62%|██████▏   | 561/908 [12:50<08:53,  1.54s/it]
 62%|██████▏   | 562/908 [12:53<10:00,  1.74s/it]
 62%|██████▏   | 563/908 [12:53<07:19,  1.28s/it]
 62%|██████▏   | 564/908 [12:53<05:28,  1.05it/s]
 62%|██████▏   | 565/908 [12:56<08:26,  1.48s/it]
 62%|██████▏   | 566/908 [12:58<09:36,  1.69s/it]
 62%|██████▏   | 567/908 [12:58<07:03,  1.24s/it]
 63%|██████▎   | 568/908 [12:58<05:14,  1.08it/s]
 63%|██████▎   | 569/908 [13:01<08:55,  1.58s/it]
 63%|██████▎   | 570/908 [13:04<09:43,  1.73s/it]
 63%|██████▎   | 571/908 [13:04<07:06,  1.27s/it]
 63%|██████▎   | 572/908 [13:04<05:18,  1.06it/s]
 63%|██████▎   | 573/908 [13:07<08:25,  1.51s/it]
 63%|██████▎   | 574/908 [13:09<09:53,  1.78s/it]
 63%|██████▎   | 575/908 [13:09<07:15,  1.31s/it]
 63%|██████▎   | 576/908 [13:10<05:26,  1.02it/s]
 64%|██████▎   | 577/908 [13:13<09:02,  1.64s/it]
 64%|██████▎   | 578/908 [13:14<09:09,  1.67s/it]
 64%|██████▍   | 579/908 [13:15<06:43,  1.23s/it]
+[Batch:   580] training loss = 0.526
+
 64%|██████▍   | 580/908 [13:15<05:00,  1.09it/s]
 64%|██████▍   | 581/908 [13:17<07:01,  1.29s/it]
 64%|██████▍   | 582/908 [13:19<08:01,  1.48s/it]
 64%|██████▍   | 583/908 [13:19<05:55,  1.09s/it]
 64%|██████▍   | 584/908 [13:19<04:27,  1.21it/s]
 64%|██████▍   | 585/908 [13:23<09:30,  1.77s/it]
 65%|██████▍   | 586/908 [13:24<07:46,  1.45s/it]
 65%|██████▍   | 587/908 [13:24<05:45,  1.08s/it]
 65%|██████▍   | 588/908 [13:24<04:20,  1.23it/s]
 65%|██████▍   | 589/908 [13:27<06:38,  1.25s/it]
 65%|██████▍   | 590/908 [13:29<08:49,  1.67s/it]
 65%|██████▌   | 591/908 [13:30<06:28,  1.23s/it]
 65%|██████▌   | 592/908 [13:30<04:49,  1.09it/s]
 65%|██████▌   | 593/908 [13:31<06:00,  1.15s/it]
 65%|██████▌   | 594/908 [13:33<06:22,  1.22s/it]
 66%|██████▌   | 595/908 [13:33<04:45,  1.10it/s]
 66%|██████▌   | 596/908 [13:33<03:37,  1.43it/s]
 66%|██████▌   | 597/908 [13:36<07:12,  1.39s/it]
 66%|██████▌   | 598/908 [13:37<06:28,  1.25s/it]
 66%|██████▌   | 599/908 [13:37<04:50,  1.06it/s]
+[Batch:   600] training loss = 0.437
+
 66%|██████▌   | 600/908 [13:38<03:41,  1.39it/s]
 66%|██████▌   | 601/908 [13:41<07:43,  1.51s/it]
 66%|██████▋   | 602/908 [13:41<05:43,  1.12s/it]
 66%|██████▋   | 603/908 [13:41<04:17,  1.18it/s]
 67%|██████▋   | 604/908 [13:42<03:17,  1.54it/s]
 67%|██████▋   | 605/908 [13:45<08:04,  1.60s/it]
 67%|██████▋   | 606/908 [13:47<08:01,  1.59s/it]
 67%|██████▋   | 607/908 [13:47<05:53,  1.17s/it]
 67%|██████▋   | 608/908 [13:47<04:24,  1.13it/s]
 67%|██████▋   | 609/908 [13:50<07:14,  1.45s/it]
 67%|██████▋   | 610/908 [13:51<06:14,  1.26s/it]
 67%|██████▋   | 611/908 [13:51<04:38,  1.07it/s]
 67%|██████▋   | 612/908 [13:51<03:33,  1.39it/s]
 68%|██████▊   | 613/908 [13:54<06:39,  1.36s/it]
 68%|██████▊   | 614/908 [13:55<05:32,  1.13s/it]
 68%|██████▊   | 615/908 [13:55<04:09,  1.18it/s]
 68%|██████▊   | 616/908 [13:55<03:11,  1.52it/s]
 68%|██████▊   | 617/908 [13:58<06:44,  1.39s/it]
 68%|██████▊   | 618/908 [14:00<06:43,  1.39s/it]
 68%|██████▊   | 619/908 [14:00<04:58,  1.03s/it]
+[Batch:   620] training loss = 0.408
+
 68%|██████▊   | 620/908 [14:00<03:54,  1.23it/s]
 68%|██████▊   | 621/908 [14:04<08:00,  1.68s/it]
 69%|██████▊   | 622/908 [14:05<07:21,  1.54s/it]
 69%|██████▊   | 623/908 [14:05<05:25,  1.14s/it]
 69%|██████▊   | 624/908 [14:05<04:04,  1.16it/s]
 69%|██████▉   | 625/908 [14:10<08:39,  1.84s/it]
 69%|██████▉   | 626/908 [14:12<09:16,  1.97s/it]
 69%|██████▉   | 627/908 [14:12<06:44,  1.44s/it]
 69%|██████▉   | 628/908 [14:13<05:24,  1.16s/it]
 69%|██████▉   | 629/908 [14:16<08:09,  1.76s/it]
 69%|██████▉   | 630/908 [14:17<07:25,  1.60s/it]
 69%|██████▉   | 631/908 [14:17<05:27,  1.18s/it]
 70%|██████▉   | 632/908 [14:17<04:05,  1.13it/s]
 70%|██████▉   | 633/908 [14:20<06:34,  1.43s/it]
 70%|██████▉   | 634/908 [14:22<06:51,  1.50s/it]
 70%|██████▉   | 635/908 [14:22<05:03,  1.11s/it]
 70%|███████   | 636/908 [14:22<03:47,  1.20it/s]
 70%|███████   | 637/908 [14:23<03:30,  1.29it/s]
 70%|███████   | 638/908 [14:23<02:52,  1.57it/s]
 70%|███████   | 639/908 [14:23<02:16,  1.97it/s]
+[Batch:   640] training loss = 0.405
+
 70%|███████   | 640/908 [14:23<01:51,  2.40it/s]
 71%|███████   | 641/908 [14:28<06:52,  1.54s/it]
 71%|███████   | 642/908 [14:28<05:28,  1.24s/it]
 71%|███████   | 643/908 [14:28<04:04,  1.08it/s]
 71%|███████   | 644/908 [14:29<03:06,  1.41it/s]
 71%|███████   | 645/908 [14:31<04:44,  1.08s/it]
 71%|███████   | 646/908 [14:32<04:38,  1.06s/it]
 71%|███████▏  | 647/908 [14:32<03:30,  1.24it/s]
 71%|███████▏  | 648/908 [14:32<02:42,  1.60it/s]
 71%|███████▏  | 649/908 [14:35<05:28,  1.27s/it]
 72%|███████▏  | 650/908 [14:35<04:27,  1.04s/it]
 72%|███████▏  | 651/908 [14:35<03:21,  1.28it/s]
 72%|███████▏  | 652/908 [14:36<02:35,  1.65it/s]
 72%|███████▏  | 653/908 [14:39<06:32,  1.54s/it]
 72%|███████▏  | 654/908 [14:40<05:30,  1.30s/it]
 72%|███████▏  | 655/908 [14:40<04:04,  1.03it/s]
 72%|███████▏  | 656/908 [14:40<03:05,  1.36it/s]
 72%|███████▏  | 657/908 [14:46<08:32,  2.04s/it]
 72%|███████▏  | 658/908 [14:46<06:17,  1.51s/it]
 73%|███████▎  | 659/908 [14:46<04:37,  1.12s/it]
+[Batch:   660] training loss = 0.388
+
 73%|███████▎  | 660/908 [14:46<03:28,  1.19it/s]
 73%|███████▎  | 661/908 [14:51<07:46,  1.89s/it]
 73%|███████▎  | 662/908 [14:52<06:45,  1.65s/it]
 73%|███████▎  | 663/908 [14:52<04:56,  1.21s/it]
 73%|███████▎  | 664/908 [14:52<03:40,  1.11it/s]
 73%|███████▎  | 665/908 [14:55<06:32,  1.61s/it]
 73%|███████▎  | 666/908 [14:56<04:52,  1.21s/it]
 73%|███████▎  | 667/908 [14:56<03:37,  1.11it/s]
 74%|███████▎  | 668/908 [14:56<02:46,  1.45it/s]
 74%|███████▎  | 669/908 [14:59<05:22,  1.35s/it]
 74%|███████▍  | 670/908 [15:00<05:13,  1.32s/it]
 74%|███████▍  | 671/908 [15:00<03:51,  1.02it/s]
 74%|███████▍  | 672/908 [15:00<02:55,  1.35it/s]
 74%|███████▍  | 673/908 [15:04<06:48,  1.74s/it]
 74%|███████▍  | 674/908 [15:05<05:22,  1.38s/it]
 74%|███████▍  | 675/908 [15:05<03:58,  1.02s/it]
 74%|███████▍  | 676/908 [15:05<02:59,  1.29it/s]
 75%|███████▍  | 677/908 [15:08<05:30,  1.43s/it]
 75%|███████▍  | 678/908 [15:09<04:33,  1.19s/it]
 75%|███████▍  | 679/908 [15:09<03:23,  1.13it/s]
+[Batch:   680] training loss = 0.460
+
 75%|███████▍  | 680/908 [15:09<02:35,  1.47it/s]
 75%|███████▌  | 681/908 [15:12<04:31,  1.20s/it]
 75%|███████▌  | 682/908 [15:12<03:23,  1.11it/s]
 75%|███████▌  | 683/908 [15:12<02:34,  1.45it/s]
 75%|███████▌  | 684/908 [15:12<02:00,  1.86it/s]
 75%|███████▌  | 685/908 [15:15<04:24,  1.19s/it]
 76%|███████▌  | 686/908 [15:17<04:58,  1.35s/it]
 76%|███████▌  | 687/908 [15:17<03:40,  1.00it/s]
 76%|███████▌  | 688/908 [15:17<02:46,  1.32it/s]
 76%|███████▌  | 689/908 [15:20<05:14,  1.44s/it]
 76%|███████▌  | 690/908 [15:21<04:42,  1.30s/it]
 76%|███████▌  | 691/908 [15:21<03:29,  1.04it/s]
 76%|███████▌  | 692/908 [15:22<02:38,  1.36it/s]
 76%|███████▋  | 693/908 [15:23<03:56,  1.10s/it]
 76%|███████▋  | 694/908 [15:24<03:26,  1.04it/s]
 77%|███████▋  | 695/908 [15:24<02:35,  1.37it/s]
 77%|███████▋  | 696/908 [15:25<02:00,  1.76it/s]
 77%|███████▋  | 697/908 [15:27<03:38,  1.03s/it]
 77%|███████▋  | 698/908 [15:27<03:04,  1.14it/s]
 77%|███████▋  | 699/908 [15:27<02:20,  1.48it/s]
+[Batch:   700] training loss = 0.462
+
 77%|███████▋  | 700/908 [15:28<01:49,  1.90it/s]
 77%|███████▋  | 701/908 [15:30<04:02,  1.17s/it]
 77%|███████▋  | 702/908 [15:32<04:19,  1.26s/it]
 77%|███████▋  | 703/908 [15:32<03:12,  1.06it/s]
 78%|███████▊  | 704/908 [15:32<02:25,  1.40it/s]
 78%|███████▊  | 705/908 [15:35<04:19,  1.28s/it]
 78%|███████▊  | 706/908 [15:36<04:08,  1.23s/it]
 78%|███████▊  | 707/908 [15:36<03:05,  1.08it/s]
 78%|███████▊  | 708/908 [15:36<02:20,  1.42it/s]
 78%|███████▊  | 709/908 [15:40<05:05,  1.53s/it]
 78%|███████▊  | 710/908 [15:41<04:38,  1.40s/it]
 78%|███████▊  | 711/908 [15:41<03:25,  1.04s/it]
 78%|███████▊  | 712/908 [15:41<02:33,  1.28it/s]
 79%|███████▊  | 713/908 [15:45<05:49,  1.79s/it]
 79%|███████▊  | 714/908 [15:47<05:36,  1.73s/it]
 79%|███████▊  | 715/908 [15:47<04:05,  1.27s/it]
 79%|███████▉  | 716/908 [15:47<03:01,  1.06it/s]
 79%|███████▉  | 717/908 [15:50<04:48,  1.51s/it]
 79%|███████▉  | 718/908 [15:51<04:19,  1.37s/it]
 79%|███████▉  | 719/908 [15:51<03:11,  1.02s/it]
+[Batch:   720] training loss = 0.398
+
 79%|███████▉  | 720/908 [15:51<02:24,  1.30it/s]
 79%|███████▉  | 721/908 [15:54<03:54,  1.25s/it]
 80%|███████▉  | 722/908 [15:56<04:33,  1.47s/it]
 80%|███████▉  | 723/908 [15:56<03:21,  1.09s/it]
 80%|███████▉  | 724/908 [15:56<02:30,  1.22it/s]
 80%|███████▉  | 725/908 [16:00<04:52,  1.60s/it]
 80%|███████▉  | 726/908 [16:01<04:21,  1.43s/it]
 80%|████████  | 727/908 [16:01<03:11,  1.06s/it]
 80%|████████  | 728/908 [16:01<02:24,  1.25it/s]
 80%|████████  | 729/908 [16:03<03:24,  1.15s/it]
 80%|████████  | 730/908 [16:06<05:11,  1.75s/it]
 81%|████████  | 731/908 [16:06<03:46,  1.28s/it]
 81%|████████  | 732/908 [16:07<02:48,  1.05it/s]
 81%|████████  | 733/908 [16:07<02:45,  1.06it/s]
 81%|████████  | 734/908 [16:10<04:09,  1.43s/it]
 81%|████████  | 735/908 [16:10<03:03,  1.06s/it]
 81%|████████  | 736/908 [16:10<02:17,  1.25it/s]
 81%|████████  | 737/908 [16:14<04:32,  1.59s/it]
 81%|████████▏ | 738/908 [16:16<05:03,  1.78s/it]
 81%|████████▏ | 739/908 [16:16<03:41,  1.31s/it]
+[Batch:   740] training loss = 0.389
+
 81%|████████▏ | 740/908 [16:17<02:43,  1.03it/s]
 82%|████████▏ | 741/908 [16:18<03:17,  1.18s/it]
 82%|████████▏ | 742/908 [16:20<03:40,  1.33s/it]
 82%|████████▏ | 743/908 [16:20<02:42,  1.01it/s]
 82%|████████▏ | 744/908 [16:20<02:02,  1.34it/s]
 82%|████████▏ | 745/908 [16:23<03:46,  1.39s/it]
 82%|████████▏ | 746/908 [16:25<03:53,  1.44s/it]
 82%|████████▏ | 747/908 [16:25<02:51,  1.07s/it]
 82%|████████▏ | 748/908 [16:25<02:08,  1.24it/s]
 82%|████████▏ | 749/908 [16:28<04:04,  1.54s/it]
 83%|████████▎ | 750/908 [16:29<03:15,  1.23s/it]
 83%|████████▎ | 751/908 [16:29<02:24,  1.08it/s]
 83%|████████▎ | 752/908 [16:29<01:49,  1.42it/s]
 83%|████████▎ | 753/908 [16:32<03:19,  1.29s/it]
 83%|████████▎ | 754/908 [16:34<03:56,  1.54s/it]
 83%|████████▎ | 755/908 [16:34<02:53,  1.13s/it]
 83%|████████▎ | 756/908 [16:34<02:09,  1.17it/s]
 83%|████████▎ | 757/908 [16:37<03:50,  1.52s/it]
 83%|████████▎ | 758/908 [16:39<03:31,  1.41s/it]
 84%|████████▎ | 759/908 [16:39<02:35,  1.04s/it]
+[Batch:   760] training loss = 0.577
+
 84%|████████▎ | 760/908 [16:39<01:57,  1.26it/s]
 84%|████████▍ | 761/908 [16:42<03:37,  1.48s/it]
 84%|████████▍ | 762/908 [16:43<03:27,  1.42s/it]
 84%|████████▍ | 763/908 [16:44<02:32,  1.05s/it]
 84%|████████▍ | 764/908 [16:44<01:54,  1.26it/s]
 84%|████████▍ | 765/908 [16:48<04:22,  1.83s/it]
 84%|████████▍ | 766/908 [16:50<04:08,  1.75s/it]
 84%|████████▍ | 767/908 [16:50<03:00,  1.28s/it]
 85%|████████▍ | 768/908 [16:50<02:13,  1.05it/s]
 85%|████████▍ | 769/908 [16:52<02:50,  1.22s/it]
 85%|████████▍ | 770/908 [16:54<03:28,  1.51s/it]
 85%|████████▍ | 771/908 [16:54<02:32,  1.12s/it]
 85%|████████▌ | 772/908 [16:54<01:53,  1.19it/s]
 85%|████████▌ | 773/908 [16:56<02:33,  1.13s/it]
 85%|████████▌ | 774/908 [16:58<02:58,  1.33s/it]
 85%|████████▌ | 775/908 [16:58<02:11,  1.01it/s]
 85%|████████▌ | 776/908 [16:58<01:38,  1.34it/s]
 86%|████████▌ | 777/908 [17:01<03:09,  1.45s/it]
 86%|████████▌ | 778/908 [17:02<02:23,  1.10s/it]
 86%|████████▌ | 779/908 [17:02<01:47,  1.21it/s]
+[Batch:   780] training loss = 0.463
+
 86%|████████▌ | 780/908 [17:02<01:21,  1.57it/s]
 86%|████████▌ | 781/908 [17:05<02:26,  1.15s/it]
 86%|████████▌ | 782/908 [17:05<02:03,  1.02it/s]
 86%|████████▌ | 783/908 [17:05<01:32,  1.35it/s]
 86%|████████▋ | 784/908 [17:05<01:11,  1.74it/s]
 86%|████████▋ | 785/908 [17:09<02:55,  1.43s/it]
 87%|████████▋ | 786/908 [17:11<03:16,  1.61s/it]
 87%|████████▋ | 787/908 [17:11<02:23,  1.19s/it]
 87%|████████▋ | 788/908 [17:11<01:46,  1.13it/s]
 87%|████████▋ | 789/908 [17:15<03:11,  1.61s/it]
 87%|████████▋ | 790/908 [17:15<02:23,  1.21s/it]
 87%|████████▋ | 791/908 [17:15<01:46,  1.10it/s]
 87%|████████▋ | 792/908 [17:15<01:25,  1.35it/s]
 87%|████████▋ | 793/908 [17:19<03:00,  1.57s/it]
 87%|████████▋ | 794/908 [17:21<03:18,  1.74s/it]
 88%|████████▊ | 795/908 [17:21<02:24,  1.28s/it]
 88%|████████▊ | 796/908 [17:21<01:46,  1.05it/s]
 88%|████████▊ | 797/908 [17:23<02:07,  1.15s/it]
 88%|████████▊ | 798/908 [17:25<02:36,  1.42s/it]
 88%|████████▊ | 799/908 [17:25<01:54,  1.05s/it]
+[Batch:   800] training loss = 0.453
+
 88%|████████▊ | 800/908 [17:26<01:25,  1.26it/s]
 88%|████████▊ | 801/908 [17:27<01:47,  1.00s/it]
 88%|████████▊ | 802/908 [17:28<01:48,  1.02s/it]
 88%|████████▊ | 803/908 [17:28<01:21,  1.29it/s]
 89%|████████▊ | 804/908 [17:28<01:02,  1.67it/s]
 89%|████████▊ | 805/908 [17:31<01:47,  1.05s/it]
 89%|████████▉ | 806/908 [17:32<02:08,  1.26s/it]
 89%|████████▉ | 807/908 [17:33<01:35,  1.06it/s]
 89%|████████▉ | 808/908 [17:33<01:11,  1.40it/s]
 89%|████████▉ | 809/908 [17:35<01:45,  1.07s/it]
 89%|████████▉ | 810/908 [17:37<02:35,  1.58s/it]
 89%|████████▉ | 811/908 [17:38<01:53,  1.17s/it]
 89%|████████▉ | 812/908 [17:38<01:23,  1.15it/s]
 90%|████████▉ | 813/908 [17:39<01:30,  1.05it/s]
 90%|████████▉ | 814/908 [17:41<02:10,  1.39s/it]
 90%|████████▉ | 815/908 [17:41<01:35,  1.03s/it]
 90%|████████▉ | 816/908 [17:42<01:11,  1.29it/s]
 90%|████████▉ | 817/908 [17:44<01:50,  1.21s/it]
 90%|█████████ | 818/908 [17:48<03:18,  2.20s/it]
 90%|█████████ | 819/908 [17:49<02:22,  1.60s/it]
+[Batch:   820] training loss = 0.497
+
 90%|█████████ | 820/908 [17:49<01:43,  1.18s/it]
 90%|█████████ | 821/908 [17:50<01:45,  1.21s/it]
 91%|█████████ | 822/908 [17:53<02:16,  1.58s/it]
 91%|█████████ | 823/908 [17:53<01:39,  1.16s/it]
 91%|█████████ | 824/908 [17:53<01:13,  1.14it/s]
 91%|█████████ | 825/908 [17:55<01:31,  1.10s/it]
 91%|█████████ | 826/908 [17:56<01:49,  1.34s/it]
 91%|█████████ | 827/908 [17:57<01:20,  1.00it/s]
 91%|█████████ | 828/908 [17:57<01:00,  1.33it/s]
 91%|█████████▏| 829/908 [17:59<01:22,  1.05s/it]
 91%|█████████▏| 830/908 [18:02<02:11,  1.68s/it]
 92%|█████████▏| 831/908 [18:02<01:34,  1.23s/it]
 92%|█████████▏| 832/908 [18:02<01:09,  1.09it/s]
 92%|█████████▏| 833/908 [18:03<01:06,  1.13it/s]
 92%|█████████▏| 834/908 [18:06<01:52,  1.52s/it]
 92%|█████████▏| 835/908 [18:06<01:21,  1.12s/it]
 92%|█████████▏| 836/908 [18:06<01:00,  1.19it/s]
 92%|█████████▏| 837/908 [18:08<01:13,  1.04s/it]
 92%|█████████▏| 838/908 [18:10<01:36,  1.38s/it]
 92%|█████████▏| 839/908 [18:10<01:10,  1.02s/it]
+[Batch:   840] training loss = 0.385
+
 93%|█████████▎| 840/908 [18:10<00:52,  1.29it/s]
 93%|█████████▎| 841/908 [18:11<00:51,  1.30it/s]
 93%|█████████▎| 842/908 [18:13<01:20,  1.22s/it]
 93%|█████████▎| 843/908 [18:14<00:59,  1.10it/s]
 93%|█████████▎| 844/908 [18:14<00:44,  1.44it/s]
 93%|█████████▎| 845/908 [18:15<00:51,  1.22it/s]
 93%|█████████▎| 846/908 [18:18<01:40,  1.63s/it]
 93%|█████████▎| 847/908 [18:19<01:12,  1.19s/it]
 93%|█████████▎| 848/908 [18:19<00:53,  1.12it/s]
 94%|█████████▎| 849/908 [18:19<00:40,  1.45it/s]
 94%|█████████▎| 850/908 [18:23<01:29,  1.54s/it]
 94%|█████████▎| 851/908 [18:23<01:04,  1.13s/it]
 94%|█████████▍| 852/908 [18:23<00:47,  1.17it/s]
 94%|█████████▍| 853/908 [18:24<00:50,  1.08it/s]
 94%|█████████▍| 854/908 [18:27<01:29,  1.65s/it]
 94%|█████████▍| 855/908 [18:28<01:04,  1.21s/it]
 94%|█████████▍| 856/908 [18:28<00:47,  1.10it/s]
 94%|█████████▍| 857/908 [18:29<00:50,  1.02it/s]
 94%|█████████▍| 858/908 [18:32<01:14,  1.49s/it]
 95%|█████████▍| 859/908 [18:32<00:53,  1.10s/it]
+[Batch:   860] training loss = 0.489
+
 95%|█████████▍| 860/908 [18:32<00:39,  1.21it/s]
 95%|█████████▍| 861/908 [18:32<00:30,  1.53it/s]
 95%|█████████▍| 862/908 [18:35<00:59,  1.29s/it]
 95%|█████████▌| 863/908 [18:35<00:43,  1.04it/s]
 95%|█████████▌| 864/908 [18:35<00:32,  1.37it/s]
 95%|█████████▌| 865/908 [18:36<00:24,  1.75it/s]
 95%|█████████▌| 866/908 [18:37<00:34,  1.23it/s]
 95%|█████████▌| 867/908 [18:37<00:25,  1.60it/s]
 96%|█████████▌| 868/908 [18:37<00:19,  2.01it/s]
 96%|█████████▌| 869/908 [18:40<00:40,  1.03s/it]
 96%|█████████▌| 870/908 [18:42<00:50,  1.32s/it]
 96%|█████████▌| 871/908 [18:42<00:36,  1.02it/s]
 96%|█████████▌| 872/908 [18:42<00:26,  1.34it/s]
 96%|█████████▌| 873/908 [18:44<00:37,  1.06s/it]
 96%|█████████▋| 874/908 [18:46<00:46,  1.37s/it]
 96%|█████████▋| 875/908 [18:46<00:33,  1.02s/it]
 96%|█████████▋| 876/908 [18:46<00:24,  1.30it/s]
 97%|█████████▋| 877/908 [18:51<00:59,  1.93s/it]
 97%|█████████▋| 878/908 [18:53<00:57,  1.91s/it]
 97%|█████████▋| 879/908 [18:53<00:40,  1.40s/it]
+[Batch:   880] training loss = 0.394
+
 97%|█████████▋| 880/908 [18:53<00:29,  1.04s/it]
 97%|█████████▋| 881/908 [18:56<00:39,  1.45s/it]
 97%|█████████▋| 882/908 [18:57<00:39,  1.50s/it]
 97%|█████████▋| 883/908 [18:57<00:27,  1.11s/it]
 97%|█████████▋| 884/908 [18:58<00:20,  1.19it/s]
 97%|█████████▋| 885/908 [19:01<00:34,  1.49s/it]
 98%|█████████▊| 886/908 [19:02<00:33,  1.52s/it]
 98%|█████████▊| 887/908 [19:02<00:23,  1.13s/it]
 98%|█████████▊| 888/908 [19:03<00:16,  1.18it/s]
 98%|█████████▊| 889/908 [19:06<00:29,  1.55s/it]
 98%|█████████▊| 890/908 [19:08<00:31,  1.74s/it]
 98%|█████████▊| 891/908 [19:08<00:21,  1.28s/it]
 98%|█████████▊| 892/908 [19:08<00:15,  1.05it/s]
 98%|█████████▊| 893/908 [19:11<00:19,  1.32s/it]
 98%|█████████▊| 894/908 [19:13<00:22,  1.59s/it]
 99%|█████████▊| 895/908 [19:13<00:15,  1.17s/it]
 99%|█████████▊| 896/908 [19:13<00:10,  1.13it/s]
 99%|█████████▉| 897/908 [19:16<00:15,  1.41s/it]
 99%|█████████▉| 898/908 [19:18<00:15,  1.56s/it]
 99%|█████████▉| 899/908 [19:18<00:10,  1.15s/it]
+[Batch:   900] training loss = 0.507
+
 99%|█████████▉| 900/908 [19:18<00:06,  1.15it/s]
 99%|█████████▉| 901/908 [19:19<00:06,  1.01it/s]
 99%|█████████▉| 902/908 [19:22<00:08,  1.35s/it]
 99%|█████████▉| 903/908 [19:22<00:05,  1.01s/it]
100%|█████████▉| 904/908 [19:22<00:03,  1.31it/s]
100%|█████████▉| 905/908 [19:24<00:02,  1.00it/s]
100%|█████████▉| 906/908 [19:25<00:02,  1.19s/it]
100%|█████████▉| 907/908 [19:25<00:00,  1.12it/s]
100%|██████████| 908/908 [19:26<00:00,  1.50it/s]
100%|██████████| 908/908 [19:26<00:00,  1.28s/it]
+
+Epoch   7: validation
+
  0%|          | 0/101 [00:00<?, ?it/s]
  1%|          | 1/101 [00:06<11:00,  6.61s/it]
  2%|▏         | 2/101 [00:06<04:35,  2.78s/it]
  4%|▍         | 4/101 [00:07<01:49,  1.13s/it]
  5%|▍         | 5/101 [00:10<03:09,  1.97s/it]
  7%|▋         | 7/101 [00:11<01:44,  1.11s/it]
  9%|▉         | 9/101 [00:16<02:39,  1.73s/it]
 10%|▉         | 10/101 [00:16<02:05,  1.38s/it]
 12%|█▏        | 12/101 [00:16<01:17,  1.14it/s]
 13%|█▎        | 13/101 [00:21<02:23,  1.64s/it]
 14%|█▍        | 14/101 [00:21<01:54,  1.32s/it]
 16%|█▌        | 16/101 [00:21<01:09,  1.22it/s]
 17%|█▋        | 17/101 [00:26<02:35,  1.85s/it]
 18%|█▊        | 18/101 [00:27<02:03,  1.48s/it]
 20%|█▉        | 20/101 [00:27<01:13,  1.10it/s]
 21%|██        | 21/101 [00:31<02:15,  1.69s/it]
 22%|██▏       | 22/101 [00:32<01:44,  1.32s/it]
 24%|██▍       | 24/101 [00:32<01:02,  1.23it/s]
 25%|██▍       | 25/101 [00:36<02:03,  1.62s/it]
 27%|██▋       | 27/101 [00:36<01:15,  1.02s/it]
 29%|██▊       | 29/101 [00:41<01:43,  1.44s/it]
 31%|███       | 31/101 [00:41<01:08,  1.02it/s]
 33%|███▎      | 33/101 [00:43<01:06,  1.03it/s]
 35%|███▍      | 35/101 [00:43<00:47,  1.40it/s]
 36%|███▌      | 36/101 [00:43<00:39,  1.63it/s]
 37%|███▋      | 37/101 [00:48<01:35,  1.49s/it]
 39%|███▊      | 39/101 [00:48<00:59,  1.04it/s]
 41%|████      | 41/101 [00:52<01:16,  1.27s/it]
 43%|████▎     | 43/101 [00:52<00:50,  1.14it/s]
 45%|████▍     | 45/101 [00:56<01:04,  1.16s/it]
 47%|████▋     | 47/101 [00:56<00:44,  1.22it/s]
 49%|████▊     | 49/101 [01:00<01:02,  1.20s/it]
 50%|█████     | 51/101 [01:00<00:42,  1.17it/s]
 52%|█████▏    | 53/101 [01:05<01:07,  1.41s/it]
 54%|█████▍    | 55/101 [01:06<00:46,  1.01s/it]
 56%|█████▋    | 57/101 [01:09<00:52,  1.19s/it]
 58%|█████▊    | 59/101 [01:09<00:35,  1.17it/s]
 60%|██████    | 61/101 [01:14<00:57,  1.43s/it]
 62%|██████▏   | 63/101 [01:15<00:38,  1.03s/it]
 64%|██████▍   | 65/101 [01:19<00:49,  1.39s/it]
 66%|██████▋   | 67/101 [01:19<00:33,  1.00it/s]
 67%|██████▋   | 68/101 [01:19<00:28,  1.18it/s]
 68%|██████▊   | 69/101 [01:23<00:42,  1.31s/it]
 70%|███████   | 71/101 [01:23<00:25,  1.16it/s]
 72%|███████▏  | 73/101 [01:27<00:34,  1.25s/it]
 74%|███████▍  | 75/101 [01:27<00:22,  1.16it/s]
 76%|███████▌  | 77/101 [01:32<00:32,  1.35s/it]
 78%|███████▊  | 79/101 [01:32<00:20,  1.05it/s]
 80%|████████  | 81/101 [01:34<00:20,  1.01s/it]
 82%|████████▏ | 83/101 [01:34<00:13,  1.38it/s]
 84%|████████▍ | 85/101 [01:39<00:18,  1.19s/it]
 86%|████████▌ | 87/101 [01:39<00:11,  1.18it/s]
 88%|████████▊ | 89/101 [01:42<00:13,  1.10s/it]
 90%|█████████ | 91/101 [01:42<00:07,  1.27it/s]
 92%|█████████▏| 93/101 [01:47<00:10,  1.32s/it]
 94%|█████████▍| 95/101 [01:48<00:05,  1.05it/s]
 96%|█████████▌| 97/101 [01:53<00:05,  1.41s/it]
 98%|█████████▊| 99/101 [01:53<00:02,  1.01s/it]
 99%|█████████▉| 100/101 [01:53<00:00,  1.16it/s]
100%|██████████| 101/101 [01:55<00:00,  1.02s/it]
100%|██████████| 101/101 [01:55<00:00,  1.14s/it]
+    Epoch  7: training loss = 0.445 / validation loss = 0.441
+    Epoch  7: training acc = 0.789 / validation acc = 0.790
+    Saved model: cls-7.pth.tar
+
+Epoch   8: training
+
  0%|          | 0/908 [00:00<?, ?it/s]
  0%|          | 1/908 [00:06<1:38:52,  6.54s/it]
  0%|          | 2/908 [00:06<42:24,  2.81s/it]  
  0%|          | 3/908 [00:06<24:19,  1.61s/it]
  0%|          | 4/908 [00:07<15:47,  1.05s/it]
  1%|          | 5/908 [00:11<32:16,  2.14s/it]
  1%|          | 6/908 [00:11<22:13,  1.48s/it]
  1%|          | 7/908 [00:11<16:19,  1.09s/it]
  1%|          | 8/908 [00:11<12:01,  1.25it/s]
  1%|          | 9/908 [00:16<28:59,  1.94s/it]
  1%|          | 10/908 [00:16<20:53,  1.40s/it]
  1%|          | 11/908 [00:16<15:20,  1.03s/it]
  1%|▏         | 12/908 [00:16<11:30,  1.30it/s]
  1%|▏         | 13/908 [00:21<31:12,  2.09s/it]
  2%|▏         | 14/908 [00:22<22:57,  1.54s/it]
  2%|▏         | 15/908 [00:22<16:57,  1.14s/it]
  2%|▏         | 16/908 [00:22<12:42,  1.17it/s]
  2%|▏         | 17/908 [00:26<26:27,  1.78s/it]
  2%|▏         | 18/908 [00:26<19:22,  1.31s/it]
  2%|▏         | 19/908 [00:26<14:25,  1.03it/s]
+[Batch:    20] training loss = 0.338
+
  2%|▏         | 20/908 [00:27<10:57,  1.35it/s]
  2%|▏         | 21/908 [00:30<23:49,  1.61s/it]
  2%|▏         | 22/908 [00:31<18:11,  1.23s/it]
  3%|▎         | 23/908 [00:31<13:33,  1.09it/s]
  3%|▎         | 24/908 [00:31<10:20,  1.42it/s]
  3%|▎         | 25/908 [00:35<26:06,  1.77s/it]
  3%|▎         | 26/908 [00:36<20:12,  1.37s/it]
  3%|▎         | 27/908 [00:36<15:01,  1.02s/it]
  3%|▎         | 28/908 [00:36<11:21,  1.29it/s]
  3%|▎         | 29/908 [00:40<22:45,  1.55s/it]
  3%|▎         | 30/908 [00:40<16:49,  1.15s/it]
  3%|▎         | 31/908 [00:40<12:35,  1.16it/s]
  4%|▎         | 32/908 [00:40<09:38,  1.51it/s]
  4%|▎         | 33/908 [00:46<31:39,  2.17s/it]
  4%|▎         | 34/908 [00:46<23:46,  1.63s/it]
  4%|▍         | 35/908 [00:46<17:26,  1.20s/it]
  4%|▍         | 36/908 [00:47<13:01,  1.12it/s]
  4%|▍         | 37/908 [00:50<22:33,  1.55s/it]
  4%|▍         | 38/908 [00:50<16:35,  1.14s/it]
  4%|▍         | 39/908 [00:50<12:25,  1.17it/s]
+[Batch:    40] training loss = 0.513
+
  4%|▍         | 40/908 [00:50<09:30,  1.52it/s]
  5%|▍         | 41/908 [00:57<34:25,  2.38s/it]
  5%|▍         | 42/908 [00:58<31:30,  2.18s/it]
  5%|▍         | 43/908 [00:59<22:50,  1.58s/it]
  5%|▍         | 44/908 [00:59<17:12,  1.19s/it]
  5%|▍         | 45/908 [01:03<28:14,  1.96s/it]
  5%|▌         | 46/908 [01:03<20:40,  1.44s/it]
  5%|▌         | 47/908 [01:03<15:16,  1.06s/it]
  5%|▌         | 48/908 [01:03<11:28,  1.25it/s]
  5%|▌         | 49/908 [01:07<23:43,  1.66s/it]
  6%|▌         | 50/908 [01:07<18:26,  1.29s/it]
  6%|▌         | 51/908 [01:07<13:42,  1.04it/s]
  6%|▌         | 52/908 [01:08<10:23,  1.37it/s]
  6%|▌         | 53/908 [01:10<17:39,  1.24s/it]
  6%|▌         | 54/908 [01:11<14:51,  1.04s/it]
  6%|▌         | 55/908 [01:11<11:12,  1.27it/s]
  6%|▌         | 56/908 [01:11<08:41,  1.63it/s]
  6%|▋         | 57/908 [01:14<20:13,  1.43s/it]
  6%|▋         | 58/908 [01:15<15:47,  1.11s/it]
  6%|▋         | 59/908 [01:15<11:52,  1.19it/s]
+[Batch:    60] training loss = 0.481
+
  7%|▋         | 60/908 [01:15<09:06,  1.55it/s]
  7%|▋         | 61/908 [01:18<18:27,  1.31s/it]
  7%|▋         | 62/908 [01:19<17:19,  1.23s/it]
  7%|▋         | 63/908 [01:19<12:54,  1.09it/s]
  7%|▋         | 64/908 [01:19<09:50,  1.43it/s]
  7%|▋         | 65/908 [01:24<25:54,  1.84s/it]
  7%|▋         | 66/908 [01:24<18:53,  1.35s/it]
  7%|▋         | 67/908 [01:24<14:00,  1.00it/s]
  7%|▋         | 68/908 [01:25<10:34,  1.32it/s]
  8%|▊         | 69/908 [01:28<22:43,  1.63s/it]
  8%|▊         | 70/908 [01:29<18:39,  1.34s/it]
  8%|▊         | 71/908 [01:29<13:50,  1.01it/s]
  8%|▊         | 72/908 [01:29<10:29,  1.33it/s]
  8%|▊         | 73/908 [01:34<25:56,  1.86s/it]
  8%|▊         | 74/908 [01:34<20:04,  1.44s/it]
  8%|▊         | 75/908 [01:34<14:48,  1.07s/it]
  8%|▊         | 76/908 [01:35<11:09,  1.24it/s]
  8%|▊         | 77/908 [01:39<27:55,  2.02s/it]
  9%|▊         | 78/908 [01:40<22:43,  1.64s/it]
  9%|▊         | 79/908 [01:40<16:39,  1.21s/it]
+[Batch:    80] training loss = 0.414
+
  9%|▉         | 80/908 [01:41<12:46,  1.08it/s]
  9%|▉         | 81/908 [01:46<30:24,  2.21s/it]
  9%|▉         | 82/908 [01:47<24:59,  1.82s/it]
  9%|▉         | 83/908 [01:47<18:13,  1.33s/it]
  9%|▉         | 84/908 [01:47<13:31,  1.02it/s]
  9%|▉         | 85/908 [01:53<33:53,  2.47s/it]
  9%|▉         | 86/908 [01:54<25:51,  1.89s/it]
 10%|▉         | 87/908 [01:54<18:50,  1.38s/it]
 10%|▉         | 88/908 [01:54<13:58,  1.02s/it]
 10%|▉         | 89/908 [01:57<21:45,  1.59s/it]
 10%|▉         | 90/908 [01:57<16:50,  1.24s/it]
 10%|█         | 91/908 [01:57<12:32,  1.09it/s]
 10%|█         | 92/908 [01:58<09:33,  1.42it/s]
 10%|█         | 93/908 [02:02<22:46,  1.68s/it]
 10%|█         | 94/908 [02:02<19:31,  1.44s/it]
 10%|█         | 95/908 [02:03<14:26,  1.07s/it]
 11%|█         | 96/908 [02:03<10:52,  1.24it/s]
 11%|█         | 97/908 [02:07<23:09,  1.71s/it]
 11%|█         | 98/908 [02:07<17:19,  1.28s/it]
 11%|█         | 99/908 [02:07<12:54,  1.04it/s]
+[Batch:   100] training loss = 0.663
+
 11%|█         | 100/908 [02:07<09:47,  1.38it/s]
 11%|█         | 101/908 [02:11<21:46,  1.62s/it]
 11%|█         | 102/908 [02:13<21:30,  1.60s/it]
 11%|█▏        | 103/908 [02:13<15:48,  1.18s/it]
 11%|█▏        | 104/908 [02:13<11:51,  1.13it/s]
 12%|█▏        | 105/908 [02:16<22:00,  1.64s/it]
 12%|█▏        | 106/908 [02:17<19:04,  1.43s/it]
 12%|█▏        | 107/908 [02:18<14:05,  1.06s/it]
 12%|█▏        | 108/908 [02:18<10:36,  1.26it/s]
 12%|█▏        | 109/908 [02:22<25:59,  1.95s/it]
 12%|█▏        | 110/908 [02:25<27:06,  2.04s/it]
 12%|█▏        | 111/908 [02:25<19:41,  1.48s/it]
 12%|█▏        | 112/908 [02:25<14:32,  1.10s/it]
 12%|█▏        | 113/908 [02:28<23:20,  1.76s/it]
 13%|█▎        | 114/908 [02:29<19:36,  1.48s/it]
 13%|█▎        | 115/908 [02:29<14:26,  1.09s/it]
 13%|█▎        | 116/908 [02:29<10:50,  1.22it/s]
 13%|█▎        | 117/908 [02:33<21:39,  1.64s/it]
 13%|█▎        | 118/908 [02:35<22:01,  1.67s/it]
 13%|█▎        | 119/908 [02:35<16:10,  1.23s/it]
+[Batch:   120] training loss = 0.382
+
 13%|█▎        | 120/908 [02:35<12:02,  1.09it/s]
 13%|█▎        | 121/908 [02:38<18:03,  1.38s/it]
 13%|█▎        | 122/908 [02:39<17:56,  1.37s/it]
 14%|█▎        | 123/908 [02:39<13:17,  1.02s/it]
 14%|█▎        | 124/908 [02:39<10:01,  1.30it/s]
 14%|█▍        | 125/908 [02:43<20:36,  1.58s/it]
 14%|█▍        | 126/908 [02:45<24:51,  1.91s/it]
 14%|█▍        | 127/908 [02:46<18:05,  1.39s/it]
 14%|█▍        | 128/908 [02:46<13:24,  1.03s/it]
 14%|█▍        | 129/908 [02:49<21:02,  1.62s/it]
 14%|█▍        | 130/908 [02:52<25:38,  1.98s/it]
 14%|█▍        | 131/908 [02:52<18:40,  1.44s/it]
 15%|█▍        | 132/908 [02:52<13:47,  1.07s/it]
 15%|█▍        | 133/908 [02:54<17:48,  1.38s/it]
 15%|█▍        | 134/908 [02:56<19:31,  1.51s/it]
 15%|█▍        | 135/908 [02:56<14:22,  1.12s/it]
 15%|█▍        | 136/908 [02:56<10:47,  1.19it/s]
 15%|█▌        | 137/908 [03:00<19:42,  1.53s/it]
 15%|█▌        | 138/908 [03:03<25:17,  1.97s/it]
 15%|█▌        | 139/908 [03:03<18:23,  1.44s/it]
+[Batch:   140] training loss = 0.413
+
 15%|█▌        | 140/908 [03:03<13:38,  1.07s/it]
 16%|█▌        | 141/908 [03:07<24:24,  1.91s/it]
 16%|█▌        | 142/908 [03:10<28:49,  2.26s/it]
 16%|█▌        | 143/908 [03:10<20:54,  1.64s/it]
 16%|█▌        | 144/908 [03:10<15:23,  1.21s/it]
 16%|█▌        | 145/908 [03:13<21:24,  1.68s/it]
 16%|█▌        | 146/908 [03:15<21:56,  1.73s/it]
 16%|█▌        | 147/908 [03:15<16:06,  1.27s/it]
 16%|█▋        | 148/908 [03:15<12:00,  1.05it/s]
 16%|█▋        | 149/908 [03:17<15:12,  1.20s/it]
 17%|█▋        | 150/908 [03:20<22:06,  1.75s/it]
 17%|█▋        | 151/908 [03:20<16:10,  1.28s/it]
 17%|█▋        | 152/908 [03:20<12:01,  1.05it/s]
 17%|█▋        | 153/908 [03:23<16:49,  1.34s/it]
 17%|█▋        | 154/908 [03:26<24:06,  1.92s/it]
 17%|█▋        | 155/908 [03:26<17:38,  1.41s/it]
 17%|█▋        | 156/908 [03:26<13:04,  1.04s/it]
 17%|█▋        | 157/908 [03:27<11:16,  1.11it/s]
 17%|█▋        | 158/908 [03:30<17:34,  1.41s/it]
 18%|█▊        | 159/908 [03:30<13:01,  1.04s/it]
+[Batch:   160] training loss = 0.442
+
 18%|█▊        | 160/908 [03:30<09:51,  1.26it/s]
 18%|█▊        | 161/908 [03:32<14:49,  1.19s/it]
 18%|█▊        | 162/908 [03:33<15:17,  1.23s/it]
 18%|█▊        | 163/908 [03:34<11:26,  1.09it/s]
 18%|█▊        | 164/908 [03:34<08:43,  1.42it/s]
 18%|█▊        | 165/908 [03:37<16:47,  1.36s/it]
 18%|█▊        | 166/908 [03:38<18:30,  1.50s/it]
 18%|█▊        | 167/908 [03:39<13:43,  1.11s/it]
 19%|█▊        | 168/908 [03:39<10:19,  1.20it/s]
 19%|█▊        | 169/908 [03:41<16:31,  1.34s/it]
 19%|█▊        | 170/908 [03:44<22:35,  1.84s/it]
 19%|█▉        | 171/908 [03:45<16:33,  1.35s/it]
 19%|█▉        | 172/908 [03:45<12:18,  1.00s/it]
 19%|█▉        | 173/908 [03:47<17:58,  1.47s/it]
 19%|█▉        | 174/908 [03:51<24:29,  2.00s/it]
 19%|█▉        | 175/908 [03:51<17:52,  1.46s/it]
 19%|█▉        | 176/908 [03:51<13:14,  1.09s/it]
 19%|█▉        | 177/908 [03:53<16:11,  1.33s/it]
 20%|█▉        | 178/908 [03:54<15:57,  1.31s/it]
 20%|█▉        | 179/908 [03:54<11:51,  1.02it/s]
+[Batch:   180] training loss = 0.605
+
 20%|█▉        | 180/908 [03:55<09:00,  1.35it/s]
 20%|█▉        | 181/908 [03:57<15:35,  1.29s/it]
 20%|██        | 182/908 [03:59<17:25,  1.44s/it]
 20%|██        | 183/908 [03:59<12:55,  1.07s/it]
 20%|██        | 184/908 [03:59<09:44,  1.24it/s]
 20%|██        | 185/908 [04:04<24:36,  2.04s/it]
 20%|██        | 186/908 [04:08<30:08,  2.51s/it]
 21%|██        | 187/908 [04:08<21:49,  1.82s/it]
 21%|██        | 188/908 [04:08<16:03,  1.34s/it]
 21%|██        | 189/908 [04:13<29:25,  2.45s/it]
 21%|██        | 190/908 [04:15<27:53,  2.33s/it]
 21%|██        | 191/908 [04:16<20:13,  1.69s/it]
 21%|██        | 192/908 [04:16<14:50,  1.24s/it]
 21%|██▏       | 193/908 [04:18<16:50,  1.41s/it]
 21%|██▏       | 194/908 [04:20<21:26,  1.80s/it]
 21%|██▏       | 195/908 [04:21<15:42,  1.32s/it]
 22%|██▏       | 196/908 [04:21<11:39,  1.02it/s]
 22%|██▏       | 197/908 [04:22<13:40,  1.15s/it]
 22%|██▏       | 198/908 [04:24<16:14,  1.37s/it]
 22%|██▏       | 199/908 [04:24<12:04,  1.02s/it]
+[Batch:   200] training loss = 0.377
+
 22%|██▏       | 200/908 [04:25<09:08,  1.29it/s]
 22%|██▏       | 201/908 [04:26<12:16,  1.04s/it]
 22%|██▏       | 202/908 [04:28<14:05,  1.20s/it]
 22%|██▏       | 203/908 [04:28<10:33,  1.11it/s]
 22%|██▏       | 204/908 [04:28<08:04,  1.45it/s]
 23%|██▎       | 205/908 [04:31<15:20,  1.31s/it]
 23%|██▎       | 206/908 [04:33<18:11,  1.55s/it]
 23%|██▎       | 207/908 [04:33<13:23,  1.15s/it]
 23%|██▎       | 208/908 [04:33<10:03,  1.16it/s]
 23%|██▎       | 209/908 [04:37<20:03,  1.72s/it]
 23%|██▎       | 210/908 [04:39<20:43,  1.78s/it]
 23%|██▎       | 211/908 [04:39<15:12,  1.31s/it]
 23%|██▎       | 212/908 [04:39<11:19,  1.02it/s]
 23%|██▎       | 213/908 [04:41<14:40,  1.27s/it]
 24%|██▎       | 214/908 [04:45<21:13,  1.84s/it]
 24%|██▎       | 215/908 [04:45<15:33,  1.35s/it]
 24%|██▍       | 216/908 [04:45<11:33,  1.00s/it]
 24%|██▍       | 217/908 [04:45<08:47,  1.31it/s]
 24%|██▍       | 218/908 [04:48<15:58,  1.39s/it]
 24%|██▍       | 219/908 [04:48<11:51,  1.03s/it]
+[Batch:   220] training loss = 0.565
+
 24%|██▍       | 220/908 [04:48<08:58,  1.28it/s]
 24%|██▍       | 221/908 [04:51<15:43,  1.37s/it]
 24%|██▍       | 222/908 [04:53<17:32,  1.53s/it]
 25%|██▍       | 223/908 [04:53<12:56,  1.13s/it]
 25%|██▍       | 224/908 [04:54<09:43,  1.17it/s]
 25%|██▍       | 225/908 [04:57<19:52,  1.75s/it]
 25%|██▍       | 226/908 [04:59<18:24,  1.62s/it]
 25%|██▌       | 227/908 [04:59<13:32,  1.19s/it]
 25%|██▌       | 228/908 [04:59<10:10,  1.11it/s]
 25%|██▌       | 229/908 [05:02<17:00,  1.50s/it]
 25%|██▌       | 230/908 [05:05<20:44,  1.83s/it]
 25%|██▌       | 231/908 [05:05<15:12,  1.35s/it]
 26%|██▌       | 232/908 [05:05<11:17,  1.00s/it]
 26%|██▌       | 233/908 [05:08<17:12,  1.53s/it]
 26%|██▌       | 234/908 [05:10<17:56,  1.60s/it]
 26%|██▌       | 235/908 [05:10<13:13,  1.18s/it]
 26%|██▌       | 236/908 [05:10<09:54,  1.13it/s]
 26%|██▌       | 237/908 [05:12<15:29,  1.39s/it]
 26%|██▌       | 238/908 [05:14<16:59,  1.52s/it]
 26%|██▋       | 239/908 [05:15<12:32,  1.13s/it]
+[Batch:   240] training loss = 0.410
+
 26%|██▋       | 240/908 [05:15<09:24,  1.18it/s]
 27%|██▋       | 241/908 [05:17<15:18,  1.38s/it]
 27%|██▋       | 242/908 [05:20<18:24,  1.66s/it]
 27%|██▋       | 243/908 [05:20<13:30,  1.22s/it]
 27%|██▋       | 244/908 [05:20<10:04,  1.10it/s]
 27%|██▋       | 245/908 [05:23<16:01,  1.45s/it]
 27%|██▋       | 246/908 [05:23<12:27,  1.13s/it]
 27%|██▋       | 247/908 [05:23<09:19,  1.18it/s]
 27%|██▋       | 248/908 [05:23<07:09,  1.54it/s]
 27%|██▋       | 249/908 [05:27<15:14,  1.39s/it]
 28%|██▊       | 250/908 [05:29<18:57,  1.73s/it]
 28%|██▊       | 251/908 [05:29<13:52,  1.27s/it]
 28%|██▊       | 252/908 [05:30<10:20,  1.06it/s]
 28%|██▊       | 253/908 [05:30<10:04,  1.08it/s]
 28%|██▊       | 254/908 [05:32<13:13,  1.21s/it]
 28%|██▊       | 255/908 [05:32<09:51,  1.10it/s]
 28%|██▊       | 256/908 [05:33<07:29,  1.45it/s]
 28%|██▊       | 257/908 [05:36<17:04,  1.57s/it]
 28%|██▊       | 258/908 [05:39<20:04,  1.85s/it]
 29%|██▊       | 259/908 [05:39<14:39,  1.35s/it]
+[Batch:   260] training loss = 0.439
+
 29%|██▊       | 260/908 [05:39<10:55,  1.01s/it]
 29%|██▊       | 261/908 [05:42<15:28,  1.44s/it]
 29%|██▉       | 262/908 [05:45<22:48,  2.12s/it]
 29%|██▉       | 263/908 [05:46<16:33,  1.54s/it]
 29%|██▉       | 264/908 [05:46<12:11,  1.14s/it]
 29%|██▉       | 265/908 [05:50<21:24,  2.00s/it]
 29%|██▉       | 266/908 [05:53<24:57,  2.33s/it]
 29%|██▉       | 267/908 [05:53<18:03,  1.69s/it]
 30%|██▉       | 268/908 [05:53<13:17,  1.25s/it]
 30%|██▉       | 269/908 [05:56<19:41,  1.85s/it]
 30%|██▉       | 270/908 [05:57<16:38,  1.57s/it]
 30%|██▉       | 271/908 [05:58<12:18,  1.16s/it]
 30%|██▉       | 272/908 [05:58<09:12,  1.15it/s]
 30%|███       | 273/908 [06:01<16:01,  1.51s/it]
 30%|███       | 274/908 [06:04<22:04,  2.09s/it]
 30%|███       | 275/908 [06:04<16:03,  1.52s/it]
 30%|███       | 276/908 [06:05<11:49,  1.12s/it]
 31%|███       | 277/908 [06:07<16:59,  1.62s/it]
 31%|███       | 278/908 [06:10<20:09,  1.92s/it]
 31%|███       | 279/908 [06:10<14:40,  1.40s/it]
+[Batch:   280] training loss = 0.388
+
 31%|███       | 280/908 [06:10<10:57,  1.05s/it]
 31%|███       | 281/908 [06:13<15:32,  1.49s/it]
 31%|███       | 282/908 [06:15<18:09,  1.74s/it]
 31%|███       | 283/908 [06:15<13:17,  1.28s/it]
 31%|███▏      | 284/908 [06:16<09:53,  1.05it/s]
 31%|███▏      | 285/908 [06:18<13:53,  1.34s/it]
 31%|███▏      | 286/908 [06:20<17:08,  1.65s/it]
 32%|███▏      | 287/908 [06:20<12:33,  1.21s/it]
 32%|███▏      | 288/908 [06:21<09:21,  1.10it/s]
 32%|███▏      | 289/908 [06:23<14:47,  1.43s/it]
 32%|███▏      | 290/908 [06:24<13:43,  1.33s/it]
 32%|███▏      | 291/908 [06:25<10:11,  1.01it/s]
 32%|███▏      | 292/908 [06:25<07:42,  1.33it/s]
 32%|███▏      | 293/908 [06:27<13:35,  1.33s/it]
 32%|███▏      | 294/908 [06:29<15:30,  1.51s/it]
 32%|███▏      | 295/908 [06:30<11:23,  1.12s/it]
 33%|███▎      | 296/908 [06:30<08:32,  1.19it/s]
 33%|███▎      | 297/908 [06:32<11:10,  1.10s/it]
 33%|███▎      | 298/908 [06:34<13:52,  1.37s/it]
 33%|███▎      | 299/908 [06:34<10:17,  1.01s/it]
+[Batch:   300] training loss = 0.591
+
 33%|███▎      | 300/908 [06:34<07:45,  1.31it/s]
 33%|███▎      | 301/908 [06:37<13:54,  1.37s/it]
 33%|███▎      | 302/908 [06:38<14:56,  1.48s/it]
 33%|███▎      | 303/908 [06:39<11:00,  1.09s/it]
 33%|███▎      | 304/908 [06:39<08:16,  1.22it/s]
 34%|███▎      | 305/908 [06:41<12:54,  1.29s/it]
 34%|███▎      | 306/908 [06:44<17:04,  1.70s/it]
 34%|███▍      | 307/908 [06:44<12:31,  1.25s/it]
 34%|███▍      | 308/908 [06:44<09:20,  1.07it/s]
 34%|███▍      | 309/908 [06:47<14:48,  1.48s/it]
 34%|███▍      | 310/908 [06:48<11:55,  1.20s/it]
 34%|███▍      | 311/908 [06:48<08:54,  1.12it/s]
 34%|███▍      | 312/908 [06:48<06:46,  1.47it/s]
 34%|███▍      | 313/908 [06:51<13:50,  1.40s/it]
 35%|███▍      | 314/908 [06:51<10:17,  1.04s/it]
 35%|███▍      | 315/908 [06:51<07:45,  1.27it/s]
 35%|███▍      | 316/908 [06:52<06:00,  1.64it/s]
 35%|███▍      | 317/908 [06:55<14:21,  1.46s/it]
 35%|███▌      | 318/908 [06:56<14:09,  1.44s/it]
 35%|███▌      | 319/908 [06:57<10:27,  1.07s/it]
+[Batch:   320] training loss = 0.402
+
 35%|███▌      | 320/908 [06:57<07:53,  1.24it/s]
 35%|███▌      | 321/908 [06:58<08:16,  1.18it/s]
 35%|███▌      | 322/908 [06:58<07:53,  1.24it/s]
 36%|███▌      | 323/908 [06:59<06:03,  1.61it/s]
 36%|███▌      | 324/908 [06:59<04:47,  2.03it/s]
 36%|███▌      | 325/908 [07:02<12:43,  1.31s/it]
 36%|███▌      | 326/908 [07:04<13:58,  1.44s/it]
 36%|███▌      | 327/908 [07:04<10:19,  1.07s/it]
 36%|███▌      | 328/908 [07:04<07:45,  1.24it/s]
 36%|███▌      | 329/908 [07:09<19:04,  1.98s/it]
 36%|███▋      | 330/908 [07:10<17:56,  1.86s/it]
 36%|███▋      | 331/908 [07:11<13:04,  1.36s/it]
 37%|███▋      | 332/908 [07:11<09:41,  1.01s/it]
 37%|███▋      | 333/908 [07:12<10:51,  1.13s/it]
 37%|███▋      | 334/908 [07:14<11:47,  1.23s/it]
 37%|███▋      | 335/908 [07:14<08:47,  1.09it/s]
 37%|███▋      | 336/908 [07:14<06:40,  1.43it/s]
 37%|███▋      | 337/908 [07:16<08:49,  1.08it/s]
 37%|███▋      | 338/908 [07:17<10:19,  1.09s/it]
 37%|███▋      | 339/908 [07:17<07:45,  1.22it/s]
+[Batch:   340] training loss = 0.504
+
 37%|███▋      | 340/908 [07:17<05:58,  1.59it/s]
 38%|███▊      | 341/908 [07:21<14:35,  1.54s/it]
 38%|███▊      | 342/908 [07:21<10:46,  1.14s/it]
 38%|███▊      | 343/908 [07:21<08:03,  1.17it/s]
 38%|███▊      | 344/908 [07:22<06:09,  1.53it/s]
 38%|███▊      | 345/908 [07:23<09:22,  1.00it/s]
 38%|███▊      | 346/908 [07:25<11:32,  1.23s/it]
 38%|███▊      | 347/908 [07:25<08:36,  1.09it/s]
 38%|███▊      | 348/908 [07:26<06:32,  1.43it/s]
 38%|███▊      | 349/908 [07:28<10:29,  1.13s/it]
 39%|███▊      | 350/908 [07:29<10:48,  1.16s/it]
 39%|███▊      | 351/908 [07:29<08:04,  1.15it/s]
 39%|███▉      | 352/908 [07:29<06:09,  1.50it/s]
 39%|███▉      | 353/908 [07:31<08:33,  1.08it/s]
 39%|███▉      | 354/908 [07:32<08:50,  1.04it/s]
 39%|███▉      | 355/908 [07:32<06:42,  1.37it/s]
 39%|███▉      | 356/908 [07:32<05:12,  1.77it/s]
 39%|███▉      | 357/908 [07:34<07:42,  1.19it/s]
 39%|███▉      | 358/908 [07:35<07:39,  1.20it/s]
 40%|███▉      | 359/908 [07:35<05:54,  1.55it/s]
+[Batch:   360] training loss = 0.427
+
 40%|███▉      | 360/908 [07:35<04:41,  1.95it/s]
 40%|███▉      | 361/908 [07:37<09:56,  1.09s/it]
 40%|███▉      | 362/908 [07:39<10:06,  1.11s/it]
 40%|███▉      | 363/908 [07:39<07:35,  1.20it/s]
 40%|████      | 364/908 [07:39<05:48,  1.56it/s]
 40%|████      | 365/908 [07:44<18:04,  2.00s/it]
 40%|████      | 366/908 [07:46<17:45,  1.97s/it]
 40%|████      | 367/908 [07:46<12:55,  1.43s/it]
 41%|████      | 368/908 [07:46<09:33,  1.06s/it]
 41%|████      | 369/908 [07:50<14:59,  1.67s/it]
 41%|████      | 370/908 [07:51<14:39,  1.63s/it]
 41%|████      | 371/908 [07:51<10:44,  1.20s/it]
 41%|████      | 372/908 [07:51<08:00,  1.11it/s]
 41%|████      | 373/908 [07:52<07:13,  1.23it/s]
 41%|████      | 374/908 [07:54<10:21,  1.16s/it]
 41%|████▏     | 375/908 [07:54<07:43,  1.15it/s]
 41%|████▏     | 376/908 [07:54<05:55,  1.50it/s]
 42%|████▏     | 377/908 [07:56<08:44,  1.01it/s]
 42%|████▏     | 378/908 [07:57<08:59,  1.02s/it]
 42%|████▏     | 379/908 [07:57<06:47,  1.30it/s]
+[Batch:   380] training loss = 0.453
+
 42%|████▏     | 380/908 [07:58<05:14,  1.68it/s]
 42%|████▏     | 381/908 [08:00<09:25,  1.07s/it]
 42%|████▏     | 382/908 [08:01<09:23,  1.07s/it]
 42%|████▏     | 383/908 [08:01<07:06,  1.23it/s]
 42%|████▏     | 384/908 [08:01<05:28,  1.60it/s]
 42%|████▏     | 385/908 [08:03<09:33,  1.10s/it]
 43%|████▎     | 386/908 [08:05<10:07,  1.16s/it]
 43%|████▎     | 387/908 [08:05<07:34,  1.15it/s]
 43%|████▎     | 388/908 [08:05<05:47,  1.50it/s]
 43%|████▎     | 389/908 [08:07<09:58,  1.15s/it]
 43%|████▎     | 390/908 [08:08<09:19,  1.08s/it]
 43%|████▎     | 391/908 [08:09<06:59,  1.23it/s]
 43%|████▎     | 392/908 [08:09<05:21,  1.60it/s]
 43%|████▎     | 393/908 [08:12<11:25,  1.33s/it]
 43%|████▎     | 394/908 [08:13<10:56,  1.28s/it]
 44%|████▎     | 395/908 [08:13<08:08,  1.05it/s]
 44%|████▎     | 396/908 [08:13<06:11,  1.38it/s]
 44%|████▎     | 397/908 [08:16<11:09,  1.31s/it]
 44%|████▍     | 398/908 [08:17<11:14,  1.32s/it]
 44%|████▍     | 399/908 [08:17<08:19,  1.02it/s]
+[Batch:   400] training loss = 0.368
+
 44%|████▍     | 400/908 [08:18<06:19,  1.34it/s]
 44%|████▍     | 401/908 [08:21<11:33,  1.37s/it]
 44%|████▍     | 402/908 [08:22<12:27,  1.48s/it]
 44%|████▍     | 403/908 [08:22<09:11,  1.09s/it]
 44%|████▍     | 404/908 [08:23<06:54,  1.22it/s]
 45%|████▍     | 405/908 [08:25<10:14,  1.22s/it]
 45%|████▍     | 406/908 [08:26<09:13,  1.10s/it]
 45%|████▍     | 407/908 [08:26<06:54,  1.21it/s]
 45%|████▍     | 408/908 [08:26<05:18,  1.57it/s]
 45%|████▌     | 409/908 [08:29<12:04,  1.45s/it]
 45%|████▌     | 410/908 [08:31<13:48,  1.66s/it]
 45%|████▌     | 411/908 [08:32<10:06,  1.22s/it]
 45%|████▌     | 412/908 [08:32<07:31,  1.10it/s]
 45%|████▌     | 413/908 [08:35<13:42,  1.66s/it]
 46%|████▌     | 414/908 [08:36<12:25,  1.51s/it]
 46%|████▌     | 415/908 [08:37<09:09,  1.12s/it]
 46%|████▌     | 416/908 [08:37<06:52,  1.19it/s]
 46%|████▌     | 417/908 [08:40<12:01,  1.47s/it]
 46%|████▌     | 418/908 [08:42<13:12,  1.62s/it]
 46%|████▌     | 419/908 [08:42<09:41,  1.19s/it]
+[Batch:   420] training loss = 0.504
+
 46%|████▋     | 420/908 [08:42<07:14,  1.12it/s]
 46%|████▋     | 421/908 [08:44<09:12,  1.13s/it]
 46%|████▋     | 422/908 [08:45<09:13,  1.14s/it]
 47%|████▋     | 423/908 [08:45<06:54,  1.17it/s]
 47%|████▋     | 424/908 [08:45<05:18,  1.52it/s]
 47%|████▋     | 425/908 [08:48<10:46,  1.34s/it]
 47%|████▋     | 426/908 [08:49<09:07,  1.14s/it]
 47%|████▋     | 427/908 [08:49<06:49,  1.17it/s]
 47%|████▋     | 428/908 [08:49<05:13,  1.53it/s]
 47%|████▋     | 429/908 [08:52<10:35,  1.33s/it]
 47%|████▋     | 430/908 [08:53<09:39,  1.21s/it]
 47%|████▋     | 431/908 [08:53<07:12,  1.10it/s]
 48%|████▊     | 432/908 [08:54<05:30,  1.44it/s]
 48%|████▊     | 433/908 [08:57<12:36,  1.59s/it]
 48%|████▊     | 434/908 [08:58<10:46,  1.36s/it]
 48%|████▊     | 435/908 [08:58<07:58,  1.01s/it]
 48%|████▊     | 436/908 [08:58<06:01,  1.31it/s]
 48%|████▊     | 437/908 [09:04<16:48,  2.14s/it]
 48%|████▊     | 438/908 [09:05<14:46,  1.89s/it]
 48%|████▊     | 439/908 [09:05<10:47,  1.38s/it]
+[Batch:   440] training loss = 0.486
+
 48%|████▊     | 440/908 [09:06<08:00,  1.03s/it]
 49%|████▊     | 441/908 [09:11<17:29,  2.25s/it]
 49%|████▊     | 442/908 [09:12<14:22,  1.85s/it]
 49%|████▉     | 443/908 [09:12<10:29,  1.35s/it]
 49%|████▉     | 444/908 [09:12<07:46,  1.01s/it]
 49%|████▉     | 445/908 [09:14<10:35,  1.37s/it]
 49%|████▉     | 446/908 [09:15<08:48,  1.14s/it]
 49%|████▉     | 447/908 [09:15<06:35,  1.16it/s]
 49%|████▉     | 448/908 [09:15<05:03,  1.52it/s]
 49%|████▉     | 449/908 [09:20<14:25,  1.88s/it]
 50%|████▉     | 450/908 [09:20<11:08,  1.46s/it]
 50%|████▉     | 451/908 [09:21<08:13,  1.08s/it]
 50%|████▉     | 452/908 [09:21<06:10,  1.23it/s]
 50%|████▉     | 453/908 [09:23<10:26,  1.38s/it]
 50%|█████     | 454/908 [09:24<08:42,  1.15s/it]
 50%|█████     | 455/908 [09:24<06:30,  1.16it/s]
 50%|█████     | 456/908 [09:24<04:58,  1.51it/s]
 50%|█████     | 457/908 [09:27<09:33,  1.27s/it]
 50%|█████     | 458/908 [09:29<10:06,  1.35s/it]
 51%|█████     | 459/908 [09:29<07:29,  1.00s/it]
+[Batch:   460] training loss = 0.497
+
 51%|█████     | 460/908 [09:29<05:39,  1.32it/s]
 51%|█████     | 461/908 [09:33<13:47,  1.85s/it]
 51%|█████     | 462/908 [09:34<10:08,  1.36s/it]
 51%|█████     | 463/908 [09:34<07:31,  1.02s/it]
 51%|█████     | 464/908 [09:34<05:41,  1.30it/s]
 51%|█████     | 465/908 [09:37<10:37,  1.44s/it]
 51%|█████▏    | 466/908 [09:38<09:49,  1.33s/it]
 51%|█████▏    | 467/908 [09:38<07:19,  1.00it/s]
 52%|█████▏    | 468/908 [09:39<05:33,  1.32it/s]
 52%|█████▏    | 469/908 [09:40<07:46,  1.06s/it]
 52%|█████▏    | 470/908 [09:42<09:44,  1.33s/it]
 52%|█████▏    | 471/908 [09:42<07:14,  1.01it/s]
 52%|█████▏    | 472/908 [09:43<05:30,  1.32it/s]
 52%|█████▏    | 473/908 [09:46<10:58,  1.51s/it]
 52%|█████▏    | 474/908 [09:47<09:00,  1.25s/it]
 52%|█████▏    | 475/908 [09:47<06:43,  1.07it/s]
 52%|█████▏    | 476/908 [09:47<05:08,  1.40it/s]
 53%|█████▎    | 477/908 [09:50<10:45,  1.50s/it]
 53%|█████▎    | 478/908 [09:53<12:43,  1.78s/it]
 53%|█████▎    | 479/908 [09:53<09:19,  1.31s/it]
+[Batch:   480] training loss = 0.403
+
 53%|█████▎    | 480/908 [09:53<06:56,  1.03it/s]
 53%|█████▎    | 481/908 [09:56<11:02,  1.55s/it]
 53%|█████▎    | 482/908 [09:57<10:10,  1.43s/it]
 53%|█████▎    | 483/908 [09:57<07:31,  1.06s/it]
 53%|█████▎    | 484/908 [09:58<05:40,  1.24it/s]
 53%|█████▎    | 485/908 [10:00<08:31,  1.21s/it]
 54%|█████▎    | 486/908 [10:02<09:40,  1.38s/it]
 54%|█████▎    | 487/908 [10:02<07:09,  1.02s/it]
 54%|█████▎    | 488/908 [10:02<05:26,  1.29it/s]
 54%|█████▍    | 489/908 [10:05<10:35,  1.52s/it]
 54%|█████▍    | 490/908 [10:06<09:46,  1.40s/it]
 54%|█████▍    | 491/908 [10:07<07:15,  1.04s/it]
 54%|█████▍    | 492/908 [10:07<05:29,  1.26it/s]
 54%|█████▍    | 493/908 [10:11<13:29,  1.95s/it]
 54%|█████▍    | 494/908 [10:13<12:30,  1.81s/it]
 55%|█████▍    | 495/908 [10:13<09:10,  1.33s/it]
 55%|█████▍    | 496/908 [10:13<06:48,  1.01it/s]
 55%|█████▍    | 497/908 [10:16<10:45,  1.57s/it]
 55%|█████▍    | 498/908 [10:18<10:51,  1.59s/it]
 55%|█████▍    | 499/908 [10:18<08:00,  1.17s/it]
+[Batch:   500] training loss = 0.408
+
 55%|█████▌    | 500/908 [10:18<05:59,  1.13it/s]
 55%|█████▌    | 501/908 [10:20<08:30,  1.25s/it]
 55%|█████▌    | 502/908 [10:22<10:14,  1.51s/it]
 55%|█████▌    | 503/908 [10:23<07:35,  1.12s/it]
 56%|█████▌    | 504/908 [10:23<05:43,  1.18it/s]
 56%|█████▌    | 505/908 [10:26<09:35,  1.43s/it]
 56%|█████▌    | 506/908 [10:26<07:43,  1.15s/it]
 56%|█████▌    | 507/908 [10:26<05:48,  1.15it/s]
 56%|█████▌    | 508/908 [10:27<04:27,  1.50it/s]
 56%|█████▌    | 509/908 [10:30<09:11,  1.38s/it]
 56%|█████▌    | 510/908 [10:31<09:07,  1.38s/it]
 56%|█████▋    | 511/908 [10:31<06:46,  1.02s/it]
 56%|█████▋    | 512/908 [10:31<05:07,  1.29it/s]
 56%|█████▋    | 513/908 [10:34<09:15,  1.41s/it]
 57%|█████▋    | 514/908 [10:36<08:50,  1.35s/it]
 57%|█████▋    | 515/908 [10:36<06:35,  1.01s/it]
 57%|█████▋    | 516/908 [10:36<04:59,  1.31it/s]
 57%|█████▋    | 517/908 [10:39<08:54,  1.37s/it]
 57%|█████▋    | 518/908 [10:39<06:38,  1.02s/it]
 57%|█████▋    | 519/908 [10:39<05:01,  1.29it/s]
+[Batch:   520] training loss = 0.453
+
 57%|█████▋    | 520/908 [10:39<03:54,  1.66it/s]
 57%|█████▋    | 521/908 [10:43<09:11,  1.43s/it]
 57%|█████▋    | 522/908 [10:45<10:28,  1.63s/it]
 58%|█████▊    | 523/908 [10:45<07:43,  1.20s/it]
 58%|█████▊    | 524/908 [10:45<05:47,  1.11it/s]
 58%|█████▊    | 525/908 [10:46<04:50,  1.32it/s]
 58%|█████▊    | 526/908 [10:49<09:21,  1.47s/it]
 58%|█████▊    | 527/908 [10:49<06:54,  1.09s/it]
 58%|█████▊    | 528/908 [10:49<05:13,  1.21it/s]
 58%|█████▊    | 529/908 [10:50<04:22,  1.45it/s]
 58%|█████▊    | 530/908 [10:52<07:14,  1.15s/it]
 58%|█████▊    | 531/908 [10:52<05:27,  1.15it/s]
 59%|█████▊    | 532/908 [10:52<04:11,  1.50it/s]
 59%|█████▊    | 533/908 [10:54<06:04,  1.03it/s]
 59%|█████▉    | 534/908 [10:55<07:07,  1.14s/it]
 59%|█████▉    | 535/908 [10:56<05:20,  1.16it/s]
 59%|█████▉    | 536/908 [10:56<04:07,  1.50it/s]
 59%|█████▉    | 537/908 [10:56<03:15,  1.90it/s]
 59%|█████▉    | 538/908 [10:59<07:12,  1.17s/it]
 59%|█████▉    | 539/908 [10:59<05:24,  1.14it/s]
+[Batch:   540] training loss = 0.386
+
 59%|█████▉    | 540/908 [10:59<04:08,  1.48it/s]
 60%|█████▉    | 541/908 [11:00<04:08,  1.48it/s]
 60%|█████▉    | 542/908 [11:02<07:01,  1.15s/it]
 60%|█████▉    | 543/908 [11:02<05:15,  1.16it/s]
 60%|█████▉    | 544/908 [11:02<04:01,  1.51it/s]
 60%|██████    | 545/908 [11:03<04:35,  1.32it/s]
 60%|██████    | 546/908 [11:07<08:54,  1.48s/it]
 60%|██████    | 547/908 [11:07<06:34,  1.09s/it]
 60%|██████    | 548/908 [11:07<04:57,  1.21it/s]
 60%|██████    | 549/908 [11:08<06:04,  1.02s/it]
 61%|██████    | 550/908 [11:13<13:23,  2.25s/it]
 61%|██████    | 551/908 [11:14<09:41,  1.63s/it]
 61%|██████    | 552/908 [11:14<07:08,  1.20s/it]
 61%|██████    | 553/908 [11:15<07:11,  1.22s/it]
 61%|██████    | 554/908 [11:19<11:10,  1.89s/it]
 61%|██████    | 555/908 [11:19<08:09,  1.39s/it]
 61%|██████    | 556/908 [11:19<06:03,  1.03s/it]
 61%|██████▏   | 557/908 [11:19<04:35,  1.27it/s]
 61%|██████▏   | 558/908 [11:22<08:37,  1.48s/it]
 62%|██████▏   | 559/908 [11:23<06:23,  1.10s/it]
+[Batch:   560] training loss = 0.450
+
 62%|██████▏   | 560/908 [11:23<04:49,  1.20it/s]
 62%|██████▏   | 561/908 [11:23<03:45,  1.54it/s]
 62%|██████▏   | 562/908 [11:27<08:56,  1.55s/it]
 62%|██████▏   | 563/908 [11:27<06:35,  1.15s/it]
 62%|██████▏   | 564/908 [11:27<04:56,  1.16it/s]
 62%|██████▏   | 565/908 [11:27<04:11,  1.36it/s]
 62%|██████▏   | 566/908 [11:31<09:30,  1.67s/it]
 62%|██████▏   | 567/908 [11:32<06:59,  1.23s/it]
 63%|██████▎   | 568/908 [11:32<05:14,  1.08it/s]
 63%|██████▎   | 569/908 [11:32<04:36,  1.23it/s]
 63%|██████▎   | 570/908 [11:36<10:07,  1.80s/it]
 63%|██████▎   | 571/908 [11:37<07:24,  1.32s/it]
 63%|██████▎   | 572/908 [11:37<05:31,  1.02it/s]
 63%|██████▎   | 573/908 [11:38<05:37,  1.01s/it]
 63%|██████▎   | 574/908 [11:42<11:29,  2.07s/it]
 63%|██████▎   | 575/908 [11:43<08:21,  1.51s/it]
 63%|██████▎   | 576/908 [11:43<06:10,  1.12s/it]
 64%|██████▎   | 577/908 [11:44<06:06,  1.11s/it]
 64%|██████▎   | 578/908 [11:47<09:39,  1.76s/it]
 64%|██████▍   | 579/908 [11:47<07:03,  1.29s/it]
+[Batch:   580] training loss = 0.432
+
 64%|██████▍   | 580/908 [11:48<05:15,  1.04it/s]
 64%|██████▍   | 581/908 [11:48<04:29,  1.21it/s]
 64%|██████▍   | 582/908 [11:52<08:48,  1.62s/it]
 64%|██████▍   | 583/908 [11:52<06:28,  1.20s/it]
 64%|██████▍   | 584/908 [11:52<04:50,  1.11it/s]
 64%|██████▍   | 585/908 [11:53<04:20,  1.24it/s]
 65%|██████▍   | 586/908 [11:57<09:55,  1.85s/it]
 65%|██████▍   | 587/908 [11:57<07:13,  1.35s/it]
 65%|██████▍   | 588/908 [11:57<05:21,  1.01s/it]
 65%|██████▍   | 589/908 [11:58<05:00,  1.06it/s]
 65%|██████▍   | 590/908 [12:02<09:45,  1.84s/it]
 65%|██████▌   | 591/908 [12:02<07:06,  1.34s/it]
 65%|██████▌   | 592/908 [12:02<05:15,  1.00it/s]
 65%|██████▌   | 593/908 [12:03<05:01,  1.05it/s]
 65%|██████▌   | 594/908 [12:08<11:51,  2.27s/it]
 66%|██████▌   | 595/908 [12:09<08:33,  1.64s/it]
 66%|██████▌   | 596/908 [12:09<06:22,  1.23s/it]
 66%|██████▌   | 597/908 [12:11<07:07,  1.37s/it]
 66%|██████▌   | 598/908 [12:15<12:06,  2.34s/it]
 66%|██████▌   | 599/908 [12:15<08:44,  1.70s/it]
+[Batch:   600] training loss = 0.487
+
 66%|██████▌   | 600/908 [12:16<06:24,  1.25s/it]
 66%|██████▌   | 601/908 [12:17<06:28,  1.27s/it]
 66%|██████▋   | 602/908 [12:19<07:34,  1.48s/it]
 66%|██████▋   | 603/908 [12:19<05:34,  1.10s/it]
 67%|██████▋   | 604/908 [12:19<04:10,  1.21it/s]
 67%|██████▋   | 605/908 [12:21<05:13,  1.04s/it]
 67%|██████▋   | 606/908 [12:23<07:13,  1.44s/it]
 67%|██████▋   | 607/908 [12:23<05:19,  1.06s/it]
 67%|██████▋   | 608/908 [12:24<04:01,  1.24it/s]
 67%|██████▋   | 609/908 [12:25<04:19,  1.15it/s]
 67%|██████▋   | 610/908 [12:27<07:04,  1.43s/it]
 67%|██████▋   | 611/908 [12:28<05:13,  1.05s/it]
 67%|██████▋   | 612/908 [12:28<03:55,  1.26it/s]
 68%|██████▊   | 613/908 [12:28<03:03,  1.60it/s]
 68%|██████▊   | 614/908 [12:32<07:40,  1.56s/it]
 68%|██████▊   | 615/908 [12:32<05:38,  1.16s/it]
 68%|██████▊   | 616/908 [12:32<04:13,  1.15it/s]
 68%|██████▊   | 617/908 [12:33<04:46,  1.02it/s]
 68%|██████▊   | 618/908 [12:38<09:44,  2.02s/it]
 68%|██████▊   | 619/908 [12:38<07:03,  1.47s/it]
+[Batch:   620] training loss = 0.388
+
 68%|██████▊   | 620/908 [12:38<05:12,  1.09s/it]
 68%|██████▊   | 621/908 [12:38<03:54,  1.23it/s]
 69%|██████▊   | 622/908 [12:44<10:16,  2.16s/it]
 69%|██████▊   | 623/908 [12:44<07:26,  1.57s/it]
 69%|██████▊   | 624/908 [12:44<05:27,  1.15s/it]
 69%|██████▉   | 625/908 [12:44<04:28,  1.05it/s]
 69%|██████▉   | 626/908 [12:45<04:30,  1.04it/s]
 69%|██████▉   | 627/908 [12:46<03:25,  1.37it/s]
 69%|██████▉   | 628/908 [12:46<02:38,  1.76it/s]
 69%|██████▉   | 629/908 [12:46<02:37,  1.77it/s]
 69%|██████▉   | 630/908 [12:51<07:58,  1.72s/it]
 69%|██████▉   | 631/908 [12:51<05:49,  1.26s/it]
 70%|██████▉   | 632/908 [12:51<04:19,  1.06it/s]
 70%|██████▉   | 633/908 [12:51<03:16,  1.40it/s]
 70%|██████▉   | 634/908 [12:55<06:38,  1.45s/it]
 70%|██████▉   | 635/908 [12:55<04:54,  1.08s/it]
 70%|███████   | 636/908 [12:55<03:41,  1.23it/s]
 70%|███████   | 637/908 [12:55<02:51,  1.58it/s]
 70%|███████   | 638/908 [12:59<06:43,  1.49s/it]
 70%|███████   | 639/908 [12:59<04:57,  1.11s/it]
+[Batch:   640] training loss = 0.551
+
 70%|███████   | 640/908 [12:59<03:43,  1.20it/s]
 71%|███████   | 641/908 [13:00<03:56,  1.13it/s]
 71%|███████   | 642/908 [13:03<07:01,  1.59s/it]
 71%|███████   | 643/908 [13:04<05:10,  1.17s/it]
 71%|███████   | 644/908 [13:04<03:51,  1.14it/s]
 71%|███████   | 645/908 [13:04<03:32,  1.24it/s]
 71%|███████   | 646/908 [13:08<06:36,  1.51s/it]
 71%|███████▏  | 647/908 [13:08<04:51,  1.12s/it]
 71%|███████▏  | 648/908 [13:08<03:38,  1.19it/s]
 71%|███████▏  | 649/908 [13:09<03:29,  1.24it/s]
 72%|███████▏  | 650/908 [13:12<06:09,  1.43s/it]
 72%|███████▏  | 651/908 [13:12<04:32,  1.06s/it]
 72%|███████▏  | 652/908 [13:12<03:24,  1.25it/s]
 72%|███████▏  | 653/908 [13:13<03:53,  1.09it/s]
 72%|███████▏  | 654/908 [13:18<09:12,  2.18s/it]
 72%|███████▏  | 655/908 [13:18<06:39,  1.58s/it]
 72%|███████▏  | 656/908 [13:19<04:53,  1.17s/it]
 72%|███████▏  | 657/908 [13:19<04:28,  1.07s/it]
 72%|███████▏  | 658/908 [13:24<08:13,  1.97s/it]
 73%|███████▎  | 659/908 [13:24<05:58,  1.44s/it]
+[Batch:   660] training loss = 0.450
+
 73%|███████▎  | 660/908 [13:24<04:23,  1.06s/it]
 73%|███████▎  | 661/908 [13:25<04:25,  1.08s/it]
 73%|███████▎  | 662/908 [13:28<07:10,  1.75s/it]
 73%|███████▎  | 663/908 [13:29<05:13,  1.28s/it]
 73%|███████▎  | 664/908 [13:29<03:52,  1.05it/s]
 73%|███████▎  | 665/908 [13:30<04:05,  1.01s/it]
 73%|███████▎  | 666/908 [13:33<06:52,  1.70s/it]
 73%|███████▎  | 667/908 [13:33<05:01,  1.25s/it]
 74%|███████▎  | 668/908 [13:34<03:43,  1.07it/s]
 74%|███████▎  | 669/908 [13:35<04:05,  1.03s/it]
 74%|███████▍  | 670/908 [13:38<06:30,  1.64s/it]
 74%|███████▍  | 671/908 [13:38<04:46,  1.21s/it]
 74%|███████▍  | 672/908 [13:38<03:32,  1.11it/s]
 74%|███████▍  | 673/908 [13:39<03:30,  1.12it/s]
 74%|███████▍  | 674/908 [13:42<06:22,  1.63s/it]
 74%|███████▍  | 675/908 [13:43<04:39,  1.20s/it]
 74%|███████▍  | 676/908 [13:43<03:28,  1.11it/s]
 75%|███████▍  | 677/908 [13:45<04:29,  1.17s/it]
 75%|███████▍  | 678/908 [13:48<07:15,  1.89s/it]
 75%|███████▍  | 679/908 [13:48<05:17,  1.39s/it]
+[Batch:   680] training loss = 0.367
+
 75%|███████▍  | 680/908 [13:49<03:54,  1.03s/it]
 75%|███████▌  | 681/908 [13:49<03:32,  1.07it/s]
 75%|███████▌  | 682/908 [13:53<06:34,  1.74s/it]
 75%|███████▌  | 683/908 [13:53<04:47,  1.28s/it]
 75%|███████▌  | 684/908 [13:53<03:33,  1.05it/s]
 75%|███████▌  | 685/908 [13:55<04:02,  1.09s/it]
 76%|███████▌  | 686/908 [13:57<05:38,  1.52s/it]
 76%|███████▌  | 687/908 [13:58<04:08,  1.12s/it]
 76%|███████▌  | 688/908 [13:58<03:05,  1.18it/s]
 76%|███████▌  | 689/908 [13:59<03:43,  1.02s/it]
 76%|███████▌  | 690/908 [14:01<05:02,  1.39s/it]
 76%|███████▌  | 691/908 [14:02<03:43,  1.03s/it]
 76%|███████▌  | 692/908 [14:02<02:48,  1.28it/s]
 76%|███████▋  | 693/908 [14:02<02:10,  1.65it/s]
 76%|███████▋  | 694/908 [14:05<04:13,  1.18s/it]
 77%|███████▋  | 695/908 [14:05<03:08,  1.13it/s]
 77%|███████▋  | 696/908 [14:05<02:23,  1.47it/s]
 77%|███████▋  | 697/908 [14:07<04:13,  1.20s/it]
 77%|███████▋  | 698/908 [14:09<05:03,  1.45s/it]
 77%|███████▋  | 699/908 [14:10<03:44,  1.08s/it]
+[Batch:   700] training loss = 0.507
+
 77%|███████▋  | 700/908 [14:10<02:48,  1.23it/s]
 77%|███████▋  | 701/908 [14:11<03:45,  1.09s/it]
 77%|███████▋  | 702/908 [14:15<06:00,  1.75s/it]
 77%|███████▋  | 703/908 [14:15<04:53,  1.43s/it]
 78%|███████▊  | 704/908 [14:16<03:36,  1.06s/it]
 78%|███████▊  | 705/908 [14:18<05:22,  1.59s/it]
 78%|███████▊  | 706/908 [14:21<06:43,  2.00s/it]
 78%|███████▊  | 707/908 [14:22<04:54,  1.47s/it]
 78%|███████▊  | 708/908 [14:22<03:37,  1.09s/it]
 78%|███████▊  | 709/908 [14:23<03:50,  1.16s/it]
 78%|███████▊  | 710/908 [14:26<05:02,  1.53s/it]
 78%|███████▊  | 711/908 [14:26<03:41,  1.13s/it]
 78%|███████▊  | 712/908 [14:26<02:46,  1.18it/s]
 79%|███████▊  | 713/908 [14:28<03:30,  1.08s/it]
 79%|███████▊  | 714/908 [14:31<05:32,  1.71s/it]
 79%|███████▊  | 715/908 [14:31<04:01,  1.25s/it]
 79%|███████▉  | 716/908 [14:31<02:59,  1.07it/s]
 79%|███████▉  | 717/908 [14:32<02:43,  1.17it/s]
 79%|███████▉  | 718/908 [14:34<04:25,  1.40s/it]
 79%|███████▉  | 719/908 [14:35<03:15,  1.04s/it]
+[Batch:   720] training loss = 0.422
+
 79%|███████▉  | 720/908 [14:35<02:27,  1.28it/s]
 79%|███████▉  | 721/908 [14:36<03:11,  1.02s/it]
 80%|███████▉  | 722/908 [14:39<04:26,  1.43s/it]
 80%|███████▉  | 723/908 [14:39<03:16,  1.06s/it]
 80%|███████▉  | 724/908 [14:39<02:26,  1.25it/s]
 80%|███████▉  | 725/908 [14:41<02:57,  1.03it/s]
 80%|███████▉  | 726/908 [14:44<04:48,  1.59s/it]
 80%|████████  | 727/908 [14:44<03:31,  1.17s/it]
 80%|████████  | 728/908 [14:44<02:37,  1.15it/s]
 80%|████████  | 729/908 [14:45<03:08,  1.05s/it]
 80%|████████  | 730/908 [14:49<05:09,  1.74s/it]
 81%|████████  | 731/908 [14:49<03:45,  1.27s/it]
 81%|████████  | 732/908 [14:49<02:47,  1.05it/s]
 81%|████████  | 733/908 [14:51<03:47,  1.30s/it]
 81%|████████  | 734/908 [14:53<03:44,  1.29s/it]
 81%|████████  | 735/908 [14:53<02:45,  1.04it/s]
 81%|████████  | 736/908 [14:53<02:05,  1.37it/s]
 81%|████████  | 737/908 [14:53<01:37,  1.76it/s]
 81%|████████▏ | 738/908 [14:58<05:09,  1.82s/it]
 81%|████████▏ | 739/908 [14:58<03:44,  1.33s/it]
+[Batch:   740] training loss = 0.456
+
 81%|████████▏ | 740/908 [14:58<02:46,  1.01it/s]
 82%|████████▏ | 741/908 [14:59<02:12,  1.26it/s]
 82%|████████▏ | 742/908 [14:59<01:56,  1.42it/s]
 82%|████████▏ | 743/908 [14:59<01:30,  1.82it/s]
 82%|████████▏ | 744/908 [14:59<01:12,  2.26it/s]
 82%|████████▏ | 745/908 [15:00<00:59,  2.74it/s]
 82%|████████▏ | 746/908 [15:05<05:08,  1.91s/it]
 82%|████████▏ | 747/908 [15:05<03:44,  1.39s/it]
 82%|████████▏ | 748/908 [15:06<02:44,  1.03s/it]
 82%|████████▏ | 749/908 [15:06<02:04,  1.28it/s]
 83%|████████▎ | 750/908 [15:10<04:36,  1.75s/it]
 83%|████████▎ | 751/908 [15:10<03:21,  1.28s/it]
 83%|████████▎ | 752/908 [15:10<02:29,  1.05it/s]
 83%|████████▎ | 753/908 [15:10<01:52,  1.38it/s]
 83%|████████▎ | 754/908 [15:15<04:43,  1.84s/it]
 83%|████████▎ | 755/908 [15:15<03:25,  1.35s/it]
 83%|████████▎ | 756/908 [15:15<02:32,  1.00s/it]
 83%|████████▎ | 757/908 [15:15<01:54,  1.32it/s]
 83%|████████▎ | 758/908 [15:22<06:28,  2.59s/it]
 84%|████████▎ | 759/908 [15:22<04:38,  1.87s/it]
+[Batch:   760] training loss = 0.350
+
 84%|████████▎ | 760/908 [15:23<03:22,  1.37s/it]
 84%|████████▍ | 761/908 [15:23<02:31,  1.03s/it]
 84%|████████▍ | 762/908 [15:27<04:46,  1.96s/it]
 84%|████████▍ | 763/908 [15:27<03:27,  1.43s/it]
 84%|████████▍ | 764/908 [15:27<02:32,  1.06s/it]
 84%|████████▍ | 765/908 [15:28<01:54,  1.25it/s]
 84%|████████▍ | 766/908 [15:33<05:23,  2.28s/it]
 84%|████████▍ | 767/908 [15:33<03:53,  1.65s/it]
 85%|████████▍ | 768/908 [15:34<02:50,  1.22s/it]
 85%|████████▍ | 769/908 [15:34<02:06,  1.10it/s]
 85%|████████▍ | 770/908 [15:38<04:32,  1.97s/it]
 85%|████████▍ | 771/908 [15:39<03:18,  1.45s/it]
 85%|████████▌ | 772/908 [15:39<02:26,  1.08s/it]
 85%|████████▌ | 773/908 [15:39<01:51,  1.21it/s]
 85%|████████▌ | 774/908 [15:44<04:30,  2.02s/it]
 85%|████████▌ | 775/908 [15:44<03:15,  1.47s/it]
 85%|████████▌ | 776/908 [15:44<02:23,  1.09s/it]
 86%|████████▌ | 777/908 [15:44<01:47,  1.22it/s]
 86%|████████▌ | 778/908 [15:48<03:51,  1.78s/it]
 86%|████████▌ | 779/908 [15:49<02:48,  1.31s/it]
+[Batch:   780] training loss = 0.387
+
 86%|████████▌ | 780/908 [15:49<02:04,  1.03it/s]
 86%|████████▌ | 781/908 [15:49<01:34,  1.34it/s]
 86%|████████▌ | 782/908 [15:54<03:59,  1.90s/it]
 86%|████████▌ | 783/908 [15:54<02:53,  1.39s/it]
 86%|████████▋ | 784/908 [15:54<02:07,  1.03s/it]
 86%|████████▋ | 785/908 [15:54<01:35,  1.29it/s]
 87%|████████▋ | 786/908 [15:58<03:24,  1.68s/it]
 87%|████████▋ | 787/908 [15:58<02:29,  1.23s/it]
 87%|████████▋ | 788/908 [15:58<01:50,  1.09it/s]
 87%|████████▋ | 789/908 [15:59<01:23,  1.43it/s]
 87%|████████▋ | 790/908 [16:02<03:07,  1.59s/it]
 87%|████████▋ | 791/908 [16:02<02:16,  1.17s/it]
 87%|████████▋ | 792/908 [16:03<01:42,  1.14it/s]
 87%|████████▋ | 793/908 [16:03<01:17,  1.48it/s]
 87%|████████▋ | 794/908 [16:07<03:11,  1.68s/it]
 88%|████████▊ | 795/908 [16:07<02:19,  1.23s/it]
 88%|████████▊ | 796/908 [16:07<01:43,  1.08it/s]
 88%|████████▊ | 797/908 [16:07<01:18,  1.42it/s]
 88%|████████▊ | 798/908 [16:12<03:13,  1.76s/it]
 88%|████████▊ | 799/908 [16:12<02:20,  1.29s/it]
+[Batch:   800] training loss = 0.481
+
 88%|████████▊ | 800/908 [16:12<01:44,  1.04it/s]
 88%|████████▊ | 801/908 [16:12<01:19,  1.35it/s]
 88%|████████▊ | 802/908 [16:18<03:57,  2.24s/it]
 88%|████████▊ | 803/908 [16:18<02:50,  1.63s/it]
 89%|████████▊ | 804/908 [16:18<02:04,  1.20s/it]
 89%|████████▊ | 805/908 [16:19<01:32,  1.11it/s]
 89%|████████▉ | 806/908 [16:22<02:50,  1.67s/it]
 89%|████████▉ | 807/908 [16:22<02:04,  1.23s/it]
 89%|████████▉ | 808/908 [16:22<01:32,  1.08it/s]
 89%|████████▉ | 809/908 [16:23<01:10,  1.41it/s]
 89%|████████▉ | 810/908 [16:26<02:40,  1.64s/it]
 89%|████████▉ | 811/908 [16:27<01:57,  1.21s/it]
 89%|████████▉ | 812/908 [16:27<01:27,  1.10it/s]
 90%|████████▉ | 813/908 [16:27<01:06,  1.44it/s]
 90%|████████▉ | 814/908 [16:32<02:56,  1.87s/it]
 90%|████████▉ | 815/908 [16:32<02:07,  1.37s/it]
 90%|████████▉ | 816/908 [16:32<01:33,  1.02s/it]
 90%|████████▉ | 817/908 [16:32<01:10,  1.30it/s]
 90%|█████████ | 818/908 [16:37<02:46,  1.85s/it]
 90%|█████████ | 819/908 [16:37<02:00,  1.35s/it]
+[Batch:   820] training loss = 0.464
+
 90%|█████████ | 820/908 [16:37<01:28,  1.01s/it]
 90%|█████████ | 821/908 [16:37<01:07,  1.28it/s]
 91%|█████████ | 822/908 [16:41<02:14,  1.57s/it]
 91%|█████████ | 823/908 [16:41<01:38,  1.16s/it]
 91%|█████████ | 824/908 [16:41<01:13,  1.15it/s]
 91%|█████████ | 825/908 [16:41<00:55,  1.50it/s]
 91%|█████████ | 826/908 [16:45<02:12,  1.61s/it]
 91%|█████████ | 827/908 [16:45<01:36,  1.19s/it]
 91%|█████████ | 828/908 [16:46<01:11,  1.12it/s]
 91%|█████████▏| 829/908 [16:46<00:53,  1.46it/s]
 91%|█████████▏| 830/908 [16:49<02:04,  1.60s/it]
 92%|█████████▏| 831/908 [16:50<01:31,  1.18s/it]
 92%|█████████▏| 832/908 [16:50<01:07,  1.13it/s]
 92%|█████████▏| 833/908 [16:50<00:51,  1.46it/s]
 92%|█████████▏| 834/908 [16:55<02:24,  1.95s/it]
 92%|█████████▏| 835/908 [16:55<01:44,  1.43s/it]
 92%|█████████▏| 836/908 [16:55<01:16,  1.06s/it]
 92%|█████████▏| 837/908 [16:56<00:57,  1.25it/s]
 92%|█████████▏| 838/908 [16:59<01:58,  1.69s/it]
 92%|█████████▏| 839/908 [17:00<01:25,  1.25s/it]
+[Batch:   840] training loss = 0.399
+
 93%|█████████▎| 840/908 [17:00<01:03,  1.08it/s]
 93%|█████████▎| 841/908 [17:00<00:48,  1.39it/s]
 93%|█████████▎| 842/908 [17:05<02:09,  1.96s/it]
 93%|█████████▎| 843/908 [17:05<01:32,  1.43s/it]
 93%|█████████▎| 844/908 [17:05<01:07,  1.06s/it]
 93%|█████████▎| 845/908 [17:05<00:50,  1.25it/s]
 93%|█████████▎| 846/908 [17:08<01:29,  1.44s/it]
 93%|█████████▎| 847/908 [17:09<01:05,  1.07s/it]
 93%|█████████▎| 848/908 [17:09<00:48,  1.24it/s]
 94%|█████████▎| 849/908 [17:09<00:37,  1.59it/s]
 94%|█████████▎| 850/908 [17:14<01:47,  1.85s/it]
 94%|█████████▎| 851/908 [17:14<01:17,  1.36s/it]
 94%|█████████▍| 852/908 [17:14<00:56,  1.01s/it]
 94%|█████████▍| 853/908 [17:14<00:42,  1.30it/s]
 94%|█████████▍| 854/908 [17:21<02:10,  2.42s/it]
 94%|█████████▍| 855/908 [17:21<01:32,  1.75s/it]
 94%|█████████▍| 856/908 [17:21<01:07,  1.29s/it]
 94%|█████████▍| 857/908 [17:21<00:49,  1.04it/s]
 94%|█████████▍| 858/908 [17:24<01:18,  1.56s/it]
 95%|█████████▍| 859/908 [17:24<00:56,  1.15s/it]
+[Batch:   860] training loss = 0.391
+
 95%|█████████▍| 860/908 [17:25<00:41,  1.15it/s]
 95%|█████████▍| 861/908 [17:25<00:31,  1.49it/s]
 95%|█████████▍| 862/908 [17:28<01:01,  1.33s/it]
 95%|█████████▌| 863/908 [17:28<00:44,  1.01it/s]
 95%|█████████▌| 864/908 [17:28<00:33,  1.33it/s]
 95%|█████████▌| 865/908 [17:28<00:25,  1.70it/s]
 95%|█████████▌| 866/908 [17:34<01:26,  2.06s/it]
 95%|█████████▌| 867/908 [17:34<01:01,  1.50s/it]
 96%|█████████▌| 868/908 [17:34<00:44,  1.11s/it]
 96%|█████████▌| 869/908 [17:34<00:32,  1.20it/s]
 96%|█████████▌| 870/908 [17:38<01:08,  1.80s/it]
 96%|█████████▌| 871/908 [17:39<00:49,  1.33s/it]
 96%|█████████▌| 872/908 [17:39<00:35,  1.01it/s]
 96%|█████████▌| 873/908 [17:39<00:26,  1.33it/s]
 96%|█████████▋| 874/908 [17:44<01:13,  2.16s/it]
 96%|█████████▋| 875/908 [17:45<00:51,  1.58s/it]
 96%|█████████▋| 876/908 [17:45<00:37,  1.16s/it]
 97%|█████████▋| 877/908 [17:45<00:26,  1.15it/s]
 97%|█████████▋| 878/908 [17:49<00:58,  1.95s/it]
 97%|█████████▋| 879/908 [17:50<00:41,  1.43s/it]
+[Batch:   880] training loss = 0.485
+
 97%|█████████▋| 880/908 [17:50<00:29,  1.06s/it]
 97%|█████████▋| 881/908 [17:50<00:22,  1.20it/s]
 97%|█████████▋| 882/908 [17:55<00:52,  2.02s/it]
 97%|█████████▋| 883/908 [17:55<00:36,  1.48s/it]
 97%|█████████▋| 884/908 [17:55<00:26,  1.10s/it]
 97%|█████████▋| 885/908 [17:56<00:19,  1.21it/s]
 98%|█████████▊| 886/908 [18:00<00:40,  1.83s/it]
 98%|█████████▊| 887/908 [18:00<00:28,  1.34s/it]
 98%|█████████▊| 888/908 [18:00<00:19,  1.00it/s]
 98%|█████████▊| 889/908 [18:00<00:14,  1.32it/s]
 98%|█████████▊| 890/908 [18:05<00:33,  1.88s/it]
 98%|█████████▊| 891/908 [18:05<00:23,  1.38s/it]
 98%|█████████▊| 892/908 [18:05<00:16,  1.02s/it]
 98%|█████████▊| 893/908 [18:05<00:11,  1.29it/s]
 98%|█████████▊| 894/908 [18:10<00:25,  1.83s/it]
 99%|█████████▊| 895/908 [18:10<00:17,  1.34s/it]
 99%|█████████▊| 896/908 [18:10<00:11,  1.01it/s]
 99%|█████████▉| 897/908 [18:10<00:08,  1.33it/s]
 99%|█████████▉| 898/908 [18:16<00:21,  2.13s/it]
 99%|█████████▉| 899/908 [18:16<00:14,  1.56s/it]
+[Batch:   900] training loss = 0.433
+
 99%|█████████▉| 900/908 [18:16<00:09,  1.15s/it]
 99%|█████████▉| 901/908 [18:16<00:06,  1.12it/s]
 99%|█████████▉| 902/908 [18:19<00:08,  1.40s/it]
 99%|█████████▉| 903/908 [18:19<00:05,  1.04s/it]
100%|█████████▉| 904/908 [18:19<00:03,  1.28it/s]
100%|█████████▉| 905/908 [18:20<00:01,  1.65it/s]
100%|█████████▉| 906/908 [18:23<00:02,  1.44s/it]
100%|█████████▉| 907/908 [18:23<00:01,  1.07s/it]
100%|██████████| 908/908 [18:23<00:00,  1.27it/s]
100%|██████████| 908/908 [18:23<00:00,  1.22s/it]
+
+Epoch   8: validation
+
  0%|          | 0/101 [00:00<?, ?it/s]
  1%|          | 1/101 [00:05<08:50,  5.31s/it]
  2%|▏         | 2/101 [00:05<03:45,  2.28s/it]
  4%|▍         | 4/101 [00:05<01:26,  1.12it/s]
  5%|▍         | 5/101 [00:10<03:14,  2.03s/it]
  7%|▋         | 7/101 [00:10<01:44,  1.12s/it]
  9%|▉         | 9/101 [00:15<02:33,  1.67s/it]
 11%|█         | 11/101 [00:15<01:38,  1.09s/it]
 13%|█▎        | 13/101 [00:21<02:32,  1.74s/it]
 15%|█▍        | 15/101 [00:21<01:42,  1.19s/it]
 17%|█▋        | 17/101 [00:28<02:42,  1.94s/it]
 18%|█▊        | 18/101 [00:28<02:14,  1.62s/it]
 20%|█▉        | 20/101 [00:29<01:27,  1.08s/it]
 21%|██        | 21/101 [00:35<02:54,  2.18s/it]
 23%|██▎       | 23/101 [00:35<01:52,  1.44s/it]
 25%|██▍       | 25/101 [00:43<02:51,  2.26s/it]
 27%|██▋       | 27/101 [00:44<01:57,  1.59s/it]
 29%|██▊       | 29/101 [00:50<02:36,  2.17s/it]
 31%|███       | 31/101 [00:51<01:47,  1.54s/it]
 33%|███▎      | 33/101 [00:56<02:11,  1.94s/it]
 34%|███▎      | 34/101 [00:57<01:50,  1.65s/it]
 35%|███▍      | 35/101 [00:57<01:32,  1.39s/it]
 37%|███▋      | 37/101 [01:08<03:10,  2.98s/it]
 39%|███▊      | 39/101 [01:09<02:07,  2.05s/it]
 41%|████      | 41/101 [01:15<02:26,  2.43s/it]
 43%|████▎     | 43/101 [01:16<01:40,  1.73s/it]
 45%|████▍     | 45/101 [01:23<02:12,  2.36s/it]
 47%|████▋     | 47/101 [01:24<01:33,  1.73s/it]
 49%|████▊     | 49/101 [01:30<01:53,  2.19s/it]
 50%|█████     | 51/101 [01:31<01:21,  1.64s/it]
 52%|█████▏    | 53/101 [01:38<01:40,  2.10s/it]
 54%|█████▍    | 55/101 [01:38<01:09,  1.52s/it]
 56%|█████▋    | 57/101 [01:43<01:23,  1.89s/it]
 58%|█████▊    | 59/101 [01:44<01:02,  1.48s/it]
 60%|██████    | 61/101 [01:52<01:27,  2.19s/it]
 62%|██████▏   | 63/101 [01:53<01:03,  1.68s/it]
 64%|██████▍   | 65/101 [02:01<01:25,  2.38s/it]
 65%|██████▌   | 66/101 [02:01<01:09,  1.99s/it]
 66%|██████▋   | 67/101 [02:03<01:05,  1.92s/it]
 68%|██████▊   | 69/101 [02:10<01:19,  2.49s/it]
 70%|███████   | 71/101 [02:10<00:51,  1.72s/it]
 72%|███████▏  | 73/101 [02:17<01:02,  2.24s/it]
 74%|███████▍  | 75/101 [02:18<00:42,  1.63s/it]
 76%|███████▌  | 77/101 [02:24<00:49,  2.08s/it]
 78%|███████▊  | 79/101 [02:24<00:33,  1.54s/it]
 80%|████████  | 81/101 [02:32<00:44,  2.21s/it]
 82%|████████▏ | 83/101 [02:32<00:29,  1.61s/it]
 84%|████████▍ | 85/101 [02:41<00:37,  2.37s/it]
 86%|████████▌ | 87/101 [02:41<00:23,  1.71s/it]
 88%|████████▊ | 89/101 [02:48<00:27,  2.30s/it]
 90%|█████████ | 91/101 [02:49<00:16,  1.67s/it]
 92%|█████████▏| 93/101 [02:56<00:18,  2.32s/it]
 94%|█████████▍| 95/101 [02:57<00:09,  1.65s/it]
 96%|█████████▌| 97/101 [03:08<00:11,  2.89s/it]
 98%|█████████▊| 99/101 [03:08<00:04,  2.05s/it]
100%|██████████| 101/101 [03:12<00:00,  2.01s/it]
100%|██████████| 101/101 [03:12<00:00,  1.91s/it]
+    Epoch  8: training loss = 0.439 / validation loss = 0.442
+    Epoch  8: training acc = 0.791 / validation acc = 0.790
+
+Epoch   9: training
+
  0%|          | 0/908 [00:00<?, ?it/s]
  0%|          | 1/908 [00:07<1:52:43,  7.46s/it]
  0%|          | 2/908 [00:07<48:03,  3.18s/it]  
  0%|          | 3/908 [00:07<27:21,  1.81s/it]
  0%|          | 4/908 [00:08<17:42,  1.18s/it]
  1%|          | 5/908 [00:15<54:05,  3.59s/it]
  1%|          | 6/908 [00:16<36:41,  2.44s/it]
  1%|          | 7/908 [00:16<25:35,  1.70s/it]
  1%|          | 8/908 [00:16<18:25,  1.23s/it]
  1%|          | 9/908 [00:24<50:04,  3.34s/it]
  1%|          | 10/908 [00:24<35:27,  2.37s/it]
  1%|          | 11/908 [00:24<25:26,  1.70s/it]
  1%|▏         | 12/908 [00:25<18:33,  1.24s/it]
  1%|▏         | 13/908 [00:32<45:26,  3.05s/it]
  2%|▏         | 14/908 [00:32<32:33,  2.18s/it]
  2%|▏         | 15/908 [00:32<23:34,  1.58s/it]
  2%|▏         | 16/908 [00:32<17:17,  1.16s/it]
  2%|▏         | 17/908 [00:39<43:11,  2.91s/it]
  2%|▏         | 18/908 [00:40<31:02,  2.09s/it]
  2%|▏         | 19/908 [00:40<22:31,  1.52s/it]
+[Batch:    20] training loss = 0.516
+
  2%|▏         | 20/908 [00:40<16:34,  1.12s/it]
  2%|▏         | 21/908 [00:46<37:17,  2.52s/it]
  2%|▏         | 22/908 [00:46<26:55,  1.82s/it]
  3%|▎         | 23/908 [00:46<19:38,  1.33s/it]
  3%|▎         | 24/908 [00:46<14:33,  1.01it/s]
  3%|▎         | 25/908 [00:56<55:18,  3.76s/it]
  3%|▎         | 26/908 [00:57<39:31,  2.69s/it]
  3%|▎         | 27/908 [00:57<28:29,  1.94s/it]
  3%|▎         | 28/908 [00:57<20:45,  1.42s/it]
  3%|▎         | 29/908 [01:05<51:21,  3.51s/it]
  3%|▎         | 30/908 [01:06<37:00,  2.53s/it]
  3%|▎         | 31/908 [01:06<26:43,  1.83s/it]
  4%|▎         | 32/908 [01:06<19:30,  1.34s/it]
  4%|▎         | 33/908 [01:14<47:50,  3.28s/it]
  4%|▎         | 34/908 [01:14<36:10,  2.48s/it]
  4%|▍         | 35/908 [01:15<26:07,  1.80s/it]
  4%|▍         | 36/908 [01:15<19:04,  1.31s/it]
  4%|▍         | 37/908 [01:22<44:44,  3.08s/it]
  4%|▍         | 38/908 [01:22<33:06,  2.28s/it]
  4%|▍         | 39/908 [01:23<23:58,  1.66s/it]
+[Batch:    40] training loss = 0.392
+
  4%|▍         | 40/908 [01:23<17:33,  1.21s/it]
  5%|▍         | 41/908 [01:30<43:04,  2.98s/it]
  5%|▍         | 42/908 [01:30<31:00,  2.15s/it]
  5%|▍         | 43/908 [01:30<22:29,  1.56s/it]
  5%|▍         | 44/908 [01:31<16:32,  1.15s/it]
  5%|▍         | 45/908 [01:38<42:56,  2.99s/it]
  5%|▌         | 46/908 [01:38<30:50,  2.15s/it]
  5%|▌         | 47/908 [01:38<22:23,  1.56s/it]
  5%|▌         | 48/908 [01:38<16:26,  1.15s/it]
  5%|▌         | 49/908 [01:46<44:47,  3.13s/it]
  6%|▌         | 50/908 [01:46<32:17,  2.26s/it]
  6%|▌         | 51/908 [01:47<24:08,  1.69s/it]
  6%|▌         | 52/908 [01:47<17:42,  1.24s/it]
  6%|▌         | 53/908 [01:58<1:01:09,  4.29s/it]
  6%|▌         | 54/908 [01:59<43:34,  3.06s/it]  
  6%|▌         | 55/908 [01:59<31:15,  2.20s/it]
  6%|▌         | 56/908 [01:59<22:39,  1.60s/it]
  6%|▋         | 57/908 [02:07<48:38,  3.43s/it]
  6%|▋         | 58/908 [02:07<35:33,  2.51s/it]
  6%|▋         | 59/908 [02:07<25:40,  1.81s/it]
+[Batch:    60] training loss = 0.404
+
  7%|▋         | 60/908 [02:07<18:44,  1.33s/it]
  7%|▋         | 61/908 [02:14<42:40,  3.02s/it]
  7%|▋         | 62/908 [02:15<33:02,  2.34s/it]
  7%|▋         | 63/908 [02:15<23:53,  1.70s/it]
  7%|▋         | 64/908 [02:15<17:33,  1.25s/it]
  7%|▋         | 65/908 [02:24<46:23,  3.30s/it]
  7%|▋         | 66/908 [02:24<33:46,  2.41s/it]
  7%|▋         | 67/908 [02:24<24:24,  1.74s/it]
  7%|▋         | 68/908 [02:24<17:59,  1.28s/it]
  8%|▊         | 69/908 [02:32<46:37,  3.33s/it]
  8%|▊         | 70/908 [02:33<33:32,  2.40s/it]
  8%|▊         | 71/908 [02:33<24:16,  1.74s/it]
  8%|▊         | 72/908 [02:33<17:46,  1.28s/it]
  8%|▊         | 73/908 [02:41<47:30,  3.41s/it]
  8%|▊         | 74/908 [02:42<34:05,  2.45s/it]
  8%|▊         | 75/908 [02:42<24:37,  1.77s/it]
  8%|▊         | 76/908 [02:42<18:01,  1.30s/it]
  8%|▊         | 77/908 [02:50<45:16,  3.27s/it]
  9%|▊         | 78/908 [02:50<32:51,  2.38s/it]
  9%|▊         | 79/908 [02:50<23:45,  1.72s/it]
+[Batch:    80] training loss = 0.489
+
  9%|▉         | 80/908 [02:51<17:28,  1.27s/it]
  9%|▉         | 81/908 [03:02<58:07,  4.22s/it]
  9%|▉         | 82/908 [03:02<41:51,  3.04s/it]
  9%|▉         | 83/908 [03:02<30:07,  2.19s/it]
  9%|▉         | 84/908 [03:02<21:54,  1.60s/it]
  9%|▉         | 85/908 [03:09<42:53,  3.13s/it]
  9%|▉         | 86/908 [03:09<31:12,  2.28s/it]
 10%|▉         | 87/908 [03:10<22:38,  1.65s/it]
 10%|▉         | 88/908 [03:10<16:38,  1.22s/it]
 10%|▉         | 89/908 [03:16<37:24,  2.74s/it]
 10%|▉         | 90/908 [03:17<28:49,  2.11s/it]
 10%|█         | 91/908 [03:17<20:58,  1.54s/it]
 10%|█         | 92/908 [03:17<15:30,  1.14s/it]
 10%|█         | 93/908 [03:25<41:28,  3.05s/it]
 10%|█         | 94/908 [03:25<30:51,  2.27s/it]
 10%|█         | 95/908 [03:25<22:24,  1.65s/it]
 11%|█         | 96/908 [03:26<16:27,  1.22s/it]
 11%|█         | 97/908 [03:33<43:00,  3.18s/it]
 11%|█         | 98/908 [03:34<32:27,  2.40s/it]
 11%|█         | 99/908 [03:34<23:30,  1.74s/it]
+[Batch:   100] training loss = 0.440
+
 11%|█         | 100/908 [03:34<17:13,  1.28s/it]
 11%|█         | 101/908 [03:42<42:07,  3.13s/it]
 11%|█         | 102/908 [03:42<31:00,  2.31s/it]
 11%|█▏        | 103/908 [03:42<22:28,  1.67s/it]
 11%|█▏        | 104/908 [03:43<16:29,  1.23s/it]
 12%|█▏        | 105/908 [03:50<40:58,  3.06s/it]
 12%|█▏        | 106/908 [03:50<30:25,  2.28s/it]
 12%|█▏        | 107/908 [03:50<22:03,  1.65s/it]
 12%|█▏        | 108/908 [03:51<16:24,  1.23s/it]
 12%|█▏        | 109/908 [04:02<58:00,  4.36s/it]
 12%|█▏        | 110/908 [04:03<43:31,  3.27s/it]
 12%|█▏        | 111/908 [04:03<31:13,  2.35s/it]
 12%|█▏        | 112/908 [04:04<22:37,  1.71s/it]
 12%|█▏        | 113/908 [04:11<43:33,  3.29s/it]
 13%|█▎        | 114/908 [04:11<32:15,  2.44s/it]
 13%|█▎        | 115/908 [04:11<23:20,  1.77s/it]
 13%|█▎        | 116/908 [04:11<17:05,  1.30s/it]
 13%|█▎        | 117/908 [04:19<40:57,  3.11s/it]
 13%|█▎        | 118/908 [04:19<30:44,  2.33s/it]
 13%|█▎        | 119/908 [04:19<22:17,  1.70s/it]
+[Batch:   120] training loss = 0.388
+
 13%|█▎        | 120/908 [04:20<16:21,  1.25s/it]
 13%|█▎        | 121/908 [04:27<40:55,  3.12s/it]
 13%|█▎        | 122/908 [04:27<29:27,  2.25s/it]
 14%|█▎        | 123/908 [04:28<21:23,  1.63s/it]
 14%|█▎        | 124/908 [04:28<15:44,  1.20s/it]
 14%|█▍        | 125/908 [04:36<42:33,  3.26s/it]
 14%|█▍        | 126/908 [04:36<30:35,  2.35s/it]
 14%|█▍        | 127/908 [04:36<22:12,  1.71s/it]
 14%|█▍        | 128/908 [04:36<16:16,  1.25s/it]
 14%|█▍        | 129/908 [04:43<38:23,  2.96s/it]
 14%|█▍        | 130/908 [04:44<27:39,  2.13s/it]
 14%|█▍        | 131/908 [04:44<20:07,  1.55s/it]
 15%|█▍        | 132/908 [04:44<14:50,  1.15s/it]
 15%|█▍        | 133/908 [04:53<43:35,  3.37s/it]
 15%|█▍        | 134/908 [04:53<31:15,  2.42s/it]
 15%|█▍        | 135/908 [04:53<22:39,  1.76s/it]
 15%|█▍        | 136/908 [04:53<16:34,  1.29s/it]
 15%|█▌        | 137/908 [05:04<54:08,  4.21s/it]
 15%|█▌        | 138/908 [05:05<39:28,  3.08s/it]
 15%|█▌        | 139/908 [05:05<28:19,  2.21s/it]
+[Batch:   140] training loss = 0.377
+
 15%|█▌        | 140/908 [05:05<20:32,  1.60s/it]
 16%|█▌        | 141/908 [05:11<35:40,  2.79s/it]
 16%|█▌        | 142/908 [05:11<27:33,  2.16s/it]
 16%|█▌        | 143/908 [05:11<20:01,  1.57s/it]
 16%|█▌        | 144/908 [05:12<14:45,  1.16s/it]
 16%|█▌        | 145/908 [05:19<39:27,  3.10s/it]
 16%|█▌        | 146/908 [05:20<28:33,  2.25s/it]
 16%|█▌        | 147/908 [05:20<20:40,  1.63s/it]
 16%|█▋        | 148/908 [05:20<15:12,  1.20s/it]
 16%|█▋        | 149/908 [05:28<41:26,  3.28s/it]
 17%|█▋        | 150/908 [05:28<30:21,  2.40s/it]
 17%|█▋        | 151/908 [05:29<21:55,  1.74s/it]
 17%|█▋        | 152/908 [05:29<16:02,  1.27s/it]
 17%|█▋        | 153/908 [05:36<37:32,  2.98s/it]
 17%|█▋        | 154/908 [05:36<28:46,  2.29s/it]
 17%|█▋        | 155/908 [05:37<20:50,  1.66s/it]
 17%|█▋        | 156/908 [05:37<15:17,  1.22s/it]
 17%|█▋        | 157/908 [05:45<40:07,  3.21s/it]
 17%|█▋        | 158/908 [05:45<29:46,  2.38s/it]
 18%|█▊        | 159/908 [05:45<21:31,  1.72s/it]
+[Batch:   160] training loss = 0.443
+
 18%|█▊        | 160/908 [05:45<15:45,  1.26s/it]
 18%|█▊        | 161/908 [05:53<37:37,  3.02s/it]
 18%|█▊        | 162/908 [05:53<28:00,  2.25s/it]
 18%|█▊        | 163/908 [05:53<20:17,  1.63s/it]
 18%|█▊        | 164/908 [05:53<14:53,  1.20s/it]
 18%|█▊        | 165/908 [06:05<52:48,  4.26s/it]
 18%|█▊        | 166/908 [06:06<41:02,  3.32s/it]
 18%|█▊        | 167/908 [06:06<29:23,  2.38s/it]
 19%|█▊        | 168/908 [06:06<21:13,  1.72s/it]
 19%|█▊        | 169/908 [06:12<35:39,  2.90s/it]
 19%|█▊        | 170/908 [06:13<29:18,  2.38s/it]
 19%|█▉        | 171/908 [06:13<21:10,  1.72s/it]
 19%|█▉        | 172/908 [06:14<15:30,  1.26s/it]
 19%|█▉        | 173/908 [06:21<36:33,  2.98s/it]
 19%|█▉        | 174/908 [06:22<30:32,  2.50s/it]
 19%|█▉        | 175/908 [06:22<22:04,  1.81s/it]
 19%|█▉        | 176/908 [06:22<16:08,  1.32s/it]
 19%|█▉        | 177/908 [06:28<32:11,  2.64s/it]
 20%|█▉        | 178/908 [06:29<26:39,  2.19s/it]
 20%|█▉        | 179/908 [06:29<19:20,  1.59s/it]
+[Batch:   180] training loss = 0.375
+
 20%|█▉        | 180/908 [06:30<14:12,  1.17s/it]
 20%|█▉        | 181/908 [06:37<37:50,  3.12s/it]
 20%|██        | 182/908 [06:39<31:49,  2.63s/it]
 20%|██        | 183/908 [06:39<22:56,  1.90s/it]
 20%|██        | 184/908 [06:39<16:43,  1.39s/it]
 20%|██        | 185/908 [06:45<33:42,  2.80s/it]
 20%|██        | 186/908 [06:47<30:37,  2.54s/it]
 21%|██        | 187/908 [06:47<22:05,  1.84s/it]
 21%|██        | 188/908 [06:47<16:09,  1.35s/it]
 21%|██        | 189/908 [06:54<34:38,  2.89s/it]
 21%|██        | 190/908 [06:56<31:31,  2.63s/it]
 21%|██        | 191/908 [06:56<22:45,  1.90s/it]
 21%|██        | 192/908 [06:56<16:36,  1.39s/it]
 21%|██▏       | 193/908 [07:03<36:44,  3.08s/it]
 21%|██▏       | 194/908 [07:05<31:18,  2.63s/it]
 21%|██▏       | 195/908 [07:05<22:33,  1.90s/it]
 22%|██▏       | 196/908 [07:05<16:27,  1.39s/it]
 22%|██▏       | 197/908 [07:10<28:15,  2.38s/it]
 22%|██▏       | 198/908 [07:13<28:45,  2.43s/it]
 22%|██▏       | 199/908 [07:13<20:48,  1.76s/it]
+[Batch:   200] training loss = 0.409
+
 22%|██▏       | 200/908 [07:13<15:12,  1.29s/it]
 22%|██▏       | 201/908 [07:19<29:57,  2.54s/it]
 22%|██▏       | 202/908 [07:20<27:10,  2.31s/it]
 22%|██▏       | 203/908 [07:20<19:40,  1.67s/it]
 22%|██▏       | 204/908 [07:21<14:25,  1.23s/it]
 23%|██▎       | 205/908 [07:27<31:07,  2.66s/it]
 23%|██▎       | 206/908 [07:27<24:35,  2.10s/it]
 23%|██▎       | 207/908 [07:28<17:52,  1.53s/it]
 23%|██▎       | 208/908 [07:28<13:10,  1.13s/it]
 23%|██▎       | 209/908 [07:34<29:55,  2.57s/it]
 23%|██▎       | 210/908 [07:35<24:23,  2.10s/it]
 23%|██▎       | 211/908 [07:35<17:42,  1.52s/it]
 23%|██▎       | 212/908 [07:35<13:03,  1.13s/it]
 23%|██▎       | 213/908 [07:41<30:24,  2.63s/it]
 24%|██▎       | 214/908 [07:42<22:41,  1.96s/it]
 24%|██▎       | 215/908 [07:42<16:31,  1.43s/it]
 24%|██▍       | 216/908 [07:42<12:12,  1.06s/it]
 24%|██▍       | 217/908 [07:48<27:49,  2.42s/it]
 24%|██▍       | 218/908 [07:49<22:40,  1.97s/it]
 24%|██▍       | 219/908 [07:49<16:29,  1.44s/it]
+[Batch:   220] training loss = 0.381
+
 24%|██▍       | 220/908 [07:49<12:10,  1.06s/it]
 24%|██▍       | 221/908 [07:53<21:54,  1.91s/it]
 24%|██▍       | 222/908 [07:55<22:05,  1.93s/it]
 25%|██▍       | 223/908 [07:55<16:06,  1.41s/it]
 25%|██▍       | 224/908 [07:55<11:53,  1.04s/it]
 25%|██▍       | 225/908 [08:02<29:51,  2.62s/it]
 25%|██▍       | 226/908 [08:03<26:52,  2.36s/it]
 25%|██▌       | 227/908 [08:03<19:25,  1.71s/it]
 25%|██▌       | 228/908 [08:04<14:14,  1.26s/it]
 25%|██▌       | 229/908 [08:10<30:30,  2.70s/it]
 25%|██▌       | 230/908 [08:11<26:59,  2.39s/it]
 25%|██▌       | 231/908 [08:12<19:29,  1.73s/it]
 26%|██▌       | 232/908 [08:12<14:16,  1.27s/it]
 26%|██▌       | 233/908 [08:18<30:43,  2.73s/it]
 26%|██▌       | 234/908 [08:19<26:11,  2.33s/it]
 26%|██▌       | 235/908 [08:20<18:56,  1.69s/it]
 26%|██▌       | 236/908 [08:20<13:52,  1.24s/it]
 26%|██▌       | 237/908 [08:26<31:28,  2.81s/it]
 26%|██▌       | 238/908 [08:28<27:12,  2.44s/it]
 26%|██▋       | 239/908 [08:28<19:38,  1.76s/it]
+[Batch:   240] training loss = 0.309
+
 26%|██▋       | 240/908 [08:28<14:21,  1.29s/it]
 27%|██▋       | 241/908 [08:35<32:09,  2.89s/it]
 27%|██▋       | 242/908 [08:36<26:45,  2.41s/it]
 27%|██▋       | 243/908 [08:36<19:20,  1.75s/it]
 27%|██▋       | 244/908 [08:36<14:07,  1.28s/it]
 27%|██▋       | 245/908 [08:42<29:33,  2.67s/it]
 27%|██▋       | 246/908 [08:44<25:58,  2.35s/it]
 27%|██▋       | 247/908 [08:44<18:46,  1.70s/it]
 27%|██▋       | 248/908 [08:44<13:46,  1.25s/it]
 27%|██▋       | 249/908 [08:51<33:00,  3.01s/it]
 28%|██▊       | 250/908 [08:53<27:58,  2.55s/it]
 28%|██▊       | 251/908 [08:53<20:09,  1.84s/it]
 28%|██▊       | 252/908 [08:53<14:42,  1.34s/it]
 28%|██▊       | 253/908 [09:00<32:53,  3.01s/it]
 28%|██▊       | 254/908 [09:03<33:17,  3.05s/it]
 28%|██▊       | 255/908 [09:04<23:54,  2.20s/it]
 28%|██▊       | 256/908 [09:04<17:22,  1.60s/it]
 28%|██▊       | 257/908 [09:11<36:45,  3.39s/it]
 28%|██▊       | 258/908 [09:13<29:45,  2.75s/it]
 29%|██▊       | 259/908 [09:13<21:28,  1.98s/it]
+[Batch:   260] training loss = 0.377
+
 29%|██▊       | 260/908 [09:13<15:40,  1.45s/it]
 29%|██▊       | 261/908 [09:18<26:49,  2.49s/it]
 29%|██▉       | 262/908 [09:19<23:29,  2.18s/it]
 29%|██▉       | 263/908 [09:20<17:04,  1.59s/it]
 29%|██▉       | 264/908 [09:20<12:34,  1.17s/it]
 29%|██▉       | 265/908 [09:26<28:02,  2.62s/it]
 29%|██▉       | 266/908 [09:28<25:52,  2.42s/it]
 29%|██▉       | 267/908 [09:28<18:43,  1.75s/it]
 30%|██▉       | 268/908 [09:28<13:43,  1.29s/it]
 30%|██▉       | 269/908 [09:34<28:17,  2.66s/it]
 30%|██▉       | 270/908 [09:36<24:48,  2.33s/it]
 30%|██▉       | 271/908 [09:36<17:58,  1.69s/it]
 30%|██▉       | 272/908 [09:36<13:11,  1.24s/it]
 30%|███       | 273/908 [09:42<28:04,  2.65s/it]
 30%|███       | 274/908 [09:44<25:52,  2.45s/it]
 30%|███       | 275/908 [09:44<18:44,  1.78s/it]
 30%|███       | 276/908 [09:44<13:42,  1.30s/it]
 31%|███       | 277/908 [09:50<27:56,  2.66s/it]
 31%|███       | 278/908 [09:52<24:54,  2.37s/it]
 31%|███       | 279/908 [09:52<18:01,  1.72s/it]
+[Batch:   280] training loss = 0.457
+
 31%|███       | 280/908 [09:52<13:13,  1.26s/it]
 31%|███       | 281/908 [09:59<30:45,  2.94s/it]
 31%|███       | 282/908 [10:00<25:42,  2.46s/it]
 31%|███       | 283/908 [10:01<18:37,  1.79s/it]
 31%|███▏      | 284/908 [10:01<13:37,  1.31s/it]
 31%|███▏      | 285/908 [10:10<38:26,  3.70s/it]
 31%|███▏      | 286/908 [10:12<31:44,  3.06s/it]
 32%|███▏      | 287/908 [10:12<22:48,  2.20s/it]
 32%|███▏      | 288/908 [10:12<16:34,  1.60s/it]
 32%|███▏      | 289/908 [10:16<25:03,  2.43s/it]
 32%|███▏      | 290/908 [10:18<22:08,  2.15s/it]
 32%|███▏      | 291/908 [10:18<16:03,  1.56s/it]
 32%|███▏      | 292/908 [10:18<11:49,  1.15s/it]
 32%|███▏      | 293/908 [10:26<30:32,  2.98s/it]
 32%|███▏      | 294/908 [10:27<26:30,  2.59s/it]
 32%|███▏      | 295/908 [10:27<19:08,  1.87s/it]
 33%|███▎      | 296/908 [10:28<14:00,  1.37s/it]
 33%|███▎      | 297/908 [10:35<32:38,  3.21s/it]
 33%|███▎      | 298/908 [10:36<25:49,  2.54s/it]
 33%|███▎      | 299/908 [10:36<18:40,  1.84s/it]
+[Batch:   300] training loss = 0.415
+
 33%|███▎      | 300/908 [10:36<13:39,  1.35s/it]
 33%|███▎      | 301/908 [10:43<29:18,  2.90s/it]
 33%|███▎      | 302/908 [10:44<24:47,  2.45s/it]
 33%|███▎      | 303/908 [10:45<17:54,  1.78s/it]
 33%|███▎      | 304/908 [10:45<13:05,  1.30s/it]
 34%|███▎      | 305/908 [10:51<28:00,  2.79s/it]
 34%|███▎      | 306/908 [10:52<22:45,  2.27s/it]
 34%|███▍      | 307/908 [10:52<16:31,  1.65s/it]
 34%|███▍      | 308/908 [10:53<12:10,  1.22s/it]
 34%|███▍      | 309/908 [11:00<31:16,  3.13s/it]
 34%|███▍      | 310/908 [11:02<27:15,  2.74s/it]
 34%|███▍      | 311/908 [11:02<19:38,  1.97s/it]
 34%|███▍      | 312/908 [11:02<14:18,  1.44s/it]
 34%|███▍      | 313/908 [11:10<33:51,  3.41s/it]
 35%|███▍      | 314/908 [11:12<28:27,  2.87s/it]
 35%|███▍      | 315/908 [11:12<20:27,  2.07s/it]
 35%|███▍      | 316/908 [11:12<14:50,  1.50s/it]
 35%|███▍      | 317/908 [11:18<27:23,  2.78s/it]
 35%|███▌      | 318/908 [11:19<22:17,  2.27s/it]
 35%|███▌      | 319/908 [11:19<16:07,  1.64s/it]
+[Batch:   320] training loss = 0.415
+
 35%|███▌      | 320/908 [11:20<11:50,  1.21s/it]
 35%|███▌      | 321/908 [11:26<26:00,  2.66s/it]
 35%|███▌      | 322/908 [11:26<19:40,  2.01s/it]
 36%|███▌      | 323/908 [11:26<14:20,  1.47s/it]
 36%|███▌      | 324/908 [11:26<10:34,  1.09s/it]
 36%|███▌      | 325/908 [11:30<17:09,  1.77s/it]
 36%|███▌      | 326/908 [11:30<12:37,  1.30s/it]
 36%|███▌      | 327/908 [11:30<09:23,  1.03it/s]
 36%|███▌      | 328/908 [11:30<07:07,  1.36it/s]
 36%|███▌      | 329/908 [11:34<16:21,  1.70s/it]
 36%|███▋      | 330/908 [11:35<12:27,  1.29s/it]
 36%|███▋      | 331/908 [11:35<09:15,  1.04it/s]
 37%|███▋      | 332/908 [11:35<07:03,  1.36it/s]
 37%|███▋      | 333/908 [11:41<20:33,  2.15s/it]
 37%|███▋      | 334/908 [11:41<15:17,  1.60s/it]
 37%|███▋      | 335/908 [11:41<11:14,  1.18s/it]
 37%|███▋      | 336/908 [11:41<08:23,  1.14it/s]
 37%|███▋      | 337/908 [11:46<18:21,  1.93s/it]
 37%|███▋      | 338/908 [11:46<14:54,  1.57s/it]
 37%|███▋      | 339/908 [11:47<10:57,  1.15s/it]
+[Batch:   340] training loss = 0.484
+
 37%|███▋      | 340/908 [11:47<08:11,  1.16it/s]
 38%|███▊      | 341/908 [11:50<14:53,  1.58s/it]
 38%|███▊      | 342/908 [11:50<10:58,  1.16s/it]
 38%|███▊      | 343/908 [11:50<08:12,  1.15it/s]
 38%|███▊      | 344/908 [11:51<06:16,  1.50it/s]
 38%|███▊      | 345/908 [11:55<17:12,  1.83s/it]
 38%|███▊      | 346/908 [11:55<12:35,  1.34s/it]
 38%|███▊      | 347/908 [11:56<09:20,  1.00it/s]
 38%|███▊      | 348/908 [11:56<07:04,  1.32it/s]
 38%|███▊      | 349/908 [12:00<16:39,  1.79s/it]
 39%|███▊      | 350/908 [12:00<12:13,  1.31s/it]
 39%|███▊      | 351/908 [12:00<09:05,  1.02it/s]
 39%|███▉      | 352/908 [12:01<06:54,  1.34it/s]
 39%|███▉      | 353/908 [12:08<25:01,  2.71s/it]
 39%|███▉      | 354/908 [12:08<18:55,  2.05s/it]
 39%|███▉      | 355/908 [12:09<13:44,  1.49s/it]
 39%|███▉      | 356/908 [12:09<10:08,  1.10s/it]
 39%|███▉      | 357/908 [12:12<16:10,  1.76s/it]
 39%|███▉      | 358/908 [12:12<11:49,  1.29s/it]
 40%|███▉      | 359/908 [12:12<08:46,  1.04it/s]
+[Batch:   360] training loss = 0.444
+
 40%|███▉      | 360/908 [12:13<06:39,  1.37it/s]
 40%|███▉      | 361/908 [12:17<15:53,  1.74s/it]
 40%|███▉      | 362/908 [12:17<11:37,  1.28s/it]
 40%|███▉      | 363/908 [12:17<08:37,  1.05it/s]
 40%|████      | 364/908 [12:17<06:32,  1.38it/s]
 40%|████      | 365/908 [12:21<14:13,  1.57s/it]
 40%|████      | 366/908 [12:21<10:27,  1.16s/it]
 40%|████      | 367/908 [12:21<07:50,  1.15it/s]
 41%|████      | 368/908 [12:21<06:00,  1.50it/s]
 41%|████      | 369/908 [12:24<11:37,  1.29s/it]
 41%|████      | 370/908 [12:24<08:38,  1.04it/s]
 41%|████      | 371/908 [12:25<06:32,  1.37it/s]
 41%|████      | 372/908 [12:25<05:06,  1.75it/s]
 41%|████      | 373/908 [12:29<14:07,  1.58s/it]
 41%|████      | 374/908 [12:29<10:24,  1.17s/it]
 41%|████▏     | 375/908 [12:29<07:46,  1.14it/s]
 41%|████▏     | 376/908 [12:29<05:56,  1.49it/s]
 42%|████▏     | 377/908 [12:33<14:07,  1.60s/it]
 42%|████▏     | 378/908 [12:33<10:21,  1.17s/it]
 42%|████▏     | 379/908 [12:33<07:45,  1.14it/s]
+[Batch:   380] training loss = 0.465
+
 42%|████▏     | 380/908 [12:34<05:55,  1.48it/s]
 42%|████▏     | 381/908 [12:39<19:10,  2.18s/it]
 42%|████▏     | 382/908 [12:39<13:55,  1.59s/it]
 42%|████▏     | 383/908 [12:40<10:12,  1.17s/it]
 42%|████▏     | 384/908 [12:40<07:36,  1.15it/s]
 42%|████▏     | 385/908 [12:44<15:23,  1.76s/it]
 43%|████▎     | 386/908 [12:44<11:17,  1.30s/it]
 43%|████▎     | 387/908 [12:44<08:22,  1.04it/s]
 43%|████▎     | 388/908 [12:44<06:19,  1.37it/s]
 43%|████▎     | 389/908 [12:49<16:39,  1.92s/it]
 43%|████▎     | 390/908 [12:49<12:09,  1.41s/it]
 43%|████▎     | 391/908 [12:49<08:59,  1.04s/it]
 43%|████▎     | 392/908 [12:50<06:46,  1.27it/s]
 43%|████▎     | 393/908 [12:54<17:22,  2.02s/it]
 43%|████▎     | 394/908 [12:55<12:38,  1.48s/it]
 44%|████▎     | 395/908 [12:55<09:19,  1.09s/it]
 44%|████▎     | 396/908 [12:55<07:00,  1.22it/s]
 44%|████▎     | 397/908 [13:00<16:47,  1.97s/it]
 44%|████▍     | 398/908 [13:00<12:13,  1.44s/it]
 44%|████▍     | 399/908 [13:00<09:02,  1.06s/it]
+[Batch:   400] training loss = 0.437
+
 44%|████▍     | 400/908 [13:00<06:46,  1.25it/s]
 44%|████▍     | 401/908 [13:05<15:46,  1.87s/it]
 44%|████▍     | 402/908 [13:05<11:30,  1.36s/it]
 44%|████▍     | 403/908 [13:05<08:31,  1.01s/it]
 44%|████▍     | 404/908 [13:05<06:25,  1.31it/s]
 45%|████▍     | 405/908 [13:11<19:54,  2.37s/it]
 45%|████▍     | 406/908 [13:12<14:22,  1.72s/it]
 45%|████▍     | 407/908 [13:12<10:30,  1.26s/it]
 45%|████▍     | 408/908 [13:12<07:49,  1.07it/s]
 45%|████▌     | 409/908 [13:16<15:05,  1.81s/it]
 45%|████▌     | 410/908 [13:16<11:01,  1.33s/it]
 45%|████▌     | 411/908 [13:16<08:09,  1.01it/s]
 45%|████▌     | 412/908 [13:16<06:10,  1.34it/s]
 45%|████▌     | 413/908 [13:20<14:25,  1.75s/it]
 46%|████▌     | 414/908 [13:21<10:32,  1.28s/it]
 46%|████▌     | 415/908 [13:21<07:49,  1.05it/s]
 46%|████▌     | 416/908 [13:21<05:56,  1.38it/s]
 46%|████▌     | 417/908 [13:22<06:53,  1.19it/s]
 46%|████▌     | 418/908 [13:22<05:16,  1.55it/s]
 46%|████▌     | 419/908 [13:23<04:09,  1.96it/s]
+[Batch:   420] training loss = 0.385
+
 46%|████▋     | 420/908 [13:23<03:22,  2.41it/s]
 46%|████▋     | 421/908 [13:29<17:07,  2.11s/it]
 46%|████▋     | 422/908 [13:29<12:25,  1.53s/it]
 47%|████▋     | 423/908 [13:29<09:08,  1.13s/it]
 47%|████▋     | 424/908 [13:29<06:50,  1.18it/s]
 47%|████▋     | 425/908 [13:33<13:35,  1.69s/it]
 47%|████▋     | 426/908 [13:33<09:57,  1.24s/it]
 47%|████▋     | 427/908 [13:33<07:31,  1.07it/s]
 47%|████▋     | 428/908 [13:34<05:45,  1.39it/s]
 47%|████▋     | 429/908 [13:38<14:41,  1.84s/it]
 47%|████▋     | 430/908 [13:38<10:42,  1.34s/it]
 47%|████▋     | 431/908 [13:38<07:55,  1.00it/s]
 48%|████▊     | 432/908 [13:39<05:58,  1.33it/s]
 48%|████▊     | 433/908 [13:43<15:02,  1.90s/it]
 48%|████▊     | 434/908 [13:43<10:56,  1.39s/it]
 48%|████▊     | 435/908 [13:44<08:05,  1.03s/it]
 48%|████▊     | 436/908 [13:44<06:07,  1.29it/s]
 48%|████▊     | 437/908 [13:47<11:53,  1.52s/it]
 48%|████▊     | 438/908 [13:47<08:46,  1.12s/it]
 48%|████▊     | 439/908 [13:47<06:34,  1.19it/s]
+[Batch:   440] training loss = 0.469
+
 48%|████▊     | 440/908 [13:48<05:01,  1.55it/s]
 49%|████▊     | 441/908 [13:52<12:46,  1.64s/it]
 49%|████▊     | 442/908 [13:52<09:21,  1.20s/it]
 49%|████▉     | 443/908 [13:52<06:58,  1.11it/s]
 49%|████▉     | 444/908 [13:52<05:18,  1.45it/s]
 49%|████▉     | 445/908 [13:57<13:59,  1.81s/it]
 49%|████▉     | 446/908 [13:57<10:13,  1.33s/it]
 49%|████▉     | 447/908 [13:57<07:34,  1.01it/s]
 49%|████▉     | 448/908 [13:57<05:43,  1.34it/s]
 49%|████▉     | 449/908 [14:02<14:57,  1.95s/it]
 50%|████▉     | 450/908 [14:02<10:53,  1.43s/it]
 50%|████▉     | 451/908 [14:02<08:04,  1.06s/it]
 50%|████▉     | 452/908 [14:02<06:04,  1.25it/s]
 50%|████▉     | 453/908 [14:08<15:56,  2.10s/it]
 50%|█████     | 454/908 [14:08<11:35,  1.53s/it]
 50%|█████     | 455/908 [14:08<08:31,  1.13s/it]
 50%|█████     | 456/908 [14:08<06:22,  1.18it/s]
 50%|█████     | 457/908 [14:15<19:10,  2.55s/it]
 50%|█████     | 458/908 [14:15<13:49,  1.84s/it]
 51%|█████     | 459/908 [14:15<10:05,  1.35s/it]
+[Batch:   460] training loss = 0.335
+
 51%|█████     | 460/908 [14:15<07:28,  1.00s/it]
 51%|█████     | 461/908 [14:19<14:05,  1.89s/it]
 51%|█████     | 462/908 [14:19<10:15,  1.38s/it]
 51%|█████     | 463/908 [14:20<07:34,  1.02s/it]
 51%|█████     | 464/908 [14:20<05:43,  1.29it/s]
 51%|█████     | 465/908 [14:22<09:29,  1.29s/it]
 51%|█████▏    | 466/908 [14:23<07:03,  1.04it/s]
 51%|█████▏    | 467/908 [14:23<05:20,  1.38it/s]
 52%|█████▏    | 468/908 [14:23<04:09,  1.76it/s]
 52%|█████▏    | 469/908 [14:28<13:40,  1.87s/it]
 52%|█████▏    | 470/908 [14:28<09:58,  1.37s/it]
 52%|█████▏    | 471/908 [14:28<07:22,  1.01s/it]
 52%|█████▏    | 472/908 [14:28<05:33,  1.31it/s]
 52%|█████▏    | 473/908 [14:32<12:12,  1.68s/it]
 52%|█████▏    | 474/908 [14:32<08:56,  1.24s/it]
 52%|█████▏    | 475/908 [14:33<06:39,  1.08it/s]
 52%|█████▏    | 476/908 [14:33<05:05,  1.42it/s]
 53%|█████▎    | 477/908 [14:36<11:09,  1.55s/it]
 53%|█████▎    | 478/908 [14:37<08:13,  1.15s/it]
 53%|█████▎    | 479/908 [14:37<06:08,  1.16it/s]
+[Batch:   480] training loss = 0.514
+
 53%|█████▎    | 480/908 [14:37<04:41,  1.52it/s]
 53%|█████▎    | 481/908 [14:41<12:18,  1.73s/it]
 53%|█████▎    | 482/908 [14:41<09:00,  1.27s/it]
 53%|█████▎    | 483/908 [14:42<06:41,  1.06it/s]
 53%|█████▎    | 484/908 [14:42<05:04,  1.39it/s]
 53%|█████▎    | 485/908 [14:46<12:17,  1.74s/it]
 54%|█████▎    | 486/908 [14:46<09:00,  1.28s/it]
 54%|█████▎    | 487/908 [14:46<06:41,  1.05it/s]
 54%|█████▎    | 488/908 [14:46<05:03,  1.38it/s]
 54%|█████▍    | 489/908 [14:50<11:33,  1.65s/it]
 54%|█████▍    | 490/908 [14:50<08:27,  1.21s/it]
 54%|█████▍    | 491/908 [14:51<06:17,  1.10it/s]
 54%|█████▍    | 492/908 [14:51<04:48,  1.44it/s]
 54%|█████▍    | 493/908 [14:56<13:54,  2.01s/it]
 54%|█████▍    | 494/908 [14:56<10:06,  1.46s/it]
 55%|█████▍    | 495/908 [14:56<07:26,  1.08s/it]
 55%|█████▍    | 496/908 [14:56<05:35,  1.23it/s]
 55%|█████▍    | 497/908 [15:00<11:11,  1.63s/it]
 55%|█████▍    | 498/908 [15:00<08:12,  1.20s/it]
 55%|█████▍    | 499/908 [15:00<06:06,  1.12it/s]
+[Batch:   500] training loss = 0.531
+
 55%|█████▌    | 500/908 [15:01<04:38,  1.46it/s]
 55%|█████▌    | 501/908 [15:06<13:49,  2.04s/it]
 55%|█████▌    | 502/908 [15:06<10:03,  1.49s/it]
 55%|█████▌    | 503/908 [15:06<07:24,  1.10s/it]
 56%|█████▌    | 504/908 [15:06<05:32,  1.22it/s]
 56%|█████▌    | 505/908 [15:10<11:16,  1.68s/it]
 56%|█████▌    | 506/908 [15:10<08:15,  1.23s/it]
 56%|█████▌    | 507/908 [15:10<06:10,  1.08it/s]
 56%|█████▌    | 508/908 [15:11<04:42,  1.42it/s]
 56%|█████▌    | 509/908 [15:14<09:46,  1.47s/it]
 56%|█████▌    | 510/908 [15:14<07:12,  1.09s/it]
 56%|█████▋    | 511/908 [15:14<05:25,  1.22it/s]
 56%|█████▋    | 512/908 [15:14<04:12,  1.57it/s]
 56%|█████▋    | 513/908 [15:20<13:51,  2.11s/it]
 57%|█████▋    | 514/908 [15:20<10:03,  1.53s/it]
 57%|█████▋    | 515/908 [15:20<07:25,  1.13s/it]
 57%|█████▋    | 516/908 [15:21<05:36,  1.17it/s]
 57%|█████▋    | 517/908 [15:24<09:43,  1.49s/it]
 57%|█████▋    | 518/908 [15:24<07:12,  1.11s/it]
 57%|█████▋    | 519/908 [15:24<05:25,  1.20it/s]
+[Batch:   520] training loss = 0.517
+
 57%|█████▋    | 520/908 [15:24<04:09,  1.55it/s]
 57%|█████▋    | 521/908 [15:28<09:29,  1.47s/it]
 57%|█████▋    | 522/908 [15:28<07:01,  1.09s/it]
 58%|█████▊    | 523/908 [15:28<05:17,  1.21it/s]
 58%|█████▊    | 524/908 [15:28<04:06,  1.56it/s]
 58%|█████▊    | 525/908 [15:32<10:00,  1.57s/it]
 58%|█████▊    | 526/908 [15:32<07:22,  1.16s/it]
 58%|█████▊    | 527/908 [15:32<05:31,  1.15it/s]
 58%|█████▊    | 528/908 [15:33<04:13,  1.50it/s]
 58%|█████▊    | 529/908 [15:37<11:18,  1.79s/it]
 58%|█████▊    | 530/908 [15:37<08:17,  1.32s/it]
 58%|█████▊    | 531/908 [15:37<06:10,  1.02it/s]
 59%|█████▊    | 532/908 [15:38<04:40,  1.34it/s]
 59%|█████▊    | 533/908 [15:41<09:28,  1.52s/it]
 59%|█████▉    | 534/908 [15:41<06:59,  1.12s/it]
 59%|█████▉    | 535/908 [15:41<05:15,  1.18it/s]
 59%|█████▉    | 536/908 [15:41<04:02,  1.54it/s]
 59%|█████▉    | 537/908 [15:45<09:51,  1.59s/it]
 59%|█████▉    | 538/908 [15:45<07:15,  1.18s/it]
 59%|█████▉    | 539/908 [15:46<05:26,  1.13it/s]
+[Batch:   540] training loss = 0.394
+
 59%|█████▉    | 540/908 [15:46<04:53,  1.25it/s]
 60%|█████▉    | 541/908 [15:50<10:58,  1.79s/it]
 60%|█████▉    | 542/908 [15:51<08:01,  1.32s/it]
 60%|█████▉    | 543/908 [15:51<05:58,  1.02it/s]
 60%|█████▉    | 544/908 [15:51<04:48,  1.26it/s]
 60%|██████    | 545/908 [15:55<10:30,  1.74s/it]
 60%|██████    | 546/908 [15:55<07:42,  1.28s/it]
 60%|██████    | 547/908 [15:55<05:44,  1.05it/s]
 60%|██████    | 548/908 [15:56<04:37,  1.30it/s]
 60%|██████    | 549/908 [15:59<09:40,  1.62s/it]
 61%|██████    | 550/908 [16:00<07:07,  1.19s/it]
 61%|██████    | 551/908 [16:00<05:20,  1.11it/s]
 61%|██████    | 552/908 [16:00<04:14,  1.40it/s]
 61%|██████    | 553/908 [16:04<09:18,  1.57s/it]
 61%|██████    | 554/908 [16:04<06:51,  1.16s/it]
 61%|██████    | 555/908 [16:04<05:26,  1.08it/s]
 61%|██████    | 556/908 [16:05<04:55,  1.19it/s]
 61%|██████▏   | 557/908 [16:08<08:53,  1.52s/it]
 61%|██████▏   | 558/908 [16:08<06:33,  1.12s/it]
 62%|██████▏   | 559/908 [16:09<05:38,  1.03it/s]
+[Batch:   560] training loss = 0.356
+
 62%|██████▏   | 560/908 [16:09<04:23,  1.32it/s]
 62%|██████▏   | 561/908 [16:13<10:33,  1.83s/it]
 62%|██████▏   | 562/908 [16:14<07:42,  1.34s/it]
 62%|██████▏   | 563/908 [16:14<05:44,  1.00it/s]
 62%|██████▏   | 564/908 [16:14<05:07,  1.12it/s]
 62%|██████▏   | 565/908 [16:19<11:19,  1.98s/it]
 62%|██████▏   | 566/908 [16:19<08:15,  1.45s/it]
 62%|██████▏   | 567/908 [16:19<06:05,  1.07s/it]
 63%|██████▎   | 568/908 [16:20<04:46,  1.19it/s]
 63%|██████▎   | 569/908 [16:23<08:55,  1.58s/it]
 63%|██████▎   | 570/908 [16:23<06:34,  1.17s/it]
 63%|██████▎   | 571/908 [16:23<04:55,  1.14it/s]
 63%|██████▎   | 572/908 [16:24<03:46,  1.48it/s]
 63%|██████▎   | 573/908 [16:26<06:42,  1.20s/it]
 63%|██████▎   | 574/908 [16:26<05:00,  1.11it/s]
 63%|██████▎   | 575/908 [16:26<03:50,  1.45it/s]
 63%|██████▎   | 576/908 [16:27<03:18,  1.67it/s]
 64%|██████▎   | 577/908 [16:30<08:04,  1.46s/it]
 64%|██████▎   | 578/908 [16:30<05:57,  1.08s/it]
 64%|██████▍   | 579/908 [16:31<04:28,  1.22it/s]
+[Batch:   580] training loss = 0.379
+
 64%|██████▍   | 580/908 [16:31<03:28,  1.58it/s]
 64%|██████▍   | 581/908 [16:34<07:55,  1.45s/it]
 64%|██████▍   | 582/908 [16:34<05:51,  1.08s/it]
 64%|██████▍   | 583/908 [16:35<04:25,  1.22it/s]
 64%|██████▍   | 584/908 [16:35<03:24,  1.59it/s]
 64%|██████▍   | 585/908 [16:40<10:19,  1.92s/it]
 65%|██████▍   | 586/908 [16:40<07:31,  1.40s/it]
 65%|██████▍   | 587/908 [16:40<05:35,  1.04s/it]
 65%|██████▍   | 588/908 [16:40<04:13,  1.26it/s]
 65%|██████▍   | 589/908 [16:44<09:09,  1.72s/it]
 65%|██████▍   | 590/908 [16:44<06:42,  1.27s/it]
 65%|██████▌   | 591/908 [16:45<05:00,  1.06it/s]
 65%|██████▌   | 592/908 [16:45<03:49,  1.38it/s]
 65%|██████▌   | 593/908 [16:48<06:52,  1.31s/it]
 65%|██████▌   | 594/908 [16:48<05:07,  1.02it/s]
 66%|██████▌   | 595/908 [16:48<03:53,  1.34it/s]
 66%|██████▌   | 596/908 [16:48<03:03,  1.70it/s]
 66%|██████▌   | 597/908 [16:53<10:15,  1.98s/it]
 66%|██████▌   | 598/908 [16:54<07:27,  1.44s/it]
 66%|██████▌   | 599/908 [16:54<05:30,  1.07s/it]
+[Batch:   600] training loss = 0.368
+
 66%|██████▌   | 600/908 [16:54<04:09,  1.24it/s]
 66%|██████▌   | 601/908 [16:57<07:55,  1.55s/it]
 66%|██████▋   | 602/908 [16:57<05:50,  1.15s/it]
 66%|██████▋   | 603/908 [16:58<04:22,  1.16it/s]
 67%|██████▋   | 604/908 [16:58<03:21,  1.51it/s]
 67%|██████▋   | 605/908 [17:02<08:59,  1.78s/it]
 67%|██████▋   | 606/908 [17:02<06:34,  1.30s/it]
 67%|██████▋   | 607/908 [17:03<04:52,  1.03it/s]
 67%|██████▋   | 608/908 [17:03<04:00,  1.25it/s]
 67%|██████▋   | 609/908 [17:06<07:47,  1.56s/it]
 67%|██████▋   | 610/908 [17:07<05:44,  1.16s/it]
 67%|██████▋   | 611/908 [17:07<04:17,  1.15it/s]
 67%|██████▋   | 612/908 [17:07<03:41,  1.33it/s]
 68%|██████▊   | 613/908 [17:11<07:39,  1.56s/it]
 68%|██████▊   | 614/908 [17:11<05:37,  1.15s/it]
 68%|██████▊   | 615/908 [17:11<04:14,  1.15it/s]
 68%|██████▊   | 616/908 [17:11<03:18,  1.47it/s]
 68%|██████▊   | 617/908 [17:17<11:03,  2.28s/it]
 68%|██████▊   | 618/908 [17:18<08:00,  1.66s/it]
 68%|██████▊   | 619/908 [17:18<05:52,  1.22s/it]
+[Batch:   620] training loss = 0.497
+
 68%|██████▊   | 620/908 [17:18<04:23,  1.09it/s]
 68%|██████▊   | 621/908 [17:22<08:13,  1.72s/it]
 69%|██████▊   | 622/908 [17:22<06:01,  1.26s/it]
 69%|██████▊   | 623/908 [17:22<04:29,  1.06it/s]
 69%|██████▊   | 624/908 [17:22<03:29,  1.35it/s]
 69%|██████▉   | 625/908 [17:26<08:14,  1.75s/it]
 69%|██████▉   | 626/908 [17:27<06:00,  1.28s/it]
 69%|██████▉   | 627/908 [17:27<04:27,  1.05it/s]
 69%|██████▉   | 628/908 [17:27<03:22,  1.38it/s]
 69%|██████▉   | 629/908 [17:30<07:19,  1.58s/it]
 69%|██████▉   | 630/908 [17:31<05:22,  1.16s/it]
 69%|██████▉   | 631/908 [17:31<04:00,  1.15it/s]
 70%|██████▉   | 632/908 [17:31<03:03,  1.51it/s]
 70%|██████▉   | 633/908 [17:34<06:17,  1.37s/it]
 70%|██████▉   | 634/908 [17:34<04:39,  1.02s/it]
 70%|██████▉   | 635/908 [17:34<03:30,  1.29it/s]
 70%|███████   | 636/908 [17:35<02:43,  1.66it/s]
 70%|███████   | 637/908 [17:37<05:16,  1.17s/it]
 70%|███████   | 638/908 [17:37<03:56,  1.14it/s]
 70%|███████   | 639/908 [17:38<03:00,  1.49it/s]
+[Batch:   640] training loss = 0.421
+
 70%|███████   | 640/908 [17:38<02:20,  1.90it/s]
 71%|███████   | 641/908 [17:41<06:10,  1.39s/it]
 71%|███████   | 642/908 [17:41<04:34,  1.03s/it]
 71%|███████   | 643/908 [17:42<03:26,  1.28it/s]
 71%|███████   | 644/908 [17:42<02:39,  1.65it/s]
 71%|███████   | 645/908 [17:45<06:34,  1.50s/it]
 71%|███████   | 646/908 [17:45<04:50,  1.11s/it]
 71%|███████▏  | 647/908 [17:46<03:37,  1.20it/s]
 71%|███████▏  | 648/908 [17:46<02:46,  1.56it/s]
 71%|███████▏  | 649/908 [17:50<07:24,  1.72s/it]
 72%|███████▏  | 650/908 [17:50<05:24,  1.26s/it]
 72%|███████▏  | 651/908 [17:50<04:01,  1.06it/s]
 72%|███████▏  | 652/908 [17:51<03:02,  1.40it/s]
 72%|███████▏  | 653/908 [17:54<06:06,  1.44s/it]
 72%|███████▏  | 654/908 [17:54<04:29,  1.06s/it]
 72%|███████▏  | 655/908 [17:54<03:22,  1.25it/s]
 72%|███████▏  | 656/908 [17:54<02:35,  1.62it/s]
 72%|███████▏  | 657/908 [17:55<02:05,  2.01it/s]
 72%|███████▏  | 658/908 [17:55<01:41,  2.46it/s]
 73%|███████▎  | 659/908 [17:55<01:25,  2.93it/s]
+[Batch:   660] training loss = 0.442
+
 73%|███████▎  | 660/908 [17:56<01:40,  2.47it/s]
 73%|███████▎  | 661/908 [17:59<06:00,  1.46s/it]
 73%|███████▎  | 662/908 [18:00<04:25,  1.08s/it]
 73%|███████▎  | 663/908 [18:00<03:19,  1.23it/s]
 73%|███████▎  | 664/908 [18:01<03:17,  1.24it/s]
 73%|███████▎  | 665/908 [18:04<06:10,  1.52s/it]
 73%|███████▎  | 666/908 [18:04<04:32,  1.13s/it]
 73%|███████▎  | 667/908 [18:04<03:31,  1.14it/s]
 74%|███████▎  | 668/908 [18:05<02:44,  1.46it/s]
 74%|███████▎  | 669/908 [18:08<05:52,  1.48s/it]
 74%|███████▍  | 670/908 [18:08<04:20,  1.09s/it]
 74%|███████▍  | 671/908 [18:08<03:18,  1.20it/s]
 74%|███████▍  | 672/908 [18:09<03:29,  1.13it/s]
 74%|███████▍  | 673/908 [18:13<06:24,  1.63s/it]
 74%|███████▍  | 674/908 [18:13<04:41,  1.20s/it]
 74%|███████▍  | 675/908 [18:13<03:39,  1.06it/s]
 74%|███████▍  | 676/908 [18:14<03:31,  1.10it/s]
 75%|███████▍  | 677/908 [18:18<07:11,  1.87s/it]
 75%|███████▍  | 678/908 [18:18<05:14,  1.37s/it]
 75%|███████▍  | 679/908 [18:19<03:51,  1.01s/it]
+[Batch:   680] training loss = 0.507
+
 75%|███████▍  | 680/908 [18:19<03:29,  1.09it/s]
 75%|███████▌  | 681/908 [18:23<07:08,  1.89s/it]
 75%|███████▌  | 682/908 [18:24<05:12,  1.38s/it]
 75%|███████▌  | 683/908 [18:24<04:01,  1.07s/it]
 75%|███████▌  | 684/908 [18:25<03:31,  1.06it/s]
 75%|███████▌  | 685/908 [18:28<06:29,  1.75s/it]
 76%|███████▌  | 686/908 [18:28<04:44,  1.28s/it]
 76%|███████▌  | 687/908 [18:29<03:30,  1.05it/s]
 76%|███████▌  | 688/908 [18:29<02:39,  1.38it/s]
 76%|███████▌  | 689/908 [18:32<05:32,  1.52s/it]
 76%|███████▌  | 690/908 [18:32<04:03,  1.12s/it]
 76%|███████▌  | 691/908 [18:33<03:24,  1.06it/s]
 76%|███████▌  | 692/908 [18:34<03:14,  1.11it/s]
 76%|███████▋  | 693/908 [18:37<05:19,  1.49s/it]
 76%|███████▋  | 694/908 [18:37<03:54,  1.10s/it]
 77%|███████▋  | 695/908 [18:38<03:40,  1.04s/it]
 77%|███████▋  | 696/908 [18:38<03:21,  1.05it/s]
 77%|███████▋  | 697/908 [18:42<05:50,  1.66s/it]
 77%|███████▋  | 698/908 [18:42<04:16,  1.22s/it]
 77%|███████▋  | 699/908 [18:42<03:10,  1.10it/s]
+[Batch:   700] training loss = 0.383
+
 77%|███████▋  | 700/908 [18:43<02:40,  1.29it/s]
 77%|███████▋  | 701/908 [18:46<04:57,  1.44s/it]
 77%|███████▋  | 702/908 [18:46<03:39,  1.06s/it]
 77%|███████▋  | 703/908 [18:46<03:09,  1.08it/s]
 78%|███████▊  | 704/908 [18:47<02:53,  1.18it/s]
 78%|███████▊  | 705/908 [18:49<04:09,  1.23s/it]
 78%|███████▊  | 706/908 [18:49<03:05,  1.09it/s]
 78%|███████▊  | 707/908 [18:49<02:20,  1.43it/s]
 78%|███████▊  | 708/908 [18:50<02:15,  1.47it/s]
 78%|███████▊  | 709/908 [18:53<04:05,  1.23s/it]
 78%|███████▊  | 710/908 [18:53<03:02,  1.08it/s]
 78%|███████▊  | 711/908 [18:53<02:18,  1.42it/s]
 78%|███████▊  | 712/908 [18:53<01:48,  1.81it/s]
 79%|███████▊  | 713/908 [18:56<04:18,  1.33s/it]
 79%|███████▊  | 714/908 [18:57<03:11,  1.01it/s]
 79%|███████▊  | 715/908 [18:57<02:53,  1.12it/s]
 79%|███████▉  | 716/908 [18:57<02:12,  1.45it/s]
 79%|███████▉  | 717/908 [19:00<04:22,  1.38s/it]
 79%|███████▉  | 718/908 [19:01<03:13,  1.02s/it]
 79%|███████▉  | 719/908 [19:02<03:23,  1.08s/it]
+[Batch:   720] training loss = 0.329
+
 79%|███████▉  | 720/908 [19:02<02:33,  1.22it/s]
 79%|███████▉  | 721/908 [19:06<05:30,  1.77s/it]
 80%|███████▉  | 722/908 [19:06<04:01,  1.30s/it]
 80%|███████▉  | 723/908 [19:06<03:00,  1.03it/s]
 80%|███████▉  | 724/908 [19:07<02:16,  1.35it/s]
 80%|███████▉  | 725/908 [19:10<04:42,  1.54s/it]
 80%|███████▉  | 726/908 [19:10<03:27,  1.14s/it]
 80%|████████  | 727/908 [19:11<03:15,  1.08s/it]
 80%|████████  | 728/908 [19:11<02:26,  1.23it/s]
 80%|████████  | 729/908 [19:15<05:04,  1.70s/it]
 80%|████████  | 730/908 [19:15<03:42,  1.25s/it]
 81%|████████  | 731/908 [19:19<05:30,  1.86s/it]
 81%|████████  | 732/908 [19:19<03:59,  1.36s/it]
 81%|████████  | 733/908 [19:23<06:46,  2.32s/it]
 81%|████████  | 734/908 [19:24<04:52,  1.68s/it]
 81%|████████  | 735/908 [19:24<04:02,  1.40s/it]
 81%|████████  | 736/908 [19:25<02:58,  1.04s/it]
 81%|████████  | 737/908 [19:28<04:46,  1.67s/it]
 81%|████████▏ | 738/908 [19:28<03:29,  1.23s/it]
 81%|████████▏ | 739/908 [19:30<03:59,  1.42s/it]
+[Batch:   740] training loss = 0.392
+
 81%|████████▏ | 740/908 [19:30<02:56,  1.05s/it]
 82%|████████▏ | 741/908 [19:34<05:13,  1.88s/it]
 82%|████████▏ | 742/908 [19:34<03:48,  1.37s/it]
 82%|████████▏ | 743/908 [19:35<03:31,  1.28s/it]
 82%|████████▏ | 744/908 [19:35<02:36,  1.05it/s]
 82%|████████▏ | 745/908 [19:39<05:14,  1.93s/it]
 82%|████████▏ | 746/908 [19:40<03:47,  1.41s/it]
 82%|████████▏ | 747/908 [19:41<03:38,  1.36s/it]
 82%|████████▏ | 748/908 [19:41<02:41,  1.01s/it]
 82%|████████▏ | 749/908 [19:44<04:25,  1.67s/it]
 83%|████████▎ | 750/908 [19:44<03:13,  1.23s/it]
 83%|████████▎ | 751/908 [19:46<03:08,  1.20s/it]
 83%|████████▎ | 752/908 [19:46<02:19,  1.11it/s]
 83%|████████▎ | 753/908 [19:50<04:54,  1.90s/it]
 83%|████████▎ | 754/908 [19:50<03:33,  1.38s/it]
 83%|████████▎ | 755/908 [19:51<03:08,  1.23s/it]
 83%|████████▎ | 756/908 [19:51<02:19,  1.09it/s]
 83%|████████▎ | 757/908 [19:55<04:13,  1.68s/it]
 83%|████████▎ | 758/908 [19:55<03:05,  1.23s/it]
 84%|████████▎ | 759/908 [19:56<03:13,  1.30s/it]
+[Batch:   760] training loss = 0.517
+
 84%|████████▎ | 760/908 [19:57<02:22,  1.04it/s]
 84%|████████▍ | 761/908 [20:01<05:05,  2.08s/it]
 84%|████████▍ | 762/908 [20:01<03:40,  1.51s/it]
 84%|████████▍ | 763/908 [20:02<02:45,  1.14s/it]
 84%|████████▍ | 764/908 [20:02<02:03,  1.17it/s]
 84%|████████▍ | 765/908 [20:05<03:36,  1.51s/it]
 84%|████████▍ | 766/908 [20:05<02:38,  1.12s/it]
 84%|████████▍ | 767/908 [20:07<03:25,  1.46s/it]
 85%|████████▍ | 768/908 [20:08<02:30,  1.08s/it]
 85%|████████▍ | 769/908 [20:11<04:09,  1.79s/it]
 85%|████████▍ | 770/908 [20:11<03:00,  1.31s/it]
 85%|████████▍ | 771/908 [20:12<02:54,  1.27s/it]
 85%|████████▌ | 772/908 [20:13<02:08,  1.05it/s]
 85%|████████▌ | 773/908 [20:17<04:34,  2.03s/it]
 85%|████████▌ | 774/908 [20:17<03:18,  1.48s/it]
 85%|████████▌ | 775/908 [20:20<03:55,  1.77s/it]
 85%|████████▌ | 776/908 [20:20<02:51,  1.30s/it]
 86%|████████▌ | 777/908 [20:24<04:18,  1.98s/it]
 86%|████████▌ | 778/908 [20:24<03:06,  1.44s/it]
 86%|████████▌ | 779/908 [20:25<02:58,  1.39s/it]
+[Batch:   780] training loss = 0.392
+
 86%|████████▌ | 780/908 [20:25<02:11,  1.03s/it]
 86%|████████▌ | 781/908 [20:28<03:19,  1.57s/it]
 86%|████████▌ | 782/908 [20:28<02:25,  1.15s/it]
 86%|████████▌ | 783/908 [20:30<02:42,  1.30s/it]
 86%|████████▋ | 784/908 [20:30<01:59,  1.04it/s]
 86%|████████▋ | 785/908 [20:32<02:45,  1.34s/it]
 87%|████████▋ | 786/908 [20:32<02:01,  1.00it/s]
 87%|████████▋ | 787/908 [20:34<02:11,  1.09s/it]
 87%|████████▋ | 788/908 [20:34<01:38,  1.22it/s]
 87%|████████▋ | 789/908 [20:37<02:58,  1.50s/it]
 87%|████████▋ | 790/908 [20:37<02:10,  1.11s/it]
 87%|████████▋ | 791/908 [20:37<01:37,  1.20it/s]
 87%|████████▋ | 792/908 [20:38<01:14,  1.56it/s]
 87%|████████▋ | 793/908 [20:41<02:46,  1.45s/it]
 87%|████████▋ | 794/908 [20:41<02:02,  1.07s/it]
 88%|████████▊ | 795/908 [20:42<02:04,  1.10s/it]
 88%|████████▊ | 796/908 [20:42<01:32,  1.21it/s]
 88%|████████▊ | 797/908 [20:46<02:55,  1.58s/it]
 88%|████████▊ | 798/908 [20:46<02:07,  1.16s/it]
 88%|████████▊ | 799/908 [20:48<02:40,  1.47s/it]
+[Batch:   800] training loss = 0.510
+
 88%|████████▊ | 800/908 [20:48<01:57,  1.09s/it]
 88%|████████▊ | 801/908 [20:52<03:04,  1.72s/it]
 88%|████████▊ | 802/908 [20:52<02:13,  1.26s/it]
 88%|████████▊ | 803/908 [20:53<02:19,  1.33s/it]
 89%|████████▊ | 804/908 [20:53<01:42,  1.01it/s]
 89%|████████▊ | 805/908 [20:58<03:23,  1.97s/it]
 89%|████████▉ | 806/908 [20:58<02:27,  1.44s/it]
 89%|████████▉ | 807/908 [20:58<01:47,  1.07s/it]
 89%|████████▉ | 808/908 [20:58<01:20,  1.24it/s]
 89%|████████▉ | 809/908 [21:01<02:21,  1.43s/it]
 89%|████████▉ | 810/908 [21:01<01:43,  1.05s/it]
 89%|████████▉ | 811/908 [21:02<01:17,  1.26it/s]
 89%|████████▉ | 812/908 [21:02<00:59,  1.63it/s]
 90%|████████▉ | 813/908 [21:05<02:09,  1.37s/it]
 90%|████████▉ | 814/908 [21:05<01:35,  1.01s/it]
 90%|████████▉ | 815/908 [21:05<01:11,  1.30it/s]
 90%|████████▉ | 816/908 [21:05<00:54,  1.69it/s]
 90%|████████▉ | 817/908 [21:09<02:02,  1.34s/it]
 90%|█████████ | 818/908 [21:09<01:30,  1.00s/it]
 90%|█████████ | 819/908 [21:09<01:09,  1.29it/s]
+[Batch:   820] training loss = 0.377
+
 90%|█████████ | 820/908 [21:09<00:52,  1.67it/s]
 90%|█████████ | 821/908 [21:13<02:07,  1.47s/it]
 91%|█████████ | 822/908 [21:13<01:33,  1.08s/it]
 91%|█████████ | 823/908 [21:14<01:21,  1.04it/s]
 91%|█████████ | 824/908 [21:14<01:01,  1.37it/s]
 91%|█████████ | 825/908 [21:19<03:05,  2.23s/it]
 91%|█████████ | 826/908 [21:20<02:12,  1.62s/it]
 91%|█████████ | 827/908 [21:20<01:42,  1.27s/it]
 91%|█████████ | 828/908 [21:20<01:15,  1.06it/s]
 91%|█████████▏| 829/908 [21:23<02:00,  1.52s/it]
 91%|█████████▏| 830/908 [21:23<01:27,  1.12s/it]
 92%|█████████▏| 831/908 [21:24<01:19,  1.03s/it]
 92%|█████████▏| 832/908 [21:24<00:59,  1.29it/s]
 92%|█████████▏| 833/908 [21:28<01:54,  1.53s/it]
 92%|█████████▏| 834/908 [21:28<01:23,  1.13s/it]
 92%|█████████▏| 835/908 [21:29<01:14,  1.02s/it]
 92%|█████████▏| 836/908 [21:29<00:55,  1.30it/s]
 92%|█████████▏| 837/908 [21:33<02:08,  1.81s/it]
 92%|█████████▏| 838/908 [21:33<01:32,  1.33s/it]
 92%|█████████▏| 839/908 [21:34<01:10,  1.02s/it]
+[Batch:   840] training loss = 0.368
+
 93%|█████████▎| 840/908 [21:34<00:54,  1.25it/s]
 93%|█████████▎| 841/908 [21:40<02:36,  2.34s/it]
 93%|█████████▎| 842/908 [21:40<01:52,  1.70s/it]
 93%|█████████▎| 843/908 [21:40<01:21,  1.25s/it]
 93%|█████████▎| 844/908 [21:40<00:59,  1.07it/s]
 93%|█████████▎| 845/908 [21:44<01:50,  1.76s/it]
 93%|█████████▎| 846/908 [21:44<01:19,  1.29s/it]
 93%|█████████▎| 847/908 [21:44<00:58,  1.04it/s]
 93%|█████████▎| 848/908 [21:45<00:44,  1.36it/s]
 94%|█████████▎| 849/908 [21:48<01:28,  1.49s/it]
 94%|█████████▎| 850/908 [21:48<01:04,  1.11s/it]
 94%|█████████▎| 851/908 [21:48<00:47,  1.19it/s]
 94%|█████████▍| 852/908 [21:49<00:45,  1.23it/s]
 94%|█████████▍| 853/908 [21:54<01:48,  1.97s/it]
 94%|█████████▍| 854/908 [21:54<01:17,  1.44s/it]
 94%|█████████▍| 855/908 [21:54<00:56,  1.07s/it]
 94%|█████████▍| 856/908 [21:54<00:42,  1.24it/s]
 94%|█████████▍| 857/908 [21:59<01:32,  1.82s/it]
 94%|█████████▍| 858/908 [21:59<01:06,  1.33s/it]
 95%|█████████▍| 859/908 [21:59<00:48,  1.01it/s]
+[Batch:   860] training loss = 0.449
+
 95%|█████████▍| 860/908 [21:59<00:41,  1.16it/s]
 95%|█████████▍| 861/908 [22:05<01:43,  2.21s/it]
 95%|█████████▍| 862/908 [22:05<01:13,  1.60s/it]
 95%|█████████▌| 863/908 [22:05<00:53,  1.18s/it]
 95%|█████████▌| 864/908 [22:06<00:41,  1.06it/s]
 95%|█████████▌| 865/908 [22:10<01:19,  1.86s/it]
 95%|█████████▌| 866/908 [22:10<00:57,  1.36s/it]
 95%|█████████▌| 867/908 [22:10<00:41,  1.01s/it]
 96%|█████████▌| 868/908 [22:10<00:31,  1.26it/s]
 96%|█████████▌| 869/908 [22:15<01:13,  1.89s/it]
 96%|█████████▌| 870/908 [22:15<00:52,  1.38s/it]
 96%|█████████▌| 871/908 [22:15<00:38,  1.03s/it]
 96%|█████████▌| 872/908 [22:15<00:28,  1.28it/s]
 96%|█████████▌| 873/908 [22:19<00:54,  1.55s/it]
 96%|█████████▋| 874/908 [22:19<00:39,  1.15s/it]
 96%|█████████▋| 875/908 [22:19<00:28,  1.16it/s]
 96%|█████████▋| 876/908 [22:19<00:21,  1.51it/s]
 97%|█████████▋| 877/908 [22:25<01:07,  2.19s/it]
 97%|█████████▋| 878/908 [22:25<00:47,  1.59s/it]
 97%|█████████▋| 879/908 [22:25<00:34,  1.18s/it]
+[Batch:   880] training loss = 0.414
+
 97%|█████████▋| 880/908 [22:26<00:24,  1.13it/s]
 97%|█████████▋| 881/908 [22:30<00:53,  1.99s/it]
 97%|█████████▋| 882/908 [22:30<00:37,  1.45s/it]
 97%|█████████▋| 883/908 [22:31<00:26,  1.08s/it]
 97%|█████████▋| 884/908 [22:31<00:19,  1.23it/s]
 97%|█████████▋| 885/908 [22:34<00:37,  1.63s/it]
 98%|█████████▊| 886/908 [22:35<00:26,  1.20s/it]
 98%|█████████▊| 887/908 [22:35<00:18,  1.11it/s]
 98%|█████████▊| 888/908 [22:35<00:13,  1.44it/s]
 98%|█████████▊| 889/908 [22:38<00:26,  1.40s/it]
 98%|█████████▊| 890/908 [22:38<00:18,  1.04s/it]
 98%|█████████▊| 891/908 [22:38<00:13,  1.27it/s]
 98%|█████████▊| 892/908 [22:39<00:09,  1.63it/s]
 98%|█████████▊| 893/908 [22:42<00:23,  1.56s/it]
 98%|█████████▊| 894/908 [22:43<00:16,  1.15s/it]
 99%|█████████▊| 895/908 [22:43<00:11,  1.16it/s]
 99%|█████████▊| 896/908 [22:43<00:07,  1.50it/s]
 99%|█████████▉| 897/908 [22:47<00:18,  1.71s/it]
 99%|█████████▉| 898/908 [22:47<00:12,  1.26s/it]
 99%|█████████▉| 899/908 [22:48<00:08,  1.06it/s]
+[Batch:   900] training loss = 0.445
+
 99%|█████████▉| 900/908 [22:48<00:05,  1.39it/s]
 99%|█████████▉| 901/908 [22:52<00:12,  1.78s/it]
 99%|█████████▉| 902/908 [22:52<00:07,  1.30s/it]
 99%|█████████▉| 903/908 [22:52<00:04,  1.03it/s]
100%|█████████▉| 904/908 [22:53<00:02,  1.35it/s]
100%|█████████▉| 905/908 [22:57<00:05,  1.84s/it]
100%|█████████▉| 906/908 [22:57<00:02,  1.35s/it]
100%|█████████▉| 907/908 [22:57<00:01,  1.01s/it]
100%|██████████| 908/908 [22:58<00:00,  1.31it/s]
100%|██████████| 908/908 [22:58<00:00,  1.52s/it]
+
+Epoch   9: validation
+
  0%|          | 0/101 [00:00<?, ?it/s]
  1%|          | 1/101 [00:04<07:41,  4.61s/it]
  3%|▎         | 3/101 [00:04<02:03,  1.26s/it]
  5%|▍         | 5/101 [00:10<03:08,  1.97s/it]
  7%|▋         | 7/101 [00:10<01:49,  1.17s/it]
  9%|▉         | 9/101 [00:15<02:30,  1.64s/it]
 11%|█         | 11/101 [00:15<01:38,  1.09s/it]
 13%|█▎        | 13/101 [00:18<01:57,  1.34s/it]
 15%|█▍        | 15/101 [00:19<01:20,  1.07it/s]
 17%|█▋        | 17/101 [00:25<02:18,  1.65s/it]
 19%|█▉        | 19/101 [00:25<01:35,  1.16s/it]
 21%|██        | 21/101 [00:30<02:09,  1.61s/it]
 23%|██▎       | 23/101 [00:31<01:29,  1.15s/it]
 25%|██▍       | 25/101 [00:35<01:51,  1.47s/it]
 27%|██▋       | 27/101 [00:35<01:17,  1.05s/it]
 29%|██▊       | 29/101 [00:39<01:32,  1.28s/it]
 31%|███       | 31/101 [00:39<01:04,  1.09it/s]
 33%|███▎      | 33/101 [00:43<01:20,  1.19s/it]
 35%|███▍      | 35/101 [00:43<00:56,  1.17it/s]
 37%|███▋      | 37/101 [00:47<01:19,  1.24s/it]
 38%|███▊      | 38/101 [00:47<01:07,  1.06s/it]
 40%|███▉      | 40/101 [00:47<00:44,  1.36it/s]
 41%|████      | 41/101 [00:52<01:28,  1.47s/it]
 43%|████▎     | 43/101 [00:52<00:56,  1.02it/s]
 45%|████▍     | 45/101 [00:57<01:24,  1.52s/it]
 46%|████▌     | 46/101 [00:57<01:08,  1.25s/it]
 48%|████▊     | 48/101 [00:58<00:44,  1.20it/s]
 49%|████▊     | 49/101 [01:02<01:22,  1.59s/it]
 50%|████▉     | 50/101 [01:02<01:04,  1.27s/it]
 51%|█████▏    | 52/101 [01:02<00:39,  1.24it/s]
 52%|█████▏    | 53/101 [01:06<01:11,  1.49s/it]
 54%|█████▍    | 55/101 [01:07<00:43,  1.05it/s]
 56%|█████▋    | 57/101 [01:11<01:04,  1.47s/it]
 57%|█████▋    | 58/101 [01:12<00:52,  1.22s/it]
 59%|█████▉    | 60/101 [01:12<00:32,  1.25it/s]
 60%|██████    | 61/101 [01:17<01:05,  1.63s/it]
 61%|██████▏   | 62/101 [01:17<00:52,  1.34s/it]
 63%|██████▎   | 64/101 [01:17<00:31,  1.19it/s]
 64%|██████▍   | 65/101 [01:22<00:58,  1.64s/it]
 65%|██████▌   | 66/101 [01:23<00:52,  1.50s/it]
 67%|██████▋   | 68/101 [01:23<00:30,  1.08it/s]
 68%|██████▊   | 69/101 [01:28<01:03,  1.97s/it]
 69%|██████▉   | 70/101 [01:29<00:53,  1.71s/it]
 71%|███████▏  | 72/101 [01:29<00:30,  1.04s/it]
 72%|███████▏  | 73/101 [01:32<00:40,  1.44s/it]
 73%|███████▎  | 74/101 [01:33<00:36,  1.35s/it]
 75%|███████▌  | 76/101 [01:33<00:20,  1.21it/s]
 76%|███████▌  | 77/101 [01:38<00:40,  1.69s/it]
 77%|███████▋  | 78/101 [01:39<00:35,  1.55s/it]
 79%|███████▉  | 80/101 [01:39<00:19,  1.06it/s]
 80%|████████  | 81/101 [01:44<00:34,  1.73s/it]
 81%|████████  | 82/101 [01:44<00:27,  1.44s/it]
 83%|████████▎ | 84/101 [01:44<00:14,  1.14it/s]
 84%|████████▍ | 85/101 [01:49<00:27,  1.70s/it]
 85%|████████▌ | 86/101 [01:50<00:21,  1.45s/it]
 87%|████████▋ | 88/101 [01:50<00:11,  1.13it/s]
 88%|████████▊ | 89/101 [01:54<00:20,  1.69s/it]
 89%|████████▉ | 90/101 [01:55<00:17,  1.55s/it]
 91%|█████████ | 92/101 [01:55<00:08,  1.06it/s]
 92%|█████████▏| 93/101 [01:59<00:11,  1.45s/it]
 93%|█████████▎| 94/101 [02:00<00:10,  1.50s/it]
 95%|█████████▌| 96/101 [02:00<00:04,  1.09it/s]
 96%|█████████▌| 97/101 [02:03<00:05,  1.35s/it]
 97%|█████████▋| 98/101 [02:05<00:04,  1.35s/it]
 99%|█████████▉| 100/101 [02:05<00:00,  1.21it/s]
100%|██████████| 101/101 [02:07<00:00,  1.17s/it]
100%|██████████| 101/101 [02:07<00:00,  1.26s/it]
+    Epoch  9: training loss = 0.432 / validation loss = 0.444
+    Epoch  9: training acc = 0.796 / validation acc = 0.791
+
+Epoch  10: training
+
  0%|          | 0/908 [00:00<?, ?it/s]
  0%|          | 1/908 [00:05<1:23:41,  5.54s/it]
  0%|          | 2/908 [00:05<36:28,  2.42s/it]  
  0%|          | 3/908 [00:05<21:09,  1.40s/it]
  0%|          | 4/908 [00:06<13:56,  1.08it/s]
  1%|          | 5/908 [00:09<29:13,  1.94s/it]
  1%|          | 6/908 [00:10<20:54,  1.39s/it]
  1%|          | 7/908 [00:10<14:59,  1.00it/s]
  1%|          | 8/908 [00:10<11:09,  1.35it/s]
  1%|          | 9/908 [00:15<30:25,  2.03s/it]
  1%|          | 10/908 [00:15<21:57,  1.47s/it]
  1%|          | 11/908 [00:15<16:05,  1.08s/it]
  1%|▏         | 12/908 [00:16<12:03,  1.24it/s]
  1%|▏         | 13/908 [00:22<37:18,  2.50s/it]
  2%|▏         | 14/908 [00:22<26:51,  1.80s/it]
  2%|▏         | 15/908 [00:22<19:34,  1.32s/it]
  2%|▏         | 16/908 [00:23<14:32,  1.02it/s]
  2%|▏         | 17/908 [00:26<26:15,  1.77s/it]
  2%|▏         | 18/908 [00:26<19:17,  1.30s/it]
  2%|▏         | 19/908 [00:27<14:21,  1.03it/s]
+[Batch:    20] training loss = 0.420
+
  2%|▏         | 20/908 [00:27<10:52,  1.36it/s]
  2%|▏         | 21/908 [00:31<25:34,  1.73s/it]
  2%|▏         | 22/908 [00:31<18:41,  1.27s/it]
  3%|▎         | 23/908 [00:31<13:56,  1.06it/s]
  3%|▎         | 24/908 [00:31<11:03,  1.33it/s]
  3%|▎         | 25/908 [00:36<27:46,  1.89s/it]
  3%|▎         | 26/908 [00:36<20:16,  1.38s/it]
  3%|▎         | 27/908 [00:36<15:00,  1.02s/it]
  3%|▎         | 28/908 [00:37<11:20,  1.29it/s]
  3%|▎         | 29/908 [00:42<30:31,  2.08s/it]
  3%|▎         | 30/908 [00:42<22:12,  1.52s/it]
  3%|▎         | 31/908 [00:42<16:23,  1.12s/it]
  4%|▎         | 32/908 [00:42<12:18,  1.19it/s]
  4%|▎         | 33/908 [00:46<25:10,  1.73s/it]
  4%|▎         | 34/908 [00:46<18:25,  1.26s/it]
  4%|▍         | 35/908 [00:46<13:43,  1.06it/s]
  4%|▍         | 36/908 [00:47<10:25,  1.39it/s]
  4%|▍         | 37/908 [00:50<21:41,  1.49s/it]
  4%|▍         | 38/908 [00:50<16:02,  1.11s/it]
  4%|▍         | 39/908 [00:50<12:03,  1.20it/s]
+[Batch:    40] training loss = 0.484
+
  4%|▍         | 40/908 [00:51<09:14,  1.57it/s]
  5%|▍         | 41/908 [00:55<24:07,  1.67s/it]
  5%|▍         | 42/908 [00:55<17:40,  1.22s/it]
  5%|▍         | 43/908 [00:55<13:13,  1.09it/s]
  5%|▍         | 44/908 [00:55<10:04,  1.43it/s]
  5%|▍         | 45/908 [00:59<24:15,  1.69s/it]
  5%|▌         | 46/908 [00:59<17:45,  1.24s/it]
  5%|▌         | 47/908 [01:00<13:15,  1.08it/s]
  5%|▌         | 48/908 [01:00<10:06,  1.42it/s]
  5%|▌         | 49/908 [01:04<24:13,  1.69s/it]
  6%|▌         | 50/908 [01:04<17:45,  1.24s/it]
  6%|▌         | 51/908 [01:04<13:17,  1.07it/s]
  6%|▌         | 52/908 [01:04<10:06,  1.41it/s]
  6%|▌         | 53/908 [01:09<25:32,  1.79s/it]
  6%|▌         | 54/908 [01:09<18:39,  1.31s/it]
  6%|▌         | 55/908 [01:09<13:51,  1.03it/s]
  6%|▌         | 56/908 [01:09<10:30,  1.35it/s]
  6%|▋         | 57/908 [01:14<28:31,  2.01s/it]
  6%|▋         | 58/908 [01:14<20:44,  1.46s/it]
  6%|▋         | 59/908 [01:15<15:18,  1.08s/it]
+[Batch:    60] training loss = 0.492
+
  7%|▋         | 60/908 [01:15<11:30,  1.23it/s]
  7%|▋         | 61/908 [01:18<19:40,  1.39s/it]
  7%|▋         | 62/908 [01:18<14:36,  1.04s/it]
  7%|▋         | 63/908 [01:18<11:03,  1.27it/s]
  7%|▋         | 64/908 [01:18<08:33,  1.64it/s]
  7%|▋         | 65/908 [01:22<20:46,  1.48s/it]
  7%|▋         | 66/908 [01:22<15:20,  1.09s/it]
  7%|▋         | 67/908 [01:22<11:32,  1.22it/s]
  7%|▋         | 68/908 [01:22<08:51,  1.58it/s]
  8%|▊         | 69/908 [01:26<20:26,  1.46s/it]
  8%|▊         | 70/908 [01:26<15:04,  1.08s/it]
  8%|▊         | 71/908 [01:26<11:22,  1.23it/s]
  8%|▊         | 72/908 [01:26<08:45,  1.59it/s]
  8%|▊         | 73/908 [01:31<25:54,  1.86s/it]
  8%|▊         | 74/908 [01:31<18:55,  1.36s/it]
  8%|▊         | 75/908 [01:31<14:00,  1.01s/it]
  8%|▊         | 76/908 [01:31<10:35,  1.31it/s]
  8%|▊         | 77/908 [01:35<23:35,  1.70s/it]
  9%|▊         | 78/908 [01:36<17:17,  1.25s/it]
  9%|▊         | 79/908 [01:36<13:15,  1.04it/s]
+[Batch:    80] training loss = 0.488
+
  9%|▉         | 80/908 [01:36<10:03,  1.37it/s]
  9%|▉         | 81/908 [01:39<19:53,  1.44s/it]
  9%|▉         | 82/908 [01:39<14:40,  1.07s/it]
  9%|▉         | 83/908 [01:40<11:04,  1.24it/s]
  9%|▉         | 84/908 [01:40<08:35,  1.60it/s]
  9%|▉         | 85/908 [01:45<25:39,  1.87s/it]
  9%|▉         | 86/908 [01:45<18:44,  1.37s/it]
 10%|▉         | 87/908 [01:45<13:52,  1.01s/it]
 10%|▉         | 88/908 [01:45<10:29,  1.30it/s]
 10%|▉         | 89/908 [01:49<23:27,  1.72s/it]
 10%|▉         | 90/908 [01:49<17:12,  1.26s/it]
 10%|█         | 91/908 [01:49<12:48,  1.06it/s]
 10%|█         | 92/908 [01:50<09:42,  1.40it/s]
 10%|█         | 93/908 [01:53<22:28,  1.65s/it]
 10%|█         | 94/908 [01:54<16:28,  1.21s/it]
 10%|█         | 95/908 [01:54<12:21,  1.10it/s]
 11%|█         | 96/908 [01:54<09:29,  1.43it/s]
 11%|█         | 97/908 [01:57<16:56,  1.25s/it]
 11%|█         | 98/908 [01:57<12:35,  1.07it/s]
 11%|█         | 99/908 [01:57<09:34,  1.41it/s]
+[Batch:   100] training loss = 0.310
+
 11%|█         | 100/908 [01:57<07:27,  1.80it/s]
 11%|█         | 101/908 [02:02<22:42,  1.69s/it]
 11%|█         | 102/908 [02:02<16:38,  1.24s/it]
 11%|█▏        | 103/908 [02:02<12:23,  1.08it/s]
 11%|█▏        | 104/908 [02:02<09:27,  1.42it/s]
 12%|█▏        | 105/908 [02:07<26:16,  1.96s/it]
 12%|█▏        | 106/908 [02:07<19:07,  1.43s/it]
 12%|█▏        | 107/908 [02:07<14:08,  1.06s/it]
 12%|█▏        | 108/908 [02:08<10:38,  1.25it/s]
 12%|█▏        | 109/908 [02:11<21:07,  1.59s/it]
 12%|█▏        | 110/908 [02:11<15:30,  1.17s/it]
 12%|█▏        | 111/908 [02:11<11:36,  1.14it/s]
 12%|█▏        | 112/908 [02:12<08:52,  1.49it/s]
 12%|█▏        | 113/908 [02:14<16:51,  1.27s/it]
 13%|█▎        | 114/908 [02:15<14:05,  1.07s/it]
 13%|█▎        | 115/908 [02:15<10:37,  1.24it/s]
 13%|█▎        | 116/908 [02:15<08:12,  1.61it/s]
 13%|█▎        | 117/908 [02:19<22:28,  1.71s/it]
 13%|█▎        | 118/908 [02:20<17:15,  1.31s/it]
 13%|█▎        | 119/908 [02:20<12:49,  1.02it/s]
+[Batch:   120] training loss = 0.474
+
 13%|█▎        | 120/908 [02:20<09:43,  1.35it/s]
 13%|█▎        | 121/908 [02:25<23:50,  1.82s/it]
 13%|█▎        | 122/908 [02:25<17:24,  1.33s/it]
 14%|█▎        | 123/908 [02:25<12:53,  1.01it/s]
 14%|█▎        | 124/908 [02:25<09:45,  1.34it/s]
 14%|█▍        | 125/908 [02:28<19:14,  1.47s/it]
 14%|█▍        | 126/908 [02:29<15:16,  1.17s/it]
 14%|█▍        | 127/908 [02:29<11:50,  1.10it/s]
 14%|█▍        | 128/908 [02:29<09:00,  1.44it/s]
 14%|█▍        | 129/908 [02:34<23:35,  1.82s/it]
 14%|█▍        | 130/908 [02:34<17:25,  1.34s/it]
 14%|█▍        | 131/908 [02:34<12:55,  1.00it/s]
 15%|█▍        | 132/908 [02:34<09:49,  1.32it/s]
 15%|█▍        | 133/908 [02:38<20:56,  1.62s/it]
 15%|█▍        | 134/908 [02:38<15:24,  1.19s/it]
 15%|█▍        | 135/908 [02:38<11:30,  1.12it/s]
 15%|█▍        | 136/908 [02:39<08:46,  1.47it/s]
 15%|█▌        | 137/908 [02:43<24:35,  1.91s/it]
 15%|█▌        | 138/908 [02:44<17:57,  1.40s/it]
 15%|█▌        | 139/908 [02:44<13:18,  1.04s/it]
+[Batch:   140] training loss = 0.386
+
 15%|█▌        | 140/908 [02:44<10:05,  1.27it/s]
 16%|█▌        | 141/908 [02:49<25:21,  1.98s/it]
 16%|█▌        | 142/908 [02:49<18:31,  1.45s/it]
 16%|█▌        | 143/908 [02:49<13:42,  1.08s/it]
 16%|█▌        | 144/908 [02:49<10:21,  1.23it/s]
 16%|█▌        | 145/908 [02:54<26:36,  2.09s/it]
 16%|█▌        | 146/908 [02:55<19:22,  1.52s/it]
 16%|█▌        | 147/908 [02:55<14:16,  1.13s/it]
 16%|█▋        | 148/908 [02:55<10:46,  1.18it/s]
 16%|█▋        | 149/908 [02:59<23:12,  1.84s/it]
 17%|█▋        | 150/908 [02:59<16:58,  1.34s/it]
 17%|█▋        | 151/908 [03:00<12:39,  1.00s/it]
 17%|█▋        | 152/908 [03:00<09:38,  1.31it/s]
 17%|█▋        | 153/908 [03:04<22:46,  1.81s/it]
 17%|█▋        | 154/908 [03:04<16:44,  1.33s/it]
 17%|█▋        | 155/908 [03:04<12:27,  1.01it/s]
 17%|█▋        | 156/908 [03:05<09:27,  1.32it/s]
 17%|█▋        | 157/908 [03:09<23:03,  1.84s/it]
 17%|█▋        | 158/908 [03:09<16:52,  1.35s/it]
 18%|█▊        | 159/908 [03:09<12:31,  1.00s/it]
+[Batch:   160] training loss = 0.534
+
 18%|█▊        | 160/908 [03:10<09:30,  1.31it/s]
 18%|█▊        | 161/908 [03:12<16:56,  1.36s/it]
 18%|█▊        | 162/908 [03:13<12:34,  1.01s/it]
 18%|█▊        | 163/908 [03:13<09:57,  1.25it/s]
 18%|█▊        | 164/908 [03:13<07:42,  1.61it/s]
 18%|█▊        | 165/908 [03:19<25:50,  2.09s/it]
 18%|█▊        | 166/908 [03:19<18:47,  1.52s/it]
 18%|█▊        | 167/908 [03:19<13:50,  1.12s/it]
 19%|█▊        | 168/908 [03:19<10:26,  1.18it/s]
 19%|█▊        | 169/908 [03:26<32:01,  2.60s/it]
 19%|█▊        | 170/908 [03:26<23:06,  1.88s/it]
 19%|█▉        | 171/908 [03:26<16:54,  1.38s/it]
 19%|█▉        | 172/908 [03:26<12:32,  1.02s/it]
 19%|█▉        | 173/908 [03:30<23:37,  1.93s/it]
 19%|█▉        | 174/908 [03:31<17:15,  1.41s/it]
 19%|█▉        | 175/908 [03:31<12:48,  1.05s/it]
 19%|█▉        | 176/908 [03:31<09:40,  1.26it/s]
 19%|█▉        | 177/908 [03:36<23:07,  1.90s/it]
 20%|█▉        | 178/908 [03:36<16:54,  1.39s/it]
 20%|█▉        | 179/908 [03:36<12:33,  1.03s/it]
+[Batch:   180] training loss = 0.440
+
 20%|█▉        | 180/908 [03:36<09:30,  1.28it/s]
 20%|█▉        | 181/908 [03:40<20:52,  1.72s/it]
 20%|██        | 182/908 [03:40<15:18,  1.26s/it]
 20%|██        | 183/908 [03:40<11:25,  1.06it/s]
 20%|██        | 184/908 [03:41<08:42,  1.39it/s]
 20%|██        | 185/908 [03:44<18:07,  1.50s/it]
 20%|██        | 186/908 [03:44<13:24,  1.11s/it]
 21%|██        | 187/908 [03:44<10:05,  1.19it/s]
 21%|██        | 188/908 [03:45<07:46,  1.54it/s]
 21%|██        | 189/908 [03:49<21:41,  1.81s/it]
 21%|██        | 190/908 [03:49<15:52,  1.33s/it]
 21%|██        | 191/908 [03:50<11:49,  1.01it/s]
 21%|██        | 192/908 [03:50<08:59,  1.33it/s]
 21%|██▏       | 193/908 [03:54<20:34,  1.73s/it]
 21%|██▏       | 194/908 [03:54<15:08,  1.27s/it]
 21%|██▏       | 195/908 [03:54<11:17,  1.05it/s]
 22%|██▏       | 196/908 [03:54<08:35,  1.38it/s]
 22%|██▏       | 197/908 [03:57<15:59,  1.35s/it]
 22%|██▏       | 198/908 [03:57<11:52,  1.00s/it]
 22%|██▏       | 199/908 [03:58<09:02,  1.31it/s]
+[Batch:   200] training loss = 0.517
+
 22%|██▏       | 200/908 [03:58<07:01,  1.68it/s]
 22%|██▏       | 201/908 [04:01<15:01,  1.28s/it]
 22%|██▏       | 202/908 [04:01<11:12,  1.05it/s]
 22%|██▏       | 203/908 [04:01<08:31,  1.38it/s]
 22%|██▏       | 204/908 [04:01<06:40,  1.76it/s]
 23%|██▎       | 205/908 [04:05<19:16,  1.65s/it]
 23%|██▎       | 206/908 [04:06<14:10,  1.21s/it]
 23%|██▎       | 207/908 [04:06<10:40,  1.09it/s]
 23%|██▎       | 208/908 [04:06<08:11,  1.42it/s]
 23%|██▎       | 209/908 [04:10<20:19,  1.75s/it]
 23%|██▎       | 210/908 [04:10<14:53,  1.28s/it]
 23%|██▎       | 211/908 [04:11<11:07,  1.04it/s]
 23%|██▎       | 212/908 [04:11<08:27,  1.37it/s]
 23%|██▎       | 213/908 [04:14<18:23,  1.59s/it]
 24%|██▎       | 214/908 [04:15<13:34,  1.17s/it]
 24%|██▎       | 215/908 [04:15<10:12,  1.13it/s]
 24%|██▍       | 216/908 [04:15<07:49,  1.48it/s]
 24%|██▍       | 217/908 [04:18<17:23,  1.51s/it]
 24%|██▍       | 218/908 [04:19<12:50,  1.12s/it]
 24%|██▍       | 219/908 [04:19<09:39,  1.19it/s]
+[Batch:   220] training loss = 0.386
+
 24%|██▍       | 220/908 [04:19<07:25,  1.54it/s]
 24%|██▍       | 221/908 [04:25<24:37,  2.15s/it]
 24%|██▍       | 222/908 [04:25<17:53,  1.56s/it]
 25%|██▍       | 223/908 [04:25<13:11,  1.16s/it]
 25%|██▍       | 224/908 [04:25<09:53,  1.15it/s]
 25%|██▍       | 225/908 [04:29<20:41,  1.82s/it]
 25%|██▍       | 226/908 [04:29<15:08,  1.33s/it]
 25%|██▌       | 227/908 [04:30<11:14,  1.01it/s]
 25%|██▌       | 228/908 [04:30<08:31,  1.33it/s]
 25%|██▌       | 229/908 [04:34<19:01,  1.68s/it]
 25%|██▌       | 230/908 [04:34<13:57,  1.24s/it]
 25%|██▌       | 231/908 [04:34<10:27,  1.08it/s]
 26%|██▌       | 232/908 [04:34<07:58,  1.41it/s]
 26%|██▌       | 233/908 [04:39<19:45,  1.76s/it]
 26%|██▌       | 234/908 [04:39<14:29,  1.29s/it]
 26%|██▌       | 235/908 [04:39<10:48,  1.04it/s]
 26%|██▌       | 236/908 [04:39<08:12,  1.37it/s]
 26%|██▌       | 237/908 [04:43<18:58,  1.70s/it]
 26%|██▌       | 238/908 [04:43<13:57,  1.25s/it]
 26%|██▋       | 239/908 [04:43<10:25,  1.07it/s]
+[Batch:   240] training loss = 0.409
+
 26%|██▋       | 240/908 [04:44<07:57,  1.40it/s]
 27%|██▋       | 241/908 [04:48<20:53,  1.88s/it]
 27%|██▋       | 242/908 [04:48<15:16,  1.38s/it]
 27%|██▋       | 243/908 [04:49<11:19,  1.02s/it]
 27%|██▋       | 244/908 [04:49<08:35,  1.29it/s]
 27%|██▋       | 245/908 [04:53<18:44,  1.70s/it]
 27%|██▋       | 246/908 [04:53<13:43,  1.24s/it]
 27%|██▋       | 247/908 [04:53<10:14,  1.08it/s]
 27%|██▋       | 248/908 [04:53<07:48,  1.41it/s]
 27%|██▋       | 249/908 [04:57<18:28,  1.68s/it]
 28%|██▊       | 250/908 [04:57<13:31,  1.23s/it]
 28%|██▊       | 251/908 [04:58<10:04,  1.09it/s]
 28%|██▊       | 252/908 [04:58<07:39,  1.43it/s]
 28%|██▊       | 253/908 [05:03<22:06,  2.02s/it]
 28%|██▊       | 254/908 [05:03<16:04,  1.47s/it]
 28%|██▊       | 255/908 [05:03<11:52,  1.09s/it]
 28%|██▊       | 256/908 [05:04<08:56,  1.22it/s]
 28%|██▊       | 257/908 [05:09<23:07,  2.13s/it]
 28%|██▊       | 258/908 [05:09<16:46,  1.55s/it]
 29%|██▊       | 259/908 [05:09<12:21,  1.14s/it]
+[Batch:   260] training loss = 0.638
+
 29%|██▊       | 260/908 [05:09<09:16,  1.16it/s]
 29%|██▊       | 261/908 [05:13<18:22,  1.70s/it]
 29%|██▉       | 262/908 [05:13<13:27,  1.25s/it]
 29%|██▉       | 263/908 [05:13<10:00,  1.07it/s]
 29%|██▉       | 264/908 [05:14<07:38,  1.40it/s]
 29%|██▉       | 265/908 [05:17<17:32,  1.64s/it]
 29%|██▉       | 266/908 [05:18<12:52,  1.20s/it]
 29%|██▉       | 267/908 [05:18<09:36,  1.11it/s]
 30%|██▉       | 268/908 [05:18<07:19,  1.46it/s]
 30%|██▉       | 269/908 [05:23<20:11,  1.90s/it]
 30%|██▉       | 270/908 [05:23<14:44,  1.39s/it]
 30%|██▉       | 271/908 [05:23<10:55,  1.03s/it]
 30%|██▉       | 272/908 [05:23<08:17,  1.28it/s]
 30%|███       | 273/908 [05:29<25:18,  2.39s/it]
 30%|███       | 274/908 [05:30<18:17,  1.73s/it]
 30%|███       | 275/908 [05:30<13:23,  1.27s/it]
 30%|███       | 276/908 [05:30<09:59,  1.05it/s]
 31%|███       | 277/908 [05:34<19:28,  1.85s/it]
 31%|███       | 278/908 [05:34<14:12,  1.35s/it]
 31%|███       | 279/908 [05:34<10:32,  1.01s/it]
+[Batch:   280] training loss = 0.410
+
 31%|███       | 280/908 [05:34<07:57,  1.31it/s]
 31%|███       | 281/908 [05:38<15:14,  1.46s/it]
 31%|███       | 282/908 [05:38<11:14,  1.08s/it]
 31%|███       | 283/908 [05:38<08:27,  1.23it/s]
 31%|███▏      | 284/908 [05:38<06:31,  1.59it/s]
 31%|███▏      | 285/908 [05:41<14:48,  1.43s/it]
 31%|███▏      | 286/908 [05:42<10:56,  1.06s/it]
 32%|███▏      | 287/908 [05:42<08:14,  1.26it/s]
 32%|███▏      | 288/908 [05:42<06:20,  1.63it/s]
 32%|███▏      | 289/908 [05:46<17:35,  1.71s/it]
 32%|███▏      | 290/908 [05:46<12:52,  1.25s/it]
 32%|███▏      | 291/908 [05:47<09:35,  1.07it/s]
 32%|███▏      | 292/908 [05:47<07:16,  1.41it/s]
 32%|███▏      | 293/908 [05:50<15:25,  1.50s/it]
 32%|███▏      | 294/908 [05:50<11:23,  1.11s/it]
 32%|███▏      | 295/908 [05:51<08:34,  1.19it/s]
 33%|███▎      | 296/908 [05:51<06:36,  1.54it/s]
 33%|███▎      | 297/908 [05:55<16:09,  1.59s/it]
 33%|███▎      | 298/908 [05:55<11:53,  1.17s/it]
 33%|███▎      | 299/908 [05:55<08:54,  1.14it/s]
+[Batch:   300] training loss = 0.430
+
 33%|███▎      | 300/908 [05:55<06:47,  1.49it/s]
 33%|███▎      | 301/908 [05:59<14:58,  1.48s/it]
 33%|███▎      | 302/908 [05:59<11:01,  1.09s/it]
 33%|███▎      | 303/908 [05:59<08:19,  1.21it/s]
 33%|███▎      | 304/908 [05:59<06:23,  1.57it/s]
 34%|███▎      | 305/908 [06:03<16:55,  1.68s/it]
 34%|███▎      | 306/908 [06:03<12:25,  1.24s/it]
 34%|███▍      | 307/908 [06:04<09:15,  1.08it/s]
 34%|███▍      | 308/908 [06:04<07:03,  1.42it/s]
 34%|███▍      | 309/908 [06:08<18:30,  1.85s/it]
 34%|███▍      | 310/908 [06:09<13:29,  1.35s/it]
 34%|███▍      | 311/908 [06:09<10:00,  1.01s/it]
 34%|███▍      | 312/908 [06:09<07:37,  1.30it/s]
 34%|███▍      | 313/908 [06:13<18:25,  1.86s/it]
 35%|███▍      | 314/908 [06:14<13:25,  1.36s/it]
 35%|███▍      | 315/908 [06:14<09:56,  1.01s/it]
 35%|███▍      | 316/908 [06:14<07:30,  1.31it/s]
 35%|███▍      | 317/908 [06:19<18:57,  1.93s/it]
 35%|███▌      | 318/908 [06:19<13:48,  1.40s/it]
 35%|███▌      | 319/908 [06:19<10:12,  1.04s/it]
+[Batch:   320] training loss = 0.572
+
 35%|███▌      | 320/908 [06:19<07:41,  1.27it/s]
 35%|███▌      | 321/908 [06:23<17:51,  1.82s/it]
 35%|███▌      | 322/908 [06:24<13:01,  1.33s/it]
 36%|███▌      | 323/908 [06:24<09:39,  1.01it/s]
 36%|███▌      | 324/908 [06:24<07:18,  1.33it/s]
 36%|███▌      | 325/908 [06:28<16:58,  1.75s/it]
 36%|███▌      | 326/908 [06:28<12:24,  1.28s/it]
 36%|███▌      | 327/908 [06:28<09:12,  1.05it/s]
 36%|███▌      | 328/908 [06:29<06:58,  1.39it/s]
 36%|███▌      | 329/908 [06:33<18:37,  1.93s/it]
 36%|███▋      | 330/908 [06:33<13:32,  1.41s/it]
 36%|███▋      | 331/908 [06:34<10:00,  1.04s/it]
 37%|███▋      | 332/908 [06:34<07:32,  1.27it/s]
 37%|███▋      | 333/908 [06:39<20:17,  2.12s/it]
 37%|███▋      | 334/908 [06:39<14:44,  1.54s/it]
 37%|███▋      | 335/908 [06:39<10:51,  1.14s/it]
 37%|███▋      | 336/908 [06:40<08:08,  1.17it/s]
 37%|███▋      | 337/908 [06:45<20:55,  2.20s/it]
 37%|███▋      | 338/908 [06:45<15:09,  1.60s/it]
 37%|███▋      | 339/908 [06:45<11:07,  1.17s/it]
+[Batch:   340] training loss = 0.450
+
 37%|███▋      | 340/908 [06:46<08:19,  1.14it/s]
 38%|███▊      | 341/908 [06:50<17:41,  1.87s/it]
 38%|███▊      | 342/908 [06:50<12:53,  1.37s/it]
 38%|███▊      | 343/908 [06:50<09:33,  1.02s/it]
 38%|███▊      | 344/908 [06:50<07:13,  1.30it/s]
 38%|███▊      | 345/908 [06:54<14:18,  1.52s/it]
 38%|███▊      | 346/908 [06:54<10:32,  1.13s/it]
 38%|███▊      | 347/908 [06:54<07:52,  1.19it/s]
 38%|███▊      | 348/908 [06:54<06:01,  1.55it/s]
 38%|███▊      | 349/908 [06:56<09:36,  1.03s/it]
 39%|███▊      | 350/908 [06:56<07:14,  1.28it/s]
 39%|███▊      | 351/908 [06:57<05:34,  1.66it/s]
 39%|███▉      | 352/908 [06:57<04:24,  2.10it/s]
 39%|███▉      | 353/908 [06:59<08:09,  1.13it/s]
 39%|███▉      | 354/908 [06:59<06:13,  1.48it/s]
 39%|███▉      | 355/908 [06:59<04:52,  1.89it/s]
 39%|███▉      | 356/908 [06:59<03:55,  2.34it/s]
 39%|███▉      | 357/908 [07:01<08:51,  1.04it/s]
 39%|███▉      | 358/908 [07:01<06:42,  1.37it/s]
 40%|███▉      | 359/908 [07:02<05:13,  1.75it/s]
+[Batch:   360] training loss = 0.338
+
 40%|███▉      | 360/908 [07:02<04:11,  2.18it/s]
 40%|███▉      | 361/908 [07:05<10:51,  1.19s/it]
 40%|███▉      | 362/908 [07:05<08:05,  1.12it/s]
 40%|███▉      | 363/908 [07:05<06:11,  1.47it/s]
 40%|████      | 364/908 [07:05<04:50,  1.87it/s]
 40%|████      | 365/908 [07:09<13:13,  1.46s/it]
 40%|████      | 366/908 [07:09<09:44,  1.08s/it]
 40%|████      | 367/908 [07:09<07:19,  1.23it/s]
 41%|████      | 368/908 [07:10<05:39,  1.59it/s]
 41%|████      | 369/908 [07:13<12:54,  1.44s/it]
 41%|████      | 370/908 [07:13<09:32,  1.06s/it]
 41%|████      | 371/908 [07:13<07:10,  1.25it/s]
 41%|████      | 372/908 [07:13<05:31,  1.62it/s]
 41%|████      | 373/908 [07:18<15:35,  1.75s/it]
 41%|████      | 374/908 [07:18<11:24,  1.28s/it]
 41%|████▏     | 375/908 [07:18<08:28,  1.05it/s]
 41%|████▏     | 376/908 [07:18<06:25,  1.38it/s]
 42%|████▏     | 377/908 [07:23<17:01,  1.92s/it]
 42%|████▏     | 378/908 [07:23<12:25,  1.41s/it]
 42%|████▏     | 379/908 [07:24<09:11,  1.04s/it]
+[Batch:   380] training loss = 0.320
+
 42%|████▏     | 380/908 [07:24<06:55,  1.27it/s]
 42%|████▏     | 381/908 [07:27<13:54,  1.58s/it]
 42%|████▏     | 382/908 [07:27<10:13,  1.17s/it]
 42%|████▏     | 383/908 [07:28<07:40,  1.14it/s]
 42%|████▏     | 384/908 [07:28<05:51,  1.49it/s]
 42%|████▏     | 385/908 [07:31<13:11,  1.51s/it]
 43%|████▎     | 386/908 [07:31<09:42,  1.12s/it]
 43%|████▎     | 387/908 [07:32<07:16,  1.19it/s]
 43%|████▎     | 388/908 [07:32<05:35,  1.55it/s]
 43%|████▎     | 389/908 [07:35<12:55,  1.49s/it]
 43%|████▎     | 390/908 [07:35<09:31,  1.10s/it]
 43%|████▎     | 391/908 [07:36<07:09,  1.20it/s]
 43%|████▎     | 392/908 [07:36<05:29,  1.57it/s]
 43%|████▎     | 393/908 [07:40<14:25,  1.68s/it]
 43%|████▎     | 394/908 [07:40<10:36,  1.24s/it]
 44%|████▎     | 395/908 [07:40<07:53,  1.08it/s]
 44%|████▎     | 396/908 [07:41<05:59,  1.42it/s]
 44%|████▎     | 397/908 [07:46<17:09,  2.01s/it]
 44%|████▍     | 398/908 [07:46<12:28,  1.47s/it]
 44%|████▍     | 399/908 [07:46<09:12,  1.09s/it]
+[Batch:   400] training loss = 0.471
+
 44%|████▍     | 400/908 [07:46<06:56,  1.22it/s]
 44%|████▍     | 401/908 [07:50<14:08,  1.67s/it]
 44%|████▍     | 402/908 [07:50<10:21,  1.23s/it]
 44%|████▍     | 403/908 [07:50<07:43,  1.09it/s]
 44%|████▍     | 404/908 [07:50<05:53,  1.43it/s]
 45%|████▍     | 405/908 [07:55<15:08,  1.81s/it]
 45%|████▍     | 406/908 [07:55<11:02,  1.32s/it]
 45%|████▍     | 407/908 [07:55<08:11,  1.02it/s]
 45%|████▍     | 408/908 [07:55<06:12,  1.34it/s]
 45%|████▌     | 409/908 [08:11<42:08,  5.07s/it]
 45%|████▌     | 410/908 [08:11<29:54,  3.60s/it]
 45%|████▌     | 411/908 [08:11<21:21,  2.58s/it]
 45%|████▌     | 412/908 [08:11<15:23,  1.86s/it]
 45%|████▌     | 413/908 [08:16<22:50,  2.77s/it]
 46%|████▌     | 414/908 [08:16<16:25,  1.99s/it]
 46%|████▌     | 415/908 [08:16<11:57,  1.45s/it]
 46%|████▌     | 416/908 [08:17<08:48,  1.08s/it]
 46%|████▌     | 417/908 [08:23<22:13,  2.72s/it]
 46%|████▌     | 418/908 [08:23<15:59,  1.96s/it]
 46%|████▌     | 419/908 [08:24<11:38,  1.43s/it]
+[Batch:   420] training loss = 0.447
+
 46%|████▋     | 420/908 [08:24<08:36,  1.06s/it]
 46%|████▋     | 421/908 [08:34<30:47,  3.79s/it]
 46%|████▋     | 422/908 [08:34<21:59,  2.72s/it]
 47%|████▋     | 423/908 [08:34<15:50,  1.96s/it]
 47%|████▋     | 424/908 [08:35<12:21,  1.53s/it]
 47%|████▋     | 425/908 [08:42<25:02,  3.11s/it]
 47%|████▋     | 426/908 [08:42<17:57,  2.24s/it]
 47%|████▋     | 427/908 [08:42<13:01,  1.62s/it]
 47%|████▋     | 428/908 [08:42<09:33,  1.19s/it]
 47%|████▋     | 429/908 [08:47<18:56,  2.37s/it]
 47%|████▋     | 430/908 [08:48<13:41,  1.72s/it]
 47%|████▋     | 431/908 [08:48<10:02,  1.26s/it]
 48%|████▊     | 432/908 [08:48<07:28,  1.06it/s]
 48%|████▊     | 433/908 [08:53<17:55,  2.26s/it]
 48%|████▊     | 434/908 [08:53<12:58,  1.64s/it]
 48%|████▊     | 435/908 [08:54<09:31,  1.21s/it]
 48%|████▊     | 436/908 [08:54<07:06,  1.11it/s]
 48%|████▊     | 437/908 [09:00<18:27,  2.35s/it]
 48%|████▊     | 438/908 [09:00<13:20,  1.70s/it]
 48%|████▊     | 439/908 [09:00<09:45,  1.25s/it]
+[Batch:   440] training loss = 0.351
+
 48%|████▊     | 440/908 [09:00<07:16,  1.07it/s]
 49%|████▊     | 441/908 [09:03<12:08,  1.56s/it]
 49%|████▊     | 442/908 [09:03<08:55,  1.15s/it]
 49%|████▉     | 443/908 [09:04<06:43,  1.15it/s]
 49%|████▉     | 444/908 [09:04<05:09,  1.50it/s]
 49%|████▉     | 445/908 [09:07<10:02,  1.30s/it]
 49%|████▉     | 446/908 [09:07<07:29,  1.03it/s]
 49%|████▉     | 447/908 [09:07<05:40,  1.35it/s]
 49%|████▉     | 448/908 [09:07<04:25,  1.73it/s]
 49%|████▉     | 449/908 [09:11<11:11,  1.46s/it]
 50%|████▉     | 450/908 [09:11<08:16,  1.08s/it]
 50%|████▉     | 451/908 [09:11<06:13,  1.22it/s]
 50%|████▉     | 452/908 [09:11<04:49,  1.58it/s]
 50%|████▉     | 453/908 [09:15<11:47,  1.56s/it]
 50%|█████     | 454/908 [09:15<08:42,  1.15s/it]
 50%|█████     | 455/908 [09:15<06:30,  1.16it/s]
 50%|█████     | 456/908 [09:16<05:00,  1.51it/s]
 50%|█████     | 457/908 [09:20<13:13,  1.76s/it]
 50%|█████     | 458/908 [09:20<09:42,  1.29s/it]
 51%|█████     | 459/908 [09:20<07:12,  1.04it/s]
+[Batch:   460] training loss = 0.351
+
 51%|█████     | 460/908 [09:20<05:28,  1.36it/s]
 51%|█████     | 461/908 [09:25<12:50,  1.72s/it]
 51%|█████     | 462/908 [09:25<09:24,  1.27s/it]
 51%|█████     | 463/908 [09:25<07:02,  1.05it/s]
 51%|█████     | 464/908 [09:25<05:21,  1.38it/s]
 51%|█████     | 465/908 [09:28<10:49,  1.47s/it]
 51%|█████▏    | 466/908 [09:29<07:59,  1.09s/it]
 51%|█████▏    | 467/908 [09:29<06:00,  1.22it/s]
 52%|█████▏    | 468/908 [09:29<04:38,  1.58it/s]
 52%|█████▏    | 469/908 [09:34<14:30,  1.98s/it]
 52%|█████▏    | 470/908 [09:34<10:33,  1.45s/it]
 52%|█████▏    | 471/908 [09:34<07:49,  1.07s/it]
 52%|█████▏    | 472/908 [09:35<05:54,  1.23it/s]
 52%|█████▏    | 473/908 [09:38<11:13,  1.55s/it]
 52%|█████▏    | 474/908 [09:38<08:16,  1.14s/it]
 52%|█████▏    | 475/908 [09:38<06:11,  1.16it/s]
 52%|█████▏    | 476/908 [09:39<04:45,  1.51it/s]
 53%|█████▎    | 477/908 [09:42<10:48,  1.51s/it]
 53%|█████▎    | 478/908 [09:42<07:58,  1.11s/it]
 53%|█████▎    | 479/908 [09:42<06:00,  1.19it/s]
+[Batch:   480] training loss = 0.457
+
 53%|█████▎    | 480/908 [09:43<04:37,  1.54it/s]
 53%|█████▎    | 481/908 [09:48<14:25,  2.03s/it]
 53%|█████▎    | 482/908 [09:48<10:29,  1.48s/it]
 53%|█████▎    | 483/908 [09:48<07:44,  1.09s/it]
 53%|█████▎    | 484/908 [09:48<05:49,  1.21it/s]
 53%|█████▎    | 485/908 [09:53<13:52,  1.97s/it]
 54%|█████▎    | 486/908 [09:53<10:06,  1.44s/it]
 54%|█████▎    | 487/908 [09:53<07:29,  1.07s/it]
 54%|█████▎    | 488/908 [09:54<05:39,  1.24it/s]
 54%|█████▍    | 489/908 [09:58<12:34,  1.80s/it]
 54%|█████▍    | 490/908 [09:58<09:11,  1.32s/it]
 54%|█████▍    | 491/908 [09:58<06:50,  1.02it/s]
 54%|█████▍    | 492/908 [09:58<05:10,  1.34it/s]
 54%|█████▍    | 493/908 [10:02<11:05,  1.60s/it]
 54%|█████▍    | 494/908 [10:02<08:08,  1.18s/it]
 55%|█████▍    | 495/908 [10:02<06:06,  1.13it/s]
 55%|█████▍    | 496/908 [10:03<04:40,  1.47it/s]
 55%|█████▍    | 497/908 [10:07<12:54,  1.88s/it]
 55%|█████▍    | 498/908 [10:07<09:25,  1.38s/it]
 55%|█████▍    | 499/908 [10:08<06:59,  1.03s/it]
+[Batch:   500] training loss = 0.472
+
 55%|█████▌    | 500/908 [10:08<05:18,  1.28it/s]
 55%|█████▌    | 501/908 [10:12<11:52,  1.75s/it]
 55%|█████▌    | 502/908 [10:12<08:42,  1.29s/it]
 55%|█████▌    | 503/908 [10:12<06:28,  1.04it/s]
 56%|█████▌    | 504/908 [10:12<04:55,  1.37it/s]
 56%|█████▌    | 505/908 [10:17<11:39,  1.74s/it]
 56%|█████▌    | 506/908 [10:17<08:32,  1.27s/it]
 56%|█████▌    | 507/908 [10:17<06:22,  1.05it/s]
 56%|█████▌    | 508/908 [10:17<04:50,  1.38it/s]
 56%|█████▌    | 509/908 [10:22<12:54,  1.94s/it]
 56%|█████▌    | 510/908 [10:22<09:24,  1.42s/it]
 56%|█████▋    | 511/908 [10:22<06:56,  1.05s/it]
 56%|█████▋    | 512/908 [10:23<05:14,  1.26it/s]
 56%|█████▋    | 513/908 [10:27<12:05,  1.84s/it]
 57%|█████▋    | 514/908 [10:27<08:49,  1.35s/it]
 57%|█████▋    | 515/908 [10:27<06:33,  1.00s/it]
 57%|█████▋    | 516/908 [10:27<04:58,  1.31it/s]
 57%|█████▋    | 517/908 [10:31<10:22,  1.59s/it]
 57%|█████▋    | 518/908 [10:31<07:37,  1.17s/it]
 57%|█████▋    | 519/908 [10:31<05:42,  1.13it/s]
+[Batch:   520] training loss = 0.416
+
 57%|█████▋    | 520/908 [10:32<04:23,  1.47it/s]
 57%|█████▋    | 521/908 [10:36<12:14,  1.90s/it]
 57%|█████▋    | 522/908 [10:36<08:55,  1.39s/it]
 58%|█████▊    | 523/908 [10:37<06:36,  1.03s/it]
 58%|█████▊    | 524/908 [10:37<04:59,  1.28it/s]
 58%|█████▊    | 525/908 [10:40<08:38,  1.36s/it]
 58%|█████▊    | 526/908 [10:40<06:25,  1.01s/it]
 58%|█████▊    | 527/908 [10:40<04:51,  1.31it/s]
 58%|█████▊    | 528/908 [10:40<03:46,  1.68it/s]
 58%|█████▊    | 529/908 [10:43<07:50,  1.24s/it]
 58%|█████▊    | 530/908 [10:43<05:50,  1.08it/s]
 58%|█████▊    | 531/908 [10:43<04:27,  1.41it/s]
 59%|█████▊    | 532/908 [10:43<03:28,  1.80it/s]
 59%|█████▊    | 533/908 [10:48<10:18,  1.65s/it]
 59%|█████▉    | 534/908 [10:48<07:33,  1.21s/it]
 59%|█████▉    | 535/908 [10:48<05:38,  1.10it/s]
 59%|█████▉    | 536/908 [10:48<04:19,  1.43it/s]
 59%|█████▉    | 537/908 [10:53<11:05,  1.80s/it]
 59%|█████▉    | 538/908 [10:53<08:07,  1.32s/it]
 59%|█████▉    | 539/908 [10:53<06:02,  1.02it/s]
+[Batch:   540] training loss = 0.436
+
 59%|█████▉    | 540/908 [10:53<04:35,  1.33it/s]
 60%|█████▉    | 541/908 [10:57<09:28,  1.55s/it]
 60%|█████▉    | 542/908 [10:57<06:59,  1.15s/it]
 60%|█████▉    | 543/908 [10:57<05:14,  1.16it/s]
 60%|█████▉    | 544/908 [10:57<04:02,  1.50it/s]
 60%|██████    | 545/908 [11:02<11:13,  1.85s/it]
 60%|██████    | 546/908 [11:02<08:11,  1.36s/it]
 60%|██████    | 547/908 [11:02<06:05,  1.01s/it]
 60%|██████    | 548/908 [11:03<04:36,  1.30it/s]
 60%|██████    | 549/908 [11:06<09:37,  1.61s/it]
 61%|██████    | 550/908 [11:06<07:03,  1.18s/it]
 61%|██████    | 551/908 [11:06<05:17,  1.12it/s]
 61%|██████    | 552/908 [11:07<04:03,  1.46it/s]
 61%|██████    | 553/908 [11:10<08:24,  1.42s/it]
 61%|██████    | 554/908 [11:10<06:13,  1.06s/it]
 61%|██████    | 555/908 [11:10<04:41,  1.26it/s]
 61%|██████    | 556/908 [11:10<03:37,  1.62it/s]
 61%|██████▏   | 557/908 [11:15<11:08,  1.90s/it]
 61%|██████▏   | 558/908 [11:16<08:06,  1.39s/it]
 62%|██████▏   | 559/908 [11:16<05:59,  1.03s/it]
+[Batch:   560] training loss = 0.395
+
 62%|██████▏   | 560/908 [11:16<04:30,  1.29it/s]
 62%|██████▏   | 561/908 [11:21<12:28,  2.16s/it]
 62%|██████▏   | 562/908 [11:21<09:02,  1.57s/it]
 62%|██████▏   | 563/908 [11:22<06:38,  1.15s/it]
 62%|██████▏   | 564/908 [11:22<04:57,  1.15it/s]
 62%|██████▏   | 565/908 [11:27<11:58,  2.10s/it]
 62%|██████▏   | 566/908 [11:27<08:41,  1.52s/it]
 62%|██████▏   | 567/908 [11:27<06:23,  1.13s/it]
 63%|██████▎   | 568/908 [11:27<04:47,  1.18it/s]
 63%|██████▎   | 569/908 [11:32<11:18,  2.00s/it]
 63%|██████▎   | 570/908 [11:32<08:13,  1.46s/it]
 63%|██████▎   | 571/908 [11:32<06:03,  1.08s/it]
 63%|██████▎   | 572/908 [11:33<04:32,  1.23it/s]
 63%|██████▎   | 573/908 [11:37<09:44,  1.74s/it]
 63%|██████▎   | 574/908 [11:37<07:07,  1.28s/it]
 63%|██████▎   | 575/908 [11:37<05:17,  1.05it/s]
 63%|██████▎   | 576/908 [11:37<04:00,  1.38it/s]
 64%|██████▎   | 577/908 [11:41<09:32,  1.73s/it]
 64%|██████▎   | 578/908 [11:41<06:57,  1.27s/it]
 64%|██████▍   | 579/908 [11:42<05:10,  1.06it/s]
+[Batch:   580] training loss = 0.409
+
 64%|██████▍   | 580/908 [11:42<03:55,  1.39it/s]
 64%|██████▍   | 581/908 [11:45<07:10,  1.32s/it]
 64%|██████▍   | 582/908 [11:45<05:19,  1.02it/s]
 64%|██████▍   | 583/908 [11:45<04:01,  1.34it/s]
 64%|██████▍   | 584/908 [11:45<03:07,  1.73it/s]
 64%|██████▍   | 585/908 [11:52<13:23,  2.49s/it]
 65%|██████▍   | 586/908 [11:52<09:39,  1.80s/it]
 65%|██████▍   | 587/908 [11:52<07:02,  1.31s/it]
 65%|██████▍   | 588/908 [11:53<05:13,  1.02it/s]
 65%|██████▍   | 589/908 [11:56<09:44,  1.83s/it]
 65%|██████▍   | 590/908 [11:57<07:05,  1.34s/it]
 65%|██████▌   | 591/908 [11:57<05:15,  1.00it/s]
 65%|██████▌   | 592/908 [11:57<03:59,  1.32it/s]
 65%|██████▌   | 593/908 [12:03<11:40,  2.22s/it]
 65%|██████▌   | 594/908 [12:03<08:26,  1.61s/it]
 66%|██████▌   | 595/908 [12:03<06:11,  1.19s/it]
 66%|██████▌   | 596/908 [12:03<04:37,  1.13it/s]
 66%|██████▌   | 597/908 [12:07<08:52,  1.71s/it]
 66%|██████▌   | 598/908 [12:07<06:28,  1.25s/it]
 66%|██████▌   | 599/908 [12:07<04:49,  1.07it/s]
+[Batch:   600] training loss = 0.425
+
 66%|██████▌   | 600/908 [12:07<03:39,  1.40it/s]
 66%|██████▌   | 601/908 [12:12<09:41,  1.89s/it]
 66%|██████▋   | 602/908 [12:12<07:02,  1.38s/it]
 66%|██████▋   | 603/908 [12:12<05:13,  1.03s/it]
 67%|██████▋   | 604/908 [12:13<03:56,  1.28it/s]
 67%|██████▋   | 605/908 [12:17<08:36,  1.71s/it]
 67%|██████▋   | 606/908 [12:17<06:17,  1.25s/it]
 67%|██████▋   | 607/908 [12:17<04:40,  1.07it/s]
 67%|██████▋   | 608/908 [12:17<03:33,  1.41it/s]
 67%|██████▋   | 609/908 [12:21<07:32,  1.51s/it]
 67%|██████▋   | 610/908 [12:21<05:32,  1.12s/it]
 67%|██████▋   | 611/908 [12:21<04:09,  1.19it/s]
 67%|██████▋   | 612/908 [12:21<03:11,  1.55it/s]
 68%|██████▊   | 613/908 [12:25<08:14,  1.68s/it]
 68%|██████▊   | 614/908 [12:25<06:02,  1.23s/it]
 68%|██████▊   | 615/908 [12:26<04:29,  1.09it/s]
 68%|██████▊   | 616/908 [12:26<03:24,  1.43it/s]
 68%|██████▊   | 617/908 [12:30<08:50,  1.82s/it]
 68%|██████▊   | 618/908 [12:30<06:27,  1.34s/it]
 68%|██████▊   | 619/908 [12:31<04:46,  1.01it/s]
+[Batch:   620] training loss = 0.344
+
 68%|██████▊   | 620/908 [12:31<03:36,  1.33it/s]
 68%|██████▊   | 621/908 [12:34<06:54,  1.45s/it]
 69%|██████▊   | 622/908 [12:34<05:05,  1.07s/it]
 69%|██████▊   | 623/908 [12:34<03:49,  1.24it/s]
 69%|██████▊   | 624/908 [12:34<02:55,  1.61it/s]
 69%|██████▉   | 625/908 [12:38<07:23,  1.57s/it]
 69%|██████▉   | 626/908 [12:38<05:25,  1.15s/it]
 69%|██████▉   | 627/908 [12:39<04:03,  1.16it/s]
 69%|██████▉   | 628/908 [12:39<03:05,  1.51it/s]
 69%|██████▉   | 629/908 [12:42<07:14,  1.56s/it]
 69%|██████▉   | 630/908 [12:43<05:18,  1.15s/it]
 69%|██████▉   | 631/908 [12:43<03:57,  1.17it/s]
 70%|██████▉   | 632/908 [12:43<03:02,  1.51it/s]
 70%|██████▉   | 633/908 [12:47<08:18,  1.81s/it]
 70%|██████▉   | 634/908 [12:48<06:04,  1.33s/it]
 70%|██████▉   | 635/908 [12:48<04:30,  1.01it/s]
 70%|███████   | 636/908 [12:48<03:24,  1.33it/s]
 70%|███████   | 637/908 [12:53<08:54,  1.97s/it]
 70%|███████   | 638/908 [12:53<06:28,  1.44s/it]
 70%|███████   | 639/908 [12:53<04:46,  1.06s/it]
+[Batch:   640] training loss = 0.374
+
 70%|███████   | 640/908 [12:53<03:35,  1.24it/s]
 71%|███████   | 641/908 [12:59<09:29,  2.13s/it]
 71%|███████   | 642/908 [12:59<06:52,  1.55s/it]
 71%|███████   | 643/908 [12:59<05:03,  1.15s/it]
 71%|███████   | 644/908 [12:59<03:47,  1.16it/s]
 71%|███████   | 645/908 [13:05<09:47,  2.24s/it]
 71%|███████   | 646/908 [13:05<07:04,  1.62s/it]
 71%|███████▏  | 647/908 [13:05<05:11,  1.19s/it]
 71%|███████▏  | 648/908 [13:05<03:52,  1.12it/s]
 71%|███████▏  | 649/908 [13:09<07:34,  1.76s/it]
 72%|███████▏  | 650/908 [13:09<05:31,  1.29s/it]
 72%|███████▏  | 651/908 [13:09<04:06,  1.04it/s]
 72%|███████▏  | 652/908 [13:10<03:06,  1.37it/s]
 72%|███████▏  | 653/908 [13:13<06:07,  1.44s/it]
 72%|███████▏  | 654/908 [13:13<04:30,  1.06s/it]
 72%|███████▏  | 655/908 [13:13<03:22,  1.25it/s]
 72%|███████▏  | 656/908 [13:13<02:35,  1.62it/s]
 72%|███████▏  | 657/908 [13:17<07:04,  1.69s/it]
 72%|███████▏  | 658/908 [13:18<05:10,  1.24s/it]
 73%|███████▎  | 659/908 [13:18<03:50,  1.08it/s]
+[Batch:   660] training loss = 0.416
+
 73%|███████▎  | 660/908 [13:18<02:55,  1.42it/s]
 73%|███████▎  | 661/908 [13:22<06:59,  1.70s/it]
 73%|███████▎  | 662/908 [13:22<05:06,  1.25s/it]
 73%|███████▎  | 663/908 [13:22<03:47,  1.08it/s]
 73%|███████▎  | 664/908 [13:23<02:52,  1.42it/s]
 73%|███████▎  | 665/908 [13:26<06:13,  1.54s/it]
 73%|███████▎  | 666/908 [13:26<04:34,  1.13s/it]
 73%|███████▎  | 667/908 [13:27<03:25,  1.17it/s]
 74%|███████▎  | 668/908 [13:27<02:36,  1.53it/s]
 74%|███████▎  | 669/908 [13:31<06:50,  1.72s/it]
 74%|███████▍  | 670/908 [13:31<04:59,  1.26s/it]
 74%|███████▍  | 671/908 [13:31<03:42,  1.07it/s]
 74%|███████▍  | 672/908 [13:31<02:48,  1.40it/s]
 74%|███████▍  | 673/908 [13:35<06:06,  1.56s/it]
 74%|███████▍  | 674/908 [13:35<04:28,  1.15s/it]
 74%|███████▍  | 675/908 [13:35<03:21,  1.16it/s]
 74%|███████▍  | 676/908 [13:36<02:34,  1.51it/s]
 75%|███████▍  | 677/908 [13:41<07:52,  2.05s/it]
 75%|███████▍  | 678/908 [13:41<05:43,  1.49s/it]
 75%|███████▍  | 679/908 [13:41<04:12,  1.10s/it]
+[Batch:   680] training loss = 0.435
+
 75%|███████▍  | 680/908 [13:41<03:09,  1.20it/s]
 75%|███████▌  | 681/908 [13:46<07:38,  2.02s/it]
 75%|███████▌  | 682/908 [13:46<05:32,  1.47s/it]
 75%|███████▌  | 683/908 [13:47<04:04,  1.09s/it]
 75%|███████▌  | 684/908 [13:47<03:03,  1.22it/s]
 75%|███████▌  | 685/908 [13:49<04:32,  1.22s/it]
 76%|███████▌  | 686/908 [13:49<03:22,  1.10it/s]
 76%|███████▌  | 687/908 [13:49<02:33,  1.44it/s]
 76%|███████▌  | 688/908 [13:50<01:59,  1.84it/s]
 76%|███████▌  | 689/908 [13:53<05:21,  1.47s/it]
 76%|███████▌  | 690/908 [13:53<03:56,  1.09s/it]
 76%|███████▌  | 691/908 [13:54<02:57,  1.22it/s]
 76%|███████▌  | 692/908 [13:54<02:16,  1.59it/s]
 76%|███████▋  | 693/908 [13:57<05:21,  1.50s/it]
 76%|███████▋  | 694/908 [13:57<03:56,  1.11s/it]
 77%|███████▋  | 695/908 [13:58<02:56,  1.21it/s]
 77%|███████▋  | 696/908 [13:58<02:14,  1.57it/s]
 77%|███████▋  | 697/908 [14:03<07:00,  1.99s/it]
 77%|███████▋  | 698/908 [14:03<05:05,  1.45s/it]
 77%|███████▋  | 699/908 [14:03<03:44,  1.08s/it]
+[Batch:   700] training loss = 0.390
+
 77%|███████▋  | 700/908 [14:04<02:48,  1.23it/s]
 77%|███████▋  | 701/908 [14:08<06:18,  1.83s/it]
 77%|███████▋  | 702/908 [14:08<04:36,  1.34s/it]
 77%|███████▋  | 703/908 [14:08<03:24,  1.00it/s]
 78%|███████▊  | 704/908 [14:08<02:33,  1.33it/s]
 78%|███████▊  | 705/908 [14:12<05:22,  1.59s/it]
 78%|███████▊  | 706/908 [14:12<03:56,  1.17s/it]
 78%|███████▊  | 707/908 [14:12<02:56,  1.14it/s]
 78%|███████▊  | 708/908 [14:13<02:14,  1.48it/s]
 78%|███████▊  | 709/908 [14:17<06:25,  1.94s/it]
 78%|███████▊  | 710/908 [14:18<04:39,  1.41s/it]
 78%|███████▊  | 711/908 [14:18<03:25,  1.04s/it]
 78%|███████▊  | 712/908 [14:18<02:34,  1.27it/s]
 79%|███████▊  | 713/908 [14:22<05:32,  1.70s/it]
 79%|███████▊  | 714/908 [14:22<04:02,  1.25s/it]
 79%|███████▊  | 715/908 [14:22<02:59,  1.07it/s]
 79%|███████▉  | 716/908 [14:22<02:16,  1.41it/s]
 79%|███████▉  | 717/908 [14:27<06:22,  2.00s/it]
 79%|███████▉  | 718/908 [14:28<04:37,  1.46s/it]
 79%|███████▉  | 719/908 [14:28<03:23,  1.08s/it]
+[Batch:   720] training loss = 0.430
+
 79%|███████▉  | 720/908 [14:28<02:32,  1.23it/s]
 79%|███████▉  | 721/908 [14:33<06:02,  1.94s/it]
 80%|███████▉  | 722/908 [14:33<04:23,  1.42s/it]
 80%|███████▉  | 723/908 [14:33<03:14,  1.05s/it]
 80%|███████▉  | 724/908 [14:33<02:25,  1.26it/s]
 80%|███████▉  | 725/908 [14:37<05:37,  1.84s/it]
 80%|███████▉  | 726/908 [14:38<04:05,  1.35s/it]
 80%|████████  | 727/908 [14:38<03:01,  1.00s/it]
 80%|████████  | 728/908 [14:38<02:16,  1.32it/s]
 80%|████████  | 729/908 [14:42<05:16,  1.77s/it]
 80%|████████  | 730/908 [14:42<03:50,  1.30s/it]
 81%|████████  | 731/908 [14:42<02:51,  1.03it/s]
 81%|████████  | 732/908 [14:43<02:09,  1.36it/s]
 81%|████████  | 733/908 [14:46<04:08,  1.42s/it]
 81%|████████  | 734/908 [14:46<03:02,  1.05s/it]
 81%|████████  | 735/908 [14:46<02:16,  1.26it/s]
 81%|████████  | 736/908 [14:46<01:45,  1.64it/s]
 81%|████████  | 737/908 [14:50<03:59,  1.40s/it]
 81%|████████▏ | 738/908 [14:50<02:56,  1.04s/it]
 81%|████████▏ | 739/908 [14:50<02:12,  1.28it/s]
+[Batch:   740] training loss = 0.499
+
 81%|████████▏ | 740/908 [14:50<01:41,  1.65it/s]
 82%|████████▏ | 741/908 [14:54<04:47,  1.72s/it]
 82%|████████▏ | 742/908 [14:55<03:29,  1.26s/it]
 82%|████████▏ | 743/908 [14:55<02:35,  1.06it/s]
 82%|████████▏ | 744/908 [14:55<01:58,  1.39it/s]
 82%|████████▏ | 745/908 [14:59<04:51,  1.79s/it]
 82%|████████▏ | 746/908 [14:59<03:31,  1.31s/it]
 82%|████████▏ | 747/908 [15:00<02:36,  1.03it/s]
 82%|████████▏ | 748/908 [15:00<01:58,  1.36it/s]
 82%|████████▏ | 749/908 [15:03<04:00,  1.52s/it]
 83%|████████▎ | 750/908 [15:03<02:56,  1.12s/it]
 83%|████████▎ | 751/908 [15:04<02:24,  1.09it/s]
 83%|████████▎ | 752/908 [15:04<01:49,  1.42it/s]
 83%|████████▎ | 753/908 [15:10<05:42,  2.21s/it]
 83%|████████▎ | 754/908 [15:10<04:07,  1.61s/it]
 83%|████████▎ | 755/908 [15:10<03:00,  1.18s/it]
 83%|████████▎ | 756/908 [15:10<02:14,  1.13it/s]
 83%|████████▎ | 757/908 [15:14<04:06,  1.63s/it]
 83%|████████▎ | 758/908 [15:14<03:00,  1.20s/it]
 84%|████████▎ | 759/908 [15:14<02:14,  1.11it/s]
+[Batch:   760] training loss = 0.380
+
 84%|████████▎ | 760/908 [15:14<01:41,  1.45it/s]
 84%|████████▍ | 761/908 [15:19<04:46,  1.95s/it]
 84%|████████▍ | 762/908 [15:19<03:28,  1.43s/it]
 84%|████████▍ | 763/908 [15:20<02:32,  1.05s/it]
 84%|████████▍ | 764/908 [15:20<01:54,  1.26it/s]
 84%|████████▍ | 765/908 [15:24<04:28,  1.88s/it]
 84%|████████▍ | 766/908 [15:24<03:14,  1.37s/it]
 84%|████████▍ | 767/908 [15:25<02:23,  1.02s/it]
 85%|████████▍ | 768/908 [15:25<01:48,  1.29it/s]
 85%|████████▍ | 769/908 [15:29<04:15,  1.84s/it]
 85%|████████▍ | 770/908 [15:29<03:06,  1.35s/it]
 85%|████████▍ | 771/908 [15:29<02:17,  1.00s/it]
 85%|████████▌ | 772/908 [15:30<01:43,  1.31it/s]
 85%|████████▌ | 773/908 [15:34<04:20,  1.93s/it]
 85%|████████▌ | 774/908 [15:35<03:09,  1.41s/it]
 85%|████████▌ | 775/908 [15:35<02:19,  1.05s/it]
 85%|████████▌ | 776/908 [15:35<01:44,  1.26it/s]
 86%|████████▌ | 777/908 [15:38<03:12,  1.47s/it]
 86%|████████▌ | 778/908 [15:38<02:21,  1.09s/it]
 86%|████████▌ | 779/908 [15:38<01:46,  1.21it/s]
+[Batch:   780] training loss = 0.347
+
 86%|████████▌ | 780/908 [15:39<01:26,  1.47it/s]
 86%|████████▌ | 781/908 [15:44<04:28,  2.11s/it]
 86%|████████▌ | 782/908 [15:44<03:13,  1.54s/it]
 86%|████████▌ | 783/908 [15:45<02:21,  1.13s/it]
 86%|████████▋ | 784/908 [15:45<01:45,  1.17it/s]
 86%|████████▋ | 785/908 [15:48<03:26,  1.68s/it]
 87%|████████▋ | 786/908 [15:49<02:30,  1.23s/it]
 87%|████████▋ | 787/908 [15:49<01:51,  1.08it/s]
 87%|████████▋ | 788/908 [15:49<01:24,  1.42it/s]
 87%|████████▋ | 789/908 [15:53<03:36,  1.82s/it]
 87%|████████▋ | 790/908 [15:54<02:37,  1.33s/it]
 87%|████████▋ | 791/908 [15:54<01:56,  1.01it/s]
 87%|████████▋ | 792/908 [15:54<01:27,  1.33it/s]
 87%|████████▋ | 793/908 [15:58<03:24,  1.77s/it]
 87%|████████▋ | 794/908 [15:58<02:28,  1.30s/it]
 88%|████████▊ | 795/908 [15:59<01:49,  1.03it/s]
 88%|████████▊ | 796/908 [15:59<01:22,  1.35it/s]
 88%|████████▊ | 797/908 [16:03<03:03,  1.65s/it]
 88%|████████▊ | 798/908 [16:03<02:13,  1.22s/it]
 88%|████████▊ | 799/908 [16:03<01:39,  1.09it/s]
+[Batch:   800] training loss = 0.385
+
 88%|████████▊ | 800/908 [16:03<01:15,  1.43it/s]
 88%|████████▊ | 801/908 [16:06<02:26,  1.37s/it]
 88%|████████▊ | 802/908 [16:06<01:47,  1.01s/it]
 88%|████████▊ | 803/908 [16:06<01:20,  1.30it/s]
 89%|████████▊ | 804/908 [16:07<01:02,  1.67it/s]
 89%|████████▊ | 805/908 [16:11<02:57,  1.73s/it]
 89%|████████▉ | 806/908 [16:11<02:09,  1.27s/it]
 89%|████████▉ | 807/908 [16:11<01:35,  1.05it/s]
 89%|████████▉ | 808/908 [16:12<01:12,  1.38it/s]
 89%|████████▉ | 809/908 [16:15<02:32,  1.54s/it]
 89%|████████▉ | 810/908 [16:15<01:51,  1.14s/it]
 89%|████████▉ | 811/908 [16:15<01:23,  1.16it/s]
 89%|████████▉ | 812/908 [16:16<01:03,  1.51it/s]
 90%|████████▉ | 813/908 [16:20<02:37,  1.66s/it]
 90%|████████▉ | 814/908 [16:20<01:54,  1.22s/it]
 90%|████████▉ | 815/908 [16:20<01:25,  1.09it/s]
 90%|████████▉ | 816/908 [16:20<01:04,  1.43it/s]
 90%|████████▉ | 817/908 [16:24<02:31,  1.66s/it]
 90%|█████████ | 818/908 [16:24<01:50,  1.22s/it]
 90%|█████████ | 819/908 [16:25<01:21,  1.09it/s]
+[Batch:   820] training loss = 0.474
+
 90%|█████████ | 820/908 [16:25<01:01,  1.43it/s]
 90%|█████████ | 821/908 [16:29<02:34,  1.77s/it]
 91%|█████████ | 822/908 [16:29<01:51,  1.30s/it]
 91%|█████████ | 823/908 [16:29<01:22,  1.03it/s]
 91%|█████████ | 824/908 [16:30<01:02,  1.35it/s]
 91%|█████████ | 825/908 [16:34<02:23,  1.73s/it]
 91%|█████████ | 826/908 [16:34<01:44,  1.27s/it]
 91%|█████████ | 827/908 [16:34<01:17,  1.05it/s]
 91%|█████████ | 828/908 [16:34<00:58,  1.38it/s]
 91%|█████████▏| 829/908 [16:38<02:17,  1.74s/it]
 91%|█████████▏| 830/908 [16:39<01:39,  1.28s/it]
 92%|█████████▏| 831/908 [16:39<01:13,  1.05it/s]
 92%|█████████▏| 832/908 [16:39<00:55,  1.38it/s]
 92%|█████████▏| 833/908 [16:44<02:27,  1.97s/it]
 92%|█████████▏| 834/908 [16:44<01:46,  1.44s/it]
 92%|█████████▏| 835/908 [16:44<01:18,  1.07s/it]
 92%|█████████▏| 836/908 [16:44<00:58,  1.24it/s]
 92%|█████████▏| 837/908 [16:48<02:00,  1.70s/it]
 92%|█████████▏| 838/908 [16:48<01:27,  1.25s/it]
 92%|█████████▏| 839/908 [16:49<01:04,  1.07it/s]
+[Batch:   840] training loss = 0.452
+
 93%|█████████▎| 840/908 [16:49<00:48,  1.40it/s]
 93%|█████████▎| 841/908 [16:53<01:47,  1.61s/it]
 93%|█████████▎| 842/908 [16:53<01:18,  1.19s/it]
 93%|█████████▎| 843/908 [16:53<00:57,  1.12it/s]
 93%|█████████▎| 844/908 [16:53<00:43,  1.47it/s]
 93%|█████████▎| 845/908 [16:57<01:43,  1.64s/it]
 93%|█████████▎| 846/908 [16:57<01:14,  1.21s/it]
 93%|█████████▎| 847/908 [16:57<00:55,  1.10it/s]
 93%|█████████▎| 848/908 [16:58<00:41,  1.43it/s]
 94%|█████████▎| 849/908 [17:01<01:29,  1.52s/it]
 94%|█████████▎| 850/908 [17:01<01:05,  1.12s/it]
 94%|█████████▎| 851/908 [17:01<00:48,  1.19it/s]
 94%|█████████▍| 852/908 [17:02<00:36,  1.54it/s]
 94%|█████████▍| 853/908 [17:05<01:14,  1.35s/it]
 94%|█████████▍| 854/908 [17:05<00:54,  1.01s/it]
 94%|█████████▍| 855/908 [17:05<00:40,  1.31it/s]
 94%|█████████▍| 856/908 [17:05<00:30,  1.69it/s]
 94%|█████████▍| 857/908 [17:08<01:09,  1.37s/it]
 94%|█████████▍| 858/908 [17:09<00:50,  1.02s/it]
 95%|█████████▍| 859/908 [17:09<00:37,  1.29it/s]
+[Batch:   860] training loss = 0.462
+
 95%|█████████▍| 860/908 [17:09<00:28,  1.66it/s]
 95%|█████████▍| 861/908 [17:13<01:21,  1.72s/it]
 95%|█████████▍| 862/908 [17:14<00:58,  1.27s/it]
 95%|█████████▌| 863/908 [17:14<00:42,  1.06it/s]
 95%|█████████▌| 864/908 [17:14<00:31,  1.39it/s]
 95%|█████████▌| 865/908 [17:18<01:08,  1.60s/it]
 95%|█████████▌| 866/908 [17:18<00:49,  1.19s/it]
 95%|█████████▌| 867/908 [17:18<00:36,  1.12it/s]
 96%|█████████▌| 868/908 [17:18<00:27,  1.46it/s]
 96%|█████████▌| 869/908 [17:21<00:55,  1.43s/it]
 96%|█████████▌| 870/908 [17:22<00:40,  1.06s/it]
 96%|█████████▌| 871/908 [17:22<00:29,  1.25it/s]
 96%|█████████▌| 872/908 [17:22<00:22,  1.62it/s]
 96%|█████████▌| 873/908 [17:25<00:43,  1.23s/it]
 96%|█████████▋| 874/908 [17:25<00:35,  1.05s/it]
 96%|█████████▋| 875/908 [17:25<00:26,  1.26it/s]
 96%|█████████▋| 876/908 [17:26<00:19,  1.63it/s]
 97%|█████████▋| 877/908 [17:31<01:01,  1.99s/it]
 97%|█████████▋| 878/908 [17:31<00:43,  1.45s/it]
 97%|█████████▋| 879/908 [17:31<00:31,  1.08s/it]
+[Batch:   880] training loss = 0.426
+
 97%|█████████▋| 880/908 [17:31<00:22,  1.23it/s]
 97%|█████████▋| 881/908 [17:35<00:43,  1.62s/it]
 97%|█████████▋| 882/908 [17:35<00:31,  1.20s/it]
 97%|█████████▋| 883/908 [17:35<00:22,  1.11it/s]
 97%|█████████▋| 884/908 [17:36<00:16,  1.45it/s]
 97%|█████████▋| 885/908 [17:38<00:30,  1.31s/it]
 98%|█████████▊| 886/908 [17:39<00:21,  1.03it/s]
 98%|█████████▊| 887/908 [17:39<00:15,  1.36it/s]
 98%|█████████▊| 888/908 [17:39<00:11,  1.74it/s]
 98%|█████████▊| 889/908 [17:42<00:27,  1.43s/it]
 98%|█████████▊| 890/908 [17:43<00:22,  1.24s/it]
 98%|█████████▊| 891/908 [17:43<00:15,  1.08it/s]
 98%|█████████▊| 892/908 [17:43<00:11,  1.42it/s]
 98%|█████████▊| 893/908 [17:49<00:29,  2.00s/it]
 98%|█████████▊| 894/908 [17:49<00:20,  1.46s/it]
 99%|█████████▊| 895/908 [17:49<00:14,  1.08s/it]
 99%|█████████▊| 896/908 [17:49<00:09,  1.23it/s]
 99%|█████████▉| 897/908 [17:53<00:19,  1.81s/it]
 99%|█████████▉| 898/908 [17:54<00:14,  1.45s/it]
 99%|█████████▉| 899/908 [17:54<00:09,  1.08s/it]
+[Batch:   900] training loss = 0.397
+
 99%|█████████▉| 900/908 [17:54<00:06,  1.20it/s]
 99%|█████████▉| 901/908 [17:59<00:14,  2.02s/it]
 99%|█████████▉| 902/908 [17:59<00:08,  1.47s/it]
 99%|█████████▉| 903/908 [17:59<00:05,  1.09s/it]
100%|█████████▉| 904/908 [18:00<00:03,  1.22it/s]
100%|█████████▉| 905/908 [18:05<00:06,  2.23s/it]
100%|█████████▉| 906/908 [18:05<00:03,  1.62s/it]
100%|█████████▉| 907/908 [18:06<00:01,  1.19s/it]
100%|██████████| 908/908 [18:06<00:00,  1.15it/s]
100%|██████████| 908/908 [18:06<00:00,  1.20s/it]
+
+Epoch  10: validation
+
  0%|          | 0/101 [00:00<?, ?it/s]
  1%|          | 1/101 [00:04<06:41,  4.02s/it]
  3%|▎         | 3/101 [00:04<01:47,  1.10s/it]
  5%|▍         | 5/101 [00:09<02:58,  1.86s/it]
  7%|▋         | 7/101 [00:09<01:43,  1.11s/it]
  9%|▉         | 9/101 [00:13<02:07,  1.39s/it]
 11%|█         | 11/101 [00:13<01:23,  1.07it/s]
 13%|█▎        | 13/101 [00:16<01:41,  1.16s/it]
 15%|█▍        | 15/101 [00:16<01:09,  1.23it/s]
 17%|█▋        | 17/101 [00:21<01:45,  1.26s/it]
 19%|█▉        | 19/101 [00:21<01:13,  1.12it/s]
 21%|██        | 21/101 [00:25<01:39,  1.25s/it]
 23%|██▎       | 23/101 [00:25<01:09,  1.12it/s]
 25%|██▍       | 25/101 [00:29<01:36,  1.28s/it]
 27%|██▋       | 27/101 [00:30<01:07,  1.09it/s]
 29%|██▊       | 29/101 [00:35<01:42,  1.43s/it]
 31%|███       | 31/101 [00:35<01:11,  1.02s/it]
 33%|███▎      | 33/101 [00:41<01:46,  1.56s/it]
 35%|███▍      | 35/101 [00:41<01:13,  1.12s/it]
 37%|███▋      | 37/101 [00:46<01:36,  1.51s/it]
 39%|███▊      | 39/101 [00:46<01:07,  1.08s/it]
 41%|████      | 41/101 [00:49<01:17,  1.29s/it]
 43%|████▎     | 43/101 [00:50<00:53,  1.08it/s]
 45%|████▍     | 45/101 [00:53<01:05,  1.18s/it]
 47%|████▋     | 47/101 [00:53<00:45,  1.18it/s]
 49%|████▊     | 49/101 [00:58<01:10,  1.36s/it]
 50%|█████     | 51/101 [00:58<00:48,  1.03it/s]
 51%|█████▏    | 52/101 [00:59<00:40,  1.22it/s]
 52%|█████▏    | 53/101 [01:03<01:11,  1.50s/it]
 54%|█████▍    | 55/101 [01:03<00:45,  1.02it/s]
 56%|█████▋    | 57/101 [01:07<00:55,  1.26s/it]
 58%|█████▊    | 59/101 [01:07<00:36,  1.15it/s]
 60%|██████    | 61/101 [01:10<00:46,  1.17s/it]
 62%|██████▏   | 63/101 [01:10<00:31,  1.20it/s]
 63%|██████▎   | 64/101 [01:11<00:27,  1.37it/s]
 64%|██████▍   | 65/101 [01:15<00:54,  1.51s/it]
 65%|██████▌   | 66/101 [01:15<00:42,  1.21s/it]
 67%|██████▋   | 68/101 [01:16<00:27,  1.22it/s]
 68%|██████▊   | 69/101 [01:19<00:43,  1.36s/it]
 70%|███████   | 71/101 [01:19<00:26,  1.15it/s]
 71%|███████▏  | 72/101 [01:20<00:21,  1.34it/s]
 72%|███████▏  | 73/101 [01:23<00:36,  1.32s/it]
 74%|███████▍  | 75/101 [01:23<00:21,  1.22it/s]
 75%|███████▌  | 76/101 [01:23<00:17,  1.43it/s]
 76%|███████▌  | 77/101 [01:27<00:33,  1.38s/it]
 78%|███████▊  | 79/101 [01:27<00:18,  1.18it/s]
 79%|███████▉  | 80/101 [01:28<00:16,  1.26it/s]
 80%|████████  | 81/101 [01:32<00:34,  1.75s/it]
 81%|████████  | 82/101 [01:32<00:25,  1.33s/it]
 83%|████████▎ | 84/101 [01:33<00:14,  1.18it/s]
 84%|████████▍ | 85/101 [01:36<00:23,  1.46s/it]
 85%|████████▌ | 86/101 [01:37<00:18,  1.25s/it]
 87%|████████▋ | 88/101 [01:37<00:09,  1.31it/s]
 88%|████████▊ | 89/101 [01:44<00:25,  2.16s/it]
 90%|█████████ | 91/101 [01:44<00:13,  1.37s/it]
 92%|█████████▏| 93/101 [01:48<00:12,  1.59s/it]
 93%|█████████▎| 94/101 [01:48<00:09,  1.31s/it]
 94%|█████████▍| 95/101 [01:49<00:06,  1.14s/it]
 96%|█████████▌| 97/101 [01:53<00:06,  1.58s/it]
 97%|█████████▋| 98/101 [01:54<00:03,  1.26s/it]
 99%|█████████▉| 100/101 [01:54<00:00,  1.24it/s]
100%|██████████| 101/101 [01:57<00:00,  1.32s/it]
100%|██████████| 101/101 [01:57<00:00,  1.16s/it]
+    Epoch 10: training loss = 0.426 / validation loss = 0.444
+    Epoch 10: training acc = 0.799 / validation acc = 0.788
+[ End training ]
+
+Elapsed time: 228 minutes
diff --git a/Session7_Medical_Image_Data_Analysis/CLS&SEG/seg_train.txt b/Session7_Medical_Image_Data_Analysis/CLS&SEG/seg_train.txt
new file mode 100644
index 0000000..d6b16a5
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/CLS&SEG/seg_train.txt
@@ -0,0 +1,1612 @@
+Unet training with CHN data.
+Data Information
+    Train data: 452
+    Valid data: 57
+    Test data: 57
+----------------------------------------------------------------
+        Layer (type)               Output Shape         Param #
+================================================================
+            Conv2d-1         [-1, 32, 512, 512]             320
+              ReLU-2         [-1, 32, 512, 512]               0
+            Conv2d-3         [-1, 32, 512, 512]           9,248
+              ReLU-4         [-1, 32, 512, 512]               0
+        Downsample-5         [-1, 32, 512, 512]               0
+         MaxPool2d-6         [-1, 32, 256, 256]               0
+            Conv2d-7         [-1, 64, 256, 256]          18,496
+              ReLU-8         [-1, 64, 256, 256]               0
+            Conv2d-9         [-1, 64, 256, 256]          36,928
+             ReLU-10         [-1, 64, 256, 256]               0
+       Downsample-11         [-1, 64, 256, 256]               0
+        MaxPool2d-12         [-1, 64, 128, 128]               0
+           Conv2d-13        [-1, 128, 128, 128]          73,856
+             ReLU-14        [-1, 128, 128, 128]               0
+           Conv2d-15        [-1, 128, 128, 128]         147,584
+             ReLU-16        [-1, 128, 128, 128]               0
+       Downsample-17        [-1, 128, 128, 128]               0
+        MaxPool2d-18          [-1, 128, 64, 64]               0
+           Conv2d-19          [-1, 256, 64, 64]         295,168
+             ReLU-20          [-1, 256, 64, 64]               0
+           Conv2d-21          [-1, 256, 64, 64]         590,080
+             ReLU-22          [-1, 256, 64, 64]               0
+       Downsample-23          [-1, 256, 64, 64]               0
+        MaxPool2d-24          [-1, 256, 32, 32]               0
+           Conv2d-25          [-1, 512, 32, 32]       1,180,160
+             ReLU-26          [-1, 512, 32, 32]               0
+           Conv2d-27          [-1, 512, 32, 32]       2,359,808
+             ReLU-28          [-1, 512, 32, 32]               0
+       Downsample-29          [-1, 512, 32, 32]               0
+  ConvTranspose2d-30          [-1, 256, 64, 64]         524,544
+           Conv2d-31          [-1, 256, 64, 64]       1,179,904
+             ReLU-32          [-1, 256, 64, 64]               0
+           Conv2d-33          [-1, 256, 64, 64]         590,080
+             ReLU-34          [-1, 256, 64, 64]               0
+         Upsample-35          [-1, 256, 64, 64]               0
+  ConvTranspose2d-36        [-1, 128, 128, 128]         131,200
+           Conv2d-37        [-1, 128, 128, 128]         295,040
+             ReLU-38        [-1, 128, 128, 128]               0
+           Conv2d-39        [-1, 128, 128, 128]         147,584
+             ReLU-40        [-1, 128, 128, 128]               0
+         Upsample-41        [-1, 128, 128, 128]               0
+  ConvTranspose2d-42         [-1, 64, 256, 256]          32,832
+           Conv2d-43         [-1, 64, 256, 256]          73,792
+             ReLU-44         [-1, 64, 256, 256]               0
+           Conv2d-45         [-1, 64, 256, 256]          36,928
+             ReLU-46         [-1, 64, 256, 256]               0
+         Upsample-47         [-1, 64, 256, 256]               0
+  ConvTranspose2d-48         [-1, 32, 512, 512]           8,224
+           Conv2d-49         [-1, 32, 512, 512]          18,464
+             ReLU-50         [-1, 32, 512, 512]               0
+           Conv2d-51         [-1, 32, 512, 512]           9,248
+             ReLU-52         [-1, 32, 512, 512]               0
+         Upsample-53         [-1, 32, 512, 512]               0
+           Conv2d-54          [-1, 1, 512, 512]              33
+          Sigmoid-55          [-1, 1, 512, 512]               0
+             UNet-56          [-1, 1, 512, 512]               0
+           Conv2d-57         [-1, 32, 512, 512]             320
+             ReLU-58         [-1, 32, 512, 512]               0
+           Conv2d-59         [-1, 32, 512, 512]           9,248
+             ReLU-60         [-1, 32, 512, 512]               0
+       Downsample-61         [-1, 32, 512, 512]               0
+        MaxPool2d-62         [-1, 32, 256, 256]               0
+           Conv2d-63         [-1, 64, 256, 256]          18,496
+             ReLU-64         [-1, 64, 256, 256]               0
+           Conv2d-65         [-1, 64, 256, 256]          36,928
+             ReLU-66         [-1, 64, 256, 256]               0
+       Downsample-67         [-1, 64, 256, 256]               0
+        MaxPool2d-68         [-1, 64, 128, 128]               0
+           Conv2d-69        [-1, 128, 128, 128]          73,856
+             ReLU-70        [-1, 128, 128, 128]               0
+           Conv2d-71        [-1, 128, 128, 128]         147,584
+             ReLU-72        [-1, 128, 128, 128]               0
+       Downsample-73        [-1, 128, 128, 128]               0
+        MaxPool2d-74          [-1, 128, 64, 64]               0
+           Conv2d-75          [-1, 256, 64, 64]         295,168
+             ReLU-76          [-1, 256, 64, 64]               0
+           Conv2d-77          [-1, 256, 64, 64]         590,080
+             ReLU-78          [-1, 256, 64, 64]               0
+       Downsample-79          [-1, 256, 64, 64]               0
+        MaxPool2d-80          [-1, 256, 32, 32]               0
+           Conv2d-81          [-1, 512, 32, 32]       1,180,160
+             ReLU-82          [-1, 512, 32, 32]               0
+           Conv2d-83          [-1, 512, 32, 32]       2,359,808
+             ReLU-84          [-1, 512, 32, 32]               0
+       Downsample-85          [-1, 512, 32, 32]               0
+  ConvTranspose2d-86          [-1, 256, 64, 64]         524,544
+           Conv2d-87          [-1, 256, 64, 64]       1,179,904
+             ReLU-88          [-1, 256, 64, 64]               0
+           Conv2d-89          [-1, 256, 64, 64]         590,080
+             ReLU-90          [-1, 256, 64, 64]               0
+         Upsample-91          [-1, 256, 64, 64]               0
+  ConvTranspose2d-92        [-1, 128, 128, 128]         131,200
+           Conv2d-93        [-1, 128, 128, 128]         295,040
+             ReLU-94        [-1, 128, 128, 128]               0
+           Conv2d-95        [-1, 128, 128, 128]         147,584
+             ReLU-96        [-1, 128, 128, 128]               0
+         Upsample-97        [-1, 128, 128, 128]               0
+  ConvTranspose2d-98         [-1, 64, 256, 256]          32,832
+           Conv2d-99         [-1, 64, 256, 256]          73,792
+            ReLU-100         [-1, 64, 256, 256]               0
+          Conv2d-101         [-1, 64, 256, 256]          36,928
+            ReLU-102         [-1, 64, 256, 256]               0
+        Upsample-103         [-1, 64, 256, 256]               0
+ ConvTranspose2d-104         [-1, 32, 512, 512]           8,224
+          Conv2d-105         [-1, 32, 512, 512]          18,464
+            ReLU-106         [-1, 32, 512, 512]               0
+          Conv2d-107         [-1, 32, 512, 512]           9,248
+            ReLU-108         [-1, 32, 512, 512]               0
+        Upsample-109         [-1, 32, 512, 512]               0
+          Conv2d-110          [-1, 1, 512, 512]              33
+         Sigmoid-111          [-1, 1, 512, 512]               0
+            UNet-112          [-1, 1, 512, 512]               0
+================================================================
+Total params: 15,519,042
+Trainable params: 15,519,042
+Non-trainable params: 0
+----------------------------------------------------------------
+Input size (MB): 1.00
+Forward/backward pass size (MB): 2752.00
+Params size (MB): 59.20
+Estimated Total Size (MB): 2812.20
+----------------------------------------------------------------
+[ Start training ]
+
+Epoch   1: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:12<06:02, 12.96s/it]
  7%|▋         | 2/29 [00:13<02:25,  5.40s/it]
 10%|█         | 3/29 [00:13<01:17,  2.99s/it]
 14%|█▍        | 4/29 [00:13<00:46,  1.88s/it]
 17%|█▋        | 5/29 [00:13<00:29,  1.24s/it]
 21%|██        | 6/29 [00:13<00:19,  1.17it/s]
 24%|██▍       | 7/29 [00:13<00:13,  1.64it/s]
 28%|██▊       | 8/29 [00:13<00:09,  2.23it/s]
 31%|███       | 9/29 [00:13<00:06,  2.93it/s]
 34%|███▍      | 10/29 [00:16<00:22,  1.19s/it]
 38%|███▊      | 11/29 [00:17<00:16,  1.10it/s]
 41%|████▏     | 12/29 [00:17<00:11,  1.50it/s]
 45%|████▍     | 13/29 [00:17<00:08,  1.90it/s]
 48%|████▊     | 14/29 [00:21<00:21,  1.42s/it]
 52%|█████▏    | 15/29 [00:21<00:14,  1.03s/it]
 55%|█████▌    | 16/29 [00:21<00:09,  1.32it/s]
 59%|█████▊    | 17/29 [00:21<00:07,  1.61it/s]
 62%|██████▏   | 18/29 [00:24<00:15,  1.42s/it]
 66%|██████▌   | 19/29 [00:25<00:10,  1.03s/it]
 69%|██████▉   | 20/29 [00:25<00:06,  1.33it/s]
 72%|███████▏  | 21/29 [00:25<00:04,  1.63it/s]
 76%|███████▌  | 22/29 [00:28<00:10,  1.45s/it]
 79%|███████▉  | 23/29 [00:28<00:06,  1.05s/it]
 83%|████████▎ | 24/29 [00:29<00:03,  1.31it/s]
 86%|████████▌ | 25/29 [00:29<00:02,  1.62it/s]
 90%|████████▉ | 26/29 [00:32<00:04,  1.36s/it]
 93%|█████████▎| 27/29 [00:32<00:01,  1.01it/s]
 97%|█████████▋| 28/29 [00:32<00:00,  1.38it/s]
100%|██████████| 29/29 [00:32<00:00,  1.83it/s]
100%|██████████| 29/29 [00:32<00:00,  1.13s/it]
+
+Epoch  1: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:28,  1.96it/s]
  9%|▉         | 5/57 [00:00<00:06,  8.65it/s]
 14%|█▍        | 8/57 [00:00<00:03, 12.88it/s]
 19%|█▉        | 11/57 [00:01<00:03, 13.72it/s]
 23%|██▎       | 13/57 [00:01<00:03, 11.83it/s]
 30%|██▉       | 17/57 [00:01<00:02, 14.88it/s]
 37%|███▋      | 21/57 [00:01<00:02, 14.21it/s]
 44%|████▍     | 25/57 [00:02<00:02, 14.00it/s]
 51%|█████     | 29/57 [00:02<00:01, 15.21it/s]
 58%|█████▊    | 33/57 [00:02<00:01, 15.37it/s]
 65%|██████▍   | 37/57 [00:02<00:01, 15.28it/s]
 72%|███████▏  | 41/57 [00:02<00:01, 15.72it/s]
 79%|███████▉  | 45/57 [00:03<00:00, 15.71it/s]
 86%|████████▌ | 49/57 [00:03<00:00, 15.03it/s]
 93%|█████████▎| 53/57 [00:03<00:00, 14.85it/s]
100%|██████████| 57/57 [00:03<00:00, 16.64it/s]
100%|██████████| 57/57 [00:04<00:00, 14.00it/s]
+    Epoch  1: training loss = -0.333 / validation loss = -0.333
+    Saved model: unet-1.pth.tar
+
+Epoch   2: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.40s/it]
  7%|▋         | 2/29 [00:02<00:33,  1.22s/it]
 10%|█         | 3/29 [00:02<00:18,  1.40it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.10it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:05<00:17,  1.30it/s]
 24%|██▍       | 7/29 [00:05<00:12,  1.80it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.42it/s]
 31%|███       | 9/29 [00:07<00:15,  1.25it/s]
 34%|███▍      | 10/29 [00:07<00:13,  1.45it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.95it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.57it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.30it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.48it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.98it/s]
 55%|█████▌    | 16/29 [00:10<00:05,  2.60it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.35it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.79it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.36it/s]
 76%|███████▌  | 22/29 [00:14<00:05,  1.39it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.87it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.46it/s]
 86%|████████▌ | 25/29 [00:16<00:02,  1.47it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.52it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.03it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.65it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch  2: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.10it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.51it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.27it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.24it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.59it/s]
 40%|████      | 23/57 [00:01<00:01, 25.19it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.42it/s]
 54%|█████▍    | 31/57 [00:01<00:01, 25.59it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 24.35it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.55it/s]
 74%|███████▎  | 42/57 [00:02<00:00, 18.88it/s]
 79%|███████▉  | 45/57 [00:02<00:00, 19.45it/s]
 84%|████████▍ | 48/57 [00:02<00:00, 19.55it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 19.70it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 23.56it/s]
100%|██████████| 57/57 [00:03<00:00, 18.51it/s]
+    Epoch  2: training loss = -0.333 / validation loss = -0.334
+    Saved model: unet-2.pth.tar
+
+Epoch   3: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.43s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.45it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:05<00:16,  1.39it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.93it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.59it/s]
 31%|███       | 9/29 [00:07<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.53it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.68it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.25it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.11it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.76it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.27it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.49it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.99it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.61it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.32it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.50it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  2.00it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.61it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.28it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.62it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.15it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.80it/s]
100%|██████████| 29/29 [00:16<00:00,  3.55it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch  3: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.98it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.07it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.87it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.56it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.12it/s]
 40%|████      | 23/57 [00:01<00:01, 27.25it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.72it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.27it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.57it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.20it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.05it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.53it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.70it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.68it/s]
100%|██████████| 57/57 [00:02<00:00, 23.80it/s]
+    Epoch  3: training loss = -0.334 / validation loss = -0.334
+    Saved model: unet-3.pth.tar
+
+Epoch   4: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.62s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.02s/it]
 21%|██        | 6/29 [00:05<00:16,  1.40it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.94it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.59it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.78it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.59it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.12it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.77it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.19it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.60it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.13it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.78it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.65it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.19it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.84it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.29it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.82it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch  4: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.04it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.09it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.05it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.06it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.08it/s]
 40%|████      | 23/57 [00:01<00:01, 27.33it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.43it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.37it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 25.84it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 25.66it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 25.43it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.06it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.10it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.55it/s]
100%|██████████| 57/57 [00:02<00:00, 25.06it/s]
100%|██████████| 57/57 [00:02<00:00, 23.28it/s]
+    Epoch  4: training loss = -0.335 / validation loss = -0.335
+    Saved model: unet-4.pth.tar
+
+Epoch   5: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.55s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.11s/it]
 10%|█         | 3/29 [00:02<00:17,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.53it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.08it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.77it/s]
 31%|███       | 9/29 [00:06<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.02it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.66it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.23it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.67it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.92it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.52it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.27it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.72it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.81it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.39it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.29it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.74it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.74it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.30it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.31it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.77it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.83it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.41it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch  5: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.97it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.36it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.46it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.63it/s]
 40%|████      | 23/57 [00:01<00:01, 27.51it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.95it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.78it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.37it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.92it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.39it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.96it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.35it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 27.18it/s]
100%|██████████| 57/57 [00:02<00:00, 26.02it/s]
100%|██████████| 57/57 [00:02<00:00, 23.55it/s]
+    Epoch  5: training loss = -0.335 / validation loss = -0.336
+    Saved model: unet-5.pth.tar
+
+Epoch   6: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.60s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.01it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.68it/s]
 31%|███       | 9/29 [00:07<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.60it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.14it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.80it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.62it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.79it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.13it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.53it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.04it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.67it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.21it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.86it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.20it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.62it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.15it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.80it/s]
100%|██████████| 29/29 [00:16<00:00,  3.13it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch  6: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.93it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.67it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.86it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.09it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.96it/s]
 40%|████      | 23/57 [00:01<00:01, 27.15it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.65it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.03it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.36it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.22it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.92it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.89it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.61it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.28it/s]
100%|██████████| 57/57 [00:02<00:00, 23.67it/s]
+    Epoch  6: training loss = -0.336 / validation loss = -0.337
+    Saved model: unet-6.pth.tar
+
+Epoch   7: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.66s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.02it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.69it/s]
 31%|███       | 9/29 [00:07<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.57it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.74it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.23it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.66it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.20it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.86it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.67it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.22it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.88it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.20it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.62it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.16it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.81it/s]
100%|██████████| 29/29 [00:16<00:00,  3.20it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch  7: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.74it/s]
 16%|█▌        | 9/57 [00:00<00:02, 20.03it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.49it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.25it/s]
 40%|████      | 23/57 [00:01<00:01, 26.98it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.38it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.44it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.06it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.53it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.12it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.08it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.28it/s]
100%|██████████| 57/57 [00:02<00:00, 27.87it/s]
100%|██████████| 57/57 [00:02<00:00, 23.76it/s]
+    Epoch  7: training loss = -0.338 / validation loss = -0.339
+    Saved model: unet-7.pth.tar
+
+Epoch   8: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.41s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.21s/it]
 10%|█         | 3/29 [00:02<00:18,  1.41it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.12it/s]
 17%|█▋        | 5/29 [00:04<00:20,  1.18it/s]
 21%|██        | 6/29 [00:05<00:18,  1.26it/s]
 24%|██▍       | 7/29 [00:05<00:12,  1.76it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.37it/s]
 31%|███       | 9/29 [00:06<00:13,  1.53it/s]
 34%|███▍      | 10/29 [00:07<00:14,  1.34it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.82it/s]
 41%|████▏     | 12/29 [00:07<00:07,  2.40it/s]
 45%|████▍     | 13/29 [00:08<00:10,  1.57it/s]
 48%|████▊     | 14/29 [00:09<00:11,  1.30it/s]
 52%|█████▏    | 15/29 [00:10<00:08,  1.74it/s]
 55%|█████▌    | 16/29 [00:10<00:05,  2.30it/s]
 59%|█████▊    | 17/29 [00:11<00:07,  1.65it/s]
 62%|██████▏   | 18/29 [00:12<00:08,  1.31it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.77it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.33it/s]
 72%|███████▏  | 21/29 [00:13<00:04,  1.84it/s]
 76%|███████▌  | 22/29 [00:14<00:05,  1.31it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.76it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.33it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.83it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.36it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.82it/s]
 97%|█████████▋| 28/29 [00:17<00:00,  2.40it/s]
100%|██████████| 29/29 [00:17<00:00,  1.69it/s]
+
+Epoch  8: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.54it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.28it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.43it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.18it/s]
 40%|████      | 23/57 [00:01<00:01, 27.52it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.81it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.40it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.49it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.02it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.39it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.04it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.98it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.89it/s]
100%|██████████| 57/57 [00:02<00:00, 24.37it/s]
+    Epoch  8: training loss = -0.340 / validation loss = -0.342
+    Saved model: unet-8.pth.tar
+
+Epoch   9: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.56it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.33it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.49it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:06<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.77it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.56it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.96it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.58it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.29it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.74it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.82it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.40it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.70it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.90it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.38it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.21it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.87it/s]
100%|██████████| 29/29 [00:16<00:00,  3.37it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch  9: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.98it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.36it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.28it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.37it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.93it/s]
 40%|████      | 23/57 [00:01<00:01, 27.19it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.08it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.40it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.94it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.32it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.58it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.74it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.06it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.86it/s]
100%|██████████| 57/57 [00:02<00:00, 23.86it/s]
+    Epoch  9: training loss = -0.360 / validation loss = -0.409
+    Saved model: unet-9.pth.tar
+
+Epoch  10: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.50s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.11s/it]
 10%|█         | 3/29 [00:02<00:17,  1.51it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.25it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.97it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.61it/s]
 31%|███       | 9/29 [00:06<00:16,  1.23it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.62it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.98it/s]
 41%|████▏     | 12/29 [00:07<00:07,  2.40it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.32it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.64it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.98it/s]
 55%|█████▌    | 16/29 [00:10<00:06,  2.07it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.50it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.73it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.90it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  2.14it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.51it/s]
 76%|███████▌  | 22/29 [00:13<00:03,  1.78it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.13it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  1.94it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.48it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.94it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.91it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  1.79it/s]
100%|██████████| 29/29 [00:17<00:00,  1.69it/s]
+
+Epoch 10: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.98it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.38it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.61it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.65it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.57it/s]
 40%|████      | 23/57 [00:01<00:01, 26.46it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.53it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.68it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.14it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.70it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.66it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.93it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.77it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.22it/s]
100%|██████████| 57/57 [00:02<00:00, 24.35it/s]
+    Epoch 10: training loss = -0.409 / validation loss = -0.412
+    Saved model: unet-10.pth.tar
+
+Epoch  11: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.61s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:06<00:15,  1.30it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.14it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.73it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.29it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.69it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.84it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.41it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.26it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.71it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.26it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.73it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.22it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.65it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.19it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.59it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.25it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.23it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.36it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 11: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:17,  3.15it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.48it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.53it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.64it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.91it/s]
 40%|████      | 23/57 [00:01<00:01, 26.48it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.77it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.05it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.38it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.64it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.18it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.36it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.29it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.08it/s]
100%|██████████| 57/57 [00:02<00:00, 24.07it/s]
+    Epoch 11: training loss = -0.413 / validation loss = -0.418
+    Saved model: unet-11.pth.tar
+
+Epoch  12: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:18,  2.82s/it]
  7%|▋         | 2/29 [00:02<00:33,  1.23s/it]
 10%|█         | 3/29 [00:03<00:18,  1.40it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.10it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:05<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:07<00:15,  1.28it/s]
 34%|███▍      | 10/29 [00:07<00:10,  1.73it/s]
 38%|███▊      | 11/29 [00:07<00:07,  2.31it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.96it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.79it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.24it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.66it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.21it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.83it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.32it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.55it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.27it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.54it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.05it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.65it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 12: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.90it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.62it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.42it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.78it/s]
 33%|███▎      | 19/57 [00:00<00:01, 27.45it/s]
 40%|████      | 23/57 [00:01<00:01, 28.90it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.84it/s]
 53%|█████▎    | 30/57 [00:01<00:01, 26.59it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.18it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.01it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.37it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.92it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.75it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.96it/s]
100%|██████████| 57/57 [00:02<00:00, 25.63it/s]
100%|██████████| 57/57 [00:02<00:00, 23.42it/s]
+    Epoch 12: training loss = -0.430 / validation loss = -0.451
+    Saved model: unet-12.pth.tar
+
+Epoch  13: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:06,  2.36s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.57it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.33it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.10it/s]
 21%|██        | 6/29 [00:04<00:15,  1.52it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.08it/s]
 28%|██▊       | 8/29 [00:04<00:07,  2.77it/s]
 31%|███       | 9/29 [00:06<00:16,  1.21it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.68it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.23it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.65it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.19it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.39it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.28it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.55it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.07it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.47it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.54it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.05it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.36it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.32it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.69it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.24it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.28it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 13: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.30it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.31it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.21it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.30it/s]
 40%|████      | 23/57 [00:01<00:01, 26.98it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.73it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.97it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.23it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.76it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.28it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.49it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.19it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.78it/s]
100%|██████████| 57/57 [00:02<00:00, 23.45it/s]
+    Epoch 13: training loss = -0.455 / validation loss = -0.444
+
+Epoch  14: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.59s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:05<00:11,  2.00it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:07<00:16,  1.21it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.64it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.19it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.85it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.14it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.55it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.07it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.70it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.16it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.81it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.31it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.80it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.82it/s]
100%|██████████| 29/29 [00:16<00:00,  3.42it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 14: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.29it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.11it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.73it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.38it/s]
 40%|████      | 23/57 [00:01<00:01, 26.86it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.26it/s]
 53%|█████▎    | 30/57 [00:01<00:01, 26.90it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 25.97it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.37it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.07it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.14it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 25.91it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.50it/s]
100%|██████████| 57/57 [00:02<00:00, 24.96it/s]
100%|██████████| 57/57 [00:02<00:00, 23.14it/s]
+    Epoch 14: training loss = -0.457 / validation loss = -0.483
+    Saved model: unet-14.pth.tar
+
+Epoch  15: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.51s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.30it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.51it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.06it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.74it/s]
 31%|███       | 9/29 [00:06<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.15it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.80it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.29it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.55it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.01it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.63it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.32it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.45it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.93it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.54it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.43it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.91it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.51it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.32it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.47it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.96it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.57it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 15: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.01it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.90it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.81it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.37it/s]
 40%|████      | 23/57 [00:01<00:01, 26.91it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.32it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.21it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.62it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.93it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.33it/s]
 81%|████████  | 46/57 [00:01<00:00, 27.94it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 24.83it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 24.68it/s]
100%|██████████| 57/57 [00:02<00:00, 24.65it/s]
100%|██████████| 57/57 [00:02<00:00, 23.10it/s]
+    Epoch 15: training loss = -0.455 / validation loss = -0.475
+
+Epoch  16: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.69s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:05<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:07<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.09it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.74it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.20it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.62it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.16it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.81it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.14it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.54it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.05it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.68it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.14it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.54it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.05it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.67it/s]
100%|██████████| 29/29 [00:17<00:00,  3.19it/s]
100%|██████████| 29/29 [00:17<00:00,  1.69it/s]
+
+Epoch 16: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.95it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.03it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.26it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.96it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.43it/s]
 40%|████      | 23/57 [00:01<00:01, 26.74it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.31it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.54it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.89it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.10it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.12it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.19it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.65it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.18it/s]
100%|██████████| 57/57 [00:02<00:00, 23.70it/s]
+    Epoch 16: training loss = -0.483 / validation loss = -0.492
+    Saved model: unet-16.pth.tar
+
+Epoch  17: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.59s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.00s/it]
 21%|██        | 6/29 [00:05<00:16,  1.42it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.96it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.48it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.55it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.08it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.73it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.62it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.15it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.80it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.17it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.58it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.73it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.19it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.61it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.14it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.25it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.69it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.24it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.90it/s]
100%|██████████| 29/29 [00:16<00:00,  3.25it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 17: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.10it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.46it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.24it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.03it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.03it/s]
 40%|████      | 23/57 [00:01<00:01, 26.24it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.13it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.49it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.62it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.47it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.73it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.08it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.26it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 30.93it/s]
100%|██████████| 57/57 [00:02<00:00, 23.82it/s]
+    Epoch 17: training loss = -0.381 / validation loss = -0.470
+
+Epoch  18: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.42s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.11s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.51it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.08it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.76it/s]
 31%|███       | 9/29 [00:06<00:16,  1.25it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.67it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.19it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.82it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.17it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.58it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.11it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.75it/s]
 72%|███████▏  | 21/29 [00:13<00:07,  1.11it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.50it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.01it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.63it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.14it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.55it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.06it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.68it/s]
100%|██████████| 29/29 [00:16<00:00,  3.07it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 18: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.58it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.64it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.44it/s]
 32%|███▏      | 18/57 [00:00<00:01, 28.63it/s]
 39%|███▊      | 22/57 [00:00<00:01, 27.47it/s]
 46%|████▌     | 26/57 [00:01<00:01, 26.89it/s]
 51%|█████     | 29/57 [00:01<00:01, 24.70it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.55it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.92it/s]
 70%|███████   | 40/57 [00:01<00:00, 27.22it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.67it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.10it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.69it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.74it/s]
100%|██████████| 57/57 [00:02<00:00, 23.71it/s]
+    Epoch 18: training loss = -0.468 / validation loss = -0.511
+    Saved model: unet-18.pth.tar
+
+Epoch  19: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.65s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.00it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:06<00:15,  1.29it/s]
 34%|███▍      | 10/29 [00:07<00:13,  1.40it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.89it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.49it/s]
 45%|████▍     | 13/29 [00:09<00:11,  1.34it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.45it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.95it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.55it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.37it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.47it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.97it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.58it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.46it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.42it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.90it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.50it/s]
 86%|████████▌ | 25/29 [00:16<00:02,  1.43it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.39it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.87it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.46it/s]
100%|██████████| 29/29 [00:17<00:00,  1.70it/s]
+
+Epoch 19: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.36it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.17it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.89it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.05it/s]
 40%|████      | 23/57 [00:01<00:01, 27.48it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.85it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.35it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.58it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.86it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.79it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.24it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.83it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.52it/s]
100%|██████████| 57/57 [00:02<00:00, 23.66it/s]
+    Epoch 19: training loss = -0.543 / validation loss = -0.703
+    Saved model: unet-19.pth.tar
+
+Epoch  20: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.54s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.11s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.73it/s]
 31%|███       | 9/29 [00:07<00:18,  1.10it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.51it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.03it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.66it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.75it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.15it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.56it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.08it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.72it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.17it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.82it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.17it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.10it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.73it/s]
100%|██████████| 29/29 [00:16<00:00,  3.25it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 20: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.59it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.75it/s]
 23%|██▎       | 13/57 [00:00<00:01, 22.19it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.69it/s]
 40%|████      | 23/57 [00:01<00:01, 26.67it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.75it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.70it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.03it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.32it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.18it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.30it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.23it/s]
100%|██████████| 57/57 [00:02<00:00, 30.62it/s]
100%|██████████| 57/57 [00:02<00:00, 24.52it/s]
+    Epoch 20: training loss = -0.777 / validation loss = -0.810
+    Saved model: unet-20.pth.tar
+
+Epoch  21: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.62s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.02it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.69it/s]
 31%|███       | 9/29 [00:07<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.09it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.73it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.64it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.18it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.83it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.18it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.60it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.12it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.76it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.21it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.86it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.67it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.22it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.66it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 21: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.94it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.05it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.98it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.84it/s]
 32%|███▏      | 18/57 [00:00<00:01, 27.81it/s]
 39%|███▊      | 22/57 [00:01<00:01, 26.83it/s]
 46%|████▌     | 26/57 [00:01<00:01, 26.80it/s]
 51%|█████     | 29/57 [00:01<00:01, 24.73it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.42it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.06it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.81it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.89it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.64it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.66it/s]
100%|██████████| 57/57 [00:02<00:00, 25.41it/s]
100%|██████████| 57/57 [00:02<00:00, 23.43it/s]
+    Epoch 21: training loss = -0.817 / validation loss = -0.768
+
+Epoch  22: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.52s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.25it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.09it/s]
 21%|██        | 6/29 [00:04<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.98it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.65it/s]
 31%|███       | 9/29 [00:07<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.15it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.81it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.72it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.62it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.80it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.17it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.58it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.10it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.73it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.23it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.89it/s]
100%|██████████| 29/29 [00:16<00:00,  3.32it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 22: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.98it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.30it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.14it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.76it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.17it/s]
 40%|████      | 23/57 [00:01<00:01, 27.48it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.48it/s]
 53%|█████▎    | 30/57 [00:01<00:01, 26.73it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.20it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 25.90it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.15it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.11it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 25.79it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.36it/s]
100%|██████████| 57/57 [00:02<00:00, 25.20it/s]
100%|██████████| 57/57 [00:02<00:00, 23.08it/s]
+    Epoch 22: training loss = -0.809 / validation loss = -0.813
+    Saved model: unet-22.pth.tar
+
+Epoch  23: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.53s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.11s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.09it/s]
 21%|██        | 6/29 [00:04<00:15,  1.52it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.08it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.76it/s]
 31%|███       | 9/29 [00:06<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.22it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.65it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.17it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.82it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.29it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.74it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.93it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.53it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.32it/s]
 76%|███████▌  | 22/29 [00:13<00:03,  1.77it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.89it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.49it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.11it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.72it/s]
100%|██████████| 29/29 [00:16<00:00,  1.76it/s]
+
+Epoch 23: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.00it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.52it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.53it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.78it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.82it/s]
 40%|████      | 23/57 [00:01<00:01, 27.29it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.29it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.44it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.57it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.37it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 28.05it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 27.03it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.28it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.96it/s]
100%|██████████| 57/57 [00:02<00:00, 23.84it/s]
+    Epoch 23: training loss = -0.832 / validation loss = -0.813
+
+Epoch  24: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.39s/it]
  7%|▋         | 2/29 [00:02<00:28,  1.05s/it]
 10%|█         | 3/29 [00:02<00:16,  1.59it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.36it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:04<00:14,  1.54it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.12it/s]
 28%|██▊       | 8/29 [00:04<00:07,  2.80it/s]
 31%|███       | 9/29 [00:06<00:16,  1.24it/s]
 34%|███▍      | 10/29 [00:06<00:11,  1.70it/s]
 38%|███▊      | 11/29 [00:06<00:07,  2.27it/s]
 41%|████▏     | 12/29 [00:07<00:07,  2.42it/s]
 45%|████▍     | 13/29 [00:08<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.67it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.22it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.33it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.29it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.74it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.29it/s]
 69%|██████▉   | 20/29 [00:11<00:04,  2.08it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.29it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.20it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.01it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.29it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.62it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.16it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  1.94it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 24: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.97it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.31it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.55it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.38it/s]
 32%|███▏      | 18/57 [00:00<00:01, 28.09it/s]
 39%|███▊      | 22/57 [00:01<00:01, 27.54it/s]
 46%|████▌     | 26/57 [00:01<00:01, 28.98it/s]
 53%|█████▎    | 30/57 [00:01<00:01, 26.96it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 27.43it/s]
 63%|██████▎   | 36/57 [00:01<00:00, 26.54it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.25it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.04it/s]
 81%|████████  | 46/57 [00:01<00:00, 24.76it/s]
 88%|████████▊ | 50/57 [00:02<00:00, 25.81it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 25.50it/s]
100%|██████████| 57/57 [00:02<00:00, 24.13it/s]
+    Epoch 24: training loss = -0.841 / validation loss = -0.826
+    Saved model: unet-24.pth.tar
+
+Epoch  25: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:16,  2.73s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.19s/it]
 10%|█         | 3/29 [00:02<00:18,  1.44it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.16it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.03s/it]
 21%|██        | 6/29 [00:05<00:16,  1.39it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.92it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:07<00:18,  1.10it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.51it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.02it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.65it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.12it/s]
 48%|████▊     | 14/29 [00:10<00:09,  1.52it/s]
 52%|█████▏    | 15/29 [00:10<00:06,  2.03it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.65it/s]
 59%|█████▊    | 17/29 [00:12<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.52it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.03it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.65it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.32it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.78it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.35it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  3.02it/s]
100%|██████████| 29/29 [00:17<00:00,  3.39it/s]
100%|██████████| 29/29 [00:17<00:00,  1.69it/s]
+
+Epoch 25: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.93it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.95it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.32it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.59it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.62it/s]
 40%|████      | 23/57 [00:01<00:01, 26.26it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.73it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.56it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.45it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.68it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.62it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.71it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.46it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.18it/s]
100%|██████████| 57/57 [00:02<00:00, 23.49it/s]
+    Epoch 25: training loss = -0.818 / validation loss = -0.839
+    Saved model: unet-25.pth.tar
+
+Epoch  26: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.63s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:05<00:25,  1.06s/it]
 21%|██        | 6/29 [00:05<00:16,  1.36it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.88it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.52it/s]
 31%|███       | 9/29 [00:07<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.15it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.80it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.72it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.17it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.58it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.74it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.22it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.64it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.18it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.83it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.22it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.88it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 26: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.97it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.61it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.49it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.87it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.65it/s]
 40%|████      | 23/57 [00:01<00:01, 26.98it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.74it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.21it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.60it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.31it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.12it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.96it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.64it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.31it/s]
100%|██████████| 57/57 [00:02<00:00, 24.17it/s]
+    Epoch 26: training loss = -0.844 / validation loss = -0.844
+    Saved model: unet-26.pth.tar
+
+Epoch  27: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.60s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:05<00:25,  1.05s/it]
 21%|██        | 6/29 [00:05<00:16,  1.37it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.91it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.55it/s]
 31%|███       | 9/29 [00:07<00:17,  1.11it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.53it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.69it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.14it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.55it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.07it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.70it/s]
 59%|█████▊    | 17/29 [00:12<00:10,  1.15it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.56it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.08it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.71it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.17it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.82it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.19it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.61it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.13it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.78it/s]
100%|██████████| 29/29 [00:17<00:00,  3.20it/s]
100%|██████████| 29/29 [00:17<00:00,  1.68it/s]
+
+Epoch 27: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:17,  3.12it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.58it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.75it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.96it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.95it/s]
 40%|████      | 23/57 [00:01<00:01, 27.86it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.52it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.73it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 27.07it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.17it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.56it/s]
 81%|████████  | 46/57 [00:01<00:00, 27.33it/s]
 88%|████████▊ | 50/57 [00:02<00:00, 27.88it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 27.26it/s]
100%|██████████| 57/57 [00:02<00:00, 27.03it/s]
100%|██████████| 57/57 [00:02<00:00, 24.34it/s]
+    Epoch 27: training loss = -0.846 / validation loss = -0.837
+
+Epoch  28: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.27it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.01s/it]
 21%|██        | 6/29 [00:04<00:16,  1.42it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.97it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.63it/s]
 31%|███       | 9/29 [00:07<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.10it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.74it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.20it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.15it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.79it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.58it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.11it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.74it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.25it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.61it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.25it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.69it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.24it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.91it/s]
100%|██████████| 29/29 [00:16<00:00,  3.28it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 28: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.08it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.36it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.87it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.96it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.32it/s]
 40%|████      | 23/57 [00:01<00:01, 27.38it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.21it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.59it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.99it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.16it/s]
 70%|███████   | 40/57 [00:01<00:00, 27.22it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.62it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.39it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.94it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.81it/s]
100%|██████████| 57/57 [00:02<00:00, 23.73it/s]
+    Epoch 28: training loss = -0.852 / validation loss = -0.850
+    Saved model: unet-28.pth.tar
+
+Epoch  29: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.53s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.11s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.00it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.67it/s]
 31%|███       | 9/29 [00:06<00:16,  1.21it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.65it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.21it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.87it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.25it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.68it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.21it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.86it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.15it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.56it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.08it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.71it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.15it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.55it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.07it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.70it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.14it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.54it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.06it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.68it/s]
100%|██████████| 29/29 [00:16<00:00,  3.07it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 29: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.28it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.51it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.44it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.49it/s]
 40%|████      | 23/57 [00:01<00:01, 27.04it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.18it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.60it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.61it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.38it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.53it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.88it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.40it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.30it/s]
100%|██████████| 57/57 [00:02<00:00, 23.94it/s]
+    Epoch 29: training loss = -0.859 / validation loss = -0.852
+    Saved model: unet-29.pth.tar
+
+Epoch  30: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.52s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.30it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.00it/s]
 21%|██        | 6/29 [00:04<00:16,  1.43it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.98it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.64it/s]
 31%|███       | 9/29 [00:06<00:15,  1.30it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.64it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.17it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.82it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.12it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.45it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.19it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.13it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.62it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.64it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.17it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.63it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.79it/s]
100%|██████████| 29/29 [00:16<00:00,  3.26it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 30: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.00it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.12it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.99it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.91it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.15it/s]
 40%|████      | 23/57 [00:01<00:01, 27.20it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.83it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.39it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.37it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.92it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.27it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.49it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.92it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.48it/s]
100%|██████████| 57/57 [00:02<00:00, 23.50it/s]
+    Epoch 30: training loss = -0.848 / validation loss = -0.809
+
+Epoch  31: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.68s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.45it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:06<00:15,  1.29it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.27it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.50it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.01it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.63it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.32it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.54it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.05it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.67it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.30it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.52it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.03it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.65it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.36it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.47it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.96it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.57it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 31: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.20it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.88it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.30it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.44it/s]
 40%|████      | 23/57 [00:01<00:01, 27.30it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.98it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.76it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 27.22it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.37it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.14it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.85it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.29it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.02it/s]
100%|██████████| 57/57 [00:02<00:00, 24.04it/s]
+    Epoch 31: training loss = -0.841 / validation loss = -0.847
+
+Epoch  32: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.51it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.25it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.00it/s]
 21%|██        | 6/29 [00:05<00:16,  1.43it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.98it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.64it/s]
 31%|███       | 9/29 [00:07<00:17,  1.12it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.52it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.68it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.22it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.65it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.19it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.84it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.14it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.79it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.72it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.39it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.71it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.26it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.49it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 32: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.84it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.92it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.97it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.82it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.91it/s]
 40%|████      | 23/57 [00:01<00:01, 26.93it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.01it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.88it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.13it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.96it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.87it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.88it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.51it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.25it/s]
100%|██████████| 57/57 [00:02<00:00, 23.44it/s]
+    Epoch 32: training loss = -0.855 / validation loss = -0.854
+    Saved model: unet-32.pth.tar
+
+Epoch  33: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.40s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.12it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:04<00:15,  1.49it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.06it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.73it/s]
 31%|███       | 9/29 [00:06<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.77it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.27it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.52it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.03it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.66it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.34it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.55it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.06it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.67it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.33it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.55it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.07it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.53it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.35it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.47it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.96it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.57it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 33: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.53it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.88it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.89it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.23it/s]
 40%|████      | 23/57 [00:01<00:01, 27.39it/s]
 46%|████▌     | 26/57 [00:01<00:01, 26.67it/s]
 51%|█████     | 29/57 [00:01<00:01, 24.04it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 25.96it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 25.91it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.57it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 28.16it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.79it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 26.03it/s]
100%|██████████| 57/57 [00:02<00:00, 25.63it/s]
100%|██████████| 57/57 [00:02<00:00, 23.32it/s]
+    Epoch 33: training loss = -0.864 / validation loss = -0.857
+    Saved model: unet-33.pth.tar
+
+Epoch  34: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.63s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.55it/s]
 31%|███       | 9/29 [00:07<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.09it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.72it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.11it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.58it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.19it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.14it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.77it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.20it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.67it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.71it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.26it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.23it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 34: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.08it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.67it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.87it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.85it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.20it/s]
 40%|████      | 23/57 [00:01<00:01, 26.78it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.53it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.06it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.42it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.03it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.39it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.71it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.51it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.02it/s]
100%|██████████| 57/57 [00:02<00:00, 23.78it/s]
+    Epoch 34: training loss = -0.854 / validation loss = -0.840
+
+Epoch  35: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:05,  2.34s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.56it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.31it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.10it/s]
 21%|██        | 6/29 [00:04<00:16,  1.38it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.91it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.56it/s]
 31%|███       | 9/29 [00:06<00:15,  1.28it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.49it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.01it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.62it/s]
 45%|████▍     | 13/29 [00:08<00:11,  1.35it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.72it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.27it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.68it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.26it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.14it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.74it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.22it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.60it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.11it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.71it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.66it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.59it/s]
100%|██████████| 29/29 [00:16<00:00,  1.76it/s]
+
+Epoch 35: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.41it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.27it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.36it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.78it/s]
 40%|████      | 23/57 [00:01<00:01, 27.00it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.64it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.81it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.21it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.15it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.99it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.33it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.89it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.44it/s]
100%|██████████| 57/57 [00:02<00:00, 24.28it/s]
+    Epoch 35: training loss = -0.852 / validation loss = -0.857
+
+Epoch  36: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.61s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.03s/it]
 21%|██        | 6/29 [00:05<00:16,  1.39it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.93it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.58it/s]
 31%|███       | 9/29 [00:07<00:18,  1.10it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.51it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.03it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.67it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.63it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.17it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.83it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.68it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.23it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.89it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.29it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.74it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.30it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.97it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.17it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.59it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.11it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.75it/s]
100%|██████████| 29/29 [00:16<00:00,  3.15it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 36: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.47it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.60it/s]
 23%|██▎       | 13/57 [00:00<00:01, 22.14it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.30it/s]
 40%|████      | 23/57 [00:01<00:01, 26.87it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.00it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.98it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.42it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.80it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.36it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.52it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.34it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.78it/s]
100%|██████████| 57/57 [00:02<00:00, 24.34it/s]
+    Epoch 36: training loss = -0.866 / validation loss = -0.843
+
+Epoch  37: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.57s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.26it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.00s/it]
 21%|██        | 6/29 [00:04<00:16,  1.43it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.97it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.63it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.55it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.71it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.30it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.76it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.32it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  3.00it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.17it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.82it/s]
 72%|███████▏  | 21/29 [00:14<00:07,  1.12it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.51it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.02it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.64it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.17it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.10it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.73it/s]
100%|██████████| 29/29 [00:16<00:00,  3.26it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 37: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.99it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.33it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.67it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.61it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.75it/s]
 40%|████      | 23/57 [00:01<00:01, 26.27it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.80it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.51it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.02it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.43it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.06it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.45it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.91it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.51it/s]
100%|██████████| 57/57 [00:02<00:00, 24.08it/s]
+    Epoch 37: training loss = -0.851 / validation loss = -0.852
+
+Epoch  38: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.69s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:05<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.02it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.69it/s]
 31%|███       | 9/29 [00:07<00:16,  1.23it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.14it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.28it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.73it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.28it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.30it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.30it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.71it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.27it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  2.11it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.31it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.09it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.35it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.23it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  1.99it/s]
100%|██████████| 29/29 [00:17<00:00,  1.70it/s]
+
+Epoch 38: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.36it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.41it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.25it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.52it/s]
 40%|████      | 23/57 [00:01<00:01, 26.80it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.89it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.34it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.97it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.54it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.24it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.37it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.43it/s]
100%|██████████| 57/57 [00:02<00:00, 27.96it/s]
100%|██████████| 57/57 [00:02<00:00, 24.01it/s]
+    Epoch 38: training loss = -0.856 / validation loss = -0.840
+
+Epoch  39: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.31it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.01it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.68it/s]
 31%|███       | 9/29 [00:07<00:18,  1.09it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.49it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.01it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.63it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.22it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.65it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.20it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.85it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.62it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.80it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.19it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.60it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.13it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.77it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.22it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.65it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.19it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.85it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 39: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.96it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.94it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.28it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.27it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.15it/s]
 40%|████      | 23/57 [00:01<00:01, 27.21it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.66it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.15it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.82it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.02it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.53it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.03it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.60it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.13it/s]
100%|██████████| 57/57 [00:02<00:00, 23.72it/s]
+    Epoch 39: training loss = -0.859 / validation loss = -0.851
+
+Epoch  40: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:16,  2.73s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.19s/it]
 10%|█         | 3/29 [00:02<00:18,  1.44it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.16it/s]
 17%|█▋        | 5/29 [00:05<00:25,  1.06s/it]
 21%|██        | 6/29 [00:05<00:17,  1.35it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.87it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.51it/s]
 31%|███       | 9/29 [00:07<00:16,  1.21it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.65it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.21it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.88it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.56it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.08it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.71it/s]
 59%|█████▊    | 17/29 [00:12<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.57it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.09it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.73it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.66it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.21it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.86it/s]
100%|██████████| 29/29 [00:17<00:00,  3.24it/s]
100%|██████████| 29/29 [00:17<00:00,  1.70it/s]
+
+Epoch 40: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.13it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.50it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.20it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.61it/s]
 40%|████      | 23/57 [00:01<00:01, 26.91it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.38it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.07it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.77it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.89it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.35it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.41it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.78it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.42it/s]
100%|██████████| 57/57 [00:02<00:00, 23.63it/s]
+    Epoch 40: training loss = -0.860 / validation loss = -0.851
+
+Epoch  41: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:16,  2.72s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.19s/it]
 10%|█         | 3/29 [00:02<00:18,  1.44it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.16it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.03s/it]
 21%|██        | 6/29 [00:05<00:16,  1.39it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.92it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:07<00:16,  1.22it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.67it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.23it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.90it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.28it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.73it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.27it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.94it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.16it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.80it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.61it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.14it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.20it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.61it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.14it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.78it/s]
100%|██████████| 29/29 [00:16<00:00,  3.52it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 41: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.80it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.10it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.78it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.50it/s]
 40%|████      | 23/57 [00:01<00:01, 25.30it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.57it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.64it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.94it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.04it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.31it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.53it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.21it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.15it/s]
100%|██████████| 57/57 [00:02<00:00, 23.88it/s]
+    Epoch 41: training loss = -0.854 / validation loss = -0.843
+
+Epoch  42: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.64s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:07<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.63it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.17it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.81it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.71it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.62it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.53it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.16it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.09it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.71it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.15it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.56it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.08it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.71it/s]
100%|██████████| 29/29 [00:16<00:00,  3.09it/s]
100%|██████████| 29/29 [00:17<00:00,  1.70it/s]
+
+Epoch 42: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:17,  3.12it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.46it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.85it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.76it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.16it/s]
 40%|████      | 23/57 [00:01<00:01, 27.46it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.94it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.56it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.70it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.34it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.81it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.66it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.14it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.84it/s]
100%|██████████| 57/57 [00:02<00:00, 24.05it/s]
+    Epoch 42: training loss = -0.860 / validation loss = -0.856
+
+Epoch  43: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.43s/it]
  7%|▋         | 2/29 [00:02<00:28,  1.07s/it]
 10%|█         | 3/29 [00:02<00:16,  1.58it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.35it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.07it/s]
 28%|██▊       | 8/29 [00:04<00:07,  2.74it/s]
 31%|███       | 9/29 [00:06<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:06<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.16it/s]
 41%|████▏     | 12/29 [00:07<00:07,  2.32it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.64it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.19it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.23it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.24it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.68it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.23it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  2.16it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.71it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.26it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.36it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.25it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.69it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.23it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.25it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 43: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.98it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.34it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.13it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.86it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.52it/s]
 40%|████      | 23/57 [00:01<00:01, 26.87it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.17it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.41it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.99it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.36it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.99it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.07it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.17it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.14it/s]
100%|██████████| 57/57 [00:02<00:00, 23.60it/s]
+    Epoch 43: training loss = -0.869 / validation loss = -0.861
+    Saved model: unet-43.pth.tar
+
+Epoch  44: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.54s/it]
  7%|▋         | 2/29 [00:02<00:34,  1.29s/it]
 10%|█         | 3/29 [00:03<00:19,  1.34it/s]
 14%|█▍        | 4/29 [00:03<00:13,  1.92it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.11it/s]
 21%|██        | 6/29 [00:05<00:16,  1.41it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.95it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.60it/s]
 31%|███       | 9/29 [00:07<00:15,  1.26it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.72it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.26it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.74it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.29it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.53it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.04it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.66it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.27it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.53it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.04it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.67it/s]
 86%|████████▌ | 25/29 [00:16<00:02,  1.33it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.49it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.99it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.60it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 44: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.29it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.24it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.07it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.34it/s]
 40%|████      | 23/57 [00:01<00:01, 27.49it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.45it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.52it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.83it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.68it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.79it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 28.27it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 27.48it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 26.50it/s]
100%|██████████| 57/57 [00:02<00:00, 26.07it/s]
100%|██████████| 57/57 [00:02<00:00, 23.86it/s]
+    Epoch 44: training loss = -0.870 / validation loss = -0.866
+    Saved model: unet-44.pth.tar
+
+Epoch  45: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.59s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.22it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.01s/it]
 21%|██        | 6/29 [00:05<00:16,  1.42it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.96it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.60it/s]
 31%|███       | 9/29 [00:07<00:18,  1.10it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.50it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.02it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.65it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.27it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.72it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.27it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.94it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.69it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.24it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.90it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.17it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.59it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.11it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.74it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.16it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.56it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.09it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.72it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 45: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.08it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.17it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.82it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.26it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.92it/s]
 40%|████      | 23/57 [00:01<00:01, 27.58it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.17it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.57it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.85it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.18it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.46it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 27.00it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.92it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.55it/s]
100%|██████████| 57/57 [00:02<00:00, 23.90it/s]
+    Epoch 45: training loss = -0.869 / validation loss = -0.821
+
+Epoch  46: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.68s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.52it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.10it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.78it/s]
 31%|███       | 9/29 [00:07<00:17,  1.12it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.06it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.70it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.10it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.49it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.99it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.61it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.69it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.24it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.90it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.61it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.14it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.30it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.75it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.97it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.58it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 46: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.94it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.28it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.05it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.96it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.59it/s]
 40%|████      | 23/57 [00:01<00:01, 26.71it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.22it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.52it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.60it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.29it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.49it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.60it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.50it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.27it/s]
100%|██████████| 57/57 [00:02<00:00, 23.28it/s]
+    Epoch 46: training loss = -0.863 / validation loss = -0.867
+    Saved model: unet-46.pth.tar
+
+Epoch  47: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:16,  2.72s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.19s/it]
 10%|█         | 3/29 [00:02<00:18,  1.44it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.16it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.02s/it]
 21%|██        | 6/29 [00:05<00:16,  1.41it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.95it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.61it/s]
 31%|███       | 9/29 [00:07<00:17,  1.12it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.06it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.70it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.79it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.18it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.59it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.11it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.75it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.17it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.82it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.17it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.11it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.74it/s]
100%|██████████| 29/29 [00:17<00:00,  3.02it/s]
100%|██████████| 29/29 [00:17<00:00,  1.68it/s]
+
+Epoch 47: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.29it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.94it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.83it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.63it/s]
 40%|████      | 23/57 [00:01<00:01, 26.79it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.54it/s]
 53%|█████▎    | 30/57 [00:01<00:01, 26.92it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 25.97it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.10it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.42it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.90it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.46it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.60it/s]
100%|██████████| 57/57 [00:02<00:00, 24.45it/s]
100%|██████████| 57/57 [00:02<00:00, 23.03it/s]
+    Epoch 47: training loss = -0.874 / validation loss = -0.869
+    Saved model: unet-47.pth.tar
+
+Epoch  48: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.53s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.11s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.01s/it]
 21%|██        | 6/29 [00:04<00:16,  1.42it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.95it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.61it/s]
 31%|███       | 9/29 [00:07<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.09it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.73it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.17it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.82it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.14it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.55it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.07it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.70it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.25it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.69it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.24it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.87it/s]
100%|██████████| 29/29 [00:16<00:00,  3.58it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 48: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.48it/s]
 16%|█▌        | 9/57 [00:00<00:02, 20.04it/s]
 23%|██▎       | 13/57 [00:00<00:01, 22.05it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.04it/s]
 40%|████      | 23/57 [00:01<00:01, 25.60it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.28it/s]
 54%|█████▍    | 31/57 [00:01<00:01, 25.73it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.05it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.80it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.41it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.69it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.35it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.73it/s]
100%|██████████| 57/57 [00:02<00:00, 23.85it/s]
+    Epoch 48: training loss = -0.872 / validation loss = -0.869
+    Saved model: unet-48.pth.tar
+
+Epoch  49: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.51s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.96it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.62it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.03it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.66it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.22it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.64it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.16it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.80it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.49it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.99it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.60it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.43it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.53it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.95it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.56it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.46it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.72it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.91it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.51it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 49: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.53it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.68it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.74it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.94it/s]
 40%|████      | 23/57 [00:01<00:01, 26.77it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.73it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.84it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.19it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.59it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.19it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.38it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.47it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.14it/s]
100%|██████████| 57/57 [00:02<00:00, 23.95it/s]
+    Epoch 49: training loss = -0.875 / validation loss = -0.869
+
+Epoch  50: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.62s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.22it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.01s/it]
 21%|██        | 6/29 [00:05<00:16,  1.41it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.95it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.61it/s]
 31%|███       | 9/29 [00:07<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.63it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.17it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.82it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.12it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.53it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.04it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.67it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.19it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.14it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.78it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.35it/s]
 76%|███████▌  | 22/29 [00:13<00:03,  1.81it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.37it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  3.05it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.27it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.70it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.25it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.92it/s]
100%|██████████| 29/29 [00:16<00:00,  3.30it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 50: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.96it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.39it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.57it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.56it/s]
 32%|███▏      | 18/57 [00:00<00:01, 27.63it/s]
 39%|███▊      | 22/57 [00:00<00:01, 28.01it/s]
 46%|████▌     | 26/57 [00:01<00:01, 28.90it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.65it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 27.59it/s]
 63%|██████▎   | 36/57 [00:01<00:00, 25.23it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 23.01it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 24.87it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 24.92it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.25it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.23it/s]
100%|██████████| 57/57 [00:02<00:00, 24.29it/s]
+    Epoch 50: training loss = -0.873 / validation loss = -0.871
+    Saved model: unet-50.pth.tar
+
+Epoch  51: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.69s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.04s/it]
 21%|██        | 6/29 [00:05<00:16,  1.38it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.91it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.56it/s]
 31%|███       | 9/29 [00:07<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.60it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.14it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.80it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.22it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.65it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.19it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.85it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.17it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.58it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.74it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.31it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.75it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.31it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.98it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.82it/s]
100%|██████████| 29/29 [00:16<00:00,  3.09it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 51: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.08it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.28it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.40it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.96it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.12it/s]
 40%|████      | 23/57 [00:01<00:01, 25.89it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.37it/s]
 54%|█████▍    | 31/57 [00:01<00:01, 25.73it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.41it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.59it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.37it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 27.00it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.66it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.36it/s]
100%|██████████| 57/57 [00:02<00:00, 24.21it/s]
+    Epoch 51: training loss = -0.872 / validation loss = -0.871
+    Saved model: unet-51.pth.tar
+
+Epoch  52: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.64s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.22it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.03s/it]
 21%|██        | 6/29 [00:05<00:16,  1.39it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.92it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:07<00:16,  1.22it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.66it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.22it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.89it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.11it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.75it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.23it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.66it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.18it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.83it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.17it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.58it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.09it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.72it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.30it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.75it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.00it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.62it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 52: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.37it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.59it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.64it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.14it/s]
 40%|████      | 23/57 [00:01<00:01, 27.57it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.13it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.21it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.17it/s]
 63%|██████▎   | 36/57 [00:01<00:00, 27.05it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 23.76it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 25.64it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 24.99it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.33it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.12it/s]
100%|██████████| 57/57 [00:02<00:00, 23.33it/s]
+    Epoch 52: training loss = -0.874 / validation loss = -0.862
+
+Epoch  53: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.45s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.51it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.25it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.09it/s]
 21%|██        | 6/29 [00:04<00:14,  1.55it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.13it/s]
 28%|██▊       | 8/29 [00:04<00:07,  2.82it/s]
 31%|███       | 9/29 [00:06<00:16,  1.22it/s]
 34%|███▍      | 10/29 [00:06<00:11,  1.64it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.20it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.86it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.73it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.65it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.17it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.82it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.98it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.59it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.66it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.82it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.39it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 53: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.45it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.80it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.37it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.40it/s]
 40%|████      | 23/57 [00:01<00:01, 26.81it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.07it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.80it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.47it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.83it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.27it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.36it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.92it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.47it/s]
100%|██████████| 57/57 [00:02<00:00, 24.12it/s]
+    Epoch 53: training loss = -0.869 / validation loss = -0.872
+    Saved model: unet-53.pth.tar
+
+Epoch  54: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:03<00:47,  1.74s/it]
 10%|█         | 3/29 [00:03<00:25,  1.00it/s]
 14%|█▍        | 4/29 [00:03<00:16,  1.55it/s]
 17%|█▋        | 5/29 [00:04<00:14,  1.62it/s]
 21%|██        | 6/29 [00:04<00:12,  1.90it/s]
 24%|██▍       | 7/29 [00:04<00:08,  2.56it/s]
 28%|██▊       | 8/29 [00:05<00:06,  3.33it/s]
 31%|███       | 9/29 [00:06<00:14,  1.36it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.66it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.20it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.83it/s]
 45%|████▍     | 13/29 [00:08<00:11,  1.35it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.48it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.98it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.59it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.38it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.50it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.01it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.63it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.48it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.55it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.06it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.66it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.37it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.44it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.93it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.52it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 54: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.00it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.12it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.97it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.85it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.08it/s]
 40%|████      | 23/57 [00:01<00:01, 26.64it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.94it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.61it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.62it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.92it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.71it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.74it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.55it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.53it/s]
100%|██████████| 57/57 [00:02<00:00, 23.43it/s]
+    Epoch 54: training loss = -0.877 / validation loss = -0.873
+    Saved model: unet-54.pth.tar
+
+Epoch  55: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.44s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.56it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.32it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.01it/s]
 21%|██        | 6/29 [00:04<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:07<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.57it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.73it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.15it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.56it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.08it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.71it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.18it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.60it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.13it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.76it/s]
100%|██████████| 29/29 [00:16<00:00,  3.20it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 55: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.99it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.88it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.82it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.04it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.37it/s]
 40%|████      | 23/57 [00:01<00:01, 28.02it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.42it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.77it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 27.00it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.12it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 27.23it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 28.52it/s]
 86%|████████▌ | 49/57 [00:01<00:00, 27.68it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 26.75it/s]
100%|██████████| 57/57 [00:02<00:00, 26.57it/s]
100%|██████████| 57/57 [00:02<00:00, 23.96it/s]
+    Epoch 55: training loss = -0.876 / validation loss = -0.851
+
+Epoch  56: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.26it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.93it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.58it/s]
 31%|███       | 9/29 [00:06<00:16,  1.24it/s]
 34%|███▍      | 10/29 [00:06<00:11,  1.69it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.16it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.82it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.67it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.03it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.54it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.29it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.69it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.20it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.40it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.44it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.02it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.46it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.34it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.51it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.02it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.50it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 56: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.00it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.09it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.15it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.20it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.64it/s]
 40%|████      | 23/57 [00:01<00:01, 27.08it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.18it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.27it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.75it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.78it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.53it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.33it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.19it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.62it/s]
100%|██████████| 57/57 [00:02<00:00, 23.42it/s]
+    Epoch 56: training loss = -0.877 / validation loss = -0.869
+
+Epoch  57: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.48s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.56it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.32it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.49it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.06it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.70it/s]
 31%|███       | 9/29 [00:07<00:18,  1.10it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.51it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.03it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.66it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.12it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.53it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.04it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.66it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.18it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.83it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.25it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.68it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.23it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.50it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.66it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.20it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.41it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 57: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.97it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.45it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.19it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.22it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.02it/s]
 40%|████      | 23/57 [00:01<00:01, 27.53it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.97it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.47it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.32it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.89it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.79it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.68it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.81it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.65it/s]
100%|██████████| 57/57 [00:02<00:00, 23.87it/s]
+    Epoch 57: training loss = -0.880 / validation loss = -0.869
+
+Epoch  58: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.52s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.11it/s]
 21%|██        | 6/29 [00:05<00:16,  1.40it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.94it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.59it/s]
 31%|███       | 9/29 [00:07<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.77it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.52it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.03it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.66it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.33it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.18it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.49it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.34it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.65it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.19it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.25it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.37it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.65it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.19it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.30it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 58: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.33it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.54it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.64it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.60it/s]
 40%|████      | 23/57 [00:01<00:01, 26.95it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.67it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.19it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.46it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.71it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.17it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.53it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.90it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.81it/s]
100%|██████████| 57/57 [00:02<00:00, 23.53it/s]
+    Epoch 58: training loss = -0.874 / validation loss = -0.873
+    Saved model: unet-58.pth.tar
+
+Epoch  59: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.46s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.57it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.34it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.01it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.67it/s]
 31%|███       | 9/29 [00:06<00:16,  1.21it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.65it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.21it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.67it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.67it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.17it/s]
 55%|█████▌    | 16/29 [00:09<00:06,  2.03it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.18it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  1.90it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.39it/s]
 76%|███████▌  | 22/29 [00:13<00:03,  1.87it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.43it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  1.96it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.41it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.89it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.26it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  1.94it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 59: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.53it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.77it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.70it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.09it/s]
 40%|████      | 23/57 [00:01<00:01, 26.19it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.66it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.25it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.15it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.50it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.19it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.62it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.12it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.84it/s]
100%|██████████| 57/57 [00:02<00:00, 24.10it/s]
+    Epoch 59: training loss = -0.880 / validation loss = -0.873
+    Saved model: unet-59.pth.tar
+
+Epoch  60: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.61s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.00it/s]
 21%|██        | 6/29 [00:05<00:16,  1.43it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.98it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.64it/s]
 31%|███       | 9/29 [00:07<00:17,  1.12it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.06it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.52it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.11it/s]
 55%|█████▌    | 16/29 [00:10<00:05,  2.30it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.65it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.19it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.27it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.70it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.25it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.39it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.32it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.78it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.34it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.27it/s]
100%|██████████| 29/29 [00:17<00:00,  2.90it/s]
100%|██████████| 29/29 [00:17<00:00,  1.69it/s]
+
+Epoch 60: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.08it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.02it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.26it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.04it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.57it/s]
 40%|████      | 23/57 [00:01<00:01, 26.32it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.23it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.39it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.93it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.30it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.40it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.55it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.12it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.01it/s]
100%|██████████| 57/57 [00:02<00:00, 23.84it/s]
+    Epoch 60: training loss = -0.877 / validation loss = -0.863
+
+Epoch  61: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.48s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.31it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.02s/it]
 21%|██        | 6/29 [00:05<00:16,  1.39it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.92it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:07<00:17,  1.12it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.53it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.69it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.28it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.73it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.12it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.76it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.30it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.74it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.01it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.62it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.27it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.71it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.97it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.58it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.41it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.87it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.87it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.46it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 61: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.11it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.45it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.04it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.99it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.70it/s]
 40%|████      | 23/57 [00:01<00:01, 27.14it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.12it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.22it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.56it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.08it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.85it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.64it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.19it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.76it/s]
100%|██████████| 57/57 [00:02<00:00, 25.31it/s]
100%|██████████| 57/57 [00:02<00:00, 23.49it/s]
+    Epoch 61: training loss = -0.873 / validation loss = -0.876
+    Saved model: unet-61.pth.tar
+
+Epoch  62: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.52s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.13it/s]
 21%|██        | 6/29 [00:04<00:14,  1.55it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.10it/s]
 28%|██▊       | 8/29 [00:04<00:07,  2.76it/s]
 31%|███       | 9/29 [00:06<00:14,  1.34it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.47it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.97it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.57it/s]
 45%|████▍     | 13/29 [00:08<00:11,  1.35it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.54it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.04it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.66it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.23it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.62it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.16it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.78it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.27it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.44it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.92it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.52it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.32it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.59it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.12it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.73it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 62: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.89it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.17it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.40it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.60it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.62it/s]
 40%|████      | 23/57 [00:01<00:01, 27.04it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.97it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.88it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.38it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.77it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.05it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.25it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.35it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.09it/s]
100%|██████████| 57/57 [00:02<00:00, 23.53it/s]
+    Epoch 62: training loss = -0.879 / validation loss = -0.871
+
+Epoch  63: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:06,  2.39s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.11s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.27it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.10it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.45it/s]
 31%|███       | 9/29 [00:06<00:15,  1.28it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.48it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.99it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.53it/s]
 45%|████▍     | 13/29 [00:08<00:11,  1.38it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.43it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.92it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.52it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.36it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.53it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.03it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.65it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.34it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.49it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.99it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.60it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.43it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.14it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.75it/s]
100%|██████████| 29/29 [00:16<00:00,  1.76it/s]
+
+Epoch 63: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.97it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.69it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.52it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.84it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.78it/s]
 40%|████      | 23/57 [00:01<00:01, 27.83it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.33it/s]
 51%|█████     | 29/57 [00:01<00:01, 24.71it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 25.86it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.16it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.51it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 28.17it/s]
 84%|████████▍ | 48/57 [00:02<00:00, 27.15it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.87it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.29it/s]
100%|██████████| 57/57 [00:02<00:00, 23.45it/s]
+    Epoch 63: training loss = -0.882 / validation loss = -0.876
+
+Epoch  64: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.45s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.31it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.96it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.61it/s]
 31%|███       | 9/29 [00:07<00:18,  1.06it/s]
 34%|███▍      | 10/29 [00:07<00:13,  1.45it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.96it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.58it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.27it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.71it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.27it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.93it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.30it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.75it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.22it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.87it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.32it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.03it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.65it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.28it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.56it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.07it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.70it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 64: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.46it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.60it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.45it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.36it/s]
 40%|████      | 23/57 [00:01<00:01, 26.70it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.40it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.93it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.17it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.58it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.50it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.80it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.32it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 30.04it/s]
100%|██████████| 57/57 [00:02<00:00, 23.97it/s]
+    Epoch 64: training loss = -0.871 / validation loss = -0.868
+
+Epoch  65: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:05<01:08,  2.54s/it]
 10%|█         | 3/29 [00:05<00:37,  1.43s/it]
 14%|█▍        | 4/29 [00:05<00:22,  1.10it/s]
 17%|█▋        | 5/29 [00:05<00:14,  1.61it/s]
 21%|██        | 6/29 [00:05<00:10,  2.24it/s]
 24%|██▍       | 7/29 [00:05<00:07,  2.99it/s]
 28%|██▊       | 8/29 [00:05<00:05,  3.81it/s]
 31%|███       | 9/29 [00:07<00:12,  1.57it/s]
 34%|███▍      | 10/29 [00:07<00:09,  1.95it/s]
 38%|███▊      | 11/29 [00:07<00:07,  2.57it/s]
 41%|████▏     | 12/29 [00:07<00:05,  3.29it/s]
 45%|████▍     | 13/29 [00:09<00:11,  1.34it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.63it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.17it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.82it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.29it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.67it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.20it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.58it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.60it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.13it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.75it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.30it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.10it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.70it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 65: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.21it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.21it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.69it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.75it/s]
 40%|████      | 23/57 [00:01<00:01, 27.66it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.81it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.35it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.44it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.74it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.74it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.04it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.11it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 30.95it/s]
100%|██████████| 57/57 [00:02<00:00, 24.32it/s]
+    Epoch 65: training loss = -0.875 / validation loss = -0.873
+
+Epoch  66: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.45s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.31it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.11it/s]
 21%|██        | 6/29 [00:04<00:16,  1.40it/s]
 24%|██▍       | 7/29 [00:04<00:11,  1.94it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.55it/s]
 31%|███       | 9/29 [00:06<00:15,  1.30it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.55it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.68it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.51it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.44it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.59it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  2.17it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.59it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.55it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.00it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.11it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.55it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.66it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.92it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.06it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 66: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.86it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.05it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.78it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.19it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.75it/s]
 40%|████      | 23/57 [00:01<00:01, 26.99it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.34it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.76it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.08it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.66it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.28it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.73it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.77it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.94it/s]
100%|██████████| 57/57 [00:02<00:00, 23.65it/s]
+    Epoch 66: training loss = -0.876 / validation loss = -0.878
+    Saved model: unet-66.pth.tar
+
+Epoch  67: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.60s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:05<00:11,  2.00it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:06<00:16,  1.23it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.71it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.22it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.47it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.97it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.58it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.44it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.45it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.93it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.54it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.47it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.48it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.98it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.59it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.49it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.10it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.74it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 67: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.98it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.81it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.65it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.61it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.43it/s]
 40%|████      | 23/57 [00:01<00:01, 27.31it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.75it/s]
 51%|█████     | 29/57 [00:01<00:01, 25.18it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.90it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.02it/s]
 70%|███████   | 40/57 [00:01<00:00, 26.93it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 25.79it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.37it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.10it/s]
100%|██████████| 57/57 [00:02<00:00, 26.80it/s]
100%|██████████| 57/57 [00:02<00:00, 23.44it/s]
+    Epoch 67: training loss = -0.881 / validation loss = -0.872
+
+Epoch  68: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.53s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:06<00:16,  1.22it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.16it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.81it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.13it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.77it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.57it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.09it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.72it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.32it/s]
 76%|███████▌  | 22/29 [00:14<00:05,  1.40it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.87it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.46it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.48it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.10it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.74it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 68: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.12it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.76it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.37it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.47it/s]
 40%|████      | 23/57 [00:01<00:01, 26.50it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.95it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.76it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.01it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.06it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.87it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.37it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.47it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.76it/s]
100%|██████████| 57/57 [00:02<00:00, 23.85it/s]
+    Epoch 68: training loss = -0.883 / validation loss = -0.854
+
+Epoch  69: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.55s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.27it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.03s/it]
 21%|██        | 6/29 [00:05<00:16,  1.40it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.94it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.59it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.55it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.08it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.72it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.74it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.14it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.54it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.05it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.68it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.09it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.72it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.30it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.75it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.31it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.98it/s]
100%|██████████| 29/29 [00:16<00:00,  3.46it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 69: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.25it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.09it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.26it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.80it/s]
 40%|████      | 23/57 [00:01<00:01, 27.18it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.25it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.99it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.13it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.58it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.20it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.35it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.75it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.34it/s]
100%|██████████| 57/57 [00:03<00:00, 15.53it/s]
+    Epoch 69: training loss = -0.874 / validation loss = -0.879
+    Saved model: unet-69.pth.tar
+
+Epoch  70: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.59s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.25it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.03s/it]
 21%|██        | 6/29 [00:05<00:16,  1.39it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.93it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:06<00:15,  1.30it/s]
 34%|███▍      | 10/29 [00:07<00:10,  1.77it/s]
 38%|███▊      | 11/29 [00:07<00:07,  2.35it/s]
 41%|████▏     | 12/29 [00:07<00:05,  3.01it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.20it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.62it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.16it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.80it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.19it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.14it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.78it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.18it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.59it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.12it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.76it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.25it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.22it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.88it/s]
100%|██████████| 29/29 [00:16<00:00,  3.08it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 70: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.18it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.58it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.48it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.51it/s]
 40%|████      | 23/57 [00:01<00:01, 26.92it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.48it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.03it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.31it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.17it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.60it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.65it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.78it/s]
 95%|█████████▍| 54/57 [00:03<00:00,  7.01it/s]
100%|██████████| 57/57 [00:03<00:00, 15.78it/s]
+    Epoch 70: training loss = -0.881 / validation loss = -0.878
+
+Epoch  71: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:17,  2.76s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.20s/it]
 10%|█         | 3/29 [00:02<00:18,  1.42it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.13it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.01s/it]
 21%|██        | 6/29 [00:05<00:16,  1.42it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.97it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.63it/s]
 31%|███       | 9/29 [00:07<00:16,  1.21it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.65it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.20it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.87it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.23it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.67it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.22it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.88it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.65it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.18it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.83it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.19it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.61it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.15it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.80it/s]
100%|██████████| 29/29 [00:16<00:00,  3.32it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 71: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.48it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.59it/s]
 23%|██▎       | 13/57 [00:00<00:01, 22.13it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.64it/s]
 40%|████      | 23/57 [00:01<00:01, 26.40it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.60it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.76it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.79it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.19it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.41it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.08it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.12it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.08it/s]
100%|██████████| 57/57 [00:02<00:00, 23.92it/s]
+    Epoch 71: training loss = -0.878 / validation loss = -0.879
+
+Epoch  72: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.51it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.25it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.06it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.74it/s]
 31%|███       | 9/29 [00:07<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.78it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.26it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.70it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.21it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.67it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.91it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.51it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.25it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.69it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.84it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.42it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.33it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.78it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.98it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.59it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 72: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.19it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.20it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.27it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.05it/s]
 40%|████      | 23/57 [00:01<00:01, 27.04it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.72it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.12it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.07it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.59it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.42it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.29it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.98it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.53it/s]
100%|██████████| 57/57 [00:03<00:00, 14.90it/s]
+    Epoch 72: training loss = -0.879 / validation loss = -0.881
+    Saved model: unet-72.pth.tar
+
+Epoch  73: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.51it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.25it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.07it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:07<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.57it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.10it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.75it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.59it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.12it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.76it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.23it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.18it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.82it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.80it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.25it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.49it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.99it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.60it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 73: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.63it/s]
 16%|█▌        | 9/57 [00:00<00:02, 20.01it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.91it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.47it/s]
 40%|████      | 23/57 [00:01<00:01, 26.03it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.65it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.55it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.02it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.44it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.43it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.43it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.30it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 30.04it/s]
100%|██████████| 57/57 [00:04<00:00, 13.44it/s]
+    Epoch 73: training loss = -0.885 / validation loss = -0.882
+    Saved model: unet-73.pth.tar
+
+Epoch  74: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.49s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.56it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.32it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:04<00:15,  1.51it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.08it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.56it/s]
 31%|███       | 9/29 [00:06<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.60it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.14it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.59it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.71it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.17it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.59it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.11it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.75it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.15it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.55it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.07it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.70it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.20it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.16it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.81it/s]
100%|██████████| 29/29 [00:16<00:00,  3.25it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 74: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.00it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.09it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.98it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.91it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.32it/s]
 40%|████      | 23/57 [00:01<00:01, 26.86it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.99it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.28it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.66it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.39it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 28.16it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 27.03it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.33it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 30.05it/s]
100%|██████████| 57/57 [00:02<00:00, 23.75it/s]
+    Epoch 74: training loss = -0.884 / validation loss = -0.877
+
+Epoch  75: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:05,  2.33s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.56it/s]
 14%|█▍        | 4/29 [00:02<00:12,  2.03it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.10it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.45it/s]
 31%|███       | 9/29 [00:06<00:15,  1.32it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.50it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.01it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.45it/s]
 45%|████▍     | 13/29 [00:08<00:11,  1.35it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.13it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.32it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.32it/s]
 62%|██████▏   | 18/29 [00:12<00:10,  1.03it/s]
 66%|██████▌   | 19/29 [00:12<00:07,  1.40it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  1.88it/s]
 72%|███████▏  | 21/29 [00:13<00:04,  1.87it/s]
 76%|███████▌  | 22/29 [00:14<00:03,  1.90it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.50it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.82it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.50it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.61it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 75: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.99it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.80it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.80it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.30it/s]
 40%|████      | 23/57 [00:01<00:01, 27.08it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.82it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.46it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.81it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.34it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.39it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.25it/s]
 84%|████████▍ | 48/57 [00:01<00:00, 27.51it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.28it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 26.71it/s]
100%|██████████| 57/57 [00:02<00:00, 24.53it/s]
100%|██████████| 57/57 [00:02<00:00, 23.31it/s]
+    Epoch 75: training loss = -0.877 / validation loss = -0.876
+
+Epoch  76: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:16,  2.74s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.19s/it]
 10%|█         | 3/29 [00:02<00:18,  1.43it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.15it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:05<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.71it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.13it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.53it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.05it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.68it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.17it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.59it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.11it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.75it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.67it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.22it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.88it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.22it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.88it/s]
100%|██████████| 29/29 [00:16<00:00,  3.26it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 76: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.25it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.29it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.07it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.22it/s]
 40%|████      | 23/57 [00:01<00:01, 26.63it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.43it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.52it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.07it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.08it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.78it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.78it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.48it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.24it/s]
100%|██████████| 57/57 [00:05<00:00, 10.98it/s]
+    Epoch 76: training loss = -0.885 / validation loss = -0.883
+    Saved model: unet-76.pth.tar
+
+Epoch  77: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:02<00:34,  1.27s/it]
 10%|█         | 3/29 [00:03<00:19,  1.35it/s]
 14%|█▍        | 4/29 [00:03<00:12,  2.04it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.53it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.10it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.79it/s]
 31%|███       | 9/29 [00:07<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.16it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.81it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.25it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.69it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.24it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.76it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.58it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.72it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.16it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.08it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.69it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.61it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 77: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:21,  2.59it/s]
  9%|▉         | 5/57 [00:00<00:04, 11.90it/s]
 16%|█▌        | 9/57 [00:00<00:02, 17.73it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.44it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.00it/s]
 40%|████      | 23/57 [00:01<00:01, 26.28it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.20it/s]
 53%|█████▎    | 30/57 [00:01<00:01, 26.44it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.02it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 25.92it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 25.47it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.19it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.39it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.11it/s]
100%|██████████| 57/57 [00:02<00:00, 25.10it/s]
100%|██████████| 57/57 [00:02<00:00, 22.49it/s]
+    Epoch 77: training loss = -0.884 / validation loss = -0.876
+
+Epoch  78: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:21,  2.90s/it]
  7%|▋         | 2/29 [00:03<00:33,  1.26s/it]
 10%|█         | 3/29 [00:03<00:19,  1.36it/s]
 14%|█▍        | 4/29 [00:03<00:12,  2.05it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.03s/it]
 21%|██        | 6/29 [00:05<00:16,  1.40it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.94it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.59it/s]
 31%|███       | 9/29 [00:07<00:16,  1.20it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.64it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.20it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.86it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.73it/s]
 59%|█████▊    | 17/29 [00:12<00:10,  1.13it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.54it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.05it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.68it/s]
 72%|███████▏  | 21/29 [00:14<00:07,  1.14it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.55it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.06it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.69it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.13it/s]
 90%|████████▉ | 26/29 [00:17<00:02,  1.49it/s]
 93%|█████████▎| 27/29 [00:17<00:01,  1.99it/s]
 97%|█████████▋| 28/29 [00:17<00:00,  2.60it/s]
100%|██████████| 29/29 [00:17<00:00,  3.28it/s]
100%|██████████| 29/29 [00:17<00:00,  1.66it/s]
+
+Epoch 78: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.68it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.80it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.40it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.93it/s]
 40%|████      | 23/57 [00:01<00:01, 26.58it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.40it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.13it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.27it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.74it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.27it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.34it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.89it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.57it/s]
100%|██████████| 57/57 [00:02<00:00, 23.83it/s]
+    Epoch 78: training loss = -0.885 / validation loss = -0.856
+
+Epoch  79: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.56it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.32it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.49it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.06it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.74it/s]
 31%|███       | 9/29 [00:07<00:17,  1.11it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.53it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.69it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.73it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.14it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.78it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.18it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.60it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.13it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.77it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.16it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.81it/s]
100%|██████████| 29/29 [00:16<00:00,  3.41it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 79: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.81it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.99it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.31it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.08it/s]
 40%|████      | 23/57 [00:01<00:01, 26.68it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.40it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.13it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.36it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.66it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.52it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.72it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.10it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 27.18it/s]
100%|██████████| 57/57 [00:02<00:00, 24.65it/s]
100%|██████████| 57/57 [00:02<00:00, 23.42it/s]
+    Epoch 79: training loss = -0.886 / validation loss = -0.882
+
+Epoch  80: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.41s/it]
  7%|▋         | 2/29 [00:02<00:28,  1.06s/it]
 10%|█         | 3/29 [00:02<00:16,  1.59it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.31it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:06<00:25,  1.09s/it]
 24%|██▍       | 7/29 [00:06<00:16,  1.30it/s]
 28%|██▊       | 8/29 [00:06<00:11,  1.79it/s]
 31%|███       | 9/29 [00:06<00:11,  1.70it/s]
 34%|███▍      | 10/29 [00:07<00:08,  2.23it/s]
 38%|███▊      | 11/29 [00:07<00:06,  2.86it/s]
 41%|████▏     | 12/29 [00:07<00:04,  3.58it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.30it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.71it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.04it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.67it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.26it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.65it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.17it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.34it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.29it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.73it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.29it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.29it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.31it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.67it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.21it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.18it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 80: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.96it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.20it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.03it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.63it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.25it/s]
 40%|████      | 23/57 [00:01<00:01, 26.92it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.74it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.16it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.57it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.99it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.55it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.69it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.02it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.91it/s]
100%|██████████| 57/57 [00:02<00:00, 23.03it/s]
+    Epoch 80: training loss = -0.884 / validation loss = -0.866
+
+Epoch  81: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.59s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:05<00:16,  1.36it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.89it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.52it/s]
 31%|███       | 9/29 [00:06<00:15,  1.29it/s]
 34%|███▍      | 10/29 [00:07<00:13,  1.41it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.89it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.49it/s]
 45%|████▍     | 13/29 [00:09<00:11,  1.36it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.48it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.98it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.59it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.34it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.51it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.01it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.63it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.29it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.60it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.13it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.76it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.27it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.71it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.26it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.92it/s]
100%|██████████| 29/29 [00:16<00:00,  3.68it/s]
100%|██████████| 29/29 [00:18<00:00,  1.57it/s]
+
+Epoch 81: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.59it/s]
 16%|█▌        | 9/57 [00:00<00:02, 20.09it/s]
 23%|██▎       | 13/57 [00:00<00:02, 22.00it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.15it/s]
 40%|████      | 23/57 [00:01<00:01, 26.57it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.56it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.34it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.57it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.57it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.49it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.52it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.03it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.00it/s]
100%|██████████| 57/57 [00:02<00:00, 24.18it/s]
+    Epoch 81: training loss = -0.886 / validation loss = -0.884
+    Saved model: unet-81.pth.tar
+
+Epoch  82: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.60s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:20,  1.15it/s]
 21%|██        | 6/29 [00:04<00:14,  1.59it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.90it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.54it/s]
 31%|███       | 9/29 [00:06<00:15,  1.28it/s]
 34%|███▍      | 10/29 [00:06<00:10,  1.74it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.92it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.53it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.25it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.69it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.99it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.61it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.69it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.06it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.66it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.67it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.89it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.48it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.70it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.04it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.66it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 82: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.41it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.64it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.71it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.36it/s]
 40%|████      | 23/57 [00:01<00:01, 27.01it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.60it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.06it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.29it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.73it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.20it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.29it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.41it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.27it/s]
100%|██████████| 57/57 [00:02<00:00, 22.84it/s]
+    Epoch 82: training loss = -0.889 / validation loss = -0.881
+
+Epoch  83: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.65s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:04<00:22,  1.09it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.12it/s]
 21%|██        | 6/29 [00:05<00:14,  1.60it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.19it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.90it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.72it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.20it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.62it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.15it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.80it/s]
 59%|█████▊    | 17/29 [00:12<00:10,  1.15it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.55it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.07it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.70it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.67it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.22it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.88it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.33it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.79it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.37it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  3.05it/s]
100%|██████████| 29/29 [00:16<00:00,  3.41it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 83: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.61it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.40it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.49it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.75it/s]
 40%|████      | 23/57 [00:01<00:01, 26.76it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.78it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.41it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.74it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.13it/s]
 70%|███████   | 40/57 [00:01<00:00, 27.42it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.14it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.53it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.86it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 27.32it/s]
100%|██████████| 57/57 [00:02<00:00, 24.73it/s]
100%|██████████| 57/57 [00:02<00:00, 23.44it/s]
+    Epoch 83: training loss = -0.887 / validation loss = -0.884
+    Saved model: unet-83.pth.tar
+
+Epoch  84: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:16,  2.72s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.19s/it]
 10%|█         | 3/29 [00:02<00:18,  1.44it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.16it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:05<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.78it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.13it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.77it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.18it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.60it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.12it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.76it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.25it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.68it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.22it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.88it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.22it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 84: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.96it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.97it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.77it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.66it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.07it/s]
 40%|████      | 23/57 [00:01<00:01, 27.51it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.83it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.78it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.14it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.56it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.11it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.92it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.61it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.51it/s]
100%|██████████| 57/57 [00:02<00:00, 23.61it/s]
+    Epoch 84: training loss = -0.888 / validation loss = -0.886
+    Saved model: unet-84.pth.tar
+
+Epoch  85: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:16,  2.73s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.19s/it]
 10%|█         | 3/29 [00:02<00:18,  1.44it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.16it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:05<00:15,  1.49it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.73it/s]
 31%|███       | 9/29 [00:07<00:16,  1.21it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.65it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.20it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.84it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.72it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.31it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.75it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.25it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.91it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.13it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.77it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.31it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.14it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.79it/s]
100%|██████████| 29/29 [00:16<00:00,  1.76it/s]
+
+Epoch 85: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.03it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.47it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.67it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.63it/s]
 40%|████      | 23/57 [00:01<00:01, 26.81it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.83it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.92it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.11it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.47it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.09it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.94it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.33it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.91it/s]
100%|██████████| 57/57 [00:02<00:00, 23.69it/s]
+    Epoch 85: training loss = -0.888 / validation loss = -0.883
+
+Epoch  86: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.51s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.11s/it]
 10%|█         | 3/29 [00:02<00:17,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:04<00:14,  1.54it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:06<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.09it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.48it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.89it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.48it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.30it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.76it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.87it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.46it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.30it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.68it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.95it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.56it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.66it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.06it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.69it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 86: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.31it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.52it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.40it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.55it/s]
 40%|████      | 23/57 [00:01<00:01, 28.15it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.47it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.07it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.02it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.23it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 25.69it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 26.86it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.08it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 24.99it/s]
100%|██████████| 57/57 [00:02<00:00, 25.41it/s]
100%|██████████| 57/57 [00:02<00:00, 23.41it/s]
+    Epoch 86: training loss = -0.887 / validation loss = -0.887
+    Saved model: unet-86.pth.tar
+
+Epoch  87: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:06<00:16,  1.24it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.63it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.17it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.80it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.28it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.65it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.86it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.45it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.43it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.55it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.91it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.51it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.38it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.67it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.95it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.52it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.31it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.75it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.86it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.42it/s]
100%|██████████| 29/29 [00:17<00:00,  1.66it/s]
+
+Epoch 87: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:17,  3.12it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.60it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.19it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.04it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.68it/s]
 40%|████      | 23/57 [00:01<00:01, 26.53it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.29it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.97it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.42it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.88it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.56it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 27.06it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.71it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 27.63it/s]
100%|██████████| 57/57 [00:02<00:00, 25.28it/s]
100%|██████████| 57/57 [00:02<00:00, 23.61it/s]
+    Epoch 87: training loss = -0.887 / validation loss = -0.837
+
+Epoch  88: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.66s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.70it/s]
 31%|███       | 9/29 [00:07<00:18,  1.09it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.50it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.01it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.64it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.73it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.17it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.82it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.27it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.71it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.27it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.76it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.45it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 88: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.42it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.39it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.32it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.72it/s]
 40%|████      | 23/57 [00:01<00:01, 26.68it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.42it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.90it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.31it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.69it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.06it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.67it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.19it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.76it/s]
100%|██████████| 57/57 [00:02<00:00, 23.55it/s]
+    Epoch 88: training loss = -0.881 / validation loss = -0.884
+
+Epoch  89: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.18s/it]
 10%|█         | 3/29 [00:02<00:18,  1.44it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.17it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.11it/s]
 21%|██        | 6/29 [00:05<00:17,  1.29it/s]
 24%|██▍       | 7/29 [00:05<00:12,  1.80it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.42it/s]
 31%|███       | 9/29 [00:06<00:15,  1.28it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.57it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:10<00:20,  1.30s/it]
 48%|████▊     | 14/29 [00:11<00:15,  1.01s/it]
 52%|█████▏    | 15/29 [00:11<00:10,  1.35it/s]
 55%|█████▌    | 16/29 [00:11<00:07,  1.82it/s]
 59%|█████▊    | 17/29 [00:13<00:10,  1.09it/s]
 62%|██████▏   | 18/29 [00:13<00:07,  1.48it/s]
 66%|██████▌   | 19/29 [00:13<00:05,  1.98it/s]
 69%|██████▉   | 20/29 [00:13<00:03,  2.60it/s]
 72%|███████▏  | 21/29 [00:15<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:15<00:04,  1.47it/s]
 79%|███████▉  | 23/29 [00:15<00:03,  1.97it/s]
 83%|████████▎ | 24/29 [00:16<00:01,  2.58it/s]
 86%|████████▌ | 25/29 [00:17<00:03,  1.30it/s]
 90%|████████▉ | 26/29 [00:17<00:01,  1.65it/s]
 93%|█████████▎| 27/29 [00:18<00:00,  2.19it/s]
 97%|█████████▋| 28/29 [00:18<00:00,  2.84it/s]
100%|██████████| 29/29 [00:18<00:00,  1.59it/s]
+
+Epoch 89: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.96it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.12it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.88it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.11it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.77it/s]
 40%|████      | 23/57 [00:01<00:01, 27.15it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.84it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.87it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.27it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.83it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.06it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.39it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.33it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.96it/s]
100%|██████████| 57/57 [00:02<00:00, 23.68it/s]
+    Epoch 89: training loss = -0.887 / validation loss = -0.874
+
+Epoch  90: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.66s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.02s/it]
 21%|██        | 6/29 [00:05<00:16,  1.41it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.95it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.60it/s]
 31%|███       | 9/29 [00:07<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.68it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.23it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.89it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.15it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.56it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.08it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.71it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.22it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.64it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.18it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.83it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.22it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.65it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.84it/s]
100%|██████████| 29/29 [00:16<00:00,  3.36it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 90: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.00it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.10it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.16it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.80it/s]
 33%|███▎      | 19/57 [00:00<00:01, 27.35it/s]
 40%|████      | 23/57 [00:01<00:01, 28.26it/s]
 47%|████▋     | 27/57 [00:01<00:01, 28.02it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.03it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.42it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.08it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.36it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 28.31it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 27.02it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.36it/s]
100%|██████████| 57/57 [00:02<00:00, 25.07it/s]
100%|██████████| 57/57 [00:02<00:00, 23.69it/s]
+    Epoch 90: training loss = -0.881 / validation loss = -0.885
+
+Epoch  91: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.60s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:07<00:47,  1.96s/it]
 21%|██        | 6/29 [00:07<00:30,  1.33s/it]
 24%|██▍       | 7/29 [00:07<00:20,  1.07it/s]
 28%|██▊       | 8/29 [00:07<00:14,  1.49it/s]
 31%|███       | 9/29 [00:09<00:21,  1.06s/it]
 34%|███▍      | 10/29 [00:09<00:14,  1.29it/s]
 38%|███▊      | 11/29 [00:10<00:10,  1.75it/s]
 41%|████▏     | 12/29 [00:10<00:07,  2.33it/s]
 45%|████▍     | 13/29 [00:12<00:14,  1.08it/s]
 48%|████▊     | 14/29 [00:12<00:10,  1.47it/s]
 52%|█████▏    | 15/29 [00:12<00:07,  1.97it/s]
 55%|█████▌    | 16/29 [00:12<00:05,  2.58it/s]
 59%|█████▊    | 17/29 [00:14<00:10,  1.14it/s]
 62%|██████▏   | 18/29 [00:14<00:07,  1.54it/s]
 66%|██████▌   | 19/29 [00:14<00:04,  2.05it/s]
 69%|██████▉   | 20/29 [00:14<00:03,  2.68it/s]
 72%|███████▏  | 21/29 [00:16<00:06,  1.18it/s]
 76%|███████▌  | 22/29 [00:17<00:04,  1.59it/s]
 79%|███████▉  | 23/29 [00:17<00:02,  2.12it/s]
 83%|████████▎ | 24/29 [00:17<00:01,  2.76it/s]
 86%|████████▌ | 25/29 [00:19<00:03,  1.22it/s]
 90%|████████▉ | 26/29 [00:19<00:01,  1.65it/s]
 93%|█████████▎| 27/29 [00:19<00:00,  2.19it/s]
 97%|█████████▋| 28/29 [00:19<00:00,  2.85it/s]
100%|██████████| 29/29 [00:19<00:00,  3.60it/s]
100%|██████████| 29/29 [00:19<00:00,  1.48it/s]
+
+Epoch 91: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.33it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.62it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.44it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.05it/s]
 40%|████      | 23/57 [00:01<00:01, 26.69it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.52it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.51it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.51it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.04it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.86it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.54it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.68it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.30it/s]
100%|██████████| 57/57 [00:02<00:00, 23.63it/s]
+    Epoch 91: training loss = -0.887 / validation loss = -0.877
+
+Epoch  92: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.50s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.30it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.01it/s]
 21%|██        | 6/29 [00:05<00:19,  1.16it/s]
 24%|██▍       | 7/29 [00:05<00:13,  1.63it/s]
 28%|██▊       | 8/29 [00:05<00:09,  2.20it/s]
 31%|███       | 9/29 [00:07<00:15,  1.33it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.71it/s]
 38%|███▊      | 11/29 [00:07<00:07,  2.27it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.95it/s]
 45%|████▍     | 13/29 [00:09<00:11,  1.36it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.50it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.00it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.63it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.40it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.49it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.99it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.60it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.41it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.47it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.96it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.57it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.49it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.44it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.93it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.53it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 92: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.24it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.36it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.57it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.16it/s]
 40%|████      | 23/57 [00:01<00:01, 26.70it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.94it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.61it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.06it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.48it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.05it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.48it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.97it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.82it/s]
100%|██████████| 57/57 [00:02<00:00, 23.94it/s]
+    Epoch 92: training loss = -0.886 / validation loss = -0.883
+
+Epoch  93: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.51it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.25it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.52it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.10it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.78it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.55it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.08it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.73it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.20it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.28it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.50it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.99it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.60it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.33it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.65it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.19it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.64it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.53it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 93: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.64it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.86it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.77it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.17it/s]
 40%|████      | 23/57 [00:01<00:01, 26.94it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.65it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.07it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.19it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.72it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.95it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.09it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.73it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.37it/s]
100%|██████████| 57/57 [00:02<00:00, 23.88it/s]
+    Epoch 93: training loss = -0.891 / validation loss = -0.888
+    Saved model: unet-93.pth.tar
+
+Epoch  94: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.54s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:05<00:25,  1.06s/it]
 21%|██        | 6/29 [00:05<00:16,  1.36it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.89it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.53it/s]
 31%|███       | 9/29 [00:07<00:18,  1.08it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.49it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.00it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.63it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.56it/s]
 52%|█████▏    | 15/29 [00:10<00:06,  2.08it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.71it/s]
 59%|█████▊    | 17/29 [00:12<00:10,  1.13it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.54it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.05it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.68it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.19it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.61it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.14it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.12it/s]
 90%|████████▉ | 26/29 [00:17<00:01,  1.52it/s]
 93%|█████████▎| 27/29 [00:17<00:00,  2.03it/s]
 97%|█████████▋| 28/29 [00:17<00:00,  2.65it/s]
100%|██████████| 29/29 [00:17<00:00,  3.31it/s]
100%|██████████| 29/29 [00:17<00:00,  1.66it/s]
+
+Epoch 94: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.00it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.13it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.35it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.63it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.41it/s]
 40%|████      | 23/57 [00:01<00:01, 26.77it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.64it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.57it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.25it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.81it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.69it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.91it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.30it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.31it/s]
100%|██████████| 57/57 [00:02<00:00, 23.80it/s]
+    Epoch 94: training loss = -0.891 / validation loss = -0.861
+
+Epoch  95: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.41s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.30it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:04<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:07<00:17,  1.12it/s]
 34%|███▍      | 10/29 [00:10<00:31,  1.68s/it]
 38%|███▊      | 11/29 [00:10<00:21,  1.20s/it]
 41%|████▏     | 12/29 [00:10<00:14,  1.15it/s]
 45%|████▍     | 13/29 [00:12<00:19,  1.19s/it]
 48%|████▊     | 14/29 [00:12<00:12,  1.16it/s]
 52%|█████▏    | 15/29 [00:12<00:08,  1.57it/s]
 55%|█████▌    | 16/29 [00:13<00:06,  2.09it/s]
 59%|█████▊    | 17/29 [00:14<00:10,  1.10it/s]
 62%|██████▏   | 18/29 [00:15<00:07,  1.49it/s]
 66%|██████▌   | 19/29 [00:15<00:05,  1.99it/s]
 69%|██████▉   | 20/29 [00:15<00:03,  2.60it/s]
 72%|███████▏  | 21/29 [00:17<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:17<00:04,  1.53it/s]
 79%|███████▉  | 23/29 [00:17<00:02,  2.05it/s]
 83%|████████▎ | 24/29 [00:17<00:01,  2.67it/s]
 86%|████████▌ | 25/29 [00:19<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:19<00:01,  1.56it/s]
 93%|█████████▎| 27/29 [00:19<00:00,  2.07it/s]
 97%|█████████▋| 28/29 [00:19<00:00,  2.70it/s]
100%|██████████| 29/29 [00:20<00:00,  3.46it/s]
100%|██████████| 29/29 [00:20<00:00,  1.44it/s]
+
+Epoch 95: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.95it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.91it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.98it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.19it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.71it/s]
 40%|████      | 23/57 [00:01<00:01, 27.50it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.17it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.01it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.57it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.77it/s]
 70%|███████   | 40/57 [00:01<00:00, 26.63it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.24it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.71it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.59it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.46it/s]
100%|██████████| 57/57 [00:02<00:00, 23.40it/s]
+    Epoch 95: training loss = -0.884 / validation loss = -0.863
+
+Epoch  96: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.43s/it]
  7%|▋         | 2/29 [00:02<00:33,  1.22s/it]
 10%|█         | 3/29 [00:02<00:18,  1.39it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.09it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:05<00:16,  1.37it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.90it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.55it/s]
 31%|███       | 9/29 [00:07<00:16,  1.21it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.32it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.50it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.01it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.63it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.28it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.59it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.12it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.76it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.33it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.61it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.14it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.63it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.35it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.55it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.07it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.67it/s]
100%|██████████| 29/29 [00:19<00:00,  1.48it/s]
+
+Epoch 96: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.02it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.86it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.80it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.42it/s]
 40%|████      | 23/57 [00:01<00:01, 27.61it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.40it/s]
 51%|█████     | 29/57 [00:01<00:01, 25.23it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.88it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.38it/s]
 70%|███████   | 40/57 [00:01<00:00, 27.08it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.12it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.87it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.47it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.16it/s]
100%|██████████| 57/57 [00:02<00:00, 23.43it/s]
+    Epoch 96: training loss = -0.887 / validation loss = -0.887
+
+Epoch  97: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.65s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.03s/it]
 21%|██        | 6/29 [00:05<00:16,  1.40it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.93it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.58it/s]
 31%|███       | 9/29 [00:07<00:17,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.60it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.15it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.78it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.13it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.69it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.23it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.88it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.40it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.75it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.95it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.56it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.43it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.70it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.88it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.47it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 97: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.17it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.47it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.23it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.04it/s]
 40%|████      | 23/57 [00:01<00:01, 25.55it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.31it/s]
 54%|█████▍    | 31/57 [00:01<00:01, 25.80it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.53it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.69it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.17it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 26.18it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.71it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.52it/s]
100%|██████████| 57/57 [00:02<00:00, 23.08it/s]
+    Epoch 97: training loss = -0.894 / validation loss = -0.890
+    Saved model: unet-97.pth.tar
+
+Epoch  98: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.69s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.18s/it]
 10%|█         | 3/29 [00:02<00:17,  1.45it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.17it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:10<00:11,  1.33it/s]
 52%|█████▏    | 15/29 [00:10<00:07,  1.79it/s]
 55%|█████▌    | 16/29 [00:10<00:05,  2.37it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.34it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.80it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.37it/s]
 69%|██████▉   | 20/29 [00:12<00:02,  3.05it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.27it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.70it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.25it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.92it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.16it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.81it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 98: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.92it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.83it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.03it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.13it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.93it/s]
 40%|████      | 23/57 [00:01<00:01, 26.02it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.89it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.41it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.57it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.38it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.06it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 26.34it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.57it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.52it/s]
100%|██████████| 57/57 [00:02<00:00, 23.92it/s]
+    Epoch 98: training loss = -0.893 / validation loss = -0.883
+
+Epoch  99: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.48s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.56it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.33it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.52it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.70it/s]
 31%|███       | 9/29 [00:07<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.08it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.70it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.20it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.12it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.77it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.27it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.53it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.04it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.67it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.81it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.67it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.22it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:16<00:00,  3.10it/s]
100%|██████████| 29/29 [00:18<00:00,  1.53it/s]
+
+Epoch 99: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.95it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.35it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.29it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.35it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.11it/s]
 40%|████      | 23/57 [00:01<00:01, 25.59it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.49it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.16it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.21it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.86it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.19it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.76it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.18it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.96it/s]
100%|██████████| 57/57 [00:02<00:00, 23.92it/s]
+    Epoch 99: training loss = -0.886 / validation loss = -0.889
+
+Epoch 100: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.68s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:05<00:25,  1.05s/it]
 21%|██        | 6/29 [00:05<00:16,  1.37it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.90it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.55it/s]
 31%|███       | 9/29 [00:07<00:16,  1.20it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.65it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.18it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.84it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.29it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.75it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.29it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.97it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.29it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.73it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.25it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.88it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.27it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.69it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.23it/s]
 83%|████████▎ | 24/29 [00:13<00:01,  2.86it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.19it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.60it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.12it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.76it/s]
100%|██████████| 29/29 [00:16<00:00,  3.04it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 100: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.97it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.10it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.23it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.23it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.40it/s]
 40%|████      | 23/57 [00:01<00:01, 26.64it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.95it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.56it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.99it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.18it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.31it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.80it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.22it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 30.94it/s]
100%|██████████| 57/57 [00:02<00:00, 23.94it/s]
+    Epoch 100: training loss = -0.889 / validation loss = -0.888
+
+Epoch 101: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:18,  2.79s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.22s/it]
 10%|█         | 3/29 [00:03<00:18,  1.41it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.12it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:04<00:14,  1.54it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.11it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.80it/s]
 31%|███       | 9/29 [00:06<00:16,  1.24it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.70it/s]
 38%|███▊      | 11/29 [00:07<00:07,  2.27it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.91it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.23it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.66it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.20it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.83it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.15it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.56it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.07it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.71it/s]
 72%|███████▏  | 21/29 [00:14<00:07,  1.14it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.54it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.05it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.68it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.19it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.60it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.13it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.77it/s]
100%|██████████| 29/29 [00:16<00:00,  3.14it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 101: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.68it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.78it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.58it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.98it/s]
 40%|████      | 23/57 [00:01<00:01, 26.25it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.10it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.30it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.22it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.79it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.93it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.27it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.11it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.83it/s]
100%|██████████| 57/57 [00:02<00:00, 23.55it/s]
+    Epoch 101: training loss = -0.895 / validation loss = -0.888
+
+Epoch 102: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.31it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:04<00:15,  1.49it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.88it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.52it/s]
 31%|███       | 9/29 [00:07<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.73it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.55it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.06it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.69it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.28it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.71it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.27it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.93it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:16<00:00,  3.54it/s]
100%|██████████| 29/29 [00:17<00:00,  1.63it/s]
+
+Epoch 102: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.35it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.75it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.41it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.89it/s]
 40%|████      | 23/57 [00:01<00:01, 26.50it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.79it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.38it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.34it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.30it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.83it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.63it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.00it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 30.87it/s]
100%|██████████| 57/57 [00:02<00:00, 23.99it/s]
+    Epoch 102: training loss = -0.895 / validation loss = -0.885
+
+Epoch 103: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.61s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:05<00:16,  1.42it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.97it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.62it/s]
 31%|███       | 9/29 [00:07<00:16,  1.20it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.63it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.17it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.83it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.55it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.07it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.70it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.10it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.49it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.99it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.61it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.16it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.09it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.60it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.82it/s]
100%|██████████| 29/29 [00:17<00:00,  3.11it/s]
100%|██████████| 29/29 [00:17<00:00,  1.69it/s]
+
+Epoch 103: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.04it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.61it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.04it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.05it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.09it/s]
 40%|████      | 23/57 [00:01<00:01, 26.77it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.52it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.10it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.31it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.36it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.10it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.30it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.75it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.51it/s]
100%|██████████| 57/57 [00:02<00:00, 23.76it/s]
+    Epoch 103: training loss = -0.895 / validation loss = -0.881
+
+Epoch 104: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.44s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.30it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:06<00:16,  1.21it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.77it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.23it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.67it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.21it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.87it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.59it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.12it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.73it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.40it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.65it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.19it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.24it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 104: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:17,  3.14it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.48it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.57it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.18it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.10it/s]
 40%|████      | 23/57 [00:01<00:01, 26.59it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.48it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.16it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.27it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.18it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.09it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.80it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.89it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.61it/s]
100%|██████████| 57/57 [00:02<00:00, 23.18it/s]
+    Epoch 104: training loss = -0.894 / validation loss = -0.892
+    Saved model: unet-104.pth.tar
+
+Epoch 105: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.25it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.55it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.08it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.72it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.59it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.12it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.75it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.65it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.19it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.84it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.77it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.19it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.60it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.13it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.48it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 105: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.04it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.52it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.18it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.93it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.65it/s]
 40%|████      | 23/57 [00:01<00:01, 26.95it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.45it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.63it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.83it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.30it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.82it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.00it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.00it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.84it/s]
100%|██████████| 57/57 [00:02<00:00, 23.49it/s]
+    Epoch 105: training loss = -0.894 / validation loss = -0.884
+
+Epoch 106: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.46s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.31it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.00it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.65it/s]
 31%|███       | 9/29 [00:06<00:16,  1.22it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.06it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.68it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.26it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.63it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.17it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.53it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.65it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.20it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.42it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.30it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.75it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.31it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.12it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.34it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.71it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.26it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  1.97it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 106: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.00it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.79it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.94it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.72it/s]
 33%|███▎      | 19/57 [00:00<00:01, 27.19it/s]
 40%|████      | 23/57 [00:01<00:01, 28.63it/s]
 47%|████▋     | 27/57 [00:01<00:01, 28.34it/s]
 53%|█████▎    | 30/57 [00:01<00:01, 26.23it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.04it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.55it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.33it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.76it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.30it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.47it/s]
100%|██████████| 57/57 [00:02<00:00, 25.05it/s]
100%|██████████| 57/57 [00:02<00:00, 23.44it/s]
+    Epoch 106: training loss = -0.895 / validation loss = -0.884
+
+Epoch 107: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:16,  2.72s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.18s/it]
 10%|█         | 3/29 [00:02<00:18,  1.44it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.16it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.01it/s]
 21%|██        | 6/29 [00:05<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:05<00:11,  2.00it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:07<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.10it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.74it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.29it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.74it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.30it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.97it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.17it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.82it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.21it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.81it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.69it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.24it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.72it/s]
100%|██████████| 29/29 [00:16<00:00,  3.37it/s]
100%|██████████| 29/29 [00:17<00:00,  1.64it/s]
+
+Epoch 107: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.89it/s]
 16%|█▌        | 9/57 [00:00<00:02, 20.29it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.50it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.94it/s]
 40%|████      | 23/57 [00:01<00:01, 26.18it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.96it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.76it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.16it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.45it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.56it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.97it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.18it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.95it/s]
100%|██████████| 57/57 [00:02<00:00, 23.70it/s]
+    Epoch 107: training loss = -0.897 / validation loss = -0.889
+
+Epoch 108: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.48s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.31it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.09it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.73it/s]
 31%|███       | 9/29 [00:06<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.51it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.02it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.66it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.30it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.11it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.75it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.29it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.50it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.99it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.59it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.30it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.74it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.30it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.48it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.79it/s]
100%|██████████| 29/29 [00:16<00:00,  3.08it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 108: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.96it/s]
  9%|▉         | 5/57 [00:00<00:04, 13.00it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.04it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.72it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.90it/s]
 40%|████      | 23/57 [00:01<00:01, 27.15it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.36it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.47it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.33it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.54it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.86it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.91it/s]
 84%|████████▍ | 48/57 [00:01<00:00, 28.36it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.42it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.73it/s]
100%|██████████| 57/57 [00:04<00:00, 13.86it/s]
+    Epoch 108: training loss = -0.893 / validation loss = -0.892
+    Saved model: unet-108.pth.tar
+
+Epoch 109: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.56s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.26it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.03s/it]
 21%|██        | 6/29 [00:05<00:16,  1.39it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.92it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:07<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.16it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.82it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.71it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.62it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.16it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.80it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.20it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.85it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.20it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.62it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.16it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.78it/s]
100%|██████████| 29/29 [00:16<00:00,  3.29it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 109: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.94it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.15it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.99it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.19it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.33it/s]
 40%|████      | 23/57 [00:01<00:01, 27.10it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.50it/s]
 51%|█████     | 29/57 [00:01<00:01, 25.14it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 27.01it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.96it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.44it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 28.06it/s]
 84%|████████▍ | 48/57 [00:01<00:00, 27.85it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.20it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.48it/s]
100%|██████████| 57/57 [00:02<00:00, 23.65it/s]
+    Epoch 109: training loss = -0.897 / validation loss = -0.881
+
+Epoch 110: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:21,  2.91s/it]
  7%|▋         | 2/29 [00:03<00:34,  1.27s/it]
 10%|█         | 3/29 [00:03<00:19,  1.35it/s]
 14%|█▍        | 4/29 [00:03<00:12,  2.04it/s]
 17%|█▋        | 5/29 [00:05<00:23,  1.04it/s]
 21%|██        | 6/29 [00:05<00:15,  1.49it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.65it/s]
 31%|███       | 9/29 [00:07<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.57it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.10it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.43it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.26it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.70it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.25it/s]
 55%|█████▌    | 16/29 [00:10<00:06,  2.12it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.32it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.77it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.33it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  1.93it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.71it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.26it/s]
 83%|████████▎ | 24/29 [00:15<00:02,  1.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.30it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.74it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.30it/s]
 97%|█████████▋| 28/29 [00:17<00:00,  1.86it/s]
100%|██████████| 29/29 [00:17<00:00,  1.65it/s]
+
+Epoch 110: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.10it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.49it/s]
 16%|█▌        | 9/57 [00:00<00:02, 20.00it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.56it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.97it/s]
 40%|████      | 23/57 [00:01<00:01, 27.26it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.41it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 28.01it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 27.13it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.64it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 28.02it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.94it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.71it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.28it/s]
100%|██████████| 57/57 [00:02<00:00, 24.07it/s]
+    Epoch 110: training loss = -0.894 / validation loss = -0.893
+    Saved model: unet-110.pth.tar
+
+Epoch 111: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.00it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.67it/s]
 31%|███       | 9/29 [00:06<00:15,  1.26it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:08<00:11,  1.43it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.73it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.11it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.35it/s]
 59%|█████▊    | 17/29 [00:10<00:08,  1.48it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.59it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.02it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  2.05it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.50it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.74it/s]
 79%|███████▉  | 23/29 [00:13<00:03,  1.82it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  1.89it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.43it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.80it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.12it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  1.74it/s]
100%|██████████| 29/29 [00:17<00:00,  1.71it/s]
+
+Epoch 111: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.93it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.98it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.06it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.00it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.11it/s]
 40%|████      | 23/57 [00:01<00:01, 26.37it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.83it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.56it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.96it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.24it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.32it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 26.10it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.04it/s]
100%|██████████| 57/57 [00:02<00:00, 29.52it/s]
100%|██████████| 57/57 [00:02<00:00, 23.11it/s]
+    Epoch 111: training loss = -0.896 / validation loss = -0.895
+    Saved model: unet-111.pth.tar
+
+Epoch 112: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.64s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.09it/s]
 21%|██        | 6/29 [00:04<00:14,  1.56it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.94it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.59it/s]
 31%|███       | 9/29 [00:06<00:16,  1.21it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.62it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.99it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.61it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.55it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.07it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.71it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.49it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.99it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.61it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.22it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.54it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.05it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.67it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.32it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.49it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.99it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.61it/s]
100%|██████████| 29/29 [00:17<00:00,  1.70it/s]
+
+Epoch 112: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.62it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.78it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.91it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.31it/s]
 40%|████      | 23/57 [00:01<00:01, 26.40it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.57it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.73it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.13it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.69it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.56it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 27.05it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.03it/s]
100%|██████████| 57/57 [00:02<00:00, 27.45it/s]
100%|██████████| 57/57 [00:02<00:00, 23.86it/s]
+    Epoch 112: training loss = -0.899 / validation loss = -0.896
+    Saved model: unet-112.pth.tar
+
+Epoch 113: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.51s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.11s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:04<00:14,  1.54it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.12it/s]
 28%|██▊       | 8/29 [00:04<00:07,  2.81it/s]
 31%|███       | 9/29 [00:06<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.09it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.71it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.56it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.08it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.72it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.62it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.77it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.29it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.74it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.30it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.52it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.23it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.64it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 113: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.95it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.19it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.71it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.81it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.57it/s]
 40%|████      | 23/57 [00:01<00:01, 26.60it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.39it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.99it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.09it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.20it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.64it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.18it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.68it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.46it/s]
100%|██████████| 57/57 [00:02<00:00, 23.79it/s]
+    Epoch 113: training loss = -0.892 / validation loss = -0.895
+
+Epoch 114: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.53s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.11s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.02it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.67it/s]
 31%|███       | 9/29 [00:07<00:18,  1.08it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.48it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.99it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.61it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.72it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.24it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.68it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.23it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.89it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.81it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.16it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.81it/s]
100%|██████████| 29/29 [00:16<00:00,  3.16it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 114: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.51it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.74it/s]
 21%|██        | 12/57 [00:00<00:02, 22.43it/s]
 26%|██▋       | 15/57 [00:00<00:01, 23.98it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.12it/s]
 40%|████      | 23/57 [00:01<00:01, 26.41it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.22it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.32it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.29it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.03it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 28.01it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 27.41it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.31it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.46it/s]
100%|██████████| 57/57 [00:02<00:00, 20.52it/s]
+    Epoch 114: training loss = -0.894 / validation loss = -0.895
+
+Epoch 115: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.56s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.27it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:06<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.63it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.18it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.75it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.11it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.75it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.16it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.81it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.28it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.73it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.29it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.63it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.70it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.25it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.39it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 115: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.99it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.22it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.40it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.52it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.24it/s]
 40%|████      | 23/57 [00:01<00:01, 26.19it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.00it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.27it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.80it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.31it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.77it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.54it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.31it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 30.37it/s]
100%|██████████| 57/57 [00:02<00:00, 23.54it/s]
+    Epoch 115: training loss = -0.897 / validation loss = -0.895
+
+Epoch 116: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.50s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.30it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.09it/s]
 21%|██        | 6/29 [00:04<00:14,  1.55it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.13it/s]
 28%|██▊       | 8/29 [00:04<00:07,  2.75it/s]
 31%|███       | 9/29 [00:06<00:15,  1.26it/s]
 34%|███▍      | 10/29 [00:06<00:11,  1.67it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.18it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.46it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.64it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.13it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.47it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.23it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.65it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.16it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.30it/s]
 72%|███████▏  | 21/29 [00:14<00:07,  1.08it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.46it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.96it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.50it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.66it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.76it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.15it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.17it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 116: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.22it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.63it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.07it/s]
 33%|███▎      | 19/57 [00:00<00:01, 27.27it/s]
 40%|████      | 23/57 [00:01<00:01, 27.98it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.84it/s]
 51%|█████     | 29/57 [00:01<00:01, 24.70it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.23it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.21it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 25.70it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 26.94it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 25.80it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 24.96it/s]
100%|██████████| 57/57 [00:02<00:00, 24.73it/s]
100%|██████████| 57/57 [00:02<00:00, 23.16it/s]
+    Epoch 116: training loss = -0.900 / validation loss = -0.892
+
+Epoch 117: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.60s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.67it/s]
 31%|███       | 9/29 [00:07<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.10it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.74it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.14it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.54it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.06it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.69it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.12it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.52it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.02it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.64it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.17it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.09it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.72it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.17it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.11it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.75it/s]
100%|██████████| 29/29 [00:17<00:00,  3.20it/s]
100%|██████████| 29/29 [00:17<00:00,  1.69it/s]
+
+Epoch 117: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.26it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.27it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.12it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.43it/s]
 40%|████      | 23/57 [00:01<00:01, 26.25it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.78it/s]
 54%|█████▍    | 31/57 [00:01<00:01, 25.55it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 24.92it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.44it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.50it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.42it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.73it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 30.67it/s]
100%|██████████| 57/57 [00:02<00:00, 23.76it/s]
+    Epoch 117: training loss = -0.896 / validation loss = -0.894
+
+Epoch 118: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.61s/it]
  7%|▋         | 2/29 [00:02<00:34,  1.26s/it]
 10%|█         | 3/29 [00:03<00:19,  1.36it/s]
 14%|█▍        | 4/29 [00:03<00:12,  2.05it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:05<00:15,  1.51it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.08it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.77it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.71it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.69it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.24it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.90it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.24it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.68it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.22it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.86it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.22it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.60it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.13it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.76it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.31it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.67it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.20it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.86it/s]
100%|██████████| 29/29 [00:16<00:00,  1.76it/s]
+
+Epoch 118: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.42it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.31it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.47it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.01it/s]
 40%|████      | 23/57 [00:01<00:01, 27.23it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.59it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.61it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.95it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.89it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.97it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.21it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.69it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.73it/s]
100%|██████████| 57/57 [00:02<00:00, 24.16it/s]
+    Epoch 118: training loss = -0.901 / validation loss = -0.892
+
+Epoch 119: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.61s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.49it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.73it/s]
 31%|███       | 9/29 [00:06<00:15,  1.27it/s]
 34%|███▍      | 10/29 [00:06<00:11,  1.69it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.25it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.75it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.22it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.64it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.18it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.37it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.39it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.71it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  2.23it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.36it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.73it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.13it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.00it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.50it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.03it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  1.91it/s]
100%|██████████| 29/29 [00:18<00:00,  1.61it/s]
+
+Epoch 119: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.50it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.34it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.39it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.99it/s]
 40%|████      | 23/57 [00:01<00:01, 27.17it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.48it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.43it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.86it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.66it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.85it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 27.11it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.84it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 30.58it/s]
100%|██████████| 57/57 [00:02<00:00, 24.00it/s]
+    Epoch 119: training loss = -0.900 / validation loss = -0.896
+    Saved model: unet-119.pth.tar
+
+Epoch 120: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.52s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.11s/it]
 10%|█         | 3/29 [00:02<00:17,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.01it/s]
 21%|██        | 6/29 [00:04<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.63it/s]
 31%|███       | 9/29 [00:07<00:17,  1.12it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.68it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.63it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.17it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.76it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.23it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.66it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.20it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.60it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.19it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.61it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.14it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.73it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.70it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.25it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.89it/s]
100%|██████████| 29/29 [00:16<00:00,  3.44it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 120: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.58it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.57it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.42it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.29it/s]
 40%|████      | 23/57 [00:01<00:01, 26.37it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.83it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.24it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.87it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.43it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.14it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.10it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.57it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.46it/s]
100%|██████████| 57/57 [00:02<00:00, 23.41it/s]
+    Epoch 120: training loss = -0.898 / validation loss = -0.894
+
+Epoch 121: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.68s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:06<00:34,  1.44s/it]
 21%|██        | 6/29 [00:06<00:22,  1.01it/s]
 24%|██▍       | 7/29 [00:06<00:15,  1.43it/s]
 28%|██▊       | 8/29 [00:06<00:10,  1.95it/s]
 31%|███       | 9/29 [00:08<00:19,  1.02it/s]
 34%|███▍      | 10/29 [00:08<00:13,  1.40it/s]
 38%|███▊      | 11/29 [00:08<00:09,  1.89it/s]
 41%|████▏     | 12/29 [00:08<00:06,  2.49it/s]
 45%|████▍     | 13/29 [00:10<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:10<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:10<00:06,  2.12it/s]
 55%|█████▌    | 16/29 [00:11<00:04,  2.76it/s]
 59%|█████▊    | 17/29 [00:13<00:10,  1.18it/s]
 62%|██████▏   | 18/29 [00:13<00:06,  1.59it/s]
 66%|██████▌   | 19/29 [00:13<00:04,  2.12it/s]
 69%|██████▉   | 20/29 [00:13<00:03,  2.76it/s]
 72%|███████▏  | 21/29 [00:15<00:06,  1.17it/s]
 76%|███████▌  | 22/29 [00:15<00:04,  1.59it/s]
 79%|███████▉  | 23/29 [00:15<00:02,  2.11it/s]
 83%|████████▎ | 24/29 [00:15<00:01,  2.75it/s]
 86%|████████▌ | 25/29 [00:17<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:17<00:01,  1.70it/s]
 93%|█████████▎| 27/29 [00:17<00:00,  2.24it/s]
 97%|█████████▋| 28/29 [00:17<00:00,  2.90it/s]
100%|██████████| 29/29 [00:18<00:00,  2.81it/s]
100%|██████████| 29/29 [00:18<00:00,  1.58it/s]
+
+Epoch 121: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.87it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.21it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.84it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.31it/s]
 40%|████      | 23/57 [00:01<00:01, 26.88it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.51it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.15it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.23it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.94it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.34it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.02it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.16it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.04it/s]
100%|██████████| 57/57 [00:02<00:00, 23.35it/s]
+    Epoch 121: training loss = -0.899 / validation loss = -0.897
+    Saved model: unet-121.pth.tar
+
+Epoch 122: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.69s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.11it/s]
 21%|██        | 6/29 [00:04<00:14,  1.56it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.12it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.78it/s]
 31%|███       | 9/29 [00:06<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.51it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.03it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.66it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.29it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.72it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.34it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.16it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.42it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.29it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.17it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.37it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.34it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.79it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.34it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.10it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 122: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:17,  3.12it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.36it/s]
 16%|█▌        | 9/57 [00:00<00:02, 20.04it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.51it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.17it/s]
 40%|████      | 23/57 [00:01<00:01, 26.45it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.88it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.75it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.11it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.43it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.34it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.63it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.05it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.84it/s]
100%|██████████| 57/57 [00:02<00:00, 23.71it/s]
+    Epoch 122: training loss = -0.902 / validation loss = -0.897
+    Saved model: unet-122.pth.tar
+
+Epoch 123: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.43s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.32it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:04<00:11,  2.00it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.55it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.08it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.72it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.28it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.59it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.12it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.76it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.31it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.65it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.18it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.39it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.36it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.68it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.23it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.00it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.30it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.71it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.26it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  1.89it/s]
100%|██████████| 29/29 [00:17<00:00,  1.70it/s]
+
+Epoch 123: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.28it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.70it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.71it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.28it/s]
 40%|████      | 23/57 [00:01<00:01, 27.48it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.74it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.00it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.78it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.12it/s]
 70%|███████   | 40/57 [00:01<00:00, 26.84it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.55it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.47it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.71it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.21it/s]
100%|██████████| 57/57 [00:02<00:00, 23.93it/s]
+    Epoch 123: training loss = -0.896 / validation loss = -0.898
+    Saved model: unet-123.pth.tar
+
+Epoch 124: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.70s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.18s/it]
 10%|█         | 3/29 [00:02<00:17,  1.45it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.17it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:05<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.01it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.68it/s]
 31%|███       | 9/29 [00:07<00:16,  1.24it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.69it/s]
 38%|███▊      | 11/29 [00:07<00:07,  2.26it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.93it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.62it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.15it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.77it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.17it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.81it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.25it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.69it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.24it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.41it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.21it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.32it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 124: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.90it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.93it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.27it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.24it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.22it/s]
 40%|████      | 23/57 [00:01<00:01, 26.51it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.38it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.94it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.68it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.61it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.37it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.36it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.38it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.96it/s]
100%|██████████| 57/57 [00:02<00:00, 23.89it/s]
+    Epoch 124: training loss = -0.901 / validation loss = -0.899
+    Saved model: unet-124.pth.tar
+
+Epoch 125: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.69s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:04<00:14,  1.54it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.09it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.77it/s]
 31%|███       | 9/29 [00:07<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.10it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.74it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.20it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.63it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.16it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.81it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.57it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.09it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.72it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.20it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.61it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.33it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.79it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.36it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.31it/s]
100%|██████████| 29/29 [00:17<00:00,  1.66it/s]
+
+Epoch 125: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.08it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.88it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.69it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.87it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.60it/s]
 40%|████      | 23/57 [00:01<00:01, 28.06it/s]
 46%|████▌     | 26/57 [00:01<00:01, 28.23it/s]
 51%|█████     | 29/57 [00:01<00:01, 25.27it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.83it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.91it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.51it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 28.06it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.55it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.87it/s]
100%|██████████| 57/57 [00:02<00:00, 25.37it/s]
100%|██████████| 57/57 [00:04<00:00, 12.52it/s]
+    Epoch 125: training loss = -0.902 / validation loss = -0.899
+    Saved model: unet-125.pth.tar
+
+Epoch 126: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.63s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.22it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:06<00:16,  1.22it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.66it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.20it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.87it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.63it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.17it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.79it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.15it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.55it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.07it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.70it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.19it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.61it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.13it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.78it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.35it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.71it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.24it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.90it/s]
100%|██████████| 29/29 [00:16<00:00,  1.76it/s]
+
+Epoch 126: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.97it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.56it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.60it/s]
 21%|██        | 12/57 [00:00<00:02, 22.25it/s]
 26%|██▋       | 15/57 [00:00<00:01, 23.30it/s]
 33%|███▎      | 19/57 [00:00<00:01, 23.91it/s]
 40%|████      | 23/57 [00:01<00:01, 25.93it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.73it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.60it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.90it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.16it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.88it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 26.06it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.67it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.50it/s]
100%|██████████| 57/57 [00:03<00:00, 17.49it/s]
+    Epoch 126: training loss = -0.903 / validation loss = -0.897
+
+Epoch 127: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.66s/it]
  7%|▋         | 2/29 [00:04<01:05,  2.42s/it]
 10%|█         | 3/29 [00:05<00:35,  1.37s/it]
 14%|█▍        | 4/29 [00:05<00:21,  1.15it/s]
 17%|█▋        | 5/29 [00:05<00:14,  1.68it/s]
 21%|██        | 6/29 [00:05<00:09,  2.33it/s]
 24%|██▍       | 7/29 [00:05<00:07,  2.89it/s]
 28%|██▊       | 8/29 [00:05<00:05,  3.71it/s]
 31%|███       | 9/29 [00:07<00:13,  1.51it/s]
 34%|███▍      | 10/29 [00:07<00:10,  1.88it/s]
 38%|███▊      | 11/29 [00:07<00:07,  2.48it/s]
 41%|████▏     | 12/29 [00:07<00:05,  3.17it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.33it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.75it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.32it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.73it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.14it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.76it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.28it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.51it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.00it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.62it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.41it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.44it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.93it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.53it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 127: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.95it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.81it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.82it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.45it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.81it/s]
 40%|████      | 23/57 [00:01<00:01, 26.95it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.68it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.15it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.44it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.07it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.55it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.97it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.60it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 27.38it/s]
100%|██████████| 57/57 [00:02<00:00, 25.19it/s]
100%|██████████| 57/57 [00:02<00:00, 23.32it/s]
+    Epoch 127: training loss = -0.900 / validation loss = -0.890
+
+Epoch 128: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.67s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.01s/it]
 21%|██        | 6/29 [00:05<00:16,  1.42it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.97it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.63it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.55it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.08it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.72it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.22it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.65it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.20it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.86it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.28it/s]
 62%|██████▏   | 18/29 [00:12<00:08,  1.29it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.73it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.30it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.54it/s]
 76%|███████▌  | 22/29 [00:13<00:03,  2.04it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.63it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  3.23it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.31it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.69it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.24it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.67it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 128: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.26it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.48it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.74it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.44it/s]
 40%|████      | 23/57 [00:01<00:01, 26.84it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.33it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.02it/s]
 60%|█████▉    | 34/57 [00:03<00:04,  5.55it/s]
 70%|███████   | 40/57 [00:03<00:01,  8.57it/s]
 77%|███████▋  | 44/57 [00:03<00:01, 10.82it/s]
 84%|████████▍ | 48/57 [00:03<00:00, 13.65it/s]
 91%|█████████ | 52/57 [00:03<00:00, 16.25it/s]
 98%|█████████▊| 56/57 [00:03<00:00, 17.82it/s]
100%|██████████| 57/57 [00:04<00:00, 13.74it/s]
+    Epoch 128: training loss = -0.904 / validation loss = -0.901
+    Saved model: unet-128.pth.tar
+
+Epoch 129: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.25it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.01it/s]
 21%|██        | 6/29 [00:04<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:07<00:18,  1.11it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.52it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.04it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.67it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.56it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.08it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.71it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.19it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.14it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.78it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.22it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.64it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.18it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.84it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.20it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.62it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.15it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.77it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 129: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.49it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.78it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.92it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.00it/s]
 40%|████      | 23/57 [00:01<00:01, 26.34it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.36it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.08it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.39it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.96it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.28it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.31it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.68it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.84it/s]
100%|██████████| 57/57 [00:03<00:00, 18.62it/s]
+    Epoch 129: training loss = -0.902 / validation loss = -0.896
+
+Epoch 130: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.52s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.11s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.65it/s]
 31%|███       | 9/29 [00:06<00:15,  1.25it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.66it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.20it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.86it/s]
 45%|████▍     | 13/29 [00:08<00:12,  1.30it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.50it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  2.00it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.61it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.32it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.51it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.99it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.61it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.32it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.40it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.88it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.47it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.33it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.53it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.04it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.66it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 130: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.53it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.17it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.74it/s]
 33%|███▎      | 19/57 [00:00<00:01, 27.43it/s]
 40%|████      | 23/57 [00:01<00:01, 28.40it/s]
 47%|████▋     | 27/57 [00:01<00:01, 28.12it/s]
 53%|█████▎    | 30/57 [00:01<00:01, 25.48it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 24.92it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 25.59it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 25.44it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.20it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.29it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.31it/s]
100%|██████████| 57/57 [00:02<00:00, 24.43it/s]
100%|██████████| 57/57 [00:02<00:00, 23.17it/s]
+    Epoch 130: training loss = -0.905 / validation loss = -0.894
+
+Epoch 131: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:17,  2.77s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.21s/it]
 10%|█         | 3/29 [00:02<00:18,  1.42it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.13it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.02s/it]
 21%|██        | 6/29 [00:05<00:16,  1.40it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.94it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.59it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.77it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.79it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.69it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.24it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.90it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.19it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.61it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.14it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.78it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.17it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.59it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.11it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.75it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 131: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.36it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.85it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.81it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.51it/s]
 40%|████      | 23/57 [00:01<00:01, 27.38it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.06it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.00it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.25it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.25it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.38it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.85it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.23it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.15it/s]
100%|██████████| 57/57 [00:02<00:00, 23.99it/s]
+    Epoch 131: training loss = -0.900 / validation loss = -0.886
+
+Epoch 132: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.30it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:04<00:15,  1.49it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.06it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.73it/s]
 31%|███       | 9/29 [00:06<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.09it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.73it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.73it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.16it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.81it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.29it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.54it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.05it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.68it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.29it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.44it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.92it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.53it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 132: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.28it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.88it/s]
 23%|██▎       | 13/57 [00:00<00:01, 22.06it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.97it/s]
 40%|████      | 23/57 [00:01<00:01, 26.17it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.99it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.08it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.09it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.13it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.76it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.14it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.25it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 30.12it/s]
100%|██████████| 57/57 [00:02<00:00, 23.95it/s]
+    Epoch 132: training loss = -0.900 / validation loss = -0.874
+
+Epoch 133: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:06,  2.37s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.58it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.34it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:16,  1.43it/s]
 24%|██▍       | 7/29 [00:04<00:11,  1.97it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.63it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.51it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.03it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.66it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.53it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.05it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.68it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.13it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.53it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.04it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.66it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.16it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.09it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.72it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.30it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.59it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.11it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.75it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 133: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.00it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.88it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.62it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.64it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.07it/s]
 40%|████      | 23/57 [00:01<00:01, 26.93it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.10it/s]
 51%|█████     | 29/57 [00:01<00:01, 24.64it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.55it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.51it/s]
 70%|███████   | 40/57 [00:01<00:00, 26.79it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.06it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.52it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.41it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 27.18it/s]
100%|██████████| 57/57 [00:02<00:00, 24.67it/s]
100%|██████████| 57/57 [00:02<00:00, 23.14it/s]
+    Epoch 133: training loss = -0.900 / validation loss = -0.899
+
+Epoch 134: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.54s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.18s/it]
 10%|█         | 3/29 [00:02<00:17,  1.45it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.17it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:05<00:17,  1.34it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.85it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.48it/s]
 31%|███       | 9/29 [00:06<00:15,  1.32it/s]
 34%|███▍      | 10/29 [00:07<00:13,  1.45it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.95it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.57it/s]
 45%|████▍     | 13/29 [00:09<00:11,  1.37it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.44it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.94it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.54it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.32it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.51it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.02it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.63it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.28it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.52it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.02it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.63it/s]
 86%|████████▌ | 25/29 [00:16<00:02,  1.38it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.49it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.99it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.60it/s]
100%|██████████| 29/29 [00:18<00:00,  1.58it/s]
+
+Epoch 134: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.41it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.41it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.13it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.35it/s]
 40%|████      | 23/57 [00:01<00:01, 26.48it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.87it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.11it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.62it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.89it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.35it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.45it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.38it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.96it/s]
100%|██████████| 57/57 [00:02<00:00, 23.53it/s]
+    Epoch 134: training loss = -0.904 / validation loss = -0.896
+
+Epoch 135: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.50s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.53it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:06<00:16,  1.25it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.53it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.69it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.27it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.64it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.18it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.83it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.27it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.76it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.25it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.56it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.06it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.67it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.28it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.57it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.09it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.66it/s]
100%|██████████| 29/29 [00:16<00:00,  1.76it/s]
+
+Epoch 135: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.93it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.21it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.08it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.71it/s]
 32%|███▏      | 18/57 [00:00<00:01, 28.81it/s]
 39%|███▊      | 22/57 [00:00<00:01, 28.04it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.49it/s]
 51%|█████     | 29/57 [00:01<00:01, 25.05it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.86it/s]
 63%|██████▎   | 36/57 [00:01<00:00, 27.61it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.30it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.51it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.97it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.90it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 27.14it/s]
100%|██████████| 57/57 [00:02<00:00, 26.56it/s]
100%|██████████| 57/57 [00:02<00:00, 23.66it/s]
+    Epoch 135: training loss = -0.902 / validation loss = -0.900
+
+Epoch 136: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:05,  2.34s/it]
  7%|▋         | 2/29 [00:02<00:28,  1.05s/it]
 10%|█         | 3/29 [00:02<00:16,  1.61it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.39it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.01it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.68it/s]
 31%|███       | 9/29 [00:07<00:19,  1.05it/s]
 34%|███▍      | 10/29 [00:07<00:13,  1.44it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.93it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.55it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.23it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.66it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.21it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.87it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.17it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.56it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.08it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.71it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.68it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.22it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.86it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.25it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.23it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.77it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 136: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.94it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.28it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.31it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.47it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.64it/s]
 40%|████      | 23/57 [00:01<00:01, 26.91it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.84it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.31it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.64it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.00it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.54it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 27.02it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.42it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 26.04it/s]
100%|██████████| 57/57 [00:02<00:00, 23.79it/s]
100%|██████████| 57/57 [00:02<00:00, 23.28it/s]
+    Epoch 136: training loss = -0.904 / validation loss = -0.898
+
+Epoch 137: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.70s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.18s/it]
 10%|█         | 3/29 [00:02<00:17,  1.45it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:05<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.70it/s]
 31%|███       | 9/29 [00:07<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.60it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.77it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.23it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.66it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.19it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.85it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.17it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.59it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.11it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.75it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.81it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:16<00:00,  3.21it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 137: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.99it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.09it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.21it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.61it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.37it/s]
 40%|████      | 23/57 [00:01<00:01, 26.79it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.44it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.42it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.29it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.95it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.06it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.09it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.48it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.26it/s]
100%|██████████| 57/57 [00:02<00:00, 23.93it/s]
+    Epoch 137: training loss = -0.904 / validation loss = -0.903
+    Saved model: unet-137.pth.tar
+
+Epoch 138: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.64s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:05<00:25,  1.05s/it]
 21%|██        | 6/29 [00:05<00:16,  1.37it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.90it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.54it/s]
 31%|███       | 9/29 [00:07<00:17,  1.12it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.53it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.69it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.12it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.52it/s]
 52%|█████▏    | 15/29 [00:10<00:06,  2.03it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.65it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.23it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.66it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.20it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.85it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.15it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.55it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.07it/s]
 97%|█████████▋| 28/29 [00:17<00:00,  2.70it/s]
100%|██████████| 29/29 [00:17<00:00,  3.29it/s]
100%|██████████| 29/29 [00:17<00:00,  1.68it/s]
+
+Epoch 138: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.18it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.09it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.81it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.34it/s]
 40%|████      | 23/57 [00:01<00:01, 26.43it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.18it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.73it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.12it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.60it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.04it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.27it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.35it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.16it/s]
100%|██████████| 57/57 [00:02<00:00, 23.37it/s]
+    Epoch 138: training loss = -0.905 / validation loss = -0.902
+
+Epoch 139: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.69s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.45it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.02s/it]
 21%|██        | 6/29 [00:05<00:16,  1.41it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.94it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.60it/s]
 31%|███       | 9/29 [00:07<00:16,  1.23it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.69it/s]
 38%|███▊      | 11/29 [00:07<00:07,  2.25it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.93it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:11<00:17,  1.14s/it]
 52%|█████▏    | 15/29 [00:11<00:11,  1.20it/s]
 55%|█████▌    | 16/29 [00:11<00:07,  1.63it/s]
 59%|█████▊    | 17/29 [00:11<00:05,  2.12it/s]
 62%|██████▏   | 18/29 [00:11<00:03,  2.75it/s]
 66%|██████▌   | 19/29 [00:11<00:02,  3.49it/s]
 69%|██████▉   | 20/29 [00:11<00:02,  4.30it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.35it/s]
 76%|███████▌  | 22/29 [00:13<00:03,  1.81it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.38it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  3.04it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:16<00:00,  3.54it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 139: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.99it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.38it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.51it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.12it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.50it/s]
 40%|████      | 23/57 [00:01<00:01, 26.52it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.38it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.71it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.89it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.33it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.86it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.37it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.37it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.04it/s]
100%|██████████| 57/57 [00:02<00:00, 23.52it/s]
+    Epoch 139: training loss = -0.900 / validation loss = -0.900
+
+Epoch 140: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:17,  2.77s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.21s/it]
 10%|█         | 3/29 [00:02<00:18,  1.42it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.13it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:05<00:15,  1.51it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.08it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.76it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.77it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.26it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.79it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.69it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.24it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.90it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.70it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.25it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.45it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.44it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 140: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.04it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.52it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.66it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.51it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.99it/s]
 40%|████      | 23/57 [00:01<00:01, 27.77it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.45it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.82it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.11it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.84it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.43it/s]
 81%|████████  | 46/57 [00:01<00:00, 27.74it/s]
 86%|████████▌ | 49/57 [00:01<00:00, 27.99it/s]
 91%|█████████ | 52/57 [00:02<00:00, 27.48it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 30.11it/s]
100%|██████████| 57/57 [00:02<00:00, 23.82it/s]
+    Epoch 140: training loss = -0.905 / validation loss = -0.889
+
+Epoch 141: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.56s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.26it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:07<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.60it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.06it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.70it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.59it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.12it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.76it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.28it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.80it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.33it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.49it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.99it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.60it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 141: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.78it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.87it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.94it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.08it/s]
 40%|████      | 23/57 [00:01<00:01, 26.89it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.62it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.87it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.26it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.75it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.40it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.87it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.32it/s]
100%|██████████| 57/57 [00:02<00:00, 28.43it/s]
100%|██████████| 57/57 [00:02<00:00, 24.21it/s]
+    Epoch 141: training loss = -0.905 / validation loss = -0.893
+
+Epoch 142: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.69s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.52it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.07it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.75it/s]
 31%|███       | 9/29 [00:07<00:17,  1.13it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.71it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.63it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.17it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.82it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.30it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.74it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.30it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.94it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.28it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.72it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.80it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.53it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.03it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.66it/s]
100%|██████████| 29/29 [00:17<00:00,  1.68it/s]
+
+Epoch 142: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.43it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.21it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.73it/s]
 32%|███▏      | 18/57 [00:00<00:01, 28.80it/s]
 39%|███▊      | 22/57 [00:00<00:01, 28.25it/s]
 46%|████▌     | 26/57 [00:01<00:01, 29.17it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 28.03it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.61it/s]
 63%|██████▎   | 36/57 [00:01<00:00, 24.62it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 23.02it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 25.18it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 24.97it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.58it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.68it/s]
100%|██████████| 57/57 [00:02<00:00, 23.84it/s]
+    Epoch 142: training loss = -0.907 / validation loss = -0.900
+
+Epoch 143: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.56s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.27it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:04<00:15,  1.49it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.52it/s]
 31%|███       | 9/29 [00:06<00:14,  1.34it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.57it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:07,  2.38it/s]
 45%|████▍     | 13/29 [00:08<00:11,  1.41it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.55it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.07it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.47it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.40it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.46it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.95it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.56it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.41it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.41it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.89it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.49it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.37it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.59it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.11it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.72it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 143: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.98it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.79it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.76it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.52it/s]
 33%|███▎      | 19/57 [00:00<00:01, 27.15it/s]
 40%|████      | 23/57 [00:01<00:01, 27.61it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.67it/s]
 51%|█████     | 29/57 [00:01<00:01, 24.58it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.06it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.42it/s]
 70%|███████   | 40/57 [00:01<00:00, 27.01it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.05it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.61it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.43it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.04it/s]
100%|██████████| 57/57 [00:02<00:00, 23.19it/s]
+    Epoch 143: training loss = -0.906 / validation loss = -0.903
+    Saved model: unet-143.pth.tar
+
+Epoch 144: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.70s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.18s/it]
 10%|█         | 3/29 [00:02<00:17,  1.45it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.03s/it]
 21%|██        | 6/29 [00:05<00:16,  1.39it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.92it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:07<00:21,  1.07s/it]
 34%|███▍      | 10/29 [00:08<00:14,  1.30it/s]
 38%|███▊      | 11/29 [00:08<00:10,  1.76it/s]
 41%|████▏     | 12/29 [00:08<00:07,  2.33it/s]
 45%|████▍     | 13/29 [00:10<00:13,  1.20it/s]
 48%|████▊     | 14/29 [00:10<00:09,  1.62it/s]
 52%|█████▏    | 15/29 [00:10<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:12<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.57it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.09it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.73it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.25it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.68it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.23it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.89it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.66it/s]
 93%|█████████▎| 27/29 [00:17<00:00,  2.19it/s]
 97%|█████████▋| 28/29 [00:17<00:00,  2.85it/s]
100%|██████████| 29/29 [00:17<00:00,  3.39it/s]
100%|██████████| 29/29 [00:17<00:00,  1.67it/s]
+
+Epoch 144: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.66it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.58it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.52it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.15it/s]
 40%|████      | 23/57 [00:01<00:01, 26.74it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.31it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.67it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.38it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.69it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.67it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.78it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.21it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.85it/s]
100%|██████████| 57/57 [00:02<00:00, 23.60it/s]
+    Epoch 144: training loss = -0.907 / validation loss = -0.901
+
+Epoch 145: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.69s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:05<00:25,  1.06s/it]
 21%|██        | 6/29 [00:05<00:16,  1.36it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.88it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.52it/s]
 31%|███       | 9/29 [00:07<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.77it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.31it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.62it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.15it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.80it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.33it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.46it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.96it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.56it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.32it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.47it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.97it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.58it/s]
 86%|████████▌ | 25/29 [00:16<00:02,  1.40it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 145: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.67it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.48it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.50it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.28it/s]
 40%|████      | 23/57 [00:01<00:01, 26.62it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.24it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.06it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.87it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.38it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.20it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.91it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.77it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.46it/s]
100%|██████████| 57/57 [00:02<00:00, 23.44it/s]
+    Epoch 145: training loss = -0.907 / validation loss = -0.903
+    Saved model: unet-145.pth.tar
+
+Epoch 146: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.53s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.22it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.01it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.68it/s]
 31%|███       | 9/29 [00:07<00:18,  1.10it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.50it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.01it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.64it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.11it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.75it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.79it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.22it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 146: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.51it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.63it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.23it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.83it/s]
 40%|████      | 23/57 [00:01<00:01, 26.70it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.20it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.98it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.70it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.80it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.57it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.96it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.89it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 30.66it/s]
100%|██████████| 57/57 [00:02<00:00, 24.12it/s]
+    Epoch 146: training loss = -0.907 / validation loss = -0.904
+    Saved model: unet-146.pth.tar
+
+Epoch 147: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.66s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:07<00:45,  1.91s/it]
 21%|██        | 6/29 [00:07<00:29,  1.30s/it]
 24%|██▍       | 7/29 [00:07<00:20,  1.10it/s]
 28%|██▊       | 8/29 [00:07<00:13,  1.53it/s]
 31%|███       | 9/29 [00:09<00:20,  1.02s/it]
 34%|███▍      | 10/29 [00:09<00:14,  1.35it/s]
 38%|███▊      | 11/29 [00:09<00:09,  1.82it/s]
 41%|████▏     | 12/29 [00:09<00:07,  2.41it/s]
 45%|████▍     | 13/29 [00:11<00:14,  1.12it/s]
 48%|████▊     | 14/29 [00:12<00:09,  1.52it/s]
 52%|█████▏    | 15/29 [00:12<00:06,  2.03it/s]
 55%|█████▌    | 16/29 [00:12<00:04,  2.65it/s]
 59%|█████▊    | 17/29 [00:14<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:14<00:07,  1.57it/s]
 66%|██████▌   | 19/29 [00:14<00:04,  2.09it/s]
 69%|██████▉   | 20/29 [00:14<00:03,  2.73it/s]
 72%|███████▏  | 21/29 [00:16<00:06,  1.18it/s]
 76%|███████▌  | 22/29 [00:16<00:04,  1.59it/s]
 79%|███████▉  | 23/29 [00:16<00:02,  2.12it/s]
 83%|████████▎ | 24/29 [00:16<00:01,  2.75it/s]
 86%|████████▌ | 25/29 [00:18<00:03,  1.17it/s]
 90%|████████▉ | 26/29 [00:19<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:19<00:00,  2.10it/s]
 97%|█████████▋| 28/29 [00:19<00:00,  2.74it/s]
100%|██████████| 29/29 [00:19<00:00,  3.24it/s]
100%|██████████| 29/29 [00:19<00:00,  1.48it/s]
+
+Epoch 147: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.01it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.87it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.76it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.78it/s]
 40%|████      | 23/57 [00:01<00:01, 27.82it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.75it/s]
 51%|█████     | 29/57 [00:01<00:01, 25.16it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.39it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.12it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.49it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.39it/s]
 84%|████████▍ | 48/57 [00:01<00:00, 27.70it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.80it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.10it/s]
100%|██████████| 57/57 [00:02<00:00, 23.40it/s]
+    Epoch 147: training loss = -0.908 / validation loss = -0.899
+
+Epoch 148: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.52s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.27it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:06<00:15,  1.25it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.25it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.59it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.12it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.77it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.28it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.45it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.94it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.54it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.32it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.54it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.05it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.68it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.33it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.11it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.72it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 148: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.99it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.40it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.39it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.17it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.58it/s]
 40%|████      | 23/57 [00:01<00:01, 27.45it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.25it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.14it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.10it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.39it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.35it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.38it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.18it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 26.07it/s]
100%|██████████| 57/57 [00:02<00:00, 25.73it/s]
100%|██████████| 57/57 [00:02<00:00, 23.36it/s]
+    Epoch 148: training loss = -0.906 / validation loss = -0.893
+
+Epoch 149: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:16,  2.74s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.20s/it]
 10%|█         | 3/29 [00:02<00:18,  1.43it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.14it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:05<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:07<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.57it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.75it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.14it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.54it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.06it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.69it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.28it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.72it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.28it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.94it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.17it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.81it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.20it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.16it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.81it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 149: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.33it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.87it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.41it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.76it/s]
 40%|████      | 23/57 [00:01<00:01, 26.76it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.56it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.63it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.47it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.62it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.48it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.63it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.96it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.62it/s]
100%|██████████| 57/57 [00:02<00:00, 24.22it/s]
+    Epoch 149: training loss = -0.906 / validation loss = -0.873
+
+Epoch 150: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.42s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.19s/it]
 10%|█         | 3/29 [00:02<00:18,  1.43it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.16it/s]
 17%|█▋        | 5/29 [00:04<00:20,  1.17it/s]
 21%|██        | 6/29 [00:04<00:16,  1.38it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.90it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.55it/s]
 31%|███       | 9/29 [00:06<00:14,  1.34it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.68it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.32it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.73it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.40it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.44it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.92it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.52it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.45it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.46it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.95it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.56it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.40it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.52it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.02it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.65it/s]
100%|██████████| 29/29 [00:16<00:00,  3.33it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 150: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.08it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.49it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.76it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.16it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.94it/s]
 40%|████      | 23/57 [00:01<00:01, 26.19it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.12it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.47it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.15it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.67it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.00it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.71it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.24it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.01it/s]
100%|██████████| 57/57 [00:02<00:00, 23.40it/s]
+    Epoch 150: training loss = -0.903 / validation loss = -0.895
+
+Epoch 151: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.66s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.02s/it]
 21%|██        | 6/29 [00:05<00:16,  1.41it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.94it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.60it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.12it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.61it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.27it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.71it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.26it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.93it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.07it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.44it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.93it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.52it/s]
100%|██████████| 29/29 [00:17<00:00,  2.84it/s]
100%|██████████| 29/29 [00:17<00:00,  1.68it/s]
+
+Epoch 151: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.06it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.86it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.38it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.80it/s]
 40%|████      | 23/57 [00:01<00:01, 27.99it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.77it/s]
 51%|█████     | 29/57 [00:01<00:01, 25.09it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.31it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.91it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.19it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.61it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.70it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.37it/s]
100%|██████████| 57/57 [00:02<00:00, 24.96it/s]
100%|██████████| 57/57 [00:03<00:00, 15.64it/s]
+    Epoch 151: training loss = -0.906 / validation loss = -0.895
+
+Epoch 152: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.57s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.92it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:06<00:15,  1.32it/s]
 34%|███▍      | 10/29 [00:06<00:10,  1.79it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.03it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.67it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.27it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.72it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.12it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.76it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.17it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.58it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.74it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.16it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.09it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.72it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.22it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:16<00:00,  3.34it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 152: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.97it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.06it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.10it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.93it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.16it/s]
 40%|████      | 23/57 [00:01<00:01, 26.31it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.66it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.06it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.60it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.58it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.01it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.70it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.40it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.40it/s]
100%|██████████| 57/57 [00:02<00:00, 23.61it/s]
+    Epoch 152: training loss = -0.909 / validation loss = -0.902
+
+Epoch 153: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.57s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.51it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.26it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.06it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.74it/s]
 31%|███       | 9/29 [00:08<00:25,  1.29s/it]
 34%|███▍      | 10/29 [00:08<00:17,  1.08it/s]
 38%|███▊      | 11/29 [00:08<00:12,  1.48it/s]
 41%|████▏     | 12/29 [00:08<00:08,  1.99it/s]
 45%|████▍     | 13/29 [00:10<00:14,  1.07it/s]
 48%|████▊     | 14/29 [00:10<00:10,  1.45it/s]
 52%|█████▏    | 15/29 [00:10<00:07,  1.95it/s]
 55%|█████▌    | 16/29 [00:11<00:05,  2.55it/s]
 59%|█████▊    | 17/29 [00:12<00:09,  1.22it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.62it/s]
 66%|██████▌   | 19/29 [00:13<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:13<00:03,  2.55it/s]
 72%|███████▏  | 21/29 [00:15<00:06,  1.22it/s]
 76%|███████▌  | 22/29 [00:15<00:04,  1.65it/s]
 79%|███████▉  | 23/29 [00:15<00:02,  2.18it/s]
 83%|████████▎ | 24/29 [00:15<00:02,  2.43it/s]
 86%|████████▌ | 25/29 [00:17<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:17<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:17<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:17<00:00,  2.83it/s]
100%|██████████| 29/29 [00:17<00:00,  3.35it/s]
100%|██████████| 29/29 [00:18<00:00,  1.61it/s]
+
+Epoch 153: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.18it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.20it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.23it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.77it/s]
 40%|████      | 23/57 [00:01<00:01, 27.77it/s]
 46%|████▌     | 26/57 [00:01<00:01, 27.76it/s]
 51%|█████     | 29/57 [00:01<00:01, 25.19it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.99it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.12it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.76it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 28.34it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 27.17it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 26.22it/s]
100%|██████████| 57/57 [00:02<00:00, 25.55it/s]
100%|██████████| 57/57 [00:02<00:00, 23.75it/s]
+    Epoch 153: training loss = -0.909 / validation loss = -0.905
+    Saved model: unet-153.pth.tar
+
+Epoch 154: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.50s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.07it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.61it/s]
 31%|███       | 9/29 [00:06<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.15it/s]
 41%|████▏     | 12/29 [00:07<00:07,  2.26it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.25it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.53it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.27it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.71it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.26it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.47it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.20it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.74it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.10it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.49it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.99it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.55it/s]
100%|██████████| 29/29 [00:16<00:00,  2.87it/s]
100%|██████████| 29/29 [00:17<00:00,  1.69it/s]
+
+Epoch 154: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.35it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.37it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.77it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.30it/s]
 40%|████      | 23/57 [00:01<00:01, 26.91it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.39it/s]
 53%|█████▎    | 30/57 [00:01<00:01, 26.73it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.42it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.76it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.77it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.71it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 27.05it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 26.13it/s]
100%|██████████| 57/57 [00:02<00:00, 25.88it/s]
100%|██████████| 57/57 [00:03<00:00, 15.23it/s]
+    Epoch 154: training loss = -0.909 / validation loss = -0.900
+
+Epoch 155: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.48s/it]
  7%|▋         | 2/29 [00:05<01:09,  2.57s/it]
 10%|█         | 3/29 [00:05<00:37,  1.45s/it]
 14%|█▍        | 4/29 [00:05<00:22,  1.09it/s]
 17%|█▋        | 5/29 [00:05<00:15,  1.60it/s]
 21%|██        | 6/29 [00:05<00:10,  2.23it/s]
 24%|██▍       | 7/29 [00:05<00:07,  2.96it/s]
 28%|██▊       | 8/29 [00:05<00:05,  3.78it/s]
 31%|███       | 9/29 [00:07<00:12,  1.66it/s]
 34%|███▍      | 10/29 [00:07<00:10,  1.78it/s]
 38%|███▊      | 11/29 [00:07<00:07,  2.37it/s]
 41%|████▏     | 12/29 [00:07<00:05,  3.06it/s]
 45%|████▍     | 13/29 [00:09<00:11,  1.36it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:10<00:06,  2.07it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.70it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.36it/s]
 62%|██████▏   | 18/29 [00:12<00:07,  1.47it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.95it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.56it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.51it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.53it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.02it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.64it/s]
 86%|████████▌ | 25/29 [00:16<00:02,  1.39it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.40it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.88it/s]
 97%|█████████▋| 28/29 [00:17<00:00,  2.48it/s]
100%|██████████| 29/29 [00:17<00:00,  1.68it/s]
+
+Epoch 155: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.95it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.93it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.79it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.95it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.99it/s]
 40%|████      | 23/57 [00:01<00:01, 26.79it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.59it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.15it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.76it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.41it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.93it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.80it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.74it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 30.56it/s]
100%|██████████| 57/57 [00:02<00:00, 23.49it/s]
+    Epoch 155: training loss = -0.907 / validation loss = -0.900
+
+Epoch 156: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:18,  2.79s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.22s/it]
 10%|█         | 3/29 [00:03<00:18,  1.41it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.12it/s]
 17%|█▋        | 5/29 [00:05<00:23,  1.01it/s]
 21%|██        | 6/29 [00:05<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.65it/s]
 31%|███       | 9/29 [00:07<00:18,  1.10it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.52it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.04it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.67it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.59it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.13it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.77it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.19it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.14it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.79it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.17it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.82it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:16<00:00,  3.54it/s]
100%|██████████| 29/29 [00:17<00:00,  1.70it/s]
+
+Epoch 156: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.11it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.49it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.33it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.59it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.65it/s]
 40%|████      | 23/57 [00:01<00:01, 27.14it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.69it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.22it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.81it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.05it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.90it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.73it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.12it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.96it/s]
100%|██████████| 57/57 [00:02<00:00, 23.39it/s]
+    Epoch 156: training loss = -0.908 / validation loss = -0.898
+
+Epoch 157: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.42s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.97it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.63it/s]
 31%|███       | 9/29 [00:06<00:16,  1.23it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.94it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.55it/s]
 45%|████▍     | 13/29 [00:08<00:11,  1.41it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.86it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.44it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.38it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.56it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  2.00it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.61it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.51it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.51it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  2.00it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.61it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.52it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.49it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.80it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.37it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 157: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.11it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.16it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.97it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.90it/s]
 40%|████      | 23/57 [00:01<00:01, 27.10it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.90it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.49it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.65it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.36it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.89it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.98it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.96it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.16it/s]
100%|██████████| 57/57 [00:02<00:00, 24.02it/s]
+    Epoch 157: training loss = -0.909 / validation loss = -0.906
+    Saved model: unet-157.pth.tar
+
+Epoch 158: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.41s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:18,  1.43it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.15it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.10it/s]
 21%|██        | 6/29 [00:05<00:17,  1.32it/s]
 24%|██▍       | 7/29 [00:05<00:12,  1.83it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.46it/s]
 31%|███       | 9/29 [00:06<00:15,  1.32it/s]
 34%|███▍      | 10/29 [00:07<00:13,  1.43it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.92it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.53it/s]
 45%|████▍     | 13/29 [00:09<00:11,  1.36it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.55it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.07it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.69it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.29it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.49it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.99it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.61it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.43it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.56it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.08it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.71it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.45it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.40it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.88it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.47it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 158: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.07it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.98it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.77it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.07it/s]
 40%|████      | 23/57 [00:01<00:01, 27.45it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.96it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.19it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.92it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.44it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.81it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.96it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.29it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.78it/s]
100%|██████████| 57/57 [00:02<00:00, 23.77it/s]
+    Epoch 158: training loss = -0.911 / validation loss = -0.875
+
+Epoch 159: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.49s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.17it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.70it/s]
 31%|███       | 9/29 [00:06<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.69it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.23it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.67it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.05it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.68it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.28it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.70it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.26it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.85it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.19it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.60it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.13it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.71it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.71it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 159: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:17,  3.12it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.40it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.40it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.09it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.57it/s]
 40%|████      | 23/57 [00:01<00:01, 26.64it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.07it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.96it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.05it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.47it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.99it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.23it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.51it/s]
100%|██████████| 57/57 [00:02<00:00, 29.62it/s]
100%|██████████| 57/57 [00:02<00:00, 23.90it/s]
+    Epoch 159: training loss = -0.900 / validation loss = -0.899
+
+Epoch 160: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.51s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.02it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.70it/s]
 31%|███       | 9/29 [00:07<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.60it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.78it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.74it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.58it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.74it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.16it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.08it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.72it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.30it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.65it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:17<00:00,  1.68it/s]
+
+Epoch 160: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.34it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.00it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.01it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.25it/s]
 40%|████      | 23/57 [00:01<00:01, 26.88it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.36it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.38it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.51it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.80it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.77it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.16it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.27it/s]
100%|██████████| 57/57 [00:02<00:00, 27.15it/s]
100%|██████████| 57/57 [00:02<00:00, 22.45it/s]
+    Epoch 160: training loss = -0.905 / validation loss = -0.894
+
+Epoch 161: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.54s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.11s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.01it/s]
 21%|██        | 6/29 [00:04<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:07<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.58it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.77it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.68it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.23it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.89it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.68it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.21it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.80it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.20it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.62it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.15it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.79it/s]
100%|██████████| 29/29 [00:16<00:00,  3.39it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 161: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:17,  3.12it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.36it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.44it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.62it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.56it/s]
 40%|████      | 23/57 [00:01<00:01, 26.56it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.01it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.72it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.06it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.09it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.05it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.52it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.02it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.21it/s]
100%|██████████| 57/57 [00:02<00:00, 23.43it/s]
+    Epoch 161: training loss = -0.910 / validation loss = -0.906
+    Saved model: unet-161.pth.tar
+
+Epoch 162: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.70s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.18s/it]
 10%|█         | 3/29 [00:02<00:17,  1.45it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.18it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.01it/s]
 21%|██        | 6/29 [00:05<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:05<00:11,  2.00it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.66it/s]
 31%|███       | 9/29 [00:07<00:16,  1.24it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.70it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.25it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.90it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.64it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.15it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.80it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.26it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.70it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.09it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.72it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.34it/s]
 76%|███████▌  | 22/29 [00:13<00:03,  1.79it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.83it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.40it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.32it/s]
 90%|████████▉ | 26/29 [00:15<00:01,  1.77it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.89it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.48it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 162: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.57it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.39it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.35it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.73it/s]
 40%|████      | 23/57 [00:01<00:01, 26.55it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.62it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.17it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.45it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.95it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.10it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.90it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.35it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.11it/s]
100%|██████████| 57/57 [00:02<00:00, 23.74it/s]
+    Epoch 162: training loss = -0.910 / validation loss = -0.907
+    Saved model: unet-162.pth.tar
+
+Epoch 163: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.65s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.00it/s]
 21%|██        | 6/29 [00:05<00:16,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.98it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.65it/s]
 31%|███       | 9/29 [00:07<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.60it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.79it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.23it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.66it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.19it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.84it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.16it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.81it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.15it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.56it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.08it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.71it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.17it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.10it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.74it/s]
100%|██████████| 29/29 [00:16<00:00,  3.07it/s]
100%|██████████| 29/29 [00:18<00:00,  1.60it/s]
+
+Epoch 163: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.60it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.86it/s]
 23%|██▎       | 13/57 [00:00<00:01, 22.18it/s]
 32%|███▏      | 18/57 [00:00<00:01, 28.76it/s]
 39%|███▊      | 22/57 [00:00<00:01, 28.84it/s]
 46%|████▌     | 26/57 [00:01<00:01, 29.41it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.97it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 27.35it/s]
 63%|██████▎   | 36/57 [00:01<00:00, 24.85it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 23.48it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 25.44it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.63it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.25it/s]
100%|██████████| 57/57 [00:02<00:00, 29.38it/s]
100%|██████████| 57/57 [00:02<00:00, 24.27it/s]
+    Epoch 163: training loss = -0.910 / validation loss = -0.906
+
+Epoch 164: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.65s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.10it/s]
 21%|██        | 6/29 [00:04<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.02it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.69it/s]
 31%|███       | 9/29 [00:06<00:16,  1.24it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.48it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.99it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.62it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.22it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.52it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.03it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.66it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.29it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.17it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.83it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.49it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.99it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.61it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.36it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.51it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.02it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.64it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 164: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.20it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.31it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.25it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.09it/s]
 40%|████      | 23/57 [00:01<00:01, 27.43it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.04it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.55it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.64it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.22it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 28.01it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.46it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.43it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 30.01it/s]
100%|██████████| 57/57 [00:02<00:00, 23.76it/s]
+    Epoch 164: training loss = -0.911 / validation loss = -0.899
+
+Epoch 165: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.57s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.12it/s]
 21%|██        | 6/29 [00:04<00:14,  1.58it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.16it/s]
 28%|██▊       | 8/29 [00:04<00:07,  2.86it/s]
 31%|███       | 9/29 [00:06<00:16,  1.22it/s]
 34%|███▍      | 10/29 [00:06<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.16it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.79it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.67it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.38it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.01it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.43it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.19it/s]
 76%|███████▌  | 22/29 [00:14<00:06,  1.10it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.50it/s]
 83%|████████▎ | 24/29 [00:15<00:02,  2.00it/s]
 86%|████████▌ | 25/29 [00:16<00:02,  1.49it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.99it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.36it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  3.04it/s]
100%|██████████| 29/29 [00:17<00:00,  1.71it/s]
+
+Epoch 165: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:02<02:31,  2.71s/it]
  9%|▉         | 5/57 [00:02<00:22,  2.34it/s]
 16%|█▌        | 9/57 [00:02<00:10,  4.75it/s]
 21%|██        | 12/57 [00:03<00:06,  6.83it/s]
 26%|██▋       | 15/57 [00:03<00:04,  9.25it/s]
 33%|███▎      | 19/57 [00:03<00:03, 12.22it/s]
 40%|████      | 23/57 [00:03<00:02, 15.61it/s]
 47%|████▋     | 27/57 [00:03<00:01, 18.26it/s]
 54%|█████▍    | 31/57 [00:03<00:01, 20.89it/s]
 61%|██████▏   | 35/57 [00:03<00:01, 21.89it/s]
 68%|██████▊   | 39/57 [00:04<00:00, 22.38it/s]
 75%|███████▌  | 43/57 [00:04<00:00, 24.28it/s]
 82%|████████▏ | 47/57 [00:04<00:00, 24.33it/s]
 89%|████████▉ | 51/57 [00:04<00:00, 24.90it/s]
 96%|█████████▋| 55/57 [00:04<00:00, 27.80it/s]
100%|██████████| 57/57 [00:04<00:00, 11.79it/s]
+    Epoch 165: training loss = -0.911 / validation loss = -0.907
+    Saved model: unet-165.pth.tar
+
+Epoch 166: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.68s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.51it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.08it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.76it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.75it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.72it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.26it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.69it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.23it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.86it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.29it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.70it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.25it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.74it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.31it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.53it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.04it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.67it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 166: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.57it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.66it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.51it/s]
 32%|███▏      | 18/57 [00:00<00:01, 28.30it/s]
 39%|███▊      | 22/57 [00:00<00:01, 28.06it/s]
 46%|████▌     | 26/57 [00:01<00:01, 28.70it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.57it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.90it/s]
 63%|██████▎   | 36/57 [00:01<00:00, 25.58it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 23.39it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 25.68it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.73it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.17it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.04it/s]
100%|██████████| 57/57 [00:06<00:00,  8.77it/s]
+    Epoch 166: training loss = -0.910 / validation loss = -0.887
+
+Epoch 167: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.52s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:06<00:29,  1.27s/it]
 24%|██▍       | 7/29 [00:06<00:19,  1.12it/s]
 28%|██▊       | 8/29 [00:06<00:13,  1.56it/s]
 31%|███       | 9/29 [00:07<00:09,  2.07it/s]
 34%|███▍      | 10/29 [00:07<00:07,  2.69it/s]
 38%|███▊      | 11/29 [00:07<00:05,  3.44it/s]
 41%|████▏     | 12/29 [00:07<00:04,  4.21it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.28it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.73it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.29it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.96it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.62it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.80it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.20it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.85it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.22it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.18it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.83it/s]
100%|██████████| 29/29 [00:16<00:00,  3.28it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 167: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.36it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.23it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.48it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.66it/s]
 40%|████      | 23/57 [00:01<00:01, 26.30it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.35it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.08it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.07it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.67it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.08it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.80it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.42it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.33it/s]
100%|██████████| 57/57 [00:02<00:00, 24.23it/s]
+    Epoch 167: training loss = -0.910 / validation loss = -0.908
+    Saved model: unet-167.pth.tar
+
+Epoch 168: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.11s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.70it/s]
 31%|███       | 9/29 [00:06<00:16,  1.18it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.48it/s]
 38%|███▊      | 11/29 [00:07<00:09,  2.00it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.62it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.56it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.08it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.71it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.24it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.14it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.79it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.09it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.73it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.22it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.62it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.16it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.80it/s]
100%|██████████| 29/29 [00:16<00:00,  3.36it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 168: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.95it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.48it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.14it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.30it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.59it/s]
 40%|████      | 23/57 [00:01<00:01, 25.87it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.97it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.89it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.78it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.84it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.33it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.45it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.91it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.95it/s]
100%|██████████| 57/57 [00:02<00:00, 23.87it/s]
+    Epoch 168: training loss = -0.911 / validation loss = -0.899
+
+Epoch 169: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:08,  2.46s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.09s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.94it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.60it/s]
 31%|███       | 9/29 [00:07<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.55it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.92it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.53it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.08it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.71it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.19it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.61it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.13it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.77it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.18it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.59it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.12it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.76it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.66it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.20it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.85it/s]
100%|██████████| 29/29 [00:16<00:00,  3.23it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 169: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.41it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.44it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.61it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.35it/s]
 40%|████      | 23/57 [00:01<00:01, 26.85it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.29it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.99it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.21it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.63it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.05it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.26it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.33it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.29it/s]
100%|██████████| 57/57 [00:04<00:00, 13.11it/s]
+    Epoch 169: training loss = -0.912 / validation loss = -0.908
+    Saved model: unet-169.pth.tar
+
+Epoch 170: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.66s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.51it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.08it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.76it/s]
 31%|███       | 9/29 [00:07<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.16it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.79it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.62it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.16it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.64it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.26it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.70it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.25it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  2.22it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.70it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.23it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.14it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.70it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.23it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.29it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 170: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.01it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.22it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.20it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.08it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.70it/s]
 40%|████      | 23/57 [00:01<00:01, 26.32it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.32it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.63it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.61it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.33it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.03it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.88it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.20it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.87it/s]
100%|██████████| 57/57 [00:02<00:00, 23.64it/s]
+    Epoch 170: training loss = -0.912 / validation loss = -0.906
+
+Epoch 171: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.51s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.09it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.73it/s]
 31%|███       | 9/29 [00:06<00:15,  1.29it/s]
 34%|███▍      | 10/29 [00:07<00:13,  1.40it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.89it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.50it/s]
 45%|████▍     | 13/29 [00:08<00:11,  1.41it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.52it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.03it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.66it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.36it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.47it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.97it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.56it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.42it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.51it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.98it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.60it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.39it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.52it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.01it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.63it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 171: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.94it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.01it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.10it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.17it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.32it/s]
 40%|████      | 23/57 [00:01<00:01, 27.31it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.21it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.58it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.61it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.47it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.15it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.00it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.05it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 30.96it/s]
100%|██████████| 57/57 [00:02<00:00, 23.72it/s]
+    Epoch 171: training loss = -0.909 / validation loss = -0.887
+
+Epoch 172: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.52s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.11s/it]
 10%|█         | 3/29 [00:02<00:17,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:06<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.61it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.16it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.82it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.14it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.54it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.06it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.69it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.58it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.74it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.30it/s]
 76%|███████▌  | 22/29 [00:13<00:03,  1.75it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.27it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.93it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.17it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.59it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.11it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.74it/s]
100%|██████████| 29/29 [00:16<00:00,  3.27it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 172: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.21it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.91it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.25it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.98it/s]
 40%|████      | 23/57 [00:01<00:01, 26.53it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.76it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.91it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.71it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.40it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.60it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.14it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.54it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.41it/s]
100%|██████████| 57/57 [00:02<00:00, 23.37it/s]
+    Epoch 172: training loss = -0.906 / validation loss = -0.908
+
+Epoch 173: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.54s/it]
  7%|▋         | 2/29 [00:03<00:44,  1.66s/it]
 10%|█         | 3/29 [00:03<00:24,  1.05it/s]
 14%|█▍        | 4/29 [00:03<00:15,  1.61it/s]
 17%|█▋        | 5/29 [00:04<00:18,  1.31it/s]
 21%|██        | 6/29 [00:04<00:12,  1.85it/s]
 24%|██▍       | 7/29 [00:05<00:08,  2.48it/s]
 28%|██▊       | 8/29 [00:05<00:06,  3.24it/s]
 31%|███       | 9/29 [00:07<00:16,  1.20it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.64it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.17it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.83it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.63it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.15it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.80it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.26it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.71it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.94it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.55it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.66it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.12it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.76it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.67it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.04it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.63it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 173: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.18it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.77it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.46it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.78it/s]
 40%|████      | 23/57 [00:01<00:01, 26.13it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.83it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.58it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.02it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.84it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.37it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.46it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.99it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.67it/s]
100%|██████████| 57/57 [00:02<00:00, 23.75it/s]
+    Epoch 173: training loss = -0.907 / validation loss = -0.901
+
+Epoch 174: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.48s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.11s/it]
 10%|█         | 3/29 [00:02<00:17,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.28it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.00it/s]
 21%|██        | 6/29 [00:04<00:16,  1.43it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.97it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.63it/s]
 31%|███       | 9/29 [00:06<00:14,  1.34it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.51it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.03it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.66it/s]
 45%|████▍     | 13/29 [00:09<00:11,  1.35it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.11it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.75it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.31it/s]
 62%|██████▏   | 18/29 [00:12<00:08,  1.23it/s]
 66%|██████▌   | 19/29 [00:12<00:06,  1.66it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  2.20it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.60it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.72it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.26it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.92it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.48it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.52it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.01it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.62it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 174: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.16it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.84it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.86it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.96it/s]
 40%|████      | 23/57 [00:01<00:01, 27.08it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.29it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.74it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.68it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.36it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.97it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.31it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.59it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.75it/s]
100%|██████████| 57/57 [00:02<00:00, 23.55it/s]
+    Epoch 174: training loss = -0.909 / validation loss = -0.904
+
+Epoch 175: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.55s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.27it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.01it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.68it/s]
 31%|███       | 9/29 [00:07<00:18,  1.07it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.47it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.98it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.60it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.19it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.13it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.58it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.10it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.73it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.26it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.70it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.25it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.91it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.66it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.20it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.85it/s]
100%|██████████| 29/29 [00:16<00:00,  3.31it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 175: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.26it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.60it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.27it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.96it/s]
 40%|████      | 23/57 [00:01<00:01, 26.06it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.80it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.47it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.96it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.38it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.62it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.13it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.00it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.86it/s]
100%|██████████| 57/57 [00:02<00:00, 23.74it/s]
+    Epoch 175: training loss = -0.907 / validation loss = -0.907
+
+Epoch 176: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:07,  2.40s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.10it/s]
 21%|██        | 6/29 [00:05<00:17,  1.28it/s]
 24%|██▍       | 7/29 [00:05<00:12,  1.78it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.40it/s]
 31%|███       | 9/29 [00:06<00:15,  1.27it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.50it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.02it/s]
 41%|████▏     | 12/29 [00:09<00:14,  1.19it/s]
 45%|████▍     | 13/29 [00:09<00:09,  1.61it/s]
 48%|████▊     | 14/29 [00:09<00:07,  2.08it/s]
 52%|█████▏    | 15/29 [00:09<00:05,  2.72it/s]
 55%|█████▌    | 16/29 [00:09<00:03,  3.45it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.33it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.71it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.27it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.90it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.17it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.75it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.60it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.13it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.66it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 176: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.21it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.06it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.82it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.91it/s]
 40%|████      | 23/57 [00:01<00:01, 26.68it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.89it/s]
 53%|█████▎    | 30/57 [00:01<00:01, 26.66it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 25.61it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 25.99it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 25.77it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.01it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 25.85it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 25.15it/s]
100%|██████████| 57/57 [00:02<00:00, 24.65it/s]
100%|██████████| 57/57 [00:02<00:00, 22.90it/s]
+    Epoch 176: training loss = -0.911 / validation loss = -0.907
+
+Epoch 177: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:16,  2.75s/it]
  7%|▋         | 2/29 [00:02<00:32,  1.20s/it]
 10%|█         | 3/29 [00:02<00:18,  1.43it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.15it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:05<00:15,  1.48it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.04it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.71it/s]
 31%|███       | 9/29 [00:06<00:15,  1.29it/s]
 34%|███▍      | 10/29 [00:07<00:10,  1.75it/s]
 38%|███▊      | 11/29 [00:07<00:07,  2.33it/s]
 41%|████▏     | 12/29 [00:07<00:05,  3.02it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.11it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.75it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.17it/s]
 62%|██████▏   | 18/29 [00:13<00:11,  1.05s/it]
 66%|██████▌   | 19/29 [00:13<00:07,  1.30it/s]
 69%|██████▉   | 20/29 [00:13<00:05,  1.75it/s]
 72%|███████▏  | 21/29 [00:14<00:04,  1.63it/s]
 76%|███████▌  | 22/29 [00:14<00:03,  2.17it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.82it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  3.56it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.25it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.68it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.23it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.88it/s]
100%|██████████| 29/29 [00:16<00:00,  3.45it/s]
100%|██████████| 29/29 [00:18<00:00,  1.60it/s]
+
+Epoch 177: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.97it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.06it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.95it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.29it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.62it/s]
 40%|████      | 23/57 [00:01<00:01, 28.08it/s]
 46%|████▌     | 26/57 [00:01<00:01, 28.43it/s]
 51%|█████     | 29/57 [00:01<00:01, 25.01it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.43it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.74it/s]
 70%|███████   | 40/57 [00:01<00:00, 27.45it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.38it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.83it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.13it/s]
 95%|█████████▍| 54/57 [00:02<00:00, 27.47it/s]
100%|██████████| 57/57 [00:02<00:00, 24.80it/s]
100%|██████████| 57/57 [00:02<00:00, 22.98it/s]
+    Epoch 177: training loss = -0.912 / validation loss = -0.909
+    Saved model: unet-177.pth.tar
+
+Epoch 178: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:15,  2.68s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:05<00:25,  1.06s/it]
 21%|██        | 6/29 [00:05<00:16,  1.36it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.89it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.53it/s]
 31%|███       | 9/29 [00:07<00:17,  1.14it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.55it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.09it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.73it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.74it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.68it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.23it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.89it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.23it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.65it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.19it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.84it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.69it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.24it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.90it/s]
100%|██████████| 29/29 [00:16<00:00,  3.34it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 178: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.98it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.02it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.76it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.15it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.22it/s]
 40%|████      | 23/57 [00:01<00:01, 27.46it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.97it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.87it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 27.15it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.27it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.85it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.26it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.48it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.31it/s]
100%|██████████| 57/57 [00:02<00:00, 23.81it/s]
+    Epoch 178: training loss = -0.907 / validation loss = -0.909
+    Saved model: unet-178.pth.tar
+
+Epoch 179: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:10,  2.50s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.10s/it]
 10%|█         | 3/29 [00:02<00:16,  1.54it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:04<00:15,  1.53it/s]
 24%|██▍       | 7/29 [00:04<00:10,  2.01it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.68it/s]
 31%|███       | 9/29 [00:06<00:15,  1.29it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.49it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.97it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.59it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.32it/s]
 48%|████▊     | 14/29 [00:09<00:11,  1.36it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.83it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.41it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.38it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.43it/s]
 66%|██████▌   | 19/29 [00:12<00:05,  1.92it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.51it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.43it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.47it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.96it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.57it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.47it/s]
 90%|████████▉ | 26/29 [00:16<00:02,  1.48it/s]
 93%|█████████▎| 27/29 [00:16<00:01,  1.98it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.59it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 179: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.57it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.37it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.96it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.96it/s]
 40%|████      | 23/57 [00:01<00:01, 26.43it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.87it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.51it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.37it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.65it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.36it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.35it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.79it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.55it/s]
100%|██████████| 57/57 [00:02<00:00, 23.67it/s]
+    Epoch 179: training loss = -0.913 / validation loss = -0.907
+
+Epoch 180: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.67s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:05<00:25,  1.05s/it]
 21%|██        | 6/29 [00:05<00:16,  1.36it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.89it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.53it/s]
 31%|███       | 9/29 [00:07<00:17,  1.12it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.06it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.71it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.73it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.20it/s]
 62%|██████▏   | 18/29 [00:12<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.17it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.82it/s]
 72%|███████▏  | 21/29 [00:16<00:12,  1.54s/it]
 76%|███████▌  | 22/29 [00:16<00:07,  1.11s/it]
 79%|███████▉  | 23/29 [00:16<00:04,  1.23it/s]
 83%|████████▎ | 24/29 [00:16<00:02,  1.67it/s]
 86%|████████▌ | 25/29 [00:18<00:03,  1.05it/s]
 90%|████████▉ | 26/29 [00:18<00:02,  1.43it/s]
 93%|█████████▎| 27/29 [00:18<00:01,  1.92it/s]
 97%|█████████▋| 28/29 [00:19<00:00,  2.52it/s]
100%|██████████| 29/29 [00:19<00:00,  2.78it/s]
100%|██████████| 29/29 [00:19<00:00,  1.50it/s]
+
+Epoch 180: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.41it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.50it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.16it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.40it/s]
 40%|████      | 23/57 [00:01<00:01, 27.49it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.11it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.66it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.84it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.98it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.20it/s]
 81%|████████  | 46/57 [00:01<00:00, 27.49it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 27.74it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 26.49it/s]
100%|██████████| 57/57 [00:02<00:00, 25.87it/s]
100%|██████████| 57/57 [00:02<00:00, 23.64it/s]
+    Epoch 180: training loss = -0.908 / validation loss = -0.883
+
+Epoch 181: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:09,  2.47s/it]
  7%|▋         | 2/29 [00:02<00:29,  1.08s/it]
 10%|█         | 3/29 [00:02<00:16,  1.53it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.29it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.45it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.98it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.64it/s]
 31%|███       | 9/29 [00:07<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.57it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.68it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.15it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.56it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.06it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.69it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.24it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.68it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.20it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.85it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.16it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.57it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.08it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.71it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.17it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.59it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.12it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.75it/s]
100%|██████████| 29/29 [00:16<00:00,  3.32it/s]
100%|██████████| 29/29 [00:16<00:00,  1.71it/s]
+
+Epoch 181: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:03<03:19,  3.56s/it]
  9%|▉         | 5/57 [00:03<00:29,  1.79it/s]
 16%|█▌        | 9/57 [00:03<00:12,  3.71it/s]
 23%|██▎       | 13/57 [00:03<00:07,  5.91it/s]
 33%|███▎      | 19/57 [00:04<00:03,  9.94it/s]
 40%|████      | 23/57 [00:04<00:02, 12.63it/s]
 47%|████▋     | 27/57 [00:04<00:02, 14.97it/s]
 53%|█████▎    | 30/57 [00:04<00:01, 16.98it/s]
 58%|█████▊    | 33/57 [00:04<00:01, 18.63it/s]
 65%|██████▍   | 37/57 [00:04<00:00, 20.59it/s]
 72%|███████▏  | 41/57 [00:04<00:00, 22.27it/s]
 79%|███████▉  | 45/57 [00:05<00:00, 24.96it/s]
 84%|████████▍ | 48/57 [00:05<00:00, 24.55it/s]
 89%|████████▉ | 51/57 [00:05<00:00, 23.61it/s]
 96%|█████████▋| 55/57 [00:05<00:00, 27.29it/s]
100%|██████████| 57/57 [00:05<00:00, 10.03it/s]
+    Epoch 181: training loss = -0.909 / validation loss = -0.908
+
+Epoch 182: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.59s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.04it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.02it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.69it/s]
 31%|███       | 9/29 [00:07<00:16,  1.20it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.56it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.09it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.74it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.20it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.51it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.01it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.63it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.26it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.62it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.80it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.60it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.12it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.76it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.65it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.19it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.84it/s]
100%|██████████| 29/29 [00:16<00:00,  3.22it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 182: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.95it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.43it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.57it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.64it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.87it/s]
 40%|████      | 23/57 [00:01<00:01, 26.45it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.23it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.68it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.85it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.96it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.18it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.94it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.58it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.51it/s]
100%|██████████| 57/57 [00:02<00:00, 23.92it/s]
+    Epoch 182: training loss = -0.914 / validation loss = -0.910
+    Saved model: unet-182.pth.tar
+
+Epoch 183: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.63s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.15s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.22it/s]
 17%|█▋        | 5/29 [00:04<00:24,  1.01s/it]
 21%|██        | 6/29 [00:05<00:16,  1.42it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.96it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.62it/s]
 31%|███       | 9/29 [00:07<00:17,  1.15it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.57it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.11it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.76it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.14it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.16it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.57it/s]
 66%|██████▌   | 19/29 [00:12<00:04,  2.09it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.73it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.17it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.81it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.15it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.55it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.07it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.70it/s]
100%|██████████| 29/29 [00:16<00:00,  3.28it/s]
100%|██████████| 29/29 [00:17<00:00,  1.70it/s]
+
+Epoch 183: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.75it/s]
 16%|█▌        | 9/57 [00:00<00:02, 20.21it/s]
 21%|██        | 12/57 [00:00<00:02, 21.60it/s]
 28%|██▊       | 16/57 [00:00<00:01, 26.52it/s]
 35%|███▌      | 20/57 [00:00<00:01, 25.60it/s]
 40%|████      | 23/57 [00:01<00:01, 25.89it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.48it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.40it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.38it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.49it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.28it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.54it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.76it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.59it/s]
100%|██████████| 57/57 [00:02<00:00, 24.24it/s]
+    Epoch 183: training loss = -0.910 / validation loss = -0.907
+
+Epoch 184: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.55s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.27it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.51it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.98it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.64it/s]
 31%|███       | 9/29 [00:06<00:16,  1.24it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.07it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.71it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.28it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.52it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.03it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.65it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.31it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.56it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.08it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.71it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.34it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.56it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.08it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.71it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.25it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.56it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.07it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.70it/s]
100%|██████████| 29/29 [00:16<00:00,  3.44it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 184: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.04it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.13it/s]
 16%|█▌        | 9/57 [00:00<00:02, 18.83it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.08it/s]
 33%|███▎      | 19/57 [00:00<00:01, 26.35it/s]
 40%|████      | 23/57 [00:01<00:01, 27.58it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.42it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.58it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.30it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 26.58it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 26.10it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.94it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 26.28it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 26.22it/s]
100%|██████████| 57/57 [00:02<00:00, 25.72it/s]
100%|██████████| 57/57 [00:02<00:00, 23.61it/s]
+    Epoch 184: training loss = -0.914 / validation loss = -0.910
+
+Epoch 185: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.65s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.06it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.72it/s]
 31%|███       | 9/29 [00:06<00:16,  1.22it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.66it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.20it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.59it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.13it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.74it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.28it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.73it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.28it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.40it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.28it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.72it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.26it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.26it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.70it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.24it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.47it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 185: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.09it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.56it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.44it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.34it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.73it/s]
 40%|████      | 23/57 [00:01<00:01, 27.19it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.06it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.84it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.73it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.34it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.52it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.73it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.50it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.19it/s]
100%|██████████| 57/57 [00:02<00:00, 23.92it/s]
+    Epoch 185: training loss = -0.915 / validation loss = -0.911
+    Saved model: unet-185.pth.tar
+
+Epoch 186: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.65s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.48it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:04<00:15,  1.50it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.05it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.73it/s]
 31%|███       | 9/29 [00:06<00:16,  1.24it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.47it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.96it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.57it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.29it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.65it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.18it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.84it/s]
 59%|█████▊    | 17/29 [00:12<00:13,  1.16s/it]
 62%|██████▏   | 18/29 [00:12<00:09,  1.17it/s]
 66%|██████▌   | 19/29 [00:13<00:06,  1.49it/s]
 69%|██████▉   | 20/29 [00:13<00:04,  2.00it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.16it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.58it/s]
 79%|███████▉  | 23/29 [00:15<00:03,  1.75it/s]
 83%|████████▎ | 24/29 [00:15<00:02,  2.31it/s]
 86%|████████▌ | 25/29 [00:17<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:17<00:01,  1.67it/s]
 93%|█████████▎| 27/29 [00:17<00:01,  1.88it/s]
 97%|█████████▋| 28/29 [00:17<00:00,  2.48it/s]
100%|██████████| 29/29 [00:17<00:00,  1.61it/s]
+
+Epoch 186: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.03it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.52it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.16it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.33it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.58it/s]
 40%|████      | 23/57 [00:01<00:01, 26.25it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.24it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.87it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.19it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.63it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.45it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.70it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.34it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 32.12it/s]
100%|██████████| 57/57 [00:02<00:00, 23.71it/s]
+    Epoch 186: training loss = -0.914 / validation loss = -0.907
+
+Epoch 187: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.61s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.03it/s]
 21%|██        | 6/29 [00:04<00:15,  1.47it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.03it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.70it/s]
 31%|███       | 9/29 [00:07<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.78it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.64it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.18it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.84it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.18it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.60it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.13it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.77it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.18it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.59it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.12it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.76it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.17it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.82it/s]
100%|██████████| 29/29 [00:16<00:00,  3.57it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 187: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:19,  2.94it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.99it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.25it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.48it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.39it/s]
 40%|████      | 23/57 [00:01<00:01, 26.10it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.64it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.55it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.31it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.78it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.36it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.57it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.35it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.95it/s]
100%|██████████| 57/57 [00:02<00:00, 23.42it/s]
+    Epoch 187: training loss = -0.913 / validation loss = -0.910
+
+Epoch 188: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.13s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.00it/s]
 21%|██        | 6/29 [00:05<00:16,  1.43it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.98it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.65it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.78it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.62it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.16it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.80it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.27it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.18it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.83it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.25it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.56it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.08it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.71it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.23it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.55it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.07it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.70it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 188: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.99it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.46it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.56it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.61it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.97it/s]
 40%|████      | 23/57 [00:01<00:01, 26.00it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.60it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.63it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.19it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.61it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.42it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 25.77it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.79it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.74it/s]
100%|██████████| 57/57 [00:02<00:00, 24.00it/s]
+    Epoch 188: training loss = -0.914 / validation loss = -0.912
+    Saved model: unet-188.pth.tar
+
+Epoch 189: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.67s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:04<00:21,  1.12it/s]
 21%|██        | 6/29 [00:04<00:14,  1.60it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.92it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:07<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.63it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.04it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.68it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.27it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.71it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.85it/s]
 55%|█████▌    | 16/29 [00:09<00:05,  2.43it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.31it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.77it/s]
 66%|██████▌   | 19/29 [00:13<00:10,  1.02s/it]
 69%|██████▉   | 20/29 [00:13<00:06,  1.33it/s]
 72%|███████▏  | 21/29 [00:15<00:08,  1.03s/it]
 76%|███████▌  | 22/29 [00:15<00:05,  1.32it/s]
 79%|███████▉  | 23/29 [00:15<00:03,  1.61it/s]
 83%|████████▎ | 24/29 [00:15<00:02,  2.12it/s]
 86%|████████▌ | 25/29 [00:17<00:03,  1.26it/s]
 90%|████████▉ | 26/29 [00:17<00:01,  1.69it/s]
 93%|█████████▎| 27/29 [00:18<00:01,  1.87it/s]
 97%|█████████▋| 28/29 [00:18<00:00,  2.45it/s]
100%|██████████| 29/29 [00:18<00:00,  1.57it/s]
+
+Epoch 189: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.13it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.09it/s]
 23%|██▎       | 13/57 [00:00<00:02, 20.95it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.29it/s]
 40%|████      | 23/57 [00:01<00:01, 25.71it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.41it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.05it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.16it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.66it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.53it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 26.01it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.10it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 28.94it/s]
100%|██████████| 57/57 [00:02<00:00, 23.43it/s]
+    Epoch 189: training loss = -0.915 / validation loss = -0.908
+
+Epoch 190: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.65s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.21it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.04s/it]
 21%|██        | 6/29 [00:05<00:16,  1.38it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.91it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.56it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.78it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.24it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.67it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.22it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.88it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.63it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.17it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.81it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.28it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.72it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.28it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.94it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.16it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.58it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.09it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.73it/s]
100%|██████████| 29/29 [00:16<00:00,  3.40it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 190: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.65it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.62it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.72it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.56it/s]
 40%|████      | 23/57 [00:01<00:01, 26.88it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.57it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.75it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.14it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.19it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.70it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.27it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.30it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.84it/s]
100%|██████████| 57/57 [00:02<00:00, 23.82it/s]
+    Epoch 190: training loss = -0.911 / validation loss = -0.911
+
+Epoch 191: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.59s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.17s/it]
 10%|█         | 3/29 [00:02<00:17,  1.46it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.19it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:05<00:17,  1.30it/s]
 24%|██▍       | 7/29 [00:05<00:12,  1.81it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.43it/s]
 31%|███       | 9/29 [00:06<00:15,  1.29it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.52it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.04it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.68it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.28it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.48it/s]
 52%|█████▏    | 15/29 [00:09<00:07,  1.99it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.60it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.38it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.60it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.12it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.76it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.31it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.74it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.30it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.65it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.30it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.74it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.29it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.32it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 191: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.05it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.27it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.08it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.20it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.21it/s]
 40%|████      | 23/57 [00:01<00:01, 26.20it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.02it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.36it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.64it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.53it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.69it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.33it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.74it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.56it/s]
100%|██████████| 57/57 [00:02<00:00, 23.69it/s]
+    Epoch 191: training loss = -0.915 / validation loss = -0.911
+
+Epoch 192: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:13,  2.62s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.49it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.23it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.04s/it]
 21%|██        | 6/29 [00:05<00:16,  1.38it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.92it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:07<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.59it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.13it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.78it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.31it/s]
 48%|████▊     | 14/29 [00:09<00:08,  1.71it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.27it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.87it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.33it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.60it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.12it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.74it/s]
 72%|███████▏  | 21/29 [00:15<00:11,  1.43s/it]
 76%|███████▌  | 22/29 [00:16<00:08,  1.16s/it]
 79%|███████▉  | 23/29 [00:16<00:05,  1.19it/s]
 83%|████████▎ | 24/29 [00:16<00:03,  1.54it/s]
 86%|████████▌ | 25/29 [00:17<00:03,  1.18it/s]
 90%|████████▉ | 26/29 [00:18<00:02,  1.28it/s]
 93%|█████████▎| 27/29 [00:18<00:01,  1.73it/s]
 97%|█████████▋| 28/29 [00:18<00:00,  2.14it/s]
100%|██████████| 29/29 [00:19<00:00,  1.52it/s]
+
+Epoch 192: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.02it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.20it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.24it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.22it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.66it/s]
 40%|████      | 23/57 [00:01<00:01, 27.19it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.77it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.34it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.77it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.00it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 28.00it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.35it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.46it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.21it/s]
100%|██████████| 57/57 [00:02<00:00, 23.26it/s]
+    Epoch 192: training loss = -0.915 / validation loss = -0.910
+
+Epoch 193: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.65s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.00it/s]
 21%|██        | 6/29 [00:05<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.65it/s]
 31%|███       | 9/29 [00:07<00:16,  1.23it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.69it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.25it/s]
 41%|████▏     | 12/29 [00:07<00:05,  2.88it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.18it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.13it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.77it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.24it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.67it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.21it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.86it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.24it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.68it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.23it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.49it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.63it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.16it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.36it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 193: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:27,  2.05it/s]
 16%|█▌        | 9/57 [00:00<00:02, 17.09it/s]
 23%|██▎       | 13/57 [00:00<00:02, 19.66it/s]
 33%|███▎      | 19/57 [00:01<00:01, 23.84it/s]
 40%|████      | 23/57 [00:01<00:01, 25.48it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.52it/s]
 54%|█████▍    | 31/57 [00:01<00:01, 25.99it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.92it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.60it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.47it/s]
 82%|████████▏ | 47/57 [00:02<00:00, 26.61it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.03it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.89it/s]
100%|██████████| 57/57 [00:02<00:00, 23.20it/s]
+    Epoch 193: training loss = -0.916 / validation loss = -0.913
+    Saved model: unet-193.pth.tar
+
+Epoch 194: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.66s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.07it/s]
 21%|██        | 6/29 [00:04<00:15,  1.52it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.09it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.77it/s]
 31%|███       | 9/29 [00:07<00:17,  1.17it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.60it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.15it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.80it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.23it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.66it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.20it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.86it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.21it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.64it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.15it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.79it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.20it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.62it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.15it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.79it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.19it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.61it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.13it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.78it/s]
100%|██████████| 29/29 [00:16<00:00,  1.75it/s]
+
+Epoch 194: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.11it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.38it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.12it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.25it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.71it/s]
 40%|████      | 23/57 [00:01<00:01, 27.42it/s]
 47%|████▋     | 27/57 [00:01<00:01, 27.18it/s]
 53%|█████▎    | 30/57 [00:01<00:00, 27.47it/s]
 58%|█████▊    | 33/57 [00:01<00:00, 26.84it/s]
 65%|██████▍   | 37/57 [00:01<00:00, 27.13it/s]
 72%|███████▏  | 41/57 [00:01<00:00, 27.28it/s]
 79%|███████▉  | 45/57 [00:01<00:00, 27.88it/s]
 84%|████████▍ | 48/57 [00:01<00:00, 26.90it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 25.67it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.14it/s]
100%|██████████| 57/57 [00:02<00:00, 23.76it/s]
+    Epoch 194: training loss = -0.913 / validation loss = -0.884
+
+Epoch 195: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.60s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.05it/s]
 21%|██        | 6/29 [00:05<00:15,  1.44it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.99it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.65it/s]
 31%|███       | 9/29 [00:06<00:16,  1.25it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.62it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.17it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.83it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.21it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.63it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.17it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.25it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.68it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.22it/s]
 69%|██████▉   | 20/29 [00:12<00:04,  2.16it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.34it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.65it/s]
 79%|███████▉  | 23/29 [00:13<00:02,  2.18it/s]
 83%|████████▎ | 24/29 [00:14<00:02,  2.18it/s]
 86%|████████▌ | 25/29 [00:15<00:03,  1.33it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.70it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.25it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.33it/s]
100%|██████████| 29/29 [00:18<00:00,  1.54it/s]
+
+Epoch 195: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.56it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.19it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.03it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.30it/s]
 40%|████      | 23/57 [00:01<00:01, 26.71it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.46it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.47it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.96it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 26.46it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 28.22it/s]
 81%|████████  | 46/57 [00:01<00:00, 27.61it/s]
 86%|████████▌ | 49/57 [00:02<00:00, 27.59it/s]
 93%|█████████▎| 53/57 [00:02<00:00, 26.83it/s]
100%|██████████| 57/57 [00:02<00:00, 26.23it/s]
100%|██████████| 57/57 [00:02<00:00, 23.53it/s]
+    Epoch 195: training loss = -0.909 / validation loss = -0.912
+
+Epoch 196: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:12,  2.58s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.14s/it]
 10%|█         | 3/29 [00:02<00:17,  1.50it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.24it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:05<00:17,  1.29it/s]
 24%|██▍       | 7/29 [00:05<00:12,  1.80it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.42it/s]
 31%|███       | 9/29 [00:06<00:14,  1.39it/s]
 34%|███▍      | 10/29 [00:07<00:13,  1.39it/s]
 38%|███▊      | 11/29 [00:07<00:09,  1.87it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.47it/s]
 45%|████▍     | 13/29 [00:08<00:10,  1.52it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.60it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.13it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.78it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.45it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.47it/s]
 66%|██████▌   | 19/29 [00:11<00:05,  1.97it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.58it/s]
 72%|███████▏  | 21/29 [00:13<00:05,  1.44it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.45it/s]
 79%|███████▉  | 23/29 [00:14<00:03,  1.94it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.55it/s]
 86%|████████▌ | 25/29 [00:15<00:02,  1.47it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.51it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.01it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.63it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 196: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.06it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.49it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.65it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.67it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.51it/s]
 40%|████      | 23/57 [00:01<00:01, 26.52it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.09it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.46it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 25.55it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.58it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.02it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 25.87it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.46it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.28it/s]
100%|██████████| 57/57 [00:02<00:00, 24.25it/s]
+    Epoch 196: training loss = -0.916 / validation loss = -0.913
+
+Epoch 197: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:05,  2.35s/it]
  7%|▋         | 2/29 [00:02<00:28,  1.04s/it]
 10%|█         | 3/29 [00:02<00:16,  1.55it/s]
 14%|█▍        | 4/29 [00:02<00:10,  2.30it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.08it/s]
 21%|██        | 6/29 [00:04<00:16,  1.41it/s]
 24%|██▍       | 7/29 [00:04<00:11,  1.95it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.61it/s]
 31%|███       | 9/29 [00:06<00:17,  1.16it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.05it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.69it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.17it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.58it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.10it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.74it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.14it/s]
 62%|██████▏   | 18/29 [00:11<00:07,  1.54it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.05it/s]
 69%|██████▉   | 20/29 [00:12<00:03,  2.68it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.64it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.17it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.81it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.21it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.64it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.04it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.66it/s]
100%|██████████| 29/29 [00:16<00:00,  1.73it/s]
+
+Epoch 197: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.97it/s]
  9%|▉         | 5/57 [00:00<00:04, 12.94it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.17it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.27it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.65it/s]
 40%|████      | 23/57 [00:01<00:01, 26.75it/s]
 47%|████▋     | 27/57 [00:01<00:01, 25.98it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.17it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.16it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.90it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.71it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.81it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.19it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.83it/s]
100%|██████████| 57/57 [00:02<00:00, 23.58it/s]
+    Epoch 197: training loss = -0.916 / validation loss = -0.913
+
+Epoch 198: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:14,  2.66s/it]
  7%|▋         | 2/29 [00:02<00:31,  1.16s/it]
 10%|█         | 3/29 [00:02<00:17,  1.47it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.20it/s]
 17%|█▋        | 5/29 [00:04<00:22,  1.06it/s]
 21%|██        | 6/29 [00:05<00:16,  1.39it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.93it/s]
 28%|██▊       | 8/29 [00:05<00:08,  2.57it/s]
 31%|███       | 9/29 [00:07<00:16,  1.24it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.51it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.03it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.66it/s]
 45%|████▍     | 13/29 [00:09<00:12,  1.25it/s]
 48%|████▊     | 14/29 [00:09<00:10,  1.50it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.00it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.62it/s]
 59%|█████▊    | 17/29 [00:11<00:09,  1.30it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.71it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.27it/s]
 69%|██████▉   | 20/29 [00:11<00:03,  2.94it/s]
 72%|███████▏  | 21/29 [00:13<00:06,  1.31it/s]
 76%|███████▌  | 22/29 [00:13<00:04,  1.69it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.22it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.87it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.20it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.62it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.13it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.77it/s]
100%|██████████| 29/29 [00:16<00:00,  3.30it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 198: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.95it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.09it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.40it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.45it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.37it/s]
 40%|████      | 23/57 [00:01<00:01, 26.35it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.39it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.87it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.20it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.74it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 27.38it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.23it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 26.63it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.19it/s]
100%|██████████| 57/57 [00:02<00:00, 23.24it/s]
+    Epoch 198: training loss = -0.915 / validation loss = -0.912
+
+Epoch 199: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:19,  2.83s/it]
  7%|▋         | 2/29 [00:02<00:33,  1.23s/it]
 10%|█         | 3/29 [00:03<00:18,  1.39it/s]
 14%|█▍        | 4/29 [00:03<00:11,  2.10it/s]
 17%|█▋        | 5/29 [00:05<00:24,  1.00s/it]
 21%|██        | 6/29 [00:05<00:16,  1.43it/s]
 24%|██▍       | 7/29 [00:05<00:11,  1.98it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.64it/s]
 31%|███       | 9/29 [00:07<00:17,  1.12it/s]
 34%|███▍      | 10/29 [00:07<00:12,  1.54it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.06it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.70it/s]
 45%|████▍     | 13/29 [00:09<00:14,  1.13it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.54it/s]
 52%|█████▏    | 15/29 [00:10<00:06,  2.05it/s]
 55%|█████▌    | 16/29 [00:10<00:04,  2.68it/s]
 59%|█████▊    | 17/29 [00:11<00:08,  1.34it/s]
 62%|██████▏   | 18/29 [00:11<00:06,  1.80it/s]
 66%|██████▌   | 19/29 [00:11<00:04,  2.38it/s]
 69%|██████▉   | 20/29 [00:12<00:02,  3.06it/s]
 72%|███████▏  | 21/29 [00:14<00:06,  1.21it/s]
 76%|███████▌  | 22/29 [00:14<00:04,  1.63it/s]
 79%|███████▉  | 23/29 [00:14<00:02,  2.16it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  2.81it/s]
 86%|████████▌ | 25/29 [00:16<00:03,  1.24it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  1.67it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.21it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  2.87it/s]
100%|██████████| 29/29 [00:16<00:00,  3.32it/s]
100%|██████████| 29/29 [00:16<00:00,  1.72it/s]
+
+Epoch 199: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  2.97it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.35it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.57it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.84it/s]
 33%|███▎      | 19/57 [00:00<00:01, 24.68it/s]
 40%|████      | 23/57 [00:01<00:01, 26.23it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.61it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 27.22it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.56it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 25.81it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.91it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.34it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.05it/s]
 96%|█████████▋| 55/57 [00:02<00:00, 29.96it/s]
100%|██████████| 57/57 [00:02<00:00, 23.84it/s]
+    Epoch 199: training loss = -0.914 / validation loss = -0.910
+
+Epoch 200: training
+
  0%|          | 0/29 [00:00<?, ?it/s]
  3%|▎         | 1/29 [00:02<01:11,  2.54s/it]
  7%|▋         | 2/29 [00:02<00:30,  1.12s/it]
 10%|█         | 3/29 [00:02<00:17,  1.52it/s]
 14%|█▍        | 4/29 [00:02<00:11,  2.27it/s]
 17%|█▋        | 5/29 [00:04<00:23,  1.02it/s]
 21%|██        | 6/29 [00:04<00:15,  1.46it/s]
 24%|██▍       | 7/29 [00:05<00:10,  2.01it/s]
 28%|██▊       | 8/29 [00:05<00:07,  2.68it/s]
 31%|███       | 9/29 [00:07<00:16,  1.19it/s]
 34%|███▍      | 10/29 [00:07<00:11,  1.62it/s]
 38%|███▊      | 11/29 [00:07<00:08,  2.16it/s]
 41%|████▏     | 12/29 [00:07<00:06,  2.82it/s]
 45%|████▍     | 13/29 [00:09<00:13,  1.16it/s]
 48%|████▊     | 14/29 [00:09<00:09,  1.57it/s]
 52%|█████▏    | 15/29 [00:09<00:06,  2.09it/s]
 55%|█████▌    | 16/29 [00:09<00:04,  2.72it/s]
 59%|█████▊    | 17/29 [00:11<00:10,  1.20it/s]
 62%|██████▏   | 18/29 [00:13<00:14,  1.27s/it]
 66%|██████▌   | 19/29 [00:14<00:09,  1.08it/s]
 69%|██████▉   | 20/29 [00:14<00:06,  1.47it/s]
 72%|███████▏  | 21/29 [00:14<00:04,  1.97it/s]
 76%|███████▌  | 22/29 [00:14<00:02,  2.57it/s]
 79%|███████▉  | 23/29 [00:14<00:01,  3.29it/s]
 83%|████████▎ | 24/29 [00:14<00:01,  4.08it/s]
 86%|████████▌ | 25/29 [00:16<00:02,  1.65it/s]
 90%|████████▉ | 26/29 [00:16<00:01,  2.12it/s]
 93%|█████████▎| 27/29 [00:16<00:00,  2.47it/s]
 97%|█████████▋| 28/29 [00:16<00:00,  3.13it/s]
100%|██████████| 29/29 [00:16<00:00,  1.74it/s]
+
+Epoch 200: validation
+
  0%|          | 0/57 [00:00<?, ?it/s]
  2%|▏         | 1/57 [00:00<00:18,  3.07it/s]
  9%|▉         | 5/57 [00:00<00:03, 13.35it/s]
 16%|█▌        | 9/57 [00:00<00:02, 19.28it/s]
 23%|██▎       | 13/57 [00:00<00:02, 21.71it/s]
 33%|███▎      | 19/57 [00:00<00:01, 25.94it/s]
 40%|████      | 23/57 [00:01<00:01, 27.04it/s]
 47%|████▋     | 27/57 [00:01<00:01, 26.74it/s]
 54%|█████▍    | 31/57 [00:01<00:00, 26.84it/s]
 61%|██████▏   | 35/57 [00:01<00:00, 26.02it/s]
 68%|██████▊   | 39/57 [00:01<00:00, 24.99it/s]
 75%|███████▌  | 43/57 [00:01<00:00, 26.99it/s]
 82%|████████▏ | 47/57 [00:01<00:00, 26.67it/s]
 89%|████████▉ | 51/57 [00:02<00:00, 27.09it/s]
 98%|█████████▊| 56/57 [00:02<00:00, 31.85it/s]
100%|██████████| 57/57 [00:02<00:00, 23.78it/s]
+    Epoch 200: training loss = -0.917 / validation loss = -0.914
+    Saved model: unet-200.pth.tar
+[ End training ]
+
+Elapsed time: 67 minutes
diff --git a/Session7_Medical_Image_Data_Analysis/CLS&SEG/segmentation.ipynb b/Session7_Medical_Image_Data_Analysis/CLS&SEG/segmentation.ipynb
new file mode 100644
index 0000000..2e37650
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/CLS&SEG/segmentation.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","id":"ed5994d8","metadata":{"id":"ed5994d8"},"source":["# Medical Image Segmentation\n","In this practice, we will do chest X-ray segmentation for the Shenzhen hospital data using the U-Net architecture.\n","\n","**Task**: Semantic binary segmentation; if the pixel is corresponding to lung location (1) or not (0)\n","\n","**Data**: Shenzhen hospital data\n","\n","**Model**: U-Net\n","\n","**Framework**: Pytorch"]},{"cell_type":"markdown","id":"7f1271ad","metadata":{"id":"7f1271ad"},"source":["## About dataset: The Shenzhen set\n","\n","* The standard digital image database for Tuberculosis.\n","* Provided by the National Library of Medicine, Maryland, USA in collaboration with Shenzhen No.3 People’s Hospital, Guangdong Medical College, Shenzhen, China.\n","* The dataset contains x-rays and corresponding segmented masks for lungs.\n","* Some masks are missing so it is advised to cross-reference the images and masks.\n","* For more detail, visit here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4256233/"]},{"cell_type":"markdown","id":"86a9cd43","metadata":{"id":"86a9cd43"},"source":["## Data preprocessing\n","\n","For simple use, images and masks were preprocessed by TA.\n","* Used images that have corresponding masks.\n","* Used 20% of training data as validation and test data. (10% each)"]},{"cell_type":"markdown","source":["## Mount Your Google Drive\n","\n","- **First, set proper runtime. (Python3 & GPU)**"],"metadata":{"id":"6cIJn5qXRYJL"},"id":"6cIJn5qXRYJL"},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"zL7PCdO9RaYz","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1733717419189,"user_tz":-540,"elapsed":20080,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}},"outputId":"f5a86d1e-1d0f-4c1c-e687-07d4219215df"},"id":"zL7PCdO9RaYz","execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","id":"6c9a483e","metadata":{"id":"6c9a483e"},"source":["## Import modules"]},{"cell_type":"code","execution_count":2,"id":"dfd16743","metadata":{"id":"dfd16743","executionInfo":{"status":"ok","timestamp":1733717429916,"user_tz":-540,"elapsed":7972,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}}},"outputs":[],"source":["import os\n","import csv\n","import time\n","import numpy as np\n","%matplotlib inline\n","import matplotlib.pyplot as plt\n","\n","from tqdm import tqdm\n","from PIL import Image\n","from pathlib import Path\n","\n","import torch\n","import torch.optim as optim\n","\n","from torch import nn\n","from torch.utils.data import Dataset, DataLoader\n","from torchvision import transforms as T\n","from torchvision.utils import save_image\n","\n","from torchsummary import summary"]},{"cell_type":"markdown","id":"09d6b1b2","metadata":{"id":"09d6b1b2"},"source":["## Data paths and hyperparameters"]},{"cell_type":"code","execution_count":3,"id":"2de5ff83","metadata":{"id":"2de5ff83","executionInfo":{"status":"ok","timestamp":1733717430471,"user_tz":-540,"elapsed":557,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}}},"outputs":[],"source":["# train: 452\n","TRAIN_IMG_PATH = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/DATA/CHNCXR/train_CXR_png\"\n","TRAIN_MASK_PATH = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/DATA/CHNCXR/train_masks\"\n","\n","# valid: 57\n","VALID_IMG_PATH = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/DATA/CHNCXR/valid_CXR_png\"\n","VALID_MASK_PATH = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/DATA/CHNCXR/valid_masks\"\n","\n","# test: 57\n","TEST_IMG_PATH = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/DATA/CHNCXR/test_CXR_png\"\n","TEST_MASK_PATH = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/DATA/CHNCXR/test_masks\"\n","\n","# hyperparameters\n","IMAGE_SIZE = 512\n","BATCH_SIZE = 16\n","EPOCHS = 200\n","RESULTS_FOLDER = \"/content/drive/MyDrive/Coursework/GCDA_24F/6_Image/results_seg/\"\n","\n","os.mkdir(RESULTS_FOLDER) if not os.path.exists(RESULTS_FOLDER) else None"]},{"cell_type":"markdown","id":"9035beca","metadata":{"id":"9035beca"},"source":["## Define custom dataset class"]},{"cell_type":"code","execution_count":4,"id":"4b3e755e","metadata":{"id":"4b3e755e","executionInfo":{"status":"ok","timestamp":1733717437466,"user_tz":-540,"elapsed":346,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}}},"outputs":[],"source":["class CXR_Seg_Dataset(Dataset):\n","    def __init__(self, image_path, mask_path, image_size):\n","        super(CXR_Seg_Dataset, self).__init__()\n","        self.image_path = image_path\n","        self.mask_path = mask_path\n","\n","        self.mask_names = os.listdir(mask_path)\n","        self.image_names = os.listdir(image_path)\n","\n","        self.transform = T.Compose([\n","            T.Resize((image_size, image_size)),\n","            T.ToTensor()\n","        ])\n","\n","    def __len__(self):\n","        return len(self.image_names)\n","\n","    def __getitem__(self, index):\n","        image_name = self.image_names[index]\n","        mask_name = self.mask_names[index]\n","\n","        image = Image.open(os.path.join(self.image_path, image_name)).convert(\"L\")\n","        mask = Image.open(os.path.join(self.mask_path, mask_name)).convert(\"L\")\n","\n","        return self.transform(image), self.transform(mask)"]},{"cell_type":"markdown","id":"969cda2e","metadata":{"id":"969cda2e"},"source":["## Define dataset and dataloader"]},{"cell_type":"code","execution_count":5,"id":"bff10e63","metadata":{"id":"bff10e63","outputId":"96662289-aa6e-4f7f-d897-9c82977cf7ca","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1733717442953,"user_tz":-540,"elapsed":5032,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Data Information\n","    Train data: 452\n","    Valid data: 57\n","    Test data: 57\n"]}],"source":["train_dataset = CXR_Seg_Dataset(TRAIN_IMG_PATH, TRAIN_MASK_PATH, IMAGE_SIZE)\n","valid_dataset = CXR_Seg_Dataset(VALID_IMG_PATH, VALID_MASK_PATH, IMAGE_SIZE)\n","test_dataset = CXR_Seg_Dataset(TEST_IMG_PATH, TEST_MASK_PATH, IMAGE_SIZE)\n","\n","print(\"Data Information\")\n","print(\"    Train data:\", len(train_dataset))\n","print(\"    Valid data:\", len(valid_dataset))\n","print(\"    Test data:\", len(test_dataset))\n","\n","dataloader_train = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE,\n","                              shuffle=True, num_workers=2, pin_memory=True)\n","dataloader_valid = DataLoader(dataset=valid_dataset, batch_size=BATCH_SIZE,\n","                              shuffle=False, num_workers=2, pin_memory=True)\n","dataloader_test = DataLoader(dataset=test_dataset, batch_size=BATCH_SIZE,\n","                             shuffle=False, num_workers=2, pin_memory=True)"]},{"cell_type":"markdown","id":"4e0a5915","metadata":{"id":"4e0a5915"},"source":["## Data sanity check"]},{"cell_type":"code","execution_count":6,"id":"90a3945a","metadata":{"id":"90a3945a","executionInfo":{"status":"ok","timestamp":1733717446238,"user_tz":-540,"elapsed":556,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}}},"outputs":[],"source":["def plot_mask(intput, target):\n","    sample = []\n","    for i in range(2):\n","        combined = np.hstack((intput[i].permute(1, 2, 0), target[i].permute(1, 2, 0)))\n","        sample.append(combined)\n","\n","    plt.figure(figsize=(12, 6))\n","    for i in range(2):\n","        plt.subplot(1, 2, i+1)\n","        plt.imshow(sample[i], cmap=\"gray\")\n","        plt.axis(\"off\")\n","    plt.show()"]},{"cell_type":"code","execution_count":7,"id":"4c1cd08f","metadata":{"id":"4c1cd08f","outputId":"d7d9fba6-f4cc-409e-c99b-9d04482384c8","colab":{"base_uri":"https://localhost:8080/","height":225},"executionInfo":{"status":"ok","timestamp":1733717519047,"user_tz":-540,"elapsed":72812,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["input, target = next(iter(dataloader_train)) # dataloader_valid # dataloader_test\n","plot_mask(input, target)"]},{"cell_type":"markdown","id":"24015f85","metadata":{"id":"24015f85"},"source":["# Define U-Net model class"]},{"cell_type":"markdown","id":"ff51b3bb","metadata":{"id":"ff51b3bb"},"source":["<img src=\"https://drive.google.com/uc?id=1iCBfTlTHv870VZVNNNOHqeNCYR54fVQq\" width=600px>\n","<figcaption align = \"left\">image source: https://arxiv.org/abs/1505.04597</figcaption>"]},{"cell_type":"code","execution_count":8,"id":"bb10e560","metadata":{"id":"bb10e560","executionInfo":{"status":"ok","timestamp":1733717525328,"user_tz":-540,"elapsed":341,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}}},"outputs":[],"source":["class Downsample(nn.Module):\n","    def __init__(self, in_channels, out_channels):\n","        super(Downsample, self).__init__()\n","        self.downsample = nn.Sequential(\n","            nn.Conv2d(in_channels, out_channels, kernel_size=(3,3), stride=1, padding=\"same\"),\n","            nn.ReLU(),\n","            nn.Conv2d(out_channels, out_channels, kernel_size=(3,3), stride=1, padding=\"same\"),\n","            nn.ReLU(),\n","        )\n","\n","    def forward(self, x):\n","        x = self.downsample(x)\n","        return x\n","\n","class Upsample(nn.Module):\n","    def __init__(self, in_channels, out_channels):\n","        super(Upsample, self).__init__()\n","        self.upsample = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=(2,2), stride=2)\n","        self.conv = nn.Sequential(\n","            nn.Conv2d(in_channels, out_channels, kernel_size=(3,3), stride=1, padding=\"same\"),\n","            nn.ReLU(),\n","            nn.Conv2d(out_channels, out_channels, kernel_size=(3,3), stride=1, padding=\"same\"),\n","            nn.ReLU(),\n","        )\n","\n","    def forward(self, x1, x2):\n","        x = torch.cat((self.upsample(x1), x2), axis=1)\n","        x = self.conv(x)\n","        return x\n","\n","class UNet(nn.Module):\n","    def __init__(self, in_channels, out_channels):\n","        super(UNet, self).__init__()\n","        self.down1 = Downsample(in_channels, 32)\n","        self.down2 = Downsample(32, 64)\n","        self.down3 = Downsample(64, 128)\n","        self.down4 = Downsample(128, 256)\n","\n","        self.down5 = Downsample(256, 512)\n","\n","        self.up1 = Upsample(512, 256)\n","        self.up2 = Upsample(256, 128)\n","        self.up3 = Upsample(128, 64)\n","        self.up4 = Upsample(64, 32)\n","\n","        self.maxpool = nn.MaxPool2d(kernel_size=(2,2), stride=2)\n","        self.convfinal = nn.Conv2d(32, out_channels, kernel_size=(1,1), stride=1, padding=\"valid\")\n","        self.sigmoid = nn.Sigmoid()\n","\n","    def forward(self, x):\n","        x1_1 = self.down1(x)\n","        x1_2 = self.maxpool(x1_1)\n","        x2_1 = self.down2(x1_2)\n","        x2_2 = self.maxpool(x2_1)\n","        x3_1 = self.down3(x2_2)\n","        x3_2 = self.maxpool(x3_1)\n","        x4_1 = self.down4(x3_2)\n","        x4_2 = self.maxpool(x4_1)\n","\n","        x5 = self.down5(x4_2)\n","\n","        x = self.up1(x5, x4_1)\n","        x = self.up2(x, x3_1)\n","        x = self.up3(x, x2_1)\n","        x = self.up4(x, x1_1)\n","\n","        x = self.convfinal(x)\n","        x = self.sigmoid(x)\n","\n","        return x"]},{"cell_type":"markdown","id":"0807b187","metadata":{"id":"0807b187"},"source":["## Define model and optimizer"]},{"cell_type":"code","execution_count":9,"id":"72beb204","metadata":{"id":"72beb204","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1733717531546,"user_tz":-540,"elapsed":1508,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}},"outputId":"51b2e491-b69d-444c-e17a-c536027877da"},"outputs":[{"output_type":"stream","name":"stdout","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1         [-1, 32, 512, 512]             320\n","              ReLU-2         [-1, 32, 512, 512]               0\n","            Conv2d-3         [-1, 32, 512, 512]           9,248\n","              ReLU-4         [-1, 32, 512, 512]               0\n","        Downsample-5         [-1, 32, 512, 512]               0\n","         MaxPool2d-6         [-1, 32, 256, 256]               0\n","            Conv2d-7         [-1, 64, 256, 256]          18,496\n","              ReLU-8         [-1, 64, 256, 256]               0\n","            Conv2d-9         [-1, 64, 256, 256]          36,928\n","             ReLU-10         [-1, 64, 256, 256]               0\n","       Downsample-11         [-1, 64, 256, 256]               0\n","        MaxPool2d-12         [-1, 64, 128, 128]               0\n","           Conv2d-13        [-1, 128, 128, 128]          73,856\n","             ReLU-14        [-1, 128, 128, 128]               0\n","           Conv2d-15        [-1, 128, 128, 128]         147,584\n","             ReLU-16        [-1, 128, 128, 128]               0\n","       Downsample-17        [-1, 128, 128, 128]               0\n","        MaxPool2d-18          [-1, 128, 64, 64]               0\n","           Conv2d-19          [-1, 256, 64, 64]         295,168\n","             ReLU-20          [-1, 256, 64, 64]               0\n","           Conv2d-21          [-1, 256, 64, 64]         590,080\n","             ReLU-22          [-1, 256, 64, 64]               0\n","       Downsample-23          [-1, 256, 64, 64]               0\n","        MaxPool2d-24          [-1, 256, 32, 32]               0\n","           Conv2d-25          [-1, 512, 32, 32]       1,180,160\n","             ReLU-26          [-1, 512, 32, 32]               0\n","           Conv2d-27          [-1, 512, 32, 32]       2,359,808\n","             ReLU-28          [-1, 512, 32, 32]               0\n","       Downsample-29          [-1, 512, 32, 32]               0\n","  ConvTranspose2d-30          [-1, 256, 64, 64]         524,544\n","           Conv2d-31          [-1, 256, 64, 64]       1,179,904\n","             ReLU-32          [-1, 256, 64, 64]               0\n","           Conv2d-33          [-1, 256, 64, 64]         590,080\n","             ReLU-34          [-1, 256, 64, 64]               0\n","         Upsample-35          [-1, 256, 64, 64]               0\n","  ConvTranspose2d-36        [-1, 128, 128, 128]         131,200\n","           Conv2d-37        [-1, 128, 128, 128]         295,040\n","             ReLU-38        [-1, 128, 128, 128]               0\n","           Conv2d-39        [-1, 128, 128, 128]         147,584\n","             ReLU-40        [-1, 128, 128, 128]               0\n","         Upsample-41        [-1, 128, 128, 128]               0\n","  ConvTranspose2d-42         [-1, 64, 256, 256]          32,832\n","           Conv2d-43         [-1, 64, 256, 256]          73,792\n","             ReLU-44         [-1, 64, 256, 256]               0\n","           Conv2d-45         [-1, 64, 256, 256]          36,928\n","             ReLU-46         [-1, 64, 256, 256]               0\n","         Upsample-47         [-1, 64, 256, 256]               0\n","  ConvTranspose2d-48         [-1, 32, 512, 512]           8,224\n","           Conv2d-49         [-1, 32, 512, 512]          18,464\n","             ReLU-50         [-1, 32, 512, 512]               0\n","           Conv2d-51         [-1, 32, 512, 512]           9,248\n","             ReLU-52         [-1, 32, 512, 512]               0\n","         Upsample-53         [-1, 32, 512, 512]               0\n","           Conv2d-54          [-1, 1, 512, 512]              33\n","          Sigmoid-55          [-1, 1, 512, 512]               0\n","================================================================\n","Total params: 7,759,521\n","Trainable params: 7,759,521\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 1.00\n","Forward/backward pass size (MB): 1374.00\n","Params size (MB): 29.60\n","Estimated Total Size (MB): 1404.60\n","----------------------------------------------------------------\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n","  warnings.warn(\n"]}],"source":["model = UNet(in_channels=1, out_channels=1).cuda()\n","#model = torch.nn.DataParallel(model).cuda() # use with multi-gpu environment\n","summary(model, input_size=(1, 512, 512), device=\"cuda\")\n","\n","optimizer = optim.Adam(model.parameters(), lr=1e-5)\n","scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=\"min\", factor=0.5, patience=3,\n","                                                 verbose=1, eps=1e-04, cooldown=2, min_lr=1e-6)"]},{"cell_type":"markdown","id":"91aa6323","metadata":{"id":"91aa6323"},"source":["## Define trainer"]},{"cell_type":"code","execution_count":10,"id":"e5f4e7c7","metadata":{"id":"e5f4e7c7","executionInfo":{"status":"ok","timestamp":1733717534752,"user_tz":-540,"elapsed":384,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}}},"outputs":[],"source":["class CXR_Seg_Trainer():\n","    def __init__(\n","        self,\n","        model,\n","        results_folder,\n","        dataloader_train,\n","        dataloader_valid,\n","        dataloader_test,\n","        epochs,\n","        optimizer,\n","        scheduler,\n","    ):\n","        super(CXR_Seg_Trainer, self).__init__()\n","\n","        self.model = model\n","        self.dataloader_train = dataloader_train\n","        self.dataloader_valid = dataloader_valid\n","        self.dataloader_test = dataloader_test\n","        self.epoch = 0\n","        self.epochs = epochs\n","        self.optimizer = optimizer\n","        self.scheduler = scheduler\n","\n","        self.results_folder = Path(results_folder)\n","        self.results_folder.mkdir(exist_ok=True)\n","\n","    def train(self):\n","        print(\"[ Start training ]\")\n","        self.model.train()\n","        valid_loss_min = 10000\n","        train_dice_list, valid_dice_list = [], []\n","\n","        while self.epoch < self.epochs:\n","            print(f\"\\nEpoch {self.epoch:3d}: training\")\n","            train_loss_sum = 0\n","            for _, (input, target) in enumerate(tqdm(self.dataloader_train)):\n","                input = input.cuda(non_blocking=True)\n","                target = target.cuda(non_blocking=True)\n","\n","                output = self.model(input)\n","\n","                loss = self.dice_coef_loss(output, target)\n","                loss.backward()\n","                self.optimizer.step()\n","                self.optimizer.zero_grad()\n","\n","                train_loss_sum += loss.item()*input.size(0)\n","\n","            train_loss_avg = train_loss_sum / len(self.dataloader_train.dataset)\n","            train_dice_list.append(-1*train_loss_avg)\n","\n","            # validation step\n","            print(f\"\\nEpoch {self.epoch:3d}: validation\")\n","            self.model.eval()\n","            with torch.no_grad():\n","                valid_loss_sum = 0\n","                for _, (input, target) in enumerate(tqdm(self.dataloader_valid)):\n","                    input = input.cuda(non_blocking=True)\n","                    target = target.cuda(non_blocking=True)\n","\n","                    output = self.model(input)\n","\n","                    loss = self.dice_coef_loss(output, target)\n","\n","                    valid_loss_sum += loss.item()*input.size(0)\n","\n","                valid_loss_avg = valid_loss_sum / len(self.dataloader_valid.dataset)\n","                valid_dice_list.append(-1*valid_loss_avg)\n","                self.scheduler.step(valid_loss_avg)\n","                print(f\"    Epoch {self.epoch:2d}: training loss = {train_loss_avg:.3f} / validation loss = {valid_loss_avg:.3f}\")\n","\n","                # save model if better validation loss\n","                if valid_loss_sum < valid_loss_min:\n","                    valid_loss_min = valid_loss_sum\n","                    self.save(self.epoch)\n","                    print(f\"    Saved model: unet-{self.epoch}.pth.tar\")\n","\n","            self.epoch += 1\n","        print(\"[ End training ]\")\n","        return train_dice_list, valid_dice_list\n","\n","    def test(self):\n","        print(\"[ Start test ]\")\n","        self.model.eval()\n","        with torch.no_grad():\n","            test_loss_sum = 0\n","            for i, (input, target) in enumerate(tqdm(self.dataloader_test)):\n","                input = input.cuda(non_blocking=True)\n","                target = target.cuda(non_blocking=True)\n","\n","                output = self.model(input)\n","\n","                loss = self.dice_coef_loss(output, target)\n","\n","                test_loss_sum += loss.item()*input.size(0)\n","                # save output and compare it to target\n","                if i < 3:\n","                    save_image(input.cpu(), os.path.join(self.results_folder, f\"test{i}_input.png\"))\n","                    save_image(target.cpu(), os.path.join(self.results_folder, f\"test{i}_target.png\"))\n","                    save_image(output.cpu(), os.path.join(self.results_folder, f\"test{i}_output.png\"))\n","            test_loss_avg = test_loss_sum / len(self.dataloader_test.dataset)\n","            print(f\"    test loss = {test_loss_avg:.3f}\")\n","            print(f\"    Dice coefficient = {-1.*test_loss_avg:.3f}\")\n","        print(\"[ End test ]\")\n","\n","        for i in range(0, 9, 3):\n","            plt.subplot(3, 3, i+1)\n","            img_input = plt.imread(os.path.join(RESULTS_FOLDER, f\"test{int(i//3)}_input.png\"))\n","            plt.imshow(img_input, cmap=\"gray\")\n","            plt.xlabel(\"Base Image\")\n","\n","            plt.subplot(3, 3, i+2)\n","            img_target = plt.imread(os.path.join(RESULTS_FOLDER, f\"test{int(i//3)}_target.png\"))\n","            plt.imshow(img_target, cmap=\"gray\")\n","            plt.xlabel(\"Mask\")\n","\n","            plt.subplot(3, 3, i+3)\n","            img_output = plt.imread(os.path.join(RESULTS_FOLDER, f\"test{int(i//3)}_output.png\"))\n","            plt.imshow(img_output, cmap=\"gray\")\n","            plt.xlabel(\"Prediction\")\n","\n","            plt.show()\n","        plt.savefig(os.path.join(RESULTS_FOLDER, \"test_examples.png\"))\n","        plt.close()\n","\n","    def dice_coef(self, y_true, y_pred):\n","        y_true_f = torch.flatten(y_true)\n","        y_pred_f = torch.flatten(y_pred)\n","        intersection = torch.sum(y_true_f * y_pred_f)\n","        return (2. * intersection + 1) / (torch.sum(y_true_f) + torch.sum(y_pred_f) + 1)\n","\n","    def dice_coef_loss(self, y_true, y_pred):\n","        return -self.dice_coef(y_true, y_pred)\n","\n","    def save(self, milestone):\n","        files = os.listdir(self.results_folder)\n","        remove = [f for f in files if \".pth.tar\" in f]\n","        if len(remove) > 0:\n","            os.remove(os.path.join(self.results_folder, remove[0]))\n","        torch.save({\"epoch\": milestone,\n","                    \"state_dict\": self.model.state_dict(),\n","                    \"optimizer\" : self.optimizer.state_dict()},\n","                    f\"{self.results_folder}/unet-{milestone}.pth.tar\")\n","\n","    def load(self, milestone):\n","        checkpoint = torch.load(f\"{self.results_folder}/unet-{milestone}.pth.tar\")\n","        self.model.load_state_dict(checkpoint[\"state_dict\"])\n","        self.optimizer.load_state_dict(checkpoint[\"optimizer\"])\n","        self.epoch = checkpoint[\"epoch\"]"]},{"cell_type":"markdown","id":"ce0e560d","metadata":{"id":"ce0e560d"},"source":["## Train the model"]},{"cell_type":"code","execution_count":11,"id":"79693a17","metadata":{"id":"79693a17","executionInfo":{"status":"ok","timestamp":1733717537405,"user_tz":-540,"elapsed":375,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}}},"outputs":[],"source":["# init the trainer\n","trainer = CXR_Seg_Trainer(\n","    model,\n","    RESULTS_FOLDER,\n","    dataloader_train,\n","    dataloader_valid,\n","    dataloader_test,\n","    EPOCHS,\n","    optimizer,\n","    scheduler\n",")"]},{"cell_type":"code","execution_count":12,"id":"53423037","metadata":{"id":"53423037","colab":{"base_uri":"https://localhost:8080/","height":435},"executionInfo":{"status":"error","timestamp":1733717558585,"user_tz":-540,"elapsed":19428,"user":{"displayName":"­장원영 / 학생 / 데이터사이언스학과","userId":"16960960525483449991"}},"outputId":"bf9624e2-be25-4dfb-e2c4-5972e9a1e068"},"outputs":[{"output_type":"stream","name":"stdout","text":["[ Start training ]\n","\n","Epoch   0: training\n"]},{"output_type":"stream","name":"stderr","text":["  7%|▋         | 2/29 [00:19<04:17,  9.52s/it]\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-12-8fb64e00c15a>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtrain_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrain_dice_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_dice_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# training!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtrain_end\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"\\nElapsed time: {(train_end - train_start) / 60:.0f} minutes\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-10-2b8a8b418b51>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     34\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"\\nEpoch {self.epoch:3d}: training\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m             \u001b[0mtrain_loss_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataloader_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m                 \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_blocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m                 \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_blocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1181\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1182\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1183\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    699\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             if (\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1447\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1448\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1449\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1450\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1400\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_alive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1402\u001b[0;31m                 \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1403\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1404\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1241\u001b[0m         \u001b[0;31m#   (bool: whether successfully get data, any: data if successful else None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1242\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1243\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1244\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1245\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.10/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.10/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["train_start = time.time()\n","train_dice_list, valid_dice_list = trainer.train() # training!\n","train_end = time.time()\n","print(f\"\\nElapsed time: {(train_end - train_start) / 60:.0f} minutes\")"]},{"cell_type":"code","execution_count":null,"id":"26ecc3b1","metadata":{"id":"26ecc3b1"},"outputs":[],"source":["# Plot the training and validation loss and accuracy\n","plt.plot(range(1, EPOCHS+1), train_dice_list, label=\"Training\")\n","plt.plot(range(1, EPOCHS+1), valid_dice_list, label=\"Validation\")\n","plt.title(\"Training and Validation Dice Coefficient\")\n","plt.xlabel(\"Epochs\")\n","plt.xticks(np.arange(0, EPOCHS+1, 5))\n","plt.ylabel(\"Dice Coefficient\")\n","plt.legend(loc=\"best\")\n","plt.show()\n","plt.savefig(os.path.join(RESULTS_FOLDER, \"dice_plot.png\"))\n","plt.close()"]},{"cell_type":"markdown","id":"9b0e23f3","metadata":{"id":"9b0e23f3"},"source":["<img src=\"https://drive.google.com/uc?id=1OlDau654MD9czh8ZJXbuif6zQEVbJvN4\" width=600px>"]},{"cell_type":"markdown","id":"f9780cfc","metadata":{"id":"f9780cfc"},"source":["# Test the model"]},{"cell_type":"code","execution_count":null,"id":"23e91642","metadata":{"id":"23e91642"},"outputs":[],"source":["trainer.load(199) # 0.929\n","trainer.test()"]},{"cell_type":"markdown","id":"0912e03e","metadata":{"id":"0912e03e"},"source":["<img src=\"https://drive.google.com/uc?id=1AwlhbRzGRRccaK0mPBD7W-5xgANjK3jF\" width=600px>"]}],"metadata":{"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":5}
\ No newline at end of file
diff --git a/Session7_Medical_Image_Data_Analysis/CLS&SEG/segmentation.py b/Session7_Medical_Image_Data_Analysis/CLS&SEG/segmentation.py
new file mode 100644
index 0000000..7c3016d
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/CLS&SEG/segmentation.py
@@ -0,0 +1,371 @@
+import os
+import time
+import argparse
+import numpy as np
+import matplotlib.pyplot as plt
+
+from tqdm import tqdm
+from PIL import Image
+from pathlib import Path
+
+import torch
+import torch.optim as optim
+
+from torch import nn
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms as T
+from torchvision.utils import save_image
+
+from torchsummary import summary
+
+parser = argparse.ArgumentParser(formatter_class = argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument("mode", metavar="MODE", type=str, choices=["train", "test"],
+                    help="Type train or test for the script.")
+parser.add_argument("--milestone", "-m", type=int, required=False, default=0,
+                    help="Milestone model number for test mode.")
+args = parser.parse_args()
+print(f"Mode: {args.mode}")
+
+
+
+# Data paths and hyperparameters
+# train: 452
+TRAIN_IMG_PATH = "Your path to 'train_CXR_png'"
+TRAIN_MASK_PATH = "Your path to 'train_masks'"
+
+# valid: 57
+VALID_IMG_PATH = "Your path to 'valid_CXR_png'"
+VALID_MASK_PATH = "Your path to 'valid_masks'"
+
+# test: 57
+TEST_IMG_PATH = "Your path to 'test_CXR_png'"
+TEST_MASK_PATH = "Your path to 'test_masks'"
+
+# hyperparameters
+IMAGE_SIZE = 512
+BATCH_SIZE = 16
+EPOCHS = 200
+RESULTS_FOLDER = "./results_seg/"
+os.mkdir(RESULTS_FOLDER) if not os.path.exists(RESULTS_FOLDER) else None
+
+
+
+# Define custom Dataset class
+class CXR_Seg_Dataset(Dataset):
+    def __init__(self, image_path, mask_path, image_size):
+        super(CXR_Seg_Dataset, self).__init__()
+        self.image_path = image_path
+        self.mask_path = mask_path
+
+        self.mask_names = os.listdir(mask_path)
+        self.image_names = os.listdir(image_path)
+
+        self.transform = T.Compose([
+            T.Resize((image_size, image_size)),
+            T.ToTensor()
+        ])
+    
+    def __len__(self):
+        return len(self.image_names)
+
+    def __getitem__(self, index):
+        image_name = self.image_names[index]
+        mask_name = self.mask_names[index]
+
+        image = Image.open(os.path.join(self.image_path, image_name)).convert("L")
+        mask = Image.open(os.path.join(self.mask_path, mask_name)).convert("L")
+
+        return self.transform(image), self.transform(mask)
+
+
+
+# Define dataset and dataloader
+train_dataset = CXR_Seg_Dataset(TRAIN_IMG_PATH, TRAIN_MASK_PATH, IMAGE_SIZE)
+valid_dataset = CXR_Seg_Dataset(VALID_IMG_PATH, VALID_MASK_PATH, IMAGE_SIZE)
+test_dataset = CXR_Seg_Dataset(TEST_IMG_PATH, TEST_MASK_PATH, IMAGE_SIZE)
+
+print("Data Information")
+print("    Train data:", len(train_dataset))
+print("    Valid data:", len(valid_dataset))
+print("    Test data:", len(test_dataset))
+
+dataloader_train = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, 
+                              shuffle=True, num_workers=4, pin_memory=True)
+dataloader_valid = DataLoader(dataset=valid_dataset, batch_size=1, 
+                              shuffle=False, num_workers=4, pin_memory=True)
+dataloader_test = DataLoader(dataset=test_dataset, batch_size=1, 
+                             shuffle=False, num_workers=4, pin_memory=True)
+
+
+
+# Define U-Net model class
+class Downsample(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(Downsample, self).__init__()
+        self.downsample = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=(3,3), stride=1, padding="same"),
+            nn.ReLU(),
+            nn.Conv2d(out_channels, out_channels, kernel_size=(3,3), stride=1, padding="same"),
+            nn.ReLU(),
+        )
+    
+    def forward(self, x):
+        x = self.downsample(x)
+        return x
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(Upsample, self).__init__()
+        self.upsample = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=(2,2), stride=2)
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=(3,3), stride=1, padding="same"),
+            nn.ReLU(),
+            nn.Conv2d(out_channels, out_channels, kernel_size=(3,3), stride=1, padding="same"),
+            nn.ReLU(),
+        )
+    
+    def forward(self, x1, x2):
+        x = torch.cat((self.upsample(x1), x2), axis=1)
+        x = self.conv(x)
+        return x
+
+class UNet(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(UNet, self).__init__()
+        self.down1 = Downsample(in_channels, 32)
+        self.down2 = Downsample(32, 64)
+        self.down3 = Downsample(64, 128)
+        self.down4 = Downsample(128, 256)
+        
+        self.down5 = Downsample(256, 512)
+
+        self.up1 = Upsample(512, 256)
+        self.up2 = Upsample(256, 128)
+        self.up3 = Upsample(128, 64)
+        self.up4 = Upsample(64, 32)
+
+        self.maxpool = nn.MaxPool2d(kernel_size=(2,2), stride=2)
+        self.convfinal = nn.Conv2d(32, out_channels, kernel_size=(1,1), stride=1, padding="valid")
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        x1_1 = self.down1(x)
+        x1_2 = self.maxpool(x1_1)
+        x2_1 = self.down2(x1_2)
+        x2_2 = self.maxpool(x2_1)
+        x3_1 = self.down3(x2_2)
+        x3_2 = self.maxpool(x3_1)
+        x4_1 = self.down4(x3_2)
+        x4_2 = self.maxpool(x4_1)
+        
+        x5 = self.down5(x4_2)
+
+        x = self.up1(x5, x4_1)
+        x = self.up2(x, x3_1)
+        x = self.up3(x, x2_1)
+        x = self.up4(x, x1_1)
+
+        x = self.convfinal(x)
+        x = self.sigmoid(x)
+
+        return x
+
+
+
+# Define model and optimizer
+model = UNet(in_channels=1, out_channels=1).cuda()
+#model = torch.nn.DataParallel(model).cuda() # use with multi-gpu environment
+summary(model, input_size=(1, 512, 512), device="cuda")
+
+optimizer = optim.Adam(model.parameters(), lr=1e-5)
+scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.5, patience=3,
+                                                 verbose=1, eps=1e-04, cooldown=2, min_lr=1e-6)
+
+
+
+# Define trainer
+class CXR_Seg_Trainer():
+    def __init__(
+        self, 
+        model, 
+        results_folder, 
+        dataloader_train, 
+        dataloader_valid, 
+        dataloader_test, 
+        epochs, 
+        optimizer, 
+        scheduler
+    ):
+        super(CXR_Seg_Trainer, self).__init__()
+
+        self.model = model
+        self.dataloader_train = dataloader_train
+        self.dataloader_valid = dataloader_valid
+        self.dataloader_test = dataloader_test
+        self.epoch = 0
+        self.epochs = epochs
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+
+        self.results_folder = Path(results_folder)
+        self.results_folder.mkdir(exist_ok=True)
+
+    def train(self):
+        print("[ Start training ]")
+        self.model.train()
+        valid_loss_min = 10000
+        train_dice_list, valid_dice_list = [], []
+
+        while self.epoch < self.epochs:
+            print(f"\nEpoch {self.epoch:3d}: training")
+            train_loss_sum = 0
+            for _, (input, target) in enumerate(tqdm(self.dataloader_train)):
+                input = input.cuda(non_blocking=True)
+                target = target.cuda(non_blocking=True)
+
+                output = self.model(input)
+
+                loss = self.dice_coef_loss(output, target)
+                loss.backward()
+                self.optimizer.step()
+                self.optimizer.zero_grad()
+
+                train_loss_sum += loss.item()*input.size(0)
+
+            train_loss_avg = train_loss_sum / len(self.dataloader_train.dataset)
+            train_dice_list.append(-1*train_loss_avg)
+
+            # validation step
+            print(f"\nEpoch {self.epoch:3d}: validation")
+            self.model.eval()
+            with torch.no_grad():
+                valid_loss_sum = 0
+                for _, (input, target) in enumerate(tqdm(self.dataloader_valid)):
+                    input = input.cuda(non_blocking=True)
+                    target = target.cuda(non_blocking=True)
+
+                    output = self.model(input)
+
+                    loss = self.dice_coef_loss(output, target)
+
+                    valid_loss_sum += loss.item()*input.size(0)
+
+                valid_loss_avg = valid_loss_sum / len(self.dataloader_valid.dataset)
+                valid_dice_list.append(-1*valid_loss_avg)
+                self.scheduler.step(valid_loss_avg)
+                print(f"    Epoch {self.epoch:2d}: training loss = {train_loss_avg:.3f} / validation loss = {valid_loss_avg:.3f}")
+
+                # save model if better validation loss
+                if valid_loss_sum < valid_loss_min:
+                    valid_loss_min = valid_loss_sum
+                    self.save(self.epoch)
+                    print(f"    Saved model: unet-{self.epoch}.pth.tar")
+                
+            self.epoch += 1
+        print("[ End training ]")
+        return train_dice_list, valid_dice_list
+
+    def test(self):
+        print("[ Start test ]")
+        self.model.eval()
+        with torch.no_grad():
+            test_loss_sum = 0
+            for i, (input, target) in enumerate(tqdm(self.dataloader_test)):
+                input = input.cuda(non_blocking=True)
+                target = target.cuda(non_blocking=True)
+                
+                output = self.model(input)
+
+                loss = self.dice_coef_loss(output, target)
+
+                test_loss_sum += loss.item()*input.size(0)
+                # save output and compare it to target
+                if i < 3:
+                    save_image(input.cpu(), os.path.join(self.results_folder, f"test{i}_input.png"))
+                    save_image(target.cpu(), os.path.join(self.results_folder, f"test{i}_target.png"))
+                    save_image(output.cpu(), os.path.join(self.results_folder, f"test{i}_output.png"))
+            test_loss_avg = test_loss_sum / len(self.dataloader_test.dataset)
+            print(f"    test loss = {test_loss_avg:.3f}")
+            print(f"    Dice coefficient = {-1.*test_loss_avg:.3f}")
+        print("[ End test ]")
+
+        for i in range(0, 9, 3):
+            plt.subplot(3, 3, i+1)
+            img_input = plt.imread(os.path.join(RESULTS_FOLDER, f"test{int(i//3)}_input.png"))
+            plt.imshow(img_input, cmap="gray")
+            plt.xlabel("Base Image")
+            
+            plt.subplot(3, 3, i+2)
+            img_target = plt.imread(os.path.join(RESULTS_FOLDER, f"test{int(i//3)}_target.png"))
+            plt.imshow(img_target, cmap="gray")
+            plt.xlabel("Mask")
+            
+            plt.subplot(3, 3, i+3)
+            img_output = plt.imread(os.path.join(RESULTS_FOLDER, f"test{int(i//3)}_output.png"))
+            plt.imshow(img_output, cmap="gray")
+            plt.xlabel("Prediction")
+
+            #plt.show()
+        plt.savefig(os.path.join(RESULTS_FOLDER, "test_examples.png"))
+        plt.close()
+
+    def dice_coef(self, y_true, y_pred):
+        y_true_f = torch.flatten(y_true)
+        y_pred_f = torch.flatten(y_pred)
+        intersection = torch.sum(y_true_f * y_pred_f)
+        return (2. * intersection + 1) / (torch.sum(y_true_f) + torch.sum(y_pred_f) + 1)
+
+    def dice_coef_loss(self, y_true, y_pred):
+        return -self.dice_coef(y_true, y_pred)
+    
+    def save(self, milestone):
+        files = os.listdir(self.results_folder)
+        remove = [f for f in files if ".pth.tar" in f]
+        if len(remove) > 0:
+            os.remove(os.path.join(self.results_folder, remove[0]))
+        torch.save({"epoch": milestone, 
+                    "state_dict": self.model.state_dict(), 
+                    "optimizer" : self.optimizer.state_dict()}, 
+                    f"{self.results_folder}/unet-{milestone}.pth.tar")
+
+    def load(self, milestone):
+        checkpoint = torch.load(f"{self.results_folder}/unet-{milestone}.pth.tar")
+        self.model.load_state_dict(checkpoint["state_dict"])
+        self.optimizer.load_state_dict(checkpoint["optimizer"])
+        self.epoch = checkpoint["epoch"]
+
+
+
+# Init the model
+trainer = CXR_Seg_Trainer(
+    model, 
+    RESULTS_FOLDER, 
+    dataloader_train, 
+    dataloader_valid, 
+    dataloader_test, 
+    EPOCHS, 
+    optimizer, 
+    scheduler
+)
+
+if args.mode == "train": # Train the model
+    train_start = time.time()
+    train_dice_list, valid_dice_list = trainer.train()
+    train_end = time.time()
+    print(f"\nElapsed time: {(train_end - train_start) / 60:.0f} minutes")
+
+    # Plot the training and validation dice coefficient values
+    plt.plot(range(1, EPOCHS+1), train_dice_list, label="Training")
+    plt.plot(range(1, EPOCHS+1), valid_dice_list, label="Validation")
+    plt.title("Training and Validation Dice Coefficient")
+    plt.xlabel("Epochs")
+    plt.xticks(np.arange(0, EPOCHS+1, 5))
+    plt.ylabel("Dice Coefficient")
+    plt.legend(loc="best")
+    #plt.show()
+    plt.savefig(os.path.join(RESULTS_FOLDER, "dice_plot.png"))
+    plt.close()
+
+elif args.mode == "test": # Test the model
+    trainer.load(args.milestone)
+    trainer.test() # 0.942
diff --git a/Session7_Medical_Image_Data_Analysis/configs/config_maisi.json b/Session7_Medical_Image_Data_Analysis/configs/config_maisi.json
new file mode 100644
index 0000000..fd1cfa1
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/configs/config_maisi.json
@@ -0,0 +1,186 @@
+{
+    "autoencoder_def": {
+        "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi",
+        "attention_levels": [
+            false,
+            false,
+            false
+        ],
+        "dim_split": 1,
+        "in_channels": "@image_channels",
+        "latent_channels": "@latent_channels",
+        "norm_eps": 1e-06,
+        "norm_float16": true,
+        "norm_num_groups": 32,
+        "num_channels": [
+            64,
+            128,
+            256
+        ],
+        "num_res_blocks": [
+            2,
+            2,
+            2
+        ],
+        "num_splits": 4,
+        "out_channels": "@image_channels",
+        "spatial_dims": "@spatial_dims",
+        "use_checkpointing": true,
+        "use_convtranspose": false,
+        "with_decoder_nonlocal_attn": false,
+        "with_encoder_nonlocal_attn": false
+    },
+    "controlnet_def": {
+        "_target_": "monai.apps.generation.maisi.networks.controlnet_maisi.ControlNetMaisi",
+        "attention_levels": [
+            false,
+            false,
+            true,
+            true
+        ],
+        "conditioning_embedding_in_channels": 8,
+        "conditioning_embedding_num_channels": [
+            8,
+            32,
+            64
+        ],
+        "in_channels": "@latent_channels",
+        "include_fc": true,
+        "num_channels": [
+            64,
+            128,
+            256,
+            512
+        ],
+        "num_head_channels": [
+            0,
+            0,
+            32,
+            32
+        ],
+        "num_res_blocks": 2,
+        "resblock_updown": true,
+        "spatial_dims": "@spatial_dims",
+        "use_flash_attention": true
+    },
+    "diffusion_unet_def": {
+        "_target_": "monai.apps.generation.maisi.networks.diffusion_model_unet_maisi.DiffusionModelUNetMaisi",
+        "attention_levels": [
+            false,
+            false,
+            true,
+            true
+        ],
+        "in_channels": "@latent_channels",
+        "include_bottom_region_index_input": "@include_body_region",
+        "include_fc": true,
+        "include_spacing_input": true,
+        "include_top_region_index_input": "@include_body_region",
+        "num_channels": [
+            64,
+            128,
+            256,
+            512
+        ],
+        "num_class_embeds": 4,
+        "num_head_channels": [
+            0,
+            0,
+            32,
+            32
+        ],
+        "num_res_blocks": 2,
+        "out_channels": "@latent_channels",
+        "resblock_updown": true,
+        "spatial_dims": "@spatial_dims",
+        "use_flash_attention": true
+    },
+    "image_channels": 1,
+    "include_body_region": false,
+    "latent_channels": 4,
+    "mask_generation_autoencoder_def": {
+        "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi",
+        "attention_levels": [
+            false,
+            false,
+            false
+        ],
+        "dim_split": 1,
+        "in_channels": 8,
+        "latent_channels": "@latent_channels",
+        "norm_eps": 1e-06,
+        "norm_float16": true,
+        "norm_num_groups": 32,
+        "num_channels": [
+            32,
+            64,
+            128
+        ],
+        "num_res_blocks": [
+            1,
+            2,
+            2
+        ],
+        "num_splits": 8,
+        "out_channels": 125,
+        "spatial_dims": "@spatial_dims",
+        "use_checkpointing": true,
+        "use_convtranspose": true,
+        "use_flash_attention": false,
+        "with_decoder_nonlocal_attn": false,
+        "with_encoder_nonlocal_attn": false
+    },
+    "mask_generation_diffusion_def": {
+        "_target_": "monai.networks.nets.diffusion_model_unet.DiffusionModelUNet",
+        "attention_levels": [
+            false,
+            false,
+            true,
+            true
+        ],
+        "channels": [
+            64,
+            128,
+            256,
+            512
+        ],
+        "cross_attention_dim": 10,
+        "in_channels": "@latent_channels",
+        "num_head_channels": [
+            0,
+            0,
+            32,
+            32
+        ],
+        "num_res_blocks": 2,
+        "out_channels": "@latent_channels",
+        "spatial_dims": "@spatial_dims",
+        "upcast_attention": true,
+        "use_flash_attention": true,
+        "with_conditioning": true
+    },
+    "mask_generation_latent_shape": [
+        4,
+        64,
+        64,
+        64
+    ],
+    "mask_generation_noise_scheduler": {
+        "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler",
+        "beta_end": 0.0195,
+        "beta_start": 0.0015,
+        "clip_sample": false,
+        "num_train_timesteps": 1000,
+        "schedule": "scaled_linear_beta"
+    },
+    "mask_generation_scale_factor": 1.0055984258651733,
+    "noise_scheduler": {
+        "_target_": "monai.networks.schedulers.rectified_flow.RFlowScheduler",
+        "num_train_timesteps": 1000,
+        "sample_method": "uniform",
+        "scale": 1.4,
+        "use_discrete_timesteps": false,
+        "use_timestep_transform": true
+    },
+    "spatial_dims": 3
+}
\ No newline at end of file
diff --git a/Session7_Medical_Image_Data_Analysis/configs/config_maisi3d-rflow_brats.json b/Session7_Medical_Image_Data_Analysis/configs/config_maisi3d-rflow_brats.json
new file mode 100644
index 0000000..94c8b56
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/configs/config_maisi3d-rflow_brats.json
@@ -0,0 +1,148 @@
+{
+    "spatial_dims": 3,
+    "image_channels": 1,
+    "latent_channels": 4,
+    "include_body_region": false,
+    "mask_generation_latent_shape": [
+        4,
+        64,
+        64,
+        64
+    ],
+    "autoencoder_def": {
+        "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi",
+        "spatial_dims": "@spatial_dims",
+        "in_channels": "@image_channels",
+        "out_channels": "@image_channels",
+        "latent_channels": "@latent_channels",
+        "num_channels": [
+            64,
+            128,
+            256
+        ],
+        "num_res_blocks": [2,2,2],
+        "norm_num_groups": 32,
+        "norm_eps": 1e-06,
+        "attention_levels": [
+            false,
+            false,
+            false
+        ],
+        "with_encoder_nonlocal_attn": false,
+        "with_decoder_nonlocal_attn": false,
+        "use_checkpointing": true,
+        "use_convtranspose": false,
+        "norm_float16": true,
+        "num_splits": 4,
+        "dim_split": 1
+    },
+    "diffusion_unet_def": {
+        "_target_": "monai.apps.generation.maisi.networks.diffusion_model_unet_maisi.DiffusionModelUNetMaisi",
+        "spatial_dims": "@spatial_dims",
+        "in_channels": "@latent_channels",
+        "out_channels": "@latent_channels",
+        "num_channels": [64, 128, 256, 512],
+        "attention_levels": [
+            false,
+            false,
+            true,
+            true
+        ],
+        "num_head_channels": [
+            0,
+            0,
+            32,
+            32
+        ],
+        "num_res_blocks": 2,
+        "use_flash_attention": true,
+        "include_top_region_index_input": "@include_body_region",
+        "include_bottom_region_index_input": "@include_body_region",
+        "include_spacing_input": true,
+        "resblock_updown": true,
+        "include_fc": true
+    },
+    "controlnet_def": {
+        "_target_": "monai.apps.generation.maisi.networks.controlnet_maisi.ControlNetMaisi",
+        "spatial_dims": "@spatial_dims",
+        "in_channels": "@latent_channels",
+        "num_channels": [64, 128, 256, 512],
+        "attention_levels": [
+            false,
+            false,
+            true,
+            true
+        ],
+        "num_head_channels": [
+            0,
+            0,
+            32,
+            32
+        ],
+        "num_res_blocks": 2,
+        "use_flash_attention": true,
+        "conditioning_embedding_in_channels": 8,
+        "conditioning_embedding_num_channels": [8, 32, 64],
+        "resblock_updown": true,
+        "include_fc": true
+    },
+    "mask_generation_autoencoder_def": {
+        "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi",
+        "spatial_dims": "@spatial_dims",
+        "in_channels": 8,
+        "out_channels": 125,
+        "latent_channels": "@latent_channels",
+        "num_channels": [
+            32,
+            64,
+            128
+        ],
+        "num_res_blocks": [1, 2, 2],
+        "norm_num_groups": 32,
+        "norm_eps": 1e-06,
+        "attention_levels": [
+            false,
+            false,
+            false
+        ],
+        "with_encoder_nonlocal_attn": false,
+        "with_decoder_nonlocal_attn": false,
+        "use_flash_attention": false,
+        "use_checkpointing": true,
+        "use_convtranspose": true,
+        "norm_float16": true,
+        "num_splits": 8,
+        "dim_split": 1
+    },
+    "mask_generation_diffusion_def": {
+        "_target_": "monai.networks.nets.diffusion_model_unet.DiffusionModelUNet",
+        "spatial_dims": "@spatial_dims",
+        "in_channels": "@latent_channels",
+        "out_channels": "@latent_channels",
+        "channels":[64, 128, 256, 512],
+        "attention_levels":[false, false, true, true],
+        "num_head_channels":[0, 0, 32, 32],
+        "num_res_blocks": 2,
+        "use_flash_attention": true,
+        "with_conditioning": true,
+        "upcast_attention": true,
+        "cross_attention_dim": 10
+    },
+    "mask_generation_scale_factor": 1.0055984258651733,
+    "noise_scheduler": {
+        "_target_": "monai.networks.schedulers.rectified_flow.RFlowScheduler",
+        "num_train_timesteps": 1000,
+        "use_discrete_timesteps": false,
+        "use_timestep_transform": true,
+        "sample_method": "uniform",
+        "scale":1.4
+    },
+    "mask_generation_noise_scheduler": {
+        "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler",
+        "num_train_timesteps": 1000,
+        "beta_start": 0.0015,
+        "beta_end": 0.0195,
+        "schedule": "scaled_linear_beta",
+        "clip_sample": false
+    }
+}
diff --git a/Session7_Medical_Image_Data_Analysis/configs/config_maisi_diff_model.json b/Session7_Medical_Image_Data_Analysis/configs/config_maisi_diff_model.json
new file mode 100644
index 0000000..23ce9aa
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/configs/config_maisi_diff_model.json
@@ -0,0 +1,45 @@
+{
+    "diffusion_unet_inference": {
+        "bottom_region_index": [
+            0,
+            0,
+            1,
+            0
+        ],
+        "dim": [
+            256,
+            256,
+            128
+        ],
+        "modality": 1,
+        "num_inference_steps": 100,
+        "random_seed": 0,
+        "spacing": [
+            0.9375,
+            0.9375,
+            1.2109375
+        ],
+        "top_region_index": [
+            0,
+            1,
+            0,
+            0
+        ],
+        "stochastic_scale": 0.0
+    },
+    "diffusion_unet_train": {
+        "batch_size": 8,
+        "cache_rate": 0.1,
+        "checkpointing_steps": 1000,
+        "gradient_accumulation_steps": 1,
+        "lr": 5e-6,
+        "max_train_steps": 200000,
+        "num_valid": 1000,
+        "report_to": true,
+        "seed": 42,
+        "val_batch_size": 4,
+        "validation_steps": 5000,
+        "loss_type": "l2",
+        "pretrained_steps": 0
+    }
+}
\ No newline at end of file
diff --git a/Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage1.json b/Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage1.json
new file mode 100644
index 0000000..b8c7860
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage1.json
@@ -0,0 +1,38 @@
+{
+    "data_option":{
+        "random_aug": true,
+        "spacing_type": "rand_zoom",
+        "spacing": null,
+        "select_channel": 0
+    },
+    "autoencoder_train": {
+        "batch_size": 2,
+        "patch_size": [96, 96, 96],
+        "val_batch_size": 1,
+        "val_patch_size": null,
+        "val_sliding_window_patch_size": [96, 96, 96],
+        "lr": 1e-5,
+        "perceptual_weight": 0.3,
+        "kl_weight": 1e-7,
+        "adv_weight": 0.1,
+        "recon_loss": "l1",
+        "cache": 0.1,
+        "amp": true
+    },
+    "custom_config": {
+        "weight_dtype": "fp16",
+        "seed": 42,
+        "report_to": true,
+        "output_dir": "/shared/s1/lab06/wonyoung/maisi/results",
+        "train_label_dir": "/leelabsg/data/BraTS25/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData/train",
+        "valid_label_dir": "/leelabsg/data/BraTS25/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData/valid",
+        "max_train_steps": 50000,
+        "num_valid": 1000,
+        "checkpointing_steps": 1000,
+        "validation_steps": 10000,
+        "early_stop_patience": 100000000,
+        "early_stop_min_delta": 0.01,
+        "gradient_accumulation_steps": 1,
+        "pretrained_steps": 0
+    }
+}
diff --git a/Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage2 b/Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage2
new file mode 100644
index 0000000..5186fc8
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage2
@@ -0,0 +1,38 @@
+{
+    "data_option":{
+        "random_aug": true,
+        "spacing_type": "rand_zoom",
+        "spacing": null,
+        "select_channel": 0
+    },
+    "autoencoder_train": {
+        "batch_size": 1,
+        "patch_size": [144, 144, 144],
+        "val_batch_size": 1,
+        "val_patch_size": null,
+        "val_sliding_window_patch_size": [144, 144, 144],
+        "lr": 1e-5,
+        "perceptual_weight": 0.3,
+        "kl_weight": 1e-7,
+        "adv_weight": 0.1,
+        "recon_loss": "l1",
+        "cache": 0.1,
+        "amp": true
+    },
+    "custom_config": {
+        "weight_dtype": "fp16",
+        "seed": 42,
+        "report_to": true,
+        "output_dir": "/shared/s1/lab06/wonyoung/maisi/results",
+        "train_label_dir": "/leelabsg/data/BraTS25/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData/train",
+        "valid_label_dir": "/leelabsg/data/BraTS25/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData/valid",
+        "max_train_steps": 125000,
+        "num_valid": 1000,
+        "checkpointing_steps": 1000,
+        "validation_steps": 10000,
+        "early_stop_patience": 100000000,
+        "early_stop_min_delta": 0.01,
+        "gradient_accumulation_steps": 4,
+        "pretrained_steps": 50000
+    }
+}
diff --git a/Session7_Medical_Image_Data_Analysis/configs/environment_maisi_diff_model.json b/Session7_Medical_Image_Data_Analysis/configs/environment_maisi_diff_model.json
new file mode 100644
index 0000000..826d6e5
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/configs/environment_maisi_diff_model.json
@@ -0,0 +1,12 @@
+{
+    "data_base_dir": "",
+    "embedding_base_dir": "/leelabsg/data/ex_MAISI/maisi_brats/embeddings",
+    "existing_ckpt_filepath": null,
+    "json_data_list": "/leelabsg/data/ex_MAISI/maisi_brats/train_files.json",
+    "model_dir": "/leelabsg/data/ex_MAISI/maisi_brats/models",
+    "model_filename": "diff_unet_ckpt.pt",
+    "output_dir": "/leelabsg/data/ex_MAISI/maisi_brats/predictions",
+    "output_prefix": "unet_3d",
+    "trained_autoencoder_path": "/shared/s1/lab06/wonyoung/maisi/weights/brats/brats_vae_stage2/model.pt",
+    "val_json_data_list": "/leelabsg/data/ex_MAISI/maisi_brats/valid_files.json"
+}
\ No newline at end of file
diff --git a/Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.py b/Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.py
new file mode 100644
index 0000000..f0079ab
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.py
@@ -0,0 +1,514 @@
+from __future__ import annotations
+
+import os
+import sys
+import json
+import yaml
+import wandb
+import signal
+import logging
+import argparse
+import warnings
+from pathlib import Path
+from tqdm import trange
+
+import torch
+import torch.distributed as dist
+from torch.nn.utils import clip_grad_norm_
+from torch.amp import GradScaler, autocast
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+import monai
+from monai.transforms import Compose
+from monai.config import print_config
+from monai.utils import first, set_determinism
+from monai.data import CacheDataset, DataLoader, DistributedSampler
+from monai.networks.schedulers.ddpm import DDPMPredictionType
+from monai.networks.schedulers.rectified_flow import RFlowScheduler
+
+from utils import count_parameters
+from scripts.utils import define_instance
+from scripts.diff_model_setting import load_config, setup_logging
+
+warnings.filterwarnings("ignore")
+if os.environ.get("RANK", "0") == "0":
+    print_config()
+
+def setup_ddp(): # DDP CHANGE: 분산 환경 초기화 함수
+    """Initializes the distributed process group."""
+    dist.init_process_group(backend="nccl")
+    torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
+
+def cleanup_ddp(): # DDP CHANGE: 분산 환경 정리 함수
+    """Cleans up the distributed process group."""
+    dist.destroy_process_group()
+
+def atomic_save(state, path):
+    tmp = path + ".tmp"
+    torch.save(state, tmp)
+    os.replace(tmp, path)
+
+SHUTDOWN_REQUESTED = False
+def graceful_shutdown(signum, frame):
+    global SHUTDOWN_REQUESTED
+    logging.info(f"\n[!] Received signal {signum}. Requesting graceful shutdown...")
+    SHUTDOWN_REQUESTED = True
+signal.signal(signal.SIGTERM, graceful_shutdown)
+signal.signal(signal.SIGINT, graceful_shutdown)
+
+def load_filenames(data_list_path: str, type: str) -> list:
+    with open(data_list_path, "r") as file:
+        json_data = json.load(file)
+    filenames = json_data[type]
+    return [_item["image"].replace(".nii.gz", "_emb.nii.gz").split("/")[-1] for _item in filenames] ###
+
+def prepare_transform(include_body_region: bool = False, include_modality: bool = False):
+    def _load_data_from_file(file_path, key):
+        with open(file_path) as f:
+            return torch.FloatTensor(json.load(f)[key])
+
+    def _load_int_from_file(file_path, key):
+        with open(file_path) as f:
+            return int(json.load(f)[key])
+
+    data_transforms_list = [
+            monai.transforms.LoadImaged(keys=["latent"]),
+            monai.transforms.EnsureChannelFirstd(keys=["latent"]),
+            monai.transforms.Lambdad(keys="spacing", func=lambda x: _load_data_from_file(x, "spacing")),
+            monai.transforms.Lambdad(keys="spacing", func=lambda x: x * 1e2),
+    ]
+    if include_body_region:
+        data_transforms_list += [
+            monai.transforms.Lambdad(keys="top_region_index", func=lambda x: _load_data_from_file(x, "top_region_index")),
+            monai.transforms.Lambdad(keys="bottom_region_index", func=lambda x: _load_data_from_file(x, "bottom_region_index")),
+            monai.transforms.Lambdad(keys="top_region_index", func=lambda x: x * 1e2),
+            monai.transforms.Lambdad(keys="bottom_region_index", func=lambda x: x * 1e2),
+        ]
+    if include_modality:
+        data_transforms_list += [
+            monai.transforms.Lambdad(keys="modality_class", func=lambda x: _load_int_from_file(x, "modality_class")),
+        ]
+    data_transforms = Compose(data_transforms_list)
+
+    return data_transforms # DataLoader(data_ds, num_workers=4, batch_size=batch_size, shuffle=shuffle_data)
+
+def calculate_scale_factor(train_loader: DataLoader, device: torch.device) -> torch.Tensor:
+    scale_factor_tensor = torch.zeros(1, device=device)
+    if dist.get_rank() == 0:
+        check_data = first(train_loader)
+        z = check_data["latent"].to(device)
+        scale_factor_tensor[0] = 1 / torch.std(z)
+    dist.broadcast(scale_factor_tensor, src=0)
+    logging.info(f"Synchronized scaling factor: {scale_factor_tensor.item():.4f}.")
+    return scale_factor_tensor
+
+def load_config():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--env_config_path", type=str, required=True)
+    parser.add_argument("--train_config_path", type=str, required=True)
+    parser.add_argument("--model_config_path", type=str, required=True)
+    parser.add_argument("--cpus_per_task", type=int, default=8, help="Number of CPUs allocated per task by Slurm.")
+    parser.add_argument("--no_amp", dest="amp", action="store_false")
+    parser.add_argument("--resume", action="store_true")
+    parser.add_argument("--run_name", type=str, default=None)
+    args = parser.parse_args()
+    env_config_dict = json.load(open(args.env_config_path, "r"))
+    for k, v in env_config_dict.items():
+        setattr(args, k, v)
+    model_config_dict = json.load(open(args.model_config_path, "r"))
+    for k, v in model_config_dict.items():
+        setattr(args, k, v)
+    train_config_dict = json.load(open(args.train_config_path, "r"))
+    for k, v in train_config_dict["diffusion_unet_inference"].items():
+        setattr(args, k, v)
+    for k, v in train_config_dict["diffusion_unet_train"].items():
+        setattr(args, k, v)
+    return args
+
+def resume_from_latest(unet, optimizer, lr_scheduler, scaler, output_dir, device):
+    if not os.path.exists(output_dir):
+        print("[Resume] No checkpoint directory found. Starting fresh.")
+        return 0, float("inf"), 0
+    
+    ckpts = [
+        d for d in os.listdir(output_dir)
+        if d.startswith("checkpoint-")
+        and os.path.isdir(os.path.join(output_dir, d))
+        and os.path.exists(os.path.join(output_dir, d, "diff_unet_ckpt.pt"))
+    ]
+    if len(ckpts) == 0:
+        print("[Resume] No checkpoint found in directory. Starting fresh.")
+        return 0, float("inf"), 0
+    
+    ckpts = sorted(ckpts, key=lambda x: int(x.split("-")[1]))
+    latest_ckpt_dir = ckpts[-1]
+    print(f"[Resume] Resuming from checkpoint: {latest_ckpt_dir}")
+
+    path = os.path.join(output_dir, latest_ckpt_dir, "diff_unet_ckpt.pt")
+    ckpt = torch.load(path, map_location=device)
+
+    unet.load_state_dict(ckpt["unet_state_dict"])
+    optimizer.load_state_dict(ckpt["optimizer"]) #### comment this line if s2 first submit 
+    if "lr_scheduler" in ckpt: lr_scheduler.load_state_dict(ckpt["lr_scheduler"]) #### comment this line if s2 first submit
+    if "scaler" in ckpt and scaler is not None: scaler.load_state_dict(ckpt["scaler"]) #### comment this line if s2 first submit
+
+    return ckpt.get("step", 0), ckpt.get("best_val_loss", float("inf")), ckpt.get("epoch", 0)
+
+def reduce_mean_scalar(x):
+    t = x.detach().reshape(1).to(torch.float32)
+    dist.all_reduce(t, op=dist.ReduceOp.SUM)
+    t /= dist.get_world_size()
+    return t.item()
+
+def main():
+    setup_ddp() # DDP CHANGE: 분산 설정 초기화
+    rank = int(os.environ["RANK"])
+    local_rank = int(os.environ["LOCAL_RANK"])
+    world_size = int(os.environ["WORLD_SIZE"])
+    
+    logger = setup_logging("training")
+
+    if rank == 0:
+        logger.info("="*50)
+        logger.info(f"✅ Training started with a total of {world_size} GPUs across all nodes.")
+        logger.info("="*50)
+
+    args = load_config()
+    device = torch.device(f"cuda:{local_rank}")
+    logger.info(f"Using {device} of {world_size}")
+    #weight_dtype = torch.float16 if args.weight_dtype == "fp16" else torch.float32
+
+    if rank == 0:
+        logger.info(f"[Opt] Using gradient accumulation with {args.gradient_accumulation_steps} steps.")
+
+    set_determinism(seed=args.seed + rank) # OPTIMIZATION: 각 프로세스에 다른 시드 줘서 데이터 증강의 다양성 확보
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    torch.backends.cudnn.benchmark = True # OPTIMIZATION: 입력 크기가 일정할 때 가장 효과적이므로 활성화
+    torch.backends.cudnn.deterministic = False
+    torch.set_float32_matmul_precision("high")
+
+    if rank == 0:
+        logger.info("[Config] Loaded hyperparameters:")
+        logger.info(yaml.dump(vars(args), sort_keys=False))
+        logger.info(f"Training started on {world_size} GPUs.")
+        if args.report_to:
+            wandb.init(project="MAISI_UNET_BraTS", config=args, name=args.run_name)
+        Path(args.model_dir).mkdir(parents=True, exist_ok=True)
+
+    unet = define_instance(args, "diffusion_unet_def").to(device)
+    noise_scheduler = define_instance(args, "noise_scheduler")
+    include_body_region = unet.include_top_region_index_input
+    include_modality = unet.num_class_embeds is not None
+    num_train_timesteps = args.noise_scheduler['num_train_timesteps']
+
+    # trianing data
+    filenames_train = load_filenames(args.json_data_list, "training")
+    train_files = []
+    for _i in range(len(filenames_train)):
+        str_lat = os.path.join(args.embedding_base_dir, filenames_train[_i])
+        if not os.path.exists(str_lat):
+            continue
+
+        str_info = str_lat + ".json"
+        train_files_i = {"latent": str_lat, "spacing": str_info} ###
+        if include_body_region:
+            train_files_i["top_region_index"] = str_info
+            train_files_i["bottom_region_index"] = str_info
+        if include_modality:
+            train_files_i["modality_class"] = str_info
+        train_files.append(train_files_i)
+
+    # validation data
+    filenames_valid = load_filenames(args.val_json_data_list, "validation")[:args.num_valid]
+    valid_files = []
+    for _i in range(len(filenames_valid)):
+        str_lat = os.path.join(args.embedding_base_dir, filenames_valid[_i])
+        if not os.path.exists(str_lat):
+            continue
+
+        str_info = str_lat + ".json"
+        valid_files_i = {"latent": str_lat, "spacing": str_info} ###
+        if include_body_region:
+            valid_files_i["top_region_index"] = str_info
+            valid_files_i["bottom_region_index"] = str_info
+        if include_modality:
+            valid_files_i["modality_class"] = str_info
+        valid_files.append(valid_files_i)
+
+    if rank == 0:
+        logger.info(f"Total number of training data is {len(train_files)}.")
+        logger.info(f"Total number of validation data is {len(valid_files)}.")
+    
+    data_transform = prepare_transform(include_body_region=include_body_region,
+                                       include_modality=include_modality)
+
+    workers_per_gpu = args.cpus_per_task // world_size
+    train_dataset = CacheDataset(data=train_files, transform=data_transform, cache_rate=args.cache_rate, num_workers=workers_per_gpu)
+    train_sampler = DistributedSampler(dataset=train_dataset, shuffle=True)
+    train_loader = DataLoader(
+        train_dataset, 
+        batch_size=args.batch_size, 
+        num_workers=workers_per_gpu, 
+        sampler=train_sampler, 
+        pin_memory=True, 
+        drop_last=True,
+        persistent_workers=True,
+        prefetch_factor=2
+    )
+    steps_per_epoch = len(train_loader)
+    
+    val_total = len(valid_files)
+    per_rank = (val_total + world_size - 1) // world_size  # ceil
+    start = rank * per_rank
+    end = min(val_total, start + per_rank)
+    val_files_shard = valid_files[start:end]
+    dataset_val = CacheDataset(data=val_files_shard, transform=data_transform, cache_rate=0.0, num_workers=workers_per_gpu)
+    valid_loader = DataLoader(
+        dataset_val, 
+        batch_size=args.val_batch_size, 
+        num_workers=workers_per_gpu, 
+        pin_memory=True,
+        persistent_workers=True,
+        prefetch_factor=2
+    )
+
+    scale_factor = calculate_scale_factor(train_loader, device)
+    optimizer = torch.optim.Adam(params=unet.parameters(), lr=args.lr, fused=True)
+    total_opt_steps = (args.max_train_steps - args.pretrained_steps + args.gradient_accumulation_steps - 1) // args.gradient_accumulation_steps #### check pretrained_steps when submitting fine-tuning job
+    lr_scheduler = torch.optim.lr_scheduler.PolynomialLR(optimizer, total_iters=total_opt_steps, power=2.0)
+    scaler = GradScaler() if args.amp else None
+
+    if args.loss_type == "l1": loss_pt = torch.nn.L1Loss()
+    elif args.loss_type == "l2": loss_pt = torch.nn.MSELoss()
+    
+    start_step, best_val_loss, start_epoch = 0, float("inf"), 0
+    if args.resume:
+        start_step, best_val_loss, start_epoch = resume_from_latest(
+            unet, optimizer, lr_scheduler, scaler, args.model_dir, device)
+    dist.barrier(device_ids=[local_rank])
+    
+    if rank == 0: logger.info("Compiling models with torch.compile()...")
+    unet = torch.compile(unet)
+    dist.barrier(device_ids=[local_rank])
+    unet = DDP(unet, device_ids=[local_rank], find_unused_parameters=False)
+
+    sync_vec = torch.tensor([start_step, float(best_val_loss)], device=device, dtype=torch.float32)
+    dist.broadcast(sync_vec, src=0)
+    start_step, best_val_loss = int(sync_vec[0].item()), float(sync_vec[1].item())
+
+    # count params
+    if rank == 0:
+        logger.info(f"### start_epoch: {start_epoch}")
+        param_counts = count_parameters(unet)
+        logger.info(f"### UNET's Trainable parameters: {param_counts['trainable']:,}")
+
+    def infinite_loader(loader, sampler, start_epoch=0):
+        epoch = start_epoch
+        while True:
+            sampler.set_epoch(epoch) # DDP CHANGE: 매 에포크마다 샘플러 시드 변경
+            for batch in loader:
+                yield batch
+            epoch += 1
+
+    train_iter = infinite_loader(train_loader, train_sampler, start_epoch)
+    progress_bar = trange(start_step, args.max_train_steps + 1,
+                          desc=f"Training on Rank {rank}",
+                          initial=start_step, total=args.max_train_steps + 1,
+                          disable=(rank != 0))
+
+    for step in progress_bar:
+        unet.train()
+        batch = next(train_iter)
+        latents = batch["latent"].to(device, non_blocking=True).contiguous() * scale_factor
+
+        if include_body_region:
+            top_region_index_tensor = batch["top_region_index"].to(device)
+            bottom_region_index_tensor = batch["bottom_region_index"].to(device)
+        if include_modality:
+            class_tensor = torch.tensor(batch["modality_class"], dtype=torch.long).to(device, non_blocking=True)
+        spacing_tensor = batch["spacing"].to(device, non_blocking=True)
+
+        unet_inputs_base = {"spacing_tensor": spacing_tensor,}
+        if include_body_region:
+            unet_inputs_base.update({
+                "top_region_index_tensor": batch["top_region_index"].to(device),
+                "bottom_region_index_tensor": batch["bottom_region_index"].to(device)
+            })
+        if include_modality:
+            unet_inputs_base["class_labels"] = torch.ones((len(latents),), dtype=torch.long).to(device)
+
+        with autocast(device_type="cuda", dtype=torch.float16, enabled=args.amp):
+            noise = torch.randn_like(latents)
+            if isinstance(noise_scheduler, RFlowScheduler):
+                timesteps = noise_scheduler.sample_timesteps(latents)
+            else:
+                timesteps = torch.randint(0, num_train_timesteps, (latents.shape[0],), device=latents.device).long()
+
+            noisy_latent = noise_scheduler.add_noise(original_samples=latents, noise=noise, timesteps=timesteps)
+
+            unet_inputs = {**unet_inputs_base, "x": noisy_latent, "timesteps": timesteps}
+            model_output = unet(**unet_inputs)
+
+            if noise_scheduler.prediction_type == DDPMPredictionType.EPSILON: model_gt = noise
+            elif noise_scheduler.prediction_type == DDPMPredictionType.SAMPLE: model_gt = latents
+            elif noise_scheduler.prediction_type == DDPMPredictionType.V_PREDICTION: model_gt = latents - noise
+
+            loss = loss_pt(model_output.float(), model_gt.float())
+            loss = loss / args.gradient_accumulation_steps
+
+        if args.amp:
+            scaler.scale(loss).backward()
+        else:
+            loss.backward()
+
+        if (step + 1) % args.gradient_accumulation_steps == 0:
+            if args.amp:
+                scaler.unscale_(optimizer)
+                clip_grad_norm_(unet.parameters(), 1.0)
+                scaler.step(optimizer)
+                scaler.update()
+            else:
+                clip_grad_norm_(unet.parameters(), 1.0)
+                optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad(set_to_none=True)
+
+        loss_log = reduce_mean_scalar(loss) * args.gradient_accumulation_steps
+
+        if rank == 0:
+            progress_bar.set_postfix({'Total_loss': f"{loss_log:.4f}"})
+            if args.report_to and step % 100 == 0: # 로그 기록 빈도 조절
+                log_data = {
+                    "train/learning_rate": lr_scheduler.get_last_lr()[0],
+                    "train/loss_g_total": loss_log,
+                }
+                wandb.log(log_data, step=step)
+
+        did_validate = False
+        if (step % args.validation_steps == 0 or step == args.max_train_steps) and step > start_step:
+            did_validate = True
+            unet.eval()
+            val_epoch_loss = {"loss": 0}
+            num_val_batches_local = 0
+            
+            with torch.no_grad():
+                for val_batch in valid_loader:
+                    latents = val_batch["latent"].to(device, non_blocking=True) * scale_factor
+                    spacing_tensor = val_batch["spacing"].to(device, non_blocking=True)
+                    with autocast(device_type="cuda", dtype=torch.float16, enabled=args.amp):
+                        noise = torch.randn_like(latents)
+                        timesteps = torch.randint(0, num_train_timesteps, (latents.shape[0],), device=latents.device).long()
+                        noisy_latent = noise_scheduler.add_noise(original_samples=latents, noise=noise, timesteps=timesteps)
+
+                        unet_inputs = {
+                            "x": noisy_latent,
+                            "timesteps": timesteps,
+                            "spacing_tensor": spacing_tensor,
+                        }
+
+                        if include_body_region:
+                            top_region_index_tensor = val_batch["top_region_index"].to(device)
+                            bottom_region_index_tensor = val_batch["bottom_region_index"].to(device)
+                            unet_inputs.update({
+                                "top_region_index_tensor": top_region_index_tensor,
+                                "bottom_region_index_tensor": bottom_region_index_tensor,
+                            })
+                        if include_modality:
+                            class_tensor = torch.tensor(val_batch["modality_class"], dtype=torch.long).to(device, non_blocking=True)
+                            unet_inputs.update({"class_labels": class_tensor})
+
+                        model_output = unet.module(**unet_inputs)
+
+                        if noise_scheduler.prediction_type == DDPMPredictionType.EPSILON: model_gt = noise
+                        elif noise_scheduler.prediction_type == DDPMPredictionType.SAMPLE: model_gt = latents
+                        elif noise_scheduler.prediction_type == DDPMPredictionType.V_PREDICTION: model_gt = latents - noise
+                                                
+                        val_epoch_loss["loss"] += loss_pt(model_output.float(), model_gt.float())
+
+                    num_val_batches_local += 1
+
+            val_metrics = torch.tensor([val_epoch_loss["loss"], num_val_batches_local], device=device)
+            dist.all_reduce(val_metrics, op=dist.ReduceOp.SUM)
+        
+            total_batches = val_metrics[-1].item()
+            avg_loss = val_metrics[0].item() / total_batches if total_batches > 0 else 0
+            #val_loss = {"loss": avg_loss}
+
+            if rank == 0:                
+                logger.info(f"\nStep {step} Total Val Loss (Avg across all ranks): {avg_loss:.4f}")
+                if args.report_to:
+                    log_data = {"valid/total_loss": avg_loss, "valid/scale_factor": scale_factor}
+                    wandb.log(log_data, step=step)
+
+                if avg_loss < best_val_loss:
+                    torch.cuda.synchronize(device)
+                    unet_state_dict = unet.module._orig_mod.state_dict()
+                    current_epoch = step // steps_per_epoch
+                    best_val_loss = float(avg_loss)
+                    state = {
+                        "unet_state_dict": unet_state_dict,
+                        "optimizer": optimizer.state_dict(),
+                        "lr_scheduler": lr_scheduler.state_dict(),
+                        "step": step,
+                        "best_val_loss": best_val_loss,
+                        "num_train_timesteps": num_train_timesteps,
+                        "scale_factor": scale_factor,
+                        "epoch": current_epoch
+                    }
+                    if args.amp:
+                        state["scaler"] = scaler.state_dict()
+                    
+                    best_dir = os.path.join(args.model_dir, "best-checkpoint")
+                    os.makedirs(best_dir, exist_ok=True)
+                    atomic_save(state, os.path.join(best_dir, "diff_unet_ckpt.pt"))
+                    logger.info(f"[best] updated at step {step}: {best_val_loss:.6f}")
+                else:
+                    logger.info(f"[not best] not updated at step {step}: {avg_loss:.6f}")
+
+            _best = torch.tensor([best_val_loss], device=device, dtype=torch.float32)
+            dist.broadcast(_best, src=0)
+            best_val_loss = float(_best.item())
+
+        # --- 체크포인트 저장 ---
+        is_time_to_save = (step % args.checkpointing_steps == 0 and step > start_step)
+        if (is_time_to_save or SHUTDOWN_REQUESTED) and rank == 0:
+            torch.cuda.synchronize(device)
+            unet_state_dict = unet.module._orig_mod.state_dict()
+            current_epoch = step // steps_per_epoch
+            state = {
+                "unet_state_dict": unet_state_dict,
+                "optimizer": optimizer.state_dict(),
+                "lr_scheduler": lr_scheduler.state_dict(),
+                "step": step,
+                "best_val_loss": float(best_val_loss),
+                "num_train_timesteps": num_train_timesteps,
+                "scale_factor": scale_factor,
+                "epoch": current_epoch
+            }
+            if args.amp:
+                state["scaler"] = scaler.state_dict()
+
+            ckpt_dir = os.path.join(args.model_dir, f"checkpoint-{step}")
+            os.makedirs(ckpt_dir, exist_ok=True)
+            atomic_save(state, os.path.join(ckpt_dir, "diff_unet_ckpt.pt"))
+            logger.info(f"\nSaved Step {step} checkpoint to {ckpt_dir}")
+
+        shutdown_tensor = torch.tensor([1 if (rank == 0 and SHUTDOWN_REQUESTED) else 0], device=device)
+        dist.broadcast(shutdown_tensor, src=0)
+        if shutdown_tensor.item() == 1:
+            if rank == 0:
+                logger.info("Shutdown signal received and synced across all ranks. Exiting training loop gracefully.")
+            break
+        if did_validate:
+            dist.barrier(device_ids=[local_rank])
+
+    if SHUTDOWN_REQUESTED:
+        print("Graceful shutdown initiated, exiting with code 1 to trigger requeue.")
+        sys.exit(1) # ✅ 0 대신 1로 변경하여 "실패" 신호 전송
+    
+    dist.barrier(device_ids=[local_rank]) # DDP CHANGE: 모든 프로세스가 끝날 때까지 대기 후 정리
+    cleanup_ddp()
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.sh b/Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.sh
new file mode 100644
index 0000000..71bfd5e
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.sh
@@ -0,0 +1,60 @@
+#!/bin/bash
+
+#SBATCH --job-name=unet_maisi_brats
+#SBATCH --nodes=2
+#SBATCH --gres=gpu:4
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=32 
+#SBATCH --partition=P2
+#SBATCH --exclude=b31
+#SBATCH --time=0-12:00:00
+#SBATCH --mem=200GB
+#SBATCH --signal=B:SIGUSR1@180
+#SBATCH --open-mode=append
+#SBATCH -o /shared/s1/lab06/wonyoung/maisi/logs_ex/%x-%j.txt
+
+source /home/s1/wonyoungjang/.bashrc
+source /home/s1/wonyoungjang/anaconda3/bin/activate
+conda activate wdm
+
+# --- 환경 변수 설정 (기존과 동일) ---
+export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n1)
+export MASTER_PORT=$((10000 + RANDOM % 50000))
+export OMP_NUM_THREADS=1
+
+echo "BraTS - Starting MAISI VAE MULTI-NODE DDP training on 2 nodes (8 GPUs)..."
+echo "MASTER_ADDR: $MASTER_ADDR, MASTER_PORT: $MASTER_PORT"
+
+max_restarts=1000
+scontext=$(scontrol show job ${SLURM_JOB_ID})
+restarts=$(echo ${scontext} | grep -o 'Restarts=[0-9]*****' | cut -d= -f2)
+
+function resubmit()
+{
+    if [[ $restarts -lt $max_restarts ]]; then
+        scontrol requeue ${SLURM_JOB_ID}
+        exit 0
+    else
+        echo "Your job is over the Maximum restarts limit"
+        exit 1
+    fi
+}
+
+trap 'resubmit' SIGUSR1
+
+srun --cpu-bind=none,v --accel-bind=gn torchrun \
+    --nproc_per_node=4 \
+    --nnodes=$SLURM_NNODES \
+    --node_rank=$SLURM_NODEID \
+    --rdzv_id=$SLURM_JOB_ID \
+    --rdzv_backend=c10d \
+    --rdzv_endpoint=$MASTER_ADDR:$MASTER_PORT \
+    maisi_train_UNET_brats.py \
+    --env_config_path="configs/environment_maisi_diff_model.json" \
+    --train_config_path="configs/config_maisi_diff_model.json" \
+    --model_config_path="configs/config_maisi.json" \
+    --cpus_per_task=${SLURM_CPUS_PER_TASK} \
+    --run_name=${SLURM_JOB_NAME} \
+    --resume &
+wait
+exit 0
\ No newline at end of file
diff --git a/Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.py b/Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.py
new file mode 100644
index 0000000..fbbf3ec
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.py
@@ -0,0 +1,571 @@
+import os
+import sys
+import json
+import yaml
+import glob
+import wandb
+import signal
+import argparse
+import warnings
+import numpy as np
+from tqdm import trange
+
+import torch
+import torch.distributed as dist
+from torch.optim import lr_scheduler
+from torch.nn.utils import clip_grad_norm_
+from torch.amp import GradScaler, autocast
+from torch.nn import L1Loss, MSELoss
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+from monai.config import print_config
+from monai.utils import set_determinism
+from monai.inferers import SlidingWindowInferer
+from monai.networks.nets import PatchDiscriminator
+from monai.losses import PatchAdversarialLoss, PerceptualLoss
+from monai.data import CacheDataset, DataLoader, DistributedSampler
+
+from utils import count_parameters
+from scripts.transforms import VAE_Transform
+from scripts.utils import define_instance, dynamic_infer #, KL_loss
+from scripts.utils_plot import find_label_center_loc, get_xyz_plot
+
+warnings.filterwarnings("ignore")
+if os.environ.get("RANK", "0") == "0":
+    print_config()
+
+def setup_ddp(): # DDP CHANGE: 분산 환경 초기화 함수
+    """Initializes the distributed process group."""
+    dist.init_process_group(backend="nccl")
+    torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
+
+def cleanup_ddp(): # DDP CHANGE: 분산 환경 정리 함수
+    """Cleans up the distributed process group."""
+    dist.destroy_process_group()
+
+def atomic_save(state, path):
+    tmp = path + ".tmp"
+    torch.save(state, tmp)
+    os.replace(tmp, path)
+
+SHUTDOWN_REQUESTED = False
+def graceful_shutdown(signum, frame):
+    global SHUTDOWN_REQUESTED
+    print(f"\n[!] Received signal {signum}. Requesting graceful shutdown...", flush=True)
+    SHUTDOWN_REQUESTED = True
+signal.signal(signal.SIGTERM, graceful_shutdown)
+signal.signal(signal.SIGINT, graceful_shutdown)
+
+def get_run_name(manual_name=None, default_prefix="manual"):
+    job_id = os.environ.get("SLURM_JOB_ID")
+    job_name = os.environ.get("SLURM_JOB_NAME")
+    if manual_name:
+        return manual_name
+    elif job_id and job_name:
+        return f"{job_name}_{job_id}"
+    elif job_id:
+        return f"slurm_{job_id}"
+    else:
+        from datetime import datetime
+        timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        return f"{default_prefix}_{timestamp}"
+
+def load_config():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_config_path", type=str, default="/shared/s1/lab06/wonyoung/maisi/configs/config_maisi3d-rflow_brats.json")
+    parser.add_argument("--train_config_path", type=str, default="/shared/s1/lab06/wonyoung/maisi/configs/config_maisi_vae_train_brats.json")
+    parser.add_argument("--resume", action="store_true")
+    parser.add_argument("--run_name", type=str, default=None)
+    parser.add_argument("--cpus_per_task", type=int, default=8, help="Number of CPUs allocated per task by Slurm.")
+    args = parser.parse_args()
+    if args.resume and not args.run_name:
+        raise ValueError("--resume requires --run_name to be specified.")
+    args.run_name = get_run_name(args.run_name)
+    config_dict = json.load(open(args.model_config_path, "r"))
+    for k, v in config_dict.items():
+        setattr(args, k, v)
+    config_train_dict = json.load(open(args.train_config_path, "r"))
+    for k, v in config_train_dict["data_option"].items():
+        setattr(args, k, v)
+    for k, v in config_train_dict["autoencoder_train"].items():
+        setattr(args, k, v)
+    for k, v in config_train_dict["custom_config"].items():
+        setattr(args, k, v)
+    return args
+
+def resume_from_latest(autoencoder,
+                       discriminator,
+                       optimizer_g, 
+                       optimizer_d,
+                       scheduler_g,
+                       scheduler_d,
+                       scaler_g,
+                       scaler_d,
+                       output_dir, device):
+    if not os.path.exists(output_dir):
+        print("[Resume] No checkpoint directory found. Starting fresh.")
+        return 0, float("inf"), 0
+
+    ckpts = [
+        d for d in os.listdir(output_dir)
+        if d.startswith("checkpoint-")
+        and os.path.isdir(os.path.join(output_dir, d))
+        and os.path.exists(os.path.join(output_dir, d, "model.pt"))
+    ]
+    if len(ckpts) == 0:
+        print("[Resume] No checkpoint found in directory. Starting fresh.")
+        return 0, float("inf"), 0
+
+    ckpts = sorted(ckpts, key=lambda x: int(x.split("-")[1]))
+    latest_ckpt_dir = ckpts[-1]
+    print(f"[Resume] Resuming from checkpoint: {latest_ckpt_dir}")
+    
+    path = os.path.join(output_dir, latest_ckpt_dir, "model.pt")
+    ckpt = torch.load(path, map_location=device)
+    
+    autoencoder.load_state_dict(ckpt["autoencoder"])
+    discriminator.load_state_dict(ckpt["discriminator"])
+    optimizer_g.load_state_dict(ckpt["optimizer_g"]) ### comment this line if s2 first submit
+    optimizer_d.load_state_dict(ckpt["optimizer_d"]) ### comment this line if s2 first submit
+    if "scheduler_g" in ckpt: scheduler_g.load_state_dict(ckpt["scheduler_g"]) ### comment this line if s2 first submit
+    if "scheduler_d" in ckpt: scheduler_d.load_state_dict(ckpt["scheduler_d"]) ### comment this line if s2 first submit
+    if "scaler_g" in ckpt and scaler_g is not None: scaler_g.load_state_dict(ckpt["scaler_g"]) ### comment this line if s2 first submit
+    if "scaler_d" in ckpt and scaler_d is not None: scaler_d.load_state_dict(ckpt["scaler_d"]) ### comment this line if s2 first submit
+
+    return ckpt.get("step", 0), ckpt.get("best_val_loss", float("inf")), ckpt.get("epoch", 0)
+
+def prepare_image_for_logging(image_tensor, center_loc):
+    """3D 텐서를 wandb에 로깅할 수 있는 2D 이미지로 변환합니다."""
+    image_tensor_cpu = image_tensor.cpu()
+    vis_img_np = get_xyz_plot(image_tensor_cpu, center_loc, mask_bool=False)
+    min_val, max_val = vis_img_np.min(), vis_img_np.max()
+    if max_val - min_val > 1e-6:
+        vis_img_np = (vis_img_np - min_val) / (max_val - min_val)
+    else:
+        vis_img_np = np.zeros_like(vis_img_np)
+    vis_img_uint8 = (vis_img_np * 255).astype(np.uint8)
+    return wandb.Image(vis_img_uint8)
+
+def reduce_mean_scalar(x):
+    t = x.detach().reshape(1).to(torch.float32)
+    dist.all_reduce(t, op=dist.ReduceOp.SUM)
+    t /= dist.get_world_size()
+    return t.item()
+
+def main():
+    setup_ddp() # DDP CHANGE: 분산 설정 초기화
+    rank = int(os.environ["RANK"])
+    local_rank = int(os.environ["LOCAL_RANK"])
+    world_size = int(os.environ["WORLD_SIZE"])
+
+    if rank == 0:
+        print("="*50)
+        print(f"✅ Training started with a total of {world_size} GPUs across all nodes.")
+        print("="*50)
+
+    args = load_config()
+    device = torch.device(f"cuda:{local_rank}")
+    weight_dtype = torch.float16 if args.weight_dtype == "fp16" else torch.float32
+
+    if rank == 0:
+        print(f"[Opt] Using gradient accumulation with {args.gradient_accumulation_steps} steps.")
+
+    set_determinism(seed=args.seed + rank) # OPTIMIZATION: 각 프로세스에 다른 시드 줘서 데이터 증강 다양성 확보
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    torch.backends.cudnn.benchmark = True # OPTIMIZATION: 입력 크기가 일정할 때 가장 효과적이므로 활성화
+    torch.backends.cudnn.deterministic = False
+    torch.set_float32_matmul_precision("high")
+
+    if rank == 0:
+        print("[Config] Loaded hyperparameters:")
+        print(yaml.dump(vars(args), sort_keys=False))
+        if args.report_to:
+            wandb.init(project="MAISI_EX_DDP", config=args, name=args.run_name)
+        output_dir = os.path.join(args.output_dir, args.run_name)
+        os.makedirs(output_dir, exist_ok=True)
+    else:
+        output_dir = None
+
+    # BraTS Data
+    train_files_list = [{"image": f} for f in sorted(glob.glob(os.path.join(args.train_label_dir, "*", "*.nii.gz"))) if "seg" not in f]
+    valid_filepaths = [f for f in sorted(glob.glob(os.path.join(args.valid_label_dir, "*", "*.nii.gz"))) if "seg" not in f]
+    val_files_list = [{"image": f} for f in valid_filepaths[:args.num_valid]]
+    
+    def add_assigned_class_to_datalist(datalist, classname): # 데이터에 'class' 키를 추가하는 함수
+        for item in datalist:
+            item["class"] = classname
+        return datalist
+
+    train_files = add_assigned_class_to_datalist(train_files_list, "mri")
+    val_files = add_assigned_class_to_datalist(val_files_list, "mri")
+
+    train_transform = VAE_Transform(
+        is_train=True, 
+        random_aug=args.random_aug, 
+        k=4, 
+        patch_size=args.patch_size, 
+        output_dtype=weight_dtype, 
+        spacing_type=args.spacing_type, 
+        image_keys=["image"]
+    )
+    val_transform = VAE_Transform(
+        is_train=False, 
+        random_aug=False, 
+        k=4, 
+        output_dtype=weight_dtype, 
+        image_keys=["image"]
+    )
+
+    # Build dataloader
+    if rank == 0: print(f"Total number of training data is {len(train_files)}.")
+    workers_per_gpu = args.cpus_per_task // world_size # 각 프로세스가 사용할 수 있는 CPU 코어 수에 맞춰 num_workers 설정
+    train_dataset = CacheDataset(data=train_files, transform=train_transform, cache_rate=args.cache, num_workers=workers_per_gpu)
+    train_sampler = DistributedSampler(dataset=train_dataset, shuffle=True)
+    dataloader_train = DataLoader(
+        train_dataset, 
+        batch_size=args.batch_size, 
+        num_workers=workers_per_gpu, 
+        sampler=train_sampler, 
+        pin_memory=True, 
+        drop_last=True,
+        persistent_workers=True,
+        prefetch_factor=2
+    )
+    steps_per_epoch = len(dataloader_train)
+    
+    if rank == 0: print(f"Total number of validation data is {len(val_files)}.")
+    val_total = len(val_files)
+    per_rank = (val_total + world_size - 1) // world_size  # ceil
+    start = rank * per_rank
+    end = min(val_total, start + per_rank)
+    val_files_shard = val_files[start:end]
+    dataset_val = CacheDataset(data=val_files_shard, transform=val_transform, cache_rate=0.0, num_workers=workers_per_gpu)
+    dataloader_val = DataLoader(
+        dataset_val, 
+        batch_size=args.val_batch_size, 
+        num_workers=workers_per_gpu, 
+        pin_memory=True,
+        persistent_workers=True,
+        prefetch_factor=2
+    )
+    
+    if rank == 0:
+        print("### Train Transform ###")
+        for i, t in enumerate(train_transform.transform_dict["mri"].transforms):
+            print(f"[{i}] {t}")
+        print("\n### Validation Transform ###")
+        for i, t in enumerate(val_transform.transform_dict["mri"].transforms):
+            print(f"[{i}] {t}")
+
+    autoencoder = define_instance(args, "autoencoder_def").to(device)
+    discriminator = PatchDiscriminator(
+        spatial_dims=args.spatial_dims,
+        num_layers_d=3,
+        channels=32,
+        in_channels=1,
+        out_channels=1,
+        norm="INSTANCE",
+    ).to(device)
+
+    optimizer_g = torch.optim.AdamW(params=autoencoder.parameters(), lr=args.lr, weight_decay=1e-5, eps=1e-6 if args.amp else 1e-8)
+    optimizer_d = torch.optim.AdamW(params=discriminator.parameters(), lr=args.lr, weight_decay=1e-5, eps=1e-6 if args.amp else 1e-8)
+    total_opt_steps = (args.max_train_steps - args.pretrained_steps + args.gradient_accumulation_steps - 1) // args.gradient_accumulation_steps ### check pretrained_steps when s2
+    scheduler_g = lr_scheduler.CosineAnnealingLR(optimizer_g, T_max=total_opt_steps)
+    scheduler_d = lr_scheduler.CosineAnnealingLR(optimizer_d, T_max=total_opt_steps)
+    scaler_g, scaler_d = (GradScaler(), GradScaler()) if args.amp else (None, None)
+
+    start_step, best_val_loss, start_epoch = 0, float("inf"), 0
+    if args.resume:
+        output_dir_for_resume = os.path.join(args.output_dir, args.run_name)
+        start_step, best_val_loss, start_epoch = resume_from_latest(
+            autoencoder, discriminator, optimizer_g, optimizer_d,
+            scheduler_g, scheduler_d, scaler_g, scaler_d, output_dir_for_resume, device)
+    dist.barrier(device_ids=[local_rank])
+
+    # OPTIMIZATION: torch.compile로 모델 컴파일하여 연산 가속화 (PyTorch 2.0+ 필수)
+    if rank == 0: print("Compiling models with torch.compile()...")
+    autoencoder = torch.compile(autoencoder)
+    discriminator = torch.compile(discriminator)
+    dist.barrier(device_ids=[local_rank])
+    autoencoder = DDP(autoencoder, device_ids=[local_rank], find_unused_parameters=False)
+    discriminator = DDP(discriminator, device_ids=[local_rank], find_unused_parameters=False)
+
+    sync_vec = torch.tensor([start_step, float(best_val_loss)], device=device, dtype=torch.float32)
+    dist.broadcast(sync_vec, src=0)
+    start_step, best_val_loss = int(sync_vec[0].item()), float(sync_vec[1].item())
+
+    # Training config
+    if args.recon_loss == "l2":
+        intensity_loss = MSELoss()
+    else:
+        intensity_loss = L1Loss(reduction="mean")
+    adv_loss = PatchAdversarialLoss(criterion="least_squares")
+    loss_perceptual = PerceptualLoss(spatial_dims=3, network_type="squeeze", is_fake_3d=True, fake_3d_ratio=0.2).eval().to(device)
+    
+    if rank == 0:
+        print("### start_epoch:", start_epoch)
+        param_counts = count_parameters(autoencoder.module)
+        print(f"### autoencoder's Trainable parameters: {param_counts['trainable']:,}")
+        param_counts = count_parameters(discriminator.module)
+        print(f"### discriminator's Trainable parameters: {param_counts['trainable']:,}")
+
+    def infinite_loader(loader, sampler, start_epoch=0):
+        epoch = start_epoch
+        while True:
+            sampler.set_epoch(epoch) # DDP CHANGE: 매 epoch마다 샘플러 시드 변경
+            for batch in loader:
+                yield batch
+            epoch += 1
+            
+    train_iter = infinite_loader(dataloader_train, train_sampler, start_epoch)
+    progress_bar = trange(start_step, args.max_train_steps + 1,
+                          desc=f"Training on Rank {rank}",
+                          initial=start_step, total=args.max_train_steps + 1,
+                          disable=(rank != 0))
+        
+    for step in progress_bar:
+        autoencoder.train()
+        discriminator.train()
+        batch = next(train_iter)
+        # OPTIMIZATION: non_blocking=True로 데이터 전송과 연산 오버랩 시도
+        images = batch["image"].to(device, non_blocking=True).contiguous()
+
+        with autocast(device_type="cuda", dtype=torch.float16, enabled=args.amp):
+            reconstruction, z_mu, z_sigma = autoencoder(images)
+
+        z_mu_f = z_mu.float()
+        z_sigma_f = torch.clamp(z_sigma.float(), min=1e-8)
+        logvar = 2.0 * torch.log(z_sigma_f)                # σ -> log σ²
+        logvar = torch.clamp(logvar, min=-30.0, max=10.0)  # 상한 10 정도로 낮추는 게 안전
+        kl = 0.5 * (torch.exp(logvar) + z_mu_f**2 - 1.0 - logvar)
+        kl_loss = kl.mean()
+
+        with autocast(device_type="cuda", dtype=torch.float16, enabled=args.amp):
+            losses = {
+                "recons_loss": intensity_loss(reconstruction, images), # voxel 직접 비교
+                "kl_loss": kl_loss, # 압축된 데이터가 정규분포를 따르도록 강제 (latent 다듬는 역할 -> loss 적절하게 유지되어야 함)
+                "p_loss": loss_perceptual(reconstruction.float(), images.float()), # 시각적 차이를 반영
+            }
+            # Gen이 Disc. 얼마나 잘 속이는가?
+            # loss 낮을수록 좋지만 Gen이 너무 잘해서 0이면 Disc.가 제 역할 못한다는 뜻
+            logits_fake = discriminator(reconstruction.contiguous().float())[-1]
+            generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)
+            loss_g = losses["recons_loss"] + args.kl_weight * losses["kl_loss"] + \
+                     args.perceptual_weight * losses["p_loss"] + args.adv_weight * generator_loss
+            loss_g = loss_g / args.gradient_accumulation_steps
+            
+        if args.amp:
+            scaler_g.scale(loss_g).backward()
+        else:
+            loss_g.backward()
+
+        with autocast(device_type="cuda", dtype=torch.float16, enabled=args.amp):
+            # 가짜 이미지 보여줬을 때 가짜라고 얼마나 잘 맞혔는가? 
+            # loss 낮을수록 좋지만 Disc.가 너무 잘해서 0이면 Gen이 배울 게 없어짐
+            logits_fake = discriminator(reconstruction.contiguous().detach())[-1]
+            loss_d_fake = adv_loss(logits_fake, target_is_real=False, for_discriminator=True)
+            # 진짜 이미지 보여줬을 때 진짜라고 얼마나 잘 맞혔는가? 
+            # loss 낮을수록 좋지만 Disc.가 너무 잘해서 0이면 Gen이 배울 게 없어짐
+            logits_real = discriminator(images.contiguous().detach())[-1]
+            loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)
+            loss_d = (loss_d_fake + loss_d_real) * 0.5
+            loss_d = loss_d / args.gradient_accumulation_steps
+
+        if args.amp:
+            scaler_d.scale(loss_d).backward()
+        else:
+            loss_d.backward()
+
+        if (step + 1) % args.gradient_accumulation_steps == 0:
+            # Generator 업데이트
+            if args.amp:
+                scaler_g.unscale_(optimizer_g)
+                clip_grad_norm_(autoencoder.parameters(), 1.0)
+                scaler_g.step(optimizer_g)
+                scaler_g.update()
+            else:
+                clip_grad_norm_(autoencoder.parameters(), 1.0)
+                optimizer_g.step()
+            scheduler_g.step()
+            optimizer_g.zero_grad(set_to_none=True)
+
+            # Discriminator 업데이트
+            if args.amp:
+                scaler_d.unscale_(optimizer_d)
+                clip_grad_norm_(discriminator.parameters(), 1.0)
+                scaler_d.step(optimizer_d)
+                scaler_d.update()
+            else:
+                clip_grad_norm_(discriminator.parameters(), 1.0)
+                optimizer_d.step()
+            scheduler_d.step()
+            optimizer_d.zero_grad(set_to_none=True)
+
+        # DDP CHANGE: 모든 GPU의 loss를 평균내어 로그 기록
+        lg = reduce_mean_scalar(loss_g) * args.gradient_accumulation_steps
+        ld = reduce_mean_scalar(loss_d) * args.gradient_accumulation_steps
+        avg_recons_loss = reduce_mean_scalar(losses["recons_loss"]) * args.gradient_accumulation_steps
+        avg_kl_loss = reduce_mean_scalar(losses["kl_loss"]) * args.gradient_accumulation_steps
+        avg_p_loss = reduce_mean_scalar(losses["p_loss"]) * args.gradient_accumulation_steps
+        avg_gen_loss = reduce_mean_scalar(generator_loss) * args.gradient_accumulation_steps
+        avg_dfake_loss = reduce_mean_scalar(loss_d_fake) * args.gradient_accumulation_steps
+        avg_dreal_loss = reduce_mean_scalar(loss_d_real) * args.gradient_accumulation_steps
+
+        if rank == 0:
+            progress_bar.set_postfix({'Total_g_loss': f"{lg:.4f}", 'Total_d_loss': f"{ld:.4f}"})
+            if args.report_to and step % 100 == 0: # 로그 기록 빈도 조절
+                log_data = {
+                    "train/learning_rate": scheduler_g.get_last_lr()[0],
+                    "train/loss_g_total": lg,
+                    "train/loss_d_total": ld,
+                    "train/generator/recons_loss": avg_recons_loss,
+                    "train/generator/kl_loss": avg_kl_loss,
+                    "train/generator/p_loss": avg_p_loss,
+                    "train/discriminator/adv_g_loss": avg_gen_loss,
+                    "train/discriminator/d_fake_loss": avg_dfake_loss,
+                    "train/discriminator/d_real_loss": avg_dreal_loss,
+                }
+                wandb.log(log_data, step=step)
+
+        did_validate = False
+        if (step % args.validation_steps == 0 or step == args.max_train_steps) and step > start_step:
+            did_validate = True
+            autoencoder.eval()
+            val_epoch_losses = {"recons_loss": 0, "kl_loss": 0, "p_loss": 0}
+            num_val_batches_local = 0
+            val_inferer = SlidingWindowInferer(
+                roi_size=args.val_sliding_window_patch_size, 
+                sw_batch_size=1, 
+                overlap=0.5, 
+                device=device, #torch.device("cpu"),
+                sw_device=device
+            )            
+            with torch.no_grad():
+                for val_batch in dataloader_val:
+                    val_images = val_batch["image"].to(device)
+                    # DDP CHANGE: .module을 통해 원본 모델로 추론
+                    with autocast(device_type="cuda", dtype=torch.float16, enabled=args.amp):
+                        reconstruction, z_mu_val, z_sigma_val = dynamic_infer(val_inferer, autoencoder.module, val_images)
+
+                    z_mu_val_f = z_mu_val.float()
+                    z_sigma_val_f = torch.clamp(z_sigma_val.float(), min=1e-8)
+                    logvar_val = 2.0 * torch.log(z_sigma_val_f)                # σ -> log σ²
+                    logvar_val = torch.clamp(logvar_val, min=-30.0, max=10.0)  # 상한 10 정도로 낮추는 게 안전
+                    kl_val = 0.5 * (torch.exp(logvar_val) + z_mu_val_f**2 - 1.0 - logvar_val)
+                    kl_loss_val = kl_val.mean().item()
+                    
+                    reconstruction = reconstruction.to(device)
+                    val_images = val_images.to(device)
+                    val_epoch_losses["recons_loss"] += intensity_loss(reconstruction, val_images).item()
+                    val_epoch_losses["kl_loss"] += kl_loss_val
+                    val_epoch_losses["p_loss"] += loss_perceptual(reconstruction.float(), val_images.float()).item()
+                    num_val_batches_local += 1
+
+            val_metrics = torch.tensor(
+                [val_epoch_losses["recons_loss"], val_epoch_losses["kl_loss"], val_epoch_losses["p_loss"], num_val_batches_local],
+                device=device
+            )
+            dist.all_reduce(val_metrics, op=dist.ReduceOp.SUM)
+        
+            total_batches = val_metrics[3].item()
+            avg_recon_loss = val_metrics[0].item() / total_batches if total_batches > 0 else 0
+            avg_kl_loss = val_metrics[1].item() / total_batches if total_batches > 0 else 0
+            avg_p_loss = val_metrics[2].item() / total_batches if total_batches > 0 else 0
+            final_val_losses = {"recons_loss": avg_recon_loss, "kl_loss": avg_kl_loss, "p_loss": avg_p_loss}
+            val_loss_g = final_val_losses["recons_loss"] + \
+                         args.kl_weight * final_val_losses["kl_loss"] + \
+                         args.perceptual_weight * final_val_losses["p_loss"]
+
+            if rank == 0:                
+                print(f"\nStep {step} Total Val Loss (Avg across all ranks): {val_loss_g:.4f}, Details: {final_val_losses}")
+                if args.report_to:
+                    log_data = {
+                        "valid/total_loss": val_loss_g,
+                        "valid/recon_loss": final_val_losses["recons_loss"],
+                        "valid/kl_loss": final_val_losses["kl_loss"],
+                        "valid/p_loss": final_val_losses["p_loss"],
+                    }
+                    if num_val_batches_local > 0: # scale_factor와 이미지는 마지막 배치의 결과만 참고용으로 로깅
+                        std = z_mu_val.detach().float().flatten().std().clamp(min=1e-8)
+                        log_data["valid/scale_factor"] = (1.0 / std).item()
+                        center_loc = find_label_center_loc(val_images[0, 0, ...])
+                        log_data["valid/original_image"] = prepare_image_for_logging(val_images[0], center_loc)
+                        log_data["valid/reconstructed_image"] = prepare_image_for_logging(reconstruction[0], center_loc)
+                    wandb.log(log_data, step=step)
+                
+                if val_loss_g < best_val_loss:
+                    torch.cuda.synchronize(device)
+                    autoencoder_state_dict = autoencoder.module._orig_mod.state_dict()
+                    discriminator_state_dict = discriminator.module._orig_mod.state_dict()
+                    current_epoch = step // steps_per_epoch
+                    best_val_loss = float(val_loss_g)
+                    state = {
+                        "autoencoder": autoencoder_state_dict,
+                        "discriminator": discriminator_state_dict,
+                        "optimizer_g": optimizer_g.state_dict(),
+                        "optimizer_d": optimizer_d.state_dict(),
+                        "scheduler_g": scheduler_g.state_dict(),
+                        "scheduler_d": scheduler_d.state_dict(),
+                        "step": step,
+                        "best_val_loss": best_val_loss,
+                        "epoch": current_epoch
+                    }
+                    if args.amp:
+                        state["scaler_g"] = scaler_g.state_dict()
+                        state["scaler_d"] = scaler_d.state_dict()
+                    best_dir = os.path.join(output_dir, "best-checkpoint")
+                    os.makedirs(best_dir, exist_ok=True)
+                    atomic_save(state, os.path.join(best_dir, "model.pt"))
+                    print(f"[best] updated at step {step}: {best_val_loss:.6f}", flush=True)
+                else:
+                    print(f"[not best] not updated at step {step}: {val_loss_g:.6f}", flush=True)
+
+            _best = torch.tensor([best_val_loss], device=device, dtype=torch.float32)
+            dist.broadcast(_best, src=0)
+            best_val_loss = float(_best.item())
+
+        # --- 체크포인트 저장 및 Validation ---
+        is_time_to_save = (step % args.checkpointing_steps == 0 and step > start_step)
+        if (is_time_to_save or SHUTDOWN_REQUESTED) and rank == 0:
+            torch.cuda.synchronize(device)
+            autoencoder_state_dict = autoencoder.module._orig_mod.state_dict()
+            discriminator_state_dict = discriminator.module._orig_mod.state_dict()
+            current_epoch = step // steps_per_epoch
+            state = {
+                "autoencoder": autoencoder_state_dict,
+                "discriminator": discriminator_state_dict,
+                "optimizer_g": optimizer_g.state_dict(),
+                "optimizer_d": optimizer_d.state_dict(),
+                "scheduler_g": scheduler_g.state_dict(),
+                "scheduler_d": scheduler_d.state_dict(),
+                "step": step,
+                "best_val_loss": float(best_val_loss),
+                "epoch": current_epoch
+            }
+            if args.amp:
+                state["scaler_g"] = scaler_g.state_dict()
+                state["scaler_d"] = scaler_d.state_dict()
+            ckpt_dir = os.path.join(output_dir, f"checkpoint-{step}")
+            os.makedirs(ckpt_dir, exist_ok=True)
+            atomic_save(state, os.path.join(ckpt_dir, "model.pt"))
+            print(f"\nSaved checkpoint to {ckpt_dir}", flush=True)
+
+        shutdown_tensor = torch.tensor([1 if (rank == 0 and SHUTDOWN_REQUESTED) else 0], device=device)
+        dist.broadcast(shutdown_tensor, src=0)
+        if shutdown_tensor.item() == 1:
+            if rank == 0:
+                print("Shutdown signal received and synced across all ranks. Exiting training loop gracefully.")
+            break
+        if did_validate:
+            dist.barrier(device_ids=[local_rank])
+
+    if SHUTDOWN_REQUESTED:
+        print("Graceful shutdown initiated, exiting with code 1 to trigger requeue.")
+        sys.exit(1)
+    
+    dist.barrier(device_ids=[local_rank]) # DDP CHANGE: 모든 프로세스가 끝날 때까지 대기 후 정리
+    cleanup_ddp()
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.sh b/Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.sh
new file mode 100644
index 0000000..cf70fbc
--- /dev/null
+++ b/Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.sh
@@ -0,0 +1,59 @@
+#!/bin/bash
+
+#SBATCH --job-name=maisi_vae_s1_brats
+#SBATCH --nodes=2
+#SBATCH --gpus-per-node=4
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=32
+#SBATCH --partition=P2
+#SBATCH --exclude=b31
+#SBATCH --time=0-12:00:00
+#SBATCH --mem=200GB
+#SBATCH --signal=B:SIGUSR1@180
+#SBATCH -o /shared/s1/lab06/wonyoung/maisi/logs_ex/%x-%j.txt
+#SBATCH --open-mode=append
+
+source /home/s1/wonyoungjang/.bashrc
+source /home/s1/wonyoungjang/anaconda3/bin/activate
+conda activate wdm
+
+# --- 환경 변수 설정 (기존과 동일) ---
+export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n1)
+export MASTER_PORT=$((10000 + RANDOM % 50000))
+export OMP_NUM_THREADS=1
+
+echo "BraTS - Starting MAISI VAE MULTI-NODE DDP training on 2 nodes (8 GPUs)..."
+echo "MASTER_ADDR: $MASTER_ADDR, MASTER_PORT: $MASTER_PORT"
+
+max_restarts=1000
+scontext=$(scontrol show job ${SLURM_JOB_ID})
+restarts=$(echo ${scontext} | grep -o 'Restarts=[0-9]*****' | cut -d= -f2)
+
+function resubmit()
+{
+    if [[ $restarts -lt $max_restarts ]]; then
+        scontrol requeue ${SLURM_JOB_ID}
+        exit 0
+    else
+        echo "Your job is over the Maximum restarts limit"
+        exit 1
+    fi
+}
+
+trap 'resubmit' SIGUSR1
+
+srun --cpu-bind=none,v --accel-bind=gn torchrun \
+    --nproc_per_node=4 \
+    --nnodes=$SLURM_NNODES \
+    --node_rank=$SLURM_NODEID \
+    --rdzv_id=$SLURM_JOB_ID \
+    --rdzv_backend=c10d \
+    --rdzv_endpoint=$MASTER_ADDR:$MASTER_PORT \
+    maisi_train_VAE_brats.py \
+      --model_config_path configs/config_maisi3d-rflow_brats.json \
+      --train_config_path configs/config_maisi_vae_train_brats_stage1.json \
+      --run_name ${SLURM_JOB_NAME} \
+      --cpus_per_task ${SLURM_CPUS_PER_TASK} \
+      --resume &
+wait
+exit 0
\ No newline at end of file

From 7c427c076c8999fa1ce4c46c44c1d3665fc8dfbd Mon Sep 17 00:00:00 2001
From: Wonyoung Jang <43818471+Stomper10@users.noreply.github.com>
Date: Fri, 28 Nov 2025 18:56:46 +0900
Subject: [PATCH 29/34] Rename config_maisi_vae_train_brats_stage2 to
 config_maisi_vae_train_brats_stage2.json

---
 ...rain_brats_stage2 => config_maisi_vae_train_brats_stage2.json} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Session7_Medical_Image_Data_Analysis/configs/{config_maisi_vae_train_brats_stage2 => config_maisi_vae_train_brats_stage2.json} (100%)

diff --git a/Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage2 b/Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage2.json
similarity index 100%
rename from Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage2
rename to Session7_Medical_Image_Data_Analysis/configs/config_maisi_vae_train_brats_stage2.json

From 713556516a50cc47c285095e178732cc0fcd9aee Mon Sep 17 00:00:00 2001
From: Wonyoung Jang <43818471+Stomper10@users.noreply.github.com>
Date: Mon, 1 Dec 2025 11:35:16 +0900
Subject: [PATCH 30/34] Update maisi_train_VAE_brats.py

---
 .../maisi_train_VAE_brats.py                           | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.py b/Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.py
index fbbf3ec..1911250 100644
--- a/Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.py
+++ b/Session7_Medical_Image_Data_Analysis/maisi_train_VAE_brats.py
@@ -258,7 +258,9 @@ def add_assigned_class_to_datalist(datalist, classname): # 데이터에 'class'
         for i, t in enumerate(val_transform.transform_dict["mri"].transforms):
             print(f"[{i}] {t}")
 
+    # https://github.com/Project-MONAI/MONAI/blob/e267705385d00ef0071cf51b087345d720af9102/monai/networks/nets/autoencoderkl.py#L472
     autoencoder = define_instance(args, "autoencoder_def").to(device)
+    # https://github.com/Project-MONAI/MONAI/blob/e267705385d00ef0071cf51b087345d720af9102/monai/networks/nets/patchgan_discriminator.py#L116
     discriminator = PatchDiscriminator(
         spatial_dims=args.spatial_dims,
         num_layers_d=3,
@@ -306,9 +308,9 @@ def add_assigned_class_to_datalist(datalist, classname): # 데이터에 'class'
     if rank == 0:
         print("### start_epoch:", start_epoch)
         param_counts = count_parameters(autoencoder.module)
-        print(f"### autoencoder's Trainable parameters: {param_counts['trainable']:,}")
+        print(f"### autoencoder's Trainable parameters: {param_counts['trainable']:,}") # 20,944,897
         param_counts = count_parameters(discriminator.module)
-        print(f"### discriminator's Trainable parameters: {param_counts['trainable']:,}")
+        print(f"### discriminator's Trainable parameters: {param_counts['trainable']:,}") # 2,770,977
 
     def infinite_loader(loader, sampler, start_epoch=0):
         epoch = start_epoch
@@ -344,7 +346,7 @@ def infinite_loader(loader, sampler, start_epoch=0):
         with autocast(device_type="cuda", dtype=torch.float16, enabled=args.amp):
             losses = {
                 "recons_loss": intensity_loss(reconstruction, images), # voxel 직접 비교
-                "kl_loss": kl_loss, # 압축된 데이터가 정규분포를 따르도록 강제 (latent 다듬는 역할 -> loss 적절하게 유지되어야 함)
+                "kl_loss": kl_loss, # 압축된 데이터가 정규분포를 따르도록 강제 (latent 다듬는 역할 -> loss 적절하게 유지되어야 함; 너무 크면 생성 fail, 너무 작으면 생성 다 똑같아짐)
                 "p_loss": loss_perceptual(reconstruction.float(), images.float()), # 시각적 차이를 반영
             }
             # Gen이 Disc. 얼마나 잘 속이는가?
@@ -568,4 +570,4 @@ def infinite_loader(loader, sampler, start_epoch=0):
     cleanup_ddp()
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()

From 721f428d3078a497044ae71968b1a74601fc6362 Mon Sep 17 00:00:00 2001
From: Wonyoung Jang <43818471+Stomper10@users.noreply.github.com>
Date: Mon, 1 Dec 2025 11:42:36 +0900
Subject: [PATCH 31/34] Update maisi_train_UNET_brats.py

---
 .../maisi_train_UNET_brats.py                             | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.py b/Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.py
index f0079ab..bddaf9b 100644
--- a/Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.py
+++ b/Session7_Medical_Image_Data_Analysis/maisi_train_UNET_brats.py
@@ -195,8 +195,10 @@ def main():
         if args.report_to:
             wandb.init(project="MAISI_UNET_BraTS", config=args, name=args.run_name)
         Path(args.model_dir).mkdir(parents=True, exist_ok=True)
-
+    
+    # https://github.com/Project-MONAI/MONAI/blob/e267705385d00ef0071cf51b087345d720af9102/monai/apps/generation/maisi/networks/diffusion_model_unet_maisi.py#L53
     unet = define_instance(args, "diffusion_unet_def").to(device)
+    # https://github.com/Project-MONAI/MONAI/blob/e267705385d00ef0071cf51b087345d720af9102/monai/networks/schedulers/rectified_flow.py#L80
     noise_scheduler = define_instance(args, "noise_scheduler")
     include_body_region = unet.include_top_region_index_input
     include_modality = unet.num_class_embeds is not None
@@ -301,7 +303,7 @@ def main():
     if rank == 0:
         logger.info(f"### start_epoch: {start_epoch}")
         param_counts = count_parameters(unet)
-        logger.info(f"### UNET's Trainable parameters: {param_counts['trainable']:,}")
+        logger.info(f"### UNET's Trainable parameters: {param_counts['trainable']:,}") # 182,369,412
 
     def infinite_loader(loader, sampler, start_epoch=0):
         epoch = start_epoch
@@ -511,4 +513,4 @@ def infinite_loader(loader, sampler, start_epoch=0):
     cleanup_ddp()
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()

From 1a9efa62666afe465a26c13082ebd51d99869851 Mon Sep 17 00:00:00 2001
From: Wonyoung Jang <43818471+Stomper10@users.noreply.github.com>
Date: Mon, 1 Dec 2025 20:07:03 +0900
Subject: [PATCH 32/34] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index f377089..faad0f6 100644
--- a/README.md
+++ b/README.md
@@ -21,7 +21,7 @@ Processes and codes for various practice sessions will be shared here. Please no
 **Date:** October 28, 2025    
 **TA:** Bo-Eun Ahn ([email](mailto:ahnbo@snu.ac.kr))
 
-### [Session 5 - Prediction using EHR (RNN)]
+### [Session 5 - Prediction using EHR (RNN)] (HW)
 **Date:** November 18, 2025  
 **TA:** Junhyeong Lee ([email](mailto:lrainsoul@snu.ac.kr))
 
@@ -29,7 +29,7 @@ Processes and codes for various practice sessions will be shared here. Please no
 **Date:** November 25, 2025   
 **TA:** Soungmun Kim ([email](mailto:soungmun@snu.ac.kr))
 
-### [Session 7 - Medical Image Data Analysis (CNN)]  (HW)
+### [Session 7 - Medical Image Data Analysis (CNN)](Session7_Medical_Image_Data_Analysis.md)
 **Date:** December 2, 2025   
 **TA:** Wonyoung Jang ([email](mailto:jwy4888@snu.ac.kr))
 

From 7c540b3979474fd9cbc33ceaf8b0f5bf0b4a1b5c Mon Sep 17 00:00:00 2001
From: Wonyoung Jang <43818471+Stomper10@users.noreply.github.com>
Date: Mon, 1 Dec 2025 20:07:32 +0900
Subject: [PATCH 33/34] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index faad0f6..57fd08b 100644
--- a/README.md
+++ b/README.md
@@ -29,7 +29,7 @@ Processes and codes for various practice sessions will be shared here. Please no
 **Date:** November 25, 2025   
 **TA:** Soungmun Kim ([email](mailto:soungmun@snu.ac.kr))
 
-### [Session 7 - Medical Image Data Analysis (CNN)](Session7_Medical_Image_Data_Analysis.md)
+### [Session 7 - Medical Image Data Analysis (CNN)]
 **Date:** December 2, 2025   
 **TA:** Wonyoung Jang ([email](mailto:jwy4888@snu.ac.kr))
 

From 2bae29dda8b0c29911f1fa6c5b194155089e2fac Mon Sep 17 00:00:00 2001
From: Wonyoung Jang <43818471+Stomper10@users.noreply.github.com>
Date: Mon, 1 Dec 2025 20:10:57 +0900
Subject: [PATCH 34/34] Add files via upload

---
 ...MRI Generation via Flow Matching Model.pdf | Bin 0 -> 1323546 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Session7_Medical_Image_Data_Analysis/[Session7] 3D MRI Generation via Flow Matching Model.pdf

diff --git a/Session7_Medical_Image_Data_Analysis/[Session7] 3D MRI Generation via Flow Matching Model.pdf b/Session7_Medical_Image_Data_Analysis/[Session7] 3D MRI Generation via Flow Matching Model.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..71629a9e15fa4f6aa0ce6429654999c7a5e39eb9
GIT binary patch
literal 1323546
zcmcG#bzGExw>C<5cQYU<-HmiNNH+}K3?)c+3qz+MDJ3G^DIp~w-Q6i52<O({v-kV#
zv-f*G`=7%<GsFGuSl6|#wQhP1896pCb^%N@`Y*sACK?xr6Xa^?fQcq5%Aw=u2Ii2q
zfLJ)W+Hz=E*n&Ml+`wZk4iyU*TP8<WW)O!q#KX%9qT>MuYq`2YK-`>ye+#I4L7ePe
zfbv{Iynp|pYT@Ci=wjmvwEE{O*ct>>1dEGvsDdFD)<72^0Y3h}C3Wq=K41?G53mi`
z1MFf2v?0JJ@aKuRI3^m{#rp5y{tV;4jPmd3IHX;@Tp%DW9u8%DYfmE(&!1`_uD>Gy
z@%|k;i0_|&3jCR;7D(`)UqXMo261r;{ZoVcpQl_reE(|U?*Kqt{QvqC{9Dch^zTFZ
z)6#z%60mB(B!G4K(+-Cg*wfX^1DF!93bMWsd2NUV1Ppwa*X9HL?FsnC5Bg^r<h6xB
ze|rJeNY2#-0u+<u0{!iuLyj95>Yo;YXZq@v4q&T4j}@JP9pVBW{p}2Rq~YOer45D{
zaR7VFp#%1X{8L2gpAYGOJ{14V0@wv>dkZO7Un8JZPLKdMCy1Am&jbW4s0#!bzbELQ
zU6l85^>PEc`g<_`{@~E&(DATv@pSt$Pb)tTX>AS}u(!PxSW8|CXjjL=(i151&nfw5
zEj&S-|GsR08vc)Ele2e%fIT?moPcGK0b99RgE>^dF18RmkRTV|pGuw(53q$ZCYo>V
zmvj^LO$ma?GsR!j<6y^Wi_WD55`02>7wgX~BpmU?CaemfVB)ES=fH_9+L!4OC0_@^
zg?LUx&z7=;%KQZL5|9jnkmU2M?kIT$-3BlA!nx4!h9y1Wzl+)#pGe~JW}noAY)A`f
zDmjIxa#D7RS2W`uv@s(4OK!<e2ICtSwOC5h(5t_k!w;@4byNB7#%90K8BdqH(BCa+
zr+<=};#y{Y?(#X}xn$-|2|7YoVew8ip-THTYPX$@*)LBrN0JV=RUT&Lys#&@Mr+kH
z+mfa(bk-i}&krmt=0RNxLgJ3FuKBhDIKA!B9BorQuoPdE6it)YV<6oubd)z5a}_Wp
znaX_ENOV|wNEq~E_Ly3V=@MswHDqFLp4f|$Tf)cNO~rxykxbqj3vc<yiuEJsmHTx0
zM~le!-(r$6cBHLu;&)@(zBEB9xS}FjxD%S*=-amt%dJ9X{7AVIx*=e~@BkOv?kLF^
z``XuCj;#TgkK@u_PWm(M00ZJ8i_RVINc%*Y6<N;X#g)iHRgnB|Tn~A7Ja2}Xwh{*F
zTD>X<=JepbcBxO3k^Fb{n=IBm78l2_Wj|q2ADD3&Csp{wF<bXG7)9LkEfI{e)-_rv
zku)ij%Y7n{9&u=x@^s(T2_+8mVm4_g?=x$lE+Wo<H1zm&K3yf>BqFEIs_ct-?JMR{
zZ?#R31a9TyUYUb?^ZQ8S>&@{&;sWBH%98q*r4xz;PED&QH`0Z32JTGfIoqr^_n)$<
zuaph@E<I%?t}y2ZY7zvnJZ&i52yI?1Ff{&9E}Xp{=k8z3?tHr}wS~<kw5Z|HPUa4u
zx2ZNVID2sM@R981rNqWf<eq9#YD|finkQm9Pw^}{DZ!6?@+iBHtW+tJ@|UHxnc?Ac
z2)YL4P0znJrStJ)6DbUBr-{$BB-3nTMMx?8>YJ`zgbd&O_{IoDqaNy<E8I2WFYXt+
zq>oll&Ek+>uRs69XojsuEeAcYy?*u!bJpfa(sh`tufl&e^2~AIP$q=FJi<4_9P`-!
zsW_g;ValKngRV`)1%IB#ad@LmkX_uHD;F#N+WNKp6NgFLsLfxy`5$-ZKTPN!gYzF3
z@9%y7Pm9DMrOKh^>fvnR#9?It;`;a7`o}i?Zz^%J^9yqG{JpdP(cd4_^<S#~v1%OJ
zUY3wQD5C?Q!QWA+SvZ3MOUEH);R*g*LP}ajR#uip%hl2q;>s%J>SWC!>jKyYdly>{
zJ$n~P7f<{D{Qakb4A|4k!`=<z>H+!(i{$J*JR#C{79JoTZUDLd^_`2G3#e`lu><gh
z=PwHU`#k^D{BMK(KP~^Y$bU!rKa37AU$&lq`cedd)80zb#nuT7;^dI@wEBYtLfnFY
zh5Y9`8#f;(fUj-}V0&9T2!L}yDR0}qqXO~q3jj#z`_B(<Apy?6ed_(IBqt|_{=aJd
zX+s+VcGd-|{4FE(*N(FZ0TA)00|1lx0h7+h37GRg>jM~BFgGygzZU)9=lln4{>z;I
zrvo7^`CkvjUmW@`wf?`hmHWSJtK5HX>;HO3x&8t($NyqW|L*<2Pw#Kl|2ES9%bxz*
z(EfkfPM&|Z>R;Q*`@h&uK_RaH*iK%)|6x1-vWEXM-~Yyu{zVo3^UnPx4F6mLfZA|#
z{Odq*@e2aD@8s&C?Pg&G{y%WeA!QHo)Bt-(yE?nMy8JoIf*gtfJ@9aI{R7w_8w)2-
zFo(9Pg{LDx!GOc01?<!R4bZvxgt-4-kbazZo1ydi=TFB$O*vUbBpk^Ukb*89le?3Q
zMlv0oY6=CNA_0!3R+za&B$KMcFeo}0LlPOjd}~aHCGa`-_G;Dk#&pQ@qsy97w_)GY
zd%c@#quYGXe9HW{mbC6hE~CyZY)$n36(()oU4g${yqR5;EfAxFejSwYJ1;IDhc2hX
zjpVp?8km{}?u?}ARvWGy*Dt3_4I@(rBx7tZesO&J>-zqC!4u@OGQ{YT!~f^8qHw*(
z&IlQw!}77zO1kLid+8`+=*8;V+uM~7im8=)wSk}Gyp1jys74}hl3qROYiUh?)T@0s
z>w2CDdb;|?zRSiIghtopv#ZzWx<)0pu-&v4_^^{<mPq}o)B9x6;Ij=TIsfrNSwW4#
z=UwOJcOqx)R~2RXMF!rd%lDU~97FfRsp{e_K4+@~am2U#Q(O7|r_2ykRU%gX;2k-b
zlZCsRqaV)?YtQ+@UJk=4^pwJo!cu{%@}@-5^KN+8_KU#?bmBU=?45$=N84({FPo3&
z96|S^wgqn)uI@LYsCBFKr<&cjpmTX<sb?!~J}S}LhQG3SY~>Qjylz)~_C6^zMF%V7
z@H>n0m6mFksdc=$smwy6E>+DTdj15IA5y8pe7-7p?)KFrKb!riTm9x{Ru479uR{5Y
z!))j8pG#FCa)G~Zeq8NMyn24T5~`%uT(4<6eY5{Qit6pPW-~S4H)X`n$(64^CQ^$0
zJYM)LpG4(lh5z*PYT{K{r^`*(<K^hXX&c8=pWixTLBOUstl0f-j%#de>@rfoS;ws}
zt_n%nJK`!-{EIwq;SJQ$bhc5fY<Vg_1bAi^mOi<1GIluUF5ipIY(B?ASb-Gy^BQ8a
z09-v4u5w(qs-uQi?}+6NXUV27k3Ub?{34nmVLTIyeTlzW4O`%UD&(l24fkT?lS5sF
zat8ZOv<ElmVFI~;4eWKh-_>*l=?|addCgkW4vHfZdCn}aqq&8T&%e)ttlEE7HaC;}
zAY-R$w!W<qfBWnF(GARHF%aGLbW`h#dTSKVB~Nrc#xoI4$Y&g>q)Jto!EQqK+~K^`
z=sZ~{Nec?WO&W0bI-Ft7K+}B*eD=I*BkXTlb|0b)kPX0)9?K)=clt_SrIO7%j5#?M
zJetAr!)hp=bSph-0@x}KW}GHIhZ&|~2}P~*Sv|87r)|%@@sQr`5O}jU2lSbP8+}oI
z1D>M`T|v)+u1<4R3tt?6xFsct{UnjidleLjFbQmx=Z_i_9_L}G)YjvPYmjFcC60e&
zm|ZzmeYg=;MikP}GVLndmtbvK2?<E1n7{W(x(w_c4gKTLtEUZ9<o$0s0(uo%123hH
zaDPw*ocG+#%Bx3WkZKtkz5y;?LEx{Lc}afEdO@^KDd)o9oS3olD=%_Ih2ZF3{xZR-
zPGwX-4BSi%9`urjQGFM!d+{+<>u|1Gxnz?4Xt|i{ljWI79V=O?!aMHZO+DryS_w3N
zY#!u$xf%G0V^d_}eV5-7c8X6CyAfYdY}wNW6MW5v=T8!blBkLOjut-KQWSgVVHXT9
z+%So=8aLw<_=(}5C6yKZ(kT62rd}o;E(7}I^+GP*ZP+28unlscWp6T#su~4%MbVyq
zIJyShH~)7i6kOgAa;0@bn83S5$Jz_R0npM)lPmQ}d(d+r+^=v{e8WbkipdtcDcTim
zO+|zc9j<Gg%VBy5-`C*Yv$uJl94@!;l=RYd5Wj0o4!h!YEtKT8n*`0GWE8RVL*2Iq
znfqnFLYcJ*Xia*;D-;AN;NJjui_<wa8xM;NjUnsQWPhSUyR5xHLmLfdk_<yN%oC5V
z7+fDrQg&S%^jjJGy`R^+U9_&v;w15+@cDNiqh|Kcf}Z{RA~8ea$$RN%2vZOPmugHz
zF11DFlBmRd(c39Kl?|9DC7m5+%0nUyFRpnTqSvOsHZ<OT1dJJ#Yj;!o_*+(xTp1m3
zmR^ha6MyFu48f@VVm+J!anmQmBU%kLLV4~F&gZdJ<DJY(;o`>KY7ouZ^L$r_=l#_;
zEs8oYvwiDwXS6l+#(AZckTDnKI#rN_om{xi|JJ2Bp#wK6-m@_%2AxENh^z(+TCAAL
zu-s=B0BU`2t{TGE2LB3&VOvXfu_xg;fS*U5xg0ivad}Iq+I(N7wRCU~hhB3L;zl9j
z&1&)ibbI}aNF_cVR;E>tF>K0>&!56?GlucJ!9^Q#G?$>)4wxm=4SP4CSisO7vg{*=
zr_K$A-=j`FPQI6JXyL|UeFtX+Iaz8-O8v1FBI13Fa<JIwOl}QBrF=&&>Rb1#!2*s`
z%aXK8Vn?#=dj4W-D9Vp4Nh(8Bb^xK=wG}R&O3d&E&dz{0n{;Y?xcikk*1;V=LvoFD
z;IHcg+$6rZQa)N2_+~6hVV%V~XvpNj6OI|0{ytu2>yI9_$l&J>2P&{Lntey&kLS`l
zaW=IkZTvKunTQ#q%G8uDq)C7^HQU;p5y-~~z;YfnI%k8Ccl1YHDJuvZSz4hmyIim>
zh@ps#Kr`&Ho<gYKNAR1gGMEvd9O-&~@^4+mpmpuHO2wLNr`_cL`uJV_)rnZUY#b3g
zy+m=lVKlBJ^U6`b%q4nN*b#!+JaH9aY7}^b&=1`-4heY~f|>cHkY6>2e`f<QRRi@J
zbf#$ZFG}(46r6+MRkwsmT2{!WVRMs^VFk9RwL0U{-9Ph)k4BX_T2RTh7y2HY!h)4D
zFHORaQI6>@LcEJ<n5o7og0DFm2fI#x^P+@Anj=uw$Lae3CbbTop1~!Fs6u9moZXI~
zUji{0Ce@=TJa$T5{GRUpq>`GN{HMND1asAc$mB^2;(cfbEQ27CL@t+=oXnne>xEzy
zgW5#P09KK8zjHD&DaU!wy<ff8X#35a(Q2F>Z8qX>0xlSc7GVcs0s!s=Y`bcZ{uAS_
zjmx0t+aUHBIQMSn)pjA%5@pbgG8ENB)@tgoPRPGPSdplSd1UmJ&f?Q>tm$>M8mFLx
zu+(&`*AbLLgcS)Ae&46;hl}u<u*{suRP3jMy`qCeDoPGUGf|_2Ij#Lmc6Nqh*tZlf
ze#RLQdBrn?AmVYp*-6v$6`@JOv&<Z+SH&!k_Rx1X$Dtd&qLQjVE1eKzGDNC;p&=SK
zZpiPtnv1t7Gf7y$K7|zro3Mrw1TYkXd5+cLM-ng1y$>1Zw3%#?e_*lK6B402eV!UV
z;UG<rsdHUgY^<Y0uyK8dmR=4O6&;UAjp7@Y2e54Xm@7#_8-`4Xas;J9=CMo`hOMjL
zi&&#VfMBL06{ea1p3<n@mkU-**dcjqd4y@}C5_rCoeD`YxizYo()@*!X=@%wVHW_=
z`=7iV-m!z#nD-_Mh$mf$kPLeH-Rsx~r0j=<-G5L~QVMx|FbYPd@-^S=j}h#HNZOt(
zei5MDS?%!m@6)2w?iPUTPeQ+;*J?D%GO#}(UAtstE1pVdlW0AlMB^@(Rxx*Kki_=8
zmZ|12*hONJr=xreg)s&(;D72*%~|u?%Y)Lv($gH6)>{v^c<$Lz`#{oyNfNk`w6wG?
zy>~Brkf~+Pv#e3TXmUh#K4+$(D5p$C4~ToBHe)0+672$SB!q|ynP-399I>46t0O2k
z*npXE=m_I~;Vo34V|>q<l3A4mTR8T{{rn(GCbQnu{p*KBrA|e!>`~r#{SyFMhclX-
z7Nnqh*v<$e$pXs?C9@dlq;FNA?q99<0nBL9RI3D0=8O7qSH|`Ip?%qXU35R1MP3%g
z=Tqwn|6n28hxEYRjN2*oiQO)1^EK{&+Q&YWTsW7FF~{h=dYqv=7XP(hY(20n#QhBb
z@IOmlQc_|TaDj-6Sp?T5?7rDQL>;68d7-ZOBjPm*)Xzi(YVv-QQp9_?!5)7%Cg&2g
zZpMzs6r(GZfF>u++T*PSmr#JLl#LrWN^Y+h`c$Hvp;4)lF_S?3iYoPyD^lYLXfL-Q
znE2TND|knyrf^!Kl*Wpi*^5G!=QY9>C8xQB{wr5p=!PbfHLt0Tu1TlH(fNmH6h-6)
z1rD<ftvd<u=bRL%OEe=ZkL|b$!guAF`>H4Q$IKm>6c)W23FT%g?Nk$HpVF%p2PS+o
z6fIn;yTjR!WVW#HUdSg}8{R@BqN12T<IlkJ<6-h9_srF4kSK5m7dJQS%U%XH2i$v5
zMf;zvbv*-YTRkLAMg}rT58n=uYFQMNhZ7?NFe)zTZk@|YE5JN+3#DyBjNr{E?3-Lx
z@Os?#^2Pi&^;Pw!2lfMCF^yo}MZ-mZ+KMF%tr#ehje`tCL%31sc|pDw>wvXWMaYfC
zOYy=B(5p6-MM_|N<#kAaakAbMRuFJG3^2jyOMs#&3Mi0;aJ!>PiGZ};+#C{?_8SyC
zN!fpL=_P8u`ce8;tN0j{?n~snfT>%0%peb&NXVl5A>c+}Eui>7(a@i^=kb0Ppbg7P
z)utU}sd~i8nEKmHQ}>s(X{cm`Ke1bibL(L%0_8FzWnb{YwVHOkf%eR#xg}@Fin%Fd
z^BTX+a=zOAW+K5?GE}NZALdT4!i~GlJTFM5W+O8Qi#jxVbMU?F?ZZ!ls;$vadI&$V
zvB2ufX5bL+w1mt{T(g%a4Pz$I8UGb`;8H4w8~U$xzBQ&(p6<#<4a+YKDV9%KZFJ5l
zg*EO9G~oy1AKuXv4(W|b6~;Th-AtOU&}Q`ES5|?>Km;YoS0l{#nn_}bUR3Qly6c8d
zKF#Od%xgFu!Ue42R63Cc=3{`^nVD(sPM2F!^+{LU(NOos?{=6GMl32`e@u6ban~4R
zemB7o7BCddaPyVS4#dvJ_Ra2_USjC)m$E6}icD6IDF?tPuemcjmG9z^GG4CJD%5cO
zjL-d99mCzo>9MVl1A;s~`oU+Hes|iA5C0jGos!e+YQNn4sfmQ=3{F7YeG|bznij=+
z)%UtL0=<QbD{KZWsbs5T&Ht<e@Cl~`vBj<@uMfU&5pT`OWZAw6ds8FZWdEH(RDiEF
z&HH$shUIjsI7;=*xXqi*TW9hc{#Pu(e<>Y#LyBKQCLZ8RyN}$r9b>nx@mzzWLSE|y
zpgx;XBMj5F#*RGOPy)Hn{U!Jwd66zrb1j>R5$-wirX_yni=ED5-X#Gh7n-z7Z|&Cy
zI|<c9;3%~2Vz4l>g|D-WjEq!GY<&4)dNtiWS8a5}GA-vJcUooEC9V`LJ!t4`p+FmL
zMh6Gb|9aS+4*y$^w+GWDsSK)>1p7urJ2I2VaIX}Rd9fx-q4E;(Y?H<$PQVtU#_6XL
zooT>`Wg%4K?2hqdaG2#|yfNv&H4Xah)x_SP7oBDogxb$WvU}`-_rBd}ft$b!u-;P=
z#BktV>Sf>R7vusi5c!Z|%{fbCtx=iDE=pM5TNxM=s;X-I{xD?hISd31U4d+81lwYy
z`?R*RP9sM-CZq)y+VFZC_e{G<xA{5?I$gm}eNoubUxBOn3-IlVFvy(ye>&gizt6=$
zsxAs)M;Os(Po&?}!)4OeNy|O_njvSzYi~H>6}K}kY#>?&u01aRoJTDiZEUxpvCncm
zd7`(=o<tFALC?QszFHY|5p}i#?wCI8=+^xF0YIvG+3?XMp{~;(h5`PU5+oyJKst#i
zMauWIleFm_|3NxSs`Fxy+<Oo3xcStgsv|-g#TB7F<ZT;j)0kN>@5Up~!?|N6k3C1j
z?zfQ*<(DJI_G#$d7k*@z1)^HdpyRwZWyWXX^*o%ZuwDRM<P63SxsR2A9U4lcywXrZ
zk|>LtijXjHe#m@Lx2fbWJ30T!YUs@<3yOoGRT`e%XS>r+uqdZA5-?mR@FFE|R|6lM
z%j5FL%%C`>C!G=2j9m|b8X0*aK8sghyzvI@vIz29%FFkcTRfTDc4oiS<mm%s^{9qu
z#s*8rVzu3mYGnAD=xtmbM<tKjdN-738}pl8;eFv3-=iP38H5vgB6>)p>gOK6h~!mS
zHegovBpRLV15Cr4+R)+sh_NUps=D0)zdR)+1#0qpIY#IYgR)i;{Sfs0v@y>ZM+61h
z=apEaWlG(W8FPL*{8+nP0NdKoz)Lx~{Mq-y^1LXKS7$`t!ZODEXPd3e{RvfwL~Exq
zKB7HSgwC?EF9SxQ_BX&ObBsVVm<4#$H0CduhEmxI|H)L&$tJiFy1FSRHq!krxe#r*
zlLT@tx&ZFieLpX@v(Jlf9jGFj^k6ooTa_+par}K6LZ>S~#zOjPPz<23tFnIU;U1FV
zCQ6AJiBwh*@vx9QVJ{Sc4c88m1fw{YRl8wI@bqdXr2$E3dd(2*BEsI!Nonk!$*%$c
zrpl_$+@`bOv0rh&>)u}VKf4+H&`?BSs+x{42Vfap%ZptH?w2hv1~hcJ$5eUoLXqro
zfp;5GeIl`R&nVa5WJ&F`*qziFHMFaFR8e_dfvbrA-eIFR0x+bzpPQ8V)wZ^hUb+0J
z$!i*bZDhUnO1d*GErRM6d{Xj3bN8!jKxT>=H#HOPlEUWuNK=gZh-bj-5HF1qgg$v_
z%7QRrOat$*i=vv5Vx=cs%)tr_ID$3<owp`wEaJ8ZzA+o)_->4))%&D!fx*z}i$z}~
zAX~LE_5GT;g9)Lv$Dz^g*>TWJG3?H(8*1ZZv1C%~^Zu}rfTQNy`}F(alf2a6AGIWr
zKBNjYJcmo)_XfDQ4{D(7i%#C;Cb+3ZTi=16Fk~@CiO+W8)-)P}r46ZNET^>?JMnX*
zS-HYqN_l-SfC+M`f%!B5^0oJUj)h0YGG7!t0b~KIQNXeC>7p-q&BmafucGOWZa}AN
z9@1z>%k|19D#eeXf+8MemCtdGm9?X}d7jTyN3RE^uPQC=T;-|tdqPTtypYWru?5r7
zZP7E_o4fN3Q_{gCYU<Up#FtiNmm9Mm^?ah<@MXI?DlnUte(w%`{Q!FJHI4HPGdY+$
zKlO--5lxQdCfCz%z*Km)^7YauE98-L&&W`tVl5rm33Gn0xIzlkS~6|50&z;NF6c4&
zeqROYY}7|bp>{{dLyJsqa4aFmZs0lt!C>$aKwW9|r@_^#@pGrPTZ0K#40sACERFuC
zJ}!$5%-<E`my<$K_koo#1dYOh+3Y-Ox1+hAc#2yUSb62q31vuozj>W3@_ps!(s_1>
z;SE_@fvP|SJDWS^z+iBU`J4P3h=rN2&NtMH@b%C{zt6v0NS_PrKa)(NP(v%gvD~jV
zb^)EPC1+(Py1r@1gCYj?6GD($*2+mIeFNKtripWK;(hnzvVVgPXJ`Ab!;hyE7ZWgL
z7SK!cF!w6H<C0-aJ_#Zs!!!uTo0m@d7<LIe?({F9h@W60Fc;y{DKs0a?=6+&aB?J^
z)=NxZ!GBU`|J{fEs=rU#a)r3Zi{}mE*vRjDz@={~x7~#K<Z?T^({t#bt9VVvyLkt2
znO+rcLkv4!3b<Nke0L2b%j*%@RB!xAp`u((2E_grCoWYjAplMRvS;2qw+sCMNPi7w
zP58Ao4g<t^^tXG5Y-JIB%;x}`H^1akk5)i1r3Eu3;Sk&1EbGQ{SK8?wqY)(>jpKr=
zm*Zbps~hM8x=F&ulCNy+2R~wHqy6{N+ODTxfL{z~PK1>(6mE<Y0wW|_qhH^>=<sG>
zzhaMNOZWpgs{l{{d~k72F2HWGFa)n0<^4Pmj#^dINQ;H7j*=<H=+7r?gPUw#HrBwZ
z-B)2YFmnHv&4)U2NVQ?IIt+)vQDSAPA;&FT`2yD|W<8rtJ<N(Rl<#=^QgqGx^9PvZ
zfJ=sU<jORcEpByT@8isavDdKG_MEs(QIuZ#TKR*7)H{GDrfax(Z21W_;dnQxKf3Oo
zfSA~yGCwQ7?)+{m$^Ww(>Ik#zi|RV#r=!}g#8<D{!K3Ncuvs?xmSzWoA2ua&)iw)7
z&AVYvm+LH~AvB)x12Kj0{YNjOBHv-PjU1db$th=>4&>hHl_(KTR5-9lp|Ztz`QLtB
z-9o1dT&M?+^1O?FJLnN2h>HzY^$i;#BCu548_(;Yv?&J>Hle*4!pRl$pM$_KQ1n2Z
z1eBqYS#hYiYc9%Jti?zRY_UH|X?<RQ%?o*?PkR)?34}Yr@NrKqD+?1G4~a=DidUWq
zB-_Z-QC(;A6Kwzkmv%#Z0zlV~*{c%UDfG(MA8}vEY0N;D(j3>Y$?LBT@y;rbKydyS
zU-{`oj}|Wme?Qy?k)pIhUM2(5U~jm3uAuwl-GvnZsA6xRPMhZ&eZbBuBfNcBHQmS6
z#Q^UN1BNycNjQ<M$#pGXL|I8mYc-jJg@5Wpz1wL;$<Z8kY5f=HC4O9A7_S_=q2Ri6
z=}Od?>GBf!8C3ijUVnmshm$5Ds)yANiuhJ%xV@NUW?}6uqr#0&3wW?xHLRo%3S14^
z;2Hw3p?V0%4s)(wls?>Ka>)5^L)~%+JhEY&eJS<pIv^83Y6KbY!mAH3mt6Y!XIP&s
z3}V)gX(nP7D?V%pp&bN`Sdod+Br=BPVXp{z^>a+0bMC-cPW!d7k?kvKMj8Qhl+X*!
z6;2xzt!mih@{MJA@cv?m+PRrj1__f)=4M4B#ihALEl+q8xO*`kF@*w4`52oVop(i!
zD*=et3#yt5%;%odeV682-yrpXLM@Y=({^5huvj%u2zfy{nO0P_A;eAg%`;Vlj@*VV
zTtA_Z2#~tH?n$OppQj1*OPXE?(6H{nWA0cFM9%6Q0)B2Q+$RW+v3?H_Ex8Ij6j<&w
zYI5nARxVS^)46aH-h#`+vqU9Q9UI{_W3QbrBq!opefPsGNGzkhSqqlc^!;y&azjA>
z)Zm|(Gk?-S#y%#>G%E=x^m9TDM8$mn`ldDlNmY6Odsz}0+ULXR66Hs#@qiflZo15u
z67mvG5>afeT1Im}(FODsD9U$l*MhK@4{>R&uYt%xRh5c<D%uJllGprDMG`kYkkG$t
z5y?t0>rtMfUSf`BX@IXyMYZzbWeyDcF<WgUFmwnxTQwmk*L{i4prU}F9ocL9n+`s%
z2BP^sn2N_n#aZ64!ER&x@)Dt`N{r4u#~m$3Bd^A!ZMw2$EIV*F%eLx*SsDm9V6tMk
z0p~Bf>PXcIW(OjPAkjfd$8OrL&;dx_lF`U_cvT5jyiW5PO9Av@!{r7XJCnGY?E^+e
z#54$r0McqLvJO_6ND8eiFqg}>hG?Sk1HQaWouTN{dd1i_5b$~b-l-m3TJ#CtpW%(d
z{Is4vkqW92JjnGea<}Yp3)!liM2FZP#oaEsQ1;Ym8xqo~1#4-Dou0UqlvM9kDd4=w
z`+cMO7=eC>%HHkUNE~w{w}WD7ftSYBxW)qzdukXEo}kP7u(JHZiEavg$1TT&DjQ?Y
z#hgzxHdjXiKVsz?6aTAAWEW^|4xbcY<@)rJv7sBL+$X3LQiF+<${}XH8xip~k|ZOG
zq%opXVs)&Y%YY!0pEOV_e$kIFp%^~U&|#V#oyZZxro|5{LjkuK{V^(=-*IlNjppbk
z4G`NkiUD7=Xn?Z$)UGJ4&F&Yz4qb7FRw_WwNqIYccohTuE{7RA41$_30J)CI!=5q~
zb*HL<=lP^*?Psi^jt(zi`$9pA;XXKY`;JvS=v<g%Sm4K)a+F89?^B0&QoQyv<+xlf
z1F?h#DjE>YK^2tZg)gsrxzPLt6<{wqZdZ>Y*;X$BDgS_R<WyTcLCG&r(Lx}ApSo#4
z^t({WJlV%%fV#^w7ETbvTYeA)f#iFr0qo*>w<~j|MBl{(PkO!jR$P|01zx^s<=koU
zHjDuX5&EnMNs0E1+g=Ngq)2jb{a9*pJ)46vwlQL@5kOlCFhbX~b^)(JsEa6p5Z$s|
zt%Py0jK!2nQw<?tDUYxKqX>0;`4^_|i)|X*4_Sxo-g^(YYwiz#06#mq0Vw;2a<Jc0
zF4KjamKxcH=%-tXS?|yLu=%`T#o~Fkapmng1{25`Lw5qx*v(WK@1+ceOIu}NP|*2o
zKbELO)~m%w$RF@_SiAKHKTuSw^gG=&08;jnt77M*tz(CWt&u{YUDW0ZEA-v;wAMwC
z0&A<`fx9ejb22Z0R*9Dyd{P#V@QqsHZ1I;bl0>I_-zlj#sUwiDULr8fc}|E${pj5^
z&*!JG|1KDUivM%Guz<@dQY>oCAyynw#BCj>CsBS1(s~74@t;}bKyZ{j+I}8rcvJc%
z8zZkhAZAeOMMp|q0ztQV`m_7J0zEwmAo#dDmQ{Q><X5_TwT1lkct%}zOB(BQ^uVfB
z9}}P&FRpwh&WpFgrR@OXhH-MkU)U-|s$ke?c0<W%#wsyB0uj!p2MQk>1p@;Efc?R0
znnZ{$^V$Mm(N2NLDa>0c5?k@EjL4vi0b)OSA<{bBb{}9Q14N(#z<DSl1;VfiSXODm
z4GQA1WVl092vBdp%X40A(Clz~qKmIIWRFlRm0##!WEhznj<-y1ca({Q%6IU3$2R#)
zvlMiQHzA@EHLqE^Ga<bjk4A=2>Xpij3ClH%ZxTr^j41j7L9QIOD&1Dex70z8?vo$1
zs*Rc$(m;U=yjF*^02V#w2oz?;ZR3J{D$rXUJo(OAkJv4b06tUk;%QBp7o@YA+1AFX
zM4v0_=RsRY-9R4GNLis`WH5x%WM92p9afJjPEi||VzmMUXX{fX@L}`1wfUUpaX195
zzv84I?koBFKq=^xm|i`e_qk}r*Zz!$lJ$^uG+8aKdY<d8)*K7F(&8Tfay0Qgh0Pl_
zAC$_a+06;94{wycuON%r22c)&h)RZ_wi#=Zj9B)CXQs$Q@!x|*grUoH0oQcy{NSjx
z#sx^k@$hJLM{1s*S1z7SEO&D^374D?ximYT>GL}!(Cn{n%zdHr1I?clOT$E85G*TQ
zx3gFt9|3KU{l_NiD+F5f=^DYukAeevztZEB9PG4AE`IKy6suGRdD1;eV22DIRo7pf
zo)d&x|GG)qFy-i1y*T$UeTE4XS3`a8P}e`ff<Of*!wE=id$fL0B_A2p^Jnb45DMEU
zd-wtIx^{w>`5CZHPJq`2LPP_!+r3}t9PJ>!D~;VvAVQ>wWWnw7{0NAYpe5s|8jY7D
zeaiP4ZtD0K92apxepsL4$<tX4w0LlQ;5?O}?-AZo#B{-OZwwkawg8ml?EsJ1Ou0?J
zhp(d_{^RGY&rN&tfOoce`*!6Zn#mYpBw*5@hR9{c`J=pj_}B1Y$3S(@#v5sN%QqTT
zGLu1RSl|-_B?S+?dMh%e5eQo2x7Gdti7b{lVvad-M%C>8rp2O!{WBW@zq1D^!j2~~
z=eq;vl)*UODcU@o?;ZDe1h3oC$=$n(>K8u=ImRT8W)Ebbq|4Y-<sm`LM?Wb4ik0iL
zkz5-b_}ULJUq)j<=t(nJk^B=I=BJ;fMZUnx3V82iqHfn5m&oAo#!72mwq%sL{pKLr
z8|A`#@=NKsVUcF(w}B%Rv<B?LuZSCY=IC!izxjc@rU{h>zBM@0&VNSnF$6(R*w#Ou
zy6k@x;LYNIDsD^^ysF)K7G&``BV&qcnmPq2gY)Olr$EdK(#E1K;^EfRL&GT=Fp)3j
z9bp+a{9TaO%iD@*ib>n!Tl6H7s<p7gPaqyj1}H_GQ*Wq70Dn_Ohn0y1XJptsT#u4j
zX;OmoUVfN(L<3#bR4ZF0Eb~Gu&>6lYs3I?Mu|GWulN`wnFwRYT8fu0UHoC>4tDWkC
z&PPw77L{ul0=>@06D~guc}D^U$r<6SfW(6Dv>-z&@ON>>7;`7ecm}mxJDs4#2K!`W
z%G=2jW#XA)Hzv=OH;hc%oo^q|!nq~Qu<4kj;qWChB2Zvz>jIr}nEQX*q?nc>o$c`F
ze*QIauiR->!p6+}Nhg}Rh_L+AiDbr#eOTJ=XqkFJBiIaWq#VDSbOf&sixC2S-k&N)
zloc}gL>@rP*>n!$)2R_@qn{q7o(kV{YMd>@Q1l6-(&{(W%qb8F^r9|tE@mRB*a?P(
z*H8<n$26}{9SQq3@AVj7SzAVk0&Wlg34K8~Jh8v6Ax*kA@G&kn76a@)E+aeSDydmY
z#~(v0%AF$*4Vj%*q$;`zjxpcJky{(j7mKTeCk3@C@7>LbC?e>Y&ne03)|#qb{D~f0
zJ%8C$m;;nAM`Dh}Wu1(nK~iya<r1c=+7t%Rq`y}~`TKa!-IM*6ft^3XkGl(ytHb0i
zsoAJ)NXOA}tremnx7;H$Hn|!n8%D5*)jzXxbc*82+U<Ors1HpZ{19W;Amq6_nR)Jc
z<Jo$3o+?@3;60kYb?xSWFNNw*0kUK&+n&$9meS-fX@!c6Ec#NU>3Z8Rd1@N%O_62y
zl9{%q$wP;utKEIE#?ab1VFhDC$*T7x$GUbGj7fQH;(v@U3Z%p~0Qow|?9eHPA8h*#
zwXAelBX<-zgDP2s`4~Gusr4#5qqwAIm!pLjNr`JLp<d@qyt<MJ8YOdK*ihslE<54a
zYDPF8RfY}w5nlYBnK-ze(Qv&c13TDd=85Ouw`=abzzSj4x|gbIor*|?l8U!rm`S3W
zPeox<GZ0I=njD(u9|}HX;(0do`Z~G0f3l2vU!{t{^m8nW#|zP@6m2o50SLk}DXKv2
zaoj$Xu16D|OGnVCL}IMWwS{Dl)Oj80l8U^~x@7qhMbU~ylf<O0>SKUI?=)H10|dD$
zIHH@ED3#qTam8|*L(dZf?xY>wGfM*?cy1GH+aWH7HL^q4))*SfhIlE+ufNnzd@@C+
zB||~;K02me5y(ZDiE05NFdTRxU%vgG<0ex2Vl{*{I*AYa+D#SY19i6HgYETNtZlzU
zbCpN!%gJ?H0xV~7!NJeK%y=?BG@_`r!l~s?<kMGGK8`W=qXFn~`d=K<E>v`GP2_cJ
zjQzI1;=a|rMCqCA0-QX+!=R*IPkz%JRS)qkeIXctDEL&QCi98d)OQ2Or7C^*Ej#e(
zeh<J_x2wndtD9UPQR!e$XGFE!Q{E3?=Jg{$M5bjpJM*<~VQj{<ZrV@nKm0#{7M@DN
zbd*wh2BBA`aGG;WMQA>O)a)mn=%qdt-tE(6EdABS(dswmKeCOzAb-gKWz71Whks{e
zY<maCoj2eg<L+^`P6P7o`LgV{<2-)nY!lcwH*aoES8{sity4sc9*q>1gD)Ki?2k97
z9WH|dTzN-Y7b5DH7chFfx=zzkR;|9<OC>V({Z1Kgr^J|)3#5rv7rh8?S`3Bui<r|I
zu8XdENa<6wkK4S>&~w^`n>0L0W5)bQ0B(qGxOEL8SSmF_oRqDV58aP(?ZE+j38XVG
zMx7|l$Rg|t9AgMKiPLNsAj1=(!WIHSeoS|SDEpEDlcOR5?i`U3I;A&pQwli)uoS0S
zahK+y9Vd$UD85#?dh59zLQ;TZ6XvwfV|HO0@Z}=mPuRVSsZVeA#R*!s0iQEUez!$e
zgjJg(XUvP!DOn3>#k-X%X;UYVh~~SLrJiPnQ#F)^VD>k^b6bi<ZO19J?el1)r0wTw
zN`($&+h_1>O`{^xq0n%~w^`<AE`6K$%hmRF-G35)KL1)!u=y61><QM1P|_?VvX=fZ
ztQ!c5L_cUmCuY}&q1D%SoUg0_^z;Nuk@N!j;^P`RxClVZc2v+TnnjMR$8rxY(pMTV
zV>47lSe~T}7^4IqS~^>H{8AUYfmSQ-W%*L6V#37%@vx(H1vH9J?%Kdq-S5tYrK-oe
z48J&RbMHRO*7?;7;{jLE<t66YcsZK;CU-61wPtJSIp<km1Oc2d2CYk`s7xlU*RCDo
zRUdW>^=tP%y>Ow%HY>dGkhs%C3V0+8H_hEBQJkeu-nx5bDcLgnkR%GtNW_-@ZY0c$
zB~i@c@7y>DM!L&0-<Z7nO%Vca0G4~C)&R$$`PoKwWDUTPWWvH3%_w2_l@%NeK42JJ
zLXB>UkM8VTJ`UIv^<H_n7595kjjUt9oD^J|*K&?{C=mqj&6I1xJNJZzWN(@SM|3od
zCm#dsx=Y@P6(Ro(A;f=M`;@7TNBGWMS(nSfE|?*C{j__=xz&v2CwfA%WEfJ5<NgQE
zMM1;?Bu5eJSQgVYSGm_;5y1g*k)w<%nT6~cm@H5|3f1yMcnI&%(-%t~Ybhb(DWYhK
z0K9%a8)R=B7-@qj>Ua4@Ds*rRp`->5`f%Z1{JV)BQCO7Ay+(SK;nSCo=lSHwSQO<A
z_+e;ol&4Z9y-Po@PZ{}30SQVg_f7oPINBbP3%lrV&wG%wd9j$Z#sO8rAd0SAvSxWG
zQgf~nwmp@*2au4oNhk^OQCch9?;`7oifM!k!waljV$28O=!{Rcs48+PM?6?#Br1%B
z%TkT#p|FXR`JSV{?}`LnHO*>nJzqNh_QfNc^LoE{&5E1Tw-<T|;PuQpkSV5D!71UR
zR*O`M4_g4as^WZb0GtHc`AG?1dkzr--=0v!7<2B8ozaXw3M0>TdwRYDfQ38Xjd}M@
zknP-G?TuP!$!7>_apb(Ol$@`#J9VVk*f=#kV&J5{3l52{MXyaN)#@fLqh@u4wy5Hp
zaN3HN;gJd%JrG7a`m*DyNYyd=Gxg;)G!cR-Cp4<GjOyuLM#sJ%BX7YHnYFmR!@Q9)
z*9{;t`@**WS=zw1!=&TQYnB(hG{zA_%3t5Tc_<)9;FXp0JP#iJ6=)*q&vxFj7d>T6
z7DA}MN&~j0u7jo$N*Ncsdwc?GvIa)bER6-t*FPX^7RzZdWg>E5gUJ`dv-()7VidZ+
z@WL>&v8fJqHoB}-PKiz1UYjX0`NiUeK$*5YqZ`%r6yk{3+An$l?%#$G=6~!ZJpT?N
z*uI?}mAY%oRSKi}MS8(_gOKdfO<;1+(}6oP^8|=7tn~IdFXt=fzQUd$1J^X?BxNPT
zy2GE_BPMY1R?Xs3V}ln}Ct>i3tQ|Y+bh}>@a+;>tNs~r=dQ$NceIsSUOOS~NrYMY;
z)za17EfM9!Bqa32L9VlgH3H-rZ{J*8ne%f4Z1NSKwYMKiHf}6=adU0^qj7h$d3%*5
zh;bS!+ia}@HRntxW6VX}EkTEBWr$29f#i;%{_w3X#m*-uR3qNo$sR<`!u_?rh*<Kk
z3V3W2-^Y{Po-7dyWBw@O%+3fEAR&upGwvOHq2c{8MyQ$TYGK#92)^iDLv^53#WE~<
z2b}B)KySvvUQVOd0C_qK7Q~WDVD~InSt+Nax2vZv)KD`hp+ty8O=R4NW%m)l>Q?u8
zVh^cyj^DT2PEf7LaUv?EOv(&wEPi+fuA}%BkmulkjE=3oKLbI;Q^73GgqI)M_TrJv
zBA5@2qbv;W*xv+_eT_#FM&YiF`R^eSDS$VIi@Zt7XRY;3O-B2~2^1gOy>=PO?UsDp
z9sVBnGL?k*S&I&QWW1qS4M&qH6=R?KCl#8gugO>Bqd!w>4ljy~)%DR_^%jpLbw`N~
zg6fkjtVEXELO1RsN&0NL<_q!fGj9Z;7%mfpb}^BC+_DQM$zPqfhLV1GxmLmqyhtnV
zY&o8SYQcm`#0PP<v;NLN_3dZ|g484EBZ`c%C67NkEQuvko5HUovBB9#Yaw3^+`DcK
zHdpO${kt%q>CmB5EKLm~yLdgpaIQV2cF*+6>4nO|<t2f?fBE8-13=q`%5{<C=Mm97
z3HZkWp$GAaQFHy8X+%q!NRn8aYUH22*kcOy?g6#rSDh3msd<O24K9J2R*{NUux+y`
z4Pb;|z{;Xx&<r*sIc}*i0heWx-C##NJv!Tv1elK}t!4+pLnu8gMFkCGKp;Z=>CP&E
zp)gFaB=jwAEi>JI`Ex4MuD5S*>kW>J+A!vmfadPWgfNg0=17Kor8AKesjRZ@OK0~u
zSueV7`q?I7cfG~O(#Km#r?fY9_{B1o=T{!=@MOEWn#MbE;E(_TY0(r{reZI<EobQW
zo7i&vv^fO?xx?`mL`!|@RW0(Bt!`%T=CrTB_?!hDX(Fx@gH(0nEmg9(BP1loRFP;<
zd3^RxMy)>Lr!>A6xO0Z~v0(&cBPP7+r&&5=%pp_SN-p-Zoy=Ts_@3vN_ijB``&8?z
z^2Iit;~#JwC;WZ=UH!x;;n{MXmJz_aOO%agh7n!`0eQ~X-2>Dqz2-^2u2yw2&Nqh=
z^V7}<N^H}4>hB_O3cu$a1V_;XFfp}Xjf;4j9BGO8oK|@{cQ5io=&uI&e!CXM`@N)w
z(uIW+6x!n?Xe^mZAjqdkYBfUlQZkTP#6KYUL4g!~JK`ZIpi=A3gG49O$L(pzvx;{#
z4EMA?>ar=21wIAggE>;Co#`m$K06<vy>kI_r4JJG<pp>rXwIY_oEGZomp8Ji^l@Vt
z^a**<&c^fZPTS6+CHL8MPWVUitBhN;-d20>V^l!qHLq|cfpm4f)dy+g_jEvb;Z@L6
z0Ljs%d6Pr+Ll~Z&GTM)H+#CF&)+SA?IHWYUnC$4y6S?s&Ama)M=Zy<njp!84ZI9Q9
zV>>zry@MoP3>2NTw_L^wEx{}nO2T9Ba9Ir&lE2zBN(oxd<Jeh@1+xAzhca*Vv?;a5
z@0b|F#DaKp)F>$lF#0zd8c4h~H6BzMnO*}TI*^c0^Wa7Poqr8UwTWnPtpbRO0V#@?
z8oTzhXrP<J*`uW<4$XL*vIAo%Gv_BOKz{;6qDVG^%@duMW1U4NbGbsE=4I9+fG35w
zfQeL6or3BOykkJe&>L>=tt#2x<E9-9+#WzLPWrjr=2M%njuWv`gXff6uPJrvqp2IV
zZL&=B18@=WAHEWVbKi@f&itGKGEv@y^`fMGG3YN;834R+a?I;^QXcEZbCw26AQlD;
z+~YR+aaJhwgGA_VUjZ}fDX}yYJ3IT!mkx^!6>MwCrw2ULytryoZ@uQ1fy?N?q<se@
zfjxeP@c<%-07YfjuPX#>_u=YZ-`fx-=l5gheL%Vr@Sl@ci1=Z09ke<@K)1LO_;(Fs
z=YVF7MLpQ#na*KG#kC^L0Vk1Zdrut^D*68X0$_aPLriQf4_<SB-MmQ1B#Dvs75EAl
zQ;v@LF(#li5Xc4#ct26(F>vzG8zn<+rr<B7!(<I4;2W2IlU{E4SSZ!*hRk5BX(kXJ
zvd+>J=HKwiHHRu);me-wdqhfdE=z<Gsc$Ik8x^>SgtbxlR^i%ydpxaD7m?W1Sd)ND
z_uOLX&|~D<k#;jyz#8smg`cF2wiwMg^C)~Y5i+u9ZI71gLB1`DR=F=Em+i9L?7rHg
zvfP&!!9E7i8NdV_G}1^|p|7S$>)F)+pbF2+^=@Ap%YV!MCp@;OL}+e(YCi%C5Uy<e
zmL}`v+=ULQ$B7?BXo|D~&i@V|_^uov82&hRcoCis^X<2vU+u4w3WoU8nqaG~UqCox
zs>*<;Jfm#|`8smb-DHWQ44$kWbMmxumYfwN3+D@@DA-l`oGg~?=-sJ^Y)3#IoFZ^|
z2_4VvjX9W^-J7OIG$(_680po4*Hg0OslOHjN>@>;2Jclh;EVDnMZ{?ke;$g#XOyZb
z=QfUDP|}l9=@U`fEmZ*6lf~jjj77d0L)buN_KAdJ^B$|l+v<M0rrA|Ew=L29i9|$u
z>pA>Y@R61$F{=xZ9_B$HYJfkm;%NbewdiUmsN!pZwoc><;V+)$)g7R@8~8h|LSO}p
z&Ve_2{MNf+?KS|ZuA>9QTMb>x_uD01Ccqss$qunnN3f#h|3bxwQmZx5>ZCt6nT$co
zO_!tw|HC1EoCKw_*HEI5_pxNNP)<p9P)170uo6B62&<<hCcQ5?KM5e@b9_(#-dcuU
zDbD7Sv|f@-Ru&}ya8%?H02>#N4F8=v@a{cup`P9Dau`B&Kk9xHTeku3sX&PlN2w@D
zwD=bLa>D6_6WX<*V``PYq&iE8NJTlDTxn?kGT^Xnwt?_~Q+_ip&fnAFvp>19`}+3z
zyDX)P6jp>t?AHZN8^bLiK}hEtzk@Q;cSGKeg`YP{NHbrFwz@6OqZab^^L#?|{#ac%
z<tdwMPk=wy{Fqi^A6uwba9~q|o1B4L3ypgM-flT}?Y^y*&_Jc2L`!zDL8-R(d~mI^
z6Ziq-qE<*_KSYE831O6j5noc^&Tso;@PSuRjGEnO%K4lZb#i3SZ|^R)kjkh3yv)*k
zktbL88U8#<hUv1<!GKaJpBPqSlQ}sA&c(nmDy9X#$2$elT3Rm<bDXJGt3(M<ca)Ts
zUyRpGlSKV&QT4-4uK`|eYn}^F10w%0q#?}h_geaP$$uiYq&$(Wlj4ouDns)dU)tZA
z@v&v?8R^Xes93+s!$~YzplO5N3D}xe782y^pDRE_G!G6lvyxANb{&SRZIO2^pV2=D
zE6a|&oLtutR4O_g+XIFP)d?54Qqd!RoB(n)-EJ(~8>foeZ1_o=nM<-xpQ3zQ$aBqb
zB8k`xw`<-`rZ}^X@>yq=Ig`|Y-#^mr_Jor0<%qv+#VnSv(YoZK^-sLww?f0grWRkW
z_5z{^K)NIio#R{;D&T%Hx~aWJs3~FWGdhOqi}{V`3dNX{Gc}F)x{ijSKpz7D2D&=s
zYk*#p$%*i-?ssXaTHc{98Xta~HxCH<eI;~w<%!<#D{CDVMBMlZi0QVpkB5T-;q^xV
z?XK6PXP+d55D~<u53J`zkMN}6><2zOEq}q-Lp2I|VaE@%dDJzfa~J}h)4O=>?!a3%
z%o&}71c877HJphl1B4xg6+R%Vp{7TRdEgz8uh=U6@!{u*r+`BL*i;CRe`nV5Cau6u
zEaE5m46+isRZq?a{EIhDAkpO9y58JZL8~T4**-l+rMbRHejwaAF>_UKiH3-vK;U(+
z<+IoLlIjvTmXRg%{J41f<&Gl4t)WVOvA>@eMSRld(wP$X_{-`NSL|7x`pYBC-<L-u
zjcW}kX`)YA72_fx_Q>_zhM}N|;|xe?;t&wTUEy8_6h2rzyK{~8bH+^hH(L|p`FQ`#
z@5q1V^S7zq+p|4*wb_V+EhC8<_m=7AZQX+FY0b&`d=+tIbe&Xit~qi~7QgZtUJcdM
z6`_7;E^$a>Q<H#zK-i>4%2%Ct?cy1u5urA8eh)%{`CkuuZ76DoXLMJ`O!y>ba%xLT
zWR>6D2lMF2pS6!tA$T3PR0$y_527QBcDDAiad0f%=Sou~1t|^@E{nCho}I+Zwk~#1
zBH78xpCA_b5mJ4ws6e&j;^!|V#TGy3AjJ`W&O)S$r_1>k>4c1s?w+rK@N&G37c1u_
z(H$3mgQc~#bzmTsacf&!TSo`x8V-B9!eTV)`o;EDH9O8Uor-^m#22$k(ps+{i;Ig%
z(K~LOla4mAQSX+9T05G^7q_yM&ch5vHj))dlbKt>PBcCj%!j{RjhD$(NiItyeXYQR
zTEorDwH&B&wXD#)7e0DM`bwP`)t|)H+4<!9n)j9F@o1bn1SeQBS?txTA6<)(B=!(t
zoLCk+(MpPpCj4+}+WlUl1o<07ddq6_d>uURm8Un+BzfB`T8wlNql9GPjB`x<DZad!
z=GDJrX+?+63S0SxoeXRool#R6v8aS>_E`DnXJ@^<y@^t_qps5%<Yq<TgBkPGQhzkB
z(fX{E7Z>+k@kfx)`&El2XQ(!}5PAsK2rD1vwKg>knNOB0_#WUMN=h&ru*>+>T2mKr
zu(QWj=2GCjA)jO07!jOT*bCDOS7x%XcM=j7mWm2Fy<=IfcS9%Uy^XN0K0<R}I}%e$
zDdBoliAFH?7<%Qqkd-E&6DCWEJid_lAu<xFP`pPVZHAeWgaj0^O~f^E!*~$X(9p15
zo%1q;gzDGs%Jud2jK_&3YGe44?e$Z~d=skO+4(tVFbymD>AN|)Elyh-8z+}nX;r46
zLQhJB1eU=<-lK<ff~%(QZY!q_ijZKQ*JAl$FkUt5A+{3r&by(}UB84Xl4A$s6BD15
zP$Xp0Bpy?^Du>DRLt&SfmqGG@c1BaLTcYK6jkqH&JB5*pd{yE&e9pAIyuSQ+xZ!gb
z{GdSZ_@b}xIVma0)!CWm1*`1njAUiqugl96H&H=BeQj+kM_r4{%}rXh7<`@67_RP#
zp`oGFRD8COty&RH7?6A1^6J`}EjOtFd#~*4s%3$(g|#&wZM@kczqe~jIGf1KG*-L3
z>qRu`Nb#Ol-5(x(sV-48a!qH`20!+Oqf83avfuXfNNVmU#+BE+Q${G}?mU|D5V_NM
z{d!>D*u^cG0)b}GoLsHBvs0H%+~?8R**ToE1q;uUD||mJv4epfYD}e#St3!RXI`zQ
zpr@yo>1n@ypQ>YKhTTfy#~7fd>B<WevWh@lvy{_$hbFa&vE*E&lgZgMJghkDa*0xl
zRStU57wbGl%!!A3nPr@*LiBOsC?nb(A0OYLjD!b6t?z7SHmfU3g4sp!b^K18jbZ${
zx{r^K{`K99-Z4F0T~T~FA%B1WkpG9aw*ZfuNfvfvW@ct)W@cu#V|HR@CuSPQ%*@P=
znVFfH*^ZeRufHsu{r2BI|DJo#^)oXXOQY_pE~(WmRn<Gcn3$7C-W$YRZOY5;`+RmL
zAAxZwz{huba+0O(?ChKo*0?-7i@JY4k4P^IDq$<>%~H&O<{^0c(VfVYvY{$yhZB(K
zW5W9pPmm#|V)=c4ety2ZNcsuG-$W>c1q%OE-3SZa4XD~<+>9BG*)JlmLE6eKEc{|@
z3}OwWvWJ%!!hsBI_T@{{LgH0PD<}d`RAyRwXa@$lV6Uh$VU^uF4%XWGI&jZmye=tJ
zx0a@+B8Gp|qXE}pA;q4mUv=k;s|@!69Zqq=mu1n<CwQP4V`fuQ(^w8)_zATXL8rD#
z8ya40A?=M97OA)#zI>r5n^(r#t}C><NCVJUBr($1X;@m8zLLgO<25wTXP12(QFV8@
z!tYSg7s#$6&uWh7F$}gp+2n(F6Duyzwpfwq-8?<@uFKcKrid@DIc&*)DpH#xUtV2p
z_lX+dl($q>U6RB-Pw;p;Eje-Xv9f}C5Yv3yVHgCpQb)S^7Bl=!fS1=GRkXjUsj0o4
zAJ-O31#{brJQipi$@}9daBN!L+S=kUiAF0?44}S2dWDJe_x?*lpnpzm@jLeXPl5jb
zUjpAH91LAQ5-~VAnf@g=&L3$M{-Z$ne;@nK^^ttxk4XK$3I09pG#%_an#BFZ#MxXU
zav)?BzyJyn62A;lH`MadsBcZ0QhgPNk{(Q2UpkaSGF$M{NJyme6H&(M?(jGVON;yK
zI^b>N?b&1FI{g>bQVR>u^GY<!7qf9$ptp{f4Z1zImlaSlsfpF*cXnZ@YRtCMYg?AZ
zL=nh6C*wD3X!n>f#A)lx#|Ea8K~vG)9;b6}oZf;L-|s_9OH;A0fg62{_1;Sf#*z$T
z4K5yh#OwD2+p4VEb~%xk;VEA<>c7$T`Hw^=e?9*hlK8st`0^{CIPT2WL&gg#dvr_<
zkXD<Xt;gjkN=s#&_ReDv(hPky;Hr);<q~0Tz(d!rk%lU0-vEDG8~sD$2bwS-!@2rN
zsZ7KmsiC&k!)Av-pTxOf-#TSq1YySDP~qk-m3~)EN3ow5Cyb^Hnb_v%DzU}jXy{TS
zcIzU(eo8a-p)bsxrLI@o^@GLwFt7Nd6LyxCkN%u=prZDd(!;0r+K8aLR1)UUUhFPB
z8hzrd0eak)BJ#@Ixfhn;g#78BNeT>7KoJO1IGV+^3926DaJ_5=`l<{udIc4=SYy(k
zVB6($^x_ylW(6qjs0q<Y=Gy#rxv>2+p#*vt)>?5(1FRn19ZBx3R*D---WIn(p5ew~
zzw*QxWp0BWq1Y$$PTL?iL6D^uP?n1yY7w}PjK4o01Chc!HrUT#Myk5q#)8=BPc%TJ
zkRS0r$ck^Z7`LIyE5)yoTHp4)F%2S0JYooA+E?K-RphI6P8ugSx?kmS1=;YZewSfz
zl7jD)eqc6F(VGn4%^am^%}G6>d4mbj?y=)Pgli;P7q?x;`9d<~#jHWqNI!7OZENfL
z_8jbC8+1eV_}Y!h)DVEy32?g61+V5~{ZSCR4caz{pZ#DpbI!qVmZGc!MT4{qamM$J
zuPdJMUG*B2|2*TFdi;`r1#**7_$U}zq=bd!A?+hOR28?{6&R#*5@ukVg6*cRu8E+P
z&G^6#wdetBCn5hh(GVl_lOtIJNVkd@%M$o`^|Hu>hA?qokLNr_Oz4;uYQ+P;x{nsX
zsURDR)=L}-Bh=Pwvr@Y?jiEmn)yAMVz^yl}zzmQs6;0<y_&Kzd^7t8>Xl{zU%mnA%
zhrEf1o|i*eiLZcn?y@c)T6HO9EIX%p(%*JDFmFsM$|Z{k0ZHiQfwCR5&L$6~+$N+h
zI}`H<63T7RlAP=(<H~wbT^ut$INGm3FqQln`V--oZ@H4CbY4|WzEv)Y>~g#=uSah}
z^qY#Fu=F|l`HaE!?$#{#-)h5*dW(z^g~ApNp}v3@_c7ZcZI4bvq!60*;j~TfkvH9T
z@uaE8<oGI*<JqUlCst8>)-IP%SH$sD+G8`<#v34#OV2vMy6G#-YcW5<5Ig0BMC#>t
z1840>lMJGUwnxVn&Xiz;xzUeZN&;Uf@p(^{IY0U3PBph#AVbXKY_(G2>fztGtd7RS
z5OdJ@6rNgVHIpqftKvN4E6QcLqkSQL14AV5I-Qb%<p6aXGJo7lNi=RHLg|J-<Y6sn
z6(q7^=G^N>o{FyIM^PMb&NF12EsBe(+u^hwntF7)%30(Lf}t350xicY#%e&#M*(Ib
z7?i^B+1^Vy&QKhH8J%7s@q!2r^{Cmz;&I(fbsn%{=m5&N^F}CAD^}#>=M4KWTQb$U
z_ocLYN+<HpgWRRCY4tO7yt158;o<QTjS|%yOYjA+UpfV`jcMsNJNqY(uQD^Y2Jl(k
zbe<AkY$Y18h!EiIRAgI%MNv0V`!VP=nV>^RZugvC0xg0qlfX(00-<$%R2#ee<9$uM
z+a%u-aUK*Hh!Q7;b@h*p4?mB-fOgaWAh2DAa#1kkG9b4=3Pd9LNdiBkabR2kglP{i
zH0$i|`hc*Y?GUm$9u8g@pZw$N^E)`vy#+beyfbz8LSmZ%qIn%?^t_nk<7PPpt^qnJ
z;d;!E{Z0KNOadcN3yIjGHx-rMg!<!HsL%>n`<)F=@$m8|;Q?WM9;9D6WW*f9S*W@!
zW5AheMqiE_xAIc^Tz}1^^lp%<>WIa%MVhBCTYRND;!7RflgLGaB%eh!O-sef)i@f}
zNPHpWmoTZ)#<<6HUZ13yk`=E!DYNocSWaFywY&c1LgTB($;~xrr$AcDYtT1!d<_S1
z%|<U_oS&33=w^2APPc2hFlRA-jX1UZ;uIXkBX|NK;-a;m;}%ey;cU-=1SaWLjhD?3
z-V)3OsMR_4YT|l)8{uHEd1qx&XPrDnnrA1<3&}r>baQ|5bPi&!f4>bXK?}^-MMG;}
zHsjx7rJ`7noG$Gev_j}?{_vX9ase?d#70Xw6Bp<!L~(mHV9aFM$zBzSrOgeh{T6J{
zMj}FI?Q-a{a{xogt-pYYTWsTczV%f}jMb4pebR8={gS38RU}zpEN1rC_O)baZ4o5`
zCUJ*de7e|IR?#U`4$9T%6VGhsU7|$qg4`S1IOYe~)z^jR)z!2_sLcecx2Yex9{7eJ
z_7z^09MIf50yM5@N}9*BBNl{_2i(RhLzdspb>AkYGrDq?3Mgn(SOpo<HFW@*m=FpC
z2)qx^kzfW?4oX#8f*!}zZGd-NzvA5m_5~+vo?`{g^ThL!6GZ5WWixAL0CB>3Xnu0I
zIjX%?J+MX8tip_W=6#o9O{_#dfM4Iq_Q28xRGX}o0ujBm>MJdjJ-S9cKZajpHhZ-&
zsu}K7?=rZDzCC=*MWj#+^p2O@F9ELi7r~jx8i@l{f5}i>N8v!Y#e>jygHx$EEA~1g
zuqqyTMiHte>6}bo>!6jTThVBufN$wCnSrejo~$G$8V(9fM!EOu8N556kqHyrDyG0L
zvex#97X6^lvB<KXvi*8l?+Q_vbM58aejpte>hOM#0TzGs_Y7{VgRq!_P`_xaCTzZ;
zt4q$(dRGx%s@T1+k^bbok;3<_8xFY}sNR;&@JW4(O^{JkygTUT<V9G`(5Am9*HOUH
z_?5hsA0|Fb!pS=~j!z*L=R=Zo4)RDm#{l>yxop{p=4QRl;a?&EDoATR-O>>NKtdJL
zp-(l&E3gX430+;DMfk-@7@b!kIH>M37N7HMz@d`QvY<Dheq}_|jKpmpDt3_MJiS-c
zv^ULnKhGvJfK8i3)E#hk++e(<Zz5czs|Ug>9}K3~W59!JZwOwDNZzZbQ=W(?j8RtP
zb`5wQv~`^g%46Xue{G(WA`W#IgNp9=`5HMn!5ExH5m&x9U4nz=<dlNARmq}Cq=G$W
zl6T;~L?qj}<!(-OybKbDbxk2qKR<5NPfk_(kv2m-$OTFt!)U9;(N|FzJer{dn@HVV
zh=6N%R6S0=2ZIS|H$dkEk-lrIA}>!vUg`TNYN^8%t&|R0gBX&VKQcDrDUvjexIuBI
zDWOUK4F0|=8c8qp#ATls#n5WE^DCR=@q(|%lyxv2zrkSa5wx@2J4?=p@nyVudh$~v
z!e+b516>XPQ%RYa2s56I%mf@d*QE>|E=WnCsV!n!jMI^zAV*`EY1b>lUrzXtMbs?P
zWA<POerk2}^B%>igrlE~vh;Uhq{~=Ho`Zqbq|xiJAk3y!p=RYuX8G#tgHT{AWNLE;
zZsi1GdF8K}<OzXMdcbrIVpR0RmqD4rn6|9i?zo14pcUg4u|oabA}-cQbrwLkHFERx
z4<;NJ#pW`BM0RHr5e#ftq%cg68OTc%NMkv-CW$TbAWc-EsZ39N+Ptql{G@<dY6bv2
zg$o_#Y-gl%In!0qkP)yrY_pjlEnoIy5MREZ&%<KF<Ix_9_Cr)XhIiJREH-vnMXBbb
zlwEg3Z3(TN&7u4`B@G{oH(x^^(FL`4aYT&_WhuIUz81T}_etLQSH_Sc9qLj^e&U@0
zP%vDf=O0m(M&InbDp&m~mtfPV@wnym#LTwVAkugVP}@Dv&(?Up*=)SM-b+b+Jc?Yb
zbg|}rxDM^sRq0u+K@;{y34aY0t8K}~RBAxQ>GL$tU(mCdda--$&l_uv>yo`$H5$KZ
z>BclLzq`qdNj+dqK^<8O$-1dQl-LN(CuBWU@v0B|MghqyfedTLyunb^@FbWF>~V>6
ze2ruuKl&-Zm@+wEPE;ci&YV)U9&Yni#<wZ$@{a4h7$8yB3xl*V7KVtbB#kR_^%W}W
zXmKNqF-}C62QyUUYSk14`T%5A2;);j54t&pdm@)*xMHed>;;94;Lcpncds_kJtvBq
zzMt`VEV8U5q<mLm_2h5&emm%1n##3t<7u4ahodSAcXw}NF^VmeN9}aNp%blNnp+Pq
z<=EfHdx_&t=TknBCL=`<Ruq+X-i=sgukl%tESg`5X<y&OwzbKpqF7)^Wp8`p00fpH
zdXd{R_!aYnA(GDr=lE+NRR+gyFYu;ybENVnoE8G53W~-)`ylZtBq1XUzGOVlLygsr
zwL$OuX!?=s7S5ZvCr~%7nRPkcylKzJp;w*_ImKSW^3+ZuPxWjhMM01TsY4+o@meMO
z#~0S#84GHAP8WZ>kI%N?Z;>@+B`YQC2z0W&um1*a7g6>V(zMw`hZy^3AJM6ilBXK4
zRrp09vcn_?iYM=6Qr?yt`tqa|Gfv%9GB%)JFwzui5i+sg+N}iN1b2Nm9V{o%ck)xv
zfOHvJx2KCE;s`Tt7MJc+lul8pR+z<2xxtz`pWpRkc#9NZ5yK47m><-Z!IVYW4*)T!
zSi|kzmOEY#T;IL^jzT*+S~2L0{rT5mz7=fKet&X4QqvZXN-$|?Wc?>O$mh-s<WE4_
zlO^TJk;j9|4!4@nW!Y?;ko#=Sk=~qM3Z-3KBYWIUx&Hpj*-;~V%*nFUrjJAz;HW>b
zj2GyPyCW)FNCnHzzdmoO4?W~)g-i5lPpSDVMZ#4>?YdC=%n+qa%g|7XrMbslU2OS3
z-=Dua|C%4ei5=RU$E>`1wFeh9?*36(Dk{humW>XYI4DoyeIx>dW`ofx$&wl8k9Y+&
zAyk!FaFd-;Ct9+~Rvtu?jrDjP%6b0z5lR^cF&r{<B-B#c<r9;Ufc8wSe$?&iigE|*
z(UeGEsz_owCUdSs>T5#5O+ogpsNXm?l8qlDbH5s#si+`qt^_Nw$0@zHE;C^QCwFu_
z2kyP0K4YE{Eef#k#Vz;OfYOai{96AOl_hoTGYGhvLW!<mL8zd3SIAq<c|XSFpGo@A
zf@vYYu%|yOhfEm{paXVsh$u1jEs^sU8P{ZLGt64>BBcZ8@kGu0AuS>Z)zKj;`i$vr
z42eZ$yO_B~U8+UflTl%1UrmJjZ=)y04uXZFIEE4fBgSfjJdSE6$TnwJCv1#zAn}n)
z4ApLOEs>@Ki!2pOYAa)gba$bu?=V;oYRk~*O6i58XhUDH2N3I0>IQLef2=Xo>Ut4Q
zyQEgh0(%?c7BOeQ(!Q18CZuR>pFhh-l*(qTBoH>RhcBW2T5r<~o3YXdwY_pM8jyk0
zxtV6#bl%t2RU@8oJgABfhH@#IK{cU0e@!Aa5p(KAW|-AjG;d|ei4Ob5r8Ca#$z@K_
zYf6-tD)Q;p#jUt7iyv5*APA~UASL!i(+bRPSm%q1Fe`isnx+VAG{;+Yjafr~i-~TX
za(GdNk`Pa$A31!^C9I0<>A|_mXfH|E1(uR@K*EUd3F;VxV>bo9-*Y(L7P%ltI2jT5
zmqX^_te+$-`z;7~5B!6h%X^}hb<`Jl*lK(|sGk`~SO@*0QenIzqdl88chil$A`!15
zFbkAJHKa{Fk|cJwv!_-+jX{-LZj2$-E_~ORzdAe}j6vM%6-Lp)k7#WTj<Kq5l!9{C
zZU+&cBARmRk5}(OT5QEwkw!hO!vN9$y4<dgUoRbG6L0vWwYVXERYe8M!eGn;h^fn?
zw!l^;o#1r<O&oK-&@)E?|BB)7<@<xR4sM=T{U=Hc))hbcT>}wBsxS|Yt5q!G?rdeV
zAUar+&8BxmL}6T0Xq;k3o}-R91&UV{ch^_{D4tDlxHGNQ{T_PNA1?d!s4kFXB76_G
z3N}9?8WF(P#lMm?Wd?fP0frvBFiwXc430k=iJ>hLimuvrZQh2nMJ%AC<?HWjiTr{R
zu{2`3<BZQM{J5*{ScP=wCt?ot*<{<FwvMG3@^vfs0gguUg@Ah>s_WSWlMllkal|4O
zuxN5jYdFl{VZnX2u$q2^;LAfNzAO&l;iBEZ{MF5uhdsi)HrOm_h{AQM9yCF{M&dBV
zw@apM8D&$7!YVJK6ONxdeASL8oX3yY<7au@_hrVxo^OA4X(D2x{21~q*ZNT#2CzE~
zKwK$Exr7B!IHm}-PGm>i@i}SQNzQ-L=<|b8exOa2?eT&p@;WoH<j}<~-z%B3)NF7d
z#2+0l9rZMFPF}$$vR_yJ)-Pu~O<91h#`&%2bSZ_O4Uc<Y;45Ij)nvJ2jRJW)Qf`f_
z=tm}qydpO4oXJJM&Ye>fU3YaJf~Q1{+!IeYSL`~5TbyQ5sKmz|txM<fdkPffDHGl{
zw^~_6x2myG8ehZ%OmhJxmvnP%>g?l1L4p?1z&NL}Cz3^?+5G&NV(rt=S&U-M&n7$X
z(^dToZL)oLwk`%*x;j_P%+9D>xrp{!SWA-kOeC=)M`2;SAVhe2bj(Cnf^P8Eo>pKN
zpX0V)G)%p4CE4!-5Zj7amVQj`;wNOn0ZNwXdvmYLQ)TEdXxC*11*R<Lw9xdLg8@Ar
zBvSsZTjwpnXbV@?r8FV;bg6#x4^LjUgk>{uGHfWq`;1U;@d2DApbod{)OzWH993-f
zA-cM3SXnj9#Nzq9*m)R3H?%Ctpfngpsj*-#N@kyiY%(e-zDHm7W+Fd;qXh_w;Aw*~
z<}yORY~Vn4g@_`KNbAUHdSsS#=+lS3%=h_o9Cp^Ke*?pW^v+k2Uh6`;mt5np>L;t(
zmxYY;Hcw<+L+Fl_HYBc=IDlcynC;l3t+S@bd{l?ay>(zo|0N3X(}t7@(JamvbUR=5
z9ym&@*8h`0Rz>G&gq|?2zcsi#)g9y-euJTjg#C95&f;$s_E_#A&(dU0pZD_9FnerA
zd>5oggnWDz7vNv2ZKVwE#6Y$LFTB2HUSy!)+uThqk-#$0AHaI`8e6lESFMU?VP?4D
zZhSG@oT|dXD*2j<Y8bu*=D|=(E5R+kY=&1JWGUnTk1~J6g@-b(c?g|px=68GNuH1)
zaR+H1I#v!eH;p7MLfL_*D!u>R+KJU$1u>2+cK7?k2%Q<O0*P}RUw5E`<_j07i9YGQ
zC0<^a@4{&VYIQaI7MKf^h#mM^cN@Jq8i5W=TTqk2N-wPL?knk@jiH#|B!Yt(5Vdb5
zCHAs<z&IqzliE=TRMqfexHR!e;K~bu3skx!Pdex%F$`Y`RkHk%5&CUC;ThZVxt`}>
z@0e}5Vty2?)(KJ$)aD*=D&*F%5gWOzlh0=|qynVKV*a$XdfEE@*1k(@+e>3tirVu7
zPpI317rx1@)SQhrpNL}t%_l`{l#KqhiXD(VTPpWdp9Pv@08A8h5;8p)IdB@m3R&Lr
z_ePU$Rxa5*YiSZnC$G#oeIeOb2Yf_vb=YwoerwsJV7k>3Y+=q{Lnyp44?vM{97)J-
z>z=8;XM@?PcTEtrn~H{}klGW9B1`Osz+KHjD&<QN`hFWEvM4Q=j@S#lxaD~PGid9N
z2?{tUF)m!_NWx9A&%m-tXCSxhsff&H8Dq{`LWDn2z%S6$uaOV#=W$*H7D@7Kex?NJ
zK@fkZh&n-JGU0sR7Fp+7CRaVxOx|FvvTg_2rzGnQd$9D{eDd8^{Zz|_XcRn?9qv@Z
zbpo}`4eaQ5VxVcAsz{ym5iydGdAI-LpvPqBC7p>)XZn>bvh2i*jp!w+7VcZtp_FF*
zx}>z*59OE`n=>hHB4m7W{<ZIm1udNImgo5ul}a7F_O3u~QAv{Z#M%@l_W;2@;HSrz
zC=_e9BM#w5hjIFe73~?{Lu+^~=;tVMAt)KdQoxoN2Cg9td>b{FyfNN4R(PA?ii@-C
z7V~P5EK8*q3S<kXcaiQh+!UZa`>EE7E?W%;4!CpKIZu1FYI`d#&{m;qE0q14BT*CF
z;N@Zzy<(49iq@xHT)wTEKXSneb6j?K6W2OWqBDoz+Z{~S)k75;z2XYG@R-j>bG8i8
z)1$Cd(&1f|^MqR`eyYy@6=dCw@N#zc^+jyXJP|M*92j;|o^b&h)h1_hYL`A~ADymU
z+UqqzO=RDm4mW-u1^<mMKxJ=p+58tQ(`0%pbIK@gYfEHf@MfUnPP29QDEQpP_0-zs
zAWq~Fwxh{fra52xu)bTj-h;2rSKdjrEzqbRfN0nOV|}d<IpJ!_2SNDoApUkGf|va~
zhoWdq&F9-Udiu?};4t<{XNV)-M59zhbAE6kne8DD&_t2sdy{r9Mi|q*Lnw_X2K8^C
z+%xH)glw<u5-XsMNn5#W<D+Unl~dzW+1rwH@+gqui-KdY4dM0jW$yLVcj6fv;#Qh?
z?)%~i$Dx4!9NZXEROlinnFZp_AkIx-#!8FlKV*_6tR)1P9x9J}LY$W{_L6TYA~Pe?
zEE2UVo}<f^H4w$g54Wdp9xT4^)0Qm<FF7`$0+#z3hY*mF%+;MUv5??>f~u!sfQCVf
zw8NlY>DRlREh$uwAm&1>kl2Za4N%4^t6I$-dn9+L4tmMIv4O`u*pOZANYsFqA$d`P
zxEp7a30K%HsNXgER>Jl6^)P?QtWsphL1AWIGPFYO{IcQK15*kZm%g9~KY+#e9bCAm
z;R|33GiLo&_nrN=hs2AdHyJ<|d;v_-!iET3jo9puQ}-+u9aRc)DUuzYH*I9++0(Em
zB8dT#D_=9a(-mWPNoz0|4dg8)M17*bxK17rpw-`@%@>L`+=|oaQ9<cz2lc%}2hM~E
z#uVQ8@JN76Rm}$wo&|tk5p=*nakE`{hi-H7zcnv6At@!xo)|c(7ED0R9NDro>^G`g
z(v*6n@z&=_Br1SZ4(KGs<J%|RrB9$aQ-^GV-{dXIx7!x7*Ml6#fMj{i9wB%DV}S=r
zL(&;$fW1lIfv&QMp6q~1NSSSa0?uwitRVAmqW9Auc7s{z+)2xl`Y|bVYTbW{)hvmR
zP$>CX9nw+cL4qfzn>f!L5=|ADXuPETeApoM7YDdYFyMt0sj(Fa3gVzrH;2plQ(}d#
z@5=JE!TOo<M3qr%BTS3-m64Q?2i%ywctV3r5RF`_xylO>=ehF9O$6-?;_((A7wp^|
z_@O>WekCLd_w7LoG#gMymw%PkeQ8Eiy@oKQYXl4fY<oXeO+Z1WS@q0khLC_(LwA$~
z5ogx9OV3kea^GFx%Z!ALmvpafw&)99Cc8kNje!Ap#uQsBJ!a3KyM8ggxtPhsuZiP|
z?VsAnhl{?4(n)9*9O5kp!4o+J7!L8jnX*G(uMBM2SSjhkSTUbRgz`K!t7O?;!?;;H
zGBaQ4)KXN(-dX<~tomw4nxND`BjFW!tR-tAkPc>uI+Q}y=`Mo$u9`}TH2*Y(JFXuc
ztVg#E?w^LMeGv;MoHw^^@pERn&fuqdpi|RQD{?MCJ6_uVok~0n`dy{0TRH4}3-dud
z+7y4DwAqGAP?pK9sh}C~bJ2FpR%4C!pt}Uzz<9uM*STy(zsWB~Rm<oX1a4>b+Fplk
z7c>AG-3V&SqG3x(B&9|8VD{ML&7}m4kX2=m|J7brEtr`7@=BC~0)jcx1!X}#d;IMl
z3Pi^+*jSzY$B}4{2M>&1g7BX8%g#a>?NO!EeEq>PB}3GsfQRB4+ecvJu7KEJAx>P2
zKNMNbQH@TU=1G<8#r|mRk@?mu0Kt=|hptsqEqt~|hOi@EV~2q3rtxZdDEwfAp|h#I
zu~BVt*@ERGI%Rk|_ro()X7Fla0<lcIknA1SaLGk;ne%a@s5?q4@&}`!2XjZRv!E_o
zA6xG>nl$%&d&c6ol!8xmBJbG=-FSAe<<e=2p&sq16C~fK7SRdWuX_2)RwvGSb8r>#
z3h(?jN`t(E5F;@^29b`(FhGjmI`8_ylx=S#X1*Lb1X+p|k}&h)oxeE(R?)dwXR<c}
z25ej?w~8pV^t;j|Br;-VfS+P3EXF+ECy<D3(r5Wllu~6p3rf>b*hTGedUzxpsc(Gt
z0!j3H0Fu(2Q{A4cgaO>f!gc9MV8(KgNDaEeRvlq8vE4GRstMJP1r#^V-)9kp+D|bK
z6qAv;zb{2Y^TF?41E^|tUQYT{mAiePc<1P>xC#~IIG}d4ia&><vq?J>r-_(kk_o&-
z{6wb6X(V7~%Yn>J^vCN0$J4PNdG4$>-%0bOLNQmcFfr^NlmPGLq6LLtwPz@l961z^
z>r{7tS$(V#cMk9O_keoC{Gl0+CGWe(luf_mkLT+6xB`w`-=3<2W{2g$UpQD!_>p2F
z1}<o35nStK4}9gh@jKMcH{ue3H+HTV6AtrHS1;y(A#rz{94T@NNJcz@#9Gb}N+M40
zLjA=Bc?0c9=+>FxF5Ug)qK(E<?lpDk1rHeEubIlwLpXiIiKvOVkrbdqq_6A7r-`zu
z9(g*Zt|_Vcq5^Y;IeY{I`2B07_;4Q2WuRLM*psq(^ehLSQd61Egy%7}Gw0pP2xi<J
zM>@(#*T@F78)ET614&@nno86%h`*Mf53m$&M&r23>vOoC0^Mlzj4b<9p}W*?0E=H=
zFF*ISVI{_FeUHB7NZag8Z}z_eCOkO%@HUJ=o4h`A(V+nIbBceMlu8Yx9iHDsy9<1x
zcDA3QDtjp~alYsIIRWR$;o`o{{}j<)Z>dyIGZ?f1gl~T*dQdI*KCW2B^SG)G>z1Pl
zyw>9{-tQ8BCwTN|bKHBUY;ZgYQc4`D<D}(bn9^!~W8hGQxP8%qVU@05e87`7teDDk
z=U#fK^wlKpj)*til7Q2C{RI1jGk$Th3~$a%#)K^`&t?iwLOzwxQ#JG{1pz5<lY!-C
za=<#jbfN$mF^DC(O!YNi6*;_K(`(xlZB<>@^EFK;_zQ~33RCN-Xm$I=?k~5fi<@J5
zleCYkag2K2q5s%g06Y9bCA|=i<1&cI+7yNm=Dfi<ups)nZBM?&V?aqQxtf^%&FE9U
zY*aeC0JGV_5lVxye&ITQd#kOpgk7^(qF-~Y)8{8)K!HJc+t@OBx081$D-xmtYkmnb
zYxmCd6g;2z%=0d4*cFn3@^Fz}GwLrr=Og3YnI1Gfob#Eyq!U*Ag5@n8yY9B5TxI-G
z+E;9i4tIFkbi>s#VFo}<U>@c--sa$8G+}pF0u=|pPT8q!lJQen1eihH)@b?%rCchS
zxN`5kIB5aNgR>3MATm5j8#0Gk+%HFi2`md(i_srD8aJ8F0^6Y`3vlYw8?+q4m&39x
z`{K~9ML)rb#)1yHhWW0v!`4SD_m1w&`6P>dbr`<RkT7wavWhiPdO}{mZ%VF=r2N^7
z&F_;L<v9~xT(xpT$>;<Np)pR(HWi-FFToWq=mxxJTh))*DiTmmnN4$AnCQ#Hv^yS~
zU%BYK(95LLbqaTS%O?fu?LgyZwx+Mgpck;-dI6J`?&W{ShlZ=gW<FpxHADD>Owc|-
z6qoW5pq<4ueuVHk_QfwLgHxs!k1(tpi~kC^TH-CfQbKwgRlUMAko_`#RkqAxBKM^f
zU)vsyUD9up{_>J#ORk!EYV0HHpZb~vJ!Xd$F+4x)WM~dSe3i+xLd)>1RSX@e+O9-#
z95*+5F9_EW*st%&;2`8<={)9wA_A5LwYr?;Hw-+_$i3HEDq|_AI|~-w!)*ct>rc+_
zCGt22+@EA3$!6go2$+d~x!@?&4RXY2<b#PQC>OH%+rN5-Ra1<%s8g~c=}on<(a{CU
zjT(FoSgma8$hASw>|~1#=~GSp)?+svbGMfu6J$Bj3$V>YYondbf%j{HYogSW;yXz7
z$I&96k+<?H6N`i1OVNsGu6=Ar2MvFCJ<+hbNn`SBa#>wFD5#xrb<DJQc~gT6op~w7
zU-~qeP)lhO-j0$YdWNx89$5AZ&3g!6Sb(k8ficP=A1&gF!+h~P0VPN?=R4Y<HT#g)
zA>~NV*IEjk*};}`a;yMnc`r2|c9U@l9%z$28bI}A25Y_FI&33f&uumeNL|AM$i!Ah
zszpUvc(i(;bN7$k8uhY1#&z1+F?QQTeEl`HGoZ#eF*SQsZvg}ju;hy6AGx!neaRb@
zM!x&`Uoamh6L%SBvmsxXN+)_<ugUK#enD9^0qNM9&BQ^U*_;~A0LReh#oxx|x(X6{
zhb3D?#WTeA%W^p}<W^GN@9pJ9$LAqVep$<32(WM-kF9y6Of1qoKW&ubEm8rjKN#<X
zJO`&O(X{;d=rveX7tm)7eAU3!%a!;Rhss@hZ*rmcFlrKdFNP^+62ki>P2}mNsm6H=
z--g5s8Me&C>TqYEX;x9h4sM;``OdnIn+giPG?-W1(Dao-L@x%5j+Q~{FXN8Zh2=NO
zdRQ|rkEYTTVVTZ~*ZmM7P-)c1c7xg5WS*H_OLw2WKF3UK1;n71ugvXhE{+!j@}iz*
zxvzS({A^Fj-COt07Pd7yci?tD+LEgK%KtR(Zaw@x2cYNfvfh?DbJDQf8!lQk*GuWz
z!R%*MgX8uwAUxA34m!>*<mhP4m(kG*t>qe^7r;z^xwMk%LtZ!3NpetkC!XO%E66~L
z@S<(5sfRPS?y>}B5p3zhqa+$^M|LA$>vO49jAsj^UwXWbP7j3;eSH<CuVWm(c!0-C
zRddeI++mc#wOun0w!Hv+LW2*2Xij4n;NJGu>Mn;Q<Zz#P4BH%8P?%2Xo?WKU%xm&|
zAA4_-U~@}Ax%ZIMtGdAK>9>AoP*BEj8SC{%n7Vcv&V1LJXIM2`b0}f4IxOFz$a%*|
z9?h$LhX{Xf*!##na?uU{FCk68*>!*NW%~nk_dln~{TEA4@efxv2b&Lwn<6s{;m7MQ
z7`Z>FJs<1DKUVz(^(M|r$ov-)-QQ_*4B~8rEWf#P4C3sBe_`YO9ZL5Bd;2F89Wy84
z$LkLa%x};P>mN+HfB2V)ko7mr=VQnJO;8=vUqO2RE2O$Vv9ErY{5M<td+$CbMz-I(
z{g-!NoyLmI4m+YJ$EJ6Zgay!4s|qzRL=)Q@u~yzO?OpbPe+lJhkosBaVc_Tc^z5|7
zh`s(`4q!?o(m==YQ9DlT^x4xB{t+F{HhzEaKoB557!PIhpWqdsn1<Q(Km02QvT)(W
z6H#Dh3UrE4+f7QcGRjHoDw&s}THhIQUzl(^!AdZ#X~O)|RHYyA1CZVB=jX3_Pij1)
zRF!$=Bz}=wuH?`Y*%~<Iva-5L2`^imO%q1hS?0S5&D5{^Xh2seBl3kP*eX3%U@JGa
zQN#qHkba#$5_HRe25I<keyGx#(!x5;EI;W;d+^}GeHkKGD$`ODt1?DVvRdSIfF&+o
zDx?kJvov=TuLv<4VSK3Fm%-{QNqxiLVrRQe(UOtz7WM8rJ@4u2v(D<^;F*~icWm-s
zWiK2VtmJNiNn`Gw7EeG!ERK_N=zXM{gahnzrZ}y+=jAQB&gVG&@LZzHAX>`lmH%wT
z3=Mx8NiY!50FQb<|6MV~{fi*hS*`vKPCtlDieJ29$W{u;%+!DwJDn=<FSM5v$nCE)
z&MfYRT*H!m`AZAq76SgQNO?=(hp;7y6(FtwXSC2BN)*qjz~~q8gl=P?>`%*jvIeY=
z6f|20U!6SfE^&JgsA&p)fRMbCMR%CeGCKUCf%h_1Vz5zifKCr_C<9i>h<x6)Cxd~Z
zCltgEHjuu%=*?kHRH2?UH>U0_@zUnnB@^*-=VA_M5fsbuUlH=7Gb%0&Q4XL)`}BX8
zU!4}{q7vPg9Gnf)2js!~0n8`)Ihf4$-bKi2k-O8vi~~GljaoVr3><-NBdWR#4F{_7
zBKYzjt2}i>j8cBU3A3aZsihZj9q)7O_V|fnGJLKF@G~wxrRPoqo`0zuT#i&3!3P*~
znqF8B+>ktlN6_@Sg%h1?>KJNL&{pg_J)?4&9wHtX%E7=zY;#dQjEJ5*FQO?_dWPz)
zwJg<POrh-w$_6{KZo=wF6@xhbsQtS7V{}>6ZLC%hrql(Y<>{Lgx#reop~?HQNer&V
zir!Ej0UA#-xaB-pa-(_9V)fEDYW@WC)f%4Bk+>$d5qvwNX2|g@^AaZErue#=65Kjx
z^=;r)64QINwk#!XAk$xX#+veH#rXS5-)O$y(TVuF39azVqpI%GRu@`-aTUqr!%c1<
z0TK$dFEd@Y6W+~UI2$La2ANS9<Kv*gcL38Hj?+O<cL?`pEdGTp)4TUY%z#TUDRE7%
zNiF_suUmX$w%X5HYMe8{z_R?FoF0e3{JP!wMz)_L_S4O2D{(qA)Esp-&8L+xvUiPe
z-h<ym#5>;`owR7DMEN{VJiSKk`(7|O`TMW!S|vR2T<UIt*!<o;Ar<MwJ9|n_h(HEx
z6qze&AjC1H;P<`*F$_vp{_8IP9~pb%&NjwQ=C(F}pz#P<7({KY4b5%-a71SMa6|r$
zz58%W{-YcH7w<nDl|Oi>CLbVFM*5HK^ei7lF#3<~@q^9x@xk@6{cpv8{DkRmXwyHs
z=nrh`?*;Sk4x8|=I8^4}V9`HQPk-<B_fGL2rDgsnX+@a+kmEzSzq~(c7h(Kk4JQX9
z+edAGUVdzq|0mGgAD5E<K}G+r*uPZD#lg<{p)mcQmj9yCe`MnQt+9UF;$Lg`r{exr
z{NFYGcU%8S)8CE$KhX5w75hKZ^tXxs*DC#YjsMS@{v)F9KWN^6SL|Oj{l`P=pDX%f
znDaO7{yGBss~J_k8vcQj{22TEOXl5QH~;m=BDS^;f1FP&A4EkKRyO*NP=nuJ*_r8?
z|G?#b5EDOempZ@M#=k#4*8NSd55(Rd=M?|P-|zGOw>ZBCTYnSmk4k<c8vjn{`zI<U
z%b)Ox5+C?PmESyJ5@tq~4+ihY;$tNBKSN|P|7E!J9{?49_XvNI{f}M$583|>Q1QPN
z_Wy!d<YfKNh?yV!%l{%~vNN&%2gFRae<WtIesHG$Ld<0P`1XGgGygJr{ZkA4V^{ru
zwI1uAtS9-et@j_3Fq!{~g8tWVQznkT!%hD-%KnFg?q8x<|Bd1Pot*zmHe+V~lg(KE
z(PsY!i^=?Fo3Z}gW?cWmX1_04f3n#>UPpf0?7xc+VrTyw74v@qipl)@6#8$Vm@FJj
z?0?AmUsH!uo%=-80i^P^w#vlF5nX=zfwq-37+lIMpXk2Wa8Ng3DFdx)Fma$L(I`ZW
zAZag$2me=3JwCpgSQjYtx31f#)6|vI<l>5wX65_fm&~`3*~!aRuJ7oo%kduP*Jj(r
zP?Sz4M;SdUEL!H7`(L7};ikELH}H43;KBNz_?BLT)H~ucoQHNZ=sG^0$sGz<RA30~
z$Z&WCCI4WMac91+dUbrH87hEwy&ixj>|(k%2d3a!S6ffKeXhQO!zzvDHp}zSaO0r*
z5)^8?B$HOgYxUZDoW0YU;U-}3pw}Mr$j8zuz(L|?aS+#6I=#3vvGiS2%zmx(1^2Nn
zxntsZOrn+kT&jLOGr_v7^5_Rhnjk(b;%d-wx7_Cmy%=4`(NOY<A=+6D37Vgl(Nzr|
z<Pz_SOe$D}QRSbaAPk82t2Utp*bt=KSH^MO^ybeng-&NA+0RrIqeRY|X+4Q@DNJ9&
zly3CA_ODtyO<rcXzGO1msWvC%*NW62#LUFaU6q%uM(x?2IJ<TjRquElQr{xaN}@ud
zV|xJug;Hau?f7b9N6o#6X|>SJ5cG8td{-i$kiHI@ts9=SRE6gKWX4Uh+XwG`+xMI+
z+bRRcop6!k;obTSokD<)*Dc2dZ1M;#urN#R9XAg4*~c(Q8-wa(tLlgHRx>WIIO<NJ
z>5kIk=5zh2HatCqWX~%W0Qc(Ch`x+S3!6dMyuDTv{V~EzvfL{u^Bw!%hF%t)-|%xV
zJ(-IS`9`i;7jH%+gfQEaif7A5#(OmePaQS<ZJM*diwYWj8BZGDxO1uY$c;`7g|vM!
z`@jZ4j6S{0YeZ;W35m&`1G<}hcaQO1h=kZ|lr$*LPz|dj!K}lzpg^BnG*U?%F9ue(
zhNAl?_Tz>?1w^Z<GGGs_jV@>CBl_W+M`+X}0vRGRLF?F?i3?*r8YfQw3Yihej?AaY
zOmM;`ac=0>nuFl4r!7euowq_YHde3cFc6=|wU8h;ddmHX#Rx4ks}wGjdINYB-=i^&
z52&6j{EiG|5H%$CXC-rQ;#~QXarNW;PJ)&0n=gxY@1kk-s-|&x#3TSfhg~0`jFfg7
z10DvW1=)&z+Shz>snytcvyvH6q2El7JkSUky=X?m>cV{!!$d5zT6i9+W%Z+iSpewy
zd2?-UBl4V;q%0Vms=7oLC-5@Nu)9KN`~Ix=P;lnE05PVA6k0Ds&n#-Zge8v)DMe1<
z7!=`bBnW#hq#%q<L|TjN1f>vcqG}UedR)|P_&7+k&O<5rWpb0_FGF<zTMukhLIA!8
z*PX=Y9yoR0-rnAxjw_U_vH-;WPA(ScL64vf!s<?4iH(>yYi_$<!pdk%=r5jXO0{@7
z9E%sN%?emcPko;rBQ_#4nHRqC7MiRB{{rEW;G|a*Zzk=ojju2{l&KD}Q!mqv>|AUT
zjU@?@HY+(MHlFse(!{|2sXExqIIzJaXtS~iI)~8FWxiv3y>N(7DkwNOzc=|UsaSGI
zG@<T18A?#d#rb5ijU*0qP7eh-(vNbU0Q{vMxT()lR5M@XoC#Efjm0}5U}WESLtV(1
zd*P9?%#Xb0V|E?V^4aa2>~YO<RM1Z_<C7Q@bFw*(&+0DQ%h3l;>P8M45mZCqnxaO#
z;(9_;0LlU{w^<FtG}d^+Ls*oORne_;#=(i%f`omp?h>Wg;FNL`Jxi-#FN4a$4fj|H
zA)W2s<P_I~9d1(NxEssIpd-@P2eD&o&s}CA&v?9Do-S5)J<^rr<>KNTS&5MqRFQB3
z>MaFev^&OV+kCXOO9Y`yNCY&!2y3`Z3B%w=tl4$Sh|nt*l>_c$-ZNl&p$Q9}gbNAP
zW1NlYO0q5=rBA0%B2y{;-TjL@QTOpIdlqWh@fmZ6NxiyMfDY5NKesStv-+#fDtEY$
zQn9{?Vif=zbT9R;AgbuACsL}T%jm;r;Ms;D4S=#fS#M}SN3#7Afe@-*z<0B0CBZ_f
zJQgHxA(ei`Y-}btVu`2EdZJlN3Zu8Ly<c*0#o0N(Me~D#T(5<~(>~82K3R(T-p0!h
znmaSEJ0IwM?ra-<%g>PyB<i|0z*Y}$#uE;*#N+fs05aj|wInER6t!xE3Ly>>t}DBT
zZ`TI%%k{hL93(bi(GEiZV}(S0;7@FjF`j2__p8SvGnwl0MGN#P#kEMg;wyy6b0+oa
z{uGGt>jFP!+C=15qp?eIg`cXl;qZ}dyFua6k(Kg3u81}dT@k(YOjS5*BFROi2z#y$
z9ftuGQlgW@H~ixfXW*z8?z^%nvSZY8m-cFlAFKCJxf}OZ*(nBD^f3Fd#Ry|v6euYx
z>l_r^MKCZgmE}*4hEI@yRqt6wTqrDT_1@#k+m56lVKPJ#J7ZEwl9|T_8e;k%1-YN#
zF!v29s9i`)(_yRzgN9bN0NK)Rm?+yj)>Otk>r#}`<gP`QW-ErTLalm4E?;-U+t#uL
z)BI>4k$91jsb?V-Ay!YW)LgXe6WLWC0iH8H%@QKrB?Hqz34w~4E~qMs2f6U+TSiv+
zX1`E?lldp|gcFG@?&9j>MY7N6MJ4mP@T;kk(5oRup>M_BDl#ah9)gaGwCgglgtKX@
ztkDkni9Q@c2V_OQUpjWU?%7l=xW6SHQVKT8oT}?OK4-j_x*@D-DfYmdDrRLbEQY}5
z5KhBl9cbt7Hl60EC&q%%=xY5$@sdEwkaO(;YjY!U{M0B%y7;|}JrEC?_a_zorFgKf
zdF+?NShcXOw-;YD?=>OL!!>Yy4Dr<Ft!g8Ow>h4d)we<+&$Rke#h?dd@)ugA#<7&?
zwWCs<o#MM*L`Nolw4Y<KR5N;_#J=Y|`zOl;0(46rY^Y}Ui0f#zX2=f-N?u3p)15MN
zN{UIYD{LhpMz=g#P3AD4QEM1AQ0*&;tyBFCJvR91RDL~Bnvj=l=)zHE0nc6~hoQ=%
zL2&?g1B)hyjn;5OYn;w!!Ndz7BQUaG0N2>0$PxA^B`D_9M>s^(Q^imQ2@?Qg<Wbfl
z)EI7mNA0@-qjyDwR$$`sDF>ZVX2lC2m((0zdB5V&^5aSZhao;SL8KzHVh~q~lf{Qu
zk6zgnhFI+UwaZ1X6PV+eupiRxeF@ED3@K{|^T7K+-3IL?C!w@qwtGG3=I)#%1YzoW
z?V;ycT2)Fc6IMcLkU7Z0HV43&Eg=BG2*6>7KiZu;=Q03!Sv8#t3*E+hj(u7|MWqyD
z(w6Hh$ybblnHELmcey5d2R-`w6GIs2zO)=Ittb~9!GgUx5~Y?Bsih~>s3tp<!)0_O
z-&ZX;0vJXSg*3kYjEyi4d?fj@fog7Ctp2h%0$!((gz)1yM%Z-O4%7QKsH$O;l%bQ0
zW(z5{!QKXhQ@A!N?$n`-a^-}|LZIW15sSW!)?AE7EH|Ov_A}gd2Ye2DM)Bl{QqcHZ
zvBaVrF<>Sj1W7@p8(c?>EQUiK-{#8}%Zw26xU4XcZ>g#?T?JMu?dc+2Dq)kSELfx4
z3UmD{0uK-q9upZ<fE!4e`s)x`c}RCN^UQ{zLx8y;lUK--aNDHI;DyV8gfAs5%OLxb
zMhiL$uzr`0+pB~TDJ^;Mv{mc?g3UufWX$zl6(wd13vcbG<)=QE>kAjL>;NBOkvGK!
zVN}Xhcv|d7-E3n(1TM7R?Q-(0C)T>x#5}`b7Glga6b!`mLf9|S43(>r?o)l5u+?nJ
z)tnH+kY&mrWd;HUvRwA)U_MV!=YWZB=WEv)kB*UcEDssl5U3d%9}dA*L60r2WVe)r
zl}Xs^ccA27pFbx6m41nB>BNTfhbR}OoR+ZjZaT7~tn_NqO53|c=3`b`5QV&`{iL}r
zb37rrNeht&I_v7loaY!3h^{-a%OMDKYD_q$!v4??JLBHqV5|@ZIwVG)QZ+Pl(-`51
z*8vog(*Txx3!ip0t+wGaqazzp$-W>5(p85v--bMt?c+5A@%qR|07k4gr;|fDT%c5c
z%R<1W-juI{Vu}@Jpp+6Qn@nF0j<>uBEMoFB8Ef7{p~=@ZXwNWRQexb70_gt61qK93
zebA+Gz!x$k0i2UJ9v<)uU&q5r(DW2II%P~G^Am+^AKCeP|8jg1;^7l1uZogFPm#Ul
zG!m>s&#Bre6H;HMq&s0k0sk<1<QQwx1i%-p2;#d_M^;N5J65h^MrKMt$=)W^*<?CC
zyeqS?s$QFENbfUmiz&zaw^yG<)P_quQdL~s!V+1YhY&p*w|3J_N;<@$&&CxU3y9n^
zYCK!Mk3c_gE-JuiEeO`2F;G(2gtpszP@tgCoGxXaiBru^mY}gan-E=0E~~@RU>nB<
zNI64vW%{yntGQGbTI3PP$w|5sAy>6xyNH>*Xs4%P+1hPN?(p^#SEt+bTn+Y#X2kMz
z>VI8x3aaz*5eHaemz|)UGjW!5EI$n$i7|*PFgFU`E<1-I-zT0s?7`#jc<_w|L)f@x
zK+M58g`+|;i(-|sTb*0i%CKo~_f1!07*W?NF@Fx_ALwUJBjOb#@1bBZ9A6g4dZ{^+
zRuEs#gV1OH(tJnvD@>H@E|-u%(*2EjM*Ax|h8;;2VByeBzXIIkEG7W44)G0@+rg|n
zx{ai{tLFg91c`>#xkkDODeXe7X334~%C`O|`}T4@mCm)yf)##@=v~xe1RR`_aI1fB
zXGsQIdUIzTg4ZT~75+2AOofGp#XbptpjzuUew2coJRH6hD3X958{_O3q(=ujPN=*d
z%3~Tg9Un+EgdK0SOZBlj+@>CR8&qHlxJs8<E`apIeDsBsn5<75AB`}x{<D@7q>2s=
zYhLCk5eiAql7if`=fXw>(3pPh>NdS67_HrvCr-4XKpV~kY@Z4;GJ8DTyg|A~4TFab
zLnm)*QT>4Ngun{}Vk<H9QS<)Md&VaX{}`6$iL1OD0^or#pnrLf`u9xsPq%*l-%q0d
ze@L*w%*es<=XrErqPyzs0NTL0ts#bMuz^~)il9(egKGFk?rkN@qMz7u){so38Gdrb
z3An~qZh3VU$a187akHW{g=(x8l;LP{y%LH$xo*owp=@ZAIq3dzfkHrurX6i89ZRpT
zF6Q#5Lj-LQUeBpK-}qS^f2}7!BE1Tbp246nzQLeu-Ynn`PN(dVc!3u_yP*JXc6H$5
zO$CiJF{DcUMHo2k{SK6UFnssYlwr5;Q~B|IY_lg5>k7ydj9SmBe}v4x#t@BM!VPV&
z@sW-=st&7_VUOk$y_I3X6+grsi65|^XI^Hq7|~dI|NJ(YM2g7X#*8@A|H*b$OyUy_
z%ttQl9)ax4Y%iorZV0gMeIMNUQ-wLghhImOAVlshC_eF9g*x&FlWb}T5@i9mjb$GY
zp%XboIPxp+tgDea&dZzJ4i$z<Ou7}|uStnfdw1L?r-^Ol-I)MSq<4u%-seb6ue>!s
zu%xgnHLi~=ukn*MStwqDs7wC(jI(ET<{`bWPzPQJr?r(T-#(IUtxa4r`0DdLe{Y&z
zM+4j#Q3a6X_#ofu&kizby8%`$#Yo4f*1k!O&uf@z!v`x%0!<DGW#KqM#q(VdV)1#$
zjsg;I?s+d!o(pW4c3nyMgU*72fC{>`Bx*)1EgJ;#$Qz<eGg1XnV{mIn*-9&0%X`4L
z!8d7iande!NLmWrf7oD5#j>$k@Jcx?<e%R{8a+KILRB7?6g4Gs`(#N#$$VOm1~EG%
zPcb3q<*vh{L!pjTLwzze=>3I!fbJ!B`h;CMF$yYcX8<m?+xF_8C16mr7RspeC<O9~
zVmdp_G%{Be#EPO_PE@5Y3m-}o*nK87X~iyun3kF*+ptXc9j3*8_LH4pxfKvM^gzv&
zN)Hht$d_!0dFK<-WsMEbC+IUQP@9A6ZdA7+qVS9rB1AL40{fZIXsL38JTKRmPH&)r
z=ZUv>+T0TEGM)CY()jU+1UPtjO?{|yvV*EhKM~P-W4rULPIOmtQJ@>^U43iS#4BuE
z*f{BH56!w6OjB0`$%S>5+>sAYFyn&m-J6$=k8i+biiblLbI9KYO@l<|U<k4*Hl_JJ
z<<M=8e)Ls$)s!tB3sI{3Sl;eX<`0S`QG8Bl>8{;>0VB%kVi{f~Y_%y9a8ZIwkvhqr
z2+8NRu^N45NaMM_gi_zP+d@I;w(P%x1L7|cou#U>B`znh4W%0=i!bSF5e-K^Aui(x
zWGdWo35%K876l8`h-ews587zyY!~NC>*!VpB%eDo9w>BuIFaWkuO=nx;AMp#P*sa^
zXkK9FQ2#lt)q})RUj%rjIkqmimSXeFn}$6IQDT^*@6lX$^v>g$G4Zx+g_?Xb`h<Ii
z)C5(FT_m!d6AXw`85EB%pJVm+L88&M<q<YOoSx1*41jX|+MX6lcq9^rk}8}{{)N~e
z(fO(WRn68^YuJFe2#TRA?qJ+NNwYiBzMO9`w$okfmPd8vi%U>y7V<jL#`rXvRrIp+
zF=-cu%olA>&({p<;awxd2ZlVC@!H!<KSsIy8!Fu08m^>`jD!r<ZZ!f)oZE{UP7rCs
zXO`5PWL~YIBkJ|*nE!{hcMPwj>-K(Q+eybAuGk&hwr$(aifwc{wmP=ivCWQcvrq1O
z@BKda^PYY7IoI``Z|ka6vueyy|5+2iQFDA-lQ#lI8|#+|zV0sZ`z|_mTw*4aUhPTe
z_1&h1NH&E3TzJv`+J`6fJ4_X-76(BAb+^VYClpH_9BdxLVa0pBgIT(|$V{r;suU6+
zF}TxLMK;DqXg(c=#DQ2)z9~uI&4fXS{^Ic9fb{V<6#b<Bv~R{^!<qWjBnM2w3-xI-
z<fi`D+RAB>qvNCBSzj#dG0h%cYYL_|!(NLue>_p4->$#wiBf-=WP<t9C>UEm(C!(n
zgI!J@y@(ZbfEVl*PXOJ3EFyJjr_GA}m8E8YH7Z?-EZUYE2Q2H#NMtPiF#v=a<`6}E
zrrR0X2!2L%3AY#>@U+N+&8mc!KXh=8EBtyohae;e^%~D{CT=RZiFICA&r*UY)O10k
zmOOiK#<ZW#lqsKO#nWk0dRDT-RU`FG5>hRZmH2x!Dm_x|oV29s=TS-d6;pqg@FAst
z8mwI5?4!WsyU$6>*A=CZ?Dbz)>6z2(z9pC(3QJ!x5G`8tx8|qU1E%mW^1urEUk;$`
z-V{KQ{+tHT**>#S;Y;piWa8AUsea{JZ8GBS+8aM&Hb>XDKBO@Av+e~)sjEx0_W{?U
z!wzkS9T4YdeaCpMhM;7W+b!E}auleERvrra%_^e)9WxO`Z=Y^^k2*CV+2;l2I@q3p
zolJMlBqmn>4~sC~b?}kRa_E=NcR~<H*-GSx{@u;yZQl9gqCBc=gp05xoIO^)-o89^
zBOoH~<{0M>`{YANx>rL8MC>LtJmQR<?`dShqzhH^pShqBk`T}ADr33RXW=}M1B;BE
zB$U!0lGKi;69vIf4p}8%P{-`QbJM4OK#O>{{4nK!{>)^G7fqmdPx%?zmEvQcY*t^4
zEO6B#rR=Isenj24;ANj!j?n$(K2qx}k^Ng7dBXBO52{a57k%+Lk%jM5gb7i{khx<I
zIeub_^?vIy2c~}K{N_jVFI99R<N=}ucOu<s{;%gpaEV!JHT2>X{*3Gu`!>#vR;(yt
zprVUdkt`Py64Q}cGz9fWIHW&<wM}|6xCz7yfc4FtoEv5?Z*{AdqGpJ<4x^~3tt}Ho
za~(O4{#kd{2x0CCY2I{L_r0BCEIfOz`|+}J5x2o5%jfces&BGa@6fM%*h$-eY*F+>
zT|$Pv<bCJg?Gx`zO3Q?EA}n*vpX|vur~mx71DwCZoc?Wq^H+e>zqs5dsbl;%oFn;(
zb5tyCO`YXT-IYEmy`6%xtjfP&j--jHor|T5C!NH9qAU|bJD0yM=}gS>cN7$ZioL3x
zrLnz<DKQ7@e+Fg!i`e{q&fn&Ivd90V@c;PtpBPoh(Ao5FTZM#0{(Ga8U5#A+B8|e5
zB7dFu7Xx8t_!qtUJgj0bE-4~w=)mw_W;2MW6EiU~GX7OY+0)s@)K=2Y%$}I#Z$tma
zG5?}RpJ4Pa&dH$uS&_ft;y>xj-zEObukubNrcR&imFmB4Lru(}WNL2ti9bB41WoLX
zOg|4OyE-`7nA(22kC^e_uJqSk{_9Hr%ldzr@wZiShPI{*|BsINpAptKPNsigM<#X#
zVM7NAQ%iFT7h-l!76w5#b9GA-7mLrJHl}|=SpS9{|96BXWBNC3CT3-3Q2z9y+Gpv1
z(k!;m$P7B>Pf*Ov$jbiLZT{lNpU>zs+RT*s)1QBP_TRkl-!j<$L8nrmI#seavUjon
z|0d4Ane{)#`5$3_|7OGgqM)DR{F_SZ68}S-|AdYI6z4xI`TtRo{}FKbm(Tts1!9)}
z0B@g){15y7&H8`x!2dHv{xewQ|4otqsQv#eLr&&z|BxZuXA}HiGW;ae|B@jaJI6m{
z$jQR~w{X<|L$Lq54F5xWpS=7pM+phpduS8Wed_%`Nh;%KyZyh>Z<hazUijCO{hNpX
zTMPW3CV?!UZ;$_MK7alEZ-XuV5BF7MVdP->`yKHwetMR*R(sp>vr7L1;zy9O0-RJJ
zH5FvhZ;T&(GI`2J7;11dA$)*>Z&oNEg7G(JzLRAW&}p=a9XnrpDn8zx^&h)0{5Sq|
zc=HfG#r&E4*z|d)QT&5ky!|I)>(3>+gFdsjJ)QW8UQZc!ZJ1%e&qE(#M<X43N8)mU
zk3x6=lKBz;6_2W^aD?vI%#tW;(w2<bMj`@O1i7I{y}P+u$Hv{jvlP~3Wy2lM)MA>i
zHDH#w0n!s*iY;gV+cMke4fT|YPVr~|$eVE%qO)EZ#ZRX0c}+-RnJ(wbk~3eX9_q?L
zR_mTXT~yZBKt2a=7+BZXEf{dZq3@-<!~2@)nT%|uA@!V@ff7`@EArEnE|!-nMy76;
zO6QL<mG7YQnSLPqxTQj0e|TtjXC{0gbTvcTvdT@O<(J6Onq;q5?QpO3vfNxVV<HLz
zDTy0XHti+Hi2J&vwDXk`yJ-3`WfExecw`?97<TLtp-e!tkFI%LPAd)tGttv~w<F=k
zQqrXvgn*};umezuWPtN!E-gQ#lm%9s>Pk~HX3V4<XDQg0Znr*;xmE^}J0+_{U?*yO
zB1C0yBy$wZTbqV-ePnowF`>V7rAqQ_bwYFd2cn6l3rkQficptFQr^bMjn?tLu(Xls
zCE3id_XVbI7er!))X*$EfPsGm#QaaWJ_k0uldCvIcV^nk-JWs<oYV%<V)2*N<Yi?0
zpP0XGGRRmq+c~4@bE5RPZgFL>W|9%6Mw>D}WL)$mozDyb`Xf#sYJ!L%l)IF;#>~sa
z<oyO=_!C223N;+S7<faehaGX{6`cSJ*$?x2_AgG%KYUCP$DDD5@XxZM%G48ECx0!#
zn~?BcPnaZPoy3}cV=%}NhbL`&;D}|K^r&V{^$#0)GQt>vlS&n!ST0mCmlEjQ*^HoR
zJN)Cfc;lG?$Ado4xZ-H>S>)_ou5hGJ6Uq=^+pl3}JVp6UQv@DYehAZqQ?5sa{dS~1
z<7_`NJ_=G1xz2#sBr)0Z4wLMXOtQ$Ny!1AO?N#l-D17-UMW#5kmTZhH){b-8=liaS
z%wRV4Nm;V|`vJK#Tv&8Y2yLb3$C6RAXj}aJe$d38uLheV-pwP+HByujVBdPjU0Em1
zce41Z7-Xx%lc=F_8ThK4XG;HMKkLjO85m{)reoik`MkbwcqYmuGhQ>B6rnSemzH2Z
za@&nNOe?5dsmVD7?s|LhuCx>Nq=+vu<;?e05G)kdj9*x$c+b2%H?@KxW0lP?Tj~lA
z+a1nLka?+8lO}S1$$T!SH5@hS&qLpFNJcdM*X5s6+gX|>i{*Zty&1H(TOAFQNrisE
zs4^uyERQ-5?D8`AS_Sd`JLGptX+L)HelXlrRfcXyTEQpBnv5G6Gcx^@&RnyirJd=q
z;bfje%}=oWuKUoDdQ*0S1J}RW-k(3_k?wV(R~o)(<wgg5o`+I|0F~=ph;kS7xR7r7
zva|%ABQ>N&vb;CAO%|1}u5V2k)La~C^u&rR%Iw+CTGOmaf7T8^*iLaWQkv9#T@b>^
z!eNf?N%>V@<{=9oQ6GV4AiD&tnZ$gyK!?F|1b>h^0-iHtHPaQfyCuGT-y<y0KPqh>
zqJ_NsNAzZUJqfbDCe_>Tl>LSYGoSQ1nKSsJb3HP6=e$yuk?aXwvEO0#_kP=<n>Q(J
zn5$P+D`<LG?Bn*tobY}@>gxht5pfJQFr$d@l#|J?QWUJ}I>2}qCi;P{2kuum{dJk_
z8+37O?=f?hjQ4761BR1EDIzFBi2IbI&_T>X2Y_<@N7Bla`kalXY$rFS1jVdA=0OPS
zp|sg^%KmBwT>YUTj>nA=PC@78M;0K-u$X!O2Qp7mxldK=W*4dPh<Lry`E0}4x%S7>
z=$jd>r&1TD3ZF7ZC*mB-m!MN;PH_$zYt7)$Iz0aU?q1(Vu3b+OU_U<O$nfw;_2LYp
za{qGD$mAg_QF3NtW(ep0uhc0Oi;<E)W}RzWn-j}GuYpNllTJ<SZk&NHlxL^pW}W*$
zDL7U90~L!0OZ?TocONBh?EV3B=EUmcl|qW5zRl=A4|=uq9u2?g{1nL>-Tlv-A1$6P
zt`2OBuy#hnrIaFr7=d?G*stShdOo#kiPn(Jm@`h_crzLv|1h}p>(F`+W1mfMX{D0i
zyPq53n0=&}?1a_6>5Z(Ht-sOeCvf?h9%U1SMP(Z!JK0{ATDt~rEw9{FIAvCiQX_lz
z!f(R8O;Ie|<FD)ACiyTq`<L&r^bVDE>Zf1W^Wl?M1UmO-IIT66S$k7@6diiOs+lg(
z<nKEX!u6loA1I!2N+^jTh1DX=rNTOU_<Lkcov8#K`fgUrKBh4&BSx&*eS36uP5QIL
zWuRQNHL+2Hl1SL4M35n_?3Y;WFRk-z#Mxc1>83A#K&t=oc*=2Ha;Wo?9_zaW>>eGI
zU;0rY!}6U;Kk<62L&WrBHFGSVnViypSX0ooo&}-&kUyu;aB)3qk7i41T!o|^4d<IF
zoK|jfzwGZJd1>mWeQtRA0LNkDI+;{yj|X?04~6{s#`_E(cR+WiM3Nzra{}_4eD?)2
z?nucM2Vg@lDylKt^u+0xBUP@H>)|hg>?G`}ewvGR7*6owplib)RV`%Hj<TjahrSe|
z<V5hV@)VcFziTr`RhY>E`DO8!hx4ia@_tYGhhTTaZwOIB2QQsIdfnA0n?#gLY0v=?
znOERBOPBkgalg+i=>?b-Vjb-rHN2GBxK$m6ub(25wNSHeGVXflsjo&%4jGD-m{nKZ
zH%(U*mf(0{s%9+=5TB?VdM5X8+n%1E4Uumi;bi-|dH(p|zwW80%ZkY_^NfthszrTq
z<;jq0QY0G6lL?xAeX$o4Nf9wU((ajvAy4`(Be-u56>KIog83w~Lg9>2$S-lo>??rG
zJf$EY+^J0|0wQ+A(Aol)EKz->83vGz<i5eWkPe9S<uM8w=2gZPqnJsR*NKp9Va81M
zY$Ns#eA^?hI;2Ez%nbeZU=l%2^!Wj*YW>C5Z_HP9-Tpy#%48&G_cDOsv6BtaDt)F>
zfl;oWV#_r%#ps1<?25@RVr?8<@CAa3;}ly3_YAG{7lKXv$X`K6ZA-#AlAQR7X#83C
z{Fui!nyo3^cgI{sPkZVsKWIO*zb^S+Um``foD_42l+E5AAwaK$3KgN3?L+mRQzFSK
z)7Q$7L$Rflt(UpqGAF_Ew`9NuGt4)(WsE!tJ+Cv2tSITi^rjSr;kwqg$=mP{x+WxG
zMTZEv7*EsM@q&Qc(`SZva%c=hxQE$#yOq%~sd}9lRb3PZTgjrOhVV;lYNd83Lfu=G
z^W0u<mM!L$)<{G-Bl2Gaou9vLn1y1636u@^54P$+pTn-Au-PS%Tql&KJ-O!rT3bT%
zxj*Z}9LfOo(VY@%=rS^$n2~k=<FTjCY5_Xa)@HERX>2*3z3p{!LUgLOZ4Y5}B|cjY
z;pPbk^HS*CM-h#=uIj@4NJ8wo3ZtBng`~RZ+^q91s4m5hV5~$5Jz#ROuGXZ@Y0|AM
zf9*s8X+XA6mgR5~PW38(^ip(7-~XN5SSr12uW*R$VYD@k%ryafvap$Y<OiIU=4#3q
znLWwFO5!-S&5*Os<%O&|o@c6TLOv97l?sbvrIeKaPIh~|T9YocwP$I#s_Lr49yWEd
zrbI?K&uJtWn-@BjNUUj9mzk)=O6HBtCGzE+kVd#x$QJs>3N6AMi_g-cO`XQNJ|;!5
z0w~N&aO}i*fo;w)IFhU-Z_!ibi}4s1o*P!rDW;xBftT&sx<E^QE51Q;kO%^Y;~$O!
zlntx%jW}{Lk7VosYEQMI<h0vpkF^pzDKBb#GyM=wZM)}=J1OcXReur>QgyZWT6*J+
zu<1Qru@Ti|i9*x*5>?$_*Fv>gw!>!U_4RjyF>*7$OjL_y<4qcA1C07#N5pwE9RHMO
zdRl72kQ{S9-_dY%Yj?nlns3$J3_X<K7^p4N`p$A-m{n02@$*num%h!hLrNkrln69c
zJDcgA?BeWWUXa!GiDn=$Apq!=f$kEL9}^G@-dyAlCxbFi86|=k&p~)<(<*X6eJsXs
zavLE8kuz3;OSidnzU%5eh2-SQoafWsk6TC5@gu9+ropH<2UaoD0Y0Mw>_TFPBQL6p
zP68b#>Ejy;+-)c+O&Ht)j?#<F?u<;&?F=|NB+9ECA0~9m&;$^;eY$7*?(Da7No-c)
zxR@HBx))=3g=02H+S)!mHMPGo`eKLvdAptR=f7y{x`4?eU5f-WRnl5fONc~sUviE5
z@M>5^l$LCzhLI4VR6KsC`;iN4WpzZ7s-(`tjCK*qj=3b%=QVTxGf7e2lm*5R5HAC^
z4xd_TP5`y1h{|S`@8ujfMQSx|uI=Xu_cH$?Lrac{h6zkdvYufEl+k10@SzPxI(Q&s
zTw(L8Gm}x}K}mu|7wK*n%my?sclV)iskiDO(o4&s$cahm!pb-$9~xl=Yj)l6v5-_2
zkv#3g)AW>QLX9Q;QvHU=ECwnqlSh&mu?>%Kyym)EBge4wp-mtW+zF>a8!7?&);|#9
zJEmxyQ7QuPE<nMEcA!wq*rKX6=S~VYJsCXfb2wLO)|;1ob(hUg++lh(8WI*82G59N
z%7EOKquz*6Uts<KAi{i+30H=T-9lSaf+C-vqoLZ|55p|{366xex<yptl3(Nb-sz)t
z@7`JQd;i%*&-Gx4=2EYFsc}s*_!*Ani!5j|0Uk|yaZ}j0fg15GZ0a#+=D~ReCgwd;
zP*(3O<Yzh9M*v>X07#z?*NKuOJD=z`l8m=I#be{!sRds~_dsy_3-lwAw0B*Cm*_Nk
zHL<0w(_$W_3eCz3=P#g=HNgyAimUh{wR0DKs>H}A(y4782RF3m$TcxcI;Fp=4H^aF
zX=ar_fbgG#9q#LPcQ}V8x1ARyKMg+*6%??q?gK)?V%e_c-^KY*=Uv4;1iPnFe6qvM
zeIY2lL?kdM;VyFG+6_QM)rk#C#B-!VBk|Zv48k4rPODKGX4=5wJ?7t|9llg8Ce0=>
zhFGv#U!QF&NF?4k@CW}EReu0OR(?zgYh3dG`Z@x#(Xnh5atb=^y#9tIGgr_kw?9K}
zgQC0iMw4xfKo^0dh&uiIXZNoXdk;@^*W=iS7Og2OLq?b{8b*Ok2uInhD5JsoC>{sN
z=JZH-+82lo59QUA`JP8Ntb>8_qIgfRVB;OkQOmO5i}l8Jelxg(cq$Mmm7h&j5$=&@
zQK<Q+9iDc<=Z@*-K~6~_->{u2O@l%)a229^dA58=b<n^5L{oWUx$Hq93-S__yrH+6
zG=gdWYfsDJ4Pk8I&#Cj>4WU=I`Jn6eflAHKKbqk;uwx~R%GtF<uJ47%j4)_sb2k2A
zjiFZ041Y))@)>XfXYK4(C-SEWL&tORicmqSyZdrWCEOu~7J&4?^uph*2#^H>eNX{c
z<WQ}9NaPf<xDLY5&o<b2h95iIvMvXw9A7KoQRoD)y}wYHv1fU@t#^-2lS?yr!0*!G
zs04}v5}ikYPkSqH!#H5AWG9I<sESJvA*Ime3fT`J5I}k)b(r<{hJxG+f%q|Ev#aYd
z0;Px-IR9!I14#U7^BCyGP$jkWBie@b4Y{M-7n_iWo^kNT-?o7RkoXtk$q`H*->>-x
zwJxcR2MfN_YNZ>+7ZY{}AP5je3kM2i<VtEMt8R~dOD|ZLO*-16GYmf5!VI7f<+e@V
z30LVbP^Pt2ytAMhJ^aCujhmbr2rosLH-nC_a^(Omy$h$R#1x{G7@NuX6PBfYQ+vdO
zjD=a7)^kem9+U(1GmB>>iMyK?zvnY{2DPoZcrjV->ogk>&BeA`0NJS;1&2Ah60X&n
z0wEHYwv$fJAapuei|3>VM*M}D61IZXf!X8fPAU<Dn8F%@X$U3D&O(?;3Rk#g)4-Be
zWedzf>fz=RN?`w3<CR-<R48bpXSX{(Or2K&d-t3%e}GcV%uS0>jSrlG#|(z5Mg?v1
z0S+^QyDE!*lWcJ1Clxp_{Tqr82GE`>lhr`sSg_J2BCAl`g%o)nhEF|RiO;C9{8*IQ
zFe>ll3jjeeF5~2b+|9_+{c;aI*XW0kLAn;EC<PNQqocn@fM8XbDiJAJy5@lwHfKT?
z@>bvrI^00SFtu=UtMX#m0uW0$WUAsPh3YM2BI-;;&6gB7c#vR#$y8XaE@r6s2G6kV
zj4JbNRXbS6&jq0%{zgr33x?IZJ)<WJf+b5Okm5qthYUeWs($7C3cUt@mOzGGmJ}^`
z-OI~*-gv>Mod?{H$2OzqT+?wQ(^@2@ddo#DNJsjBN{Dl^kOjWWsZF>Bko4lTR}GAp
zV&RSzR^GJ;*e}PKSjk!lx`Zq2shD6z50&Jq%(!`Y9OH~#eDHf1p+6N@lk!QVgjceQ
zzQ#W+YhR`ATjK_fM?DIT^$)veH(=}SB|$@Y7@b;(OA)c&_kgUTN)P6qzqE4b@>)m+
zL&6{!LX67lrM#3!sZ<fFB#`1p&Mo)WA906lYc5S(G>pOk$Kix?+nRH{qZ;$TY7Vz!
zbLne1eTpdGx>40-pf_bhJ&8t2G-v&ND4rBH;$wi5y=I8vU2zupiwpw7baKuS@E{H4
zD5Ee<Mdz5K1g!8N@sE@MDBD8eBNgc&j7U>@s8+met_d<?bvTzF7BaXx09P$F%YN}+
zn$%LG{sPwcLJX|0`Q-FOb*EdM^$rPSE)NO!#TOs~2*LTOgW#d}v2JDxXXJ!FHDh^$
zk>~{~=Wz<`C6J61eX1e(--yp^!Y~7NR5Bf=Yx@UJF|`P*0Earl&#SI3jFXCmi#5ra
zN(_l|GLQD>llp@j*!~NHU`@(OS;0ojv@P;sp5v@wBQ5J53vB9la?0vBVS-<xiiSt2
z=+03}jpFZ<hYMFpJenwl7)|MYr7#KSzbccFs*eATTJ$hh%Tgu5S?}~KC7-dfl^Yq8
zq0nl-%V|p&U+s)hb9YYR<+GmLYG}tTN5SEXb}q7`vh-w<mdf=u<UO1#Sf41|WC+tM
zGh<BFhtFLIaDy<Y7KXC|6Oz1NK_cn0kmoXk)<H0as^Y8b6mpUCWEHDo61D;tZV)#@
zAm++k|CT#&7Jkj|$}+!bS0_-FH`tFdVaLw3f^B<_OioE%1dq{vZ3gC&WU{6Eo9fwu
zZs|h*^+<6^$}l>A{+U`&rm-<9CT@z$WS9{HF>Xi$1QZ_1s#5g3Bkk=dQ>X<|4{^J@
zu1ztNs4)~cj^-}BwH0aTO2hHCVTDq6PHBx1g6XDniJl>{@FQu&cGec#lyI&WkD0+Z
z*+EpuG`uoN>&&%?z1BH0Iz|djwZZHt-VdJX-dl@Ni(vC*OJT@{MsDASN#cmvZwevH
zM#wK`-w<@x@i40Av{pZ;aSVS}75mLVx2yzXAv~_~sw|eV259)x-!CYK2={6nkW$~m
zGadx$7MlUM>swmmzyLYte2?`=>Ge?SW#FZ4WNLF+n}wMnqSOP?1Qwb{$HW&=f^0NU
zs;?*@!)?`RASqvI?G#rIDZq}d84~Gy&d=RozZrzbysc~4ZYC(e8)ZDA!|yRK*@4Zw
z&fdOnHHx3*h3(1F2+2Fo&({03B4itO-$%;~)leRuV210<gF15CQ`dmjF^=KDu(Otp
zz(^C#qBKMCl0W4G9c?ySQxt$hk+L(P%*>%3&Vz;*dc}CHK1R07UrnV-!R)PXR=$pr
z-%)(+Ml`<SQV&~SSruk3`ceu)>uNi$*1UgXHA1La)9BSU1S$UW>;9o)E|^LmEkYz=
zu+SUR>k%@GXrg3~otb1yjL`4#vXIFpW6G>;xA}9eqMsp8%7{2Juq&QJ2IZvAl++LP
zDrR6AMur*H#68>%W=F>M^3JP?C-e5M%9(Gxd_YF4qSU@fZ{Au$l`|@=8xSlO)^bJ&
zV26dW%s2Qx1ytzs9!+oOrfbK{@LU36bw#zO<TjPYhKl_h7b?&L=LCb0JS#s<Gc)pu
zK`RNJ(2%!NwF8!qG}uvW3BEl_f<J?&h92w+<%IrB(PbXB0x4}1SP$u{K20>gh^aE%
zQE|XLzH17#iwzD2(#^_P_xz^1oo$lC5{?3(F%(mTbvJvnc&6XUa#udiMCaM&Uyf}v
zj5q{z_dOs{JUb+IUr&TR&%!ZC^lPjOt|b)a`>)!H$J2Ki2r2>Tn1C5GGYA1;$7n{D
zkz5-)x#%!-NXjDuF1&Rr%DZBR%v7CyLYcz`3Hi-_vsfirlP2}$jPaeOoH9=H?H8VI
zo%?02yOher<vhmr4+lwdI{a&%dLUG!j=B=M(sX~)-!Dh0xsp|fBI_Ems9_jc(rZEJ
zLQ{Kl+JZCX?|3VUtV(87FG6wJ9I^<&bH#JmVD;{KwsX9YKtd-yvL)c8cnXZah2+tY
zd0NV&twwo0B#5#hZ>Z=M6HY*Ztv~@0a&_a?__#M5@DfvUHe~c@>XP#tvNbKSjHBAh
zOAZ4-<a-PoawL0gC=SGFLzx12q2y0=+wNBKl{e+I6u)|B<z7OJh^vOcKzPzNERb6!
zAVs93ZTJvN^24|IMzwPzCcM+cV(YTtrNbZO&G6?q1AdN%pe0Ne^f3~P9t&3J5PKLX
zKrUp)!AL+DrDvlEuL4HvktDCn<sfI`duJc+Rm^N}ybxz*0D^kf?z+;P5^l)=h)@l5
z?bUh)9S{~H7RX)ZCP_EifbPI;t<5mTo740-Dw7-Dk!u=8CkHAxX{UfZG=Ta;GGX8l
z?YWW(%@ysk+OYfyno$`-tddKXmP#(8OY0hUIX~2NYQRDYL;d4Pj+e}}So@GiHdxH8
z<gfX&*8O$;w|7z``0cGYnp-=x=IkkPZhS8l)s_W<7W#mWQk+}sj-GKN7zYi(mYa@+
zdK8ue{ZKw>tcQ!%uWh5<1Z(3*f@^#=L;PG&0sD8N6)S)8tEFH=>%0(g2wjI?<QpZ(
zNsoLNlF646KF|_932y`2=l8V1b$&(s=$qb8xHPB{Erd_(p%Fr@1{nsAIGL8K95tiz
z!U4#6NAYi<=;YBA=a?#@84f{HR3?nTlrGT$D_`ej<p^JECvD|(>$Y5c9`*MeC<1KC
zmQM~{-CkOH<anh7aj?n;j>?5#B2qG<qY+Wl$*s5V-?Tf=#wnO#bH~0>>>GxsazP?P
zUGMD6&O=jV<*9uX+sCW`sdW%iTA*_8e=OkrlpK*9jE`a^m`yTm!Jx&b1-Dk-DV}(7
zJU#_|+C8Tps%E7~gfF9o4R)`SIQ5zr-L>ap^d~yEd0FzijSC$h*MG)V`YCYg*`J)F
z1c3vi`VJ2|LGQ(5`R)4hWZgEvv&(Dxes6I>r-L%V+wHQ=TQ`&$=_FrkqY&&+ZBW#n
zgG<p;c!~c@&ge&jcUio6FA6jnTquwF@I4LAs0)dX{r)&cy7KV)UB)%P%(7FRc_-vP
zHPU$@8ozb8UN=voKaRQuMhN+q+|~IlS=)(H#7{7VnP2^xGNhLWz<M%Ldhab+!`yx2
z`>?>=O*t_;rd;|#ddSckJ&#Fj=a(bQ$Wz40nsif$n=L_mxE}5^6SN<_I-Y7c`3XKt
zmsSM#rJLsMyjM=%@AUnaL;T40WDsZzEK--;OKQPro^z6@<Hdc5uQ$o@&@J~CaBnRV
z0K{Psr}kdSx++Acm_ZgRs#nP{k{_;^C?{Y*7Wx3?y9qum3MwL}OVWq4x?9Yjs-N9C
zT}*{Xfqg+ei5IU@PB2v!yhj5CKDRpM>I$@ahvsv+T+ZNxYjs<dpIfh6e9cC^sP3O8
z+*-uJnF~neCMI+J%G3Hu1x6ruj5oH{S*N!)s;zTYnt(dRtrDJi!Zyb-3%WGgEQx!k
zc*jHZ>7!zpkp?nfIZ0&e)2ushqld=<m4-9rhfx@gri<~5hTS#XWaEXQM>>+h&<#Wf
zXlSHh3XiG_mD^bEHzp}ZHO6m3Iu@#OCpZU()H;l}r0zt*9P!!947tf8LQu`o@@hc{
z7|F!VF*m8pHXVdwdA}5{lv9a@WP8&cQ<crGY<|&I;b@<~m5YPzp!nUR&JsohI$xh9
zl&;cC>rAiTGY-7q5(Ax#5sK=4rWezDNOdjtF3~v#N*>i_4~6QwMfZWR2&Ug3yxe%<
zJ33r9pn>LVx^)xY@@5#0xOdE>wK&@JVsX62Lnu*<rqMIJ(Dydx^gGFq;q`=GvKd}9
zJqr7}RwqwBt&&zh#_(M!p?ad4R0e=7&tiVzJs8RvJxfO28HL<D02&p)R#KIs;iyhQ
z#RF;By!>i$P2^e--ZgDn+iEpx47n3SDAs9z6>)-_8ZrX}r}_Rl$kF%nRvfXl8VT!>
zwV$nQ!ivKKzfkE7PT6*?jW}tVh2eC<y?)RSwEwW7r|}j?9K<+DXTknS_{JnI|0}ob
z)sAR-aI_Ph|J*Wi_MtGHkIiOmH{Q($6OaAC6uB{*4*e0+dpop$WiM2eLWlhY0Tj-*
z;uItg&QW5X&!3_urB*-ag0H62#jiqe_$w^=Q@M3sl>szDTfx4tY4aD83xq+kDos25
z<lH`pK@M9_9`Sco3cDYgv9Xt>ry9s@WJm-RLL;kk`tJe}d620+gD`Q>=3X(+z8OF6
zQ5gC55A~nUdHWQ`1;^@J-ImW;IUGBwb~o*vr5@%zLR5X~N9lLXOKBO)=wg{%W|UW=
zolg=?^&NRj#emxYv(gnlAth7WH92p7+l;S12$N^U#xRa2u*(X2i)8#jtDEEI#D>N6
z(pm3mmvc>Z*%pNWK^LeHLCuIf6Xw(@9;1xY-}eH2BsLPy$i6VZr7mX2w`OzKN3`Dn
zHn|ZymmjiKL9uI{1zAiclq0FJo126}eIrnzuO)(^qHxFxA-tNAGnt7C+zKs%h+p67
z?Ra0#WH*Vft>U7#%ME{h5vpZ>vVragXA6XH{_<_!kr$br^}%vqGvZp3=wsJD&Phye
zYIc&!D+M8iOq)4yXV(cS^#$L|Ye%&karAz;C=5)5xdt-*y_DDb<R$6|aR!Edr+efz
zqp)1WjM*;gLNX_XXKhYYs$EYAH$QI5%9a-4pu9OKPh=@&Gf(t8lOVV~KD9OU+l>uz
z6K@-UL9RP9K%f_rLpLcAJGPwduvZC!5IZHA5h&66Ejdx6aNWjE?-!%R75FF^we09E
z?P`4ky#scEEkiPz$q_N88tmxl)YOV>$L_+ag!x*GQfG=lwmY(9ms<Sfj>9yn63-V8
zA2Gq_wSr--%1dUDRh0EtHBD#9E{oka{kt+d?x4bQ5Sm58oX@GRuD=CKUrbH%xa1*B
zE5ZZF*?U%y#gjU(7SKA-j>EmayF>Eot&$QUIhReu)M@$xu7R#bHM|lxZbnCp_MAQd
z6l2h4zlTJnBT+Mk4KayB#ox|KdaD(r?;44s5q;o9+{P4;vu}mgMUYxM@};~_@0QTu
zsY2wA-pB(;#jFU88z&7s{Hk}}%crwC%3F@cb8<Mo?ku9KePr>=YEq7J4D&u)ca$#F
z>jbZXy9{TT(ygHTgrj}TT)7Yd0Zbq^WDHQ_r>i{YTmDnJ1pFy~5d89i7QVfN5D#UG
zhPL6)zF-A9&_o)h`igOuEJD&#@AVySQn-hu^><W@L$-?#Z!$4QG$>H}{Rae*lH#hO
zV^kgi)o<}ns&sUw9f|sOqo3+#N9uMGOVY5htJMfqsbEuKB<O5N`~D2s#F+v(REB5l
z3@V@t?ycY8z0OrF0C4{R5dJWXI@)d(FtV>>nV0H!XMv3pmNu(;R0pM!Qovj2Tz%Hw
za5(3};6B+W@T1&e-F2n+;sv?2N*FxI6&A;I2W3;6Y8!8>F1-RJK9nX`YVBlG%NQ`C
z_-gZYX!S=#{|r9!GR;`i<3kIMDs$x)>U~AFz0MKiY&HPDkF7{KJ&Rgw-GMnmvI%OW
zxSuYAe*vX4@>-NKA3nhR-6U?63RL2citycrpT;y?gl%VvG9!|zFtTuwmqywk3blms
zB5h%3CU*=A(YXN<(5(Sn`KR)#LT`AT@7xTpgHIx|zmdjwoorioTnx~7R6yyfy8r(8
z_n`EA1F5GC_mXAJD5|7=XllVt+R$4XH?Df2g~<ZEeDkcUa{|lR0{JXrM+JWes(38n
z(_9~Y)}^wiNYAr`Q=#%bEQd}D*2X(TMT%N=Fr9C30`#is1+&5`zhdfQBC90hq17=X
zzjO0-`i&qVeN`>x(`qIzl6+5m<ox^$ZK|_-OycHLAxI;7Ad{j6mzI?)a(0uyQz9DV
ziUN#~>dCU(*;dl0v7k%}#`g8nsFXE<^{WK%qz}Q<1-!YswDkESs;kYzg9T3z-Z(|~
zlA5J-NXRt$F=sJ?eeZT^%-kV#KQD}A_U-4dGb;YbE|4Jv1|~@5+!gnd^cY1vK2^wk
z9{ZekBT_(lf`6iN3Mol=o6iDJsxT&`pP<IGiAC+r8c}gD%lp?L3Wflpq&iITk{w3s
zSXPjVZu{PjP+{`p#v_gi8m1;(&oYE7s0iKF;hF~J&W|5e?_^1I5FsRZoAt53&o^DR
z6$sYfCcHIZ7}2#*{Gtgjq!YT1y%+6H9}DM<dwv&W`%RtGRoCv7*{U~8x)tgsq&upW
zbH0-<n{Pl)=_5BlP=6171n2eeQB#*vl;|!?Q&E4+gJ_|WV~H@P^Kq9$DK6VjK;{nv
zyxkt_FGmX##-FL{Ua9rTtF+Mh6K8ablk02bDEmNGNexlz+c$EQu!lQlXzTWb9w|bb
ztlxUGuxjQhIStD%d}mU}1dya@=-rWH_!OvF3e$K$1iiPb=)nrpeoJpK54m9VI|Y1e
zrg~DxeOpI$u=Qip0nVlCst$J|r-mt|>4l4qign=;%hn}5a*~-2@6w)etOb9wRtTyQ
zOHKkiEYoInDJ=t<9Txd*ZHgay`{GOQ2!5YoKjYuTUQ6*{kpj4t+W1V&?Mu8v@sw{Z
znTy6)thsytM4B_tgwckv%WxQ}aZ5tQf&9I~wf;*0kujLdyGft2p$O2(z)9E8-#iVW
zP~$~Mo$pC%^NrqG7{ot<FGbK>AIsC|K0(#(iz{eLUjOMj!_fqCx!;fTot1|_svJ|1
zHX5&hX`qY<)7KcZ!OX71o8l>f_Lf$Qik2p>nrKG(xmwlZU^JDM+92*2=i}FFKGD^F
zTrR~7dWDd6K4;u~2af<GUetvdq~bIGOOEPLjP&k1ZW|Lud;sL~TtE|Lcxz#m-><M=
zJSi{mI@L4-X!&MbHcyhmD!njhd5}mffk*k~Od=wmDA7Mm@txN>nFWcL9B~zy(-gL4
zLRVqF<v<OI0u-uJiudYjCA<95ileSfZ(IL-={uH+q0FL!=b*$#d2IV_)G^@#i_!XY
zANKdcfY!Pt$W8tOg|Ap3uD`sf%zAEB(*y}O#Z)M0*hM%^D#FRhahQ26&2@U%UK5yG
zgU~{5(6g_Q2Muv~My`geundOHhSpvW>=ClcqY7+~RH_}S7Y~4Po~x%a)@AyuI~OC0
z-HR>?+^NTyAE5?^ymu<w>Pm^8WTsEdr}is_%^L#GG}~7%N~~kNQ4)j^2Fs!wYM4XJ
zL)8%J4mG3rsz$J1ZbZ485^=I~P;p*kzQFrgnjez%AVA`==7t5DHmBJZDDPW3Pk5m;
zQKe4cyLJ((Mth|#B{;IHZMnY7z<P%S`g}Ca*7^g*g-erg9-I)Nq<P6i@R~bS{pGn-
zp`;Xop-IjblHZxWNT^~Ts7{6U;Zf3XN=d}VLLq=O{Jt|$8icf^!Xa$rMaJ1WAw&md
zWQX4{!9GDQ59(v9oF?=&-HV?nICttg9yZD@?}1ZF=hCTJ7pP6;IXz|-_x?TJ-;(F1
zA!dV>x;>|PWQ3zj?y<P5owDDWL`Zu-wqz47rEsGXjf)W3Rw7@IHef#ErR0*{3$ZXy
zRH&?j=03Hl%v|=lqL?Ajfb)<ae_?_^TyKwF-+XW+NwA51-V*|Qk_s+tTkq)=3mvww
z-f45TueB*lguAqEj#2gUL3_K5-r1L(e9#(h7{S}lmxe06xu>6<#(x53)x>kH&c&@$
z7;aRd-#(;L-S`WG&zx62&U`|rRx!qH`aqktGWfDeceNj7Wn0q~9u?$_0Ix@rK09K#
zY;|Hd+Nx{5Q5(Zf0l-=ljo6_vPqy^*eMl1<wjkd&Em%~PUeC4b<LC~K1+Bf(8jDq{
z=a@j&+<9+v{iJ!^z@w(DHN@ID-RM~C+6`yMy*y9NXk?cz5;YYck{CK4VhU`SnNuaC
zh<7R;yLnlCj{!<$%f%}tYI~YwpB_jbjsNXXw-LY&fdF<Jjf^}rT0nDI^e#kIA!Hiv
zWUt6o!|>u^A&)6{92-zfVj-CBsj_QwH+z{l{1h(5b;@2ud7h|%;Dn5t2V2uYgs7&2
zTlE|kFOaH3qO#Qaz&{<(vZahZDycIKgR`fC&~?OZ!2zmbc`A5E8V06FayRY0wCgE-
z{_)7jZ&@cMBlXj6ENdYGCxer3NHwlE8cI@$b8G`;+mINb`kWd}1Z$TM0WS+B$?McD
z&ooSO*7#-83x$9;B+sajveXG$EAe>Dz~U=i6(^o{VeWAWAq@b>$Zt=VUoy_MWWAyh
z1d2R2_n-vOohqV^sr52H4_@G&9vXYuadPu|J4dsJ=MK7F=I!D0@;b|k$)dgtv{O5~
z?LNteHV9or$Xi4fAH~+*ASWqcGN@_*XZZYf4ySoaPi`}o43RiB;=#cIGu3KnK_VCO
ziqR~+kv;6X4JU|g@hlN7Ix;}P#S(K6OQ2cyn?z?D4&e}m{|p5K0&T%vI(IE>t?u9n
zH^(l16i&r>F0)A9t_E^yT>0Q^K@WaokQY(IN+!Ic=@Y>R-&$2r{zg;NuZblr0U4{S
z(FP@MFaE6R&a*0IcVZ}u;AaW)s63%%KyUUHQTBl<O7HwY@=9h`CBy;|RWD%xzCF`}
zfV?6AswhTiq~=gvH+hnK5NWEAzckN*2_t^&(74*d!`(QRZ#;aW!Y}QlFr>w;Yg1@x
z&K)-hOF=dtqoU(ChlN8^+k%8vxwv=oVp7uubLdoC(Z1r+(A)I2EG;Lct6t^EKuXaS
z=LrwrkY~-)xrJ(Ukvw&pz=tts7{1XCf;Uk-&M}=7GEMHK#TOz>Hf0n>jbR=GD9;o;
zJr}mDB6-YMDcHB}Q)a*Le1hDy)$O~R+5#lJ3~FE1^n??V6ebE~42JrH(79K5DCE_j
zuND&sW(w4Tj&&X9qHDOx^MMr6J^-eAxZ3HO!u8%Z26xHAu{7zNn_|*N98z5M4j$7X
z^EWsk<5jGhc!dZ6#N9X?uu}=0QnNXXVDp(E8o;D|cA>2hqRwgK3i}A`@{4sKCeijL
z<D;O4i)qk?DiQHLT3K)jJluXagvk&of3*qE&LM2(2JPi?v_6E8=Jmz6U|iHKTUB6N
z0u3$oZT+Yqto8PaYCwKLN(L(z&&ls8FDzI9^4~RL6ORA7`f$*-V8F!^xc6eP6#ysG
znzglv?9%(G3pIGO!9SM&5zh(tg0d?1#rrWX0|F|JtF>}`rf_tuCMj&G%Sj&=2~@@Z
zJe&$=Z?f?mHgb(m+Pa0H385}p({}hKcU+MirvpkOLxZ<fit9BJ^K<HD&Rc3TW1v-o
zO`!GVi!n*&G=<De$+$E<9KC$(l9i#O-K$%}YtUFpe;;%1iJabwg~i>I4DT%rXGLFh
z9gftK0u7?$mqd>ozSld=rIPp0Nx#S298$0rO~br=Q29k@3dc`V!U1ls^7zJWMYd!4
z_wiI1Aal=r6mOSKpbHz=;V^S}0?JWue>4VxuWR)%V2HrCXb6Eh!2D1DV^P5)rfK<<
z9lYRoL9=*qWDo*|1vS}Voy%5wL%okBkYmI2yAFJ8mX<lD)l|VE^ql**TmD9VziC|G
zQO##VR3lpr9$pK`xc%OrLG_NUAU9v#zQhz&w8WNo(E(?hoc$G2dJM<;V@u=f>Z_^z
z9FwXP>_U<O4T{)vGSvB7=k&%Q9YzWn!t3VL_twWKXsk#QWRbd{IMy+R2GCNB4=nR3
z?z#0<6F`I+bzR^&*sxWCwBB7pdE+dg8mGUWK=?>74+2U#e*H=N-3WBg#Ysde7$bm`
zUERe>=0F!&{6rRMRp@sm^?Gb9z-0`pX<+SaT1Dy?x#EobBJQ$!`%DW?W7I`}!{b@?
zPU=##7X@8z{}p#6DJS#p4tvFJXdFhw80e5KOm==upADG6+A)YQwg7A-=T4jQ{qmf(
zjTm$JiBRVtDK<^-vL;m}K;$W%2o#tO8$#G1-6(A>??jtg*H2Ylpc?L=Ac+tQHl64Z
z-_$q}#U_oy7Dn;aSbPCkIjF)Nif1C-IRAbx-X7duLP2X+Yr`O%SMwdw2TS1XGcqBU
zug(;ud;GVnxQW4XfOe7DJI0*6V-_0hV3%r;V0s|o`kLZ<{ZDj|fH!il=#k}fbzTt6
zPyq0P?GOxMr;wJ7*U`&*StT}};jU8E6=Mn|1$0}9GQoXSrDbNBpxK_3-CLLYvH0uK
z??Cc;${wjz^W&OolecveUx^){8M&UT`MTP#q>ahni*k}W)drx6DM>=%=h@Dt<6V<M
zy`+XYt0+30{926_(*ZDLM;=vp44bL5;MsO*@#J$_)>zBL6Ho9yu~P%|O%aA|d%E3s
zd67}tjO{%K#N?%Vz0*nKpNoTpD!bt2Y{p;9Rmd%x6?wGGL!N0|OjI-&^7D2wMbQw|
z%f^f+Cj+9vPy%(9s=f9^#hhpZXv2KAz%2F;iD_mK4LMGxZc15mnZ+O7vLcv8{s`wh
zq&f*u&JbKSBG|bCWI;={Fw+z!Kdv)4_hV1|M^<jD)AjXSTpF5!^X1(buf2FsLt|o@
zJR1&5+fXR!psEQP^B0tfF7(M~#~LO{GvxDPAmbovXZ_e?Tb1Q7lamh>z0KGC0LiqU
zNW`=r2R*#n*1U#nKF3?Uth;lLCK#nI9^QIXLiv?qJ^Ue<(~1Dw{+bozQdKciZGOgW
zcF*X%RCPt?L;U@uxeinV^WuQUQVrS3uZS(lwd4wYTw%FeIaijN*{vUh1!u?#6`YxQ
zR`qDE;xji%@=PpS<oXS?k_l6-N?kx8TlUK@t#3`$>C9h3;0>4#2Q@*OA0&Jha0gU~
zA=WkwaQY_;)26ZLY^_7NOO6+*fBlO2(B<#0aLc6vLA`0`T@50zcSw>6M2e<dMoaD4
zx?ejIH`kz84E5xq#gIf;Vw?g-L{UymR+#2T3bMambIECf0&ABw66zn}8+*oy<NDW7
zc|s>UYXFIv77G^I0+SDgU)erj5o-P8tGLxi%A*3>^OVh6m11`cL<{;qS5e^DG%dh!
zH5~a}dTtcuxdY>CbXqw@wFA$Csm!@)lho;Z^%v~~zo{WpC{9qd6Mvm*8`ux)q7DGf
z5a%9mOd#=UJ?-Ku8)=%Q4?UqU9&Vg+);qy|Ou1@V*96doKJ)>~+}~Ld;=%zyTyAbg
z1f}S<EpDzfY1-Ubb{9C_57SfKd0Yx*B)p}%Ye1%c$n(z}P_EU2+3fQZ>apLq&K~XI
zF|3g0?M07*v8sk*=o40@9Y1w|Oe4E$zsjZu{k|W2(3>E2_|QhqA$Vz%(gM#`Os{mO
zB>1)6(@_;6o#NQYjZ@i*5Fgbr3GY%^#}P<zTLjWjyimXnm_5Y5T-@78rirMknV<iR
ze0KNf*VN%Fms6F~Z#eO@w}mxRfQ^%w|D3pf2ot#q)s`A&2n?GyCWg>Iv_|^`d}#)R
z3yr?XQhwt-enl<ENhxzkzqgbUs(kx}aN~PC*rKKu&|zBB0Fc+M_s~#MsaYpiDWH#^
zst{nbfnnPY#UHRnul^{{R~xB88kI(nu$x|Qp)CaTU}u$SjOQaI&@bWp&Mtx|hFnZr
z5chB}bpSWrdz0Z2h9x{N8d<1ZYZo*>vl}`YKn<3H086=PZrf{LbfDl7rKq5uyHW(O
zITm0vnj7sn9!kc3M?IiAgX@*6c`Z2mf<sjuGy+=Edb5>73bdebQth4b;>L)fExvNC
zalRXcHW8aOCG?s!NMzXvbFUiWs$r>FbW7-<%&R*Po(Q%spYFIBUP)?q=AzGxUZ1X+
zll`iaQmy*Q<19S}7Vz4E!Wu3lSYme}M%RXe1d2g9(nHl5Jzk)S%AXI-w4->E3c9bs
zOc%;N(YXVO?PCbaV-YZB%f7LrdR?bBI6&GYYVhG(pvavfXy#B&1oo6SHD))o=rogw
z`Oy*foAjaSZ44cqR&ktwJ5A7f_RipQiE*4EEwvZ;0<-}un~waoaa9ebu&>25D3=&^
z&&ZqtyxJF?0a|tBL%U9cQK6`aJyBzi%@8FSLLu*_*P$W35>xqH?c@S;rENP~6ApR!
zPD^CS#{bMC=V?KKBR<g$x`@M0QuKUaK7OXL&OT`0wwW5!N3yEObW1{D{x*2(SlZ@>
z8!g!|uu?)FfJ;twkxN}q^zs%df!eUwexbObb*oeq$bb>uG~k6(p3nUni_MNd)N#p4
z7-N{33eDck^@CopOch_}_l*P^t(StYHQO{0)K|aPB$C_J93~4A#mm87{%%7Xv2-8>
zn5b$so*R|8R@w&?O}Ea0?vc0G5R)wKZ2DgG>-Q#yI46GLZQ;y^|4{&2rKn;qrhM&c
z@>$j985_eooZ}4SouLxj5S>B+K;Q#uQq7jkGThC61CJ$qXe;~M3u`;2PDai?)77Fk
zR}qJIC1T2M(&R2Lzxl9@OL3>Rmxu4`kFTTfba1Zp^KV(R`Yrv%icC;bnrSL!BeYT5
zcB}?*B4S?);LcBf!0U1L_rvq<c#SAT)E+nAt-P%|B|!;NfO(9R0!$VY&*bXGb}Cbm
z%}z`yTnggQ;1tTU)af|61bZ>ID+3>OWz%5UCE_|S-bJA`52AaBIiym1KYM7aW_ah|
zd(6v8_eUw-#J;BSqDl4qHcbx=8<b>K{GSNIHz~8QI3^bkz6#KA^$+=|I)75j2t(QR
zuz8oLQy!J&(+Bdp_|GG{pj|JXs+t|Kgg<vC@6p2~!a1W$Us(kfY!o2r<{{pVVMIuF
zhGy314bp|)H!TpLf2&Ig3C0!iN4eJHSn%=|JA1>=x!-zuS6e#sTu}DM3=ls@*co*9
zwul~7knBoyc1;r9fb&O&5QShI0ND93uUSzcTc3O3We7(l@K_8eq285K=4IJdD^SZ)
z-xA&SVi!SVXl8^{-#btmDy%xArch=DBj@$Szv|y-9u%>N;>17cB7PIGOg)wPa+CX&
z_DKg!#aA9#s<4kZXMfCx;I5W2dcpa;fN<-a+N9`2yfSE)`?Aegccll^F|vTrE6})T
z$UWEBAvr0eD2))FSid4z^W~vQa5;u<6pNx9SgS;37(VhDCE^{R?iG^8!AK?F?3Rjx
zo3JjZWfFO%%Mo{(O^9#gMAVi|c;-3gV9wOwuV_>WtqIaYj2zjgOXzUGBY|2Jiuabs
zP*^iDqJ}<&SC1yL>*F`Xp?44N7HMi43)MH}HC@DDjG_e<=jC>{LI~IdX9Vef92Pnh
zb8OHxTRMPnlzLog^|oH4;)YI*R-vbr@oZRPVCEmj52@P?)k6F13@Diwb@h<tEF1EQ
z$3!NMR@}hJl}`@)q}#0{&fV^kGX^SJ95&fxq!<Tj&Y2PlPtq_+1?cZ4)HDiPraH&!
zvzC@$+r&sv9zQCPBK_g3nh-&L%mUjjsahbHweNpb1p;sfcPnD2a*e~=>UqJI2<B1w
zR@q9wWbJz^rCrIhiMPJB3K)c*6KK!(;Q*ggwl4YLoNDI|NglY^`k6n+_P2Ap{ZBaM
zXufdSYQNdqY;LLA?@DP*bfEOjaxuZsiH(ak7Tkr~RYHc0V8H`G&pMANMr6q{9RRbP
zMace|&eXOZv*ON$rB#wd&=-BNjB)b%X4T52A)%&(=hW*KYx_bF$EsoV>{>rlRm8@8
zu0@q}&2pv7$M{`qk2K@zY0g(x*WEWeIA7d(lf=2~a6Dhjw(1Y^Ks*$-JRqZO;0-(X
zGu6m0?t?Yfq%KLkPA+AYQ8tr5_tW5%0LHisQ$wdXem>QS?bkwtf{LdHwIyhr(Nc_Y
z#ew#WYQ;0XHF^vhC~rJ6Nqw|Z;s$Co7OCk8NO_r#Q==JZ*>dLS8&bH#wgAtFYjj)~
z(`iQ5NF*5i|BJo142mQ8{(cv?MM98W+!GuYC%7yg+}#)V-~oa=f#3vpC%6T7Cj@tw
z;O-B<|6^76#jWSwH}}n*sjfL)Gc`5euIcHn>2uB}O6v<1+ubi**Bt|9l7?U{9X+c?
zX&rGGx=hSC#G@fJVj$r5E+VVN^<O;)3{x^C5_0CDzvTgbIYST4Lmx9KRy)>iWbXOF
zP~X@zCBIht{Q2W8PQjFrRi628;ijigPoH|ZWq44`+0Bp*uYDe+TEkbrAk)S@b5LAX
z4tRFGZ1+5y4UTY$8Xn*Aeb&LjhKzn3LGC2gY`3eanBO+nb!!bd)v2iiV)f9wxl(;5
z;~|Q`+rjf+=9L~wj%8;>!eDgOiXxD~ACU<q8!;19+xT2&KEp)0yKm@52KJ2@8<p@Q
z?MEu$zP6wAFP7@gRvY*njlGmLnk^9T5j%m7QX-M5{E(x+y^rus^a`y0VW&KAnTkxy
zH;3Oh6nr(WyRt1C87VGrj!iDvWe=rR&)4u@F<R+pwIb0A<!o}zHtHkjbB%?hui)u`
z?C>T7d1<%MpR@@Lk9*hixTHLB)V)|xu8%6nNZ{vH1qD5h|GeQ-)nUeJ2cEO4Oa^tU
ziqVmaP7I!e+yY_%RpIL&a>{nqJ}<efm!&!Q(h%rK4hjOYKDa>)|Irmmh(MY)!S=3%
z({U!igK<&~;$cR&RgT*`BhWz9+&4J%W#mu4^Q>vP(h6j#e##m=7&RTZFOaJm_+-!7
z2*U7t;lZAPxDWN7^=W*dYx?vL{_aYn1dY1ypBVpvJigPN*LACY6)cq>hNGVJ^CMbg
z6<!r><K{NHR=>799whFDSH?a+%3q=5+Df|`$wTS&O5rGSa%(r+_O8n}2A+!+^IvP`
z#j|S1k~$0_c&SYn8Dk+=7XNGV0F8}1YP4$QBJJonNfug~<<SqaAM>mZ0@vABKc03B
z$guo0N~@(*@8=sq^^6tgdF%CJC+OBp8-jD*-}Am)XUhhnR}g;ps@bD#vJeSE%?Vr@
zrG&M2s^9Gkoh|1s?`@j2+VGS?NA`M#Bf2V9axRG%m4a>CD@wXc%&N(HjRr;2*_-9U
z?6cb#quLIIoZehSKy8N`qqg5mv3@XCp0?<O^qd;czyI)c*r{0p7|MQpgiFUmZ+}PI
zJNX-wFYKtt%c>KpyC133&gRWb>@8C9v#x(gBSIjNGI`oQTL}=sw9$z;)19Q%@XSo(
z9d#?>!dj@7Q-)%`fe4*w8W{y{Ugw?M*VAooq$caxgzcMSQP;x{G4o}}nP4ZZ{@JXF
zbCQ+S7P4DinLjopHac~4Gt|$PUp>aXH7F}Ml)m36Vs)sns^XUr*j~A;rNmOP1}`5)
z43Gt~_}_@t;80{|-rT8pAQL&F`&_%{DZH;fjMT1am|}`;yUTTuK)y<BaFY}tRtFc?
zw(EGE#RkV{RC?uq{9cPwkyDiG&#A+5)1Zy|(o#?~?eG)+EZ^>(R6Ve{xt5Qgf2&2Q
za9`>vMS*2F$6)okhj!0031_ninIuSbRKZ`A>jfvwD|P^wIIG*ab+!UbxiQIm|Ch~5
z7gi56+HTCu3dVG}42EaK*GQ6G%Bd!>!WJ)9J}?q1)(eW1ieNlGOazE9wupntplZOV
z9(QjtUa+Hp!3svid%w7@*f&6N=X3+0Dem!;8ya)q(q4~eglHKGht~%UKfzbo1^PJz
zj$MA($C*}L!7(%>7Oyi=)u0o9VA^cY6-7ClZukyE?KE-RonP9gIqC&beT>%yG<o!&
z-&84(R49;&Ch1+#-;#4VI=8rMs+Q}(&?s9iv-qF(otHqDlc6GQUbb!zjlW?giDuC&
zqAs%P>tkFJ;|0{8HA7gx%3YR@M-T0_5g}DIndbm9InRgW<9TR*m+BaDf}5tfZ4Awl
zL{|(#e6fK^_y1D-yF@N7XU?7hfAJ-{#wZpC-#oHceW!%ntO<7}hkgFl>;7i2Is}R1
zouI$}dL&8wcec5?wcC;Q>7;SF3dL&UVNJm*)#gKVQF!$upI$4FirnB*)Pm@JTkgFd
zmMX);KjfB9${+(p_*IAlPpL)fRy+0ZZCl?yX1crzZQp4r?kFN|D0!PbNZ??U@DI7f
zZL?qH^vg|Uij@<w47a)aG{5TQNkrIOTPQNkWlCr*7gj)IbgupO_aHLzBxnM$eao_R
z8WVGORr)cE=ITpcg9;}EokDk1?Z~Q^SHvRD4VIcngste>%y7!!+h=5OjwIilZ`L`A
z(}<+x#LcCW_j8QR7&<`5yUkxk>D=c`>jr&VZ}1hzpZUOCzn($@DWkzc->D>vxmIIG
zekEa)Y7%pFn`D5v(N&J(>ztddE?d>@GM>=ELb7Uy9{5h>aeLRyP&vYa3*0gp^4z?^
zwY0>?eb$S)?C!~qgF!BVYfU;@zAO8pX4E{mb;5VoWRp|hMOh+U1l<;xC8Qxbu3+4w
z{b2Txu0|nO95HciVhq8D(N{VaoGs>fd%BeL!Jg!QRrM+Z(cJvJR$sF}8Er$CvIkE1
zZoGqpb^<B}1nv*n8&tZ?qHqtpw4QhH^`?7f_X>>kjYj4ZlJF-f0|V*A`2)++8pfjw
zK(wlb3cx5Z)hJ`>)F#y8AC}IX5g_QO4B3O3j2bXiYT%|OUeVZM3PI_vLV!LbLJO<+
z+Uom;>slk&_K;ELD@s5}yXT@DyXGK?2$qAc3W7+t1%^6`VxWH|!%R^*3MB(S@BHby
zxLf(#x>NFMTSSiR_s570c%dq><nswD<6(okj*qMjDuS?F{V*nlt>N?#i;wH5KP0ss
zHPR0_b$-wZ*`q2h9-dj+dMyX~M)&+m6y5JK5>CxOY0Xc+P)w*sv1SSLaW|?a6rBjb
z4=hJ<)9c(ZcUwzzO#Iky{WyE`-cz%no9A0-<cH(hv4IOA^9f8s^4sLBQbnEyb+y_@
z=HLE`u8exGUUtK%`);9b%k9waU%X*u;7hN`47=ID0Rf?usX!j1r?bR)@ob#?Q&H0^
z&@g)*E_P~}#;zc(9vp_*Zpy85mmzgDlbk*8x#sBJ#ml*5vc#W<2XIl{<@ps9pD1YT
z!<BM|NN}(|@RK2D+SgrfK6#%}&8G9AKl57HCf*wm#U*YxZ*NwK=cKI(vdb?7&<nrd
zraNU7{eDp-qUui#K;Z#;XT!Q}FhB@4{)G~mGC?Pq=pE!XRiZC$#EUi~Ae+e2!!TT1
z+i@@ARt7_JAr;|Q>O#wxG(_sBrMgz*N?asNkRl<cA^Q$I$`->zVD-=y8orzS(hCPu
zfZpG%)8jyo-!PfjsaJHHWVW^<^gyXtPvO){!Fq=Rl=DqdsZ8}M6qEqXNxbO4s;VcI
z>*jjD9AJ)<HgxACx?!(bd*A*JEXU}7FFtUiT(Jlftr|^CXE&ogaB<0tu;|5+A+LHm
zg4-<3z4mg54r3l3@rn{6I9-yRTrUJzh@oEn46iOj@lG69a7Q|OYhe^pPYaM<;=zZc
zA^%?WyNBj{K<jeJdKYSKceR6F2wDQ2iylKGjlIjzc8bsrO=n-Ks-+u=C5CjbPTy~u
zBCnBTGSj`>aicZD0>6=Yvv~BK(li$tWo{1Mfp?MAK;)^4%2hMvZM0SEndH+s``ZuH
zum7SB^@e&PV0JF{3ssqElO4<XPh^jT#>s%cft{0e^DBBf#`}f7M>Biqoiw|2vS1+l
z>|~>Av4i0Jj+7k)w7@rTo?J-=EMCYZeAK&tEgDRheM=Hkp8d^h^iRZHAGFqHQD8^L
zyj(U<dZo0W1-F_egy+hE!paUf(1Sh<RR#=d5dHbcrJWBhnEpM+&vRj((;a^}foXR{
z#JqlN2dI;b9&}1B;<)1(_{{$Tsg6Xx@Ih+_Hba?r-3-fd^pW2j=VK4Bopte8XKP{;
z81>=8Yg!fJmTo1UOu?$(RhtioJY%SBvv*s@0>Hhq(0&zbv9ibp*gHUCuzbjVtLBJU
zZC_B2-_kO>#52hiiW8jR{Hjaca0_$kxAtih@Ea7);M(t6vdKO=NF97B8N1NMxhVe8
zK@JOBv9mA9li7E-09C@g2#1S=M@tj3lC9RsP%k#d*6TtdKk<fGSC;VE%jc2^z;&g4
zBJ<rz21HR?keC7ZLh{gB!-xbu+OP{xqeGK~E+zYTu?ID7n_c(VfsQT-XCTYp)yyM*
z-@2myF~|ap%sd-d=!met-gRd<6s|J*dF}5VB78`lkr1$<_>dXSqI&7Jnr_j5NMX`W
z*VWB0(I=Udkak%cNsHjj{?zd>8|8JkNAzev2l0IAvKres`;Kgt|3MCCWzvL4K<_!j
zsk6E*k!bde3?h&-rkrA~F&yK7OnZ{Mw+U`#?yigi-!r*vMab|Ce2JWIBqwdpazd?Q
z5B=goeoK;diOl3ygwBPqqUMkQb}7Qgvy{_j{FAsvpzNSWdeqKDW||q*njCE1E+=az
zr(uBoaw3HpSjhaq@tHD8A<xjEHp<F{pu|CcNM?BAuVxgkS&m7Gh?@R^GnWVPv$=fN
zA@$UA31r?p{8KEfsdOj8Pkm-YPm;!^(Ca!AtqW60aIF(Z+cyTk1e+hPb&|@9Ev#?L
zJO{;;41vWSR%b_X%jU|OJ$0CJ9Z)|}iqg7;6ET_;zSGNmqR#gVW@n{OuMmdF<c2IF
zASou_yIzZV39lG<pxFAmhp))jXU{{o#fS?jFvRH(r}~w=T64=*-2k6n&_I499A^m|
z{h{{a<smz{yaTAb=l1|`HJdfBA_}s|P|mF6`7la~m9MFwZ^ZWaoDH@qbP5xRVb?`o
zAWL=w{+z<HM-74C_u>1KC<@qJiUqbz5<ZX1JT6?&ysrN2N+oI!Sggfp&v`+;lnx}(
z^4wpD&(hHlPbB>DIS%28m?wu=A*Cyf<IwI;xfn-OSv_?|wJ%zLzXa_`k5SbN6&>41
z_IW4u^|I^U=m9Gcvgy?@d=DMghJT4%X|+cf_&9ISMnHi#B?E}y{7R+OL$4NG{Y+=6
z@iFD}f>BGM&}m(OXX=Y`>5&e*s>-f1j%PxnQT5ys%n5&CaFfJaWW$9`$To2tb7+G4
z{iELV*uI1d9P*M@*ztH$0E5q@SodzKc@&#12BXm=5_7g9c$@wi>YDiJ{T_6SDXP=O
z_LJU#mvQC>fX%Q+aL9?|E-ysl2Kb$uWt>qJUFT!KD{mwO<UC$`u^9q;eI!B2`WYE+
z%{cY!et3Z0Yz7mpk1OH--OCiX0I^Q8I#pIfRB2<$p(jkDLP&ernKRXtincZiI_mnC
zyk{ajt(By=5z1|Ax)uw447jSQg(4_f6T0oBZaF|OJs8G^-@W`IZyz*jiYQID%8*MW
z$~A;&*R{Fdaz&zGgGt&X3=PoiPuKEzt56mMVb_RXUff%Q+*TRmTZ0O{_HwQ2l(`=r
zG9|m&3P=V2hIpZL8>M|=TvhMXtJza`Ufc2s3go6>&PS~2%7!~TunSeU_&GRLtU{bZ
zPy)eUi)u%bMy{G`h6=9zO#47uj1s^H+b)X*-h>D`v(5%4i`+3gLbffMbS3{qT9jYQ
zmMoE^h&~Y<ur@mWm{f~}dgy(G|NZSGry1c4F0xE$KCyXP323<8=|)<X+1xyiAXw!@
zmC_)MpxzWA6BmIm`!JZ@2=7QtB!z!2!5rWjtM|8-@5-Y}=TLSMWJb1X0nwy=b(yvI
zqZKb`h9*}p5qKdP3?#Wi@zb8@Ijt&W6bI}?Q^_zt{V0q~IqdyACYvOmB4ObCR9rH&
zQrZ(nP)ZML%v1(cM<;Z3tw(YnZ!356mu;ydE~cO!m%5oX$teJg{^`w*Qb$`7+gy%S
zhB)>U1%8?PL!82PRlzx82XycUhbExJ9hf3xa7lTS^O4c^wl219=Wbx(5w-NwheOr$
zPT+w{^9M*p6}}UpJa+iE!-507rF$vbGtf1#=llTmT{#bGI_`EU2lYJD>T<8$^ozR{
zI)!@LOnP&bXE#@(ahzn#r@+{n#P?X(eNx7F>cB=ly$c*ubF~eEmTmWIR$i}36N8yc
z6wJ+PGS0;GgRQz|jLZBnQ>XG#`u=`ZCT<qJO0gg;6J*0JJYrN~PR&UwuH2p6MU<oO
z8PJ<D7ENCfrC7?ZL*QOIpNg&~!UfWX@cmoOe`<=ssZ#c`4r&U8xgrDFWi}_k%q`Hv
zTw5*dtI;Nd7ntRnCQD8`?G$39b=%ZNa~Eo`arN}<t=<ou%(;KR2WG20(2*5uuFaX)
z4&__FCsH^V^^waC69Z5d?dBuAk#hr0cbz+6N6L-AorB4#q6yp#zX#NVo|2~bz72il
zmDRzl^x&N04#+TVJ56bX=lI!WAhHM+*iG0^i1)$9)EqnbX{hG2OZUkY&Cr!#V{KFo
zSsVKX_3FvTOYUH)yCtRz{x1g3hiVHm+YwXJpIwzvcg3#NrZj()`Uy*U7eSBSG3@T#
zkgIFFot1^I_|>@w2?samqM>WP2P>hx{LXN1MI@exf?OUmy5_Sh^DMrv(OCv2#L&g^
z%!8G9JJ9=Zsl`o?J>u&i=_}@H`@<AX)WaO7)RdBNh#O~Tbt9uZ!bF7_W$5A;u541X
zGak5>?n2-f->DDQVzF1QKkNLG;hW;kV$kH-%`<6Vo(*Caz5lc|&Z`o9LJ^><c|KCw
z_mwTx&c~)z5xh<=S4knQG=L`y$YlR}V`x!50wSL8=+{1c%aKxg8UOVjKX`C|1%vP2
zW;tt$gt@0YG*b<%D^LOYS2;}2#u8Xa#kERtiQEZgrX@`1;fN5!^!?Lct4~XB0vAYh
z$x`OWs^lyBHPbY-WzQSe>XbRtl{3deu9L#3%hjni5YuMf4(V5^Vib$_cxVSADDzSP
z@$TR45!;}vWzZgp4Pun}kSp?Q(bq<`N*0K~OAd=MX`yf!4u})vygtyDuSu&AtSwkP
z-)Nj9Rpvlz^>2|a;)~O9BARP1WA4+GdUe>eOBB-_JHyo40})3I0BaW1a%`8tp#(h(
zR1`1!g7Py=0Z%jA{Hx|-G)iey8V6YvS0~$@<?6tJO~%$}9<Cc%${^=zLyK)$cf*ER
zbwtOnXPaeHvK)UmH8|JwJmzKn=)O;Y$z>_0vI=N$G;IU1BxSL_t2RREA!mT210JCu
z)<licx1z2eQ%3ulOR&$?*F)R#sr9%|O-TfmpvSqfc;hjYOZ)^=ALTjpcjUppk6L0+
zvrN4_^Il#yezKo~RA)v7P}uHJ8><Hy%kim)8d<E`1y*!H^?Zvqr4Sv|jD%1ULWA8d
zFZEzXKBtl(`CkV;{<bXZL|jEvp<Xfiy<<gOSS%@YCTvlze7&}1+`H|sQG#cWMN7*W
zUsP9MRn8TT444WplgN+KX+s7)Zo4t(8hb+DrrbehT}9E#VTndO5n`z15J*^#%4d4e
zsNCc1Qs`+v>(|fZ9CxAIOIDYtk_q=q_AXcK$VZe`rI`Bn?sBhug{gvj&9mkkyq(aV
zg?WkYf>nPbc+T5*p-|nZ7jip`!KISa#Ou8_u=91s#%Jou{_GD3;|a`Mei}wC&0T1m
zm~yVt%9kR~V8Gj#sd;len|_%AI#sdUkIr~+0;wb3pz-_bl#D}-C!pn^W^9}4RKJ1V
zDa$-D=abt~!hqlM6;AGRo@e669dWE$ts%c_rVss>kv9<w{KX7C*;7Jv1?q<pVk`2Q
zL6;e~D3e|?uPt*d;fX{v)y%hP;LItRpotr9Y7nhQl=2K=MHeUVm&)DgQ>9YG!S8?^
znY89u8&|Zcg2U2kX_uK9ot4BX67!yKa7v7xs*|7r;Xk4GX;MtW4`CfgH=GC`DKaKJ
z^QPuY$hI@AGRq=|ODBEQio550yb3YqxYw4SoKCY-vLCh3Zi<4jbT)||*}uegl}plm
zDL}mZ*r)e|QIOQj_Z~v>(PoC^n96mMDG$OLyzBGHe08zZ-V#lM>xhW<Fc&^Hcnm4d
zv`+`uM5#bfLol=^mKc~e<K#0Mm*E_s`P<52!~XXd7y6L#yEzCX7<KmR{*3o$LSi!t
zw^GDLpux9ITvBwo^*x4op0E0IwPi81?J4_O{Z48%4CR+?T$({mK2uyG;n`Y=dHF6w
z6{Z)X3A6T&z>#jHswrfo)Lj_4(7s4Thy&4ee&z-h-m)m|LTiv+U1MT7&6}U;cZcUc
z7B?$=&^=;O5r@M=XrycdR9X4}6L^WLr{W^hpRiwHB+$>_m6<hPTKwI7mwf`;bMz0T
z8vd+y#X6BX)e$wSZYFR0LNVU};KG#&fsg~1_J@ea>@yVmX4w?XZNVrYnPp>cz-_dN
zKUPD50Os_~cZ+Fpu_cM<$O!7#D8>!qLawbgIyuRj<^F5;i^UlKPTQ&nyMx%!N7<!r
zJn))z@$ptpLxrtx%tpgXe=(6s_gen}OPlkZ^dh@rwQ+f-sro^LP>i_1;4~EFX5bTo
z+O?5o{+KRPTIV_5E{s&s%&57+=`k=O|HuSb54D~--MVH?d0s;K(W-P1hq$y9O<(dD
z-n>Lr8_e?io^K{A+gfcybVT`6NC3k&(LbBf)T!q8aU%8-6+l*iUNDX0C<RUFRL(>t
z3AYsigq|sSw{Na3r~Sxr_>vFXo%`?x{;;y-JKysB7VKj>OJD}tAbKMh$8?auBJaYn
z_QSB2ggRO-Wki=K-m+<<!BD%O%#MCAW8m4|KE1!;3`s>U`pCG*gXL(d=a5cwLO&@*
zDg&D40mpWWN>Bt5IRt)mb4;Iuw^HSfN+VdSKER721<UU6V;+-yQ2M?UZ5T)B+FzEU
z_`zFcKF87Ta)sXn`?2aQq;n?;$OXjv*_z-T%TDbB!EKvfP3MU~uU%H>Dv#2$>>4p>
zztX53KJ}6wBJ?YK8GI($r5>U0&%P?}^-3vxOHg?&H)k!CA`!fe>B&dBKKekdeH8It
zxui>C<OSLC>W_i4IC};^(-9R~3JR@<jbK!x0zz%f^~%h(xB^z!!!;?CrrR!ZIFHL0
z^#!NMs*PQoSk{$HYLpk;O2K>cdl@6`Qx;QDkEjz2#3*HS)ZkEVx?jKYYXlJZ|MGr_
z)^QfbV%BxF)u$cw!kPWR(bR7pY}ZiLXW+;ZXlEbUl4`+_;llm1x!LX)ys{XLa++c)
z^>8A`p&4NCr)Wiwurf=Qhoj6WTRNKG&cF;ugfS_%NOAYxykVL$*TUUi`}N0II4<k%
z)Rsn%L&Y2=?)qJK<(=#w_R`lj26_{*@^#=W{#`u!khVwPf*d>E1dhFb8eb^eOmYwD
z*7_!Cx=2~r-Si87)65Y|qcV00pPH|fB2KkH7vJ%i&OYiL=oLW1%`U`mb4!6@f9h-S
z!uE!er`UsDRMh9!AQmEoB6)D>SK1~An~rsSiy>sd{HfmPXS}KOkS?fPK@{>6_Hifj
z@lAe7H59vr&~A#{_LAbO9yPcyjk8%<A$3~&yZ7|hEx{lvaa?R2+5ijxn%I(UM5XQP
zIof><#O77_WqYLqldxq#V6X)SM@S`uMA^%PF8&u|)d$|!=!`n!-@J1!f8hzs)RGu`
zQQUqxTYf8bQH^6A_Y5~YeQ;x|X+woXeO*g+%y1wv9wTWi`ZsMB?<d<@=c(YX0PpH`
zNf-8u4{s}z4R5BIvb1HL!7I5=oi4+Fz&mUAw9>$MptL&1iX(;LNx>T|*q__nLtG^l
zl5WB^lO1M^>|pR`A2-nBnZ7pds2oG9bfg@rj#^I|_P9JQvDGDCX2I+i0e6$Z%t6AA
zyvC;0Q}`{{tn`QaQRA51x~!$Kz<?2=#;iz?JmXB1)LDE1nV{)A1#}mt?Mgal`d0L*
z(-&lbv&EM=n7N*2*gkv;0sWr%`4@mj+5_oewZ;V_AbM68{29~4TpnU<8Kf#qIw(BC
zQF|l)SaK2BpMiSS+3k`kVxy&XAc_=6Z73ia%X?XbFJ6iA`LO?GGU;Ej$4GHBM*Z8$
z3LOW7lVUzAd;Nu}*iuO9eXlyhLe0+msI%_WRg854`ckeR(h1ks3}kK~5CQ<dr-xYF
z^|&lM>U1lz(m?gMmV|cH{kNUc;iTAqDZF8vEZOEj`?G9z&iibdwE47rN(g`T(P8i2
zd{nLy501$hsFje2b`Y$!#}z1_Md^gJC_7&D3&%9&%Cl;fEipEdvaFjw%DZ~vDT=yG
zejqMTaJRw&s0Rk<gLT=9Z;#49-O&*bY3_6Byc@-^P`Eo@$=jwEciT7Apn{&3qY4f&
zZC&63_IK6a(P~NKYgFYG(QF=7)%<8u=BZBRg5<;$#K3?ekJNOqUko-pc*9j~^}bhV
z_MV^xr>g3x)K~if#{%?@{zZd4bnQw#Up0x2@_ack=j>DqET*A3^zSa6wgbpTb(k}E
zcsZBxRMJJ-1H6}9->h5&aHiN5V??wIYgtg)vj}<MA+)DETLzg8-p&)&MgQDUT1Zkx
zNOot$TgtgYB}N`GK}hwZ*??B{$=bR3;`cAb>H%}mmr_W+F<TmLagG`k;j@pEnED4w
zVii>Nqa+gSMzTUmaexdVQ22=jvWwhWohTy7yTLdXoeT$arZt$0WyDxc$R`U8{r8vO
zM=1Uf8fg{ix0HSdP*})c6GYZn-Z;2f@eQ<PuNwc`xGaWU6Xhl&Uc3KQ^|{0iSxQf<
z{7Y(qvsewRB`XuFlinzbV|e2D@y#Z~ij#MBH|bE&ay}@Z%c>8I%@DL_OCxcIECNy-
zo(vq#R(9F@r*=*s?h(`o6b%uQjHj+1HYSwGBX}Hs9#>1%{j`j3cR8dALjRXY{G03w
zk;BaHErx^jJn=q4sD<>BIi$G0v<RKuWH4IAG^2zQ>aX%e(uwxNX!1)-rgqCD^Uj(2
zO%$TDe3X}69Tmc}$+lLahWYDbevaps{J?^q*$;{j3r4W`o>Pl;-<Y?^Nw^Es-YuCT
z_wzx#M%os?Bz8?Kgz}4d<c(f`#MOp>C{;~O2a$|Ga#O)RzFiqeh}{V&tznp*d~u0j
zg{q*3)+U%tm>))71mSFoG{(0BNqfBOi?|bp9b~+Fgd%JLn>MX7`vJ3&Ue)0YGv#bp
z?>wS|IaX>Ji9awaZSH7716u{V68WXA6KJT}ow^uPjX2b$a0>hvs<iP57Dv*s7sYu*
z)hT;%@v`^&B8+JD;b1b@I3HjvmwX7p#Bj3}MLgF2Ezc>9g-*iTvY#H^;i6oIo<~S3
zg!K0oMQr>HgtP0lOD-c<BjC=Y8rCcG5l=BOR<oH7RN2uC6rJ0j#{`_&gQBmvZVXI;
z$|ICz?;sIq4KYUcA+j^-Ah)>O>H9X}EhAKQcHN8#ry#`8(fEZiB_<EXp;%IcGA2#s
zZ!EEAr`&v_je2u`bVG&;;-~^CE2o%ZPVd_^%_rq?F!7Yg%H)>K{n6)v*(20hdp}6u
z`m9bqp!%qHPfH7Kz&OdczMttKfgq6>EnIS>fVH0h)ZJ?dB{7*ZJf$niluz1VaS-$)
z0?Xl}xS89_wD>nUqz1L7_5(}rBYSw3b6_p}s?Wh`;O1f3xM;SpYBV$TOG6cr`*b)A
z7Hyd)j_;$@S(H=7-E>l)^KKaP#?64<uxno}yERy!E1EvCrkHon-)7UhAD;Ou&9K`h
zC)HWRkzq02wts)lV3U8;s^S)=2Sihopt^OosiIL*Pi@#u_e31`l=y(jkD^S#DN)bF
zP;Yttiv*t$J-wYH+zS>HT|lcTQt0UEm0Q|_TVBitWvJ@RJ(F{RqLWK72lBvj=<6=?
znB$r`dx)8J89o`CT7;F3#YJp*c)AwP`@>~L&KCsa$Wdr9ng8OXo#|`85Lv)yat!2N
zk)i^tPaPf~fJES>VaFGX%=*85n8l4RRkI8%A*JZv)Z6!~a@!sbn~OYu%Uwm7J62g2
zL6ZD?Ifsd^_{t<u5BM;xi*tgj`t$XNIoL-D!O==`zy5<zt)}fln;p(=-gfo<U8zc!
z@Jbh#tLxQ{a|c{>QgO7B^#xTRg*>~3MAN7^ZbyNHz4${UB~2MT!X3Aj4De_lRbZe7
z&+qh#S;L5K=cXf7IXoEmZC?Lt(PCiJQsO+Wrdu{-EJZ`JS|3e#K&;|MD3Oems%`*c
z6}7M(r6cK$FN5UihC=kp7sDyoPsNo)i;VO09e2G``i2WY9}degEJqsRmiTKs)C3!u
zpHMtAK_f)8{MMI0abKd-1I8wiVmF$=eo>MOL)^_+i6~5Ar5^n&UgZU<1aw(byMh=M
zEHD<A?Bltf)N30mud_$8ht!ec9s}*ll}uATC)qs(aL?1+Ap@a$s!@y<Sz*3ptIX!;
z7X(`QJIJMF?)nbvgLTq1kR3^st254q!2WT^=d<x&nrK$0{=}QcH{a$VgQb&+KGdE4
zv2w7)v<*w9Il;a9d(d~MP*eB;@=deKubO{jhGd@0v{&R3_AJCdd$j!c0&uF3Rr*l0
zvd)OsSKzpL7Rb69*^|_ZzhYSHvgiC7h2Z12zhB1^jS~IQ$?1o?cL>-P1I0n$aSpX8
z!Vt=erzi~3d#ICfIU!-J){DtBB~DFl>?;nj+Q*_I`7o<A%8Zf_K$pet)IRAI$AUcI
zef54tgefkRd1k&9uM<e!D9ET+qc*^5L3P6@BdyYGf@A(nB&BB5grkuxHgXi@TRv>{
z4+odmwfytq#zJv?;$4N=wEFMiZ};dOHe{*8qJeG&D=-%XA}Ja2R89-F?NcFhG~vB3
zr>jI?*ghO{#e}==2{@L|kXSR^7!v-=Kl?D$GBD2C&qpz3q%}YfBo+K9F%w}Mr(G|t
z8FTC7F+_LPZ&$w&A!;6U%rLQ#I!8>pdBZU-R{H6B*lVET{c<TZM|Wxfh)h#@gjRBD
zEXwk{`Y&Kpyrbs|3rD^2gNM3QFOym2WmgMjc~c~HLjDC=e1tLU5w}Cp=%uk&GeBI+
z&1(>(s*1bP!f&A@<H~LppiLi{yf=BmzvnifME(l4#3HC_E;d4zZ4RkWdX|Wn2+Y4g
z)#aJL7nhv`4pae{T7z4>zDvZCuY~BFPj%eGo+?pzEIElg1k?w6>V@lSv{*@5z6b8Q
z`Lz=+r%rPM#B?;SYS04#h#=$*$453Ll8~;17G?XRsU7%^%0o`7c68>z95ji`XkF5B
zL`s@q?v~!5y5z=ZybC|`h-R9EuD;pV8>^S2p~i+@TEFWmco<udQJ!G`pLvR!C}Te)
z!C<I&w+jl61R{V4IRP@1si1W7;f}EwjLv2MPSLj}44L!$6s$MkN2HfhLe+s7m&uRy
zIW4@QV-k?D78;dMA^xyEW3lEA2ePqT>QNUaGq&@K`0bz@*j0Vwb{^|pt@udWtUCJ)
zoIOsBx*xEF(Tybp&L2JI{^Oe2F*vWTo8jOX*<JmN=fE6bSZBxDo2N4LH8vXEQ|%i9
z>uR=7=aj}BKF;l;FXLH*<N*V<C<#k@W{Jx@7{MyRoQc;n=Mzhsc*Afgx88!V{s4%`
zSWAW)q*lY}JT96Vd%;y^n@pcq(nvpjhxjfsCsNq#$GXh!q`|VU0KB>lD-oa=h+LSW
zRe7gOk;R_FSPqc@HTFpeuHEe)8wMKZyv*@cnXw-yF>p^CI9hO*9GIUuP)J%6u$h$b
zM5}(*BnmPOuJZfCt+Y2E&SKwzBz;(<Iav|^p7*_HR0ZB*X>PJKsW*N%swqGMSBdg<
zILwFQrct~#0vb{!mo@IOq@jc?GCCH*ZF>n~X_U^oJYn>MaxE2LxM$0Q841M+@^`Pn
z>JryfTksRlk%Cq!EEot|*mhB)Y%R6E4a^%Uq8|1(dlp130YD1bJ-%HK2+-_`6VRhe
zSCw1eM~O2hpbE}jj-)6~$&*v$<8wk~Bm;|U1iKl`=J%&Uxu?l={320Ymb_%6-sOnQ
ztHF+zt6hg6ygb&rE!Api7XD~rwu@ZTmAi%b7f@_#T&;@)AcOxND4^n$4n4BVRH)IM
zYY9b}f7`>g5q=_aK|{|pRZ;f(rf;+l{U-aS34Mh@zJC(9Kn(QT=d9yz6b6eU&hKm4
zsT1hZM6)r5H<25K6`H2=sBpx=HySv={E?T`Z8c-u+@bip2jaPh0-&jp#^JCwu&`LU
z(gTYxSmY!i;k(bRF1v;%H9;)_iW@$EU?xPXKhwgFtOyXY=1K%vvaDuVlF#vszRc4X
z)%jk0D2~LnMN!Ghk8z!jrQ}cM$vkUJA3`^-URO%o1N7bP%T9x6OhQiIj|0-|v#|!|
zJfqvhEWaj%(e5nc|H}tCI9Rl;7(Gp&(vrF1(({;p?N3$Ut?*xBMjifP;dzQ(&aBdt
zCi_))U(^K?JHqZKagZI8&GCTm4QUa3<T{tegDqEDzmhfwp)T^6>T|>pr^H<bie0l0
z_F^E$`=BPU(!k(nwDfEl(H6=Y&G0Z8?J0Mzkgl;Mb<q$7wm~@z3`{~XVv~&A=<sF!
z_z{p0OjSZX7@9KXn_Qx$fiQIHH*uRW|9c1l7hYTPYFK9?ZNGV*NMGY^;^xPd`hcdf
zzqS=es*2g2Lp+#p^-U<-CUm0%e1LxPO7JdO$SM56hoaGMODcrohurt$^P1wb`_=MC
zAkV*UevGRTH8kY6G09ppu&JtQ5@u%a6@wJWMxK*O+AGV>S)R&U7PD)eBl9UKZ}`e7
z9ju=5DMC|fr-bNC4;$&1KyP;fVMBfx-FNwKU^cfzuFK62C0GXR_U6=FDCa&Mjljc8
z+?6CC`YkBkB1PF-^BFAj#r*Hzz=-fEB`8k~l=nblB~zZ`OOiu&W$swO$3Ov*Y#Aef
zB7fyLAWWs-2oYgkxU1*f;^X9GV;>uROV-wnrxN3ap=VQa#IZwMdx!)nOZnwX!b<hc
z^C~mB9I%eF%NY#K{BR83x9+KcvO)fF?`io*?}*a28FB>fGhc{A-N!`=Hzr}i=xX#j
zz{p%^(Y6h4^Q}OCCbfY!e2#~}LTZ2K`@WjH2|t9Zc{@10;Oqbg_O_sgNcHIsr+%mo
zbRcdH|79{{G<tTi)`-Nk=kROCw6+z$+6F&$ocdxe^kU)7JL<zCFQ`S6sXYs35&Kbt
z5%8Co5#nJX9F;O1+6K>xPO*)?C3*tOE<sd7SnXt^Hw~~|i@TH~f2asr2CzXP;<+{7
z8>ZNwC<mnaVM9Xwv$Ssi*mcdDy|PiF<=^tyM5*gEJmJ5mWAVDJw(030I^#RF)UrG-
zr2H68L`#M>S+vhYU7VnoqX5(Ze4QFbeOpMZZcU`a4EjKQID|pa%q+!@ai3mRr6<O;
zwRgix67dPd{#1CaF!?q+BsayV68o@RlWO0GiXlK0#E2QzQHW*@+OFcXev31kF&=P)
z<0P7_JAwzKLNMPedcO3?T)JpIZ)ld>Q+*mO#g9;|`jPz(S&|N?&0D1r-G}iETzHX_
zI@)$+a;7n=b2ixN5r+^0x;OvX^eaiYAB`|L=2Iy5a)aE8M)9=qk8$gd67^<{J#DLx
zj+tA$Ai_>6!6vA_WMjm7RU6EzdX{)?>xcDh_;T(Y6)ns*$B+~2QK8l+@SZKXB|s+a
z0zMUJ+^2c50t!_`9rkV=E2MWf%@WP~F@R}&SXG;f2O={yxE0+i-N#Fl16Iz+rc2cz
z*v%(Wl(#C3Qa>~oHU9Y>N%;Y8D|VOJ!=v4u_mtU~lNji0n2XZ-3Fi0p1cAGX{16MN
zzr4cUe&-(7NrtSdzpGt3B<t1%Z4VgUU&1XgkW1D>Vd^o6uEd@2E4)97lo*Jxk`oII
z5v~KIN)4Rxdwo-y9Hto9=4-Gv7A9(^s+e_HEQBIB)N%PH(6x6d!AZC*9wRhV6Q}$@
z*KGOvvo&AZvU?&hNs1|awd#oo(9tH^<03!L7Cr62-z<|ZU#K5Jpw@;uDD9nI@e_*4
zZZhFCQ=*ImvOh{hHv8RHv{y5^d9)`WD!E{I0P9?}9B3}Zan0I#;>x?zdm!8T_ZblO
zF6l*CH4DbbiC`om(XS{Jx1M<C#uLO$+U0&(64vvSp{KeWMTy<o3bHnjiG}aspE1|f
zwSo^<k3LYD>_<c}cpkVsL9ck>-A6P)lHj7)s+!k0^vE>bO<_<v!Ao|W>HHy`gVruU
z8SPRv6{U2H+)cYf$!Y=4@?3~nLqA(KSVP|K&ojeH7h?~8N#XG?b;-N(&R9gS(=tcL
zGzrV;fndoDP~~Y+HY{xC0$PT{$|AGa@1Lq3z1Q+<yt%KQCud1c_D0=_n`P}x-+UL!
z$`};%(4vw)B7IiF?iwA~&2Z{*HK${}d~lT;Q16}ZYci)w*fYl;FSy%tfq2ZhS?3x9
z+4<1lf(+@12#WX%Pi~Etsy7B!dR#^qbq+)kf~R2mdO3&+CL^t$qukIaBHIHOaBpOx
zi7{ZJ2B$o99ZN)@A8$F3j?{PJ)8DK*mUz=v_{m@vdhRaWWST!X15O--?5IPae!|p2
zORD!zm0~f5>_Ftjx2bm1XQ-ep-ew>aJJ#tl&ywQkyhfgz%cx}f7}lbMSy0Ym6z;#u
zHXhl&%-5yWtTGf)ZCb%`wQoWxT6{R%^wdk=_X;u2^3{VByoO8RR&dw4J+65ORw|Zv
zpBeEoL~f^U{&+f9!KZ|a0JBYCb(C{Mii^!<V@EUcIkyo%l`C95a5PV!SzS*eL@87Y
zVl`k}O|lphe2|rR1mY@;?8vsMIdr2c^|}KsvqJJcVcmSbgpR$R##%j<fSFDQxvj&~
zoAHPkUb-P8giRKt-@^faBb^GZ*3W+eLkuI4I@V(<5K}7PjAv)*m*l248C);85V1L2
z3|#GlN3xAWwfA!pYi=@Xb2(s9#Eby2U>+i7tX{!cC#;|ZV;D8Pdr{!skGE`g?_C)g
z?$_EHqc|&hojdSRes|NHQhxsDCrpg#Z@B6&nTNUX6~^es0V}}0kdhAaC3CH1MtWAT
zFR28niL~s$MElEj8)6Gy6!G*ex}NJ{f+j7Y#u4Ru>*W@gBY&1a3eiKzoq;!nY(s4{
z*p*lPZ}?cetbCkGctcOEzDcudZmiT4hlKa9T(r2B!Ia9x1<_3R0%lQId0>Ct@AITa
zmOlp(zK7u9)TqQv2DWp+aNVLdxIsUF{+&{8S)x@ckIS4Rn7$F=WiM>-ce(Y^*3CLr
zoryC$kuM3F7cZ>s&IqYXhEalg!v)0m<-dl<Vd5c3?c5-{(F1%YHjDkkln;Nab!<()
zVn{nBQpgp;ZOgRAQQ~_%OC~I*aMe*pcWNF!ch!842pwmvZ%m9~ReXM)v}$J6)+0d}
zq_k09joY9RlKEn?@EgyWZvL+uiO%kvFOoawW#AdBao9HvQ~KL2P^!K26L^5AAN{)w
zlbNx{sNB0mRC|=o_io%#Gwg_fI_2p7J5#@@I%PgBpBOt;Wbotwsv|okM15D7A|sjU
z+w4t|wMZ}vwOW8JA|V{1=v`Sy>e-6>)*LIXK#T#&rI|g7cDq;p3Ht=Sd7p7Pj;#@U
z1JN<L@Aph3Ljif@$tVN=3v<jq<{`)_Dx7=NhQs@}C8xa^r!OyWBF6go$+i3X20XQ3
z`vFT?Yn+%%Gc<$kfKFm_Ul~1wRC%wkz%hZpRm&bua(Y6Uq68}=+v;e)o&?OU1$l-%
z!ZsvzeM{9TC&NK~A5FEgTCHp!*6p_<H7sZDX;k0a`K%b-k@P%h%^7KR24fMF>tsGB
zw|?DA@wgHH^o_`us8LvxNJ;J(>SXxP9x6|=UKP~*oGv3xw~=;y9=T+Q8E~MAivgA!
zlV0PalXG99(^ZIeZRQrY=Mn0k`3_-9TE^qt`e#lCKiOZ)uIYT4SQg{JFA;bU7-;W|
z|K3^gU`rN(E4-#N;|)I2Aj|=v^J|>W{C=3Y=R*?zvcju(@k)uovzhRk9*h5p!(MAh
zq6)Ktd@&g+qsD$>*!w}N<bbf1BZM?Ym(rVlaQe>Oa)u;>*!aDrnXP;5&xK2x^wO^d
z@t6$zfD0+j>G)pCA<h1!Sr>}=fJQssr90T&63#cxQAtS8!exP>8^bY<X#=ys-u){7
z-ETXafO2&bdBcgiq=nSNpXY&h)M(3$oGHXO@=!roG13r<yKR8yG{0WsGZ-O-X{<sm
zebS^^9|_<4?MGL{{b8g%t_GUxbFcES5AEybjXSMTRHy8R+-^(Ht-p#@o`u7*eZ8OA
zeMTFozhHySl%>JynuM(j-y#PDCWnq4xebuog-yT8z^A->`?pYoXJ>B2SXrrM(2jay
z7%iUhbgX;dX)2t$7R5vTWSBXH&^>C$%-NV(=RkZ~?>&xy<B9f^hF@^M!k@>JxL_CT
zpCSM=4jbn|@LKKyj<Skg)c3L`zR)gc-9ITcI3VWMIOpB!65jtCmJ&*B*=GIJca}0n
zq3E>sOE_d4xR8dZdG4e?`i{e9IuXt1?<qoH#tnC@v~{3>>isgKy(TRBb-gc9@pkj(
zk9xTf-lEw{uN8KixxluseTPW~gT!DwduBBy{Ke30_o-*9NHz(%NzZ-n3dRQe?r-PZ
zmhVZQ`Yd2{4eff?e#p43Ue*TKt1Xn3sUP(*h1aRVF54aY0xYLJwv>g0HF=`tTQJ+G
zIJC4_Q{a4!?v8(oS736B7c_|J8-MwkZjc~ye3M4w)8T&4hCfd40=&4gliQd+t&;xn
z09+k=obdS=<0%Bc#M{eP+tQRaw6r#=Tj|2i1OXKZ2w4rjh1SNRBV%Z#EL4m_Dj(DA
z6bw}gvXMoe7WNV6q;5NnmQP*AgaSsA_4ORV^g3vJf>v!_!Xckc6BREosaW*)pUqNq
zPv9NUv*QN|h^dEI!uLQsmcVG4SD8P~8bQBqwu!R6h#8!(<Y^R4Rr1g)=K8%N?A*Gp
z`j+kF80Z_5*+wHz{$ksmR@qy`X8@0(hxV3|@g<|74IRkhPo)rsUpHM8;I{x9E+|<+
z)0p?1fYl3N&0TkJ!t>*XHwoGB_YDzwo7!<zc#SL4t*`=sh>Ncb_eBTT^Rri2$&imb
zW8A-Gt(BK6Ze^YepG2!$u}HYy;E_BVUanH9g5(3XWp>oHx`PO--$X%)<|lu0$Rm{9
z5PoeIp5zbNHtJ@Am7~nRGTPSA6r=W$H3ZOzjt{)5?Dl#l4)w@c84D*5pTECamXgoa
zu@%XsIzZ>P;mn(npNray6CMKEJfyIKIW+|o?2L43?J|`j(iex_n5E%-m%c@gY+C0y
zAYcbw#+w;nGXD+}PLns?{R*L6BDuq#(A~dVZ1R_=Uw!lH!e1~$zLrUVTb|wJn;=m7
zG!bF{2W5sO(@DJn(S1y`>`5EV5;ynGipe9H_km{A5KUWru*m($B~mwI)XZ#iGJc^c
z9`m^@rZvJ^LcP9tHvW+Lh=Um#JaX0-_S0m3>ZTeV#KX%vMQCR6E9GmMPlC#i9~%@J
zngyKiClqtjCUT!5%i`M!e;hGi9(01oihcW=UIzs-dzircvcD${iacAUT`4NZ|8!{T
zoF=>rl{0rL(Q=Tp@Ts{z^R(Sc9~xX6$+d$t&S{RYTzU^J4GWqX>8pox>tV((s+wBs
zW&hwhnM6t(FK%19N{t$}Z1tpN^{&|)JsQ%4m?VWO;uLI4ak#S-*gwkRBoIrsJ-Cu&
z{blr#`WX-EDHb3yP_sP9dmGIEeVg6gP?)>diT&hbKvlN6I;?JRSou5t`#rX4?%eqr
zugP1s{N-<P9Q2F7$s|}WbMoh#;1BpYifeY1OO{`_9%j^Cc31Fy!w$LilP|1k)$TRz
zJzUJ!ZzA7P_pn;PuR*t_*W{+cIBt4iX1{mdaDBAbL$Mm;#VKvM%hO&x*oTNAER*i`
zEYGv24ZR7tiSN?Q=#Q>tiyN$l5HqojAMJ%|l7Dfc^4+!5AS4UecC@;&glADVyA>w8
z%j~Q$277zn$#gY$myp>H{CSRe%tihcQF_GjmHmZSih8_V?%wAhve8f2ll{PiMl&NV
zvNKh2n>uL}ZqCwi<7I3O7}VwKj-OhwUB24#F4a!ws-hzFxQGM%>$+K14obE;I@2LK
z-5>^fJzQ&Zp@fK3IBtnt7y==F>6)T3v{}xGo9PccsIM-<L#XMW%Gflk^kf$qD3|6f
zRPgp>-p~Y~B`IA`-Qe2mSK%Fb+~3nO2G1{*ja7V_jo&O^`W>39S~n0+4KO)kWPF6l
zogpak0eYA>4FDR4)wqmx{pD9F7F56VZ663nK@#=`q3I6tS8*VxJr6`8@7&Xbwct5{
zjn!Po(J#GbXl)6Sz47Kf9lKma=W|=ZOT-iJSke~1vG$ShOPMqFw}@v%LVJ>BoCdyK
zp<xNG7{}?ohWu|*JO*fxvqR0OAO~Sr@uw4{YvbHX3+G>)jgnO|E`n>)>xb8NCFWFq
zpAg@pwo;I9-VHcu$!J2(<QC$?JO2`DYB$QR+_-p_Z>H;FFDd%Hc<WLK<NeE)s5IE#
z%81%1K!ts#!(ahtnF+1oTqic#nKa5g62kjDMxGy)6=_mm%4BXw?oJarNUf_}edDcq
zsiff}f6wFSa*2y7GYokbdHLafWeMq>M@--Y^Hj^#Bcm~$%Ps>(jU~@Z*^x+whR~Md
zw^IYzb?)Q);um3Df^zZyq89&8EU>x#8(Z8O6P@!v<n{lu1O)y&0R8_CV9d_(hFkt0
z1Y-yn&;K#O_>JA|YVOP`A+9E2{vW<OtBn0y-O<6;*wviE)!EIQRo>j*@;@MPc3wWt
z|DdV==X1W!zpevtWT7%p02~|~!02rUylwz=Bt5Jw000FA00RI3Km{NnzyXlnEV#EK
zi-7Q7$N$&>Wt{(R|EHq-4uJS}cr&qjlyU#J{h#Xqa3R?MKuzrH5<nb)ij0hcjD(7U
zf`W#IiVnoX1Y%$S!MN|S@koit$w-MvNGNDnK2T6GQIn8-<e_I`W9Q`LB>%uC$O{o*
z;oyY)=O%DyXlOtTAP5r^1fe9Mg#16=UONHrP~qT^pa^hO0Qh%s2=CxtdjaHcPm1)`
z68~w8|FOZrBOoFnqoAUpW4v8Zj{|^*LqLE>L_k79e3J|3`*t3H_znq|l3f(}y|OV1
zl@lH$;Cl`#wOCCTzRJ`Y4Tp*IH#BqtLJ*jUmhQtxdIrunkTx$Lzqo`XR7zS#R#i=1
zLsLsz$JET+!V+d>?c(b8#ofcxD=;WHBs45MB0k|qVp4KSYFchyenDYTaY^Z~+PeCN
z#-`?$?w;Ph{(-@v;psm!e`n|B7Zx`*x3+h7_x2AC&o3^ou5WJd?jQc63l4zr-(~$T
zW&aml@7{F5BO)RoqWnh}9K8EmL3oFVM9GefE2@lQ?DU=r5`c;)_C2Sj3yqpX<qY4%
zc?z9?hI51V{6EtEOWFUKuy6mbl>IMZ|6A800QmM|!oNd!2M`9_K77peNB{rJ|95Q=
zasp;yrj4LqL~-jL&ZAYVP>=+8{P;?Fp7*5fhnR)?VL^?MhiXb-EZyDoXo>5XU`l{s
zs*h%jqjY_+2dVw2m$_s6fbJDgiSi1NoKrmyH9SWN;gr)Chjzr^c?Sh=vG0TnZ1dQs
zWMj+awk7xV41D<tav%`j#H&wQrA@6H%tfYZGb0d4&?QQaXcH~=VX8kD3clfGA)A{>
zwa2RlIDXms?IJ=JEa|ZqJkX0xdPnz1NfB47z>F_?D@-6EBU9^2`5&X=Mq`v)h4q%!
zSyfnuWW_;|L#Ds^T%pq+>ez9AL9vKS;gA=)Ij$_6yJVTa4k{a*3-avJHOC*~A=ZM_
zy*kg0{d{fo?j_7S{&hU)Iuzw-`y9t2WTD<=+E#xSyWIE(n3ps4P`z5c&|3|tw=e`{
zUIg~pD(e~Ut2w$-x(J>{(Le2x+RR@q*`&*%ZnU=Ys8+3}R8Fr219WpEJ_7zzs#xz@
zEAA4yiBxP_xGQUVr^^*>n9z>~1-^M68{`-$K8n5qhCV4iJSuL(;9q_gJ&DZt@L85F
zdbBK5R#IxWrIxEfojDq*)3P?!dgWsA3=fIOYAlkxW;3M|srfVk>)#J1F3SZuHcujR
zJq%_3nEp5B-a0I*uInE^2#6q|q>@T2WzaRCgosE<mq>$vl0y!P(jg%zH7G45-Hg)R
zAw$m4&5#2O!}r|x6SvRvd*gbq@B6*3-yi2XaA5YHz4qE`eb#5Ky-(?gpvXf9=2u47
zJiH16E?&b#9OdEZu^a~9F&FdE=fG8^N@P%`k>j_N4e|+zqd5(d{6S;32x-0C;?VtB
z60F~Rw07e~l`Tt?Cgj!8fURl!;P6eH=3K+A`iGego{SM5=_1n~cgRZxUl}|C0_9j^
zw@NakzUyels>|K3{G2zjPJheC{7%wz=Op|7OK-h2=(v$AAC}L($WCu}qV2Fvg^r}>
z7b=6NN@A(Nm#=KZ5l4|ZE1SjDTKua~_3pK6G6BK_D<gWJu}<n&q5bNk?gEA<rwVtc
zq;)?i5WNu5Y>rj#?cdG8GPEFz!|NFejHYHURq&CT{+gA`So(qbanK5QCKwhbWXOz&
zCSJae>|E@v`cN1#FI&-{or)gBT;ERC3dCP6yEy0&o$LWg^Xkuf#YV(0BC#L7{^{T|
zk>;9<*Y{69_o6PUIMt7}`14>M``VT7ES`<#4kJlEDLZ|d5{(t3nPLPE9`glY%)$;x
z>n+UZ5L6u>W~aU+t!*ZHPN2>KOHYdvU04JH#l6Ftd=8M(Cl*ZEdwi!mwE~5AUFE0M
zjSCcjw;$aPY+S2e72+~mt3PvH)Ks{0`3wCcOE<m^`Cju?j&@n09g6IN#+!`K@|{fY
zlA9x4Nc4Ep#aD;{?@_vD-`g0+?eKc;amEkomB_xa-CA-IzUZX7B!Ks8*q_(!%hi_E
z<*~duwwzXfF7;v&8Cn7~qjQL_GYlhp`m?>IM42Mj+9<~Q{V($8SJ!(zj%1lJO_Prt
z>+Q3jlge|w^HDh}zF#@AG=#8CwMSFjTi5i9YE;W54u0yjY2qB=&%R!X6jpUaP3x(6
zZCQlOOr?`v5$H$TUTCaB_cQAI@`Rhx$UEdtmMKEZ<hxlW<uwQNd~(#j**5}}X~<O?
zotp2uDhtNBF^O-L$7=c`jv-_s7oMu@?)rJrW!Q)oI_^}d!Id1WYQxo)FD_75U!t-H
zhISMaLtaXY(6VyZt2QV|(Xhijzw^`-h1IX45<}e20a~0)^+--lkecQe*P|rGGvr(Z
zIpt^%RNWLCh@N(Bk(63X@Njgv5v7_tKXr2<La8x_MJ1i;<=1*oRg6%W?^O)-a5L+z
z5v9Bw`x|Eo2LpVBSf3r`1|K?rR#Wj`F0aDY_Q;BJfXzXaQMQaSHDeVmyvI0UY3-r$
z=z5XGGYmsi`D|z{ctmvQml&r}{i$S5^3O!`Z0~)%m6@%=*z+tBZdmZl)ZcG82X<Y(
zsm@%Y&jERRVCBF|+6U!mq?|Ty7OXW?B*alwV^;F*-bHZ|jtcLO@}4x^_Ul(dx+A`P
z^yGDF3B$}%LIc}pZjIGzeih7ca2CnCT8LKOl~aQ1Y#EL0w*o$Dbc@LBy!qnGSf<CN
zpD2_~>S|A=t75n@WHilJ?#6}LfUjaOLY>%2wl>zhhL#y;HH?0oAbP}<FDRjPSB^DL
z(QCZ57iPS34g@9|3<_mbX>>#tvMxC;zooM88$)5H$KR2JzQA0t8=joxUpFT=kCnUl
z^iW3SFr7;GYc-2ldzBdc5>sp2gxZux(dIR_TQM&8_d9xRR)^M;qTg6x1S8R;A`~WQ
z>hfzBsa^M!Xui$aGyvZ2bj0zc>gLY;9a?&k1|x@@F1Nk^so;MoI^SDt%skU3L3t|M
zBioFT1G|>H{k&aGH9uc^%8t&Ar}-M}P_##s>kA5ZN;eq(tnc<sab_8piGD3dt%`D{
zUm2q@SvBQPJHg$wetW`o_tEPCi>=ca83Ps5FkS3ZWVu*OYe6e>AzweeoLk@GBv$Vx
ziY=n5R$$7X4YL_vnRpd_7uqGif82gRw+2=lJ^YExtc8Ye>><_e*fy>wJW%x)CJ5Y#
zGaac{nTrQcwT8<ssZ7x$0l*C)+gfjGq_Z>S9>cRVJLlNXE0jj*O$zg}8YGV_zc*Y1
zO?STjNY2>4(3Pi&47CbNkn%UtcjY;_17vQ8?S@Djfu<$3@5>#G`XObqvy_n$r#Q`H
zz5Ei1c^Q%lDO7t@#bpGoy)5;Zki`gRMYrMFrRu#AHuPo+=w0&_y~dKg&{Xu;I+L;n
zpJ|TA!yQpm59%0SxF+zs4zrLH-Ukg?n274^6>NAj!+5A4cfxU1{SB*u6yVof%(@(F
zj3RU_6>Y|15Be36?aNu4nU2m^pVd4<c#=F3oyn>>C_4RtgoW-;vpc|O*EDCRNkhXd
znKw3*fYp`18>m0#Zx3<>8?86q$X@(b*ip_dM$!2&yt`Fn=nfuj^JT$R2WR3quTF%u
z_BrD)jnk?Kzld@|Q}ElR85lLntc#(e-{9m)Lenwr?+lQ5x(l{62{$|mtJ^j8eCaSS
zRg3}t2ZpQkm%R(st@-#Dx1t|Ntf+rUr_w4-b$aQztVCX_RCLX=qgUOyI0C0e@>W`e
z2|3B1c&`@uYtn{nNQ{gsoi4b@c1E~lr#`8n2=V%L@q@3{<xzJRARdw0#or~;sg%A}
zJIvBy-;Sef$D_jL4e#WMCaWM@<~iNleRUZ^EHRYtckn{A=RlLZTT+2$R!imHw~)+!
zZkbeNV@g{>1m^x)iLIXYD$P%dk-Oz@vQ4_=uD9`h!m6VMj9O*M{FI+n8#pt1(cY2!
z@_OL8?sR4G8DeMe)Tn#6cP3>h9>2;9En=IinuMWrRL;@{1;&w9zpROJWwGg$bw6r7
z!-Y@%<b8E=n|;7DI;YxX*F15O&pnDN>-$lVFp*grRkYA%-KWfip;hz97sl+bT%)^6
zCtFN~&w=+1koR*Vj#lp`uX9*O+qdLkL-5?JpVO(Hf31IW0HcMnp_QUD*OKPfC7nt>
zNdIaoe<E{tJG!Y>Jw#TZVMs{Wkx(U8+@0kG?3NQJ(>v1XlBqq=J-g{eV^1|O>dkK~
zzWs72o&7cS_(pZGh8$WH(srqhq{7=Kc=DMa_bm~x&Fq@EWvZ9&we7AmQ`-`1h4H3e
z_~VLsV`)Y7R_UIU)}S2g^?4X8v>4SMv9Y;468CJ#QuBV>*jiEU(@XqPCya+`Y<i{R
z`{917>Yr2{U%iP5cDC5!3my}$Q*%XUEyej!cRF~cZ@g0GI7+#?qFnfjm0{GKEzn;w
zu9<NJ+``u2H()HL(67wa`5y3Otiv|Wq6Jga4WV(?sW6(pzOh`(5J8vpqy9o~W8T50
zkUo=m!1zG+plPkB)YoihGUKAs_tcMXIylBMMsaF+4vo_1z&k0q8Y9KZ@V*ftxv38E
zCY}t}G4LIH-=%GAip`+*Xex^7c8A;&CQjIsU4jjVFwFaNAOz2t?B_A74c8qc4`*2{
z{6&oJJnd+v%b)M3S*K6C0HcOFp?i!j(ZATuv%dX{P3YyG97|Lr9ga~X#W1B)WLM#<
z*Olk@E#ma@!@gq#HsMW*7?w7tZT9bC<Wx+*$PWwD82vm=b{Ijdj3Jy_cWGWqb|NR6
zeqtmU;RY`CFN&}5f|#uSU|)$0lzFuNsG&GJ4Tkxt+)V#Q&8o2t2;;wD4Vwg9RWRR2
zvqOK>4lj{ed|<5uz_-0@y6HLxAAItaPdBiyoD11{K~)Z#tt|*)($Jq%d4rkyZVWOZ
z48TAbvC(a^*D-w`(<h*ztdm-Nl`4fA<pwX=3f&od6I%o*+e}o|F%(6)l`EF9L+C+^
zd%V|g8hA6JW$#WgsfYQ~RU3IZk(=u)UOAp!UXfYPz`$Brl)B|=qadVfW3i93g=BYS
zN?NX2yylR(FUMlm+|T@Q9zl5y*z&X4)P(lvU0J?qG>NQ5?iB~(SxX0SY@8sNY|Igv
z$~LeGSR}OA!qdHL<i<3#xB6^f7MT95Sjoj1CCzywBA7$sg`|Zr!D<>;)o4+@l+VRr
zmxPnJHU(;gqHr2R<>;oggw4e|mqf`}ZtjmHMCX9E@k5~&HZm-OL2ct`LGgCs=d2(*
zZHW*n`3Sbh(^$tMQwIoDL4}|0d~bD($%3y%fEwT-td|IN&>HjS5-iSbEts+s^*Ew%
zw4%rsyMSn02&|J>)$t`pM>E>vIS~WyOw%&(N6A*r!bB?fY?y*5Nfza3eqrNU0_T08
zPUnDTn2C&Inp+g_Tg<P!TZ-OUP>-CmT$pXAwz3hdqA|5x1;R&t^wN`KsCdmVCk)R9
z9aad}7>64U9oW62<{W%?_pbQy>&YbGz)w}XbcjD~7mvD8rA6Q=zK}w2kpK8+{GAmg
zht@{vR;bkuM(u$@W$XdX2Fl-L;+pH4l0z;gvyp2D5l}&)G;U&0lzx2F3oLp+w*Zav
zgRk`ogVOHkXu!}R1{-1*Cy<j$`>O3)@0*kk^&&}v)|XfY^kw^5@&ycc=@-jV1K9zK
zYqMq5tU1R6B<X{u5XveabR^-awK_8e<1UJPtWx*XLjloLYLmc&7_0DS#VOZfsNT6t
zr9LKhRDVpd#4q<oobYDwcoyJoLbo`>P&Zm{f+CfEXoI$q?9Q*Qh@j%W$ijYW<ji%Z
zyxw4*Lc4jEyzn5DUEf_oLG!VtdIEz*&<hz7X$=yX;wvqqbq`68<ZrfyBgk4+ewe*M
zh<mK)T4#hKdmZv$kjp<{`hR`@S`)lUzx!}W+NVvd?N#%t94m*@rL#Qlezt0!bKvIK
zcKd+(TmBGcm~HEsgQYLW&d%sLP|@1<RYc$vKYAF4fEeo*hu}!EutmtRS>2>v{leg`
zuf`W<XQW+h2_fy@U?`0>4(4zI-1`@{AP9B3a}NBvh4@@lr?sOC&xhKxT-aSY*s%D)
z(bp`_LdfjG$7irJo%0@l&B4$EF3@aPnEz==Z~D78h|Hts+HtYH94qI*$<+cO5<{4+
zuM5Vs<RxU#F@wJM#%b}*wpTv$bW~0KWSu@c>uS|yV-*xKf`n&Zc{!QPV(-<k-70=M
z;6@?U?EPZ(#PR}I_unpIV=?;ah&w>K9d%y_#}8pHLLoLSH@73(lT=k+Bp!aQ*<Dng
z%-(z{_TX;JK~aW2RxiUo?Pi?O+dYk8r=@GvqS`O1qy2Bo@8maME^aOe;W7Hep%B5J
zNtK9=c2V~oAQ3?Kz?$W;Mm%gQ!NsHb<AHqC1R(24lI4;ngoR~co7TDx(LTwl>l~-a
z$$H2+8}Y(v4wcG5xBarpd-nnpyv$tdSPRq(0keMw{=8@D=fFa0&Y54%uE^R!7d8Wh
zkC`Wpsqk{<43*HQcPt59k2VAr4-gk}BK_%XyXiSb3t!I14*AXnk;!WLQAnZ0T1egN
zynMxq62i@fccVDxdh**~Gc4zT<R(r6RcA)7f0>UjJWn9;U6hyDJJz=)74RTa*0Fjy
zk#W^<NALw{$nM2hWmqBbs`gX`HH+kVJW{kPAacp~(;~!;T67+=T<EPAEqJ4HhqX$e
z`&)|uGxhX6C6YBo)ncf!3Pv=VH8uaiI4M23OF~P?><qaiW2ZX!=5tQ^lbMoS`!U@u
zRw;)>CKVR3Ip1IDlq70HMQ+6ta1Em@H?PgK=MfS*Jki7OwmRqFs-0EXCE@Plz86&`
z_bO*Hc{$%~x4NH1CE9(n(_T${m5)@v8amHRk!eBKjkQ2!$D!h(8MO`dRku5t8ML;d
zM4Zxv8UWWB+EewGX+hYP^{TBTgP5_|CslDT*rRsuEJl~0bA9RbGX|s$zY|{(*ncY%
z%;7W~p{JbXdt<;Y{Mf3hZp6QpGtK<_UPqwpoI9OQGlIg+ncU&XuqagoNjmY#&<kGk
zz>fiEOZXW}*40QqcI^W&ve2jgl=z`P6;8M^wH?<y3C3k2L#r^day1^~>Jfn==N6jo
zWs&IA5YLl2_q;`{O@<o!E}EovgfjQqRS3L;<?e7cFX20coiG(rYiMno?_!qo;zJI<
zsh9|xHHhIpB-E4saF(*7Q&6AZdLo|YZ2rgDqkKHqPC^{IK|(-8-vC!udn=~0lLqEx
zR8o+Mg;8`?sk!PljtLZ|Nb4Rw5w&#V2aZFI(kbr^tJVx5ti!9FJ|^gW_j)X*L$pBE
zowU}-d<(>J%GnVvFDH)~SLzi}bwV_DJWm?Dh6YO-#m%fSD}LY7lIIE`k9S5+h_iav
z^t&z*_8n)C<Z4F|1FvDg8&;Z8Qf8Q)3hupktE(*Cj_Aj^SqZnq8vX1=bgW4?+}L|u
zO^%pW^Y7O)y*qx{$Va{t>@#;8?+39x1kDv{0EWssNP+e9^sk!s=dF5pw}wxc{U|f4
zA<A&;kf3Xq--&J>Ln03A!9!N<rMi=2UhkF2xVCz}F6tN+14E^5cVU9%S;jc$rUsQ~
zzWIxI!dHcnx72aByLH+j6RPcJW%;JJc05wir<RqYT(zQIF6Ln--`Yn`hAnm+Q$X^O
zDr<A6l*9RC`xDQReEVZyp^@ONEED0nkJug^d?y;O+|PUtjQ_%0*Y#h9@;uq`b$OWC
z?N+(vVjgSK3D=Nu==Nt@QP#mMM{>QzuQly5)pPEMy?FYN@F%mz#o3l__08f?)5}Af
zLF>7T4xh=+fo}1U!2WdcVxuD1gPq8zP`}rC*SYIQ1)uScS5egbqC&9*9MTPF5Gd0A
z>t)J-esd077fXkY^PU57dNq&IEMF#vZ4o({W~>^sv^NP+nXI+zr1>cxP@J}Q)<}&U
z{%ofunAQ7$<JD!m;_LZANl^Q^!aG*~<*;n)VSdmt*>USR;C0Yn=xWH&?8wxdl)q-^
z-iq_Q$J}G+8w#7bwiLjNdxR;R4bC2NGvWFg{%kb+s`BeQU2>W8{!(yzw5$MBw_kB*
zCedAH;pn8k_TA$ny?e1e5*<1#Bu|OGVG;G0Godqm*W7R3RwZ-_f*Lt|0rwq6d+tQb
zBL@25Ual}#*+t8SB$uHtEO)#K<gUY*Mxx(2wyQYqZFUqzYipkaWNGGs@QptB4w=<*
zvl^4;&RJ2o-)q=wJ3?ovWr>&u{iFOntYF)6Q$};_^sGq8vHEtS3u2mwg_kf2YvW^{
z<4aYVtRpuju*CUI??%1^^Qj&L%|-|YrW6Wzz8%@j?i?lQ$w*mi{yM4o5&w!+dNV3v
z2P$$CtOrA_q~k!d!qBp{_og`dv+Er1HviY5EfJq%VM|4&G8?vf!gT93Wm<x)4D8pw
z)21Yk8b5yByVETZzRP5UtUuuXA8RjJkzcy8duiJk5iU)b0Yxa54;2%AH!>8rRjhMF
z0&TLNio*KF_?W~U&50Drh>TNG2%^dfWd6Bkz_sJDi~VkZlaFrp%x4DAhCd#2iR+1=
zye`|Yo?GP5VoE&dtDT3OU5n}3Gi)1dF6r9jT=ryX<0XWkJ>Ttv=wIo?L`i%;ZHzvF
zsVMR~p^TqQ{s*k82yT{GM5LB~Ak*`*Q}47#%9{-ecQH5x!!w<VQE8Qe8M`p~Q>LB^
zm68{4Sca_>OW)6#!n)aNgz|4(@bWjwe|Ze4gFgo5WeFpE?^l70|1M0j73)J32c1Rp
zYeuIo#W1Y7hHc}-<{a-gd5DP`J~@gpnzG>?+Pb8U5n^U6PWP1<tQRTh{I<GhS~xPX
z7v5W~8VCPmk!DL6h~?rA^~;=HF^tU0SgZ7Op^j4$E2SAvtG+Rwc~&MrEUjXfF){J;
zEbBG{ACbkKHj<eal7s`^V+^6PSD~;L$<EOs)dtD4<5;6xqRz58GgUaDxoM6j-63u`
zbF_5(eG_AeSOeIM#UwI7Je5$l9-C8S|MP(KWjTecGnarsvSk$s{PQmt5*A_`U;gs)
zQf$e*lmoF&MX2T#J|{wo^>vf`zBdQ7&8kNQ1P_?FY8;1=lj@}sl&&57`J$lC?lTF|
zHr82iO=nay70xBTe5vdr2{$VQT=0}6c!uo=DrW)MhvMf&g6*iZ$Q<aKgGq-%dG}k_
zK3nhYTJ~!|S5311g-LV;BOhb$zWjn!1}63qx9&KmHb@i%^?JjhuMq+@QZb_v3VjhN
zaPVkZVV3RUb9I=O<WgN-e=wuSOqFM0L6yfzVe>@>4b_T_cJoO#!y-8O#?}F^5Ez-Q
zF-}g3uCUj=Uc7mLB;f($IWG*o<5MakD2*vX&$>K;r<TYv=!XO(pxC}-TzLASqMGT<
zE`5nDu2~dQp>)s1=S9}Yem;xEljNs}N|jA+lVT>yOU1I^u}r9BrrxDx&7>aL?eCou
zyU}F?BO1i#0D~_Gk9;rr%I7p$i`_e>x7MsMPK(BG5r}j_=bInm=^PJhiq~B14GSvQ
z*`+#5uT2EpPzsF36su#X-n+qRY$-DA>E}1z0dFDkZORWCKNZ>cqgVqeBV|APxaZnm
z>AW6EF|t@6ga5HY0T}G0!BA>;a|iei$O%CK<P89FEfv!4H3_vWe5k(EEU2{!nao2?
zC>N8d5`1}L`1$<%!@P1fz|Nm>xag&De)5Vy(LDpA3FM=n98naY*CTwx;D5K{9Qc%Y
z4$!H*1QEc<6s+ei;zQAJGla?wMrzvWnxzU2a<WoRcIohYbdZ~Yb<Su-2Voee%+3M(
z`&)MP_>9x)c6EDTL6+x&KOdBVZEp~4u+S`AQYI?X%_a<I2n_}Cd#I^8HG(MLo637?
z+@E=6=H@uZn9x##*oP11mdUo^%(x6@y<6zPFW7E0ade<N+XuTHrr>%nk0(9e>}>v-
z0~fOkQT<n-OB7P-7?NL(`~r&xMUB1Ohc#CCt7oJArUxrAZ6yjZ=}qkKHCKqeWVV0K
zlIbfP387_l`-HV7kyxO5yQ8QZg{w_zq^`mbS34x?S_Lh(G1|U4HVDq6B^c3Eqy_$V
zaZq$AJ?dyvwg*rp;2c-GKo%~8p$6Z>_8HPrOnWfb_iD1lTZxr$=C*TS&L=D%TqXS~
zAmb6>fN&Z)2g=`|VdZ<#Tl;M?`DKIvd0f;|fUF<Z+@}znu-5N8TMbu@QYn$P79OXq
z>T>F@qtZqTQSC4){GjAwAzs72q&%T#wtXM~0>Ew`NZ^FBd(rvM)Lt)>BCeD(jm$~1
z_O6Eowz85B`5G{DA9F^nO;;IB|Cr=@cMg<}5WK;B)z841=keE<XK_S&C%7~Nx<Vmt
zb*UVO&OVpZ6?XKMI7v#TC?SUHj`f#`5-SsvmNGmGRIMiq$5tXi1dJ3H*m8K-GDBN}
zG5J(BVqHUzXeHNj)H^vDxX2S!ys=;WF3E8$Gr3@>*LRff!!u0Bei1{9EHGM6WP`B@
zs33_N)$4<+f7sGWeR}^Vo2kRF0C*&(Ay#X?Syg0swd_koMt5wO_j_@4UjGjM)_#YZ
z^_U3N#<K{ev7o1xnmDc#bb$1HJD%NW5)zERQ(w^=n&{WUU!yrJ?alhyw2CDWWQxil
z6}sb3=}pa&4<o~?obIQLoWO&G{mh!dpw2TvvoY-oyTZi_fOz{Cy4F=VQN2{o)XmsE
z?Yi>FDz21ql1|(+_jJn1=Gu=%g&D1$7WQL@PLBfaZBC`1%}Eg=E9^?zVw6hSzG%wn
z=v)IRN7lfAHYRA1gVWN2qXK|Bx4wd*UO^!&p4^b_+QULQq+qg(JK?pmt7553XVagS
zA@5%&Re@FH>q()Ak(?1hK}3*l^^NpCr90wTaFlGYw}#owT2+h?t~6;Q-Z_`Wmat!z
z;4BlH(_-BYJ7nD}gSbjXP5I34w>qzWYd(w{t%hLca?Sx3x-~}K&>UOuSKo&=Im#=q
zH!0lhFCMG;<bQ2t02+AeC0KDaFM;1lcChnM+AW=eErbw41-`0d54NZeCy6ma=_Zr{
znlV2~vhO+MV5ou}>s7NVlYL~m!1~T`@?_tiD}MRZGWS)m=5uFC=#lA8b7z`I0%>aV
zvVl?eYzd=x6jKj_^VfyuRyRc;RJ{_!f?U5aeX=`dIt#8QIf2p~g2A5O93&%KJQ*AF
zgg#3t+=(f+Gy{R<yPf`~bKo*ILoVFVXJT_pC^U6`c<Zp8lxOSB=C)q>_T|>O*=u$h
zP=^+=#L~V6(_j5}K0XnbaE-1Dl)KWFgJ*n=1?`GC$k#@EQxCz>yn$ms-#7=_-@7Ar
z3f-KsDlpW|Q?+y8Fzsx{6W#}@8_uavK|_MQAz|*1Dkz>BjU`=?{t)M=AF_N~#+EPw
zyCbK5dHK!R*LpmAZE?6X?#@`e`xV0I7Tv^9+|_JoSu@6tF}$yvl!ESu^9&#5@i(fS
z;YZawd$!|=Ns&ET_sWMlU#WISy@^iWPrnG|Wy5NF+issF!1ifOEl^A<+YlQ87jfFe
zS-v?>U}5|_5_-F-y=fb@?vuap-MpMiYDp2lB0i@DHTrR){_aNFq^j5jq`xVt<;-U=
zx$6qQY}O3&MuMp~N=ssQXKqq-d8yVIdq>re;SA4#;7!2_2OmCYkGGrDw$<CaB~N8h
z9QhV0dQzS{%FVHBr85&IYlikcuAHK-8`}H(biWLs>dM3QVuFD-FK?D4h^kzGe?B6o
z=l9|~86Zhmhf_KWJL2zQ-ScGfCH=mt-kaqNk8Ed&_NQ?7b`BFRX7rUBTEq~;w<;1(
zIoe2AtsgJ2PDO+bumX?VLfnkh95e$}6n1B(9+6t%w>B)v5P4V*r4gKr!<TDmGfks|
zC{>SN&x$#cfZbHp2N<@W;nfFfR2n|p6rGZphpsLb<gk7YOtE{_Cu1YUdwH7dBm}h)
z5#x7V=b#l*lb)joe08T%cJ%r$7jAH41}bF_`SSn9kpF`z|DW7TedST7&eV3jIWygo
zNCb7Jpf+@$)1%Oymk_=){*GUohh)O7#!0uw#<4y&QHLj1$szr>G|qoWVr5!CGcF@$
z%|w?uODksB%_SE;Me@7%1fFQQsJft@y0fGw?+dRKX*q}B>EC}B;fgzJ;r;?ftZ-Bc
zsKPC?X1rtmGw?l*5-aHVw=AKW)JVFnhZwe|p-Re3j&s28QlSsz>dH9wR4~Vp+mDm*
zc=Y!7gx>4VexU96`ce`*lj61b@{4o=sWe<wbzR>7j=2Br=PT24PL7%H9)m?vGr>fD
zd>;q>nO0_4M>0o*JB!V0Qmc&brY83-L+;2fjGO;!3i#hs!v8ORr|ZGOf5@_6Tr-h{
zQR|<usv(~;1BaLBhBf7wb%L9UFHby;QzdTQ`_dIY8T3&ZN0g#x`;G2FE`i(+!U}<Z
zIB?5#WKXR~xmK$c7s{rY)6z+2>(_SlV_@uSwZ1HSc!;30#W1Jn71^J6VV|R&6R+oV
zV)&kh?doT~eKdcGk(4dEoMz<4cHqx+%9$|qzH`Wxb<Yn-c`JU;C#P0#)jxiqERa$3
zi2plU_(!Mz-}^JqD-YeBxf)s=`0w4)Q@&14rJ<;z2zVW1#ZV%(UWFD7<pz|dd=6C#
zkN}YpTUq+S`umsLvkW-7)emB+4QAKc<Ba|-ZvOW;`hWd*7skU3(d2o*YiH%{cKlkT
zUp<<hlW(1jS<QwWMAMpd=3pPQB+Pw`(~LJ)KCK7`_qDHiKilo90FTv*H>`73sBkc=
z%U;$igeI#DunXmyd=;~87xaHyqlre)Lf^&j;KLjX`&IM%AXlWuvV-R+?mtQ1Oe{Ew
zN`GqT6ZOjPy?ekPg`F%wR<{<6rgZllxOY~0nIen#RK+tPe35Mr#_@)gVANe{Pvjh+
zHhH4X&F5Mo`!UaIXSrEapF^ESd~NvdOT7E&pqfT@Ma7x>{WOP&q($vTlZ>t6rnZ-|
zY=d*gido1Thwts3HwvY$ol;an)<5LO>!y@!ysjNJs*5}AnW%?k^i?OSmcQNxDUSCR
zGdRhe-Yu2k1fGq75@UazQB9TMcU4O9nChgbJGtY%DWq7x+||M*TUm~}fG7p+)YbiL
zI5oDax!S~4nPIGZY^gskT1!eVUF*uFpV#T&-&N;4BtbyQrTydyo#Ijf{=nvFs$~FY
zlmUH1@~ddPcl$D~h-zSL(dA@2U_a8Xw~52RG)fmeyC`wQvQ8?peHAKbRfQo^%eC`r
zGE<PXWZNW@$^3#-%yEO{X|${dCN|(oXBQ4(fim=A`8jK%-i2RH42(H<5K0e|DvWsi
zG#9%B1==3>f^^ms`Tunl`j1qWBR02%{KGw9TOZGgA!j@*=!^N}bmQaKq>GSySA5Rw
zBse$h7`5YB6v}lGJriHUZhii*jMzVExYuS^BkLL6Qy1c-sV94DC7$7lVT_v&(bzZN
zSI54=#=L5t6m7P^C}q_5(<!MPk`dfXi_-5(Y9sheZd?zN^D?GfPUkM$<}z?UHL9!r
z)k{~K%rM<}uYu*kVO-8wMg7m*>7UR;7iG_zcOKHdB)ad()&6~{41LdVR^=?lhw2l=
zi?|GAs%2kw_sdC!0n=r}nQ<*Aj3jlZ)HO*GAp}PUw|IPg^uBfSzqaU!(UuWo){C<>
z45%32sP8mXw!_61ku51nyujxS>=(;}A>oF!D)TMxz2aCEZSrLAlabEyhUs40wv_CT
zdbUT8&w-$zu3GV~((eZ(!fSKMsQ2Y09`J%S(IQE<`<|;B-H=FMcIk<(^(2?GB|_@l
zgMx;F3{3lh)Yyecy_YhUv(`06`_ukBUj_}l=@I5=78z%L%c%wyD)Qigu#v(zH4|3%
z$F4bIr1>HPpqsP&r<niEhy1TSr=u9{@$iT<dGaWX){gnjFY=^4IM$4n^c;XE!%uf@
z35R)$?}ICFdk%hlTyqW}gkeobJL)*x)HyJl1nhV5^_&AJA1n{v-wC$e%eK>iqd5m4
zVvwVy0GWGWWhB-V|M$lBlyO2ZFdf<7+kbYPPW9w#eLtQ7@fNncw2a4r5$omIpOC#h
z*K@%Cf3?>~{?brR^0REPlL3<KL7fffn<Fm;wjDd~OevM#<hZVN?{+K5j)0kESzf4X
z6XW+`cLQsqz-w`&tf;`@Bp|RYji?@DnorTRcJ-$-t<eQq&SmMOiu6_dtwF)Um)4Sg
zWAPWqpo!hKLE`fb&%yZe_z(G*z-E)oux7G<3_iU0bWi{ETApiRzv7bnqP+Q1+*?EL
z%Du)T8PTvkq2K-s6cPLn5w(t=9T!H6?<4to5Gdb2q$`N{L%NN=e@K_X><{Vw7avWh
zQu|upjc4cwrHfwwn{<~!>0;dfCSBX&d*Gbzfpdzj{o9=W^E(6kNXrssX*Js1Vy1|D
z-#ggr$Ny?)2=>?D2&C*I>zs+FY|}WSj^yi4Md5tbYlAgfkcpzc8na6{`GI-|kr6|M
z*fJx)a*xKDB%AMB?mJppnvAPq`5|_ro_2<3X8CKhng%CJ6?UjI*7;|R)<(7rt{<<9
z=S%dG@%1RT!--<RdbdA@8~vM*<XBS`y%K+uRK{Q5@Q*1rwE~stui^g7NC{TU^*;YL
z3dmp51-u>Umdt(=@xS(&{6FXucCruut)T!J$*?_re@W;)FZ6q!8X=B%W>ZHzbgP$s
z4w`u1=}0H}lk_lVZNPXo%`lDS3{{X8(JzrZU#3^)ka3Xu|J&%m)a`G&^WRAn@|z%P
zw@I<xDwx!W@g&ubK?RTyR?8747yyS)(2I@4=~ii+1J57Y?z}u(PWdHOra$A{0zW<&
zhHTFjg}2Jy8uq=Jc@EgE6|zqrzm@*FB_YEX@%wiXiO(~c{hYdAvX(+C6VFmxQM_b(
zjG%@sk3Rvm{B5)tS{-+?3P0M>h5g6cGjs@<fqCT|CV`6<%uW9=J5FN#=U2$+#m0e-
zrq<>RM+g7UHOz-?8&VG`AIBXB<1d^8Jp+6GoZyNs`g{(+i2rJ>;2ii(rGH=d8Na|R
z${Fe`8?oF2*<rw22Pb2A@P2Onr&!+K+p_uH)cmH$tnH`$+f<t%I+Ha&2Lcd#!qT&$
zkiGS6*z8Z`e>TMj`Ae<-Fus4Smp^0xh1nG!?@{4Eg7x#4B^=1C!A<Vt@nnCsWN;4r
zHnG1i73C8A-f8ox8m#{lY)%N*OZ88&>)jG=6pU_<Nq*$WpljQX<fxR0C_WS3;InF!
zhVtfJSX!aJLlrml4a$dRdpkE0lxpMS>X~P+N7T^rVXp4(94qufSsF`aY}{#Iy{O~U
z6q{RG?~$b{GS0ImiB3iB`Epiwlx)bjkYevw)3ABGUu&aD>+0Uq>ZO?CoOaybpS7k8
zPlY-z*ut-1s+k#yyk+Q?e%H?52k}3%G4@Mwn7F2-z6_R+DOGBG3kyvixh$Pvjenq`
zE7klavE&qFgiFDn9qY!-1QNZCyo3c`Yn-wAl2_X%xL%$E?)TN$t_oi$@LlycO>dWD
zF`;XX4)ec$?;3tk6m<?fiR47kN#7+jU;e~)vS3SC@wGY=n}-U$u49wDw0fFh!jjOC
zV}CXDM#}ROj2z=LZ7szZ&zr=4b|Ziu&%B?gqn3c|rRVW6vfJf~-4EN1eZ~!pXT$4X
zXFzW=*|~dhDOSHIIku~d84D0UjpQ*4lHdnv%#Pa+3m0pcK1GE`O{}Or1G!agy}FU~
zS`%tMCb5X*0H*uSCsE7e6`(cv%PIWs@#H9nh1E0<e3<I$RN?G1I*|!#SsjFn5?dLw
zN&B_`soMj6S4Qr?`KNy|raa>U2owp%s;K6Lbc5)L`t8G;N6+Bs!-HZaUCJ-<3s*ll
zwBgh-&OiM*ch1H>Gap^_lG}Bqzla$=$WPL1Ly+4c)w0rZWPUURajBHhE1|aa<z1e=
zHj;t$9NfKk(nd+Qh*8|Fnl{9o=}md}{^vfyD9zyi!kfIpGhvHSa5@LgRY}XTik`5(
zFY`ZstoM5U5ydJ?cRD(A?lN=Qn=}Sk4iLrbeHLqn5%b+k4lj5sf7Z+MoBc|sdisCi
z?EiNF#_zxY*k9x8f&JxI*?zMVe|kb1%<|70Hk+VZUp@zlP2Xp&>7KMDI_4TuKd+W&
zr@tmHOyKfAp_9P>CEtYM6{pql4WR2EQ1j*CIcYmdIxPb;Br&eqU?&D3Cul~B_~R+y
z_+MaYHdm-JUSX2~Uk`Fa1H>=K#NNRco({s+yV%YE-ClrhcXpHF9N@q~4p!h}jhV#>
zU`3zvF&nr@ixu$Y1Pk&1R<!>+L*vuGF$%&N<k6XH_;Jz+W24tu5@P<;6wG$MhTS)N
zs6k-t+-i2rT|0jE8ecJ?$Ar<ts}xn71IA!!jj#1LgSWYyX#kx+zKf3udiB#;VbCgr
zH1CFN`58U_GE?Dj(>2!UX+Cw$hPPu2<w<V$3Nl<&;f;JrSt{ScZrL5D&CrkprBUkG
zXx@?$QQJLUsq!&%{@gQYWHu>kwoewkmvL&{jenzocQ+k^bz(E%Yx-s<m&~x_h>q5;
zm59ohh!r}jW#z-(#Ip-ua4Uy#c=cA2k?)B8D~O%zu$XW~N7%~=*uZVJzUa+lp%dbV
z$Ag<ZICvOUo;hsgCTtFboE-FFNc=?UIgtJglFIgvZjtK85Lz}ZkenWP06JYOypjN2
z#aS6Rm2$Z-NytAsE{E^>^U7wu2R{v70o}L=!$Hxjy;)qNB?9c!JZ9<-_wyltZ?lkz
z<-v@UowH$=6!djDopjCqg|a-|uPV8CWv9yo?@|^?3{-?qRG(JW=??d22T_7MA#T=D
zonXQH1G*!31kOhhKZn=Z=}o~*VH5iF(N{T)+uyTolbt5b;$^;`1FvAW*PgCxH|5b?
z7jK>R+)`1b)xLo}ormB)3K*@yPc9W*tlN$NYuuhyIDdn!uvatwhxK?az!2|?2XUQr
z-s`Hd0#7D>kY@(yt@zm&CS{l^a6ZqgcH;S7RJ3HfG$TBj?3VVF<<}fM%T+g@SB2{w
z01kJji22n>YZ|nTeANu5xt70#dzstiOnd^Xjn`c+urjQhJ@D^??ec)@V0?VPILYZn
z!sWy$*g*hpYN;{d3}ikRo+3ciEnYR!RueGkm%6TXKhL%~Mp@N;7~wf}_?Y)=!J>5j
z;oEZ{_9bk63$ku@dwkTaoB<bSaSoWhJ_ihsb^r72h3m4{!Chy{1Lwf{szY2+848Yz
z_oKo$CIrY1|G8-`)E&!SgAM-HEa)MF%ZNUT^s6wVS&x0l&|;!%F#CdQzc&R|ZhzY>
z=GRISZ@mKTrbGU$gIONkQcSkpMxk^Su~GJDhwMZFQ&4D*>xGZe5MGNo!?D_ON8zy}
zhsGgm9+Gc1)!&j5k`C`()NY(URv~$)ujvv|V=tqwcPH}pNJAwxsl1GqVX@xCzPE=J
zxL33``V!w*BkhDmv*hC3P2VPV4~`ERDTLceyM_n-BvOXH5|<)iLxM=9!ykkv8W&p(
z1<p=X1d5X!%}s_x#dQiQ_-I@13&+?L`|8<cKx@}nt`=@)?k;FN%jdnhPJY~f^K%SQ
zgbS3GfJpOk__9G?JM(6*er<#)GQ654CjN!u(YLvZ!nqiYkp9^p9U?C1<BS*~_6x!t
z%F(Cm+j7?i<7ud-w<jm1M?95#jALfA^i%75l~i+|DP=AQdh7a3=TCQ}Cz>CHFjEx*
zaZVl2Vr_$zhVnb6k#(@(@v=4V&M=RbtK4(nBm|Hp!c>>m{5e_OUf(mkiTd<vc|b1<
zYSuJf%M)(6xG{gqJeENj+45BCMGZ}IQoPY3Vir`nqh)3pY3UW+cYaj@Tjh%t<=zvW
z`BjdUqp1lWS_|aO0~<=gL(-nP<xv~y@wyNdL6}F^)}?`BinC0_0!0RF+y?)kp{ri(
zF!mhyAR2iNn81(8fa9*;>^(*DwSr=WTbJ&bD#;&5>4HYxY_!nvMx#&ne2qQ<^KCDS
zS=rGZj#6=V&N~uB9(hIE+zrXSsa2wPjfcMQNaf_9ecZ|(-)*XXoa9<twY!p3XEJ)m
zW+t9#WoHoMktcC^<yN_-2~XJcbQ9R?ALJWJ7LEke@C-Z9)Pn13z+wIIvCtg2l$q2|
zjzGL7UtGuwd`l|Ez1fDX2c82f+=ZiRdU&}b#KF&VV4p^g?b0-?ec>E97(}E#)HeC+
z=ikcJ{!zB3ITIjr)C?iq_w#A?kW8LcWtvj1PG{jW!e5_P0&QD7;-d$gYGGoIf(>7z
zr?Q-4lhk(Q>8-l6js4g9pC8nt=$N?2n_2O};p>PC!hSsRjU)7UJqM_vT&6pFg9Yli
zKcL*~<UsyJ2gaa^_-P2Pz8u{=JR7XzJt+Rt(N;sXYr45>S`kTlb%va^g6*n`v^ptY
z{s$c7MGw$surGnRP*c#9uW&cgzz!l0M2@WgqsPJk^3NUEMcY4F=_-;gjvut>l|;li
z*tTChl*dVIWYnZnN)D?vB3>mVxc0ToU78n&!|GYeQb9W%6?+cvOe)B6_Dg2VR%|kQ
zhNFjZ5Ob~w`tMRDN|lvz^nj$FL-bh1r2~p*M!lZ*ky6KTa|t|WF><UXkT=rGR3R6G
zYG209l#<dtKOX(C|GYWEXRruM(~`!baT4|UcAJuK2i~2vCF<io7yiq&p~g|vq>W|5
zwN1U}QeB=AtfL)JXI*^Ph?C=X&tWWo@<*=4*DF5;NCNl2Hb#u@I}>1&RnvA)x&-cK
zabBLZLTa8pKB7a*^6ym4z86uf{5<Ak_d(Uc{n{~4#J)M6mLTx&LMEA-xe6i^DMirq
z^1}8Bj6@<)Y{xX<wCx6iPLHlkj@Y;A^dSta3!ysm<~b<N<6A@^nefV3*!m!(5b)yq
z6T;|@2jTegu|?;=Mv_kcV|iA~;CB(M8`HUj^jSVeA@eG%q;$-vYhMT&Kkn1V?Ho1Z
zFO;a(VcEysbtR^hw;Xq5r1oYid?h#0KB}wko^Rdu5=>-!(oG7lckn_e;|b8-FwiH{
zt%=^5QO*fLr&vQI<szELNm8D9vXSE~ocWa8_;QeSRZ4a7(Jv&&v#|-wIt`<6ol~Oi
z1=)!y8*{elcKd<vnDR)Gio)7*`nj!-mj1w@Y&pT+*;cAg&Xa)|q?$8(3f)JtnB!6V
zlLrDiI16`~s0wD!G&bLso({sM=`4!zupI<F)CqRkVQ+PphVy2)QGXkEG*<s=JA1&L
zmA|J2kKhyecu2+e$uh&9$i9g0-l+(z{jdRC&uF}LV^I2q>e(>Fu9|!C$^4l^X^|MZ
z7#C;afUgD}7=Z1{iQTdju&DItc^&)ABHNzqmT=mll1rGq;P~Kske~mMrNjR!J=TY8
zy`E++3v;U)G2)!~bnxYbV(Tz@Z2MQ<Ej1sV0#=HhOpl>eZ<E^V&ocGmgg*1IwYUxp
zkc6zlC~yV^1|rkhF?S=#b73FhX9M1&emp;F4@<nf9O%-kV!JQQO^d~I@T7Ad$$nft
z5<8Mi4E-9X7PWP>v1Y{E&Uq3o$M+pkKfLA3<Kd1??rXF}>b&Ziv+!gEX9=v9BbBx>
ze=#1J<+YPLrB)Y8Df5aiGHnb!S~(@pskXAFyjSeCQ`sPxD!No3lUdmjLKCpQMd1hy
zZ%L5Me)IAqDAg|ade$UYN*5t7e)s!|E#Z<qUmgbjUQfQxsE$;2IAfaHaoN-(4O4ac
zpvE4!?d7zTG{eh+@TB*Mct)#bC({+NNIPOujQH%S4BGYS;4@$F{)Ozc_Md18tNfq+
z#WMB}{s(i^20OlOSTzdSziLe3ISV3(g*^LNFzy^sd?oy6qE9=9RS6l^;|KJ5M+f>d
zuVx#ON<VYb_1O?9f-`iP_}s7B2{g^Qva=i%yC^M|sXu9dm8JA?cm16Ebzy=%8oTj4
z!Y7Au1*)SxmIT-lQs!8j7xyiSE;_76Q<<7#5r(yKS`86)K@8y=R&or?uI&{Tv+I>J
z48!KbzB0~^FFsU)XV~|PNtbYgr9`WWewW1peR<Hy4_$Lx67;m@+TG3HQ>t4({ah@I
ziEc8{ZcZr7T^$z8%g*hte@Oihe{z5W6OYq>4?f6x^cO>UM#hKD%HX)ranN-fobfF^
zK5;$-3Gf&&HHB|PWB~^<v7n2hx_y?8SV(3&IgLIU@2WV^+ax{*I6;<Zd<r(!sPp$c
zJYV5X@LRSPxf!z=?&K^E$5VxGFz<=en3D>?B_x{TU8KRMeVyyvwUI)o_OJsVi`R~s
zPuYK&Flv_dv|IISZ<=nKe}$_53~0`>%T20nt(D4;<&1HWJRxf?8~Zv6ZjE$DQz$m}
z+TA|3XSyCIElMKCOV}Yy1+3%QgO<=^BZ`jpi`;3bceW9d5oz;71+%{QF^V6ntD{;>
zAE;B<Ha(0srhIMMjSlyFyf(d2zD^(eE4bP{`b{<bAnhPH9h<Fod-O)obetw(H#rkQ
zln=OaX6&kvuJ7v0lcqyDsJ|HF-E|v3j{0h|ezO=X+Q*7q7PDe-xV_LuWl|t{6<P0q
zRu3e3Tdy&u@yMzqmFTc&`Y~(!>QI3ApT~gww^%d?XATA^*<iS3_J>!qZd@q-_eTMD
z`SjFAhQxbQSM~(VqP)Kx(0x=?n9DBcixB0`X3QziIR^-e`a2MLcG2cHtYj^zx*kbh
zeRkW`OUm%F_RTwi)dKPgw6W!Zbhq#2^~wvwl?M-0yT9udL;W(;0lTy;_zia+kp+=W
z)~mzqplN3@$)stfYkn}uyT~MQJzt_jt8d-TO)Jc+Q-n;_z0M)w*ug4dTxh9h#`xh@
zmb`GdRAEEyV(7w&2BBjGi_+v`<p-;146pODDp%xLOC*1Xeo5z#Z4;01FF!A2s9t|@
z(LwIhrsGnY3wtOfdf^o@p-PqID?|U#*Lj^naG{C0aBhd-IVHr%xDE<-aS|J3dB>=t
zG0<tMqnX<D&X!-kfT=U}^!!B%E;f&uG`j<ltbAzY+NJlY?+4y%dJspLKl9OL1MBms
zmd^pgJsG2d8qdOWAQN^td!nAVB6$?P5ANIHDk}}8yGvKVkwUd->gk6`)Tq~0l_IK?
zADyUT)`QPFf6Pj=vEvd*w3g^K2v@4Ed!v>TqsEuVF7V^(n{A^sZO6K@6{-tsckLUg
z%j<)vZkl=MNi0jl+iE4zc-n%zos*x|N-Binx0{K4+v*+@(V-CKiMTiA&L<n^K!;w9
zUJ!oBZ1>_IV!aHE0p-{v{#Jk}DL!MMB|cJCL-tKF)|(8lB}yTPGpc0^^p!{R!8x7`
zZE?MH;@9~0W=*B|(N1Md7M6d)ulBNPp}ieJjaS>^@W?*kCSZcEm?ypk%g*ejEASI!
zxLofYp+OR^z7yP9IXPzJV1Dn*YZ4@F#}D!p%B50aHUe<VT#Pi*Bbh_)T_At(3rlw(
zExW;}nF0B%c6ZG?eyH0eOlxEgr7ov`W`7h$JU<!9;?_x#6@S6%SC*C-XIzRqEy3JL
zQE=W$;zf$bZ9aA+xcvtNC#(l>GX*<;&LMbn6NL&r)UrvYaCiIqYW#LsH^!zJM(%Kv
zV`l;L%i$e$0%sgc+r?Ko*r&XNO)ELQ<OzG7L>*fCzcTXHGMTSF`P0}?sbZ<l0WPIs
ze8b}Z1Gub4T-e%8{Fao9bB;*M!J+v?vAzQtm99MTLOhE4l@r@(#(;Oyz5t}<Yx|?h
zq#@oL@ik`b#MeO4g2}c^Bq3T;>LOo%UccTmMEv8Z;Mn(mzVbKjl*(w4mM|6~$Q>)0
zpDg*`MvMI?eJx^_bi<TpZ*@WfilTf*qMsP-KT4tEa%>yiBS_N<knY#sX)MVE#yy;9
zqwC>!*o<{$yeVV6-Oe6Bw<5+i*j`aWSz8##YpQmNDjY;w556LuJ5JTAkvfQms_Jwq
zo^<y@N1q3Y4{IZzgv%7HE*VYTIJQ#PG~g%%{5<DEJ+M@d*!H_ZD;z4Y!OqzQE&3IC
z9^uUN?S^0Gjy<q^kFT%N>7|)rYbtx;$F7jBno0W<foaxy^Xo4#(p`s<%6nUGtUSrG
zSH_eJ^L4A5&w=ABpkf~yF4ZfFZeO-<mT^p~H46q=h?};v2OAYtlm74}uYN2DK4}rW
zsRFc`&tCkKpwf%?H5>;IV96JE_v0{#?yQD)i4Gg(J9f+knpC}GAAK6?=##$&cdV{`
z&-J{7NP*J^AOlux|C_q3KG3x-7=snL>tkkTMB`1)_>#u##*P2zX#f)lkBK*#9@ULk
zgqsMq-RJ7<d2@wwX8D(UvCY`V++g9x{^xzWjG!h6_burGg-Dz_HmM)WlWCWifemi)
ze*d%bR2XIzwLt#KGs-}@SR7|RK2a~3#SwO-IpQfg{F-A^6Uzv+J%WXLQnYUoh9R;n
zHs8~u1TviyT_XZ@D~weAARR{c@A_`(#bJZ9+%UFD43Tctg>&ZF>pMjpPT_ZtSXM6d
zE}~me;Zn8-DzpVTl98EabxJIRr>Em21&Vw#2xM`b#)pGyb@Wl;yHZS6`F*IhBe(Wa
zYPteR{Bjx1FWs%eIaSCdESb^dDt@p)bilH|1I%fWfu&KXgw37-zjEQqn21a97SU@C
z+HFXy_RvPwCjR~Wj)lSK&(}YD#a>*0v)QVWW6<oG^#ne#Yib&In)nXaeLz?DwH2N0
zP{+r&ez{WbLmT%LljW03x0@>f-*H7dXrMa23qzk_UJ-9JCH?%)GC>rd=%2)m?9xs&
zE)*$r=JgN^KFKBuoG*cPF%_h^#^WI;Kw%Q_FR_Q8ikSd@V0IS*Dw}EOnJr=GAJurV
zmrT)6RH&*72eF1!oaw9$RGNk^DA;;q)<Wulh%~zVvDb|>lsDP#_Jvh3@BWDWZX;e?
zr$W;c)&L>Sqysiu)9J<xSAo`s)Virhs<-dhoGe!W%3f#*A+DM`45YH^{ev>!%qV~I
z)>Zl#@uV$Dh>?=lX;3*o?g-_$#nc&dRZ2DLZEfJ3n%(}OU;1pTM?_8RG~3I!0m3*F
z@y5~RD*U9+WnD#Qe&y4gmo8$I2*bI|H@@wWPY<5nkD93{6HenDeffN+C5eCVN#tpk
zE%pH{q$<k;&#}{&h*A%F(N0kO`D6XmVrKmA)QLM-8@<NDy(*x4JeOMD@XImQk!r)i
zSu<G?<Nr=tw(>P8`4iy<vxCY#g4DXYeD7z5<gZNh)#yfCd&yh`P|iuZ_?7*l@Si8s
zpT}ODIwqu=ldCWMyb|s<d8TrBkWTg4=B<%Y=#5k|RVG?@r}U^xx$DDtkrmOgncIG@
zDlJHNjI3ozYDZagedBZ#U(cWCKM~Hrtc$Vuam0FxS<m>u=-$6k%>iWd{<lFcPGk(R
z9(_>M^;<Y(OE`b~j|koEb$u!S>pYtmbk8VNgppgaS6A)6;S`656oeK?s&xqSZ*=s}
z%dy6>6yrGjFAfW{C@a;m%AKl>!~hx{XM}#luB?8|lbue+XCEhTzq2fK_Z)y0>s0<6
z7E&OX$Wd!{>rpp$Z{y=L4VxKxc&K{cmJR}qa@m-f2WR>#>3pnGpuXBITHjqy^A#C~
z()#MC?WW2*AYPcTQnJ=-h#=)5{9-1rRI!5;0HL)vA}F)BKfaisd_wijENi4bp(-Xi
zd<!*fr0%1ClYkMudUug`Lk30$EEk1!LQl>5Tsz&bbsm1h8*+woepz4{m!3z~rP|Ln
z<C%w;lm|zq*MK;gbr(-hy7xJ&Cw?<2RyFqn*=8)o$^FX#7E+uZim8FM{ZX~XyJ^GD
zC2~@^1&c-{hvtPy)JJp-*`o-Zk3p9>az(b1JaHT)GsvmVe0#}<t+x>-YrkYiyZ#S%
z?;RD@mc5G>0wSU!NRX_6fJ8xZRx(J=83iO3NX`_9<Rl=VWF!kkNzR~TBxfjMkyDW~
zRGqsy-EHgX{@vSeyuM?+@&2em)h<Tuz1N;=t#8ix&2M^xcafEmi#&}bG;6ilwCiu4
z@KHCV8@Kxj4*~cduU70bXiIE!K@4?6duHqp&>oWXBH&EBS@7Q;SHZeUGI;y!rK9B&
zphwCmENx7~-@GRf@#H1%5bKVxWNkvx^L4#92f3^@x8|DDL(!V;+Jj@%WMlPg^CINK
zGU3XBuzN`IlyNCJR^PSNuS6I>jQe)n$KP+Hc@N2(8P@c8A5sUvKYL9TNQCBohcs$9
z`c`Dr5>-8|FZ40zRKFNXXTpK!4i=SfLSGi~2wYZE&Ko>l7bg{F%&kw#2`^6O{t|ob
zK#Ji84iFsDgSkh^omk(UvOIPhV^F(2&6Xgsv#zVx7hmX3en!2%&Cad;W^YEXl9Z~-
zlV&K(3MQ<-z)2d2y!G0d#`4BQB+kk71rT**D{3-L<1^WBBbzR<Yp1AiHliL)P_%L}
zR8S2gW;IR2r3|$EX!2HQPxDd+alAVQyOYk^ydZJk)#ZM>4>p(kR=voV@3cIP`Nf~i
z62xlpjQ}=!w>LOTBIC3Y{bQJ+-}mCR2s!VvtVsK9ry~F--Xol9O}k}iJ<+<8J<V1U
zFZ~oRI^JPR(fx`!Bn2WqoZA90F0IQSAe0Y8{w%gJ`PbOGICa-mkTKqOz<6IV8s*c0
zvRT$U|2?Wca8;jqJulc<A)3JbZMS>{W>58dc4aB5uH!b(v8v;0Vpc*~QpK<K?0r0#
zH)XK>jzoIRPwzde91v!nr5`=KD6DaL4lX~1mQrc>oN)<y`Vu=FDs-`n>R<Px)?$of
z#c2RZ@aFkAFK_2!{|IEo>1o;2OQWgu;9OQVC3+ShTt&x!EIMj*gAM6oiQM_-ejn76
z2arzDRlCRP_{Xq!cIqnSsx?S@+eV)T-wzmGZB@J{ZtLV5d~myo=h-1*4o`NfCdNU7
z+pko^nN#dVd~1rUlm>(q7ek@8E_hZ&tWZjiQdwT4(puUokf$Mla;MTRz8Ea8R^vx5
zGN`1=<StGR;5^R5>lIzXPAx(t?-&SsV0lLCjqbsMiz=G16)5@CXRXO?UvI-VB;vT~
zFV_#LtY_vc6J>|Tbw-mY%#CAbw4LjGeEf?Gn?>&pZEtbFSU-<s>VmT?!|*!LBv^zv
zfa1dkrh?+k3z4N=fRiCWCPo()<ahJG;~QRWgjX+?G4wt6D1@_!u{Z{qD=POvOZBFa
zoT0tnrJNN4DX&!<9y;Fr^p1SMh$#>r%I<0DK470_KDsG1WW{&4GP{f$>v;qIQI7t7
zBL}vTWCS7(=WSjlvE_XZm;2l(5gqX!zA_hDKM}%_4(7~vx1QkeSnD(C;hLm9O27@G
zGI(S4^?AY|Efa>SIF0;J0Jw+M3t#VgveMd8MY4?$6=zl)`16M;Lf8xKlxh7?eb0dR
zK(ImYFg@h%YZd|5ZQSoTL)A{)w9;_g5RS*wD5iUEqbZw#_S1_irUB|Qx!-=gpDlks
z#oD>gEg1yIr!6+J`aX<w4Tu+L6KwZ5BGHIo=`>)9*abAse0Db<4n81YpZEZNHfOn>
z?m1E<hO0jVKlNpTePH|MI{jr5GhJ^w<Jw=c0VmA6b8rQ;IGhjV3Rz;pT>W!<+Ch`L
z8-dj|k`3aNy}GHGaLI|mxw>M3G6$C|ez4CBL)>h5h`C_r!c-$4u5C%u*zB8fDOGBa
z7b-YB`3;3LU4r^@w_7^<gxhEPI=(D6GN-8Ua*@c9-DAxued5F7IxIKHUK|p|vPXM+
zuV?61337kPa{SHu`C;GKkb-&o+(WYU^>uN=Q|lSFZMN{uC%RKD4_x26e_uKlt*Ba<
z3NK8R-w*;j`Y@fDo{)-zKS3r$+BC<!Z${-A?<!5@kJeA6@@h?gI}&ob{Bm2Y1^sC6
zXp07}H3WyZ%O<QK-t2Y!0TLeYyZ|&JD&5&lW9TDuBD|w%S24$_k8%**CDb{$_NNTG
zeILz++8^-Bhy~f0cAP7vX4<&wxhDCOFcEjUcD=&i^Cg-}6<yD+s@YvhcOdRmeQa*~
zz}~Q<ssiNk2dE87Vgn(B*yTi2jtS-;sBq2Gr&7rs0sFd<kI}&*SV(KM9;ZkIRW#re
zxLBh}$K2!Yh@a=VYyGEPA2#`2<uk!zo3qBK>y72GSTrU8DOAgWD7FXWko<he?`FXl
z06bEkpsU5uZ>?uEMj=c1UO*6*2FRgsW9&wbU-~KoDIbcn5gwj@zUKyMhEqH~qFA#<
zzONtVmsFijf*FPG_A8fMFjN)I350@8_7_uboxPa0!yG39B|*PfeVtlAuqvh1D-gf<
zK05<1X@#`b1u4&5FV|yx=vw5ao!FW}W?1Q;ZmqxR4%XtdGh<cKOmC_HX_a=sT|>1<
zh3#I(7@D1MwjnyfV|yo|jW*q8XLN`7Xzp&2r&4=LNeNz!--{W~3!<kJC&IcfaKcz$
z)Dk7x7MU#);KwRU_ve6KTeHwjSEaiqMnIN<fD!R?M#n+i{Q*WvN^Nt<e7Eu)cP;?q
z_rMF_T=;)N{3u|atrme7s>?o+_io$apJ$PsCGKY62{sfZtp)@FIyjE7ynDhN50)3i
z+%b?w)`H`ORxvimKrwKyh2vEA&8R!BkLTaA^i_0UALiMdVo+;*(UU>*Vo#h#+FcLa
zNH9z)LYx0O5zT(P&H`;Hi(;+{W|%@m3R`5m5CK=%W`hSAu0d<|0L4rA8eZIgfM)l^
z5c%NEGK&_^jDCIB;wgZ}a2evshw@_c=quxOrjoQMx@{CakAHUe@XD>6LpDjBr`h}g
za*ICAIg?xmR=cb})klG3E+$~VmeFVToB=@=l<|^HV+HBzH!TarG$!`8h}QxPia%~V
z8_~wRkO%;L^w()#c%x|P1`0W>Q})g~$`kkUF5dbHPkl>jit=_t)K!3uC@_hqvW&Bz
zWj}yigNfr5WbZbG)H`@UH;rz_p3mA`FwCH!%n{i#(ba+_$aU?;Yj1_=3JC9rm7AmE
z^}jQ>EH*`O;ooI|aEcS^ZW=M}y0!K`ZGn%KyCmP%#gC_tVHQ>rO>Jtgx-=}dncMwj
z*+@Q=nFJr(^+*(aD^vaDtz+Q?wP3p5%B5#EKpqpr!A`2U)LdwgQQ1SL`#bAbd`+^9
zsM2n8T>IEJ(Qy*8BTg{0>6r&eHm%jNY}n^pB{i3NsLwalMa@TLoRc>!^b;9(EgCX@
zPZIf7UQ@3w(mQGJae+)o1>(L4b(noLVC$?Lj=ml=x}3Hm<C<ob%O&@)1H0^WY^Rlt
z%wlgWFn>*ZP->yN{Z+*W1j9Q*of4#fIuvAaTBW}S@e@|eotm=2h2(C)mRenI?3?)F
zJaTyNWGEKqtzY17BRAxH5xUh>RUzOVnDY3p)aFC1Rgi10H8sg-y*bQ^RBvAoz797%
zTY!_!4tGz)O-eb5k+MG=^7cX8)>jRsA109wdr7-2t;|UMZ2*g!d5;}7UtM(6Al9w`
zZ7mJiVP&`>VNsx1A9jRdR144<V&kEJ9BFg1hT*Hm4DS|oai`y$vVs+Kndd!9>R2gX
zCVf3^AyC*z(^N5XsPENH;rez+`~_22)#J1~LYN~e*Hej=HHYK0hAm2Btq^k3x8BRD
zj<3ncFLkus7REmV)F=M{UB?%vYI95z9j>5&QqQRj33rTecL&x*5xo-LZSvrpqb-0m
zmwW|0+SL%TU6Z3^n7f$NZeOG*)D3zXn&oFEofN(uQ^$M}Cr;Jq57TjvlMPlKUY;Wg
zSW$10o*4zY(%y#(9@q!d;9Cq7Gmalo6n@;<nfNiLz{%N)FFrSC>mpjQ&W(`@1J=8m
z0>D`=YC6Y-y_-Xs`Y8HL{=1_f-mu4=L4Yc))`Od%qn%qQm^`4^`lzB+K2G+a8wklH
zyrTrQG!YjP@#*#D5fABpi(xiTd$`XoBE_o5Hk+FJ%*y^%m5r$#lza7*i|bjw?Sv36
z31f(hJ^g!`MxihekI;we5jx&8g3ri5Ko%mMg+}-m$?Zu7OshSF+mGrnG+@Myv$LN5
z@oNqDJx2|Bk%zr{coSXsB+nwE^5A}=9}h^8*Toj_(wplQ7I^bS{s4vNeNA}&phATJ
zcX4_=6TYb(I~^YEulMrhDi$SXzcSPVKg{J}k$alLazK{{NJ|EN?<fN=2~&Y55Sd!o
zPqY@K{-|os)*0)V7&>oEsB`MG&0fJ!jkmnAvSAg@WcQ<RdW<wUaN2WfN>?yAp0_ul
z`OC>GALhsls|4~*Rwet)&`^qTg7VZ5s&5&0#1@tdI~J!3mqQcARU~UF9XVI%F#1;w
z*gT`#6!i=(qz0C%!<~=6XWB&8xt)OSeA7<+Q}E=!l5C|e*rlE&yL{<u?nKRfCjQ<<
zTpE|S?x`iY3g?=GqVq;aPh%t2S<0cRfmm~0a;LYV!|ed9>a-!rJ4!e1uTmdP8q6Ba
zJVT{AaNkr}5}j>|1X<c}QKWK_)i*JVf|OZ7K8os<qwI&F*u5zSl5`IN>B*9bunKqK
zUN)1r&hZ4#KKauzPLQ|MsXr<;hqw(q**qP<8FJ+-aLIui%e~`j&tsg{#@u^L7Tcjz
zZ}3q~Ef!-+WTPrTSX%`BF^&2YiJTTK-G^h$yhUwc7dMC-BH3BrIblfWdrb7KHWiSU
zN=;qVvDO;r+>uQXH)HYzH#X@Fj@foM%tEs<sABro^<&>%`~mVTVQU)s;Yah7?3S08
zws@?B+dPbSiwmx&SOLzm_4#Z%N3|zTthUjiwhQeKl&Y=BX%4`rsn<_yd?|`zY1sxZ
zm)9o1&8JvjsE!XrRUA^)40G!Xxs0gja<&$Q@bxU_&#WK9*iUm_-)n<@k+M0qQB>n}
zxkh~l-yjO`4hXK?sCsm-@g52W8Ru{M1j{U3{XO#wB5uFb1h^bFP{=sVreVB86ES2d
zY18<q1E{iX*SK=lHq$YEd$Wu;(jJeDW({vG=wN>%YpH4HJcQA~%yyMZcw=^#SraCo
z&lqxV8)CAq@iRl%%Q1#d(>2Ipp^bxi$<^jDBlPd531c(2vr4{DKk_+`)sx*CnkuH&
zm8R;r&@^s&GBL9eN$DnL5ie(>5htoEHZwCZq8Dy3V?A!~Dqd%FpYtL^1;=AIQKQ;s
z3iUXpoFF4cTi}LI;qYYrNE)~86Ktn72P3N869YlsJpQPGX=P{4CwF)c#v1Tj$(L6W
z&A)Wn25?s2@`}g@MB4aRqiW+@S(g+jd!SLDaG=J6InugddVVYYrMZ)5=bmGN$b_vQ
zjz6|^qdZLYJl#Ihf6PElT=e0vY>BL45W~3Xoa1!96(iq0_3PNcb`m4Vs;?e!F0897
zaaiu$yW>>;26A)V0vroF7yCp<iO(4xlWr^zTbQBVR>rY@VxY_Rp)$uV%*_E%`*;dG
zA?z6%w1g|o(H;%@9n1D6<n%Zk_5HFCLG|wr=U<Ztf={BNr~N=eK|$+05B-+KzxX#E
zogatgSg#vYSO2p|Bz-6E`N2>_o!$u21^V1#6k2SWMnup|Yg%R<;aIVpjt{=wAC5Ge
ztYJHNO0zxh0(%x~$<CgnS)ZEAD(hIiGvvp%_RzOmq?@lRmXGT)m<ogVV+3y*){C;V
zyhGv=w#jtG#|{;-qC~C9H`9-U9dzOzlhiE6{kY-@90A2&YL!q#a<|Rsh2obURmrS3
zbd*sx`R?3JH`fxid9_rgS_sL=ObzTrk5-%T>YtG$zdo1zqEq&X&U|wxY2#9%*s2im
zX{$JQl5MnR-=D@y^bljQHY&#nx`F&MQ!_^gl3fd*xH;Q6T70-tsej{eEwjcrcEz%#
z%YURQ$MG&pMNP2NHNUrB+Aq`$om#$j*jUFTW1$X<o`{j00OfUYZ6tcHp}RI37c5hs
z^f=08ciYN(odr7e&R#eA?dBXM*>Fy8*d3IYG?ad>q^$~xxEHlF{%(VJWn8`1FT%sI
z6kxDwT`{qKk6##bX%9pGnWkpKoV;>#_hE26v|I@Q{s9&R)Vt7F^SAg0=y7KxeK`5G
zzE*6^8g6)9RxZlA?TwP%J^#5CB8<ZqNgvLAgLB>OB}dtc#+56cISw-&2sd**Pj9c8
zA<IiwkCjOwVYI0;_dgN+FrzfpH$rX+P2jmbq`i!;cs<n_)T}c$we{n&TiDT#af%2d
zQK+Dy#w5Sm7Bdao6Tex`EKYqCWZRmb=_hR0Z{J+M?7yF5_TbiD%t@fLF@8lsxuJ>F
zwX<|vlZaqI$+5bj9n5YPoFG2;k@&#*L=ocT;P9qrSU5r_t)|zDZLGl~Z!PiUy*_IZ
zdm7{OH#nF{d+s3#=UT5!Zz*7RK;%KQCW(!A^b6=i`Y9K0l5&~2G7d%J8;{J_7;~s5
zj7`gy&h|6*Y-j9|<9oUV>PqsJwb&(+%D#iv=bbo2pRIMk%aGhQ;oX(-urU|5SQCm;
zpI~dyyy#;qS5NKM+tNbU(#=1H4yu2)BcnSg=53%@XpcBo9E&m!FpJYOPdPG8Lm#bj
zgS2&b`_b~E!9WUI!64No{T7gW$y$RP9hPGpr(O{cy!jUK&!~7{=cvR#KoUGBkA6SE
zjS&jW->XOzhWPvnfa%BUsq)3boyRJ!@oyL1CG;<86jR-dmfx<YG6%C)?n`$sO-VIO
zoR9ZbCc2UEsZPUx3@VaM7w2J-AKBkIjjSl-^WvoE+l!d_eCV>>>?cC1w<pmr^ene>
zbSCH{kZ#qVB^*Of+79$Uya?d4*`DK{3}qh%@*Y}O<!nU1bRpj0Jd@QG-!8{kN3)mn
z=~3oLuRN=9FtRYWtU5T8bzm1YxZ=wk!z)rz`8vzmayT=LDcZQuwV}21_qA2@(!C87
z13<j+X<~Vr><?n|1u4;Z`Q}^hQ52TR7I1n^nM1|~*ORfkr*R5~Y9m;6?`%^=Y@*)I
zAdi@)Qr9MpZB=lpPxW7<%MKFut*Ekw(#0am-YbH&Id;+7>CL?RojrX+0*`7@i-y3q
z@*m`tF}$QI^=ItbCqwX9*WOi$;}Rnit4<Qg8+zCtmG8;BO04>!byPr_Kz;zmwtM{p
zJTDB1epO(5yCr5Vi)YPkU^XLcWmTTwyHIKbBzoEl$d<7Sr3$=mYAm<ZObO{)s-xLH
z*W!bd3*=QFJQ6jHW5$d-g6fbMmvvJ<@_&4ykPvZk&6B)i-_-pslHf;?h5*0mM&6L0
zhyvoy88=EXe)qF@7UN2vVA8hTPGfh*^lT=(Nn{GZw5m@;^c4bXLdUKqU~jU4g8+E#
zj6y3Zi4h^&v6!e(Zh`>re8*ItCum=Hn?fUHqyMp}>AlCUUAS|i@pT1VB4gpEF1Jo>
z%pDO}oK#uXfCss0u$+CP^5m{G)_aVYy0_?z3vGow>DRm86E*Ai%eze^DWnaPOPQ#A
z%Q1!-L!6Py-%ir*OPP&qBXgo=M`~Q{_@}+@8MAd!NYP2p7iKblJEm<YY_2^N6wMw#
z9{5`O)c(`bi@J8P<&TH`o(DAK(WgLoS~$>8K}eof{k?xp8^~l3pdGD4Fn~#t4~LA`
zH=W=1u`+)2lL~6I570n(j&eZ6>Hwm6FS3Jr13_Vp*8Upfu*5`Az)mmI(Y1UB`PMc+
z-&%r-$K@`h(u3G$apOsJ^<<QH7X1L(HXX7DcXAu9v+*wAF=f0RN77illVc4&ecI0r
zYEy_*+82n0KlxH)djCv&<8jq6eU<8&?#%#7=l=ZcDw7U(X=P*9_Yn5OsrPf%s15&^
zsncL6ph<On=B=0~8R6Z}Pwk~$-o<flkyst{Am$Kuy&|;;mQ@oGLl6Ey2ldQJEAen}
zuosSDQ8<=<&!IT6Wr-QHH=D^b9!g*8MoVFiaj$Bu%-IycHdEsJ9SlRuh>H%YvIz}e
zx{)9lOwRMczV0_IX=Iq)2RqY|%R)7*SoU$UZ+IKZIUt);34_W(d-9FbUO(J4#4<g{
zSE^n|2m!D1!lcU$Vsy7>IQOj<><>{*sYZ<?T1f@6OBWillL9n!d{z1R^~PTmzN{tb
z_20Du<4N0r+UGs+qMwOVCuVk6wH(ul&=FI{0s#!odlqP&^kq%%g37hA01-N(@kBJ^
zU=@n1%}ET=GD<xvc2ci=YE537Jvj&q@@yX6Km!6@G52JEP0Ad0e}thg(e>#V16p(I
zm*Y_M)S+>~ir9%eph0i^K?z`7eR*>8?_GoV`|n-y40#*6GK}tUtVGIjeq(56@VU;6
z3=ViOTrVOTdc7bYl^`{dlZ(AzEX6;s4K%oMY$V>LPMtrjj+U=&(6PXAc9g4;fVex1
zeX%mWbqJ+tRV<z|widIG34ej)m8{unp<=1{?8o10jHpZmoKv2u!WnU6$#37fc(M4m
zvWQ8;8t<<gJs#OQlBsVf9f5v$P+R$?c;0hwtPWVA`(2dZ2{Pmm2jI;-r<mepM0WGO
zjuUb0IM?QL%65lzGzz9mB*}rbTaGucO+&xyhX;vBEAK{+jF)NCEGP&0lJAL}j<6%M
z-(Etxdy9hj><a)Pq{tEUJ<BAvl<~dO4(&2`jrtcSCV?-9qb?W2w&n-!wHslS0m4+Z
zz1;8LZeJgpne51<!`aIP3c+r9^JE+5%*8l(Ii_UI_6>tYGpI3&e*i=Z+0)0F>*EB;
zPzCi7Hd=6(xr4yI`+7W;&CsakU0NnhRX@<TYk)HMp8<3l!(C)m2>o#6bZN*|al*Q(
z`hI8k0AD`zZ_REng^Yox`q#Ri|EbzXcX)(1%NF+=S208osx~G6pf2cD-CO=wB5yo_
z<z2DBnq+_O23{AKlj57LT}K{N0_-~UA|hcw^n<s*AezTXwZ634@(7Q}5^ND_7Q{=L
zEAs#!>H;vdOp=ujG!kmw$2>G&Qo&)g_-v!Ml3n>koGuaTEEYBK0T5@OXbKjlnitY3
z=Gi={uVrM$XH_nZ8WnB8Ti1$oq3WhHef4;e&B)k`a-8&g9)m)z0QOmD2ENg5e<Zgt
z!D(`L6$hqBNf^^313ML`j#F)+J<{((>yA`*BOaAP!D)WpgR4!|#pLIGLPB05YJIOO
zMi&#k-(>VaIHdbu`LIlK&rz%!J=|qIaVwP}YQ7$|GSmNXF9HK-DYK8d$*-^OH5tED
z8DiQMzN0jgxW1{!$9l{a-s=K|!i`MIA~A*KCTQeWxi7kVdAq?W?K3m-+n9{hv+=|3
zjxFDxO4MoA*Cne$J}rL=nef2@)ClJYA!R&b*iv436<uC7J@Wgxqdwi=Xyo@cj%dOJ
zBRF#4I#$E7=dY&gI;z3b4pH&TBUijmjFaDstW19|!5YB$jd5VJA%A9n90V8un&$ja
z8SpveuDn9c-3`*@cGs|2=DYV(=UO?3_h52qW9KY7+iGnO%VK^!+|Y@tACEaTLT-60
zz7(V+U?Mvf)`luN6fk#NS0~*t6V-A0Ci>QoF+_%*OG0Rgsjuf{;xzTq$CpVfD?$|a
z;Ts<5W;X?^#)rzwyYY}(GF27??M<7qlqihiJju#{X1Cb6XLiFs22JE1AaE)`hC|LG
z9Eje}Homk?s?Lko=$GscRc9;e*%kmkoO|dtxBp}I?V)U^$hr?{;fFB#J(3)^1uC-*
z(js#gA=AKWO=I!LiEz!Bp{$XIgp<Y{Pe9LCm#8FsfOZ<ei;&<~KX8vyah4})(gve{
zXxDKxRXDhkPXk)ux7MxM?dh<l+80kOmG>@U3~vXw39alN*4h=C*>Svh{isGM)*`Y;
zh<rhlglUWw*#RhtgR|X@<*OsCeQEb@_U0R4($U`4^oefpfV3&(?=TGO;p*vI5+K54
z?!PwJJH3hIzD*%BBFiV(O41)|7Wo}p{BX<3Ah?a`VOja`LpkCkb^3hL+@5(FAiF(w
z#a{hOm78|ISef=E#AfcOk7LDV%^JiKc?!t)(U0o^b#P*1b<Dpq6*BN6@~={AbLG0_
zkrdxOi7TcI!gDL0k`ipoXqk2(j4+L6b}!V1k2vnEyXsx;P*v2pXEHw>S&f7H@fCT0
zXFLx4pknQ*%3o6%`TWPbKR_fR4TWhVrcoU#)f|tL$S}-e8uzIDPpDYbTc5bU`r(}3
zd{UJ#-Z{Y>AzxL)^N}4rRkVKM8AI64PLRw0?VIQajCGnjm-l$$%RMXe>mI$gi^3em
zvAr1PQ4TR8|2kGdK~eT-ro0q8enGLB7n3GZ?=*5TpBsv$V|kDbkKDHn%`%acn+Zj>
zc_=o9TU`&l-CLf!v8~qi;Jpc*%bAaFuAj&?$h9@<$HPGd2t^eMM$PAr@6MlWutX~;
z8Y|Vs!WH=NF)tAwhI6wx4Hj$r%BBvhx^)jFE}HWKj0$qCR`v(&d<TYb{aDsSJTmY(
zV|QD7N~Yv>DwP%wEJjAEs<e|n@Xf)MTpPka+X>vq%(cndi_a!;Y;m~9HEpqav*yS7
zI8T8we7%u)6h{B9w~>b7u7LPX`{EkVxh-__|Au=n)D%o@22@2lq!3r(o+a(@+_oFm
z<ZUz~5H|^-{mg)Sa#Bo(@ciy1T4=@kCH6j6NmdGLpz;GW@g%gMvG(nxrtqW^W*Tqw
zOYxf=pDX>$hb$PY)S70jUq>TQ#;sKhx0$D&WcDNy?uOu-cqMH^y*aQOsuHQ{?OaoP
z=31?Tau$5lTRa`D5@Zuv-%7XPn24a(Fl{7vD--8Cyc*Os{I|pI-u-G2EBLDX2zHz1
zuDjNF=M9S}vG<too%*lWi|fi2ZbaD?GbHVs?gP0MUY+xE?Tky#y+`f-MZNPVYn@RA
zlJZ2~<(U}_v<61YnVOymvIy}xfv!|`!X#i*zeG?1!9+VkB9~+Vbh#T|py$rI*ohvh
z6^kHuDc3$X%AzJ*c=hrF)5V4W7K$agY_tL|`6Da0B^b;>Z&+$<@U@e=^)LgE+&%~#
zGoY|M)&0KrIMk0b;;7Aov^80?{4}6QGWmtCt7>A_H5}T8&x;5|NuUc}gVvO)3L+c(
zczZQw2WUGtqeP|OUYj~v?rT^RXDb&0R^bc-t*l+mzV)WbezfE!kl8s$#~(cM;kyvH
z;)p>2KhqfS9{wYadhJ~BHJ&{)H^PV_0wMp1g9wP;QX%ZrIhvt~tAorDJjZyEH8Oyc
zGiP<KuB+Q`<-uL$xV+jw1FVBFvX%J<a)fW|SwA6AAw{btp>*n$rPRJEV#}H5uEY~o
zM)=Q&)C{r|Sr$*ggG`u-Ux#)spA{st(at$_x;?Erua>Dn$It4or;(mI4$Ru<1;cq5
zK{p1>RwsR!bH)n15Nzx=K7upgh;4`RYfhU<2OYZhE{}^>CA=K?&O2hFoTk!7Ft(`)
zM!6yP)@$G0?O!ELO_QAlyWr%}jMX2sqwgg)Yd4=WJhd99&v+$jnf_I@W9;k8TA*g0
zu}#HVkad5?L!M*<mw!5Mhe>2_V%%l1iF{p@cDG(j&^vH7CA;Y6d@DvDJ|I2MoBpzV
z7eRUgwJ?7(RZLEkWYxLm=c*UW0=a7)!+OpeBN~^3QTZhN?d)m|L(NVqdsY%tOJ_Fm
znVT7kr4T}NTC0GorE5fFKB528ux1SeK>XfDrnzS6$n5MgMUj+ScGRW`cRFGY7wWfb
zjj5_GHSbC5JGF*IImOkH30!U_Wn8Q64}$Eg9*Uix>n&{-odQPrh(-cH&^a0S<$sz3
zqUk=Qa~%k<ZZ(~g!D?83`<!v>Dxmid{Cu-1Ym+pII*SVVA=%96izokfw%hf5RdMRc
zY-1hw$xL)Fi@m#!$C%Ot0Mx%KtID61jI36<&#2zdyv3Qcr;`(ltEH0~D<z<%{`R(u
zme}2jWS7V8>MDxK+DOWHppS>AB6PTee}7`DAB#GEc1_!~)l+0U-I-6(t}Y;LajZUF
zdQUnKVt?POsnn<%{xZf_4c)tb1XI~99}|=gc5`9-#>Z8lh9m5yf@O5e)lkg_(m=6H
zb7{lnMZ>Xhx0D_$ggZ~<M!qwISjQb)TtQKmu==VBl(dNSHU$@Noab!LdAK<~B=~e^
zlw~~$bpEu()nQ`oeG37$qkgoL-e|`qXPwQ?HJ!eF-1~_X{{7O$&3-q3r8lrl7q&pG
zIL<Hug6<^XWoF=~Ty1z;jnt41bY>_oXC?C;A7$FQ$qFASM3q7(wU><xyd>@|=VjoV
z^jkYyNMR{f(uAD{wf<S5gN}o85rr5>#WT5KK!VbWN_EP)&DYc)wQ_Wvfm`2YUb&An
ztrlPPz;J8|9;y)LnZGU5T2tLr?S+TY=?IFSnu6CQ8**ZoQ>)x<kBJQ8oH!xwbZ-P2
z8i~ECtP}c3!+pK|JDMA#+FN4Y7-L4kDlf}mF8?O-{#<{7^~~;Jl0|quc^464q^|G@
zWv1xodi@t)qm*o+8qG=_R!a9tgws{^(x}HCo_Z}Zl5i<HeSWdP(*D7F9X)_Kfc^kV
z>I+~Dtf)Ecu5#mQH-Z~B(TA^}YN8leK023|l$P_6SIkX+iar<l;G3O>sjaxDuRaXp
z26GANn$&#CIWV1=Y$ib2yp26yKx36OHYIewc){IeCu-{jXyyX)B{@2Ex`u|Vv4N;X
zz1ACS(-yP!Sqp&;<)XZ)Tcbm>?pdD*+HbxU)Gi4T#<cqEk>q`HP3zhNXo3|@$!d<f
zebi3gh_ESyr)B3o|BF?A&>JSql&h@w&|f(ajsL=p2QlXY_~wDv-1$7Pg5nms{2ERe
zz-i_)-a8XRIoq$QjEw($JAerQMZNT$n>&ij(;N;<Pw(fb(BP1D928^UXDK9je}f<P
zye<uH%)BvkOQnmjln#R10pt~-M9Zl+!k*Eh1{xsbY2yk&6S&hYM6fNa(~4h%s@Lw+
z@yBCu(u+7DN?sY-V(S44Jf}BhrSo7FuL_XJB_9HUtN*C4qJU?5X}7S8ZA*mtICvqI
z(O{2Tb&5qFHrc}|!PJg8Iy#1N*T^x{%y6;^QNEpCNq4g$a(!bY!J@x`$6zXTQSWSf
z%#AUAI^JA5QD}Xf)l;d3x-{_~yg;#}3==Xo@DY+ckrt3eALf-tTOUR;ww>FRAVP~_
z46?;)m{cS9qsjL>sSb>^t(-(Yya1>FV2ep{1TeL2pV90ymx43i1*Ml2nzz;((eV<k
zkuN<E!3o3tRu8Mywl@PGM3`tLgxVlNRZ`VfHWWIR>iDL`#=h3;``j(jr@%Gby4}7j
z+YS^rz)BMBl5NG-KW1A&7R;dNHr2P_%J&h6&*aTfr}ow>ke1*tBKeSpg{BsjCJ$a)
zc9|mZW#OzaCcyJ!hz7{do5n)@yqi`*o|$OPLuEh6lGGr;a-XrWuSAh<ctoRS#TF8m
z?`3@sWA3?j6;uBYUw<lD^!`+`U;uuTV#8*;7tpu(>rqRLav-1OneJb4#=~;Yo_;V@
zdeDiYH|o`)>U7u<i=okYOVXp%+VpZG#fx><^jh2vWmtuG1J;nfQrz=pdg)t5f&oS)
z2&6SWp{~p=!fe6MslyU<rac{dkHvoYiA1-vP-h|dIgladq3=FX?#l42k{%a<337o{
zb$H{fIr4O@GI-(BVV(Wg>1QjGqAz0j&-_Geteo8y=GH!P!7FMS${C9cUIU^s;aYJm
zl~vI;puo`E{Bc~d{Oxn{Ex2~CX9B9J@$K7dyJ>Dgk?#b<Ur^gWs!!$?aDAV`?(-!Q
z6``d^pAh>}I;pA2QNSyBl_n5mTB;;L6qv?)QH+B%hW-RbbWd?XAGK9xlj7NP?XjkH
z43%+1pQGESC#PCgI@}IESNBo6>Dh;00$bP(boxDO&NiT4@{WYQ_{Y|p(s*J`85ws-
z7ar}WzU2Ox!tIJz^5c+;TAxNKzI}K(e)ZFGOt?_VYt!i@^FNuMKE1ye>gxQZkQa!-
zcSFkgE~5c@;rM5W-g#`JJ}R?m2@;HMxCdk)uSy+3ORiB;F8t;K*BfeEw1<y+Z;oq}
ztKSMqGn^La40?7cf!f$TpWPh~%^P5}>^!i2ZDJ8{F|h^|iQ{|iN=CpQWlh<-QCq$i
zMxGSU{sB_9nITyRQxKk6ba$9eYbTuoTcAtbto4&wye@M;Y|K-+wHwH=q1~N`ykU-6
zmYQ2%>&y?jo_j@GgI3Keqcm99>aasD`vfx!xRb5ejn4a#Bp2_Dtf!Sx-^Xc?+J+9;
zIb)fk1&HqK*kyk!+a)qx8E;G-cv_+fc#B8D<w7KWDR*{R=MPYU-RinHW&K?R53ge{
z@pOk2fi+FfByfF<jAwYhKG_4zmG9r5p%8|V0GGk)*wJX+iB1MPSpx5=(;KG)VxR`%
z`^Z*qO%j#b{gDX?BFp7QP4kW(xeVM4MRqh$;;dE4;N?oj$I6R*HgfT*KD=G<hc$dy
z2K+cqx%@ib^Okg0>b&W3#RUkpZrkT+OKK_^0;N`iS+Us|vFfbSixU|C-)fis8}8^J
zntb}157xQlJD(hi{t!MH<wY750T_QFTpH^3+4{`}iJEO1-Fw+ix@v9=WYV-KwZC^H
zxkB=0+*^9hW|<BAf}+UM%o%P)O0$;=x32RPe6}{#+1*!0)VWWY^dd{nvIG0oaMW_k
zjw0n()y!*a6k{YpvLtDk&w=e%Rc+Gjn-=J(gC51=yZ=KUatmJf%pNC|cO>nx8z&8t
z!@3l%H``%vVJSQb{V}75q}hCB-zau7X|Z`%XIifX>|S-(*s#bt8<Exjnh4{Wi6y|<
z62;y}##dga+i5z2y$Ex!7nx6XOTPAuT2mRT1gZ1%yA?^E^Fgb&^1#RqWXE2oSJJ8r
z)bkHetCI{KZ6JOn|A@(Bk*<u}J~k@Bx`WUiu$hiMGUw)ARVMmXy<5lm+QFSj8)hVq
z%Y68@aB9h6A<*NGQctsFm~~axg<Z|cR6r-O8m9)v&B(~`CwOFE1Gt710D%V<0w^pz
zG<E|Tkam<CZ{sf;-}oQD5+}S0C;jg$i80C$KIYDBo_J${<|;S2O1@!K_alr3v#r+i
zZAdBySY(HvxYjX#IXbgh-8cmLR=_ugWVySOzhhYkv3N|ObumYkOHfRh>r`C(?F;9M
zqK{SK1PP#(dLTTlg~SQ9C&=Qm6ADt6IwX!a%{N$sw>mMG+S(_I&{MU0p=^pqijnn_
z+WN!%O=Sk%Kx@1X8c)zd(Mu9|MLEkul6}#=2ZlY7>rqFk`MRxk%Vr)oL*3|Pd8=)Y
zI;=N2wn)ZRhg}reKC%tlS1Hv!+WuSyD+g;GXjFhLKh&gr+SZxBKTqBGZs5B4G77b;
zTY)#pKKrQoDX{Y87a`|II0$Eo7b*$&=|3Qx*3(8%&!;C1C6BvHA3L#fb~^22w)<9m
z9v39uY>rtUO6La+X7r685@CquAuf8U?<>x<pN{cX8z|HeXt*%clWe%v)FpUOICU<E
zdyZ$j@90NMey!VWgZ9JCQb$+bEQM$|(w>M`to*HZ`?n18zZN=sT`3w+YhFBqbn%KE
zozNV?;%zqml3*6ccA>#PN*EM2v=M`o+{n3ot7tK@h|{0}H2+oz;Dy}CA!Xh`7|3IB
z`X|0$ed~km+4fN~mPL0--GTOYi||T4-FG9t%CwbEgDIR*lX+5M1OF2ZPlEGtIfgRV
zq_cW<->EtewgE`l787R9-e_41tW{Nkr4`F2jZ1Kx)QRvrmomvK2Jy01p{4N*qd6V-
zp;<hak5e0OZVy^S_RrYd(=0PokdD@Rg4Gb_QdzAze=p@afIaa;z_%l}87tn-En#bs
zxh;8?Aa#Y_Jve(`ZXUEPjf>OZ`t($QwkrN|wAPd<X+_dLfnUR{;H1~&G-%yI!KNxA
zlwBE)izW&N+=AGt=k<|bd$yegLx3%O@7fdm%#*^}rm!Os7KH>wyZy(=7&4^U4L(JO
zsm%c0eXE38=@k2VDQ~cIQ7GH!9i#c9W)9UO7z@x%{LvoUd1))#mI*c+^m|`hzteQu
ziXSIqlESB@G%Ysz2wk8N9spiIEWdhy#Y(?8hoB3kKe+-Fd%>4cN#iKrZlL5p=_0oA
z_XD6-04M*0CHnI(lxW;VXF(?x?LZ!ZJ@buNuVZV@=9w!+moO!Hy85tQyZD;hRMX=J
z>nbZBYIKJw+2mAQ7QZO<*B;nZeY)M3>E_Pq;6NWR7^(F+tnB0Y>b(7BY=aQGR^jRz
z4y_N;{hRjX4|mSjInY5igZclF01cj=Yo=4qT$_j0!zjxr%|Nf9w}UnV>oB7C-(R|)
zr4&%a2Q#|m9l@}n>323GKhOCd8R?UKt-2f34p2h~#A#I58<|V1_XyX=HnaxszN;2%
zm)Z4)*Zct@pOjByk`8UII1j=&!wx<wmM8n%fT=#TN(d*x;HCLQLG~!ISjyGav&fgT
zjND~SqxL};QP&u+>SZs;p^6tuQiT;OL#ymS+r~`uf%guV57YPqUbk^6C=$BT-%qLL
z5gx7L6=%p$XkD9-o@1TMNQy58^aZzXie9*0pp2le<m~LJipkF1-!MNj)i4TA2igI?
zddm|TaRc0P;+t^}vL3M~t{74DFm*VV63V^#WK{&U0lCWo-`32z#n{wZK)yQ|ahZK*
z`c-*3O1U+$^XqpjFl%M^axeGB^}BAb-0vS{;IbpjnVe4=hAXZo;OQv#b|hsQl9u8-
zy2CIVYrQttx3e#XYMf5TtrmCg=}h-B#C<-ns^;mLJAkW>+h065EWY#Sx-aq<-5r*t
zzp&0Tu6?m?+Q$K;J&N)F0A(aqY4RAKVFEa490w>xRs?y|xd0k#almJ3w99S9Uq66|
z$K1VCVmIjU1SvwUn_7w0tcfT(I_LL9QOVuPdk{5(vBe92N6sF1ZMCm9<t&Qm5NG_7
zMZcAT2me;iXw5A;S|u^PgGjDp5jE^cD%1Kt!~64M)nY?chfq&2FP-h4eWh(;WHA4Z
zIFa20Hrt5Hq5w`=A#(bv*8_+eOWOVJyF)u?T~w9l+A`GX_mo8w0(^>glk+h)$%?`i
ziP^zf^&S#Wq$llyS^Ecki7@;t?=i?OQQwx;l87xykQgj!w^3Sw3xRo%W*B+{_SH@!
zKW4t<L@xJcOW%JlXt0lY>#}9YkHczrEs?i~A;HUQ)Hs3EAk?@LKgY3=F|oTo>e~}B
zi;D|Sdl3lp8c(Xkg<zMyN~CmNi@s#~H?jITgaJk8P|uLS^!C)p^Eh$30m&&fo_<N1
zAE_(oO<%83k~Hp*tbXwm4MGlDl907*-0RIwb@1wCRq4CRo#!t^33CjT7&NvH^PxVP
z#|`JA7j9oyk~ypUHN}23e%i&q45<}Ec|pBPhuep+Lnux5z4naN-SN<pU^;ZeSS{@i
z%T(T38jmHDnyQ-8kSDY<szXMKNGtfk6=wbqU;hsYxqw?1PnBiT*{0+CU_MNq3G?%i
zu*DZE8B_imzpt;O?3|({;I(WP3+Rdhir|V!*6iw#=Uc)25w6tntIjGopHF2LSafD)
zAXW<W%G|U}b`L2fTQXlx`><qJ?*#|ha@VwHevvCOR_05u91|(oY3Jh<K^3;;XWZGh
z53L$g6{+;UrrW9!E%>a_L|F~r8nMmJP?x$|9lspkr#n+BcRu3$H#W{yhU>qh8Kf#s
zQjU_|RH%>I9#W?)NeP%h$#^0ko(5wjw9Z+1G;mb+;~}v;!Nu3ZKu1lty<ZG+_iBwD
zFTmGdNPUV|azg4D(Vr%I6kt~jjZEo2YB9jFlQZ2kxJ`sHrh9sfW_f7pWq*4nKd~1+
zsf9B@FraBn=svE!#~CK{Ns{*sW8&>1F=kqnP`Z-=^5;Xv*!evpbhdg7Fhrt}Ug(;(
z%U++`;@H2iILFO@5lJk^*o*(Wpb9B)oqvF&8=?-B0E#K%jzs&!B}&S5_fk>tUFZ4H
zTRD2g8v2TB!f6+YnY|v%K(lE3Mf^MUTAo3X#d3K(Mdsx21=dbd0p@I%UWZ18(_kkE
zY-VQtHa+kDyPM|A-!WlTJS_l5i39HiYbbbC)RhIT=vTjU5~8&0knVsw!1F?n6tz@_
z4epPy55#z95l2gbuwosR<o>?<UE2nN2LD!LFzVlbp@DIQiD+oNfdFi5PN9!FP(wr9
z+;7iTp3Lw(GGhK*+hZ?Y;ZkE%Q(BG*h@Sd|b&s$@7ZZf07W9}FG;~HBdqix=;~P?9
zw>@<zYZlg(tfms+4Y6)@z6k<r&QqU|W;GhEvRt}RPY=JuLsUjzH@2GGfQ|=-x?juJ
zY)LKbTy0|&Hp_ZenSea<RroAOK|{XDtHr5HHs)m+Wh$5G+HXKzvF3aKG#arfyS-W~
zhukKgXrqL9xKZbc#xFW+rsNe_(-6<&=1ojaRaXs<l&j;{zZPpdEG{{kyp+nElE`gO
z9G-0tTCvQgShU@z5Mf!ZO~NY-S8Jd3pk;m1c0VEvblgwan{9ReEp}6{Oh2S@JCLGh
ze^lnY{?1BGr11i{A+=2JRH+Sm%l}<fB9?HTNL*>y!E-tKxLC^!?AI2K+ijns<QVCb
z<o_Ok`EL*#nE+Iz=_+$4LeszV0U(Y5&TFNTd#J;)5On-Rz~a7B)TfsQkwh3kQQ>J!
z=EU4HABc@$*LCApl)-irMZNY5IWs*97P+l-<)mzm7ad%26BpVkgMi*}miP0@I7vm`
z{gs7wb-MbvcD};LXsX5|>Z(1#2|ayXcgPE!SYpuhjQ75%NG(txWFrOAYaFrX5eiD%
zUy#dwrDRx&GvNBA{O?M0zwrMmy2gK@28z>I4FXH5b}RMcs)WrB!4Z{AH@>pFI2Y}W
zbFeU%$hqCp3bva5<99V3xr^$&izI`MBq`S8fdaO0J&&@aC^CkR667TBQ?QBie5|bU
zbi&<G%bO-8IPZ?33L!M|gk4~}VZ#@42gF==wK-UXe)K+k)OA)i&hw68Qz*}@iRKii
zAhbn>Js^)C;7=q$Y3u8{(9~JLu&xV>|B4l%hELUH7kS+RddhtT(3l%6%weJ56?IeZ
z+bX``QMOV>V;ZK%K-7n++r>8ku*lgd6!wKJmp6Cy<-P=XW=9V$c~H&(_p)Lv^W`EH
z!p|>oDdhb+1fAEKPBSHhB-mi<smA890WN0i(HHZPFN0}ZvDUaoGpS=~4jbw5pAP%K
z&7D2+bVE7+NtRq2T@}NGEXA+S9Ss?yZjEPYtIh|y+an(Vv0X3)x=w7Lcw6%~5}*oB
z-xcAU2zV)CkG2)sy|tnh&2QDV&j4nvvz7NGTSiU%%O~y2uJ7^He3TpBXsF<rLiXtN
zYZI{FN(J>z6A)K&Wu#@FCv6<=Q0s3rl2s1VXp<Y1ysb*}w38co?`z=9vjprzP@7r7
z-EVQ-l?uJ`lA2nnArYd3`~Gvpottxak7m4-$(?>AR6eY_&o*x2gdW@&?Hc^4OGur#
zOM{k=%tLtr=sjb6EPmhk8=%9*n|`#zfetX%Q^EKhU+MoQgXkwp03^AhyMQ2N7DO;8
z_ty`9!Z{+s?7Omb)ldo%yY1oSnU#kR;(eWT%T;hxA&aTf$W(-8m0QSjc~|9UiUaj8
z!0b6Tc`h8J39&Mlj3Z$ymKk^UDpDC{`&=8EY%V@!6M_Su)cI|+j`3YSiqkD*NF@CJ
z9s<y|p1$>59IE-jgmIwA)(T}E;*RC)0!q%31jFQ2M^tXC!(LB0OMBA(0b&j@>U{WQ
z2Q?|sra5Xp<-3TAM{6n|sgF{*Q~BpQx7y`Z+BBD4>tfINN=oc`H{X$MrY{>Coc;r(
zH1d`tyBtaYy)T$A7{3|s(^=6RZI}N^AWQzUDlYwR5#PQyzuNKt;r+Fs8j9`=YtGid
zB`+&tP0v`3dF}Sdd3=&GLSZo<U~JP|jz@v82FE4K4k=`y$p`4+0E9pSeHM1kNq3rd
zW_&6DIRL=%xp~nxcgQgZnX45;7`xY@121+iPK?38CPD~kbT{s~q3}-TPPU^@wP)D@
zH_7(`jT)Z<Y_7Fw7gi5Al+RHosEaH#@kw~onsFnbnE&5B@B2%!JxelCn!o+>3=u{g
z!ri=TH#hFqV7X(51z|0p=0oXcKoc;4f9`m?jPP;O+v`zZuhs1Bp&ixkiR5~^ZY(`a
zmmuKg;mCp%(zoiv&%rQV`1N*x2>d?>;ler$bej0f?KngJIS7q#py~jay8j$SnqLM1
zR+*oH9}4;BD9ZdY_M6BrOqdJCzl`F4?X+bXqs|uvW?^^})Us1H`3~`-?=<5R_+I62
zw7P&+iQ<%B<>1Q5u~1W8h@E=r4D3{7eX$xRxoVkZZeaHyEq?n0pjKF{ykMBx`v<56
zsR!@FN%Np>mJXmlFf{+03C;a#LSFx3LMQ(;A*0j3nh?;R;-?8U{)-8D|6)Qr|6)S_
zch1{RZtUl+dk1Pxw?>*t;!OYX{C0C=w;zH1T{^4>otMn=e^W<UZVGE8*kjMyV^M1u
zKJ==|WDR^2$#n;`ZZPC-+OaVy$a4`b6GC8YHmJ@dZA@6xK&fag)|?!jRk8=3^Y=D%
zd-EE|<y&ua%{-TBw{E&*`FS^hF+9$WEqz)bpb?i+WpOo?{>O3X{L|omjyYg(KmYX~
z2KRH!0fYPX9Dg&opJRSya6g~aKkWSHm;-kH^GW?_=RguE&<n^X{(ZT?-ldTqDL2M5
zepWEiYei?Utfl##j(FqnZ{>2y-u6SVqi7H+QK>v(15zP9MN7s{w$k`U>`uK&#y!O5
z7Ik_Z3|23*;3L$+%jV$4bZ`ST&FpK1{}o-)cdBks9wY=+&>4q{j`}V<10VZoVQ)2f
zjwuys)F{E-Z;mnvt_#_g+wF7@7oO2P0T!m8*9y$!pLaMuVZ58*4nYU-8lRl10vQ{N
zV1SMN)EsD{5vW}r?8Ncql~@d_=kx?k{O|9(8F*l2EkNv;tQ1(unxjt@gv9^^oy>7@
z$#ndFn?|D*LU-|LZ|9eBzFJg&J{_OH-%m>O%jF;+PKy?Vpk+>;F3$aO&A&etJTzfH
z5TO_VG}VEpC5B)HQbMTfwoN;`S#XJJA9aGu=xvLR|JhzhFCV1=b@Lg3+&Y>F(yR;A
zW}n}hgv=pi%7fpW=hB_gO!r4a5dYp{wm5#h-@gon)g8pq3z>bfnsn-4hriYs<j&Fx
zheSKu6hiOpGtt$kZFl))0UDJ}|Gam#e}5&tNsmi9<D^TazYhihu$VSg%V~>v?<Az2
zbYXQbsL;devoYAFOOH=u1CNrx*kn+yFpA5;Exb&WlbT>}qY;t4q5B8Ov9NcZgG`x4
zfdQCuQF8m6NbAQ;Qr0m=d&Ji4x?L43lUIA-&uQ`d<EoHS4gKGK{kJXh=d2O?=g9rs
zBLBT5{$q>$IazalpH5}K?rYL`cMtx&gdsBj3)8ooA9EGwVUz}vbcs?Uh8UgLEJ$W%
z4cgQ#dsa;knfTr*@N5V6u9H&3P$R_z+?u3x%78xh9%TU5wT|m`@GJKDUVk*4x)8eV
zDXC}p?!-1uz=3ep9uw+C_5a5;yr=29H{Aji&4LJYHZ$8$Uq8g`y4*dGnS-(7Rk^0N
zx^$d;y`);-0KX=c9NhW}3Tbu#Gn=H#i3JwC6T;r=yEFUwx4HWdK=j{u+5jN>xx)Me
zME|1^{>91wl+eiW6Kip+;Iz-}hP$u%*NNA@y3S>S1@z5eS&Tj>Er#rTgO%nNp*{#v
zrNi&T&Q8$>i|Pn_N<Z0M)WBlCpPBZk?~Tf|;qQ8qq@2R)Ii<le8+QbH?FTjQOAFDF
zSBF(Vk@Pb_vzuQf{r>KJTsa{BZ(dktuj(&UcV}S;@N+|pmOCeRJKYS{_j%ns<6+M}
zmlY@~dZg+Y80_{=GLqJM@t|DRoYE-w5|S6N_5*y9&fH_ijL4L$pw2G6r@`O#j2agZ
z@lgB354C>CH$EsEkTo^uZYaUr6a;M5{Vj>$VnC$hbi_-pMGh*IRf2eO3iHBa*M+dA
z1Kkvbo(`-P+sN+C`WN~Ycz#tJXmi+qBARkk9HXmqE&HzLT^pS~F75D|)v=b*Om@8z
z2j5qNQpC~^uPWN4h>1?_>PwoXnT6tLxq@z92juY+Vm_%V2w|&s(a+yptWXYtE0#Bz
z4-*=y?e3Q7vm?&NEC(*t7YS($YXn~>O{1bP4}grM<sYEPSDP$w@}|v#b@gs$ri&K}
z#|{TG=Q3_*9-n*}1sNI&<z6C?tcDvyDGF|QM6&$_bOCxac-$Q&iTd|mm?z=++vz(z
zn0Jb9uZyIU96n^>w8@*I=ZwmBG2Kx143{5ZEB8V^Uzr3H<&_e9V{j4rjV$~F)}QlF
zgjimGO(1laHBR`^yD^hAS6bOcUal(TLj9ixNdJ@n!|-2-#<fB-mF;gQnm6OmC^dQD
z9})MsE4O&EUApPH7G~%dXXaIh$z6M2cBe<QVr|4<gVO}WSkR&OL*vzIujxS6C+)mv
z18T18uSe63<B$gebEo3<fTt~PW8Vpk9?h=5yFS`)Y%T`KjjXNZZ2<xb%v|Sa4zzFg
z4rIkC{nzis5Z89AP#DlAIyBGVpiF7rWxi-rIKb#Cy_h>oS-pH<L;~!RJXK4O_UPZv
z;_R&Y=MUq<0O+&2)p&xo0BEZ>NCTVJZiCNg_Oj4-Iq)C@<Wu`%i-uH0Ud)gm0h^~y
zXBQkma^X`Sq!}Awe~zi|D{~1v@ppL5bBEU}FQZS#Wu&Nor0-|^7ccip=sogZFW2mi
z1XVrmEha*43{4qv0Lwz}Xid4j1dRE?|G}mFnP&U{@c&`rY47?5LYHBzbO*|p717HM
zr(vu8Ehq`aN9FhgGgEg4ez-y1%C*$8aD^%gW@Nb9)S_uq7biCwSDpI1y=8~{Zmy57
z2@)eB`Q7u{2O<;^*G@OOo)n*lrN0^*dOZi{UcQvS3t?zth{8kP;~=Ic{a&_x)y)B_
zC`z|C+R=?4sD_DJe}ttC0Uju%52(x7CmrA2v=#OR=y=uh*m<pnvaf8Ws6>JMxo{#o
zvrZ#<i_3#V!_gg=$AXA-YqaK!s?67D`1lAl*w9Lf2@?3f*n7+1IGQe9&=!Nmwq$Wz
zELqIV%*@QpjFx1{Vg`#@vMgq1W@ct)u(-GUelz#pnV9);<8JK6Mr=oQS9MiVW#)O#
z$#b5rCV6`Wo)yO@J$XAuKu0H69sGXv_q89+WNz3JNn-4QtYa~)MN0=g@ZYL&|0j6-
z|L6Tb(`Wx*?>|5A5)y)mt+A7{qltmdKZkaPmPiQ9j39c@KS$i$bjlv~CUo)!W`92%
zO>CV(e=jQ1DVjLhxi}h`H~~pS+?~agoDG~!fMYQw77)unDa4dGKpcOMczJ=W&dw%|
zw!n3H76uTo{p+--ovkx)PLv76$ox+pQDzV$>%Wc|K@9Z&I${Aa{c{AAqb_G?X=3y@
zkAw}-Bt~`+u>Eb$KVMlGLBRHJH9FcEDVaEH(a8&o(kYv`JO3+*;J<c4|Jq6DfPg|}
zOpGlI1nu0lfUNW&Hb#07Gd+tAh)xJ-9dN@=AZDh2>JW3ZbFl}i{-;O&`9h~er|f88
z>tz48XN)}Pgp}xnO<XOEOcccgfr6C{44r@^O3schM$Z50Jtq(&FYo`-(|=3;_nsED
zum(DbPShIcNnsNsJ7W_%X%kyBXLArcJ^SBW|2|Ic>18S#ax3(xz7@0JtxPgAMLci>
z2K*N!gb;YLddxF}Wuc@(QpL1N=5O~K7vlo81p+};k`8zq85hsIdJ|Jcg*0Xw>)lg(
z3@-%8P;j)D$&ywq?4+G=h%yX#-e9;nd(@0bno+fkJVo|FWdSBVF-rrJ>Q5Zc?0DYz
z$eU0~!Y%MZgyf0n<>Wcv*W2pqxEU&z*2SobjpoD-sH|3Ui$Qt<&L*U#*I@y*bKJ5%
z{M}`VmU4w_tppVxWeY%_=!7!#kHx_s$(V>j*#!a$OBuumlnnH%y%Vqrzj7ujfSt?0
zd0Q(>_LqT?f$}xE$lX8JpmPkuL8hqUW9`NQhw4)MalxsjiF)4GP2~adF8j~#8Z6A~
zsk9tlF<!n89c*nHZy$A}W*nIvG<y55(iYWpR+6Q8%3>Q>K#m~5DG&E^Y`0!H^W;F6
zldU-iCw#;;p%FD>PtI@(H4aB0_ox~hBsBMX?bT+-4cs2bft7T^!j%smShl&mJIN$X
z(Z^3PY}y}W=F~I25hSJmz2&EdDkj5b;@a&P5gR6TqnUAc0`({sV~S;LP2c8?JBY|V
zp4;h9wnRsz-ucA<VM2LR$nF^(Bc#7U0~!`~YVYb1PS`nC#i=s@u415T>kD4wdZ)O*
z;{>HdB+}G3*W3F9?~Az2E<MZ{#tF>Z1_-?ma5@OCs*{p?17ubs&Br|*NILC^&CsPo
zGOJYkq2l~TM`Tw!3huA)fBa*LIaU!R+LUB0vPwdc9!!+2l?~xXP1#q=#F=Cgv+<w5
zRU~96T2LRus++c-?A`h(8J>V9I6g%hjHF5{RlGo4KiM)sTN1GKZM%Z85C5Q_^FSt&
zt)9go{pn%22r^>7+tqn}M#DGzBmpfjT*y)yMtX0Gj#Xe?JhH#&4BcWgSgii|yqn35
zkf7dY9i{WL9fxqA*s^20426{CG40#^z*njYpK{8KhhVGy3HC$&+FA@2_Zffp)X}_8
zcL>)y)AlWgzrRVhs?C>>%baP7k}8$s9sV3ZU-1jjk=QZGZcIvR;BPpu@E@<oZ)k@r
zU2cVYgE)GRH1=PE+h3&lA0p>sYvgQUXZu&AK}>YQb~XkUwtsPe0g!(Y+rKiX1^SCt
z|HT_fA!z3UpeK-?7PwB!1Yi#>fPJ*A04C9L0M|wTt#}{_^FN>nV*Ur+K+J4FnTjCh
zf4~dG{0~fmSpGq|zmW950L0&H|7B|#|5Iy)82+{cDE?pXzxYM}_Y6B5Jqu9&f7}CC
zWkC%8p`yPH``@VOzbp1%m2$AL{=G%o|FHirD*f9H{~MM5hvNRd`2TUI|J~L9e5Zdq
z{r~Yw{|@p0ZI%9ao&TTi^q&d!zftLbSM0yW^naaN|GA>S;_>el{hO};N2D0(l*|o`
zEo{vIwJBt0ZRe<DZ(wBdKb7RaU;ekmLUwkJf8~jZl^(>z%tFh+^shr!Mq0+d8W}J&
z0CPnP1SnLKe?EY7|1M)7=6_5GUf}gV#pfS$;NKhekKX<_3xZC`#nAb$W%G~WK&NJ5
z45(Qa21Yt@6ALqQXAm<Z^MBo7K?`Rmc@sw=I~#jD+rQ=zJDmhzCOF#L{Z-o_Qv+)!
z6T1Iile01aCI3fVewqQRth{g!zPLzit)BPl__I!TmX*wnfoLghC}lm>D;!C7f6jy%
z(bk82UrrICE>Pv4t<JCTQQ7mNO`On^P~WrTpyOioigEmd_(zzLH|iv-^QME<nu~86
zHF7IIP@x3=-`aFp%p=v*w}_%w3llCYtxE3k+Se0uC8gUPSDiXu&jM-DydWiMi{KS4
zRl<-fKLPEuprJ~I`{W#`uB)-)PF^^;%wez%qi5;oT*qIsfs?x0s&LCtcd75Ll%iC$
z+t$JXrNUegfh|K#asFc=^x7|r^Tv;mz|-z+N>8@Ac>sze>7yDv%Ox|=1^Gf~6o=`S
z8lJv$ubaxrOmDU~c(!eP6jwr$i$U9Ng|@o4ScA;Xr5qeMY;L);P6s0+625Or(NeOi
zJnj^CO?2%JO52_(?e3Y2OEGg&gNs|Kwh6gkKfT%=kL+d6K@`w-NUQ46C6!ch@S{R8
z5Q0w7;62Kd&)2jM@NV~hCq#elB44AZ_WsgzTpkKnWMmkUeI-&7D=s0@w0~tJ;T^!t
zEqHsVU@97`UGT}fF7-Q1kvd`Xq2A?oMIZ<+pf(AYZR9zs{N;O`>m2d|UKR9(j*_bA
z!aQ2g^-`Z6UX%9DG5X`wW`^#POsHkuFrEuBTtKUUv6@6-voZOnaOZ`tuHCeX&?55U
z5@N|u3RarbYrMbaEDIFJWMGw5<5;9nA~kl>Xinc50kcI{>iD5Mg{eV$kuZhaJpRDn
z@#{x~CT-gIp?pQk7sRYDB6%MEuKA_PXZn8oF<PE=+R3@fR3z0=<|%)38&p#j40+r&
zBgTfP+f712a}PK@c=JXNc)dJ0SXx>d8X9_gdm9_?mT5I;>*&yFH`?!yrpy${W$}1n
z^0qWJJw844_x6$iFH+KiqN0QS{iKA1kiP8$?;qLO%{#K#Q$vX?ble#JAh_#bc^x00
zfk$waoRy}IlFHoEbYEZJ*4Ebg`np=V_ENpgN(44j``gnAFyZWG?Q)zB#`8oYt7~g(
z>+0HE&z9S~{$%U?s;KaDy~9)EIp$LOlqDZD?-kejVky#?@=gGCS7Wu2@<dVvYM?|*
zN02O@$NjRizu$DGP)4axx~j7BLB?_e2L>4rZp^=~t}ZYT(iv6LC_cAUQ2#*w2A=dt
zl1I1kG|4XD6uSKrlt9b^N>Erkj@qZ6GubWWr67f=3EBer5Md%dz78~EQD*XGyS6*k
zEMA{r6hdCt)5T(?B3jK_^PBy#fhhbI_p98N7B2RCHrv(9%gaXZ$Lq(N10Z3m?OL<P
z&Av<~x5wyDhDoc#^)@d=n(nYqU$=L55;Zj|m1sUDXK<Fh<zm!A=qk+@6kH`VTD=p9
zVIxQh>)oAQURl@L@By!Dd6ZE?7-}ghqNAeHJMBxySAVeVFda)J7K=*fay#GZ`QhQ=
zA-Hu@I#gU-EFmFrb${>b=opX*%?{4;dutlPaXVd%jfs(;+Xf}3s%dw)@l`!I>5@}T
zub1Wi!drO<C9tk8i8E(rN7@{@s=1JY?w0f2&|~DWK+(d{@jCIVTC?k!C3Sa+TE+eK
zH3BlStc*-qNr{z~l0|B;C^;=HEd|A3Umvt5Mn8;n8Y>bfF$V`n%+jqAb1ey{YONQW
zGt@LEQb?F=GDQ^{Kk9BIF|}Uf-DcgYT)Tp6!5|t}9PGGXjUh$+knKi$o83lxCXW}+
zuk+qejMvxKo12@Gl9Dka4rhKzDJdo<rnS{o7$h8H{fURG9l1<yZ2Yy;hs4|*1p<t;
zb(7xfcr*lUf-ulv+IM9kJjfgFel7TB^FY?`!RRVdxMn^+K4zmo4NmPgT0JmcyhDYj
zOFP!ae<@S_YAY@MfI|kk((H1=>UDowT3Wh4JS=SJ57mH340m1ljxj@vG^P4P_vMEj
z2BKt}4K9Xw5jMZ(&i3{P)$0%VoX&H<RcPE7@Ph<%F7NMktBr?RF4{}%E{7ApA|fKH
z(6HL){}OlDFI5jl6h?#cuz?>h)8m_}%Ek~WjL3O?NgW|elyq`(N~X~&q|2`{9dmSX
z85<pCF6x>P(AUon4D9*#9`ZJ_!~Ke=Rl!MwIa%8gEX2|Hz0SAoYXE4lL|90u^{zO0
z{G&#5h_M-4a!GCN?d{PFOb=ydW+pemL_~c%s$>EBa&ewm^l=A-v$4024=%3EDYPo$
z_oJLD29HLW&wvzsM72i@vtAKoV`fh7+t}F9S0R6xhK7bd-v1G*t4V?`p{hDRTdZ`a
zH!wU*MK^KFsK%`h1LXS<q&dUPr==i}8d^n`JhD)(Q$m-Bh)GRBP0gH@a|U?}-QCmE
z@qEs8-sTH_-q7ptZ7WcBvRWu4<Sp*p2WGq)w!gN}S(t02jfF{}WI<R&#H`*ThsU)s
zAt7N6wgpV~Cw9NL7coMg-nJcJ;7fl0mM+ysM<C^LV~<`rPG0ZV`V2taXed47x>vr6
zv9y8{cCTR-Fmu{h?5wN+(J_=I9urkp|LIF;*7@!IBny2Meh#J-#7v2U)w=*t1*>hc
z#DrT&uQS|Ua4^hk;*H<iO%^c`Q6}H|bqT+X%~^p|a--V$T1!Jq%QKTH8*|0_Dr^7<
zE>=?DT6&$5ib}J}5W46)fIWZ+GSs->L$JR950rn>$&^WBWl3B4<GVv<#oHexin^<k
zt^2$1R7q)A%W)?!H`nv-xDS4Fsm9Etn(ZrdwLAqLi{-~ZND;Vf<@3ryOYT!DQ;Cv#
z?d|PvF6WqE;I2I{7s(Ab{3gWASWiazydPp_+5ngV;BQk!d;M`+oFq=@2Xfopyw<52
zgTr>OwbBYv7+wZ$i+n2&E2ID511&IJq<V9qdmS+qeiBqS`hj`L-*j4BZRP~1D(DQW
z7H4K|F8sh;&q1SUY!k*9@{ZlbPZ+;fx4vseK?(rct2ZGr)^z^t`T5yt8@v<Empa?b
zhE$-4O-MN5Lj&ON$ixKOATfrNhnU<Imr_tYGQI(E07$&X+&oU63~f*gr=I~891s9e
zlk(>p0~dG1hr{RTwz#yEd9d>!9HIVH!MT9Zmgg815e-T}8a+WlM&|l1g4)V<=66K}
zU6EZlCXJ_#+CvB2(9qCo6YJ~K9a9vRnyR)Ai&19n*F?hPsV|sBZ~>p?2h|908fk2<
zSF2wb<FSc|h}f^dd&zpbIpVgaFrM#)f<J7v|Egx}xu~J1jNeG1(yRQ{zAF7=4N&XM
zVu|WCt8eYU5Jg;Vi3<a?_V@N27+%Dp@Io{F5GR@Ov9Ja*K$SkvuCasmV{*mOdOBvm
zw_`G-Lq_~jp^oVDEgcuhY1&5M7#p!{n2lv>OI8m?Q+hbSG`*c-?1Dl<O?Ic3TRkvt
zbCN1A1_M5X>U*EAxO%w(g9rpFTeixtE5sG)?vA*iN*rEmadT2kN>8_Th163?Pfu^W
z80+cj*|aT_k5wg8Hs~~m65tGZ!8s~t!FtS~thTGT(n*wbxj)rV?Rt9{;DdpIN!{o+
z%ay0dbYL`M0@QU91bYYlr1P<uXjo{dMMpCrH@+B~o|&yU@poBnLjg^-{QBu%NJzfN
z(_vv@?NB7!h{(vu`1<;8VIUwNY{pHP^^5Z7ZZYK_ZqQItDwzK%WMO3uFmO9xMf+L3
zL2`)+D5Gcs9!{5IWfc__t~N9&9i6p`j<;bOBtTWL#!8e~Y>}`4I`<w#-?G);VHTVD
zF*KU39dVh(c9mVVMYUA@ORV_vR9t7LMvZAC0gq>D5`$5HsdiHwz7<4MeSJLvm+NT)
zrPAhKDDr#Qf;~)}U0A?qBQ2(+q%14`RbM~9w3NcLh-Jypv(I8)NhX&$ieq7AWyOtE
zdh5jxbN^e)GVv8I09b4X21sjOl%I=QXzR!jXf-<Qh>ky+>0I3lQ6&a^MIIhib=&TR
z&ylA{9tm~jKw^IcU1&$(oB!OT#kKv_+>E1`M58q?DW^*oFM#omQ`$oA7sG@$>n%&8
z9U~Rh@TRSi!qT{3Wb=05%6q{0#AwBl*ct#6g&&|P4HtJzu+h?vbCdbAjnA9oaldEw
zo5~fjKgn=g_qYlR2n-x626qC*H(u1_=5}w|S{8gf@J9nI*!ORv66b%;u{YlPAI}t_
zd{sq8Mm7yUetEbus#n~3h0X?_3PAFB-nw6f0hl{h3ZCr3W;RECc1=o3I^(>E0F1K_
zzpV~C`HA9jNdlKw78ffeSpaOkzILq9n5}-J(`(1Gy*pi+*;F+#G11ga?Yf16qmCX3
z%#<=T%yq!a92^u^1usR38W<P|3PLDP&C}g}R8UaR#@Yl75i-X~=Aj`AHC$9Ej~)w;
z{Pzi!fN7VlK<Itx-_g;LVm&>L1J=_6-&|j7Yinog{A4r<^Cf9MK0XGL0b`Bxk${s?
zDw)>B)z#I>sm1vSol9o)7<{@^BP1wDtJnUa4>(s1V5aLCa^=844IR7b=w}Qis?bp5
zUVe1UWnWg{{>YS6=x`mWcv8%W1|g?ti&mSQ$V(JX7An|kY|`IwqhkUMN>t0H7r?)N
zQ=Q}8cLl;8a9^c=A#XV*sLkLhPyoj;%6nZ4gSQj_C@rWRP_1^$L2%Vj;ChcAc{Ed0
z<D%2#*tcn`ylb@LM%KJ0W5ovv+;0331t#?(W_pSd=e4@D^at|9f`S6m`p6luKp;#c
z4vS?khG-NXhxda$Yi?B614@gK!D6LB`kR>v@OP$I#md8lt8HG%)4?>`l2uP~7Q{@I
zkYXt*Db`^}q)#>jfI-|y`>pF(DJ6Av2wgmZ9U}rH{w%N6BmMl87Q#n|87@Gg;Zj{y
z#n54q{zk~<I=Z&@dX2GJlb1)#{J#Hb;+VAvj1#%PKb95)5yjJroJLMhALC;!Bm|k(
zeGCS4YSVi{e|~*}*B|FYFON4%&A=L9#ZC%802J~iJ8XTuceaTJu-?;eHr8>CWUkZt
z{nYvIkzV|0Wm(vWN6YV-=lx#q_%6L~4*5_&e(ar}YjQd;MVTxv|02rO_jmd6F%?zG
ztj=lsVsB4m1|J~7;GOvKcOu<p4eV5*z$4Ac<Uc(;bndC?`Sc|otG~hm0Me^!28b<@
zyMhc$3adX9Y+Td>4KTi`Xo)%-3zRjr?MsO-*^jG@wO6sE`&$j=;4g)@S%(~pfc>$P
zw5qJeCgb7U_*N7Dks2m8e3p61lbGfq&ta5aN2Uxr0$4-bk&&hGf!*V>r<^|!+6yeL
z)qSg|s7SHdF^>e>Rf0#<!8r%$`1nZ6=bM}Mo1OkWKTy0pJ*#s_*fl5pZt!gK<bfeq
z@0@qv9YlN8+Poq}qR`@7D{H^m80_{irT=Rg5qP+h`0OEgFn;|lCoC-d=X^~d-1q5-
z9M#}vrN8^AW9R_|5KQ}%EbvQ_ucxOcCkZk(<M$cA!&dLBJ}jjoSsH3;Yjg8=7n;<V
zxYh=y2XJIEslAEY`+j4txjYRp@bsS{sET9dR}2FRFUZy?&?6P{0Q=IQT*v5E*tT&?
ztxT1Rle1dT*v@W4AqW1u;MPz)xdu~uMh4QksECOF!NmI928YYtu?i~#jZA}erj791
z(?Z8MV1tjPvZ$)6wz!^E*VME)?jbCccA5>&0IPuBUSU>fO{9Ac0P`)tZYIBe{rUzI
zQg#an(o=)5<6Wi}^o$!+I4_|r*{k@%HU%mGwZX!IX$TJQcrSk`<Wbm|*KKa<*QE7r
zo}aH*=<!KLTg((fLqT=YG-;RnJ(1`e7&ts!ZpFo+)9O-Ou6tc%usZ~pdm(&PE6<m%
z0@hcHi|XZx1`ZAm9QK?17wc_J2__>+DO-saz|;J+S*;iK?b(>iQZ+G@Ja^tRA#S=Z
zeYpQSZyboLOt+gxVNycpc<}&@R*Eo|#kZmDT1R{u!7nc_@8QAG_y@o>z}lLWS!{Os
zn(yW1<yPx2V{B~f{b$WhUj>+1fcCw!Gf|U-StikhR*iKjarg))5VIla5$N6f_K;Gf
z*!SBs8SVPf+jbuq?UsG!3LaEAD4*y1wQ7^_p`FfAj@B8RY(#s&t#ChL2B~B}I9J=I
zhFr%H#mvh1)G?RJ{1bTzSVJ4WgBx_vBd8-G$`cecR#a3LmbPKp>u-Bs)8}E!xA-|t
zDvA2NTWUC%zZPw)wY9bVaUb`F`ofxLlAubD*IDiZQTJL@Nj~NelZ5c0JXmRrcQjbG
zu>7d`pYlI4%j4#rp5OcozjbEZ)&hceJHXV|czt|2sTi|1@akkS1I&(%8nX#(ny9EK
zL@e6bEMi}eU%!7F?r3^n50S&{@68RuTtuv$Xi4WAR(p}pK*0s!n~(#RGy)R(9QDBm
zOFzR%c>!g~YB!WB_vp_}?KqL9$Fs$y`s?jJEyUC|UI>VYO~eZv_vdRXPmtSVA6m*2
zr&lURtFFEK9|EsNcgJ?9IcYODiy7boxDk^p87(E;4KrC2s+5Ntqsp;DwGCoqh+^##
zhWh$FY~FQMl$I)|g*jg-cEOZFW-l>5&}c^#=gy0fCK!T1Py(7F29XJx*dB{qdL#>y
zdMcmtz+D3U97~<-o!vo*LG1OD0`^)H6nUL)9xT%%{(Y^@YQDNK0;o*cUwW?v_y0U^
z-~7(}?%C)PJ_TM>-x&?ZqBAuan3zD3%Rl4yygwa!BZ`ZQ%ZybaAsQOmoy#f~i%CYP
zQoO-QZ{3R57ApJDfm-fnUa`l`Xop$zHUM`ne0_WCe3KIL*}%+f;EhN&llw8E^pxy;
zR=H;JF3!*qcL#A_SU0Ad^QhiSq(-}XfZas8CBcWPwpHPGnD6M1tSHvArCn7CUwiSK
zd11-Kib`B<d2(D23Wvei-6BN+TD+*wX60pNua`Y20!}a=KF1^{n-L(Q#pf3mRzL9>
z_J)`__gY#Gt+n(kahyp>B5=4xX}gjp(_yHoIUb=qxD^rdes^we?^-QU3=EV2)Al-!
z*EqpI$F^rpA|5ofNZ7F!crW#raLlGpYKWnuxEp7=D|`1AbzZwOnqW)Pe7K3G^SdV=
zjZ%~my2<yrE=p6O^7iv1s^hF~ooWTwEGJOqbZ^-T$hyolyER8m9D@6#`cr`QzQ)*6
ztA`Vf=+7hh<CBvn%6E{j3+Cp!1&so;4t`0F97u63QZ4U3`Fg$p>l29z4GFS{=>q3G
zb3VOz2j~^ZCx2JA-rFnT%LweR%M;N<98S|rPDB$j3T3w)MKixd47An=)xuI;KhKTK
znh{=tUM0nWVVXuN-AxKVB}nAQ=+qrnPF-vb{#L4%U2?tB=obA*myMKf^bU)AU~KH{
zj^G6=R2b&Oa@X-us%{-V;%K9T@A-tD-9rX=$nDqMHlk4<%o=#Ix4{>h3~pH=LSZs9
z(T_cXLrG)Js-64UDaNXxY)D86LBSFyvic4gJBm$SI=7)*U--4%`$kRk!`#7y)H8x5
zSGQ|Yx~1%})WSdRdu8@MqsgD~+ip`0%*2B4z7&o-RaI3PKp(ulKAW;4kkB$RPLGU?
z0KhacK}SPVmZ53^zz-o|79=b(9-nV|2}CL+7FP1vN`slPajjUvgugnqX%7ar*f06w
zquqeuYtCYLEGnXIZ5YcG?$9ZZiZnUWdA1lIFBh54NZdxD$H;E-wkS<J(Xz;}5r#7k
zp(lvwRv6-xv7Hf8Q2`5_UCUz01;ts(IWlnEu4U>1hz&E`F-NhG29fO!@w(vHFz*Zg
zz$Bxk#zWFc({_=Ww+B*3Q!)~rf;wHYoAdMhXoJY%9q!-*TK`eDztw!{`#KNs>z~L5
z{5rzPDSJ0J@KI6LEt{Fk+49`<bT#m@IT@aq_|afg@{_>`2Fr+#h2@jwcVTk!qWZF{
zCjW}Qg@t66x#i_%6_drxjA%Kow8DYiA?ZjqyU&F^bhfRo)aJ|XhV-!{@m&>N!g=y#
zCp(ibo|=oJ=P(onZpLX_G=WAq*+hrY{LR8o%HN%K(hKBVyTXO#TaFIG`}A4Myw@^%
zu=crrC|hbQZX>3A3Q(mqixh8~V~Va0D`XW`2=CrcpP=4i`nseT4#h-gq9oOW^8Cqz
zY)<|pH%CsOEK-)za=AuB{p;(sI#nG*m?nk<4SYkpO1AvWCO)+B^^Z*HJk0N$A}Udw
z8M0$2M?OSI(}9EwN?hB;cd^nsUy19z_QUJW`uDY<XMe*TFTm|9aP1WHz!Nn+S-W%8
zab>1rf4~ka=}mB<uG4s~`TA!1p@xJjNBA~Thp~=fKaZF)dgN!vB4^Hak(0)3zqzH(
z$2ySl)*fDaukbf~CV4V>`S9+Gf>h08`*S>>&1?rpS=n*GA4hqHO|gotekKDrM7S<m
zArr?lxEjufx<8~yyIyUJs6KfwYmNaX25@=_=WasX!PQl_&N8t}K@Tuw@!ju{oC6l=
z47$I=mr=c|aJtUdAP-2J@!Pz$(7^&xODA{lFnkefgb&bZ36&RSV~#T#_?eJZgi)bo
z-0!RdBUV(TOY+6v8_W~`b_8ZLX|T1igyU~RIW@DDc@Rj>Rh&wzGm_WyEA+zo4B1KN
z*1>v^khK|xi4fYt<;3xzRG3xmkT+g=hihK7p5uNA+nI`_i!qZcLt6i1=zNWYsyVV_
zR4ogpsjx-mV;%SE8!P*?oJXg3Y<0HcXh~8fA9Ny-zw#V~f=1@ZPX{8>I_U&$GF_K1
z@tU7Gmnelulqz`A?#J5QEmhnb0z)Bf(4)QfUmS^oH1P#&qKyb28nthaG9PFNI5u-(
zO|OcU$pgnPItEILlWCo#U%j<H7@ya&8O&cL5CFgAaH6kDpvzMgebZ0=s7Ebbl5oo#
z^A5Q~?gJA>hCDSlzM?1%GQJ!4ovNdacGVzPmFH3wiKPum-M7b+Ahys^n};`jh&qxF
zIEd#3=lM5Z<oIETwGD${QiFrcW@f&UT=nH1kt^kJtaz|!l^%XuD-`ig$#C;^EjX&d
zmx+ss>7;$rZgwvAAoD9wJPJW0UOd|G7wwTx0*s$PEx)IktlZq(<x0gm%`pO0qik?L
zzZ8Sz>@RxR_EBW780-R`k?K5acNm&OKIbnQXLnP<F=gcjycdg-)in~rB-o`Y`!!}M
z)rh{Pyl#GJ-t#G^8$!tE3!ipbd3y(#s$0$9W7Ja7MXxspHE|h!MuYp@u(}rc;)W0p
z0j_p8$Lf80Vkp{m_lTuyiDi<N#_D`!OetV-AOAq6rH$8SPT*uA5KBiLawqCI8>Rjd
zE3wtRuBvhz5o(ZYGjlS3v>cKc9{NM9#ka14=tyO{flb7}T-R6Ke6lN?4xRRf>RnL&
zJ=r(CwKWFf8b$ONRUw)WEoeFlwDPJ5iGSita?Y};g`B?jf>Or?NN2Yzr|yTOJB1!x
zJ~7{as|F(lCy5kJr?>0DLE!dasKO_jK<rhuJj&l=oS4Td&VZEcn$ksRkhHuRYHAf?
z<tKk?ka@<e@kgRmwx#4v)=RCNV<{wk(d}wxxeVog&%$PWQ-{|T-}$gP4fc`a{@l~n
zy(A_wX{GMXnt^favf#+pXj*l)+O(;77~I&ax)73Bhn{GII+Oik<mN57Rf3ie_;GIo
zQFya!4amnZzWDPvp5#UIG8R`c%hVh<0AUu^^c3i^4#4eUb-U5i16keI>#Y9M=g_nD
z^?pTPsKfX5gDp!);v3~0eiKwk0rV>!<s4<v4K|th)Hr_gw}QDjXB+%BZ)~Tfg@A;Q
z@e$G)7|0U}R4E?240$ODpfhynpn%bz!`fs;hnXK<Lt=ZJ$6!ZZpXH7^;)#i;44P*!
z4%b=+6};AHTtlqKo!3kR-eYt8obscOTRUrarHStvORNM#)9M(~x}AYa0WQ5Ut(Pm2
zXi+Q?ziZ^_t4sv=VmBUqo6Qwl!t7$Whf*|MY+~?=OiFeICCOrQl!-|7TBbAoo#|*N
zV;VLP1#Cr9N3lwtarLV$cciO;Z?h=gTsOmd|A_hcU=tyyI#+;q-x!CP8AGlp(nUJU
zne3Hrl9^wl<a6cw-}OyNNY}nRRYTFAhUU)EOddZ?He^HA9Q!$n`}gl@>Z4haGSg;*
z)5`Z}`BHukmZSdRNf#con+E4ADqP@KWz_cyW6~SRPwM-$((EfF*>>9RG%+79gnS{N
z?&m0wbCv{;d^u}T?-OlBNW%}&U7P@P_s^W>)zwva3^INfKr1SwA_&Rc?&m>5K=c|q
z7#Ms3d@_E0%Rxc%zw`6I7xn1}eYn-zbm8XV0nDh9ECPn$LZs=)J3SC8;Q#o63*PEq
zZ)<G@RRK$d+S-Ia-*a<`f&!*=&GXf9JE?yCvcZzsJ2>!rK5Ix|bu1PDQ?w|Hk0Hs8
z*qK(Q(ob!L<i2YCz`O-J)XReHHVlJT{T_lJxnHyxeeqJ5{c)iUO<}3(c<)y=@<WQg
zw)hdoJC1}~u#=$g-*m3atb7tk>54Z?jXrNq5R1;71|wqq9QooCIzp<bB{n>g3iCq9
zJG<a#x-`9bF~s(of;3A_OS?Q(r1Tx>(>N=vY*C5?RCg)CWXh|vJo0zw)X@nxF{Q6P
zel9~04Xf-u{aittpTOR{@rfG8_KtsE;VFY6(NiQz6vFZ)tCXR$ax5k%cU`5SS?GH2
z4H&*o_1!s+Fdb!sCp9R3C&_c|1ZRksU<~#t7WisVP^e7(C^r*YdQL%lsuOClr)js$
zmm#_pcv>zBc=Lj~)SLuYSZ7W9=??`-n^%sn8oi~{iX|y=IR`4RpwDUl$bY|L_&{V5
z&%@YHpu%)hdDay^+bi-`Raa-j6qrg5xK+!5D3{OJ-g^s^8$53JOJHy?F);&Skbu<@
zh8L@?hQ?B*LC@3eQ4hgIcjDjpVX~g@kPImbKIcc~^cJg9Fc3+d+>OL$3Qn`t=OUv9
z176;BKd{fQ20!rI=8v8kz}ly`m?lB>&c*N6th4;|`SNOjaAj$!&Zd{d+@Vuk(kq74
zjfy8+ySUgUp=5T9nN4CSLbR!+E{CDeLIB(~XI8+-7=T@J0cL13{iLHOCu~UckpV17
zJdRvb^JMqPw6j>lj%T9CT{jYzZ8unYmh~_j#-gW3R<*!qu9^h-QuoBC;l{?6e0xz|
z^4iiBF)^_PKBRoD*ok-8h~I+zd1g($U`LNFn=uLBUQusWrB7R2Y(RU0n|TNxX$(9v
z(T5Sg4YRkjvPPKbZ5)_y;kHybd<oa^u0l_Tu+&4ZW8mC7LPHM~q*0;wTjG56(-#DL
zs2Eym;l03_$hmAmD;Az@-hy>S;{!=lEPdM(km@;LnGlOIh^VrirUKp$ltW-~j{Jmk
zVl+{oGHev}Wp^r~C=c?S5tWCb!jA4GE;L0<xOsMIl`M@-@xXG!6-BHAX2~psc+-7B
ztS*~en`dWZRO>(tczLRq++IvN)AV3p`n}`u{^QHd#FCV}QQ%$7=~Ub5W9!t^ls$*%
zt+{H}(vI_AZzd2VF`ZoSd!SApv0H0q@p*EtC%UH=FUDluo5~X-iWScd;$eJ8Ux}fJ
zhmU_$uX{LnaMNJ3(mxM3wr58HKW%B%GpOuTVzL~zf2YT${MJV<tsj<aIQ@s%)Gw*4
z8~5_8E5%M0+{T?Uu~5O3^?YaEjEM4Q0l_yb+GPResbXppDe^Wr(%4)tL;GU6pv6QJ
z++m5M%{b}zkj3al7Vmh6z~opr825PUFt-Z^L51wV|1#N{y{3oFf+&bRX%UeFZ#kYm
z4%jy>FeRsv1VPmELZj5Rl((k+0gpUX``=T}cFQk>6b7qH#8;UyUR#<tj$yn`SWknE
z1We@TuXNv^FlBt0JL(v>E_<_UOzp8#0KYdyFtXYGwPP{cF5DGAdO2DrTRho_cR8-c
z?J%7E)$k^pon%J(N_10em>Mnfa!-YY89a~@Cz+lhvu<D}LYZd-y8?--J3er4?n@9k
z<Hw&P&&<jSc(BqupOhul)YQZ^fZ&|h(f)qFEVlh8?8Bm=E5bwbZ&G%X)s@gW0$YmH
z)6<KTaezyT7LAj*<9Y2uksY@BEM~}f60^qiWxwWgb@kvkxuZQP8G@^MQ|2P@X6>J6
zbzIY33gX1gvB=N)Yc9V2_DWKMU)qMk_&b6#a9E4Mlll95$IRLn6nLrl{VB`}ZuqE|
zvkxfF+lyD7--SIhT;C0BM@uJgcUwEKO@q-Qz;@Z-BDv#RLif#qT4F0TjFG0^^(5a1
z{y!A?*vP9i`h^Y)qB_wGc%&=772<ke9;1|-(_bzMQSCuXbJMMwM|Xz^sEc8q*ssPf
z;7bf}$M-of#}{aC4)&Y+hMS+k{Z#w9h9<rl)Op(8{T;tn)}iF}GQfO%yP}Tq_q6sy
zg-PEzEPvSM<5VxQ;8Hu>9TX&leWa4n3`JcIw=iSEDfs=fH5exr{T3W9Bq5*SuXWJ1
zh=;)U?1ON(!uI^kJqgxbF&u|Y+*Ov^aw!i^M0OD$DrTOcb;m?{<;Jz;*J&E-07aka
zFDQMxbbQ(nbTTRh0|W{y4X~7y)Y<9jqJ$R3IKkVmibb-!j>1CI@pKNdJ3YTUH0Sq=
zmDch}H}`&zdsv4)wM{p2weg77PB8o@FKHt!Ei4UFEn`1(*T60<7VeSQRX^zb&i(47
zoP*gUp5jELhf(wbv$}2}re{)?R*sZD=df;pxy*IWOGd*~ixa%3f>I{dT7FdS=*UAq
zTP}~|Y^6eugZX?*hqflh$>M9%66%=ho5j(b3Xc%x=a2jS*k3zx=KNs2G6s_q=v6LO
zyQulcMvVn=zf1^{sOg1LK)x3FMslAId@WsQ%A4Xg&}%UsW;C8oiO{ss4jC4GbQ@3<
zNGeuA<69<^t(3}53eNKF9UYQPynQ<H08L@g9b)R6C53wtO6OyKcQ0aU3v7Jp@34zD
z=}%=9mZ_G*@ul~F;vkV<aiFk~A9auptX9pGnYkU0{WMFLqjwgv<iYZ+W=6&hUB_v7
z+s<yk^9bS5CXd`lS#mf=o01^I_&{MH5}Dxgftd(Kh_g-*x&~3-#aMMnsYq-tRAuz5
zP)`G|WvLBOEAlP}uKKm)sc*u_9^eF=II`r$T7@cUYx8mPV1K>X=;%-^wvK$mu+f5n
z{^HXW0KIBkk#yHlULNJ+IPxU-Q{1Qv40w{?E`RV<YV9tPK0V#n(wyRuNaDr{@#*AF
zJ`~RJ4DXg`h2%ll4UCtha_-e2z5CHhx3fDlf`ukWhyHnR@H&$x-;SK@YC%QVk50uw
zVJ7`zbZGa7LGvDeXGY8D0i72kqQCkgG+!F)VX%f^fD1H$n*E}tCdGXlhihE$ZmCS}
z)b@X#{hKY8m}Ex_T;oDCggJMau6HPO#&Ox|#wJm_ZD}GRh_c#x)DiowavJQ+XtBl@
zkTR<B<oLX0Dl`SP%8D8^;@7xBGR|-bZ0cBgKOz7s<rQ2%plKcTX6Q9)wqm$m753(A
zmOd7^?b!A{jHunZub`CAI>Zoj#V&;N@f-Pc6f}~v_?hLWxe?ChaFyA7A^3&u5>MNx
za|8U>Vwc$tHnA3HMp&$VOnB@iJ*=UGy09x*LthgUmbgEzeRs8{!@jMQmScSpL}$T_
zhm7B5!6?SMSWQDEn+`!gmoDpgXr;M$iIpr_z4n=&2VMIHuA%%>T;%J2xG1BP{F<mA
z(2lm!)Xi0%At%73F>R&~E`gOgF?l^zdUNAZf@Y>I3XthlH%DQ{X(q)thu&Nw0GvjN
zsjM50CO{CDzrI-~y~GsC&AFnkj^)tx>m468^#{dupFcW$e0<l!{aW*Wyjgre2yy<9
zuhRF`Q<BRD>Fp7qKt&coE~B1<=z%cutFwJplJ02wK`M<}UE?;z*YMbik#ZYugkI}q
zNBv$udz5PzI>yNW@1>Lv#{y4&UDnY7dgg{i47yrrnF8&{GM;ZLTy&SbO|Cl6!I}AP
z<cy8vO5XBvrV6j8#k(W4#)CzpJv8fciAX5qJ?*pQ$B(Drnm?raGCD^af2<DpHn{c7
zI5WV`%j42!%SX9$szolvbDB0bB`(Efxs6-pA&6nwxh_~(v&~yDE@fO4jo)y?P(d;8
z3{bcX;J?$v#2^WF^K5vX!zWu)NU1OH|GB&*Imq-nZ(<v|@R{&hheq<vTCe^z7bEHF
z@^B7APyFF1vMnMm-a)G3Kt3yZGUWYXk6#Y2ok3IfT<UE)XRND>h@M|?g_P!t?6lR|
zR415O=Sk)ChB6UHSngtjZG`T6L1pR8_lQD?RXE)8QDj)ria@|RYZO7Iu8>Eoz+<{O
z{WoW%$T?gIMV!@~3?Ft-{nROSP`%2V7h_#{W1$GHSFvE4ZjQyCJZo3qp`xXmV+q9A
zrkavvpNl|&B0yLQk(XH@0aH>?*gHFuaIY#hrU3kk6m<CR#3AD(&8HwltcXYO)y}}B
zE8ws(24lHEfW<^a6h<={#ic**0`vuZKOCAb6ci!SJAe(_e>P{iwk(yhku~fN13tMs
zTvU)&4IqfWDy>9?XF3EJWLe#2sdR!9$}Y3+T~R(<JKGAdJq-c`k<aJ_`P(vKr+AzU
z@KU)<W(pNpr&oHew@|xkzXsgNrFmkPe}DQmfi?<*IcW}%ba}s9a!#Zg1W!&*7M-$J
z60idiDn-VA^+_-mjV2ULGO%21$>4B;(M+P%kz5bZM7C>dYg<B2dXY;H&>RByKX*`z
zXWt^y0srhma!^a7{z{m{(SLT$`Kk&&rIRaM;A4s=lD2DxOyx!^UPk?33oELC_EvSe
zWYO+PMZw?mwt~l~{{w#wsujhTJnMSXB&oie+`mvkm$o`-rR?Mk9Fk*b=$y*oL{?Ic
z!OYB@&g~Ir$jza#;1&as#%4$MDuEb1;AC(AEeRSC6GUdVJYS|oS!m4}g5v(XJA~n?
zfB^&?BRd~^jhT)5KO})k(T4yc*VuVRIvgUxPcwf0Z<=ePS;p}1)B(<pk6ANBy)jsA
z_T<m%;Nv8DT{(+~^99rTbs;7RS7xNW>4w2Be=8wU`1YW<HZR#FOzK|9e3^(2I<egk
zd^v$!aao$MR!qus{TL0bXK>}aYm%sBJ)gTdAQ}Y!1u>`_pUYKV8gp`WReQb;Mz>O6
z_^WEEy^G7)Ld6Cl_(5!%(PBEkx3@PC7Nz@S1N`>KX@AsU?e0AgHpd4z2jZxWy{#TM
z;zl}3W7@n~;CU@Og~CTZfRpIK$e>QyN!dRG!yza*8po*w&wLMV@_wwo`jR5rv8Q?e
zYyJ=%;$R4Ve;|}Mmulu#A1Go(tl2iadMZa^VRKd^*U%F+{#~@Bh<gV!OO~#puIrp&
zsKK;pZgJ^d5+r___<Kyh=ar57(<P&nU~$^y=Z6RHm#YCgAV>x<M-&wmUtV8<8&v_^
z;sBrFvm{j_txmHu5RV5qmCjC1kOe+K><*v^0UpdG`tdJ*0BMaJU>n*jGqd~5{!&<|
z(EH)?{x*x#<#%4*Uqag2+M2}r5x{CXyaYH#<3LoAS_oSDbTUV%^23qx4vO>}ATYcB
zKNH{mQf6&T|1{nbQ{dD5nk6Fe9{tZZPlgU*ijRkHKF-rv8Jsc$eJf+;uD7VCO=|p2
z1DDlWvJf+v?|M*wzYnMz`ep+`4W+KdroYzgB3D(Ckd)Mo<KIe4O<hZ^f`T8R#88`7
z;D3-fAK;T6d+aS%3~(IWk$3&=0V=W(dei)t@N#e`_Xw&K*J<UBTJ*=NILDpyri=~F
zjv_K~$s>vr(p*77LAW_*nK#0cO?!4=Xvt>Sfpv<T%uWg%0s;=bKEGKq3~G2E3DsHM
zf-Y0ZAgH_?*A?`*87QbTQIco*1OH4YsCo<Q%BFl~X1V_5JRc{+rblS+?rDzCO#Z}O
zWtdgd*LJ-ZM%qeCO(z{(L%Mr#P{TE4HlAL5#t^I;D(v`hX#fy!0UAix>ixyW1~x-S
z|KP|7E`uRd-!7Qh3pq$772Ke7>0~UCHV3|DYz<v%!+n3bsfnE5m-bQ8X~9!dcV&t*
zj^@*Sb)FMyx;#N9jg^dCV*Qn(ci(5buBeifI#mRYd+Lv>zc*g}8_q53Z#7mCJI2H`
zs)NkFgUul2R`f@n5kl$mFzszg)#@Lm$rR(ttK;G*7#Xkc7W822fi=326nshxjAxrJ
zRbn?I6d9A_?jS(Y=pPtxV_o9KAejx<2N(l7@<ims#5;XhdL+qATqIO#O1L|M=))`b
z^3I&6?GOmxkp4gz8aa@t>4VDGp}`d!okwoDO^a9SmaNt`kjr~P{XYKqM`JMw!d!Hs
zMxxmcdG<Nw2>T~Km+zi@A)fY`w5{Hb>s)284;7dX@txUrmw3)9p%F?>@S;eRbSI)X
z-*@w&rT@X2P`8xWZFV}kCYS&lq_8A7&29z`6|lI-U3hS6UO$>@2S=n%RDA&-sF~|7
zWw0M;w?v@01zb&RFsIN97#eN^v)A7SlU94NcqP>a!hEf(t7{;d@Ip5h3JCuHWHO72
zkDoc7E1@Y#bb(oX(gQ(_A@4~jCvZh)ExBStWm$^`yO`P%%4P^M6|z3}vquOqk?$DN
z?w*<Tpe0+OBYwd6IbsakY<p~)6!Q(&M{avrtIc=7xly)4>&Md@l9|_f#{|8;gqPv*
zk;%wa6W`;~jjJnCx6)S!#&i!f?6R){pMM6F^jGkK5jVjwI!4*qL{<FjI5SqhO~)uJ
z82oAtU%(?Q%~}p71=FrHTM}<VQgh)|tf+cV4m<t+`%6<AZ#X$KN6pg&i3th94}`o}
zH9~X4!CzOWtB*6cCN$JT5}h7&%k-kZ=2Ik6kSF_Iq9$vQ<oi5Dg!YDp_Y|*KMp}|~
zQZnH!eV=o7XK!<)Z1XNQknS~3zq}E-$ygo$HxB>w`{n>+!NrMEhqIPCqA@!yZs{?L
z16EZ*N0}%qz;yIQd)Xihp_&b}ALi1!_WKb7;s?LN(#9CSuhW!aTxq>uw!*VwFm!b$
zz4d&};Btcl+pRWMwG`bbhxFaX1^q0E#$&LU7!%QA#SwlNA6_i-u1zS*K@a<yHPBQ=
zC1{44f9LHrm=^)MeAh%Q`o}M636JYy8+N9Zm@@~F>Y}-v66GVyy2w>Q(z;(fPkyo5
zC6#dg#WORCWK12JVkHrH`6DWv)3zz*zZzAoSd37l<$5Ae;*C*GQpzlA60AN^=>&<0
zvMUtq#}j&?gL^JUuC&tK)^J=e)@9&|tQ6lZU3~<I)^c)k*AC8-qR4FoLA=#6=rwWZ
zo=1jcHrHQngeWOt-Uug0@^us?ar=~T!?-zR1^b`7*&tJtUucqGtC2tZqY=4}EG|yG
zPx`FdU`?gDC|pu(fM`BFO-r?Pb@Hvm4|wYBQ8M&2n(o!?@FCOIJX;z>{O`9`mbXBp
zs)~wm4=*onYRqroInO6;^De0s444bd538h~9oY-g8EUe26TB;#wleVHnP?4H>Lgd(
zsRUofG2i&OjRc0NdD9f+gIW@Yj(O}teH;86$kli6Dt|hL2RkT+f66^>cK8v$aUJ1f
z=3~8K<B;G*y(Ft_aW~K%rnlaC((>g#(WR=*4SxHk%Az5*5Cg&TAZx-{vN@TqIR=`I
z@h$<if8)=uhe#U-CRzBemRAdq`TX{Zdt56R>so}*9Vdnx>zHnVdi|-vpWo!_KY^2H
zXWhK95?r<X3uS6NL)Nxi?Y9DKq?VSaRA*;rV`7ZINt4yQLI(s?l6S_Fvwl9Nd_mdB
zEm8YG1bTtL-`Wxe!NR7e$M2fa(ugn>=Ab-(e)`}|=<@{s<iL0Qhv4`bC*J)An%NkH
z!-NxwAIMAaEf|@Vnv_;o@YQoCB5$S;XE>}&34ow-t{Vrs+c!RY+X?nUZYx*JHbdxm
z+b7kVUP-AVm6FLx6o<D7-H&HSOV%3brr9PDM?f(5QkOLolBuDa2$#D=kIPaSX;#q|
z4uw2L+m9h$6TNAct*;vMlB4{)=HL<`Q5=O!y7kfkh%(9Gtzzz2z+%I%0yRnz_u;CE
zaavEK;!kT5&$+x~aVf!~ONQS#sN(yewT5Mr=@9l8=eK@xmEV@?eq=U>_w1G;AM0Yl
zb#C348Sn|@7pj7f!C67G^X^rG$r|MvJ`CZG{k)g_1q~1A>Tc9c=}1z6O6ln7fNN3O
zC$K$NbEi`8^887CnB}-)1*8AsQ)^SohFe4DS?ML$1yo}(rSH++2XGPAoT5}NzHI)U
z>f9QZsuBUiEJAtZWF?=u(3@P+ui`!h0ZRNbGC8}4_daaPzY!_Y=KJU7>JF^mgIN_c
zG=`dSQyzK!hZW=tOI2J-Te;|&=qqI`d-;OEOWV{WHiEjej;X;<Z}1&$ZWEJ`B;9)>
zqo!BnhuzTH<!n6o2P+x}{-g;tObAWkhK3H;6O__h?r{itIqxq%_N;s%{QQDVCe3fe
zHvkV1Fk>3nczdIJ%wiRPHtLSb^n4OH+$?>|$_s&0Ru;DF=KxnxQ&0)IxVQrjZP>=W
z4xh=(_vQ=8yAN#!P+XZoJqCSx7ZF$zRjh9$V#vx8W#N(HY#JW$Qp!DUy-2Gm*_H^p
zoKKBE9c}Buvqu(|G$@Pg+;f$jB{6df>k?Cf+)?}_MVO+Qp33Tl)-k_brkWVF;jF)q
zf5B>b(2b@WVCiGMgbJ!FHZ;kXf1=RhHLcL^jFz~IkW5qZ_c_>PQ_(5{ck%s5{Y<7W
z`6+p*E{jki$MpM1%Cd1a=WD&yQi9YjN!D&aDzM6?W|c#p`SfvC(TNz(kMR&p&Tr|B
z<r-)uPk=XWqUb~vSB-=yihJ#+Q8u5Yq&=nj!%qtDstW=A1)b)d=RU4o>-+CFTR#X>
zq48b|-%aFLaeHS5JUqAyzHRvY$ym~q&auc&H?keL{cQ9w^GKNV`8ziXLGOi?aqS31
zFgVf`DnYW4HUcsZr<jcmLVPTFKSx_9rCDDlG<mB^znEdL6AYrpoM5rOjuV=a$%z69
zSHAhp(2rZXy(GE7s!-Vbe>T6m?zWq=6iQva(u=#Da(0LEyii$H{PQ8XV}qU9^QrCZ
ziZ7V?z|q@63_|{wn|yiqU?pKEB}~~0&#xve^ZIm6<7#jVUu{S#Vb!r>l6GL^aZYjK
zj*p_`&(ddGHW3hY`(iTtvvvRIOt%F2422r)9-d2V^h~||cg4+vg5+uznP-SN&9eZb
zq006}f|#+9<iL-cW-5V_bK*NM7Rf|?NKgIfP4~);53e>3o$Gs!-ie&}J`bj}EOgr+
z8%7@p`cy23i&MaeznKq+!$z!*6ObYs(7y(sqkSXUHu=1pVROb;|6XIdzVecg)0rYC
z!+Elc6BY<`GyuPCaNHY?#AS2m&eSYRk}SYOE-Ht8X(T}(8cSmXesOhpaInzEwAA88
z_Xv{xj0W<Tl_TGuR~~K#J78Zrzxm;rZi3_6AAF36+vdm8!6uCTZ{RkGt|u!shn=as
zF`+VTlWy=OF6%y6m(CEaax{euhRFXNYzrE|A{+%jxKwn22(s{h_|Ddf`!nD=H!gFv
zt~V@9e))>XA>B!HFLI^iz$GGC7UFB7a~kjp?{rT_%?tY0kUy$w&}2{q57o7f%h7p8
zo|{?%DHit8^o@^H5veW?(NAPF)Q(W5<*8TS57(Lc%#ZC#fN1dJok}%lacg;VJ;B@T
zXjAs3QVlUH?4DJ`eUOhJqC?j}fkGSG)hewirZOAYwYLuG`2y3sF-Y!A*3-udZ!n6n
zkQ=*HAZm3O+QFV@$oNlDsix2vg4rV>sKkzYB2m9(61l6Tg&sU`Z~Qi4ieQ<xk73%i
zKl%Y4{wRXxTZ`L;@7v=+fARYMc*e=$;o<)N{8>&rC<1VFa4?qig=4v080&f-kut_B
z=QST>KpTeHv1N{fb7W?5R`cz=npc9sW4GW=P*0F2j~HF1-S(LTHTQS$&CS(`f{;T2
zJ*I_4u+vwnDvJc|)Uwm~<R7LC49F(?&|_!%IX3wV>9Y8q?0XWnXRp%OFFb<@YZ7IJ
z^H=;je~`YCwdA5VxejT3F@IjUV7n-?zaD!eT=aS#^ESV2j7_Fgc(uTwE1LxgyjAn1
zI7V;%Tu!)+WF86{9O``OpsY**hejS{R+`S1Ls@URh32dF$3W-~iG=x29-?8;sj8H?
z)X2g6uZ--%y!|nF-~&zV3Yxy?(Jsi|Nx@>fg+{vs*7-l$hao)@5@;5*uJ>E$rl;G>
zs93?Fppe%x`M$WPVK5dyvI8uLRqen#_`%3i$L5w6H!d(S*`U?k@mzw1z)-oEj11CO
zLJ7ucS*WTBXOg2CCBV>By3m2KG-+eg+67BYY=aZ$Z^8aBZ?NmKsGlui<N0L2=N#;h
zQG<70)GnJR{x6EoIV|rsj^f$2t;MCaw!B=+TDEQ5Hn(gob75Pym$B?x=JVcfe|KG7
zPtWgn<9p8eoT@ejLDYgN$7osjW0swO@g2}>(~}|`W589|V8fKKSgwov`_0tCnIZN%
z{2~sA$;lDIOMugk>4rJ#C%hua_m3IuRW^IWsR9C2yTdL5DTi(~{HAULC1hBXq#PC+
zRXK568HKX7^qU-tcw3NsfpmzR+482^8<)d@S?s^xJqAJq{vnNI*tp`xmq6>G$~ndl
zHfXbLL7bOn?|W8LA%wlA=2EMldkxU24tWWlg|9^-?hTZ*<Z5E$#?NV7nl!4+vLfi@
zTegOTU+-zrd``1v@Oq@_9(Pg{y@Ov9GW%Ztzf`!tGD?g?fsDsn1=Hm>fq4flHRT&s
z<!?%?b1v~-17=L0jn?j=b1C+)p=;(Kn&0i$A@o#FEuoO8VTGCjf(dNO?N34x=p~&k
zw)VFWE9OHvVIJ+9x@Sx-i8Ahs9{iV$3CMzSSrO*iL{SUsH34zO4C=1du|AnsnuDkJ
zYm)I-{GV~8^QoQp3f{b~TK>#6vTI{0Y0(IGS7O#*@Lc1EtQdMb->5HUHh$P!TzjtO
zYSz*S5H4}PU+=pXlc!pI<!1afE-baSGnuDd=<R0U?36OTZycJbO>yeVj(crwtxC2_
zmZlS~Ne~qlt$2%6-j3~+mOZ9|&uUTkVxiyX)jjQjQ&1>t3(4EbQMjkH&(g-ZVZvsC
z>_&&zOR0(`I?XV{huvGSR4N6uaUk!_+P_YIpIiN#mdqB#E@EP`4dN2W4~s2W@=i{d
z!SJZk8hqRC*`+{m-2MA^=F@#19{HL6D<dY#MW1m_sfhMnpaDvZrH(!(oM7TgDP`gh
zJxb^c$X_qC4GLcSQc3}ljo7}^jW|1ndjz=paL(;eBD3v^%3o|7{wBX5n(UmN1Blr=
zE%yCv{@vlNMhV0=lN(r<>TlOCy2jfO?5@T8+%{%s`yl(rlbjHr+5)rB)i}de48B;z
zeHQat5XSnk{{B-hp&sZ^I!&SdVbU#@rY2QW8MRFFpo@S_#%Ru20-581Q+y@4BFPhl
z3m>(67t`T8#FYHsh&lIFlgOwe+1#}ok#9;goDok6h;$0^-8vjZ96aJfRR<uM@9#WO
zb_p8UsC35hM1-%)iNuFV*Q0Fh$F5c8r}U87EyD0)9r^Eyk&pzqbTo>rGmml*Y*NAo
zlNDESKVK{K`irv^>IKNIxeB&Q78$r}AZdPa@9)E9V5&eNdJXsihcgh|bghkXoz<l1
zcv3sGYl=c3UppP+n_N!*{!Uq;0_Q9{n=*;UUczsHV5&cA5(LqZ9?&QR7Q=s!m^Ofo
z?N=R`Ght_@(<(|!XMXgkt<U%vS#S@c5WON;=wby^&Wtpytg(aD8(Y<2<mm&VKY19i
z>p#4>>FN7Vs)6ko5tAaYZ~D;H(OXF=F)K$^PmjDJ#C#ZnkB{%DoEX(RY~GBVDoZa9
zWj?y=48o{I)g^x{1^bLSW%{nqI?uI9Am15fDoR$1adi6Zh-s+;kr9z>CflisU!O2a
zr5G8%8q(3glo8Xp?Q_OZ@DzOZ6pV8!<Egduy$mjJah{dZ_!6eUy^!m5<uvj>m<5e3
zfDZwYgz`i0veyCcnX4s}zn~Aoo^>y8!1JxxEPbiGk-0NeQpc68+Hyfio4l_i0*P?A
z`<KX};8u;G1umA<FX3E8Oq|GmGnlQROEkKFH6z%l;YIcqkDb5F{n2;&yKKHo*Cifx
z7Z9YGA#QB1+kcbLYYtgOL2u@zXnijQ?ZeTL9rNJpvKkecto_*q&(7X#ZE$5<K{A_^
zrjPdqb<%uOm0;UQ(_oBMb2COkFdd~s(B1&%xAX6-I$uKBx!>-n<?vrI-(A*STG0R5
zmZYGb?+kW6`Ph{BsV1(AOmoYJFE4wrz#S#DKRUzE+fj8)N$y$YD(k+1HX$Z%FrsrP
ziRsOqmNj;SHc|VN(dG6IiZy%0zs(XtmYF_TYu-O^hvzxFjRa-9rmQcG7qnG)sbm)5
zikqVXJaM%e2ZSLHik|2r@2n6%q{)`pHghCNH~Bs|yJI-?yxfAs+pxT0oa5n435ZOH
zJ|)pg0r48-iex%Kdg_CqAo+mN=*QQa?*a4@aDf*=(toPIz~a~~RDbam%8P|M0EscQ
zp>=b)=kxP(Oc(Pqp8p*P2WVrrFLxUjm^Wu@J|NIe?j>6+AZQ50+4Lj>ZM`C~HN&R{
z4ix(A_XOx?8+&XM96gpTt)!5fQ&d!x59||kSBf22=-xf;lk+UDZs@053#&6vm_tq|
z?rWSBN55My);W=b-`Y<Ob6)fel6>`u<ng~i$qr4yo}P%YD#20LC`3QbR?K>jktCI!
zYHCTmj(PJ4>zrw<CElA+7eLt}++~T;;X1ujD9UGd)q5IPBC4+ULu+X&@|uTS)l%=1
z0+U8d)6<uNnUeI1(bT;!gsh@C7Zz%kG!o5erC&&_k8j#lLafS@h#TYhg7~_xs*aat
zD|z3Qqv@5+YyKMhP|*?560|<RT2iwMoj$s2yHo*^56c$~Z~rYf+r)hf0AvKMHzG=^
zs@r>e2oe6~*PpN?Qy4xMW>(K#yyNDcU0dq{)|(wyEm=<=pS+JA7?xz~ANcvZ9`<rX
z)<BkoE(1Fs{4_K;gLo{MI^&N%aGQ?>`oPsPumL8*&XLB$2FMa<Qv{ih&h0q6yO(B?
zT+_?<9W2(H81c}EMn^}#zMKbmghGsrewlAU70jMFEyE6QhOgJJ(dk)Wircfh519t?
zx#Xy5O`(K~h|}nn>(N;s+T!*yW~NK>U7tKPZ}TB2oz+TYD1A&BQgAJUN*}&EqYZP*
zLe-UGvi!F*+@c4Qnvrq7)HtXG=jrKbYGKiM_zej2L1J%cdooD*?3Lt+@IE3{V4NQu
zWX|`&+Fp`uTH~;>fZUlJAAynWhi0G|q+GDSTYq>u?_tbtp@)QcW5OW8p^qt5sQsD+
zqhlJ2u&}To(}(L>#VxUuJqZyJs8!|8JVh0i84zwayfFG5PEY!g$sKw#q}!3g2A<9?
znB0AFnjA~Lyn^yxADaKjek43QBZJTBuXyw@JP9`T<q7UGjMBYmH<mL4(R)WKB~H$N
zu&{`Th<W@jl7p57Vg-)J3pG>wIS$=MQi~u3Sm4wW=9UR}DXJZ~6_xpZt|`Xj?o5v>
ze$jFs_87*Q<}H820Oam`Bck<x>DsUfTrqx|0t&__1!OcdwW|3I|0lNo;sdxN29H5*
zcUD`}&GhfO?P3T9UEa-KmJzj3TPlupeUvmLIWj{Kx+CUpnfJjb5Y%!Mx@xw&JS1P_
zI-?5oiX=tb-255x4I2p|)^F<<yat@_#ZN>DiEvM>HZpmt4<D9iXK|NhB0%c3ZXXAA
zSTtEN5V}tAP-C@e9;u_FwUw`l?7g8Q74@^Q*$AFV(Faz+p{SiBpm2@QPzw9&(j?(=
zLtQGXsK7vm-><aTnZzkm!GBP}BMK#qWaJ{Lwj7HmkUehE9qs-$cU$OS2kpM>l`*^s
zBq4WK{$SOTxx%5|#ToVvebm*7{i~;&V?^=m|1D&FSkToJxVS~{)LDTs81N0`h$aUI
zBY0rYlkH&gRl~$Yc^q{vdjM=BlH#@x1WLDY$L}fk<SSo<x)`{~cv~!+Kk9Ku3b<p9
z7U>$%^G2koEFS7NTqo08w$L_MU4y^Bcqy+|;X3D?d%Se5ZPSlrizzf|Jth<s5rMc>
z7#J8B&jB6i26R5UmPIE71O^1i*6#>fNt&^*{J87-wj)ZH;Qz|L2}wT@1`}$$ZImj$
zwfP8K0A)}pqh*X1r^&=mG6QjQ8EI+D<Ev>)o@x$UB=W|hXz!Qwz_0pG$#~fc8`Z^C
zHuBFGr*+$WXcB%!akCsvf#H%nevEnA{h!&-a7Hm#XX}eBpBK~Ph5es1r=~%qO?Fw+
zV-krg3$4l9q)0GGwuHtB8t@N9<fmPnl@~aecz9BcS3fSP0s)dPESngjn9VcX=IW!b
zPr(yU#97!uVv!5?D(rTIXJ!N`%d9i|mSlpqDh<dF;xt+BooK9}p?_{U>U(a5-LgR&
zo0xpr-IzJWwIILV)QchT7}vZcME10-;YcV@X)b6aKNZu+`cSKcZBV>J$ZSgB9iyc&
z$<iyeh1p@&Qmf*qS(x8(<}H61U-hNp9cF8Nc-tz8_2;qNPIx^>Hdv8yo2*6AeGB44
zpz4zA!a$WJjcYVslrJwYgAjMNb+p@Od3icbx|NFF$v*P`C9`}h+^*%((HO4fMG3T(
z6cAv7AB6xe2_0J~MELQ5whhjQ2Af{w&(4_$5p?~hn8rr#Lns)tfmq_HnN|k_U?QMM
zK%~R80n9?5va+&_^z_$#VPG?BKMBNTkhi3SV3NI0T!qn<aM%(h8{#MuFy@Z#6c+DD
z>+Qa)uV)gXfLwYi(ynO3pY`6_9{)jmEGsqQc=7X5vO(EJ3OiWk|4LFgq(52W;zU&)
zy3Bq*iJ;%Z_Pk`l?dqMP_^w1;f?W1xAM^P&=V2}#ZX+`{O^24ZHCvl7#wD@#uh2xg
z+wCrM8v2Aq*j#wq@`S-=E5!*;Z>7pIL)~Q$l%e^ie3%$Cp6st?kUJF7Ws;!5K7{@7
z03a^_Q_`nkc(PcZ9|y}9?2O?6$T!1#1JoKMP&S@#SXt@R&`v&U2ma4!@rrh;p{|IF
z{KKRqSic2jh`$NUXy!q8cXt;1Q-Ht&ETF{tWM=<n0$qu?OpzkP-w#;eguh(nK?Hp7
zFIqGd+x(v1`5?dJ&*dx^l*uLJuAJ9<S_|wnggID-HT<K`QKaAVi59WoPQN%W+4wpV
zlYN=j8EV}lTs^lrvyY%)z7Q(llPN*$#n|&0pToN_KXl7@-j<@-{IbJ1QLi~?GHE$k
zK9VARMM|XMfS(^_zWia9l9KqgerkS9^U*d4Cd>UONPOhm6P?sT1>p?e%<HtVwUMc@
z|Gs{C&6IVs_!InCu3#tar{UdyfGz}bzF>>N3d`H9`rh3EkS00LJp~2OLs%q165zvv
zB_<_3{T+~&fjAQu8j6R92UVr`?SmUwh=CCa76EN&VgjKarqGB1_TU}xoh&Xc7JQ0O
z0O%R*z{wym-2Qd@{l~u%5Lnp~?oVxBYIUHj#v3XLdfxCk7j!0gjwo1ct2;Qb^hC8u
z1QDtP65b<cRTLoa;UpTorEJi|$mF%rkd&0n$&FQG{>Ej7!cTs-*6@3KjKo@b=XY`K
zXCX|IC$q0X5w%AmH!F#orC-Ma8rBP%@ZxNusp4ZC_SH8{jvyR7=J$;cl=jSg3D`We
zO0xTm3oWp&#j?E3{1_HNT7B(fEZq9A2Ud7^>H;^ZO4A=+F$2{vv|k&amXIpL2h0Xj
zD(1gUKzL=@ngyLEYkbxJ*gj>8>c?!;>o`6r$jBJAfB3kf8{A6q;Tt;(ON`b#9+J<_
z&Rh|{gMQ90)S^ZU9uFrFnZJ{3pT)p>!i?mex;08m0_v9SYYp7zSpaFEiq-N8BElsm
zhU0;NZ^Wom#j?S_PC-FIf<O}aUd*?S&uS|wwX#L3)B_J6pPV<@)coDP3KDwr%+~%@
zI0ogN#Hl;C+-C^`^iM?*&6PrOT?@SsJteK57+TXARpsQ!v265@<CLDO)q9lWzOCnu
z{oE6#TK@v<y7Y}?!++uInpVfPa<>f-jUXKJplZ^+A(SSDiHp~tMtS`{J00GT_MyYC
z(?wg)#5P`DrN+zsj{+1qX-oiiY}Xo2HT5@RW4DhJzt`ZaULkAozkZ2nJYXUr)ty{0
z6=gfebyM7pRtu#4+)?*GH)D{?LF9V+Crx+lY{7FvyK1o*F+^iP8LoZRMAJ`#UQ3?E
z-WP*Z^=zPLVy^^)4tE8yV0czELFE1PH)cYi?S&L}5ArQ4qrD`gb!+6r;9PMmg{Va}
zD9pj7e^xQ)BA`;sjgD#X#&l$P)=Db)s$V{PoKft}#N;8#=k`Ieu5Ug&3hUFIg+;Ab
z@oU2${WbAYI50o63KONeu)g2ecVE9m48!@-6)&l;*_a=4cS9D1r&?8;gdtnK>LKV;
z;xLjs`m1Vn#yW1R!wTnSc<LALe%PD-=qsas`!bc^$s_pa*CV)ZSRqo#B@qt$<XtIu
z+i_nTv5i$S|G9>Gu2syLF{)tPTb#CK&nF4wts$sjJPrEUGUIdi_4k9>6e`jYkf(hC
z1dMQULW3$v7XT&fRGvQK*I|5c+=Q}0RQ)0lpbmTL{_bT`XPdXfptKQ%HPX8)es8?r
zpzddvl6>s2a<bdQ7%|H;_}-><T~C-!4})49M7tTsx3N)BrlN4Ky)l~x(7kFUULZjO
zzUIFme`*T^KOU-&PS>8k|HH%rFFpU(<P~w;B7?BYm-F9pxT*;0AHuq#-&koC<)<)7
z;={vfs#i23aK0W~HQ6!~w!@I;6KiW)hpd3Jm+Pa;L8s*XjFx_zrpJgcyr>-Js>fb)
z$wS`?{&I7gm9^to(^9T?e4PY(T*<#jE5?~}dS!@*%j$TatO_bCtDfw5^rEX^gyYTE
zyhboZ^SeW#z1s1^C{<!tmYXnBdqZkZr?G_s&*CH#tawZRH#_Z$Ux9J9q4H?e4;ixU
z%J~I9TR-Q1>dI1F<_&F{f`nW-Z#|(2-PRJXL9eZ*Go{&Z$1#5^Wj?l^g|%Jlk3|bB
z>+6GQ4L&9xA$I*A(yN|h4T>JLo&pp$I*sx-n4VwbQ7iQ~-CF-~q!k|XSz4{^GE`J0
ze+?dp?d4D)v(bl<F1c>#x12|L-1_A5idf5FFeHI0Vmf8>=j|7f4XKk_fDy?4$7UZJ
zE6xXlPEcG=oBa^*$Np1EAa|MaIbqi7s}x#<`~#&$Tu5t`#@{tWssPWf(W<q{VGKE}
z*GaaYRh218C7I@gQtWMs5=Sn?R{<5({=bq^MTIJu8Kz}4vb;CD>8z<OK1oxPeP&Zp
z()nA?JARutn-EjJJAGz>?$#H*qdh`fI(h5aJ+#T-@kxnuS4riKD(kj|KRhqX>bWSi
za`7e%q>%-CXL`#A{>HnbwjgPlOpz_&ae+}nP4E1r4oQQ|n*3Du8lu}St}lFUx-9ZN
zv~YQEcgMm9P5}|8is(C98*!$-Woh+|Vri{t9=pa|`SRb3DH$eKk)rTv)V=1NGSG?(
zTs!iaBOo2Sh_HU8tfBG$ei4Ps2(D-2U-_>_IeMOf9?6<^${w!9Y&INly4<Ys)Ej}l
za7qI2OHaYMbhDDQ9K01k(l<D5@|pNGC-#{R*2SKEd^_hVz`cb!OL&QNX08DC3B8PV
zpt#XV(0?uU1#`{FJ*x*?b_)og<n9Tx;3m3qKapYh+mu35WT9;<ieM3^(&Oy9KY!E?
z4}$b(LP)Q#&sH85V;d7)n03*<65IAcvf!Pqjdt2hDn5#VYc!jdpCINRB*&=70ZfM~
zWO3&o2`AHI5Mr~Vby82*)_!_CVtMy-`@hPgh3ujD*iS;ud=J;ZVVs99J(;B=e1^vl
zRmI?T-}6IA^lLgq^}Yu@LFr=?`D|KVUq|D%EnE~Z(vMCScixxali<fA`ktr2{I#8J
zwl(6+6)_a^^46h&PmW2O`|$d0XJY(dhPgK(*y;9d>SP^)P~POfd0>0#aCRpTRVb^f
ztxbPdP+5txL%3G6B^(zQS6f@VW8@D~MS_ELX5>_7LBlIMSu!Hm0a1NQTi6x^AHq%_
z>UO^Jl6%#PHaEMnPJV?Xko{!)>48Ox_%q>CSLUp!R^UHO4g~+u#qpMVxa%b;%j>f6
zIx`jT0W;(-8^Y&;tXcF742%J(@%ua^)UpaDR<!xKDPG=#g_?Ll){pW)#R+5%BhYly
zjXy3Ekgw^O<bZ$G<a#PgP+=}#!nUA@G!9FvKTNyG%liT!Fid0XU%wL&m|{S}3SH~y
zIA`J6f`-}2YCTh*I+OiKlSNRj)7v+rVdI)-zXdVD%`<6AVX0LzYMg{oa7xoIn(y&=
z8|J)AK%!0w`#Sgt89B%R%8p7XT^3^h6QkOuH|J>K<GK7rCnHX4J#(IJP0VoO$h@Ek
zL`q)rAgah}KTPpZ%(w`NRZ#uJQczln8#>~kq(>I(m60=CUmt;@wz`l1xn2ru!b!@-
zME;&q+V5>6E$iEn0_YbDF5VyU_xTa>;T`I=<1^57Fia2w%uo1fo>Gf8q!a>NIEeZE
zeVdtFLX@Z&f0fM#AX9lYrwzb@e9x(8QMkP{Et+{LOjV{UxS#{2D`?wlnCr0oe3d0z
zIyc-tgfsGnM$2$**(wd<K&P(kLMxIljfbAYfn;GY<Zsa$s7>&VA(`A$YsaxKUzsko
zd|j9F>93@}mZZxWp%cDph^>OSQ%PkMq(}k>_0^!?@3))?TO8RI8xx-y7%1BJr3GOm
z90#<Ej|=7|>n@LLgkfy$3=_Ou6RD}G{f|NkZ3e&~TOE?kR@efi`z4FKq(RS<Mb4@O
z&ML7ho1ts)H5dBAj_dyr>1>-~M~&=IaA_}nRwV5pmQXiIs!xCO+U-U<p#{&8&d#|7
z;mj+T{%v`HU{b5S;HxaBbp{D`%=%FA1x2@GZjZ<myqNS5SYvS%>(6NGs9F@w_hJOo
z5QwnPJ~a8f6Z>Y)6<HqLL<~9EM|WL)vOz3LldBV=zeG4ISfn8@w!L_&=q+zxVzH3K
z)3C~8*6Fd3=%}%fLh0V-!yM^p->e^7`)qXGjIZHqYF4eOuT?%wP{g-={CKs|?gDv$
zynz&_T_vi&;XriWscySF#ToCA_FgZ`Uk`jmq$1_YT}j!-Q;#^A*plU?&c}VA-hG;q
zO$<;oQY<E%`Y6Aig3j|0SG}$dXXi`*H#g#BC~4^MGLH}a5E56$-?NeK#n5OoP5fAF
z*jE;*cm0C0Y_~r=9oYGKsD*V3{$8)D@Pu&cr0j=Ojjos*$y&_#mLt*c`N6?KvVsC~
z(oH^23rc|EYWS^``MCh+lSY|=LW2zE@m~q&iz0Q~m+wkSuiahNxBtxaaEcxw_ngt3
z{aykib~Xv>-&eTazRs_^jCh|w0%o=$OtKGNvv0cm7K0O!kp&qi?hYV>#}HDIgOEw4
zQ-+4g=8x0szO{;fV2O!8>pAz)_SAk3P`6VP$`JA)Jb=_OuiXdu<=`!SjjC3-jJCmI
zep^AZKgz>yl=jhn*2f<;=;b^q=6y*>kHqH6pcBsR058q4O3=iH^EZl4`~5eGr`N1d
z4V)o%B7Pn%cwfGCe5?+R5?4cyc9Aup)UOdo&WBh%ZM^A<DuLh=wTpJX3O6gLtgt`z
zexyuocrh1zT;$5x$;w!nb20dX+7_w0T$%FKeHdz+pE;Gu3Tw35wPh4)XJ_VnZy1(I
z1XMCzg^qN^_P^cHmymgpSZbUo01+*y>HrrUeBvdSpueV;w^Mt`tm67YYXS;i^AR4V
zs+yj9GHZP4O9VS!glEeKzaeF%m##r>G|ulKmnMS4%M?;E@C=W921k1V!((H)=w?=j
zf*a1!6^D>~p)C1kOP?{+W%v>Zks{(ZEV%<kRaM0e(dkA}L70Q+PR}6S))q#}sL*&+
z=<fL&yu|5EWq!(~)GXbBW61nv9%+yQ!flTNL@qVcLJi&T=y7?@A$a76$&t{9F=&K<
zklT}+WP(GTb$Aijjp5^q$cjmyA46pwdXjm=XUShG+&Jl&Kd7+(^BZZ=xjAWYCN1ez
z6=^XIFKd<&nJnxTD6HJL8e_9AW+V)C#=9It{3XDA`Yp~5+h&iF6;A(?E43eIci_|0
z>6iSENHSKcmV_LBM>-dur1>>{*8LcUHr|Z0bh!6^%AO&m8%nSuj(gWJ9=h6-=MNu?
z$f?Af#;EXh>P>xFuJ?$)2b@;!_Fw|H-ahR1w(YyP9MYS!Jj7;ALak+|dRHA|n6s$<
z)QlE4P9sCn?5lmBYcFH5$^Kde>jAY#<9uPkV%iMV0>VRx<VJB1JjHA@y*A+{GRieM
zeAryl@9O}@E@^l~<x{(Bu4xbb^Fz@V`+`4LQ=OB#-tXM!`7B$D1r-%hplWps7=)xq
zAsrNU1A}vAs#Xy&T*iSfCjm7_X@2cPe<Qx1j^1=>m$a4;GCi+tr5WTM>p?cCl|F=N
z&<Gd9z+?D_os7wVL-qSUSzilNUcrq=ljUij&&;ugTo3nJ!wYq?3@LONgvAwBe+-K5
z^V>+BMxhl37p_W<gXOT!fAw*FXp!-X_(fk1_rMPS<@UhqZgu<8b#Qu-{pHLRxq$)w
z%cbSN*B{pnXL7{kLmfPc2w!qqsLPAHRfU`agznuhITfZ(mnWZ5b1&Je&nY}HITXW#
zS8eBB5d-;{n4@u`<>``SOXR;c{ZGyO9pm$M!4T2wm~v~w-qNP%rflWia_u{4O9}0M
zxq#Pm)uXxL++$%#wH|gQ@9Xgqlfn5qcEKT}`1!?B5bXo}l-bx*v_iWPdb`cX7dvtB
z$vOcUl@j{7>wvr0i!KlESFP^Q%HmUkhWz|H9dDc70B>Kc#8VGBA9i*f@!fsZf&^G4
zG9hX5>p$_3h@VqaMKEteJHupyr0MahdV4k<ei(GQN6zub;h-O*hLIpLyriwxN~Qf`
zPl|p)Eg|8s#A?05XV!p1R6@CcA6fBk_RA2*)frPQctjyErBAeWwhmK%SUq}IAs%#8
zSi!>M*(!n@iWfaEqL3Tu&ZkE56*07oqw=06O*}sMx&sm%@UyF2`A6lag<(ww%?riS
zrhb=>{+7DrfSterVWjii!EMfE+rNELCys)NuWuRWV4t6#(d_e~9D)io`);LWi*HPO
zaG9U-r>9CgMMMVGH?M{Vu{91~490}G(xP`Ry=N>_rcpgzI|!dqE6W>_qvJ~(xDI!V
zPM~EjYSbhhUOy1ZD{?xvUt?^fo4cz8G~`H`na-rkD`V4pt*Q_2xV~Bbdr&Bf-TMZr
z)?S@num(yXDcoCii-8Jj$b^KH)a_>?G<bt3t*5r@VR3UwHjj=*LUR_1DkXxeN2OU0
zTW;7zSgA2RZ>x7gMW{kVYJV~7(8*Sb-;+`FuGw2oB`nQnfXF#es}6ZqKpG&N<k6G0
z_bXMn<s=Ezx6=Al-TDtaHQ!hoB5wB0%;uUrD$aWvTgb2?{TvzUiTRb3%zOsF5|>m)
zw_Kw2ml#pWaPjeZfJ6!~dD$*;EyC9seNTG?+^Q$a0NX*!jHPSV${-P{fr>rmq?^MC
z6YDfdF~t=cl{dTlavTFhNVlbd08H0_sYNzgmj(fIHLG9$TIEc+j@F{)lHnhd_^Gj^
z4G6w6XZcfo&$Ym;70eNh@Esl<HE~@7krBi1F{Dz+fUo)b>{TiT!UQ>`r3@xfoWXc2
zGX;5{s0=#EH~-PM84<v64uoC1oBA!+h#}^&!s_>e)B#ZmAsJaUNlJkln5DMZuZ_%w
zG&Z`($;Cun|8eO|JXeuBu{2DKi$H9)V4O$RgF@<qa?^{>TKAF*Iq{N0mYEr}Lo?RX
zm5}IfFdHSB_c)lMX4e$9%r$|v-i+bsHniRxN}|<cz@)d`Vj(1Bf4-xR7p#8b3009m
zGz}M_dP%g2>|MDTDjmyae`-2&otsy7>CUI2MNA>k0;#>)SQu9)Ct})JXp%SvUfvr3
zqBJ^t0Xr6gBA>aENneFeT%?P+a>{U*8WvI?#_)H18ihln3is}#Pc$X1i<plW9u;aN
zSXjN~Ki{a9>h}1PbK@utac*8cyuQ9}55$?AJ%afz?7H)p7`!ie6v>OR{9@ILNN_VT
z(m^yrVW<=7?sDYD1{X?Dl?h#k$kPKI0DD*0qH9h~OB>=>1{67pG3*p3h2TBTJ8`F&
zsW}W(pP;c38od@R9TcQKjrUk9fwse})wo}Du&R`Gb-|-%-eM2;Em(`SrA4<0Rwfx>
zkbznOyqw+Ln|}@c{f~@T{;7Iv^KYy=Ye(i&Igc}=ghL03G$}`6B?SMzys`HAw#W_B
znu9UGWeEo(YHCWS^T~dx0X}n3L)hOV)YZcSoQgmuHDk=r4_t6AL*hwwf2%{S^lp2{
zjTmJ|h{I??i&CM@lmBC{r)|6KlBU~Po_8%~O%bKOo;t<WZ+E6IV$SnEdc(o-9lZ2G
z0k%GYD^XEVAak<7{q&jRpDC?W)HiJPp_XcmBokgtB)Gr5<!W>FMc3(h1qnMb{eM#!
z6&u|4Rvdax_x9cmP2-S4d$Q-evlDwv{`xf*W7qb&*qFV6;YN(1DV-WCQV+U%^}EHi
zUT@v69h9(Nz<Tii18n=!?*H-#QX(bJn?<<QFrrw_?E<{KZZpL)w)J3qUj#<c1-XGs
zG)m+A>Rc~s6<Nj2LcF|l>w>98WB<S#FICy6GU93HTT&EzL=q@J7aq{VRelPE0Iksk
zAo1Lj0>3rNKrUz)(6bH>>b`M@sWYpqs;bJ%kAdm$2*J&7Okv<ZBYqCVB%OMTLP9?d
zGq^_l!j$!RLR&QW{S#HZoN6n~Qc7O$)`z;+vK?pV9F{fsSgL&Dx^h4f1%Qm}0Tqx;
zAi~E_OG`6bi~B8d%E-tFdzFi9H{u953NtE$&FfO};$qD$zi6ANOt5%m=iElj+({;E
zE)Fo1Z*EF?XqT6%@K9KonSXS3-MF5Bm#Gbik=zy&fOJnRz&tA7)Y>W~B}FOZ>-q3+
zS6aAMziWAM@%_6H5>YBFX%4BwD8reZL#PnA-*2a|p-eh^M*JQR%fKib<1x6du5NE{
zFDYZ55>JAkYyyCuGg!a;O9*%_>OlkRYLCY`h4i~$K@~eBoEUcxVpLdzn+B9BF6OEO
zw4|6JZEf#8JUsHFgZ9q9M?(iZj2ZxHOImvRz^-#=XQ!7yt_W44G#yHqSkd(6dyFQY
z3UXg4NN~?j{AO+RuSoS;Xe$4j0&D5sy;}gdWJB!0KL!EhzNA=KN-8Q`%y;v2YP264
z1dWfR==nMWcP;_aK$1%uDQFR1;LT%Q=4e`ceM(VG^nr5z)jnc?d@t4E(UB}bo1&xZ
z0WvQq{`gzBqq+GiO&>2Wke+(Oz_15^hsemtfI*K*A<zbpu0TKy2@VDsfG*d=89<)Q
znXm?Hcm7>eY^?m}&*W=P6owCssh8xmbH5Ft^%&O#CVt}1Q^$XHPDA>VqP%Rs;iDZ9
ztNj`NTgk?yQB5c@nw^~;83rQ&%)fo>2oxmPTln85OATfgleyy)6JQRGi*Dw@-G@iC
zFe~t$%q9jVC=)x3<hxZ$0Y+m+z(rGw%9k&$Sf1Rp8KYhT6c?zKm6d>!OUSDA7AFh>
zR3|U6QUSJFegKf4#0sz`j<Ul_8LV&7j$*PL`LW<&I1yDU@a~Z=FE4?<7=cNNfrNDN
z?;n_x1|}q6-aml_7~~IOD8%jTY7~Z32<9Y6VmF&)dEH$Pt}JI8b#}&xZBSvr{zOoc
zRVKb*-nZ|jt%G%3prI<H?@DNxyQ_m0*}OSgZ>o@?v~^JRf*7JAgYUFi8z5-PEKndn
z#aUYF_oS*L$AQ1TC;P^hrw<X13(ePrG<Wrzs(BfsM*=$;hTV2a$rl}r$j>+gl6(op
zbT*^VL9rNF=qi2mE{d+-n&Iow19t<C3Fy<b|NpJR>8ZznEwajaUAcZLtQcw~LmLHr
z+D?}_6AyDtW4wBpnsb%%!HcHv&_~D3xIfF@%r8=)hb;2HmkBRx0$Tv`E{x46Og%qR
za>uuopz2{JhoE&V)aLBy$ozP+0#>3aWEf&M>t!>@c2eh)HZQa$FDV^gLULSvjU%h}
z{fMUERZD~>^ITiASv_Z}tmX5k6B0Z|J=!YlyC2!{^;Li6m9=H#ARI@=<;}ZwB=*Y9
zLua}QDSgYq;K^6s+vgJc=VxuJ&<Q@4dZ41~KR9wYfB9q$4oQuYaN*#_uUL^q)U#Ma
z*{-d1vIRIDw^)9~)?`a5osYK2l<zQSbvq^P*JPKDqVlk$0d{B^S1+Q?oIvak#SCU}
zVf?y{_`4E|g`b}tVX;YMAH0?IOgwdX`qrTq)`IgJLOe)o*dkw)^aR}zg5Sp_3!jgy
zyA>@g2QR{sq~i59yI}bQ7c+IM(W;C7@pt6mQ8Jh;{ehvSgCR$yWVrH+C5dgxmpIl9
zGe~4W;Jtz#%6xbLM@tLSUKmwHr`NZk%2cUcUz<^5hs2$7N(#83Sav3X7B|nPW@R}7
zC5P^_#h{*IS=-n4e}a70W{y80O2d<7=Du*4NFj@?)~RiYtGX-iGzRLW2>)$;T82mJ
zva}N&Lwbvm{ZNTPEg))sAmAxdt1oexzX4aT49A7Hq=}V+`iB`ULw&R&p;T#icOf0t
z*oP?%uXSZvOoU33T3UrpN}c3j8I#bSko<LRG9tCt^F1J&at*Y(L{PwcXWH8FOX9}n
z7ov9R+`r8qw-iZs4SY|eglbHhxSZhD%S%^EqfB3sltPmAim0w+>R@PuL$|=R$bk`6
zA<E_M-@(El>>O>#{~k_qzt^AGNsU8s4S$w0QBl#jK6JGs><ngE`%B5@vPz_o<{a#f
z%-`x=8an8+t_^%IhxXLlHiKr@KmpDc3`Ws$Rliy#*WC9$@yW$XUY|ZXye7$i*D1j(
zc$-=ly%u~>7RCIuS3xx`q>8Z2Xs@Dhaqy-Kag7lvt!0Vn`a9Zv<{~DEfcF^Sp@057
z*X^f<8k+-MkNhFDa;T{Vq4vw<Ee*2r$Mv;+IASyHuQX?01>Q;f9~ZMemY)n12<06k
z;@?gDg^`R&wsh-$cBd*hA=g$WWEd{|vE)@bLu0BO^!Vjw&r6!Zh3L52V#b6`y`ky}
zNx~u5(yZeo8&jv%GCCk#?}{&*@m~DFxQ18Wn?^9q6P6lyuWL8QB^pSBPF4nUP<NBM
zOaJ{iidop<{(wc;#PnA&U%jHM1Sw~i57WeLsY{b?t$_tme%EW<;9Ee%A&hjpNS3F}
zH@})YY9s51nj(^kkad=FZT~eX9Jlh`0EZ`fVXx~g|2v9uZ`Y`^44=^6RN_|rJB516
zXo1&aX72QFWq<c^Hy=m><Pq^b;*%=%!@f@hQ1xtW?28Jcs0;lVa8;)(RW1q3@xH!3
z^}!uk&m|YIe5lzzL5y=#E8yk#CLcdg)s@ddgnorB3l$ktGzuYF%2r(v7#a0%+FLM@
z%jw&#w<wV+ZNQbr^kZ5Ni@c*weu?aRy2IJfq!X%6_%qvZg?hm*`91k4*k@*Y8#ya+
zSTxI3^eCe4N^NynW`FN0dMn|MvP23(Xmh4llAy6K{R2yEx$~3iH&S$GLl$WDNB;V<
zO94N982<d`2YK)6daXt*Xg#R;CGM2?g<|RF+`$z&tz98JpUj~vd?mJ{!lz{|)MbHA
zYtu4|O4LFhxx?BdI0bRJ^S7EvMme4d6#_r~-ry>7mcFMxO^&crT3_4H@ynF;c8q4`
zExx0!Ov9l}+?V?o*dzO*&z-mrEl#m3UrFJS569fI%hB^b;YG>R!kqapjL+2^)9${x
z+>cz&S71Q+_DzAwJ4B8NH@F1xNx;%;j=^h30ViNJqk#Ewn)2;K<_~N!h9keJEKL#m
zM#`q=Bk!n<vbKxM!&A6;4U0fiPZuH8DW<?eHEE<Zc+>q@?Od!ONALm{id;_XJYbGV
zOQ>Y_dEii=>YbJjC#aWvJxf8Ghf+;^H6$^~>&Ez~U|@u)v8A;Ejee>XcezTjKTHup
z9SG(353VHN@2VIPMIKpZw08n`XJKB35`F?n(OS-^Y+9c)jZu1%qKrmH+xdm%WhQe1
zw|MZ&w!|hPk?fY=J9?`t)3WKf>8$62-F!bdzpJ@x7PEYTr@bkNsEr(_(iv2%;te_E
z@F^eJiE&+J=9tn7?_UfHd==J~Vx;BD(+sT(KIfUGSwpw&@BGFP?dH_Sjh>)AzL{z&
zESBI3<SZzgD)kcG-cHiYQA@w5e`i1S8?lTd4B@=k%H-$<5fqN{6Wb6R*wid^)Acqu
z_&a1ygg&()V6#es@4!Yj+0lrvA{EW$mi0}PJ2VD4nOzb!mR#Uf&eVuN|Kv}c?+|QO
zkv>Fy3DW3lHat@vE1U}KjOr?WN8v~enEm+z`lx6`&Emlx+*%0{gZ#+n3Xe0B$i$Bx
zN|Xx4va$#YC_%CBZUoe|oaHiHhd5;}Zn-38@P~i4|2oI2C#C7xs;L+n{7@NjO%o|z
z`1tq;E(EEmsl|Zo78(i+zN<X}l+Q_-nMk#!W@c#_8Lh3YRcg0CR#G|gad4UH^#)`X
zHtEWgY19@=UJ{ImGDd4ED@P_KfS8XEhr+`))hogc8xV+adoW!DF5}C~+Cq7X7*4zl
z)YhMlqf;xUM~d@><Z^}z=G3JSPhr~5;W>UM%0`GtB8e(TIQ*d&E^U?#&xcHR$bV^z
zu>6B(crhAGXk~rn$m#I*n7)?<LBAIiMS){rduJyo@bLraaB%p0*$lzd_c?6_Q_dG)
zr;}`Y{+H47ymsz?7a{y?1TGlhgbhHGhozr$zzOm6@X+gG^XJge&|l-u==$`WsyI|l
zQ#*dLyzK035JmyX{F9BIfXQ4zP$L021et<9x4?t;^b3_k;01UJg&vA)<6e$5d%X9u
zEcE>!cEL5&_hw#mBLLD>Rdoz5pTI}t2znBLH3_s#p;t=dSg-{A=+r0Z&{1{vKoT45
zpC(116eRiEB}PR{QxTi+-U3d5=!C{pr3lYKDzplFy5-M~a&2hSsJOAAH*^tsrvCoK
zH=Q=et^ZjFrcDobJrMK{VZfmcEes<uU9jws{)wq@gvD@ka5^maKvFqMJUKmm@AMS?
zBvjeKFz|mE0dROqNJu!hlkprvm%#Yf(`|BBn=x(k8zbMO;Pz`XBLrpUU?h284*#`6
zj8XDM8pYiCv#NfmDIGTbn-y5N-+m2L?DD>`o^>Fo>;9htXXsr6%3J`cv-i!#)fFld
z5)vBPHSiH#UP3c`ZEU<srk67ZWmHDL;AY!{s(~3jkalBfi9hoW&zPhhMy_I04*%UB
z3v9S)Ks1<}bA7r!O%Q%vbghE&r+<qBVD}fZioyUSjLE}1)dbTVIDp`dZg}zCpoijp
z0j2-_7PvZDerT15Api{&<;~6NZ&QEk)-5ns`%vLlH6*)3f8~oSx28(`DVnHu@SDKY
z#oRq^NSsThNW{ijhS!I&+K802eD&9_Ur%@EA+2MJ$FTbK;I9FDCBD`PxElA`f_521
z3^7g343&x#hpz4S?06`oq@;4PvH%NAA>@nnFCi`t{_FuvNMh5t05uUjPA4ZPz&3=v
zFr&pl(}n)<rZHdN)K1iU>(aBmy`9ftUANn}y}nG?VO_vb48SY`OTq2=YgX3qmLsU8
zxcBWAtr97k4qON1Y=oxu>+9>{V!EmKAbSm+eQA;BXI_nGn7-QO)0&?z!jN(^*@#KU
zF4!*bef=9c*-D97D80kmt<4QK2rXztf&cGUFRpI=uq>|E`aOXyZE;oK4RI1+A4`1z
zMs9tt-LzK+l)CP~nzWP@*lS<V&EOd(B_s<g3&)P&X(8GJ)dc<a6s@}$yw(K;1@rUs
zw5;|#(0Bfj?$geP&tklm6y1MpZ0UXl0BpUQS}LeN`ia;D;&Y&t?C)RWi)tNh?cy0_
zEeA9-%uVA!NmTh%@CN$6-28bN^*b!d0O&~2XVu4Gev3?I_x(2sBBvg_Vnl>iv<xDD
z-u<4Y2YQ`GM4z(yGQF5xn;Fc?{|-SwLifL2vUutt7rBE$B_ooyZYHJ;ho*#fMchn7
zMCcK1bX;z-0<Rn^3k!E1+Nb=6V2ZtFFYfaQRD6f?@i%s)Lv6gmB6;V_&Bs@h!sigv
z|F6wsZWjlL{9MAeA|DYY0x6)cMLB+4hy}<5&Tj4R^VM<Whv^GxXP66FQDe=*m3dIv
z%=|@Zf9av&L}x!Zid!^9%6ASDYyl&qu2w@}ZtlfPh>Uz|hLJ_NIRsl}3&>*Le+3_Q
zQb1o!h_B2zVpgW*_Moud@Ggyl7537CtBM*k{@WkI4?DPB5$|mlSf?&jR8>W3o6>DT
zht}eAFL4$Cf8=`F{HcW(iaeZ<_8(S%OPMrykL6sjh1aVe+zr(yz;ls$emcI6ryWE>
z68_Vg0!4$`(<W&;kB<im{rjI2;9+%rytc~GPI-e91q|k^YioHRW!F*;*3Fvoa-uY9
zn22JD{A^OCi-5uCniBn>RwN1~NI|aMO3VNCBc*$;;O+`g9f1>naBqOs4Yb@14DidO
zDN*<-HDM>g`3GrO9It5S8=#`lv^qqrKvNaPtHqu7=!aB)cGx6cSCEiyACXcHo$4Lg
zpO}~c1~9>#!KI}KP~woU`nGZNH{8KPaBwh~{xIfx2D7#F^awlRA`y9%aLA^<cWTA{
zlBc|EP0oRAsdiC!J$DG`g`mFd!IxB!8~gs3m6a7(2F*sF|BZ5Nv^f>Ew=Z2dNb9OT
zDmgm_O(84Oh2uIeL>YqOxgIG!q=L;Y(Zp%V;y&H)^IyF!olQ^+&bwd&2$&uy1D=!C
zewBUB(a_gt$0NBkvIE1{d9W@4*L2G>7=M96x`tB0tYk3^GvWexCM!JJ3F0yeo-f^&
zQQ|OT=3P#&AVy;-&4OnhCnNMBvfp^KrP`n;mksi_KPG6hkdtdQBBm3cQz+O0`^yDF
zby4LVDq7Krck9Q#a8V~13;xnxoFpmR{?5wS5=0Vzu&teFya0?1Jw5%Bo(RHbf`^-%
z-Evcu9|zco&L)K+w(wT6auoqxQueEsVTEy2sz!Na9J#)Uv(ydO$OgSqq{99JPwoE;
zs2v^4?tIt8(1Vv?wNVs)N%iBP^t;Bsz>a-^t5G0)j*N7F;nU8skWr?_id~`N7cgie
zQ7$3XN`m3>wYFyNq@T>;;{f3I>SHff5i6@h&=mysTHd|xz^irRhm_2TZ)L5|bOJ>0
zOC;WBPsLU%PF6IO%fVE~060(m$|6WMh*mWgqwQ@1{*bqhT5pWWSkh0g@#Ifea4fwx
zo<)je)M$;onApZ;(rS;m-j;6Ri%;Cs6B%>xmoKxhejs)`*tEX!dfN&dHci3FJea-o
zh_dGW(fDKN{=m%+#!UmdZjDrF%Bt%$0a~I?=&XGW0yZ4#6tOmmMFiQn)JQb6w6Kd0
zW3&JtG65F(wD^W=2`a46@bF;w{?*M|z0TO0PXiKq4OJAu?MMqMy}ESg8)e<u5^h(l
z^jv+M%K4%D^GC7K>%=_jlBq-ZNpKT`N9dm4L~#GNo5**56Al1{Dj$v<w`>A6{rAR*
zTaw(*R{1F=gTL#zzw08WI%!}FMfl57$6g=SAv~21-lav}9&maoWndZmn2C?}8`y^a
zub0|;SElgI`*fK%Bnr7*N%cBW?QTGsZkC%#5%Ys5D;yV0s4Us#w(jsL@}JU)Zg#CG
zX~pjY4+tk-7sjuLiUG#j)e#X9Lrxim74t`9WAZID%Rjstr_D%<Fyd2u+^6+K^wxT#
z$l(i=4vvpUYp*~G_e1Q`1|?vsgBGvb7X(B^Om|4|+@sC~ReUHjuUH#OQiQ#c!uZ*X
zYze55Fx5T;5%r|>cMR{CVRANBR&v_^-<MkYjQ5>fU6r;WB5Zo%U#v`)56JKNnJTNQ
z2-$Uq)NVl0q5N3f2OKaoJqZ-Ex3|~REB&?qx2B}69^Dvg=q;6_x^}7~8_mboAbypv
z&_rF})FuD43Ocfgyf}&N*MAJZo58`wWy6(9m6t8n3(VnTQ%a|B=Jltq;%-$i%?f+x
zUAwS5nsI7uw{Hnf6Q}3rldD1ph#*sfUP?zto*Z&zy5D{DAnZu6$qHo}2{nshHuf=D
ztyDbvE0$Mi*=^g5kmGwY_lvJ4gYiKv)>ck-Q2AF&oQMEEzVk0=vYGe|4QB}{JeL(#
zc8GnHxs$Ynr8TL!Ki?)e>Dlh)rKXzp6JacfF{iD!IfYb&YU+E@)6)JH`H(!NwtHib
z!o*%mz)_6gMS%`i7{;OOqyN3aa>3dMCSzlj7IWqE&~;f#AT}2OK9zok;a<d%oz#GC
z{+U~cijLkK8i#VyD-%$7drG?g=I?I=%NL9!?NYAJ99)2RRXYS^{FIawlQSGUf?z5N
z0ngqu6m$=0Ta=jco*spE`^LyXEX0Bi)%X*Zq1rQI2|C`9*CYWJ7M7(Ib+FACsll;l
zbQ=9ea3?g33ZmlRScWezf9oSkun%Ud@@AlRw7j%*U>WC29=IZu<@sxCG6uXH8#FdH
zBHsU;E^@t+mTii3P2?0675xd4INxnTaTyhw(x`@8{tr<UHDi!}FukpTrjv2|!oE|A
z&>C76wEf;kqknvS-2C^wRxZ${0J@JmEhRbG_$_e(mh|MuH-&e~dHy17-yEJIq(p2H
zb%**pNSJ>0ir5$#ZGo(*o1FvLWH)=#2`AH`J7UPUKY&8eGckRlPt|=B49#;MS$?VK
zV40DJ?`Ug_9hafU1<i1NKsd2I?+hTpQLp4OnfKyU(_lD-fkRyRWse;>{)@<+u2N+c
zBk|k)kAULKqa$ngYPKAdHQSHUB{PA6P^IbM=t^dStNXe2U#mWReg7VBKR{AWs(k~J
z8Y7P`I&=rrEydRJvoCB^CegHiH<+&0_YY%zEH3=)zG<$9OTLxWmpwYw0fTeS?Tdb*
z)iMtP7S;5Xr?g4ZnKjUp0$1|R&J9a3w)TZ~0xlEp4dIpsR4X~TF`&&XED}ih6f_+G
z&CNc<Ml)7bUw<N0J&)5w($BAJx|oe^?p`?Q_>Z!Q)f%hm#Sjl@?9(>Y`=Dt2UY1lQ
z8RVJ`Qv;Nhlg!|oghf35r_#=4*7L3u!IR&>FM8fS>jr-Q2+kaOZeYkQx+_ctTG(O<
zH^KRh6$v@2sISJc!)rCNr7je!b5q%huYrAA!#_fF1!EAP`Vx+_mr}q*=wj;=YK%ph
zbcxDs?fk%iqzeH<(OAnB1}~(^K}u>6y!x*Ayg-uy|5lfb10tw|!+A}E`FR}xm;1@P
zNc8=46ZW*c=m9&FLaE5(uQW@n@4srGk=HOyq>ZZ9TSXoQ`};X%Prw#>OEJ8Z3?+Cw
zK<aQ%c?R^HWa;a>sU;yCeE&-$xrpA_B=^AYeHf%U8Hgs!m_gte_*R{efWUn79#*%=
z*Hfkei#qUi<G>akoBCa>qAqL$qcH7Wi!Ev}?_t7`rkdK1yLF$j#OirPvDS|O+Lrm}
z3S3|p2rP!a%34Z(<yNr~-(v|TpXUbphA%unoyZpV89x;@bwL1$*hmIK=MW>cI9Ij4
zG{^3mtm$X%%~NmPqF<dlH+#Ow!S{b-I5JCl(>?AnNH4q__|8dv1*LE+Qlo#!HXl7X
z{SKLBm(p!)k<!>UkGWCR)*ew)0P$Lr>a>Qpz29}aym?jQR4Rg01M53jrw0s%3U77v
z1tRT&vFAo+=_$`9j}uA!4?&-DrX>9Sb~t*t5jfVGO9qW~rZpFW6EJz)@46kP`du;N
z1~$3HbE5od7|Tj<hEUMt1`kMGOh`clO8hp#`{V||TLmIkNA@ackNU|o(%1Nl66^CB
zx(h07J*sg`+faCHPFOf>q|<w{7EKIVfmpaHA5g!cshI`TiFYQO-p$#SwWX3j&WV_+
z%d0W^-n?iOw40-ewhnzQz5<~xO%09g3aZUk=k`^5woT00WuhjRDRehsglEB%bj-1T
zrKP2biCCko>EMiLKCu}Y6~)P_vdLQi=Z~iZdx_&>5iV7TXnAjoUghKDgiiF>dzI22
zXS62Yz5Gu=xT@E(#iu4mb|>2g7djoCuh5n|%%$G@-#fL8x|9B2z*;x_bu_8@#)*eW
z=5E2IO`}$~9kly_sO!S@bAt$#qr3a7=qS)~fl3pBJ&5c|EV2)_Xgv=PY;0^=tbjUz
zlrq#lkOgJc1wT`!>sq*`F_9kMiNWWEd<-7sBHk4+wXt95zESzJ?<AU68eDh)wj?+7
zLvYpL%QJenu-NJGy;g%!KGSg22wmyxb#%oi_np+%Y?}^BExi9H2et12M$&qLi8#L!
zM5i@twEOWaXq>dPh-N>7pi;JwLqCxSq_wF@Tv8I0I1%r-07w1Sh!9WjHyA}(#vh<g
zE(rN!vQi&eQcrxM$6uy{=>+w+!9SAMx+eGvO~?z|&Pwm>+}v(}6S>+(qSvQ6Iurvr
zZ_>gw2K+3Ib~=SvTM`YNHyrm5RnAqtVcSE!z*M7A#s589|0$Smc%{|hIQprd!{6u^
zykT)Rps*sL0+#aJ=TU8Wh^pV3d`KroeZ2Oxxj04FcgPq2L{7Mh1@Q-DO=`Sp3&9?-
z0>d%2O)ed|4^m-Wt0g~vtj5yw^334=1R>V>aVcc5%hX?r@oSpbTH@eDg$g#Cma1Gc
zv9p__++19|&6M1~6@olpMY?1DLz2To2?Ngv!2%=@z{$8gtcU7p)UxVn?k+=dYIi9*
z^YN{I-9#Qu!*?B@SZ$z4p|LvXf!gxMKgdjoSL_~e-J}OFXD<T9lwO0pR1?Hs_H-{M
z$%wj)`%z-pm;v7K>wBj{`=l}8qj-#{owwuxhsI|xUjmt+5KBekv%Dzn8hb0Nitpb+
zK=tkIiOBf^NXr+4S~xaPOXw&WI10;UWoygRrr{EL;rRpHX~Z;t0~zlH7t0yCVj&&X
z{wG(Nr<g5Dnz;K<^@ac#>RAJ)!B7u(50BPpOAO2@f&UqhqUb%Kvm6xrnzPE(=9@zz
zw7Wf}EyQ#GBk3v}@p|9B?wZbF4Ab2`-Q7LYHPhW~#&nPA?wIao6JIqwrn?`W-|c<<
z1vuy2_jP?z)ebAlo;^>aw5;rY(m9~e{sYnnqAXOkV}S8kxw$n?b$}<+v9^!VK2r#?
zBlGy2b0wazg`)sy20b)|!mWd9O9(hmeboOGPxzXcSO?zmmu7U}e}nO8b$Y}wgodtB
z3kw^y9JqsZ8Wt9|(COnQ|7!w*q6%}W=wGbG--FwUCFgt8$UemWdt>5gQtkD&fK~3t
zLoP*na`FH%)ZN{EA!()#wx$HUlGRDUF*p!0-*pPY!KP=iwo(7wf0L)F1Na{xbR0=W
zhSfu~v~<7l6}dBQ=Jcx73+w0>nW89|2$@S45ebyFdYpQy&7>u85E1sNI+>XbcW=$t
zB0oniEqydce|vc!8kv(LvNti2F>N^IyT+$zU^tuE>RmB`uvotYnah_hY9~*@yo{>f
zS=^BK9ZCs?{<5{QqWjD4>E?!SZK+YF1;i*gO5pahq>;OWg1N!D<6H*%{Ph0)`|psS
zt*tJ7PFO>0?3gx%l=$@7F55$x^)v$zZ16w9mV~YP?umwavfNZL-xAg%XWj{rknz0a
zbOtTt27gxr#3rQZwxZPuCVDG>-Ny2tyj&8H6q&;P`3j=R3?O4)F7(^XBLJ!WRR&E3
zhW?-xk?aP6z;n2{{=W{7IaLfStcdy0ih#{<A`tQEJY5|P1#=e%Qv0RawLXux0j~&X
zX9t59*cWIa*aAE@GgNZOH??kp0Fpa)F#yn|d`M`&fH@63o?UAX$PZSTfSQ<?WHUSv
zy$O}s+T1k14*3Nh?i_zuGKaBB7HNfY6#?G+;6E;WZv;IVO4=Eek<e$;=`gsFSLiCV
z)O0f4S^gXYse;5_qq%Cr_I>Zum{icHmzZ|h)PlDtI6*)_lAJD3hgi*aVMst!&0Ex-
zZHbe)NLCdS;J09=`@TY{-?>-<*mbM(13N-#BB=V?i2NZGO%n@(B^drC--(Hc_=6lv
z&|)nDB&%S?IOhYsp$5R3UO3P6N1U-PWbOie#>a61+h}vtap723f<?A&ebs0WyD6H$
zqJQN1<5-|_{P8rrP4vXP)rvUP#suX@NVju$-Kio^`^Vw!XO!`oMb<6NnXh1;Qb}`w
zZhf+N7W2KSOedCiN2b4&ocTfxbzbWrkp*-=jS~*SIJv%EQ|2>~b80X{hOjs1AtL<U
zzrqBAW(NQtOXRsi=z6B7rwOasb!5DO%?4mR5%i^}*>gXn^gB%vn+ScB)o=8wXI2R4
zD(~)qKEbfXDZcCiq_!?+f5AsFdwg#_nfY-gS3%(lWFH?xC&@-Du%Gi>sC=L)mMi)&
zGhZ`;??DFWN;%m!&ZXE~fX--RW1|~5XMk^N;A;dVFE6j#WmEF~^Ki!82mnt}e3!mL
zKlS~P|3(b`HWQp-)}qMDz+fg+S!4W@E-<NUf}$)L>2X%PCc3LE8k>Ofw+*+$$~MUS
zwYM?*7^H$&6pmrp$MNcngNmxB`F{2&YH?A|3T{!j&cn_w1GUz|R$uKaIhsNvV^h^d
z$2lBPljBM%>&-+4SM%Wr@c^XezeA238}^-Vk+>?}0T}m`?N8;}6)u(oQLna;zSqs(
zR0Ev6b`nut9jaME0;S}42_i~`Hvy7+C}dA;lz{0v-&D!BY4BZ^CG1MrKJmr(X0I%R
z&Qa4gi`X@Sxz_{56<udB!tScY0ep>Vge(R#!ga^4mLFkS-ZRlDB6xw2SP=gF00zun
z#@^WX$Utk+lX}b??2pTp3B&MfbTpdP4xk3Sr_P5u0cz`+1}I369Ol+3!@Am0_mp7X
zxCa>}Hp1z~%yF^{&<6YeSjlbIyE+#g!@1sdT!McSNC}(6zhdxkpMW+524?JG7E6HC
zFn(&-d@r-~OYl*vG%Mfp)@5-}Q%mf|C0jm~nnU*T81|_S__k(ai%()&Z}kj9(8%Qy
zr^P`nu$`p?uDIBbb8-RKFp7agLSo{@LRst%O&#j5APZNtT*n822~J1A_}z~z5wwcG
zayb*>c7{F>X|wP@@+8e-FN<*s8;B50ssQg+LnxJ_!ZMe`BD)7p!u}-R`hWxoz%1qh
zyv~MPgViKE7YDWeI}<tu7r4Lt%XNhgX>Yk9K!wA}kqAhSj0_SAisP-V5G%(t*k}iw
zo~5OwGsDhyxu1@X`nI)7B$H|q{vigqCv4(H8g(M3KxoIq&J1tV;gv{;Ch;rjMOF52
zb8PSJ!06S}+q(&>E(x1IP=ha9kF@a{eMPhSf383NrdEo)^1Kh8Y&dz}_R*j;9^ZHt
zgzXm2(404~gK$>>O*MjB6ttoqQAMM|5Z3AGsg)mmp%BUybVoD7?93z`2>Y=#f|1SZ
zxD2=|ApFbekjdWQ?^6o3eC1pnsr`6Z)4i1k)=qJ>#gpYv?v<MFLv?F3(_SxY4?&Rw
z8~zaE&+)s3FAWl5m-dI!4iCzY+Q|nmkEgM9Urq_tL$Du3xVh*0ED*c`MTCWC0$f~b
z-yZ8Bf*^3WOOS5zVXqpmL9yLAnxE2q9McAFWEhLtuzBM7t(3WR{e|Al?@xL9>=OVW
z)fZG#s=7Y>ufg>o`|G=_+x0;DN%DTHc{EUT3<f~)FBHWsV5HMXXMh(xUh5Qk4witO
zjA14JZ0M%~R6#6x)5XC=*$|cN#O1Mv9opv-SV+Qq2R<Z39xP)Sr4w=$caMr4{_z2R
z%Mlzh7D;V_dWfl(mfN6N3fkCCm;Ylbvp!ttGW(`LBSoq8MmBgcBO)U~CR55X2or|q
z>Zf`0W=0NErv()a&F7%3gui#wq2$NR?a6-lQQ-_Cx?-c?if_<M(hFaQ3`RBBZ2zr!
z!{xVZ2U>|)aMo(8lMm?fQ`eARF%@%dBj_QtBB-iPgH74*ZQc7O0~VDY-LL3s`@h)e
z_J9N1%p#Uv&%=Y)>H_Qv(>X%ckyo-fO2kNipaP$FNAeoIIG{IT4kEaSJmDc0jsbPT
z!y=DI4cvTo9)EQ)fN!X4u@}HpATm4If1q-$?gR1^=*3O|8Nh&$i-nqhYN#|ypcIRU
zegZxn<qXd7<M8317%TwN^LWYCIC}+E!%8LP_Uj=QgF?74r&SCsuqk(c227CAiZ{Fh
zxS$y1?7b01Zg`S<t&F(+I?T~4@AdsI{|@*PY}C5P8T>`~{u|kZRgw%VwLKz6G%jh0
zbRJIL_w3JL=SYZ&35mffipA4eUleGxM$VsXV0YN3;X0PzqmIuM@bi+82b28aN(-AP
z5)#r@1!;><Dx0ZFXv^$!sY)8<hi>ah45f5ikMWx_ag2`Tm!z`S5+Lpc{I!-|A<qF)
z`d+m*6h;#MaD3a#%kQ!0kyd;eJ`m`Rmpg|-p&+N_Gg8pO)YSJVdj@FZCr%NOP5@x>
zyFNX*n|!sGDyRO-2m%x^&c)ae*071$^Ka6Hfx5Z`JsU8}tOE;w-tMKaUM6&=Sz?O`
zH%_K7=`L%xzhj4Sd%fH@ff}ufsX9}E-o%nOJIS*}p`n(RsIE5_-`WVxjC1w|voJT4
zN;bbaG?)Z_7Rq7U8wp_8nFH$YV2B!?3a0Z0!P+sa3=9lFvlikMn)lVG@V`S4bRL@k
zScO3`OB(h)c*|aFF-VXzCK0|J0-z?78R9BhgGXe}gzuxsK5eShi!}Rf>41ooJR?Wi
zOoBO-FRw)u@IZo>v<IrmpEgXf(iTzHg)=5ZchZsq*Bm6Fzg1+Vz8ci4oj-n9f==gj
zTud@v?skz5x}G~|%lb1Bu==N3waPOB$tgIX*a;09Z6p%On2Y>eT-XO4@a{pRP6N#2
z@(@%Uj8C?upmMs-R#x}*uFGs_2?e3a<qqxBDsU*W3O2Zg70Sey_ey&p0`XzK0|BTo
zPBRXFm+>dhbHh!7r<TVsSh;S?;o`^nWkZ6~9FdmSEYKyce1DIu6deqYtiY&0$85PB
zsGz7gC{W2wIteH_e(KI+@VU5t{``r+XfWwbEf(OahgN0R{Vp*-E>(p?PESr$LRUeh
z@o`MZ$DN8{M@;BmNY-FcA5!~_kYD6Y2aB|r;wJ&$8;6zaf_#mhH&$7bAJbqIrcRlB
z(wH!H?z+3o<EBF;Do%Du95OyS?SYWR6{kmY9<w*kdBHki63>A3>@Y*QI{Yu;T<pht
z;DIXxF(^TRQjgZfv7?4I#P{?^U^b|Sb*M~Jwq{N|`TX^>^zP{{Pgj-SZEDsSpSQaP
z-}rtz!<~)<rUl3iyJEJyWV5~G<cxG3{4Zf8&<xEXX6>Yf-Zz}=H9fXBiyz<jJbr%u
z^xeQ1YrPgH3UBvZVQ)!@Ds}SA<)776J)cN$d+9e=eUdDfF&Ti~REpV<ZunW1ES54Z
z6k-tYiC7M9{(IAu17Rm2Apx&`QEa(3){q!|GpMn0Q0lj&@yTKJ#n5##7=1FSQTy@B
zGu_j1&AiN4wqm~8w7bg#$ttMdG~oVYweY7@YO{#|P&dv09k5~<l54olMS%aE{_nqA
zfI5N;0_5*AKBE4W1j1Z?wkS|Gf`*z7v=M+0KUaa0!eS5(L3v3h__9tqe{RTl6+AVs
ziv~dMKx%zqY`tTVFQQctfoP~&Dk|6P;l+P`YPBo+x|Wwu8)!!nLhq!Ht+Ow?82*rD
zef=t)VQtXj#PXA=Lk5O4%ynv&2Z#~kVq?F>Pg@(3qJn4&3)EIX<A7lORF-}y09-+T
z-H>c~xYRvauXH6E3;lrTyu%wc6P#fH(v_S8+u+2(F(sORUwnrbyIwJh97#;9H#W8?
z_gs69#so`mI}6j-aFr6SMABELzZz^7Yrh-qzVAF(EQg$D-_&WmDt_K@(L32R72I(k
zrWSZjNGL;+(*1by(;(?4lBnLft-T$@Pu36w1%e<?GJJeX{)*U`7+&W!49q}qNYq-J
zBZ8}1DV-g*F<~5buirxtsCT>E-Q2?J*a7r-Uau=9H87UKWId7o3AhY=b*TA}P^eNx
zkP7Rawny9Sh7tb@T=P-=SlNZHjqGALuxB_c*n?>uOR8hbE+j-&07(o2d?MN*=&lcw
zuui!-6l67Otp#)}9(~aLe)I_8ubG^ySwWZfpUv^&nr5mFZ4Wy`{}L|wR8z>U$*e$o
zgaHkF1Dg1st$B^1oqSES>)v%R^)eK2VySknJ-3Z01^u-RlWMkTu&W9v3S>@(j%a;@
zg+^kfe1zfuoY|7}4k2+8m$}BEX{>L7YQ4s!bL@g1@9Z3Me+iPTAkUAdDH|F}0sq86
z#2MEoECS{YDf#ljnYTkkjXT`8$HtkO!N)jP?DV7ig~F7!2hO_~<UDp-@fb?M2x>O_
z(U5IQXsty9X^!ZI&T^)B+cKqvcv`M#xbu8+%WvK@5Xc6;;#q>;k@_EH0|Qdl*48Se
zJt5xc!B|g}oc<t@YAyiGe)!Mb?uUfB`h-LZhGQi110~)`**sxP(XF-(9E`PmbKw~R
zT%rtDrgQ=Uf5!+iQ8kN|$`XU$eFgYtugscjRFKAJ#z&JjgI&Z-?q7^K<@0tr%P@0V
z#~$?!4Yr?+z*$JEKDObPs-nLM1x)FdD1Jue&QI`dv>R^;e#B_+h<kGio2eHLJ;ZJ-
zdnNGA^j#aR^1~~9d?_x8Y%NU*%s21emF?;(-X5{y!TwkIwG`Iq3H;|~|7r;}C7aab
z0X$}3227av&tRlI{qyq{L@_C^M}m=~J>zn3y5_tw672{^`D*aUwC(p#0yN*vUkxFr
zTG=WR@if3+vYjAS9BGkaGkOL2UGST+-U8$h9{&gN6!A8<oyV<BYRyKRKc*D%8L%qj
z=;{-g7P-x)t>Or?1-=3B4Gq>|Hg5HEf+u022m!Cz1VO$=+2Z}b#3mYjF-gr&pFZj9
zfBc^0<-#BQip^k17p(N)Dy;ty5%cBplcb+tN1Y2r9Nv1tVRAD5XDPCx{wg;ju?kHw
zzdM%<mb})XC=^iv`7%T!=dZqh*z7S%;w!!`ku{*(iA^Frrcr27#RArIrkJ<qqHn+}
zSoS!I-|JmnxpV#VMG7Zgk99&gEzFyyZ0H_H4r57Ii3KtmN0}sW9KPK2%O~KxFsQd2
z1tZfwlgoZ4k3HC$!a@NBXCo{omKRVD7JBdk0e&|jMI12_?7nS4X<$<VH16PM#~|_t
zAmJiFSr6&(`pfc4%7gYtokbuHkA=^EAr9|%`G#(zn6a>yr{^sgI|1iO68|3a@qG`7
zJ&+6AlmzOU859ELyZgO7aE&`ZAZ^hDl@FPSk6V$DELH8l06{^)F|Qb^r}KaRRc#-u
z#TW!1s~2of-#IBiC4#4~M-cK(1muooqVhR2o^%-mgM$zJDYNhE6v~xuxe{)Ts&(rA
z!1~`O7+eC-_qWhC^o5RGwuQbEn`f%&u&^?iySj24bHyPhMrjuFI3kXRm6}ASLp8KS
z2Bm$98t@B*#I5q*=w@Q%i@F1$qKl7!$BKM@$z~Asf7pNk8^N~Z03x|BfLiN6Dn10x
zmusu-8#dX6L(eOKR&F9khzJS=iZBN+n`ZaJ1<<V-6Bmb|`?I~>_05cN0o(w{L<`*r
z+9{|3*Qt^bPi35SuJ<m`?x(C#)~Tg>GZc|NpVNr)sQ-c<BFDHv+}D-&#a#X5!*M*7
zT$@+^0fLc-M;Os!XLpxV5@u9yEnj6?x1sTakB>g{zsmhouVCVNZt&QaA?wA;WE$Ha
zEG!sBNVo$wdSF!;xcjj<#poAvztoOIKE8qV1ijhS9nU=YAJS3pM*IMHBmuCNzhitq
z-rLpsRpr}%6+qxyqgn3#RisGtDGO#4KPZ<?u?23e7!M4jQb{P1z=&WDQn;9tGX6U9
zstd;Je3|$~!nhfjBjdjBa#8@DC74Uqph9DolApOb+RQf5>|Ge2n6M<M`(8RXw}>AF
zd!gTG^GXC?h#-U;sWVqB@Jk*?{B&sm9hwXe7z=qQk}WV05fPm`uL<cb?*XP%aplJV
znJeT+!i`s(5ekFzR=@HQ(I#33Tg?65C`CJfY%hkseS?6NUnBs+|BooJ#bHU3#n;PA
ztsz+q&7gfz02>KFbW9&LK>{17efiF<!<`Fi3xM?GGeJY9T7$!%R0}U4Xpk}9rn#K;
z@%AS9c?OI#?_n}%ESWX~JFBmHdFC_RA8?D53`K$uCKf8clh;(#Vj(_9>MP9lkWVTE
zQ+&<K!~RkbiAzkZ*C~^KW?jJ@5x!O)r-A9bM9R&dSN#|Gjw)Y6;ZHknUmM)rZ6_2K
z7BY!sq#&e7p($r_qauUcyI~z|y<J1sbj@6}VwCWR2v^B+ahz*WSl}S*hCU0;J%W_3
zq*nvbt%tdeo_ipA`AWx*mcIjXI#=ct{rvQA2u3_L+6M?)smKMWNx$hp_NMcuQK`Lg
z-puhl3%LEmY3EH8#}->^`&=S$+kpG5t9F`?wwxi78)`#Di2#By1d@t_1)Oy{VOe!J
zkGVKG2dF7|P|muuvdBb|L89aVPf|*W$MR=rLM?_ekFl5z{5JIFcttl03c5x$%5Fo0
z=Sjbwi4o)vumigE9||*KIiXa$pp6Icn+p7$V!c?I$oNoB{AM1T;6M5|X4!Lpz+OMY
ze_s;?y}!Vi03nrThi%+usgcc|r)zg|+Q$<Jl_}s`p%XTu>#d<?+=LdqdD@?aRIPNJ
z(@LTp5Gug6d`#H!@()MOOiAIfnjp?Zx@9rk#iQZi_yn>qfrJ3)#cpnIk((^O0I8^9
z#giTU&^1_czz*(v^{*Gw(^0#y(CO=m(aJj~8WGTH)(hvuP`|=8DN(J!bz?R$7^QUh
zrHa%{YZj20c9(%E<QB|DsFy8xz!o)#T4s?EQKM4<epzMu!)qVNM-E9pZ<dG%OfrS#
zVP*@0D9d}w2zW>D9-(ln-U%QIw0NGLoGfAv2eYIsn^plScFrG4SDgF1J2|)@pef{x
zOSUxx-Jz^izq8(#hCzP~MmpZRzxH!1M=I#SMR;^lp_n*dM#IKu8v)sP3bXkNV-Xy+
zS04fL^WqiD6ZJpC&azEyaYl`ajRiC7<x#`*Z!@qljdd-zexjkk*CniY7er1KZ6n<D
zgWaFREc)uOB2eRFa9=4>rbm-8=7xyf4<M!-3L%r&RkNbv0KU0>OK{zuqXMMrO(wx9
zB8)D8c!`I<6I_9nOr6{G1iFcVTesHs85vFZJ`litygY+i;`Oy23=x{EcfX4i#eh~8
zY!8?~3k#y@aN8mhHUCTE#!@Q<8L=O{j%#T^Y@L*L{Dj0?NM7z#_Y-HgZ742O-x+7S
zn`c}0i|$@5c8Fc+Yc%U3QoTk?UI2FNS}4oO<!7@*_-Mt$MBM9|`oHxq`$P%i@Pa&R
zd5^-w)U5XP9gGoM(NNHK=mpNTYZRPWbh#=t5O%pI=FlcduCTWLhQk;X&qw+D6v`&-
zhB2>iQCi43G)dPZ;=26-8G%-wKR_~IQIWdG7Z5W#D*#YdkYrZTdH7$=9%4L-$nRIB
zdS8`E<0Fz%L4N?sC1wGpf+$wZ=Y$6GAfU?f0}HoXfE#A)yxJQauni4s7nvbcXD&O>
zl_nZN0?XHeHE*}w+IXDB7;DiKrjEy6_wL6sPT&YXfa~Lz($`9JZEP)H-HR9w$%YR9
z@nE8_3r7x##_NnQ(=*j?d(!Ki;q)1m|I%w?&&R;kk3F-Qu&)aX3zt&Z;J-A5eF^q;
zhd%p*4r4-=5Y|bj|G-MgKt;LCsrQOp@mFp)<RbR_QNCFgc!x<M#5Gk=;qZ?@nOgT9
z@?|6>%qG>YtVXXc<hVfElezc}0|SZ{6!7gc!z5f*J@ui%;*UZ=L(K_ROg_Gr|2N}N
z2)H8j7e5WQ+G@3guv-j7nRNPmBA_uCTx_z}O}VN7L#bUG5a@A^o0NTo)aVPTni?qw
z=7cOqB$=%rsb|x5hJ2&sWuEWf3S;0O_oa5mzG4{Vg#i3>0rwgC!-jPABqT2>sqc+U
z`NPV;#sXX4lf2mSdu<G)ik(0zabs9rfWT%N9RB&qr{!Z{0AQagy~l7F3S3-C>ERa$
z>-@${T^~~blzho1yk<AR><-CQ(oFmA|9G=Q8)iEcW8m8NY*a1^!}APq?4vut5~IHh
z8afw~!mXOFzY@ZQ96JYI-rmx`hcUn#&r;B9b1ia+M!P~68g#*2Mp)eC0S?d%2zIMD
z@|TvOH9W*7grZIQhT%_{k=AdAmv-@IQTrzm23fdmX2g^BiDW9MvvNfJNe@We=sVbw
z+*vzH*z-oC@kvQzTF7cJ6g%OL(VgEi6ZP{P0yHS}MoD7)<@0sS!_R{uNF@A^781Ds
z`>m;|X<g_WusC1LzfavHknz;wQG&xjB0^_z0$ZWZ()(VwhVqNRI-TzG9U7;R@H_gZ
zH(#TJN@PayU`88Ck=z6+uC#?cZ94L0h4F<yMG^E%$(yNXgQ*c1)>SAe^MDtWq<{&T
zG||7cBcEpT)rV>yU*FKn43ziwX4??got+)_9OO_X6_ug5nCR&5DjZ}SEK4xW&g)o9
z2>LJ;2w@4E<`JPWc>xc$t8aJ12BakxdO~^-V&mg^QZdl>ynK8V2!ASv3e2+if6P>t
zXWfP_Z~JPQal}v>Pv`a!7+oilcl%s3P9mfarQW)34WJGg&&<A5Co59(a><j!0J74o
z&55CrjhVSpvqZ&h2Q2A;rNyrhH#-GXy0Kqa>ZfzMdd4AkOFam!#L;rnnb;bS4%TDC
zv}mDgsl#<V9_Lw9R?-NM#?A~?RMdS8DlUEceW}Ou-o~T;YU#LdHQfsD*5z6!rz^X!
zEc?mX&gi#&SMWVs_Xp3v5?|QR5#Qs0#H`+V=!F$LA!e)58PZi5dFd_DVCmw*%~1-+
zb>(ed1@?+Ey&Zlx5|3B;p~6kdaF-;%M85o@&Zw&qN^yX^^h+(+tM|1zwRaD|%grx<
zswhvx;tL5Ek_>lpAbD7q)GGR4#y4-^_GTDYOrI~(YPufK;+>Zp%fQGwhqb~7MlT#u
z4C^Eb;7_1Mu1`xNitYKX2y;ApU=A*XbZlxy#u&VM-=gB;i;&(g)dJruc`e6){;|Rn
z{`<aqH<(9q@4LsC8J?!cX3Vqt>(L#I%$^=FpB584UO6t|sKKPKzxMoH{J!ETs7Ty|
zVuE49@LZRj5bX8aCnrvk_zU2KETMmscYU}a(Fp)k-7Dxr=vDS=r7flLZ6=cdR;CY#
zQ;L?W`u*z4@9Yw&)%x!mKz_CC7jknFr|)Ys#as;2Zee|F-@mDm%5?b&Vdy1?;Pp@g
zT}pl^a|??$E7(RT6ul%5oq*}r-1c*N9SlM8myw4+n+$+avEjyk|2sHiKD8~=2@nYf
zc)FsgeyY(BtNESt5JOjZeG}4-AG=Vy$ucjegZc5>iVcKPx-)V_rctG!$+N5d7%&Pr
z@>L54gE&sYPr>)0_ufi9V2^n*H;|Vlk(Dgx`}t%$ED&|(n%0VM_t-iiub9)K9l?uC
zm$bPhEV3KBLO`e|K6Z3BDryP=GB<X<J@;T`KnTB+0O+kf5L{}b(tBfBy6SNF_DPET
zNv@cK-ZeTub{AaQV123+(MJHciuD(rvwIK@7ZM8Oz2>qQ_fUWx4Bh4Bya<pIL?22)
zo`u6Vy1&mM-z6YyCU+G#Iu^BWKU-#We_&-SOegcVUE2KjJ9F)r30+vWy@0LjuAq2W
z<MM@Axl=`Dr#{w+%yegLIHX({zV)A*Z2KLh_J|=9?2jXGn?-LQkaF}d;+2BvO`3^M
z+47PV@fAUA#Cxt1saAy2B_JDo(G7&+xWKu2=|2<wREsJY49iYRf@9hSyreZmRlOL<
zLWqd=ss3Jq8N4W=9-_L5Q+zKbAkYTzIGf1h(IUsJf5`#i_9b0FZ1d5g>=y`b1_PB9
z!?V(p)O5ls{>?9leRP}0dWV@wyw^EtOqHw_eNb5lGNwHrBO{}z=<8W+TsCV>a4XL?
z+=2QIOAow%ZXbK0d9Fp+RkhSw634x5%Hyh&hB#T&^Bn2+9|3qv=urm_vQfh2f`T_X
z4sM{E(+!8~Yu^npB$*-JJv@wEbhjEYpF@GeWbz9RwEKLB4EK4x*)b~}`fiv=r7%Hf
zC$OW9whgG#^UvttzTHGn8QCh}OCZLC)eKQDa;VLt_Api1l`%#`X?WD3Sgk12^yX@g
z-=6XVQbRX&FrIr5XTsr`&EtS)o%W&i3-%JXr_rXI$~Qgl^0$cYD9@Yvt8<6eg|4*2
z`e6-!4Pk(|<+FSk2HVl_kGWW2y4c|J`*E$K(2h70QUi#8?Ylq#=0rf&0fhWuBmKNL
z{y{*l@g)o5>G4*~`{Ku2?;X995CD9}%;FQjce6A9?=9b>6DQMQT1w*A9--#VKoUdS
zRALlPLKhAD7GsEa&lN{Uxob>J*V!$JxBY`nL*+K6j+)}vs^2dCtPqd4@~#lzxd5}K
z;BJ4T?JO+^s<t#RoC9BunazY`eLfh*$8{+4Uw2=OF#$T7*(cBLbC4v-HpwMa_<BzB
z2&Ggb70TzhJU~o5>kA|m*$lDU02_)shw`oaeF%Y4JM}=p@?#Fc)<?={a#*Sd_8DLv
ziHbrQpGB_2ENLZ6u~KBxve&&EbomxS`C~x}%{1rB?JXNRex<|St6aFB)hSFxuWZnc
zFj6n1v*FGIXAUn2T@aCV_Z3q*X09<;=j^lKENB&Bfb<4fgf_z7nxR(&sYl0yt3O&A
z#&Qfm8?6^%dzI-F1l#c5za<aA8K3Z41g*VTDb7e5`L0MYjW!J9*%^5L!CtR-!n_qo
zOcXcty>a$yx{)H7Lm2~@AEMY=UQu-{VwXMShCyvVz_|qo!d*w>74TENI$7&{+~|ep
zcG|>!EyaHA3pOEzZ5Vf-=f0<-ZS#!fgZ3=uFwt)DQ{nOKV;F~fq?U2k_nK7LrFt|n
zaT^E;Hga-No2cpX`k?~z0%Zg>`L8!hayCEkt;&t0k{(r}(b_B|-MphfyEg=G8fyKX
zn>@b@)Gu+bMKY&)d%?N%`Hh++65L|SU*oXv3Sq#F_F<W`2Q?7_90hhwKEO}r78V{|
z-)IXiPK3+%^CjY%GeYi|G9P390eMjGyg?OVmAWfIJ^zfWfdT2It~io<1S=&arC{F?
zI;S1RuX1gsCBAWTH42=dzoj+DD4qS?m!3~ysKwJ3sNN6Phr01UWOO2%7c84w-Zyp=
zr)O{vgYAf};jUudHgF|TBuR5UaVfVrznW&k`gy4i^R;lY)hEmOzZjZdbWwr4%dpuY
z80&O*_oXkQ`|7tM80~UIe0e`#<AsEVg5Mrp+hsKAtr=1+UqIAszB>Tu(L(j<dw`j2
za1fC>-kBQRWuU(wz5{cf;($Y?UzQx0ukHb!axOE=*#DLKeGl}j6ezZR&z62RF^bA*
z3Xq8>wD@gGo1=}-cI4vQwGwBq)_=b;LSdEtodbgKrC=P;d=l6@>OKtjo%Fn&#bsBE
zUm5WT5D*lscf@jsVLv42?_~JgMg4nrR$h?LVezbSlxq^fiET%&9Tr%|E-o(><LFtD
z*86q_!s>)c!ozzS<D#R32_Qg2`_o+^B~hUF75z3OA^Mk)OZg_HJ+9<2FD;_SdmX)z
z->{03l0`+YGkzAT=7~0DHTlTN%Tuqxlf9pQN%injT1YT9GRn@$nY5y6bQ(XLdDVXe
zcx8He<N>Vfp<;7)fJKuFy4h%G;G!V&Vg46OBp@uD%}6Caq}?Pz7=;LH`QS8QReGUd
z7mH-r{EYrg$(6(>@LBJC6-*0;YxHqKC|iKHdIm^W?ZufV5b?0HnvF_E6{5?L2*dRo
zp*bkZr8NJy1P5$xK<wKq_YAn#<r3La4dx{tZ_^?MjWRm-5mQcul5**H2n2fQ0@6k3
zsv=(!4OuNuV%vXs>mbBJ8{Z?P08vmrTNF%xjnjJ9q&Cw`;$lz*vm9NC-eKeR8f5*b
zgj?s!;eX>guznrh+n%pejlq}6Z$2FMRuBo+4vCg67w(uF3RYjUOyJ2_o1RXX5HJ3#
z(9AD!o)1ycR!Fx+-@vt@NvA!*B8-Xe2xRvwJy`^hpL6A!=S?G2xAt(lOZjIP+;a2s
zhz0fMzJBiJ2G!pN`Px?hGUz4(?Sp?e0Gmc+tFe;qus=EmhQoi=e=7AG%9qWwwD4K9
zg#-m-;7vc)vC$rn<Q!OTw-C7W>sB-1!T$A5#ej8B>%(Y`N$eyHSxC~UGLTk<O@R4L
zc@h561vDK8g&n%Uahv-(wQ(YWzkjC)XtO}7f3379H8r)Hs>|%o^h|2G%Fhbt$CUz4
z#3qsn#JNUWO`)?A_wO2I7Z(>c5J2-|VPm(rb9kza)tdKX5Cvv&+rq(W#Up`JO@XDg
zy?p`6uADOo_kJqx+!-3r*LB9>exPNwq2(K!jiBW^(%;`d^A7~w6r2E@_2`@Kuq&ub
z20OnOU`utJNkWh&e`9{1i`CdbT2LNT4F<u1vD^tK`&>n_rhvYU8>mb{8F2~{eW4(2
zl)(bpw+{3^T0(Hce&Sm|<6;T0Utp%y19&vRe$1wHompyHThoi45(D`T;jYryF3EV$
zhQf))6Sx63ZvUzt^?5kaYBEzjVM{8lkzd-|=)3Q>z-}tM3<62_UDmg6p)^|q&mBF}
z0D=dgJ5XQYzCtPt{XMaz3vyMMkx?RoiO}%i#Ux6=ea!rT!a_?&$Lai{ML>j^&fr2u
z!+3zRZKyM+Jt944p%@Ub8se<03eR@Pv3Iv!a{3Yk!}ZyRp|o)3qX?_Peebn0R};bK
zaEU#jsC=!AT`?C@M}KCrqZ{^CT}d0!L<XIxMgj}Ss##AUKw#@&fi7d@5v`yZYpUx0
z_BK1^(_SUfhFu60R%dN$X^BJ=4!#xa;JG5c?VhJthe1R}TU3Unm;jhhzgb&bOAg~@
zWE>kD{CK`KSgX$<Pe<DaK12ZNsUnWZ#FPF#JdBl9g_RBn2mk~HB`B-DBj0m7n2YXs
zz+qLx0OQpHcmZJHk;J+5yZi+7D7r)BLi&QvzqKGkCVq2*I4@W$sxKd4)RzAsfIf?F
z8h=90;NjuHp&DDGQ)7~oo$W9cU!cs2kWmASc0BfX?`HAw@^jypV6~BtVT$78(krvU
zuw+i+7(S}vvqx39grvpg=#(yW3POhx$D`dJo-Bow#Yc2L!Dawz_ZK25pA*JK3GM-c
zizr)qC#d~-1o=0ksMp@W`5~86Z163+mO316u~Y-s5;@sART8<FvJ0_gRu<H&oLdog
z2^TZ*^AmRqg3bzhe1nkYp4F;s9LWiETMhBk#%aVpL}-6RIJbFQCWlv&a;vjVa108t
zL1pb%`5#ublv12e$#QVFgert$qsE4z9dPC1+r81$HV8H91?pO@zhMnBto(>pa5{nD
zmHpS;FgfTtQ-h(<u9ju@NQxL`%*0kaFwZh6@1pfi=~B!~qu{q03_d+ld}fYJ>M1Z)
zXM8{y>I13=dgV;1WONF$ViKDS5}(ROgdz^}cR64Jx&m{Bc7@K%+AreTXTelKg76Ba
zxdeS!!r%vCnu)<r4;J0JFex1!!Y03~f9iA`0DV1Uy5!RYcE->@$V=pG4_h4}@QRnI
zkof=RGg~bXJ&-SFEXtYag8aq5|B%7FY>(=Xyq^}jh~})2n@QF8+Xdho%V~Kz#8gd9
z{G-nR1YFzGZVrNy@<~hOumqrC-7|7mbp?9~>(lS#k&s8=PZ;<jJMv{OFEAHRgW`Af
zUTbSB&@_C*dB@6HOkl?W)w~zxfQDW!>3F76B_@@o@881$3pO&Z!G~&fN2_dJ4lR&b
z%RFO$ZMQ#F2}%;cyJwcmZ8s+;Dv}ImAF5&gd8m13PI|Sqx5m)EQi+(rX*&#`JN1yb
z_&!fM+#k638mZ7v!d+Q61A{oVwyEAhz<W&z`X@5rK^14S)kY*>TdoMsKmb|rB{qei
zR9)o&^raKERT(2%`|%}}kIlH9cI^(NP1OS?O8=RD9UtBy70jA=!@tl?S^H+vGCnPH
zaBzSuvZ3i0|7)sS9o%ct9TpYi(tK8lLPp^&ef%xwyQ!~;W2jxq<F`AE?D4g=?8W&C
zXm9ZpnjYdLm6bnwRnYY)4t<k+Z%+ZrLyhZOY1OXHGNFKzl%?qXmygF@^bWQ@465${
z0qCNaxzG=g)#P9F%Z5SvyHeADD-JXejO<C<U#Xba>DWP;;{pn($a_?{r=+o#uJaC_
zZYy_Sw~j79lNsNmcQ~i(o~-!&7lf9h3a}at>`+ccD1H|2-Sz*QqHEI^7Wto)#l0Bb
zVhFm4#!|U?!fdRlC$@+?U!e-yqe4&p%*I8AbB7Q6By?u8j3$+KffyP}JgN(y0|D+X
zwUnhDWau~bICI4imh%xC2v|6YymH&L1<X$!!3jxGGH*WnO!;!^fFdGCKScO}_4RIv
zP$a99?_Z*#{;jrgx%&fLCW9;F{TS~F1>{fm0{$FX@)~LsWlwmXn0cvwJ@bH??nI?z
zQ9dbE4JZ8%jFk9Dt(1cyAK6b+|4}0zi3u~oM2IKGMbe9k<`Sng@MDLfK3mI|040D#
z@^~!TZ}~gZn*Y9OK7|vRyl8-1wK`QwJ~)xf??3=j2h8*nWSh{A4&l<(30qa)p|%SY
za=eFbYUrh8*E>2XD!7U0+&+=svqj~v3HYRfT6;ZZU%bzKKK{K`Md;M|CX}H`VNl4H
z;)>4f#ybnFAR09N6}K=nbt!89vg?0(J);En#kc?S6e+4^gz`BNE28x(1p#bxkYt)v
z{hizwVC^;WgYWeP*W(?Sip~idP3vZ}s-Oyf%pG5?$j1cY4}kA=t47bp4bbCM9bhP=
zMv<#15iZbrxCbRsub?qHlpx$qfl`|hSn%<_08H$n;Mz0>O8fQJ{baSRqn1XT{0#vq
zsX~_2a<ij-vH6rnS!|>_ET8iQ)VBQjdU(r_4sIHMPVt|%gwSWY&zVvrHFWrlnY;k<
zZQe5}7sED-o239t$WMo<&LayJzRTqSidPxo?l;e}G6%*v-o1sauwIM5ekGptfU&qD
z?P3ei>iDn4%=^M#ND3L5i16^uJiyDTk1M7!C-NTy1TNtD-dZK<Cb@Bl&~n}9kfr~Z
zqzOA~_|TV<p_CG*P(~GzX8ZqCjQ&sob{TK%Kpjhv?mW7)_ZVz`L&HF}S)2E@9ZLuf
zX%G|{(XNcX+YJOMclFhh{p03Bf`JeF^ch@c5|yU9bosNtuLQG7l(f`V^5tX40Vqgu
zyW#z=AH?L<Ejj7^)(s91f|Hm2D({&hVtsusyE?!`op8_$RnBvC>knCE%5|dE8zFWP
zo%2dvlYp}s0VrXo%O)J+B|9xGZLLUi_6ARqoJoxGa(7_2c|t};25<swnVemMc8?kU
zar*&O?Hx1DZ=WNiDcz5k3Fv2S!L5wrQOWSy{O?%G4vb}DX?86?YaLqI8h3@YC24YG
zDplSwp4G23%0t<)Xe7iyBpXF->zkdpEn&Z+It0O-srB_dI#n<NRXq4Gvcs+#GI}e0
zy&f}(nfhC7lH=W4y}QMWoZky5jT9$1CTwdu@i;Txv9IvBtT?bQ@5qs{biy6MBkFV7
zV)NZ57{=qmJ2!WLra+I$%nFBRiP<X?4BuP48LnS0&69fGw0d9C{Q#yQXA6rrh>jMJ
zB-6TUbaGTo%*nAaPK;A<1%L*&k_>h47qiL!nxK%-xKvEJR>z)<#`C1DvP5SXuSl`W
z{eM6IFN&OU(EarBTgU3^&f0Ao-}IECyDJ4*ZBFY-Yv<x8We4_wUS2cyVqML68@O1y
zXe^}Qe}j@#nc{MA2)L!+LwroH*NXn^|7_4)59GG+Nc4CcT=x4u;HEIa!+JOS#a=T7
z4LlH2_k#Vksb*}PQ#bGzte~HmW{%-jq1FBsRtD*HOk^x_0cu%KLhujUcFk-fg^2{l
zr#?Y-ddvW$XkYW3VHwiMdwRJ3?AY<~WAGh1lNHkqvO}He#Bq2$Rsmh%Iho4W95gtu
zdbNP75P-M){9HIX9_OEy%i(i2*>f_L*ZDH|UjYfK`aSFJzF28;YIz=pdP9Q{1f#f~
zL-0ji+PUY;x#rwMmNqM+uKo+`vQ%k4^-gwncICe~2?+^zEo_40b%)~!A8D51M^_#7
z!r?DB-pnc|pXUFwJ*AK@{pW%rY~){yV3)P>&!XRELfOJ06s0+Pe6T=~Ot0}M)6*r|
z_(){8n-{@mhy8joIOjja@|v3ege-#rYuTQgKc~yl9JU|;X5*GLZeCuM96@cdKmblu
z6X~O2>psw&und@@!^5^d?GFj7Eq+VT!ICg_5Y=D%{R??!nEfBKar+gkL)Bueyn@05
zkQJ3$?`CjW3qER^K~yoBCD)774}5YkkdW=GCS{G?s$`_4pFoDrTmxc_;8t8i%N<z5
zJ%x^jy^GI)2Yl0mFJ_Uyi_(HOA!&rN{S|$&ZoU>_=1lUP@hgIkIrZdhWL#jQ+RFV=
zLj<$-t?nck#0dTLY%5sGRC_Q>xc?N%`LX`tAx~SE>knW93-=~KRF9CS;p~mFS1w<M
zl86!0ytn>7YwEYH8YI0TYSGJ!V@0~%UKx$~&R;w>%k!;%M3?9B`!M1<ItzbP*Fv!*
zaEAU#uunXc4)Ac>u=d3KwJVW%ErAmkezKWI*(KpeDP>9|H+&rg<^8dsaH$)GX;6jY
z<<1rdkNzRgeI_i`TFX$d87`65R!PSnm~M=wbo*N0tK$a-25$f1UMSd)ML@LG7WdNI
z^DoYJ)MaqMUZtD6O$B+IFW#EzPS6U~ZltL8QJ}3YVvQ?94`Nu>rfNWy&N@2ICd$7u
ze)%{z71`6yzTog;Bx#=U`BJAFwHC8-kD#wUJ2o0I1z+n!$y_m&Q}ulPc79$D?t@BW
zuDVx;KK6HpvBiXqvXb22Ezw6P!)3@=QJ-ZDWN1WZ=OtazMH4!?mo6|#1+DHA?I&{*
zz!h~33gj1JGk|Kxgrc4P-y_lisCaFiX#7ogQpdC3pqhFE$}Gsds{<Ztrevkm=h6n|
z+CXIHAJsqbk+kx`!ND@cZN?ggI4RroZ2?h#o?pGb4E+?@$33-}_EThRa90^7nYHkT
z|Lq@75&g&?Y~ONopO@`x_Lq3T-nTbsE|{w?pOw+Y*{{;(r(XfLJ9+4bqA)iUPxi@B
zA3K<oi=V&M{!~NOie)0VChDzRfzPiF+zE>OOB^ceD{lrtu+y~WEKpEAznn|gaiqdt
zwOuI0GVfF8pB@_-sf0={B}(4{Nt8o!9hftJ=Jg=M>8IulQX09YRg4_SC1(h*ujCR%
zdU_39q3CN_5)r;b>Ed{Y0TW5Vhu}`f!lG>YMuVPyIDF>XmN|@+qN;rfTG!D5Nzpao
zaAjj<Jz*_53GUJRu6WBDACKe|v^^K6)Ko<;y_qp(*k!?<qb~K6MIcP9<!x2Zpg3Gi
z0T}%Ej1XPOS#`01bT{6LawmYS=^tc)B8?G|WaR^}-!OQAEhJ7jE+L`&zjy83?7Y};
zyVH569&C55UX#=9pM>BsiplWh<%Dx;@8SbKvym%$a?rpan6Bd+0vVQwmt8GZ=@Xz%
zKI|xYlt??fiC_H}o>KVsT`SWR1>LuO=fKCT-1r30mR$|!hA;hzFt8SK_~&%3dM=ew
zs7q7dA=liAR7c}>;J+1w7ThM+zY2xqfMP06a3*{=?kXh}mPiecj5RSne)xbp<iB=9
zEh&kQjh!Z|pr8<^K3wY9HYX~&6GVmLpTJH5Wc~{}El+Kn*5+8O`OTs=V_wd(6UVan
z(tC_%Gx-w!9`XDxQ2;cLtMmd<KU`c~r|t!>5spH9nB_7=rQI74^}xvqR@1n+KS0QQ
z7;z#Bvv~i2l_dE3_3MX|OV%080R6^2dNgU&)@h>4_c&yVMPo(_Cw(zy`Y*w*G9b{c
zFaabNJT6aA#17Wm&eGA~fh;2ePeo^K{ktC)TPMXQ0kYKZ=AU@Y?XrkADnad`iT}(5
zcnZWKK8=Z}00(pX$%JP)<!LBb(;VNsidH}J`mWx4DOSiMJD#afh>{3l6F7ZjMqiNp
z$GOzRzkmNe3{?sORlaM>7k?U>iPb#58KJ6_pDD*qkDU4LQ}405fWg~oWx)qex5k90
zT~cj@Kd>eAL>yA0yzt;>JOwvo=JdL*Eat|aE|9-`MCG%mijU6&O@gom{x6_Ix_$Wc
zUF}?2N(ur3#W+uvr>ATBa~qK28`)3QL^E;7jDMH-%C~?2Ar&9%zC=hHV*$!cUEwTi
z|5FvKGpL$^X9)lwifOA$@9`4V^J(YXTUkh^>Zt`OM<-=nS;*1_2?wEk6P@SY+`<B>
zq0E7lwDk1?77(Zj?#V>IgsOmeCstkq$HLZNvNkT{Nkx?Lp$4`;A6hDcVKa{p0;oV5
z_0CIN(vgE?*`dYSY$&>x<Hk0Bw=(FG-nV-rZ(Ek?PTA#b2*vaqJ!LJ%pQXnkQz(lM
zMNJ6AQ=F~z_>V?+!s-%eg(!XgQ5Vht*HRn!-keIVRp0_J@3zgNNpAUluKt0jE6+Xa
z<Zti`mL2V!Z#i3<oUdb7;V?0m;UtU?ybTPN3#4PS?bp73{c6H{Q(0Cv+GdCeMB~QN
z(%~!UF|j=7&<#f(CPRWV8`TL435P1`G#a~e(`p2yqu3e`0e4i4XOMCS_Q6?(b|9%1
z2As+Kq~$bn?CPq~hXz~#E#J7+eR6b=V7gH7_I32}AP67!Sz~1;90z$rIeC3xrS_df
zfOVk-Dl*&EWsp8b{OZ-OdHnG??(0szd05rb;=Z5hN=l|u^>ZB0dH?yxECh&rxY&G?
zMHWjKw*yE#f$r~M3iZ#YznJ9)33Rp&ngx7AAv9F&B&{fRfxK0){@<efI7uJ-mZw@M
zQs-}CD?c|S`>(X8(S0j3?G6~)zMIW^M!+rwP%<n1C2vw<B8T-9`Cf|^kmPNkw^FDf
z6E0?O9b7&YJ<G)jczfc-?iVt^4ND(cTIb_!KTrGG<GLQM-dxhr(SZb^pHqMDLGb!J
z1_qokWUDGguWPxa)h7G2OJm3uQ8xhT<cV?;l9gBey2zEkKriy;4?Yx{JCjLE9|S>v
zXYv?1lGwtWEP@D#eTbW%zdso%>FV|1YGe>9XujI*IG{NDt;hz{r{xvn`8F!VHCM8J
zVp3C-RJ<N3io_F+(pxp*4d}mMr|)g#m+vR&dMC@jn{^@}Apuk&a_S@5Q6pgP8kYRh
z@ccE!-)gI?zk2=pdGSMnyNw-8L-(P5;f2AkY=rLE6yu59AvaZ%T)~6SRcJ6wX|5u*
zXd@uFZ8GrcmuwCtUb5gB;>l%7Sx;jS=dN%g!5#2GADo_=8Xx;$dccR<5<F0^{ES+<
zZ*ur_{MMY^6RVD)t+CQ?b5oL=?~|vLH2PliN_A~5K)gf$$$;$d7cBJAg-;$Q9#}Dh
zf`Uwe%V-KUSKu=pgV?O?<MNcA_q(>RjoLe?3nSwkkEj7b?W&@CcSQ4~m7=O@Wckv)
z_tV|5*v;i7JpGmE5d{+VTTsHgvhuTEu@0<SfS`1;R*Q*$J0Wj6?lq9RM8$R6L=nFK
z2dA=@r2uzQj<ki1r>)P?5GQ?>?wc);rBa{)i(z0_AT+=;)^hD5Lkt=g7Ko|>kg|BB
zTi?p^paBz1DWC#SE<^KeuZ>!D5dAI^eYT{C=y4H1N8?=Y(i{t>3-YQ;dgsre-Ui?X
z$Ljph?w**EGJnmES~OotJAA&B_~Z7G>e=r=Km7D>g9H1mnAz@-sUlbWefR0)de^Z9
zrtRlLl&AtdZSDE*Wq4jEZF@3t#B~8r55Amw_$Gp9QdZNsjz^0YX9IYuVd3Etoipwz
z0z6kxlN66880^#T59vw=wbW|p47rN)tj6qTh@f~jM>c=I1K9GwHv{~@daA;$D=Hs8
zYy)T45C9MlDr!9B7D1^(Mo!M3LY2B+O2uhv3)(mSl&J>g=Y68-kKw6rTyL`{gA@B9
zmrPkaWD$RV+C9Of`GI=`c<dfQTvCkS=Ha2Co}LYFmV}q{Be|-4k~l0gA$k}nZo4Nk
z@%sK5ZVik%Afu+FxcG)$X@PD^$RtVkGj>=JHg(LwT628=1|Gq_(<px^cy1u=0h@&L
z$K|Cz<Q#xkVkV}h*n>gEww|G_P9!*=8QO8Y07@R&3y`rh2cY4IozGfP$9J?i-N&I8
z$tDg?m!CG+{!%&y&lLhUYpsSL?dY^J+?>DGzF{fDH9KjKcsc?YNg#FG=;(;%0?LbZ
zAYKH^UB@^e^gyHz^Z44)gR93+iXUAd@XM0(@6JxPh%aH|6Br#`Nwxu*3q+o9)*fUI
z@!>7oNO?b1l^QucQw6Q?)8|A4I_v8>`T4y7-Yj-MswyaUk_SAw8~EA4_QJ*0ke?5u
z;^gSqiTDlPNRA<Uqf=61UMS%OiLMWNx}OfbIX_3m#y+(Wvq4s#w9`vB`FlRh_xo`#
zM%&ArZ{kVC!AE`PtYr)r&&QRM|0utO>H<hST<FJPysp5_Z^ns;g!J+WadUG6@hCW-
z0O#@U-97+N0H_bd^f*&MY(_&v1MGvy8rY|z#AYw;*j`KBAOT?&Q4}O6ogs2^a{4SP
zWUEVKUyRE${X7*Ud_*{lV`Q4oBH2VO&rL}|9757UI2nUE1FM`jxPTuYfv*4#4ZD;Z
z{|B&;FlebYYAgr!A(?_;LeP~+3|2Z^TwHwoMu4Uxq7!0}a*r5~V2fPM0C*j|o*;tG
zcMe8cT|p_1)46$Jr^%v?mnC!0vRzjDHPUD2S0jQ>BDk>>nw-(BS)pz{QE>N&Tob@f
zWG(inPjp%aEBEjcpEJQV-SH9+1B7|tj)j8E(juUb5osfAV$s87YhA-~(R7lg8C4Z9
zF#52cQ$Z$T1{PDp7WX>4uUxe9yu7?2;p`w}^EF}8@M~WTwE54|BFKnGML^5}k`j9(
zp#7B3GyUVeOA}L55purmZ{Ez=?@d=USO;AwQa%6Dom=Zlep5_Tz>kpEEN?SaRMb1D
zP3XxL;oPC6zQemQxCGQN0ODTv+K!h6k#VwO59-*a?Nh>#vwmRKy+Wq+s|C#{F62E_
zbA|tYRh2RhE7tL`{)zTH>14w}xE}aa75?M<W^r|>EcRer0YmLhXGcc_76lsGdU)g_
zUcLrsQs0PrMbk3}gr>;DItfL4Wg2g2=3%kzTP`0eAQOSL!JqL-PAg-)Yi%vUlHW1d
zacA;<Y&adA+w!RoPjvEdc}cEpUE4@{5y1jtfsEJT4CIe;+@77??PfUx_UI*i2&mdZ
z+L~N8`@n0|FVqS=#(*mWRx?gPLDJP2!^I!zn??0VR{h}$-1i4F$|O}f9d6JkWE+PH
zeV(D|9?j|+-RLZ*O!z8R7MAb>D)5~EK?CqZgumH2KJK*n^@ibtT4R2Y1P90!c<pz6
zbrrVN67b{$G>0Go57Y_+$jME}wt|V1bB-uaR~$fS^SeLw>+GZVMNm+Cn?un@^%*|l
zFAI&@FRh`|e;ebf(7TDmv<&i0-R6%l@2R0jftwIGs}H;@m@OzsZPLTLFziqzC~S)!
zVfuL6R-eq7M0B29{rLKud_!J>Cy(Yv@rL*7F=FLewVwR+Ua_-lUThLLdg{Bd8EjNh
zJT3GSt-dQs%VKSY@w)J8`jWy`wwb|bJie;MdftD1(O0{STLJogotd?fR^0bFEF>N8
z@RZ|x7x}ga=9jm8WO^n=u+eCXq>R+;a|k~=+G+fxu<}m(PI5i+N`M|jb}DvRI1$uq
z(vD-FQL(2#QvRYd$aF|+PI_H>@>z=)*Es8ZKUcY;obmKr5+|eS2-4H%sau?1c5Y*}
zwMa8qY!I=9;BX=4<@Rquz)S7v%=uvJ?ZIH|LFYQBQvF_pzNb>!1I-~jZNncWmH{&#
zCRh6m7T4Ipy}U5w`oGQCgiWZfE;1jqJmRU=Bud1SQ|NwMS&@stq5hqFtk5LNW>&8L
zQCGEIAt$fCI|W=g75*oSL8cXC3l)g>%dMnS<!j&PZLXb6N2?dB>f=-BRq=<qhaCLZ
zg|YODsV(+@PIL*}9H~k7aka7gQ<SyoZ8ASV5$Ip9`!oBK*Xu7wZfWwqt7vR)E*|5j
zTw}@9txP(oj!;|HDPqT=E$SFGP!-g%9IyEBE9S}(^~<m4`bw9@xzkPtgtA<rl9RPE
zR>L1%w;sO6XS?<XsgnlAJwoxAtwnEt5=7`6*v*wII11az*Wr@U5SG+YcU=tyr^=7q
zU~F~!#H|VEU@o!l$c*rAdHf+ln$7xZnemRTUWPKI>R9oaun9$?f^R@oW=H_D(e28?
zay9(2j3dnzuV#l{mdGe}RQ1cK@|P+3m??!gm6+%;<u3&PZi-$R5`UVg4(kaW_A-2Y
zFlaQ6PEZ=d5IH7qGIFlp8(hBJ{^K7O;+5g5w31v#=xfqAAw)=$%G`7V(F^xfI!m0G
zzv)P4?Q7f{k|P}cAA4^d-A2#ui#pser{Orv%-kdmGd9f3%!!kR8QO*$W@c!p;kIGM
zh8Y@W@NxUT-=@32weG(6?7hw($CfOOq<Kg)*6$fx(t>1q)2lE<j*Tbz3WJz=_FL&)
zrkj=h>fVtUT%zGv^43JsJT6UpIu3Ca*vy~qnzo+04zel7ZSTIIa4J=fX`=dRV)aMc
z*HQHG!<-a{5_v|ZQ0LL*Rj^%d_19}ix?9(1o4uFM>I=mNQtYt}M7y`vRt%YcDSs93
zNm^EiLmEs8o00V13yH{*t|Y#+LaSU^1R6IujTAM#6107WK~16mE!qJL=O}a@jqj$~
zX6UUUt5(lZSSk;hop`uIN6nxX2JY4?iS;)M*iAU$9_9o)AnQxs3-!TQH4a8BbD9lI
zai#ZQx3Z;?=3d7%WtwQIT1*wRPRtJWsENmCNB$5IA0R6bzeACf%*pKq*@Dx;bD{50
z#HrOW-(?l&j1NP$G1r)VMTLcS-7I}V-u+fdwfs10aknPXB06*2eAKOFkCt!`8BTUr
zRmR@MnmfmTg#$`f5v^#a<`-{*C)qBJ`3v-Udi>|f{tFaS(t4{<!9e<`76oa@<tp8Q
zTeiDl5Y7+pg834?Ac7`m%aG@9v@M>3PcCB~XTei1CBTF9&O6gbJJlp!@;L82x~a2M
zw^?`p^K^QZGM^Q&FiUWE2Zu&_MB4cI3#un{zJJms(%^BO;x;d@5RPwvsPCS?bebn)
z=MMd)_pOPNc)tRbdVJGeEAsLv3E7o`?J|D4t%|H}Rhepx;tD%@tJuj!*|#P1#Ygf@
z&J)lHr`EC=oQT@6hts63Cf<m;`{(YLq&zGiLVBWrnQbbEvcqzQbnB#%rfAfJy&mI0
zd=(PDrjPFp7+o4y)R=icF<wi$dQ|Iw3g|20&@2DWDfEgNFv#@IvP`MC*uD&=&cpM%
zXwJ5dLcMat^ERox9yi4~^gE_8VAylWSv}nmld=Bm7tbegZZ8s=P~aq5otk|tY|9o^
zRP?75hK3$i=TI9n;Eq9fI=#+etS5>LdfLu<6kjjSiJE4-0$*G-RQng9&SAEO{$f|Y
zDq&c@?!345be@577{xPc2gJo19~iYP+t7LB%Q^W)b;{bX9M||2+PW|(T>amfM{39`
zyf)F=Da}{6shbg}Sv$+sV<`F}zSrzk*7U<$K`rdY1y(zSd@wX>FJU<Rxu#UkvOLRK
z$ENfhfAurWr}x+K*EGg#_$crpJ^#Il0=lYsdJ|v3Nc45MK{GB9oM<yyy(;hPW@BtQ
zwXmsUJ*Cex8Ybg0sf&W$=PgV>G#XSsvS{skf;h6q8h<D~zP?4I5~CmtdrwuzaAZge
z7t*r=Pvu7KXHU}r5_o)Ox|v|QS%(z~Ot?gxD^(onLUfK?<eVg|dntx~Jv<e~UwSQE
zxZtSz^?<wdTEbkBui^wJa43jjJjFRf7wmad63M@__CbC@!mJhYUt{a?UyQj(yZLB|
zBI6kmlsx^9hO`L3Ilg)5ztI1&XLG4-P8!!5^H`X|u!mi!i%oPqFMz@xA=H2PJ`+jb
zwz|*@tz=FpKeCzB02m|wVzEcodgigsL4`xLqS3({V_y_{;qQB74SAM~xVU_BkF;$6
z0<B6U`XOW3ky)y&NW8MUZKR{+8?gmzt5g8*vJjff2GK7MS2l?ID?v9rcLgG@A2>en
zR!FK$MGq!m<@vbk-WtuMM=-E7twl+G#B{=Y5e|UOoExEt;w8BrHS26$6e^c~aO!MC
zwM81%=(ieN)zx1a=$n(*b39q{d*$be@DaNbjIZDX-2u4ys&VQ4);Aw5!aN-Kn%qvG
z?dixpc$)_JOng6RWbw|*6Gg0E1DkQR;JM3{pt^$00B(8q)L;N@q53XZbFJ!yQH!(9
zKXS*;JUcS&bAiQ|w#R+Pw{&v#BjON#KL;i(t{h!gTYsD&K1?4_oc-Fe7(eke12fK(
z1?=GqM%Yso)HG(Bu-)?eduCY>a-d6_+T>rJLI#FSA}Xz2YooV@l^o{;lXG_;ZhD#O
z^75all2S7m??<d5V*o|w{miAfOLKW7$4`osJzcYSzW3@>b#=YjAFSg9d-iCeX2UXS
z>FTwO#80y<vV%&m!B}>3MN6idSU5clv$&UKuLr_ir2+Yh_Dn6AQ}=~zc~sK2RqP+7
zi!(x#6+})tc6nz$o$0XqkIfQgGyreHFK!ktlb6l9<C%T^l&^A>1>guQVvs{;bc=zI
z1xfBpXVjQK?!<fwGgYiJZL#%H*92<e*miT-dXeK$sjaexJbop)oC$QQwCPsE17aVC
z5j-^^i>syt)^jcy>{08PCG;7UwduZA{zg%k7dzeoy_!CPDelw7z0s&`P`IG8XJ=85
zsAdr(`n^Qj1`(Th)MbgYoMm^K_444|I@y4nSL8d}vS2HRIMn(~wX`5y=lQXRnpasW
zs>bqhjnvtga;_G0m1{BP)pdK+=WVGy0b32#3vSp=2n>j(7QxKyAC=Kw|3C_T4;$8g
zDg20!QY^S0R@58^cbdH_+;i_`dvcoohRl0(1EhDXYKoWNqHGK!JgSb-c1&I;gbW)h
zc&c3Oz4Anz-x62c9M1!aXTnZmCXc+yu}%-x6?MJC5jWwuKLLHHFfvuHqQraY=O8$r
zA1lXlio2jqy^Z+5-0<>x3!HTQBvr=nS-3M@J5pCv%wNmzQ{jTcTq(@Q*QRJ=W+7TV
zJ2Ok5g$C9ha=%?T+annj%L#KmwNlF*Q1l2T#?Iq=Q>&}tlj66o^c0;&R?5}Xc-B@I
z^d?eE_V2#TgPcF61<3C+gVD>GTxXr$3@&cTYb>fpz1m5)pD?}qtRLB^t5BhZrcx1@
zcg=MC0N_ge`7F}r4}7c0A~g~ZS}=v4<soPp_)L`w-2KXR6PaouK+*lI>=&4?D!f=7
zrY`cF_B>4hX4jn$xn&gBX;$E=&0E8i$J`duA0Rb^j%XaoiMnfaJDC?3XLQ?lFjCC~
zk7X^riiqUb-6@Ln<fEIz<7l^1v3mjNC_3Pqrw65BcX3R5KRcS|am8Hu#OU54c{!5p
zJktni4O2zDFlMC-x5w$nFBGg{4Z{bWe<e0z;ZCz>moDRWvQ1xq#&dHPd0G{~j~b}+
z|Dof|*0yE0;XlmxwmMKVFjqi~EzK~N`_ONK+B8tKtT~{+*u1(HvSTgc?28i%)B*4v
zh=~WUJU>pjX-Wv5owkKu2pLbX$pl{?`Jy-6-W=Le&F&#;_I~9PcFJO@hj;tSusda^
z8=>__m6XySo_f8Ait%`;Dov`zjoiDyebFYH&|aN&Mh!-BNz8rG{@y*RBl5HP_sdH;
zUN#}BaAPT0P|34%ysEIfb=PvW#o*~TLP?!$pT;98-gP2wnkkEx4JdI>EYrTHi`tRZ
zJw6vxkxm>o<aF$&ePI1qC~;?Mli?Y-f+$1e|A`>xl0VV471NXj%PxAUE2jBljP5r<
z1iJ!-DBWMZj(0yk>yS^6I+r-y;-XFcAkoV|<4XJG|HG#0z4iq{9TjeT^!2?8rSpbf
zUPe%X{scv2>MFZz`3Qy`qfAZkk^v~Lb>g%?gX7n}P$?QdxW-)VgG)N~cTLvm%!nw|
zfpvjJEG4mUjD{oyJ)GbY9{;z#K^#Wv)a^5BJ;&Mkn$&Bcs<l^llpjx*ohhBA*S(^O
z=nEl0_Z;nckL9F6K~haoWM*B>jK0m6ZUv609+6mLcfG~=)kQGknX&R?;&7sNrZh_s
zl4)nJ`q}Ya<15#lOBJ1`An2GLV@D!21D1p9lpCaoUlSk`WmejEC}FR)1eeUGjvYYg
zoxwk|ef1k`&|4B-Stv8lmWO<ujgEunB}4JJQ>_dKkU2d+eR)y8zG{xj6*WG!g-R(&
zmIs@r|5Qd#R2D{d6>}mx&7v9v2V9mOJ+R7eBfTAwFx3q73sOLCaQZ?MzAWk(!;+&N
zI`ZL7&B#e=&d&<SC}dFeSz%5!v->^QQC*D1_w8h;(4h(`kXXw@O{5l&JwnqiV%U5K
zd7`xILAO}YoP4IE(5MoNt$ax}SohX9c&=fk7`$X%uz;P2IQPxrWUqmF<X4;SuwH=#
z%J-&IyG6g1*X0a1-;ll$h|G~GSl4mI3pU_8BAM&Z&DkRCe&wrDd@&7jJd@{wa!FzY
zOWT(R)y!QUZJ3KNRzG9v9p<@Gbtc*u!Kugk;F~HtaS+BFhuP>7t<zCySp}ce%&O;*
zi(JPhG2ARzR_&B4f(PKS+YSRv&$mo%Dz$M?LEkk&^Zs9dUg9P-43I%Rm+-_VrCWMC
zM;oiZ?GeICVl>1U=4@<lt~>5ZC=##-o3Y&@k03f#y<WJLsH>By(~X3y(WZQkEx=Nx
z#-23|p!a&vGlk+%1{^uYu?Vgl>||gWiX0MWXnc^a@*SH1X>v;Inaih}JJw2_K;urK
z%=;+6pZQ9zC-+Lb?dry5jJ)<SBADjr0lj?nfj)n(lbM@;Y0!Z_wFj<NVJaW+Oe+i~
zfo6|)O^Rru$(S~rH*-p1-chp);hrKh$AFF2zWYMjz6A4(@EV2~@7Lz7sl=xRZAL9z
zHiXnSG(k9w(Pp7o3$rBG-CS#V;Ckyg{^A4h^@~xR(B~`PUZJP(Y%dWL(S4q#4ankG
zWmRt6625ExzVpPs^Y)gf(7TDWe%|a<Z-(=O=$F1$g-+Zo_}FD4T%G!&LpR;b>nf@y
z&)s{r_HB()CNi`4p1U*6Hm=D;&imRFjHJ<@ioSo5e_OHT5_-z@01E3E4n=5n8fWtp
z25Khq?0z-fU8nff%fg0l|1RWfm<e<W|K<lt-ysZd;w5ZEd{&)|S<-l<L1Q*AxH6t)
zAysUt<)%yjO&7L1i{kGtGx~j`21q<SM29tqFReV!@0PE?vBhA0^L5lbn+I@y0|e?!
z5t6(a5kH=sU1>Xw9DreRd+;g)=MX%wZfC*3BpZT*XO1U8esj1J<}y*`3j-O@Q<tJ*
z&l*Jm#<B>{@A!Is?}6+@bz&m=cb29bIgoBY{1vp+`NG)T3T#bTeWGtV$F|*4v7NS>
zc}QS#Ycxdh^Nrl<$eaQBDP_&+oe!Wsu;L7S9aUScS#%RG^C);V-l7@hmd<o@rlAO~
z$?nXStFC%n7wsQJx|17`PSKH}lRNPzpXpu~(7|RU?EYrYh+V5;2aRONl*H_eoTwDV
zCAjH21roU?Hs|)UuO8M|+=StN7TrF0D$xEU??i;&ZC7B`4K+@ou%VNhYQ~o42i^3d
z=?qos@Im%8fdDKvc$(Z6y(;it`z%@5$W8&nb=(G3gtq_;WIaA;GB#XF9)EvAJG+}a
zY}7CksPJz6*L63WuHBonw`vYaF7JhTQ_@1dVrVX(fYBbvjQ6`o8tY5^fuqeIRW$2m
zxplVdQ(q+n3(tTgYd^$b5;e=y*e;8jLwpB?d&P)j(;FZBz_;x$<0qBi5d9;NqmTw8
zMw7xU{$$i_`tCU9mZ)mEQUQ-rsK8h@Xn>zz%^+_<yUeo$ynWGZLxM;?w#6A%PyAh9
z@m?s;Vyx}w;IU9xN&1#u6X}QTH?`yBX5^ar>W*u<U;<2)hQx|hHOUI~de|PE3J)tI
z;U!ftt`3E*dHJH}HBQ^{I}9{^M^+iv^ksaM-E82t5mfr@Qm`~dU2ok~%*hXtOYMID
z?)vA(73Zp80pqHdPl65NcHo0baD@@al@S{?)*7%NK&t#Z&ASeh^vhPmkcH~clUJZ+
z?L7?0!gAct<DJFLP2eD|bXuTav)SAyVa6p6#5%u-(vt_Uk?tMIF_?3B`4=N8*CNNB
z@9}K<b_+}tT+M!+9b)7vWFzy%0G5V<Y~1R&5N5@Lv-azYz$y}(@6)CS+|RES!Kswa
zNd4uTH$<$wVpT)OcM&{lGMH79Vi_;e_V&uM7I+8TfGL}p<x_8@V)?3^H$S|@yk~BF
zba5N@?859O6MlFSH9Si66h=XAZQab?wNkJlxavr>{Sn67c4mo}y8Kx;jB`4WvVWKG
z*MfWG$ECYq^D-y&6bBPDW_yxQ^L&V}SM~{<Cw!jEA3x;c*fIwuYE~LP*{tZ_1W!uo
z4e*nG(N2;&&B%LI$(&d9yf>k?g<+12U5kNcL&D#fuD>{kfzvEOrW@X4{0jl6N2=<f
zjt2+S$LK3vg7&(uPY82+lQF9F6I0XGToMrO%1~Kkr`5dLOu1%a3AGoUAAwqHH*q*{
z{(-g7u2Ps)$xaQw#KZjuaSwdW?8nQ@lCXSyKQZDQT$A*;Z@7J58=DPoK4dhyJYF^P
zay^e*WBW^5CIq6w3+-^eaa89V7-Q?vV=_zXqDa^NB+WD><w1xY(<%>@YivIO&3v<h
zj{#ClAwss8>&?2FgcoG?H=ye<9gmq)5`pYYG^Kpvp3dBF`t}q?G=EFMysR!LO%zZc
zBiyOj5{f&i^w^0kwwT<jA&XJrx}jTj<PVoz#ng(}TzIL{!XYS{!k2+;z@mO_AKA^L
zJ7q<8hvQp2(p8tJ>lYZCa<N^I(S$-?2@X=`A2bpauYSQk5mhX1D(2t)s?BE`=j0SL
zbp;}%zyk(M!OpBF!lXoD+;MtN<JKJ$cAjASZs|qGB}GZSQm^pBQYv$Jer+nrGvjh~
z)8pV}vNiT-*U&ozBv(G7RH=l$eCF`pxBr^lj9od>C3!Z>0aF+3cG9-zfN8e@I{MV-
zJVQtXSd>4}U)QKUcHk^t&dfYc&#TIzVA%}X<-{3C9tQ4p&z`=5xZ#30FH1TO>(Wbb
zdjuJ#5?#fsMz&;C1pbd;r_+&;it-DgK&xWQ-SO=7^6bO=g5k3IFM1`Gm5vS-O?Jf6
zC9|H#o-l74Ez}c9%n_(_>F8v%#o6AxO2mD&C?SrOjFA$GQJ<I?uqJV_YB2sYm3Pl;
zO@3AWRg8M;#}|=L`L3tJxAwgL<1dfF^!VkZE4m9#<4!(EG*%*kYBl`Q;F3q<t^vlq
z0acfq4yE=;7oH6h@c0XVE%Y6HA9J4KccX<*=sg-|NK#I8ya<H%irvMw674^z0!o-s
zGdoW-THi3Bvs`8FG~Q)y2J<^&pP~CYAj+<ZUCY({2;OcL<EMN-DD|O$zeOmZ@f!2y
zD;90b0is)O-<L%usS%{&t|OA05Cn2CS2$5Blc)CG6E$Jl+QWN8P_U;~2prk3Px)3;
zEq5pCam@_iG8WNhrt){wPuNSqSXrW#CL07x(cP~HFrJ7C?tPQZI<+#){x`@K?Ninj
zAse$O_~!J~wB)4CdeJ%(nJ61EdyR+C{LkvTeY!ro+Tv4_9vVXApAMXX@4?$%mXF*E
z%WH<HWEKLHQdNbcIXfgOELm3yVNQiOw9CDUIYq-(y(UqiU4pm_5$Jw6Xk9&C%}vo<
zjz9Yv!*@8_ua5?LZx99ooR+Y7b<bGXU1@N(9i$dU&W>{Vpn60(rY~-mV0c4MSj9#_
z!EOj_Cb%sFsya(ok(ie@F4n7!jAavtZ4b&YUk^g+6&h+JR_-HfPi))(G^zcaV0oBn
zB-NVFkApAen6PcMOgF_JJ0>r#lv``Gwq;Aak}4&94WbH=scu`dh|C)GRfjLP^HGYZ
z<mtygj(@I|9NW7Y+<7u@)k&s)T|CiMgl^euCo>pG?@^<b<P;Nf*d(5e$KEVZHg7<4
zkYsB^gUcel^J&w4rvrTL-o*4%w8G3q!qz6m{Q{YuM`28vD`VK7#=2?IkbW-*jmnEf
zvf_v4aQ#lcdn?lqmEp04eweZn={JY-NW?ZWRdhi!dW-=n#k2Y-uq<OLW3&k;z%YI8
z`5${~Kc^<8OPEEh&w7aM)uYv)(+j~r;L4<gQ8cYjbvx4(OnK!>ZV5+sAhT86{XhHC
z&l19Jc$h1-`=*%t!!5EwBj=(KsLH9y{2)D8fOq8)t6@Z|@{vxRopMjE&!n=aS(L6M
zEmhE^XyS^P^T~~7vD%-%+<E`;4fQrH(XWp<#B`+$ACtSOWOF7?Ev|WB70nllC$(fV
zZLqp^1kU-ZsE3z%f3cbr#^C7)bcIs|7He+PheZ@&=N!(4@sOo)<WnbKV(lv)Y<srt
z!{UOLM5bK7huJuOp6HHH-&IK<n)&FK@a?|DTGw=2k1C*hQRnE?c)v+<Di7`{A|Y|a
zISWlZ@f3d<mKAVAr8Q4%;K~$m)k~~*)!{{~j(RWpOMwg3*U1lhBWq^fU5YK^i)+cu
zDdUTTtz&)$vmdQUbEE7k`?lc1k7BuB-;N8{cB;QdsJAZrvu4uxB5X8$kL+%?;pEb}
zA@+VY6p3ip#21o_l)$!KB|N|oc`kD$zgD`5!GIM@B*TxF+)O>!BjZHS>pH3gzIqQ_
zBc0mtl3me_IqC9Kffd8pMu1gNAcs`^+py+GY~TW!3gdQUDLFJlEQybNCR_)Aj}M2d
zS#|CqFCgOXag&4WcG3O%w2$k|ICE0xD9y-E;`rwl&$Y3K;?0&Q!A9B(g?=OTxWU$2
z4A^^`35I0ZCx5J#_x;T3th9n#*VDxIiP&qjb;Ghx3|N9j?d~x{_f90koOC6p+gc$T
z>ILM^u(N~*S3*0yyzfBs`1XNmYyIBYW+P)?9<zlDuA%wcLi?+z5Gyv;3QpzOFJ(sj
zUERqwTsH~z@M~K!`6;uEkc&xUiP~<3(IKkczUQCGoBf2eOnTWqH1$JWJVzj-aIKY2
zYHTls!`7ymf9Anp#|_1;)rekcT?-tpcdg#QZfrS?C;fHO{Hh&0zF#St8JWrvu{%Al
zAP6GCS&<$n9o-LrVsz{+R>3rBJKR(*FL#dkvbE;B$DyTMG!RcW5Vi6^+qM&{ngd$9
zg#7o=^)!gP`IB*6oWa}n3GOX2fx{x4^WhSbUZt0dmIV6C<<)JGyJhz0+cw8(hu$z<
zr7q!k=1Z=pPPmr`zLc!S_lMoYEkN(^`8uZN9D_l8<^g9LdSiND`a@Aj$Fk!I_B(|V
z;Chnwg$Y}@b&{i#H08fp0hcQ})<rwhX0;>SgUv3Nt=><k4Cc2-Jm1uzBxCIg?0r7P
z-(sR|ZB~08vUDK#pX99pukF8kgRvbEv_rDq!Zww6zmgHL=taP88%1(_(*8hpUe647
zy*6oKS2%86K2?*N*6Gm9*BogR!g?1^q&uo3{Vq1e&UZ_baG_Jjs#%jS!y5JWfLqd6
zy$#H)XF2-I^4T|O(T7rlnM9Kd{>#;&+tRFkLj~fOBQfHaOP#z2<slkN<qwAM_P=F~
zb6eS>xlfW+bbomHdK&Wctj4Y+W4DL+c%ph|sooBLXv*fiY3XuKhF5Xs*mlNlJoBd_
zcI@&(uVO)g81wjybrOQ0!*?@z%6JL|j!3}s8kZQDSs8rSMlb!!NJtD!BEcTv7dJ;(
z_2t|CDXj9Z;KGPJ97oK`4N~14OD92nG*1brpVkgZO%H;(vDHw#(v+6U6}l;wTq)C7
z=|7u9)Ndj8&I<HCSbDucpI!h>DtpaJX7HEl)_3gdHJYrfhMf4<g<FuVNbV;LiPOwH
z=I@B5E56~~DxG&BhZoB(p*cfwh9#->4RqG{T5)cU?d=>JtRS7~DuMb#+(xPy=q<}`
z71@XtPrtQ2KFjh6B)L)cSe@53ZFVgl>p%SBY-Z!iFcQ=NUl+?AtP>MT-Cd~;bITQ^
zysc}%H({jw6(F^8i{P?{ZJg;#BN0E^>}yxFfV#pkc!_1^8&0fFOYnF<Dtf78wLMK#
z*otc0=!4WZX5Lu7{-ctAg5sA?(%K#Hy9mQ^))-586Fj|Zcvka3mxr#s26(46tShL-
zB-8wD>FX^Cfo+Jolhny|uXQ)laHo3U*0Y`~76VUhEQd4$VPL91tXX|*z-;DYA~AqU
zJ$+wh)hRHH;Am%ip8z_YGoe~Y67d5F<$hje{1r&DvPkUv=*JZh0sMB0I024}oeb==
zI-2SsFb06w3k;C>BGG3-LmHpnTRW3^icC&QW_rtd;@n9rUX(JUW*A&geRn>?>4y9r
zqc~~AStM4Kr6~Ezz&Iw5oibe1j%$9OSMDAagj%i4^Q5n&@8H=&KOj57(lNS#^zGxM
z$`59%y9Ceyr$y$;CsUEaj7kGyw>EAiFk}H`dLgMbT(6hOFNlmYa;s?UwqZL5Q0=!I
zjFKYC>LpmVo`x+#Kjm~tTd*>ShTW#E^lW|NTfJxm(e;Uwx7j+|!l?p~epx~m<QHWZ
zFvLJ-F<&!ltf!K3tc}tG!;W_Nn>HAN*k?}0tR22Qh{kNRJf~)Py%l*)OMSYl1y*Ag
zB*C)Ie^DPOc1TGEeN%;#-q@}rStV%vV%Q)w(aEb@6THr9JYq-(B#D)gPU*#x*rWhg
z8$2)<n&}mm+K9+=A}lS2#p))@bQh0af4Gu4m=H9M#eoa}c+d}PySnAC!w<B-o(l9I
z0}8J49-QhzuIRhN>%zWfCGbzs+515eBkf5}$yd>k>3ap7)ufb6*P&zvA(~c{#K*(+
z8-VIpNZ!WhB^EYmbjhrkN+UcsE|R*eF)>_dIZ?k=OOo&VP%!-@OE+-f9+Yby8n-*2
z%?36bCLKQ#d*(eHvwo+1Pey=xuaa`V{(}lb`vS@6DdpypIYBVDR^wg)=PK(%%Ke`I
z$W6slv9z_-a!C&?W)-aCbGM6Kpo5R#GkavxrFE#B?52b5^emWv{XlzR?A7vDxCASP
z0z<@~dXOG;=t@{V<LllDtD~~lgnq}}fPcM7pTQ`gl!T@PCu))U;h9s(w~t$(Fn_D^
zO-2A}W8dkmsl6x9^dS~yF`uKEZS(xMq2f@bHOLJgi&+j?WD{p;AmaJ`jdxk(qJ4{&
zCX-ahrV*I?3!$(Ro7(m;+qE!~EZkm*X}{}3dthgf=g%;Q@z6mhG!M0a6tvMM$$9b^
zGy2r2(%xZyqdOQCsMPq%TP2H;8+j2>WS*2XZdI41>k|n+;fUq-OoG%_W+^zn+S?HI
z5LBxl;IVRO<g-?UNJ4x`VlPkXG7g?li#_bF-X5A7DV0nrXmh}721gxsGo$rd=n=I1
zdbe^2Y7q*4&ZjcLz3Lo~b;~74KHPs&e#&P*Wv03e@}+O=#4iE+YQj4>g#DBkFGy0B
zz@{h3*r>5BXiGM{$Rv<f-)B=`iG{N=bYg$FwtvuI9>wg-an<;~_W-@sB7&m4H?>PS
zsP#4{>;w4KdxbIB=aT*F)HV-v)g7EZqYszya!J!q4>fos@I&pYPJ9}gQmPkdA$|R<
zd%-Q2OKuv~_mE9&<8pZDa={3DL8%gCrR};I2cM;=xz>|KO2H(lFgjlr74xlJ7!PVU
z({LKl24CB>%!AY~J2+Iq4wIBp%mu8`Ne#ptSRJfB1H$7GhoY_7B@agUm@>YlI(JVN
z6Cgxrz6{`TI^v5nD|KsFQka2LEppiz-byhKtqz!5w>)-^kvw?tpgZd|>W6#3)G(=n
zk6#N@YA?NNYjCv^A46HQ!|ZQomU~};zpq_pzJwS6F74xg#5@|R4ZA^)#ueFV-q#Yd
zAqYVu8+F+?Y6c1VK_Hl3t0uVT`aTR64Ee!sTt31Q{t7HktjS|?H2#Dw82B;Y>{v)D
z*6<|crMm*0BTPaF$_0bQADnJ@kaM1c^~TeLQ87TW0-C)wMc{QTVO}gHE+*s3%&jq>
zwg>Qv_j5vL$cseam!r;00>|kF*9}=Lvca*UDz%RVYlDP?<_{p`tt%t?$UNL{PM~S6
z_q)JGI~}&S(MB8A$(c?DW1?t%(f!oAHuc`tafY9IogReSH^r)$bndokcfVRMdW6lD
z%&sw6Nd{eRE~0=ZMRkuS%oicpU<hp=9H9ZW8(N!PC!SUqd?cL(>jdBD+z2dzOQ5aM
zV1J5E{$j)>*g0m&0liqkQ7TM;GYudO+7G&Cb#@yRldo|<iBMZ*g3HvBp&oL&Y`8=s
z!2Ue9Jx1w@1HpFcUf19O_UDp54CkO1v;s}|=5y^n5-ErH@j8QNP|qxgm$SJV_5mFH
zQ+jTc{Tba}ph=**b{H&0&zFlMWmiNm<#qX4e=Yt5oX}bDa%u?qjst|e>g0&qP0_n#
zmD;+m!#KRBY*exJ`L#fmJgNPoDttE$-S17OKVdju#C(t1wVae(l=DbXgI`0*fI(($
zKyf**VbLhvv$^i<;<6<^1YEPHcqQ6KPU;G7beZY(;w|taKAf@umwIbkZ>iH#<Net&
zq_ze^>=qVd*JwW)f!k1(!w1c7X4INeZ<L9C_8pX_PUpF0HgRJh&Vu*3ui_Wo<C_Ji
z4oEaai(qQLwo6oPrh<x3kfZ#@t(oh>mjjvyoid>Yu^nR_v#BVv>0@i3MV}u<nH_@+
z&|*qz^W3jss}0rZXjxsZb8x%7V|Rx-4jjKl{7jx)J&1L6^vdNaeQ^SF&Vq)QH+DZ^
z_wG5{k#Xro?^9+Txizk@%u_8CXTe@7ZGT&i=TF}<UfaK4qmFfXjul0JYEIqwR5(s-
zaFHr|cMRo7Vs^s=FB;K1RqpA9r&z!(b!u&am<t{z=%nEzuyE%&C&IT|xZeJicCiT4
zNHSS|p3D9oxaDGRR+>pw{r<)0>QTz-8J02En3?&QcPhBdEQMjTny9xKe4}9Y?R>FO
zS`KSY{h58)cy-Vep%8m{1GYzBs@-);%Q(EBMktnYHOPTYcT`e3HLlv1{V8FU*G4pj
z9o~;vbBe+B&I9H6KFYwJG1|}%lZItAxHv+u?<_ONa*w^1`sJ(NoJx%0=ROuUy(0Bx
z`>2w0Mz|!6aTC9%9ff6;vIiflH?FR8sfS_O*ZwH7$OWlXK9t&A<?YZzP}aH!Im^9~
zlWOGl0g>UaMNAaN%&?W{<E>tcKgqoWN^4rT(;TO5ejc9Kh}z>Gykyw&NzXfhlhu|Q
z7R(cHNYbn+tc%(3$gt}CVr^fbQTqcm4lh{m-V1marN@Nnd7vFIyD~UrQu8uNEp^0=
z#hdDR9aqeVy{b7j)Sl^705e~#peJB5P7Ikx*>`Mv(S_z==Q&5@FdtS0rjk;+Wu<_4
zaIox}{In_NL&H|eiJXX3%!gVCASt~jH4NM@8$*Y7A1|?s_GhP(;#|j68;WN3lzQ#v
z$C5|12|s1|y*DSlW~H;%^r7M$1F6f!@H`&M-e9`YV5*JCQt-Q2TE%_DlW_g8uB26|
z<+hm8`6Fz4>YOdvWx~3m@!_aw#sKnw;^d@W%6DgF;m!KUR%6k`(O8l_Z=f&jXByv6
zG&tWRJcZ&C%?El|7ZIhlAijfvoG|gLEP@h4Syd!#nfOeCjG`y1&KKzKe7gz3=A@ju
z<u>07(=;I~C?wCw&^yN9G)P730+0QZ(0sPt&c%>LS#&i;zVi`|yz!0o<hBwI(Czzq
zu~<jZZ~G}M9!i~e_f3KSZYfT?WUMay?38Zc(cb$fGQ(}Up3$s}#eg4S>RWwR)4r58
z5&bp0F^{V>1dpRVH#N@@!N6CWAqT$NM;!wb!HPohGzt%c&$21aZwsXG9orWkMhn(j
zzp+@1tim%~bICVz*<ZrUsW8g^q$JThY~gbu%hQz0>=uO0ElWT@{ep|{HSCE_sF$c|
zVl0jv2I->}WS%wF0Pb<lPwWVT{K(E+vM3-!dJ*m(1q_H2e$n?eMGE3DbT(|j*0t`z
zQ9nyiADLkZMi}(V)R9f0-FST#`|Y)7_7WZ>juT^eb`C@ck5xVLgTK4=t`_O~Fx)(0
z!ioi=Xxq2U8aR0BH-%@AJjXwZLJA>I1pfbrL^3=u!UnPv2o=e~(cI15)xyN#Pu9uQ
z78Qw`6UYYqlM@nR)$nn)U{x`(`u*W*;ph(JfK;lns#&->dAOQcxIvmodb>-jyPLRM
zK=RV++(7O>Eu_`?f&72xc({O&069-Y1VYB$-NMxo(pH6s0|*I!6-zlex<ksOIDs60
zTCz%U0Xca7oG--<<YfCR#}4HEE5iX~|GS76$n%FGgtWG@sjY?CZ!)qD5Q#bXfRON8
z+CPUpoIptUmqT7QASC=PldF@Nx`n$QtIAs`Rt*bp_rJ~%|0|LBE0Hw-LU<@znA@0$
zJ9+CtPK8k5U;}coaT@?xB_Q%c%;N^+{!4smS0@i=h$MgP_ve6BomIot#L><9w;j!V
zSS8e1-&%Oum|3Vvi$e%&n3%djny9<GdYHNYWotJer-;bE>6hP>|Isf}HuexRvP#)Q
z?Ecom%*ot>Rl&m1%H0~s&d0^}`?P;_t9MqX&a&gO6h^@7T+o6vLy(rB-&(H>rnGD#
zh0XMm7-_UfZL~dd?T&_;?bWFuCzS`HtWFXET%^LUlM#pU5f*7yyoyCl5uK>ZTh;;5
z=WHxFhNsl8kjKVay$rN?(}Abcob0E!Qcf&ct%i6oO@m}Ix=hu)oN=xgUY3T!N4J5e
zxBZNv__I(ELg<ZJc~BT{x0VL^2~Nm|FBMP+k7ztZwku}c*<XJs!VmaLKKwn4hQO*&
zS%_S2?~UktjUfvkF}Tm6wg8dtw`cQGR4zV}a`{=%q2wB@z;A_>tsRV`vEa`Pm9dcV
z7_vtJsN>-FWrPJ@;)CKTf<#UL35tWK;8{9;(D%SmgucY8Qa>rt+Zn;V!Rxey%N~{2
zKf!3u>2Si#R*ZxXYwN5o_wwDhhsm>|+JeiK28k2-`UWMuFqzC3<CW@4^_B1}nbE;Y
zivzGb9VQ++d3yG716#P|g$*uUy^H)J671gLKa}?F*wTO5_M<t(ld)<#8(I@&p^6g`
zbl@(@IrXM=t&Hiar7kGQ;a6kNxL@6{bY~yZ4c_J-y-K#8cHl(Vp&El??jg0c@=Fto
z$C;W7yTaSpcntNA_?4w}9zlB1O>YeM(yvgUjQdj3U9E_{vbK3_fAzAnT&FUg6LZOK
z2f!<g5ocunTuXydM*R^fY`K)3Nl@mdemabf^5Z2B%Z=soz;aq`4{?g6+Y<FT-(#=`
zN*D)_mLO4zAdfr!N>6JEeun64G8{Q!5DyMXN386=v4y*gm~1#5AW3}z+02%$>bn^N
zLcvTQAp!yG?14Yg(A=!aW7Ai@qoQehF1{7ZU(Ob6mpbQPV`D$6sBnbaptG5Gel3kw
zy$kiV3Ekt(@T+5eN79%+!RunhXwz5{-3z>aJFZu@pQorHbbiCHo_Kt@l}1d5(Y4r<
zMvfjly80yb@oDpClI&aFH?*{xE5TZlpS}i(MqSo$Dqy$dB#{7B;d8T`V-#=EH)Ytx
zL+~+@=5i%tIdKguC`aRrf$XK89hCriW?0B??+Ah*+;~_!j#?@I4@)Q`pZXQ!Z?<Xc
zs#);(vDl~=c4nV9;~8JMiqRR=c9ay?AS-mwHd+MUhJCnglpoS2Dzc41caQa%%rmQ$
z(2YNdXCHF>A~ov&QLM$*62%=6ug=)6B|l)%q<?Wcibs#ak!NTsPU4ufkH%HVBy5QJ
z9vbEsp+q^-5E-^Q^-r6R>d$9s7g7xJ#=3)pX|5z+2(SaEPBMJ<If6cX8a!n;E6GBX
zr1mG|gr2MKX|#cVpof|Qq!ik~9q|#0NvDsHa(WT~y;V}Zn{9cwd?G4gcw~r|P*Rxr
z4HWs7F*ha-uzhxDSf>rW<m@Uz&t)rRWyUD9OSl>!v$&aJofbrSK>^p6%*pun%A}4D
zdEB0)j>X7hvIgN%P!ifzgD77l%m`7ir^jZ@=8N`XcZ}_?v;4$$OG8Xplxe4#=xmm`
zIeXmkKfo(bJ8&g`FV0Mw97y%=5_x%;Z1)dju0Qa<J2$QMz!OoSAUSkrX=<j!e`Z#W
z@_%EVBto8gF=^b4Q{&Xu<%e;4IlFDi)BVz>x;m;OY`;A{C-<q@39eDqeHajF*Fw#_
ztkatB<i~<1_`}|44|dk`Gj>j(daeYOBm5EdoDmw;Cx0>9<w-@TaLV##`4=e@K3^0P
zqkA)us?oHduz^#EWUffn5}vdCzOZcqnJSS6!b`JKemjgB3C)H^79sWB*wx&jUMF`w
zdafA7w+R;m6FU#d8UIsa@gZKExM*so6FpoavH%{3UXHho6Sn=dkp1JCga8r(aec8a
z92uSw!g(0>DkC&qXHIU~Bo%@3hEr~R@dZtl@MWI?BZ1nfKUG7LRJed`+7DbII6=W7
zdSkXjE17HvRP1>@hQ3sTuo{utR8*_HQI4zgPQk4o5K>WFQ%WtRBzy{W@vj?;@%7fH
zB)u@0umd(?8lM@H&f`RIa0Ter!_5bnxvjqp7B9|S*c<9kZZ_7_-vGjC$EAFNsM*7)
zWU8nN(K1`!+%oMLJIY+0lxIXAwrf-pFA7-i$(qP17dLMA%h7Q`W?Du6p!b!x|B)Fu
z4$Eq=Z~@2tRuA<{5j-ubhFzNRcj%pm;%_5F-j((i+Vav)gPO@TYYYQ(sFhyaO|`O3
z7fa^2&L*mO<s3XQ9XA8!1sWSw%ck{4f*<U%evHEoA8DBwL)UJ_(B+j+v~vW_v1)o(
z5%E2gr%Re^A=kj9n&d^>;n8cbcOQlKa_^KRlsH%xqBtz^T%L0~P~v@7*Q*<x^NO?(
zJN?e^lcTdp57&#ZxCKwb8hvtksGMwny<I!@Nlb}<sU-eHjBDE9Uc$oYq#}yeh3jE<
zIlij$&b+}dF61iAIy&kAUvR#AF|g$`vueC>-Dd|m4egw7V@l-Dcfy{YsGcumB|7v5
z|2Zc5jWPaZ?DcRobGLDF{5`e<Ia%L2Ihfcu{ssi>5FjAw_<Jzc1G4`<{(nYD3vnk8
z2<U>eXMxnSa6%9e3xqlg4+N{Q@I&gQ{w5D;!u<yU0lEL69w7H0Gy~-R1FL{Mf6xn%
z=Qk6z-`M2eAcfzj{io7${0C_z*ni6bY5rII4P@AUm+<kj{buv;Q%J26kp15=`YqUh
zfzdxB`xjGwULGz8L6(1){wGX-o8iB}^xu*D2l;=|^q<xKM@@el{ePP2pDzC2#`K>V
z|37H@=Ysk#F#Ts_|LW6!dujbgM!&KDKN$V(7W}!TI9S!KP0Vc^tswYW!pYvrRo&Ud
z%;I0s@;|EoZY<&C<of%5;)IxslZ%^$o%63O4+jg!Z)gtjPas~79uR`8E&hB!%KmYW
z0lEKn9{%lB{l`uChhTpI{NJ7htGb7&`)`Nnk3+%w&c^&V-ezZKW0kS6v9g9ZN1R;$
zxy0f&?rtg;t`bfT&Q6ZM-6%fRzY+Xf35~z;yDY?maCLV2jp+Y~`42$P!^O_|#}W7k
zq6aU4*8v!EQnFG2$jYn$W5^o--T>%IdfQk60E&tL1^@u?3IGQK1%QL(pde8W2If!t
zI}cFB_$&YWKot`J3rQeZv_4g=f6f1H2Y?FC1pw+2z{>y$z$<uo1bDbtkYz9t5nmyp
z;Gm))Bcl*tVWZ=a5R#FS5E2susaR-%6ik%F#B{v$Ol<6s8PsI7{K9-3LM)tI9KVS`
zAtEB8Afw=;qT+Lq6O%)xN&gpL;I9D8SAauUEf^>Y05m2P3?>w~A3z3?6b@pE-^Td+
z1qBTQ3kQ$z3K0nz(x3?g01X8L0}TrU2L}sL7b*Z!4}isl!y;!FgU41iL7;HK;RsI3
zdqpW;*MqAzc}B%)>iQlL3GX#N0U<RFEgd}r7dH<tAHRUaTS+Nt8Cf}X4Na|g+B&*s
z<`$M#);6|o?jD|A-aft|p<&?>kx|jfA3lCcNli=7$S)`?DlRE4E3a>8Y-(<4ZTr&O
z*FP{g^zHkPsp*;7x%q{~rH#$4?Va7d{e#2vi_5F)o7=nlhu?ZZ0bu@8>t8hc4|-uj
z^n!+kg@Hx*trrxu7bL=9!orcW!()l5BAB>fQ*Z>o!Vyo(tLs6e<WxJuHFcdt!lUBa
zpg#Yt+8@pS-zoO~|C46_qS(LcwFE$cT$s?9Fqi;Q!0iKFeh|`sXJp{t3txJj;iE>E
zXpt?2R4)AeKnDysOWqf~BAN!BF^dicanlt`aJ|DQocNa9zs0cM^C_Z~vP$INi^V`!
zUA3E`ZJJF_9U4qQ`CcVEour#EB?yUuo~3Fdn~o}*D#QNXr6FiMzLn%$mSm@~f<1oo
zHAbC|T&v)#L!yZUUqD9mpb_4k!?9(j0Z4%-u{B;NY{_0q7$<9;%yy=^?fu0PW2R0)
zHll`*>tN5PnJZxYt6W6u{Ad7OX<pm=^>4szeg!49H}OPfXt`T?H)Ym<{Ajv=5`zr&
zxVILo`lWWcu!P75uhnE=<8z5g14^NN8CmB0mrzgomY0rn-7hI4m-v3Erty!x23)V7
zpXh!Bs7m4hRK@rRxYZxuDYHrNbv3h%0;B0+%QaWB;1Kv|DT@Gl|D5qZ6X^fQa+u`1
zJTQQ2sNd*%_j*sjDCz~VIMsq=Hao*Hl&=0m5kK`!>}5l?n_=oX>C)!q#$2a^9c;rm
zbpeAU7M`U@5SpUBwQ7eU>9koA0SU9ImKHuRlmJ$h<mXejN`MF`Z}jkmB2#9@{S?Vt
zS++U*Mczr+?9)@GpEd$iCLmiG0HrkPZ&3ExbFXieV!G=1+WMqx9(n19`TI@w9tIg$
z4Brr)7hwEtpAi=D-1OkqYsp|R=Y}ZH1~WwlUt}jBB)No-ue1{GHu}kbl}eEZvf4!5
zw0UET-8Tg_{cn3zp+YABFA^Ig`G>XhmV?{)rRC|X?n5hGf7$c!yba!S&62QV_#_-w
zs%(obL_HJ1=%7wM;Q&Ur0a2`~Ct=p`r%Y8hwCW!hGWfx;RJm(kuU*uic&4e9!+Z@h
z%XEj4n>vz4VZW&cSvtNFibr)eBt3I%sIY#anZAzUFK`f45|B!}ASbn!w$gb`nJANx
zbnuQ*O1XZ1rwR-ZH#?<zJh(l%we>ZakD-bfNL_l%!=W!`QvBS!DI?~?i{&X*Dv%7t
zJ!UJyeO~<`j|^+=&P^dgMpK8Xnb(nJ5|KnqrM=cyQxs9>E?*hoonXY1yzE_|iDDA1
zL3gl|`xf8c_^jV*<{eqZ{uj@Rb)Tu>-WQsg4&@tT%N$kN@#HPI1t&q-L%<u^QTbc<
z6Q}u$1(*3fFaU5><u~}yFG5>CAtYMR<L4Go60=a)nl_jew;*_Tkf7u$iFMcB-}0qr
zqS1g->4Qc?bG(C+d}a#9n_nE1YUT2zCBh7-9AhV4wAS;3V>`^!;h~C*y+6udOh1Pv
z^_jKknt#y0quoSnD>ty2pZC>jPW+;5VL#o-G1x9AQ2Gtl6<w}4dGh`RwdfB1@Fgeu
z9q`V=&BtqtjCizECq}Tjy#?zl)hceNx$}xSu{oK9!Mm6aW!i|mx|aHeN5`_#F=ohO
zlE-Qb-z^<~Akegd0TFSfKksS=1&VGy_>@Tdxj3Ft&qyKz695lZ_}@qndn&R;rcdWp
z&TgZan0C4Zn1{a$S5k5aDYa|o{_3k1oB~<C7$xHb&W~U72Z*2cyZP&!36YXd+n?;j
zRWI>Zme^1v;<u5p!D1#1agAdhUXHJ`lEx9qHZ)yuBggnH3hkm&91|506SsZCU-z4R
z95p?KTwon!9%gxmlQy2pt`_ZI8oa}AV(@ziU`l8KAA&V$raUOuouU#^MUF?k%Je%s
z0_7dk<%*F|EP{tr)meidM4#yi84diqZ?@7pr<yd{&v>#{anF|Y^abT{>t!F?n=ukH
zKtHdC_<Qy)YxK^FyPU4$oWuzOI<s^s`(W*$?q#fRjZpxAeRgfa?9cu$z&kq%9eoa6
zrW|V%Y)tfDIR1NNtU_+8Vj#$D5OVJfyAv|aFHLiV1Sd3gO*VZT#d8uZzp@_Y;tuC)
zcDv=%3hzzlEgrytuPojDeNG$WSceS<!=Gu@0|e;i^bwVIG%Za<R8#h<;f$s{)?S0`
zbi^w6Q?M2c;TL_1^tmdaU?6L`6@*MvcDe?VG|fBsQ*I%aoVB%PpX#J+G!8d`0eZ-x
zOs^X-IAhJR*fp*k7$$}E3WnE(S^EhUK-QPsGSyotjH+lN7L2rT-@g*MMW<D%T^J?5
zRO>Iz)`*TjOzfZd+m%@RT17i;P0sNf&#jJ{wAeSD^ejQ~?<@ISL02knf&n<*zIK8M
zxYl3X1q@aWpfUz`3jHhtUyq>VVrC=ajwo*gSl-H=Gu<>}6Adflk|@m~YLqOg@;py@
z17p{(zEz20AKr>$axE*P9q5wQI8Lt&P#@pU#X;*1#^T3~-e$%E+VVxMUVc7S);v6D
zdvH0ff&$i_(A7<E2T94>&f_aKs$it*Gwkp;KmIgWseg+*&qppswU<j8-Xuh@=~sg(
zR@zgh&74wF{F6v8t>A+%9suJ<z$<QvzPWcQ{%Yp|Gbz*5jT4KnimkuNA{D?ieIfQ5
z5bg2w(psfH|Jbmsh~su>d)5)DA=9N6TP7^!7ftzKbS|Fq;1g)Ht9@P)!z#qdk^mQ<
z#*KK*SYV%YpUJi?i@+yjRg?R@d<hioF-(T7V;#saUFoo<`i|!v54KQJ1KqIH@<)t?
zJz-R<C&jKD#kme59|tSu?Ic_Kz;QI*){8Wrm21B5&}s9$r2OZD!CqFyws>C*W2f{L
zT>|VU2&gQUN~2e@TxAF8rH#6;Ub=y)2X{Z?r#gH#QLWTwNY~yr#7Iw_7B_dbXKaTr
zMWjd$QL)mBv=)X8X0y*UhM$wBZ&bXVxM{5G<DO<-nMB_LuuLX$KyiTqudKb^iQt?&
zrhB2x6@vll-5MNu;;MIOoHK4STAm4;anf(j^bHBx&c^C!I<1^;_zJ=&vk4-JH2Vv7
zkYllAw=#^Z6py=cYp!>Ni7wioNW)LSfPUOe<q6RZlKR&V;T=UM6zNZmaX1!B;!s}=
z(?||(0$JwV+fLWdJwM>)>n=8}chy>@{G9Zy))9MmBW#xv(nnYleA4l{A|O8CjSbJV
zo-La$YdQsDpu@Lr$c~|WC%Q|Yck9m4rnRRUe~Q>KV;Kl<J}TwepM7WUdCbGD^+elF
zDAKs<X3cp*pPn~0scP%!UIzn=*p=ft4Dcd_R7E40-o3f#KSBCg*P)Frc-~^^`Lyiz
zt|^DuP^77Nf0eMMHv8AZ@X2?PJVD9VA7ub66Y}g=B#a{_XC-0SZ{xpr5guXP6{)<P
z5Y(?krCV?NDDpL;TC93}K)~>`16o4iT}02|<D2krk|cvU#0~N`bdc?Gl+MzKdHGKz
zTDBfUy1llMo<UXJ>n{PQiyd}l`(4;`+{zgHiVhydYzB#K){bsLRYZwsE7`7mMN=JO
zo0p~9AY1DU_Xmed2ffb>JG(qj<^&wMxF?BrDQ73yomRPbd5xnikZ%`>fDnag?%q}N
za(HS0?+6a0hlO2q<_I-&o~V3<R-q@KB=Uc9FUgh;xZu<-Rf-N)lkL#fA}g}`_y<3U
zg07z({S5lQJ2)_15K~zjB-9dc?<|==RIVX;bmJ^b;$#%pH~jo&nOKtwKq<uu3Os}g
zK2)yqH7v1pa<3SE<7^eSg>YC1(p?v#nKJZ8PX12e+1@h!%f2v7N?R^?rWzqI=d*=q
zcwo*R=sTgrW<t#q@1a{Zww}Z5-azDx%iagRRF&@Lc%n4)QzE*4g+1%xcyFp}p?SM9
ze~E~`j9(=sck!?d(fA_MCIajf=K{EZgzKPnhrKGdA-NgQ2HKq!`T|SGviy-&t1@+q
zIIKOQOTtHV-w*vu@HOW|hHV#f4m?BR-=~W*P>XjjL%Mf~2iXwv#_Vi!u&+TLDc$pV
z9|e3_yG|dT6qjoV4}C%5jqRD8xKeEuBNX-1c!r@DLPM0Xtjt3s^mBm>^Nl|P`{d3o
zGff=1DZblym<Y;b8xt&Se8iC1_jS=vjp2G+Bk?wSGv^_SYN*Wf$#uBgM$nL+N)5U#
z2A}yG3Y!nHh9TKmOBn5WU@B|4KPt$Hq~U&^_aR2Vmz?rf=sVEohT}J7M%dR+qSXZ;
z^x^ZDFC-k^L;Xvq%cuVQc{=+0&B@>4A7hH+?eeqOBjJE3+o(esKkhsVi#yLKm_^=v
zn?_Dxj#!NsWBg>YF984{fx?>v*8m5pf%qL0O?vHVj?{jL4$QhT)kzEWJ43I;_yp^s
zXYR~dxJ1L)hAWokEttq`GOYK+!w}L=z6saK9w3TRKZEoLJ?c+iK=bJ>>&(8&Sa-Av
zGZ0vm5Jj;l`s^;Q*{!0yWkBsBifC_Hcjr*_oF^yiva5e092&0!5@&2K&*>l8va_jG
zmBgj|Z*}bcw>yp1b~+$@mclLR7)3iF)M8gJE8;7WMyRPteWT2C>=$qAwM<YPsZ+o5
z{YF8c!wVPGlIs7h`Jb{uTcx)zWH_8WEy|EtlW*n4EH^`w`_yyOQx(1Wv=*6j*(de1
z{A7I@NC6s0CAxGv&qbU1pQ@?n8ii-3eK&K;Y{>|pd&HHp9H^OzZ-vmj4li9gYU0z4
zDY_T_zj%Aks3wChTr||so1jEOP3TA!2)#%PC{2(iy-DxAgH%I+AP7oRKt!bX-b(-h
z0qJ0X0MeTv-J9>b_pEz<+;!JF=g0XmYh`8D%scPQ^X}QR_p@gwUp8L+*t}8s2grLU
zx%q2dk-3<m|MGvM%04h&m4$uM>}lreYD32CJ|0kXfJ)P`%GE0W=?w7y8>#agP`z)j
zKI$nN>cRm2B}Nbar-7*dzfA!CZ~uR)=m5|hyy@BhxvhrXMSM)w<Cg*G|4ew#$|iZ!
z>xQYp$98EQk`$uSCw!(y?Q}Xcm5E%IR03v=;b(5762Fk^h>$zO{h2uIg6erUP4j|m
zH!Y`Rz(7zx;F&7DF5ds?VkpjRr2S)mX8n0*eNAi*2iiw;pf~{vFpkP07_2Vy+fb+@
z40nw3J>LF05OG3Zhqv?((BrnMa|QZic|-l?A0VmtAK>+DPsyEw8`D1k0Zx%#E5owZ
z+BP}|o1MT)H<?o^{sC6Sjv8+%S|sm){r>>{dj9~V`LUh9?SJ5GQPM(pd>xwClB0no
zOzt`Ug;@e&j{mKXzZv}>4fG>=T>kUOI*<SRk(KG3^+Sk=pvb{WQ>}g3zET7QQjuAw
zayCYP2fAnad7=5N>iAu3iVofHt-D{Ja6>BX?O%E5p2t3^GkLCndmwx=V?mOiBt0#D
z2~dXOPrn{{i-_=NPrrOWx!auCp7J&2P21x_?mlS^mcP~i0LKPDk5gioR%PLZ)#n`v
z{{VYGPu_gcjhN0h4oGV>@bF@1<sR-x;tNWuKKpna5odH5_y*2uHun#3@zwE$E05tH
z;Nv?r+zIKT^Wy6zLvJ%1Hf=y|2bW&6FQ1p$s2$w9A>Z-edCdPK_Pg_f^}#;?gZd5E
z$~|1eeAWNa5HOHEHsQ7VUHqLFCR`b5ZG6o!(l5_4?LIj<Xfb(EVK+8p)t0<mCF)s#
zJtDo~X+PG9uUl-a@0|4=F?&03;n<qY>@6xvIyA)5@#;cW<k%M1b;Ccv6^Ca1v4gly
z(n8z!I{N}Z9^kge?{G>^WHPsBa00hT?*oleE=qmfgCk1%$M57cLv2^R5Etp{AZ$bl
zu8V#nv}Z3(I@u1Y>e85n<ZKaH$lZKhWLqj&SI-0$l~=aipiVBrJKH|IiRET*n!6eb
z9PHVMPN92+&HLs49}tyD;^w>H{xKCwR8%a46#~2aTOOKEPo|hM;$0%^zEll9&GFxn
zulrdqFlvW5<OYc5zXLXJ=imq4h#>U5O!AFzuyk6*Axlg!`5z$aIO5nKqQ`O7)$xwb
zoQXMN{pvq3dmcS6`50knyg@2OXdQGz;vQCwFf>XTX<kf+4AG-}#%=Imdpf|pyfePw
zxn||ztyu^THrxb#((r$P#<^Rs+e1^!3oTix!5f2zMsf;Mkgd+j_RzXC8p1am%1F&P
zNRCgWSGQasQ1)ID@_w>D+;n3>5tB+KNkv7<tO&hgkq%yZujYQ8{~UJ_y=c?&njD+s
zXAfJPhQU&dTmH^o$eqI@?v$&r6x^ItzRg&7zt70#4QiV#^y-o?Lfp!oJ{8+QslH7l
zM`<fADRIkUBCby1XpVBX@v;;|sGy`{-+P6T*;{Erh2tHai)XTYc-(z05<uXs{D1zr
z6tj9EmN8G|{cYF_g!S9=8It;}V))a>H!W)2lN&$<VCkKjnyT&8cO5TJLEH$GP_CJS
zSwnDu{EF)=lt(RMmHUSm?!bYYUc(dNL_~;Q=ug5No+Z&R^-3W3N;dRkZiXZV%L9Z`
zYschI&Dp%k7~Ts@d64kf<j=MrV|}u`7Gu8MJ6sqJ)E)LHhu~C*Wqqa$w{+I4DOZet
zQf{dJ`{Fbc#lNnlFS38w0k7fGNS*H<;}+p<Mmgi|54n(Vy*{RB24<x{V6Us36br(G
zJ%00iQ&z?cbEQXX&%k)-?>Nb1D0IUm&xmF{LcO|V9KZ54zlvZ8&1s<1h9i03!@bR^
zOj;bjfOEe50m;G29LC^XbP~NZy!`M?KCxD2lIPt7m!7Y@H69;?>B^d|)_jal@I{df
zah%IXn7ZJgyZg5dUWh=PhCR@+Y`5fx60a7<k3eKHf>r{#+5Ec&0=GVH>zB8kDE}oK
zo*4$cv>xJ)^u+68<pH0LyYiHJle^gN;BeIT*tqfrYO0M9RbRBlMEo+4d~+j;9J=F}
zWSBkVZAZ{9=D+VS&Fs5P8I4n$iw{cv5oJa1wl(|limFhy+nTB9AkTF9(y8%~vN%ye
zGzRz4$qmXS!{Az94BjN~m!3Ia)b_e+SkgGDL~0XZ!#W=-tUfxL1L1%ckHd2pPC5;A
zn4=c$Qj2OA77tw85@V%)8%5<%1he1`Iy3gZO6xm6>6BU)vx~-~Py*%w_6K1@n;)Ev
zUxfI{7qAE)brYu0ZWH3;DY&PUZr@l8WVD&D7P(3$_@;(Y?nT@fhE|PGXU*w>2<bJ@
zx{(y8gHMri3J=RSy2gI+nC~+^NNqy1xm)3((LfMnrj)-<+@T`O5-0mpV*det+x<VA
z1OFWE2WfnqL>zA1DH;pfc-Owh<F{v)e0|N$P4Ihc3d;mgH!Fa`9wK%NxMZ6N9*z$^
zEpq;-TrG482m+?U765w+(ytfiz^1|ZFK}41U5(cGZ(l>$e}EFz!8^HUmJb6Bs~h_k
zM8=<5_WxI&y`eiKvo@xCQcrq^Pb}(9<&`oRuwlB|Cqn9=B51(N<cYV7B(a{(Yx@U~
zhR0^?WuL3wwHG@1B#2X*_DtG*=nHtj%fx_nBt*hwuMJ4oI;k{7-`~CMmGN#&%L|j9
zO}%l@y_1<Tv^nLxrgsoup<3V6>e6oBjp?eu*HS^A2XS(0YS2S)26|()JwLRpkW$lD
zCs*-s?_amlKMNPFy*jxa9Fu0uULhdd3+U+3Uj#q97T<r=R{d@<2(%!zJI+kKVJTeP
zLmv3)0Tq69KO~iM?4fc&o>;1yx;_*W-Dlvf4z1=0%ilZF{3PamF!m8|IXY;GArjWz
zMF_?`44CO#cp+c#AD_+Gy}pOfS(QRScXi2Yhm?GA9yHtR@CDNL6vE@dmA|aHp=~J2
zPlsp}louMLz^gyp8^BznBNr%FL-Cd2a4iiY{CGnpDW%Qplup|Vi8nr`M*TcS`<Y-d
z&+_8cEp<ufD&Nt8;@!&RB~$#BU1*9$$MoTarmR8+>tc)Jn4;yMrc3Us%?^`}`oIeA
z6-U(p#dk58gsZ&>Tlb(dHL0O;()VUEfwn25Png;=`#RLQNj&Gr{M{G<C}K4=KAY*>
zbmg3vcJF+)_103T-|>hq3s=c#T)zw&FR}ExoK#4<k$HHKZaMkG2T_l;ay+ff5u|%n
zVc(K>psJCenoQm!ak3chksSntjjzb&fDKz(G7eUex)zUBT^s^*32g;{5HB>F<FsQc
zG4o-SrkIvW*yGUWtC(VBdvT;l`uWhs5MjU{$m!?CH?k6u4~~i77Hko&sEHw|gD;UG
zYi>_nB(LV~jC$M&t*Z8On3n>^;8OjnjvgUWC?bl9R7c}f>gMP@5i}$!eG&Na@#>+w
z#J5V@9fo6%xaIRooMc*wK9|M8@IikbIHGlplD8#K2qGu_ci&P_v`&RphN4rNWKiBT
zI)E1%lS*R4#O<`Z!u8y(gu^tN;~lX|w&4&UQmo5@gb^gm5@KC%x7^R^)|T>>`i(5C
zX*}Wa$Gq&el>C`*#=8shr7}t-J2R3#e1{^7OHjmvDBK2&4fi|AYBXwo^ibA+kjTGk
zXP63a!6)S%q;wz{;4*PX_75<$D`jFEh@HgR(V~cdi1J***CR<Vo~qm(?~#jnJsko^
z>xI&RF`9OkZ9ONVz}fef7n*G{<2O2gI;8&rKC9n}pW4^Ij(Xjp+TQp3ri`6#=$ix)
zo(9aO%L;rQ=}ZpWxVNH80{@JN!K%grolh1&q`Y1C>;g83JTuv?q4#UQN%=gO6e_Dh
zU@>_TOx>V<_ju!2FuE&a=FcRtx9U<@b>7|jDZ{B<IX=iOQKy!agq&r1X#<a<nUG3D
z=8aFi2K_FlnVAjCGJA8RfDNX>2b}iy(!kDIjZ|*%Ig-!g;FQeN;+K%v@8h+6Y7yO*
z^Gq!iJ^9d`4Y3CJ3Vh4sK_3|Q<o>L07j2S+`}%6U?YQY>!@y+Vdk;aaK()!m*A#`X
z+>IMB%>!$Sa`d-T2yr)Pn!7_?vYq#emzw_m$YVR?t;S#|1sF^Gj$c(wm9f$8AE5F=
zZoK$iU&}q$#q#Zvz~QxRdFyfQpx+UEPuAQ3bX3isqoq^o2lo#T6r?YEWMBgKrD#L1
zf3)yE1-hROOupu2MDxeVUb+UexRN*RPrS^~c;S-`6}A5HE_qb>_QtXHG$$ta({hW;
zU0BQQ?bG7Xipj;R&Kr?G$`|jHLS1M6%#4Y=sgoMlzTGbAXJLKmXg)=}XDjJ-nwPt~
zW%jtqF4q0ga8~h&t;r|rhml&6*^DsToN6pf39$Hy$3s<tqs1=kClG30`r1e)icwDk
znvW+sM>%KTr7BEL4*!ylEVujv2<=`uPIC8M0bbZvI7Jz_bMJd$s4eOW`&!6tAFjOj
z8S0<(83+sG-!$%DS7|tu*%L_1ZT9S=-H0~Gp_BHN&+0bKV`?s}GcKvm1b?rR8P>D2
zW0Sh9ZjEdMP%(WO>iZ7cb1rz_;y4MxY0^-<N+-KKPa(+JU}Yt%Y4An5vfon6Oipiw
z>Ld#$_&Q~*!*(fpnD<kjt?%z5D?F}Ic0i>R))7HvlqU@GAZ!>BTM&A$S-2$d0dAaO
z#J9eI7l1d!KXE#l(BWwJ1hMa%@_9Vz1}2spp~(o7YD<Z^{JEH{+(Z6qv$?GSNt$51
z0yhxI1_9_iz463P_HVxXxcLx1@?7(ecnkB?rQ1N*8#r!S1YWn}$p$yR(ciRu-Ep5x
zDYd-v{qDmmGPgM?GCUxku~qQ#3sji;2>;xno45z1aha*fj`UNq$e|yqvMzOg0*bQK
z!5N`i1+8DX8b8-c;79qs&Oso2w#O$sf{{)X)pd<eSIo;2iUI^}vklz+tYF<LFOkH=
z5wGKfn~|h+S}rlz6gG9d1q!>{nUTgJ$)kCY{eaGMmh)SXq<_GVe3givX9b6(w~e0<
zrP#w~S+u49mPGWZevDzeo1gjz2>AOCFqyt_b=n{Lm*PBH=FQ-gbyGtki-ATOM8BCz
zLP3^j$y>{7+HTcl(P^JY;%l0kfsgC(wRxfuW3OG*I|3B38B9UMsmw=9XZMcjP$<JK
z%oVp0KmR>`p;7m2L!rU<wijes&!rX|a2Ru`&r18-W!7XW9<;eF?RQq0SMd<!c7J$d
z^Hg7_77b})ak@+;+DKY=(rbpxQolR+ne`2WM6aJ}zI-487bu^ghaPqjX2>>B83As3
z-@jcd3@neR=#035O?#9)|Mj3R_@1qMeF?bKT`;|1C-_f^P{L!Q*H2s8V^qB2AMhA#
zl}P<%z~zG3_;clp#9vV2$o~b66W6OxGm_$=B8%2QFNsa0nYoyJ^A8YlHF`~l;wxZf
zBTp`GZURn(79k(tFPT33WuR^rVd?cp4EN!s_}_XwGmpH#>>uL++se1#!JTqN)i-e~
zS!2cAvrW>C@p*r_QU2poTCo5NoSrVTzYUc%zZ?0B7Y6$s>`8fVPUF4FwKk{Iqz0B|
z;W7kY0?5cT-EgiDz$|Y;^t33w(Q+JTZjZEtw13;b4QFwjDD1l;5Z0@3ip{da3t-V0
z@R`YI(<=BX*3kGl)^MqBnG3L+KCXE=hYs`d9NZV;C954FxBuWtc#yi+Yn3@JOFR(R
zuPso%+Hn6bt%{&tQ8N(DVr~Ad*Na36tkT^KWgxUVD)~;FZaI5)>#Wr%ZB$eJhx{VH
z_gDt{elrX4)i|ydK?8g~Tg-Zt{)$E8!?n+|lSF&!^QLnYvBp>kRsE@*x4FsU<FwJV
zpc=p@xV*Q|gW<NlRRBm%1Sy^Uu`znbR)&w>*A`lX7v10JXViPt%T@~D%bge3dB%En
z!sU@Rk2}=N#7}ze20!~gY1;kOM9cG%obDw=#>x&ukVho#X8W`;bx@+vi9SZkDAxet
zhk1YWD?V8^pXN_W{m9nR0N1HKCF_cayg}s(z@O2ATDxfJ7?^?6T<?F}WSjFNrTGYx
zvbi=<>O%9Qe1_h9gcWnWv+N2m3it<z4q-h%Pd<t0Tj>4!rdeU~`6jcs6!v*%!=K8>
z{kJ9dn-uo)6U`tP*s*!7O@4h(&kQX-dRqLmP<HA*Y1{B{8B^%+$CpF|pHL&?!$c~n
zW^FbyG4vj6CiGBuP5~3c6+tSspMZ6LFN6pqp{4kH9kV}syss*mBg(_qmyJv(bG|&&
zM!x~`+$$ZQdY|0(<P!%l%vQ%xs7j<+QPPodcUIP>jqiux!VE?mc`PrXlvY)`K->pB
z{9-$jCvsy#oVe%blqj*y(*(c&>N7=o6<!XJ0D!YgiVc4?sMqj|IWI3iVg93v@c`*_
zBsoEuF@9$t)7$mk@1&K#C)Fz6*T+B{T^<e7c;&3;%_*c+(xAr3O<u3v{lI2@(;RyJ
zxQT4Ez?P7Chtwlm2L|MN-IO8VYV#8!N%SJ%<g-5RzO1QCGEZc)z{<_$r>o*$ccf-$
zXl`faZu0@b%Sq5M<%_;5^FH#|LyTj=fBGRVEy{wMKV1z2+|W&mVyPmt6&g3YeUH9R
zX?f7A1w_S2xTj{zR>25MpzBVTYJt{MgfdU3xuR#xE+FqiUifS(IbU3hQGPSgt0~wg
z|C<O#tT(f`ge<sRM11(otLBBst(d%ZJGs$ulINT-l+5*bMtZyMn{^jHcv2#nP^W7C
z{hQNj*t1F7&$vCS584(&?h<5MXDmcwd^ZpIS_5e2ucIMh(jhShklZ~)if5PKl<rq#
znBsps`i>BdH1RHZMR}TBD4QWm@q@*QfANm`?3R5zQ=}!J@$5D{z9l(Co#&<V4CfPi
z?tR|mmE7QKUB7*1tr*;-zb+&2&gUh7vIMTRJ=}<#5Ad|j<9UK_>l%6ehHc3>C-W&b
zhpwrsn?bL{2)+WlDI}!W*%Ve$VIV9WQV2W*<i9ON6GT7$i!8gE%YtjlE-X%5kp#X`
zv5lSaJ@I71-<D?anVq{cIIVCZs3@}$+x5)XF+K7YH(cCM+N|lNh;V}0GLf9vzI0<(
z90%ChMFwq5Etq$Ea{HPE39=6B$JkV<076Ga)!<}|X_3TBnD?ROA;|T*yi%51rx2$I
z>Rd-Z%2AVn3p=Xjf#8%crMoE?J{}AWiQ%cGD%YX$!*<E0LK*UE^FQO2%{I>}dQbtB
zJxM$T`2RblZG1f`D382`<vjvtjATb!WLx+hX9rv;kJ>663+voI;N-6Qfnjy%k_~>o
z);L1Nuh=6Tc))wqds;bD)BJ+xHb3v}dE<4Pf7vI&V1vs0WXB5`gSMg?nG&%d0L1?Q
z51To9p-Rz+eZtVCw7cKurJw7K8vN7XNcvMIAP`9`cllN17RAr;%UK9wClCb5^CsP5
z1B3)$y|@>g`*?Cg6zWjh<_Dy0#iv%J62-f-j8%ggfhbQRLaNKfBheuqc+c`&BFSzF
zsHDbUbSpfa8GL;s&eHw`WNxt7%Zn%k1RexaDV@t_mmBOMHppu;yWHb9&i?_pijxRD
z4Jh2kNaYl8zR!<GYiKt?u3F_&x{vq#0b7|jtwtZl-bpCCc+@ds<6epqpb7V-hA>wC
zL%rbq7sX+Tx(81mEzm1}(Zyh^FUJDAKr-r}v7qIP7jAugT*j%kuh0bb&Vxb+ZD>xa
zeY5}^m{KxXYkWENI1A(+N!#^p%3W<zQ)D7qm9yuX3a9($4`=-jAlg1FbN@(Bl|$`u
zbY}n!F_47(R=726AG0mJc)~KCWGV!_GrLN;wC1jNzR`_kU6XH6ae=VbVk3MnHAI65
zrtU6pPQ&T&{sG9Jt79TY!o3x?5E;$KEI%DCQp$5n!skF5Z24ziF;RoAS7%>;YbWMd
zN0UknR8^%(@`7MzfGdjQv}u&sPzkxAi>LCCqB&x0Dd$8cm?~>`yj+{Mzbkzdq7X;~
z#Pe2HSU$YmEDsXx|0!xXb|Bmi80^Fe)#p_=ICcG0tdP#b-#{f6@SB7X<I}yx2;08!
z((iT7bIJaM5>I60+KZ$jeKe+{tDxDC8Xr4S7?jN>8mYKdp%Y$`bF%U$X4L;x=wyXa
z90uU^HxO9il@pA!sE3L3r+e()fy>>_13{@=`=bE|Y>uhHIATC>!F2h8T$F=>PklE5
z872*JFRuib9L_w@Mr#h!9MH%~ILIoB-Ex9N!Z11OY1@Vewpzc0)kd$g)l8m*7Odvr
z%jtwSNn!E5v?rT?avo&!@=9zIetIHi#q$!M<S@mR04YpT(Dl33fpd`v;GQIHMd6sf
zeRycZ)fi{SZ>V-p%ZoRe9)0T0q;~N7-Vk#Ol}ks}MTE<33Rqu_vndEp$9p-ioZK4!
zw>k!KL=`fVbNF?7%kxxr=Dv!G5*IfG@OxUuf-va;u(1(rm{IG%nMXImVz#+X=yNI&
z{xW?QSn1ARaBLZ`u*%d1Urn~EbHtZB2)YbfKF|*KLg08F6vX~*ub%C~8Sxay$kkIl
z8I%sp5bItr=zFG)-lUKkDRw5sR+JP3vB7MRp_doKEa!vt4EZT5;w5M#IIox6EE8i}
z*;)Cq%rtpIgHSi}%cWI+^MK-*FflbZOZ#O}2SR<LEv0cdQ4#)NiAFT6nNhC#jGa3|
zH^*=<NlB|8)ts8D(mcfGSXsG!h2iTuQJEApgXVTQf)QJw!@QEI6!=YXx)&ldG}4<C
z!@^VwL8K!ipSLxuA0!GUUjGtDGcNDvAlAYE0B5`E?r{NH&!<9#a3;K1@-+s|MCqiH
z9W&YP#gwU}(_`025w&{{+yf#%*^e0>bu`K5EjBR)9=1t=V=$}<>j?*b({CvY<tRIy
zH}n@^mY%xrsG@97NniS85D;{T#iWe`<(y1DSyg-xU5z8PeGy4_!k0e94TK`n<|<)3
z&b^C$wB=(D0#84Tvil1H*#LMz>{eb3h#S9Wpi6tSwnnX3n|Ows*-SoLt{Vt`n7iM&
zXS_zCP5(_j?Ub@NC2X+ix7fUmslx6}Tw$0=>M<#5HQ{p0dxUGbKgEhF2%6WnPMBhv
z{b+_`_JQco*w+I5CL4tq5NKec>FR7yyz2b-iZ))8zkbEHe((90$9bZn@G}V5K+N>^
zV^Q)ukq&}t>#S)iVJe?!Tu;o@ls})R2>Aa4#9d~Pa%Bn51MC{R?5O)nHs!pVxN(;p
z9Pf60)0z#eD9Q}wZ%r8R1bGf3^0Ek=uA=e+CmWNNpC<erArq7!+(k)J71(+$hzt?_
z-7H&H?C|0RsfJbP`NyTElAtY!gZ}{5JH=nK@*6o!>3P-sx@tUYekXh6p7qZ0lsK5O
zLgenpWtF#R7?HgFwR%o<5UpyIuz?f=!brGv-FHX0RqxwiN;5616yR~571|o)|2h?^
zBc&f~Te`C>%3soYM3ry5`O>uT87yk~o|pawL9`+m8-w&Ql`i$Y>x4WYEDrexuyD-j
zcDHM?^`Q4rXFXU^Wmg=KcL%J0=_WX&k|wJbM`V^Z?xDB#U}@c02{Sx67R=%)Y!qGc
z-oMyMBG)g#u2FQbzLBJhADV|y0(<v)!hwyRX~TB)4nZu6UJp@J2A&eNcz>y+*I^i6
zj-16GLc4$XMBGn0Eq5o1dKP3*X_eV5Q(X5I@wlacfIL#}1~umgmTVkQ&&T5kwfIQt
zV<IRx2Pv1rOlj)P)$+1t^a1u{#M37yy_=1~vD0S|mgJCTm6&TO$jD}|Sn_!WjQAs)
z3K*Owd+pl-&Po3pz=-CWwaoW{*&rixo4J7>G1xmfDRKJD@<5b(pw%hZJuOiLiAUf+
zskn<s&$%{CBu`*is?0PufkHD<raj?Z!-7(>)Y>l2RP>{9Dr(Q8(i7(Jg@NJNb+GLb
zu)VlZ?63Y^D@*Hf<m=-)(Myz??c@`8VAM65*lX@|I>-O;B}eOC^s7(9i$mpHeIkh(
z+-Y^zRHR){H~y&%hTk}M-Fg2y6Mvk@U=3O)%cp6z$fl5Bf@Qru=-OiOwkSyVlk^wQ
z8VMFcK1u89SQ`>nks%n?x2&-(7m;JLJn8li@HDJ0K-6MjqHthXxawPsC`}Vd`Op9<
z<3JEZ&h$NE>aiLiU~G}*lPq>)0??OdB=wsVeAdMv4X1exa^r4CEw3-qEFsowHlljL
zNPr%i%`Vd83D31g^+_MTy-9!~D0*ngjXmWhRpIS7TL=jKrJ`K{L!ghSH-Go;tTfAA
zwk(a%1^l!~_7lYyvg+5WQe#S#h#cETb4dwWZuVcsWy@(3l|Rq)`Yn~c8<dUqN}nP;
zQni@p#G*PEOm6BK8xslCFrM>PL1;h@bsLrZ{`0nI0!OxxIA7F;ZDW@8I|3qYVU2~7
z-OL>kO=rOe9!AYSv`gCd>aYI+l)l?eESX*2zkav3q1flR2}Q*hECD?HKm-00E^mBD
z@buvld!*2)zDdWZNIY(eK#bK<2qmLxy4sI#YiH91iP~$(08hY}*9eUmD!_X%$^H7(
z0oE)5Ux|qj7#u?;NQHke#3+kB5>`TgSFwEDa4UZ}?FZSa+`bgrefG3m?sLO?uGpe`
z8pF03>jWcR%NYbv=8=heITY*BoOv^wj)pCCZSQ0<NkKmYqVa~TNZC-tVgo^YE+th*
z#r5auAzzm)7nVR0wPA(sPjcKwXf!U>)DCrZG|S2_)NiSzTnpb@l=HQXWScQX;^{y^
zRH9-?9bJBVeL)>Fv#XQFOHeygd&i-|`0;P6c||mJR<%0x72^<ubE%9DzY6RQ@&Dzx
zmG|y7<vlSa{Gu<0oEbV&th<tSey+eSC{Dyo8-mi&3lpQVO=A|uOB`kKFwQyNCFPj(
zaeZKL-ofT3!FigeJLKd(2!yt`SJ(wWPn4dgPh1(GB`QP0)sR#!>YeKU0956_^+Ft*
z+qvDjpgi{Mk%ZCe&(qP|z|sSaZYKfnMGNk1h?{wVs-WbWnk$%&3LqOW$Wk|C=q^qR
zF(bhk3gwa7P#H6KegQ@efvv_EKe$aw#qk2)@AS&lFm!lpYwv#x8icLY2h=x;W>%kc
z=H<_xHNVh${-#qZt%6Mn(i(|~C#1k`c@AMHm`f`4c773mkiNlODm;4gH6aRLoQ`{B
z{Ao^u9HZ}V*-@w#eNs{@iel)H(fD+LAb4M3o*RGw@c+1@x)i-wf8K2P9I_w}E;Zt_
zfD8dXi{%B}+t+Oj;N*13{53mX+82}<V0Yi;?Yj}TD2$Y&b^rjB0Hl)~WvK3gY<8no
zZSG&6k$!BZQ#;`sKA@@17v@j(?As^wZr0aumwb^hW$VIJ=8#9q?$+uE34ATm9r~r~
zy6HfeDyZkDVQqchvO`CsF*NR8GYHfex_I&Orh30?Xh70hH*806qjt*Daw0*K4!$2^
z*|}cIJd^L}!^2`aMuD$lzu-VYhNHt`mpiM+s7J%~AuS#fz5zk@bs2`rX`40ar~Gkv
ziZUiJ0%Q|ltvm*6+M}Up5-|GQ<52p<WNgU}-sCmp-c%A+#B4VpSVZ`gMU^~T6A7^c
zbwg=LxuI@SJI)0Q{^mlrIFUo;YjODA%(o%)4o5!FN}HD<hYNP~tdAO#WsnUg+3^hr
z+U7mEhgRESBWh@m+kq1oj3=nX+BW&|?CFXDFm`s7qd)?LSI#180aJ4L=>BS<LsQa{
zY8xBC)%MhF3j$?rMQL;qh_BUFw;;D2M9SQ4>yy5uqdYaS>j7Yl@R*L&>q3`DD@ng*
z-8;pX@?K59EeyBltX!QOx6FdQT>?R9pS&7Hb=h+NwL;NH3ttRpfE{C^P~IqKS~$Z}
zW~26_;xv;7qp8gR$wNG)m@2DNG#_=nj*syB?t}8;*F6DWD;MCZM1eNuG2Y*T_y<c^
zA7#Xhv|ST@-_e)_Yd&>F5plv{t~`w0Qj2-QFqobf!ee&p?`4EV5n9r2_SxUv)8-Vp
zr3Y{(opyKY#FyUzaNr1vlqs=i!ywpIYS)xzMb)~vZ`+Z##l+?j$YvD(fUN7Fi#=^f
zzpvCbTEwh!m4~g`;6Vt7-L+D4G6Ms<Vt*Ex_Ea!X)>BS<iGN?v{Nc_tZ`{IYW*TR1
zG=|0?KXp|{52daU0^-R1G^urJietY!s|!Gwy0`!Sj_>xMQE6|DGO&(M7eK;gNR-WF
z)WKZB+Km1HyEY%QBHg-bY7vnwEGEN&y>@I;H)Nf;schCr?^8irU;?8FsUq`$@Q28C
zf<Vyl*%0Na&BtE?d{3)G`)adD^nWyTG2w-~R>0AWEPJ9Kk1TMR!~wk?w_@dWl{|O+
zs#}ZWHZZ~$=l<93gs2C%lVUmS!<1t{nVKb<Wp64az(^>Y@!O@P0S6TD?GU27@&0fP
zzTyA@EgHQPyvKeX$H?8%c&#F)ST&HC)qAiNJI)mDR`OBmYAusAn>|mwIc|qsoKA~n
zk~VUobwJ)a11}H|t0<OZxS|&@@6mX<eH)5+`3bvDzQ28O^@sh55A+|vIr=1IV@dF#
zYQmPk@NjUdhHPqA4kmr3-KgKfIh5_FGhHZim$~xENJL@dFj*eyZz`{gho@zevndo}
zm);uW5H34M9-XV6eBazYu%i@M2vn(cY=R3KiPc)IrL`7ZeAZn=lyIW0ww|g}SwUTH
zsn9z5_fO;5U#R9Yt&F>f?9Cs>A9&V{jJtdtRp;wU$2^A>hnDN{^zkC}zN<p3mj>zI
zl#%R*4^<}Y|0TCEb{_(<1`>h2Qbn~Xou1J1%4ph~vDQ7oe&(=ax3z+e&;*iqJFth_
zro3g`6SR+3bHYQh?F)F3r!`=RhFo)7JFG-kzE6RqEcj%Xjn3@D<X20j>TphMq5js;
zq;tJ}|2i|+!toq)3N7a{2sqsQ+iQ9qHIf!t|JY_{XZ!Qi2GK;Mp!Ey3PBtkBg+b;Z
zn{6OBs0`KXC*4tOO+PDI)c;^4?Jbd2xAqn|42#+52&KTT#cd=o9?*F|NrOr)wGRBq
zB(TQtbQ5{iMVD|h++WoGHM_pC5s_+2D#=|od-x(G$ZfH3+Qcf&(jHMGZNY)2P@!Y2
zX&lM>21@KEv<=#dcJ8l^rRkO%HWya@<?yNopTo_(FJ>;18~^|xJu_(0^K!6jYmVcY
zysrdd$D2=48)73E@EJ#^iVV3t;e6}WFBN3GH5B9J5EF!D8=It>$Q}!SdERd+rulek
zu_QO&{5m+af8_g!mfM4hzs~38<rhKsx(U-ng7nX+YY*<3LvEVv8L{`O)wUU<;eD^|
zo!{3r!kuI;zt*tdZuBAPQ^d&pDMA;U8KHFQ%AT%0rB3vdwK#qdv7UQpnAYPUsk`${
zE7C2On5Ya%x4OEY<(WHcgsLa8y&9uM{QOkfKBw-d3~|Dwm?N?+>14J2#gemIU*?~Z
z4P~|0hufELZ5!U8wOZf6U$6Rq9!g-baf4cWbz!kAuW!02^l<rxb;Gj5r`lgIB#J82
zNHQkfv>P0dF%bY_5VkhEs&y&PEccpiTV38cA;FEP;!Fimku-#x)O8caCuyWAlQVTY
z^F$NvzDBRZ9gsTOcQYsx;~3p3AxYWH+3@8{-Ae1(T_>HhML_3HM$279lp2#lt!cN{
z!$IcmzM~?o=%(M^WwGeF9PoXq0b!yndZ&dtufu4{FDT%`qNE)SJt7aFYmAft^NQ1J
zxZC?S@yhDQu(h04R})Y4r;5B}2v8Wj;r_^#%lWOPxX)qVHkLLNebdKjZD^=?&~Nhg
zUMor%0J__MuBnfWpgy|_=c?7Mjh771RT%l)nVLD{*)6K%;emxEgne?#57G^|Z(AKV
zZ5@dR_VgLz{MDXhXjY0iCPLbtCpG}}FJ9;Z8A&kUvo!6%RqLS213K#Z+fPBd0?vI5
zS&l$L8gHuKmmo-hQabz_=eK(!3iJBBS`710Rj4B;sUXvCQ|LTVoVsZl+`H7dJpywV
z2d>J=+`h%Rr_Qjx5tKz$mY{jFWheh*7g7Q2m^Kra#1MinhX-)fug*>IrLeDNNxf|6
zm?{2kZ}3#?8}C>XF7kNvWhBWU0W&EYur}EHrdA>ONAfaOv{il=ZXhi3I<??YUQz%C
zcft=bcsRR|-uG~aNkfRutdyBEc7Vg*zsuE@3jfcLfHB3NV#3w(gPU9NB~QK0w=c@&
zE2lcfR)P<5=W_%d+NbT}u7_UDcO%E_>9mJvaC9qoZQ~r|*RX2N)}nWVE}~&s;!SVI
zDl*GafHAxm8^IJ-tOb%f;=4ocpKEe1ovQ`D$1X{H{bFGQT1IunAThC24K4AbX~ne5
zIM-kbITqd^8$HiQZ-pVa5x_V`Q<^s$m1yT)?U!B3IKSF>5OQZG6L5V~SsrJyBR1~q
ziZflCjq~VMBy%vPC{1tpA1JH<VGzRSraSOQ;AiJ}w)9bX)97}4p1I+a{Af`ow$-S?
zVb96SmZE^n@vm>14S!%`LsLIn7)FjAH*Q(Sp!zDb@z`dK7Z#P%bsL*ShN|k)7I9HI
zWGTbdkk>Zf2En!$@x#xbaegl^Zz#JuQ<I%AWEH#$`g2!teK^OJX>6UT7Q2xiP8VdS
z-z-3T<SA^-&)oEz><mEqQDPyl9uIY{<U>^0HX<TcLQ&otJxUT<+PxJmY>+O#(T^;E
zl0F>Tn5aXwB?VON4rs9VS`owyVe$JPJ>(T<K#sqv?GdGq<;qm+#icivrbs2d{-CYM
z<>>foH=@_$^e4pRyP4sI*#<6b4YQ~CgR3xv!bh?8JH-7|S7uO*L)f#mYNs#LBP&%D
zhKmj4TM5itF-R!xPC#$W#Zhi%{pF|orewh!1p33+eeZGRgllUfiU!I~=ZUQar&@lY
zFmCXQ6gArzV<>4Ob-j9D_pYWaMWiOiTQeZN37;0u2eeI3edb#6uDN(-eD+W0*V;>)
zDfjP<{{WPZzn%q~hucXs{3Q)cllb8LLw?#Xg^z(8qt-PPIQ*Hc@_Tx(W5Ude>Q9H7
z#8fSjlZLD@H2$hl;Ir1tGe-8T$P&POyT;^-mPdV`Tix&Qu?Ze4u}m0`w{V27SkJ!o
zuOyUlg&Q^rJM?3Fc+`EKIom;Z-Nzc%v!78LsXYmMlolPjG&?n7crt5ra{b*tq?I$o
zq~h0X#9+%WrhLxWyY~l}gmY-t)0#uw`H`H=u(Gu6R1Ifz9;`TV^hMR@L0-;XzRpnu
zN%Bv#w8$(Vg=~SR&#AF~=wwwdzwAEPtF9)uW>rf<xFl|HdHh(C+~89Z51ojgJBv=;
z2<L!<c`d)+IiI2N5q%`t0WcMlE9uZMdxhhTM*Y&8glo;$yF5NyJnS^uYWO`T?|Hvw
z@lvu$*OZ<ba><U+_L;NFE2`LZ1&C4@NoLk|m2XP-@@pX8-E&7t$f(^2VwCq7(O_Zp
z5TjjyT9}~J=Oz>w!;|~L>tvmq{M79Z17X{@3V&%`&=C7=65{3Yh14$Lt2xQ^u$~4q
z3`1y7fCXp@200hQc_mh67jIhs0nCT=u+JL)ZkOD!%9OVp1ef%*HVd_h_<8Z)4`}xn
z7$%U=qcS7Jo}w$(nhxVxG$syTNM)L2Ua2-)gLHloJzHXUK0EEW()zGmnxEtIRJxPd
z%s)WIM;yOg<G(TP|A}+|UnoWd$6gH=FTOXF+1yewl`NrJcTRPX597~0qW3<Z+;sWT
z>x-paI<nur&FeMGQ?E`y1MJfK;dD2<gTctj^OdXboY9IS>1j(WFmX{~>Y1v?Rgn}^
z8vq<L+V@4^PuH7u>IGVJGj}Frm{i#b?i!ps+M807Xuh39eiPxuVY;@Q^_%l=w}Jy9
zU*>pXAFaV8>Fgft=1?m~WW4-Yl4>SEqrUl4)4u?ae%AG8{G_b$p|5}N<J3ntB*$#O
zJUAkK5Vo=b-)*IQrAy1HJo+BK&V2uxoVe(d0k??KeL_Vrm$$slNZc-hhJQkyHll>7
ze&I(N>NhnLlIpNe_@?ab(d=tKY6f#x+Mby-=1OP@d(ar(^{;mf`xZ<`Ki|E6jOK`b
zkqMW-slZQ1#t`ftINvTppS1tVzW<TE{)H+xGI%!1MS8h>r8;n;Yvtw1p%{cF@IWri
zW$}!usosRU@lkBXQ%CFWZbr;(`fff|$qxccRk7v8EsC!nO_VS>TZ~s^S>DSHgPfej
z2=%~ER$mNL<tM!6aJCc85I_wK2;pXqKc!#%31>GFA=wa$GjAF?Oq(gHO~^_cg78lj
z++`HWq#6EU!scgOc}fjlQ18MULxdtmaF}p;=DPAim!1l!I(~VxV{MDz*qA$Czslc!
zHay_pTzmADBmbUnUgyc;>|J2HuL8|9dc9l?`i65}QslRKYBtOI$3H_LjG<-R!n5z4
zeUC&}F=@I%og&IW5!h}W)m`0qLU4WljljO<fIsp<;zw(KF^Wxk7;H?&w&1QIcimS<
zsNI&<JXJ<Y-a^F<kz1%B+$|?HM*Y%Lj@WqNK2`U7e}|=c8?Qlv65SxFt*X6pwiifa
zgk_sd;wjvI7~_XUM0)KEf_;$(H}l;Om&CpnMRPNWdd_nfV6Rz~5Pu(4L~E{pVtM(z
zrpP)ee3x?eB0=_ej&@9OotY<2+|w@Tp~is8qJyN+NRA_OG{Vji>CA8TMUY#FKIsCq
z@UeziEf|bkVm&HN?b;g(EvCf`a1Eh{q~wltzUQBReL0*wDSXCwZdr!kc+U#@YJUi*
zzzeKm-9pT~O@rIIT7<NkQLd&H(lY<3n2MCfplE|J_dx?9T1BRhrvFNwRHi!@kQ|V0
zD1oGwg13OPEEoMRW*-&D>(VZ)bo&V`8o)ieUDG3d(hv-PzT4&g;{7r8?CCQ}{Zo(q
zPo5Po>}UmrZJ_%^fwv_6VMK&1?)i@1EbP}AFv@mq_+d=i#9hbFz6;CMgt%Ak;)W+6
z02d?u18?9lowl?i!6~{w;Hsg7IftA!fuNmO!al@SZX6Jvb5_o0Quc|XSnhsFUZ~Ra
z<jTO~O#0l-R6<0vP&eh6^zDr;Y$v?Ki(yZ!ZHjdno#Q6JNVV=^6Uf}^H)shzrjAih
zcvR4mw7r`7m`9W_ttC3#T`omI$34(;FT3tTp;G{mXZKW|tOc9$Hl|CaWeXgfr0cmc
zrQd8<>{t!|q?QD4;27N8pRYQQ`t1orFNANijv6dNbCdWODDbG-^3=_np=tRRZw?Lj
z*Iu;0QpvN)mOoj~{U$OC+t2Yy>3i|DKdVURk!DUb6`as_qg|(Q!_-9el|8S-0?%lb
z>>E(32!AS5q!bo!PaPZ_P>f`oIAH5pO<Jwdfe~(mU?b%D)wGmpU}`8jCVAsISQmQi
z$1gdbKfk%dL_-oj?4@dURYmaHpa+2sYf^x4LdVIVKg!sU#XHjF3!TyW3(g8OuiaR|
zH_|P@CCeGQA0po0_W>KrJnN(iNY5bKQ;7e#1A`z7J<LjaCDM<C%$+h#z$=AozcYZ3
z0PAelVXz8G?OgXi7Z9^g0Jn`xA~muDG^@OSRNg#{am*clvJBy@MBUY~x;Jdj4-eb+
z@S(A92S^BHj}cLbjPZ}9EJ8IO$;K%nTIF5UHxRVlgVs&q<@>rz+T|M}X+5gZhaOx3
zTX|m5I$*2(SqA2_J_&Ce2Q6Se`Af6;D0SF87IQv0F=KF8SgntO^Ls>e{jlB?<uxvD
zjL1%a5`n+w(9IJ-@q=!CEX=p<`9#qau1llyy7|1TBByS>u4sr982nzHS772LKO9?R
zoNJ@PMLEcxfb|&(#zXSuPMP(ML)}r*gBzD#!w+2OJdN?G;JPA6B{r-dC%N;=ISJ1{
zfRPce$^4RbXek7#F}<~)ezv4s8`(Z4GKL4mUKuPbFPIMEUF#xkFoUd_N{k4Gw|U<1
zmBM2M!Jzr-TBMu=%-x-%cG*4PaW#*pX0^?7PwIozh=k)=khI{tkjj_eiOTD!>7C7y
z`#(w{4LW4P#A-UsUeAehKHO@5z8zRW?R>9{pY@7xW#r2Km}WH^n{ShejT;c@iS~+=
z;%4cH@@rt*N&6ynqGD^l7^i+!i~P`<KIbXwtUw;<p>x#Ulu>+7b4+t=JgVwzzUzq$
z7PEzz@~D>)JwD8y0awr|7F{Yb55k7A>3O)M&h!h(`A>Mn#T)V!cD7I#wa@F`S#(&F
zH7i2y@n={qn>h^-cn5BGOYIxp+dEG_Z&W>@jYt;$#)N8(!mL9G5qF@97~>;`4V2tU
z-s!kBwaBN1X2Ph~3L;Bz{*!$v5Eet+oxFY`_)ud;GO?GhmPVb>YJWe{XX@}?%%WC*
zxp?!}RyWJhSFzT~pHC7mjqCU{zvwrEpjJ5tkjf`$4DCK9lgKU>l4hL0yfIQHT0sT0
z{4guU{h4<{%FhW5%Qg=Eh7|&b{TNq}tiXeyRLvUem)$mmGSf&Y!b7+KQW*S#w9|lb
zugMbo+lZ(A1Cwo?l@MtguyRnELz?lSpP&g&C{Otx5aX^^7!{(RI9-fSHdfzgnp1y{
zSoQW9!#Na}Ttq*7dM-Pe@w|S~Wk6KQx;0w<X^t&HQ%Oa9=u{E!=Wn%D6mNPzc6Ecc
z=f__j0fXxW?F(;-N<^K)?kq|^h8ks99s3o%UL-hY*eK%Xu*R~-1HqA^>Y();Bg!u(
z<FfB)M+6sZa~F>^YgWx8<xV`Mk93wRX4giKsX+^7OJg>TA%sgB`h}tzq<4P_^5poD
zajTWS7w+AnsvF;)K9{94vZ(}s_Z0#o0+GQ>3l@LT{{S!UauM&u%Ohs5{y^L6gw<*<
zKhVx@yrZCS#?sLvO^6@lY#|)dDA2?lr|-peS$w!;OU3|%;hKS~fn+{fexYx^#M;TQ
zH_XxgP;o?JkQxE231?u|to!$#e902nujFWDZyT59(cTE_YS=HJi~J)SHn2rp{42vG
z^dF!+0`nl;u;Q@g`c?s7p~l2)p}4=wAt&^}oA^m+zY(nKITKZH1>V=j3G{hs`Tplu
z$_J;P5)x@m<WF*o(tFN!Bw_}Z432xara#?VdV5MnEp%~S3S%_f@y27O{+^dj;yU|c
zx=$i=CzWSR%Ju~7s(0!$Gz8<1>RQ0dHWgB@Jo}|Iwfn&DHmny~*V>d=5+=c84l-8f
z=(x;3_>-<6c`XO$Zf1BV@!7HEJxTpN?_QcuMI7Q8YWuBG1XgH53NQ^Z03_4Wj<{!{
zF8(dGei*hUaf=xscVlX@qF!E%mOMuBhRUp7`lWCj{El*uq1gHQ9YTOuzj%uPvot|+
zY(6J#wl>~Sw=I7@_~Yd4m&e5Gwb~koI(sW(^H8cw*SHjLuji-;r$Hz|X3X_>_}!o*
zd^N0joKcB=C7#NGW5gmugDbyb<;MH>R3^(;r{+eOJD6OAW9<yLP(f&@Ul;a-ia)m_
zPKbW{Wc&P@7I;|9WA@5(k-@uQs}#(Fp>VrYCHg*G-+2j|e&4<yf}<IFZHeyKxL~Sl
z*<zK|zKe`l@e<-w!5}4#K-ooICZc`$dB~9R<;EK#KyEW-zOvNs7@1h(mdWCY;mg<x
z_B5!yO@TVsgi2%uKt_uto($MXf9N*lD_|%;{lq~M_A2F|XI%<IaXt<#VJ!X98E|#i
zfk|;JSrg@IOk>0!=ZKC>)V@t1f8em82Kz12>l2kOcer3vV`Nng;^O?|n!2M*opS`z
za+7aA@l-T}l^8=t_<~eJOXugZMR=g0g@jbZ>$4G|#||=1{Yw!ZOoT}Yx*c`%3gJF1
z9o42qwyf5Gyy>J+Mr(Q7XoEBV)$G+>n%=YNceFzee$~nTH3tHzABPryZ4x3cA<dN<
z7jRx&EY6@o)aaUOobr?O)6nCM58N_n`3tZk!ieVly6}=i3I9_aYB3|o=1KaH16zJ7
z4~$P~*L<)iO#W4<wNHSTK%Vu~g4jLBm<~~GO~h$l4EX2xit|-)_(w*`kaDE-g2~DN
zFpyTWWA1MJdTAga&h7=1x&jxKSo(hE6FRg0pvULJ*%_b#Rx*rj5F@mvN-sU7ZB!Wc
z_54BF&I7T;eVay<zX{`@fHT05|0Gp8u3G`8D@H$a)^relCV*M&cVB(&+2^wnSsdV}
zcY<--@rn#$$W<wR#N0`(dgU*gn_mBO<sU#{H0g1}wOigxQ?*W*k%Q;W@qzF-PB`oF
zSdYQ$ePqm?1|FOoLQsZgz8M3YRuBG0#w3qS9Uu;{TL9(R{P{ear%g&UPxuR7tvj$?
z$|w%3p3-S<HDduBO_{uB4E1oDXWsWkz^cY+>74*Nz-7YlxdjR6+Xi>Xui3MYUizDV
z^FHiPDRI>&c}T3b?QYO2E4IkHM(%Y1qdmRL^A9i;Ql`kYmCZ-CqRWG_>9u?6FBS>_
zi_P1a%Y#x_ifF#Y?~mHPdN?%JYlXC-N!bm@msiZJmgDsXVe(mD3ge_I3a2Zcz9<!=
zQKD>kc|h67y#b(&jASW8ifI=W^Ugb62wj91fAI~Dz&SN~{dpbM?`iL`=ef2F;{Ad)
z4=})&q-=0@tmK<CM1P?cnD^C***K$y|Gf<g_ERqes+ov@Dv&cPwCKok4h}{Wx;h{+
zH9eX)b3kMhv;jbvI|zeySIojE{{d<>7e~)OA3t%AQ#5ag85!D5)#w5(Ml&(WD1JVa
z4luimtN-8yf7Drim08b=NNzcQf?X`cw_{@`z<WtyhiL+W{eY?bAC5CdpPQRFb$vTk
z`OX1|MH6*1Qs-n|2HVjoZN-=qmxVWceq0wgB48s9U!!Ic+3oU{Hy^`C;KfXMdYx5L
zt9~{8?nBt=)Y8;Ifs&1y(f{@Y{}NAK(Npn~(aQ5-kofc9u!RWA#P@|ft>UeV9=o%)
zxk)_F5<?Q)8Ks~-)&uZ03C2q;x^TV0=eO&sAL{go8_oQ4yr8=I0&I)zB<wndj~KUi
zL`9V?Om5>|p|(8LVm~@@Cd7F+Lyr{Ml^CU@h`dFb3^_K}Og?#u(DJh`-~L?cbBE~%
z1Mmhp-&%RQn1-LNEgx=#b=WI4s~u|n{Rfb5ee~u#^Xpl%8s%9Lb-LGYWu#H0ZcBOa
z%LD@Q(-V3y@%B~yyrEfk!TSaJWeSdshA)t`xx66E`U!r{l<A&*qf-S0iBp{wO`f_9
z5gq{9v+TgB)JGF4nR?09E6P;~t7L{6T@F-KYj~kGe-^udi2XAEjewtbtmU0W4(%Ji
zI@4q|xf0*-YOwn|**7FqH#}<%Xh{X=wKmz1aBmQ|tM?h*pSTe`nywXGw8@+|jvAP9
z@Ac62#Hsy`h_sFKnN>~``EhY9*)EQIC}W2>u-YHgrXCg*#a>hE9=p!^vr>k_RG$7N
z$3ng`aC<d+MWy1QyMDvCk<-oQm-as`H#TM~vC6Ms<_nc<-TKL%Gbj<xq~Y@247#$5
zKGrOVe(H*++r>jR_tNvLIuHQ&_@Y^3Hbm1h1hTi<cpYWNJ%R-%ue16d>3{%rIz)$=
zOk=M(_<P(k%t2@#+R#xdlznw0XVV`MHU-BdDJZY2+l*4X3A44f?DrU%1c!8g3^8eM
zXjv@%lv8D=`JH)EXj>+Z-5X&}E*)K5UR&M)r)7@9WAWKvkt79@Xf}{Vib@SkWzkLP
zqBsM$kmR~6PhFZ?G@9AGn->&+oj|A{<LotV{eDFYb%9C48t<@yjhCoufldw&Fmpby
z9=9wDd^7gzh@)rivUblf=*TgZ{nfJe#;-WtZ))mZXbA9;A!}6sqZcjZxH$e!jJpck
z1_RHZLl2<~4<U{hTFl&%amE!8+Cvl^^j@Tmxf<+Ap4?W2vD)H$^3XTnfkui8<u{ay
z4rN+AX)RtBfRwTk!f2=0+Rg`S7XIDwN|H|U?HATjRT(W_5K|*y=PN0rfKMcqCC9v3
zTLXc95^P4Du8#w!M_urlBqa~xMGnBMN`N_=i!I($S2qgd*yMc|zw}0(-GGRpNaJt+
z7h!Mt)YjXE?S|s+kQ9gD?n!alqQ#55y9IZr#R3Fr@lxESxVuAIC{Wy?xRp}$xAV;G
znf+nUJMV|}AFOq+wXX9zj&qT8ZMa;~SNs)ANk@J3#R_2Ac+YBdA1Wb7o0eoMw6FB%
zCV4VbbLt0H0D<-!yemr2L{0@XMNOPd^mwtCQa48f*KbY;%E_)UR!?wD-r%5xYKB_$
zF=W&mxMG)lco&N%1W`i(5K6i)A-R}$1E0)4pFuZaxcySPKW)#u-*qpBxCiyr-Jfaa
z({QUwwC~1?!8aV*TeAc#AOqnm9s*!(nthC@i33rj=#dHUoO|p7g>RTB7@;kvt=b|4
zJ$wX5;)2E`(qwN*)qEGGk~~0T5$QNq`m6@7VAB4b{Vf<SV24=q2aQrE`6S)sYu=%f
zy#D~cW+E7A<7fs#^btxlaHTQw$pF>t=UzXn9Bq@FzlSU5$GvqanLBF64yUMch4ZrT
z>XZ%!K_aZbJc6D3I+18}5)mJlC93?A&gd_<VibZ0Ont*u9zQ_L(_=t@Ef~-qMIhp%
z`ipW5cuP`ZY}Ou%SwF6hW{zXiMA0&CDN=$-oXIq-Uot>#v|0>Fs`-;QaT8kv8NqdL
z87)ZjsFJS<Sww&M%!m3tj&iFJPMWHD;l$;s2+JCpvh>-3D^H<)M?`2+y12$m=PVs?
zyY^4SWYmD3(=wV@<CzwQanB6tyVp$`)q)|Pw1!TJm+Yb?NimhdzdXwDh#@R=BL?1(
z$_mQCrpkJ79Xh!?hztwO!>jA6U$bQ^(QSN(;4Lr3>7ILUYYJO-hu+-Tb4{<c^VY#V
zd-91Z8OIlyE=74$FjqsnNtKu&Vie$s@DLK@>&77ahj=3U&C<Nqx5iz0XM-RTD5+PF
zJ8cp>xzzrLwYGCvo4+snTQ?6aQUQm6_p0g@*=Oi8>#Eq*D#r(NPwbC%XKz#*6ArCe
zOK3+_-E8ptP@{i?7edl@5TkZc&Y+Ep{{Ug?`EO>VXTmy-x_j#+0zgzhKDEUEV-hdH
z(8Mh!Q|7!&$Mo>njpf1;O94iu#;0!jH{CWw3fvNiy`_x^K3&BmLUTC>AlgP>r(UH6
zX(nhy@DQ%nvT&^Q?xhLnw6_ZAO8A}*CVE3zfRM#afe(`igB`>y)CRtO=v!?kRGeGT
z`OvVMA0U}R;p8>i*m-zm?x&#xL4B);7Th>*oQIaCP0Uv4>4*-+w7llL7OUdin8@1*
zM!YAu7}`zkD0eQ`*L3`n`&6vRB7z{<7RLlwDG^7njY(FL(12bzJ9<@=2h;+MhjNVA
z6mV&{b}Y6!l%jZmkpe&}Py=CafpbDa6lP=RK-FJ;>rbr0?dfL_ovTYY1psaOZQEKm
z@4t}sn2{#SsqM$VsE0jJ0FIyeeti-Q6HK0YZ|j<<s@&v?_6ajUGLcitD3AAGxz26&
zH%DuxQgEr#ZPmd(SPW+%j`Uv4k8>hiO{-(Y)|o=D#*G*seDVQ~^&@i?dp#vZ5&UIT
zhN;wek}d};#a1igV@#&V>+idWU$&Y6a0Wpj7I=?iG!?qMvHu(<<m9lrTHCTxdwXJ(
zy>j}A8#8aMX6>f+EY?ym1E<Fr>xklnFt6w<tuIrZbgq@*d5!Dx(O#;ZBdzjYJ-d?y
zT*s-PqiQBZSjGD`et{Ij4!E6FO#Y6Vu@U=yN^e%XKBI+TAIJL^Sz@@e;TeBdxiW&e
z@tti(#L3W3UR*Sq;YU2v3w=n1+jq52)~De4d*{}*>e8J3HJP_7$c2GE-fHFqsyYny
zH?6Z19yx=U_IRqCTwR{w;+T?fmeCAXr@@&B7NvDV-8_l`qqmrb%DV3=Q+9I2JchI+
zsl~BI*6$7JSq9WT*t_<XDymJVn?)DoR9<kU`woB@ucHSHiVf&RbAn|tVIII!2pZ12
z9S=8Y9pkqOf_`ZlsB|IuW^T+#-A&4;cke&Iiw&u`&9+D-8)q7BnlQ0_jI1<ox5U65
zaI8cWf#+?+sK?r?LzmNZlS*R|&okGpOtsFqw>)UN1USF3)&>)!GLh_mgJ*~7CKEmp
z?oD%7S${nn>K=%ZrBrOU60(ninN}0EY>pqO0po-F8aXkr)^YDXN92+e{yV_b|CY?v
z(<>EL6Y>@blBfy%576Iy``i)d9(bSfz&0)$uO&2wC=zj#w&jtwj^X&?%QZEf==pM)
zJcz)G%_er4Cgy!q09ngaN_cLCnW_iTjDUQ0en9o6kV{6U2l;f&n(j7>8ye3hz4Bc{
z2KF}q=Y_+?vZ{zSxlH!!sIDzsmVFt17B9N8)KNyxzV->_wqdj5ao@$VM^Y`-EO!3+
zo8Ftn8l>z$4U}GW%el0(g=%&<_!8LKNjbod?FnH4TUdC0*Y`8|Fz5-nn7mscG2Dk~
zzGmCvI(esjqEQIA{H|}VynN3c;3hsD7Uc0Yo)d@Tf1>37)Ai*-*vz&hSpAZ%#+9$g
zc@sY&i4A0vbjI7vvHrR~#dk>8puma0CUUg&hv71PtMxW7Goglh`nbCQWNeU=meTT9
zR6k|WYdsWCGtqEK7+X|(6e!i6vgY_zrpeZ$$e&eK>K|p5EGC84|91uWjpjhzg9+_v
z8L3|)#BfI+8$Ufd|8$P}*A(%{?L_?eT=pFPC#SJ(;Q$#(l;%?wfSBoj6ZJyC>tA*+
z&mQplJHwulKF%A*Kh?&ywXcF}kekj}XJ<~9hh`i<MXKTm6z~)5VMYr_l-X=ZGn&$%
z)Tm+C{z`}2TV?L0wL?NqF2aPn1}ap%;r=~@1`er}pU=m#Q04qR2x0E#-V*<S61qsL
zxYz0~<BP82@5V||YFJ_O&XrRxtQA%uCvuxaYQ4Ghn4fV<mDvnYEM=;|`yIyr0Hkzb
z_c#0>x<qPxScg|VOh6w~?d*u#p?{$e`l5?E%bq!2x1S&@{l2qT3#@xx9vN?wbQNv2
zM(5^g0(26(kqVTQ)rKf}tVp+nZIe>G`ZlSDjNE(Cao0ML#YhX4{Ae4iYLw^r@^jYR
zh;(yu^`X$a;+jF6%OCzlIcj*(`l3rQUzg50jNA7^#tgL?M;$>Aqf#1+kIud~nW|8d
z@Ye*Ev2rNBXr!sWts$g}$>Bdh$68NDWyG3)-%a)1^wGm?`z}f_Qr0Zg7yK3NI!d7{
z2Kjmsy?(#qu+7$d?F@35eM9K1))d5P-^QxKr&`|s_x`3%EZ(&hPpokD!Pp%v4Pd<R
zTuBV2{Gp;&qptF!r~FvtSgb>7o~b#h4bbv^qnxe9vb{S>491xfHLqIt97!!Y_fK(P
zX4#wCm1o!hy;+t0FuG<X86UeXKpWHL&w5VW<8JNeepN5HwzKu8i|n-m;NdwBUR<0|
za5Y2Q+Kk4rnct<cQhVpW!m5cF$yR}wsc@1vxGNQhW}QVwD{|Xa{9H=n6)BX7Z*x!F
z&mzMvw@l2Fbw4EgP-}L2(QmKXd`v^(79$Yp_DU4q`;}sMy%F+_B63MyY2ZDcfP%4<
zV5Iad^hSE5y~b&Kdzt$+1wyeYQ%{j=y?v}U@{o%5&7dXOl|jUJ0s?#bTn{N>ljoM;
ztU}UnN~>@Uooj3N)~lU`(wyMoamR)-?D&|6QKIJJB@F}x(k=hpP9bnv41rA>`RX9e
zxPvOyz0PP2S%&lh3pRd$9|@=k+rGyojSaxKhQ|jjl7SJ5aaeaD$uu7bo6`YGj4%!B
zpO@xu{T1atytz1SBCz#qOH}1L#hfG|0zkn;gZ1cjA>7V1yi0i;<rBZJP)le&ywdYZ
zxM2P`Cad?#O_vdgs2|9PP86h`efGx{T^;T)<@+7?t83-|Ea%I@t?U)Ff!0vU@9U1f
z@$?QXoJ=0oV^QpqKBKMb65hv#9_lG;*fC3wQ(Ni)Mjz=9?%2SCg2L+}x>`SIM~<7T
z24_odh1xYA*1n#bEOsvOyZoSo3U*0_8dnz|QN;ApOY=-aB0mMRk@q&Wfyu#M`EPv?
z10B&$mur-M346AZJAkKE)Szib9p;t2Q|s;c=2S#yf)++Q204;0dg%So3`Mrr(hW<P
z-`|M-dEl^`t3eg+LRKOqK+q8>xdW)oQ)s|B`Qd@qi@)=QdQF<cr7V0nZt5N$5FqIA
zG=32d{pyqXmS{Ejs7p);4V~79A&c(Sv}o<`zo&$_YU?@{AmN$y(Olv(QPTbHp=Xv+
zdd&ETskWrlTo5yjFA3fm5^TK)nGpzjT%*r=7+*L(xdg5_Ris1$FN$HFQHc!5NGA<S
zqfz&Ag?hn+DXuMzx1nN~NX>wqGEje24?&Bo(e8?(_)>01_<MIKExj+TS06!8)CT1|
zwKv>oV&$d4Yad%TK7{j|85$QAgdqA)49M;dls0Yk<!pdE9}AmEwq}_B*~c?o4<hvg
z7CPZRIt6UE@BSf7O$ZRi8Hx3N>SrRsr_0ohvaR?0BF4^2U6UVqHrLuB$D+Ws0-bRO
z6+1_;z_SXuk*n4H3}trPuL9>hhSIps-`5l|6ja;Ry6VLW^65i0CE~A6*;RXI&!f{6
zV+d1$5~<(5Q<=$bTI}B5h@Q*^s9hB4J#BY|28X>mx$ALgzk3m3W*LN`_g%FyKpt^9
zN`mpR!<+VLQy?^JG>~oCB)`P4)%G@{q|*Rwo3!DgZi8<9qEgy21=hV7^&5Je8*@oi
z=vv;DGEOjT?J%KA_PJh2K#oQkok0-vh%5TV4leg{k4#&hJ1TVyEVk{L92)q5x4)OI
zDlwe?@tUIF-~dMcyoh0Qg-v_CYL~%kzwfBcr}e|ZeX{cKWzv+mpOOC@hD#MV(+MLe
z0(qZ>ravOnh0QLh|K7W`vALngU#-tkR__hbJ02+Th6elH=0S=QQgCw;yx=n_bqM~O
z(!kp#fRXt4As?oMinsG~57uAe@bP>StQ^G|dCBM*DQy-J7VSAc0y$J*YIl4JGY`n4
zoH#@B%>AM9`)SB(E(H{RGe?NLf-ot2j<NueE?#Cy#fKM;Ew{qU{WJ>3E>l3FXfpj3
zu=nFDw09kFAQ`t72^klru2(dAVr1YIHSKlkul+oi%NN!(zv>uwz8vhYhUy>&A+)|H
z+?jeB=2M4rpVV6Mk3Y3F1*v|XmEEkTc`1zaOdSD*qef!2Y$GGOKRx%xQdXpY*BkG<
z2^h3CKxpB)^n~8jp302ze}s6OzGp%hIWQQu&@+w`X|F;6z?Ke|i;i)}{kLd$^|;z{
z*#FuY@5L0+f4$VHaQ^$MTuy<{6eaoddXR?<d4APk9xf2cU^47@p(wfew2ssNXZ$+P
zPrdD%@pqniG9UW@J*i<VC`%Mv+N~(j``C1QP?B1`hB{NlRjF7zco+$IEViL1W<(G}
zOHGYNy%DPHWN#8j(Fv(YNR;kL$NR^T*pwMiiNi`^#E;tEMV-l}Es?@6aL(e%O-y+r
z;I3`NIM5>S55uX~&qLpD(->m6FK`gT;Rp_bhi|Q{?YnM3se7;NKC+e3pcsGKSgm?x
z`j-~OXpx(n2Ul#}3<Et3@DQ=(ZO0N`j-5(34Ex9zHfbY@*y#BgE0n+X8sQJ1i?!83
zmS2r;xj)KTlX%F|^c*JgLrpf*M!ev=UzJZkcc;7JSuk<#3&rj}Z7W03D}EbGF1qu_
zZU%gQO`BMe2Os{tD=usFZf9>q>9;y$*zzdzg_w70&FLQDhZm5mqQu~08IuQi?w^tP
z81YM)*HE=8){0M;@P&XbOS^?rAloqI5aP~hj9vlC7zr3T|3_@JQ$6-$=Vj>E28?*w
zl3P!DG%K{RRUmEDNWh4o>Fsb9SemUFE|G3XHTLQa?y9<tNou6nW`Ry_h;vd~K#@bV
zgB#iI$+gAP&V<IAYxs^I1PP!;U!)XTxYd7dwxpYV?yZ}e`B|$n+SYX}v$W{VBX-8%
z(JDnOm)}0>m0eNhJa>43Zy!rD29n%v-5BYrph*ToN6RPe=^V3b8e#_wc7>k4it&Ds
zL~lz=4E_=^%IMYRl|4x<_V;qEr4H;BFn08#9!N)ohS`vLIJT3o=C00rGk4$mC(_91
zIc(SWC1>kTJF&|=d5N)K{7ZSa@+6x8oaH4Z&&(6-DY<POw+qD(n?+)ZIUZQyva}XU
zxPES4i7`5RBLCXBX#2&YdW`FtPeQD@pf#64kqaK$T6-LA3z4#vqE!EpyLa2WAMOTa
z2#%IiPVFO&$<BRuO(nO0F+lb~F8E;5{dnIL#L<6En;-Xe)DGN;7~xn_Os}=k64$v+
zmNwcmh&}4trJDB1k!j?4LrCjn9W^yL+I1oJv`3p?7V&d4P**j}S{cmsx1qDvh?;8(
z$I74msBcHo9#X;oIl$Z@+u%YjePT~bOTMww!*dLL_3Oy%Dt>sl%=<sU)UtV(dWb}r
znfb1jKZ0Y3gyHkr)JA)%32+iEvRXmD7Vmb1BCN|C^Fu1@RO$%u^NdEe?*R*|5(6KN
zA9_UMO&{-1X8UZPDJ#g=ZOTNFx`I@=+)B!dXZ~l}_&R6N=K>zRvzON0@1#}Y<evF+
zWW24p6KSn$aarGc!RD9vWsbkk=1Chl`aFNS>D529*J>8O_YMNSs3nmkL4f*fQEEi{
zk$@R-0ED^gd4GC4wKSeJ9%7iOOsUEhIh0F2NMGhSZWh%e_0sEwKr0x#6|Mxz6Buf{
zYt&36jTnda&%Dy3YIn3J%5P0_GZ2)mZ6y0lx|=zQ?#MmPn#|~x!l|Ns&D&v;Wp2#+
z6mS!^_!@70Tf9Y+AtZnMjpm+#y~irFkHuq?zLJ=|?zMFGf^Wm9$2cg`#$kYj*5?;o
z1tI^~hRqa*?i)}Wrl{o;Kv(G8>41T=+ZR7|1Xy9bQ^t5KuHSKljL6mL%>5z$rce<e
z(x@?H$J&uvEW<(jc-RR!Oe6|vs1Zq@4Wj+#n|5Aza2t49bp!S8ZqKK1sEIDWOeGLJ
zTVZYh$7*VBp0Z#BT9-*|5b~bS4g0|~?18PuVj4|sKbmg~1y7ejK`q3!fw^;g-+QJ$
zq3uPB#BU}IDdJ@?2<ot5udzeW(KN6rt2P}hP2G<Cqg`0zmZ7>rboiVY(mzs9l%@BZ
z|2mrX8wQ3A8q8?r&8i=V9pB$bn#~UQ{?w}BwT(ZM7b{Gaj~c-SU|Kijd-l<nHdB5j
zdurgP9wSe=GS;~Mn$a)lAvhsEAqe-7ZzV5jN^-g!hM7{n!P3LBBN%G1RltS-O(F%`
zE?`a{-?TO?FPzgd+v3d(n<EY+K5Ck@Z}}gtzId^hSZCY%=jQP6N-WnTJLAU47<I4K
z@#ftM^Vw$3Djg`F)zs`pH)%&*3kyOp8WX+xBvTmtAe27M{(MxxT31&C$w>F+l~t;Y
z50|!4WC{w<y>H{#tl#)f&v`Kr)e=Q;*ZT5A7v9|QSdf*htcE5!LwhbwJxv6R)GCWP
z&D&C2pdw>tvmv}MgB=0gq$g5y#LDjZ``mLyrr&z=Y`*U_Y+GhB$u$VOdd-~F*ky?^
zLoo*67)6Mq$DToeecsv3W@-*e-XrVUbT(}+pIkd-xmzt{VZ#y7ekkRtv13bzZd1oQ
z&2L5W7s3Jr<Kt_L7=PH=YvtVXoW(ZOnp-{#O*fP>bm^tUCN>-|3ISk{J4FJ;QWf5j
z`#Ww_oO@8{(Y%5pWl))xxxa(2V1mQhn(OA<kSa;7(ou+PfiNa$93$5nBiY2k^hjo@
zQPGRFl15Y_TTBx*0cooeAR>BSXL6nU^6T{|t)?O<&KB|JjdwctWN)cy0EmWxRUHj-
zu6;xgGYrX>_>yJ4dk4e56J{a%kiotOK}xm+S~d9oSaLdb|L4|v9zy7-@Vp&*uKr1Y
z&o%Fcq8XnFzKLrjEFs90i?NiLONk%uIReFut0@wuj>lx`Y5>_KkdZgP=2MVc%QWFT
z@j+U7or#FH1-;L|H^|iIOl8|Y@B?hg-i4zH&d3e=*~Szf*<C*`ux2aF%oBh9`@*vy
zS*m2!m5Nl2@B9`v{~v%{CA+-t7^%@8+v@0loe{szQL`0tCFUoLHIdeKkXIEEDQ(9R
zNd(ow>=J*Akgse@SE#v4*Th157M&zPWla4L2<3_-zvs)&5mOsrcpPXqy&SI(wUeI-
zwxGIG<c@$F9h1J{DI^E^Q75^h5w`n@B)7*ZTM$o1A^iJC>|$F3!OvkA;?VgcW$weu
zhbQx$BcnN9mbR;urAe}?T$+Tk%_>f-i6NK4;P!DaN$6kP`j&IdpK&Nv0XQaq9&a1h
zt3&c!YYqsSK^NG0_~EZGEUMbREuSU{_z(j-5t!-%V0{<&A^dq2s@2-sI%n0?faQSU
za?&1C;IEs)oXJ{zG78v{q5-j1nFUlpx|_`N3q-8h!9RmsH$r<%XWWhm)ekS-0asg=
zG<P0UA)P(?ny~(G%l`l&uwmKS-VwbGr+<!x#qr1{`Jpoc81Dvl*^Cz)+uSoH;PB6b
zL3O6Y9EE0gV`C#5a;+i>4Jwfpit?AbH2k3n?4^@)S;zUYc&fm`($;DtknyyM<691J
zh3)_dXo)iP<CA88mdzA8@*h7F{|?|SZ<}kDX!y%Jpz3i>-XNsVDYs6M!PKV5m@lxw
zk4?3;A)V!MsVq7L;dJB&CL+i^-k=9ypl1+Zv;WhpGaK}{&DSv=X%(buhj_%p@LdOK
z0n!(nPqPY2pn-VIY?y0F=Fur#snyVz#C9o4bU~U%_3uSq3@To(Eh~%n*SNC@@H*SO
zXVb?P>yyy=T|-sEN_9D!BqSPbL3+6C#GNe!c-$2Q=lxaEeAxIJ%}~}rUNZXafknlq
zLiVpW7?i#ep(YE;pO?+SNuL)uDMmT7vaL{>3NxXee*hoJ`kD>mLv!b3^`0n?tA4?@
zk)3d?%`#7G1jUYHA86!dRc7UahN92i-QiNm&@d;TX#vu7WADj@udqy+vU=FE{c2Oe
zeD%c&4w2B5A4w6@2Ow0^yZrP|&hLF|p3H?rd^bN`b3Q!+&op`rIfLu();+6>ul;%5
ztjH6Btj-Gm8n-;1l>Gd{d2P*oZK-@$j{_+|pt(hyzA1^SZ2XM1%At^U7C!wm>L+QW
z1}j`35hbavoB0pmGhN6_?zZCG@uxLR=8EeV%d+u?QKnE`rhPJR_dqlU?oK$-riXxl
zR~rG@-RXH#KM$L{{W4Sb`j6hg;yqwwn>IQ@YNRSD0&l*JqNFT?wn1&LGwN?N0YQl}
z4wxMS#s0y)hIQs+x>wz$hTxkS4v{<-9WZ@Q-hs#V)xe1UyS9X8c31b~`}5<V7s(<y
zs%muq0o0n#>tI0MbE`JzSR3kCQEPJHm;v+#*%9;b(C3{i`ZmD0k26P}D}`X(yb8C%
zUa9rID{DjFa*S%^iBKNyi=tMlALQ;Ljm_gY7~1Ok;{^U({VfRLG`N6QWE-UyztoS;
z)?eG2m6;1GAlBuBvnupvrDb3t=<a<!kurZ!M#zKnvzh70!O8L-yvNk<p(nHjaxPkZ
zVl!80BcwvfX6$DYEn$k2W{J&6D?ncc*>2*=A`|QBnoBV*iae)bsh0a3J`_un7jM1)
zOs1{@Sc<m{wCaopZnitDp2V+ACFv@;i)P(1e$X8x$gR6Y)f3g4ZQq`fj+0w$C4^*J
zY?fmPlg_*cC}1hBF?d`2F0T99eDg%VvW}b`n!%l1Qq{k|s~c44CASlID9PrwZ7}}E
z*m^avNssq66ElMMDi#Ul{Ry{+D{VQ{)Yg`K&cYjF2Kk|G0AsFDoxRH=B}s(Z-#K^o
zWEUqbx_5S(Lho{3S;B8tzYw567?<SCY7N?#u;^~JPv9&WA6yy!3Eu`juTEbgId(64
z)K9aDb5_VkUD+c!J4oH+GVt)=>&@KF!<p))yNG|9y8nqy{(^xj@8m0Xgs=3rt_Euk
z6rCwd(()gCc7JEsPbI-=^6ygaTdt4u7v@zQGhW~F`rw0^Gm9}y@X66vfw6b?`UWXu
z3=T>;d)Uq|u#|wd5D;s>rhw>9fctihw!=#_hGn-mrP?}stg)0DdgDOvLC;0XCKh|w
zxrb--%*=<utdVS(pQ}W04$8)19-u(nY`$W$GWM-&)<o)C^+dn6g&dPdk#(#cWOH-#
zu_*39T>O)enqb<5pyX9>1(`z0-xo9>TCUt(Ku`*n-nNF=(eseh7_+bk6M`&(L<a?!
zl857fQ#x^X(%${}k{|HGsERe$k0TlUD=P=~d7u>=;wbmaAeJTZaVcS4`nYv%?*1BM
zd;6!}+J68VnNOQw>Vjo4ss!OC8!OSaNMR6MYUQ;6e#TbZIDX#Jh=-HzxAu*4Bzesw
zV|2^(66-1^e*O8KY|0Cu?Ry)GY!?n*z5S6hmQD+Y{>^W;9*LI`O#-9Akua3JMzkiE
z`fnYEJ|6m6mYLGTSn&tiM(e~X0jQgjOKSi0jM_D;!@YTHSy|?B13t2D_f6%pi`+Ak
zM{6uGegeq?@m4E-5thI^gSra<LWFrdIZ(D(Rhg!>7+%jNY0AE^bPKmWI=SDWnh<cY
z-gC$gZjm3857V>4U-n?W`O%43hfQ?Rl#uE7uYF%muX~Y;uo^-0;%6u#u7zD${zdd{
z<VKp)I#ZR*ou5C2vW}|=si-3-?;qA~dQ<kB+n%ReTCVd!qy%Hf+`^|curIM-zDG2K
zVCuwf<h-|IsS<nmYpR8$+q@!J?hcyla`}n4O?dcb$PU+A6v$Kx+L&n75M?J<l$?dw
z(v1k`0_0JZJ%Gq*Ri*~hZRL_rx95M8*<{2_W6)!nS_Ld)`x(*e>4`B8^kNIhPOtCG
zRecuC1`{eMCuV!551Z?DpY><&aV)YW<SOJk*0#g=mhnISg17`%PqJ<MiD@DXydS1>
z^?p*TW$v@WgDT7n4Q0IWrRR~@;FKu}BEd+5<m#3mqXYsIc8+q^ZFgHGb_Qb{{ond&
zO!k5@8M)#+|KgHS8yAy?RCEn`FdOE&@YKAFG3|1B-QY{!Tu(I1<vvmK6w0u;&IP+&
zj_f=<dbK_Kyzm#7snOpeNsB#zLLkdvXMa@|T)BGyWM>cxS2K>wf2))~A+M(+tcI5^
zZKDXZTM8!<j%<UBKMZ}C^m8mbazMz^E0Y>DX%;6Q|JdR9vWv!H^0X6?;+b7UJIUMS
z=SQ77Ngxiv|LZ}ea81!j7{x<$aviBK`)c=<V+BXI&mWEZxe88L8C%o2U+9zjok4Ty
z2*P}zW!-$jV9?DrRkvPTSP;m#<2Wg{noERJ{yh65j(K)q((P9&Yn&=hg3Udmsw=l@
z_k{)s&2e^>>u}OlYV}Hv`yFGVg_8OlO{9Vby%i^D(>`2r5~jM))o6*aaN;pLg)!bV
zoK&K9-IiAl(cOX2U5eVzoHe(ucAY*sx9lhNZX4eRdYkY4J3M3{vR8iOsg9h&Omh^2
zgN%38)n75Vm-sY~Jc_ml6%fuCZaeIBIBb*Fu9cGt5Caa5)6PvZj1#|}@(#26{lDQ>
z`;+kS!plzyqG5#p0V;orY$rA3NV~XH`Vkkpkp8(TbULBq^QO?3VTgM~GN^5ZHSA2x
zE!>_;yPNc^b$b3gkcsi8@NV;|*D^Hfy8kI2Xmlv%drP7Wqq-|ebBw1e+cyh<j@He!
z&Hw*&aTqe*Pu$2*g0Qh9{-<cSus_0Pu>rt3q1hgwf0Sx-fm*_7tLE?Pad5J$bJ;L$
z0d#z*_#5%{Y)nyTLAg{<Sdm~gzlf3~%MMHJ(s)RbnSZ214KLHU^9P<bgTE;-6Z+4;
zom!&`yDpGJYwYT+7tP4nrZtsy*aE_-z}avj{PpShufd4L-sV88ii>O2j*F!Hh%#li
zY#vtQ1n!Tq`?0p*4~+ktJt0lzjm<yYR@FKNJ{N8~=e;&;guM)KowxG;Y*}QG9-imb
znU>#vs4k{)ac8tqaO06*@$?@+V&ne$3aWwsk9yEghC0{~+~IaG=f>;iN?gErwd=z-
zecL|i6_u*GV@yaql+W8^%HOwW#<WnKL3VZ}^=3&CHP^>li}f4@KKj@8YF#K4p^TR<
z>x`(?hB%v9)-KY7--OvFtbOWO;-pPGiSh7*t*6aP%%`)A?VvSbjh`D+y^WC+*4#T7
z#Ys~EbRNQI66%M1lb8PiqHdGAjn7eAPuP#_^ZY(xA70wd6!IWeD-cuxG+rd|38A1|
zG-TkZGx`8MPy@%2%u(-F!I?F0w?*%rh|;J8zRg)bdp_jxIJ8zx+*?Ht)y7n8`M+`%
z6OCb<ch4_d-ZC<@FdaON)-Jd6e{`w(Vti(j+*}7lUUb`<sf(<p)&MKsE9cXco30p5
zwOk8h<LAbV9XV57ZsrQWZ9OIwpx9;I#+NGc%PpjUA?I8I7%Z&D@($&o`nO2;s-}2p
z<rQgn3NfCOek%S)E3QguJ9})#AbZhsbNoLACv|wq0^$WciL+EtNe}GVE@q&OP>iw)
zA`Gc=>;A$tA%sYR-94X;sUw#h`Q@f2&u%^o$;I`ms6N}G3<NqgSUl~OP+W}f^kT$Q
zhbbQ$YEJ=k|4`<Bz_4CZW^U2)5MFH7eE!~@SkHe&A4OTm%$=fiJX?|AA(#4qKtVd8
z8B;&zKqQLZvy(0AN=JaI2^<ElACx`Bc(OB!flcIzq{wE-5GxDzkShCM!oxxLs~j2y
z4^K0m%8NT4!rwD;%jQr(ZYT&l+3D8QfwXunMTwb>g}zO6O9IGkU+R_X^B5ASuO^k1
zHeZ#8?muZxg2Oz|7Afkoy+ImUi8k86_PK|7J}(hrz9!MiGwsEud>>ouFC*&4VVbHz
zy8v^qlA7?YsEg$|d!}9$9UWSuE4K4}x?G(y_zzGKy5FJk(elfsZ<)8!uLxA?PmA+O
z5=MOZbCLg|l3gP%5XutH_8;H}ziHS|hQ?{PZc<yCQ<0z@7xvjt^t*zYoSYc&V%Yc1
z;koDrF~oyR#Q`<-0<YMZ+Z(Nher1YU#o(RX3v9@vprNb1SRtG1JZt9LyRMdHVwqFV
zewM>gN!Z%R+MdNPyuu7)iJx90ui%PqpBWNU_bKJKt$7mKg_b~L(3O>y+}r!JGTR_p
z^5TM<u;XX6@BhRHK9Ty@j=Sw_0JCtlU-RK*q9-1(1_s(UOb7L)T17`cZI`d20WxPo
zG$a|^zIJi<Mf!^xoR_)`RO=d`hmQgd00+J1hi1#yHAD38zA${Y^pN|lv3EYYvw8qa
zO)RvZ=&ciMZIwg3ao=Jo2aWAW1<?NaE2A>dtJ6Ie_nH9FkeIlcPw}LmA9Rm5+m=Jx
zRj8aE++)<DJ~xWt2PPV&YpY&avOt7nUBmi&eU%0xVG6`78??87s!>>0ji{?ul31ZP
z3E?bvV#|dxtGZAiOuBt0;8?SzP9)Q-s|zCcFi;%snXd>2U}=OZiUz#ou3&!uu?fb+
zlWz<Faw4^^c7R9_a!Y&Ekwp=U@;2Aq;a6^MF3Mm*1nRtwLblMhG<L%iYKJ^esrHuO
z+jshI*bM6+updMC@vyYA`KjkL*dcT9HXhI8{Xyd9L2v!xVM6`D5i?P=n}p3Z1toI2
z54>|Ee5`9o;4#W2j;PLpN}#AB!lA?)c1Ew!h00v!W*f0vgk<mN->cdt<`<gaq&6Q3
z*#lo(S~zqRM8CT45LkS#VDqgVF1#OB%h70Epdh{Rsf_Y_@Ef5#t3@~9U4Tz0HU^^4
z7kHw$PsE+7pc~!3d?EmhiulDT8S={h*i`1~6ii{2XTDBD=3ihaR3NP1V-f1w;~YIB
z!66ZHZ(ZhuK+XJ=qFp7LEFK#qPe!Gnn8|f6em9We2-|FjHNAIeZS}}jS|3`kyZ1<f
zcEi4OZh{6VBd4Uz<?+UH!>8^}sd(><4X*Du8rTDi445}Rmt_LVBfq|jNM5D)pY{^>
z3bSjA-Li?p6Cecf?kcyvpe6Fm^^C}c2kIMcq4W0kueoOPC_jC{B`U5!X|u-wa47-j
z_)8r*p3GelM;?{kHUMStrkmN9Trwihxn!f<{amY-hCSUv(EFUAvS$UK>G|$IKakEs
zl5&$+ERL^RS!e<O+Y~MJfDTI#Vm;`@8F4EY`+sueP_`1gK)0dajF?dfJY`0xf)YR<
za=!|u>wuQ~Nbu=BXK?zE*QFhhjABrdZ>>%0`+z7v%m8%2j!Z2STUW{(x~D9%s*k)^
zy*~>a9V7%UIWBs=iz&+(I1{wr17UC+M5?O`MhYlWE-GDC=nKVIGfaI|3m4p^Le_!9
zw7pEsBW!Zi=89X}9JKW&!}roFBqN7*&?A+QP>^(Y^jCSq9UnpT&OPj(Ex5>#Zu!fo
z<?QS<xBJf$imvhE`Y+yF4f?O+(w3r0itkGW8~_iXiwHGf*6Atj0Gc64e^)C*GH+=d
z+6Nq+{Iy@xFXFp1k!t1WdLh}iBzr`u>LY2-jt!S0)DbAIFK)SfQ<<ngxzY<<>vR)D
z?ObA)pPSqiQQK0uQ#u-l9q-zgoSu+>-icN(FUH(6NXxaC=qSfPL&%V-J?PF3a-{Q+
zehf&SSZcb0X_N-c5Xqn?2r1G`d$W0QDPXTjs33~}5TLnrt1C&#Y3X;hC2bFV?=eXq
z(mVr@w+Emn#{4+|_HEazPrmgsceJnk{<aChkw*qSv+=$&s9-YiCcJGsMj<UbHToHk
zV<rK@cg6-WD_xQP{eXunETv;Ok!|Bu>Kh%_A-IyoM|@@*55Ur`f1g>_V<jh3^jqP|
zZwgyEkX3R(?2I}t8g4(!7wioK`CO5}tlF-P#XEYjA67anik$9(5v0gW8zh%3M#M@l
z#tu9OedxZbkC(B>-#Y0NGi2US!yToFkQ=`N9eEjS*!pJ6T}@#0ee+@5YXcnQMxhqX
z?F+eS-S+}Y#5t{^Q`>bCN&g&<KY4i0Nt6U-Zx!K+?YEpcO>WZ}Yo_X{85x$Wh1<4y
zR2M-<h`h#VobZpA&aD$O`B~^Ty6k)~&LHR*&%w`>W@wTvC-NVkWd|7}H^uys^(RCc
z&0?y?<tMt|hdwGVkGK)LbxRP}b=#eWAk_sEErT4N0_f*1WnC9fN-ZSI3zdOQa`z&6
z=(K`|SGOS00p-E1{&!p>ZS$EnizJ>L8b>!AU9;U-N#ZW-TIV0n%p;Bda5N6RxEe|z
zEfK1|_$Yjr@E4<fNdVyqmA1Kj!)wpIBH*4uD*}ujPH7B(b-<&7fDAjnYRyT$xt0{i
zgGK7i*F4*JRjv;D@VI@O07C`OW&k@-%ce~t=|k+oQ6au$PZ#FQ1xf)W-ISi!$thHm
z9GJ2ox&#v38A7oI#xZTjEY}5darbO=r2>J8QqSCS+FdVr()1eLX6Bz!aCwiW&Dcr=
zl}aYyOroA5A`%9H0!^i=;!$3Uy5ssXHU78_<Fb3#vZ)(qKUG2@oUhIAaafXr#`;JH
zGRJq*e)_riK*zE#ErTjBB1p&@Iex`IWVK=JrgnxyQDheL*QQ?86@&OGdAe#NrY5*S
z_-N|PEY9X`?t`M_4<2d%F>iB0y#S@<938HqE5O|a4Q(o_{5sUX*1qbwxAe{X?$9v5
zo0SH$e<kA|X4;5pT87gz@HfySuHCpd(8K%C7NvJZ#J(#!@$u@((t+P=8@Mh50G?4z
z*GGU+V9)HzO479gl0y-$lr6gwpNgoK9RoqFaJ7jg>Dtp)-?HwuwCvgC)e@w|rbv&Z
z3O*XJ)CwY~LDer4R~!%jg$)K$p37e<cZQiS!b<W+e|@p(%Csn@>jS%~lxv3Ck9f#R
zUC)6q;`7&UPuyvzViGnpqsVwv3Ueg+r}|LeV#oQMcFn3vnG4Q_nF_yHbn}*qHH$(=
z@7nJo#*k3tyPucJG~YUhvuark9_MAXz61Z}?nuOEAg7?{$8WUf!}K1dAM(gE)~auw
zYrbdFP_~K9sq`~tVbkkjSc7xVz$CiLgG6%3i|c9s5lpI&{d`4f$V8!n9%E24=&X+&
zrI?1Ua+kK4w=(I_{(9QgJ7dJ8oQTz3OFRV&?ghHec;NP5Mj!HhYKj3lN;|A#aS>m-
zq61QJXFL;q*(x*Yih8F(EhqP=$RloKX|1P#(P4c$TVR0Ad|iQ-gXcAZI#TC2(RK{<
zqx}-AnWvF=`f0jr-5L$Yg@Yz1vtJhqq3LBx#OvE|7A~s%S{&DGs)I(tF^-Rf5wm-F
z<p)&Q*kt3_gq-b71gtwsj9{Vdx^CXy^zCFXm#m(<&LoOLI()-E9pAr<yuU0;o*rG&
z75@@{Q3eGcWUlqGAl8y0wL6>FXO}LWS?lH}i##*R$3nDx0V~Mh>wM2}2(CTiN-T@=
z2lL>GVV0o;+%wrs1B&;GK>y1lT@otGi@*~tZ}f@#s$CI&xg(X}hcpkI$h9eM&zjTb
z{-ylZrMIr@V8{QYMyc)L^`IB&$GT5(IlRd!ZI7x%`VO1%)7@Q-vBoxemWnjBW?Eh2
zKp{Z_SwPyDOb}2j9MetJU9_fd8uVbBvybtyIvgP+r-^163B`C3sxvn2H7?p{64Iz$
zUmrd8>|#6E5nh-rRJJRbdjq%-QuitP2%C54rG9b`6P~KVpEO4O!7biY*jlx;1?VYf
zH2zx2jRpjNsRP2$&<oNLIFjUbTf8qMWJvlc5UL^`wL~MUMh1V28jfDnt*tEXoi?FQ
zy_*~L2|(>cH6i)a>$wSP!HnpU0HC@$w$w*3eU3sbhv;zgCu^Jlfu23iNV>5}gu?G}
zo$*nONCnsa8kU9I=N6#~g$2h`SMmP<yrItrmOKj!Ka`fk5u$d?8f-AJ7QBe2oxR<N
zM;_n$87$`ZSekNHL_p{dM;<(@Rm#^dhDf7#D^_t<kkg5$qRtxa*g9=+PSw}$G+Zpn
z*%Y)@4W6Bz_}6aexwhHgR=c$E?GzrQk$=L0Q`U+6lO1C3DkLHpQ&hs$afj_W*|}RD
z4xgmLHhuX>jeX|+{8{4r5AbD7=cQWLSLgzVF#*n9Iy#7J1#W{4S=;b?o$r7IXNtMM
zFz!m5=nHr&hbecb@6R9xdml3rfV_QoE3G?RQ}1Cd-x<8f!bo}iEppTgeewkW2tvui
z20DD!ntU6YkU|le9yqj{XRe#Mb=dnOb5YmX>{w}ufuS<V$oAp@Tg<8R8WfTiDU0!J
z7<q8&No5GObV+WV<WshFBT_brh)DFt!>xh5NjqYkwr!nEq5F|C>xqaa@a%*fWcJY{
zJ81#=cx{u6(WmYPyW-`vpRHe<5Tn5W+{wXI&SG4%ynD%r9$A_nMqa{75!Ala*aRz!
zKqSvEY@p!8ifh;)xoDYaZ|%AJBCPvoYnpq;8xq6M3gaJPbU6+z9$YKQT5yF;B`N2e
zZgF9!(HU~lsnk(sg~{gEZfGhW`dK_>1pg4uDA(2w$l>?9(uinJot&^nd5OKCTbcnt
zwIPxS*pQ%PY$xSHVf7v9(#UuDeRnQpfP{&4(q_hqY(IK<x6@&j!dl1UpW}a@hm|EH
zSVR5;XcRmG=FaN=l#n07EXuM&{m02-Bp*xQ(zj~|DX*^NRm;uRt5<w*f2Cu7&E9Rn
zMSIV6J96QF95p)G_Pq^@J14<8a#md?piyjOo|S>O$-=V}GvKg)_q|qEoTp@`4aqP)
zm1+P^@}8^#Y(or7CV`zrO0FtCJuj#C5xpgmTuVp)dJc7Z@lMCpmGri?#+XU8^Nps_
z1{F9031}RFKn(b=wG9I9pIx%8q`_hrzjPQrC%7a>>&;l$GRd*Rydn^aQvd4c3=n_W
zn^V}AP4;n_eVz`{RQcYc7i1~O_VsOdrw#Sk_71Ay(L}Pgak>+!?smnJ2|@4R0mo-T
zo+@1L_y1RRVjri~p&VEhW-3#gFhzgG3Dj^W@EvDvA*_OO;j-6jzprAaeW9_o$@^+&
z=7X{Wd#loTOvd^)GdhW9Gjw&3k!}c~>=GX8WRrQtJ8Q;VX*l55zt8nCuU>M5dm@qh
z1qfD#M-j3d()?4^-j;4F<#ZKSZtr!k+k1dkj)Z;$YD*so#a7hXSc^YDyalSsYpclY
zEtc*<=5%^emY&k1$Xk~|8fQg(Lzy%<7$hEe^}mBi;k^nC@D1f=elVxxDa|O^#6Pad
zvG>+#N5ycwijnvF(9g3>?gZDyt4jSlLI#vOj1?BWo(fx6`M7|C5^rBY^}i%D>@iqt
z3;kd1^}$pu7>~OyGt>=k3G*t!%af|8cgo)PtU1q5+|QpLW!83$+nVz4xwAH1ED5Ad
z%2Q9d(9DcMV|pI2V!l}+0X68EW%C@kqtkibUXDjx(Nk#yPgBUXAF`B&azYR6cvM()
zb_e|<Y)bhka77ccd8V7%(2!0hr)%{E?RK#iz&}sPyBPS(q^pLJ3!E5GP40nFy442g
zhWV${MgqJmjkt2=dH?>|m=80~R?vypR7F{G(L;J`2o~GhBA85dCX}D&xrSS8)qX$g
zPka$zZ8@bqh-yl-%Mn?3odwm^vcb1>PR418A};RqJ=&KADgjKdDS(W4u`<lthZb3U
zy1T8Y*Yb=GV}8IY4myzioR%L<V{X?<Q)8VS5ZwH<YbnskI8aI*z*Tf`j397Qf-rym
znx?0ryP`&DZELge^h!n=dBNSGB#IfoGD%j-MDOVXcH^NYqivl6V0-My1VNnj{jetf
za`2sz&VQQ15?SLzw;>jATa{H7?#yqak)7P>j;FznKSaniD*Ck~DwP8mrBQHp^7s4z
zj87tbWtjJEt?ijseZnknTOcOnR~dLJQCE+JA9w<)Izq4Sd_sFK=to4k4UHZg9u5!Y
zT$_bOKhGLS_wg<IG#O`f%_Bc0vYA4`neqlL(%K5j7&Z;r_Kwyq@l0JjZ$a<oY|^ga
z2toXT$3a@_RAX(kklK-Jtw`Ut7UwUWA6MI4dZ0SVS#l7h-jOk7O3N)d?z$4YrAwr?
zowP+T-AcT*(*XjyOI9Y4pyRymMqjY7c=(S9cH7?WedqY^ZdFxE`%I#k>h@javfP1~
z*o%qIEOF(CG4y1+KJ@qqQHj`~aP|yJVS4uDeOW(y!nEAH4L+F;A-o#pFh5sMFLIUH
zYGM~_^0y=a7c}$(nGHScf)1$6+S?Y#8lErkZP)WDmCH-iHuh(02+Y_|-a~F;m+bAp
z>PlcYyjh*|T8g1;*Jp$H&p2<eW%1`ipGYRI$C+7@d>sJl^t4---3_|VP8*sS)SaI<
zjp^*_qwslJIiGCjk=y(jD;R;GK#XFCXX13(TN9XhYKabFzBNzz{EM7ZZ334C&CkYl
z7okKnpHkXcJuNABN-)PIiPvK_VXOHH76@-YS|w3?W3uP!Ubl6NI_Xk}jZ|DYo-!6S
zq?eabg}&;c>rZx;9Qm`7Dxo1qjn*J-O$+{MMlg!4td)K7HS?#L>5`f8TcB|)CvBg=
z`;WC0ry#)DN@{B4XKZ%1jm$dmz)zE`gqp!o9-{Z#cACZZ#E2zER9Oa|?8GGI{GiF|
zFD|Oj<7Cl-vFda^7S+X>=!}>XJDzUTNp`tTY<2EO{{a?yE7Hxm8jVa{PJ1(ZzfAtB
zt$v;`BE{_DOXfig2(#f|Rpb`*Uge>rltQ@8lxw>k+U~~M%&|&FhDD@r+F<eGlq=!b
z4~&~v(l^#C!#ut<9xYd0JlHuRMSHR<k&n|uPI!vpp6%vfYmD;~hh+&hxmnz;>p0!8
zlujs3<BD+=CsHnxv2t@>#Ys%>yz2DXckZlZMDv!u64eVjdEa2R`<k`WbQ1fMcVkUC
z&g%hyYZe~1j0`^feZ)AIQUwZ=HyC%u$eit)LZaFJ2hgPmN~gZ-NQaJ(ctG4c^%>&|
zwCbt6jND>*T83SBt#2c8Xaa`<GcDd<U@27v;LGxnk3~wPx>Y^H{e-9hukI`&)o=04
zCE9nY&~iTYPKNgS-Q2x|C>*)+tB<eL>HdD@+<qofl_Wn-eUg?+GrmvBdp!cIe+x9>
zhI{6(DOFAf2u6RdeZAfoyf8_%xH|M$C^$)F5lHKSrq$Q$7{ppMO1NF@@^RhGVnV*1
zQ4F~Wjug%%?nHIEI)nZ8@SR?ms@vXS;F68NO=?Fak|$R3(uHmqA6I$RI7e_tRNpSn
zuIq)x{s#!1A<a=<UvF7-^*(8xy$>Wg`k~TtDnhmGk1t3~FE0=;OAGx40Rv<bN>^=M
zry5L}CR!JTDvcS_c;u2D_1`%BCG0ze<=QiF0^OSW-FMYIS$B$-D+bwC1+E?kCexad
zJ7m-OVAYVrcdheDO&9}rrU}w`n2w|roX};E$4rUrs=7fKxz+CKUscmO_sQODq3jq5
zRHbeI@xsR3&(d2l4^=@&Yhz=N-3sD@Pm^G`RU(Q`fE;VY=3aOe^|wJ+mst~&0N+jE
zP(t|TrvLy^+W{ChC|SSkvE-9k$m9FfaktIUhD=2I-B)oEgg%pvC8DtYl`A8>nu`=F
zBySNz1_YT#Hb(5mV0#GwUIZfKc5<($hmA|to*zPujq3`vRCMW{8KeT8@9yJ=d3DdV
zucx)~c`P{IYWDIZY9%Z1r-g%lMD$bA3K@@<OMf=Av9YE<=lRx2H~uR>0tUsyL+EE`
zdSt&*Kc}_YGT}$OrglMqicCjyHxHa5mf&bbr~HH`e``EaKV{0tLZ+;p1@+WA)dhPi
z?i6q*w!ad=5@2DA8hoAW`#Hm%hu`i(GXwAPTQ4p({e2bTeM`7}_;;>Jqa3;Q%WdwO
za*{r^jqSuAw5p9Lmn&TvT)n20X&5q~wweO#?SKco<3qW1&d;3CpxKQv6l2y96Tp^N
zGF}m^e>rC~#`Kf7XIQj&C}fc|pZI}Cs5tQDO~-*qGG3Ui|D@R^_Db-^sU^;pWKEBL
zW@qSM^^+$|{GhwAl;=sa4`{aAo}QQD#`8O^wjGTZKOWvzh5Rms%Nr8!Y_u`n7SBE6
zT2=?kXaG&u*IVP+8=U9oF<0Ziw`=$7zIMfXWle|5;(EyHH9#i*2*H-1Di%jCs*Oyo
zl-CTw1VNXFCX>Mey~cJwY96h_h+{T9TzQN~OqjswhR7-ETx;xj>FLbmE7Z<fHm1$l
z-`@Y##It$bnyG)Qd=$9U9d0u<x><nk5qM0n*o~}i!jup5oz%SK8|EY(%bm@Hzx}M`
zwQbX%YtAu_6Ei3T1h~ZyS|Bf784t40ePpovgZ#ECc<re&?<t;bJa~+w%GDygQd`k`
zMKiV;UN}oLb#_L&gG@)itkWV(6Cl4*pXSrAiXujc!;GpP7EL#Hth8s5jZf;TBYv;y
z;=2Mu3g2#3JjxXx{;hBLmaIo5_)SSXATe<K7m<fRh;!Ih>8EW|7VV!kbxqJeA-F{f
z-<k?^R5#e<1~DvP6p0>zr02#j&m+vPo;P&Ai)Q`dH3DMREi=d2DKpbi?zqv=C12Mh
z6CE1mXV_dGEuSn8|4i;KLtsSn11z=t(USwUeh?WwV+~ViDg9sTwA#$$Z&e+)i~|T7
z-l%O?HYG*mC7aF%@ShT(T@c2e6)dNs%AG5mxD>PbcsgKvh}2!3)n=5gnRldsyWzMO
zHEouiqnz^n>xOnzu5nW<>h{J{11A>Sa9;D<8+^9)7!B)D>F(pOfvqpu+MSOP)X4mt
z>%PZtIv0wnFWIMg)jw!N9$8!~JH8z0Ch#OkJ2X}Na09Vyw~My`($eO8<_i(;Ok0-h
zoh&lr8*1_TmYM(zbHg>HmuhfNc!!c%OeYTA<TIf>v({gehdie}b<^B+zTz7Efrz%%
z36jNLdnlu6FdQViIVl1{j;_10wHBCvc2#xSrcF+ae9v&C2o%81zggxz&eToYW3Szm
zD+%3lb}_?|!e)n-M6NiADXOPea7f{-LaXTQ8IbWj)LeHMN`^4B(Frx;m}yl`<m;$>
zB4_FSGa4UW$+7&YKCSv&PUp_zALn``?_#OZ(SI#QDMBo?Ml>`=y6<ULf}*Vu+(fyw
zi%I`l6<)f3WX&W|>#AuFN%l0-akf-C*iLt&tRU=LmMO82e_Ti;P}#}$VN5nAG;9jw
z9~jr|VRhK@LgxKHl$}>J8-CpXV>R|3Q85!Wf(lAfTicMTQG3@`wL|T#6pa<DNVP`o
zU7HrQ_iSx7f}$v@YW|))=lpM<i~mJ#a+P!PJ>T(pj~AXeN8qS@7T<JS{N^RhLuqz!
z<iU_1bx#_0dv@Z+g1#a8cN!6kJwilr?>F7V1(qKLs=Q}=`ATevYmlOK6$JSy??bpS
zoGikNZ+SYVgI$C?K3&wsGft9G*4;f}X0&Em3=h~@@&j8>XK1F6NG>eAm!>4`Zzh#J
z6;diFQU)_wthHP^Tup1_aY2UJCq)5s0Bd><d3EjWlV9Qz`a1PWuCYy#%V^&X??@yR
zoQzPg-tOS6G<Wtx2&ARIn{>a!E~>Y4o4cRP$H|7f3zFEfpBw0ueOT}2^(Z#cW=oCQ
ztA@Lnn}l0AmTX*Uy}3=}V@o{;hu_0>%lMQ5!*_$7h+Q3b@10Zox@XQ4htm$se8G=}
zc=^P%y^=ZO&c}#^s96T-)aeHw`<LsJ)GwdN-36CnB`Z&uum?ZkGsl3vB%Q~u@V@U~
z=ij6~eU0&)??aA{DBk}I{jQW0AZ<n^*qFfzT2OnJmn1|jA9UI^9yk+ea=@o`3=b2N
z$suzBV7~ktlhjby1|K;$Rt;`WLJ|Dg>zthWTgj4SGm!y^jbz|)KPmq2QT@t;p3YCM
zjtNKg$y_BDPP2)kGp3O`;_;$FG&Lkw3fA<6p-Rg?Q-XVFB$i7)#uy{J0E|ir_z-21
z1~;mNYWHZ*N+AZmb3an!4|={>p6$}{i9dxYA9(Dm{B*FZwET8WO3NzS+&A@uxk%1J
zF;c=nTQgtEEHE0;*{@CSwW#Fwcw{Vox@VjZiM~S;+h3#DU;Xq?Ub$PIpO)1&{^}9V
zq(MFa!W{B0@0_ND$o*NpAZ#CxV)O@a8VXocqCX0W1TcF?U@z?0cE8-6+W6+f7%r`V
z$Uw?5nnWxm>(uUz-KWph^sHp3)>u1Mu?Y8?#?fOa6@%;+TKQctAGW-3ja$)epH$+G
z-B9Fo&F(tY`e$JbeO0{(%*3DwfN)jd--o*ILwI=&qo*3-M@dR2wvgd{PH&<X-%OOM
zA1|{@ny27y1?<euDg^$mDAQN3eqFK2&AEHS+M0I7azxvSihjw*5;i2eMgLhbB#gy^
zdwB_`C9UaK^tfH(Om7JDzz%{u?NspoQ`M&3eKQ^kT~<g`u4j=Lz4_MhVCOspHmv6%
zAyNR(qK%#8mEv3WA9rP%Rw`)lQM+HtBO!`06y%jIFbYEmEOtRq9M+4?(X_-qJ7qtm
z^V4xzH-Xk>+!Ix5Q8xbe^o7C=<am)ePXRZ>y`&Ece=pKJyTeUzJ$;heQvh-vDX(cf
zt#4I^OIuuF{K^Cw$HjMkW4^zjjZz}eU@t0Lbg*$fCH|AlpLsXCj>JbeJ^@EP?jeT1
zb#0yfLREpZ7;no$CB938$%PN<&B-inBdZZ9$Bjq}nBA;LqYjl|9KBt8EAxH6>@{qK
zvM&k0y4$h_vJ0W;Pvua$oBeV&^`-Mx7fUMpiXa(wQ|F00H^+M`X^Zynso%HtUMm>1
zLMAqkzsdF04oj*?!XuSmB?ppa2zlAU@i+>#l-C)uiBrni>hi<(QAuy@z<vsUq4W)=
z9=XPeoUEw2JA(8IMTP)UPa?=Q;uqyW-wQ3JM<&+7Mtrp~<JqLu3MBmwtBLSV1ybjG
z;(w*Jjq1ZjLO$0EpoN0_!SWVjcUvOJd5i(M#bjKqM-x<x?eS9|Q>9V4W<8t$M__7H
z3^&M+5B{fIc*;z>dZgr-$|8NBtIH$DYnYZ9SOg5to_eh+=J_inJ={Xt$youwKsUFV
zl6#Ht`!w@z!}lR=#)?nOJ*PhV69JDE*~z|j#Mtxzt@o)wK?dOoRBfCFLBlC*kd#J<
zh=xY0jtBE<>utF_&l%6!#`c2@X#w^Sq(lGF>K`zk=NhK|vhW9m<8wzwmL}JLP6MDb
zQLQ86gm7{yZf7alSDD2Jeog|n+%YeT-+9;JZ{#5^H4nv8AQZiSVCkD#$0hrrAKb;x
z`&5QD`2GMBUq=zCEVG*H`%3T%^^(QD4#&*R^u^a|Tu^!{1yB0>gtIEeJ0{gM_km*O
z(4@><r7bD?fGd(eRV81Z&-;j`UE>pCvXN_zu?4VlS|{LTTO4T9QwnIcjVxDwkqAD&
z{QXwjpv7vN0JXNy1(Jb%?qz{!E6Cl*6WVB+$R+#9kD5B5*52PM_{AZ2(-Qs$+d%a&
zo#cGN3L9HL0|7zcHAHpu>5hS3ypdwi`t?ZiZjFGJdqia_W^7SP?u&0DiUG7C#JS#n
zNhembGVX25R%B!ns{;Lz`nhIq!yC)gsr0rVeKbzi$Nt1kPb&jnBY_B8K_BxVxryGP
zy=sJ`vrQyoe>K7G+{De`goe8lyb<_Y8vh4uY?mZWWVGFMNO$UKL4BmvG2-Gsyix;k
zM}zD?WJopoo+(^)dy$n}d-^%+Y=;+}8$5#g)BOz-$EcjYNmx{&<w*Itgqs^^N`I#L
z&I5~67g^@Wk#tzSY8jLdBesg%r%x}oEeg8E$)r@gZ=Z|1Y)>Lm-PGJyx4-x!6}{6|
z8bx}?sL?mDs{@ap*}wh{x(k@Bj4q8hro*FZg3D89?7?J%w(zd8Z&mqC7d|+(CS`{>
z@WbP;BmV(J$mA2zAhBDR03dSND%7TVT0&`ww$4Ja)_&pFs1Olan0uKh=LEKcHu$u1
zFZa0J&Iw?Fi#UB6iSGxyJJ>}f>J)Lvmvi>%HFOZ6h6B^Mi;lgKl}OOSYWo5EuKkG#
zn*ThC79i(IX}c6Ww>*v<=F1Lp6J>O2go)Wdx?R_}BOB>&6Dd_ll<|ZjaL$p2*#TyE
zp&B>^515hzwG?W#Q&u({jwf9I!s^}Ug3Z8XU?5CQb1W5;zJlGq9Gp6Y{AhsBusT1)
zcUDWe+Av=zU504FE!4TQ@scTqpX<qdvqr&^aWehm4I8pt10D_{rcIpP1HRpgGi9Em
zFwq)BiAgbxN3{4Ja{(I_U;J)P<5DooCy*yVf;Doyu(vF7%^swNQ(9epgIR};J;{Wz
z#xtb3B{xotXbgCVccz|&?}Y;f;j;PZnMCq)Nk|NF3hPF+pXksg_fpFcbGqXKnvJ&i
zUoYs4{>W2WCBmOAGO;?Dg=LE<Y>Yi$YbHPnZika9_Eoaj_yS~`7`|7OTe72^Y|2Fs
z)l=nqYi}>>eI-67C8c9F^#dJHq$Tz{V1iT4R`!Gw#Qb3csaSWPmR_5JDRpa$D5P3q
z-^pof30?atj0OE@cqG0&1br>Y^V08mrhH@>3D##o=z|4D#OdTA7J_4o^Fk)rYeO~w
zKtryUt_A62|BbizD7Ui?3e)HFRRj#l9M#wtlToT)H#|P{X^X-PnN8OmyVN+*!QrY1
z{=k&T0A<x-+Ly;abIgdc<rTbvM%8>;`v<|LvNMJA<B5u)6wsm?Bgw&6MZX1o*#`hE
zDFRo2aUMOKRkDax{*-%<wxWQ?mNyg6bU2ITE+12EWVdzAY{?XRI#jEyqyY%m5JkZg
z4gPdiqyEX($i?3kOyCOm<azr+%hyBfh9Vq)5h9Pz&o6$tb0WWYV(?K!aMEJ4%E|l1
z!zN$7T8maKN#?c~kgf(T2B=3brN~~sCJ@h&{79;-JVuwG$pjJRICMgsQw>FG!`ncI
zqR{fT;9L)>auodx=hHFS4Kc*xn08=x>&2^tz<V5@i3Y?-zK74<GRY+#&_z-7$yz7A
zwTao4{W!`k)0BPl<)k-9FYx8wO<<^^CROpcw1ByQv|WWa>0^FqBI|*IkI)HcZ=0op
zi4I%gnfC+92xI&!V=fFeVJ(V4DhZ+|;L#omwQ6oSY1qIoUSEPP3-;W9TrG;+Ym6SW
z9&*p=GI(e>OOsdJA<oc_={%ZM*0+|NXi*kr(DQm{+A!)wq{4)3)e|b6rjMu9JFa+M
zYr2(I3`32pKz$W;Q1>70$=eX>-p2<6P%QmBxUnFP@Pguisl5<S?Drp2%Wp{_Idw!p
z@PGo}b#*SWOm<ySb-{VDqk-UWB-*TSWBEEt0)}!xYe@R|Pu)sGW&Oyc&SfvN>&wot
zw*;{{a%CZfrW?iVbe)yW_|~x5E2Om77Ure_JO~|=eb)CR983;cFljVl=kp<t(Ip|b
zCWQY@q+D&yGV;OczW<_YEZAjH^?j*uf14!WR8$n?)Sffcd~BO|;OOT%Hay7Rh+~!H
zB0cuTHS_uy&bDT-Fq<Re9mVS4DrA7i8wlcwjUb3@kh^8zTU=YSwN{MZ?3~=RvQ=hG
zk4A7|+6D#q({E8r>YDJ=T%SU{@nFL7Co0=dDQg+?kcKCPL%%C<OijM(Luhw+B4TkI
z_Rz^~W~x`g${By2YhgmsScO_O-UgB-=`oBHW>1};=~d;?{{0=B7shYlsyjS@QIw4Y
zY&-js$!KOZ!G6we+@K7ROAY%w9<#r1FS<=|&26yH!EU=53#%_7g!{dv>D9uu85El<
zRjfD-BUNhewVtO8b;<&*3onnl6jCGVQ)Y)T$>nOPLmyNdnC2~FjNF=e;e7VN%}^}H
z%T(!3V~pBaO&?X%kV!K8^DYZ_=t(*a7Jfy7Wb;|Vx3qRh^gpyf-j2NKs0>wydQhi|
z)|f1)Jam*ob!u$Bt>J|F2yp`|O_j|1J&rY5HOgs*a_qzkmHa$4_%>2;lwITG`ti^A
ztMg(w4ZH6@ELu|zYVu|t00kFf{wa`kil>?x%?4T5rD-3v(^r)WtoE2B2P_qrf)jI1
zwy~8Bhn3XP>VoBqhqIfTMe9Vfx7+^ye}EtuX`3KTLM65Ya>%Hzh*a>lYFke;e-@L?
z>Y7;FSHq80Z{8TNiKGdU$0=$Bi0OE`OR&wGjW7ojUJh`jXVns8(TxlUA@0$@%mkbG
z%eAD}1t}6w^a6mxKew;BiSRL6Z|uo0X*?+5Ja_nF?>cy>X9ynYynj&?ke?HG_CRq2
zMo8frmVcF=LrxosUOYhsARwV8*6ZIsw;LNxRk)kcL<rP{6a=L$u)33x%N~E&Uoc_G
zMIbX1wz_06bA@%%AA6=;=SEv<@_-hY@%N$Obl*M}EZ0lo&Bd6W?66Sy$G2F>TD%(Q
zAFglav-_gK)qFgC#(dLJ^;)_ZwsPO?+M}pr_IW`ak<haKJXa_p98P#l8u_pdKKTa2
zN^1w*Ece!_cY$3#|B#Jv!07=b+N{Zx+vY|Sx?j9}iY15)4wbeAIfE9;En#jZpTH%*
z{sSPX!yUT#JhhR?E-UXRE_Ybb_j-plte%^GV+s7Usq_5CmUzlj1B;I+i}l#t7r%L2
z`*!4X*cuX+c;BzJ-ZZ(+HD?^^*LDQSyMzheR*7S+Zb=119G8j`lhN%?+(()W8A;;p
zhnzzXXvE7k-?iv>1Mbt7BVhH-=a^3LsMwD(P5(QYqv))s37_mYYdbjQ-wKJ|3RoU}
zUiIPFA|zSZaZ5c>xCySC=f}y5Tcu}-8j8S^dcQaGYdzfF^AK<tg~6UT5D~XJ6F8IJ
z(eNm`mUoc8ueyDt``uCR=72&)T#>%mBuAe%XN7i|0W;-$Ad1vYJrW0KG!PouNgvQ>
zRQA9}W^s#@O#VF7Ak|%fZ*0D6(0V*GIW^e}kNMW%?Zx;-y&2~#LZi9`I{qqqnRkRk
zSQbNsu(>amAD6!@*t>QMlh!CLoDL%H-GYjlxB8xcrz*fJA7YEerg&&*>E^ls!HX*L
zi5i0m)vx?YoDu~{PVD>{-^DcyE-`X%5WpPD{^1P6SyujTTc#hJA<ZM6QOF<cKhjBy
z9k^(s>Q9&GRYzt2127Lqy@Wv-pb6w$57fH3K*H8TD3w8)VStjwZEYznnAx>*wbJLf
z%zKNu<1amS0ywklr5-qYlC-nnJ{`b+%|ie*+}OZm9c|f@F|VXBe-HcQcHLJtEZ6UY
zG<p)c0ERS8qWwvk$mLuT_qeUdBZWZ^NGsNh7@t!$T>22sFr;#zsIJ$Q!!mr|E1t_2
z$-X@tgZM_&M~AgKld_cbRG{v|;E?@>;0c$N(6cN0Yh_-&oo{}a^bYR&{{XMK+>_1D
z{fyDEgx6B)U^OErXenjYoco&$k5*Nlbkc#@ltsbM#c%6W4y6^wXle|VMN`7trAehU
zI>!vCvE9C6DO>R<Mq!ih&o>;HDzK)k@Uei9;_`h3YrT&b=n^eA59px>cTK#8leVFu
zhO4lmdghD|Xy<s{v4E|hqiFcz^dQ0Gu2X@B$L=M!UxWlURBEDfL&J>o)tI0tP9w}m
zdzx>I`Zwjc#WoK8jH&-65POg7&9I4MT$7kRswD4Wg|V1H<T<3sJXtxuRtao=@Gm9*
ziE&V$yWx8;yEgm6jPpb(9ew<}IiX66quYwbaW9d1Z(I}!{mxkI<~x(Tf$o}6fBDFW
zG1w!@d5lsv@wz2b8*@jSxgqlp(Ze=8lGw*vvr^fST}MkfH<YzR<<|2!)JTZ#1b|VL
zGBBu#Q5CE#rwT5&H*wUMPd<MaY7B@ZlMNt=<9o^4#DQ7KdnfSuc-C!LowZNS*>fkI
zUz=-7#dkY=eoP89rR6O(=m7nyu^@NL(Uqj0EVi3hdYkV+%p`KDQKXNJrM+X^JoxN7
zxJonyr+qZ5GkkFpbYL2A$L~bIC77(?V^Uq2@Z~BKHY$0E6ooOqXFI>rvtaQg!<ccr
z-hsMrsGAfC&OqD<XogXe6C$v~U3VAU9GJQ(M)m|K-7<NsmG6$+TA|d&1RyMj{A{l;
z&6js@i{oSgI8@2O&ZZ5U?`E>KysS1Q-@fPv0;9tT+O6P<sx`h|&+pFHfbYk<@iguU
z@9e~)Lv7jKd;Bi309RWTvl)F~9kfo?#&V1IM4IHU4y-2RrQJPZ>HG=rgk@v{vndyK
zV$gQE^X!Jsj*jtnT;o`4kmNNWvB#_10QiXSbhlsG(j!)CC{7Y$yMMb+$w!pykZr-h
zof_e80)S~~^&Ple%u~bDGo`;V^tPYKWqi*!5AsbI61lsDLV^}p`S~vysRqUXcG@;F
zpL$_Bj&{2GNpeCjO7)vd`W?FfDdW&@MY(7zg83k1;Gz7y!^l*XB-f~*Kg8XWp2`+e
z$R4wBD`F}r*0)r}{VGnfk~y60?ZB9lXR~`eEAbKVt^eJn5GU;J4iUU|Ihtz7UbQ=H
zd3AX6?7gbETZKUG6xs#`k>`8|Za06bN|)RQZ&)nRLqo6z-dqjeu)ge5TGTj+Crv<t
zt0%{nRUy5USLa4)NX2)lWi!GsqjfTh?>s}2dpKnE7HR{#8GfM32m25rx~I!<lRa61
zrZ;!3j9xO8#VuXU^T!#=#}VFSdl>o<{SX~TpIbRE(O+R@Ux<$-qNnMu8q!Hhoi6W{
zy1N@R8YgpXm_zb_6!oiy;ucC{(1@#st@5zSK|CiQ!^8|JN83k9-G(Lx(@M30T;AA(
zhCIQZ-#WC;3a^^IIaTuSW%?S3=sws_b-u{Z!_e7U3~~*7N(i-N3@kKGx?(jG*-Wr*
zL{D}pZT}YM*3H7>(Hmkp2clHVO2nlx>DivsM94@}t-7ctNxGRKN|u1oH7EH0roUww
z<G&ouE7ASNsCMo@fRh4?#W3c!aMSrmTOn{>|BAJetqy_+f`bO+FW{tf8aVgA6D_x!
z^07nw9<B4wTInapv?sJQBb@Dz>l8v=r$}l9n1>34`{iSWJYA#kk#L+>PO6KcM<%g*
z;hEn^v$qiF-b<(I)9N2Hr%5q&T6bRy1;u#7Tiw>)1i!X*@HF&YCB_d2c#7I?Z-jE0
zU*~ks3O@_Dw8K4l{92ynkWDDy6oXbOY7>1Cd~*aFT>3xT6#uW5#sBv`Y*a~ZXL2``
z=2MKZ4^2q{>6b4SQ5UlxXLGOYl6%*<P+Vaf*GebWVdc0Yb2|6n<6uzn+e4EOyYW-D
z8B<g9`JFcoV5F|5w6y&vp28!8FY{@%5|w<XQoNG9-@Nqx1GqRRKdkmD>oISBh2`zu
zQm*tyx4@+Re;&Y02RYRe>LKpj=l=l&8tWBrhhWxu#rk>+rQ8()oDL2kbH0#{O0U4#
zc=3VbUw3I^J7ccr2QHY1$Ho2KFLb+4+#Bu%*gF^)ktFG%$Mr&h+j=`Yn6w4O)W;L`
z`#h62O<0fqQ3z%v#9Q};nM37L$-YFtcJvFb1l@C_o|Fr+;g+-1f#eJODX{w~<lV=H
zW^+%uFKR`TXA$=rTP=pd2L-Cu2EZw%*;<>GD;2*qoGVqxa;4_CkJ-vgcWJ;x*)FsJ
zZe~&ZIXZW2dUy^0-g557aBTO!TYK^_&(Z-=S&^K{nm81FWRRn)&#7D~<fx~`^VF|I
z=c5*EQC?jDY0B~bt=E{+`=Q#GbZYDCTk0*z9ob6yxr*@khV^;hz1c6u!h~EP=2n<l
z?*p+(fqIMTqkC?Yg;fs=SE4D-1sxPs1*#X(G2lrkQ{(#IDtjpI=XDr$nXSEDBP{81
z`(9ywVUuiu<utJj?sr<!KhtdW>&E}h8W}4I|D3c8*8KKSY+#BKL-4vOy#;Ir_#MV~
z|C6z}(w{s#)W5tUEq=d*woK|V{rG16R$1B6d$@pM!p8+qTR=)K(5eM^&YJp4tnKZF
zXxTT`j0|;#qJJSWd*k&_emFmm^`8iit;7?ea=VkhHypSY-DT;%t$t_nvnqnXdvHn|
zdL4}#-Apzb_hd)E?F6Ys3AZ3_AkC-y)1dX7J55ERZAHcKv|3nH{pzYU-ugcP1EdJ}
zQ3NS3>jauN!7bY-uFE?^b+=u6SQ18?azB&;Y4&D|q+BKy1lLMz<J=pB)6y&_^M(7z
zPvAmeoVM3^n2i;$QmG8qfdjzST_#$?Fn)$nx0%5nJ$ty5UBlQJ;~17k9so{OQEI*j
z^ecTQyzr}7X5%1-j-n-D$d#$?YBOk~k@&C(3$I|WxfzMke1rV9(JvW8Q9DS^QvlfJ
z-20R7rkdxMtt^<SESz9c+vmsfFZJSYnQ?2OCtqvZd!P8CI~4t#=juEyki>siFN9=d
z-ZccW|4DCYX&PXWdCFKea*a5DZMCw}89#l=iWckB)7|G!<%@>?-I<_YxjHM}vC>LR
zXJtw^+0f^iLPIb9H;JUP^CqPH{K-E1x(VILh)K%ahER=i)}O)+p6pa7*=b@EyHrqu
z6lk4Uoj1EKNj!Z<q#v}FsIs3FxEkzKTv;x*Th`Npa%U_>5K<!5$#yz_!ROE+oPzcu
zP`;a^CyTMbB|@%z;!4BNzp<@b6ONg~X_D!jHWJ0t5NsmeGhn-tT`zsO=-g_X>1D+a
zt^hH*u}yi(u3<^Y2c%HqO)mcS*S0okWUBAFIS?`4JOHUy@S5Gz+#14Zc1Xdl3KRo?
z<klyNVHe*ZW}myP|L$_<6z9~~MT~{9q#k~6wJqQt#H2OC%+aySSs6-dI4P1{#giL<
zf$EC5juDcU<#hYyTr_k$Njo>)NF&7%%YIzG>fQye7Hm7~BCfI-cTRgllJsyx@@<-3
zb<V?oPT80w*^!?g<NgK}%qu98T3yi{f#FLOMl=0~o(7)Q$nDwR)riz?JHK?9V|9-K
z6jeG+t~O!-e53eJ;ImNIKVGtXT~YzfGptg)neT}e6c!elv~H6t8l(c}(P@|u!R^>`
z(jdR9QCRc*9dB)`?`}UzgV6IIO_x;k$E<IwXBhGg*SK3q+-F(LRTnC9vFvmg@@MvY
zne?KZo{jX6AtzbXG;(KF(NLnI$3IiIQI4Dlgv_wBkW&Q>aGL{o2!9Y|tx2(BjRNM{
zuYj}<Q@CIK`RnSWK=2H*yQehTvMi2nTD}fN^(N+sZxXC@<I!f;&}i;s6>5vrS+dR5
z7$VLY6(WzCsQ5Gd@|_i0&?HM!qr7e8JkIJCd<;m=uub?udunQZ^e{;#^%2>_>DC(C
zsU~tpDkvU&)4svBi?i(9#j)~va24_~_ON}u`m&m<`ZLgeih#Au{`_kBSEk-ro(<h-
zL&u^7RGi94wf&Qb>s|d*=-Ec4!%}^+WTRd*e;8cIScu{QU135?AAFWq^3N#L*dwW4
zQwbpSeEx(C2jW?7?pKjgvU)-3?p8P>zK)aiRBP9yNgl*f7(ua7DT~mk+z&?2xE7!7
z-B1zTbU#jRnq*cR-u*H&6@*2@cz%}|c6?!FlpZ{w=i&sB;=d;=usrS=5D+|jpPa|{
zJ5Ux0_3Hny{;{=1z{n1&^~$n|js#$z?3F=S<s}4uSDl$8qNy@^lu(3DC$iKbd`j10
zA`m0GxS2i)Z`@~Snc8<W2u3B4g%xygH8!n?V||snh#kneFTC%4z8Ykqq-aqo=Qnh6
zxQycDzNP}(M_`Qu1V^UL3u9dTDOv&K@l@J6bl7mrE%e~_5s%&a2l1CXcj{X(cisf4
z)4EDtm3EF>QSF~K_XWi2Ya0bV^MF`6x!fqv>P3IsAKhQKMDvC8g8AH9+)eNRN%V%F
zD>gORh4mq3Qu>v#hh!^ndLm-Gnv?tAxGk~ek?_Vo<uSL0A*R<*RZ;bD&?@(H;B8D1
z!+b8w7E_x4OcgLxc}!(n!C0>@G{$ObpJ~D3SN0teySdKjuLQV|65iekm^in6FVK{t
zf=>o28o`qHnxC3*l8WE#c!r^nt!Mfa8~P`nOhmWagB+VkPml5!@^}%qKulMF?)S|-
zMSy9~Ts{3y#~Z0WBD`I^s}f=9Fyc)2wDeclZCAVYBgI-CyC)e`uNWFU?UP@Q1Go12
zzc+(?Pz@O-`7NdbF%O;B4Q;DXq#Y^@MqS&ryIO(IdZ$wSaVy4&%t~Ji<l~j;x4FVs
zy%GyjEs(AC&#|tPrsim{i<nfxB6=)DHu@>nIyI3{QuO2W49r?xZmGPQtv^q-|Dm1=
z`cy#-x=^Ii7%nHN+oGpplT5|!HI3}s%+E!>s+A8Q;g%%aFO%5h@(1EQ5qvMV@3wq%
zy^|@&;aiuLw^aCG`<DY{w`f@aCxKVndt!a}{`h;*^IID>6bsLU0VKBGi%9;tKivMi
zE{Ly}{o^+zy1s9o{%=e?h<ht;ZLZ28xq`Wni%C#lT6FOEKR`LjTT<zk81-OqO8lNy
z`bVs%>EgN9;=|f><MYgt-9_J8T`A-MEPL~cH;DE&_lF$UQTh7Vq&t6Tr8FH>Ete5}
zt6sCb5@VI6e68_I13f4F_L+BN7knc%hUo#FW0VBI1KpXW_vcqJyU$Fmeu#&Jl{4!U
z&%bVLPTzDXWwAc?qx^bz$g)5Pz}OE+#oo2>GP@^tzp{ss^4*%#<pVb;*)-jBezFw3
zk{#B1+O8d^=`fcO<Ni93_ypE)TplM@b8hfCk&Bys>S_v6gMClzv#t|)C}F3f11Tm;
zEJ!G1)L>Q5%rW*D4(<6klpPYbcImb^_p&j=Cf77K0Foo268DTA&8f!<;NVM%`W6cs
zRdZW26?mqk;m+NOW_$fsrO!NKLh|V^gQG-|COM{lS_fN$i2R=9;4XF1xwYB}5il4K
z0H!YY@}_II*>qT-P2{A4opmbv`99mSOCh58^tYn-9l~_>s=dB4Z*9M~lQ)d{p%d+(
z`I3Qk#e}lO2?D4)rS6F$M*75;)>}*(_J-vJn<A1s3kTDdKYPycaS#}JOLtCEQnG)Y
zBK5e;(UTeK<^inc2XtG&?lLwT>}sPQ$xc7$_)XKRF0kYBM}xHHXVMV^D|3C43|2j<
z5!ji&cl)C&7w5|DQ$|mp-h5ftn0rJO{n031q`iglKy(+~rHk7Cj0p_yCX-KK485l9
z%~gIILkej=iNM{$_-+V+#J^!)XvQwz9c{L6PAQn*HgBfEZisf;xuf^-WYY=^Y@bW7
zm&>vsDsA0|2DT4qMUbFA>BFCJb0y94Fikbfsc+8+!;&ijUxiJt!t1059HL-23>9=6
z2OlpNQCR=XwYt=O!`${ZhASj$w8O>TQRA2j$&K;K{SS~3;3_?E@~OuuQW!7gq5E)5
zMN#SlmNp_0KnAG{RPMZV=IHfHefziXJ8j8|DQ`bJ>HyD(x_|V&eD1Nh+;!-!2H4GB
zM*ltHYbVFRV+FP4PxVMW!LwhOQWvE`Ab5>QXEzm^sk{C4gX#oVZ}zzBy2Z-V9&~}-
z+%BW&Df^cNisOUYhRZ({e|nqkpG7m?F7uv`-h6YZ!2LeKrgl1h;pKYmo%MrnJkYr*
zwI>ft<}M?*c_pboFiNRHH|}9u@JO!41>a-jCQ@H}Xj$g`Psf#P9<AP!$u#!b!sJgf
zq)hD$S#g#f!lIu6U9`oXVCt8J66+#|8aNduL*9ejGVMVs@YQQS;MT!NTHMRxM?77n
z<V>l|g|!N0<<0O@S|kKOh)6w5r~A{?+D=8LA@#*Yp}j>N5nStyJ>1DyYe5dkQTVxK
z5*|BS$*=r~=}`U`2ATX*3DsidbtbQXLHgp=50*-)$GlG*RL12$8v<mk3Nt>9dbtHa
z`!Vg#-Ap22xZbkKMy@0<v_Y_Pdei}8@}aST#|4V{YRfAYyZDz;$!N6%K?v8Rum7Q+
zRY~wC!J8e}Nv|WJ<jIm6L#s7NW&dVn;g77T+AGgybxuU&oMnl7QYUD#-v#3yr$;K*
zXuU|%(q)Y%!(LS;Yw)rCVUuwYoXp;_j{LqLD|KG>@s23v&%u5a*JHfzHW~ZfyKWRU
z0abhkQiM?^sI)a;;Qqm4J*_(g%PeQ>a4C+hc_Q|H|3xLC<LyU2C8x1B0}x?Ncg_u=
zg?yH+_|1+hIhA(~T1uBoIZqqnK}5`mr@3#hPTdm-0#NlI0IqiKDm{lMUGinU4U1`g
z5>}`XQJ{x~IH2Uim+{-qw%Qs<AzoQ6mz<rV$YWSmR_;d9y$8b2mt(s(GWfWQ0iJ~<
zct>=V8?C(TpdY_h9p#8EDme)ssk%TlFb>6EbwyEY?*t_oUOQjA*s|*DWXuvF+j(Pq
zMom=Zi@nVKUNKyzl6jgnKcys>eB8GqB0KGK1|6b?vkx9Jp`i0{%PCiyL=|gL>9)`o
z&{<)1=U>LoVYRr&t@o^V$nPjiQFw)eN~n!-s?gQ7$3^gH@zFlV7UHHK&%SVQC&`;M
z?*XI1wt~IWd6c1+^T7_W4=}7qDI}jLxW4Wv`VWx281wdpp7j>m6w`9@N&{_KjmHXe
z(=e>95}3g7ieF79Z1b{gJ>f0E!nF4t!rp9&0)e;}JEl!F&C!O?*!Kc*l(jAVUv%g6
zq?%V5S|tOi6L-IxixXKLl_0~V$MUiou8||3Yw>Oh08ecf!=b57nZhNpf_#SrxqXVW
zN90M{aIU8_uR`(+ByGvAubi+MU;?9}T~Efa-|w96@Bm69m@|t{G^=EIO8qKfJ6wJ?
zsrR<U?o*AUuj`xP%t{(@brq`gT;Lc^tAOpOlOUnE@aBJ(Yv)S#J-)}kQn~Pyptnc$
z$K9}rki)Neja7?Ov+V}Ybowv;TqcR6>PU?$)}tQH{Yv1}H8qk_HxQ-%Bycr9DB4WE
z)mXSB6aDFg-1mD*#&fw(;fps1a|4h~u7OSjN%&s8Mx)Jw|Lu9(-~fzFkdZUz-~j+3
z*h*XuKCXr>r&RSjLQ+{y*@(g<+Db{Bq3FCSRw@|+vX!3Z+rDr3CR<z6uzKg?^nZYG
z+ckQFhOLv+%P+2Iw?g|9=*)akMC0p-?`$dF9m4T|`2oQgPn|P#&xAS6zRe~bG|q$1
zk_Z@SOL+hkqDe}7->M3@8pf%!W&V8=AV&Utsvp)<hlY+F_1RlgecNkkWRp)%B8&8A
zYg*&v7|Ph9i4Ip{e$pC~%#>W-fa>LA?*j~s9l!+h&y01%?|SUj>c?-MSrQ-e%9B^4
zZ<_Oujdc<{IP9J1K;7^zOxXwe2c+eTKtOos3`ZlK*S&_NS+tjD$rKCAxm^rBK+jNj
zH4w&JT*?bI`eZTeb4K|o5^DMveVoaZtY-5T=fyLjvk~rTf|9Uf>Gu5o<~8n{HSN<z
zLK?lK96O>9#maC{i-|W1y4!y+Q*wNA_#XgxaZ@4oNym#;GXIImu$X|{&TZ?ElVs5T
zpoP=(<-}%Z!ZF{pk)iWO;~VRTkr*yt(gIn)xgXMVS&9L*$DaLtG(f!5S}M;zO;m&=
z8K^sg)LkR~(9S+d8Xe!hshgmAYr~T~(NE_=1b{H-$y<Ez2Lb{92O8-x^~M6XbL~9+
zo3}PTo?sT%b**rC_C~6m_=m1-uuo?(<)^VUbk4UlagUgLB6b<7Tv&7)+h1!r*mH0+
zNt6nf^4N++fVekJ=zSYO;E+wl@QJqE6&Du2pxZrXy!>%ha?y*O3Da9{odYKHq5mMj
zZAP`msikpmS}hMk<S3J8?Q5DYG9>R^a%-%F^pXL7XSw9(<^E)-SUUUR$lt5B;M1M+
zEWg&bnvU62SKXeK_3V5Ud>KYhM3Z}NaUOUl(l1Cat(XjY4m?R+8M2cWMrVD|lj4r!
zGKeP8w0G<CJ@r_0cViC9dEFc`6>!`$nTCQuOX#{_<JFwtr*7OcIg47+{R<$m`DpsT
zOI+gpqUKG=bAQ3d-_WLynjDtTW}CFX6khpl#&0exUtTbyJ~igKx*HZV5=1|}){iDC
zkkQm1xu`n<B%BHdMY94|3ZE>NVA2rH7A|Lme8h0n62)J#3DV0~A)2P*V#HAhm!p5q
zu*Q-wr(4#!|FgE-*QH}d&INu=&uPnkL!Utxv##=4g@s;h8sjm6S1r77q=ERI){yRP
zx2k7*Crvt8GgtTHAV)=SDC!hsf5*kfdp^PUY#=v~3&;fq_i%$RqA_9nijNUb7Y<-&
z&1m{(FBEcAbTvHIT=x|iRFfyJkSFe;eDi})NfyfFEapc~Gtj`{fgkT$_-N@Co0lV8
zOgOw;vJbOl%x|x&rJ(RlhoQP?ZcR7vZ*4e^va)4qgDX_=r3Z2T^qRW;Am7=HNCDF9
zkd}o7nX@%CCAr$@4tfq!F-6}(ysc6th=gh&L94lD(54O;S4h@>fcm^-1)?4ND9z!V
zn&NKP&UBcrY99y7qS+FS{ByCWko*+`pA*e=y*VC7i^gw*R1rAR8c2()gO7973W!HU
zAoUlaCw|=1v+%>5)Nrl8pyy*{rRGPy#Rru^^FKnK5Ef?r8V<Kz0?o(u-lA8VBG;*y
zEZ-eaB{JO0r1oki9Q+3`eOG{#e|>a&swuWA)vL&SkdnmElz7p#OUB0#B>VY_w0&z3
zOS0RV!AXiU8DC#AFUlPEbtOakWMkYzF;XV!3T;?+o0P)FHKlP6_RUx}ulre=+L{h%
zM+=h3D><MyKxN=|hp(uiT-a1c(2L~H#WI=qo8H}=6;;=gE4fXb<1(l`0w&SMZ^%h6
z`&3B)79>f*?yOu7ln?J@EFiNomN#>~g|a}RB)1eu-wKmi6Yzf#86wra1f?hFzntv@
zGn$;t625L+M=b`C7I%l=H3RwdU$SM-8hCktw?yn8NlB8T<D0}Z>oRst;|2)sTdBlo
z(I=PRr`I%Isi{wAGYzZ8FP0o@Xft(JEFLbz86}{P)7!VG1|K0H(YDfO{(Oy(_!}2X
z+EmF0J~d^3pR#DTqb7^CNzRLsrk4uU^Nq2{s@#71AApzWk1xxwIeu_I;%jOfQ!PDG
zP#e9wq)#r+)|0u#H-+X{$n~83Vvmi)0#@O)(!=h2r+pNQ*~1yLQ`baQvwA6}9PLpz
z?UDO_HkAz4E_>)`8-GBKiqPZDmK!`*+t*ViUwxl;un>AUbeVO#%luMFQ#0sglJ{4p
zE&s=oP-gX~9Zk9<0Bm-Hhr65ItIgc!lMHFxS?N7X6t)BwT;ZzsR&alIe%!~e6*Yt8
z$>t<f92%MfWo4-4eit8$+KtWC>uJ;06z9Th&5Ndu^Q`Z|OB1+jVnytJp4sv<^t1cD
zDW+x$gVCI{9qmRWQNO$Xktv*tE%j4WW-p_X1b`;Py)ZVzg*?Obk=M@T<WFaWS5xcP
zZe@D9kI3DQf5bL|ImcK~-M%M=3H{yYhlC(DXHGeb%;(LxLUueZ_v-DywGzjNC$L-k
z6E-N_8lFP?=t)e&IQyTyv%@prow>=OLP$~44bADv<tXJ<$yRe?HY(9P=J@tz;VLRl
zVAY+xoXzTSh7cH=tclayGY8RzS}fYVY~hDKp9BfuVaWuhXfD4h0KWl=LGS!GKwU}|
z;R_&1{Bi5MLY7&vLebT#^$|H*&-~Jz3o{j**|Mg`an&?`vJ58V?9n#=Z!%hDz}H-m
z$P1r<986$GfAR8-9kKawaIx6sSCCM*uwc{gaw>!ozFtLJkc9dU0D|C|SJt<D=cP<j
z$m0KESM_VdUOI&V8y8EY0Vd>f)=hVP{oGVSLSlrdwWtvb+QR?8KgZQc--`}AGvG0O
z_YmVZY8ju6+S@ipa-T)~%DKsZnsoJA{7K`4*V6eqF9U?>^XzG*d>H*aEl1atDBk$>
zN@ahbKVep^<-LC1y}56%QB%{EtxexsiIoYK_f4S<aROp(+Q6qf7C5W-`ey8{5h|Wq
zc0-3P#j>woEWGPXXhEIFJ;hsl^`BhriLRd*;}#yi{x!@$*5^z`_Fk*~tYWf+8E6)`
zGWFsCHpN`C;fVcxsE6Lgtt`AW?Yqjv9=ne|t$iK-do&?2etcKMTq4|x1Bxy@u&y?l
za}OFnnfLeNw~V17?gtw2mC;|Dc*6j^15@{w340A6gbRJfUNAJ^_38HPK8vgSI4wJ`
zICQfx78T_%m(YQD6Lg4fF!ZPq?$_niCm5cmZ&7)-SLJ)g#y<-9LDFZHGi9?EV#u-a
zwO4SVo)F=(@UW2o0Qpv*lq8)01HAb6zI<r<4#&Vby?2rzVq^EkkFPCfSDI5CH*H}*
zGfP5YmI`4kryp0er#<R<ezdB=|5fjuRt=#`JZ3ga4wX}e%zoBtj4Zw>9_fwkge11b
z1hUNdZL|6QcwEC{SMOEFQi}wXg7~l-u;Y|}*D!?_T=r=E>y()^QAsJP?Gs@2e*mkh
zF7K~Ps0iHrgVzaajWhsw?)w0gvQ-xv{X<tTzP6+>+w5~o%%rKO$tLClQ$n$#a<{^)
zIor3d<_o>7A7@0*iCk?VtrkT|<<dV@?63w75h`y^Pd!<WxA4<WD<y01y+4(2??PWP
zD|DYdbd{Hmq}mV?sxJ1<?h@j@NZSP63sdg5v)X&w!O|;muG3!Y)w~Ui!aH~k?oWTV
z*<-)xE9XCUid<n=QOiQR0^k{)V;W$uah?W;;-0$_rb#*V0M7>!REjYG#OdC@ulJ;d
z&AnM9pR_fq(Ebza=MD^0czReF#6g`Bit2k)=92AJ_<i=6>*JC}vK(}~WsGw8)^=*t
ze*Bw?p(Z`4d7X}wEp&qhVbux2$vK8X?8j@ml#C8PV%ES3h)|E+x<%s)#ETpz&Lnt=
z%}TMy^+aS*v#-L(AK*}-?YUZ-`qQe7Ic~u{NhV*K(K>&<5(+I_l06H+PGG$Kwg2i_
z<lmJo|8Ir!IBG!h)rW%(A*>G(Syqzb0p$=49uy#R|A&1=x2(P7#5webxBF1n!m`j!
zES@ya$cb7K!qsM%+dXshR#4=bfnJ^Ot`nWIPmxrj$!3Hjxukw_^FD5lb380CXugN7
zr|h<B^J~K{sLyU_6k_8%4uhlc#>r-=0nSm{Bm>G%chIac2f7)@kD0c}C`mJf08FX>
z0#`5fJ**tfN_pPs@Izx{n;e+Zu?b^~7|`D_^o7s(=zgz_$L+&zPKZs+ihOQ$7Z7}5
zzx_Q&tg|}3rQmHOYq*lZ-fS*>d~Ld>oPyX4+E)QxXeOs;48%svr7r&d>CRf1KFNeb
zH`VT3xZV}&bPKWfdlPaj>4)6#0iO!-RtA_klRlq9^FR~6M%$c#?YD&#NG4JfS59~D
z!{wflw5u%&q&(AAi;db`Y$XMbW}ncQZ}>rg){jX^%Ql`vQr?HE5`OEfP4c)KVB}?&
zWW^SD*r#dy9o{vPDj8$LH*)2ZF(IwlGjBABcLag540(E8cSy274KWnKlA3Ig9D@0E
zs63S?0)OP-Hp;W=Pv(wB_F>@T<kv4oX7A9veLKn}<HaEEi=f-NvIsfBhXt>`tk^{z
z%sy`(ypwra+EA?OP`s>;$-r_^4?`r!70BAi0BhO|53qmkO>#dzTD5k(MHXJ$igo8w
zkv9N`Q*Y!(rSz%u7XPUGnxX|!U~U$_jSu)zeq!&mrL^?k>h6+74VJDpoKo}^JE(t5
z-7XP-k=R^emb3UJCPAc4u%&3$xQ;>t$L2us7Fx)gMqhrGv@8@ZPu&|o-lJ7|Z+yWH
zZHTtI<$J(MZbw$^`todjW0x7~C}OAa(Nl2n+1t`zD)K4|Y$)l7Cs89YAc;GvJ_%B>
zCbU<*wX>138COS(G7|Q!^mMArFgfDzS8G(?awcQ+>6#of#C@sE_gypimTNK*h||r}
zfWP=W*yz9B)Doi=McE6++Nc}=k9=?V_u}JD1!`q#`Kj%xT0YvEp1DJv5af2blz;o2
z<NS{-1{mI!P2JH=X~Cc*14vrP`0V)b-ox6##Lg%G0ycL`O_mp*EaufF^Bw|Fod@DE
z%J4Bsz<?fn+$!K+LXa)Qxlp#s5$`S<p)+QI#bSM+m_QMNZwuS;q6%a~*smf2FvzXg
z&$&H8FNJ)0GAxrKn(kl{7O;8!XOQv($LjSa=x5N;Q{ROLTUghOj@c{H{GG`M<Vk|<
zkBb57;E2LO-H&RQse?#P#5Sm8wBjzf30_f4x%_n9|ET>K6wcta1m4&cIayk|MU1A9
zFGh*EhCU)^WTp3nr(pl=&9Xhzo*epJhKBa!M3yXu8uEXN^z=QwAlAdXWITg=7gb8B
zztK(?Y{pIG*XI;0Ujg9#3Nc>^c1r)aMd7R{Hk^3kA|{wPOyR*3Y&v2|#_e=J&&V~_
zQTAxcUbQI%AS_ldaZf+D{FHi*vxAL3fA}hMuJ*|TDBUn)p?4(E6+@36mm3~B@2I4>
zAE%yGz2_JR>~ryG^grmMe3kEAoVU_{#veSWbVi=!y>o7yJ7`_QE9fN7>;+MfC?5a<
z6Xv&}w3wA|KRZBCVPC_F(h|78bQZWU%LPvn>>1TL4`Acpch~=(a_s9#`u}aQ<<5E6
ziy4yiwAe1TLU~zwV=surn+tUX){kZ1!k&Had)Jt0x0&i|i`g9kF8B|kRtmAvZc9%1
z)m$=aA6fR4g|XUs1uIwA_}_fK@~w37RM1G)?Pg@(3mVNlsfMZW-x+aU{QSWq`R7jD
zR8J&SI`<PXgm2HZXZBaqY`*jTDvs<mfZL0N7t{7)5M6t@0WCsz_bx){1ZEBntDoKJ
zsJd|^DrA*uam5d6q@fVoetv6)5h~z8GRgofy1L4goqch0a4^@Wx=ia2s}Yc77Huu1
zXeJU|2&Y}EMp7?47t1Q>k4Cz))JGOWpMFZ2<@(%uGQb*F5=UmDF-jYwW{{|`D#hx~
z*++drHcig+VoB74I|Y*|s*hW|xVp@4GUk{yg8@x860MKdS2=yZ(3{w(pr9M6_$q(8
z?8+jofI>@3h;Z*DYZ>1>YX$3}o;t5ECopBPQFJhbOxwOGQvPj}hQ%PrSoQ1;PyatD
z_)rPRtn~!edDLaEM_J5Yqz=%akzB67U|URRIUMuPqzNkZX!_A{MNd%E(jGPYJ56-k
zULj>J+9#IYAS>L9!%IvlbyYVIe9S;K?a9HN2(2WOT$mAVdBm&!ixV!&KGt9pbX5oz
zNJIgb6tyW#J@ghho^BHMahA1=#L4XKM`H{lvTL56U(zm3A&G*`mq?Qgb+_=7CQRcu
zzN=TI;3Nxs+V~vQ#r|1RwnB!BmUxp+hxw11^R*MswdTbBU%&PmWn!%v3%2;Z+R4Jv
zI|-E&8?su6#&VV}YMcC&EIaF=aA8R5&kaQX@5DWv`S5jNl9*=}3nnk&YliihKkYnX
z3ABnQ8&|Ly^%nD5dblr^aTEhpTQg0QCB=2uczTTw$wrOp7p)achuoklzi94Pz4sh6
znrN?u!s{o=F;AN}>j=LkQBsH{{{dT+2uLl9Tyh8}a?b5NBOQ(C;<T(hYiqnE1<w=j
zsiTCVQYtfKQ{D*N63f$hvf*nE{j(jBVrW6-(|lm8mu#8ggENi=ro<Xin{N@;eh2hP
zYOKXhzacFAy?SG4V4Gv6{12vM_3O>Alk*R7bT$I>`ndtk7%Lc?GOnksZ1o>tbwB!J
z@f-Ki^2h&beVl27kv}+>+mOUQ#H4EdZH~ayrDp-Zkxi|XTc*&#yFd77H!eH;e}8#<
z?<emqy95Q7Kwu`gw75=&KY&-k6xj#Qg*Ts!n(J*DPi&VWM!jWIZz`vQLfgPH-=Avw
zlm|JpmGkfz_Qc)ZhaPTM0bQ-ZS0e!F1?Pw@Q*b|{m`Q=#d5T5)Fgs|2*FW-uJpfQt
zQP+TNYRZ4GNn0_k0TRne15&f%MRQ*#Ycgu9&7`c12YuO^H*<*Bct8(8c`_^DOYNfi
zWW#&@fGXdipSRwfZn|QTcv$J=bpB_t_{W4Mw-Q?;d7So^4cJdx{gBGjVt+Z3x1}Gj
z9W(Gf{=3LV{Bh9RDrzc)`G@Y+Qf`(JajvCMN$OkDDOfCRIi;D}!MONfw4Eoam)cZ4
z4ao_SC;0>Vvo>qCzA%h9sd^>+_4$Z<=-wZMt>*(arDBBZ@k0sc;pHGdnJ{Lzm0`D+
z&wl?%NG!bvqScf!iVN_-m@&W&ak`|+<1s3X(T@FAlsrZGnu7%Pp}VeK{I-`+y&7YF
zwnY1Wiwv8SFlsxf?Wc_vtVw^E;0R9DQz)UxKb^;!?JXyD6-qAJmFbG+$DNFo^+~gd
zMTO{B@%GHf0d+oWn^66F2@P8`Ay-sTt7JjWZ}IrkmyTI)*@Kz)kHXUb1B?w{Of~#$
zZ~u0182l|t5@@ufACCHd0;Zd%)fiVR{xDEU<NLX`rY&a6ydPidtdQP6<j-kaQL#+<
z%X5jqWI9->dT+zx$0IUR?5MynJ-c<6ro0<;ds|Ig&X@DNJYUjsmz{yR(6`n~gH}zy
z0|qS;bK?Yv*z3{z0SfKtaP=Yh7W*$m&)@KY%&UxH<Lj^#qjfZ29Zp_2eEIeg+=w@r
zLp(Jl)20TzioH9uWZ>-Q<+p8aUN2j8@I*PgFvF&eyhhNIqjrNU2yDQt?x&Q>@A55o
zCSSAGur4QtbuDqZJ2hp00{7zch>>rr<w!EhO}ui$5nsl_?{E%cuE<LaJQ@IzJJ#wo
zwFK=IT(vRT<A^P~z-p_%`~S|e4;F%Qyn_7R{j3e$C&j5+i0<pIv~ZG<baCIV`#Vh8
zfP>{hUhB(ce3iz^s&bNrGodXAB9P)?H}1-JM&GFfvVNCt4&37Mvw{!Pp7j~9EXv*u
z?r7@u*n~c_=W%5kqK%FJKw3=B6vfM>ZU|=UyPIt>8hHKV{rT;o$$Iw6n~=NLCgraC
z<Z|$|aLcPnfw$cAk+r%NIn98wI@}yN%Q#5FZEE6EtYD%y&Qf;}THTySA@`R{oeatY
z^rDqBMy>NRW=QiPBB(b&c5U9wUYn6X<iNIakCso$t`{0M_%}WGAzWiV{Vejsfaib-
zF3@Y%chy^G{zOsMF3q7F7wB^yW<2Bjq4!`tLn`)UwEb(9rJb$%5an)#9By6_!FObp
zwZR4ISTp|TzGC{2ilmVfMAsuGSqQWF61B5qW$*Y|BflB9L>!o;1V~FeL^xpD9a_%0
z(F)d$s>`jP)qC|6lxf-(_+#K5ziNN!w8q;)u%~3xi7<!9!DX|WF2uD#a4>sJ^T_>6
z(A_TQtpK>g$$r)9t3r(VzOz6IzOH5{#uXb$k_5zqFYY<dn?G!<vrr%`;q>JF8ay8e
zTabY)l0E0fd78x|*grZ46z}A7CGm^%JYPSKw-1*krYd5{_gArG%@V*n=H_kDtpPT%
zAJhCEKQ>1uLiuWG3w(7bIRsIxw8DH(Wr_+aGZw<S!#d#!&rMB<t+D^Z-dhL7*?jAw
zBv^n1g1cJ??(Po3T?T@?>!3-n-~@tOaEAbcTN2#eVUQVI26s7=Z|~o|d+$?qZ{4b0
zx9Ze6e+<m@`*!!!&sy!>YXx`T;T?*Nz9ioIvT5)QVp|+1&SFGHH6~T7-X4>nD)&Nw
zc&h$uM`)6)nS=1J*P4S!Qrr!F{6Qx;3vScopWn+f6k>^j+@!ksafi^GQu2~sx9BYW
zMnG265g|a#-*#YVX=>DPr+q7r-p@^>-B&tQ!F4n^F6{bp-KKM;zCKHP#<L@->vGq2
zdoxK{rE7fXDZ@u$);}{IREf5wCL+`BVmp9TseFi#Fd{d-9G_<MMOwPtUT@Mt5sz!7
zGy}Uqr*k6a6^*PbiW;(<9-ZZS?LBC*Xc{^)rHGUybFKarxFTbvpQw0WLW^2@xo@&q
zH&q2a+j--iW5hiEWw-iw>U+*_2`G}3+^+B$48Ap_GsQIM?inGq%yW+}StSd!rlyiJ
zKexPg96mO5wyb@x-Bf4H{=Jv~)y*;}txlO^spq9z%xQu=?#cln&Pqd%smmMaAr0OK
z5r^+D3f%*GUD6N}6<_(Te!5Lz)-Oo-8O0RpqbyV(@Pk8E`g2aD_CpJ81=*IuF)K98
zP(F5wG;p4KIfMxNr7rwVTk<&EkoDeFdBCyAcu8??e0GybNEzSGAUW(h$aFTsw429G
zf8?V>>CuLj|JSHCLG>Osyc+40{t<CsOf{XFMu5z?^ITzsXClHWk2w?RFwyJoYH6~p
zkZ-?<P%so}Q<gSaYn-TJZDP$9z9ri=zB-A*TGNWjL1#img!{&ENFBxbZ9BJV;A2df
z<Q{JIreSQe=jE>4grM^?0-rbZJN%R`>?ZI7lX40xf77LVrJrk*A~$)*h>Sv9V<m!1
zvI0hPya2wAAk@8s!*!zw^s?^}XPy%Zawc}?q&a3W=Fnvx`h-b@7^gp%#@em{_YV3_
zYj}E_h0}pRQhNzSQ2;3`u9;Mlj!T%BVV0&*R)$(vB{6@~cc`y&%2Aw?Wfg6=$CXoK
z05Y-)sad<}Y;|tvl5SKl{juzlGJosvqz2GeU@c;GPANnD03%B><~vhEH&A?`ZHY=9
zFYzn8x!VOt14+>iW7LIXW%J!vg*f$}ROd4`=>8ByTV6{#6G!aZ<N3r7(ZtqkUl(YR
zHT7a%W?3Rg^fw4UBXC=Q)r4fi83FmL`pjkOzuXnO!w!Cf6{y~TrZODv=HXjrW+Yh+
zRmd<;(|J>I?vfMC1nSePOxLJ58X$z5(D>(BCepi)FE_$Ds`^ohM)`ZLOw=O11dc<e
zdRgj5slCw&XpTTNCl4`*?(Y4zv_+C9YZ^_#WV=`VggCtWk%ba?Y0Cj0Mw>*+4if!N
z=gU%i=~*94%RrMuiNf54(hevXV)(!5VvYmXI0P1d!8cPw?ThES=9boWZit)9lS%x<
zy5?H(*-E~QR5gCLiIZob=Bkj#sUhO1med)5ctZxSx^b2ktEj^v_F?JT{Y+u`k(S6`
z)qCED$soSh|M+uB)!Wu^0nqO|=ba$WlA=-O&R2?*`1a#^j)d7cR{IM9qRNAACmOn1
zqmehIGA-Yrm>(tfu^3_a)_q5nK%|$FbRjx~bUN@uQ5W7#klMt|ZO^?bC(nz{u9Dlk
zA1Oe(TDzjy;+*}BNd}AtO;z~;?xg;dA17R~YtB|NhH8%WMdKOMI6CCy7M9zbqyg>7
z@JnKE5j?ux)Rzy00u1*J(X49E`G?PN4r`WV99@mBZMMfa7NOf!fbLSDka23`lp=DX
zBJC)%e9L4#^}uj^tA+GgqQdG7`j8Gi@jE$HSV=kDxc+;kfQLj~7_<}GRr5`(h}1=1
zT~`6yMo*n6ugB{92_Ehj^qnvLTQn8=%fuIFbMzLiDcG;CaR$JiGFxhIuD`obK?L=0
z27lqx)Xs!bF2CD0QnWtH^VCsS&aRX&R~Y8^+8HMPg(#fbh_nCteCf|b_ja`JL|r{G
z`g)@x1OZdn(U{;!)lD}Rz9mr{A57iw?he5GBw925L}^Yc;LMOQrX5G>hVYGObTt`|
ze@LoRQ9?lgi5v3<uNpf>PFN~j@T=a(7$gW9g)2Y8kHZ(fL)Tk|l}mP0#zxB@S`xwD
zi5y^WLT~PnGb?Q_zLD2EFp5}3GClZ3=VilD;wCD&$gS;J?U*mUo#tpr7PQ;_0vjsL
zN>j7d%dnaU0oK6bDt<%wc1i5jvP2wTiU$p6I8aecCNXyInD|pdb{bL>Qo`bZ!0j8&
zCLei82g=22NI>B>U973I@&|A4ElJWy+N`73@Fmx`iuNX8FC*WN7~E;tO=dm;KYlQ7
zzmgE39I&G`81+E;W`Wk=ka)UGtJKG{#v#i{1|yQ3N0x^lBh!k>s&NwKxm@+rFYPS~
zuJ_DMI(jsfyc$J*(2(T4ZOQ7Tdmp7bXu+O{3Ds!k#!7PwMS?i!1kx(kwuEnkbug>k
zx-5Uw0%_mzBby^Q{=7tm{$7rmI@t3sK`d=rgqpE0Hih06*#%i5)C#B%^x(W0fCgi?
zm93h{{IY-ht|45Kv|LF#{S}cp5CQ3Z8bTIUM~;0%8K%9<Y6`eQB|8<eJW@iEYG+$h
z;|%UlB-e0)kGL=Moc573xszXNB%*K?H4%()$9y~~;jxJ=YHrQI=nc0T$m+=zcE@l=
z-4;%(K74)R0eYdv-#Ay_{ib~?Q#stYhY*tu51Wyeco<Jrk&?~hlaHhg68EB!@uKRV
zImPW{)(l>=pL`RNzo;YM$G8(m?YPHae`n<#yYf5i5NR_yZi#OwQSlghWPSANy7ZV8
zBUq6a4?D(ZiLL1No>an={06t4jg;MTF-}wsZlR3eth=#;GVG<;jg--kWX6<haI8B!
zl?S)=i85DLL`Unz^(EO2g77qCx&B`*az(xSpS&9Ve~-f)bGikKVr3QIR6)3BSObq^
zjdDE^t^3D3;k{U3sRhN741`U?-Z{(9XdMiSV0l#07oP7fd4;W<QC_4Mw}{yZ3ap?(
zy5_;H<42U_&j=H^D$fYN;-5MYye{;p9}6H|cQ5U_Zm{8os0!}o`-3l#rh`vt;rH|;
z$OG{Qf^BbLPoFYeo{)kscb_It*B)Q+tUbJfpGTiSHI;RzToQx#5I-2;eduS(M?Y~;
zfX_36gbLgw^-J-)ix=~g#t~QXaqx|1H2CqKb+`#vllcDw;{P!5zo$;@UbL`pP3H%y
zzFKb=Jg1FpdIyD|?nNQA{Pm9YzrY9CD3EVxYTqH#U*9|<09gI?MS$OI`QJ9KCg6x7
z*L_#1uBQikVSPS1KzxGNg}|3lZ4MUTvZ-IqCY`F_tWMQkclw?2$7P;FF#Ev;YEe8O
zzy17I##jMXWV_17!6Tr-h?Q~3w*jpYraqh^Z}yv(Jg5OE#&qMftp#^*{K9;EtGaMb
z6GLjT|8B*C7&Z;gb?|yq<I#{`Pdk5cD<X6EHaYBW8RJKoDhZcci<<PPOn_Qpby?{i
z%pC{7hn#b?U`8W{6w~x!9XCqCQN!3NwFib}vbx;c1otT1;+k^Z#K$G;!cFnn(k#y3
zhPXnE>gIEvuZxzD)xoM_@D|Iki<9$ITN#~|#6fI{*!LGRlg44No<M@J4JSzBtcx`^
zU0uJG8C@~5Tk<)Fm{y$I94L5$dqRAJz!WFgw&cp_b)>7is#}vYcbkY)pN9nLj2?S|
z@3s7#7teG+mDHbO9abd6u!80EU;A3F-GM^$$}DBC)mW;_NSLgx1rRU`CoX2gA-gq9
zr@6hNe5NS-@dd->MnjXE<LdWh-@2KP$>>5YL@2Ag`N3?{0ip$VE@jFhO+Ie#!ogsM
z4B)aYqdhz0%0<BlW=hoWi2&0g-!Nz0nU&uj_Ahd#j#0vGe-k4Wc!cZ=RMMXM{OOxz
zr+%8s8759SCPgE8Cz}1~m)OjU)k%G!@fc44=AWtK&5dos=6gEb$#l!+f$@Qg!S!h#
zEV$na+8lpnmj)wKiM*})jwV|M9Z^yTFN)2$jcrdJJ^X^3dikjUET^&+2maRYIL0EJ
zDm*OH;|y1%_y~hkUr>Kmkz@4T>3wA{n%<1N8+#IXn05%ZpHR>eSV8n6zQ_xi_9)D6
zj%Dm4VTfMOWYxSkZf&p2*gKlL+vjd?@!p8|$RQVqY%~=_y_?b4fSFiUY6vi9axe?D
zKEe2eqA1m!BUs|(_<65oPQy5-<djf)npg4SEF)|sr4XZxr4pJU?96>$7lX-c+6|_i
z7SSqh%qtwTX}|%Qm}$0H9mHH+7d9+VWM+W$L~CxQ<xW;F2!ukD9;-L`5?`5pE(E$t
zEal0wjw^YGK;E$TpzI5~dAa!5BC6Q|5v6%PUeJ?fQ5p&vzqw;hnCztY^)c;;TT@Lu
z*7jYJc6UlQQ~8ldJdW^*JF2=1zny1_Irj+Zr_I^Crau#Mo?!po2wd-{qCF9t@_)8V
zZXPjq+c{kgGNky|$u=}ps_@i*v^>&*#s?*%q(Z7Nv}?Os?`2Yg7a?9y{z)Dj3C;uq
zqb%&HPeMw}@zYQAwnAZ-4YlzaAmv6h2M?eX9miPz4|%hgoW(x-xh6;zv&_s`#)Lip
zeCZcOv*|;ycchxsz5H+nA7XKSOE)AJ>lxt>R}K|nQ6=D^XqlAwb$WjaE@{(FjCp3>
z&5qK%7j1DN{}um}L9$Z=hWux!gsXuxru<a2!^b_3qWtoGy%Re_tGPV)<&TY#MG1HB
zV{*5QyTThRN^ah(;oZbt<`K4ZCMZCQ+MVLrc*%dZ`64E&Rgph*)tqC3ZLDP7I0Lj!
z4pdy;jQC}LPvt7q9R`}wnE=fZ-YNVhiys3utdZ^sA>Zwt(H|Y%Gp0Ft-s|Sbs%ECW
zyOp2&OF>RVUn_ZkxO2NC`EVww#F{M%AC8n>c^w7ZOhY<lFZ-K8e<w_rsikd6Ji;@B
zfyODpU8K;DkAD7>PLAYk-rx0v;qq5JxrFL+SFNr-lv)}IlUnBhLSlRMeq8e$h?Cf+
zf+;)Z@^d7e%&VOti3|oG-*ZV*6QfdQld}=b^-IdS3kCP&V%lqf)$p#d7Y!wMI-qlh
z33X7!hJo8SRSw~c(yAbaT|L6N#%ZJdMr9wWxTVdjKMH2a@e)LFkM*ic3+yw8yES5_
z<Zv6z7|y0xTqc2GEVDWUqXXw7X>3N69*Cr!FvE@?z6FXP5a<iQ<m_T?pL^-I=!i_v
zrT(_a6(SOrv8G|+{WC)L=Qh5!;N-EfsF9BON*3O1am5_Dd*{5afmrpdP-1hnkD@9c
zO#)O;E{j%slv-m)2*xa$+Z$>VjePaJ-cS)iI=Lmtlktaq`;LQeWct5?l<Qz0kuS`d
zuu?tV5_`gLoJ+awx;o@dyR6T8wgIGOn<PDOMu{(Vi7Cgjo6a!7R^{*V6Oi#VB>q?W
z;5Sg-q5%FHafOepReU2piDBPp^#;FYaXCJskJoCAle_|DpH-576830{!JIulEZ^1m
z&h_&*lB=wwg_%v!V!w@<!<#A)aPjp!Y(hpx1Xn>G1BV@#8TMMpov=dJn*gKrX3qtd
z0NE0wp~PPM%%HiVAOpRY6@iBiHv=h*#V10#CULx@R2hHXutP1mCbj|upoH^lea}Px
z`Ica-y&Y|ml7*@#Fzs3A>{dLt&9#qLe}g*3+mnS^@dlKT;^?F0r^zeJwYK}kep5e+
zw#%lC;DZ5zOwUJE(jN6(wB0AHQ-c({R5^tsmm=Rrk(8--ng)Wu-+&+twO-`x8Z$xY
zzVQGB=+nf!_0z;RZL6?muchW=B!?{jj4S^(VTXG~CwA7&=B8xtLA+98W52h)JU4k!
zL^wnR+L3rP`7y{EzYzSioUhN}Cs~8z_pztoH#rV&DF|zpOiX=gD32(kCQR)3gS6cY
zmv<A0=>$o2q?#Q=;<94&Y)Y0tpEiuNB<i;d;zXE*<nnxRTgcMWaoB*{i`<vj08M~)
z+1-uC;}-Ehiz-mLIw<HH#7Z{2aCp^j+am}02v#QxTb(D~!JD3Y>Pn7uP!5Ls)uONo
zwoSZQGtTX%U~@v%lZ$P@5MIz&Z~pU8ofqd8^YTv%z(r@q3+{}<Rw$&QI0--E>uasG
zMI$uU@RhLUSH8^6*AU(#lBTeWH{O<+x|5YIR+3@Bb-j<GT=GioJomgm?-4+s3=Yqp
z#<$|xU9P<^`Wuuc1J^>B;D-6D`xEv_ob&i2AJ_@1PMN$SJ6BQ(Qsy#bsN*IRdkX@}
z@dxOj9uxC<0TK<Zoj1WXMh3K^Jt>+<GW#o@lp1=V8jM{Mym7wd0{ulL2;B14drpdU
z#3Ba`R*NBQ?^I4`4)ZBt1jEg%Vf@uHMMs)1uMl7UQO84Gp24Th{h%BCm9;_k=G(^N
z`YFjxAR7<wm$@q=?d;c|whFov@V`>+)DXc;a=>p+hF6UN{1F#@IqUct()OVPSjMMg
z{&%zYkE9=FS&-xMN|phJug!_`F+<2Xus&!xpSi;i{K|D{(A$>6jlqZyoKs@ytBTQ^
zsQE*<H{U2NE7=ymwX5wWp(QJ?FTif26`&QFjYsXqc^W&+BXrnHb?hcrJcnP2=NZLP
znshRpSDvnNT(?Mxd-VlH?B+mIx@f`3u50n9fl<H3&;BdRm{(U>P^Z0+q}!>KpKbuX
z+h>Gy4eyLpn<rB!5G%qaLiv>O6Z@qi4l{AP0Be=?_YE8fy29W(PCJ(2ZcTO7rD?1m
z=TuRg_s{8SaS&Zt2$Ei3kGb#;BmRo9t)C9~dn=$X(~d1C3g0OQpR<%4!T6&^1Bq^A
zk*)WrOOf4Nqk8Sipvzl{{uxM>b}n~U4J7!SVaaIvr0JGIIKQ`G^rWOavfQ4~(6Vc3
znv_)zfzw4ilK}(w7m}q105hWeAj72x;|+$+45$k`uy{%jAO&8vXl~Nk*;!IR?~nH|
z`TQ7G7;MFt{FrX@n6tR*0Im7%?jB@pQ8vo0c(NV+BN8R4$Y9~ND`5FgsnNc~cG7Iu
zK~V%Nyq&5#_U+AJ=|T&jBrUac!TmvXO=+9hf!MEv72S@$KpI}UPI+%RJIfYQNS{UE
zP_!6rOMku*D$z!pL<iG|K^bHeyENbIn>uy?R<q`%7}=at1K)DS*2a{HVhU_VKlwa#
z4R{2M-ih~ior^#=u9^yO{MAlTva8k%7xs0RvPb5XJ!lFPDt%Z&7!&l!->?0S{MdW~
zSeYBF1FLB6<r`a|n^IZqY3v*FN2MAzr?_D5QqVr$i-tWy=rot8p|n^mNuX;~hSp%D
z8+TKT8*6g0%?)R^3`NG~WEA{DZ<d9t9m=qGM?Q<}!w->(^QzYm&j=&i4~oIXd(Q~O
z0h|m)EE>#v*PPvSg$Q*7#_oPNT;e2gMqhc=ViMJg6_Vtw@)hwfYTphK=e`S#dsJn1
zF+9neYxgR#=l7m#64KViT~@<e=iBzy6w`z|znrkkU`sDB$W-B0YpidgOTxoO(o}Ot
zHPpS<8$a#3O%4V=bYTR4+qy#zy{4?!TDF@^a^nq!zQ;69HJ6`>iJF>ukl$$Od^KR+
z+F(mR)j5t|*Lr-e<b)k|(9G}XT_amp3!1K?1=Tf1EiG||M&Z3G7dk<GS+Dg+j4WQ~
zX+{M@)#!2gX@WH=2xbha>Ck@MVH<zKx{abZ%7rgDBR<3nPNV=(`*ZM;df2FNeC=4N
z621Ra{i15Myl@QiBJ5?AR++oVeY<4anB#Pm<vnFj65Fmw^ELF-tz}5cdoiaD4(2&t
zQAX7{r#HnV7A`<%)qoZVX3OPom}2qnoprp`l_=kzrKx4Qv>#t-68)f<AjNaxF4Mke
zgi?B!?h{=Ro1RAg>nE~$73EEkVOT75BHF=Ug#eK*z8P-;yZben=N<s1%m(jCc$T#L
zi6vFy^rbXfNBA+4FWU_J&~N?oex$hMRs9?J?hLEjuAdX21Bd$DHCe~<Zc$f>&BEDs
z?!1F>()}gb<num`Shtrg8oOxZ@0w|cQEJU<Zm8#bVkt7NT4CQ%Y6|5WafNbI0&)=O
z5)?NZJgc+-QzHKPyGuD$MK>A31KVdXnkMbm26gU~<!_*i>l*o|f$8A7XN0<^-wuzt
z^~3k<-#c~JRGRzVHzsXlV@cXkDo(Lfnn*@C`08L_NlENUc?Zus1e>~w>Rp1j<$HFw
zQa5tH>XN10lP1MW4nDr9_UI4pys_I@zR?~mURqgL1(a8NI5!XeI^@FIFi92RTv;AO
z8lYgpD=6EQB%*1qP{X=ORXO=^hnen!q{qkYG`*9>5-Hj&h&}L1;8sz}Z2?=+uBPBV
z_wpvbX*BTQ2^ISBLFJU;bJ_BZ$<ol%)YUe?dQQJRQfG+aSRe935b?CKp>D3fHyFR$
z0rKua(8G0N6lc^7Zh^@@l;MSLLL46SEwlC7@7BiJvb|xijcnZR46p1nQ?)BJ9cb$}
zZcsq!kJoU28-6HN8%Lxj`ty2Ihp<o^rmL?ySC3o*#URsQXxD(snwr`uyv2p&T43L>
zZ>sD((ACr^^4O;mVwg9c66?4s;hR^wN(dm9`0YQ@bd#B)vO;8Z6rTRPeQ_NOB-(82
zo%94vseaG!D&On%Ce=W{l?~*B5S6ikmPrq&t$b{dt_oa^En~O>3h~!Ze<M;V;nv@G
zz2FV*KNb0MJZ?xy^dOM}W{>Yq0xU0g)T>yKZEn*Wzb%&HY^YBV+FAPXW!yUcl;N~p
zed%%7Zkx0W@>AK@r_w2gi&K}1jO;~>he=n3Es0zQx@?7AO6owwJ2q;)rNH{BT+HaA
zWjmE3$r8i<lDh8>+F>$QYU+AKf@Mu@F3fnj9l>h9HoQ8N*1Fe_F81Qm?SMoeP$YGm
z%0va<1abclk$GmwqL)Awk7UKoSl?sO%u$A@*w8Gk1Y%5!WBprEcU0aZH8UHw97no_
zu)~ocy~QOCi^UkH<!PcL18*?KNpBIT@#=|knl{}0_1DI-2r8&eoGXwb*;y}_KYKuF
zLgTD<B)ZMPH152p=Tv6eR-;f#9;2fj@@rh{$oML=K&A45dR8`<BrK)M%ip@YG(|A_
zb)c&}#H0k6w<i?4Rkd}TY3Jr;?_)H|qBM>@7NjXB&uc#QXIkxZUVTG2n8Dlq8<o02
zoZ_!zHN%_?mzo_<J)^rIU6JYE^W%_J2mRB4SA%%E%q4akb$l&=v)udL&$;%{2V$qn
z-gN@RO`XPACu{n+bJa}6&*0T2@}b@|ZrpjsYKPTI&wEy`1tWb!Ne2Y;KLWI`S{Uip
z{W8WoPU0hY$z|*bTM4q=d|!zLG!Bd@>?O+c2#XTHo!kC2vEkkj_IF}l|HHV<&|0sI
z1~oWleMsTV9^pzCAQJJI0zDD+vzWeq5J|ELUYI!F1ybhWJM@vL9Jl2cM_MC!&nL3z
zB{?GwH<2}Kto$UEFZM8+kR}IV|3*iO;(Zk*gn)uj?tHA)(S`AhkXGfuHvi!CB=cQE
zR^iWo%i{ij#|FXwK51Y*FTO#ya<=sFbhk2d`sb^Qx&0e-J{~F#s(-$Th_Gt|U9H$<
zeLdy1Jk30<*rin1RbAYj%pBRJC8;>X#QsAp-v1g)&c)f2UCYbd^KTM=)8Sy3GV`$d
zCs;~aMp;&tS=!9W+}+Mh#mv)|UDnyc#nR5%hF#Ip%GuM-6UeH-E@S0k0Y_-&?D;pA
z2NfS5JQGcJZ5JJ9JNVmHRQx>re?$HgkiYNz1K)^>@1Lgts{a7<Z^*w2_x}XvU%;gm
zW&Y;<7eCj(vGGp<;KAB1@`^GlX0HDiV0IZjDozd#4tDrMEue>|m6M{gwF?!Wz(1h>
zM*CYHcRN>47k4T?!GA*a;6?c7+kYy;-x&V_spf8J<qqfN-M@HYqGH#yvay4U4fsyd
z(#6~g9-!sr>gs6a1SjslNab(Z|4Qi}S^j4j?0<i%nmNHG`#(!W&e6=qgNmDrUD4Cb
z(au8B*~Za|ii2J9uS`6t`1u9@T|S;vtXy1N?9yhg3RZSDwtpW9uuFQ|=-F9%+EVdx
z^0KR#`TpAv9&R44fAZA(w>Np<Z0Y}dEG}LSb}cw*y6`Cfydx#$;%h|3%E`me`%ey3
zTmo<|;fVvN{&_BE=V-+R=kFhW|093=9RIb{`fBF(Ru=!aIx0Gu*;ujv8zx>sF7|(i
zz{A7KF74vz;;!XtW?=;{p0}Nam8QHDyOf=$hq{%!w2PCgi}T;o3$QCXds?}>x;VnC
z1(mg#qlXnc91lE*9Zs94m5kM2{bc`O<!l4zfSaE~Q1CCpf7LbLtix4QZCpSXo9lS1
zPqzPD3ytW736j4gGRuPcZ}cyKpM=^M4Q7ZMlCrWg*!bG&85dwlWPCg%89yyN41&Yt
z?NwMNZ2LrmAx~8*=lPB`g+ZE&n;KYWV-0)U!9ov{%2R)b_y6C&-Yy-D(;nCDcAy-_
z&Eg8M_`z|^7&nN|f9yeZskXcP9P7`tZ7}wp?7dN%N%k`mA1C1M0jPro%|1YGqMf@R
zu7jP`pOJOyp6*hJ`=7n5O3D#CKBPSnS~8XKn|vZD*4q*PT-E4ueR>H?Zx;@}>&cBD
zM?gUE%XyR}g_0v6T=)$LBOt7!{-3}9jSR4T!^7U)_pD=uf~|+OSU%i0x>jwD7-_u7
zp{R}v(gSRMsnNq<DG5ye(N6_KXhu@xIipx?awusW3zd|XtKB^yO(5lV?&C3fS}-$m
z*PEt~js4K*xZ=AyntbK7!Dm{Ijws3=L(D6sub{50Bqb}Ut7sx;m^fKuD(=ObT)@|M
zK8@Se`K~ZfG|z|{p22AJpOIp0xmr3j$!&AGw}UQ|o!cvAEW%cmSMQk6PO}|U>i+<8
zi|(}7uza(RP185C$=yyzUPNyo&dSP!qGHX6IJaHTA80P=5w)V7VSd8+_F#^Z9#>7#
zYhK7CpMrMTA$O1NQPPK%9Dm$%si&3B<!!0j6DGbDS!Mq&w(B6+8dBHtsDsBdqr)yl
zet&YodBltbzyzZH3e$hXA0@y%q(E)AaMn)oGayhyz^t<IctDcZfZ8bxFg;TNBr>nJ
zYDq}=(Vpe#9!hz1fCa#S$IOn2{g4LaUt0ieb&UL!9*$dbTNaISp90n%J9bx>Ri0ot
z5R83K{8(w!a$#U1bY%1bfc!U_LP~9h?<kwD<bvB5ndDEOK1Fm_dxw#0Sr|wq&PP6#
zqrY~mb?svIg1xt0Bij{$*O7U1KxvE1yy{$RZUuUY3b)KctrvJw)kJRJpj-|0EN;(9
zZvASu__f<9{4{!Jjjzl-fh6N!+6jQeKQnykwx1qwz0YG+;>jC}0#}=J%O+V0_8^MN
zRo%dTymj-ce5wk5)e-@SH6%i5sR={qH5_I`hW7_57fR~|UZ;Z>$n>(e3O;SB;j3%&
zwcqIz<no_xG|<oLPN~5l7eY@WxI{9Y7Ms7N`%D)T(;Nz13$RKWYeV(SKJ1*wl`$||
z9-vz=q4n(*%!yY_%E$kM&0snXjqcvol+t7)V8wezeY6@~cutlDQ}^KR>Zr|b6`gDW
zDhsa?Z2crWjz#H$N6g*Kx@-Vjp1^?lZF26jmtzLQN{TF;ymybqF;}CZ-jd)Ku7b)K
z7`nJvA>nSeS!27G(KbFnFlP~Jc5-U+6nRxxG#?P-FIIq$2QMF&U4z9-b#?88wH9wl
z{w?xvln^%B-U-qYz&Ty&RV*^*Uj}tcVZ4c+aN095O9l_qCfF3QKL_w0>ObByPH862
z8KwA`g0b0Qx3Ls_A8#gpZwZ!xT)13c{*`l@ncnwrXFItsbQUm67746NPyz!HmzqlZ
zXF6FKfi`Wk?MCNuzTW-v0NJ30gwHNRb*=sdTrRzuwv8OOnlp2W4*?2f@Pxt?JYw*@
zKQO0c)(SCz4)W4G-kr=gWW)7C0lcW6;pGE<oE~b+D-r$R(DSqW3+if<EL~T|sMmsH
zc#m~e&~`K!=eA(8A|;3$&ZcuW8AbhKS~_00ou<XrI;2MNqqz3akOFM6xf!lWHwpM$
z3evJ#(vJY3s*elFAn%;S=E!-A6Zx8RE8T5f&p2oQmv+SU_jneP^_?3{6}55{u@6eq
z#WdnXA_^YSEgIuca*d$#B?R80J*|r%uV6h-)$J&AnqbBN@DB$RcyUBa8O3acvKTH6
zh|UB?l6cL6p6&7LRM$M$-3d)(@@<OtVkm}%^SOH4x}yH{pjn9PZhKwNxlPg6@7<by
zl!6Nxtr3?p0XYM+MDbJ|Dmm~H>AW(x_8Z!H*NCKm-P#nzPv@;EK`r?DC;0K!e^W$H
z@^_H(hn>g0?Mb~1iWO(x$+zBBf73xco0RzzcjRAa*N>!1kwU2keuR8qeF3QYEt=)i
z^Z3I<>5NO5b^h5GFcoMd-w9Ip9Ha|YE`0Izrvn-gRiZ5>lasDXfjK6LetDMjv$z@<
zyY~0I4&IiZ80_mOkGaRY%Qnw#-zMkv+U~|G@4+79Uw8>V2s|{os`oxH^B~tzem<+V
z_SE}lV;j|_kQr3Q^o}b9=}%Xqhd}mdE~NX*`y9f>bKtyw%DU$-sYL2yNKj+mwjICI
z$LW@Q{Ue`Ha$YF8#5nCJDA{_OwM||lK;OVnpM%t4G~qiP)~r279F{+rm!6h3o5D9G
zg~0@%nALmMQa7b3SXR5rpCnb!mJS`p`V&WThWTUX8~YC3nc?hQSgI52_>8@Dyo(x`
zb1!aE$f6@MrJYt`+C91Kb9tTlamS7G(yzj=zlu?t2Kgxi`ZT4%FD`Tg{LkX-_FmFz
zbw|cpp?S)JD?Fbyq<KqJNuD3A-1wa=rX}?_QUr_!gg=>-Z4C-<r)h&~^y39>8&tGZ
z_G~1106=+>F37T}tXbY^)>6~fYHh4a{~O58XHLP;cEsJ}Gyo_gr`_VJt01t~F9x>d
z;m(z)=Hfq@<|EU-<7Q??T~fgJH%^-+g|MUYz1WWX->f1RJw9I&D{RImJz;hzdfUg8
zem&_xu<<*ObFE(kH@U1WPrd+j##ToA3&Ii?-%s&DBetpGbTrY#3ZD_fd(gyy_ld#h
zg9Gnb)=UYazkc-<INWztolWhr3Nv+WTnuoB-6>xM7a<EhzmM7b1(_k}uj1lW*VADe
zZCwo`RWQ*#BQIXmVJ7z_WB1WJ^ZA37JN_3}<vZFio{O8S#N<Vky9&aQLkTI~6#DkS
zx257H%KATZ+rKy0RU+Loy=N(<WA~Ufo4+4fzcN8NXJT|z58nD)v%DuiO%-mb{)xLK
z59h^t$p{cgA23C}r2$Qi7@Mwr0#0Ix2Z+(a5PmeBwXc^}QZ0X0<_X4=*^8ehDWvhZ
zJ`CSq52U1ZG(kNYbz5_HTzzeT#>s1SE4{4yV7Jpv&;!uWNLaOM$?KfXo-n{&tAAPs
zx8x6)Lz~h>4bmV9=iNKpXP5|n6+<ij`S~=0-w2nNo0mZWCc(&MQt?i~&J<3?3=hA+
zMM>J0^m16F2?R57Plr%5#btFRO&ZRTCT`N%ef<e|GgKGW4%Z-rntT0p{(xPoHu8$4
zWFM=+dAR<cuUIT`9j1{a9_8K~Pmx#B{eD}&Tw7Ny5Z5j5{4RE_bLl%Zo5{_(b$?@N
zRU}r%82&66w_!jS-eoqG1!Oha-_jgS8omy{5y<A;QCpnsR5GqUUd7caFwp9^Yd8?X
zr7Q_Fr;(D)32sRCc~<J6YvazeQI3kN<VZgiTFvc7WmJwMS_y9a9JG4Rc(h7}<KZ-V
zHti`edKEN91B2IP<KBbB`sTDWP)vLIorIKuj<lhn^+JLq9g>|Xzq4E?ekt>C*N3*>
zKrs12`v*Cx3!ewG{dS9?4#zT<VIm=kBY^eW2%C%Y>Bl0vp6dsz$?D0%bPGwXtQ-qW
z%6<~ndh)hczzBGIOH#%uX>M-5|A@*@%97Eu_SH;UAUJEyWhkhF?eVnk<~};VW*kFI
zZ#yvH?nYYn`2H-bsmNfq7t0jis`H)iiO6%1mC0TG!#%uVF-^7ZVhz?l=YdV1e{o@t
z4%SY63^J0u$J{WsGRa6tSSJr=96NWOeHk)HJ1dALoX@$~`O9LtXT!cA+}=7od*_5{
zjIs`o07vQ7<+dxcK>E#(4iDK{s=tJ+NbAU^^XwX`#z_ne4N%+-=W~edYJ{rE{pc5k
zDf8Fl#J0J<-#o09Ks9n{>(Hz6G5%TS*|6jnG_IQ}x;%FDcyI_l8oO`IU2N*Mdmfv<
z_YtI62;Bd5oHVD)Sa~h5a470rf1NhkS%7-IOeylw#(({5)#ZjXUj!yONHpMN-OMPH
zHli|~OzfkNZ`UgJ0i@qiEl1!GY709IIDBZqB{WuRF%=5jI%_$|Aq?yg4S7V&|3iag
zfun^31FH=VsWQL!)i+4FzXEPF?bqe3_~Z5;JTxqYp0{)7aQ+TxC5Qm_UoiOQ9#d_7
zeoSW8Y8PxF12qcFxh7tHH470J9k*$Ff)5eMuHr4;l(bBZWSu{T{MGM?EoD3F%g4~D
zG}7BAX9J3{$hgLrFJD%LljoHd@+nClEnRu#EYJHNF1UL2_c06en}F>$IvtXht4CA4
zT{@fnmHD?;P5j$Luda0E<B!Gs9Or8-gyYHAf>t(aAdHQISu6XlLuWl@tc+y**&<GE
zQ+ALtZc<gj8Xr%S9bfO!J&y{twn}#)myONF?$z$QjO$6#7pl~Gsrd!%IJi=<mDWcN
z(I$?z^12gwLS8!dY(WNk>Zgi?>CZeiDwC^@h0E3Lv|{p0ENMI+bd|nzH(ILb4h;N$
z93>bpAu>Go(AhZcMiqqF{>nf~IgdjNPVgBJHi2v%@Bf{JjLM<YJUclko~b0lrw`1g
zQ55Z}u`C#;zEE$jQ8SnP+3>Hi%76IT(CYO`>}y#(r|S|_hL2QUThY-k54ClTZw(He
zn1ic4-2rZWvkxO-TVsT&f~JOkb54T>*S$L{ggJ2E^;=P8!#YXl6w2&Cr&IKwn3&?Y
zq_AXn;rFznqT+2JnyzgfVZ*O2J`g&hm`wO>$C0*{JILJFl(P$%(+F7aiHfPMHx}(?
z<Ifh)gBUWltiaV%@KPkRiUUZ>B^<&Zm<Jzu)A2X9ewHO>f>z)56_0Lf(itc^+xR=;
z!Ye6+b$-=&P)~U7A)e*#4&~na$T8gq+GnktxJJ+K{*?VzXnnfiJK^aIdNA8!iPQ1n
z;@hS<9`DsJX<iul^XxH(UGMC>^9SBU8*Q)l;-AZghAs^WTj|7p9*XMVg>C0v^LSro
z?f5wzhaGsZ`Hpt(iXP;Tk0^_wqz2eD-?Cn!FOrZ2zGE2N(%p|my~g#Z_ZU7i)#j!A
z1)Z*cpbkMfTn2sbZa3ocDIe^~BOegX=iB(2H-_*=r`M)T%rUn#k7uJshXDsCMz8nz
z3X>ita~4E5keLzS=nNm&->x+lAJg9s7=OBDv&MWqcE{RC+5WeojkgyPe|71*HzMxt
zl#K?ZeBi4ykH0lcxO#sjH)z%W;ed6pA|_Fnl(CvmRi9ftC|2>o5Et{D&9^?i^r!1^
zv<}>1on@-(EJni`uGbLX*p_cyn^t+MgWs>&%2#XsZj!Q1w9Heu<?foM+u*?DR9%4U
zvx-LF@@51~x!!)rLriS&Q9J#5%iw9g{0|J?n6<yZ)d9?1oGjJwTdkQU2b8^kxM6vI
zOzNMt5@c7d%fYu68w^&@$ka{qfSq>FWW{_RxiH$hPCPDT0JoCorje0m;^E?uACr^p
zZo>|sUaQAv$sL4jPF`YbTR{HNqwvnmXYK1xgU6qf*icp4$wW5X2X2`2Al0Fx=h2HK
zUc*ehq5{Ig^`MUsf@nn{T7ePaXe*Bf9;k4NPtQ52E^c_4>J0JI8O43H)F4;LT}7q7
zx#4wOW5uVxH3r<fp@5M>Ku~zu4==ZO(%SeLcon_kMBs6EqEXmkd(DJ%XD+IRUvG?d
zDbuIcpG8ycP|Zg$V{4;5c$CYIsZ1BkT8V>*lKI?r%@s2+5MT%}I4Qi)2>RWPy13VL
zTq0KlzED$b#=#Bb6M4}8_%%6ojikvTdt7+6Vnaxe79>g|?=KU3_I2(ohIpT(nTKkL
zj}OdEPtD7{0Sw9{wrra<&Y(VHjw^V2Rpp2O)8=dWAqcIY3mRS@+fG-5n@{tHQ*xd}
zTzl#)|8IxTUa_Zu;WT<W>G&YJ8{-1>F^w=~_7b|8a9>>A;)bP-(rAv_l%$PsaKN08
zR&UlpPT+=m{BL5jd#9#KT!v!Nr)1JhOm;Y~&mLCAS&3<B`p`F6OV<)UzK`vtKj~6I
z6w=ej#;QtU6vXwy906OGzc6=JUyUzp%x{JRqDkFFgHA3(KhNW$`qC34Gde}92>A;O
z?gwc~I^%GET8VeI`HHT0Yvd&WBS|{)SZORBP)ZxkH8H{)zLEi(MALIkC2lR2fe!7V
z<Y#d{wd8hrRn(E6q%Uif#fo}6PsH>*Z)!$n&!<wy6ilrX8b?mR?$V9G)b=2=jM0=8
z5AUrP$2Bey!A$Mrx96={APbHjWF#ayI=X>zMclqK8=G^injs}-^eGH?8ZJs>l@1S@
zjqR}$UytW^(&H0X+u6l>?!mCkeQ?uEyGzAQ;k*07u~#YvP<$3nms_vvkhP?EAAz&4
z&A80HvY~NDhTV;kdS3HtI*McN-R})Ed`IeUA5Ly#+7FWTQwu;@FLBJ~Ip4Spi6zCH
z3jmsJj=FpA(xXePSkkXP4qn{T9WHDE`aX~LIy0K|-3GIMfIVacRE{XeqU8rhxR$WH
zyr)Pdl-aV(?Mmtt-oE<tD-@0}5k`)@)^-+j4Skrhe3-g?nZzF!4v0BZjrMrVfKF7m
zS15yoyS%EK(-ih&ryVnWuV0RRjgIUKGlr#;_qHlz@>;OmR#H7XUq&69AOh!^8+fN)
z_TKnUl)GRhm3NG%7#TrXva&i{mFDuxu+Y4LskNi+1B;FCXU?^BQ6rGU*)A@CcJ>-i
zTOibsXCZ;#ix%xJ!e^>?eSu}9Sac8EL{I6PPgo5hTDlRkg7WvpJ*E%eJ-;N)EO&nV
z5`zbUQJ^0h0uKnrHT86W8aF>suh0~Ewym<D^<U_jXcB<5XPCf#Bf~h*FS5i}qK6CR
zRRh&%=cS;v92e^Z2cf;mm*!kv;8jyWM%a%nt#NLc7EO=`hdIi`AJ~u}F;ist)XjJ=
z+Tvm)wOwRMViHS!QVHrRlmjM%SSRzcF7#zRGNQ*FvM32oVj~O97-yfjAh?QQ&$0q|
z&3gCa3+RS!72DAt+R=Q#NHuXi(Rl=dm4eZM^G3YcFB9p=86mLK`tbck_9iLQ7BaR5
z{RDAS_Hh|l?O;!TF)q{D@#J=|RUE2GgkM15cd7e3M8<uvPHDw^Q_%99{R$qG0lAQV
znQoOUN<rgXd5aEqhqCpw_M`uqJnP!@_w6%dp@b;QG!NIF?FU!mhvaL;68&#_+YJR>
zUuFnA3gD*?itSFs{UwwVzmbHn7p=a8;|i>PZ|U`cN<}0TP!Y7f5(M))UIaJD%0Vz<
z`w5BE26Dwe)4(WPI082<AKCh@uczffH6otB?CX0&c(sG@WHffh=KHbHY2}Z+gd9mJ
zIK%~XS=T8Tf!DdzVb8*ayTM@u&+@j4xj!|NPqLCHBPmWhw<2v;yosVC(fj==N`k<Y
z$dlnopniLYz}|-GRRrLCG%xuFSZgNp(Lp!8g4VHvrut=B;Ffhb{dph3IH{f`PaF;G
zOm{5EFW|b3BR*I5s5u3vS?haOL|W-Up!a#=Ji()A&Uruwb?o5P2%n)<zLw147VWps
z40)&Jl6zTDK<>{vF|)0}Kg@Tb;@{%lO^EnN`wkYT6;kb&1d%})-qW=2utyj0>4KeY
zU_B_)ozs4ApaAwckj2Zh>S~&Mtsl$7kEBC8YVR@&w1o7ULT!a%@2{MEAqQvV{qOx#
zb7zYlXSJ^!^+&G0C?2p5^ClOH%jq}y=^k6<TYOmADwzC?@dtNCifdsTkb6=RNb~+)
zM&tnZ)|l>YFUv@_gsYD0>=+cVui)d4jcul(;bun+M3GLMflrDa7EcR@sbPM#<2qsR
zl{;@o^Z^ryp7myga=(c_OKBQ!v3aBSt)ml;x6>A2u~?QJfElca=2Ge`hC+Km0zMwY
zft!(~&0WC|pncDx@9rebNNConCX`MDJARAX%)zC|`atakzKpEg$Gy#0n;90<sCi<(
zD|)J`lfL$F`6k1x<|$~&#L6`3sr@+^g+LuzrQz93a8^xdWM}Q{X!*x)-gA9uBtHgk
zdjtc@=mN-pI9IhC-9ywFE*8%g%<z@^QytsRY#>myKhTIptrwJfHiaS;(NW`DxM+G5
zbIGtDxxXJ}f$C5$P|3e5p>`fW$k+`LZS0eYj~Vze)&_@jFQa!I88pqfeH84w{VFQ5
zNl3jsKmW|K!wm!zGKpwEsJeoI8_Zi4a~a5Smq?5|bm;aJj1%Rln9|+28D_3YfMoC`
zM>fhYhw|>Oqm$%141VKpKsm&5<ve7wx?mk=lZ&g(N!XqnsD}IEXLv>la|&Bpspx~h
z37r@2_@Z@E;_Cvj$!hL`$^ca{?Bw-YS_bP6Gt&F*_v85YMNplkj91bhKD?Ia6-I+M
zj^BRLvI<-Cxp8G#b3+NeJTEi0Xj{I?fCDVFmYS;5!{>X&^8@W2`Ma`8?#=tYz7P3E
zcW5};#&~{dvgmk+7tm5olX*3ZLSnTNgNKVh7TK<l?Ng+fgR^PQ*{|ox0DG!pH>s00
z{?h-x>({eqlzqC-v*?_JhGJD!yOyT*VU<PE6%iBwo8~!udPveD8++UHhb*pgi-ToE
zEYNtWCht^bZ`>A!b<f|P!FM=&vB6$~#b`Ef57wzlyL?*NfH7^rX>i2~u}k{!hLP`k
zpB9wd4#ED-q&~ND8}(pk1x>u&bKRhMqNW~%wsP8|f35OH4{WDaFd!@_2!sOQ>&AY>
z+n@Wb<;njp$6kG+<E9)80kua1e={DXc0MSbJX<<xI0=*+2Jbq+br!l>ad|G@O8%T2
z7=1;E+^i0-)IWbtguEr(ui#NTG7^_iyfcb%n%@SfEWALrJLQeGuFE-?gYX1KS50x9
zIZ=$`;rE`7s7iq^8JuE~Rh;d-1V$SvLIqcW71xai&9?J**<=@6Cv!ga{q5O8(9s#{
zfB5D%A_(q0HjeIem)HXU<HB2)8g&?EB6!3tBL<WdL&*%68i4YJSq6;M42iCzpS>18
zID;~q(@Mj)CsNE9YkqJA?0#JGA*qoDUy{t=QvPI{vGji#x2T*g1oLx$JGQiJW*YBY
zlU{J{JoLjy3#E(RaJbD!c%<vjv@|$T`X1wjNZzUR7Slaqd6c_jM8it!ANZ73Bh|Oi
z;k;UG(mC?!)e$zL_*Q6b&Tvq_J(J>~b*k<A3j0?W7(3wDlE9KvA-Iqe+_=O2HbaWL
z?GnS7D5B`ddwa9+ruV(7?jHQtvMa@jVyn`5P981I)hmE{^-haTzm(tT5akmu9o<)$
ztGw~L&#ovvKTe4+NJ#kTI1gy}qv`6E`MtSxCla8NJJvT913H63@hrE#zrqQwn@Tj#
zr@>0>wp+7k#{|w}B(O|R&RPf$V>t+n2DW)?vh<C#HJY$k!@`gKnMiYX9?!(?tuC{$
za8cj#CZT{be0T{kI`5l#MEpP8kD=yxZ%%Y}2Htn7UY88CnTAbf>_=5t#~q)w)2cdh
zz%@U}ZM<fWTetYGnXJyZ<yQjFHO=Z#1q^jJmYC1uZU(-tV!LJ`@&?#{(7pl?Y(Git
zjzgRIXR9akteS5OwtPIBd%+{NZx@tRx@Zp$)E9=e?d;vTUod>C*2qYfIvazm8R@gi
z0veNW2!VvHUBl<cI_u!4D9y~#?O82jCc~5j-%-gA+805ZL`VKHCDbrqJ$K6qC2kK)
zERG;}AF5*cAtAV_fsPxtgg(J0o@4OLal3<7!Co&HoHOw}jTEbUeq7M9;o}o=x0#cM
zihC2=OX9KmEo5PArhwagqID6l#`(GV=c<B<+!?SO>k^$ML)1Xe=wQbo#&h0nKsZO*
zo3{Sj?CX}=S?P>Irv9kjVo%S@fuqXBj|OQ#`M?gP9n+z{`QC=kovuvc0OA_egr5%S
zK9oEA0Uj%Q&o`Jy_hf#xH9E-dbzFMn-kSZR#0M&`H8vPK+ge3n_PB8$D9XefqS!8P
ztgl~Y^~%Zytc*`}_WKf^#qDs&U(<+ZDn-3&Ki*!lUlVetT|9^HQ+7!ukQ#Zbee|9y
z7;78z6m;?Zezxqq%>PKe)>jrtUNQ$Kdbxhvr(OK0jpU~1E?GGOQ{DfWBaOz{r08B*
zIVyKiq)m8iQ<j34@Q~4UdH$O@au8uJ$#@!vTf6h$t*vXKcqy<XHyzUAHu<uHg`s@_
zKyxQ}V<#$ot-l-xUUj!u=XE@SfVoZq8WQ)a2%U-k{HhWXRCjfvUbwIaGi745$+gpn
zwt`50eOc?4Z(^6|?W3=7aPVT#PYMI$Pv1ewdAiADWaag0wg4kR2UVCIQhBv>1y`Z7
zYLCCgJ$1gO0I$8OuhdEqFK>P9*1Xr%3VT6jvs_iwBs1qZ!K1Mg>N+6K%%KdVMhq*!
z5(^Vs2C;XJ&>4#!5i`?(h4YU%NwB2yh?XxasoO{~<xY)7_vreP({Rw<yT=)gr?QF_
zE?AHwz>suD{{HIacbk$S1!cg`WjQyT*p&v4qn9f5dwQS!+aRUehqWOLF~oOm?^0-B
zTvEnJNOvgyfP8ovXrSaEnM741n=eISveTsL_5FP0k*%z;vqhI+8wVA}r#an+qN*7s
z@F>~k@?vq`A@z|F`Y-M4YV+B%u!0DCKJuf&qU-eE@Qu*^xv|~2b_~%vV{Mt6>Sy98
z{8IKvQ!_4$tTEfH-U9@9PEj%x9)H?KjB4rD6KO-XNi!3@DZLhWQ!HnM5394eetO6J
zfpJE$o*$=%*#V$lOHG(Riq5jMnH15wxbMs2-<g1x``+TSU4s|NZB9X`OzES!s)2di
z{f8WOihZ#aY#<nCPDnc<dS2)JKt-b!MD8rf53jDT5*kjv?NzXKAjb?>>l~jXlo^Uu
zuzH&6J;4jmp7hpl(O^Zc9x=-Z^`6m8)E61$QKZ}Zp7OZtqgEkbf38Dp`S(WvTt54y
z-?}@sH-@UO;9_CqWkBVv<S(bB*ArLjOm62UG4i?8hA3C(;FEtU6WNK&svIfnI4IjJ
zX4z;hNRA2Jjs7GG$njY-ckIW~0tl&fH44O;lOLmGVdG;*VqE<d1_6}ZQ{N&@9<o{X
z#<1>9a0KAIy#nB}@)ja}uk;cW9XqS5!doOX)J$!?8#_r_ZJ292zxB;$&OI)%{ex8$
z|0Q}F(T6N9H)H8hEx7h$!C|8E_~klfaTjYSr`YEBKxPjd`T50EivsCi2(2#<=l@3J
z^{M155{pdn9nEuD>%_jYhGWd7snoHgSB0&?#|!CZJH(s$ZodUc!I%TWwyAPh{e5=Q
zr1C=#RR&-c3T>F<^v!G0kWWq7jZN6N*g<5{UN}{qbk23hUlnO*S_|%P2V=Rn3hBFc
zWHl=hT6_|ZzXI#6XS#x=?kOPn1H$X-5Z;o$J-Bbdfd}TpjP2}=fpBI<Qq$Cl&P0Ul
z>pfs+V*a0S`uZ{>{oFcf9Xv~?k>aPl{o|e9`Nm<zs9QlT6L|Cb*7NB{Arb_Dye+Ve
zcZRuOf8p+&#hLFGdLjNK3j5H<e`oq5@Xd=>UCn0p5b7KP&lI7vL!atI>#ri6bqx;Z
zaUkN7_(@|fpZAyb^QnCYF=QaC$M+Y#khf=;?;Zkk;^<ag_ns-eRnEp%esK0>UhyLB
zVm7!iTaTv+S>ODEPeQ(<;up*$ZxN~xgSR4hXb(pV>51RqC2c-J!p}bkJ>H1|Lu+pY
zzO;wT-R3-7aMZaq&#alvv%nOlio&P6!Vy-P=o!>MXw%)HXG#{rP;-+K-(G{bj+P{O
z4{+jNYo`cWI7vPp>i7iq_R6SIHsl5RoFYL1HS;%lbytAi)bcM)R*U23(=hr_<E~vx
ziw38oA^Q5)7qdD#J-xl~Wr)zQup&BoFF6On5hBy7f;*cuyh#yQii*RB*FOTI5&Xhu
z=VF!6xpFDPv?#S#nmtwFJ3IWp?^_!qn|$^x-8d(6zrXolew3eX(IB5^@scsYr(>as
zP<HY~&b_x|y0Ay<RVnuSp(q*}n#Vh|*XmjsLe9|2L7^#4oY&!@@ZFNp&$I(-KxBLk
zNlAHGPWU;4(KaboS=8=GHpX};IJUxHPx8L_#l`=i=^LXfVVZ6y#$;koY}<A+v2EM7
zZQJ(5wr!gep4hnOdGGpu<YXo1Pjz=;@9OF*|H{DkNZ30lHhL?HlJl$^I%e}10+&WC
z&0l|X)^X{-hAM%9zI4aFaHjM89i%uQ12Hn`=Oqv*qAVl>uautu*=A?m-)1nECbCCW
zHw}gNfBqsri+{Ghc?}wlP`i-&T262s(*LEwIc%DgYh@0PhJ-48HLHINd=NEg#73*$
zXkME8#PCZ+Q~}9A#e_79)fA?HPY!Q;xOr|aDuI2ez=t~-%~)^iu?Ri~b$5|yK!^_t
z-Nj(X2f7s<4V|aQ>(`?a%H+ve+zR&By9^dE*#AekV(u=NuCi9)ae3s00tsoy)q9Oy
zfyEz@TDtfRLh4wf2aaQkvZ^f_YmAgtmgUetzjA;$kIfU$lx|l&^`?>F&85T7_wqPO
zyhSF9K~h3q{*Rz<*EMF`eVOHM>SE99kL`Ukmx>*ZD=g2V3#oEC+zQ%Q9n)fd0gX4a
ztpd8JF5~w{r{=QQMZ+w&uhVK^jSaJ{*g{Wd8_r#9Sppz0C2mo|%64Qa;=r)bb7Sz?
zPSbUA^;XRQ?(w&A>=%b9X|w8mEFvOec@xw3;!^||vY(3EXKh}CrU8?%j)tI^rsaJH
z8Vupi(O|%n59nO;7=BB9e0<y@-}&>>bJy5yQP_Fh!V8YYqDq3=b+<5ZtW%`U#hsFD
zd3F<tMdU(H-G73(`FLp<H`@RKWWK+^?kgn9L?Z9HFM=^v6|1s}kG_^i843O9Q#FW`
zu!Z-W$8R)<44^7dQH>eEY5g-9Yn=dItqwv}Hu}&PzvPesKu>9%I&R8OBz{3aRk>K_
zZ8+>gs-TfN4;6)W9g0FcC(*E1D=I2u7FKw^`HMOfLOrVc(aK;lXBYaAlTj&VoG;cw
zeI($g*iRdL9;`1UMn@dF)Mj(v9;z^2al__Ib@?T7n+y0!IT@Y9I~?sSzOzsKi74x|
z{#V(hWxyb5Hk6-QMw03AeFKOv_u0y^*j3wt^B5H#OKZ&}Zx4IELc~B(QAp-B>&$p-
zV1%U$qbVsd(fq~z_!We8V2JCb4y=gqiv3cDahM0eK6g6|^ixec;3j_n#lX1|4Gw7-
z&{^++K%dU#k=H2gi0UAZUnvZ%Udn<BSd!CJLpI}bI^f8eZ*KGMumRr7++N6J6m>V3
zhIVvCC#N0a-MQ3u+jNxjeepldywgV>`s{u8+3a@j#BqDlFL(hY6-2Dp1i*&^aWAp|
zs$Js7bb~~$bru#X2*!}M451qVUg6(9hdOyV;3=cV-#ot&Bl@N<XL_D9GEOrZwUU`4
ze8bIb&j`?A0)F+rll%Ehb~l(=cuLaTI7;N(J!XJKKjZzBBKC0Uo!A{EUYmS1@?n*`
z#-n9Qh}wMLWF5B<96c>Y;!xGtg{j}OsCRv1NRYR`IA9!kuUsB@yAIo1>cK@K1W`od
zZ?x(>O$lw#i-C$Jh$I<C4#{Z0`gS$VR6<>*W$gOn%U8eC!wLa{2YfImjmP_NHvKc&
zjrgaOQJd3Qun0{iQ<Gb_Nl&azIw`WfxC_k3ocVG0T1HsW%?d=(1+If%&Mw)JlNxPy
zzq@#|ruuHPBOlM3i;yl|oK&+zeOH%P#J<e@uj7>h)_Fl7NdHpLs&J#JEb~+p_Hc1H
zy`v0r*IpQqc6!ohJp3FVxZC#j^`vI!mLWvjt_Cou5)5&^eoDZtYsGJ@A=Y_&`xl+X
z^ZgltYb~(A#l5omGF3x7_kYx>f`F_2Defj70^vYuDC=w5O$iD4;%zf~C2ZwNpT}Y+
zy|TB1n`jgyux4Ubp&%)`7dFT+v~N!k9#jDB&d<V}(7B-{2GQ{>8Htm%Xmj;oP}Vy2
zdGWC_BAus8qp`$DCQpR?#p1eF<#ox2o{FDx>PGkO-XR9r*8OE-)*S@D7ACiE5~I5F
zvV`d822}*9c}s*m8k=lu*j1!SUJcMB&&%D~>h?erO^97Fjgz(efFJf}mu4WTnn;a3
z+eM>*`v)hEL>KVq)i^oqxO@g=((&+F?!v)Vp?6a>=-Oti``yX*aUxsk{>>IYkBOL=
zigsjrTa$3_05!G{8crGW`n1F0y8QICwWm|@1NRy%&+NRO1xGlbKX3ahWP51fF39m(
zo-uBc7IUL>#|sZmz|T$GDdc9jwreCi0V+@=u#az*J>u;-jZ%hMNkw)y_wuC@ZN;r7
z-VLEHZeki8)*Z}?jE|$Ir@++XadM_6q{z!P+`gWPlwoOGjI+3CH}vC3>Hd9ZlVRuu
z*)MN$G{^y#M4hjBH1Nx&GlX+8B-vja!izI*Lb|=d>}>Z}6JGsM&oTr-wX;J5zgt`I
zt*@!UD?CtfSX=!%B82Xm44t~4KawJ+`*&t+XwX_$e|_2RZqr}~_s!Xbtzt6|y_F7m
zcC^>0;9G_~>R5pd@vWa#c+;scRLcdaUhpZW?eMgDs_KSDXw&4|oBe!kTZ+%Jf?{=j
z6^MHF8c9VfCWBbZ8V}L`0X#zdZHD`^b=qGIJ4wu0f!#WY;Op(aSnuc)cf@zMx4-sK
z`Ptt+%~(NJC)CDB2i)~GPr2`V?6!E<IHbph<80C`re3&Oefa+DxzXntccp0(0?!)_
zfd7e&cR72SD84fLvK0TX!g6-u#UB5!e;Kk<*>g-skLTV+VrkW38OAg3I@{e**Ta#%
zICS9D@|^?cww}(G!AF^oZaXbGYk-sjGud(DX?p!fcs9l6#J>H>(KGW&XzyRYlJ*Fm
zq2bH3mV{l#13Vk70z`*a%)g@dR^g{7=B(cO{3D&^XajGxk2e>ugSB+A@T!@MS|`|)
zv2to|YSvk4sW>rx^}R)=w|#8@->?t7iBUa`9e(opO=Y@zHYM8Z`1>R2PiCHP1_ybB
zz`in>DI)|8{n!1MkB61=E$a|vW#+$j!9nn~Ii(7c+z^~RJ6weYV86O_D@#6Hn8h(U
z8%7ZR(ILa??sN{g(>JpLT?6vYhi`LCBbeyk>T?alU9GQMiMu@jo+KcHI#}H7(WBE#
zdNM;cO3rJPf0NHr-wPk{;*ZG3!f{gc#|M6uTOG~VFav5=RD%NEB*N$@IlFMshc(U8
z?2HYom`86iO=={>e6A|sDf$zBw<gb|8t_z?5m`9)m2-gwC|rTO=v?UwI%fvy(Y1|L
z6;rM8b}KnLT_@JLr0JtbPeQvteS(xu?Aqi#IiJYxhTx49BMSiJtTEHnS<T%f`y=Im
zjkqG@E|44E=C{!rDap=AeK)k1fMiWjVVFHdJEGY~<l++5VXJk$39Fn{cfsMMWwIHd
zR`I0b$0{9fzVckJW!sKyT?u#qRid6rMa)Z`)!g87K6M`!$!&9hQS9w{{wxL*kJWcc
zf8@Qax&JNJA$PYOb?>4gIl>l4-DE1>L-B9Bls0ti!1iE{9@=WJ#PeTk9C!Y%CK@lv
zyc+>CCHSzQ{&;_ZVr^;CK31XuZ6Qqwik~nRWMs=iLj9w|s1w7M3wNkQm0rP<D}O1U
zeev!L)xOM#pOo>la!iT#VcL#H+Kc?tVz7WIFznz0iT&t^XuY54si6%XDuj^)q*CPA
z8O2T&&Iy{7%_>s9TI?@L4u4?xrjw-f&01&K7u@`h*79%mevEMcPk!L&)jHY8xOs{a
zU}`b$IVH%dY+bYbiY4)`f_G<`J>a#)`QeTb#(}S8h0G8qAV;}tilA|sR~rHDu9t)B
z;pu3MoY_!s+UD6o@Z&TD#{c>LFn8AWeRS!c)#2c5G&FFQY{te8VZR2*5k<Z|!fzGX
z!siH1i$_%m@VX*mMNkIRD{TC?mnkyhf#rigL0;j(f!kT@jlt@^3_G6=HBO4zC1Ms+
zHD4**;*o}dJc45_w#JE!J)7*!P@)GTmk^&&h!S`1TdmfrfppaX%%ji23nOuORG2ms
zYVuY3kjY^wa)g5f_ogy5_g&`V_H83yjm(@?*7zYLPK2NJ$QUFWyXlDVz?LwDLx4Ra
z18<L5yN=$9qC|1S`qrHB4uwAmZ1Ai&*H_td!_sv2PW2tCJp<`#yG^o;1jWX)=BBUv
z?#`dt%_SaXErKJ|XY1`Xdp7YzHx3nqa&WdA;6>HK{yK)ib+-*w`x|&A(p@goM8KU_
z0cwsQ`b@USp89<B{^R+cu#1K1sw;sZb{@8&z8)+lHu-63NV}%L{gk$J?d8tNO7U3h
z{b7%s(;H6?mR1G^=ujA8e1$ecb<+2HhuThmSi1@f$K1FwAzDgJx!ym&-0g*tnSt@0
zr^g`65&B<0rGhwG%zla@D}_BX>i)s6lw6g@hfFrJqvGXhbknhmLkq#ej7+A>T4UJN
zp|`?6TczWQzRk5zsqZn0j2e9!Gf4yn^If&&#xV$8Y;KWPDTt$y4`VPu!02hI3!LK~
zl0<f+x0K#2`~3!M%E?1JH>stymX;TU+61}ZIm(y4Q;xV#fZOUd5*^cD{pF<segEIK
zXZQD;**4R?=r~3npnvNzXUoXh$zvx{BEiepW-a1VDxc}(Vt*YeiAaVo2AuI)M4`4u
zLW;I&b*yP%HbNwiH0@hRhFs3qonis9SHLc~#oRsb1C^<4@z3-CGVgZ-@Zwg?d=V<N
z0KGJ&o#fbSjMM&odlsRCw2yemcPoQ|Fb^C48M}J?-PyP-96KmOUc88$%rx7+Vd2D2
zeP~TS($<})Ydx(W*i5bZIESm@AteRH&f#FsuQ|kz7+-}BrWZUR%Fx%|FnKt6*p8mQ
zp2r7M7CyxNYQf&u%MB*KJ_G!NL>JpoJx=;$M><O$``%)aAkE5d+l^IxGAadJ9Sr!8
z`4rM*_^xnUeYDNotV$I9=`SzuJZlNf0=~US*!9H{jXeD|f^(KMhShEzU_&^+-@$}&
z2m{n5B<!3Fpwvuk+e{SbpMjRfkA|)J(#gWrRM$%xC&0Yn!L!BF<w~3uox4OWJyez|
zjcxwYHZ{BYG=-Z(iF^~6Vq@^Z^tYZR{P$^GdGg&5bg$=Z@bc+&NzSkonXy~ky>rXH
zBKRNu7W8w2v+>rLNri>eStl)daE(TD^uz51Pko=&0U!*lvcAx?$G_Irp8exAOWg|L
z!C5ub#eLyql_2r@vPxQHW+sM0fpbCO#o1|7!}Qk0uwS=sGaCat1I<7xA1=bdgxrEg
z#Cut;Avn+=vOZip5oSR)`(E)q!O*@f<w7QU>l970n}lD`zpSkuE=)1xpz4U>>e|Cj
zT3K5naHGZca2xNym=Vv@m~U58vd4dg%U_+uA4fEy`eE$eeK%dAgY;#~ms?AJ-2g!Z
zbSbu~cxbRMMU9=_)wO0%J2zT-MzOnZVTkx?JKNlhJLEM`D{iz?-PC2(w9}m<VZIC;
zIDOl>nV7OjYZ2`h&0^BCuF;WCO$R7rBP?<3_m-=3L4oDI0j6w@bdbV*dR4^382;M}
zGJHqoUwG!42w>8BCKYy%in!Mlv-d|?clsa!FIthQZdOWas2-<fW3jccNO2SrQ7`J7
zlyCsoxbX{Yiamu*j2Kp)Y`^VZMUp+)sY#O$PvAi%lRA~ggZKFCuZ{eyN}$P%FH)#l
zJJkCQc+`;s4eab9{4_BHanJqTCKUiHZsvyO5aF}vxkA->AH_tQA|y@<SVR2oeQbc^
zLnqV5L&^MY3*!Jx+Oe$0sO3~v>)AtkWYti{CXuPfGa);w-mW}2d$CouggY7x1aolX
z1l)H*E^F4m0kJ4N$P+qt$v)nXDg3OE!m$jnPRw3Q8k%1Ri;G!U-5ru5ACWfqnIzKh
zwzUAQfP%5*NFYi2L;ataiVpS|0f$*9Cs;jFadz3Fdu!3r<(iWoIyxCfL?@gSZEk*%
zl{N@}KIWjQks>PFZ*sxU8hy`up)irAu}0s&GJz9_HD<Ii57Z^be89XP`TN2KUJj(~
z5Z%e%)_bVNSHC~%BJaU+WTvH$LgNs;Ty1~O87g|i9je6uP50w*lOBjFW?D}C*lwDt
zOOJ0eWKJxH2Qred0P(7z7oF&bgGGAWL!$4U++F2cwtMK^@?D7F{y|CJR=nwtW7RuC
zuyPJ+-Pyu<%HRgup)3f94;lA+ul<Gm%uA3{j~@Of3VKQRI;c=wV=c`|w&4l3(J84z
zld;apdVm7YB%6O;AZeYHUh7IiIM-)I?QDG7ew=kIn_B&&++iHcnw$4`ch;17(o(T(
zJeqN~>6Cj8&o=ai9~=K2{w79TC&z|3WE<<hx%kp!pk52+%8p0atOt?x0@34eIafEr
zX#L%?65Li^UhTET?*kJ$48Bc1Nq(RAxAOoZIy_5S->O#qXItNodT1#`*An%lx(3U?
zXoy>3IMb8TkMps2Uv%1}Dvh)#0UR7dg!=HW5I6XkL$4iri+T~$aHu;MU|VIc+Ty`&
zb=}cnLW*>rmhOAx-4@F~J^GR9U<<o6ofZPxkT5rz-Lv;$@^)~GUU;snyi}s~fMhHb
z$ZY<~_W_b{w(}clvJ$PlJV`}6njH*VeSLu3VDjeVxr$Eq3Aga$7ypjgv5CaX(fR<K
zkKjiO-Rh1qwk@>>V;0`}Cl?{llxkt&-s8MsKDiGgFQ7~poi4VKU`ZPjlU5=SzLhYo
z{oeKU76A%|S}5dnK*V2(aKI-ZP-f>tV`fXYw2c-664E)P5cr4D_?=v{20$?Lj~x_U
zNIyixQ&SU!eqRHQ9wo-))ju_>@#!rRy%cG0pS*&QE|e@&uvc46m9ZBzV*?9{yxg6Q
zzqS<;V|h%)t7V~P6f7`N<iF<9%dd4fhu!U(Ab~I%PHUA;49R%+92CsW^ms<-#yW3}
zSz7Ox-xcODp!{!&Qe5fqoGE<ONN7M^)@?%M46G0SXM$}5jW!9n0VCvVczj507yluN
z9dgOY4VR!Jn{v8|pt>KDUOF~^E{)%2o-Hrp=QJFmA~ZSmn6%%;NvSge;wUX1X4!6e
zSXS61xH1q=K;l8{Jrc03QIE|=@uH+2Lzz6u8Ofur^YG^(?dI&NNF)v&CmlyW3tIvC
z`DFYW+ro1H`F4sSP-I8zc*QB7mLR?)n15Kum3xP2qrnTOaQDK=-aI4G?n|q|r3G%E
zto*MqGHnbnPNf`*tA!%;2QjR;)1C;Mk^-2RbwhuG4@pO7&Lhx*eN7^m_#lTl#&pl<
zXnfTX`3rJt{0Hc1W|r($=huDOz*}7U&B3<sF1OGL^Cm;xY4|AsUu}HW`ureqOdlPa
z(%$7%`-(%QDVH}DKv(k9zWIP4P6}_EVX@Dq-R=>UFGP*zUsax(E>A^Oya;=#hj|&E
z|Je0Vy#Oh`mE9k{X@-LJQ|U;eg=EBnxgXSbt(doN=C!IV?>E9F6!;+{?zdL1zUQ>3
z<Iv?<Y2)t@x4@o3Bx)rQ#i>KDt(~LJ(Dimkb{1_s`tK5ca_nhp&+95c&X5RX-eu0=
zN)jPxo7ncDI!Z_fhfEBO?1wpXz@fvdpduj<5MG^_>g`?%?c3`sT~6Uiz9xxQRgn77
z*%(H`+|#FZ8aA{fB;hT(#tYqzREkrggcu->{Hh;R1-a?hCV@>{iq34x&UDHT1gdvj
z1$f7wX!IIKu)aK_n*LkYC51B9xd5Uv#PZHo7#mu^N-;Eepd)hkqm6EJAwQf0+^M;>
z21Dj#$qYWF$%kS?y*E@|-j3&I{>JL04juXqe@ktM4|}y;L#x;C5$*!kzPH-;cF&U^
zqR96n##n8HQE9RSHyctF53#OFH-DiZ<~QEj^boHMsBY8@5f)vDI@L7J1#5Zm2C!_U
zKs}b(I4Qf42{q40q?pM;Ny~x#yx^e@?EQP^=r@`Eq#Ml&A0(^y;5HAYeHZIob?~4O
zJ)jb*f)>*#h;4SaGpYs(WJ6HSG*tlmIoN?jq{>|t?2Z(S`<{?Ff)VayNp)6FDGx$D
z8GTp+*TMc=CF|AE{?f_aWFI;(((b_#`*+m(bNa3JRfEe6Hn6XES?A$V*rlSZQD)WN
z>=PVZ9||s%N*rSG30Q`{ECF3E*=h+3{a`o+%?FC+arZdb1T-}PMFHbJTTt)@vVCPz
z#?@4{11g>iT0Q_VNcID&jXnC2mi#`Ea;#vVk0EO2k-&B&dhb#cN`KwI!jS+Xzu45d
zWcF!234-iHVX!8ng}4wHN<1ozpY30u$IZ)N#-Nt{ZPs8$t|o$Xq6xDAZNrbi|5hSr
ztE{P!zOGS;pR==O*u&lEg6E!7%hQo{d%_HV(Ro6~L{l!@RKOB!6Jqp&s$0>Ta|ooP
zEtY@S<xn*9o7$qH5W^72&cIdkAkDcnk_6<AK|nLpbx9>8KOy>&QOjqt;$&M-uFQ$6
zU-Aym55mjbYmx}0WF!^Fg8kpMkmEMzS3&SwlHi)jL^+y>gsMLccAi(An}}?Tm$oUn
z(DW-uLmeE9kLN@*rTCL$F7U3A_x8>8r*`<7#0b%7Glx|p^<V#1@F-HHgcE~p?dq~M
zdDXzQtM&+yp;zm%#of02w+hqaofIXG(41}EQ=-RCYEnkQT)`RBw`H=`SP3HeHbW6a
z+U3}xJtRDy4HnZ%T++dUqJE*wMBCAD@wIr=soOnbsjJseTs3NO-XScqV$dLC%%<(I
zx?TdHPqbI~XD{CVB>q;@Dk(b<Bt&ce)8mzqjS1w{eG<CcWC90SoIZ`NwyLOakZ@>}
zl#B>S5%sWot7&Lr3f`uCnPdKh$K4k#W@+}h?X{{~CL4tz7<cVQ55CoE0N>)a1*M!_
z=((=APy@|Hm*xm9M!N7y`L)e0J4Z7S*I2AdD-p{alIKpuP5306fN*HP`uSi{+L>8$
zOUyr>;gp>E+ClD?6974RO}+P9N+$$HF+0lsE%Q*$h5047Dam;eQ4+bQqSL2@MOU&w
z-l8S0wvHIp+goUMZni0h^Z0PnV-wty>r2OtfQP&{uf5`p(My+kGHy2F&W0nl`Pc26
zyvo8>kC&dx!pKgcR+-3%)-*>oATzGDSMTS|t+Wgrd%PU4iMid+lAG;o#-=d8eScR+
zJXi5nqKBS<9v)P#9fc<uYB1?%5{VEBmhA{Bt;Wr>$uj*hHUDdC;t#&*vm`sg2>J3L
z*s@~|9&2g`3?f3Z7pWRW)4Imxt0@?Kr~S^?-31i?o_B~$&aBDD!}Szl6i%F(k_A$i
z$K{~I3m6>kmM~tZ)Z<-&<Am*0bkp1Y9yvI4Sv%y8p^#vRSH>OV5d)u9@2zfcNuxq-
z1%|fTtQ#X2V08VRg)KBINsW7GP$cQB;JuUy=gmt$v#E_)Inpdw=apH&c1R2(8Qqe1
zq@VvIfWsH`0oc&>!kL_RxC$5Ir}b-ytGCh0V_z>45n}ys+KEm?x<JnCi~yl4M1W-_
zGR5Uf{0|!|{tP@YAW-U#$pn(iAU|!6+TH+z-{+fT-Qs2=bu>SvlI-`_@Zf~OcyM-g
zEOsdDR?6X^xUEJuC;zw4z;{0R6pj~r=N+~KK(Yr7$H~-*@HyV=iWoOE@pdr<o{Nbt
zVQ{k}lF=eh_6}Q&=}ak9T$4*I2+9dBDplWcq;f!YSw|QYTn-vUAS*L&4l=br$Ql6y
zKJ>RJ-kp+fFjTFIde$RKjq^qYb7>sB%!GQ%zLX-^iA=QYO|s?Zs+ib(WbMO)LZ|u|
zfAZN<!k0E5-@r5x;nr$jeGX$~odkvZ`zY%OJ>7pRskV-akVN`@Y0-8hM9kUc+aXV(
z-}U!DU4KpI@-1#|pXifG&s!FyJmKBq$v)|f=pOJVHeXgo&OS!M*483ju8>4jtZ_cI
zkUI6PUW*Riyopb0UO$fyqfpXm^a=UtWG%s=A}h^uN?VVPy0OHto5`;|7=vhlj5<IN
ziyWlJeP`9YLnRKva44ab_*6wp!7&`V^c9bmgN8Y^p1NOh)v1%Q!!X3jo2M&%()W<d
zL-$&H?PC?TA~VYP0NUA@d8{{7g5sqDn^DwkaZgIw)zt<Tnh&*XhG|FBSl?sC2m0C7
z&^e6N1_Sg(NuZY&-0VQ&pL!GFk(1p7;b5!G5FoJ8zded0gWxq08HnHViEhjrl|)j`
z@CzQ`fO0l!0<prw3Lj)%8x@^y-4EvaUdicmqJAr$>Dc{B5wBSf&HYdI+rq4S*odZ$
zAd?%lxRKbowbx&SW3>qxZ1-`MBXX`*mrDc9Pdesgf2>&dVPY-sDOM@M<M&B)W!0!H
zo;oSg?J7;?@XN4?frSuV-ddF^C6A8vH^;3LGv(}#Vx#vL%ZSzl4bX-o(%kCwDLVdM
zGid-Jy@G`3=19rEQ4SJVy)lG&^r0IG>YsdfVXCYtVP|Bp$hoJ5C|fr7`$2s+slVb%
z%V6zp5t(6wt?_uAJ4|vii~jay=GBszn9Df6d7AZecCXK4!jKK-b9Z>D6&|TKQ5JNM
zwXv!a9U3-KPCwB_n#)mtYEmoUsY1%ic=}kRg@`d)+M^$?riGMPymogCbjKu>RNNWf
z7M|SbNOtbQ-!ewCCuykfvZ9brv?`GJ+KvWx*!E6#rtMMOwIuh*I9OXwCqT)W4OwpX
z&k8Lq<}S>f;hmEH=-+m#Ax6M!O_E@cjl|)vq9(gpg=2*=Ueyqrs^KoJ67~p;IN@Q1
z%-XvF>87wNYV;r6mv%X@e79|eLI`%p=%i{ER#3ugsMh*@g?@#<N1bo)sXEXD17vM7
zDh;PDI3Nf-(AH~=gsHa|lLHG!TXkGl=4QZIk-vD@d$KA9-{Qf)zUKY7d{Q2DnEx8j
zpxW>5RCY$M9c|_AxRSkr$adA)BzG%!BClSwU$tR+H!QXWUz1~zc_%aX=Psa6Z?S3P
zO|2hD&+?@k3>N9jl4BX@gDx^C(R#Fy6cX+!s^#GO&S)i?0;5^{(+C>kH4=lFYy6Cu
z41pCn8X#z;9R~mBq2QbvA8Sgk2|v@Hp;Ofq+N!Eu(_@pib`B<A9}V@)pfYxPe+p?j
zq-%ASS_>bw24_%r?rv{K{6iaK#1*&c{?oEnN2j-q^sJ@kQ`jL&LcSTF!tDniv!Ybg
zb9Akpxlx_LpytQF#ImpltXIvAt1t@!FxXTB=6{YiH<yYF!(jgcn78?*8BP-fG99)3
zRFxuylyE4oE_;=K;aLZM*PzAF!J&be>}I1DY}Z0_TYQ7lYu;01m>FVv)|+eU3_h)e
zW+4BCeqcCM%Lpmqe0g{<Z9Ni!4qTbWG02BE5U!xpg7kjI=NC_W)N#5w8=p>wcfgZf
z!LPEKW$|;eNK+rnN<<g*+UTXkiXps-f!W{C4R?tz*-+T%`MuwdVU#Vdsi0z#9`4V<
z{h&B$M4ApB;M=gr3QT2NqreVDRN<b9jw742v0|*i-^zwT3x=0<Z@5#5#tz883h-aK
z{(^3{XG!v*`CYaR222nbKx;Dlm;<G*o3PnaxH&q37^!3WDaCwycb(F;Wu4&9ZDjlw
z1{`2}yWKfCtcc_%@$EdKHA(<9$)INhzZke*(y_m)FLpY;n!=@!ti;LYz1{3R<zPZA
zrS`A7IMN3*epfT|V%GL^S2<km)uv%x4+xu+ZJd&U?wnN-{ig4%<A@KZ4ar`ZmQS}G
zJS57#-zU0C5m7=0lG(3QFn&Op7{N>-9o>dZ*5-KoiZdKJvxTmy85l7}7ZncO`q+z#
zsey&+NDO8$!Of5$?+&}d^xu!_86bb&v&p_KWrP5t#=R?mTKCpw^q55L#Yktp6=8sp
z*Ux{fVq{NnkBk^VLC@P5;KP)V>6f2!vV&V+y{l4R%T3E*my|^3yfLVTXkgs?pXoNw
zl4?mD!W$<P@;|lUy_S?!HBMiz?s?+Uf+goksB1*JKuYl(S-8itQ$xrPkX5K1&2#2h
zr#MS&>H`ce^i4YK$-Lx2Cwp;jHoA(;Ve&?=P=7z1EYyy?Uwa(0@ukshc7*<)z>lAn
zURqXQP#w6s?6|@SjUk`rac@swuDILD8;Tq(Yxpm<h4-z6q|a~Vi14R>S3dUF>oZ>m
zDOI^Fpx?Je_kJf8yDN>?-bm@<VVoS9OJu71fSY%47NFTlD#|#J!@RM*ZzAvbvvpB;
zRk$wBL_-(aKOA*AzX3Zs_xswyLx$Sqmn}-Lkp>+1_P?1p4GRgnwzl=o*jaVmArpD`
zSG`odt3<a(Apu<+6g@zPQKbrwWH*H-C%o?_hBe4M&XYTagM3Ix3pVTB6V%26pSQld
zMnrgEgPYmyby;~*`?MIvmDXi1$PTBlG;_|)9}(oFlHGwzLbwAO@hXmGC%0jbcw@A#
z4jN;fx@|+Ila&)$l;9I(R_&Km6O$4)W}}U8R|je1@Q%h8%71y+gFZUMV^UElmanm{
znh+xfxXoTC-oq<H@c$e_RA(9*lf`Q#rOUQU*2%FT;=}t$K@;%ZO=xs%^L*++!*#8I
zR@|E}ig-X?pH4Y(8rn6_jos75$!JXLQ6Rl+qjg{o)rN6}1oBDtyv~4@@hB9?$>fV2
zzD2}uxj+4_O)Nh7LDC{b8#3=sVl*$o+~lqWuePT{wL&1Ped$+AY~?n_6#sJuCO;-N
zb(_F>6oCn!o^oQ=Wr-af$_V(ohkI8jz)^H-)iDtJ;qdf2Y0w~NZ}D{2cG_M=@9!ym
zpFSmQ%)SCIf`Q@y_5?PUKzd#zFS2vK=(gl3E3Z1wWLYhXN;1sK1Jd(LQ*iYYIS~%*
zUC(U6X>EzY@%L<}S}9j(`MhC3RIJd@I~QbNdTtw6A`k$lqXB@8^gQYN?F5Z(Byctc
zQ?I5t=$m`P530ucCX%p7;IKU4MK31UX%yCAx6y?%lY^`0`RFJ+rk(OnAW-6T|Jo$A
zH&b2mseV1T5biJf8m{HZsd*uN-HUQcbV&r5l5qyo_W-_a2&+4B*)?M=-fgRSAq?p@
zaE#a?3?kXP0YKk{gjgowcHOkpY&toCR%3)6`3Wur*8wc70$e|;F0&Z#v{oACVUYRP
zRpagCo^&$*d<(4mjalE#E#}eD$>5u!p{|)ICuf^#txbu1H~IuvN$={CQ+uw?<x4Ic
zQwd@IKVe_aI~#XTJ7phVYPD>7iXOaIRRq(UsdgV3H{3$10qW|OW-CYY`p%f@)QpK;
z)icnbcAD+}x9H6z|9LxL1x18tpwh40SsbG&Z&O#B`wV&DkMjML8EErWwGH2WZ2#+2
z<+a`^^P$Ck#_zj-_Zh$|VzE8XQ)XVQ-U%8S+Sx29wIbrc9P26UcDLhYdT!u5wi}PF
z6oQHOS!HTN;MsqC`=%_r^aR;tzXQu?7&d&-Kto3VU8`wq)R^{87}b1IDMfGAG1YoJ
zRXwOB5cCw`Mz*fy#tC!1tE8S^sd26el94z+S5191=$U}}^iI@9=!AGr^vMIYkb*66
zyKdkP|M<9@%1P<5{^3YY?YD}}<$W`+kyFR=<-yD!KcC*rTi50v|1CG|xz>ECGUon#
zN}NKM3nZ9<eRbg;$$I%9TYlR+_#x99+{VE=rlvx-@TxOM?+(e=btS_{+fXkljzM{G
z=oQ*PNV+#(s+RI>XjS0`9BM-Q`A3lo5)pU1EZ@M@cmk{F$ip4d3Y?vtg(U@kflj&G
zYAj5&o(Vd7DJC0%a83LUujbO2hD_g1N|FQIqrHo#V^QL*D=YhdveCbusD`+lX5qhO
z+V#Ze+U4$<WnI=Tqz?20zcFjRn+80nf^=G}R#ijSjI-0%uK+*36YX{sBv}eBfe6-v
zA-SaVH0vE^j?a|q^@LX%hMe(DjC$8Qo|=9@mVCF%(3n9(s|Q$wfvCss1xCy=w`ehx
zFa2L+{HdhP?}j;A4@^K{1GxJV9+|grYpknjB2#BxZ~pCeStR`z)qxq@Tw6NgcbCcw
z$f^%TIcfeTtX<-vRiUz6^~W?%!x9*-$Dv~IrbV@~+;r#=&$DHepNRP@;0wWzLrgtO
zC|U(tOmF33m<RSNp8u9mekK)M(Yl;Rcu?0*2{2!kqM3R8w5lRs;T<SZvW^efKQp({
z<S-)o&&Pxa77hfyIY(O}?|uSXvZ}vR_}$1$0_I=OdRsBM{|kmv-T(ZPTU_{>05}yD
z7&^sj^q#v-c#5^M-TzjdHv4gAKJ*h>00T?zZZWdm<~M3;c#`+qc&nLh^o?idqTCAM
zzap|9mNdGuEC!1ZXWHxSvX})gEyM<-26~pGd=oU3=sgY0ay;knm9kR|Ykj-T&79W9
zd?_JEN=1hFn=XJn61a;&Q<*#?BLl@1-cas(Dh*z*Bq`d~CI&68x0ik<8Wupe4brB<
z)ogdVZ+!nZc4{v3-XFZH;w+&_{PQAlo&t1tg_oReL3U$N_mn5!;F<@fR7;3iSwbKi
zS?$e3K*3NtQV2rE0dJ1LMJ9FE5eoP}T;n!Uh($g)cqhDKA1x=Iq(T7$bHI<Bdw<9f
z4UTk4i&KyQJbwf8<t0Lwm<f!OO~UZ|ml2QuFRissV>(Ga*~7M4ZtiyW1|JT%r%#7l
zhpNMkp6eV5a94k9kpwj^!sR_<9~~#7D@}UTt&klIcbuM}J~}d=5^wZuuE<6TXA^gv
zcE3d^=(BHl)Me#B5b#7JvbI=0h&bUPg!*qh%zCNl&w+d8-0QxlBY{u>e`AD2P{8#t
zDy$wpElhF9Y2MS*jSt8l^=(wR#Ec$hnMR0ykM4N5izZYfyyK^zoiT7SC1jIAjdMbY
z$}5nB6A7X+qJTFN1xBvB3<sBE{!m+Dr88B7KwHFK@#ie`|B`&=127DNYZqv=^3JxO
z;?b*hP(LWyEo-S<=2ibjMs!ZAl&Mkx|Jv_)|9G7G%iognPZC@Rq}SVo@!IYUjL>^k
zxSA@18rj|se<#0EVLCT{&zel(NhdhUBA@en@lN8EQicn38DZtMG_H&inM@iqH9;<d
zrn!KbK(QpnFkH=eAYZV!AJqEnneJ$s-8fy5k_oQz42K`Il}w63hf0Ib<HVK$BS<2O
zms@8I>R{2=Q0$`N+os2gItp<vq2rYpRkzpsoB%$__qdg{a-zoFGcu={^!iasz@L6H
z()9d~v*&;tE>Gny6R8P9l59*Qv@MUvkLTx4Ld2d6!r1SY9NFQSE6vhMPMxhz&>%|u
z=VMCm8>cX@2=#yWqJ(z^@_s%~5>jvE?co-gV2jD{-4RFQW;F~HM;u6{h=DmSCuEU~
z>C%y_XDxTBSdAxG49*G?P=wT%05I#;uZC&w78CL-TW4cK)P<0>Z_?xzvk3w34sY`^
zC!^SCHzWB3ITr}nI)1_p6{>^Yj=9HWaspu8lPeT1;`19RL0{UC0b_#ir`L?zE}zny
zBH_Q2V`_}nulLza65!T%b$DTg?q5kcDYF(oSv7O<wA|TVbiCdwV|Jup5Zi@W^9u`;
ziGHO9hm9kHK-ScojCGn;EvCJnrDjsn{mpSbFdZw0&N!L~j)H4e*<ms=txTs98iQ0e
z?<SS<AY;ysyF<LIA*lUe4u(b2qonftv5{-f7YEs^i$nD-#^_r3!%O)1hHY(}V_{cx
z`PLsm4`5tEL&`#e{yOw_Um7}fNIfcfYy$j1_+Cp)LZ38cf)oY+cDd!a27Jj8c&|FO
zadJn;#3?Vve`wo;xoc1jaR4fvG+2}gvAI}>QHtUb#9(^<6x&o=r&CS`!?R9Klky>)
zhCn0n!c{gZMGyPem|`zLFkq1;AqOwLhQhJFqe{kPym~sjp@aIaWkBF7MqUa`acqXi
z3KoOm(Ri%m-su-0o=I+f$0C6pG#(5H<8awfS?eNB2~a}Z^1(J(?dlO6GqcfU7FG_~
zet%Kt4SpcxI!Or-$k`lwVxy@r`#cO*=rSvjA!GiVl*Qlp1azwh+~~|QlG`O_6e!D}
zWU`rtr;nQN@QLm>=J5|q3Q2s^v<9#C@GuQ2A{K^?(?{xH(-k`Jyg;_$4y7m%nvrP(
ziZNqBo;Dp4dvx_j3*u|clSK$!Qb7=RzZbWViBFVuJSi|z2oS}{&IsmUnMd$-KDm!S
z)mg$>`qQbttMG#zWw7Q5h$}yYbED9R5Gx#$i@E@ni*HI}#r%?JZkhPBvW9c@ZfUMn
zRDX%HFbbi)za&_%Q-J0{><|Q%i8b-Huj9Ubd^;YwkJJOlWwR4a`MnU#IN-)RIu53S
z3VS@0;HYwS+864f9PHR-Ts&a*y%Kus49CY&tMv5s*V>H=?h}tRzV%NJvl0}k-mkqe
zuhV>}sId6K*MAx%SYz<6oAu(6jO1ifVdKWgFy%2agp7p9F93pk=3*gVZR1#^Pi3`I
zFo%N^Um8kDcnm_h0j_D|RL#W-)P)F4+*fiOyGe#Uh27-f@m{HhtSRlZX9vBWf!l)q
zSTO^>8B5QtgKS0`vZCXC)-CEZ{~z?ayH=<$E<nY5D!t?~@783t^5)42jlp3&I?kDF
z!w_`?`Yfj{tMatSkPD3REy<_9e>0~GF~}%nFS_f-o0-u?HzPKVY{h-FuusZI;pc`o
zM!xJUsy&)S(gY(?^Y0?40Yo0GwKT!fcgKA?TvftadwyKkV>OM3H0)+{ET%L}2G-}J
zT$g<y0R>bFpigV{5YX$P5WMhLB%M;eIB-PM4hECt%i3`m0UX!zW)A%Huws-)nd^gS
zJ8v^5FamAoX(S)pm&jqmd%BI_;2ca;2IUbw#gCFQfcNYB)uABbEOaJwb$+MC(^mQe
zUF_{ZpaR~0?X#+xg$0-YV0}iyh{ZP`+NToX%DaG-TnH4Ee)!rF@K9PRel{aIT6Fx4
z!e@4_8gjeN;8F2Q#}^7mKZ`jDHldrd{*LScU|Ncl1C=BkGIlk?Ni}W2V+MaoC#F~S
z1=uNkK5GC8RVL?M2`fRmLuoYXex!YP0U9itcdNmj0CI%00{~XV<gwhB8cjG(S?zz~
zyE`1T;^t?SbH*3AaVRKQ6QI4hw|#88!#*?3;{layqB{H1m(0@=z4#h`cgS>s+R>2D
zZAQ~#lYdZtX#9>fem5Kn^Nb2*K0nP-6NJXzC}7Oo>sz*h*#CEcwi1^}h{SMdbds!g
zNX@?uu&+3%OHM{m)DVfmlJXB*?!G8{-9)?*!EyEj(K|U8`+R>%LICz5$@fRA8AP~-
z`L#X$a1OK!L_%%p3|ncQir!Wi3%`yUnMIFacD2wA{_)}AML<t(DAWA=q}R<O?%q{i
zCrymYqwzv7U2b2uaZ~V(P8_HvXe1GaYg&aRGd}hcWZUNZ+P~g9g=jb53=aQDfzvV%
zN*sKdkOFP;(k=}FW!AM|s{XOZ9czeV9fTuOY(wYpqnS`Y1uj_W-?7~GqvPgGs0YMH
zGO|;x2|OWs2__;X3S%x{+_W@1zbR7}3X~DLpBiL)=%AU8nJW-lWfa}lvwn}vGahxF
zdp9D)cztTy1bOocrj2%M!a#fGJ(abRnUq4mW^0A59MW~d*Iw%xSrWLo*4y;>!av{5
zLjunrCNFI>o`GKF_XM9`_j`(rFnz78l6+s$#JHHDtdL(t7yS=X##C!mE>00~-;!!x
zC3FM-Tf{pgIniSf;VVZ2&U#9QjH8jzv@Axet2Hy_x;Yp7fdnm!>D+v&MPOgY<m~b=
zR<{}1GY$-{-RP0P@yRaEJA_3pRdg#M<|75b6imF|T~>&%b8-U}wf4S@AVU_W`GsNC
zHGDp|_{1>RR>@CThdOysQ0?;dmRzx6bm>1MI!<?6yo$Ef<nTAQ?k=q$&qNE*`<=O_
z!-eb6((P`_CaEW3BbMAh2Zso>AbxsRQm=74VTPa39lK9z71hu%&HN!M7L>_3MaV0W
z_%KfYL1^b>N>zRALh~2b-V6f7D?vgJSr#*S9@!GhK9BZP%T<!sJ{zrbE*@`29F8IU
z522I6owJf<5dW!5u%SAXuV^Bk7*T@n?dilMy!`oaf#Mj{X#0fRh_8>e1;0=KePso#
zou|<t2QFI;iMUSBdT2<JIm>zDCjXTTDLA-go9}hF1V#Aoy-f^LrehG|*S85|F0@IU
zq;M}v3!s+CIUOH?P@z#%kI65Zy4H-1BEC#DpdR0g9eh-i(mdP->U$h-=b)IAn!ZTP
zlRJ{v{@5x0qx3ePfvQ*laivgRSinW%G9TBtmdU3a$>l)7MS(@+vmW|`20E$dqn{uV
z3HJ`AFGFmpMs|waK<9(l->C<OovwN`UpeF!%^2znk|-&%h!$;Np+g#j4Xrk}Bs~=-
z9q)n=?JhT`LRp26_LtW*T#1Ioc-tx&h#=s;?W{T_B%>^8dGcN-k3WukGwo!7!Fe#U
z$G44Tzf-qwv%5F1swQ*g?tDO&8SKoS-^v6-TZ0|lyqsW4S`eQeJw#$XJqj_QSGX%^
zxd$g*y1@we91y}}qATJ^@AfqN?j-y6<O6p~2_7ybdRz<eBU%z!v0xL%FhtLXR#u`Y
zzQ{TRcGA^v_E&NIAEN<OJK=xo?O^n6?dr!FRC=Re%mVgq*9?EBv-kMLCkL(ARMEZZ
zF2>Jn+Pq}Y2qvQ9?$Z8<_<XGJ47hdo9lvow=6E`KRb)acHJXw|#eTIrMi(NT!?qm5
zpUmyiN5ncV2IZxSfAQ_egayDcmvl><p&mDzO~k@2+$MT+ZaG#`Ug?G(bZojQ9ge`F
z5lN?K+W?2Jzovo4!Y_azN4KE(Xk+h;&rA}BaY=}1zg^wk!rDC-u-Vn8m84YkWbVNu
z;$9lqEdr*y#kQ&v{qxx^Y<d_em_+(aa&}9YPHW<RKwi0`ixGb9;N!9G>)KWVu@yJ)
zGLcI?qkNZ$i=a<sz^H47S%(uk^6`w^v_J;J7gk}X-9nd#3&%jfhf%CjKQ9$dqZTD9
zD!~G=Vki^7`dN&!S@E}kz%xy@+e{j>L^KiNc(yPnt9toOr@uj**aZVpLl~0yK1E7P
z!x#kEv;~ECOLf%x#^R*dB7?EixH9YT-N^wRTT+jM3(R$jguq-@SH~ve-f6!H^Q<cE
zu#VYAeGyHX2+<XdUWC_t1u%#K>TP_m;FNrK+XQ5+I2)Z{%xRs(->ALz&tfTOOp$+6
z-j5H+{`4T20GnVwSfpvI|3Kr}ZA&7dyEXpFAQ7Gj27bfkH4<fSQv)UK)}O{a9}Min
z_QS$6a{dzx9I${k{6?wNGL;2poOVg&wXNamU&$!TAOZ{!g+PMdBA0rXPFw`Gd^!5P
z)I?7;H@!TL2j}ciCjuI%TXa1C82<wwwE$;Z@uA53;ou|INoCTNZWfAoDstgWoDE}}
zv+yo(2Cyf5xFdkzrDo=}B_)Rz4DbwlMySoq!2AWH(#i<JK+c^oTd1R3)W*I)S&(^r
z7(=?H|Fd6<v`jl|AJ_ygVZLB{|05M{_?YKM_?Kii2cM#{L<l0&xZV#8&AAp%W(1FV
z+4p+-HwWww&a_WXj1La-uJvZyYF5{17O<&6LBU=~X9v?JVTHh_DY~!YllepzYDmbW
zdtn*0l}rDqI9YoXhImE;_?q5ndEX*R;jIwwndVQcWz?}VT)mMWS}Sz@-H^bj5fi31
z3;yDcITq}9t@9-Z{_ywjo8qISK08At3v+Tn?NA>gQW_x&Ly0$8+8+ob1&}z1(Bxja
zboX{slZ%vdzw$2eN3C_~x6<X3wlsq@Akku_!>Q?KQ1%I5b!|q6i<d6x!sslew3BOV
z;))R#7p8^4M@1h~h5HepQrgM~nt(iQVYi4XTpvqu>C<sJPw~g5SU;ImM<irSuaPkW
z6&Z@mDS^J;fGI~Pa9j_W_5Ejp23_?C$NW9x0-X#L2a^Orp`iChT2aj=*Cf0>=cv|@
z$Y6GimL(dX2{>9lGkO4(VN99-hXfY(K>nic_UChsw3NV&4^O*C|4u1>l;C$*!=^e*
zdtL=zlwc5Ld|Zs~ZF>F)>)CUwF@I(DR+rD+;Z+j8vb3dUUc1LeK82gp4d)O3vny+#
z3eRDFW!<eG{KR10?9X+3x``A2EnhRsP9y}XeO;@(cNmPt5hN{zb*#k44})=j{(yw-
z7p@__ZxQM-$f322YdtFC1J)ceeXJv2MBtZzxhtP4G-zv%!AGRNuPAgcmykV+=Ba1b
zOZP%?3FxmuQJ51JHdxY!G|D=vz_D7gZH+OJFwbYD9UCZf^{g2)#U!J^CAVxNEsOwN
zzkEyLYZq~WIc{y9p4o|osg;47iJs-%{miFQpFJ=p3+3oqb%GP7MBz*7dr1Bbwm-Ko
ze5AQo*$oco`4Vc(46wM2eSk^E7E7o?OAci|e9?rrPi_a`j0Nc3M>28*<$eZ}=LxLy
z-&6C`qR`SBnMeyJ)ZGniL8C*}BGD%^S$AA7^(;h#N_nut9dQ5LoAxGcA<*X6-R9U-
zAz3m_CVvnT<i%b}0qQ<}T$v>F#<`i~C;#vYYV~qT&ig*>0S<Zd*?TCnysy&JS+gjn
zj)l7&?OuU~_6#y>jRS1P64ZB>%varWcUoo0$-Dz2lQcdad?6=D7MocyAgk6hPb?OV
z91D7V@4c~zrtYe8BE;oU&d{Kmn~oLcn@9!dgY53k)~_!A77c@5Uk#q8MrhS7`6FS+
ziSjCDFPUUj>oG~{g_+PB<z!yodbA&i7;0$@tykz);Cc2*!gGzmnXSaQ563uemyim#
z`1$L%QOZaMO@%x(6sXQd3MHFB;h731LqH0n6zc3;o)vZm@NrD7O3D>F0aF*}Uel2o
z8TpGjYrSC4n#2VEEc((6CO1fhaKG{iTEqOT&uR^?aW^^W4E#mLn30`UOvJysT?$A1
zWq%=1k;&j4jF`$Zqd<0bm)});v07{JgiM<Bw(p*@AtLo$m|W}Dvd<+ABRr__yMh`O
zrz6T?hh}TlHW34;=_^Y;G|&DrNV;S;euQw}ZRMe0p3)>ZGNenxE)Tkf-r16b=fx#V
z?B<sxN^hq+uLm8`JLNX&sJ}_W{tBojoiY)A>!w!8mb}`V|HxN|VhoBr8ue&)Ny!H#
z>dc7XbH*-pSiioWcZ82hU}gk+ej@^|4QLO*w9(H&`;Geg>lvb+4*BMG_sa0ugqw_z
zTfQ1t_9!2Efi&uo5JroCFTnwpwQdR!@@!$EyUiZIu1%_b05Jq4+>xuDZV-|E)rQ9T
z=G1-T=JqA+gY(*RtEt;O*#)fbbuJ(GcwDHw;pLE>lu)O${QKS5Nl1TK;}-I}RKQa@
z16KN^0@R<zQu^l;oJeGrp@>ZJKe{mqJ<<tVGRR<^SJ1~G{l8d>HSG<MehCpIhQzC)
znmD+G?rNcxzBlnWo|=>E(2bZWAka&GY%Q-f`1lFxW@7|}s?g4=(RV(lJqLSob8cv$
zSWtoU^8)a+ak1FbWs7Pla5fj})x665WtBT!TS~^$EO2gc=S&l7wVGDZaHu4Y7%Q+G
zkhXKyz$2Sj*>SMg!M%Yz#lQo4cVFnO+#Nm0^|OyFCaCp?8#hQ)CDnx0F2B*_gSixM
zKyrv}c&k^h2&0`+ZY!w))JXdbG%M}_fdlhV`uQA$bMQt&?CT?f2pOY-#K?$s@sz+%
z!G^5s^L@4$556nO_Ov?|w3nD?Mtyzz?B3=(QGVQ}r6XF|UndOZr0o^hV4Et9BY!iZ
zcb~2MyEB1Smpm$Cc4!23%Q403)Ub3Zq;hg@XQdbx5-@L+-05-H5shEY8A4I#Es2^X
zxLt3b_{Lc2NFN*Z;NsBW_wKeku|VP-_A@`{-aVU;P#0Tkrja?t6p{&ukk8Wo>ab6C
z1fHrWB?Vej8Y70!|913QYCF@C{4==_-deI>hY{9I1MkyAadZ|)&=++umlh=qOc8+C
z`D&xz{;@ET4~UZ*6x<puRs4tPXfG&~mUYRM&Nr0=DmV$tw88qA2~33yFqjDLFZHl7
zkr2H6ZQ^M=**_mTt)Ee)y$Y4de6rC?oit%=gS*@de2II%^RrejtL_SP$4>`cR!eoJ
z9@66H*CIoEZuYtb9?WpY?lbTnrz?6Ne;JPn(%s8JExk1<UU*jpv#QhtPct3y#Co8Q
zBIs&(`Rr3`X~gq89_l_Y5>jJBB0YZ*B#pN`z5Ki)S54+O%E=G0dJ|?QmY%Z<{;Esb
z7;hf!a4$b-U)wJpRV%y2*^Q;WqbFoq6DZ$H#mC5WL<CronOIkq#WP<TIalewODH05
zoz9bPs|eM?h%YB0MQs?(Ji!3_Vq;srP@)tmM;5l1l`2*kg)h|q$KG2<#Sv`%qA^0y
z1b26L2u^T!*Wm6lI6;FG+}%C6LkR9NI1Ccp-R(6w=iK|9?|t7N_pQ6$TJNnpt7p2W
zy1I7NuG)M5s;X-jEGz%~bM79QIU^>wYTAYob#~5AqReKx`8A+B@u~Qca>RH1STtKN
zC=p+f=v<>VT%6+a<GI-)DWHV`TxY71-HUj3_ZwqA8FTl#j`PzSKLYW4u$Gz}UR-dX
zNl<l5No9|6@b0qCLYv8`>q*m3=5YniX|N${`B?WS$xDloHua}u4S3Dz0+mLL#=n`|
zQ~+<-Q@x?zAMd@zUK|5@-I9(<SgfoIV8O@nBQF`<#~6im=%#Pv)W(KH30(P1k54I^
zmG<6>Xx7F^fM8d9BH&)mIiXf_AeG}1Jk9pfUZPOlZ*?b&Hv&$gFs>`cP2Q#AW4!({
zKCvLVv>*5_YkRKE4Iyd>3w5L7@sUp;><d03Ef>3w@-M)^duX`ekwVSyNd=CL+05#?
z$<7o6f+<#YSI<7RcVT=Q0uVMZyo_DPExXpBxAl#nehOuZK|&Qp>(S?QlO6|W&$yfd
z<>x0@BN~0#6P7Ybdmi5DefR7Yf446TKyzh)v1PcgR)#jyK|if0Jup}3Pv`VSaBJ_Y
zh71L1_RNKRR)&2mH0*pE;#)6<PFAc@xWDQ)J>`eA%ovEgdpzk`Ex0b_)vBn&>Wmuu
zWu?xX?TAfKU|gt{w@Cx|W40~eviDKrPIR$uVKAQ)mwaSFVB1c?Ac}svc>5%W6c%dI
zBxehUi3tC+%HNK`<we1af>XEexfihd1NJzwL9gEA-}iC&J_!n~O=MpI13hf)%2_@4
zS~bs<GNWPV4s}1go4Rp>-oXneO0i96U!$;C<x6v8!F{FE*9^uK^-Kwfs48X*l%CWF
zzxU91;xb>2cQ(vCN?T~xa|CfW8O0UH4dp2LB=f)S&Re($Lx7_&pj70cz_@NEB;4T~
zRHHK)H(&n)ZPs+W+S7D6@#RPvRU*vJv)K?<x8VK?t7?2b3LiPA?U?xwaVtdUA4Rx?
zI%1pR=APqPOoDLNE2IWpWZNGvG-+%FG4tZhq`*Wo#3LrE3Q5T<Z?f&Sz!|DVeq@k7
z8HltW33kFpIZDDl`b=N)9G8tRLgkB7AdIIQ=W2Q$KtFb(EV8~3e(`|<9WM;=%B)By
ze<Z9{w_JmX1;z6nY=QCRX=*gghjyG#^K_I+qCMk)DzH{#GqQfYP<hB*ZyYVekHU-R
zVtlk}vtL}XdYQ4Cs9HEXLQus<Ve+`)yHAc-)Q{Kd<NZ*6b_&^>I#b^0{?J&K@V@+g
z)wk9Qeboe$MvQhqMD9KShJ=lcJx__rI`L=a^UP!;hqF3nvNSuYJ$;#Y59olyEf^;l
zSK-}^D_0SZmQ;ZPA*G|V0S*zjUuI%XGWg3eB`!vxF|~=v04r)BZ@E(ox9=^`)!e@@
zW3YrgAC{=xk$hE{H5T&*)~uF7z?|?0z|gPe)zLf(^5uomKXZYoCW}{F%UH+6!PC(q
zJ%m2(Qhv3QkTYjz`xU{*L+DeYvm39woPyeTEuW)i+4tzATn04yH0Hh2f%1hRo3%J2
zcp<n#88a^jl<VIVggZn6Mq=3lWSoYz(GWoEO@9B*Vs19dD7Puqsr)*pK9I_{Q&tq0
zi`>|!iQia1u4fdrP$`zzcJ!iypJV6P?aM6O!ybj(1Ga7AQ9gqaaWArsgr;30MRRFR
zKKodYAeXsvZ33R>Q8#IbaDd0{kkCzq+-cQqP-@NFurJ&-HQZYAmCr6s?txKZZ0SJ~
zuHn^H*#M^U-r1XJBKTd%P(#KUVF_M;C>t<}K}=*bR~}B~2bn+D9@8IYB;iJ6@06v5
zbhDCoj!9acN5r_@et5%>!ljw&&vZ<%xbf9^xFB~^DYrJHgKuif|Fx+>7M-N8s}pIr
z;!lQLAFbrAN3mncu1Vg@oe!vgj2@ax9U(1wUOt`jncmBzGn)7e?52V-85yY8nm@ju
z=;*BvM4XeXzjvG;o?M!$Kf-0YIF)o3IxNx9fFb7l-j~y?Ik*)Cdt+%h^N|(O=XLu%
z6dC!A6`(~J0}mi8%_bwmVwaHMT|OUPCh|EjQh_}63>^ug0?|zfRSFwk(Au9}s)hce
z;Zd^p%+Z1L&y#zs)&;7Fw87U^)250@YHzP1CnB%6-k0!S%lXb!2ZwPkClJ5CiD+Pt
zoOR%%U>F@*7+&4H#ap<zSh`T(_ugup^?PZzguuu|gp`Z{(=J%?yIJg5<*&uC7IqA|
z5LhRcDYWbl`5jH~Y9|*s?J?Byc}vprWFj{kJ|7(}r!8)3_{2pnFmh6J&dGLXvzR62
z#t+OPMX%VCkOr$q+0wC3Bqk+ergJsIT|1BiZHbS4g;MKttZ2`c357*k(QE#n)&oxP
zfw@Z~dWSPV@n83*6(;7xJ8^V<t&XCyoy9D?I)<phHO8%%4|NjjjIi3+?0vO%$@7&i
z+KdpvA^}v({kkp&pz`-qYuQ(p+V!D!7zF4Sj-}!Y0UHJe#}~T9xoEpc`}Rv?Q*Vq%
z^elZj+Jb`N6VU&TL8hyn>~5+w;~#p^(O{zY?0hg^9k-{^m0%oTg6MQrKLbYlc|%K$
z7KoJe=g_FmW;x)#kG=h=rbNrJ9IsYyQ7wi$NTTr0y_v7oJtg?ygSUZ8TxFiY+(qr2
zs?2=S;jD$JoOqUjxM*HY@1Y1Cn;VR8y%W64xN&;wUh$KsYq>hU8%Xw5;`_t`D9iPh
zwwfTA+q8~Ivt>>wOzbT-tCLqt`+~qQychb{0RE`y_jm4-=IRo%77a(R3itwk<eDum
zGcm2TWqC!NL>Whj2_`|zNSSY+>_6Pa42zJ*ew&(;Zq5+yn^|HU@8YwU7kHXm+k~S?
zU(mffdx$)?TjHQstqexITD6~ZJfmo9lf%UjJz=S-@g(z?9g&HeZp42Lyaush#)U>F
z33G5Z*l+s^Eur9haHA6r%=xTq3n-ue%xP)2lbo6Fd^w@w2ycHF=q}Tzx}C4OTf4WV
zwuvm~NiUTOy_6trW(x;Qo=5cI&-mEQ)fxh!<ekm#>hHeZJvuS~LX>>#<8}L%)J1xe
z{Mv8|T21!cZ7J!~Sd>}`n^OeMG*wkLD{2KbW{J1P+C;rXB)LJH;OH6K&CiRz%(u_3
zUp_$*TPN|z=N|7RhX47AIs5S;X9QC#uM1LLfO;(%{rv>e;RoR2Qzu9CGfosSk&Y*&
z(%4|T!Z!1j2|5u%*#N4z-FYmpLz5SjG@6gnA<_N&^;3l{y@j~idtjhvFtab`kQQ^H
zz~$}D;OQFkYw%E#2kT;6IX4y+#Gov(-9JP<_gFTL{Ot<lbJ09#Y*bnB6Bg=+qj=(v
zYb`!-bRSbqr12P#Vc%)HY{u+9+xb-$%3RN`AsDWc6k~1~*@ETuzI*H<eY#++Jkovd
zuc#BfdZd6*NLt5wfX7Rk^hIQ9<>b<+FAgyDPVXe`qT8ro_KCpGb`I6hfzr(?6+{X(
zHr_m$L(P}>mDv1mzH`v6{?zc%;|%m@>leB?((5iXZ8+x0NQ+ArEL4!WARB_~yXxE>
z0mkoh{dpU$f-<BPYpfslJtBlHU_0f;7CS0$TpPdX&U;D<30@}d(|V=f(k>L|=T4JK
z>FH&f7xxwZ9)FTf^oj8;CIfB1lboBYOetUmMkR)X9{?FKe?S}_kY(mza`(O`K2jlh
zVFC$8<P1Ck&yTTnP0Rt+VO$TOl`I*y0Ua@1h;|$jMyEd0K^Yy!*Z|3y;)f@aTK+aR
z4j<;&FN+N}^>;RB*;Vcb!Q$j-Dm5n8#T8#c`ei?aJ~D3eCt=Ki3-(t<?2F<9IGK87
z+Kv<QzMHLI07E9x-t>I<6?9Q4c9nPle0r36t#hze$Fe&>@(Hs5{4-l$m!no)_y}g9
za{7pNh=u6~A*0BzZ5u7UtEWyj;sTPJ<DYKMR69W`#%_Dx)3XYV$Pn|g`%hz{OCsf7
z`{<=%2gqjyJWVF}v_k|e`DhV9Osn0ln%=4&6+_*aY93M~5mfJN84l~&SzhTCwqM~9
zdR|w)XD!?}{m4L>w6$j?musLqI#W}P9I1xtWA^Yg0=Mw$?sA2KT`f*l4h!3Y5T!b4
zxUussD6Z1;dX@Uf7=Vwd+T4x+7bp;6Usa_x>w;@-Mb^~G5wzmbCMteY0V?L!ewh|K
zhQQ*k0>c>Ej3MlTeJ!vhsicWF8!%d58HQegT3lrAY4J15#@DK7yfuP64k-n0jtbZI
zcKrdvCDknmFT_dfzW4Lz-C)f3ORdhY(uVg_qILv0rI~>5AE3?dZ=2q?%qviSO6_4~
zW72N^dD6AM-`K(TGO_TdQmQxT&gKezFDmXGWP4wQaHGf2PkDZ0z5Su~0|oXQYsuPu
zS)dbWq~Cw2@dQl5sD?W%vGq1FjWxLG+ksd(`RSrAm^Qz2r4~t`TNjTw*2Ec8e4M;#
z`m9xl>Pc|-Rg!(JB@>tyd-tvlevSJ#DB$?y54dJ=WOj_8v|L;M^T<#ee~9qpS`}Wo
zbj}+HY+v88CR1*)O*#9ljQ4)t*qAkcQ=1c{wJaP7>Gjc-$$Ld?G&C3}d!ZcanMAg!
z**f->UytQ5A5Ms!jc~vBG%B|#dZ}3y=>Wc}fN?kFbj<fpd{pBJa$+upTn!fXov&AW
za(?toBs`gTdeNi#_J2qwE%>4xCruQ4(Kw|a8N*%kw)R>_OCunWiD7bDdRm)ibtD6q
zDzmAER&KqJgobLcYZ~TyK_Tb_A~(G6fe_z853Ms<-jQ2e^*Xap<P)Y}zi6|89!Ba~
zbo)B;`we4wJx0zZSIxm3^IX8Xq$O|x>m>Sr38+z6D!opG3LnS<8zDzK^Pj&Wh~jjM
z1>S;?`5gJ}RXANK&5ZAIRh3!wUDR`1E{?Nn+E+0{m28yYg91e7LXz=FR55Gx1)GtJ
zF$DH(5+fKq*r}euR?gXVkxXXe%)VW}=T?5j)_#A$4`lzG*&}I`+`^%;s~+}I*%<zj
zX*s#Z=E9vcrm%HN42u@_6|8I7q}y%dY&3JgvA4TGpOxFxl;`?GIb0uo`kWr1YbFB&
zf%&K^rf9(7L8QyfR)HwF=pDJ}`<Vn2`bd}TFYN=TLg$6ydU~qLdJX4$yF%AEvhA&o
zO>I&6nn<5k#lgudyQ~(flL8T1zCi)*XBw<39FBEQ?1rAoKImu_BRiF+f&_$>5ArBP
zC{P7j>wQhBq&9f>B95@H_Ia_%uxup3>t7p8Zvnfz9VSRC624o7Z{f*d7|@U&7~QL-
zfM~1vEqkY_Yx*B$_yByGMHXXI{h@=fc0&l#=E}B)o&jtpt6BC9c(K^Hqyz#@X5Q%p
zugf+p+EsA287&YzgB(QvHsx^8J%LzZChg6&m?W^@uVhvgd;9*JZM(ih9QC}{Tw*27
zS6O_#Tzh86$s)`U^_z#v_BwFu+m|N(?j0n@)rLNZI7-e&<$04|o$sT_YGCeL6^T5H
zUU}q@TAmq6@E^^MxK~0avG@iTP3Q&py+N-4_YTKWY?&f6KaRr``hl)Ysr%G9jUv&9
zg_;t9+~YKBX|bM<oo$iL0VjB`U%&RfVokr*p<Fd36DfW^SmAFUm=eI=2OoRM%yLq5
zc;gi^7)3pGTpe{4hdey5-|!2#+mc?8i@ZkqK~7DWQyUx{ijYr9td1oA&O?OPqb~>+
zJ@oOwl)L2{o(IOzJko8&*7aqd+PuswHurn?v2_z0-1o5uN!y+=j|cVDS?v*!E^|B&
zx0FG*VNUCO+t^{JD7-b@v=G0cInI79%VUT8Mr_F&RFGGC{M?uRZOB~Z<|s(*y5iGG
z(<U$*bgy56;tu^PE4M^!wplDlD~VDsZC?7c{<)%+v^Bk<ttrRB(LMc^@M%er?6NAs
zJBtVByr)dwhdVH4jAv3srE=1o2k+~Qa&N|TkAl=OdQn%p3gj`Jvb`1Vvf6TgP5N;Y
z(l^B2{Jq}#@R24Xx!4!jMs4Sh;pQb2CkiQX=NbY_aU$ofqp0KO18WqI7#$8;`Z<|l
zW1W83tbX~P8v*F}agomUhZQix{{H+c778ejoCV)zP*fpkqZ%`{x;eV%=(IQb4gI}Y
zr`4XR*XG!!G`ZpERFylwqR}|8XRpqKW6M%!g#?51tIIZRTYPnEN|cYse}r5j2k#~n
zknuhC1>{2CtBiiIv81sj){nnpwGh~4rM^#{m(YN#^Wtyhn}Nvx?ubhjy!xg@%%hW{
zY5BNibG0M4a;n4Gq<1V93je}Cz-?!za6kTYy4+*^N{2^qzNEWtS1{MPLcP@w;eNcC
z(I5lUrG0)uZg!;wNp(47X-n+d>igqsP=N!z;n-J#z2(bGc2FIgcZ-*F`p28myER23
z&~R3UrpN5$e7jo1XvYFuj-}A?t5>yL?S(%^Og3flWFIqpO6)R}NeNpZAFEVDE!NE{
zQF)RUeE%%g4u9pwN^Ih<CR&-FzA_C^{AKdQLK8sja=aawD&aQty>x(YcvvRlD>G*P
zVy(82$^PW2V#v)}B?zHv;e~A$-6{E?hEeLkfIHTBwQaa~2Xmu+6Qe4c{_sCln2FU7
z1A9WWccI9<>hS)4Uqwg^ubFyUzo!#+D~Na%4E{bEr7r$<Q2$}NrLW$B^>}h3k;}z)
zNOCFNszo$l?@?Ugr6fh^OM0~EFre}z;m7(8qd%YCzV+`g>6@O9q)44iD_41J9igJ%
zDr*{*FhY4s%?t7gFSh3&jT>WDUmgm)lSXU$#!FmsREZ6vMR64u3Z-^!*3__-eaZO-
zALP*~*+xKRu>)+~95{PT&&6H`2X`{n+-qN5bAYre?J_i#-!&yjUuwX!3)LRnqvQUg
z94YgbupK{jaDet>+|bQI*t~M#w~B*x$#-`*tpQvFC;R2e?UCq6hh8Q0@;XLOT|I~T
zuXXt{Y&y<3K-hl?{jQTzm);shLn-!1OkWz(rt(%{ZXR`6(&5)0-X7jr3eVx|6RTgV
z#<cXqzLzKn>cHZt4n&%c@I^0z(2?pau#&X=xXezj(q`XZZN{3K{XnP7s!F*^u-GPY
zEG9{Ff%xj1wDaa=*0&jJ-n3%dyvx-c_k6*ag*vqPhObx?<uduLsj)>z6S1q7`Xu_i
zvSt^5=z9!w!~)~Snz<q^=r}uCYO({KT@5wUqi&~Xq(6GZ_9M1Wc?|1iG7#&gxmatp
zHG6zHhL-wEYIszp)Ky!yXE}Z<tmU$X1s)#0-^Nd%wrg4XOB=MWx;2$ZTF>k1AZxnH
zLzl9UxFK;1OOc5gt=I*ekR}O>kJYwETV&sZ1O32(=f#;f87fLi((kJp?=Sg>%f0}$
zFW%bZGtdX7>mG}B_rG1NMi$c=TH7%7+AtQzhUNSSIC%Y{K4)$pwWVq>vnZNbAc(m|
z9do9x)?1%@vtR9ZxIw%;t>VA$`HLY;j9Y`sqvY*!q7v_EU*Ea)aK!!fVfHmiJ_*3H
zELmeQ5Zt?m-+}X}$F~Fw$LguE<Y*29>`5Jq8OEe&@Gw6MipIX*uDM8l2@NZX&L^1(
zy2V4oMK@Hd#^|9)HWj@E<^>*xi<<epAZ#v1v#O#&%zGmk0c9H*4#~9-a}4gzJGQ`p
z;wxuK*<gETtFwyM0C)S@?1+}kFtqs*D8!LavOj`e(Xluv3vireY<L?H2atHaUTQwa
zuQ*bnU}fFWCntnGmx`Inh8eH*ATO3Xhq<yIl=n(~6=xfwhWVb-7Lu2-ut0;xdm*#Z
ztmGX&w23;5CGuGBv%hK)DBa3V5_8Zse9H8i8fjZYmQm#R340<%Jr2)$T;@}I*}+kF
zN+Z?0uBu`vT*;@f7B@&5*_)%?z_)vzwJO{-{!6K!7q!frUJGl4G^aOsz-_Nky08IF
z`WFYEsxAS)MPrDf_j5nj-tuO+B%@mBO0>3T%y{oSbI({w(Am`{+1oZi3s^FdBOF0e
zNm=-nDgwhnj>OYcS>4RBWBO0gsM7TkjHUuVw251*^oDLZwGzV(tCF;4XlL%DOE7>s
zTBWfi(7w8|+s9w1zeAp$zp<4J8;L;ZyRec%Zvm8nEAC8um2zr3OzOP{Bi8!{L!3sG
zHA#Z?8FFngLk05!f}WK#Pm|!V<VK+(a)waR>02{7`W-o1VAJItF}xNE3fQ!Ra^mUE
zV*;J&|3MPOfNNN#UKqNi&~Z-CpDMd?usW<>y<CoqrBAi3&=>i$4r#w7@$JY#z7<lU
zgqNVnbgP{jOI5t2q&vzv#IP+TWkEAVJ~Ld?%S~+d2k@4S3B&Qjdz_<oKfuI!lI8nI
zOPO--?T3D<R&vK02>5uWG~kYbx4P6`oISJrn0~c8NbSKzvyapAn^6dbh~<~A0+X+k
zWm9jr9rXLIuh+GF&U~`OenKQ|GI3{ThhfE;XBB9%0dJOL=3{-r5I}k_LS*4#UmD(}
zFDEg5R)tU0Jx0<$5=84l;9|nTlt6Z)X?6$!r+o$V<b+m^$8H7!BMwAy*o1j%L}d!}
zP@GY@{AqvGgh?&mnOtlBFcpbp!_g$KPl@7bcOYV`HwY>FONZfs1z--oJ*&hu#0}{a
z3}oilflNe0dCpi^2y7);62gK5<7bwqY31b<xr=V#@%SgTu_!@5st^JXTD@&l&bx|z
zZX>_w8sLW=XqpC0=1T-2KmBM&nwkx)Xl^W*6V57+n1;ahU3!OOzi)f{s>u2#%T>ok
zOK&lCNS`GWtsvGp&tOD8FrXukVj9oQf?{k;c0ziTYXw|Ho_~Y~{c&9et_yxK+0oe3
zg#(1Lr^IB*cg#1(-7QAHdI4i4dsPQXwNCb}V8<UhB*0%3I5i4!UMX-(Mvk^SKX#Ed
zu?AYXfHk1SLwE&iz1<RHqgSoUiNo;L{)D0?2R9In>J%>19XJ^pcvwQ|^%}zibC-M9
z&v0!Z|KX=_`KxM-qz_5BL0_`ka#+C~78wb~c-gezG4Wx9(^?M=?Q0#J5*L$FuLBj>
zfpY)S;>_ca5yx8|*@Yk7dn@O__A)O`KWv*r>eh4lwmhuXJ86EjP{ipQi^wrOty}`E
zTPm{V-W>PunzSFg>8VXAVH)sn$)e2Z^poL|yPt7j=kf63QM&L(BMTPn<rgbZZ3BOR
zOLfkDLid-w1p1HG`@S-UQZgi|p5KyxC||<mT}t6^cGo<HE@FDAQ-&FaR7?GwDeRU#
zsl|D#nIzpj?&A-M4*ccEXLn7z%PkDc!-$V8JswJa_gAE2`|EFuZOTp#VW{DzC?++f
zT+6PG*o`m{N|$+EWZX^3W19>H<@ZeQo_^P#xaf*fZ-!~*yYq8J1iVPY1TS-%tH<oZ
zizoty1RVh;cL$Pw*`=b`nN}f-u%ly0yT3`i!MN@Z={FFg#9tBWU(dTdF1bt|9l`E5
zVup3t-hUit#eacXr(4TpaE{>c?juba`6^qoi55R59V++hYNOgfMP}AtTZQp}567SO
z*;hP>mICTh)hNn*NBH;Gt?xp9EV4Dqpl!-n@#`+z8+FlUf6GixDZ3Ur*U17DD>g8@
zC*PlE={Q+?b@VV3D17W9eIzo$okL5MQBNdWl8Mqn0p@mbQg%=epEghDKNuwUP&OWl
zkw(j*$J6TI(i48ftvuE<H!s<nuLIkHAdJjF^ZM4imY#aUvkX){kgW0=PY&qb@lNp~
z@+Uj*r1J0SP3^(nqKryD#^Ko>Wh+y1{?9c8J?orjVrkLRn0v>!e-6<1C|s}YGStEf
zfMr)vKIe|g7BBFLwZ2S4TKQmL1xnKcWN@zihoX4>Y&YN;Ps0I;L2sK527!frDnosy
z4pif&n{*nz(F+Ys5Rim#63?2*P|Mj&0T6iR=Qp|SB5ghZ$U<*&3%;aU5HTcAm8S_>
zMv}!Cp>S^iiNZS{f1pCroq>H?-^KG0-i-`5!`sPXjDaoaZJ=n`_wCKrsDfqEgN)uM
z*Uz=l)5-2(ndC7qDK%bkwAo-{0gYUMgpa(rt+I4x4rvfXOXRM`oc(=fIi6nT&(EZq
z=Ff^NZz^^9=;is77W9KV*un<vYS`LiQUy<6@f{2<^XFD3jh$XwJe_G1U7nY;*BUAt
zumZl52enGByG|i5CK0>y%TxEHDB#)e){<_bW!gyX>(fe&>J}-~(7OXX|JHV?v*j^3
z92|?4ZuzgpaT^7qt@EPIy8?#ujCI1l4l%GU>dS<+4(jFPq7s7xN-;*t+&yKEzpuCa
z<@1tuT0vg#-ek+{c36^h@6^qeria@3tqu3Y)2Q&73z^5P<@SvlMgCF-Zf&a?90}8`
zFcJ2rg=z<u-F$AgWh)$(iYWf5p<5?9v%zv`woDK~Su#`1*t+Z}cJcMwQ~$qhZ$=fJ
zrZ93mA0&*YjhA|{zgi-7{9!Civ<Iq1;`ZQF|B7I1mcdP!c&O%$f!FYeF%MH_>_Xl2
zC7V#`z?js8s{#-8(qhJtK3&R>v-Y$-^oo?iSZ?Da@%}Mv&YMI3A$R^y{2yM#C!1B-
zDlM@XNF5Ab$EAzsx{5q;x7@l2AY>7$N{n~XcRIIRe_KlRyFtJUBso56bPThG2)syu
z|9yAgh>4T^WNSgc+0)P6UEcu5REmZrSBHjtck-x?1)OKFHtt*X{AX_Zr9{tU@sgqF
z=1s0=7{KyXBSv^?)sGXveiHXMib8Ex2z*iLF`0N44Te9|dKz8i3ZNjVQ%}O0z$Wz-
z1J>nb8$2UpcIv~8krkjBgxSYR%8XAVCQLfGWqOv23KdoMM`nK*BxQ~&uV<gd{#KF^
zv-z4IEuM#%9jF=)bEaB!Q7mkwxt7>%{zUfeZ%ubLHkN3(WZ};U@WIdBo{{CnJmb^k
zR7-{vt3QQm3^LJ7cAhLl2xJvK#6=!DnUJod%D~3Pi5x64REhBT<JX(%4s9Jz{MauX
z)VHu`wQ&hLYLkjMHXude>z6CHJ#JTNV`)iwApY9y8RX_YARq=Olq_89uxF=J{|TAD
z#=@O?Vm|L?+1+Q;2k}KBx{8Y*)0o~mA47U{q%@>^C-wJwuPc}`P==cAJ{JD~;N$%?
zFO8Gih6+Z#F=9A+*gK_a;BCIPtZTKWZGjg-eY0>A9=xC9ar^=BEI_9c0}ZvJA2R#O
zwjpYsw>-)c#SkT)hk_*gF6PQuXJmT>H5XbG?xxk}bjF|Sugow_MC-rKxZH!}8sZ$3
zNw?f{dd<zB$dTY|DRlPBmg9c5GTF{j{TNwM+Fq-Z2>noB)+pB4p-xIWtfhImqt{rR
z_U45fOk~_6>Qvy(p~{67KkFOAIPqUP?9wv!<-pFgiZ+A6MXC-+W2J#YYdHtk7_|pv
z&wnN>jK7pa=tg83Y}6GV^8}9>ZsdXhD`ECY3je+Tm$LZ1x^9wT@hDS9_%oJVZ?m&t
zy{8;XLksP!Z$ql$J#c+wvpZ=fIYZY`8}msxshGusQBTF!iJA;A9oGny`_Mqa&UQBN
z-eQZ|b{Mip0Co36+H1TLaU0rYag2nIk|8J6ObG*I#Ke^xSCu8RT&zB$lugR0GgXkw
z8lT-q=b0BsDzLt`8QV*3EGsJL<x$8)UB#W+gFQ8{{>6LmT62dN5@T=h^zF!7w%fLz
zRmGUpW!ByPXQh^Bh22@OHxGS-rSJ|v>zt9;YXH=Y7ge<4FD)}L_c!RbdHCq>ceY}w
znoz^#8;L$=H7xeA7C7~7lV2b@Rk0^H%$NOAt!Pn`t>96RuVe!%?*4P_softt^`R}<
z+%*iBzp$^J5>S4M;(EI-WEETx?lg{{Vo6nsCdQ;AdxwC+UJ=6)+pAvksHQrc@_UWL
zoEzkH<-(};;YOPQ<NI1wGzI(E#Ocz|LJ7SMjV|j<@9(*u;(5SIT|E7Mg8d5(f<=B5
zhfMg{`(rP)q>ORIczC;?1B>;Di84nFLPH!llk%O<A!(4q;oq;L*bDd=q<!ki9wlwg
zeAC*)-hU?xs&lGVM&|Eb|A`6z*3vPTs<;aDta)Ewme&r~%rJQmGOp3!&6#32*1k!E
z6ts4}&|E+^^hi#{<l}9sVj2LuDrwf5PR9+9++8X|*E(35n3o{KJ{=2=cYoq#jtfa;
zWBPbvj<lHn_S8@d-AHFKc5$`W9Csvh^LTn`PMp)H_Xg<p?>$~VIKSwkI=rQXF$1Ra
zXUyrF<>>OK<@oqk8iolk4&TTp&KBc`AAGp%Elq}CnnJ{#$M}c8`m0Wbw|Rjo&kF$Y
z<=4Q%zE4uWc}ol3n?8$o|84^JwlxbSN*T<+0U~e`?|~6TgRqGb)XTam9G!W5mCcqc
zF=3FMWld8#L~*_QIDK#YB651g-ip!eqzXi6Y6m*S*J%obs##&Zh3Y9K@7_G6^wjLy
zeQfD{k?r=&u|sBlJzIj=g65^qm)jm{s*1Ig+`kq@h><?ZgnGDc#x3hr=@9_L<qNru
z`{IH^fu4NF0)|^6h{4gZmW`3|#0li)U88)=Fm?Rq1z3ao2KN`?9O7Z!tdz&YSW+)D
zIDZ2|lGF9jE$A&TkecHxwZhw2cCEI{@zQ^hr_^o{rbNAvhkkH$83L_*$qI`~1hsOh
zIhEMyW>wOB@2Y3yfMjHrUMFsXULc-)k+<UM^)fMyl%uIfX-yBDx5Gh3<}wcpNwx?J
z&g)DZ<c*F>SIB<LoZzy+3oKFvy5$X5r}E-Gj=k_@JPIed7*4`lh~;Av5mC&{amkpa
zIW0vWkCy`A_&AVX6it{Kh(UH3x6^>Rd{}wkB>y>U62kM-LfHUh>1fCom8(b%c-g)<
zIUD#GRk{1P`waI|^UXB8)ig}?iP?fPlk+yee<rc?oR`wG?j6YF*w0>-&7JfW0owj(
z)P;w>Z={EcE&sXw&!zgfm)=XqzzfphL`>csrcP?aoLsL;4hH|RA<#ZQ(_UMwj`@hu
z5Uq2Pd~;qBwiul0=3H?yUo7LHCMR=L&dwIYf(efwWob}`oq(!lzx87|6VibO6k?pQ
zK{Copw~eyNXT=1uo)#TEsl0i(P^*+BXO)Op45JVmbeW!ko*5g>$i`-Ku)6GT$aNX=
z0Uqw;%-(vY`wzG;9=WQ2Y}RsPwHsk*JA<?Faau?F;;iolx?%4E!>Tyjh?TmmG*lI!
zMO9r{nZv^rbuFzFt&~*Fq~sUxgX$We=N4E&P9H98fvtc{chibZ5p6u4cS@ECILlu+
zwt_U#4mi7PZPj_Y=xw4vGsV~AR?@J)x*bt8xK$1Owfj<zS5Du6I)8=t@_+y|z^hl|
zum4Z*|5tf(0(NgO+6^u0FFFWk<r<PKtYg|G*`;NntBW>~OP{*YJJSN+f0<%vdq$b5
z?Cp39+Qp5}?AWEah)iO$oCPl}@MsPVEecrfpWzh$EXq6W*tjmZ_AgR%f2JtYi|~P_
zpKILQ?nd<Q{K|+4|Nei^>D+6ZU!>|%1p(IB#>mmh!C2q=@1?DQ1p+Jw6Coqv-zy#-
z24y!pV+MJB)0ZCyV;d(zX5gkGgQBsct+Runu_F*g#MMbm2|&Kaz_pkX8zI}@7-CAC
zgq;7pVrL};4&XUHJ|Gz<Cu0X2Ag=spMnd5DN3f`^jS~<i%0kHeHztE9D<L!c-{(b{
z37P&zV-RH{WclY62O;}kIzWo*at0R0hA&S`SOXbl{!9oQFPZ%N_cIeAaQx4|%!Hr+
z7U3l;4z`9$#!gxc^1`AF%Eqov|9C?1A19%IoFsGzfv7UZM&|m0wys)0N{oaY%#4Jr
zjBGlD3_?J*0j@X#L4Rq8IoLYe0Xg~0w!eQElo*s9^lcpNUf5^o#vr7`AZ+YnZfLA1
zCI}>~tZ(25L{V~ba5i-M2a6pES^4<>Lv_5Q{IBW|HMat|#~^A2uv^&J(ALNpSYlyg
z>SRX9#LmI;^4Py>(>1M41K3+r^ph7xmQRux^EcHcZjH~c|9nQ!Cau6(JQSLQ4l1zD
zuHKMz@_+C$HIdMvWp+XR{aV_-(sXpmB>}5LS2?z{wP^LE3+D#(S)33F=4mLe4!c$I
z7b!aWrrpUbToMBGI>;2gB2hm_@#aUeU(cm`zHFIvgQ{*DCgKw&;wqhBIOw%lt1NZ=
z4Y~kKxre2th($&<Z>6##_q6CXYh{T8lfMDXI+)ehm?>9e!D5YB_Xn(HzWY0KjKt0O
z5H&1Px?kV7KRlHN!H-r3lMcclvu1Ps&@~G2JzvxuxiO8+GM@6OpYh>6IfdArP;rHc
zaZzud_KRGf_6vjOL_SI3rEi?}i=iRN?JyNiV1X*Ow1@vh!*)IQG>-T!C&Y#pc!xa)
zov#G>H*8Bk4;)Byb2Au0H|fY88=%mgNo~c&(yCbHA{)eum_iM@bvY%Eqk4c(+ymp3
zo#NCQx%xm@<Z?f(IJ**amQ1?eTVE4UAlu?mu9cr=)t-!gWBzU4(V2LkOoq_ut`Kyj
zNeDhYwf5F2L5<uRZ-9oG+AT=&GJvXxmJ~l%=3OkLGLj=2srWHU*!sXkw35p?giLS|
z<3scv$}=xoFqQm9tRr3etEWri*y`%nhqaMYB+~Wk+XL)zB2f&}(^54%*NeB|sFcq?
zVy2QlA(+W^NQXEkzC(YyK@SunAS~9)7^WCHxiaNU2TL&(w-aH7cY{UTIH^itE=Nu6
z6jk-M!+aLhBjnVqHs1S+F@H9(daK77n?>_o*$)|!v}D0InpmdOxL?*cVJWB1bY-YY
zPB9rOJW$4qiei!!DuO7sc>$`z(wq;El$oXSYKR*)&&v#VcpLI{0!pJx*sjX7z*1<K
z5w-<M%!}Cgcd~3=Ec1rU@b(V|%KK<jh%{rggQ>*FqxG%F9PKl0Cs7oybY+h5+@e9U
zH|J*GiwHClHU}ijO2o|Q(to6D-Z7_R1l)Y2HVav*q0C&fx0iqs71_Y2Ptm=kjO=6~
zQ5jpq7tI%Jq9-l$>E~C6II#yY>+^M?;%I+O$u0CWq|p3fk_YFwB`?3?S-*jRel2oR
zg--^e%D~a_3w!j{JpVDMxOUGPfTX;2u$ASKwT@+edX#}%eg~z3Xdm>RNJK8d44wzC
z$d74)F3>_YYTrR_%`?nyl7e{p@HgI9&Kg^0QChCPhHOP5Izf%lZ+K;04c^b)9YZwE
z>Oszw>C^p^?WE2aj`SW(Z<j}ec*Z|arLR;}{qB~QG$zH#4&bKeP(vy4O79pUR2K8>
z!<UHSn&iu!nybiNUWa#^Hw~##&04wru}%{0C)4m=Yrc?(ETLJlJ`4;p!eaQjql7tB
zL05et7pL6PT2_eMt}}wG0RrVsn&S%Qbz{c5eoS8{imC7~kahEv@t|F~#MWDwtUALr
znGkjKbWYBn4JeeMwo1H)n|a*c*|fsvgHAG&nDwe3hWOFEmUR_T(L$i{jgH{!i_#XD
zx_d($j|wA7q<dKQ+J6B5c<_eWs{TvXk!f<*+*R{sIcI!o!<LME+k0nC#mg)e!Im6T
z$%tF%YZjcO>{Ozz@v9O9@o6Y?@28ynaJ2WR3Axx1IsH>vmEs>2g<%QMdFQ{V`Y%Zl
zDW&pPD;oA^?>$;N7yH=NOLpD{2zpE+jc2wrFz0%xa@3HV7l<*+u$3IBt}hol&|uEA
zF?*1w+OtnV*unL3Cgkg}xnPr)uesPG)WfK2kOASv`o`Qgcw_~2!^!#7PC92ZdM;<t
z&8+NWCs-}zQ_ML3_C0esKXlevnD12~TobGZoG!Hm-*oDXlCU&gdw%1kIA7$dEfOnl
zb#1%~joGn#9mk60A4m@EcG|bkm|R1IunzH-#FJF-!P6Klb#s(S1&?yQ^R3daLsP6K
zP1cvNLy19V(E|ToqDXC8s_22GiEd%lH^dea)u;chIfJv;iUi}F@+mNYG&jMRZ5bYV
zfm{DE2n%oxH<Cx?tnOESJ-&P*fpt}gJU7SId?k4kZ#6YpAMG(+qQQ3ft4RavV&b4h
zQSB{G?Eo=bsEMzd3*}6lUw(mpl@?YE|1%pqx%zU0{p&+xMlO%w0>5AE%ivlkIv<EZ
zrR5A%lvTYBC$0bBLb?Y2itAlHl%LApbKgx9=2gayOAn*ul<Dt&<$hTmn6AqYCV6+u
zA(OT-g;6ZYUInDK@K9o)NFVt#2c$E8vfrWP|24*7`Hx1;*~ZYx+}7sBXcDq82-{lg
zo7=otS|-54irBnZIW58$8~$H>ff$0e&H#u3;?o27=~)1CNe@5+dUgN=&~pO!MgN&R
z5QXC}wjkvA3mphK{^AQl4h|q`MM93hpn;I%FKBr&`v1WMzdZIoOz?lqE%SfMtq{{o
zK7e%p@qIxEj4vUdIT+c1g#Y~v+?6F{`Zq!^8T(%#^xq}>52BnL?5sf9=>P5fUl4s^
z!~X)&e@pIP$^Va%{&#o(b4g#A{{K4Bmn#0>M)bd9{(mm%ON07fAo|}W`;R*Pk4@`8
z5&Daz|3&DZF#E4aF*7Kc=^L5bm;%65$kxi%LCH?v(D;AC(0|?jXJjE;TZb2UVqs?_
zWMO5aXJYxsB|9@c^9$w$@IKIM&>{pNtMT6-Anac<M#%9Z*Z|J|kFEMI{{C0S{vzXl
zLUslvX9K5~4#r=k&Y)%vyrV|Q#LB_WAZ~1KYUV`9%E9qp=UC9($x+_eLCDtH&erCo
zTk@Gf0zl#pcD66bn9xMu%F&qNzX!|AEF6q1?Eemz50<W#P+gz4?fbrv7LIamr@h6?
z-nB7vKqjR?dX1zkFYK2?h$#Ryticri+DVitLf<Jyb~!yYeLf{Uy$$kcbJ$>U>;YP7
zX)Z10J(l}CSlUjl8+G^4|50bE1sEvSyUToj_v-&2AN5w2qm1Wgy~bw=V5&kcpsj+E
z@ug(Hp38sAIfc8uQm_8Ktb&U968078cl%h?#_O*GupVj(Utfa0iF~-{>x+T@Mn-d>
zBjO7Tg}i!&PIPdOD~10mq3kCJ4eI?%kb1z+V@KNmli1f6X8b?p*>Z(()liOkG%9;M
zR&jx-I7y<&5Y%82r6{w6Ea6$PFnKDeQBUyUo<Pogqc-g%GZnjH4lT84y7+WxzCH2+
zx5UsN)}gezGx=!w{73|gz-jkfg?=6Ke32rkKgLu<?SVPLdi3g7^p;p%0^`tm#3+f>
z<UM715+Vsnd9Lw+1su$5ur~J1Rb7=zq{6gtC)ys^`SVs%e0|BCv!%h3BpT5qt8?NJ
z71}~PN>x0|$=wyqbER}!gi-OM;uAe0>P~)tv>TGY7>Wt!eTvOB>lU{`<>dCw9Li%@
z*V;L9&GFWVVdnZGKz<UyQ>8d)G=ZGvlB-HKP}|3bF8`JfUrJC_vsfmT*15cRH-;Dm
zQdw`!ZV~|%s^u6al&sM%UD>x?U3#f37qpbco~$gZkm6gT$v{uD)fgnayXfo|nKo0M
z^M|X2NGXRI>0KHod-NtM^N6Q?ZGVsB*(8iY!KNZ}Lre;UdX!p5d?1p<E+32D$6wMy
zJ`5H1ec$jF&UB8My%DKn;C#*$7O7|{EBA^Lpf0Fqz{nq1nS{(0b`N`nB^_|d$eGFf
zBqJLJ1Mwqd#Z=~?3gSR0P{Q=H0ul<9Q;qYmyxh^rgm&kKJ@(A8A#|}~%sIKrd6ay<
zmFSCBq6${p@}}aE9_MZ%3Tb8q{*s@5tSjw!hRAl$P6i8_r}YYh%z}$cP(2#Q(7!VM
zn>c=JFgpo#*eTiDYXsFxgHws-<`++T9>W?eX`cFqOc{<lY;3WbK^Is29k;WU{C8&8
zR<<ifiFC_E-eL=G7GqBg6_C`_`@Ok`j|8m`k01GYJ<nFb&rhLh6r?vPMr0lh%c=Zl
z1pGxfM<|tcfy$fL$GA=!1HNqUYzSjC`>FZb3p|^nGsn`>_8&G!v%|Co2gj~wJ9rjP
z2CTtVFq;By=fwisyRABJH+^n5JB7u84L~mcWV{7BA-pQ>a2<Njs|;P7GPS&>VFbp?
zqhSA!XkL}z3f|&2UY~=S<D;h~<EiJ_mg7a0iW~I`RpgFxy4z8|{r=ci0g}W1UO2U&
zl}edy2yeyhPE-TU6~E3_r8;MR%+jci&$vu&nfP#oG(nx*r7FLr#iP63qHW|+ni-u3
zNG5lG_xtycK0M7wgTKZ5_%rH#9=&FpaonxB4HvsTRY&A~_kUg8iM1gZ`djbgP{U~q
zVyeZHSv;66br%cyqEOtDtYe*%ShlZhFLL1Fb$pl9)(^*g0ylT|x$QHk_c7RSp*JQT
znZoNWI&G}2wJ!KaD_oWB=VoJg;IP+^xP(Kn(m1E)9?zjO5fS91HX@Y*JMGa*yNgO~
z57XM+j2^n^3b42CBqOxY?AN=uqPF__?yDV_s7fVyS!TBp(;7*`rrgzi1Di&`O3R0C
zN?NG=5qUnXg;te;rpY%=4OZ-jy2o>y!!p25>UeA%WN3#eL|cJA<{ci+knYIZE$!vo
ziIi-Km8b9dvlz0@rpt3FoGoK3_gjJYRx9nUCbSA^-JclEX;6nL$=CurbK!Dwlc|iz
ze1DOUD<r>bC)6;Hb03JD`jD*rg(xXzJ+A6>Pq-wlOX>#;jDcc|eUowBcyuQtW~`s4
zceHSUG0SrFWR8=;0JC7Qss8Q<n1!Uz=BPTEv>-<ja(cP0NDT=?7G>JkGRD~!m+&>R
z+W56D#@J{fNJ#U4rgZA|1heVGDykIYV^DMbt*K^z(gp|`mrzAp%Y1bWdElv{R--<h
z9FSFPWxvvih|4&V-|r{2ccP6IiP~n)39z3^BUMEYyp0?ekR@YMTAy<PmICHUz0;0X
z7tjZzXcv<Q#$_IgYb%M5iLX1LIRyqrD^U&Tm0yIyRXeMg9J@4`_N&b)YHGw`4W4i+
zN}X3*%lbm-rb^-2)76k_PpKn*PYOm_2JbHupOk?29atVjG?P<lR0~h$t7wV4hNn$Z
z*r@hcqi5xb4T2Oev?VyH9LovEf2}Gr-YCkrxSN$L=BS0Ca%J?>MYmXK#`u2f^i(5W
zH*+#bm%#myZ&K)1{stjYxG;1FxlK7Idg3<xou(%|XQ+f!wK$YbysDUry4!n6)ab<D
zTYFTjN`Adv3FbQ?!|Dm~SR~;{Mbk!z9HKvP^GQV=kj(`Xw8{U7ifBOP<jtWXsl^b6
z7OKNgy>TE8>Di!aq=gEjMiHf%dKXLDNf<i&{od*|I?2CTb=NS_?Qk*t`y+ypj#|l<
z5eEF_8Q1ll3bFl^c7Ap*v+L-yn5%Q4z9-jpz=fergzoM>zU^2RbSYh3d<(|~B$LzU
zY$N??_nxIj4&Tdl|51y>JN+E-meLp9e89D5EyNc?Y=K;qCEpn*^nJz>zoKf9HD1k@
zGmY0{`xW=w)UCTWsfsOyl6*XKoQo~ZS9?d7E84a<Mts&+dCp~Iw*9R~W?MHEDaY-t
z56gECx9-mwS2Bn`ZA>fm*CMyS^+2c<hS#{_9vVwm#Z9}#`ZvaVfqm1CY9IMhGu@qj
zHlAGidU0mFX&XIe{TbAZ`7z@zihoRx--N&Zc2auwwoc=KTg(WqqfLY74)AEqaxVJo
zsjR{#ae20E+~otLWjOcnmSBd>hrjwfaR{0LzP@8|X~X+&?bok|`VtfjcGc(4T6Y5!
zm)4H&uU$IpL)_FRwpb74<u)O7&bj+)Y6%!OctjH=8GcD#eVp6d^a(4Cq?wqtV>bOh
zTt;|YVmay4Q~?OXbo|QYZE>28>fSre8-r<fJ3lMjp7*M7NGe*1s;Ucij=rp{>xe|e
z8wOe@HeYEkb3LseVn7#~usD|3P9>(FV|hr1)khuhI$sa_ZhK{q-0fL;OK=@_#%Bl4
zcfR$%9oekjV&m)*ldHdmM>AKuhvMw}SkC1|ZJA|eg17CQdAT;~2Vt+Uo(17uI?fmF
zac_s+LC@;ZET1XdCFpP#)`2@pn_Tk_R)5+`MLc!O&)3_}CWM)P9}Bk`^YKZy-ZI*q
zOT{><@w(4bty&^S6YuWc)c%q)Xz4&M(o3HI6N`r}WH>B}XflQqyWoqkLI~Io29}P7
zUN#ojG*7<v9d)8)P7+joHEW4;KK1tjR8lU*z%LQMWC*DS0`fRkIz}XK9YUkB!JK(}
z-;~XAm5iBaHDX3dWt_1jSZ$==Q2odUV~abm@lG33j>_o7mMbwDU_pt3o3$;gf=p$}
zCk<WUV}=S5*_(Eo-+4RbBn(o?Yx(oUv(ro9-&Rn&2eO(p@em3AKpv3|9%qb=uuAzt
zg0a5=&+DHkMKX_^AB_xn%qhE)P&`*IV2z(7M2A%mSEpy0FqA_3*?4RIyF^=}M5Sze
zpVb6UN>Dj9nO?;kv@ZVB6?%4VW3;r?%DlfHCdsL!-Z^5v$0l9oYklJ+7#6q3+d5w*
zIg_j&#8{`o6t14HG_W}YGr;r4U+mM_Iu>eZKvi?iXrbg-!=?%d&8WP_RDC2F)dOa1
z!MMSt@Sf-BY-!0PU%FBaXL6*zb!sZ@n;HRYj{~x5Z{GkbY+#UOy_AZ9uOHErq_Gv2
z|6pM^1qCM$eL;ZiAEZXX6ji#~qG|W$g<h7^0Yy<{wIYL}G@khQE>u!7W`iONYz0x1
za}lPAQ=<q%1wmbja6KeE91NtM@L-d&aH){USt%856gml%bW*~YIZx(TvFuYaWV}PX
z0!mr&P<?U{@NcjBS7At3UXf%UUrDLXwS>NT+bW_28p3Eld_J>TiKf06?eNyKHMP`T
zoiB*+P&_mU9W>UeYchJA*jg^edwfg5ad~uT#?JQOddp$a!R}L3ZEh_ir78hZ0iP5c
zb)gtK7cRDJf5)|KxSu8XW9oWf{nVa&A9bDPbGbY#XEP>uuw;LDK494~?{m8dZt!-0
z?#w(&cVk!hIa5!TmyDyk=;HkxIs0*jcFD^7&h9d~&F@q8?N8gi@vU>y?>-zLv+TmM
zx#%wjh4$$|CE#5Q?|Fr$`RE$a%N)0x<MizeSFy@L=p)rMP8O$flFI$f-Stbv{raQj
z$5@tpG|tDaC({)d$X)$Du!%?qA8P|=DM%~%rki~<1Wd8toUypzqV_n~u^9yo>ptC=
zaeZ);HL_x>tya-qUB7x+9L?YA{=E4cP0$N;(pwWW8Nzjcg<p9%BRcY^MW&T$#HK5W
z!{^<VrbVN~-cZ}xP~n!ivahGI%J2R2vb#xtc~yGifda%1X+lfclTQ8c`3f|b%0F7`
z)z<EG@%<8Nn{G5Br8XjE{rpLYMO2AhL&esHyM188Y>vy-{G=u|Pz_qiap_C4dr{Tp
zW<(1d#&%SOSF@?Z(SFrqrj7cufyoy5dz8&`3#+G><x264wAi2a&hhos-1WtwtHVKV
zKAl=iHD|X&&D2g0w`)P5Oy#oI6S3BY<uzCmi8O(Q?qC<A6T!Okcu1yG9k!#MbxNDZ
zIsZZP`Os?H$GeQ_GR}29rTi6Mc~6ttO?f$y`=Z79F-mKiB%9{UK5bB&^`8;A>-lys
ziDPkzXll{8fOeipEF??u;Q02Ir83h+h(VL$fRULxX*6MEe;Aomk*s@KLYklxr*yG<
zyGY6(C)m6%zO;nl1!Z=oi+MazNk?NpK1&)6;s(;1sU!!|s<28`#URDqTUnJR39KO#
z)6uG?4`x!Znl_k-ztt`c|Feqh#^q8gglkxcG--f(dq7=2BDN{9!-FqHxSo<vi;+$#
zOD+Ptp#XE2=js*HnP;aJPERzF_A?Cw14$?<*C9ls3iY0w#&KW8ibA0$j6tO;?7$*j
zWynZ@IXG<!6FFThYhU9H+sN0i9k(Z%GJZdB))i0}L^yS?v6HKVr5KopxT<*{laFR_
zeNro3;<<+Q87|Rh-&^P3`lTs~Q0CQ3Q3>X7?Oe~QZnN4tRalH7LhT79V0TbykvQ1*
zr^FwBmY|8HQczej@`VuBHpVs@WJ}`F(mLHQMbJf*vO*;bS1Oc$$^*}-`YjFj4>y$8
zyTY=f$3n1ZwRF@|Y$qhtg;V0T*ayyIjQN`R#7fp`KGVeZPe`!(iH|tn8lMJ$LTGjY
z^k<t8_De#RsY*lSeY;d5wbSN;RO6pd$-C3YZ0vq%8NXa9sC|b~P2Rx@W=~27YT*w>
zlfq6bqjAW}YSN?hTBNZWz8@oxilVQ>R!TQW4iVhyW(*v_{w{o=2v^Rmc;S$#B3>ih
zmy`g&-2Xea(QFw_P(6gOc;uOwx)t<+tReHelM^OVC!VvTqkCO_S8WT9TQjR8ENntJ
z5YDS@hHZB=5m3q%6vS`PJ-0#=C#{Iz!FmN)Jdi6Rld@MJ9&vlMKUDeTT4*8_LMoFM
zU%sf7c{_5jiI?xQKl+f~<j(A|@h2m)is8M9E`i7MM)q}w`x!Xi8t3>3tHhy9FMVN=
z*Uo%(NpMKmw(ARII2OTwUF-_4>GNF1yiWc+gkMF)qqxY_{m(H$;u7YVIYepEr_4{s
zu6wpaDs#G0Ubj^5{I)wp%L5UW0B6F5!wJ7At#vAaeX^3r)%jSB|M|A#VNuTKW;3xX
zilGb}d+dn5ppYQ(>M6zTdYw&4%!E3_)8*^Nw7Et8)~Zw2jl9ivUe-C6@nPFiwOobu
z?UhQrM>gB`I>saaRcR1tzJ@RJ=xJ^C9{PDx^zio<3j-pq?*GNtIdx|mKUq7Sj%~YR
z+qV70wrzGgw%t!`+qP}nwln>%c@Jg|=6@gkKES=MRl9c8u9bLabNlh@>*7gSG(!Gn
zhv(7lX1h*I%9Rp3`_wAOjM4RwKgHdAYu}Tz{m&WSPV?TKfR|-~Z~1C;1UdiJneX+d
zsju6wy)G6$Q~gOdKi|*A*M4u@2>;f{i^qdSy7x2cs>3FM6U#O>bgb>#1fBwDJx{Nl
zbN=<Cb4&2=)@s|PTFt3xi;B}U?s&h01_4zf_ad2bj+u1?(#7h-MsI42r^#i{DXmey
zZr_)??$1laV|$-#O<$^hu2z8uy?obLSFhu<VAw=_!p^Pd=Bm*^GD4ejZXO}z<uM!F
z{Sz)r)BWL)X?+5|<y#NIC)P>>?uQn8`|R;<>Bj$$9pOJBy*H)UZk8mQV+mM$#ufiO
zp^)9%@{ise<<lj}3BZ?*6!SRclyrC;`5>9(AYQYS4HA-N17%7g^-WD`rH*)NT*F;X
zMC!f)uCHoHvaD)7bj1tQRGp|R+&aY*R3g~KiwD&&A&H`bssk@c6x%G>xFOnGT$#(W
zo-(*yYD5}^^ja#m(T6Hx=Go*vq&Q8g+n^+irifIvzba~)`^q>8Y9nf6_gg;fsq*Ag
zOi*QVvV69ta7=2}fWv>9DOG|>XEtI_G?Z;nDk`30m<jx$;2=%5tOr?UN%PPsRW{H3
z^pUna*_N4H8;(`zk0n4$#>}8oT)!%QvDJmo4M0OPC??IA0n38!XRha3H2X_#b_B)7
zQaF1$*>Y4DSU5d}(hj6`##zUCXi>hm&5O&@w^1sL#3Hb=NmnzNWIT1&4W_e}*5=-9
z9pox3U-S0TIjxF{>z>nS6ITZw)nv*5ssIqB@hS1P6A2cXb0XB8Qu|L3^*^^%fl0p-
z<~Gd4x&}JEz{ks&WLiI7NS)xqMT})EbS%-dvHGQurBA3E#e|T_kSl53YL#}DXryqm
z*#{0Ff9<>cg=(UgC&~*)rL6EE2^@_d6i0Oh3n$5<ki*dQG{c_FF?|zmLt&WTD>#3l
z*_usEfQi6&Vv)-ui&RY#HDeX(H8}Nvuw?#qt6S&DHCoTRVGdh^Ww?E;aR*Xk65Lr%
z$=IW9a&N=Q@;*7`jCwhIZZF_*eIFjnl7ERFv0%MEG~B$AhV$9tf0mHnIHm_|-M=Z4
z<8%Eyo7;}w_Io`!_@qDu7PA*b0~()}`3X0^nu`_OHe2mW^tI=;=eU%+{M|gf{MxF!
zbLr&R)l@iUZ>|w3zET;>*O&eGR(5V9gU`<nzc{vEy)xVhY;7%E(~c;z1bAQPXHMw8
z*8=JSQUY+fPA@BJOPy05KAxxHJKRf{1T^;VVr)2_yeI2R(|*rrw0vdye-4*-#>Tus
zP~edj3$LbH<Jj2}bTm0v?2fr?Eg%>Z7n>zk9u5Uxv?|;ePsif%xtwjh&Mp*n13z8{
zue8l^C@?-Gx*4<9>8vKkR69Sc#_H(YH=}c7=wro`8ti0UPh_m$^Yb3o=QH?t&gTfD
z8$P)A_-E<P)=BFh(?>gXyJD(`Il2k7{Y$9*6UQAYtmms4*gJi)n0#u=I<Ca*t}quT
z1JJh|zpLafh3tQ0UBGHQV>A*td{4*T`|*}lYXP4xMuB-L>q8yqVujK3`D~x&&7ufN
zV?!APcpImML(iN}!*w`T9Oh=SnvL2~d2FlN1e>)7)1@EviH*nNK7kZ2SKi!Pt%_G`
zYd||~wavAGY(M<$PJU+o)>i+q#_J+CACmf2y^YLv$A;HLyu&W-P<evQF2|#n{un-e
zoan%EGQMj0&*I4MFT~Z6BAnx1`p?rb+xEID+mlxRr=w7RyUTZj=h5n+DZgc14kTrh
z0tJkauQ}$o$&>rsQ7ig$1w`iU4u|Z^bqTY?Q&0%?iG-0AgwXJYKQ@W$$y!}=BkUud
zX7mh!g;qqb(tDQ)8m)}d6S<0}MllLg3dQ8(VM-)C%PFOpLoDc|0r6#XMI$Y<yx3yp
zN*3W|P_;Bm*mY&L%=LZei-$aHYGj!tJ9mRmNJ14d2VyX?sw84PJL?`!`08WoI0vQl
zzf$EK6#<$??HaTIZ8;-0iPB;m<w08P<&0eeD}YX5*NoZI43(N*#QFd;jwc*2$~Mm7
zAij#$v5A^W9qDk1If*=8s1G*&b?A`lFBF??E9>$uJ4`wpN_>AJR1gUaSBw}1W<m3T
z*V002(@_z68<3!V7(>#zYI4#N>Ja_V&RGLTr_7aKt{9NCuiU~6B-Sol-*XX@LP?xU
zrbQMbGlc<wg#&Fy)hvIismv&;tQ<6lDF1|!2pGVI0;|^=r{;;IR^#fpQkGCkZ*Sl&
zUNEi2MFVXNs@RR|<dwovH;YK|3JZZFsGqBXD<l0r#xxZ*0?q74gQD#~dm6K-&9J%~
zhr`IF03;^vF5WM4XKfG>-6iDh?WLKd5K9e2s-Sv!)rGe!rzMP-m5B=HGsX2borPI(
z56i1(XsWqME0QINqDD;Gg$kGM-q+zB8Fl}fFM`#4u)=7q*(hXGeA)2G57}!xMdt+v
z%5#9hfwBk$AeYue=z<F2B>q#^;2Kf?%YNCR8>?7bVROo3>o(3Sc<$8org&c9Z}Hz*
z`M2p)<(BH<H2py6@AGvU7)hwR(q_MK#M#;AW#w{r)cOp=^RAAluoOGpq#$sD?O$En
zP*17odioOI^p-JpbjgFLxBWWGv3lKIeP-qUVrvwrcd$LFa80wi%>Cl=Ff)aF4w%>N
zO1GZhwyEHpPh9piJFm^|#=Ev~!HiX1;3GKU+vcmU%9}E#I^~PAfMPzp=#bv;x#Bxt
zLr?=WX9+aF--oIr9N4ui)p@@S9F49{b@uxvSIyx`h{&wV<<~!62~=JA)ObGhB#wRx
zY<%q0mlNt}2sEzp0056$ieAh7mVTM+<&Mv#?U*q!mnDrZ9<W}Q_11j(>fhY+&dsL-
zk1^AFz_fOUqqAL_c@~zhN6j-(TTR>B#nwoDzIVIfkXb+PCrHml)JEGN#dyE<DVpt1
z$E#v}7y=!ux7*d|>+KJ~`KWFoA_d1bU#sizFmTwPfQLu-`R(c@b-Fp5@96Sck8keT
zsNh&n#7j)@tP1L1^x%Ze`D%i?MuNUh!qIbKh8!1~TBAn)^PA0jQ+pY1sQ8or*U9Tg
z%<NC=%%=;gmy7z*_;6;4-`&0ymdm>-V*!fqm+*@!=mML->?<2@AIIFvf_z!R466%o
zr@f`qz~bg61m5m@dw2S;(o*m4m4w=|+RvNS)yP<Vy^pVA5&aIlc9+iQlhUzs-`>bc
z+jWj8_D8$wm4;G>{V%#<^6T%W{S#nt@%_0Ltn=exa>Q3-^1D|R@cesc?5Uce+jEzH
zz1dz&84MtqaPsP62czF-R2X&|4%+nF=8^gjy-1T#=J+y3HRA$#(V?aDDowgpx+onc
zr{@DRcK1T5O1r5E!YoZ)SwgT~j8NuCF}*qC9=cPo@<nVSIO;_!Mxg}x5f)zA-`|tR
ziZbB5LLCFLtej%xZA-t<u;zk~NJ^)2=S$@vfI-uT%BAQ{K8WihyD+2>(UZl@-X}Ob
z!#l3bXhynP5Sp~=$WY}AW3>4zW+qk-cx~bd%|#?aA2F4Re@A00QOJ+mk|DB%7!Kdn
zkZi|Q4uV+6oa6!~f!4F5p285?Ws0YjW}Rw<2t`Y+kL7CS>dWYhCZ<K!w!)!uYiKU1
zT$gq#<$%Q<75hm=8;-coIgOYEazOdykSZpf1mUqdl!-}Tl$DF4tr(0%`l80gxDEBR
zL()Q$TZ2or)_Usx&FjJ3Ul;vg1|Fg$=?T?9prKOuAwew5dTPco6UN$PI&X4d&+|N9
zo1|m9v`(&RY6cq_y~504k|sPYG>42lYXZrE7tFL|6E|tF<`iDF_VC=OWFKg3A1z4@
zmsb;|?v)c&J$wkOP*-4@tvMMPBds`tE>(>By>T)X9PX`uz45Yw)3&8w4?~k_uVnws
z#7FRzJqrtqJfbtA2;#qoCF|Ton8ZZgO2R}(ll!yVrL$AB`=}kgYqIKLD5-lXr@*zs
zxw0vB<7e41;>nqxz_k~DG^K;|N4P9g=G_!DSBx17EGZHyQ68I5$Tp>_gCrE&&)GPQ
zy@!J!5wwYR8(s+VU%Y9tM#Y3OJYJNam0)v@u;3GU5eVE(OibMa0o%v5GW_J^N7YLK
z&sG%y#ztE?ZL^I5(c_!xoyK)<^^?A-)uJ5JYV^L0W;Ur&><s{HC+P+7`o+j*9<?lq
zGk<p{dlwhsT*YD<-{f<S&?+C?i<Uhq;I*ZGHSLw3lc9YXSzFC*jpvQi(N<?)wI9IY
zwqneh{rEVZ_b0kFp`34x)fLBzX`@&{B3p#BT<iPm(BN?Sv|Ic1=jm{KvSvyLxo(Y#
z#d@g#ZukPFYhpA1Q0Bcv)7$4EbCqfyOA?rl<|Xnm;`KJIltfA+S-ZP;ClJPeg#!ne
zj^D1m_{z=Ur8nQF|6XD5abaA2DGkuP?vr&sR;%Bac75s;$}t3ukQc{b(0yI}SDP6;
zFD5=Yqt=6$JZjCFomK(0qBh&MSa~n?3DPz$JK1<@cpEnPo&9#M#^(wB{LgRuhd=T2
z``3CPa;_iMEF|Qm-_@@_Kf9*{UYi~wi#K5Fd)3C3Lk}*3-c9VU3NKIY{P8v3QoCZa
zqCVcX7Tz)M6%gjztm{e0^gU=X397e%F>urDfJ^x%_eR!dglSvbt0wU>RXB&@4#&03
z<1FC)b{mt!)6>+@WpYy~eYoG;<;AtxmEF2<a$1U}^K|;%t#%-=S^Lu5#uhQ}QR`jJ
zSHfNI*<$g+Wy+(w$TQ#V_H9txt2LjsBW|C71H<L+6Is%lx{ONS+a-U$o~Q0Mq|<xa
z>TT#JA}*Xl1Uo}zH&(k(si|$TZRMKYAnPF&^_!2_Qc+b!?ZhkbjZc%6D)WktuQg(7
z{+s8&U&@}~%mR;?DLjZQj|wW8ZirBBp&ulBItW}AeuNpBik!J0US@!jSw=K<bpWHC
z9m|0mIV)3g677af7=i9raDp(}+$#(m32*OSP8c3We1AWq>urh;5yb^EiB0JS^)eWk
zJm36yA(l{x@Q7mgfms^WK<j-+ifprDK8Tob6<7-83B*q$qnjR&q&Ru@(N|`ExpI^D
z53g1|2p9w66T?I~X1p=TJ=FylWc+f7RZw1*oZ1m^)_M#$%3vWXeVT*%6_9M%G2_HK
zLvBHEDGTOMAt#;(=6IagBN(K@&BA_^+bjdVpRAT}2(4P<i#K9+@mqx)(323k0g$t7
z(ilME<1k?;31X5sLy}<8yfr!m8VL;}7i}BvbR{b{M^MAM;oc_b0{-MBlLOBUr%?6d
zX}7it2>94)Fj9;G`@J>;myiHhXeuIEP*&*xnj9#sK||2Z_!y3#B$VL8p))8{OM9re
zA~1^m<Y8FYvVY+b&*o3!K%|Nc9Bkn9Cxwz^NoeT6Gbz!+xM*XLjLuE}nL5TP7N$sj
zhjF&z!Tm+bR0astO2Yj=k@x~d#(IkD=3>BIWFgq_4Gb-b5T}n+6w$B)t`=yLeqc)l
zg|6^2XN4rzl$arm<WMDOGUJ4W)@rbyL4D!#68`-DsAO(|#9vQK)8Fg4zF#3DL`A(<
zKkxBoTk2tAvY+XEES90moSeknymxnJx6ih=DsMQ4<R_$R(`r*!aVc-gT~h%lvwE7o
zKWy`zzaI%b3j<39(@`p`*El=hT`u>nlVio@@O{nprCYx@7IO!q-;S<IH|qF%1q1h{
zB(Glw$Gb9=fE;%YBtC`f+W&5$wz?iU$ei~%eXsuTzAkK{aem~WwpCfj%yyZ!kFKs@
zd#E}LVHXEY&7^~MB?DLa+;o4^UrvFGbmWU@(4|@#yQzUW!o?+?FRHdPte#Z;1;HGx
zX{>T-&2zZ;nM~)~z?gq$VsdlAx_mkEud~wliQA7Vx9z?tU_ZhJTV3U@V87M+v4?o9
z24NrNeSO^|KhcydwmHSsP(oK#AE<vJ2LzT{U)s5wC%aYZTLII&+mxFYZ8TSXcE*78
z6V~S?R``FluRGr3bk&+aH^22hKVmD}f1);rmaqMVO%&VSSo2!te{;#XZuiG&eXO&1
zSC0&w7LPoud~AJt{V4O4zfa3CwK^Z_TYWdjFq4mchEGOthrnbXZdTAeOKq`;uEzL<
zIP@nUS1eK8S|+<elizNnU4vCsmjiG-J+#OgUUwIl{1|dL>HLc&mgJHm`Vg47e082@
zq#oOA&*7H5T*W~<VJZg?rp<E~IRRG^e_U;#K|{ZFyX{46RicEka{p${TRBLIeq;1)
zA`z~CPNy7f<=roWNaNNlGp%8IsqC3(;WF9gTuxSYJpvgr2BYvykI2RmO0(G9twDi#
z?w1u9bD1gONs;Bq8vIvOUU(cUCpgn@18QdljyF)`(3tU!wIX>i1t!fz`i`eZ8;?*W
zZyz_REKV4RI4&I5GX)NZhrx{vJ7-9vYfMIojt2okftn^^QD;2mqgOi}8#fACm`=W6
zs_}ZuMQekU#7%1QL&;Kzl)L8y9jDVr@<%PHQI~j+dQPQ0JqMTEpI&W4amhgr7)~$b
z3+D19Zeo((WCt}tC`$>9@@@%msbJ;>ndyrBHc-w<{NX)h2WDIs;;q7C$qtf?h><i%
z27!za;*>Z*BJ@nkMVSL;RBSQ`4H9#~V%9PEUMG}o%4RMLA9}sMozR1f--*+1GY%y2
zFZLe+q#+Th<Vdh&dB5fDl3-3;ykZzQ2kvPtBkKALjjN`Qj)HVYgVnqcboYw;vOwf2
z6_iRzsSA7Um4g>@0)Ct_{a}bI$4sb%DZ*AhNs|u#%g|I4?WD$rS`vWVtW~uiz)2Zy
zoG0l7riev?BZVz241-E007@OxX96LBpui<0WnOZsObD7$>!E}LuUfPxNKU>QW`Kvc
z5~4nG(+5Wz2oH1T#Oj7?Qf%m~;X*bdN5&jOL28J)6)!j{%BX2{Fk|w@Dk(E!AT(U7
zgONzEGbALnpvwQmNdb_Qat#1U%=Lj}=vVagN~<*Zn}h%3QMN5kA|O66##VNM!Exu=
z^CBTOJp8rXb*B@@dM7Q7<HY1@qoG`&-Eg(VrLxQ4Tv}<bs5kQa^`S3YzVnRoHT0{A
z+r5;`dFSD4soG!V{>}2`Q)=7{`gX$NVK!K0`+4)bxQL<Kn$Ndd<9$|D?^}FnV-O~J
z)y^4n-Wl-Xs5|WPsnGVX3!-!|&iwon^EYMXoKNt$9=o;x)9iMivrefVB`fXn`?F3w
z1(~rxCGAIT^NtLnN44Nfg|aUTT&{jL-a4))fRIxi#Q#L@>qme*a_G-D;MHeYZ?*h3
zqB_qyN13gPl9O*~b-ACVpNEwpk$+boOB|rVsOjm>8@(`fo<bewEyGV`{f)$5+<ouU
z?R9Brb7~e6$o1v-)690o;p+Xa&(vw3&+b@dbX3!}l(^8DQ-eF>7wo%;-0_(9vR|hf
zqrE&qx-acrl~)D^%e%y%z(7}Xb_e?GQn&4OcW<}axA$uGEPh>}o<%oj^UMCir=083
zpylVX`#s6EyR$pYL%e}!iO;W%x5m8N*0!$aqxrn#&5XH(&&9#yyF|}}r=h<oG=#Bg
zHZO(#9Q2=-ee-R9Rq6nF9*YxS#+E1PUgce9w>p;jQ}L6dcQngI?L%$$YUmv>p4I7>
z+P22i?OoTf{1ensE8<$^YMI3h?s}`%+O}@%=Pqkya8A}Z<qx-zP1F7k*_te@!5(^4
zlm+z~C6`_b<>{JLgw6HhTCJa$w9dXET-@c6g;<6P_8HgRS!b%TV5sMHtI<lsimATN
zs*g8*rbl+$)6O%szV{`gmPj#_IrW;P7^(1?L84IZwCKRGoqHteMbW}`4@Ozp1i>Iu
z`ZG#4`t-Dc-<E#)^a_Pa%%!~a;`t;2czBqkI2tIJ28^Q~czVoPK4}bNidL2w#qpxU
z(4LGyNm3>$Rb*JTUYyW0IwC2-HoXaer7LeO-t?Zbhf}(=^+A0U6eMQ=3)3JBo4z5k
zhuofU=Snq(6e(z2f65=L${9FhCYZ2+;{4VHwCXefgGxwx&Yx-JNRU`9gPl1YoGK}3
zr~uVSY5)kH*&j+^x(snqeTfIy-W(MkOmJRf7*HsY2e0m}<Bb+pNK)>HqX}CNM+q4(
zY#`Y2F>?fLde;U>Bn@*}a-(PNAKB)qqS1+`7?Wf77N0HNQ05BM(^DLL;sdl}N_cXN
zw0wyu5Va|Z0;6=boSGX|v#KGGlLfqBngTRRN~7QBi`<oNzY5I4!3LPYk!EjLL1Sbn
z3?svJe!?mX)#&8ZP=lLc^~?VHhL8BdAOLrmFCfW47UHd$35H^WLN5XdgAfT>fgZ@#
z9*UK`#EXw1Hz@{bFv(CAQO2=gMp%ksBtuFS-=CvorDl(b;O`S8D@PWjSsFv43p1OE
zHD#>v$csyomL(aUoxMguAR~;y%t?32Aj$$WF0nzDJ+4hd%KEWe$Shg7N9bZ>Y~&aI
zvwM1bhs=K`oAHVCA4>f9dzxS5F5d(EJ2V^z2RelM(*s)fK8kjWc6=f^l#}Ql(9F?e
ze7$_1*1gS_>yGoKBk|l`@712g34GKZANJ1#I=1Wa4mWsN+-=>@Z*Pt;Ro<)3o9sB)
zyE#~9Syee(RP|40Q#l`(m^)p|TRbbjwCr}>DVBd~KLW3-Un;@f?1+YUGcv3;v(oJW
zY7&Q!Dxa|w?|Lf*eDnAU<uvx5k=1}DU)58el<IZOtMjyTEIa!Z`;xY;)dy43ZxxAG
zvFldA_1nPS#>!*yGc^w^{gytC0LOmG$Qa#&?KIq0n`hDPVuW)sQ?bBYC-+LaCQr|F
zmc=CQOP$B<e1=%@k&yA~-q_+=05I$B;|FHP%1M`FeS51!<NNwtXE)vY>2;ViEXLTn
z+Z)fh%s71Er0c(PQ#!bu>*v<zSTt4m*JZZn6O;5)W0O`_jTr3bZ2KT{T)fNQH-YuB
z1eJ7A65`xrQ}R)<+?_vN`mf)LBDs0M#lRN>JJDy)m?gga4BTcHI-YJtmIXy-fNb+@
zi~8QbY}yqUc{`sqzc<F^`&@pw)akLb(pF(3sAgZDeCIV7OXw!DxR|rJ%#~xuzk7{k
zE}1*8Q-Chky8?>2ZE<y1&nJ&1&a2_z<kjFGF<jx<pB18_<y;?QkDEk&J;T=~L`d|%
z9d;hXzfM}{+BGVjjx3yDC%8H*UoU4X(nH?U`v&neUCmqaXsU}>xNVU{UD*QV)b-h2
z+w#~X#RkyE8ZZuV)QA6dp$ON#y>5@WPFA`+b%LOsv0?ZZ8G27wpizQGAm`^#SVNSH
z)hsJWa^ZjIBK1<3B_jLb#JixOB3dCAFB3_mVZZci$l+aZArN1$>53x7Vj>9vVN!5!
zl%ad+lFJMg{~D8?2*oB@^$ecI?)nUq@n<Yp9HqmD5eE`;N7Pwcr8U@~^!LVlJu>T~
z?>U$eP4wGvT!7chVjI?uj$qfwXm60yM^5kGsN!@mj8K5U;SHGHg<08(=SHf67kk|d
z8=ym~)^sT6;mT6q8pzx;lB@q=qzE+Qf`OWco)AHS_#rO?USbKy7(60b1R_fEE5ATd
zT{#DUNupuPttV_s9TX;Q$kDFDF5w^AOo?krM-4+cL7ZRO#93TGYB4nrNv-?E>}pmx
zrB_;*e#^-eTpAY^WfiS7WtiYR<Kqtsk^}976(og&b;u$0Cm=_|*kmNK5e+wTr0*A0
zK_l)91Q%`Y%Z3S%8a(l<NmewWgjjIEAm@iv+rPcD^-a*+AW0FW`Pd>;3(}T2O;xiX
z7EKF+l<;JVE*a$UM<SBOJy%mfPMJKGaBOjiVGq>;t|_v6)Y85K&OPM1G?J7sc+fyV
zDJn53HTi79KzvuUYq82*Ne;sJjxn}0yFN^h4~&WoM*V(xc<HSNefX#uqoWH<N>+ya
zpaGULc_Na(@RT$MN!tIYPNY!&>6}#V8@d>Dby?Yo?DcI%JB4tD&D(4rhI*qJ^a>07
z)wZfi9s0j5m+OYN#WCKvz2Unbhn;P`nS<}!a6X@}4*ne9`G^FQ(+qmtd_HDX9esxk
z2`+42<mN}qq2lIO2xgbbPRsNzRp0OHlg-|Nx@{~B>gLnF9Sz^-;{UE+u$m90zI)m1
zBsSv86gIDaX6k?3bi<rCucc){_D(74rCV6KoE+L84R=jGRPp-U<}Yy@OzDr=wrO;`
zx1KGTecc&39*_3@gCI{}z~_;Aw!UtrH6O?F?bgzYs+@eF$ot)B*L^1Z>wWE2sre>+
ztgfNHAFk<R*5ll>srNJuWCPzey+2!*li}T7^x^1NWmDgIr>^_Cd9euDyFOd=rS}?H
zW@p2CY_z%m-K75HHDz|ig}^2c^ER)49mDufy69wn>gR7oEFU~uI=T~myzZ`*mzqB2
z)J0YeMoyH6Qg*M-)Mskp$G_#6;`|D&pLB1XZXS)QjOxowR%bWA-6H!rNs-W-^~<26
zH1gmR@f*i?BaUN^zLNyra~`F8`y}{yWnxAE1H9cywOb<VbWc8;OE(4;^)<F878jeI
zFb@;xFFPTpOLDU=1DzOI85f;2_*=qjAXVEImFe2{cP2ldJX77K88R`BbQjtn0p(@u
zUo9bD86Q{V-s$ZF2&$>jUey@9yy~9CUf#KW?uEac`0}=AA^ub`>0XO2wp3MVs(QFz
z9|k9TlQ{!pW+grS^{;!ZG%ejzs*eso75KRtU+?i;Y_@sRhKpvPS&j1IGfjl&6!3f@
zjS4B^q2Yq5k)R-=$uptCIRSd$qIq!-;(Vw^&PXE$NmfaC0ch4VAhw=F(S9qZd8c5E
zeBhIT%oBEH>kJ5<I(E&v>E-Ij*q78rSgAQuWG3+@#8s-q`RQy97?kw}X$xpn>~iI^
zM@MK9Eclvgta4s>liUbNtJF#0xhD8vjPt@aE=6D|;fGLAA-WDyDR{y}pn>^{&NgOF
zg3;$QGm$N)Prt=WOw_qaUyYmsu=V3bU`+R3w%{_JMAhOk#CXZgX3=QL`vx2WRAJao
zE$)yZMA3*(87YJaj>4Th1I{iSG=o=9$DfW(-}}kLV5%cG)l^l@U^xqz+qFni12j1E
zTc~)Fs-ZDYd6O|h!pGu%f>vUnk$@@VKxc>GMIa^yGlQW~dVt9F4z^Xo`1|EjcZ&|!
z{IH?G+^JV8Q6-6lp$<~!o4)=jD~=MKLAkSwOczVPe`5j4ruD1umWZC7Vf(N?*uk=Z
zG%k^7&uGD=?gwOv5Vnc%FpG^ELo`HLEsP~7p`?7VIo==T`KVKAR3Wh-ltVS5<g5|3
z8g!gOJv#@iM;tEvR3B1V!0BGP9h`wAX^$heGPB9v_co!%z(LC(2D(TesD%eRI~^a_
zFXH5=LZq6B1u)s6TGWAoh6neU;*ZJM{{R*1XvNUX_(e8r-&D@Ee|Z=C_Fkz9ETDj=
zr}VY)wG+CVyTBm@!oL}sWlipq3SB3>-LnpU`F3W0CXWk8$=W<@vrhRNOTa}<yV7pw
z*P*wIvE;6|^-e%F;kCf`utM8YXu4ZjsgC#g;<dGRbvtR(tA%^}`W$JYizQrRM#{6A
zp6}b`whD-LU=I%|^jf5J5>5ZCOmnfB{woVbWEn-_8;tf82slDrUEjYT+druNOB+L}
z-QSOu{$B>K&zRlc7edM74LoyS`d$96{`RfKRR`|=AJNydNuN^=-iK}A`Y2<I4K`F5
z*CA7#l=T0Ci-Y^e)6J{({F%D^tO`0mHWW`=pOy$r<zjy^du;VR5@wY8C{cE(0+nm!
zVs%Pz^}io3HnRy3qZ>K-tOBD}v<^QCia-1-qPnuOFUst@>)knwGDcD!gSbOxanJ+8
z&Pp4uDpJMs)+z^MrYD}KRf;kuX;w3!(<wkOB0nb=FKsa!LL{Y9wX=VwswVk9t=2EC
zWfFO3m*cXk3O;|koK5W8cQ*c7Yo{4o#vMt<c#O8V2fjw{u69iH)NcH;3^W=3QX8Fr
zF609&b#3%EO^teNL(x-UUCf_azpax%Q0?KvHTrPjd1{cI?*8`d@viK)62Tzg+n}^Y
zlMs3Rd<wu(nvZj|D>X^AV3Ue)vH~qmu@>0sY!Gm2%q{8Dq=&0WivGAdRRC{eRuI!P
zQy0-x|9*HIOkafkQgk&_b9GmL|8Lc^#gF693N98*r3N``Q)u9LYIz!tWhRcpr%8%V
zC5A(t4tFEPU)`nx8w~BJm!#n@TXFc4nn9D;bcGQDvA-;J{>K<}Y5>`Bktm6z*ss_!
zd9JIu`}OiD_M+LLKb5?72x!<)`~t)jPzUM3i~M}gmF~EW7=BjVh{1DpGvq|0^zsPY
zDwS<u<lts;#5mFck*VZ({dqN%)S;3fD4>R#_@Wgix%VboKefTgopaD~QB{+q(jy9f
zacRYXkCmX^v@e86*UrhCw6!>R)~Zhw=P{j@mWG><2lkm!m7IWL9UsIe-cX?cNe@^&
z@TAp?hEH6}NV9U<npU<f#B9)eBZ_uQsuxg`N+SwFa)il3HSnlmkWntw94j}0R7qMF
zX0=L{DkpzQ>H#IJ(nQ#@V!JWKMp_*w9%b2oA&{vo;p)ZJG^VN}tX%2Yut^xP&=3Nr
z_I}rB4DI!WpO99rk09514|_xT;aBn+G@jL$+MosulHA<&qf@DZKtjiR#L&mv3!({w
z_ZgrN!RYlASaJGb$qlJ+nn=<LYCy4NN%h{i+DY088!O*}p#_R0mks<*h(u9NBZ<;<
z+aL<#Fc|cUkqSyN+Ahr7n%c?3Hi8I}lKI0Kr3(6_*ylJ`%XyaT&Q$7nyV4T$B2tk*
z9M4(-(o|zX{h;Kyrrq}+n4&Uvsnys%jp?<a`fcLrbvw~Fax~M&k+?2U#lKF7Jpt%i
zo_(z_b6~^S^y=JfT4@G+4yvzzcUrp^c@+ZJ>jSQQ^_;xE9=NG(<ToB(>roOzGP@pE
z1hnoHJ`6t>nx_S9^f{$awDu5I9fs>*Gr>6os^8Bd*FMp=uAn|nS4W~PpXbXr)ua+=
zEpD0tvDK~&49kf2;>QAfo=?sHW?O+I=6mkh+i4fn;2(c>(z96f1un5O5=s|g&l?H-
zYM+g#uT)f~&u=R+-!YHaF*2?%bl*13XKRk183=u=rrw~+X-DsjAn(tbA;0zm$E{73
zAdd{wch0qvzl9|}BRiKAuEeStDw9U+&(n0ruU`{>{|bE@im|<-SbdX*sdmxx__IEi
zaJ{6Nh5;{>cz%a&ZfbX!?tcRHIZZR`|6X(@ut7Vj#8EQx^|T!OJ9z5PY99rmyzl4P
zT49Pxbo!xVr-Z0FOIfz9yYGw9PXE2<dUDrpM<Gq|m4N?^>+=!OIIn-O?<XOfx>Y&7
zS=7~w)zZ}&!I$m!MWnwa_p|MLVwiEXYGeII$LIPii@RQzhE`fER^^|froE51E@#B9
zHNAKKZ5Qs}Q`-3!*PhyKR(9pBmG}i`&ULeg87D*X&g&k!b&AB}Q`cF9RVU#oUF<j0
z#ZR2G0vW#D*=gCD+c$@35)tIx?`PT9a$jZJU7rp|YnSP#H7eCqO|g{<z&jUTKA%nr
zKBYnY3j%JI*Nd%*NJtU)XJ>Z~wC^{2foY-Pr^$V*sIVnkMcAMO&a52|;XNjd%rTTX
z1Gb*S`xe@+IEJ_qAp%TH@`2<Zl!($cGRjnjMKga;NJMXyvSKY+N;Sm6`^$<B-o@M?
zd(RZy7Dg1~|Ms(kEBuu<CJd?MwM?1KqH}1Q;r6W46{K>v{HqbMr-5?*0^KgCc*%j5
z2J(|*$}oy%#H58*h#dS#dD2QRCdfXi{6M^2+VnWc8Bj@LMh1&7S^$bB_j~Vljtqj@
zpLBf12K5*_$LHrl-mFV0JisV9vQH^d(h6IphiuKJC|@FWfQ$_0fcclG(r`D~U?Fbj
z(S~FdR4zLQT6=$2NESkKVHigBUIC>jSgraYz&mLuPF}viD^0RV4YLj#6am;f5i9V;
z{_gNM@n>cwxkSo#3G<C3QBW=xsiLV+j|KYuAgdQW1687Ul7kIQ>b4zvISMt-0(UA-
z%FohLXmX&MHDknrR`9AM$fyQ;zyL9h&YNhEiw2|&=?79gy_8_K2M^sLr`{n#0Cheo
zdOU%d3^s2S7Mje???n(5sK2a#{vyn4NRq^4WuYjHd*B)vttz8Jr~FHoEoWn<P8<L`
z1<e=TQFB^^`?0@G)tkQ>mgtGqFN28^%QQMHB}_tTdXI(18yr;l`vMi4%7|ok=m!``
zGAQZ~cBMakS|Fr+;Xu4;S-zlwFPZSy?}h*88dFI96F>TAJ<XhII!sJV{&udG_C_|g
zm33G5xnG3Wy4q{HXlt)jy)VyS>%b=?Ky*CQ^^=X(U3-7}Y9QBB-EZR7Mos^#DNMqq
z>*#~aZ$h^#>XzA`<Crg3Kw!bVW3wj2>c8oBg|~-rY}!$xRpxrr^nYJz^d&oMd++3_
zU@;G8dXLGK35&b=l?{f~v!OM^1^-6Zw5K#RpsI4NdMYuiOW{$Vc*-*_t8Q3dYOLOU
znW4)$P@t6K;m2LM_4lTspQ`-0?p{ROB-NnqFI|>*c?V@_D_Oeyw8;J<3eD^-XWOT-
zkG`}@_L^>a-#V50`xlFYwWqP<@U1PI%2<kukTQBm3OD%euk(#|M9fgFI~}^Nxx~}L
z)MxhRswx}1Sx-ZKfA@ROs~(=As`JIq&BH;MM%FdjMUK>_12Y$l*YD%IN&%iShU!VK
zD|yI&*m}0#`gAai{3<skybMOAd8mix_q1_F?qcaKfoprbZ6xIXYToTr>e%bWUkg+h
z+!S)XQ953Hh9`VrI(7<XG>on{E2#9gH2NqZx$M+=&8E;1z&mr3*@fRyvirML-&xSb
zyxFkL+;xI$MVB^qQGef?^_?j2MHnz)jrz9AWT~Fg(O**4%q7#AT6O(B%<=uDw<vll
zm{<9&{z&pTrw!k)?EX&edHgx&P;v$@{Z_~zLD4lqvz^88*JrWbZU0PZeUs;xzW;ld
z@?@U_VAqkBxNx9Y?|cDXfGyQmmDLFFfLoxO*muS6v0GpGSpDUYZ{4Pp0#M{=F1TM^
z#c^D2=0e}LG=nY4W+Ed@U8KFdYI=^9deNj>3VF<ssC+w=1_-sP>1to-d;7eOnooa!
zj@dV>w9~9sz8<7Z1pphjJ1+RS*jd#*moW0WzsAqG&sYA*-I-ZcWU)N--4#jX0mst@
zsa?#ZTB)-e4>Pzw5yoMbzoFoB`V{p-6tM$;=Njx5ysQ+6x`2vP1J<3#TBP&UY;)o)
zEI2V@iAH9G>2%6SK)Dmq|0JCvtrZVJr=tbS8a83Kpm1Y^E=fM{GV9aqg3?AT8?sOy
zyh12-Mg#54q@7*!ScCC*iYSBG=?Fn7$IuAI<s?QEUvP@H@>NI4$Sai*YJU8d>LXWD
zR3eC*G1+@>ku201Dw?+;5!yP3?Ay-;ON*3Qg2s}@*eVq*=bQz5!DUAH8x|fF^UkLI
z_h2IKT#kfpG?$#Sh~=kH*9IL%QDKJk_#9|ssnjT@+=+)kb`>fMI4$T%6hLXqY<HT1
zg<)n;s`d9mznP+_Mo9SJtP>P3UNVlo*<kSq6c~3TU4qr%zuwS*^nt@JC>h{r(Hfr_
znd6N&4lShng(fbeB54TP<an@7gNaa%Y-nKc69?=TlsqOZp!~!JR!Xpcs9*J%v52xc
z*4~i`$7T<MZdx=SWk*y8?++Gc?q1%kk~r^#@czuHQAJQ9loH7S9xLJ8z@CFDdUUDr
z88JI7ek7d0utYz)ju~i5M3LenJD9rHRFS9`QHQ8pj;5v&okU8Ifmpu<DK%co@33eJ
z@>~lrRsvL+@YwlYjXc;A9mW3WsK|U^EZSe5^OcaG4?Od(>`?iHxVX6Nvx#<2))rx5
zVZVykQ~c{fa8pIVLN%{%<J-j-JXfSvq1)$u<Tllu<*(+5t86<umniLfScW9en!!_T
z2?dE`#PuU~P4Q+Ok44E(bMzXlze-p9<+RgLFHfc{8j1%en>Qy<PS6~49^(++mz{`T
zWF8M9V2yXTR4xt3nq1^EnKV}2{Pm9)z>lokh+fQ)kIwzd=c>xB%5v`+p2ydLd4(u<
zyEgkPmndAbCT8sv=IV|NkGtnk*lf6oE7wi|4_DU8(#Fo}dUJ<`B$nb)Ze2dRYwY9w
zN;@5OwuS`*i!;~fNxaQ1@9WO5$(S-FbIa^9Kk3`O=6Tz^vd;}b9o0M&yFS2^BDwaj
z2tDVd%co_nv(+$@_i7`%Gees1>5TV7>+ulq3P|(q;PcSV130QQTHGUeF@=4Kpw2BL
z_rBauL=@n^%77{HYhk;!^Y;2ye-?$@&)7Ul>ZJY@)qHxGnHU9TR&s;_`xOWUINB;#
zXk(phr?_V>8+atVhSyn+&Booi+tpIt6&MIR+S{Dm4Sec6Nt%rdI~}gQ<)-)Fly4=;
z<N={lX~rdQs+FMk9RfG6-M)!i8M=0ta>YyV!{)vwSgq<_RQg$m$zJzmAypfnNeZJ%
zcUGVHbhfd20zL97qfzp4ewH20-9;vr+8dmBKbue=yuWVMe?h8e(ohe)=XXKkCimu*
zPBt>=O%i?>njGBs2U|0kUHNsry}aiyQuMcG^$BY2|DnCn*?7rub9uX+sNdddp^NLP
zs*ky3U|@c|6cvVx2_ABWhGWvD+ZorCJsi}~7EM5tFWA=<&XdKeHjuav)Jo+J5RFsX
z7xF>*F)ip^$oy;hs&5>SBaAr0`dhLbf)=J>4r-peR85h{^&CD}+$jTX>et?nELBQ0
zW^kJ<K@t-|>e5j&kWF@5KeLc|3r1&$U=Z?kvY|qdzb=h`|IG~jhz4ZA#fFiDN`x_x
z)1Z)wcM0|@;ktI|2&jt|)gpuXB_t768tFdF83sGmiNX{VnHE?rIDyjZjv$<w`29W)
z_)Uvyf+pG~*zy<MG$CJ=n;Kp4PBpE8RuLx7BR<2&)E9nPQS7K#riDCm=kfT989dte
zN862fr?QpOU8@e>K(r$ay+$r6WwJ<W?;=zSEj`X?6lzffwwE|76EoAR92q+;YMvHf
zx@whZjEdy`U>{g~M1^Y@Z7GagR_|UO*}Dgp-3OQaUS=Gt4g|26WgCTgm)Fb3X->aZ
zQz_}df(iOKzH|Z&8UZ`}L5HysS{f9lJ#!~-NNb;Au0PLc`*$u${tvAv)I9B81dX&T
zDig(E2YNQVp3Xvyok>e<N^-tG7*q~0(MT%O6en1mWDrw=q{F+f_7pj<WgLtZ-D-FJ
z4;_pUAb~<sq)cnlMf2MT#i*rdc=1tWIVeaaK^oM=LefN3{UvkGoZXUEy@`@#uu$W;
z=J@OXfU-aFd_N<Ff`W3V0`$ZTg;1B29Ek<+c3Q5cRkd&OzZDqm)!5m4nw3`D|0@4f
z+tfKMFn%(<S*74uCSR&+_m)Gff;xt%JJ)_9<eRo8z*uws+w+rU?^<)9Hpy*w=zb`g
z>=ZLw)eC~hnI7M36H#}>{WBu*sHyWj*5AVSHrrtN{?m5Jo}iy&yS&7|NzL24{`#nY
zbNY+b{n{Te{aM3xo73!IQSz<n*iRAFVLP;>H?;44zuvk`*a-A#PvSAV?$FipJXmVC
zwB4vM)t`<;>`wpNm86hR#R;3O)3wgUX`cvDc5*HjbQ{`#y>;pf_W~=f&#UiW*U+F|
z=d9KG!0PNVuNT9fl{9%1kr9_%xvz4q@FH;5MEh;o`kF63JU`t=y`F9w@+NdWC9<5X
z-7w{48IdbeNX;CL)|E=8XA|DahV~_k%LO`0u~v@B@pea0$?CIW_tw68k;Pz_<ohhk
zszQq+r#_Vwe+TS+TtuHd%Gw`+UOhVpeOyN9Z3GyZrDz(%9e_i^G<!R7{B5GoZ3!ZF
zOKpfH;Pv)-IG9`O<Wrk`Jh)iQVjhGXziJD`SZrn=PUr%e6tK<6^7c7B8E+R;lmPd4
zfQ|pgJK{|OxS+waVm_h{KorvGm)Q`_Ndo|;y46SCh$+z*8zk1B-l3U(l{Uu-Cp&SQ
zFLjbmS{9nDu~F?w1_M^*7n$ww0d?VH8U6EFhyK8fjx`(?jB)~>PN%zA@8tGXbkpA2
zrq%Paqi0mur-$H8$ja~i_hbKp8NFrA;iuG46B(o12@g0q%0EHEVNytxVqS5hfhRU`
za;eTz)EJw>s5q8BiCUw@Dy3i%!Z5-{bg`s`2CE*R@G%29rliT+X_=QSB37uG>L7xH
z($V3fqe!qUwl0lCTBM3KE8MN}WNc2NC>f|A09tuVu_B&HqIo94f3E$05p|l|ZwAxx
zRaFEXq^djEMFcX1p+ZJSUSzSt;{YTj>V7SkIlOA1P|;2tXIMaE{eI&EV>@+GDJU(S
zo7pQXIh{;(9_9+yp?E?l@Pu4xOo&K=Kq)VLe6~LFB7&6+Dxn1hVlxe<;7~&_qIbgm
zxKyPc9RrpUBuPUu`l&^VN-Zy-4^&B%{fAf|x#BM73befgBh$Y`h`|EOQWJJs@tm9p
zSv)3eVrW=Q-LTyOvT?;0T6B}$8aiR1u2DS>QEQBpQJxf96ifmo3dvs=+GwXN#2!|=
zs-BU&pOp21J?J46A)n~5j#+r#LQx`TATb(}iZJo`;1OpoxsVB5henN1xhdrBX%h{k
z1VO|zi=$&`Rav>YgK@z=^f5ug{fQ&t8b9L8F-~e~J*X3kb|^JClm5uW$1zO|P!f>D
zrv@qborA_W>A|6Zqp=c3k|G<Ad_cv6^$y1;(u)(7Ap4L742AvL=`#$g;WXk+jxm!E
z+nE;ATm+3yis`55jlca5NKvxV-P`JzOwG(}e#0|O&*>HzT<e0WOx`bpjaYQokNG@w
zfA;hL-kCa^Fi}wO^}GT?R!ZQm_M^{5yPfAZwhp%C8kAXDjX51s?oc%^#i+7IA(#V}
z?{z#A@|uobqpjbqW$x$EH&abxnsWQbUi1E*?QP611_ur^XdNract2qxba@?*f8+T&
zXeuQdO^uwCW!n?1GQmHq5>!R;TM&4!I5ot3n=+j{6g&GBH8$t^(60QOFuT7izr2Fp
z_6qTbsF`}w8@=XtJerDn*n(T42W)Pf`}tA0c5TcgtS;-eF6%a8{i|Q#3j?ve7>r-7
z%yT+(%!_;vG|hLEOdl7oHX(=4ah>rkU+=}}@XxM(%s-s=Oc>hRStFR|C^4@eK8|^t
z;S#bP`JSO|s=Z<c(eb7HEnBR3iZ!#d9384%BNN-!_}VWHSX6Q^1t?|r0Y?m94}M-m
z2-)>qO=5GejACyhp~Zqm)9rXz4H>-_FYC(zV$*-;T@@96RaL#ceJn++A-L;1yD9Xl
zB^FV1c0{tYK`r6))Y%turLxBOt<K$Vz3ykdXJ3YcuNj>7Oukie>6Jp}%fo`TV-~Y{
zdK40NHg4{?1{%o66xk|Xl%N<#;xI2|H}W|S78c;V^!}dGsh*dWR$hLYV)EVB(iF&f
z8Ma=2glOmEF?V(Gs&5s5b!mIhWFYKpXWP6z@pwHuc$vLfIsE&?Bz^?4!--w}`DF1`
z-x#y(x>WmlnETR1*#R1Y^g|do5)?Vki2DZ^niWLsuU%APy}FdZsh?z)C{=5vy>*Ta
z9tm*7H8S{KrO^RK@w-?M0chkAMz3JpKvBV7>M-b5X)i&FTcy&%-vvDks5SJ8nncO_
ziaZlu)f$z?Cgmo6@H-4{qpUUwyjxl%(=>H$uFO9(?FUOp!!I<f@hX1{fd+?@;si@B
z!46X4q-)CDtgO%jb6DqTb6{@5U0?&1Xvxh%pcKgKN_7`!_inwxvC|X&DEo$|>RO2<
zS%<xvn9;{C7(|qF^xXme!h~)O&zFz{@0k>>jMREPy3GvsLNzESN*m3Ti9yhy|A}+*
z3lfSi$Qd`V7oBCKq(>j_C!>;-q)HEy$)f_p0T2FniygOuxd_c%JfFreseuxPC1ol+
zKP|zC1zR+X7_2p?m&`;H)t~~ZD1mMX=8`oJA<{rn-~X^y6(1MeA+cqY*%(j%mR(d>
zDxv>Xipj`^nTSGqf;2UT7pB(wL=so-2YJ1iQ^A<n1d7v`0UjR;EC&&5p%pQ$TCfmo
zx(z9c$iN-8DBVwR%G%#pQ^le4#9*Q#AK%@#g?T7glvtue+>D~0v{BO(H7i&?pcuG;
z$MJ~`5gPbmhs++gH6Br%vU@$@2e&Xt@~Ou8r=v`xh6@ATySs*3<#A-f#C^5l{(%Dj
zxA2KlD!;E#L%4pDi&{QxMua-QW@%v24O`S5zq~BH8oixUP%!&`dONTbnVoFdfINKt
ze!k{W&&V@Z+5WJ?_-IG{F6Vo1G&~i~bg4mRR%64An(&%qUT_M|8?0>C#BW)uVa{4!
zPTIo3li^5}_pE09ckY7uO&t=`Y`8j!olva-FM3jdm;cq}rG1?0`Olf`tDxuU*OS(*
zptG90*!_OfPUYDr`$=eGN^$+qL~wZRPvdL;c4r61dXJNvqyb-k<_3thc~<z+%3`^5
zg|lKz(?RgGPwJi567y|_QuKubZnyiNJXG_j1rq5ucH+*$YcXZT{SEwS$zJKjFByAp
z6U79$ZVxf+apmTR`^%A4_?Y<H>*v>50d+c#nPf@jbZUI%HH`*h`+Ur5ya4%XWX!Uc
zuM0Ej!wOd_a$+o-H{pev65txm<;9sbt?l~dyukTTQdqlXm0LomkweS-l&hv&@eF@L
zbEEZJ$=rRx8v~&qyXNMCekbRAeS0_W>iWr@OvB^Amsw4_5n=_`96O<f8G-pxbeRmf
z3T}2%+Sc-tuYQ-~$_7ikm^zFOdST-$esEqwMN-7uc;qCcaqCY9*U{Jc_Ny!}w6tA_
z@+BWYDorP3`DOO@W7kC?16rXfBu&P+m#%K}A&^i5E;FkFc^g6X$?$RPMsa+iSMYm9
zQRUKrv6x0(a~sI!)mXG|d3{i`yt!Xn0cdbprD-Etr+_D;NZ$Gami;$NZj+>WGlP3}
z3sB-mb5d_aNn!*m>+Zm*)58jr45NscmdV*iZ`8U`TpUboej~{N=D^d%fnv1iyJ_-`
zyCnOSqD)b;640$K#L&gY%yVoxKT*um&`YsV1jB`uPfg;b_a6!l&Agd6sKj$<hjmXI
zK}}jjMJCGX&@r_?*-KS^&4WS&4uQR3<*72Oiw)P#kg`WmkOgQNNmJJqs3sAEpQ*4^
z&<cVT7dW;8w4_R7^bW)!I|`H$>zVYIG^$GJ=2bmJAF1lL;6zwRM~nBZ9v-Oi<wQhq
zgJ|)8AnC{5=A9T(D-RalraPg8kde-bf+gsUjXPOZnnC};5IX3SB=zzzVU3h9yAM~U
z-vYn2$w><%b)dpS03Rixz*RM(%C<6Paq{%R4d@G(O8Mn@wt~g+!=40HEPND(D>%28
zgp;yyzRWc(QU~JqpE#O06EoHTnBo~G7j76N<VT{`C5thGI*$>LFo~t|GzipT(&AFX
zSt?PVwh>CX9N59|BbTz^Kz6+_Ku%%_hL*T(286=Q9<(rZa>VdFv@p3Q4QV+o99a-1
z3H?A?i!{Lx5y=QMBvdq%1n3<tOPyapc&nG&QA|c?C{;M&0YU2@U_BBtU_PSCph97W
ze^j%;h*OA3i4sS1Q4<Y82gJhSN04p|Z~m=bMr4KY{of?1ux@BC>~3@}X)<S>VX2#&
zd(_o>)1~o$8=BK6=vcW2j8{*w`fjXp!eXgS&?&T~5c+Pi)YzReO!8wB`mN|aI7%#q
zUShLg_+$qvb$6;YcAYRD{dQ3?)Ae###?*0Xk}rs*Q<X=P+raANV{Q2_zRs~b6edd6
zv2EwXwr$(Cof8`;wr$(CZQHi3$$RJPtot8&?cQBg{gk5m_B=91DlPWa<kf|h`i`!9
z@&aSV5<TjxdHZurgAle%Mby!U6tL7tLfKMrZxZuH?RB5W0+}bUP+E}5XQh;gQqRu!
zuG6ccEhz<05VNX+@`;oCkwt(ra!d6#ezs<v$zeJb<zrFN)xwod0_;~-c9g<QK~hjb
zx~nw?zRpiFA&*Pj%!*pBL*r;Y-g`g^OLcL7c=vh`j$RTBPR037#;V?CM$6)nxpik1
z>AoOlm4XgtBC1s0eDWFc$vc!u+0^1H{vT)K15K7Q?5>ENqDCid>;V7c_My7GiIIy2
z*JF?4jhdz@bZG7YWF|+?4Hcd)P*@u06z~V$Y8BcJUaxl(t*zOj^fywKJI!D_G8qnY
zWtqh0mLv3(N}vFEFDpIj&DRHu1I43TkE26R;QfKeemk&<;75Oj*|>|^kFK}sepS~6
zmzAOpu9l1P@MYIb(C@>mjq0Y0<Hcw~?-j$Z*QKH1jE}AxC(C4XFE-g|J8cf#8$Bwk
zjfZDmONdR|=Ec?ag^J~i;T$jftFe?7(dF{=cSkNRE~v_>w%~a?xRL-$Od<+io+TDW
zF5QiVL)TQWr{Zp9?8PECo!<plFPkZ-c%h)}pM4$9^6b9DTAOanYW@@D5N{JyJ-hp~
za`s$soB+cyx1~vsido~rXl`Wq^ojv6T#!`h%;IX;radE{W;t5P$*VLNTW`ZZP3RdS
zw34w_toV77Tq|~Fnw?n1-3g4wwh3aFI$9A1V@8jCLu*cdE~T6}!pVC1Oj|_037F%J
z2nYU<FzQ5622gbMVdHdcvT?tYMG}zovNx22&;rU3Vrh~1@WFjQ!1DQf&8AR4_IhD7
zmNc@<31lZ@M=qvH8B@C`(FMywwsQ;(!CGmXy#xti>iD!jra_qMJ*JjB(d7AI+mU{V
z7+ypn`hSKn$(3t1FB{k8T>$4`cTxY@GeVIO4Kf(o52{ljQ<?6Hhk|%H{F9bD0Z@lc
zI64M)zzhJ&=iqK-zn1xXnSqBSl@n^A+RzY2pO8x6Hz>{+HmxKGRx(X^D0OFvH%~yM
zv1X198b`b9Sp)|^f}oE*k><gXa3~qKq3i{lL0z<PU<lzwno7}vP0lDD=1@2n2^ATR
zQ;~$gLYu@gl}ecyh7>;pk{KyWN~|Cs1{7N$g)Ah<o*o%SFK7)1C@Sfd;TI9lk5T{`
zI}3u8j8H#*Mi}om@TWjt*@+PrmW<G_Y)}}48!EVP;4cm{Vs<3t$!;B3@G8I27^1>w
zU}+2CnqAP`Ya($d_Is4vsISETrN~+@Y|Zsc;#*mnXTdI>G~s%7r%$_~WwX&*Z`G{Q
zRdGmqcd!1mv{g|xZff`IH#thQ=xE$^WW_(rakc*(#aq1*Sxvl<c-L%JV|%EKWF5sz
zb=i=l+@l0M(VFMs@%Dhgb)M>a@Ul?Ud<;y61hoKNItNKsl$#{$&}MqhxA3ZM6VcY>
zdpHVjtS!G-Jzu}@VZdF&qi?ovY_S+T#q7y1>rF+$)4)q70h*{3z9&3l`;vd_V=kl*
z>t8iDTGZnz_qbPX{wUPmirDRVhQwr3Z?B`nP$OdJux)dfZDwcvrS|IWx!dS{p9@?f
zLCpNI%aWa|^6%B8x5=$?H}|o!Z?((gyU9RrkZJPe;Y2vNVR+KThQQlFb-`Odr@FDV
z(#7krZLdf%KBE!PyAcnvX%1*$NnrI*FX@bvOsh!uq<8wv7`FA*NienWsU?`zuB!J{
zQG5}xIf>eU{{@sy7%gz|bct;^L#N<mrBqhUQ2D!<TwD(9Pk-??duLgFL=hZu)twHG
zoogtWwCH28*&I1GCoi~$!}F$XZgI9Q>N4ocq~5rCdu%ZJ<#?T8rdwoZWNhlKwsJN(
z_t+jOud|BJeJ^~Cxs#b4nG7BIo>#PU^ui=V(_Di!_4KJ<GB2`#B*V***6`ttlGDbn
z1$<xk?V4(B+<{D6R%GgFx3hFP`#L<}>g_P_^kBlE!RxNIK7U0F6Jis_3ruadYJZ*O
zy^H#3<}M@UeLi}9a^JR3b2j&I^JnMI<qJRvnCLWeVxW;wZ1n?!Vxa*l147xQPg@@=
zuAM}}c^NPzA|iwXmTBAmUZ(D%inA3IGm~+%m}n`zX|zikOQ1(Xyn5XNR|=NWKn;T`
z@sukK5S4B=<#^I_5>B-IN~1{`44cHX{QEu5)4|g9{{3D(8)ixAAQG0LlTUsUiq_5W
zPHP-kuxZnNfM~_E_|nh-2NA{1a3WpIMm5&#3F8x{e}PG5Ov=mVsxDh%IUiTUfJ9s=
zJE$V~l=IcC$*revi|xxqJUoh2PKg(^va(SS<RLj6paugiM(q$_<Ni7`4rIjrB@{P1
zM9$<+j0;+zr$}G1%T6gDy|b4I2Z8e^EZB2XCnWGs#u?}(9v`w!k*Q#Fghk8FmUU!o
zMd;1-C$IjaAQrwSoF!!j_>bN#D;XK2j@FML1yVxT&;bb(fLYihf1B_~(TImMTW+io
zVINk#5Nb@3IJSwt5LE!ixUFG65{gteTq-g4cI+=PWpD=IDhWihV!UuscNNMX9m#aW
zLP1hIBy<viz0|rpY2wU)fCmU^G*QAoP`Hfz6Gye=VeEq<6hvu3(V;QiA);jDzqy^H
zfxv=!$4%ClIJ!Lo26ARb#4H5Z7#_J^vD~>mgSgdKW6~J8WXU^4G?_7kF$reT#SKNA
z`zmrm95T~|I6>0ON+WEfd&kT-A3RQp|E!&IrknlQT_{M%x4K07H3Ssuu@|%Fw~&ux
zycM7Kj{`nK>8w&?Gy7)atE1b8xaO4SPEeQX1H&L@#-o)<==wFq#@zPW&+kUNTM_+T
zd>1+V7a7=alq|6a<dP1N?C!SUjhw3}ANmsQH0J1TCJv`6JMt#)dbj#p2NFmA{pXF0
z$|B_MVJZ`E+_R*$ii$Z_>{f+b9!l#Ra*W4|E5pF;Y3)s`JJy1}N_7%gF1;Pj3+&P&
zu#zoDd|9vV3Y~I?)gI6L)7>K}a`mO;GcUZ+)-`QbrqvDx`X-tjw{srKlTW_q>ko=*
z;0@&0%T+m5IKNTmStqtE$NvcJ$jnY`u*|SBwt~navV;K;g5-Y^8;nIZ>W{2ruGpH*
zhPSg?l$dIob(gx`KUf!+&s~3vQz}Y5HgY(9d%K)_P8K=%bbJ%hEiLx4+^KbamA4sm
z9u60*78M_t-u8-P?QLh-IN~E&oXecSvf|zUR!rnd?`)yja$ou0k8F6m+Rd$#B8Yky
zrawNRk6|D2JPU;;Q^sgZ2qscIq<fz+GQFmXg-f=JmZ0;!K2F?ZPf0H|C9|m8qsA8;
zs>99aK=$=3j0vxZ>v*^EA3h{#kq!}|4>Z`x#pq-W9!N*wW+-`WAc@@lRXLSgiiU-r
z-U_V1M{fAS<cK7)_pVjODXS>MD_!n5-%9`J7~U{*>A}@@tJxdR!p8Tyol=}RbrYEi
zx^IqH+*NYi03Xv_xcjuvX*ZoR<i*{C87wk6I>Jy1HD2BHKq<XUw37cKitZ~i@f`^+
ztW3T*qAGvt5B&kCG=KOWylxnK123M~v24j^VP=!t?)p0Xj6a_S<&q{?f%(OZZtdpS
z#ghqn_brE303q%5RCh&R)x?a1#E-re-7=$ei_;c7ufaw^<t52Em8qjLYH2T>Ca*af
zfQNMw+HqisZIjLSw@Q}k$|*-6;$W(ynBkM+A}Fwj0wE*_I&$f-)<_s0QW6`&FJPmn
z%>sZDvPX&;-<EbFaRMGGU|i@xF$mHM0L#lqZI}i}j7y=?IOs_q+?Ek%BSer;7~hu)
zVy={gkwd_t>YnUt8hPv}tF$f{O4;Kw#5a!<KBrm`145YfdTMvdV!bxTsZ}9^CwT!=
za`l2Nfvx$gW;e!%J)Wz;CMCx=GITVCht~^0DSJ^yhRw{3#^M0a!yN<G9v+a$<5k55
z@fOZqwtzl6zPaDxsPr*-Kpqn(rcxc15-a02Ap~)I;lyPGofwR1!;oC~At~h2N~f6N
zF5l%MF=g|Q5d-byClx!&i;@c3#m7!6hGIg_;ZYsqPwt`n;W*I(1sy_ynu8)436G`$
z&`}eK1E}sAoaZ;%C!&NY$ao<#!UDa80MF+eVL2h*k^>|`pB#_~BdCZ0aGnA*gzMW!
zz=(GKtItt=N2@Irr;Q_wSg0kj6W?9iyrk~U+c74Dmp_z80aAt;MMQ)OJas|?7-W|?
z#sjv1>$Ko>Cp_3y(0AGP;eM<Q1WR$)RX65+#gK6GtC*bP3ZLNlmlT%=^ceQJiL9P1
zZu4xM1Cq&}tpLN93BR7=KPn-Fe(h|nr05gIvooVaCLt*~KGSrflZmBGK|#SsBIC%7
z>Ctq1Zt^(eid_@+toCcRdfe$s{8aU4@AVEoHPDHkr}Ic_gT}HYZ+8x5$ORxpANswT
zX7+-mj7S{BY<$q#Tne$_j4WXkiedn4FR{K3mFWF!yhZGEY^ilVrPO$R&As@p<Eu*k
z+%j)_mQU*`^_WL(A~QdS)qHUAw!Pi+7X*BK3eGe&rSN&ZO~vfcP){yk9OLL061q6k
zYIftmM={!GAgN-gm5V0Z*8yQvHCgNeW=4xgI!ViZWs7DlBi4NtH@^K<y^q5@7<E8?
z7jBCSZ{z6GHxhE3I#?@v8}rZww!S}dTiW^&JnNF|GO&5IT1nW+`njn1MP2ehO-^~p
zMn#D0syFl7Ojg*4NN2jj>%lSW!UbhNjmv@O`x}Vk*?lcyl4!_dD?CSAzM(Pl8zQnU
zujeF?eS02GSD^!%obZ1KbYT9tYCX0DAHwEwH!et=GT>V~ntuwGm5Q2eTF6G)#q?)q
z4R5}`RImMbzZ?|CT2HU7B<XLxdrSSe;7VSKXX<WusxI*oU!Fv`NGFxd9tIbrNif#%
zZ9BC4zCV`DG^6>PN`4XnF>s%h|F9Qs-tW1zOt!FAeA9m!u{F)aZ4GYTOrym;H}P{r
zNBvygyXFulUw=Q4<h~KNpX!SXU}f^wl=D(01+k_Z@$xdX;ey=454O{1eYGw>^ykq5
zrTo-CKg0Lvc{#NiO*MR+wazX_qdOhi_dgyEZ;I)4z397J)%t?pO<k=2243E!O_OEi
z5~#+INOTGez)xQpe=+=j_WjNpff`{-a{(ZW4yn_El+jNhiq4_|DjZ80=dPdCQ*kg$
z7Zo8E&@0-I0>sNyN)s8|#`0)l+CK4G(25*qtpVF2<8wxc80G&c)55_ug$)5l3BrsR
zG470W`<p6kxh2sCNOH)N(+YSd5q2R{DGF7}21t`Qj0KRY^7}8jy*^_#O^6V*SUxSO
z53P+C3JI!0{c*t7!BbdNzF^g<SH%(z3P?Rmg`&-m)a@_AO=;)Ph683gPg#5&rw0Yp
zHb@M@uP?P4zfx=w=s&Tj-)#hT2fq*B8J#?lhf*&Muzt3a{%+y+sf^bpDoWxCI2U~V
zORFJHdql_-4tb`bOpY-n_>Tbq9JfpmwY}~Se12%a6y-1u(7zpHA~|+r;uOMRzl%wv
z_`qaT)CW@-e}b+W2|FXyL}OF407(LR2I4G_#3(6fWB?yyUCd&q#e$pDBm;_&f1+II
z!bF+um&8ZPW0;if)W+qzhC%Kj;g7`f{18=?3FnzWP%I0y)1mEm^x_r@fPT?DwrWYQ
zf$9nZK6iC&P`6_C{f0njdGQgWyUbv(^ewH44nT5iDP~f9tbb4~2N~sU`?N4_ee(%=
zy|9u4C%|)h|H7Ydjb09zLun5Upw#JyI5b3G&y3Dc1o!>HV2o2mQ-wq!Z%hS1PI%#L
zrl9B<%)U7(OCTm8IXYQ?pp}KKPE1V9nLh&OJW_(yI)S)g@Z#uL8@E89KY0fI#`|VB
zxIk5yv;=Z4YC3jbIma@7^*33;l*;tHwY@Psm~J>rN(opx!D&vTJvg%q+q0<|V`Jk+
zudwaQJA;P0N)a`plPFhqmeKtbz8@)x+}UdTe%w;msOnC#)D#O$FXgeXgCDE&exbH$
z_U2=pL3&adF8A>CG^}bc-+MhwyO}1<Z&DQHsywynxiZzVE+bjjOp@nJ8K~-bI6;>+
z&KVuMb5wtj3QOj@4%XwXw(TZZXe{{qQMpKDE2GxpYW03Sa4~~!hF-ESmGj7~7nZ@w
z8_n~rrHUleHDHtVvLe13x*6$BLbAqPCN#II)56VibB1H8e(|T7)$rg*{_)G;$l8RE
z)%OwL@~x<ZD<R{FrTzQw*QsdyRg8&3E%1+`s3y<d5?fAoj#sVU_x+{akB!;`pRU)p
zuZx`*#mvsj5XZ<<+4n`s$w`iNxY-v*J*kG9Yr>rq3;2WNv3m*2{Iq}D<=UNy=2zp!
z7IXc>{?cUo0It8&EXFr7=*i%_+Vy!#nEy_fn_aPM`MPUf#h!rkQiuI(EWEI<2I;$=
zD|q*JYXh02r}EFEUd|^E#Emn$lZNQWV-J3$-IFzA#j)@ak1H05&12TmpdMeh^Yb~^
zbvHOJ@CJH^rn6ftW4M^*!ZKY)v#X5}e3g-22zob;O=`xdk&z$~R4^6(@H5^RL;q(%
zeiPYV6LiG_EKw5+8>Fv?j_0p*le1_?Q2MG)p^feLCrxa1RFNn~WOPjafS-^tHgbl|
zGBgef5XecGzc@J4mK$6)-Y8U&Tc(d*EzAp-yqQJD<{lYSkz$9+uoXj^LXM)@s;CJj
z5g*+4Z<`lkolpQHIn<P8xB|uTL2cxuA)C-RYJ`O~oE|~rE46IV{0N$Acp)z!S)rQ0
zNoBz(A);ncK!y!vg7A!SL!Cm2V=aNE2~l-S5b>-RQS<76-?}(|4uY12VFW}QoV!2}
z<hiQifU1ac=oxZvvCXG#f;pvi9X5XrRk^<V9eG4zkt_MD19Oz4c;RSJ{zHsuCy79(
z291Q0K3rYiNTuMv<O;&%T&b9n4Gu%7Xu*OyOdL3&UIj=uUZ^#H5ebLw^4x!rBEn#*
zGAR=VmJY1g4hT6JW5KC1f@sLm4TcQ7|F-*@>_ZDXtxb8U2^%UR8H@djl@ae&6YO6N
zDx&ZzX9FAo#rflk=^cyO^P__t{1$)--8`G}8S)b-)+9j-B4ZE&qA8)^8%M+L{A7&~
zC5@FUGy!NN3nC)o5}ApULQU@fQscyd@7?{?WYhr=sjD}bq$WoZqU=Simz4CIRb<A5
z6UL<!HO|$)-lSHsV1SGvY&h*-!3XiG_xt+@@na0eMkA#L)I?BEGi$^wtP9zk7#ftS
zKmukhf%Z-xng)A<_NUdaE80eKoUZQV!~L9CLPFxL?YT2TDS+yCyp*7s4bf!_*SM-|
zmX&&U*ceuO*!J>HyEhmp{%pHBjnO>IaDbO2Z7oat01$HR@Tj@CvVVV%ui}GY??4iR
zcXa&9@#}eJHg<ekuQ1tZ-5k2U5?hOzw8kaq;!Bs~cdfoHba!q?#|P*Q7h2e6)gYVB
z0(st4^DUYQ_6(Q-EZNzBkCQkh!)~u{MfGFhO2FlwQa1ROIFL{a&og@7zik3~(0AVs
z)!%OgaSJW)>C5@wZ?lgBXFu=zh%^VMyX!`d8$42@Mzw$!nrra7%PdWZb016?Wd2_h
zTlu-UB};C_5*yZZwq?JV5i~I^u=iorVl*IppPtv1nVs*6ua|7TpT?gxwdo`(5x~LO
z5hmwL`@-3oXeYnvsWw|X?be=UmIQIK10QIhL_2ul8yRG)3(zfWXZW5MU9Y1YPlJHT
zAquu@-!B>Cwia1Wg6yBl^d2VXE8UrXB<#-(<5<lhXDxl!#8KGL=JJ;Kf*m!I9Ih<P
zUigpVnq-cWz{SIQ-MaDl5T}!a7B1(6jD#6{-PdMkqEdW9)tvYx=J1f=K>V)y;R`5>
zKN*P#jo|5mRbRZ`U2tcokIa1BTxJQg{K~sozo653snCOh^>c{<YtPxe&q$3YBXe8J
z1MdJ|bBp)mtB9MMPED=%;-ZrXf)}!sLUiyA&vQ@*QK#DAoBcY}nj6`O1(P1{rnVw2
z+MTeKA6sV+vQ;+9^D6_ps@0mQ#wVzAK{D7J+#T=#9VzAGGY_e0s_C0KQ_DpbZ`N6{
z1!P2zAh8<3OQ*G7wsE0xHE4nu;^B#u#-rrI9B9)b27}5f939kvOJIQJ>48Ko4rj8E
z4cb+IFL0I}GCcUjZWbC0O{LO=v1;>i!T5`!E>_5o%>NPM*MKQFlS+xO_b){#o4orC
zC{8Dw%Dd6vpsJQhC1wgjW@(Qy6j&Shwr!3L$NJ$l6?3TDs-O}ZesZ!@#fRM@OW}la
z(@@Hd2Uw7d7XK;hkx;)-3d(9%g)|8d*{p>~(Jlu=tE3%1hK_z)W#@xDQH4I_q>Tg4
z9fu>p_~@&B?CcLvUH`U~>dr{BG3qOqVFdIKrV5OeLnFHs;0+KfYB1)cQJEYN?85_*
zgeONZ`)&?~eL3w%$%mCbIfR@&;}-r6`4<pM9-03+9aZSxv$kPKhBqt#(8f>)K2e6C
z)(}bK##EXN_@KV5_gg$Xf-olL4kk8$772k1s#`^aBcu=+ff;<yEv=i*ZGb9rQ1pXt
zOsGe3|Dco@G9`#6=v6os7lzCwXM+`9l{+*e)Tk7s#-1jfWmd;R7LW$khckh$S6C$C
z2MG#MI92e{1?&U~rP}8TqlZJ##S-T#2Ez%-gGLxd(*8>T<x-#OORwXMBrjAFGIj`o
zYDmo~`3o1cAV<O?ALH(w;k^|-wu34?1L%(f+U)!POqK1QRW?qM(}O<Rg+$k`EUvEK
zE{a>!@2O>j5%j3#Y+;R&c-Lcr*YU34tA*}l(SVX(7^a=e-laIo8}{q1t_~$fvqT<y
zJ>U0c-(*M*48~l1OQf4Iuq^GSh8Ek`BaW9X;ial>x}CSiI>zM;3_Cll>xCBS+6z<b
zs016>u}~AUX!eD)o>_p^b7AgtEw9B-xrwM8X3`X*CSal?mM+nkmkU_e^R<!^ojs12
z`T)J=<4muPmxd}PsyX|ece2e<*)4MiTQ2Udb8TIvr<Yn*q%g3kV9FsUYL;#8oK~jm
z9*#2i*1mO&yztlXr=^FE4A@_aXV(hu#`1@m=7_VAn&cHrI18|M<O#Wb`?gb<7J3Ao
z9Z~x}O_s`uZ6${+e0~7jK#=U1Y1j8A$@SsE=dOj~gTu#FkAyGN4R0&g&n`iMpeAj0
znFu#Rf#-#(X^qeA^>Jy>`oB_iF|+`y?CXYbXgf9UCXE-i>E4{lCb#LX!C8@%HZbi|
z2}be?jP{i60esfu<RvaPAFrnyqndW=jn2Vp-$%o%wvKFkpGIBo8E(F(e%i<s{m|!h
zduqmI+)b<)I#^Y);PirRU8Oa@HNuUi+U$h3rD*@+5H7Z#Cd22M>@oeVAhDb8ylP8B
zqrJ5zI#p3#M$2g`Iay^tt;h>AH7Bs&4+K)wIh-u++oh+;^+jf+&Nn{vliiD_UcSeB
zYPcn&wRPb{G}(`7xp$Q|%OAn_U2LX$E4m}kD<{oKQXrN{SOi9>Si}%@07MD9DvW`;
zQT6AqE?uf*2#|a149S7L(5XNL{QZs|kJ4cN62YF8UIFR(Nk|rNxej6L<FW3q9vzA`
z@lrCJ0)_Ef*Tq~iP<XgRf_sf1qVhITshuuxav`*f29h}3-ji7KTF!z2qlGG~Lr5$U
z6=>oh(zX;4A}VF>`r@DoxV0K4q_`PUtP%7&hz*chv!zCA;k8Yc<ry0JLikihC@X(4
zZDAS(MXOsN$^(xfc6co9t2AM(Sci%jZF#N(!(vjDBxwo-!6|me1=5L`128I55?f(~
z?T@vH?TZzFpjRmfIYWaf3>qNb$jup$1bN-Hf&7K|0f2x<l;Z@X`N5cxMU3i&1>s&}
zv!Un{)D`K|f`Asx-$0;1qz=MRIKet0vNK#5PXOZKe7;t3kmAAw?i3&r{2tz66@>qE
z@2W>AZJo-u2_H&w@QosFgb_zjE|kq&JV+sV$abY79yJgG`4lM|nbjIuIz+?`f#V*f
zWbYErV}>GAyU{k42EzL9`>na@vqt|TxHrcD1tOmyfC?8xN)L#hpPN0B#QSaZm1l5+
z!jJ>Be>IPb4dVCXPgR7dM(}3d0Wh`t>xK4@njbj|B|kfG>EJ#wlo}UT2oR9`)eEQ!
zAZ$ARKK!jRev;6XK{OSdHw}2RWu;C=;*I3li%%|#_&-5qCEN9{rF~Ih;pbwG>q>A`
zIb}GtV9Q5gcKWq{la?!E!(ZpCfxG)?Q@#D*m-CkE89ZfO><g?_%}l9i^+^Uz^X36Y
zog5tNjEI)3D;#%b$FiNB*__fVOBnBtyZwZ2tFh>@`ns^-@9pmO@Tork6J%H9`qf0~
z=i1_YU7CZsJA;C~M9NA5eG?aE9R&=#YHn|j)1|1596gWv`JVY@o=%_RO;PICzWu?f
zm>X9+xuKL2-~yt`aL&e}u?5~=+ogwf4~lNOmNULxe%?}&{0dP@OvZ7iH>SG9&XCNE
zaH3(Xs;IrL%Kk>x#BEL(gY}_4b#4iLr$+R!LtxXPwti~Xa)HgUAfY+?Y|qs)(kL-K
zSft1r;tZDq@pNw_AO6PK=XG1#vDG5qY;)l<wUw38*}2%tqSk|%B4z~CB#+nQ?Q8j=
zz}s1d;p<fRVRPBdmysPQwp;tAi)X>fxzhGA(cVPG_33$N>`ZGZt4Zf&(h=S$9p|po
z*Q>~8jtVYyz^MKiHo0dF^KrRpWTmAgm^5Kq*m=uoCMko6Yr;Q>^ZCGXdP?<e_r?@r
zE7J*hkr~yZ%;Fq%sXAqe_sm)ElC|24ZT0<+K8I@%i_e-WD$f!E_eMr$ld`gb$aVTt
zQ3Sp-U8D=$(=?xz?ssM9U#XG1SdII6x*Tn1$+E?8u$lX(IpZ7AAn!6x?)A+UZq5#e
zi_J8ylac`3uElY3oo(wS-O4MU9VR|tI<6U8_y65b5c<p#XT>>+C_yR*tVWRJ&ZUk0
zu_|l-z;cFZT{uc)I)wJ}FQ+g%69<X)o?xROCBzz><m&*1$C121G}&U5$B~sMj1mgg
z1x=_xzQU!<uTUSQ>%-8BmK|8uVgXND=cR55R;W=o|E;g8{3jV)rh@FyVNDq!SP*X>
z=#`+Vrl3Nmf?ebhGPw&QJ_-puU^hGNL>nNWV=?@+SUNc*62)De1xJx!p!1KA<T^G)
znR^t?-2sY(U%z1vfuOCQ+?Q=<iB2(kg5#xJeL~b&ytqzYT~shR$!oJO7{lD0qJ4Gv
zpG&e8v3M>HaK7RnH%*FY5SpN0f8DH8Bhd#(r;y+H&Yx^7Q86!ABwk3M(<v55#kdir
zVo6)DI5xfl7`3AQzKG;Z(=s_6s6eQdvseHkNZ}qj)=wJ<Mp!t%4uTsBNFce80fN6l
zX%f{47|8`S$3&$6eCSi6g$rh6MNtAa0K?42baUXz@h=`bp?qW+dC~GLGpd_`Ure<U
zQzSP?j&|vv<I>rYx1o`>H<CtcKZroIq+Iz^!RAQuCc+05GX2W~ThlTeShuf0AEu<>
zG0+oye*k!S@-su)_z(j{eRK8?Hkc^^0aXL{u-WK+2}EMd=s}`Bid!#+X9)1-9ErZz
zF=Ky#a(T47!Xa%Q;WJ|Z{%J*JfG8oOJJ8>UZ(^dJ;mpg!qC|Xr{JrCqJ9-J2+OJ|O
zx8(}YQ<@GNVP%pzZvRl$Y-M)dQc|iLPEH#sw*G!GjJJ04L;i7|d?iVYXLVMIe-Wpd
zNG3hiH1`yx?rGn;<Z&@Sc|2S$NE*Gos4ji%+Kd<T+R*`~inUVRJm{fX_^f0-1$v`M
zGuZJD#K$<7&E3KtT%NWhGM)(5Ni8mN3bw5RoUNGFhn<DBlUJK-8P~aWlj@;_t}%HB
z+p^X!>o1S5rCw`cRE}wDF~L6-t*4|QIBmPuLWhQ;%lB5M=IbS06|Q7@m9Dt%t&=vZ
zpR|Xy^sD3CBW<b*J7vbsO6v<s0rm#Tm%WbQou7|{*6##}Ylj(msR7UgJMA@kqbsb7
z9!qI!Dll7b=_lEQ@e_q^4Li>jcs+RT9tB^zwsy_sGZu#&US3CUGKS1V85(6WRq!{D
z<te{+-)#*~JEphqqP{A!zOb1|^qP$A3UuBu3DNPzo2qP1*BGZ`NPAZXR+Dq__6a)P
zQyy1${c5kO^zCHx*3)NcjfriAHs9U1=~S}HsT6E87tRp23_;j{l<J<J1G5)AI*{e8
zOnBJq4pp6fy>@;s`OU&eu9HivNL@x2?yx{!nevOawqq6d!@9cjoNQ>`9u>BEFV@Q}
z$hS|dpvW;u5?U!-qGVgGPwS(#87^7Am!S4gSl;tt;ia9Q*S@vw_3@{Y`8HQ1ffE@j
zw~o!_)ASgw5j)z-tnx6|V^TH8nJ0z!b?Q44s}*NOW85perdHkmx0rxWPKo*bQ6`rJ
zBx!ONv}a8=LJ12h!lD?gidVMW#4DQqQ}9+PSvL+k@-XOtml5H7?oJCS!4Jf_#h4>@
zeIR7zoM@RaOOjVU<kY3G2fsdEfEE`Wej>=TRGd4HefU1fT_oK*Pl@3IJ(fF&F&o)B
zY_T$~0~aOL?;IZnSjCEAHOJnWEZ&Q7|7QK}mPU2Ta`8kMlD(iqid8*lQ7VFmS)CaG
zM=!6d>5u0;89|4mP+wm6=RRyxwSjC9by+G&fRcot|AoA5f_A%H&ShLpV;bqS%-usY
zSVwx-h#|Dn2@}oSAfM1s9JHkr#r~IK4Ur{O2z46Nd%1rZFyw~}1)?7H&k|I-N|_oR
zF-RLBDFCQJz0HkYEURzM*xX)RR+IqUURjE_I2R3ZR~Rvg3^?F4jVumDpO>HlA^~^^
zb<SL$2!GrUm&u;s$ip%~sG6X+Q`;q`rc#wO<@k`<idc!MUcYsgX22?p92dnDkl+d-
z5?!{e_)c3|Up~@7<L@`XR&g(R?GTVS?f0R=Yh4Qnd!P{ME;NC%Eu@NsU+8XHEKyOL
zT|boEAEszZ2y;+KQ2^sy0(WA@#BhN+e`A7pxDy;v=;#LkeG|B7C24(q4MoZvXpujR
zjH{tWtPF#t`?ssayx2wYhQz-ZcV+T9WCaKr3iW9c`=Z1jKR$4|x1YZMOO{P1RO!-j
zWYxrE8w}v#q5_6*-WrIiW9^U+V`o9gz_i=3x%{cLcsiRsS|0qjgpf*m&_pTuhBdW5
zfX-G;!=r2G;nG&%D}Lhi=fko<k-1o92P-!SeZ-WtxkN2N2R`3hf!cF)i}zA?Q#Q1v
zAhrq9>Lj#?rMCm?F{h^Lxp`(SgVejk<Gsd~JpeF3Tg%qrV{mvZ+1M(ofx(!i$s>M6
z7F)CZG#X9oLfh$c{&M<j=4SB;#GrWkbu{9)G~i{#<K~%Jhd#_>y^G45{{_FDw>I{i
z%DEsZ+y}p=OF?skE-}yorhK;j`*162JN&B9sJp|!$Ia03ev|*TG1-=rE0%jfduPr;
zv_>+Kt!jg7Cg?+M%WaXVOZ{mJd}qd4Cc<gZpNXYHsYwZOVNu-r@h&ZRXgTS-zUpq2
z3;qLt%OVEe!n+{&E$cMlL4_@u2f8af!wNMZq5R2eFUZ)cqrQ1yyOxo+h0FJB>t|%!
zn`OjZ*BuCdqL>&gjF=D+o1e((KMUP)`qWR<k{PQGklA6HBmR#-EC6IYVJb<yA%chk
zGlfRsKaz=N98;1QvraCp>a_JP^{XbkWy+;o?TsEz{9W(&ubh(Uwo{J{K4&vFH%l|r
zrLXVTso`xpDl@xQyC=-%i_C)XBlzHasjdt;*BwuXyQ-q=hp8uc?yHfv_arW%dS1&{
z+XVZC<*k!f`^K*`b3@GvBRm?~!IEVeEHYc&2-_Uo`xf2g<(%dhWsCz&fm~a2Fami{
zWK^1kgpHZ#m>sD03<fIX;hO9!ZJpGw+rzs#&lehOvjV`2bbp5G1ht9Hl6JHGeg_>|
z8g&HxgUp=jE_J;wjD(S9k$0J--|U>1-tWbSuSdawEj=-UylV{*qC6Uy&NBic$pU<p
zCq(JCi6C~iP&<wck%X_0P-gIEg2WS0G-U2<O_AV7a3o#;@mD2H^F>sJX4PC|R2d;C
zDX(T0vx_vUzf|$;P@sEprfOaTE0MNFion4LleCeUTrF|pEte2G!1x%cOHwv9)LMjJ
z^U$GeAO?9t12B%Tcto<1(JYIv)b-pC&Oo5Xp9xPnOq+yT%OE3s#gELR?@d=&HByON
zAE-q<qAo<LQM_Bpp|S@Mbw1fW@Km;?S(Uu<*n!I!3qRc$Ng#0_hyYuzk0Fd623j+C
zjB~#CvxN36qx5n`-w!6(Pg36DjM<o@-7n>y@=_m=0$FxOK%suulzmPSOcZ}pFS9u(
zSZW|x547zC@|PclmYM!jjMXA1C>wAv9!i`_NEeZDZ(7a$qhey|PYF#iu|Ec_X>E*p
zFQ0<)?tM3_VTst>z>CzJj4Z_ug)~`W3?p);U={W+5@}C-=V#cGP(dBWDKTkrE=&c=
zJR*sh#RRP5SMpAj2Z%?Uef`Y&<LE1y51h#PZvooOVcuRC2ZUe}EIBD&ar9I1w%dzG
z+Ij$yM8w^g9xVEp0BY<Jg_#0am!JH#8-m0naZ`~xGu*JWRk{k7%EVZZfa60O%d1Ln
ztSA{V_YWaNOwhlwN<wn(h^RcjdZ;uFX@O?*8l#NFN1@9%uWarAcA#%v&+VbH2r3GS
zGdmr&DqTiuSL((PAH1rz4F%qm+ZVBrsd9*fTJN?_GyiSAD6Xa<PUrR_x;p{0qvf2B
zk7e@1=kwR`^`V#M5?yMw?bQ9K>5}NEfE`?1y=Jbe&|9b5Dm^_OXS2i}W1}rQJ&igM
zuJv=D-y^K|CFb?a`?y^F5y7;DPs4>jeIO>ZJC?*3@VBc~Q&E59^z5QIY`v&(H?h-n
ze0#FWUcCdW`PdrYI%$OKTxG9&b@BH2;QK?5c4w!QeThtjfg9J+HHDQnItE_OBX}9w
zv}G&;4b8f&LD43{e7u8aii~-WBdD3tlmT80xCH(2X2)_LgnIMcT&dvvyZD9<UCxUd
zE0w$0Ezi@w=GD!p%yW4b6&{|m9O-&Exz}FDxX$xyON@>0Cw;58pe64BaG<*6YI0Ec
zFe@(o;)`X2L8GVmBC_5EC&<vqJb(SZ8}El=Vcl$t%#~TGLb$D$Yt8lM@$$;8b|Vx%
zM1g6=ut&3ZtJ24Dlc7CSa`6pp<XBD{uk)q(OpR-Ff#ul|>#BXW)uvw8p_%q65|&zS
zF*0WMg0QH)qHCbaYwVORnA&wqM(MBv`@Ew<hb7ZTv_0Ot^43k~qs?BAZsQ4!@(zZ*
zMTQ%db^G73+F6B2v+3E?MULAf8+LGUiCqhf4ZCb*nd~HXpcwzew(3plOzkQ221=FX
zOXvWnK0l9^)dl7AGLE95foxtLTitu7<@<!b+jGF*lLh1}<DbEorj@#{I|UGRM*Jz_
zgg^keS9*~0Spig!mVXyV`Y`O7cmwrgXU`bQ7jyz>Xn*fOn&SFY2%SZnB}Pgf`rKr)
z+kueFhD9vxNzr9*h|j(V#kvM`&y4Fpjj;xFDiSm>uaXRM)XU};jtCfM1Q6Chw@Z1h
z8B#G)Qx)Z@3{{csA)yLXtdhB?+`u~OC21hX!;oM_44E)M3BWl^g@W5cEinabhstpW
zi^Ow-{ooBIEYI|*s8wRt&B%%v90)Rj0(InP4~3RD!hA5BRVU6HF?Ec+QnV>#CraxT
zfo1>s0Ujt>aOUyL_NT7~0$h_ZJZCiS4=9rZL{mtF94CzK!r>}u+NbIOr0|E<3UP?k
z;E>=*92^_h`jttl+BU|^)NsUCu<I*@wGELw7=-_eH%;_w^TMemACf9M&z&Wd*aI}M
z<UN5%y$7p(EIFO@-B0Z7#!8&_6!Gbz-(@Xd#i2wfHmf({O-KdVN7S1OQxu=o=0qwk
zNl-B_MS`(Dp8qnJPbzF(3RE#~2pXn_p^i$`%g@_=4OKQj&LCE#1xuJtF|nWyV#Wdj
zKXFLqj?Rr7qNG{NG3qN77pq68xdtyh#1RKIE&^}|EsPghBWD%V-*?!>U`QP$fQ0HB
zG@{`5K*REEOa+u6K*v{<lmH^|4-`PkRfodz4>l?6yyFA|5l!*G)le4{z0Vkx_=$b?
z;+3oZ&r9;H;knsA97;+|d~C@k!}~G)>Y=fodg8Otc(ZoCUB5fx(vtcfhfDKt+gfnZ
zKB*i~V<XDIT2j;&hN;`Gh8&usuNuy;1*bYq9Pa<rB{Kaa7PFyY>XSz&ieif13psev
z)79R(R9U{;GobELspfiG-Y=7acJ*0vJA!^$YPx^%z{;6iw0(9Eq;%T()M4+^-V{V5
z>7@}8x|++96x#9Y=o;FNjS!*$-j<T;lR|Ef=)j(Oz!vN&a+&wq7d5NuZtMdyDi?y0
zGPHf}|46yfIp=LKQuki0j~L_1H50s25gJ|(${w4r>-o6fFPb2zY2GCG{4%pbUk>Q*
zOMiQ*(eCDTzA!$#s&0mkyk<&_ZYO%D8ht~a9*a87pV^wM4<z@X8I!!?W@BaUaroLx
z*lRKUQu0q7NXYd#&jj4kM$jE7-wKcE?rC+d+1cUWvk-x4gQrPQtgyIBv3k4IhJ7h9
zE%Nm^Js0;XTUNwgW+v{eSSi-y#e4m2=JTs)0KZy@M@3=`3Tmoo-S}&ru~_#9-HA}k
z?rBuiS|;HuN(Pv~(T!pi?Fc((tCax-Zn!#lPG<MD^0f>6>4jEKdYKyaIwZ-&MAcm8
zgmS;i{F}7wiPqgF$~V949;}@?(TCaSU~}L8iHE>`@wB=!-Su7TO+d<GRNYcoVV{$0
z5Wuwg!B`a$^&cw&Lxjs*pr%vFvlr2-Sm7yXGM4vcX5n=;6?5l8wQp@QhR)CJ$A5X3
zS7qe{j1pCW!X}e)%FzBl`?YltfPgi|vvouCq{+CCZMdjFAvp)ExmT9zYE71vW5@7P
zg+9$K<C|Jb>sm`FP397yYtN!IY?H(pqi&#K2D=)TUl^<ulSeVH<6B9L)wl!jNEiad
z0}wTLX`&tZygcJ({8K`?FBJWV>0`1kDO49gOWt+Kb^gLejyki3F7#=HB_fzkw~a{B
zL1Jik8La>^O@&o7%9u~7=Z)0JKmZbJdf{Vpn(j-Y$zuOZj{8nWYDJ;uNhe#v{G4gK
z)1a{wV$;cs@Ff1WVWP>SZ={uhd1Pz!9~<S7@V!BjkqH6Gu*CvmQ6$$8$6*AyJzh8n
zR7dEYqxoUDQ`7?U0po6ztn6>353wAHEg!pIEntbE6cCkYF{E*W6{iaN>yH;6IvK9@
zeR8+To{$*+>D^>&t#!ht5c|g&DexUA<lPk>MT)mccJx*&41Z`I#{H!pD~S%tF;wKf
z_9)b}faj>rpW^rBpf0G6mUp~zmkTF+?uUhej?;kT?*gV7@Nc%EKGlzrMP(qUAvUch
zbq%2yP0%&B^j7=Wzu^ASnhOBI>>&~Z_zNQ{E(`^zmYn&Wy!uo|pOl^r8wMox(W@<x
zFkn6{O<j`~BPlJd=|+A%4~SnA^T58m83Uv+)*-Ny8-|&S4m8nQIX;UP)Rf7jmQfok
z5X`U6?{{G>EW|gQIe9pgjQ<@YFaIT8bhZDREm9>sN|@apDk-m9##5RRG!bz0&Y&Bq
zUz00?;|U`nKSwbuKlf8wbk{ieSLs0pofIK&w0#45e1&A?aA_cT!e6#$&#Jq+pI0T0
zH{Q>5_}|SLW|+eGTthSn`Usuv)o&vy;4RW1o~rvM=q><uwD{>bZ?_9Uc0b;S9aeW@
z>%Fbj^%f67_Fo)V<19z&d`=s<ozr->i!MxZw#z%djF*?U2rvSGZDWI&=}|D(&&<@U
z%rY)|-k;OpY<6nvI^JAg>U?#<c<p_>uD1F<r#n<gdTx^8Oy?KlOk=n~>yD_3Lj{(f
z6CG6vJ2cV0yzU+@ZtkrQhaY<xJ55!ayr|gQ_b<ASGd?I;AGK544$ihMWk#)@mKuv|
ze+Nx=rIoF}PmbU0WVdckeHb{n{`|aaAM(*8I>Xd1=wHq+L5c|2PP{GXS5z-`>xgJ!
zhP~*v&wVWy9B9$uYk$90^;}O{_bF}w876^UDANyH4N#@5Yjso9_+X~LUgR|DX`8DW
zjhyyspw|g#86TWP!hmFN?&KO@?p!juw;4$uYFmUIvmoG{_G3>Iye5c%_t`aXWn*I9
z4pV_twg&oiS08%_nc%jAxCX9i@x70Ioe0I@0#-_1@QYimCC~OROM4R9deYp|zkYIU
z^()ohTz|gxE{1O5eLgQPaCu*B$eH2b5-R1aGF8Frp0eg(E4gP7jl7cvX%YKNotdY5
zv)#YaCFGq+)=J!5n(Oi~ux+mS$S%EmUpZU<%GY9uyUfgNDR>l0EwR>`ybV>wf_=FR
zyY?hnmrU<na2fIu)IvQ1Lik9EOzfcrvJC&mY02~Pi5%mQV5o8%MQPUe1Hj$ju-1YW
zM4XpB$hLY-PDwOlObFHY2o;#&+8Z+otBI`+xOGTEWCBY;2iJUD+Yr4~h~L(C)Am>0
zK?ym<DHTFWsc{1U&>JKO^A?R=xTwRidKyU#=O@Bu`%y4QvCx_!>OfK?XYrXQ)hr9p
z;KEh^l_ZTr`uT+)=1*i9>$IgJ&ie5{h3bUgiz^to6>}b$fr+Zl23I!q+DtNgj9tWL
zCjMjd5tZYj1~N{0P@r+CmXSNC5SPRipa2|w<p4S*fV_wJoSpeo^OG1uhjc3`(wW!?
zH(RB1GG$f(WaW~O#jR8(+qkH1NhDg*+#t%hgP)nkyM%Co=s+<W4+EOl8#!Z|)RnK=
ziCNL#NRAP?xH2pM*8{D_Nssj;$vg8=ni?xRDIs2RHH7JVr{`psBM8p+FAYIMd@5=&
zx|M&;%&NkH>8ZGU1sVV0tfGYm=Ds~CH*#L#sPU^dY?HXY0H8Z&ZKqJ88hjmGx54-{
z8V+}*e?=BbNJ{a+NnV;YT8H)otN1I7rouG?5Slnqz?3lsU(6(}aA=%3DGDqtq2Gi2
z>k6Fao_U}jbSxAoYt~X;5)<TkzcxIk$Z-lv@6M<Q&Xh>eZ)`Uq-Y1kNXC{G4LQ!#k
z$7-g7-Tf~K3CR}?;a_n|oehi2xBM+ud?kw!Z|{_2UY%DNmU8#Q1wEF}?}eh8Z9U)C
z1%&36A~V~I$SkTXZZdr22l<b4I$6H=)%(UU`n)Xe?zer%;fyk_y=lH3zK<T;Y363u
zRyEi58ay^;05Cd@mY<imI=f$Io()V_BjS=<AQM-acHI(PFe_p$e}&eosMB-jIRf75
z+u8A?&4j2}65s<&s@xDi%VSi!@7e9Kzy5Qv(L0;g>_ilOrrPUs;v^!VKutC$tCPcf
zFJsG&*88dK;ZCrU>r)WROYMCR+-|4C6SGi*yiPm2vf_igucO|SRo5w&vkZT;v-Bvw
z09sj<n23FqyY6jPu|t^QvGW!G=j|zr&eJO^FWv2-*Z8vkZTy3+d|Qh}n;t;>?158>
zEn>vmu@BVa>}V5O!<Lqor~P^Cw=u2qZ4<1l-^vJPq-eutsm`$j*K9itQko?D>;1R%
z&mBKB77%t>Y1-})`@9iK3N@(<b^>>Dlkd7{GsnGl@p;Q|B&Rw$^Ge0j1Rx`f{0OD8
z#p35_%re4cL6~=LwWZ?XTuzCIu|n6sv8Y{+l?;zMn<MM?LOz&+&4(X49;d0bel%Zs
zO7Lej0(@tC?X$dKGc+H?FbbqdNU!?+yn@c74_Sso$G5ifJH*|z&4+&*>jG2n{rO(a
ztQ<UKdZmFI;it0fLNZd5?i-T=9M%Lre_1x&)<)O6yY}sR^lA6dn`$74C)BT;Xm6_O
zU`}2?4D-ADnX{RXH&}Lv!P3MdOd)jsF9P+S#-(erg)tCX8S1&RLD@KM!Fdz~l%N7<
z7u7KZ=3QqJ=t9I;W9@W_s^;`BVM<*5aXHk_MXP}pkJc@j;b76MaiKoYP<AfWz+UEc
z?dox;u#Ic@cR^L67;v%+MiQUX(>FMwE^=ckyT%b9$)=zP*E<PBRJ(fGLpJq_OWlIi
zLUhgk<$I_G8G(LD{WC<WjZGbiyD=lI@zyv7%Y?zDPB4xl5h?k^Zi2B<4eA<<(%KAl
z`;uq{og%Ahg(+rAAi`Y4Ocwnl77jOAivAH%Y)NQHF}OT{HWD#7<pn@p>GTOnCr}Oo
z<g%G5Y<P<SFOQ%|0(*emO|>|RH;>RkK><yUGzDB^-pP+s>63|^8|%u!kAv#5?pb9$
zSEE*%<j~CTr_*CHp_$?yJI5vv$>Adc4DnV`LB`QhCuv)a=Zn)vU?g?ZzoK|x!i0DM
z`!7fK5d@U55`<zaO%p@!hG6f0X?hq8_2np+u>kW%G?`KIu&lI6u&NEWfJvniRn>$H
zWohmf7HdG+5cc*tO5p;@#YplTKv;=kv<Cg*O~TyY>czPjG(iaCe#Q`r;v&1D?wFyO
z*3>B3E~oSm|3q`cm@9?(moJo6>y))&fJ_e2`)5IvF?GopBtW&<3q#dK^H4&iV*K>q
zESP^Ioa;WsmGl`oNt6?-7ptTsUkIMOIb>?_g@uJZ|Jm2~`;wB9?^<M5q+F<R8RNq{
z*hnM51bgXKPX`P?SMM+z4AI$m7G>7jd^<iT5fAu0ZXO*TopJB7D@!Ujn@&MS?V2B-
zldBU|Wp(MHQ}YU2+@slqYMZya!n}kn?nM{-QL7Fmi7bmc?<IWHKgV}DHlLb$n!o@0
zQX*^ys7Bm_C6cPy@QEqp4>LYYwogh7f1bYwoel~}cM_(OgF$(xbVr?^)38;ur@Ate
zr1$)h(mF`7Y#B|3&|y_Nvh#X6pWc){dhz=oW~Qg5v!3bf8n?QfG^9wLYMf`C-Fj0i
z4y_l?MudKz8(pg=LMem7e%U4F26&)Hc?NdyPnB6c`suohbRnKTC0IX6o|>KAg=<Ll
zzeQxuH_?w<(K9(wGZ1r~r6n%khxKD-e7<IeCMCZOv|8KmOb%v8mszG(@JEkiDscyi
zF*RH4_?S?#q^T+G9COK<{hMjw9gbcN&*G*RyXmy7NHNh-ARh1KT&!%e(lfAfwk@<*
z&(+P@4MyigQ|Q`4E5GB4K5mDp>-jg0UozaTZFH$92Us*^$K@>PW!ik}l;~77<uN6Z
znZNnv_Wb@deWzO#mJmY=%8;m#*Fdy#R@~_9^>$j3vDZc_ZN2WkcTgqO<6Y|Iu0S(C
zNVoNF+RNb5^tJCWaCLpVRlR#BYj?VQxS1?iXT_SPfS99?S95j@n_1fCn$U#|%8ISM
zKk>zq!xY)KdcP@d<(e_dco1$Y@*!-J8~reMV^w4HaX^G^tehZdnb2BAj9HVY_wW2!
zxh_z<K5$@v{7pJQoWO`npBh*kYGz^A&sw=TIY!<wR1$5Vd|B&?Ag#|y{fjc6RE!Q}
zRiX`*R8S?g<SC9GRoyal^BmT^Ogd=EA5qGJxIKE9!7_E&RC_5<TYCagnGrWzGJ*7R
zxg2|QpR|LXkd(Ai5mu_Q5PFa}OvAe7WpSA!K%l5|O&s1T41fp>_mHLhFO4#PMk@Q|
zJe|tM3eh^JtOjWM`m`Drg$ebkK^Dw~y+pZJe^Vgs9%&kap9@XR5Jf$tK9YViC46o`
zpD;+K&?M}xB9cRCpC2P=Dw;wWYBJW|zNw)9UM#YmG=UaVBy$<CQFFY!=s30kpnk{L
z{RsAip0C%{16j)UIv)1rGg}Z9n4wSvHWPBrP>k=gZw-sC3K-N~6j-_{9g0MdFjk)Q
z^l<5_I!j$>tmiDD%3iN}KSL-C|9(KMu_bSoiueI8Fu@D-ofu_PC>BqJDB0B`gy=+s
z*3_FZ1DqsH0N{KYLM~=d9q``b{}6Ug?U}W~w(i*JbZpzUZQHi(blkxk+qRQ8X2-T|
zCmlQ4`PMqwM|+>lUofwFs>T>~S9E94o^rN+t@&>~=cWXRV`OQ}R%7CdICzG=-rl-<
zRbOrTJ-|H)RUSMcKoQo$qI55&7$qK)N+E=-Zq}2$C`2DJ4?Q){IWQFK#jCBjco;S?
z^am-taK2GIDQBFvtO-a{G%|l?kTUeCq~kA8)wCfw0aCv9?^ulVE7AWdE?;>TFY%yh
z_Xc*;iFaQLvF|-uX43@M624U5^HP^_Z9S3zLQ<Dn5ngi|Hb4I9$A|I0haVL7e5kgt
z&jdqrm<;XZ<7Y~B<6gCz2v1F4?)l6Cy0ptYtD|9Q>NZxBrfK~-M=s#qNz+LUKc{W<
zDMznztJUF@@0uy#z2+^`;sdW83yOV7hgs7vZ_H+OheN+ayOecJpL6RX`(BMfBXvr&
zak;|_dre;Q<xzS@GaKjXF9Ee$UbP-wbo_1Aj^O^-$?G9`z*|*s+qv~Q{ruNMmw<uL
z+qIDI)6!b)SIzap%X3ie{>|;wsgF%NHrGX!!Tb1VOAp`8dH79oD^~&hFyACpUMLr?
zaigI35ZW0gu8N#|<}{Rb56C#jZ5LHN)9a0Bqt#)P!=Qx$(RvD++d~32gmSaf@!H~X
z>=duHt$_(7B)8+*Cn~K5YW~D#rJiSzu2(^JrK<vU)jKSoBQRxd;dJDBy=lYYw+wx#
z4`hlx(1F+c`0Th6AR}EgQTsUc1XvsbCkXI12VO2IZ|+AtIFQLL#}MM8%2_(!9T}RK
z<}(moj<?tu=@2l$cRm*#B|;UX@&pQO=6u=9y9r_aQCaU8C`71wFn97X<C0rlX7o~E
z={i|mxmqNZ9}hn&y6c~N7zf<ME=v#d6-OlN44s&2Sk}z5Yg1(q!1>iJbm|l@X4wK5
zMCz8GVP%Z&;`YJvjRF^1_X2z+{&_*|xrPG#%?R&tGT4_IylXf%{Mlz77uOcU^=JyO
zBD+hoxBs)XhoMv|BL|5o!x_*jiiy*;f7;kCmQ5muDp@g|&@zW=)h<JI5+Vx`vcVRM
zv!z-lBC+8H$&f^(*koHvh62h^_uuIQbE27zm4vDb_%9G%s$o%bYx7PKVC$1;W+=6!
zL;w6{?u)_|ZsPfCbJ@bR(4?ujK95&XrfP$^;!NHlU0T#=gp8FxMC496!p_<fcx<i6
zQ`z)dCwC1`>59$|9Rk$)K+~-NgZyVK!vhS$s;yTUf=wixG`F4*%&0hgdi&R9Z(pmD
zUg!AASHut12gh{)V#cmoW(tdnmO>wNxT2X>roUyVRC&sn8z~lgn3M_a1jQmr)41~k
zA4EMG((LG3#L&4yEJ-W3e7K+{#nHxXqg#D5Q3sA8ruO4W;#yBni@S%@!?(%8j<&v$
z#d-H-r@wZYBLiix6FExiwAvMt4HgA+3e<eQl}u5xJ3TtedJ*zhw$u-$s3>qjOac&y
zJ{4y;MB>2k`aD^Gd25pJS+fOJ9JNK}3P8ru&Jl@MnX$8z(V9O|gC;C}KCV<S-Z*7J
zzn3`^UY7k1p*0>e9S($0$>E+dV+cG6Io|+OVgoE?n;?1n1fj*qA7A|lSwSS_B4`!K
zpgnUHG_g8lMJw_$M!w1uO<2g3@dGsU;GZHpq~tPFfkH*lNI>Y&DI%qOPIj8uaT?M(
zIL7Es+KWEagUbI~g88N$rVvmCOMSUL^0El1w?Dq$BtU&{I=<>~)*~~hIFEa}aZXnN
z@3JZ%wC5G&8fSl@MlWkTz4_h@m6elA&X4M$eJ0{qa2Q+M)FxhDx*E9zb}OAkrg!N}
z1lLfUokCwi{sHsU%dRh}e81S4P`5O1^gA0j=>8fUmmqm)G_v#aarAGssvPz!$l%&h
zVohpM<KZU0DgCL#-?{B{sQ0v?#x15hT0~L6?gR#W5C)i5SKnNY7t(yd<Cpu@yFZOD
z_SHM4Du1jF!u8dR?F(#Y-wZQ^Neq>qzirpp>DkO_oqwYwb(1$T)b;0E@8_WOQm@@y
zAEUq)JKLMRJEjfnGp?=HF12)5T5r{lk38SUM)tafW*4(!J`2wGGxbvbRCKTNw(~YM
z>*GD1+a7*c;IqnCC0DwAz3(;R{6wcvSiFbNdf;+!B&!!pz5EF1p7U)pzEBzlW?zn`
zL<EMNC~6FjIQkmdJDC~^?g?FWa&R&0;8!v0uI6~UT2<9%;q0SUsaw~|8niP7R1!HK
zFARBrsq_@XDI}!LD*B~SAzPyr|Gb(~m^z_2242(3ZOLc-d1>GIl(#arwc^;5kCPli
zu7H?=xhZ}SFP<hd4MT0Hh_7zjJigY29+>_Uyx*r(>(iP=#y<|F>!SHF86_*HXX~hX
z)bP~8aE;jGSK(q;S8LE~I0JI(v3v|g@-u@^VTf=1?koJ`_0#TdY6+a5%w->SxNGFB
zOA(nWp<re3KXh><YUTiOg>sk2=vkMRdcEm**HZ##{XVHYd1B_l_XnQQgTdV0!~BuH
zZs-$6=m!L(0H;7t@7>-D=<!Hr^1F41nMv8TlG!v0>R*F}i)Mjzysj>8J^^Nqc_6#K
zp86DvfW0qUhu(TFfvAb(afERcrPmLACV6sqGtP_|Tq=FaLu)cyzEsjfM<!J}Y5vf>
ziXBayLHGnrs6F)HWZVJ;1?aNaHgJiG)dcUrIWd$~s-U>;1shiyEn>3lQ4*vyYD6W;
z5?D>$wt<O(_Fq=wzA37qN|beIw$`Qkzi7Ynj!49=HQZ1prvC_e!PSFcG@aPFRuGDl
zKvu}1w*&`tRE51l`;_<RjAi|eG_&B?QHB1}8YiY{3kl)4IOB>zhzt4lf@)jkO}{GL
z6<?5LR_H`}#P7>64o6G%R4~Z1oWnTd0EX!Z(TB<?^XxmcmD5zBLrs{(R1Iw<m0C3|
z(rD+uS8!K0JbtBz|B#}(dHWfz*@k0uC0$(Fud1$LjFkho(Z+H2(0kF6SlYrZ(Gq?<
zE%)Rgt5MCMrq?<DxEGb|?$Nq*u;f|PkS8fY&prrx&}$pSi6b<TBCNz8vtn~V8Nw(l
z&-*S)3zG#X1SJ%s(#HN=)VzztuFn#$VWiz#p^~KDiG$DWXLf?)`d78m<*Epa5QoU`
zNci&Hjp-|%4-O{7H6VV_iUv<Z@Uzr3i;zHM&bYEoYVqCG)R&s+dy<WHle|qwf@Kv4
zNJt{x+Eziv$-JRyKhmS8KuJON&d+N)<|QfhVq_UQA%h5l7eW7wjiQU;0_Da|_2WeW
znINBy5k_+OyLuBkv7-CDh5JwE`<Eyy%Qaqpx;v4&ZHJ6_g;;x-63rbw$0ntwK1<fj
z{#(Z=f|R5^IEk}#3ha6x_7b82pQTqqu(U(qeL0d<P*I=)4;QPq;Y%*f&3VZJOOepm
zbnNHq%RYOwnFnt2-32bfy4lTNdOQ9AH|tzwiq_&<<=(+AXY>$VOJ(=iddlduQ?ql;
zUSDIqAc5pz`GDKn8fi$~D=jQtnsLcQSr2ILpudTtV}=&0M$$g+?7sUo)D?Fn^t2XB
z5~F4G&`jwpPR=B#HuBzQo_pckdJ`KZpITd6GQED<Fo|WGsHPf_+2t{y5rdtXRKsdm
z<Elg<o^;EMI;^HUcMaAE!*XcAHtMjf-=phq)Acarczrptk#c%uxLZf_`R;5W(k9O3
z4YA~!y)00I3$Jwcw<<?PjpvDbF(o@?E4y+2{FuT#JZAxupPA-$ouUHY-$i7`0!SN@
zI=$V@#j|Xa(S2&gyL@`Nju^XTKT^)Ee;4m!qQkfqigR7E(Y51SSC*4P5YVIcV0#d5
zy@x5`Ut)4PeD=x^5)H?H{N8JbMfmh18~C-aFl>&ks-f^oknx_{u-N5TE6F8hvK;-B
z((HX-mLpV!E|iTs5A0!X$sJ)Z#hI~@xuDI{&R47aGJJ>l%Gyk>1$0_Z5^`%-3$q~-
zw-wj?D84djF&V8C6b~aD<4E2CnaegT!+rNgUhRZG+}Ar&-48|f!c#&&^LdtQe7ni7
z(nnq}Cj7?BhPzL-ykt;)?HdSLk6`cBl#9@ru~~CD{nLS2xHth7m7WDdq~Ol)S5rBy
z0gWLIB`8>?Eeg?~tSnJr*zun`Cf@2w`4Q3^DMk=1IzkMb=hnFHk%=){j7V*|CF}56
zN~4TWVmO6-LS|OehWK&kkb6{+NJ*inkgPaWO$D+PBqL;}Ux`UN{Y;Qn)?^SDGlrYa
z8HvMne=vz*glh6Ei#b8?Y)KQ%LM74j6hSGp1}YMqJ;dzX%13nna+DdiB@62YL{w>w
z*Au#sQPqPmXTkWdODfnSqq{{hxtO0uZes4DSjFv!WPP07e<YfhB&BJXW8k2*8b3Sz
zB5ITtRxD((!bQ&`%mwFF8VkHnmVyF}ME3the@%&&AsK>3c+0E<XdOg4*VS+Y78^Cw
z+&;fd5p=gdz0JNTK$Y6d(Pf?C^K!8GyO|vA4ej*%1QYOP;leC#8?51C!g^H>%EL^8
zi~n<JOnbJ4z>pU$g0q}4uiYFeAfDS(()y-u%T5PM|M^j?j?75a?$vHy#3)I$whB$e
zo0;aKDl~xsa|6$fl!6sJB~3_9mJmDCpJ=RN$b_U^l9uvK$g$pCWe1NN12v2M!AV3(
zDSQ`LpSN!X!Gtvu4Gv3+mk=q>^EVjH5lfjhDJ~MZGXm|}2t7D5VSZMM${V*vRnP>T
z!f9l+9NG|`l7!{caw*=A3r30&DNDMS$&!%p*|M7yr3%~-2FufQ&3>7k`mA*2;e*#Z
zp|eJ)z(3Hvi)Gg0zY-G}=^eUWn*RP_UEV{MhpAPyd2+-V&Xo+JFrb25GbsHgwI+j7
zNOx(5N|kM(HTn>SF)VOiB{0vS5l#~Parl~@9e;;|qf(vveUo0}>sZYd4I5vcV<O-B
zFVJnnilTbTgmzoM%lM{+lLjj8#kMCuU*2H8pgao)-<Dg?KROOPQ<Hm^)9`GYTt}M<
zlq@Htpp5MEaCPl&d;<&0X0G{#w4u%ftE=D+Q0L@Jh?_NhG<QB#<GR@@7@x`leh3R}
z<K2N?NJ<xpsaFwMr>QxV+Sjb@YzbXnAGbUg`U$9nlE_pndso-sX;B=pv!jrYX|9(0
z`J@h%@YQkYzfATn{IjNXh~NF*o&VLzxnS;K>-iu?wtV5j@xYPmiZhNg?45}LX@SlF
zm94^R73yL{&MA;T!(@#ubC@&5npU!`b2I4qBwW0H-6${XF)t1KHnq6buGYht4<9NS
zum12)YG07&bfTa`cyY$o;Q?s>3hCZ{K1C~913=V+NAO6i%p$wJug0=a9MP(=(*{1c
z{p~06ZQ1mdsnzFUV;LrAVsjHRQQ&J?w&+<{4=}0Ns?~^M#AWuhrretv&MLARt|~+*
zNygSPAu&*uHycl1YAW2(1z*LsFZ1BW@GHi!@aae)$zfNycDC5p91v#Lh^(ws%n~%#
zMv?5I^kuYY%d|zau+a>sdV!Jlg@;QRk~jgac-W16TRx&I#W0w(Uw%B6Q2iI@OPv<<
zw;|HZTu6px@^qwfSBF;&QXmz34ox#DR5?@8D0MF~!uUHCPDp8H3lU3K+_+#{6DH4b
z(~6(?*Fp(v)3u#CSB%n;`PnFaPKabNQ`MY8&5qi9x6hluTFTzCxNL*7K`M|5+EHym
z9A{u(@Xq@AR=Z^a$CxvEP#<z^oD4JCoH(Mu-I0tP89@T6|B$`8zjtQ+nLInK>KD=(
zLTfy;xdqj7Euj^o*2Tfo#2;1jHGlob_*+n29C2<c7C@ycyRQ>DClvU&3c5L@rzjt)
zYF}RE2@)<-j*N8kE{b-wX!n$&XnuYJ^Wh)i4sbi=K&}B-OJ!QdE5;H+AqsN%a0r)Q
zyN40I^Ov8;E%JVkhZNH}e4mECN0ora>1Ga(Aq9O+-42)CPYDhO->3Wgs7e(A+8Ti@
zEw8%GE@opKYxlK7-<2Da0go6e5Qx=-7Se8Ewv?J5jEY!V{|Z<UP?TaOW)AU7P2jS;
zl}MCMrF++-0)3U?2_F$uhm;T?g1%t_?n5|fS*DzXnV!5--js3)=@CCJhkFsJ4o*S+
zJIVcXFFYLbVE5(+kr?4dg;;*2i;dvwy2B$u5C*9zrBfXoxPoAgpe3B7ffB~_q*n5b
z2p5aQ5QKDhDdZ>{N+?28as4Y&jYEtdm>y+-Jj6KatCK{|6ti%?VH5^I!nOcE_H>ej
z#?1s@zg!a+`#|mJ&XrI|SZ0%4V3?`#Ab*wBUnW#iQmT#iEm(h-VN^CS7{a%6SiQ;B
z&tF=247dmT-hELsHDGY2PvziPRDb5i4O}O>>@2N#8ZV~#D#h76<fW|rAz14f@{^oA
zC|)kVp5M7Uew)nPKX1BSaYA|dl}H29^jv|Q{Oh*w?%(4btzS5lsn;Hto4?&6_laWD
zpDd9`lUCHt;L@4myTLA^8&^4bDymsc$ugL7VvRG5TtIE?Pg+FX^k&*c<L_Md(UsRl
z_N^*xl#Vjf=F0?mj(9RH84M5GbGyH=O3TQaHe{yVF#_Z&fZcXbvhMK0&_(CwG%#eE
zKF$8#gTso8u38XiKJDKeUH4vB>~0o_WgpLQTJfpP@~kXKEcf&v7fnAQo0~(hYDJ%W
z5)W~8!?p?G>1>thao<^2SlilA;nUbt-vfThc>G|%TUG&cjJiPw$07|i2M)B8)6?N&
zv(xrLC@#rB&#6A^6zFi<pUW7p-b;5AjcQyejAO8t@!0UC^SzQCT)U&Q>o`}<KCeyu
zNc8K6*qz-u$__;umYAIDpCD3SHr!`XctIhxt>E<79Qn#4`UjdUAUXs5Qvtb<%|4t|
z<Ra(ym}TfALKIq9GdbZC$!77F1?QcG<~Lk$2Hlw<Tlo;$Un$9J^7TItLZb_(M~~U|
zA~N6>c%kj3e=ef{-SieTXa5Ma#j4%O!xc_y5d=;%FP++v&x9gziiQ0nVvq|cZ3Po`
z%Q>=^lIXC`o&IcTG_C_bUh>mOabx?`;MCM%45UitesTbzvt{Mt4!?68kVFd=rHu!+
zF+uweNyhb*><A3UETXQRY3fx6a|5_x+QcU{a3+CVmdfZ7fO{SwLJBfQ?ll^7Sie3t
zmNiS%m|R1ilzuI{`m7ov^<)hNX7RIg5Qij{!*1q!sCG+25#w)KZd1)E=n*a8;-Ydr
zsiFln3^bc@dBkZQvz;`lK`h2&h)fr?eQ6?a)6Sr(CG5}?fjKzWujzSZ-B~gkJzf=&
z__blAT2((J5(SiHMwK%McA4-F;)TlI*Xg%xv*V02(?X3XD;A@<HRKP_1Z%ZqZLiR{
zMS_JENKsPBz4!nhhK~~%W@Z^0;%0Bmd4xHgD1>dkZ-(meQ;k5|roi0WG*oP^!Ze>|
zPDeLqJrk3!m&?QIrHjJU_nxHT<(tma^)#)2D_<+$S^70)CC~=5E&aarz?mJcKejKz
zL@MG`9y2vobYSRmlB>Q4*I;J|!%(o?nqMjbcAo$u@-{I&^z9oXhAR47B?R@TXLxK4
zOr8r8%KUE5r5H*j0_vxT%aatRAnhYwDx4K0S>3i53QXj*`O!fh2rGD~!p^BDB<l|v
zbLuFggHBvwaY#0{d`1B#w5<4?thbuEEV`ltk~xz<R8m0`zS_=+^lS!VMoJ=oQI+_!
z!v3n<u+jr2N>KB6@;ZZj87YThW#)qu5|I-hhpyc13Jd%r^|q%ADgGON&M+*Oc~Jbw
zYi;7n%E~mvb6=NwtS+z5;bgqe`&H0X=HY8CZUz%w^PA$0vy0l+*v3-wI-OR`_)r$6
zpG)ZjmU=$@%F0NRwC5#e^?t5T1D}?bz6m9rNa1|Om`rN^tJMM63F~Xv;2teF*L)dg
zew}R%xo2_ib`KRawcZ`e>f(4L@={>i+y#<JMYt;5zq?pQ>XZbO0}*9TvX2@<wL)#Q
zEU`4XI9!}-jxSv6mQ-1p%rjTvPeowMMnX_{NZZ(rn$z_;hH$xn3m$hLy~Qdu&56}(
zTi#?i?p0f(^yd}Smo{~)Hwa`-VE!k+`)`dc9&cJc)(p+f-5}g|WwQfzn#xMl7g81U
zOl>$Y+Ij31Vjh>DS{4!AS*u)ux|lzs<r6~>1@A}CJ(~I7f<5!_wd|IcC||`d`mWYU
z<xFxqmgAG^ji}Q#z46n%{cY}sHfu_>D;9oGRUfeOueUl@l)v>e5a@gR3*kB|@#*WY
zD4B{?2eddn$TMW)_&9fz8k73Eu-?igdd%39g@v`{Pp!it^ac;hz}>Ic9Hu`$`6YV*
zUZeOQ-o7)ag4(MQB4Pay8x8fq*-Dim?-~->E|<w=_1Ma%4*s`)M#*zJ<<MM$TWbr=
z<}YP?rf%zPZAxCDw2-@dKx)bOx~Vn8{G_!!GCfK+yI<Q>o4(H7nfm`$Xu5gV>blw9
zPWIBr0eFzY^^f)>r%8TpC$DL4qnCD#53b&w#eD@q@`1EhE${zZU(E0n4$)a~xgh|!
z(}zdBl9us8R<NQKJ{D<Vl8}8`l2rM~$wUEBQ-4aC8b7pwp;O69%(NBY9%xbPN7HeN
z+~a39bpF9}#p><je4p)(S~Dj1T3YgG;{ZE1LY#hLC&3VvNG;U*G$NxBAapTy{lb+)
zL)6p=U+F(`hAzb_8C0qtx{%#9fA^Qhz`;?upYoKTsgzDn2C<2fct+-cd!Zu8TDgiB
z#u#+2o>Rc*H&IsvMig9v;onC<onGt%d<Xv67J|wtFwLlatx%tvEEH>*_~2=!9mWb<
zMa*H$x(sd^R(X1%VR5z9!I@TrK!x`({5y3#JT|)p(MXs-Pjg?Fa%)>)6IDt3%M5nl
zenCr+4~vWS3Jm&NTbgjyvRluwsRptDUV^hOmhFeVwG9q}C;zv%84dyuf0wVnKNTGO
zpH8lF@wea1jb62U=3l5K3Xy~+JkLd6afTE9!DQdjxSu@e1$_zWlp-f4sb#rb?=PeK
z!y97o-AA-oSH8Z!$jJF3Dmk+lBIOhfAK%PgqB})R-@8Lr_;w0V-K|r0Je<<_pc+&V
z8%B_FNUPfe84}P)ORq_U6J^pAiLjG0VPs?Er2MZ1=e9n?NDXP9ExnKQHIMi?3B!Ic
z{3xbyo@wn|5v68PuoiSWfF|XFQcjWyy;CE$Y40rS6<$kqgpU-w4dra>j3Wyiz@WGU
z>kc>1`5pgrm}D=uFTuT|SxHG?u;%)BHu1m>0r>&0={!4vKk>V#QB_@_>ETO~VurLi
z2eqKE?&)?p?ZFRt=^HD@e#^8w?7{C8Q1`Lf-E|wxRgcNF75jnr;2_Mjlh9OutOwL%
z=o6dgpB!*MPN#QC1u;K+a$lS-e*)Rof2`tm=5tpOc{up{?7X}w=Vk&y8aSo@ac~#r
zzK|@_U=fg`=f}_W__@$~Y;i~9)tBFv5uqY1+~-)VWRif?q})QnT4md_=w{xz`F3-u
z7Ew%+dO|?o@hFh;#e~xB%_vu$)coF3dhhH`ZvW1BB3>o0=^C-YUYt%{q&{izu3zC!
zSFQI`&7sgyO}yU0&#;O@6-J)FV8IB*BKX=p^E_7++rITG`|NA^brI044er%59QCza
zTuk#)Un<vC0pB%OM`is=9lYK~0hTdU)b;H1wNIea(GA4Nkxlj%^hYaMJLmNKJbchG
z;N#}9rxWF6%HcNPy`N)z?#jE5C@SfB^Ll5Jvnd}K`JvK69cmz-PY#xsm!qp~9RHFD
zh9@TFkhZd#E0EFIs{An8^pQ&wvmL8m!zQPjv+;Ux{T1EMxvb1+U}_TXrtYC(k=I;3
zzWg!RmCE*kUxJt!O&|BNsl#HjZC^VHSy|^)7dEGT?2XnX()}|91hJh`ovnyaub1*7
zsw}B>!|ZHS$g*&LJ7^aBwEcDzsFO;+F|&VK2QG-XRdo}qoTz6%AXwc|8PIUO1pr=O
zPdLbOr1L-Dit0_Pd_>te6)IHNBZ?h02|pi+Pf@fS`VFVmnzz`z-O)%+nH5md2ag>R
zmM8slh3?h6OyvQEPT__q9x3*M7&6z83Lf04tz13@fY&P9o4{m@g~+LCHpUX3R)d~B
zyV#Mq=g3bmLBF5_ge#NbT$#hB$7nes7urD3dKo~_$`r9=k=xVyi($)or2?x%lpS_m
zh+u4phY{CVRH^UO^IeaKqx8*LMtG>lo$@Xo>+H)$s}BC1YPyAX`bsO$4J)?65!%T%
z<K`@>(Xan$eH(<gQg{B0z_fg4LVHKJzcCn%HIzX%eNV$Z>F60UBUs{$1RvAY6|YF1
zWW?wtLDf%{XE|wX;$joWU!p4gf%qZ)JK6y`5mGfvp1wZ^B5y#*sH|f90}*EA4?xvB
z^UM{6q6^M1US>b>`z)XCSN~m!ib^B-<Wpr7K{nQUy7h_{`(`Vpmp9G(wTHVsL#+ST
z!RI&)hW&y@zzlBN_OBfGs0&)cth8uia-+L1IcYi8Qd}!4aH(HyamGKAV~Ejz8(MuI
zkfpuZ<a2~YoXTeklw9r8z1`ZMDeNIH>N-5wqgt(h`E61kac%f>m~vaL6z@+k2I(Cp
zV+5?A*|5+qR9y(T?I#gErMQHI$*<O|1zb?1cWXrQD+r_pP293S2@D13ky@tWOvqqA
zkoy>s=_p|8t_3$7P(a=y;{-uNvOQVfI9{$z%i|>>f5r5Xmkt$^_UQeIQXsRq0P8-k
z-cJxKCnQJe{r&&xZW>m(Mea@(?&DO1f00B&WdpTI-+Q{7mR7clLOd(VN3vu21hydM
zp9+X-hWwyA&C>#+@9b@x11vA1eli63)cAC$Eh8JMCq?mn62)|aBIj!uJZx1sA|y3E
zExW6Gs!^m24XhHc&NV9J^w`=sjIMN}VYO`-Wz~6KKbR%6EF@PCNj{awr__b5t*Lpu
z_Dxd!G}y2ekdP13=i^h1bNzQPHh(oZU2zDfVSQBv;2j%O!;~5Nx-PzpWu%-HPua1V
z*i(EUlQqGpW@CPJJ-cWrZLwFFx-L4l#MZg4i8e^HyWGOP)WNBA^=`|kWq{VNf^>K_
z>P~WoGz}-dGDh)ZyC>*gdrkoGqI^BJ7jG+_5H$`bjLsXnclcQ5T-0&UBduvz1q*n-
zeje3wQ8CWAKr<3%nbNy?JeEE96E6aDwdy7Oxs?7X(b^KrOchL$4p=(SJ-uIsTLcQ&
zqw3Zq7+@8+hetL^=W+>C7p*?G#ZEu)cFr)ZmE<sl^1&1HUpVL5<2txC)ABuU)5Lx{
zK}@_ON;8O{?8iZ)#*+w3HJ~(Dzjm!L_dSOKXAGY#Z9e`?8Vc=Aa=HaqE_u}OEB)lP
zPyUTfLo?+4+K%I`J=Sbdd7Xp(I=o<zO4$#Fg46zb6gCTZskS(;Y+2%lS~Tq&If_Do
z*wZP@{c!p)pzGt$S3hBdqc#%*for^B`0@-d#M&_MIN!`~ct$3coY5}-dN$c|RN3oU
ztLge5#Wyxl&Y4NJtJ^VH0h`e&@`s|Z5mV^E?rjot&H!OTd$Bfyn*Cp=;7@0Ow3Z#R
zm}`}Gc&|xu)P<*SEYfl>?j`&Jlq)9_8wdh1G9f+8DpkSVFG_w~tT-E+dv*B;a6&OK
zElomvy@6iKF*0AcdEL|CnaJ^Aq0H59J&sShEg_WMI`48ZgYHfeVTII;dPTS`Tc)N6
z)pNx%TYo6eq?q-$gbb?@Gj9lQY=vO8{82&-3&_ns69*iZyLg9gI&sG;Uy1ajy+aDk
zXDe(zb<!Z!b<ud`Hih)-_);K58W`YpmwQ-MEIWI`z6BDGwLC2H*WPb?{6cBc#Y9R@
zaOT4i5S34yee;=e=j(=A^UC_e+4qHUo64WpKOJh+^$pH>S=3zIns3I>UA|r%&#z<M
zKVSDA?~Xm#6#xNUymue3bNl0drK#grw4oNAzSd*;ovld&B5zy?toh8DnVzb-S!3To
zSM%W^JTHpJYQEFA$3Oq1%t1m_vW6&3St5;F#om$@;Hg0~yj*j6S!N-}j}N*u{>n3`
zm=vQT(ep+8f=WG9nKMRAh%<C`<|ZP8O8=GVv{!vE67DqGHW2NrdF$`46b2dW&crDz
zOq3KCYSi0c_yUEZU}~=9>}<(JIb<X_n?KN5r3(%lQcCvsp3?p-vxn%IlnPVRoe4ZO
zXKZTBg!tD@s27zaqp%>op`kIMRqfx4|Mw_06}|Mv#;TL7zfC}SO=X4X!U#j5@0>Yh
zhJyiH+~|0t<+sx`+JkG2kDWF#HKEI^F|2BC+!!mjVEsWuR^$dozMz`qq%i1dA77SV
z{w>0!E|;9bhT+KSqrmeFm<MpQY}j4)%(!@sGx{rW*uyTXq;LZ;re<_763muP4{u{Y
zE*tjLz21x9me&umJw_`Z;7=2)JIKl!2-)Unns_hq{kYz4#5W5O)z2yaI<GJI%&)#(
z1L3ZFF7VqaPc<#6%CF*S#=OTRYU%l&FT#74A9DyQn6n8SA1B74@KbEFAF_e40tTmL
zJe4pxDfe5HWx9S08`9-4o>|oL)q$_!KiTifB%qcn&!_#RVI8Ew-QjSxB1<R2a2yn9
zA6~8bH`*WfqrN!jSsla>6%)xr_HM>(&c}24nK%jK-TB{c4f|FD6SBRQ6~ljP84Dn>
zUb?Xc-hZ7R88{ehot;&7)-%4XUYuEf2|xVxJO5X4@2lxTSAeC5(o%hQlC18T9$`z0
z0y0^BsH5<OGv>3w&RFN#ZsBRAb!Hv+YNzs4ska#<(Djuah4)!UbT8L&9joRSP_y#x
zdhmXmE@lJZtBl26{<ET0^?4QyhziKEeq&u>J6bahZ#N{9e*gk+2*Sg;u`{a8NOF4J
z_h$|p2DQ?ZT!h78hr0nzdE*Wq{AGw)?m)XI0DW8W3U#XXEI*X;b366hV5qDR6@@1m
zW4Ar;v0PSaR_k|I)B3wv77$<&-z~}b>#UM#;O1sFL%KkXg<Lsn#nv%KyOgJISJ<f9
zX32(Gt8^O01YVlgDlXRm9ghtoPH-?gOu9i7kpT6ejrF2RDJjZ|{o-cs4^Yy#qr4eq
zB?3$g<(o|4x<`HAB3o&j-eSLI)!WgObzh3W<Dqv}xlT^d6pcsa`5%Y=j~FUhq*#|n
z#go3>Xab#1vzeB2lugzERibQyRKsf4C@B~`WEvmm;o{8;?0*J*Kx<F$!%lnP*T>O~
z&Z#tv3Jp@nvnd-4`4J<YxLaEyHQK1>-`{J<O=TA)d$1~23=_);57=h-g*>XI7*A1=
z>vFOhD`u`Xo$eLG(kZC=jK7h&T3s;Lcs%cw23|<SD82-_{C~ZKdjMUFv#L2<cKodh
z<kE%?18vbW{=`uX9J)e6i%bsWs~c%#H%SjG3OUuO+(gSZK}(KyA;FEd=24454Kkx-
ztH=G)i1Tg8aWvJ==A}+HpmX@}^uFoH)acpJ^X=Y#e9zneocuf-t@sE%!btV$>EylH
zKUy0H4CdcV4Ymc=l0yod*%+kN#ZH`Pya7}D3&_y``l6Jh^m$S9q~dvKYN`rFn39@|
zgj9zc9c@e$jS4*|jQd>~$cW5af5Wj~(#f4V^&t8L1=Ej>X?J*sRuc~n1QtdD-C<h6
zz3ByZ`F7$-jb(p<D1(oW&Z2N~qxO@VYA={7Sw(;fHOza$BG-|A@L?g@N+4UM73iv3
z^V9zv>OIciwU{E&pDu@2We!SK69vOyDWl9GL>V0aP1F05bx%swee(5<ncjm?ccur^
zMur2=8j}}DN%tkDphL{a2A}4-_)l!c4*%qj%^`KLjw{>F7rB;omD^hQAF<i1ZD$c(
z=EG8uRdcmj|7{csk3-zoV~;mA_VjZCo{n_iW-kJ0s26AJd|K5t32?FTuVEdR<Oy&Q
zEYIEtt%YOM1JkM}aJt#vGU)m~Omw%mI+^~mG`!4S+k~i=kCkd#@}<Asv+1~h+|?r%
znUOjp0<VW=S|aCXv&`xeaMBmtOn>wlJTyE-Ai(~$A($RVTnT{Bjig^tLL+HsGfME1
zx70-&XJ{))0sT4b>BqwL_Xz^nbASC+{9^3^wLjKvcD0*O1zbLJ*e2KnHf}Ooh+bwR
zQs)uWw5sE0oaNju6KMGHueKQ(RrOR@YUZ~MD@a@oO>H^LSIZl;vA(Q6oOaOYadG!%
z_gogzh*#kdTwHCmsvNH(s`&w%{m52^rU?DpUS{nLZT`$v$l?@|Ui>N2sA?hi=I?Ir
z)baYdgLW)s|B8aR-Lz8d;(?n~!c}4)SZpf6IV!KlLm&6`m@J&iVaR~QpWe3K_yj#X
zxBeWqTieWNYM2670}r-iPjTf~B>^L`Z`lrs_wz(=61Y1N)TS`u!DaRc4`{O66A@U8
z`?B0aqf`1c(|Mj}zIubdvlGn&tF<Vz93&P!b?Z8iQX9}~-^*K&Z!F@F%yzlT#eT#;
zJHlF^fxPURdQ>#Y)}0+G->7bTYZ;09DM=y{`teDJKjGK%w7cCLu&Vi?AhF&x)?WV^
z7Z?T#T>)i-{?zdnCZ^k6ptAOvdUTq3B(^*Hwp0+2n6T`!0{pm)@Z_xj;jo~BlX<Ye
zpR9I>CX6_}dPc>jOeLOF5J}l$?1o&W*S|2E42r5^qo!3csopVAQG$hlS{-itz=S56
zr=wk=-Z)ANZvbF>J59Db8`_1nsMaR4uuQf^N3m>~uiT`s<u_zs5jFE|57X6*Q@-?A
z2pQj`4-Oh?nm5DGhG&pU;F&?Cl+mq#$1WlQ;~5}kLm36BS2SI&0|qr)%K?*Vl8?UW
z-<;OykqHpsxzwRqBIX1Z1KGby24TuPBSK0`Kq?{Hy42kzX=e+NtW~pMvDSOV<<COh
z$P46E1Sv{Na`<wbkLd10JqGjOSL*i%hhz3kT#_9lBFKwO5&*`QWaqM_{Of^Jnl%f4
z-AFGlLu=Dd_MPo*50)nW*M^2hTy>0iTdUC<xdg9<w(l={6GO7oe--;LLx)m%`f=`t
zimgT`^4zV8jGIPF29sOA@!@c*MU=CqCE<~Z<doQ{h`)yh`xB-ZiLkhxBaty`MNp;g
z0&n*`@phxbFe!RV;(nKQRT2CCC9pb`Aj`W;%8ELf@FYeO4BZveY?dOYK<yv*GW~_r
zOD`npX^R*mJV^{~gv%X_l^Z8GE?18dd5TGx#|Z}jmsEPu1t)}x4Bboj`fcpVgs2}h
z5^j2kYI+D3rAVgfM}_WxQ&m8z1{qyEgm+A8utSpHR$Sys2pyj!NkjVs6|D&Ekl=qh
zC4fl4e~uQ%x1)7ElkY*^PX1rLDJxq}P@Yn2TJn4oKW)ZtmYzjE*vfLi&0np-!oJjm
zfEBe68e51fadsY#^J|*svBn`ta$|oP%GCP+dDvt0@q;<Z9usuXaQ~`fU9D%{)LT5&
z_Gx;434bD>rIkJJoJZQEUED0H>_OHdaAWnL_JvJML@#rGK$xO)sI{Zd)MoVP!D{b%
z-kq9(3Vf9}%cfDiI;GoPHHn%;8dl$&tc7|+7tI&C+!5Uws&es^t1(!@N*NYG234Y!
z)?cslkZWE7Ov%&6JaI_s{DtjqBcP}<Ng^r9fw0JJVYBlLC!WHqHcD3Td)Jyw=1csW
zgk(!^?{p4DmtBd5z;oEZKtbK%L-lkW_DbDv4>Swd9}5Olmq-WL&1~bA+0ETghqhoc
z+gN&*cXfCdi7+`$BKwnJeJ*vmbyV8Sz*o|?Y;D!zTs%;(d@^6HB-vTfb8^i?O;512
zAGoZOJf(!Cj!)P<HiYG)xrmt-oI+vQO)(kh*Poy0;h)%KSJ+wFVLCvIXEgMBr&~SW
zPMoaiKFyhLz5^l;Z9XeCuqB3VhFWr$V(fk2to=9zZ_-YM5t%SxqfoRXY0cKJp~5s&
z5&9Eqv<i)Cvg%dOvnn*LQ#Z1zRDB4%bb3gig9jc3%RDYVffPPCnMRq8&UOB;^RaMr
zYcVD3YVFDByCacC{v(X)R#gUAS&P3Yc*gMAo>*0ZSUj>DI`$u3pEEmkHm)O$zfS%5
z!u8g-bN&J_)|IR|rgVmtR~5Ud8Df5;Ek)MzgRVjKy}F#D3A%`VM=1=+Dep0vS|)^l
zaxR!jm*&`LBvkuqCWjsDqPP|#1;<sP%qQVjxM`kca$rG{o*{N?jx)ER(GxW-{{AV$
z#;%;MWT^ats?3uc1sX!TxAeO@e{3(Fx{5R+Nle&Mxs6|z9X%Fp{JkLohN3=QihjI5
zQ-+H-Jf~ofqaD>KN>LwV93zWJGU^826@7;n(g|JPQ#Z+HDiF+2hFSVl4A(TWk+o4w
z^Z|9Y3PKX+u@lX&nU_Z(DQdy6VW%unt4+cME~eLL7&HCLzIR|hmY^7y2SFeMh95vR
zDe`ah<^e4!j=yv`oYhc4K`lWb1OCg;<@Jl#@4i2n!5&b{|6%IyJ=o*e=682=QV4iS
zorfc!o$6l6>EhpH=egc&RraxxZs3~4iKO&_?3^S5V5T9ZBmh+<+nN><;xA<<l!|_1
zmJcSe{vbLoNKq9X^BfnJM~GV~@T?<d7!BfL041<M>nbJ9VVjv7`Ax#xi=}>Kr7n-n
zhx2=Ute>dfmRf}(CGvz83T0xF4?*@%s8F44=b!j-a`Sm5_){M#uaoFTcI4loV4%HR
zs-oQla5JDNEKx)dnyN5JK@*UILghasWvLn2yHEj%uxVn1p3a?E8Ma{A93Uo(FZHsL
z=+P9AsmjUar>_^fl$dD<5dWXrGNPYrWiG$W=uoa%<A8f!!zO-ackb79?#IU^s}@tZ
zwd}e~)@@E9H;t(gf`kJMD1YrcPI`0~H9^R?Nk`|Rr0hr{*SBhY%~pjpW_H;##GoDm
ztE?1(ic0j^{UlR7Bs=`wsXDiRy^)2bm3iUr#Veb>wSTL3b!mWmQWwLt?vUyxE0+SX
z#&?H6NALgugrPT5eNch>!*(bv-!c07DY1KY9UeW7><ei~l*Y}O8HSa#t`<{B9jGj~
zfF`=chh62*rVoX3-A+`ss;;f@yNUQqUHe|a>chNn`^6g$s;C?`OYS}x<MZ8~n(i#s
zc?;rbBLCd3XWgFN;V(ytQMIUjBJ9xg3?MRX3@GUE2pdY3((~?CZ=7+wzS@(yQdKy4
zJ8oof?SR6-_Yd!UXP7$rQsob*pV}Ak&6XKjI}|T|X&1jYUG+Q8tvQ`mGcf=hkKL^-
z2K9YJ1naQ;iW=!F1$x|{hje$p6FfW6P4>7#;^&cY1i@*TX9Lh2$uiHEF#2IVo|=!r
zas>1;-k!ZS|04JXgmCKYV>*;)eeAB^ICOVD8jGGo!gj2TxXWv&R~4O;kIZ`kgIjre
z&pI`*d~r=D*;z0cUTT_SX^*yitT5CrlOKH1&mBPNrk2?a1fcn~oE^B+^1G^IZT#2z
zbrH|0>3OO&c<SXm>Jg$Yh|oF$O78eST>UWX@$8?xN#>MzUlkN@YPuq~4I5(?scedC
zkLzsS)>Yc-nW2=g|N2y}fWlC&7@J7PiDD@qnBL)GR`AD~2>)}v2NkV}r_x9awdIph
zrc=q_Bqy6UJIFi~txV80OJ%BqxL3{_r@md1@%@;q@Gl&o$}gv+##1TCYh4(C{N@M@
zpb%OKb8B49&7<Kasuv_UccQ&k<Q1=(0)G|DvLcoa&C%T>!>XHcsM4H@AWEf;0}0Cz
zyC+T<Se|Lk;bu<@q8^}2a7WaIWXittr51m%>Nozf(mfaCLZS*cV&%yS+tZfs7+c9B
zKa1kl=A|XM>qf{jKpk))D`QzP^+vOZaY^AS*CavonP%q)p)?xD5UZfTA<rQ*lvq$u
z2+ze<VEyP}tlWgYK0-5a8^=cg_UIaX+?*x`yuUEC9AV_}>FMd&chz%rHLQMJENyUb
zU=Y;C^0xOh_?TP_*}lB~u>;07&WW5l!~=oto$CHpOAr{jBjKp3k)FxC?N!jd)6=R%
zgwC2oY!vdrP)vjtS;l|DClZeGp5t86=)@xBC`gAJLFy+iOtL!n2eMQSg5W@9nXq)T
z{2qO$UhZ}dP~pLU2Mf2B(IWDeQ&NBgQligj$ubJX95dfkg*3v<WQlTrli`H}|IkX>
zS3#s#3z;4g-!UAK3HBk08Zp5p2=$TAr-@<y!lTHGnB|lPk(3=h`n3<=Dho#mNXtn=
z7(DL(H?l#>N?1%uf|iuyy#fS8zOo^H_~G+O{WlZ!h=gyYpJa5F3l$U;=x6%8_6qPE
zSzF=jX?K6z&t5!QmjV5mcp23ei_7#Y(!AOlzq|}S;-t)MEZvAle&i3^*q$A7aGa|z
zk1kOm+a;Xi7K#V0rPH&=%I2fYqsWJY#*C3$%$t5po`!au<pXY|xM7(2mRgr*9v(J;
zIXkQd8wGLdVR&ZaD#2Go@$K=Lh|g8hCJ(>uZq>#o(ppHcn81zR7|GSped_(K$`9uX
zXDNPm<RZf9tQ)<f`Bcx<VziqY`VU=vY~@Ett}HUovhecgny|s-F)wFd_jvS_DRwca
zR6Sd|>HCOX+CRLeQA!H-`1q+Tt_ZJf)U-DtX<=t*IoRdyC~3<?J-2#rgRKyTuF5Qn
zcH*JC0HpUG)_GPaADkzXQ*o|2w)MYFzV&vp^j6qb-*7jR?GEqF+wI&9yZA)=%N|Mx
z>{`NkMBLZo^#{OpBM*L5iapm`jn24)U;fME^6-&wqIr{e8#`_jtTY8!rjU%oQ<{n?
z#Y^~^#g)U$+BspUm?UXd8MW`j_?C5>S<jpYhp|09*b2B6f_;u!$Y##gYF4Yd!7hrD
z)L0GksXLxuT?C#k729&=z_5n6)+L^FORU{(du4|?)KD0Q`B=}JZHJvZ2cvT+YMEn=
ztg36(D@`q;>qZ-IQ<s<5^xZ?)^;_juo0!3u=!pHLI_!58JS&4q%u+vP@t9gO<z{S*
zrhjwmOi3nPrT7O``i3<PCCBFjuDe};53L%#frcSH3HSzbRLaS<8BRZ?JC*3mtIfP3
zIsck<oDwQ}kYKUKh?#HBSV)<5Q3s!HcC^(T%%we`kGv6zjuk7NL0qd!HayeTji?&E
zCSyOJFhm(7WBm76v1xjeu_DVoEOWSwC<}xjZMp+v1TDKy@ciPLt0fSdGYXnC2--KW
zk1BDe6JM*k@4{Gmg0^1A>|ffXc*mNW9g_zm5I_o(YIy*wij67nSpx1UPXJXZRQE9e
zk3qev5$B*VHBd}mYED66QAAo=WM*;r8#$p^RNBN!s*VwOf8gY+oRUfQ^bf!cPeDe;
zb~LeyLRD&V%P=HNUs8~Gn@6Qp8hf1ODHG>95I(*VZ=GW`*0Lr}q2X?SiT8eg+33Rq
z*J0x_?V#7y%+gV}^>sUYV1WO$-L=^L>1%f6eNcLSnJbsP9U*BEd=k&CCNo2Job>lj
zrPwtHjDeX@(-*5akF^rjYf4m}ac5o(qMRFEabScjA9+=gvj;j>;!rt+%NZobsUG}2
zoKyXsXRPxMf@;{gLB8)-uQ4ZbQnHjJFBE`FKhBv~fQwv!Cq|M|xZuw4M3$8rB>}t8
zVmG->>>vu}W#;b!GNV!u84Qt^m&G5hW6?C;3|uwQDM<8wMo-9GO<O{Xv*$r@#CD|k
z^IvB$T8edyIHqIdg(!<wFc<e1MR%cAqj7~ZaP>vD6QOVmg%*ha+2cJ!`91$daWcQ7
zI36c6JScjJ-viROJ#Le^Y@VS_<2@{!nChCJPTjW>+2D~6drqK=$Ogf0W%uE3<<w9c
zes(hI%sZ_?JBZLm$_-!T^`^dj+!M0kp(hcJ6TPS>K$0F6L9XH9GTq(={blW%Z(Ie*
zZhq3r>s#t8m<a-<v~{L;aJfq>aEeZ!EWp6xYdANtxD>X`j$Vw|xuMm*go{06&7E;_
z+1P~bVb%I$6TI~0R|0~@)9s~CaLcu>eXD<HhbI=teOBY`f`1%hwd3ODu+_*A&!%}t
zht5h9?h4xeo6|p}%EJ7+(ZEXu_fs17^E{FOPfiv6VUXC2vf#$xlWMW0wH(XC?#^>1
zu@kFChqAe#xQrcPe1bzN4c<StwH+mRS!1vo4g)P$bQf-i95E;7dYa{f`A60#flMOj
zZYnONR?<q_5-p$CH+|Q-OPS*?naeFhuD4*B?mp$(P2yRf+|Ra{FPkpQJBJ`*tp(Ji
znAgUa^kw`88ni9wRUcJdA3qgpJH{0(E2C&#J2$E?ub<FlysMLkxJ=JQ4(Uq$(dGCV
zS2WrRq<M?VMYhz*y3z~25@rrYVJaGINO#$Hf$~lgU4PVzQQPGLHw1r{V5#fkz@?F%
zIN-e1XdR=EJ=!-3ta4-a+<ID84YKMvG{wjfNz|$ttOa=g6D^q?ap!s8cA(;*t3$_Z
zVA`sKcja|c7SLn!Z`hXJ)*wYd?YzW(?6mg!k{K}9P%6}0(u6Z&8Sgv!U%ka@Y^KCH
zXi{u`{8XF`Kp~Y_ai=QVlstVe-KPI*zHpgSc#^hFe#qh<X0bMkt_2v^gezVyQQqz*
zy^MoGl<;7DB_}t^xat0i-N7w$kd_FF5*m?5wd@}U=yh3kCIw;Db4~kLWm2<}ql3LN
zR_a_GB^-K(TO2U=Y+*8SW~NX}c);=;pQ1fdTCplhwpL~RLK2sqI6$0!hRRc$&Ewg+
zjJaTj+R~bS@id<+h|JhMg_3I+Fwh(k7rjCfjY3i8KoCBh!$3u!7hi8nzG=M>{$L)f
z$!~TP&V-fbat%F8#!{XAv3NN%@ZOXZ{S=ccJE;&Q1&{(K8wlJ>Xq;j;;-ZrH!V9>K
zodkW4RBa|wVpwXHm#?+8xAT9$$^Ph)A=v7;Gsx{~Z)fv6z8b@*Jrd|N=&0Y#Zk^?-
z`+k4!R&f9g=fQJ8ef6kVk^+*zbPVT&=|E=WdYI5&l+odBzQ<vY?J0TANJ;<jgiKAk
zB1~vQ*fUVV2Rle;`Y<I`(8wv-1C?L@61hgw;5qo$gCzR|jr(@)6b-o>%KiYvGwV5-
zFhzq7(@7^TLR4IFF+t_K;yh6`k)p<=1d9v~jxfAeJB2wJV-+XZ7y<g-eS&OCJEnrX
z@%n^HD3N_HWJr{`@CSy|!FTdS2gPRSrgu)!_wzSM?&OProg{PWxjj}fMeazE!G^gy
zt($+OTI?h7`o90~-sRk?MPKh)s|($A0bRvbQeOgR^->4H;A+NI3;vu@6~my2)+J@E
z@<&z#9Np82z(&u@GE|EdFng};5`Dh^swy-4C}*mc^A!&>+Unke0Rd8%!2^Ab0b#D4
z8i6gjYe<?x2Uo*cTT!1=MxEdA1qao{dwOf)Ok>;sY5%bruc{*Lxa4YD&f}bx<X2-2
zvcez{If7f5FC<e={i?H`eXS~#jf99xSJRS^>}9TArpyFlbOSqUy@n~$2suuM4lzQf
zG)c4WG?o?OrZH5d_w~hs|7?k0*|8p6BS$z=%^F;QD)MdT_vxlipi=Wk(VC{po^ErK
zKey{k@B_h<fG=lbb3Oms+=UmDpCg}dTUSG)gJox-wP^f~tB~-5C-_bmIB%w45({~)
z8$CWNxNw4hsE@2wfLhIKr}E)k)Y_{yJ;b<&T*bJ^*4A}cv%l_!t+;}>i&BVhjQGT%
zaL7VyKK^bc2B7+r*F~Vr2=MUHuYtFRPd#mjd1`xwcaa>*&-YB(=|B!dn>vFxI~AiL
zAO6T#lMk*dQS0W!=gezM4YWQKD`p+EzT;4&3GoFMRDQy8hBvr%EfEkuNhVi|iqR-{
zo7jH%zphq~xR5K}(7#(AR(ZK~Z}twGxGnLPXBJZ9;Nk`ED;`UhFG3|E(Tw?zmg5&L
z+D2gbx9L7En#^bO1Rd}%4az@TohR3X12Z+sm&G?cYTG+X<ckC`VaWb>E-FN3PDU=8
zEZB*lHO^s&r*|&4Xtwdf=Whp;rY9~eH_{0sPMWJ?nle^mAF>9mTT|kxCzJP}x}g*P
zt*BTGgaHofM90%`O1I1vq9UBj$pDi74`Jun9ay+6>#$?nwr$(CZQDu5wr$(CZQEAI
zy4m}D-*=4l8{Rc*&Z?*OHEVSB77$^dnp?sqQ5G<9`pNmn+zqqk!>3D*hL<*j2#{KT
z+bZB>@--f;xkal`m(kZRdGZ<J%VWzc;=;0jIqIWmFJV^#3Rr<FBnR>FZ>lI_1)%E{
z4*hi#1t@!C=lDBX&OXvCfgwCTjQ3|;;ia+(i%2qi)D+|@!n9i&XE+QQwyiBX*-TB#
zLiAHeDXKTmi5}4#Y3R9-pnXWQCG-O5iloFGq~FR@!Ani<=>NPSm{FYxDTtdvCx&%&
zLbM`nW>JaY>}Yx3k7uE7cRyEqzi+?4D|`K(&l{WHt~X;s`7GS{7G-G0eBU<*RAZkk
zW3Tr|Z6;qPr)bdw<01k}hq3)$wtrq@TVsc_*io<^N1KX_g3)AAvH8-K(x93j?8~Ya
ztp_An_}0Y{<`~2G63fqCp^ZhKgWFz&3B7Ye_YR8KugDpxMEqsv1sKuqDcX?iKP`+6
zAQSW|1ao+1Wuqx1`EXJ_A(L{aX#Pk_#;;d-*R`e9ki{37GXu{g(Odrs&Xgt`)+-|I
zuB4Q7Av(;STf~00@c-uyeb;Ry27op*2$h!9nrKLJm7rNlUsU8VM$d(7NlwwAzvn}m
z4Frx)k~>I}#reN)NItIV97pWCEP|^q@pB1o-8D<mE*}fgm0B;)yx$+Y=dN<)U0AX>
z0r|?&yq?Ly6ePi=TzFd__j1{8CDjvQj9vh6a3qy36r6(yfN-0O9IuU?2TW<YHdhqg
z+q*rZyXE`Z<vg{^WSiLfc(RP7fdB3hN0(bRADJR&K}tCH6~(%;Cyz8oJ6I>LWX?Ux
zu$!jmzSJ)7_-4cPx`VgkD=i7H1N-Mm!wQJuoJ)3Rzup^%GwcSYTt<>R^{}2ECn$f`
z4)1%G6>!Jb5U_P@@x1FDI$Mo;J5N1Mn|vQ+dwLU*u_i+dwv1x^I3ky1Z@iJhxF4$R
zqCQvqFIFe9xZXeHEH|yXce^})W?y>Q<auBQCz9=$eWV)RI$C;pzm66bY7`Y)zL#6K
zX2eZo?5k_rOGT9w6qfJ|k}+aFPtjD%F|cFSHU?cC7{EhH>rdgBD`V$C<_9inU?8v*
zASqE!^i^(OpJ8UR5iGN@-t0!?M;5X}RQM#OD2Cvc;kNWR!5x9PgQ!c#ckZT%8Z9Je
zv01$?o?u)Z+LbSnW^exSeB%L2q$#PgTT55@TDx8^dVF(ZBjbx6hrN7q?fh*x@!D<6
z`K%qEe}dz}<kiO8c4t1uX8g+MdZ`{MA|ijlNb73tvKYN#pdSyX6oh7OCXBpG4TuI?
z&0SjhH+~*d^Ds_Dgtu9IIgCM&?H(JJCV*zVrQ0aSs$Nl?ao=Z|cr16iQyvvS*PAm~
zb8RO_LA%f34~?l={<I<86Jj`2CBIOzFWOZh&|H|Sxii*WUgV7mYNUwonc>Vm;X!d)
zpf_NCqEV@C%CcNUV9B|jfY7dsU%<FDmEt%W)_-MeD4c-J%cn+{h;n+G_ypY)0F8(!
z2@}EC%cGc|W##(kUlk-t^TzHmoZR@eFM6tuAo;u^zbB%)FYP1@f91V<pufbjK6-Ri
z1gQ#Hrbi=hRDkgqhkIr!ek&l1X@kJH2^180N+R~72p48}EXTA6!4si@`V5?c0<Nco
z+8Fbu3XdUkHQ3)o9qaZzZS9%fVWM3jhe!UtgQTM<rJvaB4{OUo>6bsg3D&NMZAM0(
zw_%j<f`U5Yg57z)p?OdOesMu(`fjwvC_tk=)|;Vj;+X6UV%Lon<JEO*)=sYVMzfj6
zF3(TzU}@7a_kWp}P@iVst5Yt$?JvT&Uf22um0(D^+eqC%ckxE|p@jtnv7e~gn3*4$
zXMbvL2GaYJllv~hUfzv23$?Ibd|wUk1|}{pW<v44-@elq<z{kyZwp&K{8YMsbhf?Z
zI%>6D{My!COqLwpBW%t?6bdM1W4&0O_pIi@p3zQ@y58Rd4E0q8zP<USRV1O$qS=_>
zt~gXv+|?)j@5dabF3Ki?vLc9zi@_HAM-}A(_%X;UvD2tWu@MHIFPoB&Rq9nVLSNq`
zl8Tt;E`!^Q#O-NAfKJEr`8D4OxY-l?2c?3Qi2{xH<m)&GGDW}&pkP(tBk83<kin{f
zP*#eF)kJWDa6CY<e*O^Mvlxt6F<%&TF~JZ}(opD67z?EN2PWKE(FY5QOWJr~lJInp
z9+yD?GabgQ*Y?)@RaFlc#{zBMY>p0l<#7GtjpdfmUBO4_73|zeAIs>f5+Ecb)K#~Y
z<VBjXw5C#fJ`7<~Uai&ie$ubk@Ov2tI|$k9Yqx6XU<ufFY;&^73-0x4pfaHQ#Z<kV
z^7)uu?1u<2^2?b!U96Jv`jqfAOJ?V2zn|y2<kl0nE{gscIbpwDNyvCXm()c&x;}iE
zPOTY=V`PEC95q8aFl#J$Lg+NJ{I_KD^gM)>q}Y1ema(ee>IyAa^Frs__s{nqX&#l*
z*9Y@3T4Q7@pV#f<UU-clN>EkMvzIxhME++8bu&kC&6hJlUjk47<$F?vC{t6ItLy&z
zNuP6Sxhx5mrq0LU!Ei^<G_w~~L&b#bscX{s=E#lr=|v|naY#fn=Rw(^?*b#c%pG$w
zOQ&-#Z^!)c85*IBw2i=^>iTwVsPFqjCf!cQ&)##Xc8Tf^8$y?-n8iY5eR3epMRx0F
zWo1Mo)t=}mu9VR9U3s2_s0%%L%!qwd{h>@raT#>0&+&Yi>RTS0x6|9zp<7SK=f2Uj
z>TKV(RF@i1Pg5{gcBWKJO5T(KlID;&0$Hxji8~~hbw8c06$=yPg*rqMw6W?k%t~zb
zP4(q6^=_lw?b-ISiVyNLO28*HNO1(9JpBol8LwKgI5)o+P{*Z<bM0RPO@;7|C+ix}
zS{~YMCT++L3Wuy7`-2^~%5Jio6*j(}y5*04P~T1`rQ=#&cjr2z&%@XsM}s$kSR+kY
z>;9x5v_HAAPoI-!J`d+#NNzLdLY@`D6Rs;EuX8Zw&%*6{-;wLr8{ZFYAgl%GA;1Eg
zS)53zSNrf!AZd$5RgdaCfSC(4B#I2o@QR}+=iSoTsr|H*Pl)$Vk|BbBlO`cZV^c_q
zeh)LID0=$?<zKI+NVSnP4-4)Un&}y23YqtmMl9K)M0;l-2*oR-i%pR>Hb*0X2qI&B
zs|n7iCPID7n}^UW57(L{N+klvsF{*;yzJYRD$E~Mq7b4Ivrm=chqAZUK1xWrl>$V8
zkWqS&uo!isyz=0)wgq)T!6<JE$%0Kij07?P0!?F;U!R|}Ld)q2LWNxonUYue@vF*X
zBnIK&?#OB1q(y|4d6=o7BDgzRpCeT3?O=_Nuth_-;$d<#G*dGak-nfg-y?Q@PR!i^
zD?|L-woHC6_RqU~Szj-?Ek9r5*?7I~cQ3PaKX+FTyFd0%nWb8*bS}Bt_SQL;yBd5x
zE*|@SHMKmLmcBhcjcUfBC&qylj#T)`cAu;t>eA!+18h>|Z*xms6@%;Tp$~>|KnE|y
zN!XE03+N4!^dv+=D$^sTgW|7<Ky2OM_HC4uh32#I32CcAXWZct+94DmgAEFI*!-%l
zmdq{0nHriAA^qgA%&OVR<3Op0;OO!y9BqFFR-B#-o=?J<+S(*l4FnVfv=In0M}!*n
z?=bbt0d!)fB;pb*Sj#h}3y}>TCh|mptZ9$|qDkZ6@+y=bB%K<i=2Yl$v-t-Ie<NTM
zkC4T2h}H)PA^zhGR=?ga1O&d(n%&cQvhhhw;sdq+gt9aeQ(44?g@>R<+ZQmRkG1uE
ziR_<0&z=iIZ>OxQPU&Vtem+VwtqwN@h@eVF@WG5>n68RDR@}@w)VOh^W_nmh53{59
zTX=Jux6z>cRFdIkZydd@V{>!=jxjZpS>kc8c{h2_6e|jzC(ArmH^G^4$fbG+&f6<}
zbUvcVYI0WVn^LK8SzGkE&0S~e>ip2_-ac9u89^HA(U_)1+>+KEC#Mb`&}lvmytHtk
z*1pL|5q@<^Fk<>|qBowB08c`@Fln6`z)h34%vucsd;12K+%9M*#1FaK1wL4pzaNGl
zX8O<=BErEqwsSKZR_84&5#Q+Pedi%kHZp^nww{iv0tqE*i@)uM7~b^CcC$sIx6^LK
zH^GqEHOIGdcLye`-FPl1ZWhM&oZGKjGOQ2!UVF8wet&*z&9<zPT|X_nnr<0XBS^!@
zvqO!>?zVe9_GizQc;ZQEWK&5&owBa<rt5W3tI$Wrm%pK7(y=prl|CioF*lb&9cEK)
z+eyTEN=6IjJq2BA)wnr-gulAl4hQv=SZt^ZgB?_kyT&JqF`^)ktu^mMneBdWKAtYD
zFaB$vUPQxe?SMSaLl^jC*1jLL;HP?^)rv3v=%^CeKYFrZ(Tp0-Y3=|U<EnRaS3B>r
zI8Bx#4nja6Xq|d-(RJ|B>lgd^@8BCY*wbmIjrCUB=B7=#)!V?i@H!~M7|07(Ul7k@
z^mNTM88FvL(!EV6DK$=rD%cZ0ueU4h7f(Sar&5_XuR_|>HB7uDzwe8pCL|Cf)FbaD
zzpvtO@diW_st~g>$6@SIydZ_wf?3*Znwyc&VnDKCNerVsD02Ts%-jJzI7#1vuqEk8
zmAy*9;8+L58!xqg(w>>F*&Y#Pux#m`MG89~q2D=a>|51WWl~1sjH_$22a-7?GlPi%
zH6a+^PVSgS4!!5j+Fn=K+%r64v6xrlT_NC=tw-E?aOcZfDiv;*s=^{k<(_7&?3l!k
zF(d&n{9V$7|3{1tFftz>tk8H3Lhq(58=R4In%v647cLl{QYNE%{22*Kf#d!@^chcj
zRvbuR5wQ~Pib7^LF{PF5?d-PT<%%l>0WVO-2gm*2ZxbmWgk*A+r*kFbLNdd4quig~
z599q(uhZ|RnYY>-x$m#V#o3+ApNqs~FD@c8xn>=8jP)EoCm)Z;&c(#lz!tGlwQ7^D
zha_o!4NXhc=lU~B{@*}E3PQ3UKph{Z13A-=yzM~#-+yISpF;CNe-j>nL&+)H<J<v=
zLDhBR{}hNA&`U%eK}s-mu%ECf-yno%XhhIbf|RDFkewc#sHBuX{NmIJ*#m~ygy7$+
zuoVRWq5lLM?31u0<C9kLX9K_ZpC<=n(jzORC#z)(?i0MZ3tC7*GE?!I{NPe%2=~uP
z@%IV3BLwRb4Ke`UYoM5lQCGJ>i&Ty{ScM@YA_NK$2^_016UwOP%+kyhOoA$B`nFnO
zA>2D%z5C#LB>ZQZ@eTfkRm8J+Xaoui3wO~Exyg!Olok_xMUCq|ncKaf&mPgPafo@m
z8>mZJ{U!Yf62S1wKE_tv!X=%`uqljs`yglpw=+3V!M<6Wn>yayjBX5>OFLKuy$-7&
z$Nl%GG@6-XnfajJ(Lo*)>*MUV{_^KEG74t(U}{`K?WLsL4$=w+kVDGR4onW>Uhq0I
zrvBq)b7n^!>pD%X;8f-Hv^0kA+#IPWYmZb`5Bpg!L-{TA{DDI9Ixlaoki4GJG(}@k
zDk=pjV@73m+ilZUTbZTlfcYz?hAXlzG;-a|Y;SjlIBFSsItZ1<R#G5elcv3{{+NJS
z$9z}e0SM9H@oq_bR#@%B-@{WOJ^NW}MYoUSxKG*PXZ(R<d`$J~SmXJvBsO=Wmb)d<
z>o@hW%Jnp+;IS{9m`7FykvTm1-3Ml7i==0vG(7Xxx8TZcmtX4V>e0i+&2rP-!*hSM
z6J0sVr9~RP^fE1rrZuSe0n-A?eAgD{>ge<o>U1gB+w~I5dWhblN$LVJQ)9!Eo{o;d
zi1(*>r=T$3gk%6p_;8OgNqf*7(UFWj>idxjPfI2HpxUc_XJ&=q;t*;saXN@qaBw=H
z6J%8pXsE7Yad%FFH3SW-Ll$ZMX_+O)vMM{nYWw0lt|kpsKO@YY46ihC9t25e#VfWI
z7S6&zPw(3I8)Oj;paY>_DufE|M3%)lng#l%RA$5VTeqCPau7$Y#AG+dlsD@{wrckh
z-LI4Tc9!lx8LUYrtZ3H8bqnUrVM>%lGe-2WAxso01u{3UE?TR#g386oB+tyjYSqSY
zmbg3(ge3`-5lvSfu>*uj6~kmrQ&kOd^M)Bob8E8}R)&v@bpf<MB9KWI^u&;&6qU+x
zLD)2sW2d1%m{K7r=n%{Z(snI*b<2Vns&*unUCb_dE6Sry5cHIoNdZJ4mtDBQ_IW!i
zr3oQ-ZKi)l2bkulEVtBqU=%u!4xS<cB<-W>r~~v;qQu$5<YIyG#u5XqWYP?!TvTJ*
zJsF3r8&}&@lv#ic{NXXqNj6@)S`sP%Nwy1x4)@bBG%V&J>MhFK!Qil^*3mNk*eOo<
zPi~)&NrsR0BM~PSVtWv}XHl9g$3gHaU5&avDrakc4&m(l{Ole-KD2UoIUjR>9p=Ma
zm2kB&H8NnPe=t`|VrrqIr<z^5H~kzJm)8du8yX`B)=m5gg-uE8$UBL7D40sAAI%Tq
z2immdwHYe%u#*xq+g|i1u<QVg;8KOVHBnvQQ!=4vV!%rA3IY(<!>$gJ(DhX{1r4Y}
zqKO|XClx?V>LB?oOf+2`I6zXh0BFf63E{&<StkU+>J^4T%qb!P5FMm2lC&Yq<A$lp
zl2XFp*%3f=6SD)xq(asH;+u#dT6$QF_-bRs{Sg|BjFd+qF+>%aC@7$uK;G#1i&!Kt
zI}|@LDNv4L0+DPoUe`eFtfDJ14T#K_UNAT>9kpF!BRn))fBE8ZB=OMjC;S!VKcuad
zBNG@z1cZA*`;wfJ5zmb*r_0ElK4A|Htf;N=_IaZ>=ktq$a*gud{C)Q3e9)64hLHI*
z&ol2^P$s{s^eFR@<GCjAt-1^rBQxlXz|)ZyFWC+73!*c0Z#*Ao2KK!bO`GQIR!z2_
z4$pQUi#3F~w-6z+n(SY<tn}`R#mkUb&ANyIdG0BE(=?0Ag=`H+t*(XLT^umxgIwh!
zT+%~+OtR_fouZysV#c<)%F4R4IwxPaH5&V<>|zwxLP&IeCE2K5E)HI96<SB~vMb&0
zSxSZ{DzS{vq@TAMm%d%jT!xTvOOs*U<jz5&1V%BBZ%B7keLl`PTT=&f&3?(sQ_C}N
zbt7rbtM}m%@)%F@ZPVDlW!RDl8ZH*@xm5&d5;eG;bq%h7&oj#JEVZrJq-n+DR_&G-
z&yc92!&X`Ea-jz`VBpnV4DIhJM%QXzOONZVX)imUw`xk~tr|y}<``-A5mJ^!*%fWe
z3t<))qrLslt;Jy|II#_}Bl#|b2tJM-x0mg7T@@A8weWmp9Qa4$X&GRr>%(iq#?aFo
z4DY$^HcnHQz8I6t4YBHNrD>Yi;P5LZog<D46OGjZt*lUW>1~-UTc&~jR1cJHR9ygw
zyajw9-h@qCo8n6|B!j2&;*Kc7_9QbOb}ZaS2&<IkbRX<l8QVcvK)Z>2DR|k)GTlB7
z9tM%yjWgd@F^x)b1pJvL3fjr!Nm38L-sLk?y!}_*sb4Bi>ghu|3MWDCBx~fAVFr*M
zRneUZXT-d0L$FW(9e>}FxtizYbv|mLy0lO}au;TES%`(Hg4Xgv+^{LV2aXG8z#wI9
zZS5f|i;|a@)rXfwjc47K&JyDgWP1`W&8VzGN0?}+aLp?HkRXtr_yU$NY+`m;HA7l>
zk7>w~T;U&$5c<+hi8^rqdr*v+717c<R{WFKN{W~*O0FbZ_>M7sQzYF{S<#uZFr6%G
zk9}Vii@%5&nkmnejBa#>cG^q|`l7@kYZ_m$)^a=?NBSE{hLl~9xv)ZHf0rHT-6UQ7
zqwO5~nHA{~ps46(I6SYW3+L@fZ+Ma(OqG|uv&z_|O-ZZBX)v|Zg7N`kVmHyzx5Vpy
z#sf$Bc>TV&Pw{wm(7(>lv*~PgKW=MteO~sLXKiU~Vp^wQTC?>s*yj;)AcE~$ayt>(
zw<i)M1&YfAjdf{R$bf;SWF@$}*eEF}Nl8iBdhq#L&~?H40F)vT;ZU%*W5*(bi*NgZ
z56JJ7l|7hwzv$UWVw5rzHC(Rx>Q{Z$G5}QPBjo*=`Kj};6I8;aCTIio^HIhiVN)ac
zRhJe3EG0B-TzDaps7BDvHOdtQxw&u133z%anR1oGQ_u)d7yvXaaVszdhz+VFkzo-F
z3-&$0K`_9J0{E9IAc?C0l(WLA_>CSF1QignQzgqJOcfu_gSHx~d{puZ@y!hDkI{!A
z4>$f3llfm#X^2A9qYiOb09@O~mc3q6vjc*xX2Z@)?Wf11!p29TeTAc)oNuuo<MfhL
zN_sTBvBS>5OEkq~ONtb_8!&-YY&<t5sRiS&8=cJi&HCA5k!<_DYn@w<L;fI>5AE5<
zO6fX+ba9-m01nEh==RCs{^PA1j$USU=9@ZCyWp*Hn=Q-C_x@4S+2G~Cq?<PoovfPH
zed~LOma^lj^4UyccKOaBG<u1KIp1pcf!i#Kf@o0%QzKXsTf@Lt)ivmD>eoaLY>s}#
z%odt5<yjc$YznUG>c$qWlH~Ge`GiT6Nf`TlrJYStv~2%hoAANpf3@0F`<PfrW0r<(
zBW>RzGHi_i%?pg49V0&JdzY0a!O}sjh50#r?3?v6F7}!$AEjoFv^FN&m26diu``(;
zW0eMTU$MA^m=Xu6jvTdPdDwl#7@mce-}$micljO(Ki7Ld@#;9LEYgiLwcd|}4$*rd
z5KhGL0O6%VT8~dcClYhdxY!h&P25=C9!XEHqg|~Up$qK=n{<N!PTCm1=N~U~X}vH9
z99N^A^yr>)`SMFz7UvLKs;Zs5-;!J$E6jIdJ7}F)11rbBSid}P7v4IqrE21K<_Ipb
zD>Lol-_ZUhYLSr&x)wJ6loVaV>iQv8W<b4`@?KPO;VHWBSdd$jmv^Ew{g~XGMXVL1
zG&+D&^bFve;$Qpz{&x3G%FW6ef~jE4*gL`_(gh-v6LA+ueHeaOka{`Oqy~%%=7Pl&
zHu@7DXO=*jR8^;A1KJ{m)B3h^lFfv?as|qm^5_*iXYmr~-mn=Y#gpBnI)TBp`Wu(g
zlr>G`nr8CgfiNpKwnA29tA|7x)oVAn#e-9~(3?P*O5z4CneCKR(&Pb>7|OZpW$+e7
z+{Kb&DVOrpR9R;M64>Ouh=qh`D9FegII!KwcJYNU$VIRL&UNmt)q!Frok!Tl0v+Wg
zeIhfh0szkmh!P5}Bzy2E351*pYmme1cN93D`js7&RsBz$9==FIqamllB1jPepOAUT
zilm~(5|xQ4NsJVc)1~r;&%KHXKIC6x`DMGGw9ov8zLq8!03ejI1AUG<tofR<Hx*W%
zr&mvSJ-+X=w|sB!fmpGvE8j1fv$@-vKj$xSKhMWscDXwn9DELMpJQK@P*6BBXk=hB
zEiqfTI?v!H)a*de{>7yH3BPTxjfRpBiLoNre5fvaYw>BXYe!vCMs*G5?he@GG^#TF
z2}TK5^idG7iUELIf1@sj%&kqiAz-5T9FOEbcMd8(mF-0l**hV{s>usYj8pZ~^x5l`
zMMv`k+D(ST$(4Db<70=C8q|}uRcrEE0OC_3q-BNxnbeny3GK`CyA~vdsH`gAh}ltr
z1&An<v@wBo!f9xRky8ku9^?Aj9;yQS*4RONM5U(}!2v`unr4KTCBQ{AD9nu^D3mn|
zVgnla(IFlGw?^W>R5eHK(nrCi?yJP94%tAdwk~;j91pq7rflGyx=l;hP2XL|?%rN}
z2Hf<D5dvYhDnn}B&W|)ikei`|IAefuZQk~Kn^_Fsfh(#~aEWz2^3@6)T`Prvg=<Fa
z=>MzQW~=vL&l}!f0%rRDK2oBE87sG;*@E#$4?$?9xAEc1kN0cvvwCyo+aRPG_6DuL
zb?hJ(2#1I&TCS&)bF*7j3E!gemAA)jf9Y9^{gP6I)7G=toIuAmpBcu@7Az>j0_pO(
ze7+~gp|ao0*J70Nv4Y%LTYxuR#QvEDY*TAMb956;7B-hOr=2FvZdp4Q%iPwI1Oi98
zB0=k~35xPt*xnQAAlLq(UHgC(56l-*%!>Y(@#A}<*6x&cX3KNhx?$Lk$SvDav?EqX
z6U?JbHIe0F--QHtAhpuN5o1YxjiY(G{dLLD#>Aa{>E+busv&~9QlAv!g6PpVNy2QD
zi{tcaLcdN>isUIqx}ByOro817nRN3l272k0_o(A-m0sj#j)gU9g<Om47LP-LRE;8`
z_~LY8rVrF3!57#msWPIT*5&&f7TPJD>x;_@+=P%dfNT*0tSnqr_jd1-!S06qWW$cz
z23s4`FORqp&n{*<iZwJ)Di%nq7cbYxti;A?ZMjw7%?{=t#q2YAOULO>LsN7=n%UPj
zZf};PL-`uIJ}rd!vgOrdVOO$*V`|zSxu{He@GLF!Gqb_H8PEUeBPR?EPKun$K}E2(
zBGnQJ(tHDXTkr9<t_|AJMPl~!Ad}JvW?}(rjOlGZO&gVa5)g{aYxs<8R;Jn<N_(X!
z#0*t_#Q1;JHS6|PnLOlfp2V~avr}e}$ilIDhpZ3Tk}31R2=9nPmj0>5OZ)81Kzc(f
zW6ILpFye~H;29Ik&y+}@BZ0_E;m|OQkpm#jAqpp+5jztMo5GG3G^w$T{|pcs%sBz`
ziV8l}UGZ}XB<X_HN+{7)pz<mblf&|=)~sluC-$$g6~o}{RFCo8i0H$d*@+<t#Qh}?
z9FoP(B|<IM)0xF#P@*Uz-su=PVO4T8;*ujMrp%I%my%8q$A{gu^V?MgYx(uEIN#Qr
zy>`7D9={$RcYnU0`FK6PYfE>%?`MvFzD}!ad%Qo!OW|s7q`O=iKfa^$6Kz_&O5e$Z
zLad;4#F_s9{6z!6mk|4f-moXcJE5y7W#nRJqc+~kFZ6s_fH6Wo2TVKzH@u*glChd6
zRq%|-*BFBgV1n!?5A&=0dWvYmlEbRHLMOd+{`?)enG{Y1YEy3!mnlCwE-_PEK=eN&
z_jK)o)P$x6H9=NMic=uRiPq0kTvks{q!~asi=#x~k(r51Wo$@>4l72zsi2_Wi$Hq3
z3zp|Csz?&39a0tOZ-+-k+&=*fzGFuq+#Uhq636EO$}0!V05t~$P0R%gER-Pk(k1<{
zm$X!UFzEXQh7ul}u04HlKM?<C@$w0I?P*46^&h1a7UrJs_vO7kN36C300g-XzD{8_
zYFK9WUW*0*ShY~(nu*hm`Xj3E{bU&X!V&0&fSila>1wcF(B?%hWJ}~yf5dc=5fhp9
z=12R(?s-AQUac|({M@YCX}NS!<zJ;N8(7r&Chntqlcl{vDWMJxV5YS+C^OBZx(&g&
z1`I3!{PfMm75PGjGx~<9u~LS{y-5Dpn2B4n{eFcf#WR^v_YFy7*45nZaG6s576f;g
zdH7z!{t;_!@BIA7VpVg}J<hchueWid&}G*?)b6!3WBcny$t8xCFh0{R5x-?SX)+)q
zy?W<SBx2XrSB33C`E-Y%<3@Izb_YLL5QMO*3xB`5B;n(F&Tc0=^K1b+3qmRsdYSOz
zpfA0(mK&3Ip1yZSfhf1ggLmj*11r;u^rWToP7@%A@%7xW2IC-YzHQ`~7xe?1?JnKz
z45#>_G0y6+qYB=aeQVs_lhQ6^P%dhaHH{{k5kM_wupUF_E93za*dJ9MUK9xB@UsgS
z+51HJr%{9OSOUN6V3;#ZEUL&61$UH!vSKuE7Z<0_eVA=`8<JHI<Hqac#;4cd`m(0F
zc0GfFlF51gY**eLd$OiNcflU&v?Y+ET2-b+o0G>`4BUV=v^2WV4Akf}<?G9hBf_GC
z*ErYeA8iC`jbw_eZMBbe5SEO3GAt2%o&;~Lu4F|I$UIo$WDSPC-RdCPb|0t6L|XGj
zro^c#64l*z-~Zi{VnVeKdA^?s;EJ;aq2x)0Xgo<PMS?C<jJiZaHfu33t+nuSUNXJY
z?Ry$ek>w~BG>J*G1?f^avplU@Vd=^b5TtaW0i~7dxJ@Hf@<<+ES#Z-Om6enxiN?Nv
zep-Y>Lr$y*$^(lPY1X6>Gbjvcc85s7bzGY#X`26>rQrVE7PEH(pO(B4_u@+(J9?1X
zhH;P0y?mhBf}w2BqDWTC>{!P{+aerXro!+MY%UE0-*rRIKr18^l4aa0RToEN6BCSW
zoP)wdx5p|LAC<Ip!Ot><4Aj;hkB}4Y(a7n=!*hdg`{7^te*H@no;JiIE7=|r*<zXI
zGO*!^DHRk%Owz3j9_Bg@^D?XBWo3K#wkhZLc`?8H?fs^=+w*ZMmh1P@zsSd<{yv+k
zhTH4)G!vM;XoYh+`)Zqet7+Z5t$CGgt)i=C8!y=oLW!ajOer}Lv+hMtdm@8QOvb~D
z85f_FhJcogoSg1Q->dKEIwGK~(UBF^Ko;k%VeN<D2qKq~`o;Hr8Q2OlxPj)rNNSN`
zG3wf7Y;{W$k0=r$wdIv~u!G!69-qYCR3>rV{ZdCgj=9nlLL#+8wBw4sDWSt<kzLfy
zX5r=KwW-_1>lX+H0+?iW7y1bL-S-1<N{nwP#1KxQ;Dr>2Q1%+wD+{-S7AVe*?Z8Yx
zNK1o^je`k^j~g3`SEUR&X`GHt2^B-Ek6~PslUr`I0BXSq6AbCkAb^29#DMAA8-x=Z
zIzkG1h13sp)Yb;V{GyJC85v4pg3OReg+E4(=n;=f(;p0;PXJ_Ihfg673vEOSx{vg=
zXe0#u_dp*VND|Kh;N4YN+_;O+zSZjT@X_dZgM8y9@yjFS<rC`M!Pw3CB_Dfxd+S<c
zWs8j=uUzPQt;+`&?jL`?x0On}7tHzgtX{jAm6Hg@M9EDqpix25GMEIy7YD3zZOBKi
zssSR)lmswU>T&P9Zbyb(`sI9>-VM1>ZmuhvF<mNPW~8TT4h)kwJ{dC-7Q_wpFxfkH
zfvCi_vmx1o<6)0Ma_R;LYhT*<cTD#)mbCkySEH?{EksO>!`xIQe_JijJO?xu;|1X;
z$9WRZ5nymF>SX2DS01FRFm8(Tq&*h-{-rLw%)~)kk+g$x!nBc$zRwzVNFf~#2L5!x
z2+~Mx9p@$>ft8By?)ayx?vI%5w&nR(yNNii$t3OMgM6DwTJC90K2wR#dp>b@P^QbX
zrgDxbZh3rPR+XAi=I`<`mE=*vKO`a&om09DQQOK1Ppgj3(UlpG(OGn>P0d?Z;OaPa
zU&Ui<oA8;FVRc${EZn0b+3|KFuJ#sGXAhilL#bU$t2Jg18jKX_Nsb!diXOt!nyrLg
zKTg&~+EEX$bSBw<c}WYcEDmPnO+^`_9J}TilO**93Z&KEJ-tRNTAaIzz+>W9=vFM4
zAsqi=+T1f4-5lR9^fps<RbO<iP)(bqqKBbC_aKGED*t^vZdVFbBc^mO%$M^!-O}mM
z>aok+xl)iPAf#B1E_QF#(eHYc+<bPEmmhz>H*y*TXI4xULST$jv@HQ({Gk_`^gOao
zHyquf81iwUwCfwEDk;^bHLF!NmGy4E|1af&2SK&M2*b(QQUsTMG#CfP!n(dZ-8yV!
z<ji~;%X*5+9z%sh<;of?#3)3enL(Z~r*`jjE=V?y9E>Q5QW!BO<Gx4dhRgs7WJ)?+
z-I5v8#<W%A3}FxE)j1-gc6PF`vLRti8=fI?;;5d3Jtd%)q2#_?i~tiP5KK5i1KtXd
zc2<_fVca!ZUS12GrldptKp>q&tG`b?K#{6_t+G^6;6(oue>4BD2x_Ii3u`!hBJ9pf
z{R&?TJS>aaSA*wJEWl1>A)$_Dk^=5<0j^xYN4&;8E*AsoUN(rLG5{$(syi|mBvGK@
zJi-3!p7{t}LX%e*r(>Rk?^rUR<Ez0N*x{lf@DTOqHd*i2B;WU&``i8`)9lav5!^0c
z_oMykRCf0AZ7%og<kbwDk(uuIe&ui^mXy%(rS83B^Y`NU{5YptFxkx#h?!aSMzYXR
zvF{YbH_AcZCc&*Y`os74pqQbd;r{pSTj)1w-|bm{owFY#okst^Qh#3j-ox0}<DRuZ
zJJU);Clql?c7mOsHL+d}WSo?wpy)n`{IW88RqY1w6hc7~adz`~8YnWuFpUCectMbn
zJEZ&pe~@)D1&w@!kS3!xV7wEg2O0WCg{mZ|bwe4_**FNJ03m#^gmHs7ZA$jg0mDVd
zB+SYtc@gTPy<!kqLSiBSoUU91JpTJfw*v8Xa6&51V8G!0Aa{DuA{K(hJTDcHqU4FS
z>y7u<PxMaT+z;Jf{BhXq>@WU!r1h6j3Xg}Et0sDD1GBwjud4yV+QuZ)G;1Y-^yfj#
z7u#tyyV7s%OnXWO%X*W|MPjGb<JUBpW%2cXKK$J>&`eh|2h|*{I^N2p(<<j0rH1_d
za{B8qF!^~YEew~y^}PXJ9y(2U!YZl0zFN#4aHZ2(L&PpEXEWrA4u83H-c?zbJDd8<
z;`NnXoL_puD#@C?h^C7~eiGpnNDfq5L8iO@g10<Hi^cvs5s&eHpI`YElVf0WoaCX4
z!0?y-ihK84K1*LAb_<fViQH1l7?vmAl1iZmU-T)ZF5L>oMbN;EIgcGhhTKx#IZIt%
z!mjUp@gZd7s^@!`_uaMl*23xqs>6d9Ct0VOso8=jw&A<jqZUrfS^y6HZgaW84sE2@
z$J6Sp71jMPSn%2;mO;}ih*#?B=TQIniPW76&#%M&HI!<Q%QelIZfgmAHvOU0eMeIH
zu58`!;=<42Fy{F}ra!$=-9g;`C=<r!Z81`Y#MBPw%hq?Ji>$gYJ4?;0M1ZAyT1ktR
zeP)YXvmQ**Ib3>YMLmwqYk~c->#V;#S?Y5UCQX&9m4#0|D7LS%w5A(7vm=CmOr0PF
z_n=_ZkvC7DaBW%4k#bdcXW4n~QO=P{4P_7xlJ3XG_UMG4{9aw6#jvHf{A1p*EAie6
z@=!<9mQ=_WFO?I+Eh`OiM=|N^sW`M+d$A7Wuz2_sC|0+jnEDu4M8;y%@A{*bVYSz5
z3vSvYS1Uu$d8x{EP_1)K<stvStkUy-!Z-{DbRfhO3u*E)V;S<O%ND}K7}->Q$3_fL
z(GtO#q>jm=8OFd)8RM!vDrxO%(1AaYIu<ZSZ<v7+<td)h2&*YHu!)Le2TWzo*|Mh&
zUM$wBX-U<4rVRAl)u_cQK<3`yQZvix(2_COiBUy(fvms2dEtO$f)pnMWnK7ClthtH
z)gvrwqUv+fatJXxe?=5)W*s=S6Q+o6&r&H3!1!l|tEjU=No{(RGraeCu6es2k~iWb
z;gk+v?mR2rjxAB#O4_mprlKn&@w3s;KwkVgB-oyx%0s*#!sK}LFqjxH95R7IhjD~G
zqS1B<{Vh7e1~{g)zAxfetWW;lE}}Hn6a&+;>+dHgy6@Mo#9F<cuGgnjrr4a^kAIn7
zYQBCi6Ni^vXr^S}%=P!ROsjOd>}*}^E~Zr%j}9-@@eAuA4_HoLSd|x>Y}lKXJL3du
zegusYh^cZhg+Tmxz4w!BJ8K`4Y<zEL(L8D9!lbbh3Wn)~akB8VBX~sJhjLzY<X44m
zVRt;pWq70v*uQH_dve6IxEPcYG5%Pw+b{B~=~jaPFpl$hYeaN9BY`t?$3GJMr$#~t
z!~8|8sHA-8*(iv%VxVTluWS?<EP2%KhLF6#K;p44a<Kl4{3VPGI-wh4;4mlw3kLX%
z(BV=A3JEMg4GAQ{=+#l&v@<iJY~#o((7qgM8AAH?2;eCx!1*PB{dfuI1hf>jlKDIT
z`{BtW^*vlkay0?rTeh&8%v39`LYwqMsV3AcWy_iJc}{(c`SBqA-uKJQ`F@;*>g_(h
z(#gH;?;oJ47I~jpsE&U}!^uHlDODx=xS7bvL~YTm+&fn_8k=m}xHh(X(K41yi_q@k
zw4u=qmuiGYcI?fX{rR{~hqA3^KoDPM#+nR`^mHz@d*-c>|EV5qZ)aX}a#?0_L(M~f
zB`48Ra`Sw@(F5Rj@HxIxJp2qXGq3!o2=^1t>e=SMvQ~y}z-9+|!aIqP-n4?6pfPjD
z6Ag6tub`FDW;Une{Sv~T5IjSEGc#Ll2OEC7yl$r@Hm0OZ8Fm{4NgfqFM`l4Lb(#7y
z99abNK6m-Y?9aRQ_Trh@B!Kj$yPlk+Y?MTzCOg6JUjAko`PN)2xAWwib=R>{t>-9V
z*HSF{qW5#u`y~S+>6k{teZ&0}FR7+dP-a;pz6u_i&)lsW^zXc|SXXpiarDGrMh@O!
z13+pk!jO_9BfsqL9)oI%VHjHGV?RyD8<ERsFA=2;ch3>y#E;=+z_&3<w~iL4EM~=o
zd1C`s4Wgr@@ycGkj-Np{zE3BQH&alzHTNnYPwrFr+9=-#O?aFr=EwOJ^;2Qf^9E#-
zNHGm~rQBG%ZgmgeQ^C~L?Kk@XrviCKhV0Jwrka8;T-IZ};q_haHuc2RV}be`6*!%L
z=CyF<H*#sFB;R_l5$Pxnr$4NBt@i_daL7M)t9x!FL@$##SUwon&qFg=i~psSE*b$K
zo=MyX4cR+e`iK3gs0&}FCdW&(M39$4`6Psu=_l+vVkU=MwO(0ECx)cNPsAD?$z$4A
zgwY`_8dyp<FXSAL2TtAd@XQ!4aX8ATYR4=Lj}M24ocxm@JwBwGy*4b6lrC-p+aL-q
zf7eHf5d#|J0izm<eJS5>9rcfpvqxHj#WY|tVr`u>AS}|vpy28AAGJ>hC*DDYp^8f7
zvSb>%YukuF%Yi^uTHjb$EN__ihx-&RCL@K~;BTm8b;&@jWHMya`j}QYovC9X{bHdc
z6+=PBI)#!E8Zs;hKQRy~7B|9!j?z+YBJ_hX2CN7;#!0-K+nW&=nHX(=i|diDI@+h%
z<)-&$=jW!e)bIUs@>bW^<EKA)*4Eq8?ee|*Df!2v;~}-SrMJs#zrXp(Y_e32<FVBG
zZ>LU(S>W(0)WfQy3k&SXbaac=V+|dhJDXXyPW1$7Yga}!2K@<|J)q>8f~REGUvcCB
zg(+}&0%ist;st)Av_!acz|e4WK1#Z&Jb^QJdO=HdO=0t{pZTStJ0HC6L~ndZ4{8`t
zz=E`Jbwxs4WZ-DHVPkoDa6}+n5yBDlS7oN)d|_b6c7e7vw_#{*)C6)PGVro~W=(2j
zET}(tDTpy;<^5^$Kb%UaNg#41g!Ir+(i0A+Wt2@@n32LY1`SLM>x_y4DHe<k7KX@(
zDdOM}V7Gh~LU=|aUw^7lNIR`Nb+qtu@r+h)opz;uJ7ezOFn*8auPeW_<b%V*#|c0M
zf+|ODx9-Bbgl6vzZ58~0P%#uRTbI9h(_6ecZ8?)z2pY5=vOo0g4`W0wBruYzxz2nw
zm7&YeLou_*IT=<qw2Ewx^Dksvk|DoE^_@UL<Dm*Vsy$2T@rClS^)u>7IyydokDI;Y
zmJzM%tgPQLfhUh$u}X2pOX!CAEO}h>%gjn%9^MUYe*Ei;6LrtPQxmtGz6N(T2<&7O
zCLz>Lr~XZyr|+CRBgh?gTmBN(7Xo~FPgze~D6yFu&2T%mw#D`tg<m8V7g+m#knGpu
zNsqk&eIvlpG9#pi6Qk2=un*fkw>cGB1*j<%m_4pYI7~=VV*2A3rQp~yN;mf<Brc@S
zU*81qnN*?!-RLoV95=i{)#8281N~i_S$Vs7JRHX~S2+o$bHp5lTi(;&Y;>!c4cOtd
z_7hfBH9Q?TE97Zx4NQ*JnN(BXSUG@MSeh(cvO^F~uxw<=8f1K6r{9u!96R*dfMhGy
zC|R--{GHFzMj$_|y*f#<)##dDv@&yRd8;%{)KiewWr5|GmKVJ2^v@XCH6FhP)(HYO
zlsrYU{UXzuhMk{;giqYQUhpKKfYTN!XO>a9z-2-61hteZJGYj*l-PC?pkej!DQ(HO
z183hSzqd4fEvmh)Gmq0pTPH|umuO8JFxgadP~ceiX{>XgnO7<|IoOP6ok#-umb?>-
zJL?768&%$$xa>p)T2;`RX=rRnLK3h0mi}+NA-VG5J5J;|2L!`M2(|BnC*WGn8*)L`
z!m=6B?JU-*WQ|n+6pS9v+{`VcG&SI9SGS`O5zT{CY--QCqNbfFH-c#2cT~udvTdcu
zVJ#LKNxcVOf~=N>6)XR{FeH{p$)02hK_VAda2R?i;-Fy|7nH1B!E}ChWy!Ueo~$|~
z#A4qr<LTR+nr$bfWZ>Y+A~q%t?}WJYC$1jgg1j(EZ47ezN))VR0%$QGaH?!?smUKd
z%MW=WH%w-MJS@jBYCdjGxAewp9M}4gM&`bz>ex1Wif_3L6ER$JVR2ZQ8W%2FlFJq3
zHFIo@XH00cUZCIDGmAfAN9UH4XNj9xk+bEX+tuXZMT^VN@1L`o;qxX}?zWfx{!HRy
z>Wq!9R{Q7YMRDzni*^RJjvm&gI-f&Lf2WGJch7=*qsmti)2)Tbc!+|Ejme9uIjx+A
z-IV4`Z4z~@Cz@E-DP;#*R&u6Hg((U+iZ3!EVi+f;EGN7acMdd!B)%{(PY^tmIUe={
zV3fOhp?GVh{Z-W3(r>fY(h#(~;a&XK+#$s|n804r5Qw%tCZqD~fJC@>@m7#O0z{H=
z3xK|ALliYD6!vgnHysJ~n4*0AIf5(B(&d0zc{~v4jBUQ>lZDELvw*ZSLpCZ*_$CxM
zq#TqCU}!=Az9{w>34XpgJTb-)G3f@gabdF1>cN_I6Gt{_IPj$bSS1L5nGl}l!T(24
z?F3ySK7=xLIU4Xa7%a1Lt*%^@S8a6Kyy-W%S8X+0OzJQlS(|6_HPU@8Yjo1m{K;Bt
z@SUGe;_U^r!AnG8*^IFMq-4}@So5ED@=%+7zI+0ekZ|P)BaFZwHS92y^*po`UH}dx
zB3`>AH+_Wd&qFjiG!`}bTmA*V`frA17K_T%z`#{ZEwr+|`|7C6$`VAC^uEfqS!*hn
zZl-p}H)WNH$<6fq;eF`L)MZ<$^aS~0lj(#D7`w8$xZqq;X4_SwpKx;P%8W%(7M7!V
z;F;0OZ^g3JJ`_5cG+!}%Mta1%<;D`wjWvN2cMMfH*0rOx1eG%C$(%gi(pYVpWpMtN
zTuUa&Ns}BnvuJxs7~t|Zo%dP-gW8{_s@eqxX5|QK;)%VxBZNp}B~1qy2|_OSf^M0r
zXUA46m)b2;LTIAG!P~F6sb+tm$;*Vetwh}3=_6~S8Rj6WeHo|m`LC1iTJ-bRw1z?&
zVanT)1;$q^=zuL%XFf%1Ik8-hbQaysN^l$)Xz`QV_>I|kvy{#<OYC+`WqXGPY(o>8
zP`QNUB!BaQa_apvg~P4j{8H00(R8(<a?T5>De1~uIi$Ki>X1c1ZL&scXl;(7rtqP|
zQKvVUQP`LVK3#9aR_kZz%!m`w#EF*9YJQv~X0gh)0s^)8JSx3u={6A=mZX-L6Xgb}
z?d~idda}P5&k@yEGMiT?8&~Iy8;o~KDia0ozxm4lq!|7I*o6x6MfKyx1mJPQ|J;8F
zjKY-=i(bA`S8A&lrm9J)BQhdZpO~$prlKCFIS*t^Y$3LiN=3MtJa);3!=qIhkAx?|
z2XIdlAf`k%zd@2@Mj7nwy>W7&q=baYN^znJMFL<UP$SSr3&14S8!$5K01R8s;e(}~
zO%Qt&Nr!@zpAK7sKxk;t*W;t0;2PyQS&yX|S}PY~tSbso(%@q}wuw?z&r4O%j3P)N
zP~;gY)Qb%m0V65_^@RZoIwYw<+5UQrxfm}k?W=J=jjQ$Bhuh}m8x}P?bSkZo{iDhu
z2IlAmmBFbgpfi!jfq{t0>t>+nu8elMTEF%02Zp2Dt>8|2+gcw>r@4MSKk;H&dAqya
z?=RMmM_*`pdpqx!pVrOL<hD7s{ahLzp9jXC&U?UQa<oWNEK)0anXRgKzD=Cw)dpG2
z$ZWJqA8>0|wb3UuuxoO#@i;l=I+xhk=hxg_3tX)#YWfJ2$?Fu6WwAy2!~lQ?UxI^f
zW=l=Pl?vVWghVQw?O;c$C6?_K<1OZn?#wmq+28B*EQJxdVWLL`8RZSmF}^YW#UZQb
zAxfMAGO%0!Q4&XsJ2M0i4i?P!7}o?7nh}gF2S*?qv`E#EhAzalV`3N<MrYzd@VPs5
zq!oYxo;M-{$M>T?AxC_G0Cet}n5kuJEDoz{z?PPv9k4Mu%`kz46$l9`s~2bGRuLit
zph&Z4wEW+@VTtjQiBKuV&fjn1QjLr3BtpgTreQCKse#C?NR!bd(OumViE>w#bKSuE
z-}zDa4AP%^fozprit?HX25aY&tn+aUyQJ;jB{hznrpJR2)zxY>n0p;hZw`V+^*B9!
zdo+wV3ow=hWb?PAuM@usZW?7EHnz=`Wz|)6J;<p^;WiSJWY|`f$OCgFb=$16EP`5B
zWVn{Hpi7rxvXab;&<HIzNo_32X<`~T9r1E7O6;|i@vi)nk>H&9ixyuQ8L5&g6fD+0
zAsJ|N9^8xy^fkQ{?#x0ul@+ROGN(zjDvCd8_a%$p>7|~7iky|?z48MWO2U``>~P7u
zWo6E4-mYK!Wt+U`EnDW-S*&Qvkr;_z7w!998C@~wHOr84)yfqk^5TerWsdU89f6ly
zNA_jJW*YbBN+HJ>7O`7v;M7<4Y;K<p1t$`G61L>$=!%ENN;y3}InQ0BT!qwBZTx?%
znpB~eV{^Dvr~;(Z9Ja5qg>YaAW2BEqmu8b%Vw6=~I_go*D?lw~7s}^rs9G8n;&$L+
zCgXr5RI}Lq%ZgiR?>#fuvPn11b(C(~TB~h*ml>F=GWR6j7uN<Ue#^YZgZ~D9M<-sF
zdU{1(w);chc-tD%{3Tzal&B`GS6Zwp)3Khi%K(yR_Q?(=+n^y!Q9)`g@<GF<$z&2z
zYY{uidCu~lyQ;dTx<l>I!gf&-DpVV>Txnw76fQko{-tUAd5*c|m%+ie#R6d5Udz2d
zeR%P4+n@R40i_Y=CrTlAIr0h&56MSiy4DD&nKO7+fd<b|7i*V_t!MRVJ2|rQjSxq%
zwY3={2}p^zBFT%Q=E#XXn1ka2<6w9miSr+ceS8+|hX2I_7(_bk3p+$cUW;lJ7XYGR
z<P81A+{X=D=SP6%&Ar^c26zip_=A;<_P>?CO4>`4aTf$&X%&JguQ~ilWmk%(t(~&(
zP#Ro({s`oQh~!OxM}`CqM&QV0kL<&t6tBhQ*pi{GjH+!aibfM(t$0LxfJ)h}+2<Cn
zre@??lk453dvr%9V3vgrP{U&W(iNNL{8>U;{c7BtRpsmTJ6>q!iiD%h{WAT2UR=lG
z+xoC|GJk4(<a(K{_4}~D$J3kbYGZJ9@%)_MI9NQ-m;1=DMZH`>9-<`ZnJeL4-t*nc
z_$gfvSx&sr%DfSqTxJ{P$xJL<JiH|OLK^(awux3bBzj79Eg3bfxae$j3KE3zFD0y}
zPZ&afHSjb<#7ZLs!=*4HaVK`rXexvjCMQQh@TAtpz@FsY!*gGvqXgwA7+iaGhb928
zfsE+CA;ODdF4>z02hyrAHK|*#2cZqmt{B;$l7>Md8yU>g$Bz~;Sy)(0vS+}W7+_p8
zZ(!%GlMt{O*ynUOa&!pM0E>exJU=4O#9bW%J{17&h`6T}YEnq6IizP&fJdd46bRnm
z6DEfb7$I~h`t=r9$Iiz7dW+j{Nj!7{KmC%1(T>d6f4z*YZ+<P4qoZO>12x=sRGT@A
za@J}yv2fBCi~ap@R=I3>UTZ9GN4}Mep_>Vn=#80V$s<2o+?(t-^lEnreMkrlzXRR_
zR&kdDT2Ep0%hlV`+h`;c8W^F&HOS!gR1WSyUJxHiL)Bv$OJ0klSxrK!ZO*@}pTfC$
z856FIiA&cF8msGPM62hmF@C?m(Q>4|F;4Jaje=}tdNTJhOBqLH5~xE*{On?kfqdqp
zb9Q?%<QnQ_W|Anz{c5V|ZIbuoi>`-fMC)<^;tCsSZ|XHBmtWW)IrS}n)s%zn%4!qW
z$@L(cWeS}qI|ulKA)o>G=Eqn*&;MiWoT3DYmMq%UW!tuG+qP}nwrzCTwr$(CZP(PS
z_x@*obLC&&6&WY)iL(U_wDtI#%WB!nZHPr@i<lg&9korXIVf$_vRE}kk1akzF3M?q
zIlRvH#R;diDJ-fjbqMaU`J_tcF&10-s5=BuU;OfG1@LCrc=|frrGJ;)Imyp#UY3u)
zyZsmj`a}4d-%&O^NT`9UoSOJ`^rmbokXuw#tD<3ZoNalo6%_lK(M2aVb`zG-Y8mA`
zG}0Ojx$Yc<SO`*Jks{u?6(Y9??9(ecC)*TkI;P61&a2XqVxk;UPF_o}#uY#3heEFB
zWF;#?R6~PwckX#yo@fhCX-bf{r_8cdnV)H*X*XA|wb$5?VBxHp@H`ilGU##F&dM%F
zJ<lt>H#O)viK%FHEStWs3r(Nu8}~@aJTb<~S&d+qkdduWQL-$l;wK+Rqhc<lOgB~s
zk1zo@kQGk4PEd9Jm%CYl`eb7;$Z0=k3mmZh2KTu4O&IzagZ(5M8Ab}Ik3k_k(@V}E
zuF(=hQ7~5NAYQ##sW{38_3J&z^9LRUDv8svN#d|{Y~bdg51#;$S7SnwOwTU!vsWYM
z_rLizv0c@?0T#NJ4f`VEp*mF4+x!ssgCk)`OcEI@R$%|*mk>f13C<<Ypo~tlP-F_O
z=72&>syHblDzR&CViA!BM{F>>-sBXjQD8EE->~a+m^WK+Lp+R4%-^rX<|iFNMLk%P
zHI6&Z&qSGceot<KJ}MNvB$-@r-Pz0)933sk|Ev)X@V<-pP&YbRE2mW00|#(UX|Jt~
zrR%=#@x(h!Q$x+w<#f{bB-_HeQf*t=qqf7d!+HNQ)6>e{al0Ss`TOwu`gy*`x94qO
zt0=VKOE)Lm!^Od=A^$N;rG0ep#m*cM<se;QM23HPsy!=NWirtHsH!aC!!cS`#pGtD
z`X(w{4Z~6s%bI6SgN;|q@z~1wB&=9$fVwvli|aS%fmBg3Y_3;z0F0d|@*#2Huejfr
zxR2*POz@wte&$&=hg+W>ZqNaNgjsa4zckR2l13W}VD(_m6mpOt@^R==9D+i66!A{@
z;ZeK%3~q33Xu`uo1bpFUU}(uWl6@m#Z)QbWYUlMTb9jO3mH1fZ;rJHvc8dyWNjb&I
z1xA-jm{fK6iAwqY>PO%z_><)MD5K^@BEvt%Cp?+!TnizwH#Y0lh_0BZ0rbUdPH<h3
zF>nY8a{K~a_<4WN#r)v@;=@TR`3v3rZsbcb4*6kTQ`@Xb*N<H{e09I9d1i0y;4!98
z((TRJPQ%)L*Rbnkjbv5`B~a+?g4M1Hn2RL;lFCMwCofCbZeQBmiP<ahdva3*5$_AA
zL4CL#NA;oU85uu*(_a*}f~GU6^k!JcIUdFx>8ZqeV`iCRreO0x3ZTgpQ(X!wEUU)Y
zDrmK@%=wLT!5Q|?$g(L5lncsM;1c=&fMOulIl2}Xcd2Sxa@~k@{8hqFqHv-|SCHhR
zy7zh_(>BjklSfZbQ(w4KMrfIhl|Z#%X0U(&O>AW7u=Dgz949S0s@=07c6F;Xy2oL*
z&IqtQXbpCu2S$!2kb&n=0KTUU3_e4kUz&a22)3gUG9Dij3u3MnS`$VH3YkQrm>8Q-
zm@LE*wLAfH9b2%SM_EL^9}HLe22-s;O;s|*qd-8Y{{kLR+jEOB(uiWNKNwdCiVP;s
zEZ*g#LrLsjnSW{aby#Ox+IA~&b}IkYu^jn^V~PloLOC*SA_pQ7lTZL!4lad0kOBao
z0(hEgGIl=NGF(*<>^1W|lXxoZ))Lkd*aN8}Q<Jeh=?X^O*p=EOKH<Yq-sozado{?n
z_YET~e2if#=_jYcQqGK~T_L%kuuuLMwQeIKYF!-$pv+q-u@x5*DG$dLu3fcPl}^^B
z0iK?hc$lez5%vz{##5)Kuu{vGl6N_!&?p1>U@}yW3dxCpRGvn*%qZCawtviVu(|lT
z-bGqg3018lx_F#Gj_m%pHeWw>iDPZADmZP&m?to1ENZD_=gcZiKb6na*YD6J)}*HI
z(Iv2$k&MBrK2|X^r(zLe+F>(gDH@V=<+}31lNE@wk+Wmk)A9I*no5h)a?jy2KC&N>
zoSfX$^wfmdTrG+vuo+-rV0gf<&z8lTjh0c2CqPbbUPgyN7J(Uo<Q_l2X>Wx`iJ3Vy
z3E%KgczgIc=q62oiCF{GTl9xvYJm535&Z&9v?i(6E~v-$5ZH5qkw&!pk;`*%zJGm<
z{nA3K5t9l01~Q*bK-eEl*@}W!9Soi>zbrz^1A?g^6A^xt{|~tx9K5isF&W^hK&(<@
zCg$#FyjbKzspWJz(G7t3VH!tKkC2^bZGHBI{FH-uY{1}J5a3L(KUH6uKAc8zj%hDx
z_=-&b*0dqDbvSA@afhZLVHFr0Epui&BlqJcZ3nBa)XsY5_F6`JQ)3h7&*$B8;K~Q1
zYf8uW#n}D|>qm}nhws7K6zayxPczTglikQi#6B-Zd5#i#=sbff8v2VlIxO3KDEgYE
zl@BX6>);R%Bk-3u#+#c`2k+c#^~htl#1CK18g7MG&oYl@Rob%Y)*7YNjVSZU0mdkh
zi<uqQpFgk=#6fw8+6D13*-WxOMzpbfp|sqxIrl$@R;%HVVzX;>IXi4Qt`Lo&?(HFK
zo*o}VZv(5_X-+*7I7!%^(71?kNQctWVKTCRON2o5@Vc=$$at2B;}7HiLde3v_zMBS
zE9PO@SU?bFXNE5Zes0*^eSODA;@2h!88HA!_475YMa^5KkZX5pt*O<BRZJJTl&3=~
z?{9n&w~&mhfIx^ueHI&h`ianMsJYD=TADm7V!eKBb|zpg$LY#V<bl7<r2zJ1vme%m
zq@|~O*H#2R(P=GIcp;qR-x15V#oI%ZEeb^~Xj@Pz@8aD+X>`xIbXn9&s&>_~s3{dq
zR#rZU=uu1Rvj8e7gwpd6kIaMfA7I*Ad1nIAACk8i&|ArZG5pOM&4rsocKA4&KkWjL
z^fhMlp1okI3uh2jPHk6O=$AC&10&@i|JvSuD0f&0M&px8hFYDLNjqXV<|NOU+AQ~c
z3a|IR`hC-FSxhjXlZmV<V|!N3Rkq~Rn7A#c2EO>1|42AYAv}uGvUmZ44KBmt`1k;;
zQ<Mk$7KGUIP{#OhM3XjDTRtR+O`uh6TMyUD39Rfamz;A|+Id(@%U(%}p`NGUd>Soj
zKwQd@n#NW-hBk@G*3zTL5UPePCIa45M3x9cU7;NB43)x#=w2k1sFN2|C<9ED#_;>0
z@?y<E-dI+u{*fUX-crs|VuJWJ;dZ+ux_<fQ>o)Ka0DKTFv<QOf^)2R_`T*8BfE<66
zvPywvGX7dZG8=_&36XA1p}mqG{kAcx<5W}=+JKl^nDi4^W-VEPQ>oNLJd0*47BJug
zR~Dubi_v}MP<P;f|2*P(Jf%%8C_t*(vhL#25e=uiC2ck$5_3Ejo5e3Uz(Zi$;^_3@
zgBB_|TEh*0DI^X<S4J#{SZ_ldKjQE<DyuCdiK9+3!`9B;OG_N*_tfZB7d-<^KE_oh
zA3rTNk@dx84iRG#ENCC~H`|c&YN*9hYLov)GQfCP`TFGe+Ia5em&7WHMg8NPT^>5F
zk1jY+z92lg;^{`aMh}qe!%yUgk6(6E5KBq<*aJ&16S8BXM5DSF2=_b0DozMI=oks@
z7zz#jQ+9gjl|g{0o0XMzjf?C|LvUX&3KtwNKQm1g7F^ga^6J^&#fATETEni%#`f=P
zl46#=pZj$}a=YhAsvg?(y>HBZTqz!DKs*daG9uOpIigQMfdDbwh-g6p9HSP^7BjNG
z#b2v^^wtGA1e9q3zrjH&_KylFUn52BkkE<N;Sn9BXHNsp^$50iYzM)YfSGx9e7tCU
z^@$)A55K0G0d;p!o3;`Br}J9S@ZT}j7Sx@eufFrG_^Ya4v+^>liRwy-YmW7fr-$k8
zw8llpRP_ogXHIuJGuuiF<Fd<6F7`28YU?C0PDOBkwUC}@qCD(t=ZbpU3{b?D)LdoS
z#suHi?5j7Y#P-dEFN?9)tP*>2A2xEs^>ZrKq9iJYGOP%u3W^b{N^pVlC4yTg9)Va;
zkkGm|h(?>kfnq(v9G9L}{>D;n#fhIwiazN~a5fM06EE_O`{vU9da|O8X-})$=c>4=
zEvS|WS;&=aXvfh5-{T+{%wLHxkYP4m;C6N*K>@}#wad^Oal3&6gE??|e1PgX`)(;8
zh-}C^s78xik`w;`48XwU*|HVpH)&|&Eox*>%B~mHP?jj_nv#?54n}v&dV@}lRucv8
zOzYK5Dok5Al?r-d2QVBkv3ooA(>i9x4|14KZyOzP7|T)G(xcg+PqPVs-5D$gHNnZr
zNglI;cj$WpplG?!3_msWTG3urVZe(&G9b3718x!vJQaBoTEAFaB$b%xM->(18}EmF
zn@UI?bqi&F0#wVhS1UIOm#16AgVzZvbzP2*I?D(7qfH1JZot4{gZray@1xsvp9B+_
zq`5P1c5$Qvnp5l~9`pegB+)^HL5F|!QWe#+!F+$onb-8NiE4WTnUzvj9+nBq<6Q77
z@%-ZWL~@nSbcjG2XN_9B^wr3VkNO6vtt7@at1>P;wD8fP$0&#$@7?>%o+q!rcXnVq
zy*4|85vo9o6@m9qj8AIlE(^F1`h4~@3(NtR@Qlf2RQSoq6;c<<#l^}g`!cPPY9AbR
zzMnt~wuMCH8z4f<MS6;!r`BP{&TNZ2o^L?u&@uSSQt}dwlqD_O$3-zRn;D@WDnA%K
z;9+`I+|Jt*8X9Uj@`Mt`>;iNoSoiPPF-fz8)wtP<dwbeu8fPE#n{A4()s3%{2r@Ow
zk1*$vAiKjvT1oL?|5ytuvO*HaKDG1cDm@wFRS{-8U=nUGlB7UX$?w5|S6v*(C7`Zq
zOM0}_ynah(YYiEVPh^O$B){`O>EPFmb%gg+Vcx`1DVIMN$GsEm$mODtda!lIQ!mTi
zb}F$Nlmqfl-TZ!@_Nm)-vYtFCOa7yAsSC+BOlsm-&hQWo)JCA1y_zrac^BLrW4c2K
zp!}d;u*6XmU(eu~R)UL#JUT|}tdQA%OU{Xm2QKfv!~TKgbIzOBTl~YC;>lHwmLs>Z
z;n&#R11|1M-9x^CycRrK#$}915YXHDtvMQeBA&mH7+{vTUJV#BQvQG^e4=$?K6d~X
zANV$SIFMcv8x_#SiG`u1`|=cMAaPmpzZJ>qopR6H#=S4q^CfEyaax*=>8&kpHn!J{
zl9nOkjs;ZeDk~b$5B9sbKlu26V^V=e;`aLy0kyyS0crjBW%J>1q!_r_Ib6U{H~v23
z;<;6g<(JgHqVS4XVs(bQE8{pSNecX>#fpZ8dUUu0b8Q6LzzP2C-C{Ts(%qVqQ&aZs
z+5lm@@d+~O?I?^TbX)c_T?bxVrGLJ!FMc|`J`FyO8~#mk7CdxRE!Zxf^t>6KEJ_Ve
zrtRwfq`l1?bh#p~>^!EaBuzSq+1Z%kP3|ih#<z=Q<&grI)EDTW*8Zdr`jVqa)ulli
z=m$#iC~5Ghj(S)n(6k_9QrvS+Ajxtzx=NKFe?GpMTzxC7Jxq;{2);qO526^@aUW*P
z-$-SQ2#cR8H|m06iYPrOI^PQcfWuupptEmdJXcUcVBRJ}qKwdDJAd6{?PpN3lQ7|r
zO_6>YG_LOOPO$?5BZ~ycZ-0k(`{d4|?SLUACn{5c5{u?Y?++(BpD|%atA*tu7dt?d
z?K5QS+vQq&+`sEhZzp~BRr^@hSXejhkIB3gTvc;YesM#emziuTsNQ>J8s(PPlK(nW
z0@4cU5V(=h46(snAcH(`A6GNdzmPz`d01<RLYWUy7ahz5d>W5>YfNE&#g>VQ39eY5
zTUuZaGnl7Bktx*kr1SA0n<gIZ-8`3aH-*_HS@0wX=2=vH)-cHQ;V-_<QxxtTfz_7v
zu?^AeJGl1)X!!lLa(8c`-}J@wn1~^mc6c%AXZK&Ly@2}SzZq*m@4iD$W6Splk(ZHH
zbo?fd&F6-v=1j}*)+C~qiYw#J19Q?*$0SouLs^pcZtbJ5H<Q7BC7QbXIzhkv@J<lV
z&HXnKTtviVr74(>Y{X32?Ac|W^KB8Y#T?5YyN2BSz!bSmsGEF~<JmI+DzoWYpEFxl
z!ooZM_%bga?~77-jpQi9_SBPs3E<ovNA?{^;6U_y)fLun&yP^o;f}=h_jUTCQ<H=1
z?bV8!WfLOfL?-DrrJ1<Xedc5x$;7xTOhLsi0KVRSYv7a{pom!N1eWP>&Eu}wdP5nX
za4I@t*62!`*=R1c&9$@Z>e+gC@3c#NAJr9Wyf|%z-s7U+Lc~@IpI8Z%OnhUq<WCNK
z?_!a_3Kc!i@m4?&9ZmR<1nfIZ^hZc=T=+pjAero7UtHyle2l2+ib6-Gi~M653;eX`
zzOXVyNzST&D@V`|AyYLJq8kKwCrU-(!_Vw0#Voy|(?rovrN<v`AyQz(I4A-}g&J#k
z4iBVfM$r;;QZZ9V#AFzQkQ&<qW9__U`zg0B(dtE*+?>9P%re12MM44EBFlm&HiQNV
zSPznAE!R%8R4X-;(VLoE+SzA|NFoLYmDxp4K5U@e&AdGiC0<lB?;AO=fLYN_9DBZ(
z4~io(S&h67eOIT%zL>7OpnOhoIbt!pk$7X`{&n%ySPTrhlkoxs_XS=~3S8|m*TXPP
zGJo0)@?30C^>1^9$k<T|`Fh;|u3bic(;r!JIdvMt-#_bRp=8zD9ZfHLe%L>!bTMr@
z&9uGgd3kH($PjkL`0QFcYI0=#dTK_PZ6{(4xn@KJ(!Sx#jI1y?0K|tx3+cDu=~U&E
zxX8%TOyATEsrU}<tTM2h$M#-=AD#f>f#VJgc1?Lid7s(eFCcZXfLq;<<mi>DS{a5q
zB(a~&1iH0rZr~qo&JJ6aI==~2EEAm!3iaG|9_(t&=agP*YIJL)p=xBJI(2&<e2$+*
zbT8hE@2@_JO+T7`UUpc&cPd0_Lr8ZXS-LDJu#OHM`UB}xX|jGE7v8p~tp(qc2n?{0
zoiP7(1_{y8WTUpTNvo`&$elR`S3Vi6J9RlY)VjB~IjM!{E*f)bo~K*VtYfK0r<q+u
zqeoIGa1vWFQ?_NIWf?rMFP%w=Ae6@urpoIJ)VKH@rX#%z7cP{f>1q>uUz*>&oy2s*
z%<){CvCatjB}T5;i$U~8+Xc(Pz}E7LzCbD`o~5g4Nf0n3Clij(MK?imSe_kfS*dt-
z`>51hPaG#Lum>wyTYf)QU43IlVy;MLS6Mu}QrK3a#zvdCE`t|ds^O4lhd?lv3V>Gl
zbsa6LM0XbW@hXmQnki0FFTKE`DmNaZGGq7W&}Dx8?Dp=Q94G$@tj_=q80pd2W~|qz
zN&Hae!O6+XK!%9hu~CPc4PM5buN*UN=fVqOL6`!dxj9m`t|$k*nuyydw#KPshOEia
zoY;1#33<+HYsZ}5AS@76%gw}L?5zrNbM~5@#NrUtROBX*L1PMPPum{*Hy`0IC)=}Q
zE^pD(ymy943gz${%%mQ-`R+$E@wJtI1<KP&l<$^A)xtJQyo(T}ce9qdERxle7c-Hq
z^8BCbY`#=GG*;n6xJwh))R+}G&7P;bE<UK}{B`PeTGwaJIk6JTbM|lXD;{V}+(@+e
z2<I#y!rJTOnNK)QydXVdG`6z(!~<>DXY+E&Y$lByUpZ}UKZ**KqKTTp?tqU89pz_r
z5hS3w!A-Honx62fp>Evm#sQIhf4~x|{lXu32=io9%Vf3K%BjXPHj>FHw%oL=r1@Xc
zYJJwhxA*+(?ZPqD+}E^tQ>SV|+Jp77vXBUp%!2qT1Qp3RczNfiFtr)@wIr1#6K#rA
zR(x;fiuw4;#tJctF8!pa@|6MS@2mIy@c9@%94sI0W$+=F;{<i<@H^|_lM<ZGuMVt&
zV~)}4i_VHF7^yq0o3F2AC#{~WIul?o$1<gCeayNZD&fOXwRPPcY+r635Pz2AqUQBL
z@WC)}PPSOrr;eKf5|!fh8?T$6V)nx=TnuTdw^qsrPqg&0eRxSNs!Q(xh!&En>H6S1
zDP>T{5)c7d^Fc}`y1zE6#?~O>p(yqi&YP4Q28NfCvz{r&f0VZV+XI!*0Cuz8cs)9L
zUc_qGe4*&M9~VfT@Ho^raXVg4-c+k$!Mff`Lc!%Bp4RRCg-gZe7cD6fYB=Z<6RY&C
z%&CzCTOX(0<02x1LP3B(xrmMc>G$U!J%wYUn_TH%-y1F4@qRxWGZgjld@rnubp_+0
zNrMMKoIUSg1v%D@sr=&KY2$v_mFMpXSHg=njo-|*Z;p>g{qtuAvbtGUtGT8A0q}J$
z5uv^=oLsI3Z#;Rvx-r8nGa=XU%H9SLt~UU#L1Xe_toPV2K;2(Pi^vb$IozK8#__H+
z0f$Q`)|>{)mf~LXvQK)&bQ*mKQ)FID$4!^szOv1(yT;44?P#hX{VF;=`RLBp#^$ru
z>)~VSYRA^9Q=|LtX?nQjrFokR`~CXsk~j6_hlA;@wcTB?YKNBcvpj_@9jE^V+-q*T
zVL9w#<(0`8zg{t+49CwaHVr)Du;xIJmDwF}sQ)W|9rPtblqI=p>udvX$hVvKGn5pY
zb-4b<>PY!qASAPm(i=I1$^}reRHTGM>s^{*wLx2~e0XqTz#5`=nSic$fgEoY*39@P
zy<*1n5lFLfMtww5m(7FIA5qW_{ux3;K*%XcAU!Y-c&NZyG5_lbJP4w>kdG><xCGx^
zyqI5*C1*;u52=uz;_WO0O2dXGI5|6Lh@f;)i9UNEc%CS$u$^Sms7J30|K!VRA~UaU
z>AsxwAsVyb{0w|*RKPe;K4C<L1qBifolGA_nMBX%F75Z%Wn#iR<li$hMLDJq{RZ5G
zl@W()&3=24E(l{8mY%)_Iy5K>vb`earYR)BPTc?>tmgsv*HSuQugqZJ!Nrbpig0C(
z{vZgMV-lPtXtbTU1>8Eaz%cb`()3{q*>M1#S-LiRIA8_K>f=>OS}OqYe)9}Em@C!~
z?4@N4FEXV3dt3btOKQ9pHVic6{3!i{*?WT9l|*!TrJTu7sCeEt%Bbw=#ND*~N=ZmV
z<yxzxtc)EtZgEQCfM_iy{w@YUNP0^{dT&>cf97x&q^`J+2EBNrnx6aooQaD_sBx5S
za@|5s)A9b5E<F_!qpp?T`w0Oz$VO<K!mhoq-(JwZ!q`nIK%?vJdHFq)+9kc--T8QW
zc`<UWxx?8RlKf3jNLj{H7>JO)#<0ct>1+A4L;q8)n^?;CDh+?UUBqz3@{yK|$S;=%
z%1`U2Wj+QCC-ry+b?I?Y)@pjw*23ZllELA^v-~6{b+VbVq<?9^t`(PcIXFXrl+tqX
zXdc~um5QV>frvs*`79H<&oS{dDk5XE`Ut_to*%3nYk&dStp1Lu%JuI;zdKb!*cC#u
zCQZ4UF`n%hVvBO;iL^>UMae>C0D?x*yel$bmAC{R7&dQSwWVAsvZlvSu}=H4`jbON
zIYpQMwK`uUDDghI8qb;5XX16L?T4#}*?6%SziXNR&iHq&odR%vTd1)ktmF>&>-$y}
zZ|o*T#hNG`XYYu?rdDqD($He_`($_wC?w!bN@ImHJ+M)Zla)twzJbH_)eG)4f9%!F
zjVtOuUUp;8`>?<bRQ_gM&JB%k5|YK`x$B6f)YRB^H6J7cV^p7HkVn*k+<PE(!*+mV
z|Im}Y{kqZB+e0BUEwoQ9y-RcpC4s0wO0ZpMaIi3^7SIn)lK1viUysGBRd-iYO3BEb
z?1nbC`S<+fAL0o+NJzgbRWNus5f?y*uzi(-bA^Sf^q8%o?5*VT3q<p!r@UY6Zc)b-
zDeAPB&-z)pV`V63-wGtk!C@H3Ux^lKNbZt5-gHC9{KX>yP<*4}Ft+?cB!b-iG&!-}
zGqDy!IX`6jP2~|-OiMr77et$>+2`Z=8yV!Ce>C4;4%dnL!_$5a*0naqrN_?_zfqcd
zTbz<Jx?A5a&R4LB558*~A2qDBJsqw$L-WJD+df^M3y;rlue`^m-8ZK_iu1MkmF@~f
zA1i-ef^k-43_6*bVlViLvK-x`<cKqMWDH|>2M5afU#LS?hSxQ(jxe{5CMt;kQGO27
zj7L|0UQ|ICy5F1q?g~^pIFUK)OSN&BoRUpf<w~&4-4dm-rh-!X0gHRrDDi1u&`%@n
zOf)Y8s9JS;CCae>^#y!j|LgclG<s5xO2)*2ABS=^=>IMnI<hw_l#ho%dv}0{KQ0sj
zGE*HFIpmbDnFLFinK@w+nMm)zR{ke^N?h33PS483z);&=TUorVv@=R`NiE*kYUGKT
z@j2rjb6&4tUkfl>o^mKUebD*IO2sCC52ixdbX2WWi3Ca*q%Mq%2f*|H9y!OEaViOT
z)J(Xi6=*Cb(;ibm$4qylJvY^(aCHYdhr3M)9))R!0X7}2Ke&!E>!xnxzY8!Ps5F85
zo?Be0ok3ZX!<exccgFR9taC&{Hf`Et-Lpg|KtG!xcqtno1G3A?q8}YD!oRNT^~kt}
z%9QA7nRFTH85ecmo9s+-4|+2{rUcw%H&Cq0wMc<3CFP-Adhls6MvXJD=u1urDn@7)
zXD@|m8vC4`xfxe>gDfcssuPvw9M7K}i1QmVD;qaa*EKok$+{9JLx)j*Np3@WO8?>x
z?0%irBcoWjR=#3Zm_^UAs#_RXUM+hQB9tQ=H{9($wjw*J*(`r2Jyu)IVIdQpmBKw=
zjtFf(8m?*nm>zo@vwBrNqViHC<9d37`u<$2iKgZyx*M_`dkc$!PVp*%2^i@K83>@H
z3rxm2G-#5>4H=SEk@-R#f;X53F3P*Sq9IYN$WahJ2h6OzbBZOhQ%DzxC%d|MQHf|6
z8z93{#;ZR8lo8*V0T9=zOjEyzl|%M&U8k2#jaFyU(0?!Sc@Q{mArAH|zAga(DC47M
zO>}jl*dbI`lIEMT{Wz~wX=*=oN;jRl*egZGwOP!o<Z>`%g^a5YAB!8h`~Cy9{WsUu
zU*n}V$C{O-YKJoHBK;R$5s^&Xnv>{DK*7`AlDR`z4JWhddNflef?|9s^D@4!01d4M
ze*iJ@_#ezBQC*=BPH0YdkTdw9g{uqif3CZLBXbu-0Z?+ZoWK4RjDVLIru4uBdbYCp
zn0p^5g?1q`t<w?%W5f;+?b9ic@TZ9A6$hBNx-l@tpkZc0z=ZZ`@zp)2f$<ul;cc&_
zq6dnH!+?mc6A!v+JKJ;m&b?=KZIN?wUNFj_h*rvG^LXF)&n2SLsH=%)HA8SZ5|0vt
zp+oTZjW%A!rxf2jzxQ--K6zDmdN#NBU=A4;&`sX%7K*XbriB8Md}~+~V`o^YgV{|w
zwiIoAv(Vlf<j~nPq<D^yX;BTT!UNL$`)b-s?df03nG<?nEEH}=9Vn5C3J39URUf}f
z&!3`Sns>gwj=8+LI=*o++1~E_HZr;XUVNNN9%`w{-rCCkRj8}F7#}vKUlo~*nvBD$
zt~IvMvez*)*RYK~4Rntqnt`w<6fGWyA6@fxTuQ1)MtA_`MtqP8ffuE5@bHfE1)DZj
zUDXTTUAewII*)d>?>@Fy{c+_5u}d3bTjhH@rFx}EG!KeV|1g0s7RpN|iZ;0WE%>7%
zum}Y}`~hI*uAAX#U306bhu0#mT{za>1d?igiV{f;{Zq59c>k*XK%n0fldMByV~Pa=
zEZ_k+nek~Q&?4Z52m6Q7nIX`@U;z=}jhUkxIB?)*XJ=HSoJEXfXoaODYe^R;$0-c1
z+6l6F*NvO)ibJY-(Z|!|69QMZV$m$(m53`AkTbz0!yKFD*E^O$xxl4Z{Yy`bGbNj%
z8Xu#b*H;i{09LJn$N9hKKYlV76LK#k@re)cM=OaK9C6X++2--e4z}d*nKXF&>~~&F
zj&jB67Kx~ht}c@@6T$z?jlgRVL}|<snPU?qjl<W#ECh*^(!~*H=^o=5+TgGfz+rC|
z!A(FtC!iPDOq%WUs|~x^X?K0sYHmtcDxLNYZmvmW?$#|wy8eP#SwVqVNr8$#Qt;t{
z6Y&WkoNI$I6D<N;fZ+ombcOv!{_A04zKCFrWM*3>blenQdb_@KIey_9ktCv%;CLUu
z0ZzM4KMb%7tO1Mr*k$wUwl#L!Sd`B>FO$TcGn!5wmXl|hz~#k})bD|tkG<)Tr@`47
z;}#TRR40^OK&51ZxVMo}qhRfW6B2<h$)OYKPF$34<rAs312YpE@bGd<_mYem7YQFZ
zGbuu*R6s*Z?D4S$8gpClE7!+aYUv<qvXp!3+(HFzMaFu9Vq{Vw>Dcq&;r|n&HlId?
zMqC29P@w5h2dB&l!t_L-BqBvS3}i#E9-LHeOqI5E=E6Q?k+sh}rYNT%gYUwiBE{PP
zK@BKSfOv>Rb2m1Ft4XVBtZX5=y`5c8U~`Vw@1Jf+mNzq=6yOdljY}yD4J8fi?>9t1
zU`I<4FTGJD&op~;AZk@1QL|@K?c3E>!dU4oS&rCfv4mQ9q?34MR?rVGfDsI{x<Jsq
z-^E?mggDwmqFjS!>7S~M*brWC>f0AF@04}2At{l!Y)X4{D(Einc*#t^lf`LiwoR)5
z4eMe}l6{O4N2u1;<<`4Y@~i*5I3(swpD#FBR)T20SSv7w9D`9km)7plaUV%Lf)C3~
z*z}Z30m4&L%F(8zPzpi(;Khhq6|~Z@@rxMc^rhO{1f8?F`S`eGzw+JP<UD!Fg5tXN
z+#75Qyb;dg5`bzK@e>?4ixOkVmWmEJc~9;|2u2_C5<?)SM?i65C!7XNhfjcM9pxkL
ztsX)aCUDp%G$@eOj<w^xKKOW!H~cwCyVxMznz?Lq@`=5vYmvqBa{ZYLJbA{3;jv?S
z?d09vHNWxpwSRg9gQECT(s(QOOA5TcA5L}|nV=q168#wrkyuv?AQq>eb<lAsV&<m;
zvyvGrjfkRo+?f&k?A1bIBMPa;fKXWqe-ciu(cy*x$|iqytb%ISO1UgB9%5?@cK*x)
z5!bPWwsFpNGK=SMu6f-~_`Z0%wpBgOeyRF)exLsLlv!i?uy^=a6%$?797_U<BikYa
zo5Hhdrd2-^s~ThTyzJY?{kWye$@{kYY9!gUT!L0z=%NGo7&=^*s9-c(31c<wLVl*-
zWYId!Wx>j&4#%5;*qu?!`+Jm6m1m$LRj@WmNyfh{3<9Yg9RInQ>a?s@weRUgkpYbV
zMFnK77Y84Mg%a!7O-Dh;J;cOtJER+Y0(5BfWg(mWINdh-aa@_XjBInE;vR0o8}0yt
zIvm)a8|%Yzitt^8N5W3S&VafTmjweD88rQykk^;~W>n;F2!~im6;IJDbg3Buj0OjW
z$RU~z$%Inud=T7T$R(nJfe4&;7(mXh2M=F{iiB=#q(U99E=kn@7MUECFmKevAfzt>
zcQJKo;u&fP0zr%zSto^V)3UNWcDAzm^wRbqd0!mz8KbeC7N={q_H@85xV;ERN?Ni4
zv1!pUCVLHV?iv$k(|&ttDomX1w3D*TdvshJ6pt3JfTfvIwr?m|%B=P7ROQkeR<T#a
zOx$=EbN=XOpa~=5XE*{$2^%c<XZwsrx<Wxm$qyt6A&&*8tW3ig$4mg+zMKV12LR_!
z9O`Gf^escftQE5|MFyK2HjLRy;5jt@@bE<n&L3=TG1=LaNasons8%yGc;HDkm4Ioh
z_;40hBn(Y0F%dnmgo}Ss6;|mZ_R<~}|9~p51yx~<Cz8g7l=A6u$1$retQOP)PxD_6
z@Z8x8&nQG?HCc;0gS1s83pbmWe&n=23fsQItF|c+j71Fpl$hHUN<3m|b$caSB)=Gv
zE1T3ig^iYp&yiPKPYBwgle3P3Uy-a1XWZLD3-?0+<yJw>n?%`ANoo?R&=^oS0LXC#
zsL0kb6`e^xYCIh>`Hkp#YXN2r%wc{0Wlo_nc7AqYSz58TZ{>_+9pE2#L?zcYR(8F}
z$cb61fgQsVNvW!=?3S|1JF<4wa91Sxlt6CERwxg201A#1VgVRF83wUoJ$1~d9Po~c
zvVb?GiI#@K<P6+|qVedB@EVR0ASzW<<SgrdzenkqXBgkN%AA7bZ9k}0)?+fig4jjV
zY>ck-7O=&tz@$DJ-7^k16Q;1)<~u_uP9w6bim5zKTzaE&4F8kYmQ>Nbn$v&zNM!#s
zR)*=1Bs^)w=0m__iRrlibospg{al?L^~{dtqyHnkGcXc3-RAy|!|v{78~3tSkxgn)
z<Aw*&2WH2G7Rgr^5wR97ygG}(Bo-rEMr2f4|A#0SmjqpCT4p*70?&vPDL?^^KYTVU
zY>}+zk8douDeTW)ucz;}tHVt;Y)rTBwDHf;!A&;D-FBW~=+?Jezh_P44tvYQNK7j>
zm+MiZ>ulYG&Pjt|ek6UJIvyE7ioAe(S60e9zvh&iruUJZ=$IXAK>=W~BA^G56g5L6
zoO3&wgRlKNJ@uVK7u_Tc41@jG+%2R#oGUhj(mzy9kdESlKN}McnryYn7~65l?*?ry
z_T5%bAr{W-t=w~;d*_E;zuD=oINct1R*ug0-kxsXDTbD1FH7T(4Q+W%J{u>os;V8J
zq8Ayw?Mp3=1?HYZw~y6t1y$!8Tm2o_`Nl%uR7@pIJOMfATMB7+Lj1luq}GZEpcSi<
z-bELJ^P!Q$9}n-rG-JbY*Fy$ZUC#+m9_O2Q#-75RJUTop6&d9NE?@!_W2u-%;EK?~
z^<00LzZs|jLn5ZeZ=VK2-f!{w)CP}w89xvOXn@_Y{0H0ot8Zj`_5~ng3H}t!6MWv!
z|4|K?E;Irz{t!NTB_c4$jZ;E{L&C+t72LB=bASN?ouUSt4)BQ2l^6NjoGx2o+&iUY
z=31mNK>)sr6fHqOaXd>81kfU6TsFIs8Ks?auTs#aXl{nGdhrH>sjE3eOGgG=^$R7(
z0oM-ggO68Rw5h2|T&StLxN$s5J(Pxi!*1%N$M0FKzZh{0=`6#S`K2oQ?>jYzkQQid
zAY?I1sw5vd?B!ZDZ(~48AsCIN*AY;qKp(*=#*@3N<8#5ZhTUi$iD4hht}}`*o*JNY
zZr~Y~7e%bf{Xqazzrr-h;}dcVBcUDpD_e##hwL3)<^{DT!wcJWzN^ICYc!R>f<F?}
z4<{)d#f2l(S?afRtSvnm?@}|r<iLW<$jQ;U*BUNKLP24#+TYK3E5Gh?-2XsQ9FCNh
zE)1u^zgD=$X?Yebyq7JXMfF^}I<G#MSRibr>`NiQu*zX3uS%Lwbccl<L^9v(G|lrd
z$k3S}o|r(1CH=5bbPSKwQao;jFxSu(K-+G3H$?rfC2bz!$4mkzSqy13`foUT$#POt
zg-Y0&3dw+eMc2kU*Ty0pLGYXz4*jv!WWKhtq#iAFmtKiwI9!Ib;SF_@CO#LrA|Kkk
zsKnmH4y$FeIe})xT71b8a?`Te<Wc!wG9RBu5n*#!KueEV4u_g{`%)>+!YXsx76>zg
zCOMhW5bwL)@6iRGg^=vL@rBZd^K!RWEEM+4LSRy9h&GU<04TEfa`5?TqYyd`WihM$
zWD5pG+Z7e`Xh}acV61FYdd5Z;M+JLBk{y<Mx=~e!<VFpj>hQ5E%7htI%Z_&vYsm}m
zd#hHKnq1Wg7QF%FWTUN4X%kw)frQ%HLL&x*aq4oCv?sl2i4e+!pxj=7;>M`*#zIhw
z#f!D8rmw4^jvxS8V32=Ltu9YuSfh+f8<h8pcZYO4kF6JBSwK|qF`^~%4TMF9QqJlq
z(Qq--PjIz@8^C4MZGA<5diD>o6qwMkFtRZaBBFqH4-jhS&dm5+1b|NcZt_}Ue#V|+
zMQ1+uX8i|Dcit!FzC2+(F1e39mLIMvbVl+8Wjec7E_%ZgbAw)LLb0EqyC4DviTDX?
zbYLG~KM(u`arEQ+;O)Q))ohDkX$wS6AwxUhl-tdk;>QjN@4`<Y1z{N-W0cCeIM)*_
z+UuQ~?g+A28C-gFfEmx_NOUw>fV$<xHx})AZkTy7>b+bY^{S=Y8m`?Q-8|mrCZ|p+
zTwQH;Hrl?<Qe%0)55JGE{M(L>J2DM%t*fr6DmyVYzTHo3VQrkKq|<snEFPCNhBo6Y
zs%sqbls^lD?`K6k<Z00POwQ-(9H2^LVce?=xob?Qmhi8i8csmZb!IlH_r@7@d>O6h
zH16gMJ_hydT^a2gWP8>sGOraYXOy?lkS3J|u<lnu!0jggR_uX{&Q?Q+rLbg<iiuJE
z(IF%ah0pi)v-!xY6_Tw%<nQ(z@Y<1Z;D+YJh&$fLy}a;V+ae~%r|8|G9O1<}02K-v
zGWAc0BM(QiVxRz33Y+miF-5|AaKvNhV%U~1`dwRty=ZF*t<2n!u>_Tr-kK_op-JHj
zrGVQ53h8GNm3I^sFH<M0nq!%5ngZ5HmJ_TnS~wJItF5flNuxejew0vGL0PNau)H{Y
zw7h)(*nA^)R}}OSrM8tCrER`^zsth6wFpa0T(G=hG$%J42%2X2f}6Oj_iv~Qr<DA3
z6@D->dx!i`0j)qphaAdt6ti+8yL56nTjHDGc|x_HN~!L?f6DMFun5+x3YH}>Ai0Z^
zU}#SWNEsM_=OKOFt~NZgXH=HxGcvU!$KhQMx-3U=dL$S`HWEh6UJ=_*R!uWT198)%
z)Lih%#z}G5=-xDTK4og}3+b~FSJg+tNzqLSCKHY!q-TY%(w!bJCP^?S7AB)-NM&oN
z0_FEsP#l1}J=Do8ULl$sqXf%a%7-qn>i#RcmnUC-UJksxi{xaV#?>lxA|*&?GB(A~
z37AuXZ2C+6$;7aDBL@F-OI(YWUFy-QoCC%cF&Q-)){(uLm3<2y@&r85Da4K9N|y_^
z^>!Kl&aV1MB&#uKd4IjyZ{ut7aT@Zf-#70&jS_WFw%%3@kB$&|-y?8jlHZ~}nR)nc
z=<(;X^mWFU{-&m%(<z-qq&>`!SfR3MiSgpO+SqbK`=50PF@0g_M7|~)y0ltGQ@PLZ
z5M|#o$Ih}$RjJr%P089Im{FrFze7U&5YF)YhJNssI{&>NqeGCbsJ6=y6=tnU0$uMB
zrpjB0bwd3S1eiq7+s0ZpgQ3TI>sWG*u9-v9W6k3>TJ5vttuXPB+K1T1ROp2(Yw?K&
za_gFixTHBhGH{eFN{KD|?E>;p0-0QdDPHb_hLI>!HBCpOV21Psh>4Q|-M$5oG*mY;
z(~pOX=Pz14ok#yP%*eyVg@=m^*r^WhRwLm;9g8g_EisC}@aV);&q0xGln9tQH43zd
zsI_DWS0&;g6WIKI^+DYxG@qNM2o#7IB%~mL5f8o(I2?}*kAs~K0uAHbVe9E?z_#sI
zr2Qs=dw;~<Z27*PdX9L-=(c-(k8@^|&ioam!Etu|m^e$+uLJcLCk-%|eh<RsIdVRn
z5v_Iu?sPntqWT*T7kbAA+#8<|nj$uFF=Q!-6iO^vKDe}$+TSljs_llE>Csw5ad#a(
zUxutAttev3T%UzmmRihs*Y`-3lLyf+`x4^Q$^1HI^wp@{%&ez=x!k>Z-sb9SU}b3L
zV)@x8+M?t0blSI2mh-(6n&aL4{do79h-v8hJ34-R_Sx)sb+G#$Hn`e1I*RO&j%As&
zR744to-^0;;VAT4mHU(%$}S)zRSpWqm@baE7ZdVOli6xa>{c&ZKW*`8XMT4ZVe1yq
z!b-4mU72Q1X;LCwFMntriMl#k{dica7$|q=^j`M6Uf}~hkIN+d^_FHu(?~)7Gomas
z{0o>!o0U%zQokSwkx%0gap&)!jYIkj;@$<_;{kqy3)zN-bh-zB<V(!;XyKv3g_!|;
z*Oh>Y-PeC~Uj|G;pC=$gOEnHZ5pgkjj3JmjU~o=fhNUs5(c$Bnum5>gYr|J%QN7UY
zY<I|vC0{VfB4Bk^*yLm{LGemZFepSSF<(lA#DSiiogm40cBWwsE-*Syvn+NFwf?-N
zqe)t#Yq+v=GEMsnUccwGa5oh2sWV=QJ%RcyMOj%{I*tYL{_ha^$tA8xsx-+3q^)&D
z<ho;=tdL8TA$2z5pbdVA@F<8l$i1pYPc)Qt-aKvA*N>kG3_;39hqWKO0_FmSF8vWY
zT1oKhs+3iRC=vK|K)9@m5<*RXAoKeQ(A!obA(Jf$>)5JA*&dDP4kbu}tIE(Z(HYd4
z;@wLyO(5;}!o97$C6oK+%WurhF;p5Sm!g-RXx;%D!X3k6aGeu*sg<@cM!=TFRO#kc
z=1qC5Y&0oVQ|j~V?K=&)bOUAvci|Fv>b$W5ONK%U73n+xD$-XsAl6DDktb-f^aJ>e
zu|U@dUkBsU=*|Xj3;vl3nlQGsCe1Sd7eZ)WxO=&*_Y3K|NEo;v$9#^LuFAK_GAPdC
zpi})F%FT^cFmiP}SPqw7?9z9uLGCvssZA8^kwyFNl-kTCqHID-p(6(j1wI&7PjfM<
zIGdOKnf>}y7$1!)O|~o1cp@;d$Ei-BwYXwfrgDMhuC(;gGXAMG_mugLg02RK4%UVp
zWAoxUynWkU7D2)AiIzvAA&==d(w+hf592zauTdeNSCF>jg9TLJkFhBgg5I(fn6#KS
z)=}Sn!xCtIeJds<Ign3>-_&v~<G|h0yOsLu{#FuV#8{*O4V9jrsw#vZ#*Wk$mOmL2
zXdScY{WG^77#<TXBYAL-XXzcqDQ8)aglM*wzUso{xyXtI`nRFapC3i-(<!-evA4{8
z<;0YzD|<_4V@HSo9G&2@6AaVZK(5^gL78n9Ez@XxhEzBx5mE8JATi-9P@UZj_+KQ+
zK`%QT2h3<6NNPg<8`>WZQaWgClpJgzn2B-OemwGUdApek@{_*_w67u_9`3C@AJ5q>
z&;Q<ghfYpEaJ61q58c;K7Q-!1v_7A^UY^$!OSrlaYJm7+a-ck3xMyBq|8ZU)9eB_`
zc3L_}Azj1XeB#4rU^r1BIgzOl!$nmu+sbJ6*eKYH-?fl9C0cu_B~VfwS(&8qQjx`{
zrj{Y<u#Ww~rXJd8!C=uK*A|HDq+3@QU&pGYX=X?|933`s{E@quYAU{x{uq=J-C10H
zo!|Ww`TBzO%<(S%J`c?xb!@_<4k6ttK7qOF%BTlTuBUOB-fXmT*I`y9Rj;W@JGEhE
zZA@zlG50<SlVG6&P^U*7Hj~$FMB<H~UB0KoL^6SmpO+>1dI{UJ#hx!ID0(3Y(%rUv
zdu%o=3T{!+eq1Z`Fjc>v4b*jVOi_;G;qpMEyrD^66@A0xl%820FJ70Y#Xd?LQhO(6
z9nIMp<7mUZbQK7S;B|D0y?N^jSCtKEzkYIA`}LUv|BDxjkRtL|sP9Fm|AQBWphgBx
z5W)2Z!5Yt$rJ$6nno3``TI}K2C9z($vUz3GwxPjkkw7iW#N%7br>JzXA>ue#?*@V`
z#OkNkB_1_Qke%o4gqRpun>l%ZP7_ftuUOWsek#EniBGS$iqJN+%C@>izkQu%_S+;o
zJG?Wye0g7gCj3gNo`Musl0#HYmoK;JI9BGtv9U8|dx!nzE*Cl56opnZ2o?b#YKz6Q
z%OAq|Z?~7b=KL%;tYGJtls{c}{S|jlU?>nlcbvauLN!C0(%>VLTqZbWLwB8+-Yv?~
zGyhHle-*IfBM|UDC#?3Ixeg0&or)xpk2Q?O&{%A?PnCoZPhs-&_lvF&E9;{GA4uFP
zyKwpu#S?8B&-)^tVNAU@Nuw_W7irOpB;;#phr7X07@q{+58c%_T0NtMA}qb-tHR%1
z7asQU05}zn_vC@(@Nz1}=n*aN#x;W?*9*wY%Y|U#YGJ6jQY=f+*k14E*w<tsJs9T;
zfXJn`HflGosJIN?RlU+0VxnSg+1r^cy;@szPY@E_@2AYQqno)`52($)0V?&`7+^Dd
zO{eJ*?GP3Tk>qC+02i%lbbe&}zjGyGu{-`${~)qUdE}-LEI=|VY~m9O)34ddas&?D
z$<V{r-#ecoeW$-14{s!2Nv(@V+>4-n&W6z5z(kf>NXJEMIjD;9$m;yDYSD1F{_w!&
zg<zW_4PD1_UZr>%=U3~?6;_>3R(WM;ZG#(==DohTEqDNTMdxOqld?2wYNBL9F>1`^
zD$qGk@#?bU^{Nuw>}OzZ*_Cy#I`WygB-x>FI>2&2YH?LOk|epQ;J=V79WiYrUjEwI
zAXnXta6dgHG<L?<*QaoExISD_tMxJF8`YjAq}<tgzu^UZhZ{lYMS#t<s|WL7PpW~#
zM2P6&4gY?B^_5)yXOaxreH%(OYj0vhhtHFdo=^%rTu1v}5Aa98EiE5z1&@;Tfk=P}
z96j|I)uZb7T4hkQfu3JWij9*ND!~h%Y%GwRU(|i%a1iCU{-`&~u~G6g<4t~@k0p-8
z;BeXfiHmT10bKd=_1O6sT#Kv*xVG7|F|BFM=0L$lZo1Ly%`4#lfYthb_>SCOG4Mm5
zrNkE>;SFou&9>W}%uCcU)!ueY>_diSW!3%qI<62U<b__@TwzoJK@!my>4LXb;0IQ7
z0~Tk}TXtG9k~mEjZ-t8lH_g}4t;1W<I4W>i$Q9l+o%C!rJ3lY0X|@;+rEtM<iI#0?
zfqC5YX@9$t)!g~&d>i;_^m>Dxcvd^w!nrt6oYIY2^x`o1d6`=CjcW1v%s&`z|332C
zn6@iWC(cU&vr=A0maIzl!=?hJGV7}_xTT<V4h-m&EZiz=AdPT`rHIDWNW(N9<=nbF
zq8azXG|kiIvNhc)#rN(|ds|ECo=CH=@u#B&q>XGv6pDsXF16`rj=dw*xHn#u3?vRa
zm%^KV3M0E6&x+zSLDt{*Rxex>iWAMf=zz!Zb>Y0PUI9*REQRaEN8P=1wErXL7fTxg
zN<=|;IxoqU4jJwO4lZ3Imooe}P;!+BgR225bp8Mm%j>;Ud&K}bQj$)}d209F&|Q^w
zT91qgs#i(!Y||yAQpFO}tZsQ3U0S&cF(L%qvf$`g<Dg}Sd&B1~y1Op4fI?A?vMR>H
zv&Q)UF-SJk$8WKqYViyeax<ae;;zY#WSSrf$*B=?vEgog8km`(Q)`L@C7q5Xhza<-
z7+c}>`8I56R!u1@4x(e$Si6<^?T=OU{fRI|T7Pctrm?9hbq7M%=@`1LDjZxYjHSiU
z4h%kP+Vt<%=29im;^yFF+H5;8)U`#SQBT8T*57<h?pTM~)$;a%@|Gagd{HRuZVZgZ
z_w;SsuEkjmOnQjXk7=X00cX6xax5snLY@V^Pxm`cPxpR4wsvxUI#So;4Xa{3IMB^Y
zC-2EKmTi{($35|z(YJ<GEq4w;BLY}y_A^Rb2e_XXHyRYmpo8meXxDqT);zx5y6d9x
z2}hiei#f@}knS#SeCSkY0r!JcX&sOs-#VT-H{6yE46Kc9kXXkF3B?16Cl3!T_Z8vQ
zycHIHdThMGR}meU&Wa7MzF%Ik3WB#hd!E)3^o{u$-yAOag*nGpwVC)d)Cj%*0+x9t
zh@rWos&oF5Gq6UbN6l*t4oj+;PpKb8sCtK`jWObImMUk>^h12MzMoEXcPwzS+K90~
zLG53w-8u|S3QRAjdsrw`CzKfv4vS^-%B$pgyBOXb-1il!+7na))xTUUFtqO6ZG1zG
zbmpGz>MkB+(<=(<mKsVy#`_wlD~}>(<EhN7iuc5_>3|O_0tYpUn{TSPHq39_%jT_A
z1~G|39mBG{Oui}MKYW0`WDh}dN-V}^V#3GQvI0u7lk7dD2GcSzVTINo{-eL<c9HZf
z6g(`j2)UbyP%D*h(FeWS;P8)&Q|Ia>?iKF)^2p<M(}E`0%XCECc|hSxq5|#r86Erm
z$ol~3dmrq?*m@^a#rMD%YtA656>sPG(Z5rxJ49bWw0d^2FHWEzyW#dvyyv!vFaL+J
zbL`FpT9$Aob|$u+iEZ1qor!HtY}<Te+qP}nHg48B_v^)v*uAQ%d)EVflP-3l$II?R
zzeBVA(uw}%wsI)<4pj4Er)>BahkcQF%BcFimdQq)sI|EuICNC9J8^}-b~97g_vQA<
zY@7S*Zt-ld#u)&3GR-=g4_U$<Cxu6qNJs*RN7&2vr<)^|X*xVt*e}?m(J%9+UUe~<
zT{l8uJuEPaz9~1G<|{R_7yG=Hd>$^(?;~gqr_k{Ea(^yA7TfyxbhIpTS=fFU@9+9<
z>wX<4Uakivi(z-36EA+Rm3FU(daTi9ROL^tgFXjW!myB8c6H>K=;q58*RIqi-mHU-
z{;?WR1hWQ+-X+OCJZml%M{Nzr;^Yo(DWG1QtYG74bKz}R4Rp55-o;ez+F%-FOKilY
ze5wVUCP{GjiE6yEw*&JzAv4sZ6)cDy70AWuUkRpm2cy`|E5a~V4FX;OU0wf5az}E@
zf7=Oa{&f;B@iaVJEjgL*;yqOkXP_M5;yNP%-A^aG^K+<1*Jl)A#8SwlEE!l?ki?BL
zV;Nl+lYZ*u9A8QG_Ilf$v;{!mZ@iui&DA4*jsP{ar6?`hmg5cQ4<bcK%$v`jwwJhB
z|00T!1il||3RljN)gNYDq2wl{$r_EwyL?*X<oLvD|KfG^k=R;@-BuXKM!t)nY4_na
z8_B@hJ2-g2f<7ewO_G#me2SuKisn`q|E*sYGKIF9(YD6-!5Bn|HqS-=m_wtXn(c($
zB!-pt^tC0X*RA=bWf9M?Yn3QN{eCmF%yLFoKfgp=I(Rl3<Yp`MaZ8m}T5#Eqw|eh$
zGR!mpAO=p<!#e?S^O$_}qhcd?QEqbe+H&XlQs#Kv>*Ki$nAQX{WsJ`-!4g)3A4Wy>
ztcl2<Kr?OX_b4HN`(-uh)V%%&0{rOAc@d865=LPZQp>G)2%J&91Xfm-p7)*z9fE2=
z@z=`W+HQzzyG;d~L%IBtMQatppOPriy7-QdogR$LXffOxOzc6O4D0D3sKf8^Xm9XF
z@8dB1pL6M-8Z?`+p;hPb%M}bO2x&Hb&eSbZLVjRjWGZn%q%s1Myl_x)NEZ2D5|F~k
zGAOeVKt^R2S1mg&1FP}u*0x$tQ!zGQj&h6%qo1vAZj;YpJ~6!4Y10AMYPfklZFPYY
zB6=Hefe4<&`BzogtwfrALeKFvoj}Z_(U%}ef)sV{Oh*BiyLld1R2wvBFHk$DC_AfY
zxx)b#A~q&@2-KRfdb9qvWq)W-V-u$*PM{_SuL!Ph<m-+_UJmTe7cFqhg%#u2dI&9y
z5;kFG5SQ`lb|Yg<pfrp)v{5@ie{}#)u;Yh&40CL>hPIQOy=0nr@he@buWI%07>Gm{
z3I;w#-#FaSMNK54z$OylxPKiecPO&4eb@0CA@32gRM|6}8E0waEn5UuI!EFW2J3|d
zLvx}!mXa&A0LdyH=Q@~+o0Z|6RhXw+#MoPjtrge38qbfhXRU}&B%u4nD>ttG8T$&W
z>C}RYAz0?=wXAXok>pt|#Y;gni4`Vbf3J%JSLDItzCXkH*`7U2K?DaD0y#M31~!!Z
zg@py5=HI*9x%tgW<L@saNCsg{>MrzKA<dl-3@D{y1v!Y8SLK&JUM>b6T;$_JWCVLo
znnx=EEyL(=4C1o{?hGpU9VfQa+lrWNyU$JSG9|#xHRb-g<Iv}7BI$--oQRqj81W~!
zi}t}LydfGYB<FMwG!uC{3JTVNhI(rZ8DGcGd3Jh+>qyN0SppqVju_Z9R`7Md8h^ib
za>X=X@npJAH1gomvlY~{0n!RS!8%#g|D?@o1*e;UNR#%a`{*u5di8SB7hT#vG_VI3
z?r-MDO=%Wn*0B`SQrvnjGfO4xO?oD8R+F(kt-+J2#1yk|A*kYe`Ru>VEOdE4*3WLC
z>#(?1%)K&v&Z*D)+KAhpIij<ym=Jpl`Ztf?1bmGB^djbvHvl_oRTV}K1X>-ETtn7i
zi!QYVwg~xoZ;t3JAP-@O11S8+sph1z?CN1T#7DBRP#Q~b>i=H3c^|^UH_PTyr}RDt
zp2%rgqGw<qX<2HS?n_6L_>%$4+S5|n4S{G&GI6&oNJ~m6B9hTGv;P7WkBkl-k(QHz
z7#t*V>ly#3eQ05l4eJ%KV<tdK*>eGVSx3nx?^pH%pv<n4exONvqc3jA^A*xhO~G4n
z+iP>JT&%5Ox4!CI9DLdR02u;not33$#qW*Dyt2}QlkBOOoq|XLLVJ@S&)IPevuh$_
z!+yHv!jcZ}j)+8%Ma?eCn>g5?`gPZd#e5=#x^wV(e#jWHISS*J$aeuWA-r1(Bj;ES
z2P1K6{2XRd;{FF7!84pIYiov7vH()yv~{QG!O;OM>m~#B30L*;=eHG}%cR&<R)((0
zt<Mun78B6j!BO^vz`K~<LA8Ls=7a*aaV|3$V_Y!Aro@=YprS>vs1d2Uz8EqrB3I6t
z+u=Fx?;yR#5#!Bj`Nn_l8kmp()~VZFGCd)D{t76E7+#Pfv>Po(6&^LoKOG>Svig)3
z<d8w+BcZB8G((3xf=$JcDLDa#Ga_0Qn(vh~0|$17!@tW5fr0e<jLJ`y!zS`@RCiWz
zg&IZ=HR<0Oi+Gy7gE<=qE*jV%(;?uR+&$&)>)X#w6~)MVoBU#Vd+vf+*hlVyF3KqZ
zC+*j}Re1owj!`A4(uw&fIi3Ld$}g~t%K8TgCOEy#vkO8Mr4Jxbx>%|#HL#Pe1MUD&
zi8d_R5O$C(!Bj#K7w3NUkQ#xA{B<el#aFCX>*ji41>7>tct&$py3`yDODk>xQ&c{C
z7m%e5<g%7{!tT^{nWAuWeP$Lv4zNfgF!CO9x}hfRv04J-X3gK8l~dkxFW~tYx7q8%
zc6ZX;1Jde5i(h#-vq}<HmPut*NIYVtv#NE73tp8-y+VOPhQu?7n1=Fr*dV}a2gi0H
ze=m~9uHJg5(of-G5%c#^5w<kKwRODF6%Jq_)4V;fwFn*@WeXG>aLYefWS#@>@2uJi
zj<i~id)i5baB>4T*o#>ZSZ5@-U6x2Fi{B8|^tw1hI-B@98<e*zA_;^IP%x3l3;n$m
zrvSOtJ-0wu<&b*GIT;}05ag!(0wN;6bEnrw+)^emT%Ba8!GXbo3c=sqLCrFO@@VrI
z69nlvO~A4Pz}W{wbXs;^cdun}xZcC2J+E`Z#bJHk2iAwPGP8I-1ika?|J6J6*04J{
z0MTQ3fT8T|9lJ2R6pk0wlV^Hm$%4Ilz6S>5bJ^boG;akKlbtKyVXtF1^L5sK8a~g{
zJ$*me9e?f3Bv1~PwR*WHaWwc{VpsfK3~Mi}c;{S|5fb|5_f~6mEAKYjT;R>CPCMDD
zTtms=p~@($b5$+AHjzo=_+z?@EB4;MSu3Vxq_?5%(Z$KivUGPB!$sE|(&dzj-?&^I
zhN0%3w2m~`RqWoZz|n7TrL~gr_XQGpE*#`9^c*us>s`ZwzkE*V3%yq6HyWo=ET~1U
zAJ<C_smYfI(_S>(=}Zxvoqzq~hzY4lt3U6i_$jjy4fut6i2ei-Ml#X6TdeL6*`d6P
zWv!jbG~1$5$FdqD9)*g7Vew&HW@5Z@LQ3(YXf9LUt^p<~HIN$C;NVx@m+ozLN`Ov3
z^2fiyR43yO+qJ>_G~R5H9Y`uXX<CT-qE|r~%=D0Bi*nLOLmkJ0lH$y9V)3K~)$)@0
zV#_ltwx{RwH8Rl0OBrw@p#mJGf)F;@IW7REHRt0(4S!+b-0K<m_{7Liq4iRQ1#2Ug
z)%bpTZ_^rLhKG-b>%f<j-9gRr(?}ft{>=5UnA{I4<G^g;ZY}Ube>^Y773Hoi7>C2*
z0M%<p3<MZ7SrC?%%;b3YC9JjI@N@3-o!$SgSzrCgFN?yaO7PASW_<A!jOz!}(7_w5
z1!<({Sc-zFKp;gW!xQIzg~73^GnZqc63m=RG54#~rKB=Lwg5zGH!zJgv2xbXm=LBQ
zR%O8<DxTOvnQ|g|R`XI*{V~xsh`G2HFmoMmW~|N)nx?G@N2Yv;)?d$VD5;T?**s%_
zwKbHoP8b%tw{o(<%>S2H+kkO%BRe^qBDpo3Jy6a7UR;Z^E_CvZ3cFSuWr*g0x+!UP
z)Ng@A(!0pGLxg1saFIUYUwhHQRi6lwVI!?ojhU!m_0NfxmIxa)%<Kqe5rhF#cy`Gp
zymb&R<x)y$@Qy^ish8UUL@0JXEqITe2t+%nM-Bm7uDE&T-ADyqkgZ^x-+F$Dfy7B%
z!=1N-nJFw5ti&u)jX>%wmDALMr<If~d>6JXh`~yAa$%vS)6A(X008VHD?blX$HXm#
zmC?epj1f#)>SNX<PQ#6jnjp}BeE6?{(Z8`kuKL_){A^z$cfSiiW5{;2y=pM-w~UlG
z&3b>-^7~nEq#d_LVU=wTxoyV`@#3C1G^%2DT2NOaXxOyaBGPG_{}c~N1uH&;sbn09
zbCRjc%UVGOcm&}eD(bU=H1B(32jio4+nnBn+Rg$G-Z<4W@NTN>)mK@$^U%tZ{<`23
zkk8BhM(ZSg!7jSgtN@HvrOyA1nhj=hl!Yu!EZNdfk(~e`MBScnb7S{k+5WvVQh6o$
z6Cd0pz&Thsk>Pe_l;KmfPayf9bG=ZvDN4YC9Ub>uQNmom@;i}*bP76kkUlo@12(uf
z`8XY~7A&@F^s*f|urD9Ad;#07Zy#rY8T0Qpgyi0|?ZIuJB$+p1PmVAyJeSV91tZT}
zH{Rz_&mXi|{-}QOgzh#zz2sXQw5E5Htdm24=<*o!;;TuUZ7rWB=I_a&KL3KZL)raC
zu8-38idXOb7uD^FlGo>C|6|pR%7Uq_KRJsdOBpOm9=oA&tqX*@i(!|H;Pr6TslOx3
zFvc05qTnYJUT4kDVVcp8n})!vqb(6!LcZ%NKTVV0JL;dJ6}!gE-NjRU7xgz|pPuze
z$NTcZR4;xLN@O4tgMg<&@2iyhoQLs_GhBh(@&@?r1aF9Q5;X;9n=k%Cdyg18>{6og
zMP{oNQ=~8Hfh(sZ2A4F0Xe;O(SfVp$kq2K3e~((_nf&)u+=SO@lP@%<BG)}twpL=S
zv5IMQ_79cGX*{00Na8pd*kO;Bpl+i+VOp64qM&nHsH4D;?%#e^KLw#cVl0qp0~6N~
z4m&AOFpy#G&K)CUawc-XIF*ter8!z<;AEWQiSwW#Se1m-(n)z+W%BOr?w4hIWrAA!
z`_a(lpS9VHBoKtMW7{*{tb}X$m}i=34fLu>TU+hUTJ~CAt27*3yqtCHKh=PRl{N)N
zg_H`fHh+&5OV5Lm82gjw`vUUObF4e2zwXw8J~f9k!(7nrIzrJn9CzWIBi3Q+Bc=|U
z`hhi|EhGu0<ZLdwzOYpb?v9}D5%AJNG!g$23`(5+?PxN?G>$#(1nX#CU_nHCQBM^y
z#AEsv#!*VREZ#dTHYK)!A}o)F=`<n*L7VB(XX_bstQE?n4N*9Tc?L;qxf^~*AEKTw
z@Wh87=bg|@Ya-{H$^JgWlnhpxTTzjhL@Xph*Ud};34shY@qQj|<l?f}1IJ)rk0c%Y
z`Tj5?)riGf7n_kGalKt8INrayTAIuZ%NGq4g+6zpgHA$?&nu!~LPjD_AWSP<K6s+V
z@fQY~@-VA|jL)$hR!MfxfUB}(_luc~3et@=IYGjsu2H|LiU1b=7(Qy4lIwFY>nan?
z)ykQ@h~XTz3C$|jcs+vdeDA!Lu$|vrsg=RO5z><{Y<kjIm6pc3Zlr@E2FS@lQ_H;)
zQB$T{TR)z{tPKKlo|JY0VSB*+ty%H5pHrbSo(4r#d>1S+a0Ek<va8!ie(gSRwa#n;
z#VLe1)xE>5g`!CX!?&`PpMZ|~(nM{7a_-zNO*_%Rp~=K>hz3;hpy3S7dJYw}3M8Qi
zPWlKGIs-`scV+?0AW>XJRV|#_0S4ZYhPo10uWfy%V-GznBFtU@#a*FY88k#_r2>;i
zh$TQVgImD!^)V4CTuq$4D>IL&IYmFbKH<~0(s^q|>G^R_1C7G7hM(?tls@NL_=}gk
za(NB2W9|1M$3^4k=^-iT?_7IooQ6VnOxQM9GF^Yg{fJn<xp*t+dPo6~<DI$tFj89G
zWBbvo##9nz33HDwGfhcl2R9GJi6oMnwA@hFeL?QsSQ37Dbyah#3ccLtR~Q?!X^xz)
zg9(Wgn5n6+t}cMAp4TytS;{;u?{St6Z6+_O(=8Ff(MFLu`Zk4Jb0Nakqv!tiw(}!9
z*jRW(xZF)APGTw&8~wt0DsGpn<>gRTQKJ@9JKGVwD%tnb%Nu@E<MxKd=2r6MrV5`M
zr_b@u+T`SDfc?6SuV-~ybL+|x{bF@$A`EVRDeaLqwyOfAHWM>*xw<vyS8A)_lHm4v
z(zm_o&EUmJagnvzOk1;!PUga9!~JzkEq?SeUk(!w=B(`>FxM!mkPaOkX?)6QT_k?f
zF@sI8(@L<>O($^}MjI$R6B(z=Uv6UoTed(RWeoNX6R12)?vfF#$!{FS8X(~Vgx={g
zAr=X-)Q>M|vPZFbRvj^hEP0}B_h`ejLd6a&8Tm)~72qwhnOjn16Hu+uSQ7!!WdYe^
z);tapL3M1g8RD3spw&*@3F_1Hky1IzB*6E?ga&3C@jSAN`VNwP+8VhveHX&~9@3_&
zZua)DD0tY(=mF>nRP-i9oSw!r^o(VCE>4w`&NR}_+iTnFUCrLt8GKvsS90^%=jX1r
zu6mnQ^_47B)~vL0;bN8GokB$z=NCB&*zJvLzY<LQ^e>j5EbX{~r}ZUz6K}-_sXS&4
zsxRY+`G)egr(?27sx3#ROAZ^MUVD>;@UJO;02x2xX5<eFLMlZ=q{?!J0TylkS>ouN
z;Id&y9dWy4oa$g%orZhg7#e(IXLb?-yoBVF%2rK~V+3TDOq`Q6YfWl`^e(!xvWhC?
zXp%L<;+<6DT>L!A)-~*<G;=V}2uS19NzFe)W<&v#1Q73-V^p`BaY;*gqAH9@-LT(u
z02lQ@T<ed81}Si*#<DoI(Sdl#*oKq(SwixWiSW2Is45Y-!z2guz$fV9y6~Zr!HD#-
zI;q$}6vJ+bSrU>k42}*5dU$YtpxL$JC3>C-%zG$&GWlZ4*Q5hUA%n^=!17bwnDH<C
zP(RtpuPX*W=3HGRPKJMR;flXs4Ky*oY-5gW5GYMp96Gzy80+xK(>bzwJ5mGO=!j5}
zL#`1Y(<I^C@!W4;9ujvy9yG}~hiC<D3y1o{>!F^H&E83zP3&4?r>~i`q69&5M&hti
zjsD=?oXsf<+C%d42xP|0s_JtDQcI&{WkUprfFzV!y%FeY=yF69BhUlq(!04Ij`*=4
zC+1fpudizkQj-P9Adi-}aAyvYRAnu&Ol0&yLi(a)ZVIZoBqr7-IKT)N7-j-VT$=)K
z^JQd0pfo^PE*{osiyJDcRGNR^g9toJ5=5q<W(!LLqb4m4#HKSrsw{Tw{T0p2lFiyz
zwnv>~Mv^5_!${Ypf)ZxHY-e0DxgcwD+C2@<lor5X=2Ot6)U1cyP(H(5l=KbV`OmPK
zWu%9t$j%HKjUJVZk(Q8t20sE#HXUZT^M@7Hru;?<MEe~o`9d*ANps^3PSlo(5kmbS
zCl@9gHaR8-s$c|6h$p*q0Lq942Ib#F9B1K^mXcKs>V1Lka3R#qwjJSqEO=+j!{>f{
zG1@HK>YfQNZTAF`<4V_MS3C0gIdFjx@8A{CLu=L--TB=cLVMlP;(fDwz2(5`sklT_
zU%8G)Ytxas>hr_kS}bVv^>X}PueR}e9G>jv!~VR|zZo=rZgwUA`l)hQ8hd!#+j*MY
zZIn6B)Oq@tUAdq1KWBYh{Pp#Q;_KPqGhEnd!s=s<w|!{Q))xEpSofLsM%V77=akWf
zgY)L$<6_XuY^t^j;ofeUzKMHS={@)*w0MDIVg9?^fT`R*m8H|JIjEV~v?>{q+@gd5
zE>8*65-`BB-#09dXst1Yt5GEs^lLLn&1q!Alw=~9+)C;^#WP{J5)b4k|A|bYyu_<6
z-KScsl_9*1SC?@kp{+Jjvv&7=pr-0AF|TXnDLHmYv-n@wcBKJ#xf!3r_|i+C#NX5C
zOzwW#WI+ScFMn>iV-Nh}vXUKYK72laQJloFR49-yASNzX85%x_VvIC4k69KiJ53^C
z=fKooqA&e)371-iPdDAYuH(ks=jo_#;_36UG-|ZbEJDjyvvIRVOVxAG8oH>c$k9r>
zq=ET)0)20-F8tvG%`#1pM*b;7nv3(kqilJF#f<H-iiKHD)00!|ZSu!_i$#B6g!u6B
zdBk^A5%LuzFOwZWXu*4T6vY16tC2Y3YU37oKiL>jbcBHhx!jf~EH#eRPAYgMq?kdH
z*H(u+{ySiOJq-aVDf^b)OxwrS$&hVeXpX^&s$5uJ<_zNFrZN;@U87u!LP*tQ7*RmN
z#nc(#N<}Fs&O`&~Z+KU8Ln@MNzP+-HDIDw8KR9fpM3?(CNf%uqB~4p=1MxB!=4N7c
zpF~r9M7B~jVSJ^1>=85UQ=hqxHqu96_I0I-NvmbGq+)=O;vV*T`kSez>vxx6qyFm`
zcWcBy1-(8mU4m@73V=VkRi^Si#7x(`+w;d;{oX0o$)W@RRqIMy=s5|Y^NR(`n>z_q
zrocpxa(Dhl5I#}EwP+vS?Fi~@+Vas+afb=X7lJ<?rAbI3np+dSOCnKv#Fl9!7$Wjz
zN6IcGW-s46)>-*qRX@aH#P6z9qg9B>_I1N0sq#7A6<zsBuq3518JYn!3Ad;}o?uzo
z1o6LF4}qNv;?1*SUAO2@j}Ji!YSc9K!1v9D<ca9zLs9h@#`Y-UJ`3Iu=dc!=(E{v4
z)4Ng`wjAkcHs$+(NSssS>AFU3yog4#b6X`9&mPk1qbw#zC`720kw6MbJZl*PIt0g|
zONgPzymaEz;(8NH?Av%Tjcz^u<ri@)Gp2G0cFZNC&B>Ny1FruXr8|uDg{D~><YnU$
zQj{xDwf$9=44+XivPLXh;bfKyFm6mLo+A>xN!giv?F5-bSbYKu^><kN`cGm!f*Hh2
z@h?$B9wR3h)HHC&Qz%@i?B*5q^%PW^BK#mZ=st?Lh#c832#5}gC|zulats=TY`D1C
zxL8z_u+NL*_0+=e<{&YaK89b=g%UAX4oW861tFa8+WAb+$Np;DcmH-M<ZG(yn3w(9
z;i;wk*;e+)T`dcAztHdYKUWuaJ9G5T<2%jL++Kjmq0<3i@MO{HGN=53tFC0vJQ6L1
zgqlUkuBW6wzMkILa<*R|=92=%ewqPtJndai+9B$<`LsUwW`Ak|g0Ppxt~btgx6OUN
z^iSHW)t+#1*I6&u%rB2R%zY)=`Ha(*ZoawM-_&h!nw4k@f{6b<=%`GlsNN?$B?DCN
zLsja!W}3Q}Klk(xRt2WNbT+rD3@w$~nk%muB)J|~>Z2}1NR5>GG|d#vQdbyIyVTU}
zRDy9#Ot`HGb*o3S07G)gf$0t)ud(c?jN`Eh^_n_7D>!V%a2Z$RQRyaGEQd{Hbr7AJ
zTEuR(*sDg;I;ny@x+y%VRv4DF%xvJaay?NjCU8kN-i<G0DL(C0rriqNRA_5yC)stt
zFkxilG_eDL${yKN4ty{Vb`j59=opS`uX}X;Qyj;lVn+hK>eRqtV7n!wa2}G^%0^0D
zYPvGVbMiu(ubO(t9fRy;YR}Dor9p<aPVqKFmzo%6x^0-I8lI=%rl_Z*IM9NRn%JJZ
z)mPqQTC=*h++Ux|<@mH(zYiPF_2eqKx85$kqJC(vO|OR|2cHnG@IK$Cfd6y+d?S8;
z_<lT3_v4*?hkVC*sV@dZ(Geha0y-H02J9G38h9HxkJvTnz`-nk33hHi3W40Agtanz
zt0YI7hdr3+9%OChZ1G-><zXzj-T=)WoEMGGhl_P~)Qjz{?M4F(3FI&&PB}6Bt(+p@
zf|RYeO=Y_6k;0`_#w-=Gl&Fyy(&2Jt&#%8@*yiu~<joyyWv972nld#rV<qmb+2$)8
zTmZmdb}=2CI^a)1r+;XeRW+B{b&M*gh?Ds9{Pqk_I*g2upGew}#Nm3>{Vz?WDqlGN
ztz{}IV6ib!+O%l+;ltOXjdBScZlY@JH_>Z3nhG%_!v^68s2*umm69MgXorRN8Sdm-
z>E`ca`rP5Y4LTUHoJ{CyT<UayBdZ~h(~PYpQ$n{n*Lqr&4BA-Yl};87WE&@$Md{$5
zY<9q-$p(~eB5*CY>1!Lfjp$T1NR<K!Koj+9+?uFYU!aKMW9Io&pq7&iqzYKN-{|k{
zL`>EOxG!ePj_uCSlUfqLCjvvo%T#5gxazW{y}65wkXXxxkToMmr&av*(iA#l&Bam`
zE)LMVZ3`S-A=yWdUBMI`Me#JuYio!0d*B>xMWL3GL{+>Co<#DQRQ5wa#iK(-bLxwS
zL>HoK6A`<wzC23zczDfnE%Nj@B4VDGMrmTzwvE)+ZOwG~>m{}JWlUL72CA95u0o8j
z&@3s%5${lC)e@gSX{JF*pzF}Y_|sQKWwyVt75W6}|2hV!p31v8`z|Zy%&Y+(;};&2
z{MRDVOW>peSpu4zCL@9-HSWMXKw{`)r{9JU5BG;El9Kq}FFcLbts;rrTy-~TL*2e2
zAX^L~rs9hOk&$lF#5Ms8!lH?jJwrPD>izDw<(O@c{!<~fXQr)`EzYN}5b~*kHxH8+
zFC>TE7mb+aZLjYXpWYp|7#**D^&WeGa8B&cPt-^z+7y9NDBQQ=>twU;?QKu5?XP#b
zqwK3~kCxlL-AgminCEQo`_%}(@8QRH1m5=cADnx04$sDBSEIwb?r}5SsqbfWcdK@H
zdsU`93~v^O`>X!NuJ`9rnVUs$ZL_L(jiLn`Fv68NZhgm>W*<sKtEQfFebQZu$!ElB
zmF==9d=udMa-u3SRrQVexq1!Xs$z4^Y2vGK^+{|sYcsFBlZN<AQcUVF-JfYnnC=G3
z*E;2L4tjn3&NL&i{$qo;#S($1PC+Ad>>i6~ziM3gq71kR$8s?l0VcMSdLdZAu~CAT
zLZ9|L>GQ2D&Suujb~4c40I?^EWTYaT)7-Dat7KJ`atLZ8?5>R=cw$I3=KK;@upmvo
zYsnbefd?P)LOb@iw5$Z-0T~zwFYozd#K<--`7n8=3omXw7Jilk2OVEU6<JR7;{Y*_
zh;?|^V%X&CXw+_R)KBQ1W9{>`+0Xa+I(i9Mr|bHhPVxQzqVs)!L-1vPdAt4M!{7RW
z{XSic$qX-b85nJPTyG5o06w*vhp+Krx7V+=bsRTZxf<Pt`Z4Pl^i&uw8P{h1FCTX2
z+spU;I(^;!{pI_Sa(6avF#XtlrE<jgbsO_N#W!%U!s{c>MO@EJJUdBw)`CA4gzQ`O
zuuSDOBRFo+QEpSY`xX_37nMjeLX3d~{Z}v?vNWrJYKcc`Rlv-c=K**EZhRu$Y6sGx
zzOh4R`gwNHV2%V(7q3o=XkOTxI9Ik-F_3v~`5n7Rqiqr5TiAi4gBe3~OfZ%ZFEtHr
z+P?96zL}n<IlKta2SQisHOE^XBLiy@kpIWN01|4O-CccM)f9arH591fS((WYMM6`_
z$~ub#q#ZP?S0(dxc=|?Dn#b3%AV_`PWm188(2$lfZZUAo18(W!a8jD<B@83BjHCHg
zs~?TUuZDr|&9w9wReM&Pua9xq3-l^%_qwGiQpZTc^jPUiArj2_WvXdPUOB|j&Add^
zqAljweI&nd!*rpx?f$YjjCRP-&U|$5xs(@W!khzcB2q%GPGt2s>955$(g{M#F5?ko
z+y>~{PQEN$o%c-|p9h~Wi<im^_K7ob9ysq_;X=r5nsPEAXXBDxC26;py1+oUvNFmr
zmA=;Zu+0ynA^`;kg_K}s0YySW0wyFO<QFHH%`gX65We0l#<DWP-bd0l;tF$R<8$Qk
zB}?dhp5Em0O@<q1+NKu^ah&psIm&w7y5`yDknAbXk54ipn(FU=A14QS^jA1bo+JpQ
z$AX4m{4)~e&D8d^j4kI3T$e90Zvw-I64fOgNf-R75oLavuTXZlu&>D?jG{WgTz5Jc
zH-LiNPjs9BqG8Z;<nkULF0$&u6`CfwPb!T7ru<kE(kz<7L%`C6j9vIS5DaZbL<oW}
z9~P*j{nwC%o&NE`1H8WeH;ZP~Nr8#6(=THq$v_}8EvjrQ8f6~T+@X9Vvii@n1eAo!
zg}dA?C+QcH=`s0V-=CK2)_}gpW@5$>*u8QD5c8S-hZlDIb^<4JMOH#6<byLDeHSR3
z%2e3;p(FAY@Qk*03(sE<22J)9o{a1yb3f-DgCBFGEG7uK#7%4GHLt^1*<!FDvYfzj
zTAFxm6~(#kvu_D}INQ}F554~V_}))J;pxReZQ+Zj1!^p^+6<~*-vxYLFRQEdDb#Z1
zEqI(31%+p2#pgxE7MGW28_KkJbjCcV7V90GKHjAc4FDtemn*hs-K~$KrH3`E=YfO$
z>n_jthuwhI+TrKqYMPkwHpc5sc+W2q6E8njmdr=D7H83coaDnP+1dBQR9(!rN<3DY
zsMPpixftWbh};0dIqN(-W*Dv!*iMxUPQ8R@tCHQ`xt_PXnIWs^C9CD7kDI94xT&M?
z;9-rhN!^J0_^t2{R$7}{2G7=D1$=ah;1(%vX;phfn$nwJK1US96-=sfvLKQ0wP(U7
z=#wmPwYvk%^n?gos@YxY0+*&FOM6J7FzVh`L;Ac48F;jma_414&&fs6i3y$>5=Ov%
zN8ComTn1F+TCkC{aH3DehcWi&MXxlZ1AQcxDbOTK^NP5r=V->3`VI%7SyLP-@{_+?
zf};BD3qB#3otdXkEj++i?w}LQ$fcT>2Bnx@U42$Gz3h+IQ?@@I59ce*&d=qJ(XXOn
z=pK3B&jXHq-|d|YynQUHw6>gtw!S*r>-CgH$7sj&VL<9nTHZKC>nHyFvPA?BsOQ+*
zjawQmed~&AC{g4rV?`i7+%e|DZSuUh?KT*`zwe|RwLyKwZN0#X({o<Ey}As$P>tjH
zLUmIUbrX}5L-6N+Vbe&dyIh}{k=On?;J;jVed98b@mesVmURhc7pj0$($-(x1&J&%
zHG#TxbgB3oo{c(sQg=FP=U1N#8&ZBRTa6%F%rCUB{kuM>fz8`O32cTaHL1Gm8MP6Q
zR!j~N(LABr6$qswV|Fkj=dasbPZqTSD)KC*_uVoCf+h#6@cSewkRu4Lx3oXQQK19B
zRkru|r0Q&8YNm#fBkF6?T+sBi_g{=+fsC*}9dXuJ;n&+<i<~{fvD^Yk+B2G{MlBsV
z!bIunkzgcV+w8N6L8!lpPljk;$8=FcjV1s5)Oj*+FC<Kx_{>wLO^a9MhMu<W*ElJP
z=UJTJJB*!(3NQLa>9*}I+Y7pV|29d(iF^R_LBjONhele=3X}^@7V$r4fink}{UM>G
z7iGUJ8f2#R0^B2WPgxGrJoO;!c+vA0UK)U|TPOf_e1`4=8-Fy2wfe$Gs$Fw8;Hj&_
z4@+LwJ@b)}q}OxL4zHlxXHnyY@ib=mWl$Mp{q<{#T?Xx9^(69A6;sg1NYdV23q}#%
z+uPey1!=>t;MNmij~aP84zj>{*n*5lt!eV(#2#<dvQRY4o(cJc8F2&<Qz#YkI-O=Q
zvNR6?Xr+%ySZT!)lS3H-?Vlx}Saj}xNs3%Awe2nsYsUDfbfhV(g-!!W{s4dU@|)q2
z@WRMLsacBGi=gc*cRK$<J%!e1f`wwx)M8!Nb_O)3MTPhl-%~m#{a%P9qlWb2v>0jx
zZFVAgs1qy2m-{+xUbhxAd}JeqOtFH4nss2cgHd<{vN+2q8_ey2f&`ERIwt~TYVw>z
zYiSnNXod#K7na7B2MGFRnfrQ2WX30iXE!9Ke}gMLJMf?$7~>uu<Nm6Fc9>zAXJs!p
zcl&C&N-|ml@Tzp=JMISV<$>t=R+V|qzBHs=qF14jTgg#m^Oid8&J>N~<MI`_i-?^r
zoX!s?Ellan@|GXwn~n~f@O87*H}bw7jW%B6MRs>JznSkY?cJ=L@QWGAj2=B+ttZS~
z+sN7Ioa?0KWOvouU(UW<Fb#b$ZC#Aq4Df$HrsXXi)m8o5aHqBGNc>~5Gqx7ENP5bF
zqRtb=*@OJBn0&ip@w#$Dp0qj*UM4Ad+q}d@z7|w(ozDdWCk4}23-z?;A@faL{Vbz%
z`#5oKP1F3ht$6&!DCvO~F+a43uH3F&#;GmqNHKt3tF(XPcQNd)P_TLI)xR06cuxep
zxqlBCyGAKxl-}BfiNDSX2cQ4R6DzD}xp%yakXqjTTg4l)QfpH7qn!kTPacB1QDDN1
zC$w_+q^d_unH?Z)T!_;GbJM`e&q7H7VH=NVzPnjubXIx1k$<$8ubbhpKg?|Fs`H3l
zqg+OQx1ONs1SXWv>G>!8QPy$U>$U$T-9MCde$Hm5>r?+^<!I5?3@9&F`5^?49VFHc
z($sPB1jxN{wXOY?3|WT=E?OqBHku2`=$FgZeLuPyix9J9OHn&A6HBRn+|<GCu&?Y?
zn%mH(qr=a((5w2Je2Y!`{}@v*79!!oM6Wmf2pZH&-TnEA_}}%(bF;IzU+>r0n3$a&
zkAES?#SriRsDTzSt6HW*Oz{s-FfYC5ud5(cZY^;{(~Sv|zUR`&jAwp~BaVPdlMpdX
zZJ>D`kPrxF;;)X##Jh5s+I7z{vNFxc3>WY3WIes1*xLS4^V3wUsZE4NMmJF>87K*P
zZSdxbKAW0(;soqF^eitxm!czsNJJ+dg;zUAwg6nTqf+D^ep(URJxzKq&+BisnODnP
z6X}kvi-E>Wp`i~*NP_TFnn_xkR-YUMTR0RNQx*JFS>*V9@$4UBV<BY$@5J^%0y5EU
zL%Z?fj_JomuvP4wN{Mk=xFI9VF0k~F(DgwB$cAH|xM6z!lg^e3Tkah0CY_Z+q!0i4
zGX)<3JlKud+FPxVUmE6UQ9YW-VIH2Cvpjjl9B6ti+ql}efc>G+*%S6gj42J>ASSRQ
zrNHp>7^{jW>V_F8P%+~n)uj~%`t(tLY>hTs{PNu;M!yh{1I6ip4-lcKxADH=^m|0Y
zK>lS&0sG{#F$>UWKckwS;%E++p7oBgWoPS%OrrAfVb8KwNH<|gLtw{}o=&iXWOI&K
zep)A}@p`j7TEh1ZLR^=p`q4wJr0EdNls*zGn!x7Tt&|%`%a(*sx1V<;?0ik5Fu%v%
z=I}?frDD%Qqn;jR+~KKTTQXtCG59alJC>N`I<T{j12UmDHFZRkjCep?t;9h~BC}OR
zE*N=<iMEQdC$?%}Zq46lQBzpwofcK$0DPYMNF|Pw#*a;x^-{af%bKV#=SZ=(f*wB%
z5dMa|u-BkK<KKAu@xhH|WWxu-dklV%4c-K^T--AQp!?VOR;6dwPJLSI8$yFz6I(n?
zlT?s)NQby5mil-$w!fNKXXHD)9%|y2z#!)9$;6(#OG{e5FIM{@P<h|?R?MjM%ym97
z^gR|dRD_Nr>(c9j77=AG_lLWAJ#HKil>p8vjWi2U1=jqZeM_&HtL0{Y5YXCmZ6keM
zzmEc12f6AU5p3w9#<WZeMV^O9w`mbJa4oV^F|hQuak}}oYO|Xjia*|7oF#&U=d?GM
zRQPmMd=mQcN00AbBMo_t4CRTeDJlr(-s_~=%f>C!UqBvTx09KUIRZO~9u^Gyf7bdH
z(XQSNZXVGzmwnHZ2NPSUQD2oZZ|dC2svP~+sWiyZsxuyCu~}K!Sy8lf>^+Z8@1xo~
zk}4mX5@SS6_C(}uTy(i?bZ%i8+!+BFM#8QDe`98IF+6SA$PDQIP06t<Arfa%;_D}L
z-Sa#+)<T!icunYhQK~)Da-|%UstIIIF~@PvlL}2d0eLK?bZpvwp>z2zs4jrGyt7s_
z3^W!5uwW$p?GjM?_*OFSR9jQ+&|h6McyM=+fF3))Af=K#Tqsz>T_N>SeT{;Xt+R#a
z?XCCS#m;vaDa!zu=HL{xwqo0;&CFDH9**~?udFDD@?|t~RWfi^Q8fJLugA%i7(T1d
zm!qk3TUB(k?CtK)m&y9YW;(TOn~6zy*oUSVak9dHvI-G}E%iQt{wMq$fWpx=KHzf@
z=;3}a|2xJ^!Ph>^Hzb3gJs5X-`ni~-UFE>_pw3d*XVGN1y_vjDOXO)qz(+Isjuy_+
z%4(z8Hm9Iqf2xb)fNc*Q^K{x(ks|SV+ZW%T|6->tv3j&(T3#5mF7PZC3PvEFJOYYQ
z*Sc5OkC+wwEF<*h2m+ji9n_S=)3c2W^CZK^8~ScEheu{_cSyYi0>EA)j3-C~6d;6i
zu>oc8fX^kXft#d&5gMRdCEJv;Am8Dxfxg?7Y7s6}%GOE3toK`4X2N966r`e`Wxjth
zKOkKeTml7H_zuYlor9&42HgvPw;%l9R1!2p#g1%-P_2+fgRBz@FH<PNFbkZSOybbP
z;qXIVsj|{@X^UG|lM5kYssj8FeZXrzwItSs4W<LtAHR8aeVj?iXuvQ3S({&wl8b(F
z(yD&;iUM`Y&5TP9QOUJ)ISPbAGXflapEy-@BgDR0Z5?r**0@AAC6l8*BRg;n2ZNg~
z-2?YL(WVf~6Fc|k9X&0He^6Rwt>tn_h_lf#v2Gjj)soIGGfQk>m4!uo)6k#6M%4<K
z89yz2mo~|Q*u9^G5sHi8(F{Z$R7PT+j{uOur-HPoo4QHc+;hwH9Gaq2gcw|QSsy+-
zi`J;hg$4fn<da^UwB$ULW{Fs>SaAEEPvk`iWTs-?h^Mb{!$@h{Hb=3!2ciwT#!73K
zgAe;Ie<x6$GmV#Z8LEjAs8RCWop%))Krd^k9toPD7Uqe@$V+=+*3U3VVab_M83#=Y
z<wY^Oh=ye8mUQ{ZHh5^KV*W|clGL|k!p5^xXXA)IdGJ51P@ps~IyI_n7S7|hgS-T%
zdt6Q3G<@<@{<q?yUg;a|ryv-Kjs*!IYw>{4`z4mKyW@8tkIoGlkD4SY_FIvN_~(zi
zwoDa?pg+N~ls`P+Ba)6Iv;Dy9)G#oBcSRA~XOTmEd|^^`dRAe4aCB3CS?RVgxNnr$
z_v0V|Mv^=RpQEJ2>*2gUBpQd$QFQeoKK(SUT8=WOHP>p_?I8U2ODTMqj$&Pl6MW^}
zP2GbyzqCHV6@N2lYb~?2b*-^wql<I9o_>XQbH0=JC+4|3>OY5}J6_hMjoHGq*3>)E
z)Va#UqtwWxdpR;u+g?-OEK%0{(?(|N%e>UI5WCzX48yUKTy~*hF>9>kqoHYKY4cZ%
z%sF#ZkFH#M&vc{oPoZfb{3DE}aYOXhMdMeWC(=<(=)zK8R(2_Nn-2ZGdIO^Z+bVS`
zMShe*!rNT|*ZaS*v5RZAn&KA9Wh&(Nh`u(8I3eQ=eYDcFY+9rEE9SVP!LWOp7l$@8
z6SS6g=FWtatuzVDF*DWUVKs-nBhyrCMq^vJzfDNXYAEwVFxZvSm_>(^wJ&pusz#_%
zssh!91IlKgTfh7NB1t3e9!0CgfW2_&vbzq6s}0Vcg{R#X>4%mV1n4HV=Ixgnfgd%Y
z@e%;d{6Z>;>213y>vIezkP$qs_e9Y9yg69!w&wAD+c_){qr2*8;k$bqyjzE$)1}?$
zem;5K2TbG9(oM-mBhYN9YixP6e%(z*G|S<4d)ye`KRw5=dYBzyRNh@2F}fg^6$WIA
z{gICBRVt0V&|Z=ax#Sy?Yo^+Y>b`FJ?uxy$*{Ab;9iQg=N)bu=z9RDch^XIxecehl
zZVvy5)^>>+scyS)dwvpjDIdY(8kY-7tgWq;<9$8YkKxnR*4~iaa;Yi1!M@d7Qrxnz
zc^Ax^@qT}TgN40FWSQxqWeUJm?W?6AYjYfBh{Tabqr<0wRDgz1tQFTlObV{-A)~LC
zXe){RJW7ey<1qqem6Ie-Sz>O4e>O#7_}yzj)C?N#UQ^u(*E1B)(i5aIc+Vb)&B9W*
z@$a`3!3Oe@qa_NRzFDPUDuVyU24pmOCPpM&wAnjEF>BC_<7B~wzA?XSk{;;xb{INV
zEMf|<X}vm1Id6b>XoHoN4GnMRFF{BJLkU?|n>S+>LJn+vl&gA@tK*hgm@Fvo6GI!4
z`FVzH3G0(K23$ypSGZ&gwqtc<dkJG%sTiw04>(%V-$occ&%=ZJ1%~xIQSqjthS`8J
z_wYQ#-i@ZgA_t70;OHpa&Zt*I`MN9e;pvRKCKOQtLljudq%Zu%9m9!xW|D3NHBv6{
zAcbq@9=CJDyN+RE@1u!cJ{=!&Z^!l;;&F7WC}=o1DN=qyC@HY05)zb)C@qvuzwm&Z
z{08`beS~vqigGbZ2LZes{m<EZ26SM)w6T>Dh8;Hu6EUSpL4O2BKDQ=VNQ&5`T%@0H
zam&$E`1>OtCk)F|Y~kH2uX!Nd<ajr8Pgb}cByicOMo<Zwv;J}NS?k8jfjXCACLG2=
zxlI6g-yQobl%Z8Hu`*pFAvH@DF#$ZPVfZCo$??Ezejca6)-f#erJJ;migLX8c6$#D
zVho_ju&%s95o)Zr#e=#GUx}&`$-X>aP-!D_-_NGBDX#FciMaMHLov}X2de`2v^-=o
zR9GJ*KYr4%My(VQlD7wcA3qvOnCM?$o;<<K^61(N65|{j-e1T^F7(}e=;mer^iy#2
zlYj;p#fuI>7U>Er7MV@jb`N)~6LwrqkG++{e!VOnx3iI15Z@2w+Ybem&3Zjuj_2B?
zqH4PGG8`pkRdz@D#go+NqJPO<!BIN7_@EFHBjN^kmSX5v9X@uh7a37uk7C41<#UTk
zR~byW^E{h>khSD&x;mM>&O<V6yY#xh_MS%e4~r)?diyPQBSc0cJU0)eCMz>OX^6HI
z9rpvmg55a^WD*zDC2`^$Ea&o!h!maC8vJo-gveq2B@!eWesS6~C9cvMNFzjK1T(cK
z-z{;jXmP*6{gT>JX5pCOZXQb0HK3tMoRWjFU8=J&K?1msd{Mfo`8tUamKTJwXlGN?
z!P^3e-bNPUHu_jiTbV<A>v^o@fw1F=NRx6of8~paTcDaXs?=f+Je=83H<Q&lQ?!=x
z2<oCb%7k7W_mEJ_GhwfZ`^lij{V5mUaPV@4Gpe93h=6ZZJy;N)2^#iD*UYSEW9hbg
zsHiQjiVA8?f8+4xGT{%+ejHT-hd{vP^|{KA@>GYWPuh(ga-_M4I%p7%l2GmL{(hQ0
zA8yv&>aw>rFfwvBI-GUdT&h3!U46UlJs28!*&DdHdt6*sa%B^}wx#Cnc>h@4UzXF`
zcz<dS@%6rad7tFM-CvF+rOA^PGz>z{A3;PxIz8Jvw>iJE*l(IQc;_?m^m2dZK5Z9x
zI^Msa+tPfR|I^`fdwY#u{;}tmg}SkiZ}>kZU@oya4mCd)82up#*XzyIwY9g$uj{g|
zH{axZ(}KRWjv3co@3L}ow%<Qx>#bg5ZRE|jc(6hs<+ULPh@yiOUZR*#<c3)Cm~F=Q
zICU0Ey5?w9tbKHm$yQ3kAKd)tBTLb=%?I)rpQxY6S<bC#xrOCNG%33vSM0jQF;_v;
z{aB$X^E@9yAb%LNeSRa}g$YC}L^ha6Q=dWTumy3qDw0IdMnv_t_x%<)tVp~vRK{cC
z-cR+Bw7Y`+w{`{J6T+{wN=m9sh3`>}WJVHESy*SAVsti=h!vb0*&jG084(^3fkZ%>
z>OUs)FC(#X0%z$69Rt-PoxVPlDSMG71xc~-cm-fKhXdAtVp>|0PHYQPe&q2evZp>|
zV_skWx+~8LiCcAw=rF^eMwnJ0ZEj!D`zQbN<2+v+h7c9kU+EE(hlcTA$-kaCK^-TG
zr7)@?2t~zznRP;m;R^ngU<GQ9ZI|H(N&HrB4AqDrW~O2T$8Vl@guA#e+&8HG$u1~V
z3glB<gEGWaD0)bn3)jRw?@2n*=RjAk!EGuJg)Z?L#4is`A2S;lpjK8=lKg}t#W)dN
zx-^DG%g7059AVl%ID}gqn;K%$(KZgttw?{3B;aIXI<O8=2O5Q%`ur|L6H2_0eH|+d
zPC3bgkmEH#X(j?g75NWVPI~M#CE~nTlIY;X6_t4-sbP_oz`?S|Vfk$&dy<q(9AG~T
ze<ARbqp_$_k9CCV=xL~73<Ujy_Ggvw9$KOSFWwqV&JN%uk%U@yryp%#9!UjakaK3g
z?AUZ6QT0{2vm;!Oip9XdxW*R?j~)ctr|)6bHg||*BO{|=9l$oXL^L#mscUYWRTTXP
zG-^*9OMnad^4C-lh=Y-!Z(>Vsc?7|{%GPddL-I!fNhwLm?Y5kKRmYU+@)TU>7T?_F
zOn<!ItRj=MC@Z5RPW!s-4KNhdqq8W-WOMsyPhX6n3aQ9=6ARtzqfaaUR!*HvZ>{xk
z+<)>CyG%Im$Hoq@bUlp0o2uHzz{kGG$?<OJy7Ou>xjL+5LqLbv=FV<&ZMQZlGIXqb
z-Bmune<ZwRuC`QSTp-hpW0{g14<Qlpc5a<>xKQV+C!qA|pz`eiWmERU$lC>1oI3>V
z$ds}&E8^Bkiey#_d9OuQ3T#DtsH3nc<#rYv(D+N$+D&p6ryp$ca5x$d_+**398>*M
zN4{1&&uECuR0>FC)(lOj7KSy=9iA6oW}ndIb(ZJXpo~gU^5P84yIZ43`%O72V?HW0
z9)Yw5vjaziSA@EB885D(b0H4qpc;Bb7ivQdYk44$p+tNfm{u1q{R?7KDV;$<I;l9|
zpfs6grJVU6-;9ci%5Lo4E+0plIY^c@i^>$P;)?BYnd@6W!XpYwbT+5Umhv{p9&ofh
z(6FXKNmYZkyqLkH<}-hH!(Vw_E{26hcD8!f+9qY+>&c07^dBXrjeni8^vtR%*Q6rH
zjN*!V@MMfW32i9~?WhOq!gEo&b%7K${#U~a4H}LFaNg4|oA>b8oH0BY;c`56<+So8
zw~FVz)!}}>HyE*8t=Ih{pwLmjTm!BV_&#pV7RxU|d=zL_x4y3iT>q%}Og$BKeLk%p
zPv=~B{rJv||IK$&WdH&BJ#Tr&h!%9dD`eoiF542>MpRT)5&aH5Jw5IA9XcNV{<FTh
z+4)I&vHi0l{e0*1X8LZBoids~>jY68aOzZSV8K=8oKI5Iqy1F}NYJDe4$?Lr7>3cX
z(T$T-3&6Q^WZvOdER{o9Oq3fv%uRKI%}x>-i4duk5nRh3ZA5JV(zCUKotmD|IrhWi
zA7oi0nYhmMlr{H2<58sOIo*e=nvtG!>!4PzZAN9LK-5!Q4!a8?eGw@$=g+5C!7LQT
zZx{adOFfIDvf-hlf+VN8>OW8z%!&f#1sNqZtb_8`t%|+bDJNlfR~GDRGVU=c)pd$T
z4nGkf34uC?1|V?0amBCb3c-nKP&mSm2N2ds!w1;YM6~ZOpTX472GcgNhJ|4uh=o=P
zP=E=Zh?1xLGC)B^S@*|6OhL_2k-WMBY^Gag;;tSfCclGg2cEcv@*s|oXft#0)H1V_
z5|T&gXrN~g-XFpz2Gi&7hn-?5y7)MFh&bf~$q?l;ZeDVir7PALqtmIk_y<K8sE3*&
zJs@q20$X0@;KNr}9!yNfa}w3PgVdiv<&B7wkQv*>j^(?NrfM`JQ!W}<m6<?K(ntVJ
z_klS!M21@lk)k-HMkOh7v;Rhg4kk<>E~UmRD#V|VWqPd6qUuK@qSO9D0&W~152{bA
zQRZaLUKd9n1;7+xW-~06HQgG78cLf82rJQMDrE-c0|L=z3}F-t*yDAT58*WR-6Jeq
zvbyPiP5fFN2;<TD3*yCZpf-Ppc!Fa}QA+`y7IRxj(z;JAo3&x{-SF}O*gZW%L&fv&
zD`FoG_!SmMh-hc_UwnO2bS7=rbSBQkwr$(?9ox1&v29Fj+qP|cV%z-l{O|eKs}Fmv
z?$rl%?b^F`6%Yf=KB#O{5E+I)F>4<XZBn;Qep*3JHrn_AD%RQFsSy@Xeb5lsfB+o=
zw3(W4AU$D@_t~?*fB0PT!XK%Ka){WhkNv+nlxG($;3pV3ijEu=RVBGDw|?Wdv9Xjs
z<@Rp}ZE`FCpZq@`5e@sHB>;>p6=q&nJZnn{C<pX~IB53tBU4K|)kU7mi?$8|&&IXq
zsl?>8AxsUs>w~?m<i{!q=^6H;9NH*$JDbs!=0D=y@v=K-ovd6BN4HhW!HriLjnRis
zRc+aI!^^I9GP+9J8mMkv>>W+D?z!!*1qpvjAUXBY|5Op$l{3~fq;K!^s$a|475)kd
zijj`?YXj``!Nv0VAG3hFlB4@J5Ork0<Rr|R8MmG5sKj;{TP5%HId{S!_beuHsbzHP
zN@<!Hj3DQeBZjR_rqcQE$3fOgz+p){V^2a%MD_-XhVh{(N<Ewsrpg}wP<8l8_)@uK
zztGg1<n#w@B*z?v<^sO%4mjGrkeRzOXw*q7!(kD|qBI;e|C>vx8>GOgV9;lEwV{f?
ze9Rp$5ciS3^(sm%t@dwnzU#hsFJm>|AFs*K<5X`ER7WK$Ff2_`);!w0wfZ@og%pHT
zwK>wI(lJ+)z0S~}(I$^J!7eN6mn%+1!~tv?uA;<<dZD6_LLf|&vdBr(i13*dstd;y
zR58FnigCJbnFcyRjL^A~b$nv1Uj!=$1!Tc9T5>p}i#*Xwygfgq8@#-RhR4}r8T0qp
z>-{k+3kwVk%){ejQ&SVYZm0X(gN3N*&?QKIL4mKX^%`C}1O!BKQd0AGhxg=D+u7M!
zNJz+rZ%fNeHgAXb_>Zy&9{;Lw2k#q*J3HrER`Q3aUXPlrgng7x#JJhX{qWd}y1-5?
zi_ZG`yFLi~OdgNBsSNgxi|S>$mRCR~$1VQ%E&lNE@V8F%iB5fae*WE0#DT}3KVOZ8
z$2rR?7Hk@E2n$r_g7p)y^1s50NPg^J=u_AN_sc@vLl%$C9<?Ei#2+`Is|v#SC=3CX
z0yv{0enn1(7Il58o0;ug4$!>66o&SJ0{S-R9m|l4o|%y@Vtrzy__B`_V!oijP@8a?
zg*-Ew_lo?4$3>{`i-9&AYEFpc=}FAOq3mm2TAGC3a+Lxp6;e)srXXRsif#q{oht-7
z(TFvprzMM5;xh~!)I-(H-P!OGaP0!&SjGuH@t>waN~3AEp1J$7Z#f{W17#cg$jm??
zWu}QDaY*8TegQ+F#33LFK?VgODTHMrV#YhMVZfwD*<@mPW||Fi@2_R0y+2(k(KgZb
zcv`tV1+%j9e4i<+Rk*afd@WK=e|vpCo+hoGn`SGx*sLI9wXWO+e2mIl>MuqQ`#|Jy
z$P=I`F*yu{R!U6HS)I&T>|(lsgU{q2Um5o?NV%RG>vO45Q*rw-&O18YEVo-pek~cx
zerJtmv>o1_CJWlLadkCVS$OR_V3dl>apf{nx`|J^6|!m`p`iX%K#yRe((n*;w-jhZ
z-qLy~gjPyUVb)$#R^JMmEFeExD>_IYN{mQnW{N=CHENu2Pjr&!lnsCb0s$8~x%Ha6
zyYCL?&d&~w@AtURlXR5h%uv7(+!26Ep~MuhPxkH4cMy7KBu@{p6SSuzEEl3;;J4qy
z2AGks41oSUR9^D8If<&V3Uo^`Qw~Dw2NLx4xh40-u1&=29oZxz>BCh;fJ8*XaW4{J
z;x1|ZxurpIkFb<W{uD%To=_R+BGr*3E3?tz#4Pw{#~e{UZM%}!AbqCP_lGipC{l-k
zv0wp%JB>q#q*x7tC+jQc?Dm2j6d&|IvG75cQBvYp`uZ^R5|HDonfZprLa<3pY^@G#
ztSoHMXCGvxUilAli4a%q=RJu){AGb>nBK@j!vl70X|89jbz^dNfpF&p4ve+|uBxhJ
zZba1=Uy4$WhVj$nB4ctaC!dy-)Z;AAqd#|7S&@b>Eyyjie@gg(dZjL*AJdb6(Y{@D
z*qVSiq{nNexCC(qvP#X%!VLvWt62{D`&Ua#baDhlxW{&Dt$XS5Xz2o;f2-r!*ywZn
zC{c~S>uW#f=PwOpy>9q=)qRwpGh=gq-q(m_e>HyL%i(`Mn7bGgTNx*^^2@(C13I0I
z_O>Vc6N}<Q**W#-q0rv{oVjP35L(0|vnFA)uO+oDPUYCj<W!eD9R|5L2sN)Gv~2XZ
zl*F-<547J2CbrhHk~PO8%+3gGo*ls~mC&$3UA45(r=aN`C)%MYsCg!+wTJIKq4Sp+
zZ|yIrnvWqnoBIaaK#v^Ik^5x%o0q)HDd+0~Quxhh@W%q^4})n?!6qNfeLQzcG7h&B
zf2~~^+Patklu2n~>;6>C1+KNO5v{w5kF_Y_POx?wz!?Okslzo8uTgAJ`9|Ey#{yCQ
zNyTGra~>6W?fi%Onm7_mt*6*-fA*4QB(D5`!a`fk6_8c1x7RSQv;ob0t}!;iv*+z_
zJFs5wp!wtDu!w!KTV#mhB8BTJHL<seIu&T<T{N@zuw*Vk+}&jpUKK5%(IzHWGA_cb
zq*45`p5~abj|ncW`0Lo6&5`qHM8x*@02k9CIiGamUm_~K&2!uL8X`<pDk`e48@Hb(
z9`ac4!|P65RDVM3bxfc@?)M#soR-!Gxv#KeL}5%}_d(^>ryF^<`>i2=^zM5$@AEN!
z3CX+4=(WT|H{xA}=2NE!e(;4FA^w`MYgKwnOUqy&6llQnO5X<lMf>>n_O=f6*^g~b
zY=CITIyV=WtM}>UMhm~ax<zLICIr(M<=)<J(BFc~evZ$bm*a--t<E<1Hoo$AA~q6F
z`Y7Jj*5=#|wj3eTIT<Jqk<5WOHXY*?iE}K@JPmp6Wd$)^^{cpm$T~PTi`H}=!kl0Q
z7zk`(p*|7FVgUjyn7c&H?r_y6X>iiXTE`wq+0?*<7er=x32}na=`^WAn&_%nmgxq0
z6CtVN-WTnCfXd}%z=y8+tH_1($fD{FK-2|UPFZ_aAzOFCx^+nu=af(mCJ0d}<hZ0@
zLdnE%YH<_x6SgM&o;t<O7eag<gcCNR&s_(2gRF+Ew1~)sb*6;N_z^Kj?FdVA5SF5%
zPV`tnX<hWQ3fie#=MnG|oxauDsfyUqlmSK>B*4tj<ZPJJ=@lJPFO61cT$ObmSYjEB
zdW5FL75wkT-ZEG1j|@e5<|rPTJXYJRbreHMcPOh=UlLeJGTP%d(d|vuPlp7KsuU~y
zqSsWeXkp`Cf^+Lp82iHTw?ATrvSXF&FHNP;qLGl$k<}OI$=fD>!SR*i)_lC<)Zerv
zB*ZPodNS5B*?47mWZih8ER>cJsLf<zNVEaODr-E?Vm_MH$TdWbdrwoIg)fViK4`G8
zvb13nIfwdZCsN(=MSc`QLUc47CcNLe{EkduA`fi!@Xl}r#%zPbwSx1*$^Wdkl(Uyk
zTzrMxQo|8`=ekz}643@Ui;aX%P@Cx=s;s+<00K^FgV5&2u8~@!wK?Sx$^Qp;tEmWZ
zXo{oa2d;e}wdojP^rTo3_YflinfW|~6p&D%VZ^yO4P|`@TFA9!s*o6nKmg}Pm_~>{
z9Uy`T$hmQ;2ZuG#4E&ypZ?$;SFX=6z;pwqy<sI=fz3Oua`tE`XUy<}Ia5#0Gz7ns4
z=;CmAe6LG+#GyYd@M-OpxL(ZiS2_R_?zSy#bvl2Mdw=Sj*hX5oDmv?u-uK1l^Up#{
ztk_OxR@ZaA=lktJ3;nirRVBaYY(tZB2e0CntJBGJ*X^3P(njpDE)A@A3*+0<Ywt{r
zcs6YcU2`J-&hCdv$=)T``8m{Jpu^(Ch_q8;l^l7-`n=K5)JqzKhm}(q|8nrADVG=X
z=49l<DJf}mk>ZioNyEayw4*zBUr#Jv2z261{~2O_lL+ZQH;6N{JL(_~bG5{omFJVG
ztZHO-eI-st-f%)*4a9vH$pRvYpD@2VxJYhR*jTNhb6l7EsiN2p>>O5An`HFua_S<8
zz>sv{l##t*D?_!x#Z$6LdEtx3Fop!-R)v&pEeRZ2xeSY{)oiTJ1x?nOBAEKI02<&`
zwA_a!%B95l>2&nWMSynO%MesqprRR^Zkzk!lGu0o8R<{p2P&*zX1xJQKR@$SEa2t>
ziA<cM{9Tt%*7X-tjV6hAQMUauP=R%#y9z(xvGBZZCewNPRF7kn&)f0lZYiQdt$}q3
zyNYGy(>R9=Ll*so=Th$Nm&I3C#$E078dK2%(d9(du$XtVskcu(aIYDdH~{VI--WK*
zhA-+^s-m8r@0*>3q+}MCixktI*V}1Hj`!1A2StRj=f{hKp5D$D%iQ<J`&qq?jt&GI
z&T7@on5P7vR7?(sgOKA?tOsQe{nKWSZ)U;|*7Xq6zKf08S+3n5d85?p1$!C8zs~e@
zb%+}dCnY8Md_Jx`<8N-`UmT?nAcX1MkL}-nN$QT<ro@z$l@&S)3H3w76nS~a28y0?
zyy^ra@a9)E0GpB(c#^+puB9qDpeIcWr&bTRaa%!H_Wl!$L$Fr6#%O_c-51sPiVh12
z4XS=UQYU#$ulS=GSZzNx@z!4aUQWq7%YT<Ch(nU3nE5ZbsMPOO;wvHBOS{l5Ie~m8
z(TTXFhNWX3Oa)AUPJJDvJoh&Fj~@dB<TNdr#`flQkZ=-QJZw$Xg~!K%nCcQ}6$#kD
z6fKI~76u+g-)lN`P(Tf@H*1P^cF<@{X~s~?R=>w=K($!N`@cC#1wv<MYfqEoz{8rr
z608|VqmNA<>PjJqfl}UDnD*e2rxmak{G*q6gGxR`!gPcx5HvA;h&9PY0c<7cp}Ny{
zcGw*lVXnXD>hR68hJ0uR!@m`ybZoDo8JaP=BqL^K9U$5jw$3!EBh#B7-9OJrv{mUk
zgy!Zia_goEjiU0O-OG2E61R_w@-EMs=N9)Ee`ztyuReVoNsz;z=lP5YzosKht?*$2
zB}JOobh9tM8rD4v5;yomLeWpa<(yF80VtUQ;*d;hCy$NQd#WrKC07s{&;VS+OLUTI
zAbBKO%%kM&ds<vCP1c5j#_PaW4(Z_(7>UKk7rRGJz~OvQd8tV0VS7{E?b85i>0?bh
z{~f|(fS{xiV-DnnFy5aCwv7j}-<2P=U7&BqVsOY6>I%7N*A&PU-l@Nu&#P^`S^m7w
zgrU#SlC3w6|DjFKR%VA$11+%Gyx~gh-|b+Wb4a-Bdpm{zIkb|Dd@^MgLcX^#!c2+J
zj^Q+>AzZBi%26Mt$jz-6=Jgr?d2qtKNy(JqW^I$6*AyVmsRgRUhlFEtCHnKe*Q=Xn
z@03FjsMI@fL}DeaXNSy+?{;sG&Wrw9L&wE!3Gu3x$;jopswg$AE(mzt<6LV{JT59c
zq(9+G(*kP;o+*FVsnrJ+K6)S5+se_wwf)>5NRCe{yZ-ukdap6dsd3xxT5$YUnVF7U
z%V?0cYEj+pPVem5{?PAy8`0XD=}zl-or>1dBKF-(|B<cV-6?-L^WXP5Z!ZJw&$%DT
zwSaxUw!X+=Q)+!G{1|0RdrupiEL&50x3X8+f)Z2RcS|nf3R-NJ?nb0Z!a9T@I!eU?
zsc0N&jXj-_h05H;F=v^R0r;86D%*(i1I5pB89ZMoRfICqnL0YSDU)HBbr$`uf_9f1
z<q^>p`|8Jy@Q{qNZ2A#Wf)5i>UuD-QVOvx2a2zuQF?UTD751v#LiyE6K3g@u^ph5J
zfbmNsySe+*=%fyVxRE~mv)WppfW^cS-74C=8d8@417SflnnXC4@G5*%r0;9TXX5+{
zTyFBR(%bVwBkKG4@gbEF5qo1w;y_0+<5;HY>5i@`W+2)%BL2>e0G~7aC%>Ku<rlT%
zIS4w^vD9I07XPds5r#OxEcf5wCsB8dCajZcy#~4-N7mcPGMTo8C8`|fbYH+dU$EPU
zA(FeZ<o#}LigM(35kTqE2fIQ$_`V=+g48qzIyRH?D{%6EAc%0k<+f8*Q&aN^I~G%2
zebr%|=*CHoiHQl1&$l_1;?+`mk-=`Ks;VkakyN;DdvVz752B%=fsBkiF)`7xk`%S?
zNqbBDjeXI^|II<VRwLviuK5%lx3gG(JoQdt-1GZ&6pzTk{D+$(Nt4x?d2XfnLU*7k
zi;0;&r|cAn$9_rDnEcJo#*>uQ&{%`Vkv_EzwXK?GR32$K8(9?YigROAJX(&Fy#s;s
zi^u#LuXcRH`x8Ro?SkCJ0LiB(j|l)_nO2%@T3-A$)3*e$)U-9voah}ezbXv0eHN{O
zb&5(1oMkNUst1<xlRi!BNbJZ+@v%YV>sb4aByE2!bs*}<R#`m5r<|pE!<5twDL)0(
zTrY6U7%nZUYG=eWtu#efZA;H_JKWn7GkB+m_vt68E!eN8;;>jbS7aBzV?UPa*f4kd
z?o3|F)ZmGV8lQ(d5*rG$1cnS(*q(+*H|4GD<hAML0<ND3DD~Ahk5*s3ip-E2?a-n)
z!;2v=!Y*EFpqwi5&hO^*VJ8Rjub;oz+l4xq#Yhs*gH9}wCU3`FN=^f)n5B5Q>Xg`i
ze4Kc=$_TwfI^MHJM)jt7A)c9-WwVnm_~KXASfphsplIpfDbg0o)eN)yT;nn&!|jr!
z)m{)fn?S!r|I_eNSB9py$>+(!JJSl;?^1uG<E?K>Yd}OMk{D`XLytNza&qv`g9RJq
zp4jW3v-vZTdD3CJ%wBeaDR<X--0OW6dwx$n#R@~+03*E+q3h}h(rj1j-be?39AM8K
zcHfh#Nrb1?a+hnI4i_zG+FeGKipo*IQ;mV~@+xH21+BEWzu)<xxTo(N2{l-Uo3#(7
zscY`<zI|{~w-VW+gv=7bp_Y+Ao8rD&#-xizc-gmWdHT!x-9KtFP~G3~2{>_M+y!c$
z6)GO}G5(8%k&*U)wcvdJ<}BXPFITC)xX3{gX*!}r{6taVk$ZE=Qwb7Uo)qM)R7{NN
zpF`5)9K5X*yuIzszqilz>q?XEDczK?8=x;9X=O$qjJ1|daju1>{+8YvxP)Vkx|r!Q
z9kf~jBvV~#p*_pkR%%1%DJuO`9}$q*|4KXpFCmS8+Y|@g++kheOS%cYNQPv)le6W+
z<na6+GZd;qZ`Z`5Xa45lbhMF{w^Xz|rAAz{O~b#H>Cw*G&`9C)YUptKewtU(bq_z%
z<I(;27#Mw8{dHJ2rp0~fv?05)BE>Dv{Jt^&m&>W;P1=t2fHwR$zKpCj?4w*bw$}Jv
zM_FDA1pwa3+ZLT;im@iVAxkW{`~I_Xd^6QwXiyB+GU<9F(_$@gpN%T2EUz`$Aam7V
z_?4nZ6DyCByS-yDucY6_odqnblvx3vUlohgG$c8<OPvwiMJTXO?76-4gwA@Ni2O-|
z`B0YrUX<IWh)FUthk|a91(l2*E{PV-iL8UdMq^YEOHyV=0#bnOD5WI7lADV09(D-+
zy}jq$ZlmmA#(_du1I0A=FaGERmi?5}tw`Y^Dz>bLMp>fN9n<e|$8}B1ULz^Ak|bWa
z?7XzoL>Ux=zx<I)Jb_Hg4c~VQ#%m2X!Vm;h<ekZI!YFV<<M={jxVgGtnfcBH!k0LA
zgK9XqSu(7TG^83K4^ScL%07N#2gx&O<!X}Xg|Fh>Cn|gjP_0so^ZVwfjiXj5xcpim
z94dBHqMa|KcMr#&TQ=V++rIQOdf(YLjxRU=wgkp)V<LX`HS}*T)*5;`M{E!AeO~&e
zrfk@s9B+07{RM8`9?!SXLAs3iw_w+v|1Z$HJDDrk!ZxU?s`~$!hot85e6^($OGQfh
zD3>DO@vQ?z4#<qlWMXE%CzQ3g`((Q)mSCS;rXKHTCn|uu+{%tQL5wt}&z3HFAIYcj
zX5_eTOjl<M)q){s`iD5F0IeP$+&~n0>=)rY)VCMZc8lo|F{tcf<|31?#t4*`$p=Mk
zbA98s&k4*()VC_L(j3Z01fq*`UXU1`Q1Ae<IZ|V8+-^C5K1qbuID^0g6PXMW-9_cE
z?@j@wm?%)r4;>rz(`(~aAA+wxYI}%-0T;bEo7z~4B(>2Q_a;scv}S)vEW)*=X+WVz
zcs4xh30K!NeMnM%f}$!t`em^K>^%n;r7NXxSPR#yE-WXvCC|?T#m>XKN`cl_XHH#f
zhVqlMgicR&D4@zvSoM&%H%Ufj8;}yb!W!8;TW&&(m8wk-+ZhQ^TpO(M6L5gRg$T&-
zL{1beB${!Csa_gDE3rL7gCBt&2Y2lahi$e%q+Fp)O;M~8fTKDFr<CDYX=Ya7P>ZSd
z*8~IOc7FqJm+a763<vFRoI;B@d)S=+d~NZ{-++X|?OMZKAvVlmYtGjk5BJXB{KZwl
z8Y2pO)bG8j!I9az3VP@UIIm@0`(T=x&ZPv^4vRD}wii`-B(5?@647G&rY7D+Y4BkV
zSrC>X6?=uy;AAA+1doB--b3F(w>34(tAG<YGiT+%G7AKbQl3i*QOF;jO?FA)AKpf&
z9D{tYa0qrJ{(z*lrzcZim($PW+gM>78cI;&5mYu?P*h*csxPzaQ2lOXqWd4#MI;bm
z#OpizIcP^w;h9MNA|KHt&c(CkAVERL%KRmqw6AnOfJBWfL3s;T%0PhjOSmr|nC8cu
zvzpU9<2B&&gZCn<$R(ExZu+db*^-R_wL-wf6B6je)?HEVsI+`IctUNOvI2jFHD#Gu
zd3jl3Wm#p3RoMkB4dU0#aC<37YH1LdpGbax6SvZHjx7HtzHr74`^O0G?M#M^uSe(C
z*5=@{<@4k~Op~~<ou2gt4LrYx<9$g^GrF%1h8MTb{n%slV_Nd->177?vl{y!H0hM?
z)AV*7&f3%-9=)!0H8zc@4eKgjM$S&6E6Y*Cn8*yzbW79JI(o<cZablxV{RFfbe2Nw
zXcYe-obM=>nsVjmaG4#!5G_`fo?w%q5bTxsF0#8g*)cv7$+&qGWiZy(G>FP^V3x4B
zO6%u2aAP|Kb7tOBlIaSLG26<8ik4-Yl)ejc5`L@PuR2;2VFZT~E}xQ&ZAyViuyCP2
zd+rUj6>Mb<a9b0ySOxQM4UWNS$)cE|Dz7(JA%#!Y<L-jnfp7Cwz<=-wi(>=Mi;*%b
zajG$aKY#rVfSo><z~^+k2+OaG%0Dylt3#BTnAVzR&_3f3`{GbNWDq>303!&UR}EK)
zr=DM-VP*>h8wJJA9!}3JKJgZ9f1}je*Dt;+8BEN%t=5+4E=(=WP0b^jNZ?DM`3Bp*
z#pyc|sxJ2#w^;j~M(lFjGui2dXlC3y&sfTrCx0_E7C;T5jt*~#+8w3F)wr_tq<8pf
z=IXkSXSdURc<X+h!n=kW50A%la(MW7cR1k&JtNg@Wo7mB^fVHKyV1qktIzP?@L8|=
zi81c(-Eijfb%b)v2lbA7zTPhAqd0}vZd;u2r{^dB6gNITuJir$gLhrF6Fp$zbu_fJ
zTQ1o@HRJaIVX@ouZ&EFOe*Uo?5*nHdVK&wygBo4oa$MQ~&4g{05p5zcnG(VC_DCK=
z*D_^$3x`T{D#_vC+{;U3l!7P;@U@|b*TiGqK1+}-hr>=1N0jzrNa{0tN070+2TO{S
zy-{%AJyUy}K!ehVg#>sBP;#@O14qMg`P@ac;xVs4KBsAh6TV&<3|b?$>u67ts=*ee
z=@o!-SSABh$h(0xheCXC1cvt2(K<b}ta`3<iUWkG3l+qKT%Kn<ZQP1nrQFygIwuiT
zpybbj087i{TYB#~H`~v={C&b|VZaa@80tCVT{tc!DrA*`X%hC=bKl^GNT@-OhXaQY
z4Y;1VPF8#dbrjJuHz88|7r2s-U<(pOw`(|HxM+4)##1Xb9E)=^4JQON6TWGV5S%Fo
zQ=Imn6UTPl)zzZ1xr&+}!9buzgjJMF>}dTRd0&EOCP9-zlSSLlS4Fo=%>4=4B|}M7
z6H7G#`|Yr7oGFLI>yMTZZ>)|7b1K=^H)At^tk21;I_UwS4n11NFqT%2DDJb!Sr`l#
zW<x768ccEw_Vm|uslVVu&@~2(q+?S=IHJtXsdNLU&xdZTAs}ICyU)6!4zl59CP+zj
zIlA=pl*|Wt1&Lk5<l;plDe)W9i3cJh<AZ?i%#<K4D?4h&T*QNS6u1iQKDaU2YaK|>
z97<R>WL{`WAFlsp&<3Z6jTBDx9BU2j9U|!jp$s~wZ`4EqyEN#0@}UED3A=w8kEj4<
zkBAb#KGFwc{G%#}Z{>GDR3w8RR=o?LP(!eh6vQ$Os!sB;&H%Li-k`56dmEIU9cB_A
z`AXbRh~fq^vqmj5k(O!}JeXZv1p5z_F66vC)NM1^`#MLvh8CAbM(W8qHKoRRDiB?E
zqhecbc}0PlnJ;E(g>|0C)cB;VF8%x>;xD$y2PW9A;<V7;_*X}C{G~fyd@SGY2B)V}
zb}ONJHLzRQ_-#w;xB4!w?_={-RdrLBx7pPRp2$W!RaJc2ct682^F!0`N6Fhwnd7Xf
z__ei^qq2x(^aT(Q+5+!A?w0hcngg3jv>mNfZ5)eh>Jb-|JhI@<vi<;UkB2F$VNHmF
z4zkt+3+6-%zU<!=CNIBRZ(>+hwH=cV^aRC2_9cAmF_V?E#qg$MPYvB!$<~_mF9s?8
z#t8h?^v%fY9(+0)O>19e{M8tH$lCjWGv<&()3jp_<*hz(MclVC5x%e)mJO4Wc30}T
zr9u7ZQSe$8y{3@mQJP%*0QJj~EQe!==y9HLe+YAl647X>v<=M=gj|3IGN+tuMy;Xv
zTm$#f*+;dof?i-ndHC!MERlJHB^H-WC60ZiEkB<e7OpmOw#k*VB%<{XtIU;leuQ&*
zyK_K3%2~ZseF~bwJJE(_Bv7YFv%&;JcS62B-QnclekTY}rqKKt9MkjDpe)YlIR2DB
zk}<A;cmmptQXfviOQempOvCELKZituM=T(@)Aj7R=cQ<rC-ng81In!8>=&Z<Ml!i^
z<C>1kZ^8~r(jUEyDFeo}q7vWthtt?oMNM~oOUw7|2)>&UB@z!0Pxr^&#MUM%T<HA|
z_C2F3zmi{U6Th5$#%IEJ*1w5=A$nfT@B<{DD&ltHQ@n`}*qbgq?)iTI@Y^r`V{g0r
zX~eFKmG6fIIV9@G0eF51*X{48@3B}ZKLG+HBiGaUB420OF^4g^?~nVLYtQDbs=*A&
z?zi*r^f|(^*PY;>x6>!=fB&`lLKp4Y@O2yfuf$9V7}?(`d$|@&eui&uZk9U3=l%1L
z)$8lFPm1#0E2-{JP9lzYiR^wmSza$ifnth}a|IJ!YS3RojYRe81<!t<!fVBDYz!J)
zSYmZ~inuIf@4+hZ*<OEu*47#h2${8!CA16|swM4*!`WAv?R}OG#f4vPhH0^l2}dS1
zaQBvJArVJ{8pxF=sz9EU)?xYZJ1UnC<5yZz6`<B48)mFnZ6fsnu9F*Ka4M^HZmqF%
zs7sKkRmu-3azmeT3%8O=chJ_f6cmuPyF1|Uz^Mw?oHj3qCqOxRKLV`Zo$s%{u-Q=?
zYiO*$$SBc4@UrR#<_@>U?VHQOu4R^Ie%yT|oRkk2nrJS`6zcm=S>*;1_E9km28&dN
zB-Q}{Sih7;AnByZ%!*W*UK-6qvc$ZA!cMa_P!m>CLZl`IhfG=`6!xJ3lg@ITM)!|W
zmJCdTgYiQ(paXSb=J*D1oedT3nw`G`5>Rw5h1}Cl2k+z|aE5`#Rz`{jJbops_{}oV
zrEo2+CUR;>q#laaO7EUL%?sU5D=(?*`Iny#T_gnci&#0NkH{O*VhuFkJL$s*)rs#P
z%3qzHZv{TD$Omm6tVU!O+G2@GNXbf$83*QbQTD^;w$o)K)yB=(K0X6nZi$OnL-X$I
z1_ICK;zbm>GSa6aAo28bJ+)eEKT;=;WEA7{eljO&ECY)i6Mtj616U^UQ%{W+di#Oh
zX9o<Hv;Ci-Aaf41U7uzpJhED;G8N%R=n%3<4?!L#;V11|jnw=^ofvWx^%>Jo)pKZe
zlFQ}*_PHIU!<ZBQY#Jr#1PYrhFlKu{(`hFc;7^alpx_L9z3UOHWjc|=4dNu^N4U*H
z&-DZdZ2B4C=<49kx&=6iU35%x1M8ehW3VKyIy=kj>cP~v*R=<()BnbjU(hf}jg1e8
z@h>j?2A8)X9=4rE-SW_>`>6E1>l;Y^5u;TDe0v&P-HaV8%r4HCnnt)cTCQoUFJ)vl
z&2e|pc93IvENy(Zx7yfN`PdiQ+UK2&>b%^`?f0ffF9ynr>x=u+{}qizY@9DfFZHLT
z`QOGD*O?CX1NnqVYaJcYB%#5f{e;EXzoHW2(3yu`^Z2V$%RN)>i99IARk(jh{n?`+
z&ka1<h*P4Lm_TpH!-XkH5!uS-Xi9TG#>T5_A_L&c#<3|#u%mARTrekHvuQ!6j<}}|
z*Q%2}WSIsa7J$he<Gd$F@$edJAeodgTGW%AdfE&<{gx(g^r-sjj>tAA<*;%`7L9m9
z>pzCu2=7@--@6)%`dYCA5kVeL{HfmT$y*AeqfB1hds%HhQ6G(G%6&XGA3CE7?o6m{
zNOKz^GUy3T$_XVjNl$(}VPEe>XjMW|qz-Ni5KHKHRTc@p6T8m+EYBrY?Dhvy(R)`P
zwj*V)i=W~8A9O*HWRZY_ej`ap<OYZ!hv|x5<C1V;Ea)(>wf<cN>=`%Mv1{GwKAnn~
zadx^Jk$2Q(xqoJPUd4w}ezRvgeff5$aTmCQ8dW6ZQ~q8;5T_a?mYYBVL^S@+D&=yZ
z5tSkomISfM4PxXB{K5Hfi;^r=X|$y<)RYPP71?H`K+H_(r6n<Ji`<7I`7_1WyY^b-
z{K16)(mSRhU^LMjj_&iHhFQRMg8TIh-+R<?%(LFYz(8LhgaZeT-oKivDpUl7T{9qH
ze?70;A>GUyOBG|^<}O(p&v3iQ6JH$Lt#*|k=Yb#XqnerL@$G9U<=DH_0sG)#7vY^I
z-SuWCyvVj1Q4ibA=FZ2*26wRjwL0e)qSMccyfYUs1B1MhlGC-auaCWon%c<By<Kyw
z9t(%t70!2UlA1YXeSN*Bp4q)&+x|!_K9{rkHMoC=+x3;G2_$HM9RvMw__f2!jlq{D
zHU^B(Ld2lk_bMbWJG(epqLY(TxCoKIz%Kp4?e6(`#yhY3DJ(2(jr#Qa52C+b{OE<Y
z*N0CNMszE8-VOExk>NrQApWKR>?~i%s5QVjMRLhbD7#Ll17AUr5KL@$4bWmlQ=Sd?
zz3vG6D)jF?A>WT@A7%_sWf>_`EUN3G=UT~+qbuNzO$^U&Z2VaT=9-;sSq8rJ|AOZd
zzY2;ChIv}NgHo9-Q{mAe+8QCBh1}oVF4x&l>BX4ooyv6cVz?V|+FP(2`XloyLzbV-
zoVP23*95i>5E$TsOl>Y5Ri}A5vI2|svbw<3As;0;k4cwB{o4)VNg%f%E3K^{=<C^#
zr>0Cg1FwoSrzVe5MQs}fwl2GwPb;G`^n^=_OSWomlRa1`rz!8hYWU3wGK<xTif*Nd
zO@oJr6(nY=4tKDwTNb49w*}Rexz0xL2^7tUs+DDf1#T8VmpOp$N@q<*r_Cys|3T=Z
zV-#(b1nlarERDP{G{e{TD&(ekfmHFQ{n-&N+gAIZ*#=rh1of?hbTl`%wmS=+mEX!!
z9}XH5>q0|M+bcuSOtKd_sAA%Ej{J{pxq6R|%2<priKhJ@giVx+H1mP+fW6lIx>+2I
zCXF5!N)a+dk=FJ(@vUBL*4obD3?eh`S*3;nXw2}!m(n4jaeNm#uHwgB>VJd-&C9gA
zEWX--@a>4&&%3p@R7}ZXp+tc;xb+1EcN&r*qVoziR~~p8CV4Cz<&>b~jx>U!bJo)v
zlJg|fZhyecNyE<zNahuMP@P5I^ka2AbM(04ud;)Bc0HhrCp@<wYQ5fD;T+aw8nu8j
zVufMaEQ$*_?z5e@zC`32s~uZQ8RS*DG?XyQOMn@d?HLsBnpW<YSi@NAf;lw{cw`oS
z4MXLDTQH*8`<bkQHLc1=)*>*o`?ZxN#^(jMMgKr|lP^VY-<<Zw{PNTs^CUML%Wv$A
zG;l)o5fY0ID+)*wk&b+A>-XK-%drq{__vlTr)9hybzkSpxzFg5oNGRHEwASG2QRbB
zxyuQ+tI6x`R<HM0M{&_1tiSK}?Z-ogGe`C#7y92#!E+UnuoX^^T5`*5SG3V=ZVu;f
zDG*f6$9FZde`Q?MXkivBT}`)g=rqe_y5=P|E~v39XK0Iu5hDVf?z&VlzX~WaiaS>=
z9UaZY`i&jO71^A#5j8=Y&823l0}V8p2CNjdtpw5k{A-RZy-CI}|I40Ru~rdcF%Y0r
zDpPwQ!0DV@*&&aPW<LEXu^`xb{kXTdwm$j#lv%{*Wrtdd<mA8u|7G3y@73^A!axz7
z&AbZBp)GwjM!G66C?;>9Sv^&Q%eAHH;*`bPlffxJa&cQjbwP#zB@}3ZrmNo>hrS$Z
zth@|2nH7oKjI7G6GXM0T%*>n!-SI0RBPyk$KB(&AcGM{W(kK~)bqr;!!Qw8#J`Gt}
zn6$BxLt7n#k|czS5`dIgs+1^-%L%Ef43B_Rw2tu)29?2*N`mrHL{Ks{$SWk1NlcDT
zRyfMmsK1sqbS#oQ9Cum;jr8|~0342>bj~o<=pKN|3}3-xW#CC~FnhdjV0t<xIe962
z;kbjAk&#hXCLuAAn4W${?)K@a+wB@}XYCW^`62KTYa8c#I2!vyB^F+DJOiXA=k;5!
zMpb#+!|TgH>aH8<71qJSF03<Uf}8ysB>p*hv<B9~!h+}d`1JJE6(yOMFqqvJ9t!Gp
zOZuh8*H5bGY6$Rc8u{^@^Zjuw913@9Nk>orUB~4yH8wVOsnh=wSb^cOy0x{nptx(#
zv9lQa<=^!|fN(-<PU*$`f_{79vh`)s&0Fy^8rMaXy!&!~y3o*N0|oB84q~_D#E(;)
z2$4o$LzHT|u@XC;w4Dj+Lk59tjO1|Cutk~`1j9}19b_3Enx`?s1mjf;)jCh%45YPx
zABSuLxB838ZR`X@gNL-pKQpeo4EmU(VQK6vte|e0FPCRWGuk!8n67^{sadce%2ZOb
zGcsptvyW66t{}N2f(4%ZdkW~;C+{z(*n{45!$EU!R7^m77+TpssT3D`qO!EK;nCYr
z4fICAa_-1`Xp2{vKQ>O{HcRqu%PeCx`J=pPr*I?-#1SdT;YK3-I19)^^S2C6;K23w
zl5=$q$6hucQp}nqBVv>`!334b8Kxr?xK*Y+SAm)3xy}y-<mnBn^$8l?#Y;9i-SLcw
zpM&~zh+^PN2)QPwFErQvL(AC9H?{lghA@_<c9O1cx1hXEV9Cm|GXu(dj1{<vu%!V)
z3?zfStz1MdbWOE*BGokq+S{uD%)nlbT9_R5Fk1#i^vOm-@{N2@7aZ2APZrT8EorXA
zv1w7Gy4NP-YkhSllvbAD;(ykyGMfy%P9)sT$gYU!(4nNN`btWtX%XJmdJ93Fk%M9|
zxk*0*lKl|?JU)eqw(V8M=%@k8-zoXi3WXs?s0#*g2J=y}MoJQFNL#4za!3h+&;Tgj
zPnB1c+QcF*n*O4EL>y)JY5an4hgQht1!g9|Q=~;ifg+F7C$G-qopGbo)TJDzmmzRc
zwG~klD_Svwr0YpiD+vWJ&xE2jR7S;^=XU?7*M#~ldJkrSBYJ)h=wfD!e1;T+vxvsi
z>l-r43_0FU&|EPLVuF7pv@sR*8y6I3DwKM5d=p3Y9wikn1?dGb)fKe~hTX1VKB<ve
z<&{w(@T}6!qAD~C^L@*6y<-GC*uP5xL03xuHFFl+z>;|Rp~F;7@HRA=VUNIvN3c_{
zTiP)O0F_Tw-Qm^SeBf5HH2eFo+A4cbJ8MseyGhye_WCZm8;9ODrcW(%?Jec=-6!L8
z(^}JrT8|gg-4dN=QH@Eb;f>VH%R<^!*4)ulRj`)Za!qv9r1FwY^D<wSx%aS5*OC_7
z)ZiqeBOktv9^PmguE3;h!Z7)m%_pZQtp=o`yRhw`JZ6-VG&W6vvw=Kq?t!iPr1ka?
zV&OX|BnJwz20jQ4%x~Ig*t34}m?MEd#DOWdwXcldXNFmKC{}DAhU8SWEX(PL<?InI
zmw+anAyOYz;q0pke7pi0`jsdcMXEbuGV{9gM+yLxYeL$VU`+SCTszk$kLCu0M6vF$
zzAdv98M7tb{)TPJJQ@_G_od2IMr1NFD@h1BiUl5&^CCMUvs&#Ok3}_+xltjCwbiaw
zDRE5|m50L0x`asIy2xVxUSaT)f?qi3rb1vYZAcs{1rk~@^lz0+g>udU1)9<@UXmyT
z#BnC&J9MryN=iFVMNoHD(x)Wtgd|{fMIx~m<K}dh!%oMt!89hxIVs{MA#c&blkm!h
zP^kkji%`HJE~lg`;|&+BUmVJG^5@iM_y@=ZKSRoMc2?FQ_u=Q4^iQi&Sve^uzoNqP
za=q#ET}Brh>E`~1pG;FvkNpQ#nVBo9tH0hi)*WAS!g*;Amm<cwV`unfB>yWx3F)q3
z<5wI#j*q+v{sYn`|C*wkTQn5Z?zn!_hT!1fXVcG4{POa028<{Hf+U~V?QPw!tf8K;
zl>QnsC#OWYd(_a%yZQ6;^E4S#4UM$Gz=5wSrp}WXvsE=c?<Tc>2M2{+UA+7Q{@1^N
zfaEDqvH&^R**?&kzk8f)ce<wz9m%`J#l*mUjV5pRM?Ag{ieh79oj5YHvT(oJw_{aI
zjEx7WhYCLKRs`%I+x*A6+A6tuAL_jRaz!ouhFH^tkjXK3E6o2$B!aDi{iQ1pY={xh
zd+Lt^sMwtBpZgtY$!w|Q<%BCNqg_FBm)?0x0sSt0*r<O{*RPxCQn(S#;tQZ@++?d;
z3U0(>RWcW-tg#vQBi`WTF3lG-w-Wt|Uak!Pjex4=ZP8coRqXv5l4hvkr-{u}9v?>A
z2P&2TyZF|C$OO_5ntBljz7g|IplHEJQe2b2i_~If64d1r>V%Fj+5bXNzB`$&D>dz|
zJ*r<g^4R$j&8731T6&U@n$I>x(c0c4ERdon>#@z2=SYEgjhB`(U@cc)uWegSSv@#%
zJR+Z{5T$f0@4-KvHIwfY`Bu|-&?4~8MP!PVRgt)9#q%56KiLw<XaEvI$&HX@sB9;=
z$=T9$!NHI)Ls$P_a?B-A$#VXob^4T$ZGWgKNc)eSJt9=)t+bj3NN=#XXu(xHb9)|Y
zUz^DTp)=kD!92Xo*b-m$QL*ieh|uxI_+PSg%0gqR`Fm9}8dCY{X>QIH1bHO^dRjVx
zHb239K4+y8EsYc<267$ZfF@x^<Pr1niIzIBoaRA|{B)#ps~cV&NsZko9`}FBhK5}#
zTAHRg4rm85%6?p=^01eAq&)pHHkiT85Fkpk#p;z--a6`dbHG?7ah8<osPq+NIlJdG
zmlSFG1@S03k(Yc17&g3|O#@uRj>>-k5*WSXs+_zF4OMj<{K*FArv>l8{r@XWQ(+LZ
zVv{uG<~l>zbflK-;AZV$+0Njf<dWl|`N=ZrJn;y9=`K$Gx!)4IvIUe&iB}dXUrbxN
zhfjb?MZ9F9x}kF(6Bn43x~^Yru0bP>2*WP+)4&j2&<18|%SLbY6X)(~C+WP1%KT0R
zmYxO$HyJ39iieD_vy_CPVmlI2B94HH!W7!Ac6Q_KYI=S70(Wb3acpwBA4)FUD%)5o
z;p=I7c^SV+oXRHeD4^yRW>#xQv(;|DeK^XRdddP@UjFfjOq(LVr(WU0YJS&hVdF#|
zh_>>kO-vrr>QbSnYx3q=@d{Mp)D1jDGqG!GK;vTAR5gdkUS5gK(lQ3nEh{gZF75~Z
z<#yV@kYK}Q9wvx7Cg4^ac$1&TmYFrBvn1fFBxm&|$yf{zdO{{Z4r)A-|8DYHRBuqi
zUMQp<G2_8^uc9o;!B9f6YSzZA)5PRWL3FNwS<?tLR4IXfQaa)&5{@4Aaa?srECqo&
z6%sx!uj?o%iaiRwnxVC>*D7XWzD`lAOt31ckEm8r;Pe=KGzt1by_$TopAeY;z=}@D
zK!Id%o3Cw2sb4{Fa+-yDu7^=YL0(KkW9BT#_YqN+JwlddM$}UZN3fZrh-QXL2&fAf
zL4@Fw{CpvJ+D{A~F`*)1p&(+KO&mc|3EEws7yPa1f2S!ZK3ynTW?WWEp2Z8o<;^6m
zM5Xek{8U$9Z168iDfFKZ1fm)IULiaZX)GcbAYN2t!V71?B^i@TiiY>ljo12H^uj~#
zE4JtBvF3w1_L;um0u21;YG`P9x0Cz|zp|kBwX~$_xl2t=ZP{P>GP(t!Jsd$vcEii@
zO-<;m5%Ce$-o_@XIlP;mdKWnGCcU#7@S#1NqNMUPm^{w$z1rKWM`ya-_V@Syq2%ua
zg`b|z-(TM=2|jTKzGe7}H;(ZkA>ck8Uc3h*7n{^~AFochxLLeSq0itzK)!DaIZs%B
zZg%~Dp1GDwNJvoY8JR%&vf%$965RUIU1x^+9egMI`274Wh~J&Bpxd62fnAf^+WMje
z&5=<bkruZ}M-`E@_s@zx+CTgHLaHe*cdAzYn?oN30rW<qo?Vv54uRg_R|{mpo0wPx
zC6y>L53LcZ@TGU$@TmJ%uesJekNK)-tH@d$$o<}{)g@_#$`THV30;8$LvE;Xl9tLk
zGH${)tDDK`-0|hcW<nDcRtZ&A>MsF(p7A3bOdRshGlb74CZ@mr`eGg(Xy96QP8oYw
zo1F}^Tkn<nSDb7;og_s0(@W79Xo77HpA%k?Tf+DZs;u_scd8oHSej>H`vePiE$IzN
znU_ZC)MD6VwyU5eISp=&H%;&kZZpi!1M06M#_>|^VyWSyj5q}?<rpxxZYs;dSun+z
zCC$%Kg}rkcDv}q60a_Uk$4aGT{W6{CbbWk$l<?ZD3b+Y{;wE6OA<SBZ85oYIki3b`
z%%LdmDj=S?<rgfmw(Uubi${Iq_ogADY9{)cDb+1D-gA#0S}SZxB3)qy=uX4pbR(vD
zO1GS*LixodGf9EHYS`Fpt+>(A9*;1ihG2yZ>nvBDns~j*GNa*gz)C_3(wF+y0;^p-
z-&}sx3BOQeJ9wC8ql3k;)Ptz>Xn+r%$rMVD`uxN4t&#P#&M+#S5mj+O$`z$W#>La|
z-QX191P3+Rv2)Opxz*=!I-0XXWn3HB<&6zNj<o8g?C<Ma@7LEI>eDe~ED0GY>F{wn
zG_(+l3Cqj~RWvgWFOOj%ZwCJDL3m1h+K496C@Q&CTt>5O-=m2gX@Rr7nmg68{k54I
z=V8?_ZN;!#xGallQG1F<gk(eb-Hh^}%9mD}i&va$jdf0USa@k&Y*j^&Y-|zZ<{Gmw
z3t`0-$J4(#1^=S%i&8_Ra(E>S<0Kc>C;`P1wa5)7k7A!_548kC*J_?{Ng&iPZ=~_Z
zII97)Tnjyfq&I<fE^YcGDmUfrBb#k_RexyeT&cybYK8_T=F{S2-pz@g&-c#o`5!*N
z_no_wMQ5>nE}if39Z*S)jZt>P&_Ay_kH=H+=QrgkQ@xF*kmTfuI)0hIpEF_;EpFJ^
z_hNT&`+ZeB(_W^pIMsh*YCC7=m!y0+RDHVexRuivl|Zm709n*hoogvudN^aAOu0`q
zs^h;QdYHpI=X2QlKo$JSWGU<>MWeyhkF*sgFzDRCV}E9JoB$J+l7TC>AYSVjbyHDo
z8>zHvA!jD0cS{0f<bR2oCu?6pRl$`ZVpwLbh933ac?J62863Qst{xokh;`St0CaTm
z^!y`w227=)Bn8VWlt723vUIH@e%(I~L5a+b%C+-}_oDEZ3ll|`4Z8tY*SZ`rCwuTs
zbx@1ST?AgLYP*fienD@9qR@;32*~(yvYxL6<XO&*_cAgXiuGv$cZ5vU2K9ie&>*3i
zaYWHWi;^OznarVZsJ_8i(Q+Iy4eH=b)(EvmX#|u>C0@mRA_-(pQALhuC)E&)%pvM?
zBGN^BauJ76fXPJUtTpNh;CQwe>$JVR_s8>qmc2g8KKtc*`?2=3rS{m+o&RdPD{^FC
zE{m&)AMzhG!PL~$j?eq08~^=ACNKI^OV4NYcOJq_xS6S`&#dA$#aCI%U48!D^!`1(
zQo;|3w+ox_6h3JBu>kbI5Bm>RZQM1aUx!eWKOG@}fL67F_PQ)s*5Q*9)r{=A!qE+X
zdYS%x`dV6A<mBWCkb_Q#JKnxQqdR(UQo(vu#KeIsrG9?Bau#8BOZZ<m5VW*apE(~l
z?UCQ)y23xgfe#V7za8I&9V_eKkq|DPTb}>=!MY`_+1|}iV`E~D&_CYJYjyy=rzY5&
z^6Q6SW!zW`rRlOx5{mQDinWzg7cXZM@!Jq6?s`%D9E5BnAR&<Of?z?y_Bb?-98TwA
zcu-}BkWlgA!lMKEMCf&*IqWgoQ(z<$S<7?L*J&2(mWGdS#)$>;;Z2NwLL#Hl*1No-
z$2bEo3GGyOT-YK(U}x$g9iXNd?p$rZ2KocWd8{%2afZnNLhMilXtk;yM5oQWZvSqK
z`F=FkRZ|c!5DRfrigNSvx1OV!e))(v?v3_aZ#YKtj}wxrBU+J*z=O&#rA#2D`gW~-
zxB<R)+OHne4+Dns{L4|C1(+k*;y}qxhEgFE@@Y*L{<(gF_?tZMNz%q6sQI?ox{4O0
zzkIg(<xi^vqi=?Vz<Ktb#zp!0Yp-H7F>HUFb9;x$l=w65y|}WMCu8U&fZLUL=Iyo6
zZ&jH!`dyU0^up;0#F!AEBxy#6MvJ~ANlSokZ+p*ucP_C=k4#Y&ZrT|$K98d`XIbLc
z{ZibLUC?3{t&f5DNiIG_A=}!1b1wea$Qlck-g#r?Sz?TZt=_G)jfLH!Cgim$@6cHH
zn`fNT#rxS0{kthT<*lUJ(P5y+;M|8Vb@v3t%o=TJLsnCB;LP+HhsZ%tNndA9K-4~Q
z)GN>`FXWUTmsguy2|2ezF1SM{G)<=lJ&Zz6r$!CO4u3Q6_#_FPYafY=*q5U%-yr9Z
zt=X%)MoE2IOt-zC?oo)!Ud7Yq;@yrQCBq`Hw6QK8HmELkm)*j^w8IpaE0>FylWPQ)
z?nt_-ewtPa&6=_Y3XxpuR6-;p2d>M3Sm_;NY>=~mLUdSIg=~1N?*))MC@kw`ltYYX
z@Q7w1w!<R8f>L~Ld|AD21d`AmRuz)axyp_7*Kbc$O!*tXrhr_5jFy%Xp-it)m_j2j
zK%^<b2-xE4>jg-E`W3z266ozG1Uz785X<4yZEfgyeVtxi^zZhn@v42heU5$p>@mK4
zoL^$Yn~k`YfM3<X>ZRG@pWA9=WVO1T&a-#8@pZ7h>o@H;B)63eJmWpsd|fI|MTX~B
zDJ)uhe!QW4Jh^<_u`0f+I$THB2baB7O?|nr<>6y<Ddn^=>DreOdp0pu!JW;E!Kv#H
zkc(T$m@2Bl-YA-V7~4FkOKQV&I_G1EK%~PTDy~#Os77k=%F9!eIF#j^#>IVFaNp2p
zM!n|ePK6Y57m&`Vf?roFySchIvvr`FWK6@8xVZdnd29s0E>1NmnsME-tBxh&-r(U+
zCVu#zA2xleFG}dbwJ`XmM&-~_X4BHHnnU<H1&(FS;Om$^pp8@|>|e(qW0U2JqailU
zQBp{=NBzj_N9o50Ck0iPq_-BOCx&#F`-Qam*I320M6`v~A*pIc6B2}fQ-e!#2|5EO
zDm&|^hp?^}y9>tOIawZZoyCj$9Fu)0Bt-!29(c42l%wd}{#Fu<3B7G?;uKZE+0PBD
zQw{h%c2t3FhQlq$;gFtJ;J3`1QAm_&QTfm3z`i&LN@~1Q{?WN@*zO2$uo7@=o6YN)
zl9~D8h>(z;{<g)BChKFH)%pBm_4CRO>isY$ZewGEAC$x%@iQQLNBuub(O3)0p4|NU
z76(IDSJ!V_U0K;Fd~KF{rSd%k6BAKUQIr2i*f|AP)`nj`opji-opfy5?AW$#+qP|-
z*mgR$ZQFKw&iAjGo2i<b`+c!b)vouf_j%U(ts4eJzMnsTmXws}F0(vS#f69OnjD~g
zM%dh4cmQ>0e*SK5_J8f#&CRU3T<B3prSam$-QqXRzjxd|S{AyN{qtgZX-#Gpf$OP%
zRr_9Mz1B81Uz;v}{ou&q!_knbP`F&H!+Ha0YwJspR`H=O#{;d=r3JS;Ls@nd*U*qI
zQT7Qm)o0O=(vSvWfo)pxQ}9jm+*e_wWYM%*$%w{Iw7BP>QeH)2Y<I<0E4-^kv@4os
zwMF95?(&FBLnOS+);JhmYU2>#7TS<t2T7Mh^F%q5IY}}(2dfK44r`;|*I~`1ZsX+d
zR5uOtY6~=w)x@SeHPK%D>aikZWzVdEpZ(cY0ujN0tF~_&1v7I?HdS#|Y$Z5WDCH?o
z98^e4w2;u9b8l^22@Cjz*NLkZhncD{EL`;bEnzC|@x;V1TX6}Z0&XW60XF@WcdM(J
z<D_3WtU6<^{-ebdhUkwkSa<(66nh>FcgO!l*DR^Px0eEI@CwPXe3vL&J#V*)2_d-)
z>M(VzSvM)CUQl!lru3=D?S<o=2EStDS|PtDhpSp!tX=50sH=6xqc5gq@vLk-U?k2O
z#+o8;V4<EPncr4-Bwz)c{ozaEZ;GV(h*!T!TP%bihM*vxrW-uDPl#j4jW*9Etjw@;
z&jx32uMyYv!9IFFGF6c*VwUl95}=bW8u#b;-ZhXj8n37HWWl_SpLhl2nIBNqRu%$$
z!{D5aM(rKl#`H-$xd<zVSKc=~t2f>hE9NXoFQlD=<sh-^HAj8tU9KFCy6b-p$Ur>B
zv{j%B>F{v2JC|d(4!Go=%lijgo#pf)+Q@)#EfE8Bl=Au=ha-10wh-+=)%khUNE-*o
z)YbD|y3P#Vo*5-sZKjo}<C4y#n6v(JHW_Em5gk_gpk?`}6hN459m#T4NLW@-R#1z3
zR8o4DjcRD+t}v?9*QYqSpt>|6-8-VWG@&;vqpYZK*-}@VyGpK~4pQPc{Kf!BvTH_x
za%)x>b%?K!&W?c^y=tn>^#523`d%OT+615!K5b<WT~^6@zL_&pBc_$Eai>Al(j(C5
zV7WgZrTpvhd83c3y`e++{GPjK*R%G1)<1rmz69RT>(?X8V~6S8YCfOVAI-9t#j^Lr
z9;D)qG2F<9+nSHu&?~O#Rd=-2aSafpT~)R_l%33~pN17r8ncR$R2UDc!U2lASI5y>
z;K`Kw*ZLHua4KK^Ym6)CJR9hnkw58B(P@%)tyb$qRXtq@OpJR%wGvlzaCicKrB6WH
zO2n9w0brRLmfzz687&W2>2uhp)()3|!c|7)d4`{?y8gB|kS)%Giwa0vXvMM6k{diG
z=1|?UIrXffJznM39jsQ>PF2VfnnY%L^rIaEgx31IPmI4sH8M6HHkTu-&I0ZeuIK64
zY+TdZ8l^R}rff||>LmyAbNR+>b@A^l`CzcWO;2*@N7pkc+Sanl#q)q`1X#BR_POK~
zP9F;G+a%39BiM2!Xk@d7r<oy32{9|l{Xse93K$FusnWa@No3BHLTAiED*d~;0&$5x
zhI-;2a{f??oZnbu2TLYUW=9Qj6UtZ}yMs-M2zUXweDNOV`X##~W8|@eQPjKBSgGPr
z4XkMHPbX3MKEPE|T3@c4uDZHA=O-cx3N;ng`*D_?Zw>#x*XrJ$K@a=k5H!2pws-Z;
zNeGt(f1p?#em7(WaG<%PeGI(dt65Qc2UD!4Ws>ziP<#y}AAS+v<LuvWBYM!Lx!G<*
z67`A_(JB7vIx$%paAKinerM!=N@d2u#OzxB`pSwN-<Hnv)0hbWgM8hSQB_?=D2~f(
zs5LS&3i6YuqL&8xx=yCP+O_Ham306YQB#wXpI>6n01yxmT}ocw?mW8n+o_^C^MB1Z
zh?)`-5+ulD|4MK`1H51U-K|@ysj21h`Mgg=qJ5UU{SxLGSX^B6O;sJ3h!!e%H8=qp
zTj_gjj`Kh}V_Ir@1at+QZBa$!yC>&uD3#DKgq<t^o3pQPfsq)Df0;};t>i>+G>u3U
zms%Yex?Mu^F)W-(Hs8JdO`uUa*?6TPOSR5$2CH4$o|%fjKw<$^p^ygyaaL6AL(vrw
zs0r>qR&9RygDPN{Rapm=CdZ!p>f-u!{~iJUJ-K7hlJ4PTEZc<?^p*~F7Ihs|H0uLw
z0~Hs#3x8ccU+F?%ox!jwn94Y(oKRGp(WfY8RWwIxyw{5iLgdvp7I%ED94g>a53Nkj
zMQ7kjki;t2?WS#!KlJaWdKM*XYb1B-`%b4L;{7?07<D3=mb;EG9;yHV>Ef9&s6@|1
z<-t0^lcB!ctqr=m8#p$%`V67DC{n-ErnIx@^#JIxfSc$cUOh?3P#ou$gtQVXb#OwS
zv0g(KJPpK4D%<vr7KKH1%~_p5it2Cv=NhoGDPo%w@7P~#5IEAUXTvN;Tx{KL40s~+
zOvo~Xh@vrcB*BQ{goueC0f>pGgq}BB{`C&@^*Mv}PHE5NPAA5RdMGlOad@Shq|ZZR
z%-lQP-s@X-d2u-&J3g*2Tf5GRvIY+#Cx5Ic<4SHO-J<@4VbQ>!q{)zJv1YRiRdRru
zAXCqv(dyFdNRpxt>g_p|s!?k$D?+Aaxi2=`yPd-1ag2&2yA<w=CtwI-x~n7n&e%$i
zs-~T07N(*a!V87$uyeUBcw})Y>{jtb<l*r(bN5Z3o9den?a$)Qv0N?BZW=Z8V>j~#
z__*QiM&?fOJKeG+=)PO@4Zp#2Wvp)pkl6%Cv}S1vB8I#*v)BrPQgRuWw72D@q#T&(
zT;sV}J#zs=Qa(5Q@_cA(=%Z(a2?>`qiZ_?JikIqoQQuRBg74j(M;9YpTULSk%%Sz2
z%INF3rTkh1e*$uIrd%;&S*ad4vi8i;?XM>pLk^hkkwH{U<S57D5iw@$F|-g=T7NO^
zF=87qj<>`@>P)gO8N;hn=kn15Z0X&Y#4nO}EJ&>~c=sU0V5*x3jNt{lPy=*Fgk|7=
zG{JXVTrzXhW8=83Psj`}-^)8@YVlIrc{G?f1vheRJ05(k(f4$`xmWzl(#p!)%J@1x
zK0T2`zuDZSk73)&%geDl*wo1Ud|d~4bw`&5!MS~Owt9`&MN4ZFT(z^a-@a{qS2Nz<
z&u&5i^Rglvn1F4K{VBIaZ*LFtq^w8xklQnnwNl|#^5!OBMwu*_FG>BStisQTP&GdE
z7qjgw_IA-OUzvY3^Zh+e!ld9m$dAzpg!YLby~-c4`|}#@;65aeU!;=^%%MB+OQKSF
zRX=qrWU!~DEu;LMPXsq!BZ!qwW9F{sRR8FRxo+7nzZzd(fp0#mE6CZ*n={|9KPrON
z?DJJV+q%phaP^lqjZcthvnR354_oFRy46#AX^nMTGvz_*Wlpba4mCL`T2c*+WvulK
zmz0E&Ru3KqRy_)RK4xDnPG^)&NL;P(I*ea+bvFWH0SQF%#|#qAa@FT^$iz>FNn%2a
zgD5g8P??7A8DM8&2;_JPq2rRIxtm~o?ffIJ%CQB8gV2or*&Om3xD~VswE&UC1<JI!
z3f_x!Do3J^boGwY?ahNQMkcWalGs2KRsm%4jEa%-L(z(4k})bKyN-=-HpKPJQn@@b
zGP0gacPQz^h6W~xAS62WFBKP<n9MqSB_*W-!iNRdY9N<fg)aVYOAmO4L2WL_#}`vC
zY(SE>S;qJ9))W6g{GpuaA;REM_3!&e?QQpmZ=!oKl6$I+-wgj9<ODOHOrc?6e(!#d
zR8XQ3KzG#1!34CwP{Ge!0pQVl;<Wql7DvGEt84lC+>ZGT3n8qmtStWwjF>ue`K(wO
zHm>z^p`oJcZUcxW4=yihM)fT$-mIDf&1P}~1oAPk=>?3959NM3`?UW6g~`as5Q08!
zMWYTTC<V@Hb8f!m`m}tjQx)aq2}xKP8SgxsP}6>MZ}2(ke|hLnTQuG#wRrGVxBd|a
z7$pd*%l-Y?@VS8dC{C6v2|o8Rd0#OzoVZ4~?Ox-8qFGxBbn}!`a2tnfDqNz1J;g?J
z@mz8)QCZqtYe>OWNQF7<mnWg(R->z_ok21fI|6Z60BAEQ?KrGb?DWGEBNub$ZsprY
zdp?SDmS!SCKJ7F)@+Tw<&iJTcUvDnnHAa<62iD$34m=aH2zntV^4Ds>AyQ{>d30ue
z!3e}5=k=wf3U5$G7I!kdFv$;6!%dYS?ePYxx!wK_t0Pf2Cd+r;<&*x-_kN;$>?vYC
z0GRUel3*!~pH6&Gk{%z8BSdOHtQ`1b?ENy9_8-M%7i@h>C4@sC`1BMl{{9kLqw`^A
zNXshKb#LaRoIj8jaA=5ZlokdxiJTBN1hnQ-rN599#GvTl*%0Cw?gK%TCODgYLh47a
z2zUAQL)0=6T{vYK;Z9I$#n7w=$r&Dt%D6aN4G!ree33%4Lq8iz;@uPB6LCdp^x0Kh
zx3SDig$|%|7(}=+)5XQ%a>t?U0znMJyfmGLvkSglv%27YnYO`>O;-;H#zK&p?(L#$
z$<jaivx#j7<7U>H6@|`zT%Vmx&Img^d(%BW|E}(YLC>Y!<Fn58a<jhunO_3wP?`9-
zZ9l`A*8Q;Y_vM7E?z-mSsQe3U&i;a#CNKFt9a$#I5sZ8s_})9{g3o0Y4@SSI%~18i
zMTC*#zKEsbeQ#{v-sgU}o(y$5mvF+TamFBLzfm+&W*=Z52`Sx|JF7j3QxoqzV3$CM
zMYD?hm8?KgY*66qNJw~hm7OO`72B*fDSRv@JV_2@N%_%n-hzD}Yo_ugBli<aqT?c>
zBij!HmjY})Nt!7cno$kjX&!!g={c3hguL3;UUH-Fuxeop%-|19e)<Do@HB$%Gv{u2
zB1&e#ZKKAQr@ouBshRb2XrVgJR@V2qZ}a0Nj19eGXQRW%>gqh~M#szE%F43d<zsqw
z%c{{yr|a8uJ!P+6Y(h$pH#_f)Y-=^M%iF@Srq;E)^>dlUEoUar(z0i1uw!ZbZRF2P
zP)bP(*`+Jk9bcMD#OiJQLgV06@1|DA7^$93>DF2P){z2PdD(o|-;VLs2+G${^JAb7
z#nEOMlQm^V{Gt?4eIHqikU-rKM8-+_m9l14b^lvPRE*9fS#x1z4D)L03X5buYj?`!
zR#$b*(r#(W;QSB0Z*)@qY?7N_2JcQpo^8=RR?345+KYzEihHyzTX}cDs+6${N@iFk
z_3L0DJgQ-Uyh~#e_pIi;dg^kD0?0h;OT#=zC2>Ke!{qk8vVTlLXLMx2r~x>j$TPG8
zMCnLhMk2gWF<`P>+e77$vielGa7}WA8I6=t5povtxi|t>I)MX*(-o8I+bww?DZart
zc@wGZehd|BO&X0cJL0FV9yF-~m7FA)gL107B;{8+vUwhnqDX^~b}}J-PGvPbH>D1R
zU%upteR~tG?&V?DbxSYY&r~V>T4Exi!J#3&mO9YOtxh%lg|yUEUzKJdl;iVrs_btH
zpkwM9V{&n`&7tT3&HYV0li%>Ty}f;ho;gGMTZ2GxH_%YV-)>Xk-vg|~x3b8$fc7>v
z5&y(d4Eqf|k)H_B*g9X}W5e0ooG4yxgHkPLV{7ZnjOJf8JiC{P->^q_Le2DfK{Q=*
zQc_-mQb^jpZ^t-fN$pvU9UY!(`bfJ#FK*|CeP&jcpR!baV<U=jU48xAb=r`rM^D@C
z75z-tX1kNvYN*~TIB;^}dOBOc>-7u-hwcbVt?}Y|czC?6)Ob<S(QU_k-EUcOawDe}
zMoqKS1ZraF%}U127t9E-@!y<lwHY7az}U-1I7-_5ajjL+J@<FnOG?ka?_*MgZ)aQS
zX%wsmEJNYnq(M+nPM?XQDG!RhR41!6lV@RCl9ifi{_x?6kl@U2I3xk-%}Fv>6##QO
zSg<E+!B9L9v{^dhaitDiH$J&inE>6h>PN0;a}aI$dtVF%%9W&{#Lu6!pq<2=jK>Ia
z3k5$WkdRoP&LW9aTmMa$${AU<00Y=Xw-r4}>6d7dC2}pN$cYdZMI0A+3uz80M-jOu
zDXogjVdjv<R4*`H>RTMR47}Byq}O)<%Z(3=KD2JK!v)W7)s@ky6g0q|122;QM~ltW
zxX1%rATjEgM?^RbaZa7uChn{8Eg)?U5cL!yB%%Y=2b~5M!1jka%y|qc++xb<VVA(>
zB%SPCW27La2FHeKKMr{632<|ua%@zqA?4Y7o%Q{V0$Ew~)@Lp?Y-)u;H`vRw-E*AV
zq!<|gaRW(tM=R8BDmr4)>9{db!@hGic1ed_Ms*n?><>TZ@crs(y#Rv6xUG9V4g1SF
zE^ZQ<RKXx(6!(K6T~C>dgN%|ALOUps+}$gAB=AEjT&Fn+k%9g%qNKxUwrqj(TShGa
zLdi2y{tG<)-4MIE1OUin56)UWUSc@KvY+)T(t`R>k9Ol@42D?>PO(5&&^em}uOfQx
z2eIlV350c%p?QvwadLH<Rbg0CaHQoR7w>gz_5M1Ih&Ydmx(<H5ts7>BeaYy0pjPih
zN|Z6F@Xnr)kR!&EV+Mef79mYL&KV{$ip6?iM!t7OadSzgYe;dP;YV?j1S%@sA)$1a
zmF(M+5F8euT=^39Kg;KT^~KK)v_sj*y}A809np3{yK-5?D}K~QNax0F<Rf<5R-9T_
zx)f;Gwm@BMPR1NJGu=L~-PAC7)Gc+fQEDZizfa@*=iTwNdw+YdK^zC{7TwJK+<h)x
zVc+TW__%*wN+};%c+jJ@0g_JeFVZi~yP0@i>^&~#%_lHx&+kn)syjcmDyP`RDr@5!
zRy<2XEw0q4Le(O<SHw7+l%nry`a^6Fr{$LhdTL4!gd1ZHRp4zJTU}$7xVEG*Yw7I<
zN||<TyY6SVpC*sQg4<gHb+uu7;W?ImW!g&R8SJ{gW1=cQ3W<ULO5$A;$S`@4ts^5}
z>%ymU_6IL*64oI0Abtu~A-mGVu!@kt1{AcCNu|Ck-~`=nIFV9Qf7$SEeuti$U|Tfw
zbOHE!bAv4>EUx~zOeQCVMkRcbTzWHkHFjA=is2NhAy$DoHre3eDa&f%tIl)eSJRzU
zxKKTp#ZGJ=j4ZuOT;N7<Qo||og09R9S8f!K<%*VJ6^mEMyU1cl;!Ujod+8q_3V~5P
zyB|>;d`6RuOfDCHkNBp29;{ckz)COXQB=UKbPG2xR(CwdRh0eZ;;a{vDRRsh6#Qj+
z+6{yD@oY4Na8f3YYB^z?=MD+*jHf9TP5Caeb@lT7QM&v7w4!HJufA75>i6$s$H&K~
zv7rIjP}q8b4}}j?^Zoq|M3;O<m>UI>{}(NooQ(eKD<drpRRbIuz`(!=Nyz{d-SX^g
zZLfR2Skv425q!CHBmep!*O+=7=)UJ!ir<$K-9zr*FC)Hb(Ajl)_#t^{MSRxom13BE
zzfg_4@wZ765fRZ#^Y9WZxL|laUluH!(br17_5S<d2M!nve1RYnpg{F`=?*1)ti|W`
zY|=9O^Jh4Y3K}0DKM?k0I_IMqf3cNcq!X>idd)`xC}HU_6n~~@_xkFnBL?+-9&3)9
z<@u0BH@X4O-2Z-JuPV*>%nZgoa$8aD$XHdA>i)<oLMW%3hi0pQjfRo$0=r{-Wf2{;
ze`?|W@L|&)$-1Jmn2WuPyD3;EkGyg``|OHQ@W^;V_;>`8C(^Y<87due=7B}aGWuU~
zEfK98+Q>-ogL}hwEo5v-VV<X`#3U|Ze3Cp<h*VT8fiHt2dr8_{$r>OfM+7qTl|1M7
zUE;&rokdqXGX*=Ix1u{Wp6ZH>);6KvocX}ZBAD5#3A6uFa)0bOI-5~Vya?pU4L`W8
z`sXT+X0Qd`X~}LUVNXc?kDlGfDp12HtIOVD*VKnOpL}ssTKqq>E~2Gbs3*D9KO1u2
zUg08BRCc5CNvLo77cj&P6jV$C<h$IQyBi<Hr0p>mm>s!Xg5f>+CwdVeBkT&m=jtg}
z7!whXss>lxA>I5<!R4(g7tb6U?J(5gBq_wq{`yk}oN4?9m-a;JS>~q{$T2U7s-Jb4
zRhyQ9@e<Fod7`9ym=OvqVQ!2-@HjxD`_72q+l89>wKdCp2?a&hP|cBon1EB;cSiBa
zaU<ossyZZ%?%8)?m|T)ms)NY~HZ1t=qDnp_!`5LRMXR7Hnly%%a#{6D=XRA0w}1;g
zak`xPcRor|;??*VV@k>ulK(UV>t?*}gzP&k%cn9*U#|sh%8n-zQd+uMP7auC$o;`D
zF{iz7h<_j=#7!aiVx*9;CYT7rk}4Pz3l_3pt387Euc(!l4WQ$6kw1t(X&>mgk`I22
z)uW3BbOb(nxlOFZm0z}Lh<rNJ>4W&#lFS%F-poMaEDoEazfX{=Pl)CyMYSj(IH`hq
zoxk=t5#IYJUzH!k9y2|&H9X5k1{_;bGD+?pz<lp}4IIUiXkwDDX}x8Xd1X+C=*&Vz
z#Rrj`wNZ7W7~I$NMs#hJ8$BD21jU{Iq{?Pa<U6UkFsvj(wk?Ml>A+wQWuVwC58bbJ
zc>kpG@y>NTxEq~ReJG7ft%+`=pJ}YIZm6Ys`F=P)=uEq@ri0t*X|O-L-&~x(eRQ4}
zR$O?R(Ba+?j=P?$W}8>Vwpj2<u2z{|Cvmaxw>QadW@hNvnsciKcolUxG!_}QSDTk$
zEUZMF9}%{iVRARhXnXGgYL=G*#uHTC%{*kq3=9b18P)OG6>}JPVD&WV@bXO*nT6NK
zFfbq$Cb~r*Cg<NwH|5D48+zlzW-tNz$JkcrndfF_Xh8r<W)_mlUmd<VJsjfdXSBV2
zcImZr7rzDB=`f~c&1|0y(KQo(eP-5ApWo4G@^_Zx#9~>F8L0^j*QpGMqD3_?o>9$<
z2o4$2xkL<N9;?XNTiLhJalV_H!2wccqAIK+sU$8}l28$VSF}`><t7e~&qqN!+B-}g
ziKZ6cj0TsQ%eCwzY)*376pIT}aa_oo4Fn7fVXBGJ@;raaF+*7@!eSC{k#C5l)Tm?*
zD1~HEE6RM<Sk&x9VoUYo2}^}1lKbLk)QSue+@%Tbe8KI~KrTYC#H4BYBytO>0TBe%
zoY_h>R=<U<Xa12Id?E%dX)+}R1qE|-VB`rPmA38o?`umXzf(;?Vb0&*9|&gN+I^r`
zrf?^!(_UU)M&B9O+|;)BdD(IcVNK7-(D?Mv&(GhI)D>2*<44d104^&igt{F5eb)XS
z+mQUf#s)3OZ+ZijP02eVqH9CIT$bLfw4|iuPvLLB?w@;D;q<6LxE~F=)k<SzboA#l
zClpgvZS9RD(45Q2-$5t38x75r4b-PAmf-!La=APTYHEIa)K8C(-Y?lsuSGXs*I_33
zc>LBj*O!;xzLuB07<4^8bkn(6SsT9|+-fbiABGadi|cGa{hpr8DeKJurBGN~4j7iS
z&d_{p?VLyt3oZj-ybcNlR#6ja$LKhFF&b><>gKl4L5ZsV(a%sltxaG6^q)(=t%Wyr
zbaER0-WAWvmjzis_hII`Veg-091*AtoAuGTz3v-zYom5qE5Dxl;((usdkaDV5M@Wl
zrQxA|IZMh&i5k{Rs@e`!LEQhO4MZ%uG%`-X#!N`VM#P=L!gINbvnPlaa*Nv-PBXR7
z!p4tgdsKxl9%%xUnr8u48m_@hFycKSD-ko^T!#x{OU(Lt1qb`b;|K8#4@fe!v9W_E
zz=zG=JVCOV9e*i47~HbWu=EfE?Z;yRVA%@je@JuLNI@V)f_*eK{HLWg#Lt|WJgK^o
z_IS_TYB;C3V^ZUVAXT;rD}NY22yLa0-93f>fO(%2I192mx}YUIFP(9%J4~*aRd#j9
zZv@HB-pxL?W6S{uK)k7+Qxhu{k=^6G?vB@6&kNuuXie@Ts?|xj{9?{tr)pDE3AKTn
zBxmpPPmCpB3E^5U5p6t3JEhZBHd3|8k#oqeiCXda2$x^UZ)!}|WpQ}I_x9!rfDWu9
zI&Y7WHgt2rirZk-Q&&X6vCT_E8OtZ6lKgF9fR)>!&mVId89#3-Bs>eQ;_V<{Dl0$=
zu9_Mo0@l#5cUDiU;9B?4qx#IK8_<M#Det(Y?CAx-VS9Hb871SLLxY#4h8#R($}Dw?
z%A9jRY-;tg(Di<S)PBc}%{}`+YIe#QOT=BaZ|gW|5+kwtb?>-(+pKO`Q(gzW?%1F#
z!o4F$vu#PkzuxtfFC!f+5;zZxJ&C53Nxrl>{Gn<_IDYb@aC{EtgHUrn<b7Hm9EaM%
z=Zl$*ZaQ|FP7>ag2_yO+g!2Z06*ZDA8GgApg~A`$vf^XU{$Z6Z$r;T>PVqH@qF;t<
z`eSq5tijZusgOZ2a>wmzc1v3y`9}*E#N1z#Q{}@`PsdY~&tc3`dfPp%tmD3pCYFso
zeh@1yn=)&wgVx(*x*r%9$K;nY8bL2q8YpmsOM4KyD49d!Jv=L)_W1_}b{G67nQmOS
zH%<y}Wf5PKuuM3*bD5hwDYM<-6hVD8(Q$S(%9GJ14-y_hWff?SS4W{;d&sj}g71Lk
z23bW-IIgNjGN+m-jvZCDnL${E#uZ!r_mV`jECnalvVg?V^848<%~Ncy14k@JSKRg<
zbtbk1E)E!78+r{=WHC$)B?LwC+ESl}*_}vP0~4=F<q>I_MOK*yckg>Il-H2Z!?-uV
zMpKih>6f<ChNjXMob3{J+@gSRW7(;Ju~2MCYED*7!%_qaxk!rW^VzrrM=rmWt@C>S
z%x&?VgBUTEo>B;`sLQwlSYWW~{_i>CdZW7rDg(oL1xN(3D{e$u@)Z626cclhByPO7
zYlR&!FT*4FpW`<%vxqZn@{<cHlgTNfmuf@e=JPTD$)#s>%ra4NJmHA!YW`Cd=Rqb-
znUBH>GO-mzYAOYyg1{86uaeFyzgRmz4qH1jGcimd8U1E(R}GGRAY>8>Z0Kg)`M0-^
zPlyu*zoi-Y(4D)tg99UQK&u^x&r1a>V`pL#GO+hkE;2H*PMs#G4{=pY5X|=h>ekP(
zs|wJSUkG(K{PoZqN@7Qyz>k>L8ag`T=y5Rh5j6@tcKzV!ttfk|BZ&sXfN-m6Cn@=#
z?9|lMy<Kg8uj=dSR&5NtygnXu<9B=N>m6Ry7%)HkYx}V%44k=v!dCA>#OK53uR1F$
ztDplA$NyhZQUf9q5>nT@*ihuw%QX=>xtRLxT#*=mrQIhL6csRwfZ?kP^wWM0?mTc8
z=(56*zfy8?YOJ<=yxjES(}E{LogOM^SRi2vn~YMDw}E{?d~8F(ggrpfl`u1FRL7sg
zmqrC+DlcEE&|rJ@?Un6md+a<LnsBo>z@jSeV=3V!&muz`kdS(+VdjZZ9F-XRXBQ?}
z?fUvqvU!+BAjT9LGW&{oYo}XMG4gXDC}^feMmMwY5Zj_0sB*GVd4>jEytRTM0xsY=
ziu}-V4THjhjXT|!F$&?rzppn*mPcVC)}wSzX+FisJz#h<Z05k~p_w7+om@Bji5Y3Q
zz~rhT#r;WwwUB}Wv_%|YSn!j1xS1hiAy<+Dri|T!)8HpK!}Tvrt`2+{xf`kZO=8lw
z5LJ|p*}@}sZY9-MnhN5f=CoB+4RWmX-=Wp>ZLO03iJMH0dXFAbneUla`s8ydzr0$?
zPTs5QoxDTzTG^K;*AG}~dG)qC)w|P?SkA8|?P8){U@OCk^5dl&mYVzs)Oq!p8mt>E
z4_tq`41A%I2$IQKhDHdSdl@n1jF`=L62!<shJpQsoDC_TKW6HMcpo0y?uItp*nkLy
zCnc?YUk@sxe)flG%i0uVG%oWKxuo$fE1s&Dd=gDIvR^r*|2|1Roev|=Ib|WNh;Dt#
z*>(1p)WeqWy|m5&ug)bRe!<ZkNW2nUCnAs-=6~$gnC>#Q1n6Qi=O2f4H?3m;IdgOb
zQ`k1gZA-`W!XuFLU++X+)fRAfuLSvGMQ*@$S75O;HH0T6!3!93Q?HHQEM_xM0zZ4X
zPOT+4px7y}j_Xs-8e(~f!5v{zx@!^O)lDWISB9=4Mz5yEF7aZSK6dT`xVBU{93s6%
z(Y4r~%y49@-8-v1F{=FHr~r*ud_l*EP(X`IQGr8K`o-o7bqnXx#JIAsYF0sweC7C4
zzmtD<*M3&xE^1vdA7#JqzTtWueYs-259!@tj!Xj<!8{42h}5S&&cmqWYM}W!$6-C@
zVye}yen;=buZ8vLDQU^ir^nkZrgwGq&iHPASbRd#Z@|&%1MT`0GRvpcts<4)pw_p4
z(>u-=5bMyM$|ulY?aKDtC`kqx@n4mo`yu(kRT}5(2Jey+pdO|D2als6FL&JZRK>+l
z*{TC>>N}in8*1UzDT_%%VvnjiTL?ML8o@`509zLYK1>7MGZLL^qAABnFRZ$HvO`yP
z-*A|>-J;Bz?J{W1XK9sdYm;$TpLH8D&w2(M@H_{nh*gQWEA!~fV~`V^U#fS(1r^cP
z8KVIjx7`7$KR_)TvAXA=vIt7uCR<&e)pl};Vipuza2oMlQ>7PoN6eEqcrW81G*#8n
z;8Zl0hV)&9>;r2+30JpQ_a-xdScv<1)l5r+nwHSg!?-d$mb*pBFwGEdbAJ`Icti3B
zl9X9;w8H1Bm{gU(oVhp@w5aV?0vh_H!phvjibQ*HHq%=5rpZpmhL57dm#~S;s3cE}
z0^cravBt+X{I4cb$#=Z2_s$H-5oF`I{XrM?Fx+}bIN_nVlXI*DIjZBR$bC|kaRBR%
zbL$tvZx*^8?}wPbP@n+<1yhPDD!?2C8a8ELty-;abA3G$g!|6cFK-~`4@iZn;QaNY
z=l#5rz9%f-5vX>z^eOdx-;PdpgzX;!WxSPk_O1pVG{@uqe}WeH%uPX|_salJ^6hJ`
zj_6TQ9N+uV(9L%D$20;?892Kf(0?G<93CFNT8$ea(sM}+?M+UUKrwj>9CG}tc;4yq
zP{O=*1wIcvqKS!#`(-ve8%>Lfis+-z`uQC=vXhe+fvGQGWnD;TKR6;!LUJ-H50ZOm
z@7QH}J~9y0`nwZ>?^VB*v7g?~+<f%N(QjWgFfTAL@aNFW)yB)OqO<Rp)pbpxBzxBU
z+v`(*s@~$Cotn}V+|S8K+<XJ0eH!C)NFr4;1<oeP$9t6nk8pB>t0GH9nANDp<R}U`
z5Cys-tmGJ~AtlYnhWn?vj4s!kmX^Mli)8ad6f?Y?lqME!#tk?=7U&}egQ$xtnc!fB
zf!c<&)2n&RASm~UkAECIoDT-YBvo}CPjC-Vz_2CmP^w9to>D|ah`t7n9@<{RoflUx
zIUL$KJyH^9rl{^Y1E7~d+H`TPvx;2rBhy)>qMY=0+GlJjQtJ8hefEqM6_MjcblRKJ
zBgD;^{u*`9Rf~Sp`j7IEn<?^75REfa&Vrts#9JmoGa7i{C||585B7N)tZjb^3K1<c
z63rwh;`B?vS_!>QO$*rW(Cu{fJwYu-DkhXUv6>T}45$9Brm;3mxwPkVke1C3t5xr=
zK2jC=)D&e+Q(lrPwR?O?@h{YZC>sn7oM*7tF^2dO8ub=bz%c5gGQI{ipQrwdn@gbA
zXA!nbDbd}$%UYr2GA!YyHWmLjI>1$*dSDYAVL}@ujC{!BOmFhx5euS(4n?I}fk`vp
zQ8y=|w!Z70Pd}X#J_r^p74i={g+;{@s}YWqR8h1}qxBDKh7t<!<4iD$EAo%vY$`g0
z>OeEFt4$#Ek2XgKl$kh!dkLT<N+841bRxQf35h1fjUe2}hijvYT}k|SlIHL$2=;)7
z^~bPoz}gfX?<Jis8z1-&#xn_*OL1zUQ0RH9(~MdysoC9-MgI`k(kH`#7~~||cuTKr
zL$4}`ZB?2v*{Km({#EIU(LK8mc%sKBK?WS3wmh;~+06y%!70`Ez?{Uq5LHM2#uqFl
zCCVXz0UzIYuX&?t`LbD%FlKZ}1V}4$(GRY(O7R?i&qbZ<;-pQ5lO&?)cQIVhSOY2^
z#Y-KkfQaT*3QH4pHM8eUCW~WBn-x6C1yG=-vxLmOH8%U0*jV}YvD%x+`F=SeC!q0u
z9$NyIJ880?a1u`x_dP{=t+>AoUqE851@XQyFB~P^EV<btUpL3fmMp^cIo-Ib_%)7u
zu}Qrb6TBFg{j+3#__x*xpWjmBd}Y1graywWAl+PEvdUuQc~khZokdZ>0v-I53Mv3w
z2sDC7BtZm)@JFE<j?v%Mww1xPrnFYdZR3?r?yS-XQ`V{^nFeP1=iv*x>&<rD9H;Al
zI@6PHH*Vh#J6T`V?2xO}HisEx`)GV-UYboL=4;0O1kS2_Xl&m-c|LI)ULjP;`(<D%
z^3XMp(4E&X=)O2%-XeoE@=WiUAoH=|E(Vv1VxuR&>azMp9AxH{X`dm29^2Y&gF+A;
z6fs`&snfe^Uz1CV*UbhJd!<hDeuWaSGm}~UV&gcKvUv)6-PZtp=Sup%Bb}RSzDB1i
z!$lq-8z@~3ACjHa*HK3p{(`Bz7t~tWlATtfENvty;AA9}^wBG>QR#|kEzJc0La)J1
zRM-JNAhdAJjqj@4HLfRd;O%85Vp*ml*|Lq?fpgrjioPT7TRL}UDN4!PQyZPF<~R7W
zxjQLTlq&ejKfbQ@Pjhu({d58Vf~_@dDi8K?QGLpF+$vk~0JF#?-_HHRji*fV(Nfu#
zJoj3&8}3P+&disaECyx@`5hIz+BIkGuy(N%6@(4F$liaLY;0_YZ>DZO{}S-<<6e%^
z^m^)kGVt=+7#owUD5Ba9v^$*~cFOmDh&e|JAuCMwHbk|zbGE}bUVP9m_`J;g&X2UL
zy{+%^jkluYFDM+n8~=J>uO+FaPS`<ATlpVFeRK?j(R3ic$pvXky`?Clavt(U7Zel-
zUB~Z@C6V`bal(fwHL)@<yk0iW06Ap959x=;Y<Sm6YHBJ$jn>#^>D-)x2e46_|GV{*
z0tAH6?NFU2RXf2P*a(khYGi5o{^`|~5BR*Y^GgyZ(PzNC=RMqLHK{4;DhB!+j;FHz
z(c^EG=J`^!iU<n}17DH;)w}1iBq}P3zW^{g?&IxkJQWiY^N*m&#235c1T5*7Ri}f^
zWIB<EB$?8BAO0avk~S8tfYM1RvnVg2DP<3(8qeP@(rlWT37Nge+uU!vKpX0Qj~1eu
zoLJi&{GKgpcU&Z$B>318r@OgSQ}EbWK!HYk&zm~`Bc_0SMFNhE9l_0EHa4^r6n(8d
zW&iomG3HP)iT-%Lvl`CBoNi4-r50U}t7ZGuOB}~a3j?Ud;IXhL?URzgV17iitl{*Y
zGa*Q9@!fD33BhAHzFUkhZx+9r<3VMXh%TOA;oaSc(t?`RZO-vStKbe#C)hX~ok4M+
zMnq21-=Pcp0+QMavW_yYpI<gnqItD`UZHSg94uKGXw9lX!e{v}TdVNczQcl#vyhyw
zhqaIdEQ2>n*`NC7s2wQBsB~rFUM<U=gEaZ3tBhPIl+>d7=AQ3@n8(&{>^XBA=2~pa
zALF3U-{=?DWKy0PfjNCF)@+z8>SEG>kakYNE@G@%7{*GUa^Z&cF6Wru)Km;5Rh4jI
zvEfh);R@sh>PDkVQ1gp3(a+h(l4+yd-&bH8f=DVRF6}Xe=AJnARZc9FOxulvXpHKL
z+RFRJhsZ5EB8SO4SV}hf*D1e9St16j5*VL{?mgV7*5lBi&8H@=<Yw$zS`a#jT98^V
zqT>{>q`ei%7)g3?R{vILPYhQ74GV7l;t@=MEI3-10q1L$rvT(^E@;Kl-~MR2uO}4A
z4m%!OI}XK38@>y^7pe0DaHMo;I+6%#xPGn=+#eZnQ41s<aw6LCC6;zX^LSNmyCEAc
zCzVaWt5Dx^`OJxIiC(xJJ=-n0U(t4%@4FZyu+CJI2+?o8g;!pA!5>5Bz$!PtTsz1w
z{9>q&Y~Yi}ug=7;IoYc@Fe<_+0btk}reYoW&52Ctsj*A$aLkKSuS?U8tFV$habOxm
zK}FCY#QqidEDBm;W||z09^4*->BEDuf@mJb&_#RX)ye!fu)JhvJ8cKQ7&j~KR>IfG
zV}F1kr-pAQRkM9EWzBqT<NAG<c^Wj)1;_}!!oj1?bJlsw?j1Vc5x6~J4CeM}s!eOt
zlF2|RyCo0udGYMTNFV&c(?y`hq!^}oZM~j-T00(DBBHe<mTffw-z`$3URCrQ2zB*0
zQB|IcYW0df@tPkhv$b+!dE&2{7EG<=JppZscY8p($p<@VGhJ&a4ZA9^zA$mVursD@
zW;hcg49IMf`eqyYh*xrI1iiBWdvY?pD5bd;ySfznO769Z{r0fB#?EUO@0Tq|V77Uc
zVy?O?@Fc_H<6d|4if&&P)4m+-na^pS{!^=j>D0XBlRimZoeoDu^--l6UJ_EBSd;X;
z#(~glZP$xxH1Lx-DJno0`}mKU+zp%q?~&(qaL(MeahC8;Vv~-Xo`*dh#$mBWgc~`(
zkE;e0v3T-W<DMtgYpojrHv$?)pUUYc%)Qf2myc(8+3pp6d#Iv9<%SR5hRp5!nT+53
zK@inN=>s3_<nV9=D(CKQ7+(MI3tFf|PVi!TH3k@2`)i77Jy*YR+<)^&SYN{<Af)}m
z1I}`Z62yBB{wU}bxZ4Bi1XdLHzueqj;`qN`!3_?<5utv190frl@BtTUoqp3SAAdyO
zSP7j7{WL`r4pKf&>~tk`{|B~E3zv|0bU!`?%(c0Y-sgn^rLUfxXoB$dFwnb8vF~Ey
zn;=4@&~;C3_*+*!Ls2=tU9Qs?9>&B7IMo0jk>1{`)@nVpQUMki@FNuz%n}`*yuW+4
zPf?&+npO7q_vgG!ec;1zaWkim{+@}YuKX-g80W4|VFm6B;bCC<_w4=NCiHwG4q6);
zh}i#pH=BSPPXRs0P0h_m8s@%%%|PBmm506kt<-{I{*sOR@#yj>HhVWNeqO`pU8ucg
zcn4Ix&|PQ`{>iRDl=wyzANJUsZAi`U+)~<?biJkk!q!rL8rSP7`=R5JeuMU8s>kG3
zJ;uhUGOL2B)|acphZ8HMs4z-HB<uW3aXulG)sO)(?Y4MSi2By(vp-QS^$WWX<q)1d
z8s+Bps!TlWTbx9o6BivH--l{>cOhR`<%mlKYk^DEC&MSuguz$iPnSc@Ka5>~7@4;F
zBTU!APxlt|b25j+*fyt7jGU|MeJ>bomjvePIiAzbmcia$UP0u)5d%Z}F_E2li!N{`
zA|qLDGKMqHo`0;-2<=!r7L<e<Gj!R!^)9L)PQkq+s4co^Tal3bD-_|OsQdcLRv&5f
zFp||&ZPcf?e(ZygS!5?G?$<8$%Uyi<tH*)_E-@5Ae@V}D?>qwdwu(2m3~40i)PvKU
zcA>vScm;U<Us<C*Rtik!5b5YJQqsFund|7%13b|^th^}jRFUD`{C{a@Sx{E{H2=`(
zi!CoHP8l>Vv06Bv0%Nl!fBJkTaBX^GrXk6-I}3cAbstNBRw)XHQPALR5nN|w)fz?3
zp0I|iC1i&?6oTfkZEd&HFCulGj!Co#os`5bR&06D*S4KGEK=Fw+|yv7&^N58t*Rhr
z^^4}=5$yo`U6Yc>ahYF!?qTaDUqjWxQ%H#rJ|pK(7_`r<(``^TC~OI-9v>ZBh{~@u
z&|kP%Kq~84RO(v@<Nf0|x9^p~+x;CW>z9G3IR9Wl#dMI!^T|m&zjct3BTrJ!;@6d2
zXlQzIC}xRPPm<T2_<jgT)R(45D#pHSjwAarhZ(@Yt{}tKuTM#Z^`Qxgl%sHVgq(kt
zp7$^KuKA%=8FHyBjfB{e#FId5813v7>*grQ=66$GV`SKxLtPjiEw5<>Hd_?6P1xH#
zm3Mm^s4J~)@JzRhpN%xt>*;#`RJ(x!f!EXcV3E|<3&gn{LLI+``afe2r;G9TqmZK%
zDc*@R&M4-pDZuo{!qrPwMGwABH_t$KD=Gd(Wd>fkk8|_UGp<2C-u(2C)bJ_+Z#)Y!
zbEuS4&3=Mf2d0#fj-rE=8Gpn^3;|J)=J){y7osAW_~^u09t|=pMvNE+FKLT+{ZtRx
zAL-Vm>6V2L>~V;)IzUgR3H1Ftu50%Bzcb|hJ9H;gSZ2;YJq=V{o!cMZGzLAaei_1F
zU&*IqiYH<hIy1|ie7NP^n+okL^L%}P&+;HQNR==7P2Lq54a{O4y=!e9Ta2mMSFkoT
z7?T7M!@c+zl0}xz%Y?euZw6Oif2!jC)jL%**>PEP@|hep1SU0zwK6j;9PWzDjXahf
zdueEW=l;>tzu<1&P+%LIU^VhS>}_c_2@f74t!|>0*^@jq3BCfPicj|8cL~~3Xcaj?
z&ZlHb1J}xOfJy1bdBuXYN2rbEG-ESKd4_Y=TAcZfPV*A#IyOs&WgK?T%is6gimP%-
zv?FXZlj&=p#tt$?$QK8<9W`ESKT2bz2<zjV%*;fYPNIbHi6bGxc()xNHNHxGPDc?6
z)a@RhkFdn>ssf<GoYISgLK#XWAVj3NusQdVmX-ZxK(i4N3gj_f93MXqxlLhrZ+1WW
ze#=rzT=}02v6b+lS$mh`12D3o6Y+^5R3pXX^*lK}En5Ik*VLry_jDU4YgqAv4+|0@
zIs%QV(pI$rPAp)`2S-N8DJY;oKz@FMd3t)n$OyZB34sjDn*0GyCR9(rRgB49OW5C@
zH|+ZK>%6THV*Yp)Xk~cp?d@?B+Zc<>$fPvC7sL}$Cp-~Wwgb84^gXZprOnCb=a!;j
zz!t%K79akYyE}(=(zmxazn%I{a%$>}?w9RO=d*b^YLbRb;Ga&zv5briy^NH@kCc(c
zd5WaNIW3Xv__~(;FWF_tyYiHFQ4%J<k{UaR>~&S>-BtrrY)sm_GidXUTB}8g<Ikz#
zwOXEh=bQCZy0fJiNy~0Mdoa3qRgC>pEbBAOD;3>+5xTL;vh5no&cGyb{LN?TpP=4}
z5u>Kgtmv_zf|?9}kFKZV==8pQkN`V${WMI^(h3NA4Yxz@rzP$}s|%7vNag#6#<fz;
z6<UUo{v)~iN%wmSl%R-Dp|hiCzQafj3a!qNtlKv~a~wltNYzwDA>5J$d*2SfgC^#2
zS<;EFt#1<b2`|T!`<JctB+35LF$gv}Ui1{FWne{y9xiCEe~1no5Pzv7&qrMke3o&p
z&2WzNnegD;DhS`BwiEH<*_h!)NCU{*gc}6}Zq#=K*ag~TM1s?B$~FV$Qq^J$MMy!M
zlwj3LL3?y>Iz*1qI>>`NN@ienrzakZkOeDOOlMu7NJ5s0G{{tM7h=)>Rw1HYejY{&
z{G2KgJ;YHwcl^8b_jg}sYiE7T0C)Aq%~id}tFwVnc`?Kyuly!6KV6^<3GO{tjkWO&
za;o5|fxm^xVKPmujrqNYUge~reB56K85Cx?*gBRn=q`CYEG`jSKc;`dTAO+2i4Hdt
zeWBN|mSS(W*4zO+>B(&I$fEwCe+-b5_GPL2vL7p(F`d2<jxBDC>blGCR9Hi%VBNhc
z)p9>_jY2Tw)F3q=f_BaBZ_B|m*ngf?8rnnf3jJ^W%Qs}mwP)?N7joWWZmq;YIcSZP
zm3rpDwfbjmMq_GAc<N<8fug82X_}yk*8oZX61w%JuLF{#Z9~tV>E4M?ZTA?v#*a)*
zjlkGA)pL$@x{rAMsIap=wYJByyH`s}TYOz~f#E>uH^jT38SH1(QqJY`hX!R@XS>>)
zJM+FbH?S;J;6xBMA1N0`YP>2G-K~jz#gJN!R7Y*tv!!Nc+oS7wZEB34#-0GK)z<m$
zWDRcvr#0+jkXnitfgs$@8fHbU1Apd@&t_O}ppUGhJZ^dTxFlR^!X>Un13~qIER3<M
zAeB#IV}YD_L2;g_=as8QXgwSUqnVBdY8_G{dpom|=p3_CXp#oI<iy#Pv_%5tPg@8M
z*3wF(<*@r1f!#u@nOCjk&qA`QG9(v^cplbZ?ZkX^t)kwJNFVp4xd}*kNhp*Fp8B%q
zWBE5Di>rgN1TEsgkl%rI)%seB4@*Qx*Xje~$-DCuH%g#yR+<ym%m!^}+;dW>ZYz&3
zhVLa3ld=0A-d|U$__Ybc2QAZXh0E#=jgyo0y7NN(9`I=JtsXOd=WVHbUNu~uPOvXz
zI998bT6)T?QY>l+b6a*6KJ(Kn9BYGMJ^UOmZA!uAs^I~iHSHE%EvC`)^`~y|Vdl7Z
zOq`fy%^2R25uOTYmEM`fwwuPaS_bY-iaH@BW`isoVhddK95;?b+E3@4E)g4?H97Tz
zueZY)TL%y$=h8AA+1ba+k7ltKtd$putS)*$ZUZ1*=8p)`+}hgO+}zyyI*=fy#|Ay#
zjI^e~);IwpK{5}Fb7YY3USHc<T6SFIF;|b>UU%M%dpbEdI5;{cK7D?^J><asdtd3r
z#>O7EIzB$8^7JJx{UqQ0f2&|Us1U0AtJWQCEG$aZG*^g=abSN9Gel59Q4z6QHa?#>
z1$|y-red2SFwn*?eY$scR(=cI_6#C4ql)H-1N&L4Vu418-tqqT5&rLbARV`<N%=Sk
z3lnp+Q$<m6e0tjVx39{wnS?~dCPTX!MJ)}n${R1Lgz{nh4i_Pzz~0JN*^dTC5Rf|N
zRa2nMHp{yO+VPH3{rmJoQi;^;{`~oa>N(xpadv!M@_FX-x)(-IOQ~ldr)TMu<)tBR
z>je_IwrfQS|Ad(1B0)81j~D2qBt<!NCscLA!li<l9A9Nrh>`<XU&p}mFB}LXFp0Af
zjjK+cZ)dSmi%(VcNbqozqA3?eOjk5fkGGf+{mn6z=o|{wUqCx0ief2$ger-K14R!*
z591^(f;GQTG(rtw`zn|bCMX+%cefw$M*9jfM@>n{&BM7$N<jzcuzNMd?J6j?u&;~S
zP|Qw5fP<JhU`XRedbJ_DVVh_!*=TWGgl#d2L+1=qjJbkJpxg~+P_|WM%}SN28lS2Z
zJ9>6eY0)k1Z`}}Q;Na#BT20Au%?aGj#=6A5rV&gT93p)ah3H?=PY4Xhv=Wvzq}myg
zI4tg)fe%x)harRobthpCzr?~psH&!!7?@|u#ECz07^0$t`W3f2zt6RIsovCfB8|h*
z#)t~zIvVcu(+%m*Oa-nL#WZrP1GD6Up`@fa%7oJa0Szd-?VO#&wd0vliZmn05F0ch
zgyu@X0(K=QoUu8&q?~9b?p<{O^;I-Zr~;c$mZzKcs@?|&wG%@F?oOo*cDn!c2tpFp
zn@5gqLWnM7C}sXI4!i#s(o<I5U2Oj}GJyfOebPqbFjh76Fef$l+sSI-#)Bu>sXAA>
z0M=h*TLC4l{AmS`W&Ag@7iBN=B`e2Zbgk7<KE$eW>?J9|h}F>_B_89JDfSiEo<%r$
zsw$Pxb`S!HS8EvjJ%1vFyhda7ywrQVVL^0^M+mVQXh7kgjd#JEQ=uh(!<TN-XGg*{
z!MHHjGx1my`ki-0wA|GbgJ@;3@GhMH+Gglg_Xi`KT?aos!Lx9g)|nYy8Kqh3Cz{x#
znVR5ZTjZPs?gEaSyTZUJXtrd(s=5avMo|xDjlH<@9`?SMvmf(e<QhxFwR~Ib^i&%t
z=;eYbiBNx{i>xxvzR~`accVX_KYEU<mE+Y#^=aX|y94Y^@IH>A?`uQ8otvK&)_GWT
zc=_14%dV50iP8RI*jP-gpHHT>*g3}hH}CzBoa|hb_7=R{wKS@Gi14(KRa~T8p5DP2
zlygR%eo3i|Tb5F1r4?yKzQ%i@mNh&w>+?82M<)GG9NLU6XlAB2vNoq$q#?duj^0H!
zPW8$42!R#!#A4_Ic99FeUQwqX!bAe60;5>R`*(E$2*zyX3TZXYfJ>#%>c&E`jm6d1
zT1WeIA@|o{sgI16_U5^Ck+OgLj?TxC^2a5}9}<ciJ0Tf{Q?)P%^r8Kp)Z+{7Wm8~-
zGCht_ikxc^Ib=oHMn0(FCO|j13?0eN=+e$(Q_-vSV9coC975rJxuE14$f)gZYMMm2
zX5}tAH@8DCG|Fyzao!J5Dj0bgvBHDnbYT+L;XF@P*hT6d94OVN;zER*l6C<Qb^DlD
z-t5J&oU<Ynz4R^=R`A&WsTI~~78N#`9M1L=vQi&1fB#>6onw1uZ5O0Fw(X>2+qP}n
zNym0Nwr$(CZQJhH&fL!&^YxwmBd)zxt*WyMlIL{Poz-k7bPBcg(W>(o?6suJT%AQ;
zFqP$71y}d}Q-Gn@RT6S?dLF2K9U%mAoMhk$5nl%-Pv!mI6RR3{rZ;A=)Zp7;i1>fr
zX8m^7ZxiMD6usfnCy#sPmA(sa|F_nA6C|YM{(76AoSbY_e4g#LQ{UPuFC_*1>leV!
zkm@DdkSirEjRq~_=GHm@2Llc5Npy6WY1gA@(ww~i^|JeOJ=&8_!T-14;7x1o;dB-a
zdT=Hq*YEpHF4We1r4#VW$leA3ep>Cm9@KN(xV#!*u)u=jnalyhuTO&z4}R^Mz~gqT
zhg*XqE_|z#SDOme7isM3z<^NA(HJW=RWem4apdsETb1u4|8J0ZiKNiS&&r8jaO_th
z3lLHY!YZm-|3F*k2iov)!gTSGB{=DY0VWAVZ&eDS7aL8sUl{?j@`5Jro$av*FXXC1
zI3q!<tN5M|=vhY8%B(>6qg4s>eL0iFakw_5ae6{#s4nw}ZCX6Pl+YkT<y$@y4FyQL
zx+_ssN2O`3TqT3>?wVR95eRT=@bQTIq{a*|Lzy~&j$*|668aeyBh5-RpC9*cefg3j
zsc5CgVI{|&=*Ul+V&)`pd00Z{w`ssII~t*H6B(YR1S`uL>TwZ>lq(sp&!^HFf`R_X
zYI=(CbQMYT<=tVguY-{WD7F&<mo`%7R}sTY{++j`;=G+gb{7Hd1)3cgg}<8&Fn7@k
zoLL2MR9YjEMpRlt63+6QMJYve7}<GZ;`BG9tF=#*Y6~;L@f;k?!Z8qy3kvFM5Uv@?
zCGgugxK&gn1W|{$<1kQGfY|RaXDbn(3yd3Wl?sF*MOiO-<n+g0h4#0!q-lv(O37&*
zDX9N6r7r$HNnEe`-a%q3#E8w<t8bl5U;>euGWKamG!8d>I|~w|aulS%%7R3Qy3a=)
zVB_xJON(i;2g29!2Ru7|{oqDu^C??WH3g_g3I5?<F2g!<$_7x1tMlaBdLF9&L<<)Q
z(1K{w&MS}h;(ncZ@RBrPwSPfX%{s!ZIzKAjv5{230~Ta2tp48i0U5Z-C!SXhBD4%I
z7B-EA(O8pf$k`YhQ>gl1Vy$5&;`Kh)dA}K$aNXy9Cq`R7d~tp~);&90JEZFy&2ie%
zFA`!jYGC{$l*b?@r)M^QAxv!y|Mf9uj4i5#ZV=i=QhI68S?6r`V&mT%neC)vZxYZ9
zHuMNfF@%?flbmvbl4+5gv#*P&jDMbio@Sol&khId)lN3%3tc$8(TAr(mKWf&UK4nh
zwSzbY`MVYFST);lIYbKI5bZ(aI8vLIfbP+kjsNlTWI`OL(R$E!m86QCx?A1TVKwxE
zKqpr(3l9}xNlJ7q+TmbZFR+Zq(x=ERez${Zk&B3^HZh}yR2jW-49DgUv7<gaI4iYf
z$5$$`251$Xss_&ZeV#!N+8CeR{RuyjA6^^RCjGD>w0JECuFL@SHz|>M*;zmM5LGTC
z84!$kf;o49dikC;$6D#xJGllIZ(&0EU!)f}n2(Q($b8wpDoeHP3kKtqSid^0Hv_b%
z53m6-gd<NJ*Ua^{i4py5Vw$Mm<{o?W*Uq(5hbO1jWE=m~)4E?mQp`3>+|yFXjdp}k
zo~^X+qUqX-IxDRG7e*^F``&5m8t;jXiL<s)a9IXu;e)U;LUW&CR$n4o>=D=+Ubt*c
zJ%moHGCB&|EyHT$?g54oY^(N+3C7Up^~;@OkAt|{j`~fg#H4Pm$3t`-Y4)s2%K5=A
z1s{wZsdh~OD}85MHxvP$iU6q$_iT}Wqtd7A+|Aq*UMm(8^VUV~5xXpwI}JgHK@^u$
z!E-Mg{;Cx$q|;*seG4Y$|BAeX@e*`&bPes}fL))E@YgM`iE{g;Jm3Q$#F)}Q2bba2
zi_Z7<_5gz{V6O*2A#QF6Ffl0*B$HhLi+%E>uZxRIvLA(JKzcgX@KRTEb1}t_It+!~
zEz|$!;zl2ON&5BnaH>?!LQH%(moH2|h6E_+hJ=CnZ}*pLEv>1MQ>^al(#aPc8%}w9
z^BDHEO1FDnR08z##KE9$Zf^SR#uut8?oZP9ZgqRLPB8!sZ2(#Xv-ymLl@&g}uZ|i8
zdZe2x>8kw^tBbSqWAEtsVrg?zQw+d=*H~Ioa?<H1f&O0uF5q5z)_b$I_$UlwLdrDD
z0I<KUG)^fdxzIJfndeqiEd#8~$J&$2%gd%K6E=kk6!+qfwK*XN*GvSeD~JHc$CPR3
z@l$mFUHHWibOJjvmNt4?{1ziC`mTbgW>VBr>)Zi!g-?W|tdbHivVt%sL*vpaTVZC;
z7=8h)j%B%q*3(hV2Dy@V?5M{%d4I@gM$65Kcv3(&(J}>cmPU4V4#dmk1eD~1!O=%O
z5z?N<wLMM>U#4D@jshJ}Atk2*%WsU|`%f5+oe4N;=~LtW$c9#?&wW+4V2@jAT*hba
zgViy7Yz-77^PFX<0taf*uFPq#(BK6Q9Znhk{RNd&I0DPnZ*Z6v9-w4B4TabqY#vB!
zPSx3De||0ZS>L^4S*;2vnmfWX6ZO^rPg+yCg(`N{&HeJOA}As0(1{TAVIfJIg43_0
z%j>j^EyZF7LT0!IHPo1V3X4fTqO*{Lqd-YSz^$%nhnh<L^&ozqiJ=3|b$On4X&Q55
zFzifz@95&FZpi|U2}vnJlCsTbzN=I2b_ar)sQ?5Sqf7*(%n@GfQe*1l57ndpVq(K2
zq#IgzPRtUvQQ#c0$;n!$M3%Taj8ikHR#zrc)yuf7-&=*4qc}Kgkzf+5PoKF}LmN_8
zch&@Ew&ByGAL1rkuqh?4>F4!5N8u@-w<2oft!E|Z09!;xUk;Zlg`or@9uh^=x16wM
z&ik5W!_gF?J4|lsq-1|3`fm|dOZMR6m=2Z;3RK?4q)_7Qx1(fy&;pex{0SGiW@;Mq
zbwcoiY;*KpR#+g(=7JEnRriMszeS~k2o<;9#;dm_p@`*upX=gM+8=KQ#!;oT<N559
ztkg?_Tx8sXn`B&MD9WH=U?TrlR2IlEP_tnzZgb9ejJML1(G>JjRPmruGZT|g^56nV
z3Kbg(TNOkqTBxLWpz&FX$_e29g^7oY$b>npxm)+TO1~&C3mF+-9sBU<c*js<9T7;J
z>s=rk!r;;sQP7rSuouDu&19r#G$h)lgch<Rck1si>VzL3hM)25JA^;6Tb<mm{l?=X
zA`f53IbN&gW<{Un^DEP)`=RV){CeoJiwcP}3kE*RRblllechConpJuCFmZ)XkD_$)
zge0|gQ8v5gG<Jlkr?*N#YHJEyYQkwH%k;a(kMwFf`gnS{7wC_v@HD?l&Epp@D>&tz
zp49HiGM#;L99`1gJy09RsI?7=ZyIrOVIN^1(xye0_8_fIA_fintPBldZ3^7WY%HN)
zOkf|r-JfU$pOV>zbeR8ig){Y6>h~<P)sLGQDz~C@I5`C!t1dj+AF8O28p*ry=g2_x
zvzK}#b{}OLO$|aVDe)WCf$&X@r4NS6tB8wkxPzr}k#XRRu2~OtWJNl=-m|eit*}Ve
zIYd4^-!(V8tZ-lDRbN$>pjQ`U6j!QRm1S3ZE<DVAL((pj&=#H@dO~^0|Bk_7%ix~F
zoA&&Bl~4Xs;2g_xPC#1O1rSksuhNiDU-fdX>(!ctw}?ZkA>`aF%bF_jO>()W_)8bR
z{4RZ<R5n&yUVLm9qpZvdEu$E%qJ(h|lFA%%PBk4w9K=w;z>p9DY%(NJ5d{T70YTJL
z5A;wDQ50rYr;m-T9r$yfZsnURAMaTgU0>VY)g8O-A-2_BuE-~hnL5887agx$pXc>a
z6^Kf2;C$ygJs-c$dZ|M+=9K>0@zi@jmbkv2vArZGH&<UvOY4nb3*cM(K7D+AyuG~z
zy!;5-<I9O#a+r8{$*b|>CXUPPU0q$<YJW&%WM#+wveVLDcb=gG1lE`^k0L())%NbF
zS#iJ6@2sWt<M)K>w$8q!{AXp>NBYmoEbqAPa`zn8RW0&8eYYLs{q6VtkgMP2?(p$^
zwZ5*|ZuxjLp4j_&Ouy+Haz&;><-#o|OoDvH5_H76xVYH5MGOc5YU3vmh-KOwE?24p
z+Ab5-$&kaj8#8RwUkxh~v+-kZzHbVJcR$X>sqJ_HJ(%sEfU4eCfJ*)I4B}n8ncaFj
zmg(&3`hBFmeIdT{HZ>(<ZC$x#<@0ubEUy)%4Z`dFdJB-Dxt-7ERy-<masTNDm%e;k
zyFT*Ks%o5Trk=(Ejf^}YAf!M`1v3eXe?;X<OL6^zxgUdVa4Z)J5%OpMY~TqHja%?#
zxuJ_@U`(LY0y%9WEjK^XFitBt0hg2}W0<+gh>jJ{GB?muFyLc}!9@A12I2)2WV#<W
z*Ksm|>ew!A=wPVt^)py6!?nQxltyYJnCez+$|Bx>{)f=S>bF4(3g>E4yjil8bRIm*
z>ZE7;6YX%aP*h&Ef|N{!u-GY4ML1g5^!n)0CmAeabo2zIi=*3feaXX^$M~M<ygOX}
zFBlL}azZK1#Dn=wu;ir-Gs9*D)Eub45GM(T6>&t52Gl~1zhrbEauZc*R8eD3`^D_}
z5HeA~CnS%d9xuvD^u|2tFX*P&Auf}0qRXr<F=r>p#KDXY9Fl8zqH6sOU`5?ilo1(M
zqBUn!G%V=mFml0c^PY!-F@{1kCWEHeYf}6xEAWbsg_j3X9xFCqa||K>;-ZoU7eZ=j
zT7DEv?2j4wq8D*BD+tV5lZgEuK(Tjl&$A;ZYi^aE%GESznpOOFz3g!PI{KEhgpMYY
zfKp6>f<O(y%uL1P5)xv5G@RLI=MOr^;%A~rh!vj@S}U*$`4)D<C&KIJ8?#JJ`#eJt
zUVAZ(vT(In1u}Svk>-+A)Ml46Qy_4inoLjnP7=HpTpVKZh+I^d$Cl)gK|N7eS6dXH
zL0;3ZLYBZ}V;zStq9&%QEZD6r)!Efm|MoVb0Kr8mcp+8K7Tg~K*5irB<|*WlLqWy@
zorMZE0S`fVn80X2!p4LYB{eZ@jvh2p^ebP&ohmCWCsbIKqXbQ3sL0E#1EUoU{kV7@
z8j&=vYl_gqk%NPRY-C{u3&c3+!%X~jG}ImB7adUjjIV-tCN{UJYnZE22yF#LoL+in
zHYRZA{?a8Le^%}!&C^Jch6?#Hx_BTKn#Y&Y$S3O8ILHint*1#ZTD13ClSAdt_xAk0
z@AYe(y`hn}hoh;_$;rsjSW+0jzCNjlFq!D1xU^3!n^*}sD6pKQHtqAilKfAUyH&G1
z)o>KN5m>b-tX#W<0>=h&HxuxU1|?pXI9B#a<gg4rw=5N8?scfVYcjmU!wm|w9xD?c
zKGOF2Y0R^lb1b^+)4nLJsB+?y@$7mQrOl*-+Rtw>j<^wI;EW>1f$-Nmv?C6lZz8PD
zb%FM64Q`sFCDvso(B;2wZNzS%tRk?TEjAW`EKJ%&SXCATMX}Gza`Oz`MOy)xfiaZN
zA_U$&>|c8HkMQ({Ej$KvtF6Q{M6)<Xz6z_FHwk|OHsCUuQ);Bd-fy&VIZvvc*4Ga!
z)cc+Y-v;j%PeKv2@wqR2-bLQa3rR=b_o>JaS!PmkbOf{2fGDX#3AhQIjyT?Ly_z>h
z9Z}KZrKB{n-4wZc^L07Lmz7DgxfEPY2-LMT^NQtoYleFBbleUI-;O?1T!O8l6WboF
z+MLWKE6BNO^s5!v`tVn_uPi?z2V3|6r1ISjJ%A9gTORhGaO~>hGH%k4ouBXJKDg%c
z;cX{*4@lRcIe1uyeUhZ=V%Ww0mjl_w<tAqIJ!^jbBc8K%MRs_2xEVkV$VhWj_j*1_
zDJnWlPAA02-@V}Vgc0EBe)A%SZ*jpqY{$ID5k$NczUry-lQhuwy`=pq9sp?9>*zOt
z&__l_M(dyLcfDC|A4+=to}^YS4UN|~I~SNuO4AmU;m@<m*FT}m6WiNgvRq0>d4nNV
z`-AQH*~|Itn~ozB2h8PlxJIf5m5>=DIea)`Q1l1jrba$dHP305cRVai`017lG44>8
zM1|wa+0!NsO)N2j%+*zHH0CAla7iU616Kve&fVMjG3Z-$V?DSR*uOR{GjdO4=|cz)
zv2h~4aJrY@Jo}Y_z!Du?m)S5%hF00gm4HR#tw`)ipcOL|lM@(kqV=at%N4!%3GR@V
z=XUl_IuRh)*+?7l-i+T;2579|X5Cq5-5rjzi3Zepqb%8iHGatv2R4q9{vIhD>8B@0
zu`q!ncMG=85y;5%IeLgllXdU22P?z?tu9buKd4W}RKVdfp<-;HC>U#V;P5JIbB!m%
zL^Co0Y0LLz>;yot{76Xd6VK+jk;Jpw_|PmNAua?OM5KNnpOOlF3RPToD_sA{u`NHB
z_MiI~w?us>$aZ_SmJ)aw4%{c8>b>tHwZ6y|;=&@l-HE0)U4vvV9dXT+AIXAAg|WNI
zs)FUstfG1VDGROf7~l;lyi;66hb}~3|K@ajh3o)RU0>Z9zApY-KzeQDfUH-JTF2f3
zB#A7j$VATt_KERcD*R3^SCT4IG*P-UUj~csNepYgou8zNtfeKIQp<#7McNAdugU8z
z+5y~qndH2M4c;*SB=5kWHCa3}WL3{(F?ySpCsl|)eTs*k$QyS&#jkLIN}3ysF4H>l
z7}@Q-Wgb#hn1l7>o$P9NGx=USor-+bN5ApO%&Y=C@S?axS;~zmP~o^@tC-3^4bS1f
zH>nMKuAl$Vsl(bfvtH8SaHMm1b%qXO>MY5Hhqt&rya17ee29na*uT7hYKQ~4=+7PK
zR`{?mVUV}5PL45v>97T=qrf=d+J~0`{>TWAmj3x9#&<>!s((KmND$KR^jf6zSp~v7
z-p|0_)2z$fGf6!@R$g0UK!L9*5$w`ZBGarXDsEj`Sjd<6PaWQad-utA;z_=_cR%p)
z+vwt<a`Z!_ZtIOqRqb>%LM*J*hN)3ckj}Tn)~m?Er-hWeTLe4%jO|UHOH=&KBo~`0
z9tiP3d*+vlQcxbH-Vl9U(=xo9T1?BYn;F!5@W^%$_xiMfq5~40H4$+wYrHrm)KOwV
z_DSg8KZZZ2Nham3hDJd=OzP{)>NJ-9vvMp=6B8Y)Rvn{pxO&dB_RsgW2LqnxQ`G}+
zq+P#`I+fxdG^TXQYBAB4;2)toz&WxXroKgIY;yA=#j~>ws<&)#aZ2T;mUwm0eAss0
z*Q?ym<+QCa@*`78{smTrH>z>&fk|y)G6}@(Mcv9U@E4W4J%;V46K9;l>&&g2m$}?_
z1SV`_nI=}A78q~&us#LO{Oc^&Y2tkfm_2V~422gX;5usg(M@&xA^!72G}i+%*N7cX
zU;%pNg;DNY*Z*jAkne^#AvFwsV@!WT-Qk|*drUKW<b8bpwFYw2Y@v^MW)bc(yNt`L
z;c<betR8Hcr&nIs-@@`|rCp=62AaPhr9<DRwe*S}UOVG@)2=s}dZl`B01?teJ9mQr
zcw=R3jDnWd)?DH2kMx(_|DG|QZ)Xnx1F4#V!Wy*f{=*yh7dRy)rS>kMHu7wLFqGdC
zdwdf>kbDGayZrWheE@L7v#|a|FS>TN-LXMD2z2oG7lB;V-kt#|Y4Lyd1;21<OB<V{
z)`V3Lyaro162Dde<2SWe7(4n|*l*rCI66bz!3b~9(;<#69&^<0oj+ap9#TWyzt##g
z#u!r>tol!I>hck&DBU|)GsL>84>sb0SB(kVlh`MkfHw=c=)~<rAcgo6S7C+n_Jsiu
z^+Og<5&=_opj-l)p_==l{!wGeL$S~o$FKH^uIy>4gTQEPT$x+&fc8|UO=MnlvE_<8
z_zg}8?LVe!E=pYlBm4({a5MKo>+R%fJa<sCp=kz;Mn*InL>IP5_UA=ZG0<QWXNH#C
zPKxDE#N{LYnV*o8g^J?w0E59yL)S?4$2JVLrW8EPyOy6(B%sP^36<<r4Dcht?f@H~
z-F7A9^wbm02-A5Jb7mQ!8Sl(R9+{aK|5-cS^9=?I7-G@^QI4X=e}eA8!fW<-6%F6F
z1>+#h2d5y!khDjeX0h33ft?LCA!|7e^qQz(8k0d}BJQcr;v6}hyYDLEaUUelR(-)H
zRTQ1gHxdFKq2_AA&Fqnj&aMixMG32}**>GtFN@J{gokP(#|gd%&brM%DkfXuu(fhZ
zk|{EDl6tGAJ!Jn7(|XtXbPt#i<l-wCNY1&-yeb>8PY9aS>CzW7h$L>vu`gD(?ppbV
z)~+twduU6NQ5-+R^WEh&a};h6KjAEDwe~9KEhb=l(sbu!1HI4*ACVz(_0!;rad1tm
zt@xGMAFNq;R#sA}MF{A#h$pAjWu}6LNgYc)9TqJ(E5m~RxmycGv3)uQRmR;JWjTxa
zf1{$6aY!S>R6UDy*Z!rWAk3cA^rqab?(W=Z-@xSD{Pf)5z|?7xY#)*`H`=BfUV<w9
zxjrk~EmNy=j|rh)1C)#GqI#o)lN%%)j6D+q>u29#Qp^({NVjlEDt+*W`-vGopS#*&
zV0httF$1@-L6nbD3<BaFqU(Ac3r`Xl>miYyfiYg+_v$&+^#Uw`T!gM_2PeLTbMEB7
zVN^)y1a*<2QJ=EZ#JRWhfk!b^myoBk%$Vre*RR&ssvhiX7N5`Hqy5DI`VTOAElq-z
zIsN7cJz4ebn-CV((!zg+B4myl5+7fO#~8$eFtW0(;xKmd$6Z-QKD=uV@)8;GN8isa
z22Mx9e2QHZW_HhwDeY+gvhLr^!2gaNDXsM;h7mYm=j@zkXBke@G}`f5q^BFxxiXiK
zvK|V%yx6(*#vy+*Xgpa3KiG#ib15_q7{jV!#MIxDpp{o1qWK)yV9~#%x0$4+6aSd6
zw=F-!r+GwbzDs_rO+E+93ciVvpPfTqTk^lW5uO_Azh6DS&Z=v?gjB9<YPJR!Okb~T
zE@mar_=P1FPlpRXU!20B#_I9qnK)c@Eo!p*sSdM9`?w1!Fyf+`!?19fS>DF<Ke^Rx
z+$<t+6<j~%Gz|KEl8NkpouN!HlJozfra;BzPjGrK`sWoIh$d%}c>{sdo#^^V@fb$6
zcX@{x*+{{T($Fq<68=)IWz`M=0O^-4&`~8kE=4-i7pTD3yTr2mqiV+G-S9n;*%I}k
zr%l|>Y3Zf0$pn45-rnr>`$-u%c5L6={W+KSlai6~KFxMxz?{k<HpQs__x!e#aAZq*
zPyK&!IFfXobUXO*t;u8f-<o5#_^8m$$IO6Evds|0V4?L0iTF)9JUqPJuc)$Z+D0l~
zUfmtU$l*W1_Fa7St#wUJP02ICa;0`q?R9m87+xPa$2Y$A&K8v!rKP38!Nb-}Z!IBo
zl$7x>Er4TJHM5`{(`EbLt5f5`OV4qNdF;3(Y;;mb;TQ2whq`xZd?}$?gG6c^$h3hZ
z$R=&z!7h07s4?o+7H2R1Rh`iN1Bw}WjWdc=l_K-Ci~IY413?|{5>qu1PeB_ZfmL>0
zh&gq-_Mh{}<LKz+mf(mXqcqDmgXb%o-i}B2lA^le>EY<B%VdPUB>_uGSITBYaB@Pl
znu?qf&+e_jv>K2*SQm+@2<UDtaHnfJ@een6ODT3}f$U0k#U)U5RRl~$E^fq0qE3>5
zOP+p3#x=T~HH_Jxh5ome^!wJ`ZJpYP=W41yfvDp%AQoEDAbrcHG3i)pl9D2ny&a+Y
zz=6pb;wBt<$c`ffK98HdG-&jq)Z!;XGwu*sYKpTkk``4b9ls3g-MPNk?NCZn37Wkr
z9S$X%8}WB3G-4#nhjlVgf@+dP<cU8Kx0wE3jxBYKge-WClhnBC>x_A-zMN?Zi5cSi
zFDU;0D-YSlPqw@z?6+X#);Qw$(7!j3kj5Z%z?Y~-EC4wjnLcrHypcvf_cC<zUR^%;
zIQt$V_%BjmYkpvy2!9mAxo`G|L#^MKGeJ@Q)!FK#_AT)czi?WxC`-UNnFa!;!g0(A
zadnjfK=s<<>sV>Dm?AFV8fR)LmQY&%@*+i<;G4{AYU%0IFi@n*>vpG*wDv&$f?8tI
zRpU$+lezEr0u}cX1_~Ep0@_qR(FZT0o4oHBZoBpG+ydW+rn1@s-a#L(v<QEYt+>HW
zNo8l_-^F~5?{D6rV`!}8(*lpN!Vah*8;`G9xxl!Z(QjF7YV+;iP}n`LLJ?qRpepE-
z(1VqsfqUY`&HDTb{QKoYj4LA~)2ur+6-`4U`#8I<bq@MZ<E_lSE&IWN|H`WH!f|k6
z>nfwTbH6?DnhXQ@QwG?GR&ajibuY}yfQD4F7tC~00Jx%om=g;KkeLhoIgUU@@k|lc
z2dW`ZUlJU{H6vUF-Fr&eE9cWV6*qR)92~o%y0)Jg`Wtfx@fFBf;vU`HLF74AYR48Q
zw%<OeSw~M!&6h4RJw8_Ll+|;7QaUMou#u4d{*FZD;_|DT5la8yWY<)Y4`<)EYa$KV
zc>Og6|3pF8_uXCj4!6_29R9~p|3hULw%GqwRQyAPs6|`?QBtZyoZ8D=LYFs)5y(ME
zXM1cSu?d;Y<8Aj5DOFqXYhVk^@))$+yA7OwGb+=rzg9lJV>6H1Xo}fkoDgJXPRH3v
z@$)K5MNEi)|B3fhzWc1&hYS46qTp!OnpmIK`SXhZ-;ZnV(&#VN)F6cU2sZ6aJ2z<0
zHVrdNp{iiIt;Oezh=yfOR&E#DMwHVUe6>d&k6$KL$2Jw^m5#R+3H477qYBk?6w`?2
zV8W$?qkHcy-w?isU)OfPFVx@!JUKOGLEJTQZYMGg9eM>BYC~_kM|%8$JZ|uwBCKB#
zYFUTHklDs@C^x6M{4u<qh}CVquScER@EgnE=JOd=zuhX4)OGXkEYs`n){ZA*LeZ9X
zJna6P6p<X!$Pgi07KeAcz1*~OXQrlbc|8HCf=2);k^4~M<u>I1`yzV3@SA&7RaLdv
z6s4tMB8Qupo3|e4F)%Z0>*{``ugt6AdDsCwT>xkU;BZuH=Vzr|MBn>)zf`X5s5m(>
z@qHW0|M>J|X>ENYQ_$JC+~Q{H>8T~x(%bvP`o?}F+~s~J1mKH(W3SshtI_SQGS2<L
zL@iib`WG6|Lm24@3b{XO&MMzc;jkdMgscu<LiPsL<#BoMe3j>9y7<UQuY?Z4t?LS*
z9(hX(A!^Bo!a-dbAD@J*cj_w~8y~i3hh0MjmX<Rk$rJ!lMbI`t3*RR=HS&M}XWnb-
z*LvraC8qk8vurnpX!3HaZtmJoLzfz#75_sqhoFL_7lAM|0i97|)7k@%7jt3$o8IuG
zs6+buor`i%E+O$iQI0pr>UQq08a1aV7&5eMg8xwP{MdU1rj4mXbCS}cZnSk3?LR5l
zbb}N>r`Bc^3$vqr;LwyUpt+SKsWtPs<e;3K;@aMrKC{E%DJWM6dPJaxDOk|x($aAJ
z61`7k(+g~j6ICT7toW=ee9|(sCAV0D&prh>)h>P#bhqjwBxhA`ya8p0l(mk-MY-{z
zct5YLkJY=4O?}^mrwc5KOv`)0ju`s02@R@O<?Gu^UL7#*akNYVg~WwHCRA{s`VGIc
zr(R8CEHvxYZSmO~nhw-r`H2YR(9mKE>;icKh%D?>zOm0n2yPIS%2Qy{B1rB8FU_!m
zb%?O1Xr>%V@Kwx%KWY$CZ7y(wJdlp;cWkD=jc!^UHemalu7kvlXX`m>&4AF<m9^)J
zETK~D+}Gao<kp+hC~F<RIeQ`H2~Lgd4UqD%s~(UZCv24@f=?Y*j~ZSZ9J&-H&W-N4
zJw*mab<q-K296LNT%!<C=t4-GN|^$bSG%EQ9uDUPaz&V12|YX#l^>_`*ndmlo{dAg
zj*xYff~6;;gOLp5gr4JwS)_}vRSXB=MnBF$vpfp?TKMhXf#lHf{d^%HKlrI|3ao$1
zf|F=}80i~grS^T{2`@g#lNHxT;@?_bSy+-d*(_ah9YeQ&j-5IXgA3!(i;V9=JIMH3
z9R+qJ9E@9(loPDY<8oFI(=gzf!EO>etuzRKe8Bzq^`f7L-e*~xSqt)#J~p>3IP9D<
zdg^G$bxVdoc*`G!ks5~SkW@D)fO5e|d>cHHRaK8mBdu3UUh}dn%Z73T(*&OVQ2edU
z!}8o)d)}K@D?%QZR?wFS#|CZh6)%{_62F&ToQ&9y>X1I1dL4C0gx<O&plhmxwt4Y-
z`i`%MJROU+h7!S(hcXQ%es#oir1-a{-JlQF>9fXz28xrM!cSQ2w^hM2vDlz`)w85j
zcT;#PBTLtSHGUNxy_}2ozx*iKxz14v7~a6sZIoFt(SMD%)0t`gcT)AJAC85`>!`M_
z32WT?oLovzb&(HmSw)d3U-mUBOCu$WI9z3{OpeRH+Ji&`RudhSr+5x(IXWu;t(WWA
zGj&}$5B8l+tUqXlZu3NCHM=C}Y}3+Rh<HJeZnlG!S)}3f0_b4jl_Br*9ixNIsClKc
zCe&FmJ!Qs)F5_ZP8Wnf-8rg#eD1~)SRG6kNO96>{i_m?M5I_~7g(^!8O^=yLP31S)
zMA&*_`kb+<>Lyq|gwJecj@&su=U)@V<}lCHflJB6E-G$b8{Y?Y#q8W%TrED7xxVk`
zY9bSW%#?tDe?`T`tR!Dc=xW6OywN=0{s6s6{QwRvfcEd9aFk8AcL4A9uUMh&cIYc}
zYkJRzxD*P0EcwmpKrl@2&)1E9?ix2pEZ`i!3*d-VQ)GmMgxqXX)-{f&a360pTW^wg
z>W50)pB^0@Rr4_OXXw1R97c}gq6-iYuhX%Tr3P1H4JRu4;-fk^CL<>4Bu$<DIca)4
zHD6V(Z~M^Igd_c<MQK)y6Ai;x%+6}KWUqwY0+yky7ZL&olPSDhMg7TKk(VWrs>49E
zS~~QR(CTQgfCfs;*=9~-{s09E^rs*-s<~wcD@PCi{M2DGoz^{A0nEC<Gn<6__e>KR
zjUyNAv}>5T2r+-gPF#)s2)<;{8JU!YYEHWH#+xzNT|U_M)do?`R_9Vee-^U+31WEX
z+$>Ua|2QmR3%CJBk)_G$1|M7F>t?EdK}ltJc@L<CBt+)!Vh)@j%Nq3cDX(_so5Vun
zhE$el=d!;Jl4%o{irRjh>2MnyXYQax{0cg8=0_ts--Yj+w`q84=uM0rHS4522zqPS
z$v;jv!}UW<8RAkj*?ck|CZW%53ZzXQyg?2B1Wr*_P{<6EV=-rYI`0)e*cN*>Cy+GI
z(xZd^r=>I?rSrcD#Y(kZ2K7*RlMP|2Dd7U8+@RzYX&bnmdERdc-Ia@7IU`Xm8{S^~
z7hI){jKKj-)?v|c8i^Zram>jHYz^qr#8l)QFp8v<A*AT(kKT?MP3_JdzK&dpTHKmn
zX&2PhBC0`T5NRH-23T9rf4okon1r`74?%`Gg#CB+)t*<obpjiO!*)FDDKedV3!lH=
zSaXWR5Hkj965(I?sz_^#hL<%hHNqx9VKMWtP&N`E6U0u(Cf0Rr1f3;lYy`;Y?QDE|
z7{k1EJ^r{p{`weTX&n;yvN>}xFD;B$k2-X&8Re)bA}5#z!Y0*uns$Zib-k+d>+{G<
zCF9xww<*mnsttdrqk?Ubb?Y?3aOj6~<|JCeJG(%>vV^}~xN~Ic{ezM3H_j&f20OpR
z!?;C7G0`;!p=B*3bY?=bE-tF&VS0jfnyOJ)VAB_)9yd=08wk-jS4ub)2==5~E{PVH
zy-_dNjaF}CM3bDeKr}?_tRR*l1(CUS)TQG|tHGtfv8nM!6#W%6m`OpeXSU3fbt<ub
ziv{J<Uh5#H(J>d{!6V>HMmjz3#HN{ypmhe!*OLHt_;`x2Q>oQ6=((oPKT4mAS*Dp~
zl~zq&U1w3LJEiRu{i3#R9r+p{H+C<uJwf;|vox00rn{o(=M~noCP-jim3nt^Dn;Cz
zkg_9^RCT;CQjtMkY#eKzO5ozci*pv=oVtshM_1b#5jw8KW}LqGVxsZs%xZ4PA;=P#
zyKS<YS({}$iJ48}Ot+{d0#>N9)sgn@9sTJYy|BD1YK<yw5!Ejwql_OUD!Qb4Qlj_d
z!fj>Xa2B=iOM3V)@$OlwI!nOe>yTTsj`ep?ik+`*&%ngO+UO|1_}t~-X!%fFl}?|f
zi3PsX88ojlt)k3df#=vq>?)$AX4|xS@8Kg~tu!Bf^-Hc2K%HHwz#&|gE2NoG_1gSh
z%RN+S)q=-d=5?xb&573b{U$VhDAN2_RV+ee*O;}(DjH*YB5A2Wj{3C{cMWi5ff;b)
zZkXir`_|Fd_oK%1F*iRwI53iyhWYg??w=<Q{tv)>f8A2|bNxThEhY#*G%d4TWBdRL
zcP0Rw2uLi=^8b8>LYVmS9kC??u-{u-fdYj5p1Css@sJ&F7I0OTk@>uK>;2H5LAPb3
zrJztyRrPtgSl<0Hr1-9^yJGgZI~a~97LNi%{9ggSeVC6Qa*}hYZ+SC@Ud#ZoI7rDg
zt{3;V;=fKSrsQREYP3v|Ax;QVFwe9Ht4ivf!bEqaGlF=^WR$V?P6V4v>JUtjPmSK^
zpvWo6M@I_ww3MqHWYajQ*g7d%kpfeN_T6(u-qhT29<SA%eGu}5!;<-iC5$_+1_Zmn
z8dA2_qJkhSr>-QIIRtiOq1!<51N)g+Q*XGQwj>%V5)u?}xE@<6dzd6LrL|}b?ls?v
zW+yT0;ex^G>Oh{H0}@uy&e1d#V=`rgpii;Hgr<Kms#UB=Wlf`5d2_wNt8$?WRLJfg
z4y6)7x@>%E*A3C2$+yfQHNDxqN|M`ju$BXW)f%;N)XeQBa69-&`Rw$vL*(rvkUlAo
zwxc(a#YnP}A+er}lvx^RtA!rU0>^N}<GCk^TSyq$8CXs>c#-bOaI%z^?LyCnSz7lo
zDQe?P?7aT?PORmMp`%ZpwX<ebIAG~3a4KYWjU4e(e^})^64`9YfEE9u9vK=I%#Vqe
z0I?INO*Ebvi9ai%N9GY{fgj?ul#x%~$pBDaS$==CN2ft@J*}I1f)=3%qH6yhVm)GD
z?rN{82bEAXL7-7W9M3Opd2q*SWViwt&|>CcXK3Wcjp`oOWI@T&H>qdpBg$RGhuVU`
zML-~fE)Jq11R~=$;*^3!7f63>qK}mRF}OP*Wy)d0xf!z{u9W@@p(2JJ(ZpmM@neAM
z!577oF0GTQ><f=Ocq+Ybpmus{F6jRn@oRL$7qB(cEN~?^mtji7m62l`KeAvV6ORx6
z1%*UP5`ZX)6%hB!lmro30um9KFrJyMe}oO5WAkdskjwVw&r48tdAUbyMNKQKUF)vv
zbk|k>=A?S53RUvOg|}+y1^>(U&+=9H?LQgFp})qN0!ZGD4yGycdIA=jB3x8_*okMM
zQqVxdL8R{;{(>`D<6@5u3Z90)cor&H#?q1;G&Ph!U?AzkUaF|WE5VOS-oydTBa8<B
z20*<C#QFWm>HQWD=crRF8ieoard$;at&ND-_#p7n)KO96lq}G4t<^Anki6GX;*nMq
zaCcQOm-SFrv@_`jisJlt{!X7)^4_NJBz(_sAaPp8XtfUrL@&-YMZms6ij;uH*ef#2
zjHn@;GDbe%{c^LdcXA2fE59JfO`3^PE-9Y#V03f~YbCwhz(cc@_H04j`d5(?t$MxT
zvND7IF%11Nf@!E4=+oBMO?t|&G>IRDuoZvjmR$8TJ`#^V7E@s--0$_}eSc@X{x6d_
zw>o-96_(}^j`k5|6to=W3rl)v;#BAT3h3g>@<iP&Wv6Ca3k{YRFTt-SM>je0sG!D!
z@h-mOa9V1%rpw3L|5k_h@o71omeoO2m7jlyiCaz&(>fcCO&$iv?uKC*<*5OGs40kH
z+!nh}Jp#(b*k(rq0|F_{UUZgDQJMShaeR)s;}7<G3%OjnRkyjF^ODA)&!!WB$3`~H
zi$mD6>r-pJ!yI$n3iDHo6Lj4h!^$D(+|+MfVfx1;v0Ke<8EfXWM4~a7NL;K$JG1as
z)<4Y)wVeh~Fl`dh(5RiG2TfO)W@<DrI8gym8*8|R7O?XR0a#g=_gnxo9J|rQqR+r@
z-3N=HnfSkSoBD`;-HgwnwtRrH7y|Fx{%=w~Ha6EEsg9o#fQ{-$%B`*Tw2$-l`_o?P
zz9!-`LT3jnKG&J^_7PrmM>SsfTkZQ^-Ea8k#x3uAHI{ejjy?MAyfil>JspqJ(Zs>w
z?v#(@u9my|=e^bUwIn7cMydSsN0a8Ao@t%n`QxUy7hoU#uI=*ude4gq!R<(7e=aJ@
zQp1F!qMWYS6I+T(a#432>YppIavll;URnkU2zZd9f<CBl%ti(QKA<gbd@|ZPGyA7T
zFW7fw8ci9l`zT$cg`j96-OWiz&s5UD!QNPcIAlR2?^^JFFH8k2m~bvMu7Z-fye8NK
zTpkw6UJzy*J@*@xqcs@T@^@-j_^@@Lm<LFrE(tQ>$CA#xOEPwVtnb?sbb+AGE=w)A
zo2(ZZR06bwOog3}MMqwj39=riWWn{sUUhj@j6@H^EdL~9@dXndNX%?idvD#ifHI|s
zq^7MMRaqBgx|!`CNrwY|fgV9MS)sUz3n@8Ex)P`zmN6psq+gviEImp)n@3XYLyn9V
zGML>gVHq}O6_i3Jre?`SX8v07E<yJbHZM^;n+Nxhh?tmDh3YZPRQgr7(tVsy9H{1T
z!pCkdjGV?cU?>s0*g92`zsNxj{8=@Rd8_T86grji@PE_KG>lRC%E)3B!RDlACC0@o
z1W|#uo|-&?!)=Lt#wWa1%@MV1q0Ob{sHVn&0o_upmCDj~^C>dcL`TOt$jKd3fol`l
zIAhjIqPsYRudd-WCU=2u=%8h;gU(2n*q4h~%2O6%^=|#mG_w1113O;RaK5m>W1`?K
z!U#2RZhi>EIPI~lqvFt(Tv=Rd%S6+{*qeqFjc7VB+CL1FCPo(D%h2#_;bbUGs=`7P
zBKS730d7c4{3E57fY?m{p;XCFjmmGH6>6Iu>!TXzW>^LTm8FAyh;`*ic`x*fh6D^J
z3HT09CejJ!fgOxQ8wlvcIgg!fql0w22JB4aS5O`E1$Ln01c(ne%!7etUesRFfjfjz
zup-PF?KV9P?^#3j!(EyC$9v$Kpbl$ii@#FNkT4oFl{7YU0nl+yPP~>OUC23pOuL{j
zXFt(inE(?*5QzM5)sUM}LO$_zJ)Xg7>?>I0C6CE?%li#j7j9ITTeWBqv<n$aekIzg
zUaE`saA7~Dz8uXW+8Wmv*ay#}gkja^3!P~R`E?a`A9In43*+4^RX%JCA2$9^1I?zM
zu@1IP2cNFHYgmWu*l+icX5NYJa%}GsTo0m@OG$V3C0lFaS5hc1H!ar}pnvOj?zy`J
zzTCWBl&Dd2?`+8x$(anpLjACK?)v^7?MvUo!q4h2Xse@Yq#`?3iF;7yR@HX)+KkTz
zJr9Zh;C8o;x-oOxs8N45*9NFVUN=BZE%Dh&Ycfris^F1a(bJIilC5&KR+2}pV>GU)
zlja3|uF#x*r*wF^M=h*E1gwVWQL;0Kq+ghxr|I&U9lL#IEtbDr$>Y~Izm3iB`#?m-
z769A)7V5gZx{6|Pxr({?z+C6h#B0BFqgmggdQICwt8T=4_Y5xS&~Qrx&OXGX=MPJ!
z9rz^K`1A?u7R)HLEf22VtD-(k!jpM{T~I*C&)4_bfGn`ovM>m3;hL@cGMLkqw?&`&
z8iTef&i+^BFlYJ!bHGnvEdD#*$jAtQ;S&-P`t_@CYikR^d=syx7?%TLCzn7@67ZDi
z$4_z--zU;R<m}{+by7Kw?N0c${d+9@N98w~_Ceg`^WoZi$OJHq+#ikmafH3~ruz9^
zAJhA}$jtsw*Y|$r3P=0JG$(yA0whLhKnN8ME*da%g4ob^_KYI>YJ;VVm?>vCOgBAI
ztxG-d)IGK6LgFdgc6K8F9KeT5PfG_Deawe!D+GbZZC5dLci@KhOB@d-Fk;pBvWr%`
zW}$jD(yK*&GW2o0IF|V#GoT2WG)G5L4+pypO3dHG-EbgA4V0JEdGEJ@_5?EVEUH4|
z2%wNin6X20M+eEOV;nX*SQ6P5`>*ul6SYR_=^@>J*|`N({-CPW2yvp#XpYDfC%Gck
za{zakr7Cv_PQNulJaSt;88kGJ3vsGy(uYK(NZO`KA)S=p2TB<lfkO$@f=n;1KIARg
z#2TnuZfrbts9Q|A>g2<&EC`?0uWu)<G|12oD(BU?ll<b?OMUW4bmz3w%c<jCZkN<E
zH*cJ5R^mC}botT%C8Eo6;#5acTSZC;r-0PNx>`#!kd2gF=8Q4sZ6^Un4Y5RY=uA0J
zTV4SN0Gj+(P?RtgK_aKHkKAQxb#26x5x2|<jNXg97{}M9j=Z_TGlXfLNXh<^-($`u
zXyByovUGG;xrJZC-eZl!mM)zrkk|k{P;Dh&)M6w|$xvXH^#Yo+V#f~IH@ER+(iB;i
zqzH?r6P|`zh-Md7U}y(8W+v<-jjH{H!J<`EU<=EG`X`M3z__(F{7n`vlNc^uN!XW?
z(l_U{azsSF$cGd5q%emjt0by`e53<?E8399w!S{c+nL)UI&z>~3wi&9lv5qDXRgAL
zDIkK(dF}h+h|~Jg9Cv1waGIBPV2ya;@|)jlYi-X-4bM5&<jp=T1-Z;t?)v(=!qd&h
z<E7*1(6%ViZJDrVk8NH6deuf9;r1uq&LB?2{*971zf(7q2fRDRl3JHFi64fXn{ppC
z5fn8s6y;7(TQJn7rsB&FM^>a(=g{Z-+r6UkPaGs!gVKGa&b?7ozv4-K4;hf&DbT)L
zn+OIJPdV?RTS2`)9F(yzND0=ywz1;BgQRu%vS0Z8o7!rr|H|Uf<YiT7Vp?`Hly-3l
zbk8k3^AUHH5oTb@KxjU*3SO?QZW3*0p1oJy5w2HkdJ96inESf&&)OL$cG6F6Yn{|o
zmP9T8H8Z|b#oF5?HF5~(;~Z~goNcQ<Ze=6!b&>9--Du)lczM`9j*z`PXYa(ijS|en
zl&v@Ix6G7M!_@A~<@DrLE$q3q<LUo_^>LEFH;CXK+^>BOPfd%03oy`}>1-``@A~b@
zvi#&J_<BAy28VwYZE{N%-NHJelX<C?+&p`qks3TC&fHDqy8UFDAfv!C89>uO$a|E<
zCpevuL2BL--B>374t5BMIWgb8G{z=7%A_;MBQZKB`cw44Nvocz+=DTr$FKtHTnFjm
z;KbC#%u!-l;V`1MOE{&;ymY6rw%#~zF55_B3q7NKoS9Y0ut+t}uWN0hTWqIgZn<J%
z4&A^u<=jHyR41NW*OyaYy~GE%x?0XrvRh`XUu+A+-fHdCIcMckusW&Zyc1kuxMoot
z?YhKu*6;=O9iN)q`?-7RcRy&B%k6f}1295|+IIcC%2tJbQvC0yjXsEAh5A_^5SQfP
z<^4Hj(ZYls>F?iPZ#0L<40*lsJf#jkIOj2sTZ|djzfnhMjg%G6LrP!M<0}e98$Cib
zI9reI!tOsZ;lvkA&>M*WW_F^!cMSI?v(s3kGXnhE{cBQgNm5BvG$e(k(ZjQM#Ym(&
zN;fQ{AT=y9BdU7HVkKcKDGNkC(8*7elj9w1li*o%3P~2s&C0i<629?~yI81s8QH0D
zli;gD7`0G{-i8h9H-#hEmb(a$Q*+^r{^mbviuP4iTxF9tTY=j|X>~9E>QzRtjTKdg
zs0W|mWZ!@kb^)ng$cp<k+trAWN2Ln)FUu;36ED-1eR^UrJ;m-+3lXU{2UZoSGB9hi
z6B8sp7##F&YHv@T-#7t<`H80<PC`Y7;$fJh#?U*?|Cm2X7c5KH^EB(=Ao6GkB&$Ru
zN}xJmF64G^q8wKc7fx*B@@s%s(FF}zT6Ewrl+|jC<KUvy|5bz<@oxI{rS!SQWAAXy
zgp{h4L%e%=Sh=eKrluc0n*cXqRsNJd?GbHUwvm1b8}YBOXM+|Sc1UXaY?fpa4S0PN
z_IG5C(ccI+2&q(A=zk7#$R;WVlvrARUUjAw^t7C-EB$}&&A<X(ffmonGk|4x@X>X3
zL!>(jz|cH|r_;fP=Oxd_s{p%PlSe&26imQCUhEqRmE+6A6TX6|!6|Y?bBxgPsGGqg
z*--U%GNY;DAItA2jHkt06EpC-w(>UjlHzzZrKIc>CU(}~pv0F$gUIL8zPY!i=-3{+
zG+F=9<n|rPZey*LjT7_eX8wiG+o<N8l*)BR<;6uUd@?eifRWR`vQ?MPv@Wq1XSwqX
zj7wc6)%w^5+IZ(Ws%He}g_*{uIVFCK{lqNPI2y2H^l*{+=&JqQgxlvPf86l=e$B7K
z7%K=~$w~ryH)ug#9f?1LE_Bq(b(wwqu2V(qBO}-wld(?=C1>cyz3J=U4v+B>jZp{7
zp=S%!vpK4TZyw{49{O}wNiJ?HRT-+xL38C*kh_tsbVDyjSRK)KhUK?Syk^q!WuEpZ
zG2qWm?&;xf?+WXzUv4ixSY*fpBQ+M=X>Xj_-9D+2Yiuzy1Lxun&B#=1&l0tKsR%4q
zLR58nz2Z)o=AQI1df!nQJ>48=eOa-iDd*eS^mV+s*s9xGvHzJou4nhMwZm`PEuQa*
zqx0n|X&bif-iYqzTzfL}aQFJ#-6E5#MIuLwk`m&DaW)v~ymHkOvOH7qg#Q^yx$iRG
zw==Z#H69;Lv{F*{4?or+as7SW@gbA*(TUVHTyJxd?zHk>Xeo}^@ZNIx`wf}bwOih;
zFV;Es4iN!1XwJgz{Gv+><D*lf)5<)YoBboFIhKtPf!Wb{<-V2Mh{*1JTYkFOKIwyo
zax|gOGzU&3YUM+D$%&;WIM%3qYBqDBRTg;;i&fLTl|6KgW3K|fjr#F1@KVG0BBNbB
z*Xp@t?21D{POJr^s|e1SarLwU`bJSCdUFXu!L)qKg_W~K4(EUG6+8kS-;wzL-X?va
zK5v&s5x#bEzaL8hl;|%6%g^WJ-tYH+`U5i#3}2F9|K27BQrdbzQE#?acIT#qUQI=w
zW@4pElIJ{wT(Sx=_`(?Vd~p(~Dm!5}qyu?m5#+%06uoH@hK7(;dFtEZ!DRs^KD6^t
z9qf)#(^^)WNHjO=5-Z}q9Ln$FA|)eh)NbzJ)FoxhjWsw5L!m&*^LmgxHWeEx&%qLp
z$$IB_vsFm=;8LK91Lbki^vKIFc$N`#T4}66Xr%qFqc%yEDV}pB6eg~k?3s@K#IcEg
z4B6dKpZgZSv69jgkl@3jhKAQq-4*O?N*X(kXl1l4{@@h!b{RGlB=IhC(&az8(J?x0
z`g^<)TdcVlDdKff`09b@_yUy?artc0<!=N(gPS6H4;U{V&?T9pH4my<d6L(nkCCZJ
zV>0Je)W%iY=B+#$$-xF$fzXIY(X#aqA0|M!?RN{~w}-(ZXLSBG^F@z;;_0z3HgA*f
z=94zxt`)0l99aRihAxnh*{4hJRHKyE=RRpHTnv!q@YYrJ;Lm2_;?0r@Hg#K?uHiR)
zv4c4dx_{4u2Q?1(v_fQ$@jq>@%V7GkB)$mue4f@du@+{pu@Ki`--Hn+DpX#`>M$K<
zO|n0B4z~g?(i({z)wUj*Rwrxsr&R02)fttQ^<2Yz0YhtN4^Wib#~!n8U!HwsnhC+3
zu$9y|-*5$FJURe}7SQLR_f=J~0;sAZa;%Qe_~6v(gma{gN5^Br-$eYlDE*ljF&~%L
zqy6Ytv;7d*1lioRhx_JNCq^|c7j4n`#hHH#Lu>!m#E0Z0&cE4{hbrXy`(N()Pep2h
z@2}9MbeLs~Ho-l@MMc3)HOU8OS`MOg!7R|D7wK9=Ow#mXqF6Z*QS@~(kRm~hVU^hh
zbO4=UMR#XQlozF}!kzb>DlkC3O4*_FwfuERr<W4)pJ8H<YdfiPbjB@bQ-qIER3%Zx
z%27NntujzXE%TRb=*&20kZ?t523b9l)vfON5UhzLS|&q4M)DeLXsv8qP0GrK>R$#w
zXca!34YAO-k<6KX-bP)#DNjRiOPjrhyaq5~>n@&qU7ad^o-1BQSocq|>{Z#uMp-2@
z6uFW6o1wmwnv8ujd|&4Gx=W>wyWCw>-`)QYW9QhM36yQ&*tXNLla7s!ZQHh;j_r<Z
z+j(Q#wrx9;s`-BJ{D)KfY^?RH`1{Gt+3gFP%ZW0=Ep_ik?rs3b-Q?RvMsR#6_sXW#
zi3`rFY{?Erpl8<Vi~7=M#^daAWS}FF<WMNTFVF8XTlr_#33+fyyr(by@p0%iQ^MEF
z0B5<`XHx|StDp))wkn5_SkKgDixo4N6<^9a5ldog!3gxS+PS)R5l(MW<>q1Y4k;n2
zyZD*=-4r<-FFgLk>Y~F-`>pNKrQrqIfi<p$KKiwzfSRtXb*c<0Q3OmKx(=SH;S8%|
z-A1?t{(ax0*6t9|0qjj0O*5w{>?JNhfo!U))+>fZx=Jc{0eu@LAhkGqQJgam_2J7a
z$t-5CN_vKiO}QeMEhF;r*g4KKJv#YVvobops_|ze2&$d}rN=~Qw?^bVxZdq~zwf6S
z1yJA7IC_`~`_3Kq%yXl8sEHuFlHC-%hah-+>=O3q*~N0H$9O7d3?<-NLG!YLqxYmj
z*;m@R4vZ`@)klI<2#FgfShG<|Cj=oMz@dlmW}GK~`cr(Q(AUtMp0>$E%Lo&sa8aY(
z;A&a@MLL^PXUrJT*eA^^aL}Ky_fJ)ot-7il7dE-07>i`o4KOtI3#sK{>FVML3Jg&8
zG%E(Y{e`?lw=1X8`iwVLr|QC)m)GpCE^FPg1;34+k#Xm^k(g#<$GYC1$06(sTst7g
z#uELppc&RRN(<bMFgY3)I9f;%=U@%rY5w7H=c<bGb;4fQ{xrTi@^%3rF&w~a3oZXn
zWY8@W4>@X4<1BpFg)%?EU%|cFn}JabwpmppOkt=DT+YK%6(D71`r<1P`#n*GbFEJc
zb5O8(5R;y5V<PVIX3GBU6jRAoMbFVnjS>`VcBgFDt&Ps_)%D`#La#xf0v#<P3M$@a
zvQKxJLxrgeF<@G`;SLw@0PG0Pvx-*_o}3=XH|HkARvWozXUql<%w&}wPs`K{8(nH+
zPEp^ivUz^i>S#!bw8<J&0^ftPV?%PuO;YI~EHhhity{#a%I5p6?&$&%EhJawUZCyn
zWD=#g$oh+f)g_>!_6P29br<hxO;cLQSCYATFatI}5#1hFy4n&z3)lkc66@PeYN?*y
z344)!*4H%GhE3^J7>g_}g*Hl^2{-nN=@hMx@yN9TFJVxO)VyMxWp0%?!5=y@H2-4~
z#RTUPwjpd)={j^h{3qqok6%+vI$CxG`&1Kuk-yBQ=sSBBIYrw@O^#Qovw&i9>%Qg&
zt98l8$`@J#ify^^#^Gj>k&WNLT>k{6(2TImXR3E~tglaCed|_;hY=CW9vmOEY!qrz
zZZ5cF^%>nX;#XTDK3#NL-HGt9Yc2%=ma-nwA3<T}_CEIsO6xYdo3Ysk%BtVjiP*a2
zi+`?4f9d@z;UCx{yUwr5Ee}e^9QQAzM8@)f_Sb{KKcR|?jKmC4yrL7bi_lsEXk9TX
zxM}&1PyQv-9F*3YJ=Pkj0F|JLXg`GRvq3lV%Js>L^k5cUL0^nvKsjQ!$bpN{Yj!{r
zCI?jbwPrL)|K+{10~8GTdFefP+dX}42D8`Hba^*%Fp81b-zQbrbbENZ7)1G48GkHJ
z49zFaimi7{SBHOjls@O6clv!)?L9gri;E=F-ajp+<y55eWsrS;?XKLHO&vlEXri7a
zHy$>lcnL*x67?}K>L(UnTsOaEOGDK_uMvzYc56s_@##AF{d+J>eXDKSg|0efqq}Cr
zysPVYrcRuys2NfPE>p2coK(w(FW^spw)q{VbqOXZt^PMJd<$Bqp`aa@ViH6)qb-x~
zvb-$qz!1~KWY2ADCx37i_Ko~&t;_XVOJ}L^-hI30cEZ6b8Zncfi=fCtVGkJw4q)we
z=bT{m;aiw?in)4*th2E`N#8r24XW9Z;(RA`@NR|^bG~drSi*_JtSw!(#AQ?bcFC0-
zJ^Vl;i<SRqT`#vn>SOFPW8yVJ<cIiw%y0hP9~8bsG;sXvkd7a>v4-kkl>Z`yn4D^~
zW%FZYS@}N?h<$-3RSyiXT<T7l&AsU=n9Q2XJ)**<=){bL+}N+m#7uequnv10@kKFm
zra#8JzT7rO?Le|q1gBNA-+K7&4q|*AYGUTLUzL^`YC9<T|H)ODdK}y9Y|ncw7eh49
zT24jBO=wq;RTUKc3y{-9Db8;M<o!a|!)gF4;i*i}M%6$1!khHP&yUCU6QKuDQ)d=`
zipVBbutF!D-0t61uDotDE@*V`EbOD7c|Eu{rz(<N%~}ZhWnUZ!EWfNc{5Fb%4%P+R
zJBb1W6$f2L{XBHz+976%tb@=KIaY#shc4M2)F=Fn=T01eq}9-`8mdl!+6?CAXxRRi
z7@EWO;I|A$NEZPI_M!t*@Q86CX5z$~DWEb7kp^89ubZdF1qEbnHGVqgC(?->bQ?Ol
z*Hw<8td9l?_IA1X*x7c7D!Ms56=#T2FR^gcSyr~(+RSZGgxCkBM3B0DJ^B5*IOKZX
z-FKm<zQ)84N9_izUr&uoUu-JStq{OZIAkOD$lt!%S}HnOVK2$)FR0bslo&H^SZQA@
z@|lSv{R~7%eF0TPSz(PIN8d$Dm0ex2`Af|k&vdcEi5L&6f6BVc#zH<dE%e+oJKHl%
zzBuR#9StL}GZs{i!z|-3z1N`T>YY(xg$nnKKxn<VPpl+!?C4ABmNKT@e)-y5BbtM2
z@(*YDQOqe&%KOce!K^q$ltTxoMe^5omoq;?v$-FFEZ*!gucd*-h4raz$+dAQs_OvL
zVWa<Os3@0Mr67xU{L_ppPyu)jZsLydMd4$7a)sYcQJ6jRbM5?+!P_xIo4UqIDvtRn
z-XEj6jD~Tf-Cs;BK7YaWa}8l^qGtbOGYQhQ{gw%nH2eqA7|?%P1YK3CPCyYNc;kPZ
z8CYzQFq;y7GBT8ziz4@~jT`BCy>k|sVreAEgilu?NWT!IS9yz2?iwX1CIXH1H*YHd
zxP7~s%P$@{gPbU2e7_}~xeMRUi)-ykAmrT=^79qLQ6k3E74RLoZZvK7_8h`}^-NsD
zG>*~2twM9a5E9yT1a~F5s5txLZugyIH}&ZtbZcDaBLcVYLVRQ@OpPwZyXbP>J2I5`
zb*QtI0V$=&(_L(St$7T;nS=k~-twJ+E3_>MzAf``ZOw9R%?69cG0S6R*8JJj@wDiE
zPf?MHIiIyGA)_b$&KCU0tbL+sw;9OzHePmRCi#I1?vNz4m3YseM*q=I|1#@-XhU^1
z3#6fu+NB8kCaw9NhW2Q;?&PI7i&Uf`Jgoa;(0Z~wR<gLHh3;Hgs$G;d??#C~ygau(
zHuaTeIIHxr0sA$+J}e|gF(bdMbnjkU6`s))zU5VdztwWjw~JwRg5f+i_B3#d1UWBH
zVifx<3&3Bt=dL)fEW<ZGIxfBhad895UY_E0LHX!Hcv(o3JVxcEddeb4NHsF!zNAZ!
znrg(#5<btW<g1=_Sc<4LnR<k);WEi|SYUcBo&5RPh(bSj@!b7<ImmwcBVQ5v>!=aA
zc06~_P~z+I5u#AMdO4OrLnvu>$464u=Gbjt)B!yXE$IRK;XLC!G#OG+^5lejg=fl*
zDn97oNMKcfdK-P2>O2&j&33BPNG`1joC!7=p%LgDXqVUT?EDfL(+TE(46s@l7X@n@
zC}VZJj~gCCbr-07b1Pl9hG{I_7AR^Q+e!nyp!4&A46oelWapW;qlovqS7b4mM#cN#
z8Tq1LX0nxXeJkCcFpfqZ_p0+xg0?O(bfmLVRHfU1DK8JXFF;AR*Dvpjf0uYch>~X@
zOXUEFE9<9<tJ%<F>LPuw+(S{`UBvndJ>5fuI?x>;OmA5d*#6j{Sx8$y^gm`66y!<D
z`!&>Vi<|TR2HKB>XoRZbnIs1m&!a!$GkZfzn`4Q@MWE~a@efdY6O)iNuql~hCN5JF
z!BLS=NO>+vFmaC;GX4C!wP96U51ZJIm+0iZQY5nAcac3@Ay^gEdQR*j<MiyV`2v-J
zPE#2C60|<x<Gd+S0cZl|;?DrG^O2S`iN#j4D!Ci~+L^m`^(7!(Hq94RN3MP#(p&)%
zZ3_8j8wGl8O7-=Pw)YDQ2Q`bxOek3i!@je2h?f;?2?Yu|4p@z#Q+Sv{8(VMwl_#I#
zX<>VFAx{-26;wjaJ{g(NY5oeY8^BZzcg8nMcjNt`<vlZ#Mc&us(TADaRU!`{HRYYM
z4H*@mvnr!>=TTeDQ9#jE-&#^wFtcY3hEMpqR01}pnKeih?D^Bq_2Rtj(n;sRy?#T6
z5nuXx@WHgF_<2hNJs&olI0y*^1RD4ZTp5_iAIwtxm*@2)gi)YF?B#$%eMfzQ%JY_$
zUY3(>%y-~~F;o9Q!8DIs`u4<omIwWo+Mtb*Z7W0~3+!3aw9~BB`}_R&S$$S7og4#h
zqI~2yJv)O2*J(RsYP;51sx~WA``)nuI-wgo6C?4B?~Y8Tm9B5KsOc3u91olfZKd70
zR{AOScE^#vfxfAwD+6ugqi%a6l<&$yb6k#$Y|MbmV%-~3;LA#kWlHdsrmO)m&vHY@
z3?IonDe?Ns^B4Thr_{`d)WV*<h14n-GIhan>~r9d=v;^fG+jJ>t=AL!A6X!VjCOTQ
z;bv~)YkFYKNpqW?Cg4!@($Il1c=HXGc59EnwZtMLNxL{QXOW<zvoLl-5wA}IH;&<5
zB$gg5k{^E<@9!_>0y*DSah|zPU&Qi$@>ZP<{J9GH_;igXqI+V|{*jQm`03xAt{n9K
z{#Kvz@yg}l#PxZXy+7-{hy<OSUE3RKG?l6L{>}Mr==J${`w^+LsN{OSU98S;1Iu>>
z_jT@ms`n0<X8GvEK4}p<P-7Z;p8r@>307yW&p-8R8T~Yk?;0=nr5}uGX<=Z*9{!Ae
zA0XxgW~uqr%_C1x;hsS}H%j@XExNXCd_m}xluz&n(N*U6hbCK#v_TGjxob)Y=3Ck3
z>#K|dvWHt{=i}ow2M5On8|)o>T|<|(N#(w=#hJ04k?(q+`gYIiO5gO*?D)#WZAkTU
z5kGKe$2kwL)I{|CB`r#4)`l~KNXAAfLY=vrL*QAuo93#c56>$fpHLStn%D491%kN{
z!@p>g&eciPL|hK1Pu=%*A7D58sbsolu0b`128LB}28(a0**Br~oA<USiMyhZ4}Z<B
z)Hpqxwwrwmp{@laI=ZT9uGQDix%PHyeh^ww)~Tz*38bN;T{{W^G0=GA?2Q=FF-o=!
zWo4NWgE}DSQMN6KQv(l?y3=?v6N_kJaTHoQK)F1Lwn81*=b&wPd&*V*=l${6ja5b!
zXbw`eoNybCHIL4x_8QNe{ja0$<S_P%v^#z2_0NkH7<OSdiD-DrJH=9Q=J|=DzjvDR
zo8u&)`x&F?%v$-C+*g-r;bO-i<)vtS`$tbzrWlF4Te(MJ>D)aF8-Fza<P~Y6%_v`J
zqPyh;lQ0H{0w}&;eo}VsNUq&RBoFy#I0}nr?=W<U%GMHHd%jKq_c;LATCJd%l(RqH
zMs$&aS!zzm(b@eVNCAOk0f#dDeWi%G<6r??bz-S=m7<28{)cW6PTUJ~+V)|N(121D
zB^*HB3|3U1_ZPH26ZRLghv=%dGx_Pup}n<3ZPGk)cjXl3Ls3fd%$Vfq47%(tjBPEj
zGfrZG1b7|tr`*zXmLp2Ku#d+$u7=id!YAZj_0(AVLxPI&g#4c$Xkj;R$W&@BD&}ox
zEOTVCBn|n&UX_;tXEI~&-0*rZyh6XFGJG>iFOghKj4NOZ7lumXDJSjzA6*i^(ZCcY
zpt#_kI0SM>#5q?c06jcmek*<^CDY+=zD!fh1PXf!V)NVgWa@X0OQt6)?r^If(omd>
zs|@TKMhW2AmF6)012|qw<FxarSff2bYe9ni?{3FGPxn&}N2x_lz0>E#<F=Eg&(GKO
zjQ2I1&0CfR8Rw2uGv{&Ev-<FsuE4UNke{NqE<jmbzYQuZrqnXfb4`u!;f}3|DWSK@
z^!RA+YjurDT#AZx*tL$`afO=Q&@dV59;_I%CE9IUzb<eVk^f|tN&!qnX4agdsp^%P
zmZ2F06yu2OqTD1)1Wny!m2*muj|JILKWka2^ae|(N0|Ink#SS*q&y`qe7kWgc^V>}
zV6hlmVf#{1#&TinpSgxBE-vFD(Z3OMs#;8P@fKDCZxZwhR6%2=*Hd<da<K}!Jei&y
zJ(-7#a?fgX_m<1=>jd&a(;)}n?rct6+u!#S4_>`5rx!h21V(0wAI2?qmS|7L37_hd
zT!cydN29u9#@low(?{S^QvW6&zYt4krk}|PFDol2WUr=ae-zxFt+fvyr(?IxA1RDS
zG!Sp0*l&I@gV)DHh~No{&|CNYPwa^8PmpWh?TlQU449L0m2_}?>1UkDZXHUyzNrP*
zu)@PAr!D&nP8b*RqUza))(mi8FUF_(2`#)c4%8+1{72XP`}C8whdwOW{>>7JU0EBx
zcjl5rWM!v?GFv^A`2+;G|1b}*F>o#}jExLjUUh?_gUezn3aV;yVmlie!sGLjy0X`8
zBK0A?C_q2WN#>P}A}h7t21VPG2oQ=G-3oLjfUEV;y>!di>jiK)45jI~C*d7TP2Ky~
zYb)AwYv|%a_@Hp5!d_)M3cwd<>xZ{8F)kF$yaQ1z*Y?~soHvypn;u1Ks*;!N9Cn|b
zE)x%=;9o(TycvnQ*Bp0;=R>^9h<<=iIpP13bQ)h&OP7D~#fMSfBaTFkRXL;mTe$(6
zG~Yo&=VY2bRd(4A+A(1K1z0JrtZ>seA)(+8S&!A^%}%_OaYYIjM{*GAxYJag-|?f1
zI?;C1opkqBoPFoxwJcxvsC{!?@7$2@0NYH2#G<a`8#DTfqhGhq7kZmFgxwJTI$<3#
z?lz?U#efebphoVTV%$f`<7r^9gaPrY%XW9uX8c*C|8W^fJ()2=3$X$c7G=q7Ga{?>
z4BPhQSYL*g<la+Q2}g4lmtbk`=fw3FQ7E`++n<;6ka0FD2*OYrk<+D&M)yh4^I`HX
z(l6TK+be2G4Ke>gzl9AfvSQJ!21|=jH$=s(M37fgBX8fNwh1ViI$sB-+KacyB5q{E
z3CQLj9u#%ZdZ-$FIhf(eh|zl5{><mv)g|=>dEQ5f$sVf9F&21hKG^p?VPiqy(gqwB
zGJlLmQK=t)2iK6V5>y()-&(4G2x_cHbEl8}iXO7t&tce)v{`0)4f6h@Yc>_aU(iM~
z#<g}@BxaT0vnM;<T1fnC0&WngPE|KwOdzg%rwS}HQVmjeMm>>8NVa03Jd_Unov+7@
zW;88i#%h0}HJ*`>9GEuR*_MN~?4~J7c79`PSh&7l?CK3APlv6arK4*zfWPDg+;#KZ
z6mmsQ2j0a+VN*+wOp(=>4=Odyue*{pK?@fiOs+CH#iR#ZU4R&n-)mAm>KwvAxEJ={
zpZ&M~1ao-0r)y|sY}7jgOw3EpcbwF=`=-{G`&o&5$kwK)u996w=zP3{q}XZJr9xyq
z$z`*fn{|^NdflY@6C{lqq+qSG@H?XMD+`zvBT%oNTNV<=V6{*RxbB%t<1AeW)Bf1V
zO5IAp{Dv4@WfkFE^wL@a%Mb#JnGBX4KS6?@FhfHvGQgBvn+s}^FjVL19P&WI7QU7+
zc`E+Nz$-?AwrPu5^l**So+eeJ8MabOxb^~T)6gI5C_Y&wR;A9!Ate|^ZD=qXDt9Zk
zSjRbgWioOoy_H|7TMxyd=qXl9a;k`Ec~?3<pF5_Wnw5Xf#&K#5^>G>cJYIiwv${yU
z?CMuJs|>3pHt1AE_|~EG<>svaJ*bs-%=-@e{iqk}hB(O0T_3vMpf#TeD>>Wy^m3ZL
z2aS3kw)AxWb(6vRbJCmcb=+Q0yw%f{>0H6m?%$JJ{WwJK%g=l<PI~h0Jnj`f4@Y~P
z<ardQ@?@;><|Fgwq;lt2Y-81G;ofe@SC-U}77^R}lD}WzkS;UNPB%6uA7nN%vdSzo
zX?FCmHrMRY{Kd|TzH17}rD^aws=$}MluVyY9*zEdpJ20rUk%}zAzA_sVHU#muECkE
z<Jz+H$a3f0v3pWtgk@@uR%V9b(9bg4Q#r9#JhQ;HNlUEB4`n}IC%)V<`-Cy{soD9O
zRA1pKZ`7a~cy3P8Z>sdE`B`DVuXTA;EY2Lcv|b$@!bb9}?XaY#((Fp}WQ*C^tf|>O
z7`#2*B$vRzdVsiaHyHF`IUY<Xhx^kFnOj)+EcyHAulKO}*q_j%4-&BrtEiHqttNpt
zPb4Zu#H+}Fn8Qbc5U%S>SFsD)yL~FFzo2!o7J`B8!H5)+5t)NQld*-h_FXP^<MHg0
z)MmY;-@P3mP*zfR(Qi<JX8IRBMc~`}gVXG^iibu4c^O*X+EYu1ot)<E3>FDrf>Kp*
zw!Vz@{RTq{xIQ79Q}?EFa|R8$dfml$5dTL<U`!=;V6Uo&k_W&G9sprd0u{`psZWbt
zGGz%^O8<BYsAp||arbEx3m&(Wv;-n;Q8^H$P)=S*h!-bri_ArvC{w5C;dqJ3<Qx;6
zXzEW|OoFeIphOZTJ3xq>QP;j4Z}^4{Wk5S#sTeY1FYZkQsSVTQl(y7Jxs5C1B&!CN
ztWQ;!qoV06XlF%v8Zu`M&YqQqANO1tQs)Dr_X9ma0vQLSzlAkcVdT}vYYJ0dW)nO6
zm|K4W;bDncKQZZzVm$xg4JGtfpbYM>r?u99VSyP7i*IZsSMuPVZUhJu=iee(25C8p
z8fYQ9*=l=t(Y`k}Kfx+>V#9+8rD&{(&F#z(v-uKi$=S&76sNp3&xR+IizDUo(lxVP
z`nuR^^AZYqQ=${djIf!&O#V{q^OufkWHXYy-DqA6c3_Q-u?}Xf=ZI$U{X?b4{2ee1
z)DXVf=plUQtV2x*h30lFZ%~L0?_;a03%e2gPv+NdBe}W}d^=QLF1$_E^MQAEr0)8*
zUR+goXm~}Ie_3aAOz<kJ7rPlMjgu0)S;~8!D|T}+_A;E1i_68s1Fdsl_4=!J`z5CF
z1+ER!^}dcP1*&@u3oXlcadu*GixU%NcPlXg#54#HG3*^mt;-=Tko;0WNZxusDhlwb
zZ7xV?j5=Y6n}FM2Ewd2N8m<=wxervs`g#UQ#>^2&NQ6SjDD+Thm}{O38WKd4`5=#q
zgCNXGYXvVBJUnH+ldOwMOcSd()Jx=jJ0+-EON+-|Mcx@7Hhi5fZ|B?kS|hdj0%Vq|
zm_EYNT0ad!Ki(fp>+PHyY$aXqRN;-c)V*ucvwHYujs@pR2tW6)k4yiLl&alF_>Eac
z9eNu*JyU*sGv5=69|Ox)x>Dr?bnG|g_r<sT)RvUo6q?sM(;}9ccWPrdy=N=mo$ize
zoA8T)-^rxycZSo}xZ6iVqQBcabItMs0BZ{OY2elUy5;=A4e;++D@zVzEjB5YeSAU&
z+9@P5XLsHeon)PQ<<@a)9Q1B(VC;8v!xkhLcJL@?lB7bXVL*Or3N6DFfq09VMmd#d
zcZ8tdi0umb!CiQ+sv~|bX|IUda=$vE!coRa4eEgh>r>b0CKx2Z+$&Td5$G9L_h~t^
z`mOyOOl%b}%*h>s&H19&6brCWDlt;Mp(wZ+FLG#Kyyc3yU4E#*w<Y#rP}9-_KEY8*
zO+~fo%IDG!J<+4;zGK}Oz;anHAMJs$y?_K97PntdJF;MEq{nVHx1KaD&b`smfB07W
zB>Xfue!0mE<Ogb6^dIhPaSW`&Qc~v)V|$(`)!mksJol%9i%>1B995hw{Y(dJo<R5r
zJE2EZ3MTVo<)30z!*;lBD9bC$sg^QNos?*-5*@OT)%KGszx>Q8yEsB)+&k`=D}(;j
z2(t+n!huMxLXW<70jC|ZHnC(rJ8FI;mdm<&*oe5x@j|6%>S}=`(A)g!p|k7ZH4E!{
zcq;$R)EXuaJE*ddKXbCxwisHnP6$i7?hp+GMO#pTG#3|9Re3hi-+#>}lut(-h|8h;
zHRGv*UG+O30<tG6wjS*GcOi?9ola)f0klL?!M)trkIuc4K3g%|yf_q$tmmw6^}$(M
zGXigGG`O+laOoirpkk>3*<MofD5E=&>6m-5sk^%xs}gkhQho&`sRvq2RkKZIPg9YQ
z&}V~itPEn}(g_G^SIYZDo)y<Dz0Rc!l-eRq+yX2zEP;v%hZV<%15GMUDuRu68z3v+
z&s4ldvj-$DhF)$a*Vx=3o-@y2F(!9T;L?n2P*Ek<&Nh-2-({9?h>)tdvxCjTUe3C-
z=HV3QqV{JPPfamo5wpVP-H`LkD$HUK@%<Ys^^r)=?9FZ<CwR;rS7a;V1TX*61}|s@
z)mWR;hAqb@aqw05cuVdsDJSAv$C1A`*iy-6tmHYg*gloI0*cJUT}nZhK5XK_vRucv
zmPIcR#D;CtNZdu9h<ie(J)fO9d6RWD8u#?ts#pq|TvJs*Xo92$8jqoQQ+6u6`0ujk
z-o?SCuDOY=uCepd*w);{)@t`;*L|{YY+!zyx^IbcXU+v6wTxGN&Uxag%YNTxaCBsL
zf>(TOWs|OdfqcDlZEkgKZfA9Frmmc`qNpUFM394q5{H4m^)G91VglFBygJ}d!aY-u
z3GkzUnxn(O?_UM{3<0{R98$m?Xo9k@Nm_ej*nf}#jLE8alm6uQ+=?-#T%iz7NfG51
z+ItxE(V#JvG7g}k94TP$NuiKN)JmF?hPV|QW3bUZP2_Q+k*2j7P~a;Bf;Q0WDIQ@a
zd~d58J)t6xQnxR8*800~+~s}Q=0kGF2j5QBuMUAeT%p|SXTI&ioLKpp;%Q!1RfM{A
zOG9t6#fx$j!W8`rO5oSV4j<+kt>0x%mpbDW*A&^^OPSWuHPK9JqNNPYLkKLD{QSxU
zRFrnl@85UUC-Yi|<7V}nDpSc3@76+1B=Q|;+kE`;9bX@dFz51fua@yo{mAE!5GMZN
z2mLUn5Hl6~ytqmupR6whSzUoeMc}6+^TEycA`9*9P8J~SL_^~P|LB@t*251ecy7sa
zIAu<`2rP33G4>3oPO_P-xhea{`_GeYQ@gt=F46TuE2#34feDV5jzX%Ni*;o^b)Vj<
zQ*1h%vy6B_t~ot|usH|M03}+jCh8eMKV+v*>e?9&vX2Odhes`eaV=IhHR>okLHNq@
z>NBONn(&RvH8{9*+mjvq3&3@&b6cy!rxBMeAZnl$L5vIe!Np!rr|W+xJzQ({M%`cw
z+?pb3w3bDKC8oW*S$r0~zG-<bCfp8xloqs4QN;NWGq=liFy9Rb{Nrg}RR-N9B?)6e
z;v#HB?2YU@0ufJir~F^c{_;~XFtt^bsY+j?#Lu**o657m#BKcJL6AUNMoE%JsyDPw
z9Ti?W26kkOzy?zihyjZ3d!s~=)weK&?rKceM#@H8PZjiQx`UU<q{+6Sq#a&bh_p1o
zFKQBTkr9A^A3Q|i1<^_8#sPytFGY^WQ&dNR3sH6H2@>8qx?#^*pjuUJq_1;4VT>Xg
zJEu@fNY${2{z;W{o2OmiF1^#wLCwBdJ*wAFl|84ZP*qKF(<>aVOvQ4RsZlsOk_<sJ
zQAy2zX&m6vr!Q|0%1L~3Fs|Kfz;_A_DM$T@wid@{rrs&<P>vp-5+<j3LU{ds<c)n-
z{dL>qzs0E%{#JHlp@ax0A%a?Hg5-+T`U@>5l^LWYidn5a1LDo%jE%z#TJPBtsCuau
z3fHJ0c9>1>ROg_ty%xbWDXJ*7iWJrVl60>}nNSa=Ixrvu2jv{LjazBPJG0P9l`c-I
zP<vK+I=5Kg7<-P)D2Bi?U6c~YQA$~m1$6e-@aj;0P|!;JnVp}n%0H<XZKEdS82)kD
zCZ;Kd*eYVOAGRt%9iN1*$QgksWeYtZ<)~!N@Onw43HA*e)yQ#>rMB|PAE^i^wDEX2
zAJ@uDe4t&|LD?5R679o--qXg<Z;Ad*Ro1iEbm=XnZ_=e8s_NkytaX#ciS0hZ0pY>k
zx!d-_{`4%%DtUG(Ipyo1+^fImw*D$>eClXfux1;IwG;cSAOpn;J)=xX(`+$KCoCMq
zzu^A9UujIps4Z<Z@g{j;?jM}Ir%)Mt0c&R96zQLNO88q8#WmV)Ya^&O5=(wGBgoyf
z!Xq9@H5~?U=?v?n%IAlLcFPsc{I`QAtdchY8uMJ6{!SouNmiat!M_l9nlO};{$Wg!
zW6u4_iY<9hg&Nkq^q_az;#(yJu8<tZmXFj%`Y8S0zgNCB=O3)|Pv3*nKL1>~(R|3!
zOxT&46S4<is~z0w9)SgaoL1iR6Q0Db?3X+&-oGY6LXL%=7ZA1#e>-w?c}HO>g0dmL
zO=Z*YUr73;Bm1VK`f8(eW$#y2&Qp{Cd_DO;zMwtqV(QpwwzIKSB0ZngNV#JPq-*HQ
zvgxC<WaMHI`Eu*FrD3|b3karIujg=&YI`UmQ$Iak`lOc^@T>&?WT7Fmv(P>Y_fDyP
zUJ<*vR+{A#R_C+v9qfOb8Sj9XpQin5pR`;**e92;XR8kSJ6)6yoH`-Q-+At4)H#Xw
z*q1q(z)M>oQ<(_YVMN;*^;qq`k`AHUec^VQa0t(gYjpfhe$`g&hH`<A_i3xX?w9ra
zsD`AJ2&NNQd;Zo&F8WL96UT=np^WBEZs?!}4qS8!EpXi!6gD7ING%SQcR&h<VC>12
zXb=|#RoY8B3Q&Wi^RQ-V@ri-y%fHMg(X|lAFF%fndRIY*>C0m?l7f?8RP?CPyGBCz
zg!Nlt!G;wk%4Bd<n71Z}O}_nuc@O7<od&*Tw<TSePh@X~FKQlvS>JAfkQZG<@Yz*Z
zxnv2Fg=s<@v}J86I>U9dH2tVM{|etT7()N0ww(yeJDG)E2NzoR3RbE22Jd^to8LUr
ziuE7!kWT!3`VAd2o&kj0MDOp7ar*ED7zXJmOJZQgdlw{e=*{Ftm5#*#!!7t};D@S~
zs!w>BE6Z?6<Gj?*gcO?>Y(tr&_=F-(WR>S6;o#$gw*sYtRp2zGz({`Rmb>zBSybFj
zr}t0$=#y-F+B6<*CE4gGI_jNGqa32~KAvPQZUot+^>2-2p6sBxP*FDBW3?==AZkf&
zEAGK)d3OpJYEhNnQi6^{o^8%B_MRx+8l_(gFEQ#;<t2^O$wQ_|7vQXJeqM#Z31HK8
zO0Fe!6`Ej8itrBEo2&ezb9jo~{ev)=21+-%ccw<$b=(T$zXKeffFs*xc9>St+Z&36
zdHHSEK3e&1P6H2Cdyf|6r8skWU`Q#(z#5Fyp|P8OChsfmp%W+?+S>JHyVei~dS2u_
ztAkFJbMVP&W(_77273tS5`Jj=<stwvMY=rJ1PDao)}Zq8-8y-Ji>QDu%^o{H+lXmg
zZ+Pd`Gt^htgE*mx=!|R#fOes-k~Bon`edh2VDw9wD&MM>n@~9=L!gqz7#~g!Nh%NZ
z0s@YMqjZAfuW8mCa@+;(HiCb>8VUcjNX_N-^osWMV*lLs`0yVFrWLaBp1mcLO_~&2
z^{uBc*9S)@hxsO##aXwmD}wVA@6QlG%$7|oE`+X{PE}1P&dEk+)xMk9@b<l^P$y)^
zs8-a_?wsZM9&84X$|N>;)pLQzV$;$V#_ive4sj>zG=AcK0QCekC=}XbAuXIr#2WJy
z`R&=(*bvxq@`pS*&WisSy)~@R^C1c~uY}5$fZe>L9x5x-zB3S0Af%I~C*xof`e^mV
zQ^1hma59q^6S3rs7R#S9Z3_Qsu^R?4PVNO+V4S?&htYMfRx%l-%hKg}one#%rN!0`
zSh``ftSDmss{8MDUeD?ynvn%)u3bC3RDK?^Oc>Ho<3WE*LpwbWU#9nMyJvoDi6sIT
zc$t5bGW*RbY1iyxUu{J1zW(2aF(1zqb4?;gL5=q%n0@!~8}%J-x@?6w#S*Edfx+=5
z*93C(RGiF#f#(S#NiWj0KD5V^F@Ki|<E|+<YlbK*_G=%@l3&XRiW2Av$t1Dx`$@6#
zcm<es^RY!I;M}GhkWgR7z^xY{$=_HzAJzY07)Yu@+(@DbPT8U$F387gls=rmxt%{`
zs#{U)pJl#qAc}ow6o-@R*12Y@9<gs)!!<~|C`H??oMw2oy==F%qZU|Y?404H*<PP|
z@jjl{=*zl_sLC2FKPI@?{Y%>nSl6iI@s{^2KCmj*(L3+8+v>r>b^~zc^V;A{I>)iQ
zj65wxaYTBS(4aAEHHV-759?)*f?Fcq1%ej>Dr-94bhM%Vp?dC&a2s<7wk>o$BX%ko
z0%fvAq|FR_x1%5l?hP;2%-Zz9g2#)eh_k<E1h@}7S=GbPeMvV&1E0Z$!@Cn59Wi{u
zeDz>`7CAW^8Dm<I>n;Lu#*o+IVPT=^DYKB=W<jap78=p|OC?yR#_?Ox3d)BVPJr9o
zH)o(Hf+kExEFMa63@H__GR_(Ipjz99Ac+)OrWI{kZqym>Jm0&|uOK9?#lLiBBd3o=
zD`QCBF4d$JzFEW`5@zI-#WZfQq8vh{tr6bS?Qi1ZW|HbM8W@dd_j1PzTW@$Mlo_K|
zIzGJJcSlIXc%{2heN^8`N}r^^udY%<>bp9+yI&lHJOu4d6z3rLd|EcuGj8ZEU%n;2
zotOaZo#YA?9JJb?RRvGCLLRJ?`2hNM#OxqSS_T+MwL*b9dl2NozT?NF7r}EL171cn
zQT<E$+)FyS(aYn7e=NTgTVMjo^}{lQeLilA!MNE;R&@6e<2N@qdZ8a}Uhy+1aL+)|
z4~_Y~T<em1c}!+0GSb89^jqb&fV=(Rz_+ZgGN;W!D98ullGJ2!#kduoC+xOI;ak@;
z(K7sM@|XZEnFwrZpqGPfEadMOB0lN}M3sLt5&<jFuMM6r+vT&d!W)T`lRu#maU3+o
zSQB{X1~egJ=uTmld>pvXe_3ad;>wEI-4;bve|0!|X(tNUPLiR=HXol4^Zd8+)R0n9
zt*x)?s8I|pl(KM)j7;s{Rjs#3f6&?xS4HdK!%b*MN9#N?yr!M6`PuAwt*-VDudpxm
zu^%LduIJEJbuY2b-`+csi3N{H-p8BtBy*W7NvK#ziwep+D?8)lmQHZ5ZLm&IF3so{
z)+#1}76M)r`T{vpoh_XX&>pY8&APj~=YGG3#<o~Au*|l$4Ds$o(Ogxv+V2Yd5mrC~
zqX?r#Mg~`q*C3AA8R)bZi>3+3CO2i1Nq@L{IqLFi`E2gIzIM+(4yE}1Gkto>nSFG_
zwM=N=S#$ro`!?Yi=;fesNVFA&Rg#h9@ccd=g1JlT`4>io61)VKx`B*zMAK^#1bGPx
zhH5;rT$H@O!e_QHZU)!Y8N^h0VchNj1}FW=vz@&aKQy51-6!&0ti|~LuFP~vg7^6^
z@kP~n+7aCLCYB!FX?A>^9YRY-+VOYC!lz|I1K*fubA`9JnYCx(>zDR=FOr34rHg~#
z-jvOwuiv+5jk=H1%jtQi5$C5B_-8jksqWTU#s$sFmdxv^*pW7@QTF18_>>Rt*5jG}
z*UM;CC;)bwUams{@pd3LJXo^Ha%bypY3`y?nkY$XCGjExK%#Bp^66S4Ffk-0a$tci
zCruRJ8~5Z*N=62NOjrSdLncuy86k^xSD~25w=jH&L%F$0Woa7D;X661z4`QRj)>Jn
zHM<;<Pr!kK&jfmaeQvRLtbMAhZ@)6VJh3o6v9&rtHMX*RuvjfQ?#c1S@z@;QT;5up
z8e5!Ln;&KSJ+?YNJ$L?*hIJ3y6FKx(?y-Qc4awoC0T0$jGm~iOvtwou)9VR^Zv-9N
zJj1L|r}7?qR6V82WPIMOy3^j@{S`%GI-t<bY0ao2?^Z1H?OVGc!Ml>kFE2Tk{J>31
z_R}*xE|yhTNN68a!e%Iu3ZcP>9m!RDu8Foo?HqA_u<xVGyYyf=x#=H9^#faYcpH3~
z1N-RQyWruaks#F)61RW@by$D0L9#k1$`e=w;7<wxUn=L{Ab-P+gt58u-LAn6mgIyH
zsaUsX7Z<Ga9Lh1O_$hzvKw!w{a>!SWqOM*a_V(XLE-6)I2~~P)tLuteyZ8NdmsCh?
z<QQ^imX=DjU=_R(pg2Wtmey*lof|7ligDPnF^;;Y<P+n!p;DRB&vTSjOgo$-_G2^K
zYJOd>hjpcHJEWSjZBPPUDR9bE$_FvkC@WxT_wCQRDKw26{JZaipN%j%tz0RUEIn90
z+HH(>++YG-xInPov$s|Wz2Q0vW&L1h<Dc9$F$fr<Ubb}6GO)!dg^_Lq(w(0M_bFxt
z+lV{1sQ1a@FM+mCe&6<_#w1>lDy5pqeq_RkM_%MJQ`F>;6?m)P<ubn$Q}7GMZurHU
z)gF@VavrE4i7*S3*pACr+iJc|R+<b7JZ0&v{8ZPNdg8J6r6d=btpYIR%zn12J4rwr
zbV5A$kdSX(A0X^}w2Bpe{Z65HLM3;tAMOw;x*6>RgwYQ+k3&($E+6gRxNLar$q?H>
zBnP}Zb(pOloD#X9sGJ5(1s5>OMG~)s&5Q5g>L~S9&d&B?$*)}_=EW10Lf{@j(I_nF
zQB7?0_Fl{H0|h!NB4Gmjd&k%?Gzl!II!+{t##tWoAb`}Mf6oC)PFNFOPoY1r>HWB&
z!+lm&Y*<K-LH<18y{#cfm+9-kDXYSDSM9+knqgggk+pul^TNcjcU+!EczOIevtigp
zX&9G_omjeq_tB*~v(B}yx?1#!bl97tGzyNA5)ums^=RkP^r+NM|MKDt9o~`Mo=a=z
zj7>Q#L71$C`!y`VFf|p5fXG%WnUn(HXyCA8rMBB_R$}lKYN&2xMwx+`V&q-M4Ql_(
ztV)u~i}+3%9t-y}IprV&vLl8Vq6Nyy%N?G&O@No}2Lt?hb#n0C>G64;SP~j=+9v9d
zBiI-3wxJsJL7Kfr@$9V9=(fA|#w9L>yU$%7ql12Yh<5zRa(u~iJ(ZyE*L7|$^>mqX
zUSk{a<?FSxTA=ZoDNbw5hlQ_$41Kf=(PB5<p?CS|kp8}3{V`m^%-?b4^$c(8%Vv<g
z`XE5{ZDV(}*?6)Q;dsAv@eyWU)p?tp;CClknJN5qo5uVZ(N+dMSnBOs`TXelEs1FH
z!!(;13oV4`B~lPJ<|D_4vUmK+c{<|qN@>lYnQ6lZuj5hwphotd_*>mD_hzC1>ew^8
z%p57Um**eTOK|1L@0*+<KJG@{VW5_Tr<?}bGP`tt3l$qadhc}GUhXn=afi*@(2&x^
zSHaFQm^mNw;8<Hb&py*;UR-45d6*D0ip^Xx-B6uIzNRa?%ofdCkZAwJw9!{LG+e~A
zgGVwHRB0u<z(yYIX}X5}x7u4X|6U?ESv7XHX>x?NV@c#9jd>CC!KzVO*2|ie<tt3-
z`v-}W5X*YNuy8jU^kF+*rYDDbR~KS#Zti{K_vIia8(0q@hX(xyHH<1@<gLf;6h~K(
zX##QpO^&1z8SoNa(=7!jC162}H=GNu$1*Q-#i%HBROH~}aiB~qFyM~1`(<R1i4_`$
zy>%k|Ea+jH1xA<F7fpG_jlgaq#=Q>;GB_SuBcim*XW?mM1AWn6D~&6cj30c}AI#==
zj=0&_4O5_6;d<20@d)fJxLe_Nopwng`UI`eqp283NdvE=w*5~@T^<DD%hm2HMJ)0x
zFT^tS`#czx$hetXyBaPLc31ImYHqYa4N>S#mn^!9mDX4rl(kUkU{T#r7;lMG9yw^T
z@s^-R-nfG0mMBd<G-G!ffu3ptH>>`-f#Ho!MfPh0DKXUTp*mWwj4p^>C12$7kw+9C
zaeGzf;!JI>VRzX9xxHWF2!D48d+3c78TdMz{^uN@p?D~)OiddRb=Nb*Wh!dcG_C`?
zJL9?jSi&NBs*t(~LMy+iba#;Jb^AUljJ-8$b;@_M8tV(-7tZ>a?mNT0ruHBtSW1gX
z{{HxCzT>9N%~DSFt;ARe*CN^{ar%Bud;{27wYR5gs&4;N{HpSv%~;yvlRW+!qrIMj
zv<jW=l-D8KRGDREzyY{tvs=bp3CYdaU6=V%>S{?`mie`#Es2?iXWz8yvAV+Vh%}uf
zum!wl<J=QcnOUA^zAGQ>2gjtiU+8O8<z_5TS5X3`rqg@fWAL_)rdT5p_<~M>yJd#q
zrg4zdb);iB7tNpdb6(bc-s>>kv|8`vxvegKL{*(0H|MCI`{ny>O-xBIs}Dbx%lxe>
zxFo~Bcgk5mJH>5tUwMbbUT<%G4dL*7!O~dMW@2G+Y8*(Q#!dWz4fXpgbV>ZJjams4
z=Kmh}bsbRo%l!{We0H+RjxO{ahH4ROu3$rh^)H7fu<xGcgI08lkf@_=Rf5?F$-iWJ
zpupOrH&_Y|nT52gO3zB7;p+jvV^#P3W$eu-BQ1r7RETTbnC8*p!XlI$8yUS@^E~T#
zzg(3ZycC-`n?BCAM#sfkx_mtz`oGRPx29B(Eh-^drKz^ch--|XYlcLZ)TJL;-mU`Q
z-Vt8`fW7z7v1bO?>`U3r3mZQG-(!te)OWky{k0itjVpW0kf$Ak#8b+klw6WgXh6#I
zx}G&F4}ry-qx_w-0vlkN(JS%u%k{gx(a|BIwrh!{ZkwKWZ#0>+e{<rIakfE=sVQJi
zn(ELoY>p_%o5;nH-pmDyjcBw&%oIkeT+7N)d9&%G&s{W(hJh8!X%ogekFIN1(XM#=
zAD-lNc!3#tBvP0Jxx{D%#%!dmTS_&PQ^`wDk$+~{o1h2Izib@XiKYM&9|iDV0%3Z-
z&hn|9kF1@rTjV*O8re-uUB)K%bv$R5wY?eu8jPo6+b(kQuC*M_?D_iTh6k3WY4&G&
zx5xn;_XIjn*v$=4yr@0vXj~qhh^PnVDfRql5M%^h==KnK8_<M2R^4SZC)r3t%MfOM
zA*cEC#V#gy(dWitRiXV@j>qpdQ?gfep)Wx?LYa~4=FGQ879v8cu#k`;NIlr^w$N@h
zX12J8dWy0dmW@wA&DvSgf}Qh4dWl`t>Z+9~mOV{JQeu9mUp>c2(3W&FJ`F%(tShWx
zAD(Pdxa2VR8NXaEjS3)RQP=isRN9euXNoiP-%@e2H2A>GXISNpyK+4}dwji?{Ax7j
zb`++<2r6LKZW!sFWS&W->VT>8CKcJl&`5B9)KviSr+p3IhfG<;{mN3M%#cB^xK{lw
z`%P27<H%hL<ce_$wuapF_E{QU#j^H_HCzEwg~)m}4iRe0s+%4K5Qo^GbtOiG?)Ylr
z0LX4VT36A{@V?NVIYC!Y*uQY+k+npIA(My)UD_r@%40P)8Gak>57PzY7I>9U1`<d~
z#JlVWhV%#jf=8bE4sC8L{pH_$X`3ExCT*vy*pD-svV2ZR^j?b}cXpqMgxxmrQH8>r
zH#L8DtbWX-@|W%TwN0<Oz_DTF)OmH{3kw7zi*Noo2mNJz!wnmkUx6(A3npHY>k~B>
z1Qr^cc)f=^#imZjuU%eYD<p=Z>@~8TwFB5z;Z*na&(6~jNAgJyRO{6UG6iJ>Bve-<
zp__?%lQCHvH0S>y!AY4w{>E~L7(GrF{eIxkfqn{I;5d>NW}yxA3W4#m7^TbH)a!?8
zu=(-8>2Sl#|FJ@;paT>!VMDhedsf1{ShCud)M}t_NIw@xtI}QH6{pV&{yUg!Q?p`^
zD3}jK{tOD%%CICZp`=<Zg2s;+d9o(J{kH%4)xN#W9y_LVZ|4>t>$(rj@(!!dt?UT*
zk9Ulm1|I^7{HtSwqf^?FvZm0*Nam-bqv$o&Ptv*d7+qLvoo5-Fn_V41jgXaXmE0V>
z`PmBcDXXxulMto<0`2W$*Y$9`hY1*2+o0i{#!9;t3Vb>;+Y~TG6A7fWTLGiUKY98g
z;2Wr9>?h+|gH9tDH0OqRykJ}NQEia*-Y0S@glMK07!SJT6R*a#R$JyO17+h5`Y=v=
zeY-y$=pW7Yew=U4vi}G(EKa}1wh*tk_<bAe>iF(r7f0DYdG#ux*^?<9Ex*`MTC}4)
z=hj;Wtvwv(Jf9^l&vf^XvbqvW8W?P+5pPHLf2^ZhQw+xuU8-h$*@ZUnBd)`5vQKE|
zP8+?SF|THzp)EMsm#EwHcik!oTlnY_rdp<MK@7{QO89Y~pUua1keJ!f9Ds)>i?Q0&
z&fgo%6xC|C6$6{_C=!X@_2#FxOJ6<%XX;=7Xtl`AQ?YH16Mx+$&(Wz40(Y4sXHf-<
zBnH$t;zhmKE+w&R6Zs+tyjR$NBY6U+DkrQ)jBe%`TcaqIa3Ggo{f0*Cxf2(4e+2__
z&O_&mdk8uR`}<;*Y@phX?VCJ<mBC?0A|^%RwAq0Cgy|E-@50pR)NljO#0r|K7xkOD
zzDxhe=m3uGPNw}rKJQo|Zy&0|5*=AT!);$PI?yOPnSPF=aDx=j`Ut1S28^#ljM)*%
zePhSVlYM=i?H#)zkPd?WJjy%Y<^Lba0%Lj%ip}&k<CvbvNTi{xX(LdV4(8@Im)4|K
z!`ItHQH#vM1<3`7dW_h$bMzZHR*wlEi;%F%?_2)p0KiK}(Kdcnr?Uc;v;-XA`IGdN
z^Xw2OHuU#a&Ks?tq#<iFIcU9ICWe6z9%h@vjko#lRoO~?h|YLNc1l^MQb{pDKS#J;
z;UjK&WfBRAo0CI=cM<!6{Hq_x(HYIr3!J!^CA)7z#6V{djyRfY=fG=l@h%_&*auA|
zBfTW`z~}-h#TflZiFWZVG4%s!*(-RBw|w|aTbW|=j>s;@J2f?cg63$#2@{$wQCK(0
z1nf0GEhK_3fxS(5^*+Ox?o;)=&W9_)Q5>*6fz?=0RexKgG2?2n$qcsqYGC^3K_+Fc
z42lpV5YV@K%eI4pnLz9+1%xHb&|G7}`RlI!ETViscm|C|aP}X|M9tgYmNjV8?a|ei
z={YduuUr%1x;Ti$jXV6qj7oX7{3{FVFNmC}LK3H6mv94HvY#zpoOWqPs1hU55`D8=
zbb<2M3hHrM7%G7=bf~#;dXXzF#|B@Qf7(9)GXKP+?<P2+c<?acdRO0Q?inY;soXet
zh*NPclLNaF3Rr5=BG1!@<YMNNv*yT_EEJzsnB{|i_d;iTk%cCSw()YZ50((f=)O75
zOYUWjERLgt)nYSlMgvI`5?A4;QNMX=BbqbumM14H-m@Qv=s7Pr7v|67GxyWM+RG45
zS;SuEOfN0Yjm<8MY}`a-*LC@KWi-^40D@aWlQJ68$|g;GXlziKL6z{GV0~!zY1*5U
zlT}SImX1{ELxv=#VvMl_mcUhD^zje*939~*h-;9+h0x6o1omKw>Pk0eI)oWGIJn?<
zKqVqWiUvhI-iKa-`hXoq#MK~ZL*y@qtl|2|-VR$i0{TE_#DC+fiAzG$B03;pgvKtB
zPDxU0LWMhP^TcM-9Ae5as8!h}Y;7lhzYTsM4xi=7+WI=#z3;B=UCrIx_J`;Aa(F)*
zU%0EiJPvFugGTCY=%jNVG_3tfh|m_Hd$^?Z3y*T|lJ)V7EX_8hW2ngH-EeO%NX1x^
zLxM4*BGc+@oaAe7&*5AhSI0i;-q6uhyh~~3VtWFo2)(R{ZDqhTb_2X0ajV0SNw7|-
z;~v6h{FZYI-QLLXc3kmu3g_hbi%G5iDRhYoatjv7%1)N9iv}N2M<aRBnM9rh#YG`@
zwT%`<BeAc1;mj*LDL~(l8+p;^%SFk*Qpm`LvU{eEPZ$%?9)D^D4Z|8t;ZCxLqBTBd
zPPObct{7sONOowD^$PtMuYgUz^rt$7xH>JD2Y7Ib(ZMwv*i;R*Iz12%4hZ2V3?=3Z
zK{Xr0m44{wY$|F%|3uZL2E6<bK-*YzjvY|_UaZ@2%Q!Q(%tj9|PBzs?vdBv@woR?6
z(X%xOXR@zq?ucQj6T;F^z|Ydc%c3+*#?zvS7)|;QW9QT)ShOYK>N2}*+qP}nwrzFU
zwr$(CZQHh|;@-D;nfVJRcC5%;XJvlFe1_pY_!FmI8$~n!4+{nmWwIg|yz8dWhA^{v
zQK_WI50clP9z18lLQvmEmns-&kWM$|L>8M8aT%NpxWzj_l%5aDCU*p<FCU6oCQ>)y
zb~RZo)7)rF#9316kh<eQ=V!{CbcEukI2#rh6(pBzRo^YFHfT$WeL;Fxiezti!gmAL
z-2f|WEh#QVjF@pOH$^ENb#*NkPoGZ&cp=@(@5%lm3+jO917~E895lyUgRzrZYFNMb
zx~(beOTbXvk5JCB<i_V%tB1tR8A{Uo&-v`|y%A#<xr*~@suSZ6MM5}@c{Q<9zCE;?
zI(GE>-*{*sPf(*K;tA&<&doo<S^f%2TXV{sH7bqvDRy*YG`7vu*kg8+8Mnl=hjKiX
zIbTkRhiXedPM|4(xRwgz;q4aKdN9#RU+@z6eBm~gqZBdN$3)kE{c+<YduT|x_HIi~
zNK99b^Qc=hMUYtVUBie^5f}7d*`Ohq8NPEh>sdSR*D2c#w8$!^rl_e)v9a12on32O
zD%$r@+Thmzk((a*RUjtpbuXcD$|z$6EsJ8ZXKJOmfD3SamEin+xomuybdqZ$GBd`h
zhgVTjo#iuu6aun%miA&x!hlLbLUAHtZGPW51LR_161%qGMR4|31l7$$m69u9J1h*&
za5(xF`-x`qR!n3-?L#=1A3g}BWQK`|f{_3%21R2~RHm#UjnD{e0NQ}kOo*4(STJMf
zR{$glNT6il&0N6Q;LRCvwl1+ep~j{~<J6v`|Ctp0u`2z&QxQ$DWC<RQMs4Dr(YBvm
zS*T;4Yk8?{v}0gkSXruPU66HcFeDE8YjE&kuH%gqGi{L)*9`8WE<y?DKk$$5^tCg8
zbo}$w!c4$IO;1KZNf?5Il9+Ukl5wV^l8vH-?#KdU5iz*F0{^3c(3TIos~#{Q-hl8P
zVE`icHs0=JsGQ5ClQc(5O*uQTMheL}Wtt|oB|1%Z=h?@#<3^X3<j2|d`KP<}=_}zt
zG$ckA$IADq>VBf;y~mg8{RC=l$JF+V>tp|V?|t>aX9-1@xH`Em90#(3q(ZR-t?Z)T
zzYb@-)ay~t`g_meWtz@AO`&!TdItw>8$-ob3fhXw>9WlD1@(9tX?{fXi*@O;KzVuN
zV_)?*mpf#*;oYSPb=4sFIJb>M-tsi{yf69mipuD3tKxxkGgfjFDP=?1qsY5?lcpn2
znd}O-6p$%Me_|w2%OI4Rq>QtZH0mk;aW$FY7GKL$RzM-dCyl4{it3k%K2)@uE2rT@
zLyxEyzYh(l1t`4UJKz^Oy12h<`D7lqS!rewC~BQaOEt-TTrLwB<dPX=o}|vOOoqQr
zu`0bnFEqloAV#~^e;V8s6w;O!5o9X`Fgu~eyLzoq*`go1LChP`FlPcfCZqDF8r2(X
zhQuO^nw4&5i4pww?)q^E%8oI?KyU!kkkdcPvnWnED$F?{fxB<M_Hr{#1jsr%i+ueJ
zaQuvG;ErKL{{Q2;5>cFy&GXBe3|;tH9*wxno5o=fWvFtuGu#ugwU_;GwDp*!2!KY)
zAx#G@so7ZmB=?qzjp2CfZ+()b6NAo&JKE#n0})%2xCn+oW>Vyfw#~Xq>JO^i%%&6l
z98W$d<&u|WQi9Xc)L@2^#)!bP_Yv493yoKoYg<}v-q%AYyUf6JK#fGUi+0)#(6dL$
zIU$8;;vvgUKH`l(h%u>Sx>k8R7nx`)(7@ZhVP@@bMS3*3M(5tkJ3I#4GtW3j<8l*I
z<sr72fhULjn`uaA1n3W3#ZT2Wv;>ZVAW%v^qN$ECp{ApVIfCVA-`>@XCZ-VXsKk*M
zt|GQ8t17}jisVYhTG-Jfxdtzm@J0)N4E`hVJpdB6KW(v+9)1d@O`g(YrlC8&8$De6
zH?~5M-TU*-K4o-t?eED8=3Woe(og)nca4<L!BgM)nDIa(RNk<2?kzR{*I+Qx;83dx
zY0?}Kl<Q7(Wo3L@mTL-&rW`XJFB%xpspVA}E-<W3eJ)LHv5^Qy^nOewU8$e<G{s*_
z0hw6K>uBgA!;U8|{!V<FsF*Yjb4;u%0at$slnd>MfQWUEa*q&Pv25C06t~lQ6bB>j
z+G2$hbKO<m_1Jj#X&cci%0Ew<GG`9fL>WmV>PTT9WH1w2fP}2?c+Pnvh1v+2--+6v
zTUfTmJw*jvzI`0uE)FmxAoSdw9Bm9@Nr`eI6|XbK#s^h8y9cl*jU&%9sMwZQSDr&_
z3v^`kv(&TXy=(aMBTFk=g9E}d+wwE(W3$V{n<^81$}>}I%i}^f@mWbW|6Z0v>@M}}
z2}oIMn+Pgl5GiQF)r^tE>DRQ2&<7Ftgxw8<#Ua^9%IWC4!O;SMiIa~9;`8J81NQsE
z^xQb;NS7IK^#m)R`}r~{486y_w&c+7hJ^8;sUiaW4`0_K>gFQ3Uh1`I6|nhM<s0c_
zx_wn|&pC%;ddhmdy&o!2Ep2b8y?-2TI#yn~-Jf<tY@>ZYZGOyLZ@2DdYkU=dTrT=(
zGiY(KCviGsxqTSzA5?N2Vm(xQH4OKMnDNj-xW)-*$cr`-zQ7DOyYiZsro%e|7L#y}
zw{zW0yB~IIAKnO$0-WEb#-dF{^P||uuNjV}4EHj&_GnRxcdlu;-09lOj%&(a&*!37
zTuFMkl)BUsy1JO(kMGvMvywDDp?=)ly*k?(OI2j3o@=6^uA`lJwc)vT3@gdtVT(5w
z{l`lV=Bar)8c2ftV2zf8lT39i%ft%#=P?}=$=2$Y!v2gB&0LCQ5dBo7@%MEX7b8P7
zhk|Qr)vBh2NG2C}Cl)z}rU@4oD8^TqCK>(~=Xm8VIa>mKV3H)8qZHpIA9u4Xo18RJ
zJ5?eGBGzP}w`#v{D*t3w+`U~4tM)%pchQ)EX)oN{vt=0MYMU4Y)3v4NS!bJ?g}pdT
zFRfHNwipJwp<<%<Jvq~Rpw6N43}y04GSi<sxRx<+>RS8w9a>3u9lQPCz6%IgT7@rx
zdUK7_tJtJsUCsftu#Kdj$R@4}<2)%q#F-*-%5WSj3A&DvP+Rc{qP>X8!I6K%3Rs^A
zK;ch0mFNLE$XKcW>phjH5u+Fa`LW8+6`Afjs=N}2c%D49#`z_N#rcHfT<oGii(ale
z2NAe-!itbt?7@g}li;+*b$~SC=Br6Oql`a^WU87G2tkYTYF)Q597=<7;7|!Eb-_7B
zb#0;qt*zcHMq6mOgjojz!c5>yz%Jz+d5{gT*kpZV3(bZ~Q%tlp1g9t2X!u{LB|dsU
zRc+--<GI65o2PvNyc#-fVWowRV!o!#N(3F|ysGXxXziV`YunHh=fjs3;3V8T7ScRH
ztQ;2n+BVo2&UExlZ>pq9R!4xaiqv$%bb}ffgEA|nKN-or$WcyS5)z4xB!~LEkL(E#
zSaYm0r(p+kTNV0Ogj?dftyfFYJo5NO0SG`N7;7IVL@75rJ`1zboUiBC->Yoc3Xw5=
zu>xgB<{<7$JD2UPoAXXi-t>?fTFEf71xcJuC_)FnypLE$Bn5l`#7l6qeuZ#9_eK&c
zWX2u<>>(?10TWSW)cy<j(dK#%<{sC0=_I&_Q#mDJBt_xu?3*=fs&fNV=a~c2wY9<H
z$nvb~GE>J1FSL9@>MCLq{-mkIler53@j`!Kb<{fBY}!PY>j6nj>KYW%4|z<uQx7``
z<^&qrD=n%j1b;%w*Z(@M8bHL1v@;y6J@C<~(SLpm_4k1O{a?1JMTMNk!a>ArfkWva
zg%}_sXYcB4nra>Dq(C)-czIi^iGR$*fOa_`!mLE{*l{2%;rg*?T(Q7*2M9PM0j@^#
zJ4f@$R@6*{{~(~D>>Xh4W7}P|JF5j#Vj(9W8)M#h2gM)q%RZ14weKThkESYdK#dgc
zlf4C<@!&WsPaEZ{>SqhXvL2aLJZ;t8f6yxP{&YSb_}X|q?eN-kx!vw=-6h<}bbI%_
zEWAYgczIvky!3d~);c=4-tlg4zpXr^*rv4X@S^>Er2NvN8)LRGGkSPGSf8>&!i$!=
z{p@jU71z9Nt51^S8>o-5Dm+SzpF}PF^%chZe@s`n0_G=Qx_ib<6q%(FCG+P7Q4C|c
zR4-vT^Yh8@gsDo&$n=1OhW;9;0(;(cz33#M6`0%9i@P+ivv-tio}_U;a52toFg7G~
zak~c3c<E|&zU$_s-S{5A7*->_ET{iVp1WORzCR}L&d2TH5y`%a+R!Mltz2L&ad;+W
z`;goWGQK`lyStNl+opQhg?RZT_g#<*f;|Xg25>WePRrByK1olk9^B`ZU0c{Nw<K;)
z6iq)BbESvOG`?}^7u^^US3fF0w=|Sn5K6p#9}U5gERrsb@XUYvR7Pl|#+lVd+vR?H
z(^`9rQX@lgt1G8P%-Njqzn6R}DdS-j13<uBK}ifFfP%teB@Q|3mz$_@dom+qn<G<K
zb=-TXR$D}brg6`96B1E8Zn?r1+HUpOsULk%I+)f-=)%eOIIP^C4}|=NeV~OGjb}hd
zB?3ctLg75&oTb6YU3fKA@lUszE7zKcP=#+c`nwTM42Wn8L$!#uT4iG6NyR5hbP)|C
zw!u4{cb<~p#aC4R{p0Nqn>q1MI@Cmd(>=VO3jNefLG<PJ8*QN=;QRNFb`!uOeSv{J
z0(<jq-%168wVPy%bL%)B>Q(0S$&$p>R-J-hdCu@}C3{b?#lnf~LscFO!!Wt{9E80b
z-70l$=$cY2GbwfY`>sp+Z}%Q&Igr9e_VXo~d-qlhVwfMHCbKxqd#ga<9W+?L^OIwX
z)AMso!!t>~7C~~rzFToON{TZl;+t@gu%272t3KX8SSAT4(P5<83{bBMtk1UTGtH=K
zWFk8~tTk#%$k2PIj+9}#n|Eg!_H8CMY8tL2;J>|~?LtNc5h`d4U4@_tNGbCN)z?qW
zQZf#5S%$rx&esOxd_!hf1j?jK(zMuOFcM)3urNyWqr$I@p<^_+i+gP^OMM%P<h)7m
z3LOMc6rYxk+8SjxL-5b|!Vu!JzLFc?X{$1`J2lU=0ia0&4i^yIa3P(6y9%8t-*6ed
z!K6xg=FoP4F>?JmId$F(?{@!^{rZuKi&-k^55Rh9>>`K1+$`D6!eI5gUEt!cIlkMg
z;DExISgi+8m_by6)mPO*z_(nH|3I6K*3}%UVJK6iC(jcAJ^TU?K(pZWwQ6^0s_7KR
zTmZJS^JeVopO->bLD<ll(JyTB`q5g1Ap}eX0&G^E-+3^qG!0mg;r(xqS{N>x?DUyf
zs_Nz!trq**kq;LWpgq^szDvuS7Lohq95HXDs-=P=oe;d`b0^wO&cW$QlOnAMYn}^<
z`I}15?(u6SL0t^8(N-S4U@3G`5oiSrJiW)nSDr5dqG|>4t^fImXaH!30H|7uifWpo
zYAc%Ki6y`x%JjkQe-Ylwkf|D5B9amUaej$Kwv;>w7B+~~XMiqhtpVvlG=t<0Duq*f
zon{9ov5Be#TAI`faxekx!Rvqb&MT`T!Z<lIur$vu&ncxafQ{k{SkvhQyPW4QGwcMV
ziU(zk5<uU?>Q9Hs`-su<VSpo@Sb#luL*I2JCmh$ZiTu2cWcPTdzdsG^cD!u)zFjPy
z@2xMu&fs9*aR1o3eOjE4d<~;wddGmR=uTcgIXj}9Xz*ZRU&2OxwXWU0l^kDPg-<UP
zkMP#eE;daqH*#U);v6KwDjhv5&%ZsUK1Ys{W)&r=j|LP4mk~_bfwp$f)>Qu0QbnCt
z#_Z<I>EIOO)}d0*DuDgFiyBhODfsrl%}y9Fq)2$_iv#QBv;A;)lE=#X)afz*^YDGS
z6Z5ij!>xOI?fF>eYteIj`gZ@@|0AS&qy=VKN$+0F=v0pUY}4#m{clq~TV6MLdz=3p
zXQYLBc#&v;&gERp(!HaD*Nv+ct(0)T%wt1T5!u4}6lT_LHYM7n=g<64-^sUkY<zaZ
zrQn37g~<LnY_(;1m<51QV&~GM6f{*M47GC-E>|L)GICcn6@mRp|ItLrOloPxg?5Lr
zow(M;y0x)9`M?GRj)Tg(0Hno!$)9rpJ@Ps_adO{1O`DP-uNnzFq(Uy?8A9ivEMi*V
zF>ui`Nu%?zdMD$1=0gYNCMYH+LSME7T3SR8-33YUk3vgIzFyqD!4t<miTkSXPjnir
zgj8Di&`<po-h)A2a=^Hs#?KAQ-?0SngObd&I7JGv%%;N1wyKLYFuXpZ_awHb0gTOE
z=<gA0A4qJ|Uvecpzx>DCo^%Bu7S$iSlA=^h1fQ?^JMF9}DAFD~hzbN=D^{P1I9H4i
zFa!g;P{yUO8HK(~sv5g$E~I%;YUsn<HA&fcsE7paG_wBqJN+8<@g71=AyS-f>`y|L
zw!+pXjBOZDG?)U(eOjW2CFM<rzChB983^ZBn`S~ogKXL?S89`{q&W0nkkOo)7MaHf
zNd8v$V4@<FmPe}O;uX<Ru)WD$Q)wC$oaQGQ7l5xn&tWWi*m831JymfLIkar`abj6i
zm&Pqgy=h{iU>cdMJ@sT!Yr~VckM6PIuI1VWQ6(J)j9P1HINoPG%11;pG6otC5&}bH
z_frH9=JZ5;QY(_vndr$QQ=0a(kj4U;+0BVcMD(ALn*jU6nX_Ksw~gOkG*&gNqX5v%
ze!%io4=8nZ62aB{4BIhXDeEaPe5fu)bmw7av?1O5oi<+d14I}nXTel+0!wHf&+c56
z3}Y@%dSx2+4Eb`C0N{&GYzcOwHSbEByr|i0xJx>_eKO(Wdald%>g+}^$iZ<Q@#oI<
z!%-I1z8_wbmr{<F^fg4Z<>q54eN8x5IIBCWdK>IdAO93#n!b!^3tLZBL<$HarT#W}
zL{QJhh*+?(;}=hpXk`1yh|8;}%dv`p_e&NuP}tk!r={6SOa|qmx1Jh>q+11L9&&z@
zebKU?t^0T*-1-zA=F(+8aOb{SgTXUnI-kg)a)uVctgf>3_p#0EFfMAr?wO%oqs5;7
zV%TPSdkFzts<;1|h{;yaq1Mo$Qj&V-bJD?rz64-$7BqTu!w6PV^RX;9t29@;;3PZ1
zWy<<sjV`PR@u&&%$cS<d%w-=Rm^%onP2Og9e!-O18CUvQas$spyWo*Y#wHS5c1|MX
zIhf64Fgx8J_rCT`q*dvDJoEnjd0#2o+DgV%y?7y=*uJ{4nW5=Qzxm+(0LpPq$(HT(
zX?QkySt(m$4Y+8Kwrw8X#<l*wUw9Z0bri&I8<`QoSdS1f#YlN~D87IY^pf$0s7V&d
zGyxo`=T37wm4fo5A$Iu_lQu1*ZdvBDsMV|>^y=+xM@W9*YO^xV_qHiDq(`WqiTUPH
zcia=L!b=1F&CtT7tijAPo~}vj`If&oK)L;`bFqBz6`(q!>#f1|YJQ)xbpBE>Ie;@=
z@ORq-(YL+Xyr?5*d-~doYff<_yHekEv1IPvJ3*mp)e6k3GJHf30`pDMXL#K&*`y9Y
z+CR*#b(p#ISzDA`zkxG8IG&a3rrdL94j8i(_&5G<xqZh*i|M4Q31o2_Fwf2{EZtNQ
zUhkl=&S%Z3>iu<bqD1RMOF~rbrYZigubkC@itNkgd2-3V_JCS6qpg6@Z9+?A<Z_<)
zWp3|_uUs*)Z=pH^C?wP$RQM&U{YRT8tJoEnh9*+AJ%}+G=xqDnIE3T%7z_Go$R>hm
zN5>%O8lnFmQf<vYfZyPxJULJ*@wUo`Gj@Gkc0UMyqqr__j541I1aNP%6FprdfWY8p
zU#5ZHXIqc0og_U|m%YWC_2cvBe|f$_5jdDszwIMlYQl$~N7Kp745e-zOw?>uQkmG*
z{6{0FL)`hOp`*CEGRNf08haTMnaMeWANKBOuVRML4vx<1m?AMIM{x8rk|>VPGGiwo
z#feDW^9Z824&X(u0{906&kAo(y}a%>{I0hnk%)~SxvQK4JX*&%0sLVt+{JRq-PQwh
zc7BRFn|!-W55FR&Qlg9qWMIaac}*;;Yx%HcRP};9iV`=<!ZQEXM7fwbyNha=hFY(}
z^4TYO?L-F`Qz)<q6=76;0-r0}b6zr3D%f_$>qv{710_m(Lo?6TPbWDj8@?(pOJR1^
zJw4?@Om~N__>-c(`eR&KlL7*LFq%>4sU>s7mmsMu2wwQg*D2+s``^w*{cB}jZj^3<
zKBCBg2GIYrXcX|+isx-GcXdx@-H&anZKTaCY@S@*l$!BxUp4>LF`@gBqjzf-XKtVU
zAUSSYU(*nvId42&+=Y1wDh2Mr*|${r#x-W|t_w+l-iL#jgO--dy{e%uM;K3E8%;V9
z9O2P%Cjy^pWMX)0YYOqmZ(R8BHeED)`+A6$NyI6=coJ|aVA2^+Ctg3AAuX?O>L?9l
zN?d9EJ@5An$xciRdX$qA`$9HkVpp*Vt%eL8vy6uZqG7-mQX#DyK=fY`xgXZZ#jb%u
zfl=cjqa<cj=I2xuothO0Te-#(_0Z&a^0{-|K7O^+(RWIwlXVZztDCM>U`0~LIx-<X
zW1<Wi{BhKsz5n_@L#|AFn`U6xmPWh_HQWm{*&Q`kJAYJeNc5ZY>-{4=VAmE=Pro@G
z7rq5F&JSSJ7PN5I@Z)SWFd2W~U4KCPxK_O%xj@6<9&<pL0KWF~yeQ^}O|3P(A0bbG
z=`oDNOEg7j7{<DC(GsfSLMQ$$bWjPTq>Tdk-M;nO>-*fe_4<Ldq_v~v?yUK)m#-OJ
zSXpU%o*UPm)S9=is|sJQ*GK*Cn**Vvp6tq>_HXwtSMQ?{zmgu>&rgTFhrRiQr?H~8
zInfM6UL0uL>!+vaMJsOf4==8JU0M%TQI7L)OZKt3C;E25v}<GYxT7=qiN(Qby_;Y&
zH`zbVS(>+H6-FJgkI_RPswabe3LB$xeZxZEC0*%19?M@J!>jtbUnfUTDS`(T6V3+@
zL83F~+c-;=w2rLLI5k%`za_Hh8l^s*y6*>k#~E|$%0B2Z@r!&D)UpML7HFSK-E+z3
z?mk0es10?LdBr$7@_Q(l-aG0?9+f3|MV*Zog3_5$BK~!1fz#57b@g$#<ZBBhRM!#%
zjntn<kC{nDe_(^Kk!|>FO5L}W;+^6?bgm=o3p*#>A;{Sw&=t|igD&oOd`n22j=LN0
zk8rC?vk~V%zbpIG8X)^ql!a}}w_|6KkY$-z>Ns5gx=5muo9|PQ-{ch_m>c*bwIezm
zbA0<L<)34hXDf92r*<5wD~>5<v&zE%Y9uNrcznVozrZ(*ax@+!-m`T`xLZ-SyEm=c
zc&`cq-K`))1VT!xJ7V3hh`)ZLzIwm}Kjv>OR|t4~e1m})9$M58g1r*RBddo=i%TJf
z;_IyROou}1j~a|;x%Fik%rnK4C+?B(+T-@usB`k;YX_TWk*Q3%1&oB0RYjfH`ssmm
zq@Wx6{3xOIWy!&&f=*n7*s)kvAW2LMO-1oDTiuj-WhVvt31GuZNd3vph$kfv2EF=y
z04HQnh?!057~be9%_SO;?Jao%8KgLU8V`#=S_ake7Ssr(#?#wpLxGjkhl4m)b0&kF
zn^Q93%pnHa?O1hddy7Y+dhJxKaA2OLco_6l(gE0~ocu@0%D3}SvmVqYreIiC3<kkg
zXNun=jMi$X-UL9JbGqZ)`HkPyE*GtDC*KF~E<O540AjF@kVC-z2>Z7WudOkBdI|pV
z36=31FXMLlAJDDv4lx}pO6uF%8S@%hsJqDicChD;utT~DU`6m%j^xCv+vp!hfM$k|
zOfA=TJ%jsaCC|Qte3cCGSQ76YjHcNo!;N+{a41t7-*+3l&#c)@onBhs$9HCuTPhSq
z@mXe!fpZ6<+kvj0tl!In=%Y}wHj=XEd4~CdR7ihZZN_z@RG$UJH%^L-fVR3u!N+ZO
zY?ynLk<9**iSKsz4Z*IZ>u(ujQ3v9&xH`n$(%ATzw^Y>V-bC`77ciknhV|W-qrLt~
z#*8bk7;25VhU#(CNs60)H?n`RGj8bUYOAZNt`5-LoLu`p)3g7{8odHjn0MLVZ-%wL
z%Uk>&$Iw9?g3fnlq|Muvlxc6)#RZ?6gQzaCEdZZoc3@z9hG?}tAwW+xxE5w<3`*kw
z>F6^Qh!3+v>L+BHoaXHX#Lf?cmILQgdmjb~6b2^t?+wJuLF>e*fL0T5q8(%_{{2pK
zIBkcywPX)`tvsZdVWR88|C$i@NTdp_Atre+jWA6OgXkF<;}+`V>GNLmA!zcJPH*)Y
z>UmI<1VLLE<h!QY47o4I=KWK)utyZFZI?B9v8TFtHn(*%zKR?#Zx%2|2%&IMU2$%x
zrEsIHx-onoyiQA24l-tjw?Cav7F3pan<a8ySZtS~ain8<K3DG_N8W?bw?|wlbD}kP
zac}=Ndx(yBoVT@QT9I**ruO}*KL;#!D@<lmGnO}6Q5ySvxVwY>u--V}<+fPMHkf%{
z?poFMx%aYhsPT6Bg~FO%C1kg|UI@}2ckpm$ZD3fdX`1b<Vq;xfUF5<_Em7TzVuCJ%
z!Ro?v`fNGZ^0cxuv27@FGsQA7#Iw|@@V0K#si*MjCUZ`PrP~NiFZZ?1{Z7a1y6R?7
z)|xl8agmQx(2a3VwD7#HrBE_MprM<{!Rb@2AACMVENg-T+uTZ8N@v;6`p)FVr;HP)
z#2}`lY0J$XJ!=}{0wO~vajmDzBTdtpQpqj29$NBZqB3vQe!lbIcV_*ehZCfkELASb
zN}q4Y_jm2vvx~iVk>{7|bMb8^g-)PW(i?}P?4cI|y|j#C)ESUa2*2t~5GZX(lx_@%
zGX#NCuE%+hmkr477bSj!c|7^Bgz74QKfZy8IWr)XV=WZ-OXlY0Z`VKfM|-}nr!^B0
z;hAoxz$?F-CU`u)Uexp*){j~*s;n=$UN0Zvr*r%^cQS>A*_9zoR0I*dL#4fjvuLwe
zQ7QyYC9{2$P=uI=4!Cqt84S#0l-K>W`Z2-W%x;UrX(btZ{lc^|B0iYI%IG`50)qO4
zROz)g7<zmq(M>fpcXkbkT~Jax3n;G_jz>~agMVjEl%Xp^w?n354sdKD!=t3&2+%Je
zg~52f*6bKVN(Wh`d_lYZe+AYM>|Gj-Ys|}A%2=BMaAgd;&A-c*Qh^ijJah>~gys%1
z81~*)ZyR?n*O{>XCZ}VpY$G{87PjUEOGi!+zKS5V17l6uG$ADDKEm!pnd@l?@H=`M
zu2{K9TPnFjFtL|$;N7E_fMiQQoX}N}wlGGxT5qxqyU5RLz_AOBfV9Ndr5Ed3Q0_D2
zONHAdkL|S}Axm3Hk#dqPWR*E;6LTv=my;6j)K!xg(ob-4EkHK+A2=Ts3nkU{RvNha
zT14`)^0p<4-<g%;g#<ftd7}d*rCmuGy_msA2RubMl<hMJ#_Ew3NewTq{82Q+nzWNa
z7h3FTBwZ=@_kXR3(SgN6S)-?8Q&A#;#hIQgW=b$Ss;@rAXW|;(V#4q3j@|}kiSd(j
zEB($g(z$Rlz5XtU&P&;xr6P5WQ4+W`6IlEEPtF!BYGN#GfS6#W3?Sk?=GGj!ZHRiz
z*~zp$pe#lvQBqITN17vhTVqKcE{$!tJlu4VB2fP65{N+?o5XT;@1|APVZQ0>y;V4E
zmC%{npNfz>PtP($t+OoTg&N>J+atDAv6YwrCn)T%bT~AT-j^UgiyvOYqtV5s6Qx=r
zH5(5dJ4gz4IFX<C{=CxsJxoQ|weuVf67PYXC^M287`$zK?DZg}VC%XVQDBA;nB|Cj
zsnHweA?O-B)@7{;%G@;Bzsj_b`k<a->);YhOas{mR%)4N980kbT-#0cGtc$4S96QU
zm!`O$N|*0WZkNYj?q=<c_10}q^UhT>bj<??s>+Dy&a@2fGd7Rq=f=+QbGEGS`^er2
z%rx(cgKp!U%lL@OhDN%G!SD(L7{_m3$SuP#vL)j(?2n6}<WVnPOL)LQK6SaLpkr&V
z077gAAUY=XrW-4h3zxi0?fl2x8Le%Hi^>`=PNkQZeeIW3wPk_3Te>UP_M1iL*O<}Q
z!r8Ftg0}21=kMvaG?mlCMESA$1?E$Xr%JGCb`_Tgb8WLK>#{DR+(^b{ncCJQ76z%)
zbYiqM3l$U6R6V*O@v1qEQ&x=H)Qfc_gI^k^J~qHLEHTz%4M3EwANF-X7Sh%q1D@yo
zVS^GT8t)UDpMZi#_l1r9VFyUibyy^Ta01wBGqLP$S_ch*!^Hcq2NDE==oF(YB4p+w
z;z8OhPEu!i5!vg!&cL+%#Pri8u#})En=O{)MCWAHA+2PPoNf!RtM=;F(0gb7GRYI<
z8K{h`Bw%pKfyKit8!d<)S4Oc%fyJwhFa?J>gBCFfhujC##%YK)G4emO;Q%w>Sx(j<
z;ID+4uU<52;@!$|H50ZyU&RFl4?o|(f8B16PJRy0bmJ0`+3%*otAA(Ke|Fw<DH8VB
ziLqt~e-sr};x$(+7kJ<`<Te5b4hM@E0Q>1{iMZJ42_oYM4_!s})2JpSX4hhq!}}+t
zjFOm(PCz}!DK7@}w|BnhZ%@sEFf0KZq=3gq2<*rUp8>ZKT;Jv0o%|g8F_74=&2c4}
zAc)@9c`GA2l!xnAB=PjZ>ocl)exhuXRdoO*P4@=!CHrdyiIx%CnuY^{mnR<+Bk`5}
zIHSj;xky<3l0u^xBOGWmEwG(uinUtsFcbBZ*b|LZ#?*wz+{E|53tCAT@u3lpueB5o
z)02vD5ij5~;B-+0(*Q}B+xKsipMAHsTORF|pB$T?A_$0^5*w!s(<%Gw8QZ<~9UA`u
z>j>{VnL?Yc61GllmjFs0ipqc33~+2p#*FLTKB=i$xR;B22Ydg4$B6;L?=2&5zOW2>
za(a{ksX|bOD@u^RRIHoH#DT~?+Up#X!(>)n>6OrNzXFYE)Do%Mbq!W#74GmjQE)t-
z$mhpAqNmc~YUJ=&5;r5CxY<`V9DOV1BP(()GW#C&;~~CYDGWIRX*iz^dh*5n;FJJ0
zIlw5n!7eF1#y*#wp}lw}66Pqtzj*o-lZ@bE1Iq<qc@%Fcbon4rD1%Q9gI_+1Aa-G^
zfPOc#8q~9!jyAE%VI~TYmR^=qYBvwdb-ww)n@60dz?%dw0Svi)%pE5?_gN8;FoGP$
zWz^L9a`z-{ao)$_8W?BI$3eyh1LXK5e)*4F%7Ke-f}MuLvWVNo#IU%y1XBQevs?v4
z#^SetD@5j4hr!$v9}F}m9tnO2?7NOeLdwOPO9KQN^4CKG<^aKtd~T*DU@O9pDi1$E
zJKHORe`X4lmuM8`S|U#aJ&ccq$X<3A!VA!gx${Z|b*>-~#>ckyc5;T8wxbn}z#H%!
z)9XUQbu3TOwg@GKJeWB^<>Adt%ig_#VYzg&&zY9Os<gVUp1SEy)!M%F;9h$_7#7o2
zwKZM2eH=V}pC4UcKfRwGonO8+vi-O_xO(AyqD8r94OU;kHdge$YzxYtgD|m%YN*O_
zoYnql&hx5I=UB}6FiPXy5h}t1uySEZi##A2o>EQ5<<4IFFxP*ddptZq&7k%5=wW>F
z%KOo?|2#qcu;sP7!j8GDbj5OB!hK?`{w>+SdUlaff~jhjv3dDR(=<ub+`V<}%dq11
zX<+5q^k!Dz?i|Rxmd48adc@2YrKMr3xvk~9yzTV{$m-q6W?#r){7*E4e8z1Y?<Eh<
zsMItugjr5;QTwYt>YiB=SzPrB?7AEl8#xgsP{?T};xzO-*XN4!t)d?kmPz}{>TwDQ
zS$U}O2{$xcx6S^#{*qW;S{z-l?Zd-0F|lT|&HeoT4639iTsN+r>Yh*Oz4~^_Q+1G;
z*oe7Cf^BQz-;N?kW?P{fzuNVA6IcpOsh6+B#W;v&lV<dJOTV)%2zTo_tqZe|5S76F
z-}7KLg-eh6W9q{!Nt7M$6LKePhXX97%a8x9m7L#xb{sx!f0&Mpgk?Gz{Vjht;NWn0
zyHL(G9SExC$>`U$`>&OtkdPH3V=v&?VlTF&(9B8DNytQXrM+ARZuD_Ol0_y5xuDx|
z&G<!~tCQ!sN*XS}`Ov|mV`Rri4;|s>loA>iVB_w@^hPKtigoi_b(6N1p{_2<GZr$;
zQ1i_9bkKxug(YBjYq!ZX4Bg?!k#~nl2r4TQK;qCRtDA+1ht(7o${5s}OdL=OUvlB_
zdMm0)xbJSf38~2-cI5xy(B-mq&78%^qmy@Ia$EJ?;@p!4)8iM9hyC0AC`3?W#BpgC
z!kPwz{Q<FH!Ui7~JBPg_zD;CoU~xiVS=<_Sj?yJ!!J+M<Np{KH;UIBFA;r&V^jcFQ
zi<y_7SQ>2}A1WN|u64v#PJ{2anvY3q4HO0r>XP1@A#Vn;+#GFwS<Q0-)p({#g`ghn
zl~aFD2+lz4Oaw&G3xB)Xd%BWXazMtKUOF|kupxpAJwQ=Mh$8i856Cp^m-&I)XExwL
zCN8Y%92E^y2pSm61-#GgQwd~1(iCN4q9^TZ1CCbeb_>;Vdw4<p#tTh}=Nhy>`}*ea
zd8GDJOpz#PU@}yNt%eHJ)@kIg@tu5xywklUZ0DJVV+$`N8b{4RQBcl<%`M)xveXA2
zIQjbKAh!3+i6m$urj6pE2J$8~34)I`#`CtXiTnJdP)Wtmh&tnwR1^e*Ok^HX){1~4
zGfk*i@(a_mv}T?uq(tT!73YLi*0+VfqW-Ip28}xS5~Kq3kH>uS;;MDb8VhP_bdc7u
z;SF#B>A_(Z{=znehlI@C-og9n>ygHYv3jdSirSMQ*dhhi0h8$Qi^am=qAH<XDk-G>
zk%i3L)y6RE)r!}4%U5@?r$Y^x2Kz`U_FHw5ZOow`9tn$ter{rbadyxPfrsaixzlk)
zM{{qS(r&EN0R`osp0U6=^LcP_H+9uB?bPoou`u;9cD)$5KVIH!SdLQN!p(8DnL&cB
zo?MZom4=DozP`w~I<vsVMN?0;p{&+2!*y$0dbcVtaL=<d!MFA7IR2`bEAh;7{wjJB
zME)>lwna0>T#76Tt1K9rFS%Mnw#O3?`kST3qA~BW%Kqi$YJKD8elRwCko7VWlCv}U
zyG763$iaAfI-R4%Wzp%$ajV<-NY#C7ZSweVUYU;BiqiZXCcV1KnEI-i!VICl*{P4o
zKE|51XL&!hJ$}o;25G>4e2c4$o96N%4;h)pr4X@c{$}lX#!dunzk&I@v|*VeN*_e<
zyurQ%f37Z7gX46u`sWWLj|8j)#}7j3LKUjb^t_!PMof}!GH$}UF`eu<?LCv0^|Jla
z`Sviad5!DIc(4YY^Y-=h`I7a<!#KnEK#2P{ensOJaVinw&krBoYjoevL<Js^U;@x3
zgBNtZ6;dw-EB^br{9hWe20340(hu5209~?njM6sEQZ=)RKaqYoGBR+O*08nYvl~an
z&Een9-U~A@Q;U&Pl~U49ky1rblQ!rRJ8^OC&u>>VMpH;rPB!Khn{xE=3oISQyLc7o
z#d%=u1l9$4P_xYSDG82A@AJxU!2wZJgYP2#ZAioF<O<Iy0uuDk!&zqJWol~`?7^&*
zeCKtlMF}+%`At2e%%2O!D=Gr}FRhO+ZHJQgWuR}N-w!k+xE{<$^8s2OrbH`;iZBHD
z_;>sZv|oP=?HEydvu={s&__&68pFg$1b+T~O`g4PIx{3MtApSg5w{#SJ(5HeOT^OE
zsEDXb<tY*@ky!dC)w11;owh`r2N?{*za@b?k$12K1;9`mAJ(P3pWr}K|J}zRFU!Kl
z9tskEbzl`}Qbr~%Pm?uxfN)ch5LdMM$Zi$urT>j*(OMBKDS`%rH#OFiwJ1n5zeam$
zi!6U6{CT$#l~N#?VzlHgv8B4w2}N9MEsj!MSz3?JsT(hrf)8@m@2@T_cGMX#;l%uD
z-{_=FSD%7ke5_?(SUi4`kYMCSU*?ynEN;umslS^r&{P3(HVGcI1R(Ylb_sNFxhT1q
z9=sfRJg*-cs*aLL#9PZ{C)WRW7pu%bR~x|jTIXJ1nRGc#L5IA|KYby`2=bXZ-|XMI
zYotKKyb-XR&K`i7daGjtYvgEux1|m9FlSsHS*IoQgaOH;CZ!Q0hU+>-JJ2btzMPJ~
zHg*M;^%o*J$a+lqb%-dSOUag}h@O%TArpcBUt0l(rvx~1=po;NkHPD-9x^>CB0M%x
z6ypm=KE!ax>6<%@nTVx^rHCCfxW)<UHdbY%!8;^|z&iqr1HN56P|(G6{(8gi`W*&s
zbizxrU?`V@yrm){`A2M!`ku3h&||9~<gA{sL+G1VAe5{w{@@sCV`7f-eAcz<CO=|G
z$s*I^b=y$ygIb}P1OeL3aY6zboE$cy%1X+{yFN`vGp;TuIbP8qDf1K%?x~<Scu}J8
zF8wyOl7K|6FVZ9d^8x`od@A;dB(+d|X(@24paxtm3Z_m<mNr~scyMsCF^F=$peBPs
zdBG=o)GAshZu4x0Fl?WGulyVUBzZAJilAh8)ig}mbcn$Ke%W=dl5C2dX9yGRj)ssz
ze4BQD2mYq;*ofF)8*2fHu(Mry7Sv%{8I|px)h~;m?V;h<wSX*B+8r)$*X}Qg&KHOC
zogN+E=5b$_=C*cv$5wimI>vhD1$v%sACJy<ZjNRarq9RkSy$TO9PB%stA*_>*PnNT
zqp!2i_3_71yA=~==M*uulLgn6jqIBjo($tg#N9S{_TOb}LfBix&=RZYhi;Ge=jYJa
zKtqA+PS4xj*|4jr<qq~#&rS~a&W-zh>(!e6Pj;<qLC(+8Sl2>S-&Mk0(ZSqOLHANX
z@{#iLH`bR^l}%AfBTXrr?&&t-UGvapRM5L;Lgv}ncK2TsTjhHNSo@Y5{hAC;rI|z4
zMOs%V9VS>B_=<>ttb?j2gT8is7mVuRa2>uaF;mjiu#TK+AR+M$K8f`u5mc)JYbZHK
zyV+mV;0klQA=EOXJ<F`?V)IHPz&Hj5ndJqV*+sb}=Jp~eS%w7}*@XC3V$Z|gB){Oc
zn7G80fcT2G<k0ImjO1AcPWn0yXtGZ6^R-0!HLDTUk@4bE4{+*ONa?0D3nOIQZ%tbJ
zh38lEPE^LXs||=bB@tj#zp;(*1Z95YQhg=U@5@0z=ou;MnIYDVmt`Imee}I___SA5
zq~RVdausoq=2^l+KtRAndND*e?VIbo%uU{p{0^Th3Al>c@J~Hc*3_O{EftW=I6PF-
z6)2{K)X+hvXI9NnK-j=DGQzVa=qyC!h_BU!P#j**l`l%&PafvTrtF2!LQ_dj0gx5c
zJ3sq!JTQpceJkj~=O59t<?LciABT+*1_Kir;3nw$l#jhT$RiW6(Qs5(`@>srM?%C5
zB`l;j9<dj91U#PV&W1X~fq{5?5sr8VaU^W+&tM^GBT8S7p{$@NW2ExPx;ujTQ0(5>
zS3a%JaWJPhb!K3?cic)@Q5nxUyUiVAb2waInu_3{qi3Nnl%5MSMHZ59eFe%IQI?Ux
zrk}t|<2O_iHTCx~e*J@z1YvIPvX3DSPrnm~X*Qa;j+86E{{D5Y3l=RvsWbE+sk`!U
z1zm)on|1&;5-3De7!&i7Fo-;h#}3ME$>q64XT%x$?fv)ioSQ(VW+tVfUB<TSAU2!m
zAVj92J&5HRq)j-L)j;$uKYg+gRV7~DhkPH9IT>UzkV%SJtipVzkXgR-vg*A#e%|z{
z5iU-{ugNCILOF>bd_{qS1b=J1H*lgNAOx2Pr6&8=6a)m`KJRBVXqRuP9!NP%SvAZO
z1B0LVv0~Vpmfc^x=z-Aap&$8h+S;DJ7$pf$U#eV4q2ui+!ofvKTsgeeIfaR4abbCu
zdcNl;;-)|9j?FPF{u*B<r6KKL?gtBMlWGJT`XnYbkOE)vhV~iKcDe53g)b&XG){>>
z{9A`)T;yNBAH|lKOT$IH&4xF;L)FnsN3P6>X+CTi2n!uJG%Rs~haY5)|ArnWtfnPw
zV__X_%F7&=gcSq0k+K|UI?EMH^n#X8N`Q=YVt!+5or;ARC<HA0Zwhg%N*+$L6RLG|
zgB1t-PIUz;u?MBxVM|6(_w8Mq(mG1a7R;$sR66m7qGaFB>u|uUq2<v-(aXvA{c>vg
zLRaRwXHA1W>-*&GTGz#7r>3Px{kXGp#{I7FZp?c+z<gQt1+1I1tA~@>$;J0@>+SLa
z=(r*25mnOI_E-qj(U2DPSv2ix0_I5ZaWZasJZWjJ;b81wX#9EHPQq@Jsp)%deRE@=
zsi)KJd8HwuAs}grHGhP6=IfL7PWRjX#vs6zWq?D6jC*UCce6YT4HqQ_`penq+1m3*
z6%&zEvWt}S3HDih!jM=p>eAR|Yg%u>M0d-8)>eA8iDjXyev#R+rp>g_!mZrKr*Uta
z&h+2()|F1G8E&5P;$mDH;^y|X0|_qzhP&jtuqe&^$T)7kp{)<5RT(G)Q{&3YQyuG4
zx3~0&8vm9W#3=zHobv!apNPayd3Y8Vna~!s!pxWgtbvi0u~ngUZb5lvAr-Y@d9n9P
zL&rPAI|o1yrbcu<Yot>3xq7)pKVM7KwO28f<JQ*pu<+2T^7g2*%EDK1eU^WGd3JtQ
zSJDcz=L;pvvjWHNhKC+8HmS3;s5ZAGp`f8O%tgk$-rI(qWC)w*%tUon;NXq@*^T2^
z%CG3TulwBT!1A44O7soFOW8&rV}4T#5EB#g;=A{gK^U({O?a(7c?^+t^gR!}f-}2|
zs*r}Y<eE*AAs5%N!<$_ngO}4t9JC=3;EYcw4llOj>AKbsqruyCR%;|9q8@CEO7x!F
zU`|Zqq2p2;upLlQ**kScQ&7ct)(P2NQ}Z>n<R))j?f7ZM)Lzj$Owz|jK~}`kSxmCc
zI+)-@X^Q$8V)Z&mwvSaQji4r(qNfg_V-Da|>&2SX%NRT@537#!;DM7r2*Ne<94pf?
zX;Nte6Rf9$6S0=t5j)>x(Rbs@8n{(&Ro&qHP*qEZ<HrTmw*U-*)CT=Jpr})1)`j0Q
z+&~Q2Rxt^DCba!zYHVP1i$><B*dMXOp+i;By2OE5DUtgeLu{M0C-Ph^vA~H!yQ!@i
zV-*h?WHmGmzQU#U=M3MEugo`>Pm&$dg`oqJz{JASccA9F<2%upQ>db?u8L$;4jh+`
zl5xI`YI%v0MY|;TwpW|Czr0?(JPRRwaI~)SV_cu;`DHl<yURMFGEQ_{h_)RqxR+wH
z_V~yh?*|y&HM<H|s2i01+9qXiHawc`1$Ge7;ak6bf6b;lcyVg~<j(roK`d((4YL}W
zJeiK(_4?dGJ9N>jX(HCv{M6d<v&CifnRq}e%pR^d@z5eckbzOr5qctuO-69cD^9f&
zdk3{^IgO7ZM>q;JhKW(?m`UeWM1vai_;jhV;;3H0_I)@4iydfioZnU=0P9{NNBJxQ
zssI?>ePZ9#iR!BnR5Xx#h?b~8b%la|jcgkPjgDHWG@PWg3(Q>X%Pd*BIq}wF!~7ZL
zaZ2@+Tv}l&IJ%NklaevQ!NP|_!OSN?&hQ6mY2tO!%V?FH-J7V&r*WA#X>nHxyJvb8
zE>L=fg#}b#t(2x!c&Jtukxx(kf_Z1vI%zmMbHn8jC>kv39Ote`RWMFp*j0quSEF}-
zEU_o2wz~`J?i}lGL$=;nPcUP2t+6|`vUqTGJ(!+mQapdy)VtLzJvuf%jT)Z&IIf%;
z?%vLKV?-ZnmWL*`HeX^+vaevWYUs0TP_Cj5ZNXD}xVF3)+PI`@c5I5iT9;j0sZ!If
zbh540FR542t1l;M-i?1QYfw#nee54wT5f#Uy4lpy)qS{bUqgm$eKfp&9`2`+4wm?T
zdb&`*+~j>52Q+kXz1^Fi4sKs1eP(7p8crg%N-8o*J049pqIPa(aKPP36|-tqMpQ^%
zC-$qmX*4V;?bQa69#x;8RXRDaG;yk|2T^5K?o8W3IypcywAkeqCWqoM@_6d*>(%+6
z>-+$~cdOg{8k^$$6Y}b!qNAhyg2$sUSpdAr7TKxhk%D7gpH%5;m0yAd<HH3n^WKNo
zA^G9tQhQn|7)RG9(Mrnzoq^HEG}+MG*97{Ppz+en0i4-^VBR?4Dlh@O$)N<bmESb^
z*rh4WjXBRcc)~fdK}ARatO0swCD$iUii_4zA(Q7NqRZQ>MMz{shEY5`WKd8%a(>3-
z`IogtcNBLX>n$nq?>x2MKeX<Nf5(m&)z6G}H1EE}c#lA=<W1yZx>q?r5fKrO*ki~-
z9r0Njv=P6rhi%D<28R`-V0a~szkKL5IVacM{Vq2Zq##}{tgeh*5b+`mg+`(x?ioSk
zZU5~iR>>a~V4%<{bB$6=QZkFG%HSzukt2LcPq(2g>Rh+RCSPGUjFJS+dY77pV;%Dc
zb8b_gzcbl06*q%Sdw%vZMUp_H`ZQrbL=lgpIO!%B;D2k#D6q2(sw=C$M`H4OavoZA
z3gtkBf;z2*bVLEVc`Q4r{5(HRmI$$S&G0nS!K+P$kdizJ;5>L_C~5(0HD1JJmNst(
zTr<<K`(3=3388&L)w8;@F~@Td8s+X8)$$wc_B%1tAg?GOhYLk5f*XgHcrYvsi+b_V
zD(0j1;SeTaK_X3oJLDfiCBKdc8+-TVJkosh<Dn^LnJ0KVD592B>7{2X%Z}U{Fh4O~
z54LL!a#A6w3?s)duk~huqcSaIM((Exd7TI>VnuPeA_b$}g$|AF3>fYUT>6i@YrGtC
zp1fh7oyu)*`Vzn>Q4tfngf0*iSzVmnWI6L?0Qec5x&-@fLt_W2RAUM^AKyI=OsdsC
z9=p!wS@H{d0%YN8SvbvU@<lQShftst=BmnwlBLxm<1IUF^8j`l{44d5TOLC-mpRGv
z6W6>^=PK3hQoHitM@L6DB&26iEYpNLuLDlW#&io1+RBvW5S8G_yp7zfEx(W_Yo_|b
z5!6SaN#*%(cV{0*1yHJEQM46f%A=oO=~A>eVrUuJ?+1up17cCat5U($S|fa81=aGI
z0gbWMO~Umqju-+`)kIniQ%kC34a_7!k;QR|S?Zz6Amr8m=7wfa#9FG}Scq-KcI<n;
zSe#x_^1F6VHnZ#&6JXq)y~%%HF|$V<E@D`j2wPw@)iZQt9)dkUJ+MeZJF>|C_BfOL
z@<KuObFYDGnH50P)&A{A>>tCLG;50J$lmbSo^)U7-N-&oSI5j;%fjMD!&*Ikjl5)(
zR8-08!g7E394-~~;ktzQdbhs#e3S_8eT!?)wE!;5P@aj6dUVkDEHciDO{JyJq-PD$
zIG%P`XX;2Ds$kn#Hkw%ZvoI>PENruC4{cjs_^^}s)V6<qSE%x0_xRFqZJg<<Yoq(V
zf8HL|{A_Z4=zka)d)0kCs=Y1@9UcHZFAF{nAPU%;9}_jaM(X-3E4dBQxQ`OpKHNc3
z-d$j>VF^qRsOP;3yUw|B2r3b!+ooe2U_rUxB!j$!(DP!|!j@1RbIt-~jxN<@l2%N8
zH3t6$^hOI4zR(b)J`&tYoQgDlq(;KVdQ9q$mG2~=d^)n`hEb$t55Pm`4r~*Tr-g&C
z4=m&0*kO2)5(5(+G(RU_TRVT-Nc*RRgq2tnk7b-I!cYfabPS4ba{vD4__;I_3AV6?
zLu`q3VQf`)VSP&+)y%*k-!QkxsI1VKq@dVGQ9|)T5__uRj&I0r;6j5T=b~b?qMUq7
zMRjg@j$T!cHC}nP>n?3*%d&i26-kMw=W<;_>=P&Dn<vFf{MUow4*l4s9Kg1u-_s%-
z&T|sBk>pswK|qATJ_e5Hy!b{&$r5H<B;>-O>L%c!y{5gIHMY~X;4zZ3HUnx;idE(=
z6@=O5@le;J_xVypcC2f+p_@#=fcf1UC{e6IxEo%T^FdMk%dCjrX8Rwm&Ve}-XkFIH
zgcDm6+qN;WZQK50+qP}n6Wg|JJGr@as!pA~|3ug7w|n(-miC!;xp+~%h;>n~Wiu?*
zOdAZS@Bhduw2!D8aE#PtkY6vJAjwYpXC8MO^}*OBZT^wrDlgt^1hBTB5R=$5GUW02
z!gZ{Cx9xGly+9)@{hRMvM}p1()W)fA%<M(<lqWfR{=Mk*aIKt>jEk;fE_#3~;->AZ
ztpO9;Lh%vjl{3wU-o1+M&=6prwDnW6%r7Tb0OR6H*+IQDI4kby^kF9cBIN~DM?<Gq
z8>NLRAHOen{}cES7pOzV-q1p^We5wm3Oj?cVI;Xa22>CdRlKLyFUZNsMvjzn5k1=c
z7$$w2Cr!WPbB^;BwrsALdOo+jv`&OjbOg5_*TZA_4Ul`hAAR{m9GX5Zm>ythQwDo&
zVCwJ5DtNkooI7ao4s5DAA52V4X|`sXYd>u!(XOj}9QyLJ;I!XTe6iN~9AK`q$BNJd
zQfJeO3poo8&Tpd`)g@QK^mRNk08#O*Bw}c<pGZx;t9pLzCo8*QrPIp;%yI|38~s|}
z0DBm;IDuF_2W!(LC<fI`9e}EOVQ~G1k7K7U8_AH6euN3jRNKm9h54mTG{dGHxJDlR
zhwNV~IZI$|4~wEW@yYl<56qHaKXO%x0`|JsCh+8{0MNkvUf|Y1x*NQsddv4)68v9#
zJ74zQPaHCwnM!76ftI9Lh&hLmly?vPC%(6gTVd^hF;)be+A(Km52Dzhp6<?oO0E^+
z#X-d5vn^QJio4(DXXKHBoQdFTDIA(|pZp(*Vgv*w5t-Zaam&5`4*qf}GAew~P_wYG
z7&kK8Qfx3Ux1X7nmwGFz>*}m4$*M>`hL<&$#Ziv*&Ng@lI^I~gR2Uz8e(Z`1T|R%(
zT*D=^!lxX;4S4u?+ue=NMr2(zL_HzepAHdiO?htGDXSRj09;;o3hIdLbuKtk8i&bx
zbdfjku5b-lU|N)+$l{mSLT!r!>4x88)~I6E#qPTbA19OVKf}NP_n=Wtqn6t?S1*s7
z>pdS2pXX7gvY=#i=(0WD&o37jy$yWdj#i%+6*;=xo~++D-rM_=$yvsCXF5Zl5MZ)H
zuj7-A5xj3U&cW<>Z3@v`CseDe)7-oSR-v3^8kQBqc7w>JM;vDl?#F#yOPg0~0l-VP
z(_G&lwO<BRHrjHv%&|<H8SHbBo5)k#*x6bAiMx^5{*@^4f>FIPV<ycK_@Y9C(>A4&
zRQz52#zm;od>z1F7c#NgaDA&Q(`pc&ihRI8UHO$XrZyF&?$Bs^NP4D6B{gZ6uz%Oa
zTgxOsr2BiDZ+B(<XxED2^!^;X+Uli7rH|9_(4k^5j?FGB3kls$MNt8v;-t1!grpUP
zt<t!*CcL8OfSGHvBqsa<k-EgOinR74B`t2BbAd5Fp!q3vQZ5U?r6aI?{<le!|3LpS
zX_Y@FE$%-|+Krp{J1Qte-)hYwveb<;WVqACPZ^yg#ThwcWrNbv#nA+@2uu+|Ht=6c
z<j0zkBvMVM4}z%7DH7urQi8(1mo1uVa7uO5(VZql-<ynBh*&vv<<>ysys8VFHR)Jg
zwMe|n`rzlOQKs}IvgfIy6~DN;$*BgQO%4~*fjqB+_fy*ZO>@mWN4gmmrjjcwNaMnc
zEFw@*7LdbRJuxY-7`OteV6k?qPY0FtW$HG{m&>Cox;XirzU&-~fa&+V*jJvJ?-va4
zdpgw?w!L8>qKSFyR}-^J?EkBL^kyL(K+Xtuos^lLlNt;hvbI|>i@)uOUszcl%G*c1
zXAs6RV&lp0+(;9jG+WArYw<>k`R!2Ubcd}u_{W_5>vgnm3^E|~)Dkwa;&@L*mm47y
zTX_L~cV{CW@@Sv&&7J{stlIv}f)FKz6>NAZgcJTSLn=12K47x{5|wh_N@N7m9Rk_M
zmesZcy)G%OJ_}LF&iW~dEO|0&;+k-n1)As;R^aeA5IhWZ>-{ffe1Lgoc2KcOaA1p@
z{|g+ildBLB0;15oqm?7fT`9;3t2x#i+4;7Kx+W1JRE;o#a>rJkLHmcaW2U1>MVsXM
zW<!lgvCcyBLzy-NIRBnr_9&FT*J#4FbtmV?S(_f9nW873vL$khtxah6!3E?lnOZ^&
z&6t*QRivJlpQ(zFI$hSotRNd}6q{O$XNyd#bDr)3RL{X8NV~uk=Dr%<akc65MUMWM
z+=U9A3G7cl9f5B~xl47$*r<P#623*ao=||@w{K=(Ac~q9BTRY@@U9Xe6f}Z)>CQ*w
zi9hIH9hl7Q5Q6+U4ff1m%<Zil3_*tx!ye3s{cbBOo;^Skf6aV2jbP4^)Aq`*D{4+l
z%VCvR!N@z*$}p+K+;l-eH-EXAsi$tsvntLwG1kL9-jgvwJ;yAyi%Z80J*$LRPP=Hx
z$Z#FQpOv7eqX#l&B#d+Qm0jkIK9_*hu$>U%Hn%mG_OweR&c7_~jICm6Ef_V=XMaB`
zEvt~ZG-fw#OzNCdWL}SK>XvDx(dt+e(K(LrB#A#3<^luedLb`g1?LSU7hN>xYfLn^
zrdWLLYriK~cUI-!=S!;iJ~?~d9*p--4n_ltN+6h~kGXVW>Ut1cKI&)Q10|7^<>1;q
zo~WP!5%VJB4KIsr1<t|{+++Js?JbLjbmT%rEZRgg{o2Ybok&t1ICWmBvs_ae=_ZrX
z3m>)HA2xBU!*o|=RM{34?)VnEr+RSm388V#2z-t#Tr3`zlNhSwK<K`me!-2w`A92I
z5nRiMhlMbW*Ba+kZW<PEz){xM7Qd31turgHN=MSuDFeaR-8z7%9~UMUuTn?6s{xPD
zhR!&DgdEZT%!dMr@$1!QQB4dW=3iG{BN)LxE;$2YmsxLORiT-cZUOVZ8|Q(yW8)N*
zJ1VO3cA)C&?ZGI{&s$egU(~Tm#U#DlF4HR~s_cAw!WxqckS2tAZvV@qeLR0I|7cO}
zj}~S9rxuYJA4tz@`S7t$ghf6Wz{}-4pjvAotgRmP@&Z+Hf<GAeyy99C(bcxH|M~fe
zJntx{!j#2LUr&JTsf02SE-&GihSmWA5Z)hTfbbG8$&dP|H!q1bF$1L`#mb1jr6>pE
zmp{i8>R728j^W}OLe5~NIA|(ILaZw8S^tfe4NgP93>dYsh0H}q)wk<!9TPs<A@(R@
zE<cbr#pKxt9<9L9(6unT4ihXqIxNBFHi&{j`I>oU>CJrG*xlXE+I~bT;T~Nz;Wg(K
z(T0pMVIHQBvDDD?>)_*9-p-u;XO~&^s42QukA3aVuA#%t0px3uF!MZ|3}>MMRqwdL
zSt=)(EOfk7YN=s2Wpufd13wo?v=a{42sL{$aJ^o9Y4MDw?`4^4Af>X|!c#odiHhk<
z8PF&^fY5&f|CP;6leO)!!TeB5Om0gK>asG1Mpwe;sv<vUL$@N1<(i=VNT0eC^#@66
zLV8ISjw1*ASU8vi7YBx|ahSzPgsvvSbl@131>4G*-i5N~xDb3Y#<yRK!p+Rejl^Fm
zi$4$4WO(evLQUt<DGplqP^D)D6AOQKbc<YsyRJfIMsq#qI3e*8H|1)0kYGq{G3!D_
zVje^Op&pVCsYICpi{qJ<>bn&GSm4+I&xNnd6+d~PBnXuhl{(asR5(s<PU_soaF1M~
z2Mt}I7%p$n!#b+hOy7c}Jjh4}^3cv*_~kbLgi(P8CoQ=59rk}tMk$!TL_}Lj_-Q~o
z4=YCW#c%%U$;uVSxHNZ|maCd+$Vs@H2?C^uD^OJZ>vXe7msE^}|7FJN>wt+foUDXC
zV7Ll<+XxG&8`}$O5OG&e;^`jEEBL4Cp+Ws7wsDvPXDDP_f;uU<3Uz=R6a?AV%IuQT
z^0UZJZm)yV!Bw67c)Z+nqNYJ%nN)84rIjT~(N?g}<4BX>A$0yuW&NJv+%ddyi2SsI
zA~}F)^*iNRc)UhM7B^;o>hDTWcm0*hnjOx2t;yc!&fHc0dE|^i0cO`2u@uAT-cj&l
zcJRA@_`Qd7-&bK4?A)NiHcW$-S^~p*64P=JT8q&|*ch&?OQ#w_XNL6;AaQrS;OV;O
z^!q7e>)!EcwLxX~XzAsv_q2CzIYvcB6e~s}bUUK;+pu;&REt|dKy&X#2D9+;BQZ}w
zNtH-Hzs?{;#%=77ho8kq8f(3@r)5m{Phq{f`ly!1*N(gBwx^<~<?fonIWmJ=4K&Ln
z)k#Ic14CGYG%B<;ih#0|eqvj6QD<aOepp^-O16JVRa{v~0MZQqA_Spv9$AT-rhvMJ
z^i_FXequxzpq6GtPUS1w{~~2BXS>T|aZ6=$NkUTcHM%;#vg=6OOPxaKuw#tPb|uyF
zpAf!`9vdo+D37G*JHBXc3fO?gn@@vFjzCz+Yxh4>&Od9Jj#C_lR*qA?<oWpd<e`MW
z{6P^BQU3x9!1(?ap5SQCYYG6Yon~jBo~B)jcQkN$xJ+-Zbu=9Zqd#jn>ula1ZKa=b
zUY+hK_Mj^X2zXU=%P!t?Kh_X#JhwNPHg^l>bvVl;S_gNC*Cs_ZwbZ!CwZ@d>G?YaI
zB;~loh6shmMQ24vhZS^XRcNLP#D3%0`r4~!s(&gkt4N-wwBCKTJ&E1bfxkX}<xD$#
zn7T0^=8%mfxrlT~@h;*25W7=&8LENwn?1m`5znGKeh*KVd>PlCo%F<DR~O}u(&M2G
zT6T7s#<}O&Y-U=ZOFhGn@LiwsT9lbzk%KFi6RJVpH*@+%&b_HCWp5a#AU#F$fs?zQ
zz`hmNy^FS)2z7%SMS2V=)f`Gii9-9h^2}3-=}+~q;UyplDN~@vh9)Lzr?)@q&={Jq
zX0QFC6SAo*inuJ8xuJAt6^D$pglZV45ThH;<06@N@&n)W>mTjIS{3J;`5Km3YG~o*
z<7xWvSE7BalxTXMo`B1&pjCsRXeZH#LI=!dU-Npqk?UlSs3L#FG|=!3lUPYnb^AQR
zEZ&gQdMGyAHQpIHIF;-B6dS3)VSi5kl3I!FEe)1jTYq|PrR%|azf2+X;bZ^QKgCFR
zo<DS}F$j@+B&j#0El1u@$kskn=iQ>*l?}}78I~@0L0A;@7JoO4G)vo>V~KxxS&z<Z
z$e+YZf9s|z<AmFhVYypx52}iF;Zpy8L=~9p&y2sD{@Ga&T_j>sfHCH7!VW)?QF{}V
z8y(N~BmgoE&U3g*T2LO+i%WA=qJdV`(hifs)9W)4=5XsgAeV-H5-<5Neo<oPc|xxk
z+Yav7)hycc%5O$CP61>#zgWRk$Gk>523zC;RJyn*C=!%Usam&=q_QETv|L{{1L7bW
zXiPiuE|%sj5SvwI(|KfW;sgz6!ydgyixB%39PF{E8##O1Su3%H;^mivJ&sQ{nH~NJ
z1%6K+YB^i;bp{7}D363f$&q6wlNA}R*Pb4<8`%jcSUA8C@+GOI3z3f!_d8%L3VTJ}
z=ulVobO$S|EJ0HDs^&vFKaOy+7ojdI#<R+n1ubt>m;#mk6_X{hsw|Kf5p9Tl)zTAT
zqi<jy@4_EG+~UcpJ=w{{&<&x7E<vDj;VNlOExoA3(8VKzAp4^UQBi0nI|=l0s<2!!
zTQq$*K72p_$FX4jJU>9Fw_p2qo#imbbiAYneQLWptExfw(hfJ9ym(mz-6V+bN~%XU
zg)fUVCz6>vZkCm+E&_Ycg3nZQh&#iQL$_#>JR|1$B?gXzjH@vTtX^T2eS|7$W*)Bi
zHV2QIS4ZXR)$RokKG^$vb!It*YKQBvgY9m+ThZ(3>tXhMlaG7r(>4Y!n>t!1)SOA^
zp-TPg&g>no<lKt?BU03DM6iP61(89MXKVx0*0pmakrjVx7@`GT1pTN`fkEXTNEEF8
za0u67{8Lt&J}Y8TVRE%SVt6)XUapCZIFI&0%{V6aFGjKnnuVdc&5fDGA^eGH0@k1O
zaB--Ab7+d}Av!3bpr%eUH@hjQx+prXr${J0!nCt0A;ix-x5zp*Oh3FpxwA;T@GHyC
zaWfXE;e#69LlMX0Ue;^k(J=F}?;>k6>cZ1{6L=R$RP$U(whN&_Hfusl+DW_0IpD&%
zKwIk5K=9Jvv$87FaN?q1Ld)v%^HOUg^FpeUhQ?Z(P{`ll=Zv9EFyvRK|2yw??fZ82
zP4;x``FM?JbQtSE_3hg&d7tktEL!&>hw?w9h#sMD?!;s{6`T|UrCixoX30eaeTssQ
zo(Cehp^WHNI?y#YerWTDT%K5;0&j{gd|AGw0vd>b3mTk)|9Z(>_LrWXA#%4q0o?q^
zIPB(x9kJG5^eS7OD??M=T=2LtLC;K2G*JiuA4x43dzjFG5r$vb%2*w%19_$qT>843
z3EYbkK5!5*bal;_t(j(i8R9rd%o&`52q)6a;xzqxaQyzGOgOkTi(n#4Y2L&lx@afK
zc9xGS3yq<uU*M2sbarHO$W-%kSp0FBG&``R$}qWG$ESVaLn8gF5RVJQ98atwiLkDp
zTnpVS|6f}b@*GW>Y5Z;y3V@`#PEGnqm8{tGc(AVyztXAL`kL(Oj0(I1KE&F5R028n
zhSAE!N0>`<pfpxF_yB##^#&tK^ubR<5M7EMAP<>=kJe~e&fb;u$o}i4!3DzgH|}cC
zflu7#Eooq`FI!zJD0o|dc2_60`}dKVx&Jcd&~wdmgNq5`zG2h8b^q~7*|eG4&vszf
zA-P<3d5tcU2=<ALW#14%?7uhybKQR40t0(nqrJZrnrnNv%XX6}l&pXT0)6{tOXMsW
zN{La!IB`+OXJaK}UdZ^N@dyhE%FtcEhT$_1{HScqq%x8sRM?l~ErQX$my6$04>RO>
zX~cXZz4vEhr`QNlFr~SzMRkID2l>%|JG5ZZ262-lLCc*_d)1CjypcM7072S~P8_P*
z>+tYGlKjno^J9NA<9EVpEeVO|Erp;fo1o=Nq|dlThO1eq!!%vsa`$R(^;Jeq8yWi&
zBcOED1FQP|fTVkGS@hI+X$Gts=}X)3Iq5)v#wE+>rbN&Nl+oT)P{GO8Tm3$XERb+|
zdtH8Az+6~iV}EW)?O#;4ZFTRP@5G&5V_sTa<(Z=E8J-#=BO0_Xr9I4(USg`=+1OdA
zmZkksmWFetnXc8j3iH`G@@W|E`EGDH_gLK*&2Ol>N`P)U+U>iL*zORzY72ItP2|SJ
z*f>OD<#@CpHOb3x$2Y~DTFjzF#=VHbz8+z&Sk#1{8Gm2EHOI*#X^TN=;EwEEQC~?-
z{L&YFY5DBt^Je(?)K%L4l6kyZe8@oGuh}x|_U`%WkbZ1Y3@>mSeCfN1O{de=r%G;%
z`N0)%a>a1{!L_p(#=u|=U?(KH-nM0DA9%hWM>`s6_;ZPD=``rDdBCZ~5+Pqds<`<O
zL{R4|n0d@>bDu8aPW89{=Jj<c>34WN-nvS}R7{vOqa)}BLztZl9PkVH8&?Q>D)?H)
z+4-7gng;|>WhddIwUrdjV<tCD=H6tbqjp!kwH9)f7L+x%KCO)%S&4&W1GZ?uiqd_)
z!m^x?qF%PHyc&OE*x(`Y3MWvd_OHqh?noT|?&P?)W$nj@&!5DThLGBJa1GN{<Q-Gw
z4O4eMVv#|T3D5|DZ)$RMWUaGfbFpiA0KkSvh-?6=W?UX(ob4v+d(PG@%s-J-yZM%J
z#eZHqJ#&0tw=p$ewjCWE-+*Jg*S+noM_KQbyJ_TuF|Gn_Qanop8k#D}X|f?^Cn%l2
zzowba4^ssMX5Uau(VmvIvhsum_9q*`0F16)7%g2ZP{MALlL)#l63mCN`K-qJoYkOd
z9F1EyI~lW4=zV@gOZ@aN%^6flB66}ZGl}VAb+E)qzLN9jy)Rs%Mng$@7F}2zdDxyo
z%yo@C+zD~OX)>}&6U3!Y;V#dKWEh}%aZ8g~>BqE>41jRUko&hH-9r>f$+fc=&3tfR
z5D7wn#Oi)I8Y{G4q<O#T)5pl?ywgOw?-S4(1MFxnFS+8SK6qXKa^kd>=GH=d_k|i_
zk*D+>?>|*V#p1R2!i<-xEHqlK)}WnG9GO#%YOKprgF`m_j-b9_{9aFs`=5~rWI`&~
z-cT}I0y9mJDlGYRBT=SLhbeaqSz+o|CuCEQsN8uTEz^G_F~Q8xRh`|pk2$^0>l0_Y
z+QFXn(hKXWEj=zOD`He70P*e#oNq<!Mn2>X5!<9FHoM@q{svDpl@<AY#l%heyh^zU
z0jgh1w%{b_$``P9hPJSksA_Qu9)%k2%t5Pr9#W1-X3}DCVOofM{6OVz9f89hn)BC1
zU=gFUMTb@P+t0q<E{$*2GJ+so$Qw&Z<OR^`t9Olo?HDrBjCIxXy3FyFIupQQzqByK
z*)3L)D23Fp=cR_t1qrBPmp1Er%Su(Sx)Qnw&UAxX@H!ZElc|Nl4ay1X=d)ZKX5Y?E
zzPb%;V><&Oa27<SdBZVUGb^euLzel)2;~?x?7&WbkP`lczgTO~vSDn0{Mtfywg@<7
zNf|SmCRRF<k^kf`Q`aD)4q<+wxr%s!+}6&-P>(QkaUg!PxCV-q%|>Pd`hi~R-a(dr
zF8q<DvGvto2q#x|`^gp2Q(b!VW<ET0qC^l>k`OMM9poYBN6S&07n9tXm6efM*w=4v
zY94Fq!R?p~vxqs@zgamw=tPE&hlrsqf;2X|2`su&&v`J(YQz|zV<6~`4OHvNO0I3k
zmSU9Bg||i}Iz$v&`2YG;W3243S+<|6xWWz-HHIiYJnohYq3*hRnf%ZYtU#GL(zw_n
zE)9|la|!x5ef`t#U%EfO(qA%&MwdH2T5_T~-`&10ThfnXNMl?`j^UMiJ}sUzj30yK
zTy;s**Ar=IpqEo1Na=dq0*mqqpWe^1qnk=wTXiJ*TT|OxHrpCz?JIXn-_C~zC%GcL
zxXz^>wH^~;7`B5pk>p(m&R~tVKiJ&>o%T+(?nVeKw8D~H1O=Orl$|K)7&wMDDQ1W9
zC@4F}$0s%}Y=i5JQ<EEWE4}@*e55Oz^RqLnbIdb?RCAk5V^h?8D>x^X83vY+clJQ9
zoPD0yc~eJ4TcoL4As0ZK5B%szl?W+uDW@lz7Z<<oog|&?9-N$i_NpkL8rqI4Q!XsE
zG4>Nqk#P-n@XYjb4y{#@PIt?%k4vwQO{(#Bjd%6+&y4g=ZI2HOj1f<+;JVEfZH`V3
ztju<*Jr<i5=iW%FJ$<Tqe&}xkysb6eZ)e-p__Vqo5Z@0W&OrjzolI-SKQ?W4Zm#`d
z@YdT}A1+ocG~KrPf~I$AI|F;SHavQrU-NlUmUR8_bKwjVOrcotkcrSY@MYM)Agmqn
zaE#MhN#e9xLxb$|;r{EHkNZy+imTw9c6h>z(Zzb#;wz3bZXE$BTU;m*v$E*BaS1b3
zaQKx#ta;K-{#vGj6<Q!xx*&W6@mO!g{>-HIBycIl0V*=0rZza{fKpW=^W<Wc4Y$PT
zKcks?xp~wg>)gzGY8X^*dN9kpV&)KE(cH+eYGYpINrg-NBj%u}#wEed&=LIKRp2Q^
zVllsKwKEM`4?2mY6%xY9tJ08VyNh6X$(~e1fdA0)Mdd}xN=XzeDZ8HSu8wom>p@Da
zZKVe-qPChUul(w8utY`18D`q(|J*gTxLSSM_&WLu2&(Gsg2+;cqVCp!)31!o>5RYR
z+4OjUt+V#bcii>UbF8wpuodqrPD70>Rr6vrv0YdTfMO?Lp(a9HTwiT&QrKEkK*Fuj
zYhY^Fxmk<8tHurO>=BqMbTOG<zsE2$la-_afx@-ytvGq5)Yv(bJ6#&$m-V0-U^0zk
zQPha-HPZ8tF#@C1uoJOP3rNiFs%2+ZjgxO~VV$%R>dn1WU+q7|RI^evp@U!&N?nLG
zH3FOLvaj9RV3iQ@@W$X|N6UgUC;{L`cD<_(ILU572D7czk)NfR?T!#2>6Eb#@aj7r
z{H@XoNn#C8AC#y})fqc%h}NJwUScv%aa?TryuqSG&L~%yIXo4{EL$B7`^u9KJLOm%
zVP|K-TbJ`E7o!{fO+n!d2i2+Xv%YYlsGXoW1t2BR5!w3UgsXO`w1Sxc(dzun<i?h&
zGN_VDsEVAE+RS(6zO4CyG`*&$zMY?X8<@CHuC4dYY>q4_3=huD^#5=U77vPdrJ<SV
z9GRx=M3?O>&*gXmR=9%e04k^a|K_3<md3ZTGZLd`n`K0CE8cV*FlCD_IMf>~Lg^Vu
za226&BO&2DFrJe-1C&RH{PpzfOsz`Y!W13=;Q;T|L4l8Mr0+w<L#61nLw?h?kvQ5@
zwHT?TEH<`GmMO3Ve)|*l<*!bBVETufK(E`*?@`0m&riQi#nCIr!QTC_g3q*q&C_SP
zn3lzUKAY=qmGUVQ;R?!fkx)Kld()Ao42c*2MkDLOyF=2;?t1A#i_8-X9K<In@t5m`
zxiFCxNGN++G+VgX;8ZXs>>FHkPfC$H4F^$9eI<1Q4^&?KAW8GXf=UOl7Nc}saVlx)
z+74(tfXp3y^yBlLGwXB9?4;f7<Q;R^ExaRhvqT+S<V@W&D=R1{2MI8@;Kvo3X*)CV
zc2#&gNMwr`<V=ind-9QI1?h+qhH((X@+k9t*-dxKF+#HOpUC^W+*Wmz_Oy56c8%9f
z>6qro-}HV(?758A;~<mwQqB?7kv9arfwgjh37XB-0%2J?&7)DJe{u%SH%43P#_XJ=
z>6?E2S)V0L0Ghh0USd_%)}*P@-TPk;7BIh6^x>|$g?Y*Nsuo~oZm#WqbN6di2~QPM
zjYPtb{H&FG?P$u~>F?+LeUD;u+KRQ*xf4YlU71cAe0NOv!6DPEEX!WclTY2kr^n!n
z$$A0Dm~BjQwxte?La?|d_YQvh()3q;m$MI)qMrkmzevm!3;@n~M|E{zk~T(R+KADY
zh{%y^1Rs)_7!x-k8uy%MGRj0eSQxeGwKvypI(^`U;P1W5+%2DNWeoz;{D(QZa_r>6
zRhtF_UR1#VV5+2=$4ahb;G6nS=&muz1)CA~38gDBu=VY~_@iiRhPYS)?9!txybDi4
zRU0_$YcW$mSz*+{;<Q-8(riNX>~92qdp<@1@Ni8^p;C3HJ+be9+<-`PBqQx9Tgt1U
z0p^y!h;NJ)10-KoW_r`S`?~8$v=&L%_a!>fQ8As^mhzMHa<W0jT%LP-$E|7T2J(0@
zI((y;m;;o=G(56wMw0y(Y^HSf^vpx6=PatkwnT*kZpEx1y>!EyyT6gezA{eHLHNy!
zklPEqn#Eb6DJssVLn7`d2Y=F&*vOz@31RJ9R`>>@zDmlv>7HcloJM%#cP|bxY^;B|
z_HKFr-R`}{Qh8uTnvtf?mO0T2fy1kDSqR88=E$M2T16q0&Vu5cW@8@i9K=wphOUh9
zU&1UpB(Su=GfK5g@#e7s<(!^>->;4bzd4paLE4>53+o75D%%Ou=5V0O&wx`p(^Zrc
zd-oF>#7;3(@XIMt!p)3S78zZO>c`v8*1TO;&28E+v&(L?by!t`*=#gcxXv*%ApUZx
zb7EngX|*i&TwYtCpxYv07h1Z^k31=Y{85DW=-?Lx@xarjq9f7-!O>Ra5$ewXcV~O(
zgndB9u42OZ*uQ>j-*lp9yLG6CpX+%HD>%e4B<fmsZ-Kqma%5S2c5Y#IobNHTA73On
zh>kAmX|isuGS02z{kH(BXNih?$U}*4H<G&Flvb4$w?90OZgKlZf4Z&JP{r5H%je<1
z&qUw*#w@gDj2K529@`pjdgNz8XlTf4Sr@J-LpupQBjDa)$Vm0Dy7bL2-@Fc<qnB3o
z?sfC;o|W!u0Y_tGW{eE%Pzi*C!>_eDo|Y8N;^A@=l$RpRx6!zR(fKg4<M|pL8(yz^
z&rXK<X`5FI@%zDpf!EHLFN<X>Jdt2firOLz;DbR@ufVi6dZ4UBl}AH?3r;zRG8<oU
zHPQlM<JGR%{NJ7dm8hI!G;MUlJ+V7@zw0&!s9AZfjWDN`3*6cHg*8uM&>7Z%&n(1j
zsHVefnJ3{Rq~IZ^A|a=up{Jl+sGu7r@0nuYARAce$Dd%N>g}FbnCqEZ_if5jaE{gD
z&X?mY1!x+Bw;ISp87H6S{eDf1mdQ9>uRrkLm+yGA0lc3lGLdPtdQC)#(S@`kE%iFP
z8hUiplQUWtt00Fnl1`r%XW`t?-pt?Fe2+xvMLUwk`19IO`_|#kY~m~p)by<6_jWRE
zt`jd$1S3OFM=AgL@smTolv`F5JxXf*0E0b<f8@r~ZmlCx_xe8^SI_tU$F4dKyfzs{
zFZ{ydo_+cwj!=T@WFNY^k{mMwZtrvv49L35>;QUXyghTLSU30W<d$rYImgv6WQEYy
zOkPUA*!fT~A@UqPLTSm;q_+8owc|e<e$k#vZkbYZt7D~vRSxVW27#4JJ(Msis#BzL
z9ctVTCB&?#cVCa&;4A1E9q;T&SRDK^CcVfW`k6h{_yEkwz=hLMv@c{qHAw_b0bZZx
zx@S8lPjp%1l1BHH&)pCFJPux)#`?EI@~@1+v#c)zG^8N`Xz)>?yQlXazRj=u)sNkk
zT|b9sgTxkjs?72%$dPZahE^0!UqvJebpH<;t-8>;(G5@`wanKo7%dqARpY4m4aeFk
z4UT{%@$&q&l2CZmq8f)sb&gc~%r{{w%<c4ZX?=dVkUda)CX**ff^8VM(21}xltuuQ
z#@nlFdb(&3X%PI)fSnmCM5YVn+1u^MS8HKOdqLa{fXyXPD7uVTB#BLwIo4n#Fnvmb
z8&yR~yaxg|nDryVL?IJtTnfsYhxX+&AZ;2zEDJ~S2R?|}MqIM<4<7dW&ff%$SW7by
zNQyIjgyn&{UyU?U^UF)h@NQl14C@@ap~-FwFa_e9x>O^w%kM+M6oX&_xiiI-Ks|jW
zvB|Lll~s-ve(D0@kr7lCF*!IN=3U1ae!Io{zk`Af-GDi2br#<UbDp0b61UfNq5Kr@
z%U5O*+;M|9d9e{w10+bbr<9kU`W>g3?WY?aLKz*~y*EX5x0%=<hH$ifaB6(duh5n!
zv8}RrHvDZiY8Ttg&%erSc37CaQr1O)h(uMp?6~&z_}0&-T52%2I=$DoX8NYN7^jBT
zm*-|CZiBNks-tU8S?Ky@wfSC|Z;HQoBJB9I>AtzZZ;LsBt~*$7*<b@693t*Ad9<*j
zt`7#=ftWsl^_zKB&@i7c=V1<4vky*3S4>MP!hxUIBR=>k+4mYuW^<4Cnjh6SVst69
zrQmHJZk|jE%TH+;8UQT?hkm{cG^MDGXjeZALc#BClT9F#EEd4xBqC1CgFm=;uf(0x
zxyS^p1y;Wqh=4^>kz`XaW}*GB)`9F^((Dm2TQ*`oy3wy{+%LNK1luh-@6Y{Y1F6<}
z=GQ}o?N`12FSQ1VU`)c}DP=MV-@K%_YGjnecqq5(D|UILBX`MTP-RNQtJ_~b@H(%5
z44{$IdrA0z=midLj|O~SsOqhX>)~bh6%~(*q3l*D<y?5V)DOu?K>)+Kia8#94hmy8
z^Fw*Wq}|<E`fmkaJW3A8jMJcT_~s@@dPY_n%EmFN9>wIn@X*_7mj+=laQkpaN#LAD
zAXrK-+kDR}|6j24LNIwi&{DO=fm2m5cz@Ic`K@gr8RuIV=Nf0e=9M!3#Ci;!c$VNQ
zu`~A8cCPS}oZ6)K=%iQa#6=L0Y_Tj~$VmUfAKa=ZrjN|jox_E<aU37;7)o*N5INl>
z|7~xOY@FK*<Z6HN-#OS$qYFjP*8dH_cJ`JBGegL)K((oXS)JW}h?`?6?tZ-jgSqYX
zTXOFP<7WIyTqsPl5-GX%+Qt_YoP|wh2KrX-cCc(M5Sw36eR$F^_LzH4NRHp-`QJ5T
zeA%QQ*XmdSM2-GS8+X0#Y1Og$F#a$n83%dyE;yF9BVv>IcGk9fKH`&B1Ia*^aB>I7
zZNd}5BKBqyGIIJB*LQsb1uhKqXqwF?dT8_`sRo2@hm(X0D{$f5$_J|>{^3U@7)(&%
zxvHEz-9&1s<R^mQMK3~q0VERurEL{NV+%*kJnSK8@O4Q!)X`l%jX4y?LWsnuKNs3o
zeaSqW8RmOCe%9>Gd!11CY~*|Sn5RCC5=EB;dXgNsX~1q<_6~%{9Zj3VeRX7^&2hOG
zY=4jVSf4S;$t#J=WT~xCYhp#wTy@RO&<nJUe#6KuuWrvUr^hSqtf$QqmmSA3kB!ph
zKtzIZMXf!Vft%TRU-J9}9We=#lz=DNM;Pj-2bx=0tzH%FMtpEbzdno6^Vb69rdPb4
zNl#{e*VS!0G`qLuViP>w#u%KvAjvCK<Fjj@3h&#Ro9NBEe@`+c(WkZIN#jPJlbl3~
zT&ylTN`LdoTH_o8-qN8p=no0h<)Bl5^nb?g`&bXrGb5*`B1c0|3dUnr`=>A6W4a;+
zBeF`aeb!vbWhqgjmGE5}3jP6Hx0OstsU!SjD$8owT4Lo^(4mQqLpmy1-+!CuB#cE4
zEB}xD%)`GgFbc5IZ;{USGAwNLjSO8S$BE}=+x~NQxwj;3sXpImw{d!|_Q-WHs1I@(
zu-5bn(PSNM-kqJ2k2c}G`bluEmf~b~?S*A@>|LZPov17OKKvd13+nwp{OYdCx|4fb
zVDC0R6|8_(r%vrnXiou0Znw0!>MjKm+DwiOKjKrrzfle?GzQ)8_ZpXb@V6E(6}G6R
zRE>~UVNF^`A^g~V?HH3-no8xD%$DJz%Y6KE%BZfjS+o1AHYjm`PT3Aijr<?>pr7nM
zFcUfl^9u7qKiZ+xz`#qE$VdywvH}{<Dkk23Psup#Jt^s@aOwmd;VLQQd#tUbW2YL8
z%(UoU%e()m@SS3)ef^etodSNm#C5<_H6D==@~o;`%9#^dmvAxX2UlCkq6-eAqD;0-
z`jQ>xOy^L+k{QlH>m&@hjWswap222RWqaDlrZh&~VP9CGZyMB4H(P8bdi8IFQqP%;
zN|{$9Z%3h+Jxg?>xW5<%3>)8yB3ES~#GMnZcu1LyG!YC87^JH_foSJ<uRPka!G%q_
zrrO{TNnsJz;Gkzbx-l4()0AVApS!ElOKb17SD(ll%dk3YpxQq|nub1UD3HhBacG`D
zla3t3T>p6gUC5N@s4E78FwC}cAE>CuQ*dvFtDHrJc4Xw3WG0y_U>kznHTgrRH!yW-
z7+@DjU-uFE0}Y`Ow3E8M*(`z+&@<(rK=-TlufOuDpaM-d(vJrsBve#aB`?w0AHF+Y
z#vK^K-u0SaD|mNNT}J}aevUwmjg7R3rr)t^?S`1~nt_!jBg={z3;b^e?}_0*+#^sx
z@<5!p5aZaW8bJ#o{Uj&%a$7i9&<Ebr)qMr*#|HAt5vhH=eM>Lv5c7Nn0;J!GbgqAU
zV4(Xo(pjmCAHNtRaDM2P2g8-C*6>s0@O);P5#yoERN>|%Pa5$LW9w+6;rYV-S$1Sf
zy<W#_>R#~Lsr%S7$1yu+YvgaKsFMAF)6#%Qoi(r^`a28?NKA$3XA<0-@M4Y1*cTBR
z7S=n5_Z!K2?Mm8(ur^HB`tylx$W=f|_jyi6(E41b4qyNG&D6}A1I8$OZHSL;qct;Y
z7!uSN7HHyDm1TDnJXfS5QO0$nBlVq3hu>K>*%|WMAiafcYR_a1o9EZ<(XN180X=lv
zQC&s`GM+UUI*7|~i0vc|Hk~24==JyBJp`q>A{lg>llA@EnWIQEe0JrAj%#qpr~)k+
z<LPAX*+!1B&PV{j-pJ<y)_WD0tAV|*^>ujV&Ii*TwzNIAGM4|_mH5fR{J6*GCd5bA
z>j5uj>$_$=1DUe-q4j<3^3U4L(U^xWRJGepgT=2z^mys3O`PrbKGbkAEt;RvWeBor
zok*{T=nrV;`_H$q4#)i^7PP<Y{C3-oHBWKsbVJ8q9~VJjlAYBl-@IpT7SBV8j-eH>
zT4V6r9&&|M;_viWkMB5dVPA0>E*M7?^4A7sQh}HEbsq4o-7RE?&mjMJ4fA(xPOGFx
zf<<hXF|fC-;JeTfrcZ7%^bgnBR5BAX+7L=}VqTr-&=RRkFSlg3yj=8%wSGwsx%$j>
zB?LlM3wSxYM1H-JQY)(9d--=icNAU2@o_y!H=7x61xP#mNroSc(abdwsfS*EUEkFW
zuKT>GeSXAtw(ju}9u<BS2TMC<qRnFWxmpR!TqxY<`?9~{`z+jn7@qn(3E|^;UTPa~
zJdVy1=gtNi0VVEETG7f>$#y3t`^3oou%f|7^=8dUm|s!J6fc8lF-l@9Ob%!eAQ?Pd
zBx!pIN4C%^FQykwPZI$mL(G_$^KE$XZ@`g8{}cB87pZ%27tPVl*JiFL6rXHj6#K|H
z21;Oagb=nfcUrKk`9x>!Jf{b@^WF3Ob@<uE5JVE?;ToMZ*<dzf{k*3Q@I0tzwPcWa
z)<O-d@z#Fh&EdUP3Sbml2FHHR?}|CHS{@Ek6tL0*9W&A0P;t^(cC=~|F8Ev8U4-gD
z!1Nw6+&eslG=-85L<2xDKoEMv$98LE>}p5z0<?Z+gtWK>RC~Y=|K!8mo;&UKqSx?9
zETk`y8PDB+yQBNDD_@(BE<kjSE>hhc6*_qG)o|n^!GjzN&KLr0s+Q5^(#W&}ESZW=
z)j#y;95(veSPeQE@ZFg8Q=P+;-<hEkKKKg<3u>_P`udbkq~N2LvsvdrQ~gma8ypXP
z*K9B{Hxh@<Xase53oL*VBo&2c=Bn#qq=(c^rHgAClt6kYPXlw$;s;~*BNRpm@y^Pr
zmi#3bq*takpxopuDj_BOjp)~y7E58Y75%Z@qa`~7D?P?7&5~yv3f7uK3kBxW#{cOS
zwh+@pPXSEumKROv9c4!T6&<OhVr`8DEZxR7F*BF4-<#4@6`f+1Sj-$&MHUxSTWf7>
z?~h#Hza$(;!`^j8d?(-eT#axc>n5<Vh-qnUSh9?kD<4ZDDhll(LO>Ad_Xlb(=sGk7
z`N!*05FuYgd|t(PkjEJ3yS+*21_&t=&@WJ7Yi3l(WCw3(YspaA=ZZ2oRVB=%eKuOL
z-6VT2?ZI<N?OVKf@$L18?xvYOoo}MYWrTR-e4S-Xe_`!Lz5-{x%WZvbW#4p1%`Kkf
z7wA||TZQy2D&naZ=d5md(K^DER|6i{$h)X64v(vKOd}(nQGm#q2WL*6q74NeZX%+5
z-rqV<U;n*xZ&#_@W^-}#vQF&)e*LM}zX8`2|87zA`C4cDHo^a$mvYWx*U#=Vp>2tv
z3Jw1C3U0mb!OnV@u^9FKCuZS@gIUHz@w`PPc|c}=>{C-V$CE^FEw*iP{_HDl2dY}}
z@wBC9{mn`N`(HG-_n$>;*(&mp8mUUg8G&cKg^l(}mv=>rbkU>tPa!NeTl4hqX}I2f
zi@DqZ_li9JN<As>&P{aXq#Aa5m*Aop!+@M?pAKBt$CDJ@-&NHyDZ$$~^e}I2j6M)P
zN@A<GXzQcL2tnzxOF*!MCHqso8&R_GfD&*Q*`l?Cxj^f}z)PVlNC{5tZjOm;dEX7%
zhf2h@0l&by839^1nZ`<0o`o^zN(5OxVkajwWMd3<q^|PggiXO@lCar+g=*({G;;oW
zd@g-;ahx}!_#SefyTReSva->Y(*9iE*z<HW)ZpR%$fM$#9B#%~JvKtz6r|G`HVCZ^
z5d<~53;Nr6ooV^yr}q>`ZoE`T%63Th*MbCeSrQeHyLeeVIxaxxQ%Ok$^gR^5@aSoZ
zqKvK>S4v`hVp@v&Mxqy{-5r^^I3Lhbwx}Q!=QicOPURKQF{CDyo!cTTx^EN-Y%PGh
z9&AqCgOHrg#UHqM4;!EvF0X@gBKXr(qjaxsefMl(+cc;~hPrFK9ELDMCj&dREbp^6
zoGfi6{Z|Fak*bew>Aue8TtHX9#@33E4x`bMS}4?Iz>J|DIk|ZnbzzPjp)xMj<7GV3
z&7_aj<T&xyQ;Zl})nVy!>|AKB3HakL5NmsA9jAc4nl^%lbq$D}qr&c}y7Q=Sa>U?C
z%ia1HMSxqeltWQ;j*3xvDc<v%sY?l)Dk-0)x|;_(bH_qg^JS_AvOA$&b-k2%U=<vB
zPD2sbOY|wj<5ana{gl<!5JY9-D{rq-j~-?DgW5<y@s)hYBnhMYz>}I914}@9gmZT*
z)7w)zU7vNDyaCNZJ1#|WON;g;UBXR62i1L|aan|y5d-IHhKo%%^*99i{4dP|WoMdy
z&{7z_NnYYGBe6?KPj%v3LyVtE$fSDtU@3WKT?3>l`WknaD~;kdyUb0T!RA<ho6dKw
zyr>#FQ0T^Oly@jy1G!;InZ&>z9Dp1E8Z|}P!V>5+4%C63_QFUocG#=#!Ar@$oexBw
zYYY&~5efImIs(bv)!FLWA^`$0gn}ucqsFYP1(dq*7$CI${xR*%_Vzw8L_O2{vhi#z
zBhObI*WK%{+d4ikJs*qPHAFBD2U%e?qZwGlw6s@E_1}Nbe~Dg5^puHODpf?eHsu^=
z!C$Bv6V%&bT^Go{N#jW&@I6qnN**%5?YZ(U=4Si6A9~dNsfgESnU5rPmPEX(E014u
zv?POVLGpqSoGu>AucV6+8_y0bDsVkc#UHSTpXPme5xDW|S!0COxA9$HY6o5J)&fkA
zWXGJQIK6z%)L2KtPcq@9>U1xx<##(C$T3)+<-L6J#WXd`SCzX#4H7L%4^`Wmr_{B}
z_tiVSEJxJv7Iok))&dr`ygOpX3z%S0N~SNQpQo0ltTdCkaonA=JR{j&8?Sqj$EPv@
z%r_~TLp3SgE8E_?SE`Eb=bDdV?BmOm?-%nf8eoLN_^sSuiEvG<r2HT3r&A*~(*sdi
z$2IP_SfC993PA{eaF?Z9MOE87(6ooUq61fm*JlFu%*6#u{{dYa<FWVZW}MJ@)Of9t
zYJg=LFBFe<uf=$EEPPssw4VyBaLz~!fWk8j-NOe~Z+31~Z2g>-77Q8-qOkW#ODSm?
zd<)<I%=9NAMM6ks>G!{8sElt7^iRyCDIU98&AK&{Z_aJGZE|yr=Aim%S=FY%XOy<)
zQ;*k^t1V{?x5mq6F{_u9hjEP5a;-0{e_F1ouD9!61Pt5ewAe5`gwPJLST<NUMK$)L
zE~)VsuUpJ}23oYFMm?!d=Q@`6?6o6d6rHWb^|iMfr%=ub2dNh7Bb-_oBWJ<gdw}Wk
z$n|6wR9`;RogV~U%%|KT(H?5^W_X-hN$j;jmv{qSUu44*eAYBF!)a(NMyAKQ9Vkv8
z%;hBDy0O+`GvHcX+bPKqp=5^q91CN?)t$4@K$4q&VSwSEDbqu#ZK=Quuq8t21vStX
zN2M1Gl2S30nTXUWE?Hdn{b`g-11YUdfrI(SoW5t%SBAi4E~tcpoH%`|VbW#PQYb)z
zmZ~J7$VP|N1Pjket<fear5n35?3S9f5-3R#)BcwTPDE)kJ2|`rH6M|pAtXATN#FUB
z1emuyCqn(SU|V~)YPvqaP^!DN{*e&Dr3lLh6{br`$(5Jqd`x*KiF+qT5y~fa#o<nB
z{uWdOKzl2}*pSz#DS~~jy0K<A6zn>!%keInF=^AvHgj5cD`j946WCl#GDSYai&MSK
z?-WVJ-lbHM!m~*f#z{;B(2fzr*rjmiTnTUwlkJCcQ;(xJbmeE|!l)B396O80^~^ww
zKxSzI#94Ht@?$p1jZ+LSWFNU0*@t2h7&Sx38U-I{>>RGO3rY(E=*p9UC(aCas;En0
z;E{k6l1`5!V<n@fph-8t1Q0{BmC0))osbl@7Jk~`-N345WQ!p|?9kW-X27Fjn3Oij
z(V0j2Mvn~<I4=`G@Ss&(w1*&vfq%Wvr&FWQ-D<5U2&ENVG$48|di_w8DKaZP?QKYQ
zEwGB|1;QzlW^*rNsnH4p6_R8AqP6S-i)yd8w)PCiZ6Du~{eL~V!kdY6A?+r$wTNwP
zZC&;x&@=?uFi?<|QhSAxVG-v~wAlJ~s(?*7zj={D%MfDX-S?^I>FB64=JTv0)>X50
zdBw3vsTM-YbL*oI>5*ogK+t0Z#{O;Ny0HI*#ny}lU{mQLMUL1EGflvUHO|K@GtyxQ
zAn2e=p^@8Run9uYri6X6?)f$|Rp!Gvq{4t>TnJ|l66fmJ{K^|;JmK`aKM4@c8;v!$
zP-g1ZucM+CEF3CMs-~MI^S>*r4$`%m5OEikdJ{9+K-RX4Y#j!W^a_h}fLc7mgp-pJ
zY^{*RaYwg(IT`M^^p;tN&3r2OVcEjQM8mxep;)_0N$^&ZYVvAzeJW$%KqDWCqzrp%
zeg<y=T8iyvCeoIqr|}OBbO*!|av8{HhK6nu(z-g17RA%V#{yHStt>L^e4eb=YvBs4
zn1kQnO{?G0o&}$A?c7Z#=2nXp;=0D0xmcUJw>G&}3vd2~Y47a~X+Zz*y&k#O(o&q8
z$1d&0p@f+UNMQo8*ym#15?4g<5T-h~NX=1=m;!o`vvjdFQk^`WAuOZ@_ODx8V<Iz+
zGt3A6V9%^=FxNqiit#D4n5=8EwdX{|c>Lw&?dI8;p`8FT$dJDib>Y(8)cj$zMIaO!
zPO4a1EwMGzp;?U>+kpntCFVL209==kdr(=lGjQ5)HLxI9eVJXIgf#BlS8R~?TAj_D
zBL?79&3dg2p?7^YW<G1&_7@IK{g@PdRgowgru>+ubNIYI`r44&z{o7IrgJ4|6%-Wh
zUeyugaPi5iNE#4S6@E)~&j5{>G%C?sK!+WsahtVa=;)L9yDd_bhg0ITwl#FZKurm&
z9NBA(K{Q3anq??w5d@H#A*e1?T2utB{z1Y5r`N_UomV!mOI9yC7Tg$@*FUANun?PL
zUs~tbGdyk26sb}rGYG*xJLP?@9p%p55jMWOno3{Ag+SIgb~J?OYbC&>4JV0omh&gB
z2UTKThb>ORRV4BSILyb^Iak)qme&x^jccymK}9PslQyPgRg!ZkJDfV*jY1zQ(w3lQ
zc4@d1ASXFDbDV;b@PVXwr$LG#5&-dfz4sNHk&9?`gkfn`pEmamlfe?%RA7(B7OazQ
zgy!b;{wqJ9-{I`SSwRpwEc8esrb;o&zxC8wbuF1t3W70%1SmJCtQpMgL{et^oA2d<
zPIPihOIP7cBQM8Mvq$FqW~;MQKC(b!iV~hgbpR&N$Sb(2he+7&CwC^e3ppG6h&7N`
zH13IAvc)%hT|7JbB?~(fLt8&D1A8++$swKZ2xRS;4xVI&jBYXDCfGNai-3}>Bx9Ca
zpFGZeL6Jo-yq;!6taB^xaM2{b$%6jJa!Y}#T)Lha?6MWqO?^wN)Ys&M;h2z753fsp
zS%Z9paxvu}&7?A#lL}jRXn-#QrGvU^{9_S6L7RVn8TZ-R(BLOe^apPYpZL}SzWujv
zbSKj-i<@G=z~EpP;2550iDYXel9Y9$$cZWrq@TW~$rLdUmpbS190Y0COwa20r^IP%
z?d6^OnG!?x5d%k{(Uy%G1;CMVqJ|Ot)$j<ijOCRqi1b_}_F0s7Y{%?m3E2OdFAEyh
za<Ps2vwb80!8;4|Krvf>Vb_-?#AV^!nl@e*|4=h<<>hOV0zYvB0nrB&-zr#Kk(H_N
z=niLgi6`>rm5oYJGwgk@6EHozqqI7DtbVbiF-_}rANLl+JC))-90lxFsL8}j3Og+h
za(2T>*t#izp$gTqp|Q@MrguFH*uWOr0O;Gs*}<d!ePzV|{VLQ=wv*Y;JEAYUkka~>
zB=gbgUeUmQB)MW==5=;^i(z99SM3h(q|76Fy`rzybYznWCwv2QemiDtDXg01W+DnO
zk7M(-9&Yj%%1MWBu!&xbwel)m;C;j&Zx5jgYkR6VlMmr^n+!Qhs(l^-)fKL83|6TE
zPvVj&QogaH{NKhz>QeQYZrmiF>YIz##Flj?e&h1dO)(O{OmVjumq>z!)c(?QC?WMp
zb$v>;zv?}`vIgRJ&qI@}Ms2sZc%v-ie9-@+>l~W|QKBZ@?r9s-wr$(CZQHhO+qP}n
zwr%&$#*N*0_rpfiZ+I#zPu5A@s}q${6|SuIS45i+RfoNp>FRl>Kg?l$B@7=-QEx}&
z_mgmNOp%Pi4&Z}(QLwS1AGT{ro+jM&X;8C7XnS4(=dxEhsU0!ZVHU6VLphlxL&^Yx
z8pF}U`S_(%!Hmx=F(8#-!09HssE?R5d;d8y)9&hX0a+_wODR{0bpuf9<Kf4_>w01a
zgja}7L<l(!HP~@Gkf4PK%V!DDKxE*#*);x2Wp~nA0-)vWnM#QhekXHz=SpTB`OW=X
zO9aCHV{MgbCz3}YN|kj!zUd_h#Sn9<%*b(y;b)EVA6&?(xQAE5l^qvN86?0g<Fewp
zasDL9DBc8dii;Rfl2zU|o_&^z^q)^v+NJmL%k`DZLY;3qs5=Dw<mWK@hMQ4n+q72A
z`<Vh%R!PyvfC<HSMs7Fs$a@j+^4p-3XsS5`sX1ol9b){*1qu)3?#6dVQ6-Y8lZ^=t
zkBAbB31s8OTUin=<^c`((GX($$)^w)lx^0|6Eh(^XkKM#C<~`I(fHdIM{d0ie51y!
zO7-x2KRg9Jl`^1}u#x3JKA6e7M5fJ7iJ3*$&PN|2^pHp>V#v(T+H7GUx)Xf>=h(?z
zF)Tb;1J(kRj}6r*c!ro7P=lQJWAI2jgVyp;{ix1@b#RrdN*Q$QutGMx?F2(*Q2F@$
zdf`0B4S>62)wC(`eI)XZa5eNHd@_g`FmsE6O#%KTB#PqVaq6}t$Zf*UHYigQ6>0D*
zU}MCq*IqvM2~lYkdZuRO>@*_let86Zv-J82Z%%$H8mIprv1aT9W;CrOLAu%G)!fWf
zHB9gi(%7I27I85CILC*iL~>}TMAc+}<TgO}qHNH`g25>0Z1)I;zJK@rkL`l^3U~HD
zEf>vO*&*0(e%eWyYNR`JyH%m?4e9*mEe|>`Pv^7spRMfaWYJbw+#OwwwSz5>tIe*@
zf?~m9lUN~Z)`7Hk1Tm|69}kXMMxFiaZzoY6TL>$-!%i~>UA^OLxRsopSDUBznqcVO
z4BqNs$a_#!r<v&Yn{fK@A>jw#wu<fBNixomLuBu}8=oOtrdai&#}^vL<31_hcC_k&
zLaRc-oM-1Wjllf^EuMmnelWzM0sr}VSML?>cJ2<T#L%%0y~d%a<oQCzqcN&dSJPr?
z@~t&rS&HjN*gF#!I|_Fm%%t{t${JM?)O{Md>+g^KZ5xVzA-tS>hP(}|hSFmHVN-}=
zD3zOTz;^=6Dzv7X^DK<*>`iY6Pj8>g@%=^K@0X1m22QH^P)?H5CScanWkny7HnP$d
zWT2@@)jw}8jVcqRt%aERSti4Ez~vVZ*j#X;u4hd=rLC7E-!_&}S_Nb4ZwyQa4^q#d
zM%Qm;3tPLgxxC0CtbxYTE>u2eIWIeO1Qs>dDam0~*B0(j+99JdBgpvx&vxo}JI>d7
zacqrvFwhXnBe2~ujApOWFK`g+U20ASnn51MhFqOIwU8fpW_A65zNe{{{i?jC)YPFx
zgEJ=IBWAbP;62eWo88&r$>lvR95GZT$SjKF+JO$yx+F`kuFkP<+?7y<)Bzdm;Pa!C
zt}0ZFi5>2Y#(iOoR~#Ry#UspkFaPnRm~_`dT(Ts^xZdA%Eidv|HSzCQih-Vk%DuVX
zPWVg2q>+)lHX}fA%@H4MnK*m=h=#69Ji3Myl7Ly10mT;qW-6?;tZ`&TQFUL9N1jex
zDTB6wqwJhqDbp8KE<%(w4~Y>o`A`rJHA!9>v!g^+1Vv<nPTzT6{HF387j1=^6a*Hl
zaVJqahJN*A)S?W9XUnz=3Pqk!H{t1548B{x>huOdAVuY-5+~|F^%&-h@wARlvcx)*
zo{%uQF{}~`l+Ln3)&>}PanqpDFo32(L}}5ONcqJnGy8^&`=;z)%o%jd*-G(tD~tR*
z4pTe`1_-m|RpK}MbP>1sa%?Jd>%x^ob!lnsGA-IdhghBN*)r$D3oWIhWR*edZYjPJ
z=M23+sA>t?_%jr)D@tm8pe|;`P^&|gwAE9yeyV_-=3y}vS_<YQ-2}NiU<56x@)`aM
z8IU4ADpfTOe^<_eRxvj*VWy3_&no^Poe-=@S%?Mlr}3*+;j51Cp>&YQ!Ea*;;s>S|
z&n#uxD4^85d0C&#{&7ABqcf|(V1ygYkWeHPvkOvSTZ-rH3t=CV<AyaUNeH}(fzUGc
zHQa|yG^M0S%ma{+X%AoAGa3k}SP93e%Sl}^B!V0>#$Y2G<4eCcli^m<f;+cqyWm5W
z+w`e2E)Kf~ATC~6nYXYgPd+hGtI^uH9`ay<2`X7U=whsxdi#AF`sO|V|1l*;PR?dd
z^3oqxt^vSbLS$_rWBMQ^-!}LAc$hPD*URxTa5K?%bSv@`_Po<!Jm;&hoK~J(d(Vra
zK)D<^H1*<^M$qQztI4<6%#`%OUz3eC391H2VO^*Ca3jms!_3uU$!K1c$1}d6IrX|C
zK$@w{B)iH6oP{sdYuR5T5T0E;x!SaVe_;vl;+S#zhGnFxy#v1F+V))2L&GEImFxTL
zd}~T;PtW=8^tJoa(1Ai^cIXzfm{!CS96EP#V|Ud4R9~WX<g&eS#ZXb<D8Nn_!rJ>Y
zU(LM!#$2TLQe~&aSxvkHLK**|%e+ha3|CkTj0}4TB9&s}G-PQot}cfAaLRO1*5wJ`
z6?0LO6ly8`gT5k7(%019!uimLtgGvB%i+!PdmxeA*=(SW3GH)`ai9rxU0Y3Hx}><^
zqwl1jY4}WYKrFj%68yHQ`Lg#NsDbVA4b9h~BZo#a<k#5Ph_b}e6-}xaQO)%uVtqM^
z@qM4DDN+e3UF3r+<H0C0%Ff#G$+r7qmKeP<$KYbnHTGZ%D!zVxw&X4_>HIWt_U8el
z$)?N$xo!t0lqAX_vry0#@1kAYS>707y?=7#GZ)Czmq%K+J@)JZqq!j0`rS0U5Cq=K
z>E1d8zoEJR{q<Ms{ZJR!_2*xg^W)zun6?+kb^#GYynR^S7FU@5M-@Ed<qP%ryVaYM
z&nk%ZZuKF~)e;_-kdQNyubtC}KA^!Q-{1fEavgl0tvp<Snpby`0F)73L2GP~S+6E$
z><rdKBD^7wZ_0;h5n<WCPGD35kTaj0IKu9oppi|Hv`TTL%r$_5Pb^vKA36MtZJeGR
ztS%vuGYav<K8cdWB()ejd=zOl^%tiwM4$q}F!GZvF5ad{dKJ$gnTc6xMJ4qwJOCQO
ztaRH!Nlxn`0~WzrTsE`AG`GWE^cx62wQ-`%Tac7*akEu>APt2fhtxSe%TxqqYFi$;
z=BH3nC*9aO9iph4G$8TzfR(EJoV@63gj&<q3XkYO5XxH^P;)tjb_z<&oy7K#6%-oX
z9=|juM`?u}E9Y=$3aaC!IaIz<I7k{fI;&>caNpBefjjWHyn{_H)Uzv#xJCrMFd8R5
zTKV<3D*k9Bo~eC!DyL%21;20%MjjNzN<ZWr=&7HoOkpLYe&0~YRwA+-36#Q^Ic@2I
z1ZO@d2KvzIyh~*CHc6|EGX!{fP+C-r4N6oP9gf3u9Ei*)X&Cys3G9+kC^@F&)?I}<
z1CN3d5?M{1LM6&|aL|Mie#g*`7UO(WX1^Y{bo(d80b9FQfYyzW%0wk(M+PPHSjr8H
z7%tPUy0<e{;JuOxi*c^%<HgmM2uy=i=;R?`Omm>j9KG<^hLMeQM6Sy186V^YX7k+$
z0<02%gpz>*c^rs^c1g%qp5U?sWn>YkDt7w0<&X<hXMjd7-WV_WG>83Cb9CgVC)T?+
zj&DL&Isd^AAhx^df#pL9Sa@Wlr*z&0dgd**ydkXBQ77hRLk=9o01WI>9Zr|*pz2O1
zwue0@i#`gHS{!8rS5Rz4<zMEd*nK>SOslOW#6L?SXmOf@!u9PdFs*bQORl6`bYUU{
z9l$s-qPbubK*%~aBg>6CK2e^Z^u!d!Ae<WSAXJJ*+PC%IMq|TA?9F1O81lNl=}YMx
zIKt4+f=mxr<?=t-o`5Itkr+iZw$j);JZo=PDc9$1J~J41<L2mh&be2;-(NG+=fC4S
z-oqo_+nzwZk<wQ~F_lK`WeLchq|90PrS=}T-LE@PrD!tvrepZ*gZig(SICiM@=C=*
z;wp{DHlq=TYctW=I9o((0GV$$-Jf=<onWX?N7M1s+P7YJo@uRtxOd#XS-bs!Cy!zG
z+`11IUiSx68y6d8f`Z_ng{1uO`B~>e&2?)0F&_bJKFoEFzgJtq2%}W4*Mt81+nm+{
zr$cK)y1(z;xn0hj*_bRCR<)+BCr;<cvK+<?vJ+gURD`!Qx<6<A*>F3(#S9rFrzhzv
z9RtS6E@%5=r`MhCG%kkCI|5lf=-BLsjQ-1pRr|t1E=OsbFneqXS(mw1nKwNq-rv#T
z%d*cM%~ixNA}sE{oNPT%#ca7%d#zmpHSkzAcRgEwQwlyw|IYiVLyW_u+hkpNx>En%
zHwU+}I-t1@Wa6IgwmH6Ek4=-mz5_1SnSX!7{2bFSsi^Q=O&oF8+!&#7{V^EIu_V`S
z^YnSNw%FHb_hvk5du}Yo?X~w2HbzbwjA4nlz@M~S1?pCuRNtwkLFjoi-)-jQM)$S_
z?i4Gru3Jroa+h`TP-=A&Nt&5wHVN30zIxvVxj+uxDf*j7g^F0p{gXC68$0Eiw+_C<
zJH=Dg9dlh63ds9dCw4u1x0#DlsFPxeXZOUfqzF|37GzUyMd6F;c2oxuHhe?-Z$N8I
zhTK{hHUE+jta+%ooGvVi6@r*qAq85#(q|!G{5+)5R`quynsD}g=^e1k@23gueW?i3
zW<ijnKJ>jA91&(2f<tW^RFw^$lur)?%r*cA6RqW?-;&XKn$b<73gOt+>Xt!8o2Obf
zWJ*4_Rg`%0A+3^QhZ);MaLb<Dw3`(zr=GRS?XsnmKC&@voX4R5kO8lMq~q4QbL^0*
zj|#KpV9#{=sVG7#j3bfaWBZSt^1FmBTG1pZ<|XLu6uv5$7=N-Ji}@}#dwo1vqXxzo
z#6>xgyP!6s5Lbq&K}&Xqaiy2Wbhe$L_do+%D><!D@HrnuiAG2zOI^&{G7LXgAGkG)
zeGDvk%yIUz$lT+}46r~y6veSTWj(oo;2ROO%d~xI1yfap+Qsnbc<ij1<UgeZ%BH_W
z1wmp&g2FaLahsTJhver238MDgH7Is(DW~0qWXN&DB%U(bNGfoa1ujA6>A@F{v6O(s
zFj5qn%CeI6<PyU&c!D=%<^8B<y$y}pL?a^YxZzz^sIyf6r*b-`W<gQoElh&7#~^7n
zjUXxWB3CBi!aWGJ5qh_pYh&1pN{qVMZ$ex&Cy%M8%8!M(0>vAqgDE%}VUGHY#AkC-
zMQs`Y8quPPH8q8;lCtn>BB;Qj)=*RF$@%L2y^AfuOBK`oDB}0<F?RED3b}RMwK=|^
zpdeHx+vj+ZwoJtp8$|xlfK+soH}{lh&3!e62Lgf}v#>=4A8N{^kr<LSId|U7v_x97
zi$W0yy#Y(fOxW!deWZu1S?4<U_dS@m$G7+7{d6bO!&B1b+GVzVdq4h1zV-G{?8)a%
zw$pXWdTzCgS0*=Y71#-w<#&6sw&!~s*He(waMw|nGW+1!0)eqW#)S|{$zNn;$$rzL
zr8FG7D0yDdYvz6><rayMx2TG1M3W54ih-#7qul<ewimrE{a=&Y$?&)nE~;>NCzWrT
zd|G3iwPg9xjFj(tpUFGN^)M4c?_ZRf5s5q7SF17~7vHPhALgHN?b*%K5mIp5*XLQM
z&i*C~<aVC0g0GLM<lP^ZUY~d``%$}B7l-%m`##_H;$OXvUu1K$K>bF`JUq6aB*?e$
zi)1IxIB3-j=*0$z9emi}o5oQUD{sjUCx1(5r0|CL_~^t(*FQ#;ar|jmf6k|274^=Z
zFOhsZ0XtY-g3j;>tjP*M9Rn8Ob+4G=+>D(zJXiMr0{tEkTz!E3OpI_N&lRkn&Tup8
zflBXx*d&WHgl1sMT;Ej5^4dk9vYyshQ_YwYn^Y57FQQRgg6DAW0fN&-FmCOQvL5)p
z*_O+yrt(_!JUjA~T+KxfoaNqTX}U~0Ca)ErvN2-$9=x2KeYUdr{-mDjX9@h{^WhUw
zaO>oin=Xk5O6knMMRX$8xjl~BShnfDds$AETAv&p^Vxxdbd`5w1By?f&8zj^Pkzt8
z<&D=6TAPb&6}ON2pZ;;)elMr+Fw$=#WDVs&oAzok-fWqTQFv5MMM}YgnPzz4XvmBM
z2`w*S#p~u(V9URK(&v2GANbWh+TlkYcUIu=6<1|S;yU^IxR=T+u91}KO$->NGV3l>
z3XWU?Hiu@JX%VQ2(|@Bz45j*#(CI`#UVt;eH}en4elnKocqlVw6c`{@r{2yyDC+kc
zsxU#&j_Uq-lC&n%NHQB$mFB~$t#fc{>7v!L5RD|=5HQ+|1q?|Mk0c`gJM?{lpl|T3
zm>k&lRHRn9vW%=up`IOh<7~zz@buW6H_13F3Ub{fKfzMl?M}&qJ4P=~IT8uBp>eGO
zQQ+@6#LTxM6m{#xvErFX86#ws_`<-_=;+-lC5nG$LhU#IeU}V%TCU1?CQx_*mS6YJ
zf_7T)zmz8U;^7mBm?2Ly1pF8RAkXEC2Ft6h{`>1Ret!e<7^2YtKsqFB)S0_@ZJH%{
ze~V&edI(F~K3GVY&b(HK!)ioG+K#RZ!xU-E(IyJL1Y9<_dz(1m2$qoyV!x32Sc6pR
zS(fQ`<3@SF<lhh=VjD?D>E#CPz$9LoJ#RQ7y~GbH5>kU&P?^1U_<i&Ma*|f^0RA(h
zPzYiOZ)ifWXEV-2c_2*MG!kPmhXm-sM5jfez_UY6T`l2Ajw8_tt9UH1QVDkzn{B4j
z^Hp%aHV@LvVraH?xrU13jqT~fLmNZ1D?oV8GR?H~OwIZ*=KU~E`uo(&^>t58`)ziu
zPZ-Mue6KR>YLbbJ`7Vk<PC?;uw)@k0{M^dn@e%I<r5gAQ7Sq#F{^jPoY{<39=y20h
z)+|gekS<j+tjO8<+OxIu^PS*XdIIsr#;(~1*N?%^Wn21OwAWTY*qDvj#M%h>Tr%h~
z4<{WNvR@@qTEF0h8<aD^3+sK7uAXg;?!Ms6l$bY48#SAo!9m+P<0)n7YJuuH2^p=x
zW-lzA5{UWo4et-uzVi7l?sy!jyvpKoDw`o9x=!jl1<hSnf3rmGi`@1Wd6XRK(0P0;
zY5vK12uzwt9@)>lz4?7wiNr}h-M-reSl>vh3Dv9oJMu>@L=f#@AbKA|2aAH2X=~d3
z(ZYG*%JX($<6|pyk+IkJbUEGeySQ0a*e<Hq=OwfE`}1OrYiz%UOe$j5i);6Lbu~Me
z>O8TT^encvD$7Wo{rd2#1a6r6QL1@&Ar<#$@WgiU2&c2MZy83-<MjF5ykLpHxT1|q
zQk!t_1WOqr-2g)?pPv5c#;ElywiV&h-@AWV&x^AEWdUrqgrr6))I?#MWs}(aYghO8
zeAj1}kc5YO-d98F))^-{nm2cq^+;E*q*s;Hqo<+^a>M=$RD-Ymer8(M_w{{NcG`vk
z;zsChYHFc(gA3Q4cKE8(Vmf=d<qL>TXl$F4%NI9oW&C6y2cZ4iA=Vp97{d!pXYb8j
z@CTPUb>$ECT1|hDb-8Zq-OJDy@9X+(--ak`z{rIh6d^<{zudH2&1r=bu?ekTGNIPV
z0MI3EF3qm(xrdr3_NM6V9MrFp$60+yM_@Jfbo2DF+!zAg8$9On*tL}qzYc&vPwXHz
z$^yL|A0Y;QVmJ^q1|NaVi@hF%m1#n7#NT=XC7AB$HJ>Ks*^4Ggy{{DsX(&7i)U(aD
z3X%p~uoP_C<G_ocFctMtO4Kt()C(xAA2^UK+;>hu7d>q`g-cbHCoxS1ExoA4jq1SS
z&ae=obyk8XGnBv0##NJS%hl;HWvkW`j`)M)p!a++Wr?j7Ak`w0rNn-Xo_08-^hbo$
zG>wYh*iiEG;;D4Sc_m6$^sOF!<dJ3Mg7{WLAb%n^OSY!unW==SaaHkE2TFX1%XgJj
z|L}KFm`x03j^CMn)E@$lRB=0TEFs|qG%9?gsWxQQ7rpgiH9HN%w31^)Cv4nDah>&b
zw81pu&osdz2mJKG@v@N}SG_y&rW?T2=W-qw%50K%>*Vq3VB4!ooneeRR~yXb(}TNK
zN~Yqw#7yi#X_pPdzf43qEr7FE+NK|;KwEA)ngpqauZWK!a8}3lJ)vi6x0&FUUnk`>
zlMT8*|A_%EIR`pogekWxPL?yrR~mUIBZWoJ%dRt&V4sfNd>560gB)Of4j)04{O;lL
z=vG&#v0L5n{KLfEkE(boEdW!+V+CqSOq|t0dJR)-uhj{r^q0sbO#=2gz#kAqcsXb<
z5U#>~_RZLs-k}=Gp{TNWMMssM(tMX`@Rg%QSXg&7CwDfIaJ+R+a`MMPlKODd_Gq5s
z-&fK@9BY7*{F;PP9axs|-f!y%>8dxF<zLchWE2$cCFkb4_exgL+uO90QDgt6UAote
znNze{S|*^^<QvjP_`q^I2bYO<j0_F`Z8+9kDQIaHfq7Bk?c@{j7X^QpipZqor|fX)
zUV2HLE5;oSWl68@l>PO6^F$eqRh8Jq$&!}Zz5Z|Q4oYCT7wD)t)_Lq7yZ58fP1l`X
zE>{LzE2oZ1DPnNln`w+Uap~lAUDH!+#ld~?^e-o>pPPmqj1GsF8*0f}1$EIL9ECWG
zo7}APPSzB6?}5I)HTtk>MQvA}u<Gp`?5;2N`Cc{D7VdHsw#Cw<5&c24G57g$ZtkIU
zZxjSf-tNU#Z^l=X{f7V&){&HJ4{2MQX_~H9Y;_!Vje!pPTiSE#_X*%YgZ#=VE28)9
zN6Ez0+3Z&r*pB=_$5YyIPcDUaOLs3%caJ1k-yJH;to1g)(g`M++n35#p3iBt&Q@0f
z5>eXaultGat3|)rp!!D6pUB+e5nbQx#b3obpU6Xu!gEhIm2g(f9GqB^Z-+@cpc7XX
z@{Nua1hk#oZ|v6<%sggtUXQY1w>*$idYN1uUq=eYrE7=zpf^U&(x_3(8xI9TK*nJC
z(Szmk=xQr_MLO4rsyp0%SEh6}BI^R|Gr_~ogZD%UU#7NBpeKrstf$tRBUgt+sEz&S
z`>7{zi$$!b`x(_e^}}YN6VvT?+0WHgVTen>j$3v~K&BxeJ|Kf+Oa|Nlov!Q8J=gk9
z%`Il6XAdpk&={_tx)+X?SRKo6e#DaGQPO6-Jn6X>88`Ceg&=PGFyPt*NQ>YJ298po
zKn{PuR{i@E*t)C%0KGrp#CedK(%mKX0;1+j^c(=8{6QuWA?r0JD;wkRP#`mumW9`&
z_Hc+@c`}xUni}NdWx^&}!O(BH%5S4nC#1kNf$=XIn$!A}k9DfL=?g#}?$z@0lC@~Z
zjsDMnU*_8RQvFsZRi7_{AUL^=^@hgJkaO$2WU{C7uU)bhMNTF72?)2)PCK^J^0S0&
zyvNtIm#Z(aTIx;%LW1iI=cwu*<-{2|_xz4O+aCG#bpmmZ0z%BB4H1vLQX3+@2Bx_g
zu7FIvyTbf29Gy!Fu(@HqGz_vEf=#9twx!&7Y<O4^)}iT3-9tr_lLxt%ir|cw`I;5{
zB-hyVgsVE~O`H165!h-IX4RE9PadFBDmdMzCBhP)a>HPmj64AwM^=_;j>hZoOY%DO
zq6Mw4(uxcP<atklPQCDI_l_@LRva%T`0cmjz>w$QiM#?t7UCgToi!`jj=>S7p2&a`
z23aeErQw~a)jgTcc=wvPEb~$`)sh2Z3|%nTXhcht>f)Nj2RCjLRb=|@pwuc#&x2|D
z8YO=C^~+=sMO`w)th-#~59wK9{{_G=m6%nmic8xUw&W<vd6%SvW8(V1$4+R_<#l3{
z3MG>UNS&fgyW0>c--ta0+pBB3;FKApDAxZU@zdn_dc%#i2?)@as_XOqzeNwqt-_D3
z<H=NdsP>#$ldbPL_wMj8oT|M1{BGg2tVWFv->XnegxZ49?KUTGCU<*aUw|ylGv)f^
z8WJdMIPVhansl`mh!@+s$2fdAn__v#+JIsKo2lc>^L&I4=RU?tga`4w?>pafKPY+#
zH6EF?N8$`w@Iid2h97}t>*mLb=If7_29=C*vYeCH#j@JVVX~xa33y`;G;_0!PW`0$
ztF5f=+pza@v9Zw3VCJK1Z!^<<YG&i_Gc&r#O{0v?)s@e;=)j{WC=}D+^ibHlKu8iR
z?1yzj%gPskqg_gr0%Kke_VGgL-a5iTy0E^708Vb$)yw_q*yy&^*@*Z14d*t`(^Q(l
zCLLQav=upaE6_=Jm^l5xV(be>w<hCdZ_~`&!0G*6R0%sH1vbWgISLdvq2ZGD4-|s0
z&lW@W#HeZuFso{SH+Kp}W>u5xJ+zE5jPU~NPM4}$npiO~Fsv&iNxR9E7JHC;qkpN(
zl5Cf8-196}zIFw?t~G*8F9=<Hk(74d1+%>?tBCCHoAb;S6P|>_?sb;$s?SAxe(Z(q
z6Y&hRmDcLZlqUG>NeK%!7DrP<Q~Tjtd38A@;<+@Kui9)}Ie!g+#c9jy)pA@Pf>!6V
z%E3d*??VZ9SAb=-UjDwGgQn4BAcM?!yLdO~?HnI7`b8OwtEVaPFPOMa8H?66lGW(R
zQG;&mbT4X#BXb1N?1CrUt7xU6txnrt<jatHThN%^77+DMp_UFxpXJG)VT+U1_P>jR
z8<%T59$^uJO-Rd-PMsL@BtWbzDZJLo@o~(?LmwOjz1Zg<ici@NEc|@@hW>IKh@&7Y
z1(dLH78D@Xa?~+<@_)1xXppvR4&dGQP|+fnhD*`S#Xu2;&Zy7~FrCbll&BoU;1x8m
zYk$<emmO^kinY|o4RPsR1H6o!Bm|q}Fq7I0QEi-+pd>ZPv<b|5WjV3xi3EXafp3=!
zI-7%dD4pjbpvd{hxwf<U-V{MhSru$YG@k;>1euKRpVH^K_8nnWqf^up;}AcU)2dLn
zYy&f=@9yQDSynhQMWs3D5IhVscDRHrXBq&YL@ct_<|NIt<n>bok04mR=K3W&tGH3+
zV^LYI2!@VfUQw?(fwQ6k48*~8R0+_ihVoEWi$h7b#?lHIZ$OPb;~2TKe7N&9qp6IL
zeVjj2(6aThrzOVe!TQOq#D)3_*$s%+&gzv>vzEj$%XK%{D+sC2<ti6a0zfd(?6*MP
zw#g2gV419nhhU62F$+jr)Wb-*-a_nwsV((4$f`Yr(or<cOR-rQVgF5soXc}w%XP3)
z1PhlmAlfx314GQHfyXXzEIPAdj0QCh_=<Y-2l+T?h^|iYULWT}ld?n-gUb|MA@O8C
zhMQYn5FHQYG24U}w)|*02dr#W_VjsBwO}ga{bJ}bb><T$t`ZI@Ov3#%7U%Ckx(|t|
ztM$u{`;?X!|Eb@Cf?C7b+0)_pqs$k(e_2-tU$%FjNl9Lf-E_*ZTM@#4=phCGc&mv2
zM<ega%G-Au8lSd^h=?B?HP-y89VK#6J7xoBiY8>YWtnLvrzG9>&J65XhoOM5VP;Gj
z+xOAiP1#ueFPspAiR`oV?Utw6gl_rar6hAQ$zlc%($S{gE#vOq65%JO<x&`FZIUuY
z^BaqtJ?sa<;BRwttw3W%brHgKahMtmQr_-?Ui(^Z?vfK?(oa{&z#Z_~+1Vaf@K!HK
z?hBb+rkZaDqYqsg65J_>FT!gh+KPnp0d|%TlO2t}KCfQ~1I?TvZoU%?v*s-eWeR^H
z6=I6>xBmWKG(X~Y8RA})LW2}hw)Z!xM+OEf8d|M3#zdaWNqk&rMoOmVw41r+bDk!C
zrem>>7@DC%`o-l5HC|xekD_IN1BbL}Ghwl4Wk5<}1bH8X$C&<^vS&7nI>LUMAr&=6
z3QWwm!2zt*!bF^TU2r=I-*@aO&T^Q<6@Qg#A-c_MX$5Bh2GeFZ(b2+g1p8M0!;-$m
zUF#wJ5|J5(#|GolENUbEw{#{qMtUZF@uS}wkbe!Kmh8x4rF2wRv*)4DXyRS!kdiPH
zzc_7etw@yOW?JWUtgap2`om$%lNf`2+UHq&>G@T7r}=T8k(1<cNa^O~Y@t(2HXpk@
z(YXQ!h?I*)fQWcGf^ehUr7|@MDwyV9y*>9qk-V4daih($QCf|<EHs?^hi4bC>1b*V
z+zDycP4vKu7?W;VPh;B`-uXHNSu6JdJN5><*<?dd&VTNXTlDhLp=cU0t@|bgwl&J=
z>vv)tz!_ELKoaX9=$Qs9WQkxWGS}a~<NhoRe9Ti13|BqhWe*t8b%{IStM)<~u(HG!
zr;He4%gr<ds)GG?;na}wP@)~V6C1Pyc)@N#@}LHbuooM#umgPLEEzL4<`zCW;pa>W
zcYzxAXzhypWensPz#TIHa7ZZe4hqf*{gJN<-oRy65^=6jh8#vL0TLeh^HiD^*3LzE
zJ^DeTllJ7y+Z6IT(2j)?RUsC&jl+Y1FB4|n2`dQO_xigKj_^G7Wg3|v)5e&oKtV*E
z=0Z#Kl!8$X8>q@-<ou)1P>G~|q)g6G+S2NYQ>4(~#|iOriyw3=QyvbMSsezI>I*no
zXsuO~=#eJ=(!ui&{EFO3$ys=3m9x<NV2ePVDf<6HAc98K9bQsUt$na%85sd=Dry0d
z8+kmk;FB(L=j7ON%+|x|j5Zg3u+0^s)(;t<2-Erv^{Lhzw*@fs7F-5RA(2amngL5K
zHVjkC&{}fkhhSOR;CB>xfYPR{jRc}RO!&1jXOh=ZrzD_z2&jCdmPnPGz-<`<p2vUw
z78;eik(HFA7IE(s#J4I0fv@kON&7*9m-O@1ULxk&QNn5je-1{YcrR1S-~}y%6#@AR
zaHL0s#N(qNLdr#Wi%YxBi_tbdUPv{tgcKD*c=`2L<~2x+UcT6-aCNTMQ&W6!ynK1-
zV2St0>vBuHPyAlWD)wNi!vEfDn;%t*lIy;|<d$6hU%c$pw>0s7&IdWOYN0eI3lYQ(
z<GC=f;z~xfHA-SQVxcmoiypOK_YIjg1~UiVSCKf3=I*9#wt0Nl^kEgIeI%^_I&|($
zt#n5a)1H3|t8NatZMPdu7xlKx?$7S|>{oP%nO2=~CbTv9Rg$o7)DVt%kOAA;rt16}
z{La0*`HUiKu`%YRUnwI5+<4!pdDeOyupBPb4qi~h`K|}Op6ZIE<pMB}orf2@-t6p%
zYj0g$!qQR}jn{o`q+QY^<ABd?F?qsKB0|qjTh&%Yl`IAv(|ubtnNsB>O?KnB+(=hi
z05P^@g~`4bS$rKi^&wq_7HpyC%d+waIDlVbX#I>)d%7x|vzRQOO=iScqj|4LU>Y^A
zsk<;vLzC~lz$C9hRvf1<O{nR+o{XlSK|WgvwQbHK36pr#Ql2PGtp=PQ7hB)0>k|7e
zw=yU2Wa5S$$->y!*hoFgW|Ux+=Q8YI`?k-L+S6@{b;W_{^>rf6OHrZ0U@{aUbM>Tq
zT8)8nS+gLIoZLnY&lVZA%b(6&$XRS%${HrTCb}Xeb}X^C-#5VY<jK_v*!1|i9g74%
zem(asdm3t^okFj6I(Dutp(`I_qkv-xL2Si}29IAa8sE&qXYkvNk*;UwwjW?&2591<
z_l~VjJ)bkmTc%AbHZZ67&%dpscvDD~^u1dyqFp%x_fH1zonZPO>so9Z9#t^F$vzd9
zg2GwW8h)O|S77YbCbjVX5?s5Hx7MR7{X#Y!|LLj<S%)TFoVWJEivp9Qo7X}Xg9A~3
z!#zn&P0-m8wWb<uQufD;X$+&|z?sSmpbOJbFboERKsb;mqDm0<zcWi#yW@Wdm%zAd
zQ-?dXqh!uj_Er;1iYAf+tnMS!l7fO&Gcgw6;u1=2Tf`5>U<V*XLT6n7cF2&GU^d?%
zL@?0j<2*=p5yxjyckGK0gZUJ;h~SKPCPuKZ?v(L|(J23YymbhId7&{P;+IV~hxm(c
z+du1+uXw`JTF8^xUp=z`hDZvjuag_XR6bN)QAau)Mo#XcE~<;TAUYn#FT5C5O-wa{
zE-u9x3g0Rq6FuQw(~?)pO~ToLW4<XZH-lc!4!UN3!9)(|y^=Dl^KYjEf;c{dxXOS?
z>?Q=ctmzgk%OY%uadX4TLX{AmO>zj4Ss^^$oO0#V@?p$LHo}RK-!YnCV$}z}gE_k5
zWr=-)!g&BN3LdtKfyGi$3mFm<dC*CEHo_mTq(PNJ3E+X4BE<DV0n&2j!J&n$rv5Un
zl7k*PnnLG8engkT4FD)e4hA8<C`fq)P8g}Syrv~ckhAh+$PEO_`t?YEOwLV^IShz~
z8p&-T|F1p>UK~MYN!g|RiVE~<0PYOD3%@2MZsG#gdp*&qBrxuB1xd2{dw&KRMDX$j
zJwO%?szrH*g^vZD(U!8a%eBWxcYC6Dk?&hk@?HN@^4)04u-`WsrUzdhiS@)%a?hT~
zNZgu#^QvA_8`mt}XP*@Xx@HS6F;QC19-J&sN6QY)NxO1*eNOSoJfY>B<@5c42xo&8
z1{U<J+^g#t4;tNd2WlDx6VxI>QtFYZhjq?biHs-KkAjjWa5RX;#P5i#ao#zGTs|vf
zb%=+hO#9atXu?gXRfl(#7CXJXo?BQSyVvPY&U2kzK3P`K=(5v#oj8bCdq01!lR@5C
zu-P%^#3QiK8})yUVG^rD-{oEo<fPfx${lzb-i>p!Y`ut|5V0wG5lB5_Sh3a&X#x#p
zi---GAH)0Zi)&sLtf&{5k2KwhanA>x4u#9;Tvc5z8`lVoQ~+t~Tg>mgYt6NLEw`4k
zd*Rt^Embrw^+;A^MDv_{1+P1vq+tT^(Ew_IFEVwB1wsl%ME_2Odc$(H$#}{E;%3A(
z(=EriGve*^4uk9Mi7~gQ?v0l1!(Q{WJy`BL_Z~uTy5mgNu5S<FxN>A=V1?OVot|}f
zCqQarM+#UR`8-+>DM6``E)}!lNm`lOd9Zfo!Jr|x@8kKm`}zK(gO7978fhF^Ho}QT
z$IHxc;g&=}bTkqfD(cW(wjGb&#d!t{%%-s<`BcjjvC-mB#?+J(RCMgpYC#;^tWj3?
zm5q5G4Z}jGrD8ZY(0!ze7GhI@o|L}Xkm2}As8pNU?W{jB#2A~jI%^%ChdIdHYsS49
zTIy%KJCYG(6F@+}6`#qLFFz0;Tg}gkuQR?LzmLx?swg-36035XYfIBHFNZqXO|dv<
zMs2g<(x>bjDmBYhX}IZL-p~Gt2Wsa#7cNoS?A<8VP7=csSn;960?i`|6pTm!W18GN
zBMt5F5|u^GwC;yD2k)v@3XQV2hs$ogT%6i1yIlpRP=2(^OGbSY50#XV_*^UzgF<r@
z`(Oory1=ABu&V));dhAGn5W|4?TdaV4FuZ8Il(EF2Dk_F4oM!!={izXgaq|ufIW5m
zTGVKeVNAe*REq`V7DV|qccKtTGKKuh4wGtiR~}0BqJGBVW{Aw8v{a<aCKBnwGALYC
z#%zCuh!M<|2QsysF*vtQ2rJ4__|>ha#>|}SG_$k{(T!8$Grz=BEz1ZC>@o5ggj=>%
zQP27?yysT*?FB>gUF(Uh${+vq3EH^F{%e{N82J`PLfBi1YG;>#S`*HWPC<Ziln>+5
zKcL}gdL%i9=zb^|rN=~s{vgLj)hdlgceMj%aVFT9W4ToLFz>cM7-#Q)R^MNzYZ^^k
zJqiW$T9(dA^^gXIg`;37!ZJH0;SMsl-Ofs^$4CwW%3CMTDP-|flgwha<V<g1NpGuE
zE=EqOf###3PB{IPey-qvYHU;2SSko*xqyh)n395OpVnHb;CsDDA7y%LY`7riA&?;&
z8Go|@B^d%@Syc`SKmKOA!-B&&OkE{kxXQ0M4-r8^q%25fVNvjg4M~D5P`ORj*FCE{
zx&!p=X0jusGK>SgL8Y4_YXA$eMJAI%E|HWKR=!b%XncRtUYqD%UjKVEHJAew)Ci)Z
zbO#LdbF9&)uMRWdSJ||R!qNcGLY1DZyk-aBk#Kvzq+D?y>e6AQsL&IGy*sNYn_xTZ
z3tRk!Eu{DZ%ftT_+8Y@8bt*RzBc$lk6{#3VNlbiPnG-SXtFvRXoWUd@Vym`lFr0J2
zyz8i8o94VvVT3EZr~C+3N~o<06qoq^Fp4!p{<jUY#&t0U$NZU>JJR<v>uBL*YQ40w
zmM4Ba&rtH?kr&gE)c7W-$G1cq>1tD2hYzHtjS9tftESgWU(eFp&d5-Ciy86+>~B=@
zOp7I2$vN&WgGaYEfpBemYwAHNRvBBL@$%O@E~_msL)4UCoMG1%+4@E38)#@I%A6!)
zilNPmw!k20+yrb6N)m1UL$*Oht@_5MT-mDX`TZzTF^-N{#<!Eo`$=N;duJ+IO|8{#
zwh}93uzw@ht_a+WEB~A^fv0)GN=)BFl*gL#<&c_Cg3IfdrfZpW42PL<6{_nFuDkEi
zQS#lKUE=4@cygawALHg$!ATW_o1hPNRpO!#&5y9C5fSa-s^mB$MM{v4+$H~J>~m+0
z*O--#4*4bKB$Rs-Q%xECEV&s@{p)P!^K)@+?_w%{yp!!N&aYK;{86-PqK<jMsG;)*
zD+e{2=C9rRYPa4csP*C|HLxgfKdkTIp4;zJW$KBJXBwC;XoIdW;sMPqc?@^{&ENE`
zwoSB7bZ1$gpYi$|u&0l+dY;=q(`PMMy6W9_S6xsqaF!RQlTk$;vbqjyi-}j6S}SMT
z&CuI>U(%MFZDxYz?JIUSGeWYt`)5Z&skUMKgnzP*n=iOpe(W?6C-+1AkEN55LmWq%
z-M+QT4udoj-OzWx9<H3$eIDUI`;G?DN#Y{N_)l)9g9RGeq1hT6M4$&&QGqNLUXrhO
zwG$FglpLnc0X$uT>3B8aYUh@ZE#1;3Y;Yiw!$pna$Jd^ox|1@bVzH$04_=B?kBZR3
z^O;2$ax@!RP4I%as4~;B=$D=?F1QF1#0DH$(zaeS3NdQX{0dw&hQ?MB_Pwm?MdbD8
z1kAp)T8iZ4Rkda-Tq^1DS*sbTY+mlHG$Q(u@7+AFEEL8<;K1efUD9|XmNxTy$PCR0
zn7IBvT-upf=$EErE<$o~-C1BU;tE1Pk!u!Q(e&Ej>ESBTCt^L>H`+CiZ}s=vGLg}+
zYJOZN)qHH6s|-73TGWradw-9qmCj<O#?}Otk_=hrSGZ`q1hlf&P~+Sj3TMhqcL{ob
zB-lkQuHi?wk8^vd#p3K#3rn*sn4W@&#!hqr@%bsvSDv_rC+pLf7K_ha7}`V`qILF7
zi*$v9-rD5ByobRgG>Bm1Tj7H@gCZD?mnCmd55KOUQqMu1vMuWyNkajy^U>NKtV*%J
zQDY6YfpnYa%d8x7TZ?7ZP6$+lPoyFG1n)#Oq5m=9NCTKD2w(*By2FE8AQ5z?#}~(+
z00Ot8h_s&K2n{2w9TvK#B8HhW3k+K~KiGgIPA~H(07g-hM$N;!73Om3MN~8#Mp%~f
z3;grv5dx9!PwOwABIrbxw#<wtg7RHpHqB;IQmx_W>*G@5(dMJB0Qv5#*~D%?Y1r?b
zckh_0Ao;F0lksRL8*`h2f<n(lN&Eid&ODNJeKO(rak$K1l1}ZIPsQ6gn06DwtguRK
z4*E(@A~u)i)K+3>E|{7x6BgN~PWQf4Ev%H>3+h~Fa*>TzR-E0gm!p2s#%`#HV#@ed
z9u9WbhRyk^(#tLIdO|(-he6j*@Q(UQQA1VQscp9pCXQYRL-4XJ&`!^6aoNxJQEN<B
zoSN;Q;l0b+vRLNTF?t1ri)`1C<{s^5YV+h4hLHemgFGfjOB*Z*p0%7hJU~sXO$=dL
zfFU_`dWJFT0=J9veGu{$>RSS6HpAE0R?R+H)M?yq*W-mu*AyL2hNhd5%D#-0=3&g-
znzoS?<$2iKIC46R#bMK{##@~)A`b5Jn_H+D8nsQ)4^0BJ5Y1cSQyr)(De{dSU$5Vf
z@Z`r>nwP3rW(l$DvIZfsBVTmhhyJO<IHPbypY?JcYXv5a@*jxFx#8lwbZ|q{9gT9>
zUR!SN-5V8gJ_6t=UW+rSiBPcm*j=2O6gJHm?T0Q5xUPBo*Xbi(t5H?73xvcD0;G@X
zv3UNns&+(n0e!eO7!fGJywfz#!)BG)NtEzZih{n0Jaum^t7xb|=@m2IKxY>dy-$tY
z)NCEOL^#^<8hkOT=}J@E?arU!^qbaD=ERzAOo+ZlSsd@TN?jQo-NOuPkox)g4kovU
zetpsKpQb^CecIY)ZlAllMt6nk6dljT-{nwjOEjw!5&*?hk;m_fmpDuEDQgD8I}BaD
zuT#g-|D+9<h&ek6Bh*)+b{lR<yzYcP!en1?r_-@<MivkudU?$VVFM)Oql1|d6_iUF
zG*?n~d+Q%LKmSz5J3c`4yzOkAT-}Nyf8!(x$fiF@-JNxJaw6g)#7BPWE^0!Yak-f>
zgM6V$=x}Fo5Slj%^hH8gZoZ)9@FoIBj!>?`NlSiN2Fh{62GEKC@am39GX*nkWbg$e
zRdkHh(`#xMn#LffYtK9Zlzj9|VpBNogqWMOhD2D*`mQ0Ox(7#%B!N59tv1snnPvw~
zYH`^zFM54!SBSFb^n@5ni7^sj*j1#eQRrzl4D#B-O-S}83Nu=1txMp%+~PQOkU&-*
z-ToCecx+QchB(jixh4G3*cS6a2VAC@#0W)FEO8@P&4x7nVS$blg<*^q8(+_ee|!1)
za8~I(qmJ@0IkraYLjikW#hS7U<N%{E)P@MlR1b{X28<dP2G*8sXd0D`IVqt+GLpF$
zRWcKg;0v3kBI2Of7NpaW_`&l3{wp}*t_lDp^w}BEwozEodz6CV$_$t3M*$l~iEPP`
zF)mgPWhux14)@C3cP~d0qgRQjVoQuHWw}FdrjoN4ZzQmtQ@<{oXihnqB>yyDI91#5
zzJM1yty~iDLC?EkiEu<d7;NCctA(7PB2zLkWv}Q%Hh_tYI5;WttnAPDLhS$Xg88Eq
z$re=YF^*PYVCSm^^s~RefP3S#UQ*<R&DoP%nnSdkP#dDqb330z+w*pC_4|AroBc!k
ztJ`yn_HIFT+!<SP<%U)YrYa#Zp_=`3)qirk!}j`<h_reu6FZw7?(%*4zKW}YK6_nk
z*u@>Py1VS;^6VrcOT81bv6CIs{n;<1NXgMCgfaN4!{~Bxf(ikbaAMlL!W#_kF~c_N
zO}9=|fio{}fZtp_yw|+JwG5_Rd^9#89wX*s<K8o@&$ivzb-~*|u-13r=U|58t6BIf
zjd`IGx&NBE(%Yrry#xEI1m}Dil3=m{FX5_1$81o8CgAp0<+A@;=b)1CB$#H^Z8DQE
z`&J4SsZUt>*ubAAU9E%8*Kmx1Uq(<}GUqbCR9x>9M<=I;etr=EXx&-a^hb4ce7Dss
zFlpxf1GBfYfU}@K6W`xP+t)#kB!cs4JlFc$F|Ls<(7T!2`+VqYr2aKwM=Uk}4%%GS
z)I|O#{JQ$MS(-(5XM>`{Yrf$vB>zXT=S^lfJU2z>3pDF2F5Dnc06{f|GWRLd$KB^y
z)fBB{EwtwIek03iAy(%MXORRKJavZi=lgys7l*2Pidz}Nv($s-LhqpMW=!NQK5M5D
zig+;n`}d>FRH1H@Z^AKRuD%fKlo)G57cy~tWY)Sn>lZ2NArH`J2(UvW@J=lYc-!zM
zy!z8A;<Y(7r6o5vL}BRD9Ry*Qb<^&|;WANqU0N_+G<Qr&=j7`?aDJBt^B>*{0K4~w
z#^$zXT@Fi?P)84Qz2Am=y)mLP+hu44785|_%;>{^??-ZLY^b@~9cz9Jp^mvNbkM3Q
zc3Ix8W=Ma(WOKjvzjwE*KK_$EY{`5EHaH%#9d~lY73d-&N6Nr24U3CGpNr_B4&vs)
zcTrmj16ve$eG{VgaqoQ-1CVlmGeejs%|!?n9LQ#M)eRB!m7xE$Q{=+ZGM)*O#Lk-;
zv#9$kJwcIy2odRhjDt>0p$YYA5o41K6{zWDK#m%oeha~jBdQ#8w4;}%n9waWqek^*
z4X>2UpPjRCE)<es+Woup?T+P=i*YSq4-6o}m1duYtF*y>g5;CxR9~BtWLz2zM4CnW
zu1fF&?fRtD*f31mi~b;CqQvPTZ8h){cZrKSQ;JJdnZ*Wd(V&8;PpFi$>Gw64Rh%5g
z&7UOT5Sq>v+0xB3(Fio=<}%%nQUihPz2_wp&?`}F&(+X5uLZ<}`DJ4`lt#F<54YSX
z3w4>;COXzEckaQn&PgyOT=}6@IGC^uQ6PSEwL{{eq+AmJ!LHqysn+*<ePU5+&YT=c
zN=k&)u1qX;KE$fg;6|DfF=pBTyf;}2NpBhmuv%S=L67S=I0a3oT`7EghGmIRqB`KK
z&gkI5)R$1*FDO~wUm*1PcriCI9E?;j4W5cUy@+SxkCqcz9)C6|wy2Kuu8PJWQo)FH
zl=_kqWz)PK&&y=MrdK0;7uLOw#P@6#*Q6*7U>0gXR|xk0abNlAb`H8|C=hhu>-j5J
zHbcH}1i*|350e-kdIQ_s_qPk}&r=|q#)(X-wZz0~{o$wkh4=&YyYGJpO2U3!{U?Cz
z|EJ&~KI;c!pIBQgxdXEo695-|*;S?eE;j#uy=r?rGd_D7_o5)&!T7iy_0`ILt22wu
zN6EIYR+qQFDI~=uE2PjI+yUWz^<ux)jmh#pMtj_#c@SNV{rXIdgwy3k!LU#Y7fO7%
zsq8i#mbl&)mF*0`aqHJ7U1lo6IftbpmEk5}X->7J@A$QDKBi@*_GJ@&H^Z=_wP@db
zyvejwe58?6)Uo&8cnV7gBroB)RV(Rj)$gjQad`SPS+Fu*!_OHi7LR(Fm7>DU-O%~F
zhEbWD{ul%8*!Zb_-rMDTH2;3IUEX7Z3Z?UirO!b4B3I{Qs&&zSNGSGM;=KQIx8|T^
zJRbpiJs3_oQSpLsNi6$ewperYu(UBAd!o@Xisant)A;Ou`xZ80G6G*FIm-G8?sPwM
zkvzK6^Lq{9Lt!VyWh%>)E6uMVg8FZ{m<<-LXB~_AC_r=a`K(BCsgv<{REG+Xa0PI9
zC_MIe5V8ADRDlNJ`EF|@UscLeNNHluqH4!3Pve8XlU}#&<A%86O-lFK^dfL(;(U(t
z`<iIUnK7wfzu%qt_;tu;Z_f7B7k1^HnX-NKAVe111|o)LD&=P4Y;f+8wpj$}A?wW2
z=4Tqahqibfc=ML{U;7wjTdw-A_ig{mor>~C`WqF~lxan4?>C^jN-$B3j2z#$pXSL|
z6;s-73v4dxd7@5aG}EqHpRXqo0;}U~10yP9y;kcEQBpzIR+;UNc5h3|?!$K4S((&p
z*4w+ET&0s+L8T)+f4)co3R6{oD4zf!WWM;|V|Mj50WocqhBG^o;3$Ge$oncSHF2Dh
zGH6(GfPRJw1Y$aLFgcJp`ibf<3NZRuVaoF275}{}19)^1HnJq;&Nb+PeX6-h@chsR
zYh<ZRpi({*{0K)QBR1As(VmV754ZKYfK?<$)}dwsQXE3IB3q$Q<0Tht)e&$>B0ce_
zvCM+;&b;ZKs&W1TO$+EUZ0Qw`+Vz{&b84v&8C5iPLk3uR=C^^i;BumTWT60&A*G5n
z3i83e(sFbmC>X(7HYx&sBWO0<R9n5{P^@KCa6T$_GLjp6O3IC;Dw*answ9l39I#wX
zqkcDog~Iz6z}809h_J?p%y?yey75rr^Y=6nYD#>C7JK4oNQcP8MQkRJYDi#|Z9f2)
z2Xc9+%?Z({xJxj;F!4|u1GYJw1rnf%6%qYefN8X<2RSIysHgETs=a)c^?Plch>Dvy
z)enWn7r(?m=KBv|N&kzrw+xFTXxc^z5j4RA1PBCQ+}&M*26qVV?i#_}-50muPJ+9;
z1_<u%@XeCs(f9q%xvq1TKTA(bb=6&WRd>(M1Y&)r5I|3_gV{yNoC%H*|KV)>B2K|F
zl5c?{UNqEUYzr<VQ(Xy9H^!RZ&%R0rALs4b_t|lwuz>D%gGt5x^Iq-hlatd^cM6W;
z*l1dvRukE1p8;0J5z-g(TA$=Gkc(7;gOGyY#P1YxcuKzrM|%g#p6an!;``W>i>lj+
zeWflSr|_PFvQZ*_kC0(HVvc8-+s8t(wljTn$8|=0#d2FE+k3&C7+Jo0uSUMc8Tu~?
z&RsfHOZCXevKEfM)r7KRea8J<(}jzPxnMv!xWUQt1|94dpyiG074K}Mvj`2%JQQ2D
z0F%g>tz0r_V))`5=BY^tv{=#ngIZtjXj66U+Ft&IWAOk<!kV7U4_l7>j)B|2;Zk+0
zrSDY_uB$4PH92+TJg*j~%QU$g?0p4`%~Ug&?)Ek#kjUl^>^U$$T9e?k_Ld91s?dKv
zN95`_Bl*O%?rDT1EjZrULvtjg{rh>H-9Z@BsSHafLtx){46;{)EQ`RpuMTxvxtQPl
zGZf*?vx|lMIoG=;@3lr{^YHMoZR*UZ&rZA;#5Hz1INYy{9_F{@yPUff@RN2Q%<B(E
z1k`5&niJDf*cNClvfZk@9aCMY+z&R=EV!FY=9F@dMte<QG~h;B72MNDSYPMyvhEux
zfg?c3sjz-V8KB5|>6!;;V`DD*&15lTd-x}dV|m!2{qqb++ORIz%7!B~orK)qd&YJr
zipzU5@LSw1scg+jTZ{K7`?O*ThJ<6o;JMF7UM%9jwYWde?c}|exJZ&(!Um^nKh>)1
zZMo9g-Aw2c(H2tT>=g|yN5YxyeL9xOWAxdc3AHKJZCh}9r)??+U3u5D^qu?wW1N2y
zJ0k+#+$VOScdUE8ei?fgXBL9d)p_=r%&##K6X~VJ@G{K|`NlTiiGvxcD!F$p;hnPD
zpAt^3xIOH7Z8sH1D*N@u7LTiICbPcCkjub=!8fT$XXCy6ozUo{pC<M`1wVHyXLu!0
z*n6h;Ec12^`e$=duZOJl^Ey<;6m7Nm6fL#r4%P{DLdUpVd_H^{3Q8`8CqsFi%KJmK
zJ=8FSwoa-p$k4|GsLe=U+Z40135Zh(U$`cLH&5+7T}-H<B%Y$A*y|Bhz4z(InED}>
zj*Gw`s#dQYU)6;GBR(_X;2J$I8o_3^E#zXT;h@kz46>2Hi^X}T=SDZL;-XH_X4Q8b
zP=o@eTn{o)e8H2NK?djU*|dfpHU)#UEp|p2pE5KyR?AT*Yk^7`%>@_qikiB7)cQ5N
zNE;c&SD`oCWdUhv{uy9jD)<fITwL4>*||zwPV5eE6n4alfw4TRRmp|ulb-xM3Ab@R
z&I3I>3`r5#M8`xSTGH#_R%}t7{$HWn#wd&+4cgGlB2DQrg1DsUzR=D)!#AdH1hu;k
zVe|!R1b@;E<C)4weICK`;duJf5Tz^z&Ri*jN+M57Wzzi96avBvjVtQ<J41o0%s2UY
zMe@<mb=`VJG)aouBE-0|l<(lBt9<<w;0R3NhAD(N%c=~YsVO$jeeREWYR6?C9QF);
zCGay<`ll4kvW%-d{C?n-L3LXkmd(<LYxKHQTmql;?aa1KeLAuO1zc$IQSxk;#o^7X
zp`+KRD57%LV%G>5wP^}xM12o(4*Mv!cNpGR+A{dUl7oJ*iNnyF$i50E@ZI~j6_sxb
ziiEf@uz_6Nf+ulBlLg{j?8yHS6B&7|I8Z2wXT;*VQzPKbK5-w8hRVI?CH2&yy}e0F
zs(GRq?5q5~JZ-eol8pu9I&?Pi9O38DO+T4ci2I>4kNX+b-Zk3d-OF~C3TId=MVZ9t
z9!@e#CU`{C)>EO=)w*gJ`SED@X}b!BYY~IfBB?6*q2%xO_PM2(k{p+?@-CB4e6GSt
z6bf(*6AkqPR-4YPhu>ycESBP^3*4@@f$iiv3}tP~<y@H}xELHNs_vdiF6304UWK2t
zWnJlIZ)go2e&xJ7X$h9%wM=XnN5)3pd6y%}t(?k1)N<_m_G*AqO+$Tn^6*ggc4mx~
zH}@=Meqt&GH&oko@BGfLMy85%UE|tK`fRl6<@Ash15d*@2O~q9^V#Z4y-2Sjhr5M_
zTrZn3Tg}}%i_;;AJEF``!wq-)Lk0B<wI*H4?Ikr{kKJy)0~NEfip6)bOk>n679OdQ
z5q%%?g&9RjdD`>*#3PvBMKM}Yupcm9m%pYtvU(^8wch=TUwWaoz=E5^@$7k1Xn`iW
z?*iB%FT8wT+w)<6V@M&a1e)i~nEQ5!g<GAbop8-!vDfv{#>6uaLR?<26Xyw9Y2U%U
z`Ef{b$d8~CG$;7MTc5M#UG<>JuD4+yoWB?)d~i^M4M^9GXAVOhl;7syUvOfVvS5}&
z)1B4CFJWQLkTU=5oxcjVH#iyKQYb7b7;PLJ8jE(oI<TN#e+oOms2uEUT8g+*TD@8#
zJT4i6LXssHkkV9e^_ymFRY_(}Te*a^3=AC?eVU+b)kt!ol$D^_oskvQUQzm?<8nOf
zcFt$I5caP{&VORGzDG&TuAr2v{;DAFOLVT$Tef&dI=6=~Y$3X8@~rA#*3@s1d?P4(
zp|~_cgOvik_fTvl>4~8NREb350`1PN%e9o0@stz{CO_GJ1FM?a*Od__sT?t+rIyMo
ze9Ws867f-hV%YgciXfcdVW)x)pGu^!=ds=xU*k*LwS|Y9Cc96FP$wH1{m$~du1Sip
z>J28Nb(OyVC$X4iRjC&-L!EJqx-VYMwv*7m<9g1&gPfi;PL~_2GZJO-rat!F@``n{
zm}g#b?SSGM>V|2%aezRk;>jl#Uu8kHGVIU9yJArWjT6k<HJ#{OcGMj|%6*ALx`H){
zpdZ5dCL{{Xsz*3FNi?4pDAjQ7qSweU!BVR*gR>RIVK>F3xqDXEuru}4gQg3gxQ@rX
z#+?*gk!F96;Hl71PgU^4H^x66+dyx_Ox4=ngoX^$xTno50xKWW5960)ne4j)HJTyT
z3CzCdYlzRh)f8Lvo&_S)J$>0(!qX3;9m$|4Rq;VedMz7g!`0y~L$7?7JQD}r*ku?y
zpT;79Ym|`Jnh*XWQEppkYRyO28~y|q9H-jXH;plqoE*pf)s0A&&xIq~THD3sMIO^r
zu@?&4!_0>E#g+b_7d|{Kp?RW&K8_Kna@F~7`^7jHHG-ggV_+q94t!ZM_?LB)#0cKY
zlKykYWJhGbU}x>orC7S>*-*lirB4wsYbuA_rdSim*7e4SWZDMEFd8#*J?4>r^@{AK
zP-eyDSZSp`=$&j{#UeAj#V9m33#`WgkA2*PLlW<L8^nswGH<!M$S|gih47@OC-kHi
z4S%(iJII1tRqbuEF-^!v*{9>?-F78fySI@lC3zCQxxhn@;sfzL?xJLO$w{1VBKI%*
zDK9L~Us9!TRahSxE@>1p!j0V^fU3$?ioxr9V{M<CN~X=uK0l1R>?0Ay4c3~w#;aE~
zvfP{h;BI)EoTe{>2u)U<tbCy9ZkwIqFh-`$bVO=ddBH1vY1roLO}3xlA$)HhTI+d4
zy>%;`3=ZIN;IcoT83>)y&N!<cQFD7Eor=!N2NTMQNYyU5;8PWmS`!y54WmvZw!HUa
z%dX|LxY5G1tlrtm{Hj&|VCG}7U=M$`2?1ITVtOs>3gZ(oGfK}`NjWjDFI$!q6r$%|
z(+!xLdA)82tKxj2m>Oq}1*^|7vNeJ+gvuz4(SzfxLA{#(Oah-W33Z_P<rHXojf-Ha
zFjB_aKV$S^gSC`UhHwkaW{J9{YYNXjPC_>e`gGLHHins+ZJs3bH!!*x4yBrr&M=;E
z-6R|s85nfvS6rOz8l7voJ=`7`mEv%2Ojc2rlxp8yn#owXUq$T1-R|D@q()3JX*yY0
zOx)(?q$uARuZ7Auv3N9}Xy1>`9G>TF)Rby9m|tV4vrwhn;PYZ{YcxA=?<#&UTyR~;
zVXvvRDs{Eo^SHQMS<<q#a2W?q<5`-j_Le*A=k{BybQfZ`g=MHYR+pcDOxfT%sNefA
z)Xk04*`&!mPzBCB@5q<j-p^~=Ulh%e(X#8A^(wtuK2tW=vYE@oC=?yic9|$YE3Dab
zJ`QoBRp<2ro@W11sT8N;_P!wn!RuB}bv4M&^K6VcjJ9E<$~5Dg<oaWh$AkTe|3@oq
zmvi80$oRHDX1egTnMyVrd*#MNLmRfI89UP$&Bvd&4RN^~`i}+Da(E4!M9DPk4n#-!
zrP)tzqKUYBO&>0Qq{ZTJ_y2f!^f^$5eQo+^Rg#wXZa459QArw|jnGznhEcKx{cUaG
zAT=oIY6t^isG;n(Qqt-z$J)(O3WM?YU;M&$LvQRiz(%il4}!meF~|EGx9dl8WjJcG
zk4?K8cYITLZ)<y4gA-L9j&`?gf(mo{6WuzbFk_XI8N4R*j<?x2tXzIx<M;D=ZF8>{
z(xxf6^_($w#Yh#`UNt8+>=+HG%R5@^&F7z#;(pL_RqdGWxmapGya%%mBszNzjMFFW
zC$oBXC+~DoHLT_YP0`|>4Hw`mQKztfJeI1_Za$CwRztJIR=b|zRsYajp`@5%dvQ$p
zT1h62^9}~7KmM}2$Sj#vLy^^TT+mFp#68_{;ZD-*wKnrv;E|Ko2W_X-Hrn}elhtgD
zq4y=O)~C_eU}f&p3eZk!ey!QwZpVev{QYig&xI7O(_KJ~Qrx1d$5lZ=IWy9rW}}68
zjNM4D31&&H$*=wh<*D~-*R%B`2NuoSb#tP;m4}Hpm>M2k$%RJmae17sFA-XtW-H%)
zJm7ZP9)(*|G|Q2SxAh#D7_-9VFuX28vr_lyEy8Xi)uc1uRSks`97<t1D(5V;Og+4N
zXCT<Z>TwD_q2)MP6kp0$XW8zg#?(^rn6F}fN|T~(uiil~*(XupX}#Z{LA0Lc(K-2T
zgNE&BT2A@i`qJQo74O!#B1uO~=s_$J4#QdhkF%_>o1-)E$3a}TWhdG<Ec(+kMrjT`
z;V3m4_7f{XIRiKvHmlwNZK1sR=Iz3a(zQ41k$ys6=05kxo*skL;*)1;(E=v$*YBb-
z(KX*neE5Rjkz5N!e7!gjg~|KPb1bTqD&g!Bucl=Fs#VZwpvC4`+~7jGe$HmJO48D6
zKXl{XdQ0$Nsp@{iubTUAf91K1*E;)sCYsmrVdlf))h7N_<HbaQ*R5_x%iYe|#@$r?
zT2O8yr`2c@nN;KHIv!Gs+S=_ew=hq(tEybXd)*@>r<B77mbZ4BKQ3smjt^BAd@z@g
z>{*&@t=kTfauzPVVv#t^ey+BtT#hUzvd6u2u^g0H(m1W@ylFhRwOMVHno2#nR?ls|
z4Vs#LMZHjeKxSomw+2(;IH`Rzl6N(fEbDsI{_%b)cc<pgc0j7Hw9&Gw%IVHxt<Y=X
z8ed9sJ@%z))iJ9&{!Qh1Xj-ZEQEy<7_MEkCh0}7J#mjw6nU<BAjG89LwONa_`Lpj2
z{Vk1-D=uiHJZukLUfQP@+A^1lt7N<dmbf;(%%5Xh*gkeWczWzCax0fMKV)w)EAuw)
z)gNtem7D(P>`JDb;=b03+8ItNZPc(kSgD{`%=5aQnG8z1HXk?QHM{M-I*&UzxNS+}
zuCN2{urG3X=#OAN+elv5bes##2;0X!j#6b+(kj<oU**+2O<Dun`6esfp2mJynmhS<
zuxQHhGof*P0?q1jZFonk?kICWrucFJg9J(2p+AS$@@^qQQ@inMwI<E|798Tl8RzLW
zsadg4bw8y(biw03G~ef>AeB_T8S&&p>iys}r)50Xg%PKg%k5{*hnr1W&*s&D+_$QX
z4@YEO<{tOG>Z!FB!|O8}Jgho5Mkn`+4&pafFIt-Jk893D=5Dv1OR`eWxm@+K=4do8
z_v3lZG2KO0OvZ#YUM<3ia9v*4-rTQE&MFS=UR^{c+Lhi;yPR>$ylUD_^;Klub4tC?
zYKy^dSu-!tE~`6zu_t-j^%`nernPjaj-;pLeW1MktcQ)XdZdG+*Imeu96Xne6eosT
z<FyPX;Y+#x00bx~D02PZwVBg~a+j9ZT^dx4?1gUl?bWH|a)>&Kc(0Z!53}OYFoCMF
z-CwlR>?9-2^g-poMST$96i`s!t;Dz2$?B0BM3NC940!J(m)oBM#ptBVQycS<zuu?@
znn!QJs=|}e0Hx`4a4kF=vBXo|^7>itMZ1AtbUy)#zlvX;vDXmSiNq4(7;p&0w*D6F
z{U6cF(PzlVP|DGzHGsN&x~Pv%T2fVAJ?Z-k5IFb!vyAtmjatC*b?kr0`U%m&(eYZK
z5?`pM1J!_oF#O-H_qj`!dyJY|ibKJzt>|FdHg4zzv0wKNbwdN=R1@4@YpknBCJol@
z&QR_z$36>}nhfi0piR#A$hTSP9Z~iac@5Mndk#!gwJ~yNfBkJzS!G_H@$&O28%by}
z@*uxo=i-G!ydxPN22D&PX-(Z{%5OW-A#D(VHltcSQ<Ib1XkV`H?zybq@bO^N5|4Uz
zpX7W^QwDQCeG1fLK=bsV*@$U8%E1_@wwODvGl9AqpP6Czymh|QnF8v5`QAEIXq)N-
zQDP6E+PD*mMREzbbf%8(uR^Tt6c*_7@2cO1rVHTF_p$>8s9Bf-0H38PuW>yN5D6Jz
zIi|6WT-aw6-Jl#ji)a=pb51XS0Z)!Vk{%Nb3z9<d)QUMoL<C0YmbpBu`=WeoBMfq+
zm3sGRZ=hNLr9_XHN#Nm0!51SPpc<biU^jz?NEs@LZ*C^=j{xvrsEBUQjJ}P_f#ogj
zb$syxsAtX(bZ4%7EFL6pu9+zCXnVa2DiRYpiBw65kOrKOLOxoefbvQ~7*tL3XV`BJ
zv5^JjBtu{jpaA)lM+Gzh>`gNf<DQs_o`NU&;Wxz0Q^46EiIKHj0P-%11&nmSz&e<K
zfNOP;GTPCZ6(9df=ur`UDh#T>jeZo-?zf0SfQVem$4#%no94=iqqDPWIE3}@lt5_w
z03u7L%*Mi$N`XdP|J8_C`8WX`yt%8AnCUw>2)t@yaq(LuQLlY_4g*9ShzGhTM*bFu
z77&M|F7o2$t~q$Ek_5`Ox4WEyKUN?Xf<s~m++(=BKat!v_F`iJC7Un+dNg{TlB)o7
zPlin0+Y=JF{@+xh*^6l$+OYG3_>ECxrkK58(ecgQwg2>f)woUcmj98V?#Rn;5<mR`
zGfB8T>Oh|qaD-V(7lVDYm>&7y(CIrxiC%l5rjNuAM1;4E6nl?Cg(n>D*qN06nrM(g
zUqCL54j=$}bM1mv4dnU_3PWI$;HF6B10FeGP<hf%vN=TCcmRtWKTZKE0RrE139Y6Z
zA&3MCumTC8c}~IM;O^h1_$$WQKAJFqYy(1A1CGo^dH^;<|Njb*aMM0HIXR2`4=J3R
zw0P8B!~K^+T9ytFdQKc8B#ex5c?kdEH@@FdATD$M+hyJZ%%*${1s{8OLh-1GbbMQz
zp1bKUV8DWWrEHG~cZ@#U*)uAw&CSaby8-M7l^u6>@`pSkg4l#lOCU&404Sh3Gm~(y
zVbd3i@5cz)fRc`Y4sape`tt5R6L!yNWZ;Q6^yqJbU|vLh<l$FM<ibW5qv(6m?RXIA
zVjs9+00z4CY~dkv4BG;%#BlIZ*-D693PK{gts2ppd?tYm7XiKcAQ92sd*1mNh#~!p
zMcW~loPbK+(7H4bk`ex6s}P(;fMMhgu)dx7#|O#(?#cY!H}Z~sFlc*$EYQ+l(RBXs
z1;#d{BM(tx$Q5qLl;HXG<N_BYKaBrI4SMbJ1ZWK9EEMG?R7_6}9t?tv_DdcEJYD~f
z(R^^UQUH1aM%(hwrjYpNkPB3TS_4x^jLcO2t<}5F+W6uTAbMjc;)>CkSxgXZgv(z4
zCWk)*?s~SF{l=jqZUo@{=X;PqmtGBg<=~9XA-7AL{_FBH*S=Hl6G9)s&<duN7y;3V
z0Cl}3ApSg4!X#`~Mk{15h&(wnlON`P{&}-nCG)*#wC)m0Pd0f+Gd&8V;{r%bcBBGS
zonRmzojF%&61CY0QN-WR_s&ys^TfnNA=YP)Mc-eX=6c4PLjnyH6akw4qeAdpnzZ)x
zv~Fksb6&agIQeh-su(mLDE`x#Q)D5|3^A;s(5K&xxJ{-GO*MKb?b7nz$LiU)?^`)3
zK6;=deguIkJkY(43=r7=usWdK8~<lL@{nQO2qhz4{$_E&d<&w)Vvoc$jRutcCfRXL
zkzF1_{&Glc`<@Dv2|kiJ;GTLHC~%QrNH6C?K-up|4%oLL3>H$D^dDVAChaX10q)MJ
zs;XiE)LA6I?yLMq>yQRIDUKT@3t`4f5DtJiAj$&r7a+!O6J_2J)KkX-tZYC>2&wrW
zpK{@}_MH5DaeKkv7tHz6nACmX9s>sy)Uh`e1RiBXrBj8P{brB7rwwhq0(vhDzG#8~
z*t10&A7u|_#NwM!LMZvkqP2m@UqwVYzAWvGY5b?pWSahl-v2szvGCvHkpT(;7*6Do
z>ZU#ZVJkobzEr|2k2Kr9PlQS=`RJShK-ur)3t-Ow-ENQ>mVy4GYe?ZVI$$z2b#*gA
zNOE9c=?u8`)XrKKWbROtCQkdacmC&^zhTr25Fo$Gzf;=_qJPCIq!I8TQ2!@ny{X96
zu5g;acm>=;u<$FE$D|q>4f-h*0hi!U$p_RsexS6Q8UTtn!xtl1_bu&AURb`2L)WI+
zj>g|3U%Br?sJGX7sStqm+QrQo%7`cO!fVNY2lQ9iA>_-^79lTWf|Q54itZI<n#lzF
zsyZ`k8sq-}I&Fc{zXF}_ooZvskR?NSkYZ0G%TD1ZiE>m|;vBTPK6%^mpBe=J1K4GO
zFh*=v(M*v?j5#7Cz51#=`u4-0K=z1FAW;3a-q&!jSoveO-SW16|AWE!-syuPiw3R>
z(oxH!=K+8FFW>s-a9^lMoc_a|o<Q-D{9if%JSgTrB`7W~u6mb=qZh-#+6ZJIJ7>ou
zfF0R^(AhIvR0>b=PO=AYZ&0%Vf24UsSDhWuxd%}FQyGxAcnJN?oih<dfWF}({y95{
z@%hyLDGS8nP~orts_F{>@$|PIs7X0Fq$4kZr!7#Fql-@(dML`m0d&L1eVsi6{~i{|
zEB`de9$8qxgMC&{B*dEQ1mUc+aDX_X`Tt01V($bzo7!P3AaJu<AEFl^;di7j^%!ca
zw(Z?Gse6R<sz)hgo=XQ)qW@UL^ft0;B>ZL}^uJR!*+dD{im?6FjWRN@g{La9|Nn@Y
zKhq5NJ+en~0O<pN|4o(ffHCzgCY8Tx;jvlkICLL)J-GF3NJG^7AI|v5$$ya|@J2N3
z?L@OoGMik(7aTerK4IanshMKxIRdefod4HxnCcrEF81bfHCr$+G0m39-pA|cgKT4S
z!oj~@{SV~qEzm7bteSMD_%4Kx!1|~I6ZphMM;k8{78E#l`caV}&Gmx*=2ws8@W}W7
z0x5wvURQnEm4(BRk*Iq&-+i8@NE^|im)Scw1X!xs>VxVa#Q*Ok51{K&DbE@01_I98
zI1d`LwEee-E^n`=S|&fBn?MrvM?d*rP^u^W;u7_e<oa{^o4d0vj=ha4v#B@yy-v+1
zDF8m#Q>mw^gd6Op_C+fXT&9(zukJjxfp|#pp7Y!0f5ZeaF=yZJarcj1*@=xVEO0w)
zD`e5EiSk}Y)RBL!j~3{AjCbD&fd~F+U&n1Cefo47h~C7vwp%eY86;nMaC5%L_z*+&
z^^v@xt|gp<zbm7tO1#2i?=edAFnk6A*4J8e=-HcJDI3uefLh_K*TgJRQc{^XfWvuX
zbqbNYs*O%eQ~|&F&-9<EFoKby4@%~jfJB3ILXT1N=cLbom67b{w_&(k&ep(iNh}t#
zjV>ph;neEY^VV%II}swtq!P35<ZlYAgU`wKZg8v`-z`V+{KH?Mx^o&D+$IVXJ8>&q
zo#o9l?@0GAW|7cH-*3XBEZkxNHd;M%tqOA4b9-AI^MNOA=)bbUe`K=jP0PK(ze5$D
zgxCE^8ot}^H)gZR@u{g&^&06LOHUkIaq)ojwCl7}{I@Q@B9M4*>C&jpO-*}$(WC+{
zT50CYT=FZXPDX)LlAc5F0#lLlF}rIB`_n7K!ne~a`AYor-I<Tk>A|?{<%NZXocugH
z@~1dg*o=nRG7A3-i$ZzN2Pf<APJa{8lyGoxkUe#;xDIKgk;?qvoCfNb<LRc{-VN}~
z7n5h0BqSt5JU2y$CWZs55{W`z|C?ujr!C!5Ui}*jPD|7bQoJ{FOZQ5x!20z6GDeOO
z2#}^zAC#Nsd0n&QS@@u((<W~KvI&4!l_>G!^PzP49~}&;nhpTt-E~E6t#d~phDo4&
zqn4EgJm3>H2&~M^XKu?!t6jOk@=It<nH5th_PDDzv!UzUon(Ks9VWdpCE4`BsQmB?
z99p2jLF?dk;z?tfO<Md_;+Xs3bDBo|9@H$N08v@sSqI+`a9A5l8;=7ufjd-nKLYfA
zJda3eG}ZnUP=w=D@^b5sXLsssmO|yVhTjl?O$a`g>p7P}wpGL?8gO>@udFa02GVRP
zjm6qu($SX>Ps~0{#h#ihpQa{h@TvCtA;|B;SQxKV8_a)uec5WlQ<*=Ryq+djZoPt{
zpY=7wXRg{JNpqHJu62f^VH*L5UV8HPkW`TUg@otOKC!W{KjuV75Q%P=ib<b&hdH|J
zO8+>x=%OytOuKMfhG3$8kjZ!|6Q8ErbN`)PY~)aVv_W4iwQ9N04UKNTc-rep9`o4^
z7Ad$dn%naI=5o~$rpu5OxPRB`H_4<Znnv5TUXpjSNG_JX;oB%zJMt?c#6f1S8z4Ia
z#Fcg%(jGbJ1D~EFe9G_^-fge=vDm9n*|PmG#)w(x*RLvZ9Rt~!f%4Ya+21cI1b$xF
z1YZXjZM8E*G!z%*9yLv8o2o-h&i*m$2C}2v;N<pr^tJLn8b+;^lOHhn&jcMn*Ni+i
z2pbhC0jd%jftf^)*&P&AonfeGeSLi>_qhsp5f+`!ch)8Mth-L<PSKxc5rBC{(S=1H
zQ*S6JTZuH~NpcC7lL6Ksfl>h)5D1&w)xJiYPCh+Tl*Yv}Hx-;M{!byMf~QW6d4sVT
zJINpN^7G+DwiSb)hrZ?Juv%>VAj4hN)Wn?@V&AZ^^#L94aV-F3X`qvv(3bmaGj3rJ
z&^w#;&yykErHKi!GC*(SvqLu*2edRaZ@HYyzHfMp68xD0yFn^d$IjWCib2yQ-pik#
z+Y?L<>NZj%BO|{eV8VM<$jJZI%T#dXuLPsiksbNKpdcBZt6xN-q0a>Eva#$6+<_-j
zetz+iW50Iw6)qb={X|d1;}QZ;I%9b>;90ZIokjp<bndvFl6h1<6qS@r-dCDUm81_l
zU1#(ERnb7Vyq3qABC!6g033jucLXg47^Cs96`|{`+b(s@d}d3RpOJg_Bk4ntzwgu_
z_qyBBhCzB;G!!SVre@mDdNaVfM5R_K_OQl#e-x(k>j(bGOPNo<=^yIX*iLCni4iFA
zV#!PUMoLn$uRQ?mYzddm`nxkbgDU(VMQ-lzd#(hKy?BMIqM_m53P+agIBNp%PZzG;
zdk2RzWo-{I?=f07&F`q<4W(U6>4$OlqfFugGjlHa8;g^{sC4v1-FyRgv*XC#Q+fDj
z(3+Z>Ue{x3Z*Xu}0RuYhOfEDynLXtDk=Y2bvcNqq!=SdetzuExHuGs|DJY8VJ?7ul
zBZ0uZ@~sk-IE@wz&Ct)q@hnfw$tfvp6<Dh6(c8<mYy$5q=&6hEaZaKuAPEix=EsFC
zs(~mU5bap-F%|+8Tzll1LK!wZT`CA>s&2R}l{I53*eM(6hL`7Zi%SUCV0tf$Vhv1!
z<QW$ivdAa`s>rXQ$H9<14#*4zh(C#8MokyckW>@-%_S}^t`*sT8H;w6apOm4*Pk0<
zagt(*3R&cJc&;3$2W&D!8o^7Fss>VL?{sGv8VPTPt!{H^2N@?Xtno_vr2OudoT_(z
zB4@9A#BhV|<b&iUkbYzfK0+Y9EW4#JB;iXtuFxjy6j*V8=qb0IwzG!AT0}?hj)oOW
z4c@*g#qeaKo13QUFSsO$4Vp08j^bJ#Jil*|!>tWTH+1Ue59R2^Tl>ZcZmAz_o0H2_
zKSY)-8STsERqTI>d*;Bz*(cytY*T}`Hv?z63d*W&jSqG!u425-lQ*Wn;L(os({4Qp
zkM1Zs+B>vv-z&6@qeX66Pn~+(5hB+cV7y`%lRCmuN->`BB_+ysG`0JC2aW8OrR1i5
zYaR*SbB}S{wUXFjQTsHPhp(<Zg(>B}kI>`O{T2;2z8J$<*gqTLyx636k1>0=Dd`<;
z=Os>ns$N~ym)ySC!QrxD=kwyw<632$Tf1R7?&A}c{CL{QTM}{RCgtNB0nV1!+OVW|
z=#2*B-i~V;Y^$jXR-kz6&+mNacTya6?38&JU0ji9PM>Ub9s04Ex$MDg8B_SJnd(Tp
z-H|to3cRpULffZ5rc@Ell=b?eAg4QzU8&ZQ<i@{~74hyjQKgsZA6E)HC+MpNdhTd$
zyqXfTzdTQbM{}h>JKq+51)xAX5FJ&4EeT0jdGXs){sT|FHC;|@TQcuy9nm+tq3sx~
zjF<LItkGZRrps96Gj(Q**iYykSV|&B-e5Edk<Ej}V#{@cDkSDOLtMe)epYF6wxfPp
zUVHgCxivhI(_fr&vIaj=6w@7Nj!JB#iuLp^55JLIb!uRL8}K^w8Hc-DBo%H2j6ByW
z+9ek{wn>tssHslB8KbC*$%{U!$h>w#(1=RUbRl<xh1bX0>MkyB+j-G(av_t8J&Vmb
zQG}*<kGv<VQe+5%b0GD3t?W|X-;S;wUyUIS6*-=Ug#nw;)K@#@M95j7K^?LC4%eW4
zB5gBx`&HK6R^%7HxkYOe(bLZPZuv@TDkuE+MTJ|Er9@$59l7upH*dIMvK}ZJt)eE>
zu%6S2AcT0tAd^0Gr{RwAe`)HPMoaQ@?#{k7hiJs_K0QSIp*-4JFUq`>^y<TQnDL={
znwjIKOuuk4-XBK_nxG;%TIX=AMy~jxLSS2JL9<O46s@n%AZ&6?)q$J+3<=+!)+rm2
z<I@jEnUuE1nNx9rm5{n`Fp;@@9y?VLQec-1J7RHbg}Ny2Dpcc(anAld8OF^{EMYTe
z*M+C~*uaYs6>_yzC5Sh~#S9}KfcG5|HN9`9IK_D6LGM;V21$CQ$TIy~C3))bY$9>n
z=y=p1oq`QfdD>yg$Z%^+t6iV$zKg1<h+kyqHre_fGIz0=wa-Uix@I5Hd-c&t!VS2a
zrFu>x*MRSVtx3a}!*<!)kO+5Tu6dKW{-7`Afq$F>B>^0t`gKbfzdYJlG}A7!eKHMe
zU{ldhRYCwya+`a&MGNMY>-NXRv(-?K37Jm3UV~WFx$ZC2>O=CQ2a037aLVJr0t)Gm
z*m^-!Bm=9|TO4|cI+M4DlAR-hA{ei>t_`Rq1GXI8hTL+c4(4qXcq(`qrX)J?0=d3M
zpHX$hmk{%8i`TQ5sY>*Eq18|W8wP)&lHD!OK+d^kBpMv|GoF-8oIc6Sl)XT5OSE*(
zBQ-^ORjM~4-;4-P;x1SwnaT*%=}r}3cePF+DPFY{T&J2R*~9sp;J*s%$Bxb+V$E#&
zZ&BArI8C-M*<Tbaoq&m(c53E#HpiHjsdLhznFD}MBED1fXf&(#%e&55MF<65$sdRP
zRo2bhv}RwZyDfiur@|wpl;#|4?B=d66?Iw+bk$7<VpiCLp5`1u)j@grXQU?g`di#Q
z;!9rW#UVYvxPev2jPM_-g&mO3JTY%?tY9Ow9v(ZCuZ?e)5Dm`D$ZMT(CpV^0vH_wD
zaS`wEx`}myTTD)cPiSoCp_y4GyGW@_)@kw7lcoI^PLln;t7de;jd_)$XEp-X*0lo_
z3mDtZ2R9Th*Aejw@z@R^o3Yt=N9p<4U@CHh%cxyV$xgO6BqF|{VVc)jG9xViVmLw;
z$t^gV^GUy41?o~e7x!hiUu_lx`@`)7sxFo!9B>xD*6*y7j07)P4KfBq)W#}iQrF`p
zm4XL5rHf{lPlT2paHLIKXP(||Ri!Gh^=@mKPLd_PtlYC2X2nU6WWhT5DU&C-cZA1~
z``Tkr{{5C;R=KX1F_LkirF+lV82^|%$YI}F+x1=zbFocaFLslQCFbQKi-%C6Q#}{V
zY7z=#ZiorP^5#y)Q@m#G2zLE`5=wp0)!)$NKPdl+{)Yhc>fnN@=qqMUN<FI@DI1B%
z0g<K1kHGWP&Gbbo`V41xqG`9p<2RWX)B{4gGp9UawtZ5v8z>pBK`eWD_InqbH-ybP
zq$U?qbMF(u+4xnpQva$U{@76>ApYH~ZQ_`^Qs7oMII$YN&eETZFg6gj4;)V&5HXVs
z6i_5=KxM(%+kIU_W_VT35$Bdhdk@M8UK)?mE#-87>(r_zLM;^x=+>&sJ}>vH6>Ck0
zG~8So6^SuL5`7{HwKu)u_f1fsopSBh&bj2(3z6Lxe`;ij1h9~!`fXla7Bw7^WDovt
zQ1cZnE(Rc?SL3dsMjC<G7xN)WfX<&}09YaXO_jTG|72Hu>woe$D2BheF{H`=r;oS8
z?;OlDzT(PQd&<3XKojk{i+R6#+G~Ej1hKes^iFv4`iZI#kKEl=|Km^uyyfWnn7?J>
zv(N`UCj?TW4aW(^UtG(7a^pN|C#Pn85Ch!72^TT$pES5C0*wCR_tc@vKU0C}ekV0h
z;dmakC)~5UxUmdRw~QTQJ4neqdgY<_Qe8!dXuBp{nx(6ctUO9)GO7j~ubKxM-F&Lo
z7xwoY3_=gvX+tG8N<5lF`Bpw%+Y=geqSG5V&^WC`Uhj|24ey`q+AGD1-1EDn^r1C0
zHp4(t3_jz#hU6F#&R*A8OPLW<ivczD-jM3kN>?X4b-hBy?T@`6;N^oZe$@tJ1%VQj
zDYUtZw#H*4DLD?NH~Z@kv%g6IN;ZL5HPBIgafJeDp*60-T|6_i(o2eBxVq&>H_b})
zRGm6UW>9raZ9ydMR-XB^l{9K=L*F;ko098e;=VYG!%93BM-^uzHXw^Arj;zWcWK(t
zakt{&aTGpDV4@sq48t}AL3jgw$2NN6K&t!0s~Q&;;vI$Ks>dZ+df@575y*~3l*n)1
z5M)9r1I?pMUN>yU#bA>+qlz;4Bol+c=T#b+!of1R-F}$;?jiq$t*CQPTw<GU$8z<0
zT<aQkD1~y69G~8zW{yDgtEENjfdH3*&bR(%A`Mnn#7{tG#w>B=_Pb8LtZG~HI|!v@
z!;`y?z)Ou!c+RhbWzw_i`0;r~4cZpPegvEJeKTW(0cBV?eTiFIGL9bmW7%<Hnb=5D
zx~-k3JR%I?mFX+86*~q*5wN)S%3q1loHRnj%6YLH3L&)VxFxn0oV7Ui)_>ut4gZ%q
z(RHIl!Wl8WJMl{6b`?eZ>uDm7Skyu5$dX?2*#O6xX~rYfa2MWx3?vteis%WXLQ^Mk
zwJ<J(H5&moqPF6CAqu0pOaWd}Ct}gxV1mDfa3fdXI7FmizYHaOB5V?KbD|#-x{K>E
z=CS!vte%QQK4Dd5pU0W4r^Vu8yd#^a(^4EZ+Li8t3IcpSi_!kL(YAOWmUU8Y`BFWa
zWjbCSwAXvU!!U}rEZ)S5#f3=a&S(bohyy)kMC3au-(CwMoSrptZN-M!7mO~%-r{%B
z4EIU@aqK*)0xA+U=;gc<p>^^A1^#w)o$NmO(P~DQIQdT4<(xMYM8H0MFy3@w>fMYV
z59yLddCD-Q2kjZ^>fdRTs>bT|W9t!<bw1@8u_uXFjg6%GAvOV4XbKy8N!%?j;Fg2r
z@xHl^m^0&o+Rrz=2FvgFf-aq9rabx!nQLnd#X5|8Q6=M5X8yK3YEIjLU~J?zcG~?p
z{ndJ85#<>p4?W?{m-@&3Za&>e8p!}^p@9DM$jj#V@#{nAHLaX`x^=6bHg}MRo3dP*
zHfjo~<d(%~yh34MF*?IvIVb%%mnts~W`OBT^`2(KYk$$win7r|wlrQ}_NK>{995nc
z(n^UMadW3K1bGCDUgLNZ<81IW+t$Y^!~R(ik59&NE_Jt;!Cf3c97EzxMjV(zmz^uf
zoQ~xe`BJ$-4AH9FEjdvL2v`Fqd-9+V{eWO!lVKM0fzvJ?{OoC3L!pG=T%m0h@oxNg
zewjAOoRwsr({G}^-^QkjTSDu)<z}WMWgpOtPnc@^@#xCq+EZRX9!EX2G;umw;P$OT
zrFykF%-WA>f_n!h=QNfH2VT!vyc4sbTfl%;{_y;n$%_}C2nJeFkxgE``aIVLTf!Eb
zZW!26i`7bC`;Dq6l8VgHal<g-#=QWUyXC@M`Tp3g1qN!VKFjlp#o>)n)MlLdNmfJm
zhPVO@2za|&&bLl+>j?c~>vWJg8@6AM9yyHh>)D%=hv!l>N2hMQ!DD;deN<8_hq+2@
zADkL2TJm#sYsR*v93+&Q^<&HLsjO%}T}QdhI#eb29TV~F#1-Kly~XETS~$gb`Ic}l
zn7pVgnQirXyN^P-gjK<-Zh_5=IjF#chuXu1aVTif`jO<9idG9?)p*Z2MWVJ0F5|Rf
zk598aQPNnm)F~@>RD9K)$3rBfLwSCVsZd8+H#SU|j-LE#PN=Mvp*;`Yjq`V(p;}9j
zslARia@ojYyXD?bsH<|Nl*U%MTA(^e_HngL<NN?{+IQApmuT39R85wFNRtL|J@vu5
zOUz73Zvu`6;X5cA++2}&)$7@#Y)_x0Rr|T$pExv_8sZ@cP4!46Qfp&jf3dNRJ40%8
zKcX@y?%rTM=o5&Z^qWWbYP{#(A4jf|lueA`v<qpwbUo<Z8HrnFkz-F1I<Q)QXmsp^
z0erjp6GheZ+y}_vpm(uUZ24FX<QUq!S&uobB<ulksUC9w7@tK9IEjY>X7L>I-{Hg7
z2^R<V3=2bX$v&i^j#vyhxQ%V5=`-Yxpa1%JTP<vGU1c=xlliZgNI|MNr#?F4PwU5^
z=-u}F2>KP-<2s8i`On2JLlDx{?Z%EV14n)^>4<}ETyfxDxWSv)Fj3ul+{Dc86`Lz}
zT-w0%&x*06ak|6(9xMTiOZ_K<@m&*K@V&Xb#8(?#glS|0mWyAux&~gAp9(Uun^_!@
z4!pR49C2Dle=LulAcX58`~0cK<X$%HCc0xO?%|GSkY}%-;j&VNx}^GvR2O+|t;HIs
zm}>2Qspo3OQHL(2G_Y%hH^s}6SsbE{?}d0ke81?q%A-0FOVo=naO5mz2)9X|2<Teu
zjXJ9k=6dVUg;{vTlqVsUQ{(VNyzrMkx=@9yomA}cREy|F7D40eu0hN~@dJ76mhf35
z-HbvX`qeH4Zdx$SsDT$PM4zG@+rdM*RCT5kY35w>WW1rS4x~?g#P9T34OIs+&+1aO
z?s__HQPSejJTM2m=7LKqqH~pR<L56}#<-tUYM${l)%R(;x<R_4y?W|8G<np$MSN9t
zrLBoS%DMgl|8S<0zyX~RIXHTIpE7r(5m7X;GF!8#oUsLs8))+#^C*zYAOXy}cU}>~
zu|FL=t52}do>ii3QU$(aPDn1%<`O?24>^6oq}qGvTliUQ%_U=##f}+rJH(+M2aP1?
zcrHk%R@j_Z(&aYZ*Q)pZ$GN=h5*-ta5cgEk+?(~CZ6naa2QGEsLAfl(S`%CQ5c?c*
z;w`y3?iIZ!-zH*rVZiQ|PYf-WvK_@eG^z68W|Pd9RMqO2?*&&dS!%O0CB8R;#sFIV
zyx`=L%-?x0-HEy7K^BKKDDNn9s=!$J>awyty2;4h^;M62!J6`n<_^{9U^V2#J=D=U
zR{p{pFNN$*Oiw#@lu{_)(Ae@9Xs_kSD6Fj_I%F~;O)w6q2sOR&lghwK=XgHrT4mT6
z_hF}IV1Vb2FRRWa*J*W#2LNnXabRMbHbi>5&CB}ebGVe_AA}{dd5G;)Qla$s>ZfB#
z3<|2Jrh<tqR9QU}dK0v`pkw<tHBRU9vGV#or=&amjKj|%_ow_+Or%<4HxMh|i$f(1
zlB$*aRZ*xEG?fx>z^z{g63s_^omLsT{7g4_5)&Kqifg8l)1+nliVCbn16x`!=l1q^
zrlH3g9yr#6vMvH9;ekn*CbiPQbY1eEa&WanT0;)AGMc`>Ih14}D7Zl+%Bf;~Fdefz
zdVMSOr$v1Vq}@-4&~Ev+b6~<Gbdu|E!=$9ZsW{})55Yb=&WWF5eV+1I$V4qw?k}if
zes7;*Jz3Q3q&=5ZH8gQbBnXabFyA;u20HnkCXY8|to&%tatg_jlE<D63RNrj%a2Ll
z^Lp+T1GS)T6R7)WD6*osFYq8~p6vE~?pTvQgrK}rrD7T2Tl<Iyl(gMgA$9-r^C0=~
zTtb^C{UyACrM{iLt%0t^<5w#^)0gmU3<PupkKeesXcb(n4QN$l^h^yt+S7h;(6fj9
zFK(f0WI(HIqHk|ZK+nuZD`sF~WNc5sOh+qdWo~8r!CLpD0j-dMqsd1Dc~Jpc0TX*W
zSp!=^D+_BYO9M-L0ybK4OM3%bYb$eIdjkSPU2{7FS`ia-pomrkQY2*X(MsQd_Pv3n
z5ikM^11l>J&rA5fayln3B)cR_uibP>-egMU=TsV3icd0$Wrr|;<d>8{Gvy)yR3LgS
zg-Cvb@C<Xt>e6>0ippRMU;`ZWotnvz-Qd9;$J9=p)|6eE(c6<CE5ms&uF{j_x}mz#
zGdquxn`9&R7A(Y}lk@$BzFa%`LADK~CKNc|31vU++X>|f`@Cit^~#4U*%%9Fe1^cR
z?j^5>5X!Z}`m<rj5^{e*=Cb1EH;XXJ-FMiRr;ttgZy&q%7aD|_70;2+i;OQe(!4xx
z_G&nt_HTiG^41cC_cYgSffQOdXH(iICnt=IjJfX<${qLAj`DxX&tI$Si^h|&btyTq
zcP6RVSb6{_8;lGLN+lWraVe%Zw1F+!ZJ&@nE8N{(ES(%5zd*#2ZE`&W?uerSw&yDp
zH!u;ND~{Lpiyn2!?S=8)t5loMJVoLPkBl5`a5@OVVbw$Cl>h~O-G^t0B|!Ps7e{+A
zS**U+8}s4A2RmC^&)Xx>0i_nKP78kR;w9?1@6f!rhk}ldEQ7_W6{I;T_4c6s>O94E
zYUJ0bVDchhyS-F4LjX8s?Fz=Ou$W^hY*tHGF;*T<u!isRS*QIj9O1DV!fM%g)Cn9m
zfx%$8$a~=#r0>?r4Zu+q3XzCHp;EC#422Bu-Kp<Oob2As_d7G6N$oR4HQp&2Wcat-
z9tRB*s~h}szPmpA41+|YUTwZm@8I_mc=grA661-s@=6*at>Z3mf;^ba4jhO#Z)OI~
z0ymQ-B_V{_zL~ClUf3O48_hT9U4!p&io#^F*yy4SJa1mB+9DJY8SBMFql+ElXqm2{
zp?rMc*OlfGye6e$HR04$`%&>JZ7=TV>7xeeYVeg#P)nFxxDj^kS@oPHrD6eaKzT+c
zoeayAZ?wio8Z~~<^56+rp*I>dmMI)0mBfPKQEWgCkCK)p8ur3ZhU=&uI2LytNMvp$
z!L_Vo#qy-@S5@?3ybLl2d%yKK9r<!pYqKi5)T3JWEG#M)kHfz9=O+XT#R4Ya2IuSf
z&qazEFvkz)b5@i}Md?4-Z+0u9qN0Eq*STF-GTm`vDJ`v3H5mJJyx6FB*zwmwJ?|z8
zGoP=ezh3!ajd(c%<tq_U-S2g|>_@MlK~8Qe_~i=@B0f(eFjWAXJPP<KN=Oosrj?wR
z8mFQv3f`N*OIFP$i)MPr(%u6PVw?)qOROzcZ*p~*D$$gTrBeN-*HKhN1q=7YE1gO<
z{svv&dZY1<A{^nXF#rU4dDLj<)-P~#HC9M|^;TRv=}LM&ZFcu-d1YbdSWs4mhNMDA
zU(lq}PZ5EWJ)p-|u$(4+B-q&48_9OBNC{`eUAWZa!A-`jC{wVn;ZoQ`p>*F;GRvu(
zb4qRSdB203(sWv&@4^+lsdhOr+=IqtMwrD4+tF(hT4$T$fH~iJfJQDdAm@Gx^Qv8{
z3*U=t!VWkbm!@4+Q7K8`c4Hys*eR;`sX3M_6Q`N~Opu>X3f;5!JHcJc&3*$Or=$5?
zb;wgSI{weCh>E>O(Q0oc4zG7gwO!U?a#I{<KO^y60;hj{+8BNgq+Xe--g2^E17HZT
zpGSl+{$%%xF(sjZz0jdsuF<i^h-=f*=k22Zs?0`y2_snjUXcPBcnT#xS@0IL|1Z@F
z!x^!2!K;<$DYP1i(&5gvmn%Vjh}fU5Z%&J%j3a*XKYgoFZH^t(s$3`2;(2#{x;0j)
zTnf+%?@2!+A9n)S4c4<;+mn2zNj9f2%V&i~vp(5r(ZyQ0{;kH(B&;!$%dblQ#s_mX
z2X;f;Q!QQ(l{)_Bh*+dGty3Cem(*&N#>46SVw6$XKe4p6UAMp_lCfg9Y_ay4*HCN|
z7vDw!QUDc=<w&(zMv=+*%KaQ}d&P;!BrPt^pPm09*U-plD4zZ!;03yk$Dg~ja9GTp
z*J6~rZJ=%g$WjUL&OsR+OqK}iw3_wk43l+sMzHMB>(d{@sO2)BRi(m*(&Z-GN&sio
z37-)fookn!hr>F<tbPtkXZ?7hn~uh8I*!F|o9$7Vu|f~)FbOt-X-D4jCkiD;32sB;
z#iitD0LW^>K->MO126{C7!MUzzwWx6C|<|cI4xJu;2mHw=EYqf`6FxjhtKbx)Hxrm
zNT+aQtQOZSy7p1mXgEwLf+gcbD1NbD%-b!8h%J`swvkFD{?sVi-W9qU5i!}HuY(OE
zB$xxdR;l}rJXq$_N94##xh#?F+bw|MxASw4d$uvPoZk(gv&odb6LEFDj-^&d4{s~6
zZ!D@c#V^|?8NMtmZBFdwxA7q*oBpNd9i85E(0sE!S&T3OU2jw39P*@m!Rz5}lM*Cz
z+<bf72k>DG6am)Z@mid=bm+Xt#lmT1d%&21^N8Q)R~ox5MggopLw&(6C+n~-GX`-H
zPGVo-&vvGAF@N3yFDg8SC|9NIJHNnl<Xt`%#Zs-5pTvl^wzjBG%in~L2B?bJafvCR
zO2;Flp^B9@oxd6R<ouKIHrZs!{p2NBVnDA$wo4Uhwv+|CDIM+MunkErc5N7Cxg$Gb
zm>}G{>h{)s9U1E78zzE7CX2pk(C0(%GGe@K_ro7B&)!WE8TQ7}KCA@M4koeEva(`c
zy+aH{+!k%vf5QJU<n^cpa7#d%*FF0H!V6-H=Xa))MR2fo47^ytIW>tZUQt;BbS(;8
z`yum`h9ER5V}SAoswvtIbc+fWr%v8#C})XUl_|jaZMoAQ0^`m?qY4WA=R+8zIv%X(
z!m+=M_fa!2UZUAWMn$Ps0tdh<-+PGgnqATZp8sNp?IBMkd2__vBgU)?{qv+1fi`?r
zA0|-N8l>xpIU7krhK@A;@y@zG>XpiigK(!ZjbnxC*+yg+1cdL{5_>65i{eE<*u~x>
z@C$fO(EaTx7-siVnYehzxPl`zqBR2VS!nzDn^c#cCrMGCzZ)JbHl?{Aiyy--GI-s)
zBhqYIunJ$nqkYGt8mI?|CECdGT3J+8Skh9M9m)#MZABY+WhQhExgrpP@#*!Ry(AKU
zxh4YTVyItiWH-i+9kAwh!@jGAWwhM-bzJpX^RGzYG_-1-v%CXC2k=V;mGSIZJ+1v%
zXHEHC0N2o8;D~kK2nhq1c<(PiYWCF=QCSmDTZBtU8O>%2BXCUs5hj(}ZAG;`y`AqB
zc*@!qdEyW{I_%SQm6`I-@$I#*6rpcdHA;ZsLoOz{7ye0d9d!b|$n|XdiPNElGdelu
zV0&3m0+jC<w~wuW?8Ok6Ub1^3@1E(+CA!=ZtwP%O$%X7sT!rh`+l8fo$%I#*?qyiD
zD#0m{MNSo~`ys$unNyk*gFpe&x<Hs1CG*5Ye3czBKG`4iT-Q3L;0bR&(5k_4kK9xx
zH3^v*gF*pnI)I)`v^Ie2Q?|rPN^YuaV3LYvt7Xi9KJE1zEa50pSc9)pM*)OJsV<~x
zLYUldt|T`(Sna7uyNqFcmQkIb6DV~{rM4nYu+q|om31{@UHRfzdeixvd%~vU9tnBq
z!269BfA=ao98BRvqv}lo*|oE?QNi^Q%r5IV&D(TwU(BDRZT^g}hia4r*_)FH`NKUU
zKD`I-f+9gZgigB*ipr*Y*3Tk=`gYt&<8z+Edt~4z&D=^-g;|mGGgFcLv5+^4?#<P7
zg^k|(!>b^@hTn<8xxSf|`&F9FlK#Z?qdPwqZ}rNTwl+B4B_>&$kr!wv=duK9T$?NF
z14+d+o{e~nL(#ncly^LduRANqLn|nhio`51Du%HnmU5rwp1|c2GKc$-c8oBg?jrK-
znoyP@u7wE;zctoXQ=7$5*5EG`D73`Kl^l$;*C0<_gK}v^ZW?)plr)9FhV_zYVHn#e
zS&oon)8?(~=DSvrnxvu(^Y5GDwM@xqNvvj*h?sdsRV!@c^i%;>^;R3}TC!(lP&%Ku
zO(^6hEiuhbIjR<4ydZvN56;T%{Vrb1<zvL({R)qxEIFFOyp=xG+i~#?A{!i4iVQ;p
z)pxUTo32YfruXrdaqAZ)4Zz|Rys&;>Tdv*W>6{$OOhhE;Ul^lm8yo*FlTW~<N@Zh{
zLf50XD**o(&rY159|al%x+id)bZfJ1eCo|hU&u{jqfcl;ur$CjK6q9l@YDI#HYH+~
zcaoj_R3tWK6Vu;58hq%0doeUD=f?h-^tpmh6am>TlT8XqoxeqFTdp4RPv}7rVW^3g
zS1(wFD4K2gh+$Pn>+#{#5dE3n2ZX2djZtsskQgHd5cV5$>qwv)vIylL6Ei!!4kKW>
zsB!CH7{?n_hhYd|B8%N`y3h)cv7y-x_xMFXCA{tSf7p8qpt!oNZ7>idxO?zGaCZ&C
zA-KD{yORI`g1b9GgS!O?5Zr=WaQ6`0XOs86-+%A@?l)C`&D2ay)tu_2o9@$lpL6zJ
zYd!0ER`0{LNj}D@j~9<y877;jT&(Ea_7VQIi{L<Wf0c*2gchSUi9&d@^RB=C1|2sf
zU0*?{l)u4xd67W~<1>R_5%2tdz253c#$Ti1FLhU4QjRG}x3AkZI($wH!mnQhr^UE2
zsQF3O<PS@Nu&0tK)2!REwXgm9>vKcyG=Oz4tAn3q!+yZv)b1*#F{>1`wVA7?aQjyC
z5p@?uZpF3{ce|jVKsJ+4gVXhUkYjjy^i-6;kQ-u5HuZ{t2=%WK8`AP>Qlrz!FrS3U
zut3JT1mfE`S{QGbJ%8m%c9uwE1qLY+v{g*XarxuY21U1kS4?=FQ34qZ2_@*@Xun4f
zzbq9Rnh+n&;GNm^&p3qU{{&?t?di$G9vK;s{OXGN^9%C#6v{<8@*SWokl?PWVeQ$E
zWHt+QBGPhu)Lr6`#ro=1V<S3rA67itRb==M8O}wKs%O%Egm0C(o(qYhT#fJ^zwoHZ
z>`vy2_YZ_FVt@0J6ykIvIc`#D-hR$q4pk@8+Lz8y%TGK4U)1jnjdp<EE7R`|pbU{3
zI{t-TjcJ6L<wxR$?ui7Y115HzyQeVijuP4k?FFF-VJtump`oFH&c`!MnVO)7elaFa
z9B7z8r%??+edR#+7{rSFS7VG@fW?D1lty0O+QU4T3WG&>D^9JL2@x!Dh8E6_IhUH1
zCD$V5k<T?<tYo&)7ef{1U|()sP{qLQzZ{a1K&wW>A9pLiW?^scYk_WZ`3%eaBeh)m
z$MNlr4Ryqr5~ya23mke)5xHLeu8+O_h7QE8J%y#6nxJ;|2OJbiu|lyT1w0;^M-K5a
zHRLa~TpW_OeV{6m_ArV!+NmcT&zYZ!ViOKAN$(|Bxd?}CozJc7_zzpVSO;SYQlXLQ
zB831?aWp?&$7fe>LSmq(P-E>{DLKus;86!yU^_XSw%*yC&>4cI*|f-@m~8Wv3RpT1
zPtdL?iF!k7M<>ZG9Al8SV*@HZ4ZePfv6jh3^C64*##W6RDxn|XlGntSDL-1xmgtA!
zZw>M>%2zv0jUi^ybKIzf`aEx;58l4glwcB}l$e<VsTUQ5B<Vt>L(8-1=*r-7eJXkA
zXOk?1ZuS^wkl%MyH!KK|$zGWbr;3Ic>)3u`Z-jF-vUu~{esE|A)1~n_?puF*dX48A
z_#j{*qE@q6&oY6q{ccGA4q&~xdy3IY5`0N`HY`8NRI7~O086s1!4bIF4>0bb*DJ4&
zzI6jQjg5Q&eLOfOALn_~_iP+;1?44Iv}Vw`gw3M$M%*Muwb0r#q2I4xLx^hrj38G}
z;+;r@6#oPvbyZcl+xuV|>?h%#2rr!s@#4GiD|c&%1APjX80$_ngNq^t%CtVA)yJZO
z8*H|(D;5aVt7u<8XldZT3U=2oKR7smM!=SLwIC)bhoW(##96^@l#DmQKUIkH4BnrC
z4fsY``00IYjF%xi<QspX6Zr{j!#5Idwm{C;&wf(+{MNggtQ<m3@_jYIGn{l%>u0ch
zhH(gz_?)(g+B+8rFgZgCg_;evGT!KqedKWd-8%=-aj`;eTK>kTk<W8|hLV}t1|8*~
z-k{?fEGxf<wg0}p(v>X~up=z#hfS}EnqtiSa5(lE*3Epmt+gg%J)8M?95NmY6m1oi
zYAj&@ROZxBLY7W8mw9+uWBT)X_3S!}i2^3ABRFY@`$}B8q&{&?`52YxL|ieGL?vhw
z-qeySj3f1G<B&tzvLS8gME|1Fu$g!h?ulnkT+a+pUJ~LUPE(9=W&2hoZo2<6u$2&F
z^|Sa!GO-aS<Sm=d6$5qV4=<7Se7Exe3fI5_W15*gM7T8@jl@*M%HI`a{gxYznF(?4
zo(D72^?Zaw-CLOjwTg)E$=%M1i-z5%GK|#WSxMcmpp6%{7eFrZj&ibJ=q~J+I*gz$
z8e$~C!kSXAHJ&>?Lk~1w!{nhG_p1%Gqgsx04bn1D_f%;_db5Ws70f4|t#0H*#dfAn
zgm1Yu0AUO>f<mA7Y7rIjOHW<Lv*99M*)T8xgujrUd>eKVF#dqpo&2`w1KmoDZn5|E
zZ>Z#ZKXqKJDs+{`_m2-hUrIJ&1$EAF3=@uiaWatlPVhZ|YK^A$4bvX)g^1qmRvE|f
zMN(lh4m+ISS79p)IAQ-)QLB$k@Jzvx&%UN|+7U^J4x6})tcZK?zf*k$OYQN2;Mqv1
zdoPdwR1CzlR8ufhAH_wTy^bbPe}|X^zagAJJAlTOCiW1J)?vtrTNq-!uOSD^ljfl+
z92B>_F~SeWNKfHjGZV7<QNlB4=AQLY;*#YQoC?#VFbeu@Ag{b`>Z&u}>z$snm-u8|
zDyonA@dFW(gsm+@=%A93ONnTvg!4oIHwjI$Bq>Q#8=^0)vc%{-uUAJ4o_mVOLY);3
zv@Ef;hT*r|^y8mRtc_4@t3FLAVUBHbSpt(X>47)Oij4Kv9#v<_fmEB{S<xV`Ch~*A
zGOE$6_`Oh>g}1pFr6u~6_DRijb%~0R<T))TC5TQAkbDiGPO|+`w{_{5_SE`N@-Z}9
z>{mM_4oVyhp>ByeK+7AMA~@9H?Z%ov4@Fu-pI4WHACBh1T?^REv)K?KaHqT1gmQ_z
z9VN%I4!x1!50r2p!y)>Byn`+3S36~<BHP{2M!v3JHW`zb_o|U$2q|r0irs3hGXo=C
z70u?O&5}itz5zT#JT6<0voRUOyO)Xr_O%Ze^e^eX{S}xjSK=p~D7g@d$?#285A1j5
z`FXhC5Oqey&J@THyd7(%_TxNn%r={d<cr)dmQgQo-W~tIQ3bW~qg(hRmDUQAKEtrx
zm1A1F$cvG{fPMn)+Goz2zI0R`(8|oTU(CpsiNp;rKRK_6kcc*lEVub=X8DE_3ueCi
zsQg32{P~eGKc9f*B-I|>K^Vg~jqbgjZ=aN+4*bOYk9frVW5pwtew#~vQ7&dN8Nl>F
zt&|%LT4k<UJYc(Sc0Vzc;N?zBObn9i*S>=^{}Hl6?dMSFDuNg{DaagQzM+Zw><g>y
zPc9EVe%nl*wi<(DJhr%;QO5LV`YTeu{NSZ|QJ4#m{4|7$c3yBXMxi^4x-ewP%Z$U4
zv4OmZdf4G~j5+mwFL`WCmOMowO~A)9gU6-FO&@=9(IZdm$FH@Vk5iqQW6OLuV<K^i
z2ts81aQb|Z2+*9LnB4=<v~}q=;|2^})EMLHap8HDY%F@<&{w$)a8Vw0`wdIW%3NL8
zD`*Bo<?&Oir2`-cb6cSFb6UnL_e1wE!{2nG!>AJ|?aNu0-lms^w>USl@fsY9UuNk4
z#-wHuZC+{Yi2l~Z@o+B>U(%i4N%|Z`QJvHd@Qwv(M7*waKgsW?rdjX=IRag{B@TAR
zKKDbf0IoN(64Pg<8^NRq3B`&haJZ4=-~-G^x8WPw&VZp(WUN<7<T<byju@8APL&*3
z!LxA%>Pf$Zi}$jtu&C3URC!@U%E8Z`D&>C%et8`wf2bKqtZHQ?$9oaPu}u*op^NA*
zd|2|;fRHeY2O?nOCzPsclcNbL3qQZk($T5Wty{02?2AA-o>+I%GjiWbg3f*3=LWok
zk~f$)8a$8#RTqn3eyx{Mw9bBTet0E35d5_r7QIvb6OVg~*>BsdOr(8W-T9Q2Xb(n?
z3c^e7u4;kh?DYKm>iwk}vr)j=LL6r|-y^BL!cNHuyhz0%^^OyXbX()$v@ZGOEg-3g
z8XKvog_4G>nDeqLD5?wj+=v_HgWN`5K|!Z1bKq~urjP*5K)k?|bp1#SMCdu1yQwjp
zqgummc*r`T02n}-YSvq)V3a^Z+mI&Qzn}Y_!bpI8G2<P%4*&ktpATVYRmz=uF8~Fw
zkug-`j$86|WL(zGbF48d+%GmhmcOi)vdh)fIuj_WCjMYhHP6X_O*JwwkjiE;K3`{5
z*Q_C{_&tMQriO+Ozn@X}+~tL!h(+kRJ-9(kET%4v-Nif;gO~+vm#1o2G0vvx&6KXA
zd0rV}T0Ppt%Kov*S;c&E@O4o5<8#C-*^vLNoUs18aw7jEl*k)6KB*p0vWg<3qKT88
zi=&Z=6A)KK-JQjioPpp0UW+TSld%8ULtKfQg!}JT9Bd@u0lp_70Ge?I3W+V)SDu@N
z1U&wFTg=YZ8N4UPO2YDIPew5|5*Cg>-xp&iVP*R36*CFvUoTilnE!r@i-h~{FL07@
z{OKBW^0(9j8vG;juyB!p$J25D`IDQK1U&ve97j7NB@<^YMtKo2Mr9Ls=f8G&``453
zUr&-cB%o0l6Jra5w|4GY;Hyj|oGeTvYyfJL0JR9nK(@|KBwT+Th`6Jji#_Q2kLdL0
z4@M<MWk&;BC;O*MXym~ttn|NhCD5?4fuR%FMakLG#mM=u%j`tL{v;LwUF$C~>#5~`
zh*|%$#>C9U#{I7vQ>(^;?SdGp4^H;`*+PGs?5QP2A^mTWizI<@&2<3+m_GU@^Vs^*
z<*!~3FT0s0#)GvMjisfYDgC5yGaE@69AOGr-}QEmL)kvqSm(X4YamZvfqW3PC^OZC
zGyP~Xk-KnxYQ}No3Ukaz-;$A!li(i~9x7v(L|a$MQd;`;x@qCMb-}=l#PA2^Y@v%H
z^{N(>)O2%0eIjhl@I|en<e@S_dYHEWV}TJzdQ}HBNeWVLqlDJ5P9C(M7VQ*_q)by@
z1*Wbjr6yFCw%;#3wy=Xe7$N`6cPtT#gmIbT2D4MblB{<cG3qc6>#>i&JBW{4GTN&L
zb@j};^Jd)Ulq&41Z!-tg<@OGgmiBM0BcsIKQ&uLJWf+#O`Hs$2Jn!;h7v7!Na-^=8
zEPKAMDbZ`msG;?5XXlxS9C2!b(Z-m#&Fn9@W;Ym6Nb(pph2Aet)<G5;<@J~TaAd%d
zw?ObAuX@58xx}e4X?GLt1AUJ@E1fD@jL^b;ufuAYQK**{pHixqSu)E{3%xGs(~s!H
zRhzjlj(DK*0#Hz{ZE_%aGb(+&P+?{F*}M$6OJhQ%Yv@O9dEe?5=ULF~B7RC@RQmmL
zNCVTpBkhjZSsf-Rn!2+1gWz4=espEWrjdZD-7>O3+pYb;_Z2>HUn%=f58H!FV`#kG
zJ`iMs-zr`B22MyIC-fgzr1+p9+27L|vx(DtmVJ5UrU=(pyIg~)Vcn`ace~7_9yz*z
zt2OoRFfYY&j82oGQ*(H!(u?V2!)k1Q!FqtHC)oU`|9L@v@9P1RYi~9`x-F*Pw=AYJ
z&?5pHxT`a)>8EoWLD&<w6EB^<;bQ;T#P;bmb|dejYH&;A{^7O#&Aj8B2mU6Fodxzs
zpS|65F&q64m>RfPk4LE5d~y>O7%kEiW&&`>>B6<Nx&~M{utfKD8^^Q79LP$Yk^{$B
za}jt>n(a$l8*26$&kRSqA9-Y-vu}=1eA!+OAGaGQ&snSL%kyucDo}Dmw02kKQt7<O
zSKzlS+`y~B>v7&?tKqg)*87FgqH_@Jr%gx3$-RKX_b6?Ej(ks+6=`6JHl}Gxn`XQE
z$w26UR$Zm7<4aI8u3_IgW0e#|>ATX*?NdWOSW%-32S*+Kz3RCWXLrt2oUZGM+Bz0I
zZMygfX)IFJh^ir}@*GFkUshyF<MxD>p^2x;X?bQP`-xMMljnPR49cPS%mnyQ9ppn@
z>?`>PNa35~Myn%)KvDmSBd=1fUGq^7jV{Um_8gmJk(au5Q5ANR3<otPlfdGe6ornJ
zvpRpW8}B3J%8L5$=?N8iZDBm9>y8w$10Tqw%y24gDKj;imgMn!_F<H5OtF=7R7v@j
z>dfg4h4^$%u9!*2s@Ik03HT`TY%WCEMzCz0&k(=<q=6a6oo-ptjZD-Pn2E9JPBB|4
z?JSGMW;+*Ud81;U#=W$^{&~iQhD1hwvOJzDR=h|i|MKT`K`n-jmRvmku&G;W0ut8f
z4}&|B%!H_&^gbtf8@~31*P}Gw#tuWaXI})Cx!1RxT9XiU#Kf5GjAQd9W#J-!ufd9p
z6Qq5og0xX+wsy|fAUW|hoM^H0H1l(>;Ru4U%7kK+je$x-@GI!(6EFB*2b~?cW>}5V
z-K^MZZQVsJFr#1S${Ham0%1>7UfwQpCqg2-b@S=qV3yp&jL>wWhx%;dejNTSJ)T3A
zD9^CU*_+Jwwc{jZ`Mbkxh7m1!CZudaFg+Km{lOFjcfFWXaJS0g^I`M`<!4q&KhIj3
z-XODEZ(OXv)KS`_r3A$Pu#{h1qMAm;zcl?(bn;OH_bl6^ctahYYf>w$@ZRZV6}M%-
zUmCY6;~v*M_w)#P1v)<A&V=%-SZFt|p?4A0OXhty_{N^)T9!RJ638+dX!LNUBr|EO
z6Sm*NUmt5cKbSmN;g>N`Kbt?Nn<w1MEnwO){&*)u=>);MTt|8P@NEex+3lx*S#?2W
z(%EcdQo>og{dh+e@$7S1F31-c_)>31h1vq*;P*Q|nAJno`xpy*v+KK&uF41O5o6>+
z(Q--p$g-oW^NkN$baqC4ZiN)n_=ci3HSz%i9isJ}BRwWu(Z}@=hv0xb#_(s8Oc1|f
zDZiRs!hNW#RabvYLKtD4ryyArlaRe5iIOjY0w42fPnrC~Gc(taB+6H&pDd}fFHNgh
zqm6J-jn`zgI`xqv-o8T3q)PlgtkYnWt;Sw$3Y-20cQ@ZF9Tker#*ZAW<WMI2XJdRM
z295H->I>TIyemWH)i0Fj2bLDOdGj6}>J(gPQc^0~aRbdP-S%)acqQMnZA_$pw$#RV
zDy%S1r$2jL$tEk{Pky%-cUOo{|I><DXh~e!Wiw@J1fjPK>UBG1zV_}(iK|3Cbvx}L
z_CTebde-OK8LuG%Y#ON62Si?W+pJFey_AEN8D+cGE49&d+S_;M*q)e&S(4{A6h9u5
z#p)gYGcI~U7ylK6U2Kh<E$nQcf;<T;qllf2fraf8GGGRXK-BiB@<WS+`RVWf{txW&
z*3Jc#tbqL)z~>CC00l9C))_beN@3szpNsw7JlKWn4+<jT`h$E(xc=Z660SeciiG<Q
z2qNKr>O}Dgr2G?Pc>3CZJX)52J6d7pr{e&-|Md^_F3j}w4i_gAJLu+LpTVcHB+UQP
z(bIwbZ*=rOHTzem+?*V2ph1RzdHxTbK3#_YjZXiixqmeOUuXJ1efsZb`gGC%pYHU}
z5dYuS>Hl=`|LsiwOsM~jPXDK7{~FVOo?8Fj(LYyd{hb>8F)0>CC36E~3tKY)r2k_%
z*54ohy|b{Lo#T@|v2rkxu(Gi;Fth&kl7oeT<q4yM90g!$EfN5+P5%4`-us7*k#PN;
zHvIFf{*xg5;|lyi^8aV$V=T;EEdQt<<7DRg&qw@!v4D)3nVX%N>0jafK?bxlmiXBt
zG3ncvaM&+HwZfq2kJuV{yQz=xwWXk`WF4qcvm0AlS}>{b-%5~R(tLIDeY_n#<7_us
zKKi=oV|N;R<~6!)-#K#dHHDYe&1~yv<Zkrw4GHQCC`iBmcb~XuKsC@)A>co6BWts}
zPW417Y*|=;eiiahpN$y)9=iV3_fPLUy-bGZWBIqYOvx|$-rcOe-p@Mzw@*Gm?A7kP
zh<aMB;Geb+#ox&PtuuX&|K1VOfA8q|e`{*)|8ELh+2sE(T`Q(Ex3L&@E9J|}O9BFp
zfz1?%NA~)M<{Vw~MQ4i%utv{By9_=%pR0NMpc9s${EXJ`)4S+cir<^V>%WJdeY+kN
zz(mBkI~H8BM$}XK>{(~r9}8~V#`tM<jdzetRN(#HeJ!=e=|(y6jSAU7z0N~;gl?0y
zLveI$AUyVugsw-^;HMf|uze7bdi$&IYot4e$@oV{lfgXMgn_OuavOR1F|xoG*N4=C
zd^zI9cCS;-$b&iUa;if6{rSzv!-Z6ZbmYT%_3(jk;x^X0NrdfT1HpJ8{=G&yPC28E
zAmBiaXZ9YZ>27hgUChIMe>`Gic`_K!kWV2fj&j7RN(j!*IZ<{bi<{Z<>c<xq8Z&t0
zi<1)vH#ZcixB|mK#zL8lv%8b$*xMCu%$P!I)$<8VzShY$5Vx1R^lf1@(lXp;vP4sv
zqnCizsx!}Iw9CYj#<5vKWw0TY^qt$=nv%Zvyp&2{p`c~tV<8bj6GRDw`sC;T_^^;s
zsX*Qr<aC;V%g-WPE76NWY`)N}5{gN!u2|q{yfzU%Zl6TMBYbmq(BSN0{7^&qw9wtt
zhEC0J*=N1{m6=KgKUD(8Y-qOF=yp{Y#>IL$u^@NcL_O8BMOy*c@%k<WdpMO+E;Bte
zj$4Vcuy8p~L0D>Tc(_b;82|qEjELQvO6D-6*~QX<9hs2rgHGO$@{IP81XkD<0gFy9
zrNK7y4!#RvP+s2tXgXeThjzL*S=HrYITK%(9(cON*Zlr=Jy^ZFW#?upY}@nvV8kv5
zQ<jMTZ1lDv^u%hqsC<#}O~{?=wpja8tNU6vxUJ*Lk`4><!CaHmegU+TSrP-*TZf(3
zFK2p+*Mn)jH8StbPYk<zqL8$zoX5&-(q<}j_`zh5!lI2f<*BuDx|po8N#nqz)|OCP
zn@@PN=R<g6J#ph=B9#<7jbI|?y{>m`Sgyk}YE>gslGsqLqgsN=UUZ@q)$;xprv0r>
z0ENU%aR*leG$obk-LJ)il!@Zj4)W*dBqyix5+87>xE9~~q-ZRD-;Z%f6wR%kz%?C_
zW34tD#77}Yxnj!;xVg-SU}8*Sh#GodFZ@(t54JaC-E~AMgj&8XZf=Dd##y@jf?s{;
zk$8O8BqaLLCBKn7og5Sgo$u#MrzJ8LCa=a6tJDNVMUAfBdl}DckEHg$zjXP|s<G3^
z{CW50Q%-=))VEts1(*Fv_!qyjm~6x6<{z#|>owwfzV_vLL~th3E2XZfRj8^|&bq7*
zbOU2ksJ*b!+8@Arzprw~J|K9!9GlDL>r3|I&1~Q5(4|_fM2u5m6s;Jq)9;;a(XeW5
zlc8uKubZCuH==RAJ)4JzWr@txF{8v2cKJx~<NINxi42-=1kbv+vj*c?v+c5!sg+!J
zC&$nzUSro=YokOVN__afElet8(74i2p(i;>gcpQCKVDlLx7I<verCO3avquw!^iSC
zhy3&lnx`!U6UWookW8;KVb3$i1&jRdzBf&kK6T-|FP=`}=cXm5{qE1*!l<JOVgvHv
zwCxybz4C*}ehiz6Fn$&&>UOz`Ooo|Qj3%e`FKKB6w2mQb-Rw&$MfwnOa-*<rcJOOw
zJJW#_`<t6sCucDibX8G$2cE{I4v~GGf$;bYx8Q=*TTP_it4{Pp0N;Zx66tea?OFE)
zbE2zD_*M>A6>>9T-|v;ybg8DTToMQUqRAU9<@3Zw#!N(7Ryg~MNoIHBXA;^}$QBQu
zJQASzo(%P;6WU)h;&l`e%Sz1OGvQfuqkWEekezp{(O`w8!%}<5Ko(=gH}V<}VRyr8
z{~)=&;a`2;y0trlX6*~Q4f`>3Z6510g!4_>+#~r40XL<6{jn5rwwn1pi)k}d)mJSc
zMV|`W+61&yFnXh=GP4E}Xe`hA)YrA)f)GaBHfgXF6$i_;#~8EBhGRv`Y`E0mL{1B2
z=+KK5n_?I9yVJR=mDpN6-GBcgx{WRb6DTMl1**|;l@7%5S7ehxY{Y`!DIuc5!RYn;
zVn5l636T@_-2G_*PWP{kq<{Tw)DSqps*vr6MP8^eY{vA8Ehmpt8Zx`gpCR4kPLCHz
zr+h34G^+AnZD+_&!3{g}8Z3RzRs?kS6uN@vBLoe1^g}(a@9>1u?F!%Jaov4frb}V9
z(!(y%%&&Hh3OBfdPiSemjiAM0U#dGwN^l;`AW*9laTS4^=86h$Z`bp?yLLZB1Z!1z
z51KVO;>*v+JonPLvC*t!lUs$$+D~ExaN~`k5MSQvvM;6XpL5wlTK)T64#z$I^;{6&
z8?>2SmiwDC$1r+(ds|mLo--5(waQDcahq(N1hi~<7*AI2$4Gvulqr!Ihk*`h$G&Fe
z;PGj8#%iNu{B(LrY*%eEocj9GtR#_f^#)O|M#{((O#9O<6{tGe3)*I6$?Gf9!~vJJ
zKA{$7LS=vU@<G^qNvG|esmWr+6HHYGExN|;WO38``6;>+SDUkptGMrt4x408BLaVb
zY%!bT6*5o0E)rIIK7;hvSq!ua{udN`gWeNJlmgs*RY+l4+Cp_V0yMKtgGyAWyJmwd
zy{nhuNgtn&Gci$$yYL>nI+I&H3FShh@9>vRe=h65toZAh7To$_oRHZNatb0omV<pP
z%ECy(X_w=V4o1MV?Ko2dq$(dO#DgAYTbu~=bj~g=1m16boTu;MW1&rCFbrw1Lx@6r
zI#%aeM2nS}m{?_gmb-@kn*SeOg(L8@;EZNgOXP_+FxY+8wQO<#r<&p)Krdy;8{F!;
zd&kM&yg3}VFf$XJXP@Iw9*pF1z7|3^-?Gz3AvnsYLz4&M?OXym?8S7M5-kXB%~i%B
zYffgv&h~g4bI0u#XMNi3-Z>JhZ~gor;$BC($@kCmv%GH&o2xuZ(qy-4Y-*~aUA0oh
zqKjT$UakN^fOx4bQEaKnMJY}Usm8Q_ZF5ufray*X3s_3M-BMAq<H3GX81@kPqHiK?
zA>GdC?2b!&X{m16m%_r=QG7OgufB-e*&PF03XZ@R!ef=UZ>E*r8Y*3Z{=JRyoldXZ
zI_PYd`t=%@MSy=%jJn{tk37M2EFL{k3P2xqwsW28^zZLb2NBYkg<)V9!X>3DrTAFB
zpB)#TV1l^v#T`CYrBFP`vpH7-^{fR)jwlj|Kqc4RK~PZ7a%eJ-U=l&6#*({ulB2p@
zfGpp4M=ex?EbZI+%bzXM*p@Bl>E%B)r*P1f@+3%^(yHaZuv_c^cW1j$>nF8wbTnca
zfw+FhT|=cdX%@h0`bq@^;zwjV&r=%1@sAG1Kl<(z#rJ=dBeA;(QCQZ_YP#R89Q`5<
z2*sv;TR+*=6>uXFW-1d!IA4LSSK&D<AGrEDro5+Ex*u2%ReUO{#%{cypE0Z$67kLH
zT~ihgy(L${N{)V`So#$kwOW0+ja@5nHqc0fY;UR<AFCbl+B4WlkaFR%aJ1K>#elo<
zy-uGTbmbmQVC@CLuUn3pI#T_a_?h)gU5<~r0dSACzB+-7gRoFnN(-M0dw`QNq%c2*
z*5qS>LByFN;g=MaN|cD>VW*BeBIGxmO^Fl7q7@Bq6izX#bwPsWzO-B3S=djzyZ)_s
zW1PJc1wzj8a;xz0_})~dtMJpWwVt-}&Ojo(T&3QN@sRh4Ln#&oc9-SAuYfCTwOoF9
zz>w-R_8zZL?&a#3xg#fVXDBF9^<`T3_-%=bTl=}Y6Zz}R71~@2w`oLdRz^bBgw}f-
zAqU3x#mXg`fd#EW2qlZ;pD*=2A;3|)*=iH=m_HkC@p#vKHE~9qXHxiB-NzPVw_MMz
zlhbt9G|?+bx;>g+q}YTWCblb~B;d8D-oTIn33;bc<XpBgT{jp2dnOHH6oMnb8eyCj
zx-{w}?e7kQq99XPEFk5KXX=MT9cHPUKZ_$`<Oz4@aT$q489!36-&|i;8}q(j6q1&$
zbvMSQPd1w+2XKH?cN_#{LJOuX>W-UY@g5p($<U9f(Q7<40O1_&t0fca!#<ChELF%L
zfL+L#OT6O)M#X3&kD6e0yA?s3L|?8`LfKNKEFozzGchG@?Llzv2)vE5E0G77VD*EA
z@4tWD8!JNyR4rFNSt{EPavD_7l6a-E-0DG}XAFl;qjlr;NVzxE|Jhq5rJ`5hs~fHL
zhW*hkxySXv<)^}^fh1bm{Tkpthy@$1BD91DQ^;HB*wjW|h7OwJ)7Og?@c=IlrYnox
zh1}#l&QOMoO-MdR(R;mxtL90oAR0u$5NCGTR;hxw3er^CMJs;%yu0GN?Si9OgRX?}
zW%rih)b3Qc<!FV1GMR$DZ_WpgbKmkedc|E5;`>R^rdl43-7k!HZ-DgZbbBb5WZ-dr
zidJ0?2lHwE?#1M2S*OKa$lyn4JnKEDv$xs7*97+87ag?AY+Qvl5R_{tVaJH-WgL!I
zE_~}cA^&4|I6_LstxT^a73SIG9I!~`QcU*mgadq^%Np9aq;cpaV#q{$5b=O7mZOj0
zVZr)Yf7B?gSrJs1_Gh&MFk9_O%g_71hDB~z(P>L1!E-z6S1fv22=uL$*qn^DTA2+N
zRurOpr%+JIq>5kKO(ZxNiOD_sYSx!(T(j99?qMAr9Rcv3CuuUcHJo=G##0R!>l*d0
z>*oB5ah_pl?`@d4Y4^A&n^A)5_bTCA3(uv`S~z-o#I7b_R<l4$v0jJ|(bf|sgXBtj
zTlRFn#!Oa^$<4JN{gG&wECabhv(0$*D|emcw7yW@FRP@HLg}=6$<IMxrW8oJ^<LO`
z_(l>7=w}}&pvieWSSeQ&;1+5n$(~KLKe~!X%eHWowYIie=#EyL?agIS?60qX-aPd2
z6W;=P*C?^zmC0r`2%~s_+(_dI+5hrBYblZq_cUI=?xfG#{kGXUWt?yO`7L#6Tv~$_
zo>uipEH|s3RRT4uRU+M8oq>*zNJQ7|<*$V)4$CwqTSN*8na-G>j^7bNyrI<{cI~#J
zmTTnOh*~V}dG<zzQ`xrivjwCtRV!3oc5|V58*K48+_Qs&VG%$Abf17=R@^+8L@S-5
z*Y0}WXCPcuT=@gS=XUlXS1hb75MZBidgHEqTSi}Fj(*^QUn*4KePOzLH9p04PLo1-
zN$Dh^uz!Ab7Ef>Yk2x5ZNqHB6w%}8h$@jI_rDQfqj*ypbqGr*5fpFYD3h|ySp<J}4
zz)F>Vx!S=8z-NzLDBd^oxigSl<z{}O<LtFhf#v+|zM>2?)K!D!Sh^=XQdUv_iS-Zi
zB@O~lFTdGnd?xKl;eJREBm0pJwc0k!GMyrb@ElrLM$Pxj7@z#>P`5Ym3ce>GryD?#
zE;EnmH25rrIyoC|q2AK}cb5JPXH7FIoZVK`Q@_29jkV632F$3ho+s%mVkf`5frU^}
zot5y^yPa?f=7(txVlx`!AYk`p3F<8=^*$cILQV~rUS{H}wQHz~wY==Bqy~2*KhA?&
zf2mw?p)+VMHE5IwPu(9c=VUXEE)#rBJ9nTWoZ2~PYq!vh)#7$Bv|F7GGC-<wEcJEW
z3KJnVNk-k$<ROe1kk?|**LbNJbr%wN<jiKWD(315yr$r^l-JX?($&??gV^on<t+b=
zeS3(>yZ=iyL|lw6h@WLVN~R|Qk3RCN(>|pT(MUR<Wh{Cr2N7Okb*+;mjsY&EhcO@q
zIubeay^tKPZX5EY10~vjj?IcgDVHlNlvY7rpPuhdO;9)0&1*^I-POo<=L@=8M|Yc!
z=JstzHK9vkGBWV`eR`jYhPsl)DR-1Gh%GI=+fK&@KxK8-gjE&v9S9|4urM&Qwt>9_
zT8lh&FV+5@EaPCZa8v$<PVrQXJiOHAZ+3~9cRFOo^AGl4#do-w*myW382X-%i0J)D
zDTO_7&mLMnizW=KWO9G{P<G*Ob=fE1#ntGR#P+GZJ7;G`wJRCgUSt$9EYHongP-LL
z)lQyKd46tJBa7UNqb}3%+vJOC8<IMZa~ufciuSVEEj7E+W4KYV(k1xikZ9zD<FUv`
zA08PP@67552nbZid|^P^XBYVsMZs^XX|#`G4AOaAlM5~q<tRLUZ?t;4M)_WApI~?V
z?(RjYvL=XFTxghGnyqkSbo~s_m7d6AjS+22GNsEN2)KQ=z?m^F^As4|d-e<zEdmtm
zoI&1Y^yRQ!gr`s{=LJZFs!1*sn??J{?H`8V>gxPCE6CJQi!U$EX{&!s(I!^^ph=*`
zqkSl|N@|+P1`)kl{sQ^eQ{qDX5nwm!*{ul1G~?04Dc3m}n9DxQ+cktXLL?&)GXef9
zQLPK@hKPC>;0cy<po}E8;(S;rN&@K}jdl@9dl=8yfJvAUnO<@sR7tFO4}JqN{(FR^
zherFYJhsVKQHUjImOY`t?LS*wGA=JSrG2+%>dw%Z(>OoYEI4iT*_Kp2_i`OSR{ZQD
zWm|T^$FfGAm!j8YSVJW3?&VcOqV-6qp=?S<=b&fV_jb8GWUXXiyvOvLVTlk2&81lr
z9SuS!u%n@90iQkP;ymg5psBxt+X08{bEz#oEP7ooJ?yN~=E+Q}sD8%kt5B=F9?S3H
zg_03_Q?ok2XCy&aRp`?>5b*Smv}cPYQ*$LBpA=QC0O(w=M_j7e_7QrHK^uX|6yoJr
zjXtqxt1!Y*%oBt=ye5xlen5+>EyQJIx18fh#P?G84$@}NNZerFPC!Y4=(T4pZDLJ@
z+h&S|V7btoV1t}e6C~2?R;Bx2ljD(aNmP8gRAF&>+0J^FD#?s;b%SZO1p1W$`X_KL
z-7ctxbF4=jh7;LK){bEPHxW3vU7Vbh&bGV-ZQ$kZem(FXtbx!yhqb#krFbPv2%x_v
zc9xCg3K5<^qZ4)JP6sYuc2-uF9Uwfa_v0wTjV{N%wXvP=e?!;RLAHhVx#lHxM2S-a
zM`3{U(N`t7Ar2sIses?xgS1>lGx!Z<ssO}e`Pc_v9Bzd!*7{~}p=gf8SWITO8AKs2
z6a@5Fc030Os@pb)Z_*lkh*N^T%1Iw;d#QHM1V|;)&TPzB`B=Qi#L8+gyan&Z^WWw@
z4L^i2kK^W0hvF5xcbN@AcE<+{C#zU-N}0V$y1ol?O#cOYR~kRWwgbvVnjas!RRg@(
zYF|ojWj`Tpayc@Gb++t{CtHNa8%+`PWxVCKK(6aGenA8uWCey6PEWWTBq^E#^c>sq
zHgk;vz~DmD4&dO+7s-vZ+P%fUSDQ##F=j9$tbEFZ#+E$(@zygk+;BGt@xX3DDLe(g
z&YD0X0ukCN5^ylu%nNHMNt39xv_TW}z}Q!I(7M#4*Hj(B>bfax9hE`n_+(FJ{sF2l
zHfvk!<J#^n9&l&eN(F!iPUWy=^H{t340}1(miNjC#Qy`=R1N_T_tDMncO=~Jt}G!W
zlRvJ<7y#JvQjjB@7=PrI%0PU*9RflkBVge&X8j#BT0Eg!HUTww5IP?pVMl4i>{&0}
zdD-{t@|DZ|cQEz~4K|0z$Gt~{+}5AyIsr6Qk3Q6d;AaO%IX{x`RH$J-Mkb1A^`XK(
zR-$$3*6lc(c2XJYvRX=w*DZS%N3YWOuo)3R-+G&Q{aNy^DCa7wvaFJmUrY3&!^wG2
zCCyJZ-H$SgNYS1260kHsJ_QDj?}*X|!mamIa!pd_WijFIlPmgO+0sboD0Ty$Eb3^v
zK=;k?w~!|Ge$e2Bs`k%yhS%NeDOcRo<3<{Y>elQ*NJM>xnOu(Y(k@)Nx~$5peQ^>$
zxgw}j7~?e8G|`5UF{7kruqJ4{pD-wECguAcu|g#H>hEQ6gR-?2fC!$Z(@;-kDSXrV
zqj}usgbn*ATGSB8ax|hD-pBnJ*lujeAa`?v8FE03GB1&MEaL4$12c`r&(d=<pXqs%
zUqouIhFGXY(|T@_;4Lsw?ZLXRK)_x!`Ziz76aPmg!(VAoG*b@55Fl{#!~@5_7wY@n
z(WkN>&a~hRdw`rF$1-66#81ArAUk@0ccj%V1A_}3rV+D+<>#xa`auUtWQ;b=H|Uf(
z9>%zUpSN4A4s@<pp~=gmi`2h11R%jVqcrY9ueJrbM^ES#x6j%qzqb_LQHXlK#$p}c
zZ6L5*c{sjTJqTmm8ckvM?jxzQxB@JYM)aB{1cIXWLK8?L5nR5L3j^kiO8Pa3n|E9v
zdXNaNZf=SWLwc{)PDc;RpEEVN?tUd91bH{Wy|kXjonjI4w+d}MA~y}K=1CxzxYF?t
z&`-~YD)~WvdLm5sm>Oa%|M*@`n5hel9=Gj`*ts@(8$Gb;=9^DslN(p53C>|0dt6P9
zTb1_@BQZZyH)0ITIJK0qFN+4$i`Le3FRbCiykO|+@$bBtcl*a6|6n}(4arxw={@n@
zw>Mb3tL(2dYK&7(ClIv?KU^fMt_pX=;2Wr=-?ABalE)mq6;s(-U3J*IGT5q!@kKk`
zSmhu12H306VmSDtPk=|%%r9{cqW@sPZ*|gV{!(-Hyzd8F;xSmmJ7&6INjE#u=6Ugn
z@bb;&u1&V$Bx+Q3DWy6XXi++y+7HlkndHKg#rMbl&!<Ys@uy8b{Rz|PEC6|5e^7NW
zS+|)S6h+uF8Ho>x@-|2Hz1(8&o&=2L7p+3oHy<7WBc!cc`!TNTV+D5fN6BO&ydHq;
z6!RoHYzD^yZu4@0m-gK#cW>1RU36h;b8Ft5a8W*dg%>rHRW+vcTJBu}iwR)AzbamZ
z4a5B)@0x+2du(aBT8DMp#Q{o=ru_^c^5`83IR<5YR}Yq3$xZZ0r0W4`<evwubn?eq
zOpaOk*-e*MLv7yO@44f<L<^<YDP>QiXB43|YrxyXYVg_zkm;*aq+A$#XCdT#ueW81
zw`T}1`_w6x*`9O|o$Fs0E;r7*ZCM=MO-(X%M0n4!wIWj4ztrT_T7TaDCr4XqOrsg>
zYIBun{w@@g!Fb<qib&~~ynUY7EY`MwOd~@0UYH&9FPSJ?_AhERQg`g$w*?{Vy{7QU
zs#*`g`i1E`-t3~Mii^$s=qwix%Fa`Nxda(;4*AE$9`nosZUBZx6e-Ew_zW3ZQu^T5
zjRmcmapcnzL5sS1s^gqTj$gmW{1=jfd)d}fIuF-0-53#+cROn~6x}gfF7R!$=+GK5
zmWJW%DfyuWYL>&xUF6X9-XO^Us6iaIDHEaU#YN~@9oSnyK#m?`zAkf>#3#a|;aw=Q
zlz(g+zr4n%7T@YxnDHiePED9dsEp~_Jq|OIgFCYPSt(@S6TWuO;4TdoHo+>z<pNiq
z;f;+V+U_c&$zc2z7Qe%tIz2U*g<J}7s$N8RR_RvME?<=C*oldWzmR~~cP`v+nB!0A
zdsU4a;>@W~=x%JQ(=rqul9YtQyI{Uq-3`DSn0Xm#3*XmbnDwis%*TsJr3WrBpNsyJ
z*&x$gk2g&q?a#z9I4bJ3d7Q^qB?GKW&fZzhb#I|qZjI-9yp*x9(+-lsUl$jq?@!a2
zcqQxNXw4$W$b*V0pra5G>gpssgRueG0s-+rC%bXls|xGsr&*7br`n__>M9KgEhObo
zKi6*i=6n_=Oz=CktC+xgiz!7<pC`8GCRJsSaFR*<?_@`!a-;tqkD&hrAIo&3q~Xkn
z-l>5cprnBiu=#uFxsLS`YB>i6lqA-Sr*Yfv*syKw&#X;D)qJ2>jPE~LNNv%Y?NmC@
zxrn(dlkRQY2<a})r3DIWyi60xfHDMlW057kChE7FQdlNJaC79s;RLLy`v$#<daR~>
z^5BGp?tsDp<U2m=$rldQ<6Rkr?*(P(Q^|?&BCO{cGq*qDCns)K=tPABOft0+Q`J*8
z^vgAw+^ha<cbt2XoVDn&y`s*`%j;C<!1x!|$Vxa~{!GSNOqG<Yc}}9_BfU~{{5+0U
z<i<2LQHrSy{-A$)Tca5Oo?{I&oOH#2I)l$eBH)WI+u;3Q0W2eTW8!sWJOfteZOgm`
zwW|?45q#w2i<X_ULb&&=AB!XLqnsD=e{T*{|2Vf&i@SDoa>97o5CbI6dhBnNnNxJJ
zemQ|UQA5EE6?2wfm8W|C8ldS~2Xa>gney%g#mYCnhbnlZM0iHRLOnnU;^*h5hKzUw
z+V4FBPb`n@d^QQ7;pSKi2g!JC>uUdEM?OHkh!C7(5<37_!KYmtkf;I&cs%AG23b*R
zyI^L2_1(?bmx=cr?O&?)L%9y*!TlMcwxR?+562rV<~g9=0=RZ^)uA&-uLqPm+$=jC
zGA>*7cj&+R-aY!_hcTV@9)nT(Nsa+@riKPi=?FrH7SC=)p*x}bTg628Q&jn?&Lp}y
zFb5Q!rgqQ!Ue4CksEr<Z4$fBk^6So6XXu6hJyKgSq(v?ouhyKShK>%k+}9R3?Udj7
z^~R`ttl?v6skbrRH6Dsy-53)->jmJZo0~aWt4ch`?Ok`BP#e)gqrr2qP^RsRL*UNp
zO4RIKY}7Bq3||i5>K%d`8y0-&{Pu)1kf>5vrt_7=HPYWNwQt%of^CcN2a(eBg*EX%
z#!C{A-vCV~Hg@#fFL3VI?XDZmtp(DVR4W^=Sx%LbO3(;-7$a&u>+)2sk$?%pcI9U=
zlOr@jgE>3wV#NQG5=7%OBKL2en$?y_7^(C`iTBLPN26g}=f2xjocf|l9W9s@6%|!c
zfileB;&%8Bx*?)et;-YXY5lZkZy?26o=8XSarB1G7bC@Tba#L0F&*zvU#V#C$K+6Q
zZWrRMnS1k6&V^FQRA0T5aUBE!=FYx^tyqbew^k2(bv<~tg10uV{Oq?!*1RnB6Sqh9
za=u|f-6Kn&c~r3v^W{ssb6&44XWKZvR(G;UYqz>4m9Q;3{~=z}gm+!RNJLZ!%|7>`
z`eh%WQx0QD%`-<-Vgz)WQS9_ey1jwaWumVS@Rw$6f^BJo$+~AZm-lCM`VnZ$)<6WT
zNq2^(cmOV{P5V22`?|W;zsCfuT^UA&PbBC^oR$~JCP+#9dtbUoNX2!`NaPc=I27HU
zM!gqq|MEGxvq6bcu?WaG=Yu$TGZ~vPr0^S~y|Duz3z~;TL*7cPSfXV(0`(I^Z}0E#
zt?$i&2xyY?B)vl`Tp9?CI11xd&RWX1n1pR7Hlk2j^K(s}lqi?8aahN%@39MGMe>Dw
zkC^xnOKpTVURX$A!u<HKj%3#g$b0ARr!?ZpWCuZxcv+q?cc}dG3)gP$QOk~mYocnk
zaev=0TSPr_32}M*j_4qv84W12f1d}RgHZD20X0~R#$;+a`9vH5zVFnh7L&N*CwUxT
z6b@CWiA*#yY>LB?pvxQzCt^)LZ@2U~?oL(e{*5}njco!?qzhATzBEkKh>cG=Q>GNA
zf(Nu?cEBZ^oSk7>qdtj=99}AX8#BGV!F3BQoE25HPQIxpI}bMdg_$qh+%AuFc(Zt1
zuZBxLfq?)O9%m-kj`4IltCA-!l|Y~f<;`d6jlMcpV@5ezG8Pn4GIAIK+-2PA`Q_#9
z(L%qh0t-e<MN$h8j*_b?^k~G)C%#aGMGNZb7K<B!n4rO8W-4Uu)TG9k`AbQ&T$it~
zzVi)+8Meq&P7y4&l#&JPx&FN~g~;Kd42)KYW|MVQqOqPh-+F&gJY5cMAut2FxA~!1
z2b5fuddnc<6VR>?mn2>N7X1=W-EB))CEl|uMY1Eb4s>`5AxoXR1DNMTcn=opfQSf1
z0}53iL1u1L!eO+WtFY^$E%6vK9&P#ilMu}yCuyJP#iIImy>f@7C4Gz!=M1KkzjEfP
zA6nqm8s&xNbnd<$Y)28#7W5;VQ&#b}q^!txY0WT5SM1l#LIQZaA{jGGy8k_ZA)Q+?
zgIQ<3_D&m4{E0Px>483HlkS;tw~#@A=`<ZqY!e3(dn{-N>3n`l;a~Dh+T;ZSGFPxT
z*!it^UarRj{w#TNj1LED+F-J50qQUlVqpeuBtyWq)ES2E1Nq;+<0tv&otf;1zR(LG
zeHaVDyo>l2jW1CKbniRtBmAvj6?fh~J_o*U%d2$$o;4x!bq|lO8EOpaQ%fQ)7U~tG
ze<(<am7Q~t8D2`5kH137m0Zh(J_S^`g4WAVA>Pc6v#2+sa&mI15s8dyiImXs+0IXk
z7ZU#@6fg#5M52(-pY!qYnZKOP)lgbcGdF+NVz9`_=9j-q(}Lb+s?q^@4UElPnVRq&
zyyF$9Vp-s@DI&hXLV7VrEB5~J#PlIugCZ2?^5Oz{BsnHFwrxkEt6LJI2a8(!lc_q}
zlbjZD@J5|pPzodKFCCGF`lEOzhl^s4KlF~cg+3mK<;VF?8Dd{R(NUgXEdWSJ2Nv;5
zUo9{e=t-t4bTX*9W^-TF^Gdh7|Bfy|Q8orjpj09{pz54~vJ$#5X|Bd=PYQ~E)Yq1r
z52tE)wOpgCtroVj*(_%B<W5=z_VnliL$TU^ekDyW6|eiE;$GxDZ$(?}ly%LMENapF
z+H|_8kV@8+y)((Vh&QZ3Mn6uq0FS*T2W__j2IS4C-@`-Q2aJdWz27h+Yw!`h&NB`H
z+*-M2+eS%qokg|WWA#2L=AtYSRrP;A@!c(%w1t!(Vwz^oGT{4#`YE8fs!61lXXp4?
zET@ZXL%g0}ueNGgI`oCdMdX^-N1?M{<zmAXOJGa6409qXcqK9yg&%w+sOpkHWKRZF
zK9yPEHxhux3AOte>yzjS<qHx{rG#(#W^d;%oJET&ssc%Xd9=NeDpR0GMj}#AA4*~A
z|N4B0O@Y7{hmg9pJU_}jICIN7A)H0ujTq~BQ=OD*{d34W6P>6Nmta>qNR`XvF>;G&
z*7e}y9cIEC6~f&MJy6z@0pv5Sb5DZv*!B{;mP#?3<c5z3X0r1^)fI3O$j8g%2va6i
z(DDlp)!y8@t!!^;u@qy=c*ap9Lw7OHQ*+?E<j5i-l#*iSep4bDn%JUQma_@eN(eYi
z5yg@Ik(g?-D2-cpUB4)OannG36bY=Rmlb)km!S7X(uOUM&-i~vB#eA`;%6<f`@jA0
zuO29$ur>$E!?<3KIFZv1t#3h20}dc)>FR)+Yh36=zidXMPQ*V2r6MpWJH4(dCgbHY
z`v4Om2=GqLmZ#f3`CSjroVoj-8myc_6*<;QsesPR<pQ#3c1TUYdHxLQpSqP-_Gg~P
zwz{p~gC*G@jkgi9_+3hYD8m+SGK0m>!X}o#{6v99Y#Zsd=RJ=9jEg|WT7P7_hsAfs
zqE|FtI>>UhS@^1dWc@J5K1`_oGL;Jv)R9#LX{>QLWfSBZabG|tkOZR~FTxXWYxDT}
zULUv(5D5;)A;`qdY3<A~WNh2Lbm&iD4Yr;k>4+LfROVqZOM4z|Ymxg(x*q8FEW<zg
z;%ElZIgR+;Zn&sV?@};SXw(c(Otx=9CBQ1+jBZ_R%yKd_GtcyTb}_{Bn3L}KvI?w$
z9&bJ^HW3F1_fwa3MN$e2x?v+rU_9J#9pqi#ffh$Omcdg`4BSGLtxl0u&Hj@xc2tnu
ziLxw{P=K1w{MkAGestEnZJs3V!N+rO8vcq0*M&z<FN-0AM_1r|&{iLaD0xS6gwf5J
zwK4nt<2jK%?IJbbZLDRf7i`OZ+2v2V`ipOYs)gay6LMam1i0d}tJUZAW^JJCWDCfl
zY?nzrd@cu-hXnJ^cA&)3WFYUF%R<bv`s-8P)Zu51h80IZTyPD9gp}XK=75yh6k2R?
zE#Rxkx422F{?2(zmZSM8)O{~oFNkm{=VDRh-A33jLFYAjl;%3f(xC{n?@k}+6C!OQ
zI=j2NjK2TqFfO5vZ}hpB0$N-?8n^@5TvbAvhUe1ZVv~*0RVt{_F{br>1wx%urBfN8
zg$3z$(1a6O?3dCrv^Z@*SwUuUJVo_JVq;SJuF!?mm1enmA#DeKL!K}SwC8Nq4@;F2
zZ5}txvJ>f?*Esx}E*{2374%wPnM6Y|`B;tRboo4ra6LL3nM(tpU|<kIVgcV~I^|e@
zHBsyEc_(QU=?qP$Qh11(K$FaBl)eOqCFm!oS%_A;_rsN+byeh^>`k8O+jUqPMnO^k
zJRtXhqLkduSn4A{RkiCf)*c<VFB((?$`4GwzkbWpujXPkArYj;cF(iv7YuZ~1kd0}
zto%X4Fi^ncucc5Ny;r0iS(3zJ6ib81_aqeSNSO|%B{mZA8|%OK3{^-yvja6F{mv3e
zEp@$^f|!Ui5`26zJ06w_Zb>+ds^L;nf_bhjiT<?67CT~J94>+cG4^uu?3ZS#l$&`W
zB1#W3@yuo+ai?o$$#w}fD+MItL^7{g@}(7^4`+YV$}pHeB(N|=6G2(#PcI?V;wnB5
z3LEI=9E+9eNAJ!T(AYarraEEJT5~mt&1aF<o0bz~Q;PfQ;W%OcmgC)2xxr!3pL-CK
zC}hu&3rpY#1DVPo;&#Wq<L4aPq%jT_KizN|M-Iu{yExlIyc8Sn`UhcF+ubiCb<*3{
z$JK7m7BE8Ou18#z-&;@8sJR*e_X1c1HL%7I)yQhW`94+A+@sNPTUT!5JjaLL_!-R4
z1u2|?SNX{=q{B>*;6vf4a}AlWEA>UIK0v=xIMoag``jV%4iTRXo<=5yEIaoF4GE3t
z8_{q6q5dSIRSzeG+$Y8?HM}DXbTvmyPWKEI#`7Zr3nS@iDXcpL^%%e3Y{9dRvn^IW
zMCpw2N)SZV-tXiB^?LIq@9#a@irI@D*%5@!hQR0vPFHfsqpR386#P3i`ITd<wwvDw
z&7abP;$SJvcJfr3Jl3-|L_!t@dvCoY9MMI#oko!)Zxb_{qQ#2sp;pBssiaOZ;~JK?
zbjC+`rk0tR4U%3+GV=DxE{T{HFRwL!ar_<hiWus5Vm*^u^eFN*TkDl!2@0?Gn>MFO
zq)o{f4Cd<KhGozFgd{yzLLQM#!ph5INs_QWo{hAPbJxRkSS|{RBlPl~5r41qTJl1>
zE*+gd-bCxSk>KuoHJ%Ca$i@kZ7l#TSWY-^47b=(S8ZmgOyQOUw;EyB8&{~cg20~^E
z*-XYo5=vrn;V!imRQCjI5w97@ma#TS)adjEJdI#{`Lk6@a1N}!D$(BUG&I}8Yc?dl
z3>Xk~Mm4x-=}N1&UYhN5%jg_pV`k9YB(pW{$(TG0MIx?nTwCIi{9G&vGn^Bdm(P%V
z$$w2lnN=`druXWY({s2WA;0^RL3#V#*SGFXQWMyIcgj^n=&K^W>qOU>^?}y0nSJ)K
zGT5>-kBA?<XZw#?Jk)AIeK=SG@wol+$h-k<gLP0#=JV($=Z(wwJIXHz;k5#q{+oJV
zC1<m=)$RS+?D}4(eiiNu=5!u3<;nW$y5Tiz)agF0zjChuod$0IM=Cw{qyKA2TUKkK
zoKlnWY-Rja1ik3&^a^A9)m@#M9z`7)F@z$|$p<Y@4;M9fdf$**cPA-2mg`@xS1Uf_
zr4*wj_q@l^6>h%?-%h_qQqZkYx=AGygx72*8$PvC^w>ndR#0`zI5=)Er+f`fmL~17
zMw9k`@b;ErS#4doFp2^qh=71}cek`ix3sjtLw9#~^U#g7bazQVbV`SGclWo@z4yEK
z`Ofddb@>lmbB?(0dyKi(m@GSKxVU_N_mo%`v(41PE2mKph$9J{d?(mqS-CIewdBQy
zf*^CDDQS@oaMua8wb#CPZ{g?{M-Su)?n4yD>FKQ3s6B4LDNs>F(R~i`_N3|NC!kZC
z@Mty$r&>{*iqCg$IgJ)n`<nFD-1BtL?Z(L$V%l%{MDxqe=${T%JbS#+gAgw1zRYtu
zR+<d>z3M+dVH$fuib%fPyOAAq&~(0yZw^LfSq=-Gj-_$KZPj5OMoZP{5x{Cb8;%%G
z86X}GOfj*vsH3&^HGMHaj#a|5tRXan)ts$yKN^7Rc)3XaC=p+P!}n>8f99nKFhg`}
zK7ZURdtp8&+fOWB`Xb>+sti31(fQhb*nQAKY3tP~dJs922=kj!WL?FyA5oQ`CVe*^
znhVb=d_qBzpfmVde8~$RKspfnY7s#(DEPf3hj3U6Zp9s$5w^a(JL$-LxpXsdmcnFo
zH$B>+?tgK;(;trSCfbv|x)AgR*zu1KMqr3#Y1hVL>8)Wmq1`BSW7GQZ$A|P@aB<!4
zQoXFyq0n#$;rB2Pn1@SX+OqpLskyyn;{hiZ-f^;C5Dww-yv^`v4rYM7V~8>JW4&1E
zQTHu-Vd<!#k7RrQun=BV!wS3vNP2z!D9+z>_G7&J>|B}}zB37Z(-F6Yo4qdP@?dWl
zDyB2viwc2QpVkzP#GY>32WrDqbUj#1LU6_i5-`gDq@5la>V0-pyZ6OmQ>9Ud^YKrJ
znAPqiy>v3C&;|6<u>Z(Zz;i}c<t2^>to=?iJrhDmrjld8^wEp>bmb#+yCEKCsnC#I
zlM3qHoUdT7tE+k<5z(fTlYZO$KK|~RSbkdy%ry3gTdf8KM_p1dtX_tHUn4fI^o&fr
z{lP>Km&fG5cCl(RgT;aA;*EO<#@M+Zv{Wz&6AcaOHvU%!hKKBAxe&lBk&w?@u)qgA
zPRsFdPzW*_iuJKMLIFsN@f8)RW)Uw>TjK8%o}?ZqPl)JrC8YQZQAcxy&t0wPVjFIJ
zB)8#6Lv?#21jRIxfBD`>sO<HZPGwZScKgz1vM5N?<6`3uQ~Yg8OfX^%(K`R(&naL9
zDv8^++r&M%qT=&Hn!Fbca2B34QIImOQ=|udVDbpP=7;nWft`V7I8mJ$eqm7%#R+D{
z;m<VtnhJd2+oSh{cPP(1movx@<ys~oM%IN&^lN(f(SI+}h{)zx9r?AAYOOoi#j11r
zP95B|s-XD>`=!71VM4-)*PHsna()D)yuJ7bnrW$y->ZFWd1w$N>hPaZNQ9Jm)%XR4
zUiknE<>(0yBxjoH?{|MNnhC(=-8wt&F`0TXaB)6L1jc0A!ft?=`OdOKVZ#SVl2?Qq
ztJ+5*qhJPWxK1r>pw2wSLFileAgCWl3HEneLH=r%rC(YU)|b@e);i--s)nxZ<Mv>m
zzNY$y#3BU(Fj6P=6iCq(I+O^!z{2>U2z%@{b2}k&iPtvf<d5-MuF{)_a7eKaG@-QM
zdPTMK)hK*T<2j|7YKguU9$T`;S)nXJ|2ISeWSh^VcXs03x1d~_nZ}X+S{Bt+DZ#M?
zIq<mD-@%!NshPINeT<8WI!qbO_JEnt#12kj#uJlRK*hh^rGL=kIw#Ky8|TSeEG&(^
z_k0S0Gq2pB0ZV0!$*m{SXxst^A;iWe9&rB%O73sE>7#yGuVTCX&1=Xu*OK8KvT!UU
z68gzxpqAlKqj_YfDhUwG*`Wx=v2fvX-!R9JX3<!s0She`1TJWxc(x!5Gk|6QZI9;;
z*jJzU@QH9GP_U06KY2TYXi0aC1eo=Tk^Y&_4uI>hhiMdGK|g*O#XwT1InM`}heyt(
z7TuFmzFNnNOM<?i3BdAh=YsZG?CS@s^5wU1#>4T0Odl3#ENC;IL5qS4s7qYWv|*Vl
zYu)G<Z)QoEc$srYg@%FgpMyIlRJ-;xs|*^OgY1jHhD3<69z(gogy|x7oBoUAdA;tA
zz||f{M-nffU`jKK&Mvi-k+zl&?=KhKj=PkGxladn_h(g9R2?PXxAK@`j~M{#gu)9$
z!DQQjOMHM=?NOsb;fbu2y|mJGWEPtXU8e`Gx6X#{vpft&M{W>W*G{45EoNX|A7V@s
z?Dpug<~)7EgUKm6uvp+iFFYPLT7HGOX=;8nPZb3h1Oh?De0sp2pZjC&03;GJCKIJn
z9gRz3|9W`k53%vYQII<T3Z14V<}F0w2PC=1ic8|-7cA;vvZazIZHJOAnyfOiBWuOn
zq}KDA_b~=;)41eySO~0rJAtii6M>OEK1-n{YSRg959GLqIPF#nZZxVaHVHXcOpXZ2
z@SMN17;U~$y};XA8kW^th@-vtqInvFdRh@-B>Ouh{$3vx6wmQwvW2)9=uVl&1NfEg
zB<cIxNhM9Rl4uw1=#ry@$LdYmOb1iAFXxq(h2V|=u+U-it%vDQ)8S^>R=}RIj4O?r
zEzN4#Ao;>9FvjhA{q=lmnXbIkSGn*=$9yxjhO-ktlw~<<Ut1$FZ7<7hE;2IP?OxQG
zG5j+nGc=6}U@Y!3Zza?CwjA?#xakI<SZs5RdLjUy&$!Bw0?IBoq$yXrWy$jR>X_Kb
z)DIYzo<>JY^MQY}NTqp4eU$P9zg!l)$8d2d_7?9IZcR6!K^9$uenbE7g827VM_{K)
z)Iq5`J1B|c*Y!Xq8Sq>HnN-k-O4C@Y2q4zcWJb^cFvxm%_o&q-YgS!b7`<4fIe4%M
z7<4{V(}S9_4aDA7?w&X^7??fHsW0;4P^F9&iHtmmTMJBFRBwNtpO=;?VH1wKZ01<n
za<Lw-nJE#Whl5UMHkXU2k<`R4d*f4qodgcze!b`~7Sy<v89!S?io|bFqJjE@P1b%N
zrNT(yNQ?je=7MlH(qsEBf_Lw2gPu+NP%2=ZnME4ff$cF(p8ZUs_6_&V-J_X2(`{2N
z{D!mr+Qk(=@qUjp;?v&6or1n<>Zknu-#4M2P3Aw9aQUM2mkP;u6+Cta*~vrk8)rTA
zu4*EEr&9@&*{*0c4GrdeHr@eh;;Q?4cP|T*>Js?K%T7$Y4G6!PMSVx7IIWf+)-Tu!
zwwZ^yS9f`c>2Ifjyj)x2hfeKH^K+OfD5+bS-s~rTA)m}vTpM7fTewit=)M6;n+sev
zeMv3RUo`<*hC2rsQ@!5NykS$J29ocwblR2IJ9NlCf4Dn#t%Lq0GGA+nms|!^$yKJ&
z^7&JEI_>@Za;3Zzhpi;eaYalT&ngp$L2Zpuo7;(?hR{i~dEccx=X7JJ#eSV==BWD|
zo?7Cyrb>)P<Ga+pB}~krjVjZbD0cYazI69B^OFs1ixeK4sK)jczthLl^v4mRXoGiX
z$Th52rCwS{1U4`p%w;<}dbE9SDg=oB_W><I);Bv7KW$hC<I&6~W$%9U;L=y3(H(({
z3sfxIGt5L>IGm1=efN>DnbhynhYpY0ZdbcQcSaX-0r^^GeBM3|+^pc-Xzv5V8KSQF
z^t^t0UXT5s%D_De*8n`|OP{XbcE^aJD)Ju8Su~U0!ztgED%1z!P|aA*PEb<CkCkgQ
zJxS;oO-quKZ#J!`iuGIAo29+}xO_LC*T-m+*%vM9a1(Fq822?@_Gb*zs~MDwzO=-w
ze}Hbq`)F)pFt<A5<<<m0Us$TKQ=6c$@prLf_yLePqF^-rT(j9KGFFerW(SeRFwvRX
zfZ8NLAMQo|=j-GxER=sC8x)krzpVstrim!%f3N}tWsCT)AOwVQ3E@8<Rz3aN2rJ2d
zK6GyI7km<7fB$@(`hWEB?(KuHB%6HMTlBDLUM0%gwvVXo)FHEdU(z$P&0e+KN5_W>
z-9Ll!{bxBWm6^?IpI{vn@@O_a&omYqufIJqyX&WWZ}G8k`WOAmkzM7>h^TXu#F#M|
z8;Bi1JXNlYHL5G@8hW*GIb_qj*s!ASMCnj?;%#defwr%mx4>Hu$t;T0J6;S&CN65%
z7|h}^MWVrVU^*C1k`PjUJtps-WJpq4bR%xhjdI1Kk+*Ut8%v#i&sGRNMC>Rj=3Hy8
zwb6;-s*Y7XT%J5Xng9Gq;ef4*uR+2DqwWF4fcKAIl9m=4cJ=Bh8Rea=8k0)X`&;GJ
zNUe}+fqUcaACUL8McAiU!1;7MZmT=jxVo_sE=#$Su}$)7D$XQUE&?wDUo1WNHTv|X
zFuT;^d|~6YEu&1tWE^W?uwr&7bYVB4lE#<1J21JhO!OEkrRJJTaFf&HfW^{$Gd>Y3
z54ph@b3p5SagfR$!}E5==eVh2ZJiix`nW7=xq~M7(r>YZ?r}=OBa72z7rv&wJS>DN
z#0E0Z-O7&8SVN^}aUH~+KYol|y(r+;)%AM&n4Cyymk!MM>jv8M_~YN{N)UgCYhrk)
zS2SgSW#(Af#IhPO<22_Hc5XdF2hNB>ufr04+7frgfk#sa{r4znFv~~6$F$jMuUbW;
z4ZH@$PSM@lbE&f7ozs^fWo?0hp}ekR{9Wx%Vn#4yw(`Z6zh70<P6<+GkmURsec{kX
z{#?-HlM`sCQfH~KY$6Y%yAO(6*i65F5(x^;n8b$)h5sjM>SIVm##&a~LWB)#18iWk
zu%jU)^q3~^-I7(6qwdA{=y?doJTC8VZ(b{h0FIv*+KW?ROiO3$<wd%YA!U|VmXvdf
zR6cnVP*1@k4nkOs<w3r7AZ30?pjCI~;NqD7>AI8&ZZa`&#CoJ;hRj;0hA%<rh^cs@
zrXkJXzAY9oV-sL3S^obS3kdrv4BOVF_xl&0-FHNFQ4*o~!r4Pxq>8C~>At<qC7%b2
zTX!g1VQ6qg3vKpD-8P9`)VoP4Gi?Hf*a<&9+S$e;W%57n3s$J0VQ1CJX3c+f$)-oe
z`XH|#S$!|!rU5tt@GjnlQU`W@$M<74PAL#V@dMOX&_C$}S)Xr94a2d?sGw++4H^J0
z7@eDFh`Q!-m!H~{@oh{R&w;(?dDO5!78G7}8+$nVLJw<q-M+pD8brbLan$>%Sixc=
z(+O8Nk)Ai-sB#KPW%>PZy&?Q?QklFD97OZ6{!F=RYB`Ktq1IDYM7m$jVWD(={sx<m
zbW)L!cbi0DBT3S(aa8AR@o_RHs!PnneMre_ouZvk-Otn{6G}d(_jA;R6;WNi$l7$l
zRayp%rKPbIs3c4)7k3t-cLTha*d-mAe&TAY3kkRfDI>T5_IYytlXz=mS1l_AQK$_P
zpq-MG{>26&05C*rBZ&Rgx}4qYNi5CJ{i3OqN=msf8`Fv~4~+2`$DA)OeJZ@;4=%CY
zwy${2luWwCgR{vUj7$6wS+gW6KgHQ#7^OnK&uwn|woWe$Wb&8};|3dn_<_JcQ>s|z
z)z3}jKTTe4&cLYtIV0jv#h1vR)}Pp{2JujShj;4|nWc@+-Y=133fxc5#F19Ftrcm2
z#XE03eCb1vx6mO0irQ!_yJYKF=<P7E!8%B+nI8PUg9Ih~kF@YqKjQ#%q@!bSG}w$i
zpE~hA<EmC%^Llg7L|dgTdIRLD6LntkaX8M;ZPUIBwW)rKxcD@Qu;6oR0_xKnTGEoq
zl-PG-GQrWrc=qp-mEPg4(v5L~k!K8chUM!tCS5k`)dyw98wh~#dMJ!KuU^wSS>l*e
zVjFf5z&9>zj6jHOu(qCX#I`SO;#oq<t?mPW6+EZe%hjY?bi(iH$Z#V)Se+E&m$<v>
zhsc<fNL<LqaU$Qy=8IID?ZS&y<>_G3(#UQcZlU=JeckLacCY9Ew?8&8Wxv!bY#nsI
z%Haj<N31+zvoLnIx^^ByQ~m3FNo|aS!IQCl-Pn0|iJ(GO8`7US<ZC}NdG@w4J6a`I
z&2lt|h+Bql&+U5HufY{IHi^&@J&Nd?@As+usS<0t17cSA+wwfD@@fj47O^X)l=AQ~
zGdIQ(%QE!qGx#3m3nwU?zXN1aujZfunk%Su0-NJU6HTbotPKs{*|hW-rw6LvU$QGo
zezUXyTpf-6hPmd(q@bdy=o<eWR1(hrfM?5q$R@9bYPKV8p+9gJpa@{iAXy-3u$70$
z6UkQ}T;DRj82DA3g1s}mUmH`W%B&o9etmd}Ew2(|*Vu4pZ`V~XzzDELxuoFxXQGlI
zmRWK|_Z`Rq$I$#|GSfhno5Q6^twvMdQkp{0*;-l=f!J$xF&-fJ2wlwVZoq_0Q^T+E
z%~iClj?)v*t4E`q;ZI+;+%y1u0!p1fQfv}hd*%Oz4NWPz36!k^a`%?eS|qa_T`#)t
zR`QVIXb3g3FcWz=SF7jbAVqtI_(K=|Z)pJpAv2hAwlVggazhSm8J)%d`qLv1f_SeN
z#GfO#Ui+b`ReCe(uMGhJ#%;7*mWt}a{T{jFr|YKg0jxWY$Ae{2UolXlVeLonn)Zdi
zAC{@FmyBtY>e|lgzUZ?qW#g<if!^OCw;#4DDUF%MUEmH1r86!Zj~FN}phhMx9`|^R
zV}BF`LGesnCAv+RNi25QrUC@g{-gwCRrUc%U|Sf{b78%S(OHDr%>tw4L0hJ&-rZgj
z5z<Kh9Luxk^?zJdxn#kzKyFb7TW}`2vedj{)0$y(Frl91kt*FOd7${V^1$A%-csOq
z#0hn~B4?a`U>aL!RIy>AGwi7|r-=!|S*X9L2wv2OcGq)?rCG#4XHz(K(EPPeVRd%B
zAqKLQRZDdTVVM)_tJ<+eu(!5U(CGN0xqY@%_8wu3fS6=hZv$FIQR%k(=j$i*R$uRI
z=2vku8&J4iamxT)LJNnAnT8kcL+(8M3JyVBS}gYX!?TaS0}eTxvC>J+QAsIoPvq%%
z|Er^T@UUP{SM;fruDSWP0TJr%Ip)G<b28NfP1A)n@@^)Gxo{YRWawd2Qw^NOspm(7
zdZ8(<Ud%zAUmJvASGZ$5i-oULF3>(|fN%pK+li|x&i+qw$ap?A_2j~DEXZnKY?c+U
zLABbU5~lwZY<v4<UCu5D0sL@VYdgH2$23!jy02Y0ibetR*<+uZ35*QG((*d)n+){F
zB?3T`zHKubh@_XCO`xRYCBF4~DjK=Nsrvh)<mGx_<%TpGxR`KFjxcSsZu}e;^tbyt
z8Jmh`Zr#}6O(&J`Gop42h#AN9mZD20XKR^dtzQya#ZFZD<Uq$t8-Hm{#Nj>T*z9*r
zmDt#(l<M>eFCIYv>N%Y)wq(qbo63a5<{<7T4n_60uIVEHkmK>jZQChclFsqwnizJ#
ziCpfQ_(=$%BtGd8>IxRSE)q_I%M>p_)35@$9&o;5U%rc5Gj9;rBTAm<j@vjbj$mk*
zE^MpLeZvB^<_ECcN8g4x1Z*j!u;1F<s+ThVnHN9ZK^nTf+XQP#Bt&$F8lWAN4<aA5
zFoGmk9JI#W;`B-2*y}Ykq~r0<(hIE90pZpqb(^Bm_3ec0)0pzO9qBm<SnO!q<zBs|
zAt(F8-i2OmiNwvVYsYUsZX!s1gNi@P&@QcK6;8K^eWNr-Mq}Dks|!db$f;s&I{+I<
z)TNmOjL|NmIaBZvuj7H%o?zd}E0)3h7RwXy#btmREfh6=Zv8_1_Asm6T-VI^cw)L{
zO~yWLasR1Q1&_Gb74_6yZVdCx2<msS>j@X+Vf+b;`Mnep;6Bex<N?7$4jvp>2~#Od
zJECYT((=-}wU3~Oq3ZVQ22oJOr?_!9{k!8-%jlig<lAF_p0t%-KKNBhIaj|s-z+P2
zDH*e#Ygh#UA(KZB7A;Dmi9shSX4oY#jg_7HN(78{F=X?~tj;J(wz+~RwuojZ$htN|
zqBde%70^-9t?Z!E;&6DE<7v$br{f1pupM3YVP*shV|&5FtX?b{LAaSZ&3qgg)2+w*
z9P8orm)o{;XVptNH0hC;EL!8M9`nH6SwI92XV{%i(Vv}IVv8$9Us0QCPr<rjK@Gw@
zi3_+3*6&s94wedr6A5jG&QGaEQc+RCX7Tz`qfGFI_CP8&F5Asd(4RsqJUi88$C*BV
z?&msy;fnET=yU}%eYq}?W6?CWU$^T<{0Ud<nRiQ4vIJa2LaRzj>mYO4j#^tjBr9$B
z(~oW)ne7X0x9E+>N`=(a*S9j05hTV{s!EU(z)h&Gx60_7YyzY*f<@@t5XHPZ1+rUt
zr)4?X3&2+K3Tg}0cn-TI<0hc7$*cE2sr&QSOqa(1qi6B#eZpTH-d`Ezk-A-sY_36h
zU1($m6i7j7^YZJ!T-|91&5WAz5qfzfwQGXk5ff%o%>MP#+jCfkv$mQNbiu(Kq96+x
zw-S%j$OOnyC+xsQI!|whnIT5w;@B+<ne#AzI*e!dp9F7#*&M3+E@?%e10CDHp&D*4
zZ+ZK+^V||g68o=^K@bo-PTZ<<v`(Yo@n&SKwZ;Pp&njfIDxz4(BrA%70`I;r8EtBM
zmqs_(nQPF?sQzj&=~kwS2lR&IWhC}ZB|2=`=4~5jjsfGP#fQE2&OzH+N<OKz#o553
zxT}4r3U`1`9*df|uG)k+8F%Ykz6ltKgzt17RFLXb_pQv21o*IaI=WN2HCYr>C)T^;
zEgb!*I3C-z=cPSZ{)wsKD^Wdd@Tr3WP4<AT-F&f;y<mLOF*=8f+_u%~+#85Ok;(hR
z^en~d=Nzgvb59atxzHE7k^MoQOxcWdQY&jVjqAOXQvcoQsB$jOTwP#}25bC-n{MF^
zr(8(@zE{a6QqR~mPzg>ar{ib?wtetN6lX@+;>_oh0n1tPlzLQ-(t(#>sU1`r-b@2C
zE5wlipy}Q)TizOnGDLY&x`F^J;9-q?Kw@H6R24TJ7#Teqc{-@@>=D&-Qn41N3i~>%
zHYXPP6$b|`8yT&lz(zW|xX(TYOwQzBTu5iIzr<1A(@~-@zDLWaA-ZIDJiX}ORvj%K
zL4tnxnx~~VLK6p!))Z440H`)&IyBs~bn(l#?Dze_<02Ny%A(w!fKliT@ZlV)zk0w7
zV-FH~Y|fTt1jClMk6EGl^+29!aYa{Kl+T5i_w~>8&a)b|k~+a-dJL4%47ru*FCQ=1
zz(l21mZ%05#-@IN+{6V{q(C<mlH{W~sk7mTby^kz<zmF19;qJrPthYg9atyZj!$d}
zb@sPSj||`plvZ3zBJ1<|iSIVB4ry?Yr1}LsZf=U<<>uc@Y4S;&khkGZkV^92o2yhg
z^zZL@D4v)voL5qE)iolT)frRr?OXy1@6vn{C|}4do;;>wwp*$}H`~Z+tw3HO4U`zl
zr{fcD8j*QoZ<50XJ{3Ci(uUuHdKc}7@BNF*Y(6=hWCXr9N=3UPZcJ96xRMbyQ`8Kt
z1cDUgcOsuCw>ajk`KUim5){htEpeT3tT$h)hpy(ydB6Juf&ecrQw7yVASy($rotvB
zCa8&7418sIx>b3EoyObB>awbAm$+Pe_1E{Kv(H86l}eax1i+unHu_D4-HEP~Gdpr=
zUz)7sH(E>gF_O4*eXOG`rLV1^;M%WM0g&Dgsd5@1HSf~#APCelt{1!m>|yF=6w4B+
zzIbUMvXZvIU}pCmj_SIi9h=PR&m9w>@e*cxoA74xa}&LS$nQQ%ZTqq(<_<7a!;4T*
z^UeN+*uENzca!DMoV?eUzBo5}s-c^C*>Kr{b3Igm8s@@nJvLvk3p%8OcGMH1>c#!6
zzof$s#<<aTaL(e@JDF6{xhI{B(^PHGIf1A5ZqiVk4A}E(oL#_XeaX0sqJIuZR!oip
zR3{`Y&Xz;>jt@+LsvXWX#i&;eaJBB0kG_hVyu4w@0QxNI^=<Af-w1En84t<U9a@{K
zaRK^e`9r_LK?pL^*=CS}A&>y9h+n6tKdU}N5*}bz7QV6PfPPBa;>inG{ThTEI=1Lq
zHRl6IMmjA#-W>(x#gXB8HK4%gaAl$%MZXy>#|oM8Q#VM19N!ctx4LIwAR3a?zynED
zU+0nu$eBM1EKCgKDCcc9d86XuOabH?3N|x9_Fr^`FXVuSdM@|&_02ZX&O5ZBY$J!}
zpR{>81CZDaLxnxV`H2X6r35|u@coq3jUVAr-4CD>=53$E0bUM5O4#Rfi3*tey!{Mu
ze;f`Dp}mwF3gbz*B1<Jez9Se4H&Vu2Yyc0lW#M7b2cGoNgtW^bb9KcHgGSrHTaM`c
zCcl0*?K|x6G{EGU_KmkFm~X8vh0hNTVbMs<qVNCSpUDXsQ@f}B=vjG30K%Tm6gEJ4
zSWit0f=>=Xp=BaEGxM+>R>lQYF^hi@A3?Y<!<uh+%1w1`P4rkd1AyL^3X$D1fXSt>
zfgpVrw9^-Sc^fvgbes&RQmNOaG{&#PGL!+wOk1Z5jrVYJobOI|De0ucIB^0QvFiJz
zn*=LKnbb*-FUq~dPXdR02C`vUsu+Dn&w!nUs{a8r4j-Gp(t>dLcUt%s?WCX@&6%3v
zkuZIeyY3C0h>fO(kLYzL8L2wz%d8m@y-9&suEX5=(O&*Z!t@{~_H8$;>~n8kL<X4O
zFbau+9IXk?`WNj;zWLE6tF2>BnnOCiI2-;~+<3N0NQ)u<jvEo@7g<&8m%sB-`c)0M
z5d6Eb@v)(Y6uit)W&UvEr&-A@9Muu`ex!^Sag6(8I?Q`uZBS4|XM|u`%n`>w5z?r>
zW$0`;0Br4jXi-to;9NT(?YEadd~trkJS$ahTB2wAP440X;dh-P#gqF4BFDe!mcj}B
zAB*|okuh;owy~Mq7xD1C6L+!M4#N!11x8{XIc6Y-9(t4RAsm1uW8(m0ied|U5WWB1
z0aj%W74#?P{aW+5C-%qVYC5l<65`D0hwf7k*%R(w2IR|w{hPSQ3BG0g^}l{o{;RY=
zWwh>_T%48evuFsakM+s&7G?naR>3auyUD~t=pzYoY>&I8N#eD?iu8IC$%o@d8f7os
zu#VpU4y=`O2~eH*%Dygi>>A_={HSP2+4LcY&&~cIpy!5yie_9&l=x7S`aBP*FvE+m
zub-6chJvbMoA<q*T1g)rLJ7gkeTWyY)ee~Zb+e=8`F`g~V0ef~I?NXBZ<FKg3JPm{
z1OOC>(5SH}?X2w#M&O*`+v|Ue@@x@?G`!$y_esEz6}5DKwt%fG0rGt|_?I{HEOZtT
z-LOV{fvS*t9yjIJ?z`P{6=v=~>F*!&PxwG%1!;WD{wrv)e1sWLn>WuaacGV&m5nTT
ze>*nknIHrA?><#F^NFDU=19Z!E{3yH9U|$^$m!gQvRxnEF#XSUTpw&LMeN%BlY~Oc
zW;rrkR%)T&zz%$f$Fgf}QR>br=1g_~Ej$v5*kuL;JSC4kObX;E`^6l<6BlQnMFFj#
z3zM}Ch=D-JKj>q>aW0xGE&-szAN<j?Al!QQJHoajA%TH^1I`GYeRIyGk>pvL4=<ud
zol9131%(UH>7)q+G`E-r2dT|{(`xb^ATK*5_Xp9R`f03uQY8X|OZ7Db`Wj6#T2IDz
zZg)h%)JE#9sE^B9c#6%SutcDOHTttGPey7=UQ}ER8R>6;{mC|zo<daD=k6h{bFP08
z!EopT=J&#afO-i7D7XHAQw-k72yYmWETeg&3Om&OpJ0>6vR5sCgnG~xmr?`F4OG<{
zSd8;KfW~FQ+qv#2e=_u$?<fFpZ=&^V@b8*S^2g+Kbp69~1G=Byq-g3psaT6qz-p@k
zknDZihYwVuyF?(x%@k3!8ILVJ)2~l*7Ag`hNK*HC5>N)Errg-=>4flxZM<7I&*B6d
z{jh$kbDKbSK1J7WmjZC|Q}}GXB4PUU2gu~!0@en$H};q8MSu?Z3Pd#R-i_!TrLCm`
z00#hVVB5Ut_=6*yBur97?9&l2EZL!C8w=T0D*)MK$Ov%l9e}7%F^P=4s9)=yINXs<
zSt+6GJst3jpo)U<D9OjzK#B%(`2XQMZaRpC(#NkOe^q9oUje$vTEG)zc#8B#&0TU5
zCg&By?u_hC4IrU38m(n7Ec9a|AzST#%Am|=VV!K6KrxQtAaiCUioAQC=tvaZy2>60
z$)>T1-~B)xVgvqbze(b+YE*U&_O~-EEfqvf2fj?7YlmUZXjzTC<508OS*p!TrG>J@
z9sEFY+k=WlNlk5XT4TBNQUdUPZXc{b8Evu&CieH3dyMOHPeEb)$iqT%25sO%ZBzpy
z@(&dr9EHAuxtRad9N*78{V$AOynxAIBtC-(<~n+O0Q#rsb$xazilpR!`yfmZI%(7#
zKBu|9vl{^ysK^t0gydG}tcS*0+-@}!7_^NO0PKYX=q%39RCsXn%m0r)^F;rmPw?B{
z^cjC|a9QpHENmqG1h^1+zXw<undI$dbZO_)TRFeiCmi=*5><5s5!o==^GJZ++_(TM
z69v7WDV2LM!Y-6|vVLRCq$&zZYFgEMVpl%XzuEPMCO-q);NkANUFijxWH-kssuKxQ
z#7nHUNM>AX>4_)MEsgpEx>fK1BnrP6CqaZJVXEkm;UJStV6Xz5N<d^<Kb*O(`zKhb
z%Vm6;jJxOajv;~@KgO(Rz#NqhAy<pQX%ExG6<zn~c;K?qatG~du7{OtwAOvK1eG$~
zps1+IMhkGrWvOga;eM8#1~lZ=MVE4{^+=c|fYaTW!xKTtgfm;?@mxD*50qy?p$9RY
zb3HIrV%h+li~R*Z+eo&nU)wvAfsm^FlW5k)t1Aaa+EXGcG7&ym?c)N-f2xwb)-)KH
zpitf)f9)a<aH1y*Ao8^P-SnM8ZLM!SE=pb$1s5Pv*Ja1)#i@!Z#dQ;XqYxFxQq51)
z^PtK_IY`h+((zVJS}|Wq^2Vg1EArl>f`c=MP{9+ebZ1h)c^6?Ec>MaIox<giP_Mr6
zX6n#wu?KeedMMFmlWyr`XaKV?$DqAA*Gp$KJ}<Ye1hPgVTqiZs4ded0<vRskB$uL?
z?7TMYrC5XPoY{f!wZ5;5Uun7*Y+8!;-Bvegfz|qH`xb6%X35vN^7?$`K6t#%-0qb7
zj^}ed4R@qmd=yLx6k$TGUURh;%G<@kP@aLF>E1*~?8tDzp<gA>u$(7G;Zirjy0+D4
z2z7!?%?N{{4A70+`N5yNpFtUZfDI0XyvK8Lj~{QVm)NV9VCe2RH!ZTe<ajUKkW?_w
zSHR8>Muu*SeL6=+`v!qR)2%v9hn=LUs!sD?#0`Irm~RA_OTz?<CRkE`PlcYEcz&11
zZ6vztL!1-ceI-I-PO5zlTX&;*5k7Aj1GPp?^DlRJBN~r52kTrhv&Nde@{i!Q&R_&_
zqnD4{FQEj3wLAR1h={rQ!Ln%OA9Zz$xvWF!dY)q~O35_)U?QXReSm^O{08}+a(sL&
zBJDV!p~D9ncyWLd8k=*Fi+1xM-A#4rNssp5MaQ-qZt~U64OZ5q_%0U3*XNFYgY2o3
zP&z3kJyJYMTRU$B+3dQLIrRA6v-(k`LQd@K$eFk{zY3X@x@wAUL@XXt8})pOxFyj@
za+|QnHg)Y7PMmxhj6g;KFw)a%e7`{geZeKUw>Su9MRs8UyH-g0U)gz{!Rg^ZdBS`K
z`I5(djVsoakxLfNH!I?M5W!uW=l7PS{qHi9efS&qYUT!$nT`Xz^tCKQl><T(zrnB~
zW+p(!`YSx<GwdQPG|fO!C@j74kcYO|@rq{U6y@cad0N|kcsvPXJ*>+h&hK3|!OsRi
z96+2+Q!HJni(kLPl>7|v71l?v>?`1b<oBte?8PIs6>EeezrJqdEJ&4J|Gmx^U%U6!
z@}7Ht_V>0<)b!U^Z}N;wYlAR0eDUsu!z!^Bf$4l9B#iJv`RRpG1y3UAcb*5y@ekF8
z-7!IYuAG(>mr30fKX=vCrF<xuHYe{>a6Ra_2#Ksi?3yXEQk~8vvzXU%yT7iE-33%M
z_bxUDbw;PY4aY9UQ{U(KNw8=~Jous4jH@P;3%Ek&|Fmy9CVvO8^igaUz<g%9ZsBbR
z)*c@coW$(MO?W?H!a{v}_AYVbtERcM<C;@U)R%#pWr{WeomU&*vt%=;KF7LH6A=H`
z-Y@l|3Vm)ja%v`l8MN5_8sFMj!{ndw6O|f44v6KG9*<(GG0o2s^=`N?pWPx%xn1@)
z_WO6wEvKE3^#;EVTRbk@Wa;1(+9-n_g!-?MjjTwX4+3UWb%s$V!mLP6yZTq`QiLpn
zHyt=|s}jt{hfP@=UUhe?I;8t(XBX!8-6Kc7{_i3c`$?1opiR(cRb>PYE8^0`B!J~n
zF$Ho2Az!uWR5x}=8l)3$Eb)O8dq6xd!!?s0w{Ciy0dRp+y^%8h<zW>t=|$8b{jUfx
z+Wrd`UbEo5cBV*>ZCyA^iuer&P(s4mI<NE(P;Loz$t>@>*zigW^zs{x{CGtfXk&mN
z_xjcM)Cq$B4D!s>`3ni2lFBw5*_y9%bS2-20a%+=04}7Nmhu7ii~dLGa?Dlv{<ZSV
zx?f2>Z%Wf)U(jR6N5A*Nj-Ide`R~GHnE}i>uwkewpIoN{Vly}AFn7w;m+1}EM`#MB
zc}#qKk2T*OAil)J$0(}9&U+reR~q@Xv__Zrxxtrh{~wXb>|;M;R-0^K#$ohr24mg?
z%O-xhuppOE2Aoep2d8<1cyMx+uchhi-Go{4VMezSR{7HhyfYPh=g<3VjIfU}G2F!a
z-ykgF|FWE{#SIsxg=&U{UcL#pdW0?mGk`rf164);GN8X7X01gkJlv<mmkzIlC5FG=
zMQ}o1XKC$)ep{w;!1=h2@(z>ntvUygK%bulBb2=ybvqqvMITIHI)*)Xj#y#@hwjiT
z4|;ogNa7+n+SB|jq=n8SZuo;hlv--2wfh`B4QvDET2$(18+ys@adkdy8<59EgxFdB
zN3c;S0MJk@o<vzF-bANz$gf#eJtjukCO6*?{8~5*PdRcldWl7``ukS|^fb-ih+mey
z81Ekm&3eHMpsaPH2yPfkvwy)nD^zmqf1OPm59B!zg4pcldwI|S6#?_!u{SF3Fgs&B
zJ$ftqOFk5TO(CSFL%^<+nc(+eo{iz_zr5A^B0FSuc4?RHQFf2GbkD;48H7dpFUV}F
z8i2z(I0S_*ULN!97;a-)Vi<PTCu^P}|3-QGdLjXm_aQTE_XEN<cgHv!!c)H!g;11%
z9{ayZ^3zAG<jt3F{ORblJgAD4Oi$b@53;AzVS!UK5fe;o+;jsWQU$+u-E{RwY|29#
z5DU(pi<#JCMZL*;`EYn`c3N%~6+k=~MZr`~XJVw`R2%&f3<mHG3Ib$y?Av#k1p1j%
zBU$0XTe=K@sYJYT<1;YP4Li&Jx7rLMANO^84&0Q+PIpMcD;(1ItIN(xajYnkPl@fZ
z>_tUF!K6b_XAOA%+w*sr-&C1quZMxmhS%C5*AZXa;0T53ia1D3(GE4^&cocu?S{<G
zAK8!<gx!*)N<7lIB)Ddr86BL?!y?harfVMa)xMhj{t{cnOzUG+Y;a~CC~Bb40HSkr
z|AADd0xz#?Me_{y;3Sz(5^~4{7~K={EI3pgE*K#a{0~uygpaO&aaWl7gc@T75QHY1
zkpHU1w;HR#T#aO<ty?b4!aH^x3OsKjVi<4grz<h2K7yYWFutTak6&8{JWa^e1z~P<
zXiJBq8*pfgK^HPj5i$i~I)He}B@yo;s$l7{9k>Q)dCofP?f6{oxuNdW$Q!2<418T)
z&V{>0p$CX8w7wHnAQbCmZfog(3SJ^u3MMK-3Z{>AozKH!b4rdad4T-$jK}!~dHYKL
zDV1D+6f}5&sC<pY`Z?vlDX*Xlue4K2LFbr)?xO!wu)y5<yp`2YWjpjD{y{~!d?I}M
z!>;)RWfMI@;!%4^SE#k*fEOPLDWZHVU;k@$?qGe?CR6n`ABBRmRYsLV>9yV@LegRQ
zN^{wGtlf&5tI=ltpL&1+pj6@iQ0X(Oj~?W!Qq;yJFjH)k%C}EuOu~*j87r%fOB+W(
zUBFdrMu#GAstOMM!=exkJUf=>uV5hHzp{(=xndL`z@iE5SD`T?<jz$!>B`cbVnqrJ
zpKWU&5caoTYJHGXvGFE?2KFYuBg3`dXU+For_Cq1ics%m#J%Z&9<7o7%<<-s#ch|v
zkih|Z1G&L_5z~Z3Gz_Xn&nR*gj7ys1H0?dcdaWOsrBTfe%DnV~`&&zaVU^r@SSgyv
zttlq5(^o5+Y8$uaZ@A!&G+=~apx$ykZ7^93jLCi%rgXAgp%iZw)1%}jd>cK7xITlu
zYX{u%_Jard%VdZ5VarzLiB32q|H6;#oMIK!9S~rV5cHf45VTuMN9W8;x&nHGpBsAc
zzBH(9IjLS;rZT_K^8?Shi(M?9QjiL(YAnQ5FL2tVs8cFLJ$|Q@f9BgJQ3!!^-S&n`
zi)q9jgqA6wc*^$JmA)Ab66?}5c|_OEH;Cof9#*CM%@S>6J5;3iThAx(Nw4dJ%sp-g
zutNEy;VGDKh<WmZy?I8x`xBM5vsDx9xHf)a(NkyLAQA&ke2$$5FLD2|`(L>+E$NqA
zRH>D+XmGavYiJ@#^47lCfo9ic&;}A6h#Z>r`PNT>tnzWn2Z5jYn-g9D^IQajJK~Hl
zC(>F435%Hbt-&c-ivSt>*7(E3&;!fO0mIpJ!C_s|Fu{UyBqFD8UkC?L-YiCvY7>#X
zhMiG5u5P#6`Vahy4aKOwJS21f;!0!D0rH&}G&h{ajfmo}R@K=xZZ<DRSmn9Nc9Ef%
z>ILNQ9*YZTluf;AKP_OL%;!ZUUl>381R#_)g(vy?y71|bXez<apw5qa_0}pF+gzKK
z6g_^*8k1x=bKXs~=d;R>ep<XOvEM@RH9T*KFCqo*My2vL+)4t8I+(|z3y7vsY3ov)
z(1Z&ppFQTl;J>N4M+cBEk_#|Dpd6pUg$4%)XNg3-!Q=iSBNH*HC@05cJn?-Bcv-cy
z^+B$8cI3Iby1JyKB=7y1d@PM8?vpYeR5f%)Vbp1I!jP-QyuHT8fyLUj`*@*+;LNHV
zj_%_)V`sTL$vQ5LcaL7GXy+miU22dZOO9UIvrOE=v_EjavK$|0tYv7t9Xy~IUu|BU
zjG(#LN^}6}29k^-mE_vac!aQ9cD?Im=U)545`gj&=~;IZ4nuF56i1>Mhpi7N%RY$-
zIbGdgrp4Vyb30Hn;1F<s1S3ZcZi=DVXF2^6y9VH$@pFAi)14Ec6&T}^&LyL?fs^<o
zds6{=KI8!+9W2+!|BN1*Y3P>f8-xOVTg(sfLh#K6bLolbx%&VnpoKmj07ygv{CoBS
z7B&K#{b0X#$$UH)@y20qy8PL*XBGN%baZ>e6oykJ8k<9@E*E=7Iyy{NE1hB3?7~7q
ziiO+zC2$-Z9EHlI2cuaBnSv@p8$Z0D>k5IJ#%E?`K9%fm3?_$Q&>xKD&}G32!H7=J
z`{fq)NU`qpu@&EV%%-hU>?6(9n9Na)y1|7Eh8{2Mx*N~dh@al3+(sRdHik`t-wtyk
zPF#O4{wE5nh@zuiM{NrDh1bN4S69`J7nwt?^ntQ0sHvymYUnzYRIDd5CO+-a<ykLq
z$exJK(^NVi5A~K1FkZso+JB6<U6N8K+CU^90;@cXGQ7p(_SwnGt0KHmAMW9vOOf7-
z+E>NcTop%4D3=nP_RhQ6nZk0zuQPg148VM^`B%ZAq5v_`D<~=srgFv0YL=ImgR4u6
zikjT-ZxR@F0~2#|b4yAN)_S8c=rv6Z4A=n!f{8_HESA#V8yXpLaB{}Q7tR4Dtf{HF
zxVXS~J@Vof+W6JxQzs!Hu)5q4@TtV?$?TU0tV~SL0rO=+`Q=&!G|QF>)dV}-7RlaD
zujWFoHmw%Em@+*Nuyf5}z1CLY$iuiiPe9!2ED!gEG+&&L$?d<EqE!XIDt{y-zxFnD
z(Kn^&ElTID>-`YN?d(S?R?2V3dai=`(CSV~NiW~G@+|Wu1yj5Hc3KssISUduCH@nU
zQZ2-yV)TB85crlPigW7*KeqO9mT)e+4;V$H-{d=^r4UTZogU^K{d}2(vMok9T^R7(
zKvQ&WN#>;x4BQ%^;{`u`203c?Mv)pSHZ(MxZValmxL$vk&0>MDIu)z#&r}9jO^oNs
z1SQ7C#uo8gSsl0ezykIX%VlC=X>z-@XKuy%`RnR<_2gY5@SXv1<+lC7{7hRLKdA{Y
zW(@j|amaQwe@J*t8c#g1_a`j{Q()+#C$qM?2AVE97MCQQX|hYjZ6=R?7zh->eKk$-
zvt?w$8ZD7ZOi$^PZ_{Elvy`Q^azbpIp=T7Yw3zdt7nQfiWBA&(l?WS~(E`*c^gGPu
zd|R2>ckD^9X)#E@!Tq2wC?qBil700kF|V^W=>!;M_FF~Ow8wHFdIkipe!_czMYG`}
zDlre`3zzd>E*md}`8R0)m3sbM?HwIlc00CbTe8Nh>;3Ud?zd(sHg<=L%>}{N;Zv83
zE)hcDHVR70^0G35?KQ5$rf4uBJDXvBo$YoMw~+99U|1MnW6>-IzubrP$pwn3bHp%!
z56ZsqA~1V2hIAXi;HsEfcZi(rmCUFWhK=LTu90`?71Q-}eZ&>3SGfHo->(RoMy@);
zzGI2qp?a0|?2-hR@KBmbY>X#&>vhvU<;Sm2+MH@Mnr$>}!;5@f;0ddj(Gt02-8*Uh
zOA-r;RtsPYNMezbHzr;nWGaJRXi`x&Q43)hvW!K4DpN*UPjkx2B4Eb-7b-8mk(Zb6
zK7aAzMI>nd=6p9)_x5ai{5uhGas6C%(v1QOE^g}Bm|PBxCby`#I7%^5bV>>WpaO-p
z;iV>$B33JSY2t=FCciv1ss?m*1KX*XsacB_*&J}OpXwa12>GD!>Cf(>8>pDB`g#Rx
zTh}p<#K&@2WegKPot&9y7R+3~BS3gk46$2c$y-k>bOJ~?oo`3MlEx7271S&TI;Bag
zpHKiuFYg*mD4ur}L*QB!<Q$cQ=|&8*$^E{T;`Q;IiUZp3BLO{-BtZGnfeeIio=ke&
z)LgU6!eqLUkx?j2y88_!h7e3fW~R=_H-3^V^%hsB=t)}M`8q3ss$fZRae($eV);?B
z@NgPDXx26)$t)pF+h5@pEoIClV<B}6%$PN_WPqJ=H{iAJ=FvhSKBh>V#A|XQ4er)k
zzNHNXO`+pN-vJ?0tpKvN=+3O7GwgjY<~MJ)a7DIVJCsHs=3KU4JC8w3NnMY&gRb-F
zv*b;`<@QrCu{;AgS-(+cjMgPiYmPvS%Z=sg@{rqoXOnr3#d$L`Qv@l{Nzdq*^>D$f
zHS8iG^N`u($ZtVH`~<(3%Us{p5s3JfNqxNN^CwkyvwnK&K@SN<)K#J4R2pde`S7|`
z@YF?`S<pj}gKnT6owm3{A5$?h(Z0(m_OTs#i^7mV`~o%HjFR5DZ+SlO-4pMMpJhwg
z{*T@qg~)VH)mKn00$j=^yQW0Ahh5%#*rJ~N(u{+Sv^h)VVli%%GT%{*6No`yBW>U!
zno_X|>~)453z;U_koQH5Iw^`#9caSA-J2ve3TZHf%%9mU-5Y8{PScY3Xh!!Bsl;;j
zegsDmRax8Wk9-8`{1i<N!sRQaci1mz@BOFe8{do8MCE}M+DF$e>0bI3-_DqMvzgbs
z5Hfrnor3HW<wZ>X_yF%S88Y9{o(Jxi%l~!DtHkXQw^vCIy1rZ-hs|L2hatmR5@xfs
zXrTK+Iu;F&y2oljLyopKP$^V!{_DJJ4ci+~$}dicD42+V02Jqce7GMq28IIA2Kiox
zD<1?Sd~$W=7Q5%P-=jFfyBtK!bPoIs7L$_to%Ub-a`}q+2((;>i(x|9HXoUVn`@Lz
zWgvVG--O3Bcvs!-mbmykIPJc>AhfL4TG*((isu|FS$E3_?*sz^MUGD>x1fT%)BG;q
zDP8_lOXWyv>~*DiG|7Tn=9_q0E!hVbr`F~?JrC~Sq)l|bA@L98i0hRg>6or~^o?{)
z4lkwm$El~7ecn0Ml-K#!9fr;Brvd%?>pvAy&N7XIU$uI~$6Fzi&N?|n2~6E}J{X>z
z+2FC}_^9Mw>JEug^^MMi01YYAjEy~f^cZ5r9{hz+l+&%$kVB0(fRPDw#!XG_t#f-<
z&jjhnm3)ClkD@{_xmm-LzU~WnDo37EGeyYh%##^T)e~Z$Qi;7hrMYtxG>#1Ed3XPf
zRM$MselQqGhRX(_UYeWa;_az$4p5>1Wlpofd-4X0;ZVf>KyJ*MA$TPIHYipUih;gB
zo+dn%$@&wqhU&0dXmmHJQ`=E4jL~p!92QP;uMm-S60MJ#X!n<sGpL$M&_+Ye1gA>U
zCtZ8l-x~zJY-K#^WKP}EY|>e`@m9h_Lodfm^03?-R;IWBWsyNqI`LuXr`IP35P48t
zitt4a+p(Lg<9*a&3gMQ3cP28++G5v_*}zY81~vI|?=Zu`X;q0n&H7>M#v^wIX{2&F
z-nSU?<!|3T0V&2Gc`FNl#KQy#usDMs5@7!K9M&Yg6jP;IrA@H~c!K9pLjLqm5ciUr
zMeoI%-79g8!5-I<!caD)B|DR>8LAl*ihgeNtR?3hw_TRjVX^+b$MYZK_KrE{`<}zt
z8m}a9w?{cn6|^d$J&YZ;=dpG~ah@tBR(X*4iH*RNB)=HT?Rk~lgIL2^mx;Wr_sn-U
z<0I{us;DrXe5ZqZ>O5KHwVG?tr<m+47LK!0qwP&@rqvFhM99ENdDnSznCC2m?`!QE
z^DgU-b9ov)25WV(5a?v|NC9EyfX34#gGIWocdkgVf9FD3MF?cz>WQkks=9!Q9jhu*
z8kJ%1769zX%tUUYkL&l;pOu{xxK>9;y+hJL?Cz>vu%AH+LOG6Oxs9XTN7=5x(ZYL*
zY@%ozS}ba0*49(zP(57uXIl7Rpx<oFx5<!7bsUM?i##sfT1Hq@r8#tz%2oA&J$lxC
z!NEg=bbdDU-9GrS!EVEGe~{8H7$FI#*%wlL%0wiy*ob4TJuGKUqx#P3;qWD^=6Tl*
zU+{~0+A~ndG>{4GxguVAf{sg^F5Ym$R}?Kayp4NRV>SiP%*8IQVO3Bvd}ZrJU)=#j
zzifxKP8$o~a!BkfTTK{zvGCe!;$m)D6ND%w3#t=w9@Bqwk;EDo4R5`nLiE$0_~crU
z@~yWxP~|*#0y=cyx9<{jS68}14D<c{{q^*$>AijxLhf!4TLr<4OiU|vQ3VABo}QkE
zt&c5I$!x$RP<~pJpw8Z2BY>E2Ic*&;7aa1WlI?*pcsg1N3RT)2Fm7)5;kaBETcf%+
zXWKI~ra3^JU{3R;?3=|>ODd<`Cjo%GYpvJ&?(STjF8uubfUgP}85y@{t4$j1_eXy4
zJzTBs{44}i+vadOKDXn@#KhTZ1fRZ&Fz}9cb2AU;GEjp#&()Y!S5*O%NpWy-2Muv>
zaJYff&dVi_R=3*=!x?<{s|-Isp|4*FvKRrTRDoA9Ou80uIO&Su1%29WR&z-_sO?z%
znn>8yQ=~DcG~RkBZPw&GuON_RJ`2dd=`AKv+#uY~A}gZR^ep)SMDnEht|@8nT?|t<
zSp?*af_T;lF55NyqHK3gc`BkaKZN&%BF4sV#$oV~qGsA&0kx7beZBeQcttC~T9{z+
z{N}CJHBPbFsyZeA(uW)(=S>~%7o@)??8Q`qhKrIy$nzsK+Z<h~GfI@0g<65~ta?|v
ziq9Bni6NRTyFonjM?D%Ekd%bUTVk;*|LBgdr<6<>7c@eAyJ)0Bek!#<P#f3c<~MSY
znHG~)Z;3-|;aeSA!x<k};CQCr3cjp|eTJJlt4SO<5!ZMqm>Q)4Ri8}I`2f2!bQqIN
zP~{UJ=Hj>tZ>IooblqjexcFG0CNJmPOw-R^19j9)@4I9wf_9P0u^-Rcq$UD~NI0?M
zFH1k-Vcuh~R!{)dBut+?=qjyS7!9-KP}j-oSqrX@tPX)i@blo%=ioGj$gib|Ivj+<
z+!EP+m#AIrFS?+fguc)pp`R3Za&iI)_{*K2yF;m5H2SV+iOK91Q6MRK`O2!Qn1zRj
z`&$a%$+0n_tTb+?ldEH0W@cv99YF0K>gnl;nXJb~@ZJPY&NVt%2>azUG&pw!qnXXs
z06}sFoS+>YT|L|$9!DN5H1>3MLf!-g25!w%=K1@>IQJR`#>Y=h<jbqon%^HZ9<F#P
z2$8X|P0r3X*lZ5fg$RLXnq3@v!f_E15ibvy@KI1!_Vz+Uk15pD)CzJ0iTDGA_kCax
zgF`}G9`2l6UGD;siOU#QVR(R&PmdCmvE4V?mc7AjMsq((fsJu4yw4l%Ps4o2tMPj9
zt<6BpB(MI~uA^!(UQ$GdNEyA)1Q_9_im$qoFE&jL(Vq5@%k@_w5Qx%$0x=qILwZb`
z3Lr)W#ojwltCvx>k6Nun%vpP}tKXZrdTEsLq9hp8=cP04#QMbN$#?1)HHxT<-E3dg
zA5~;#2UcY%;6|LJ7=Og}F0~evJe%e>Yjw#esp+WWVn#Q4d#DL#Xjoj*9pTg~9vF#>
zVq+<#QQi3o2_s$={r?_+6=xMVZ#`_~7gKjlSDkL)*6Qy}2aGu3_-{C5(p7w#i(tlL
zFq&R*S=)z(w`?uD5#h-?H00CJk_Ag@U*OJu4?S%maVH2{v-ecXIAC#ETe+rUQj8L}
zW|H?&2O(yM?RSKStW^LJlvj={<lkB<643qfIw~&$Q%SRwloIGtyal=x@E0+7{jXd%
z)`bdO`DEdJzrBAdx0?X;2Fh(o`N-&KX6rRkDJdx@cEh(vilDcePU~nyF`Yd<mDsm8
zH=JBtGD$2!2=!L0{%L88%35xATO%32etqTQ0%qaPt(2%#>XVX_x1LX`>Fet^Ivie{
zoT%1Y>j5!gbr?k|0oqJwx4_%6wsIm|6X55<^UifX`sJrqHu{Pm)!5L`(b-vUE&_++
zlai<+=;B}@WYWUabf5%Mq*@Js?tVJRj)sO9Uje)vwqa{+9i^cuEDU2rLrc3qnzb>C
zTC7$pEuIO>{X3W}GmIu80kIGC>nzfkvh0DIUoN6A=gL9{U$Guu<aSRFW76H9&&{N}
zN~urn{(qExbyU{d*6y|d5s>bZl<rQYyFpr{TR`F^MU)O-y1TnOq`SMNySwkgz0Y^f
zckUQ>jO+i%kF{nz^Lgf+tE9-Ycl4l%;2dTQ%K2E*Z>MyLo0rYB<OhxsV})BND%nE@
z^Hqr@1lSJjm@W7FKLn@2Gh3IuWd<O0NoP9Xbw>Crwxg9!Q(_H?qcD6km))haw^*{A
zB|%0W{Vp7pVi2YZz9px5QVRNmswh5q$hG5fcSCv!KV-7d>0a8Q%U;0PHkELpn%11y
z78HaU)VmT9f5^K(Wz%E$0%a0!z5CWu5Y1(Y_GOd+%@Oa-=SF@K8m7>9*TydtBy^_c
zcSVpNFg`szGbQ`xyK8t0S7T~Z&EYi7Ir2eAOVA)7{MSSJxXRd@(pM&@9cUyN3f2pd
zqj9R#uD73Yr=xl<+=QVB6BLhy`O)Xyi+fi1Mi7j$Kgw;D#Q!n|@ABmSqV%_h(`fqZ
zvBoC+7rmvds@l=kMyp+WS~029I;{%w4fvPC`~(q{UIz^qgc5h}g}Ah}pixm`-eBuk
z*Wh5tOr6Vx)!cZE-3I87A>_MmvHLRYVPk!<KmX?$<Fs>|m5$*Va@3bAJI>dqRE;R!
z3UhBbCcF46Xo-oxia<e}dZ!JN8@D?#9>(f=GN3F%J2)0y(VEy;^`)ewEb1K(bTqL3
zBs49P<9R*t#&!pmMM@A+*!J+;_xhO~4*X*e^bd7NJ9NDG_wWjk^lWoT6Q;&q|AWFR
z6bx|XwG$lHv1@dO_5Jzv`SS!rWeLMQ-@51(@DfY0i)6Y7=ylL8Sl@pZ?SJ8gj)*%y
z%}#ms)H%lB?qz4Cyi50w0EJnsp5UN4<|<j}ktrOT($o7iOrZ}>lru?gmL$*LGUsNr
zBr2=_n+Sq04na6hZ`-=;V>diif9~og9KS=M^WyV&g9AP%@zqM*)oC)welD6W#Ww5Q
zS9$IJ)UMS<spf`vQXTTMe$`$99RPoWm;mXtR0c$bOjq1HCJ|B3(XK{ifO=|z)>^E*
znct@s1;AE|ID<W>d>Ng#Pm>@JXztUbx9Y_{$>nRFpjxs8mC|Fi1plkOJp%(xG!R=s
zXTY^H52UByV5Ar>;;%a*F94$X1s*oZSRB2+y|M!lV~)l}Nlsl|J&xV-^Ff(0;u8sb
z`-;LsdNW?P>+;<tOa`4mpK|j_r2Y^*09QgXe)_z|DO^50J`T<50`a9+$$$*Cn=;Au
zTkX(lsU0D|YqeMip>^Zcx*r;)FQ0n}07R)cD|nX36NMQ9Bp5&~qy53adUlgeaF5?O
zFG9*QhR+d<cQ4whrm={K+rcc~)c9fSEj?kTfJTxey?wfQqEPZbb(xSzCMFJ)pG><y
z5HXDHcU*eZ>PF~GKDwLUx<VC9KoB#p*I=J%CiHYKW4ShS1Z1VAroU}}&=tTt`8aDm
zDoYe3Q0vD;AS<w;p!PL%S`20>zV*x8&F^jX5rBNOWe^RR`B3Fw14p){UjI)=PX5au
zp(QOGn}l50BEFqDz9@v4<M(Q2yAA&PN~Mqm@=3q2d8RqDDlN)A3;(`5Wl@n##v7z*
zwr8W0Gqh~0jQWg2)g(#}$<z=UHEF2AZ`M+Vh=Us^n<BbIFn8d=g!@(onj@0-f4l8h
zeK<)?Lf{X?Sbvi#Y1lnIJtHF{U{C*ik*<K*w~lHaKjk+fnQBEkh>*qh0DLj~y%~(x
z&<3cmWX%DLHMvkL;K#gN0G5y-Rh3|6WrctB%FyTb?qbmZ3jSrkAi&Cd-_g+#NCP-p
z?#d_?14Tu}&r$g#cI$mOb8AaW&CXA8i!G*0LDo=VFPkdXdzNT6o~u$$55h?)dgD-e
zXCuRv#$M%4fJSJ8Pvx+M;h8o^q{5RrSmK-U6aI9*qQs%j;lndE%@tGQz=Hv1j3{o@
zpFDtSOfU3Dc-r&C#@&bc-Ly`l1oy%xjIhcYMjqwVE@cVoP`CS|Qf7Gd`@6L8+WF-!
zjP~*`)yr2bgE=r2+B5AnG37b3{Y2iiL)_$Rh;hr>b9D%E<OjK9n24apk$u6NgF0_Q
z=%mOGA|3*|DD<7LKy6HQ%NVjZK`qnewJ%T|rta@Cyv^ZoJUO5_!`|$J5ymH+e9(M$
zh80{>H6R;L@MCYc?31s0>f4{~Es82t5F!9c8ve0LJ$5IWybIhb!oTzy7Oe3PEH4q4
zqgF1?I{hH~n}vZvQRO!p`5Ml*kw{p71mvzCpezTSMXV^}<KqJZ1FHk!pnzbe#sZ;s
z6>0)kWi`LdnG4_<XuSd~ry(2G)6^&P4K7iv`*SsA7#!7gle@~|R>ZOVZrm&Ie%oLj
zb#fGy9JHjYtW|riok~491daP)wXIM~V(}x=KQ6ZRJ=XC+ql&J?>cAn}aQZZM<5+}{
z)d+{p>JpD{VZ`anKzzH=KnUMqc>;H_HW`9F+1g_cHOucJyV-5M(iZ(*Wx=pW#^>Bw
zL?$8kI_+Su_vsP_jzOR~dWjEyc1WGEgpZOJ(o!LyCbIXYB^0XfWXE2~DT+`DSPlBW
zw%&bS&ilueeoI9B2bK8IUpgY41Y|eGR*&o76f2L(&~~Su`=YJ9^EoLAdw+*eHeTb9
zU{Ad-`BjK`-6wzPgyx2D-+sHC+Q5s`cc2W&Vb4`GK<5nFm0G*uPt@kanH96F(qomc
zC21x6-(U&eH5DE1UB)9=;{98j2vq#MJH<pI4c4Rf-~yy%Yirx}J%PtDQoNdf_PMnt
zgB<kgawS9^J?1t4rJyQQz4i;0ndxF9U+0UycrG-#<5}yzcdr6S!V(kd!37i-|I&`4
z*Iuqh{boFv2;__Nt<g@fajLt!dtrePa1xHW=|ctrc}tjF7j)P2b!O40JHI|9ZSo5%
zrHvnqR!n6zI8A+*u5(YBgWVGPhn~fyVFpal@7v&`qsjWv(`Cx(boXT*N0SKQ{SLcQ
z<PT5)FVA&3pR%Q%#v2VLB2nJlu2`k0*?DH1P@g)JuVsKz#d(lXl!2Em+WigdbV`KH
z4$il7mQH|FT0RlWeg)#i@o{>O{l7^?M!>}~?o@W7yH#ED2$NwTyuMrCA^n6@SXk8Q
z-u;Paqt*|O23)&u_^)ah)Bf|#$&#Q!3neobC3Kv?&Mm(;=K?=R`1Xw8o6%#sEE#xT
zYyFeH^g;UDeoBaEcRq<xylEc!vc`)K5PlfDRD6+Wmwmkf%2)2G=t%GWl<O#e%5@yW
z=B_R{V;xyp+0q7A3>-&C$Iv4M$2@CvNi_qb{@5RQI0tvo_RZn6lDn24eG9rwB1U7`
z3e~-nlTp3QjMUWB?A8l&8B$niXkppeZvmyeA_`*FTu$hFrP#%dqVgga)*el5v%+Kh
zd!@K`_vTf{S(8f1bHbU;J|P62WmAFe>2--VI8nStd$L6WZy*#4t&tN9|KlCMLw^7>
z3=Da)5s@CYF;2RGbg^84;E53v{?)6RiTqF?md}M-OF<F+(}U1vY!1+n)|f;V0ka{Q
z$8ezQIbxu`62BT?k5S;qy=mQnMW6HbB2M5ILfD%4<*{et6ETBauGYWxlpo3l$X1E`
z?_HN8JaCYDCdK55FzgQ$k<<8&y~aM-s=j}YLt_0J<&%(0IGU~q4A->OjX|QNj<(1)
zSdD#_4HX^5i1F_7%=xvD++|UcSxNH`Xe85v`E=iauCAYfwJ;3?cKPPS5q&Pt!TNgf
zhyzN=rT@*SftTs0zp*f=si}cL?cWEANk5X6<>eMP9B?O?^jf}nLhs*K)Bc*9!=IH)
zlx`}1A_Q&)v}adKIh<fY1dC954S=MlqJrpH518Fm5fzMBcS10x23HR-0HyN2o$|g9
zg2&zXM4C_ifZ@!%17gU8Kqd4FBL?xef|xV5QQ?Wg?C4FPvd#z!-DB?P@R#muW_w#B
zbvo5NwPUP5z$77u$R&-cI3v_hoea`%0Erz?KyAw|fpT8WbhWAz%O~LjXtv?no`6^1
zqJs4E>zPs|6_<F7eKqf%Y1%3kjnL)hG3-|$YP0-U`JVdp5lxB7<$n41T}15n;AyQU
zW(2|jST8SG(Oj<6$e#+dtzO%&ns;}!rHbw<hLFwJB-D9=9G0IV-qz9G)m_(O8m3qV
zjfZeHFVJy7$!K^Gon07u`EZR<S@f1F)#0Zt_eu+{ia}36;-_bWrrw(oBhE#Ty_SXz
zd>yC0>i1bt%N*CmP9U2d^Hza&4+BcF9~VkZuJi{LCr@3{DKV(0l$>cP4`ZURa*pqW
zsW87y&>vtvf(FNbKm+aN)zwm8fK3{jj(?vQ?CY6I%YZ#V98gm`@y-eZJ>DA;@AC@S
ziPdJSWo*20vN4z@Kp`9Ny9WfQRuc*3*V@|7$i1muv>N4bj;7}3WwfmW1N1yRcy|iQ
z%41d5GCdYnvQ6}GAl@<L<_>*1Dwf!(HFwdZE7|S-RAVdK@1=fT^r_Cz`%rd>k+}eR
zP7}!9;cSnmkGQw!L}1$3Ue~ZNt_<N51N#i#D+Q<;NWMBo#;JoZV+JA$5FJDpTBSk5
zR|gPg8y1S*bVr0o9BQwQ!g8pWethgx*3|g*`$DdtNHwZ`gZa`;*PLZ8K!?^eaG@rf
zKkp6|to`QWq3Utmm*?mq?_U#<J2_bR)u6YNZ&sLBpVV__p8>-$Mn6Zn1^N+|j|!Z^
z>j9B(+~Jd?&W4h}G1lVt{s?qtK!2uDs{9AbF6p?Ij*DNjhb8)KwM-mi#x7QGA;BQ_
zkv!cz=O_`|N4=sQiE!L-_Pu)z1bOIeDj^9Ad%nBxdFu{>Tt~-^da{rbULF~SLHO=Z
zz~$QWpH3Eu>kp^FW|OmR{>*nf9cX8Jl_(H6AxpS>PP?Hq|MDf5)-4&{gUm8~WO7Td
z8F6KI`YxFwd5$z^W5n2@VUlU)FH-|u>ctBt+g`0t58qCeUfB-5@E;m7hhEI&m`MJQ
ztzew@l?mt8^DFrONQT?A7L~}R0$f#g$3MlP`XMOPUhU6R(5xgf)T75pgi%2A4ubKy
zTFf8)9mumbX_=W6*KtS5fSPJ>xftl_2~|8r`ffg1*o|A3O-SpHaK>b{u+Vq2pMoW#
zoJhdq@Nq@E&SAI@H0&Iw!oQLs4b;uM+BPkhJjNy&q-ee?Q{$N!^Zs^@@SD>zYM`LI
z%Cv5evV39g5M_ShR={QB31<7z{ZX1fqLc-S`KKrEP=Y>@iv|uit!E8mmk72kyx+wL
zA|K8!A+4OEk$LAY`22_O*AFO9UWojZPZuxhydsLX)4Dhhruke|b>W_L(E5y}td8YX
zC(n($gYge*q5WwU-4^#!q5LE2yHHP*=o3GRGjirFxt2riOb+&$<bxa)E`}s6&5!nO
zS0*-6MiE$RP$_F0tgRPYMeiN@$RTmrVR1P{ZzFUJ#?ilENY4rxnwIRy@qMOyhKK?u
z{vJmAoOulF*RWwCWe-IccwH}8nDA^O(BrCTvR8gkbJobYZ&S+3^xUS{-M(|u<BYSu
z{Dr!yz>v*I*)O|pl>6*KlO*dz0YOA~7G1SSOeUlt=PL%Yt;J`(>`$P2A?AVU6@;R8
z#GySV#LO0-_q>mfmt9fgZX<&*CwI>p@K%ZVS1fOt+kL6s*KWv8I$;v+U|{m^o*0)Y
zRLeCGk<Iw{U^)Em(6NIZMV*mr2u`&{-@07O7_%)EL}QLc7;=avw1ZVkNtPg#Z5HmZ
z)L_89Kv6!riww^7W_xm4RgiMxec_e)mP)}hgnl1>hF9CkInS851&NXO=J4Ip0F5^P
zUqzjTF2jc_u5wrOSNjv^>SGn@`$<=%>$xo5eZ4lg<X<qPG8gqt-RZj<)JB66PUIJd
zijs->uI{HT)r`(5RrFw^+HaC%{I133aGbKHqz@8GDO~KCqFNPaVxkz8BDNSLgQ?zP
zrsVBAU>?g<io6hp>C<}#)${Z08jFmi*Y@YGx8G6in6a9#m5?w%BI1`WJhrg3G~5&2
znE(&kb}0jHAC$RK9_Q1>dU|^01<#*0=c|{A1|stl;o{-toh>ai1A4=c@P~W|;BQBB
zQBhIbP-0qIS}0^<e}jlR`{`Op1p=w5s5pzH(>x3r&6H^ka@<W8n2_}C1Khu<ij9qp
zpusQJJjUVG`zWsKpb(s%`e4b@hvHYWE)ILi0S_j0E)y3#W3U+hvzilhJDr7IYPWNV
zV$ei->lp32u9Ii-bhcydo}(S*>Fgg^%334%)+doZe0146aI?2QDD#N6)S0R=R&$$M
zAHb1{*sz<j7@&+Di|VRJsCnP;zP|C3bQ`o{Gnd6FFJX2p5!#6_p#FZ<EB6wkrs$LP
zt&tG>X)F(%CAP=i7u#y0S_+ZL5V=Cml-|MDlamxFb?YqjJ=$r{Rc=!`a^A&dh3e1q
zRAgMszW4kKN1JD5oL!55E=)Z<fxg~GL0;vUJ%B7$YoUpa^PwrqpgVk|eiwE_U;@Un
zq+d>|B_R8xx3`diNT$w0Y_k1FOi}l%&)N$N&T^5@2i;NVmO_)X-lds)l-td8lTnez
z4vULmX((VlS~|J#h{o%*YD8g0bwpur#KHo3dR&q`yZepGFiHv&f8N@}5!4^OV4L4F
z32&_lYz1|VFDI{EhC2uy+(Jp)3!4b`+=s?H5IBUOOytyzTa_HLhCcf`9~86%Eww`9
zAT9?C27}oTZ)cV_{TeZi(&wgF*YrDXj`z(BNRdC1c%2MxTC8SJ9Mbd*PYyL<CM&QM
zwxI9aanSi`ho2F_A8jH7J9Ue%rG04HF6SC0B{aDgT#$Y5+G&b2^AZw*VEg;p&xBie
z-YY)~T9P*gQ+iK0yhRdFqp;WHj1S@S@4Zp$>(mu!<y0XJwa&bdH4SUvj_ak`>ywJj
zasPShp<pZhPT}>?BHkItj{%9ZG*Ngv9Af5=NO-;oQV7DL&J7TTj$FXga9B(YwX~$>
z=LeQ}f-0X?*a#@-Gcz;KjnT2NdS|);j9%&ooPdaV)8+Cjt+@DjT8-HhAy};dZy`EI
z2L}qQ2+)%B3X|41Fp!p(wy&q>_~N3fv=mSe@5$5e+WgQYT)v6>c8$_+2CAsKAGhs*
zMy2*<T(B*d9Xg*1^X?tdL8jB`=5X>mFO3Sbh%i!DvgwHl%qmtUCMt#p&Q@W~3TuY?
z@O!X+TtULhYJa6O#D?w~M$M1p4Lh7~pk)4xaF`8c1C0xqx)McVU1gG(D-7nuWvT4c
zuw2x6U+_G}9{S~iffLpjp$=oivBzWWVlxz3TRyF5A;9~^!~KcU!AjuHdt!2!I%b_I
zMKno5mTPB*dl+p2QGA^{=CLxes5kghIUX0ZM!fLbqPJ(GfhwNC0*!e(SZ-+gbUG9F
z_c4n&=G}Kmn~L1aU9mJQH+3?5TUB4sx!;h@qt}`z-h4UnV81<GpLR1-Q}B>qiIoku
z`BnQeH9*^dH>kQ{ve|t9qZ(dWUKy>B!+=ys4e5o1zSq~T;+|y^(>?EDyV<^5q%%)(
zY9G(8;^qp}9&-ff*oD@|qh3i@{Cp8j#n~=gQl0suR|{rp1TC4-kO$`5g{=ZMnC1Gh
z=VN`2-Hs*{@(tsudXDjt#vLNVv3#}F0XAB|O`Fo{pC+!S&rS~gQjlv&P)osfv-HG(
zPb&<Q8UFD8L4)}G4VEV{2)wvw7&q=FmzOU{97C)>@;*1~s1Esdx;iu;FqHGTs!Y)H
zW}HA*D-j>(x794q@uu_3=kL5nyT()$x=&s5b7@1)?q@-!8j~Bl`dT5-1{*ID_OpIW
z_{;XtuCGl5muNTls7qE`w@$*qn(y{$pHy_t$T{trZg{@ZP*>UxHqTf7g3>!?;kIWa
z&(;gUA+!k^q$_{^%;;X#TddbQbTO6-`<n2}_E`7aoYrYGVt$qNBI*E1a2FW`#r<LH
z>*}D$$cd2*sT_v1C`Azw5pHg7CrT!!({`s9Pv3!NI-5Pv;3{kmcujwEAt52t3gs@6
zTtqx}c|2o7LzHb#R-mVXN<B0*6tp4&sUlY;FT`5??rI|`At9lmq2b8e)6=uM9t?A)
zwt^1E^3u{&!YUxRJ%d5`6)mBrHVMj2R(^+FY*QmsQ^?jx2I%T^f4IBok7X0mwSfRq
ze12{YD8X0+1VL5DCnv2cK!3K4f2hSo#)*ht^P^zA>Aw;V(?G+Kx6C2to9=Ap=2~{P
ztzHBfUyc`vpYvj4zKH`^tD8%b(BT2gLRM~W)V+B9R_6A=M9tT(ugxRC>+uj$@Si{R
z%zg?EvG|`u9J@N%$f5n!((-Ac9+Ib8fXim4e0P0@MW>-GFHc;nA|lcb+^y+q9j`P6
z3PqV20&GQVD=8!6AvjJjeF*%#g-k?BYNwOP!@%{<-{9r*Z~6JO^|rVZpq5rtQaTcP
ztD>TUgM$N7Cuzl^$HN^c85AxK*JEO000bnjAQcOi78g%$M24-1XEVnn4OXwTh!I@%
zn@mqnC**VXW#3<H5`ZuvhUVmKr3m>9<p_03%F2QQF>{}tlG3Mg1Ef_Z{SM##N&r2<
z%R^(485tSt>+9u4k&%(v`U?vS>FGKR&X)zOo9G$o>GB>*o8Oa1Td3)u35$|@3!{B_
zXU9MM+!SAaIBv}RRlbflW7AsDTS}^q`2mvcRf@G!$sgdrV5F=8T4MZeiwyTX)gKlB
z2JOEAgLn}rlFs*LtH5suX0^E!rR&b+e7ec!a?V)zm-m<JQ&Z9J@6t0e#sNa0*RK7b
zp)nA{vH_Y5OilZwb|>CaQc`vZA>c4SAE#F@{a#Y(c(9P|0QB0xiqw#k@87>mCvaB)
zIDCD54dkyxM!j#}b>$(DtAz%a8=$~e+vp1kwLW<UgU|08J-E@?*}1c|H9a+z+yL#^
z6zkwmwtHJ^3?AkKrda`qS#S{Zwf3NQlxqdj+YkC~9j(BnBOo9+pYQs)D@8M<6roYd
zT8w2wBO+9soH(s`J?>pQkT|)yd#>xUd7eNO{N4;dR~_1pGl7RclnV5qmSG`Gw_*Ko
zdpxq!E9UaRtQFhwU~#!sveQ_*AKC+u6++~KpuyBfxHV#AIAIJq5n&i@S3~1;NvmhV
zF!{;j0bS^ZnY^{ty&h{HF1FUKl=|w|5s{|(ciLq*++PZ<_@~~c59Kv$X4XkFH<L36
z@w(zS+E?wlxg0F}w^UIRA*~qW_#(iy+5YFZwRlchU9)y;!HjU?(%-61<Ka<GPL(pA
zu*HgRWKg_KQOm_Z#$o*646e}YO}LjJF|tZ73A4%hREdxeT4%7h$=~1R+r|5u_rPlQ
z#^0?8fZj~uOY((k^!j$yOZSoSm1CXGut@7L{ez?hTl+|IND?}AV1q;WwKvsiT8~)U
zr8*4Bv!^9Ml~evC&gzL#eoYbknYbhLmmzUb+S0MjC1%swdCtWVwR=XjQ4Q76LKA|R
zovGoujLLtTn<Q98qw6a1n;9TNcnxh|gmo%_zSpw&vmynKIKRFOn5=Jx6eTUSwBcbT
zT_wG#hvDMm9<v+x<=dJ!pB1wbN@;bY!vD5okQo^V*>~+J=tdPveF@H<V_2A+8MPV#
zT%gv4tmT^%ouVtDgZl=iqf8f33xk1P%4vqw(Nycsn<T_(rb+u!)3NUU17RKBPKhVb
zCtevG$T;lS@44WfKUK)EB_Z|f{R{ep#-xIn*?Kumi}FGcE1Kb(wurg4WgL~OB6!`m
zXW3OT@j;iQh8gh#A3fy}3U!~9->caN^R^|xg{V^#Vg36ubL`W42XSWw|M^fdNdxSf
z#}C#1mK#muGq*>Z9rAs2_pbLayv{RsKl}Lke_NWAF9PQFOE#M)f6VM#*Yi6guR;=V
z9xRQw<P78khM_MTN2IH5py6uhYh?~W%nHw-Pdvv&1P#pQO{|nwm<xWIJf+#pB<RP*
zvlm&YdiX0=Di6AsDW^}y%u4^v6dzd;vNUV#xgq4xccjDGH(yz`V=xL*co72;0pyFD
zt-&ww%vdf@w(@_U)sK5}cFU1AvrS}C9$FpWOo-xjTM21}?VU(90y0kVHzNUPH{=Lg
zDZ&Gv($CyZKMfHg?X4jzj(9eW<A4i{J)Qn3iPDZ}8ScxFXZnAx5;3+4`ipH$doy>b
zWm<_>eC4YkdJYe0*4P0VcQ;U(YE~%uIn5_x<{q0*w@Nk#UMr7(tYETS#<CRcQ7JBW
zo9*RW01id^+)2>(YO$0eh7#=BD<G~A4y%=GrI3+1X~kgowl!)Zk)TJj&I$P1HCFo)
z+RS_~uJna~<<Y=d!Ny0FbQ?ip5_?K!C)9;>zm#w-n5X0)P@X{*f|=~}T#4|Bz}G*2
z>P9`T^I`n$ythoO11m`Bk%POG%m9t(<)T&&zSD|Z{j6Ims6G(TnU3G&2ICt@^7t!<
zzxxGB2OvpQV<Giny<z@PZm97m6wOgEZ=RI8OeX#PSKD}XeUxZ5B$oe%!Gw&nhRuRS
zs{leh%wytHycUT-7#SZG!-@v3WE*!U4AUeG!)0(oYh%f*PYMTxpm{`vgRh+vG!P_?
zWrQje^tqg&S4HQ#M!MgrP|=~4yl+L}P;4<Iehh*^LA9w|EXN2gHyRIKMM^9rQp1CA
zj|UK2Eo@LsW`_e8{!s)VZLUmI99rrqPkE%Lo`T9t0SUMd6{!!YBa}qES`1W9cRx1t
z7iFVi@*+Jkm~Oz^9hNLiDo46ZqFQltQF3rlDrpai9?!0~R>aK2$TAkh%&`=#q-GXl
z7^pHIMe-Z{D2DTCue9WoNWEB*;DpgGd=Vl!AOGTfU+_bnslqU?z?b|JeYt_%L>eK9
zmR5rUIF7j!rr2J!j?S%?*;Ci~**itCGtD>GVRtTQ#)XX2jtEXI^`&oMZn{#F&1kR@
z@P}UGGZ|Jt9h9LD2<(6UQby?2;)TB1MUG-djlK^O>ZjDVQBO#oskTLTCHtxBrxo6u
z6aC!MGcG>ukP7r4?XngVd<*LdY=qF<7a<m`YIFEL1LKSE;ayo$SA8R%c%p}dU-AZ#
zi6u#hyDb*}|9k}+&Worwxo6K6!YI5;pCX-w>a46^GT{KbTVvtyMIa4m{+Erc+Eb2c
zEB}1}Owgrsp2;y000$%_By#Ux!-E^h#R5sF1LTKe0rl|qkH8UVOwQ48+$B;jmaQ2-
z)LeBUbg;|Ky{@<Z7<xhytAQjeYV+tVCN09EYY(j3GZpS~$<O)8aVVDz@&6dYwyXhJ
zG4^rH8t`<_w<FI<T3rNwxj^+?fk(9?2zy&{+i!>GKKOkTB>w5&u7m<@E)%n%g#nQ4
z!@ImEP%1iUzr&lu=s<~?*0@x}!Xj)hGg73Y)QGfmr`FPX+K7<%rJh}11-D5M-!S)-
zS4$~u=gIz^hNTP`N5eC5UIVuhM%nN~KpcDjx1aASfQFo<Z9HK13proLOL2>u01U(^
z*FeTug*A5Xv}iZqPo7K1HXBC^*@gx0hXm6N4rWG18Dv4%21e2zRIZTIZ)qR@><{$B
z%oSF{QVP@o&41uh9s?9Q@{fbOQ&Nxc;9V=LuIf2%<7I{QM9ueQf|^>4jVD9maL&)+
z=?7n78gEN-rh-6$_9s=K<oI`q>w}YdQ9v&Yb9J;fU&x+G9~Q{BOS_-6a82EJ(=Jo>
zQ@}Va6oeotI~MU?00HX5tBc)`zISQqvU8KJ%R=52_J}F`XR?qoVw(DCk}G2T{Ix)B
z^J*R;3`N43@&Zl<00)$3*9(ZO81>9$O*;lkr);N1FRYgja%z=`D}BR+MO&VFTOftw
z2k_wIA+b|1&mo0dFd|JX^UuWM^t=-Xz~*;>!+vE0QQf|hy{;fJ=1X6M=vGxy60i6Z
z67KDTAbzAp=PC7S8Kx7R%d>>Lrc42(6t)1~TJK`)+=g`|#Vcg9|2)Zs8%MMNeWoc3
zS=Jn=ei4$o<zl0Xou%v5V1}|{(7J1?#OMg;t^e`t4ZbVBD4=6PQ8BP<&G};v-myRX
z-p)j#w9=bCh4YpR(~*-MKlg7R_r4yUD3?bfk-XeMRnKlB<4_u(f8ses@>uU{Huxg=
zJo>UCU`2AkXv~?hgJ|w5`>r%=P6C#kY28?*_6<AtoD})E3E+TO>4NOcXQd~ToY9RM
zDaZjXXr4w;9$;Wl(pwayMKSi34x)vLOYjWUxAD<$O~LgA6Sd^bsYI4CuhxiDzY|A=
z2(Md4=GFo119Isve0F4<NNit(S>?ay2Eon+>*-RFaknQWQjgHcfg*NntkZ*KNryT|
z6d>_*1=IMpIID5<kwW|%i5$Vt2dAnbIBulD`3ee6g1e}&zJcN3_xJzv`+?i1;jet8
z+fOS{NWmAzXiB}EsEh%;-ta!G$~+dbugHH?J5!lW5BGSwW?-xY)Cf7XJdc+dDA~6z
z95%ysD6YGo5o*S|fhL0+(Q%~92N1$hzre1_0^+#CUu5dQ$qm@zo{@JhBXxRvA%d_j
z3*hgQGBf4JfR>E=A0%S245eIRTa*SMQ9$97yN~qv6=|qtT*K)O_ZP|@|N1?#VI5T6
z{a<1XmYKUbzg&=DIFOQ44_TVYVL1GyoCfOu&VQ^D;H(dJf4oP6^zlmnrf|G1scC5N
zcpUOmQkIB3Zd0}!zfO@8GgC9gp~;wS9F|4?&y`XJg`v1$6;%*AeDuq`mRHwUUHJ$o
zF#rWy3GHb2KU;84J!16!myc<JEC@sjE|6zvK>|e<*M1J_BqO_2i9LK9tG_N3CD>vC
z<J~W@LgsVx(yy=Cka|lHqlwv|Q)}!A_!%r@ob%{ErU9;|6~<ilPq;S)@a$g{>ti7M
zpRepX!I%EW?kIy!m{;K`m>uJl1%beIY-Jhe$Me_!&sVC-rc&MJukF4ps4Jhe{Ftn=
zD45rY<=neB({NaUY8ivVdpm|XYJJT%GP$x8I_cS3NxN$?qCS|MsM=Y{owqk@yns)?
zFUX|kO<Q4UJR!1JoD!KxKbRdc@ExVUh-jO%@UF~UXmVitqn=igo*UhlHirWH4g^cv
zPf8)X$mZRo&h~dZ{jYc~*qBU=J~X_+o$0~Z^9uGL=ZYX(g9tw3J*nG*>+P7CX$kx6
zoR<SQD^bL9_v`(#Q{s|VH9xK-lVw-jjr}9UXfMVYKnV@Y94jWJ6#$NBJ}a(HW@oMH
z%UD`KUC*mU=6!*)r2@bzI`tE6v$rEuG*u35FY*N_9l$Ehf{TIl6SPe|DYqN@1*~cK
zJ&6Y0j6g-Dzite^4d3NOk~T%m?@%?zTh}Pp4Zzpz{tC_Qv90k@RZ#RbbHf*{o{5Is
z&QStIAx+AIe6HHntsUDhOKa~1u!O?VY9U&4?0zUm=)~FQDKv>kN-F;K8nMlCR0P@c
zn&<g(zZ)*`oD-6%&)ns^g1G>d)C+IBh;^6*t8=K!htyBQyMZ7a_PwQVG964u4TsUl
zy7!8vZFz3&J^}nx)T)s`Pa$g<QS4Ajs;t#xowP?JqcFr#b-|S`@Y016S$qn9osRhG
z|F$R_ZOT|6%S26AjE=6!5S~rfSQ=$jM&B>~q~Kf}DUCS~($|GXIx-+!8s;hl7|m3x
zs%3HiK!vz}wV~u&$hzLD8^jK&(C>~qLoDyB-8@hq9=~Haam%Z<XCG;gqfH!9EpHf|
z*$=`sahp(f>F-0L=W!vJ|9HIjwWIn@jSqpa76Fyx?ZGolOU-Q)^OMX#D^D1E=e(_S
zC>UfrnkgT`jFJpJ=AEl!J)ATZEr>M_YhJcd@Ck8O)*zI?1+lN~idEKKL(y59^SJdD
z57RK*f|-xh&qdz42MKe}zu4>&EN~q@nu5Y2kEihs7;G;n94PIRbiI|BrM@S~GR*Gr
zIIE72lDjk{=aOIowEOb;>w!B7jr9zQWc>`0#SWE1cBAxXPDv(eFSfd$n0K(C;<$@I
z&I3${d{9_Plz!gB6SUi%|Mk>dUJsC*%C27S;rb@x8CNx%zS)#pOpZfCLm@9Y=w13Q
z;VX>AZ$5(n9)3_8z~c`FFi1@kkbEqm=zwPGyOKvj#fZfgCe+@rGWHbWVq?WJyt~*u
zwkm=Mbr~e*6Jxa>33f`5Tx1|n4|F?S5V~q<p0X88xC}EYoWHqS^~rtA0N5tyzrwRU
zhVnBRPwc1ebw#X|YD$K^ZONjmzOI5W!w8a^H~GJ+k?c161d~#tMcg}$gb9Z%%)(OZ
z5G9iSI1>RIqQF-nmaNlFxwF9ezIT03s<0TAcA4Yo54K|Ds_M6xiH;<TT0WSIa!AGy
zP&+u8ed3MNOl0j!egG#rQ&&nuckmrPNMnoFfk2vWv9y@QYO%0lfK<&}dE`vUZ5z+T
z)ci!&nk^%e$bprj6IF&>0zwGrXQB`DLFr{bhnD!QQ}|ANCJ+(IKX+%oNQj6O3+;%P
zV>Ok<6^m8rB?&1MLqzq`&iN5Sf6#%aQ*xjdo<O0Y6AgV2f3;>vgzP6_Ws7ew@Qu20
zRiziMGzA4GpR-AcmBdRl{PT(}Ap69rS7dJaifX(_Lbz(>I6BFh5`EUNaZ$M?&xYPQ
z4w9^qwF8H?;V*k4EZY5F?u+@yePhvj-U%Pw8z(*34Fi@|fh5e916k$AzK(DvEF@=e
zc#Om(z;aopYRspKFn)1~dm3Hs2TxItjGx%(S8$I<lX8?kH-J;_1!|v`3rN^AHiike
z#N=X^X~)~@Jtwe4QZK-&hWb1}5eYa<d)+dRg^jh=zexA}k4#gUQ(b)thP|J`BJGZ3
zyea^rHa(M*j<=UbKwSXi@Y&x7H;6#8(`|A;+nKbUtt==g*aMSW;x~W*1pFHqmdJbO
z1q0r~DygcHlag|w6urN{52KLT9!e2va6ZFjx0tH2GseTi6A%!1sVyTZsZna+=jZow
z#$%-;XqW?NaIcV%FvVRzV^guSI~*;y&(}GM!M>k8-<=*E9%ka;P%Afl_wGq9Gnj{%
zoV1=P-va+J5KlfYx4nGz>Q%ST@AR)-zzkzexNB-)uM>iC=f11nuSeQXBDKs~Wd&Nj
zD<CoNGB-VPb}ZW1IqjCDT4^-(HaC_rk)ac)>PNu9Wgn9~OkvReG$sja&1{?ehN|8^
zHEI85xygeCWskjPuQDm;$u@PxcE;l5CDMWjJA&h@FhtWFGWEe$w2Nl4kW-b{UOdLM
z3QJzSUh;T1M+LgDNJ{_2mUJ%Dcw9ee#pu_$4Ha&f1zPg$l!ICFRqY%)wX*CP6|Z@U
zl`mcWAJk{u$<Sh|ijpLVnY)+V1M?y=46~r&Qom-1hGgaS^hDve%~prcZc9<QydU#L
z#}BRcqGU@{NFVeh1`!Yp<+jVg`FgBk3*x!)ORL3Hp-%zH#zq<LWg-dGV@ZQzkiJ5<
zJRi1gs1zRL&AZVm09xZmnqUwrQZye9Sv5tUu>#hU4DsFA^&FoPG1w~3|1Q=787|jZ
zYrh(!2Hua3{%`MZC7f^a@Bljw0b_o->?`@$k~)M?116tVN2qvsc))T<FuzbQXJ}w>
z3fSic7dgI0AY*Cl0iKo$p8wMIJ`jf`m*M5jnaknt$HDnlpVz>$jwsA0^7VH2Vpxm;
zJIeoXYiDhJ04C=FDZMqCbt24aF~zv8`qc9yAj_SdogEw;0QGETWd+FcPCC$?Nq~VN
zVi^cyVqq~?W-NDV{MGq~JFm^MFc2F|DaGs_F;!umu#$r>o6hGx{i+(1y>ChUokWOk
ze1vRFp{R@qQNU1A>5<Yc?Qc2Iw9Ln|-mtwn5vkk$j(|A&k*A2mYFw)>CIyqt5KQEH
zXS+RnM%NLsN~A@YPF^1n={PkUt?>iN6aMTA#3oTlgoC$XT$we3tP|2z&lP_grZ<ag
znEF_1*o|<qA>(-d0uoX7d6U?i)CdJij<6S<j4Q~>N7^3S3JE>YR<h=|n<Ki_NBUKF
zN%$!gBw<mpZ~eN2VHi<ksNKn1aSh=S_T1zWS6aRkt7+WCr(+Kovw!V+J1XhRWBYv=
zA3~aA+hT$gM?&=vjP<2J4{t|_aSXp0GHmkAR7vp-$zr;EqUmfY!UgfoOilk8-ph0a
zu@`d^keKq!NabrQhPfIiSPaQvw0=TGO9(FC@$b;zO31~-LnPo{@9}UCE+3wtzP?_5
z90ZZ>3?&PT{Fs>OE-2|>JlD#>L6gIt1|Q#29U16?1dp_iriugrHG!Lp3j%0vZf<sy
zp}!{SbsOUyv{)}XR2Xy{$$o$u0Yno>(%HCwaHia^LY;=(vwLP{5|VG|@XsIP8I7IY
ziOhlh(Xz<4H_$%?E3)J~LJ!{%2)cn47AyKK?Ck8>j8s&9GnZf+1t9Q2IV5Q}PabQP
zu<7HTGBZ=JHra$(aOZX;mbz^>^q%#{w?$GRyWh~p>Pp88=k&uKLOU%dvw$2{0#fs0
zg#CFzExbH_M4LGGOqg`W=FXmEoih(_ehQdC&3P_;s#wX;1Kyv)n0s#sF#mz*;uOq8
zwzw1+?JrU*<QhWIwxK9^ey<La=GXb>Z->|B%R7K*zZ4lle<wm$1S;C^SkK;x`ea9>
zJoc;<c3dUA*Uqv!?q)vF0v?y4&SfJu!5B+wO{QXm-LEG@_TnFaKO7zvOYGUDRdTR#
zyu`<xS^?<kC5>BZoLD#-svg;_$X@O%$QOy~fe)+0_NiiT7?S;Jwx!|q7M?0<@q&70
zACQ_rlJ>SdK09m25=$L=1B4wdMG)o(XJ_Lyc2-xv=OG{>f?*@~rIr_Fbk``8l~(hy
zm72F>eu;^M1#<%fNY-EwO`48@p8o#!WP5f`I0^|2pwKU*gOmV)K*}{hJEHPg)5Bee
z>cGZHP|yz={lRFaXIPl%=*je3+uN$jLqzyG)t}!00(L~p(Jyna$^95p`{iROpQh#!
zzP!aHg<f5RBX7mp4^#e-%EVi0`PzCK)ywF-QZHM5ce&KxR-rDCMQW9n)-QFAwP3eX
zUdclrf9*ksT}2{QsGuZqoB*5f+L}yN4^=|)vC!o*#qSoT7`Q;M`4zRB&PZhABOX%Y
z<*;uxs~H_$T^*pI=_u?yx875&dF3q{O4h}XL<(?1-etrx%m5QmJ7Ocy91RfDxQ;~?
z4ZVl)CxjJ&LRGvDBr2IJQ;AUDzP@`@-@uDhy}-T~J2Ovdw4_{E0<y{E+`GzHsxG*F
zt?+;{{j<x-UD~)A<l<^4V4iaz8vLo&PY;)bTy5Im2(~ASLd*~5(*$tAmY@7IQmrm-
zTG~KWO;y#*JQ^*r+T`CW4J5AoMAqZQdO|Z4c<dIx_Q34ms+;Tn{(d#M8QF3l><QwU
zni_@P7f(HNOwS}=0kJeylTN!9-8!SFC<Nws|1Q_OdKEN^mUnET0}hR*-#*Gj!(cS>
z!W{!eouiEiQIrJjd}&{u8hX{NacLX#i&QZq)E%4N!un(@7R*;K42OXw;e#5>EnE7U
z%b`EQ1dQkWnIgy*Aw%POB7cMHzt4LeNcDN@-RLY_=b-Nvd&92fCTi)PrpbfQX=&*P
zK@V`4NKC!C0#~74IjQ4Vj_<J-hp_^z{J&{e^f~d-{-0i;{PSM&DfB3wPY@NVej7w0
zEZWm8w=Bb<MSo3*lOE?WwK6k#VfFL<V+Q*V{MyRi-rf$fEqJONr?7uu0JDIN8Fit>
zN0(d(O^-Y$Cui&!ym8BBHYWZB2Fy}9%(=IEBaR(|B|^)`K)nqh{89ha2(W!)w$icX
z6%}Bgi%8W>nepoy3NWN8LJkIv+XZm3vGIAGigUNaV0}!BvWAf#)v_J>VS}4DM=jNT
zzR!Cl4f1a|I(}?a#YmKS)SvQ=e8|gOo|)r3$GGZSxUD0i$Je9c9hxJWWwdIKevCB5
zWFcfhpky71XAL$WLEEo~pJb+ozL8bA^~WA;1uYaX`8Y{9l&&!veGx+WB+SQ%h&lPh
z7r~>prDU<i*1e(|8T?VE0%nxN%#vmQ#teOov8}P<@StC3B3>RTRf1etG_(74tV^{V
zhlj4A;weQ$WuAFJqo_)D((SRLBHRh)&*f!cGj@}L1|NXHVWIT?{nJLyPh`vm8Wk-S
zfx_#(3_tQPhoWDpb_Tappa2Dwyyo?0Iq?288~r7fl(aO`ib!4_7W-}GVM|KT$!hMn
zcWppz|NGaI#4on?7>zO^zX-_m^;baGikhm~saGqYt(I%0So`lv5bsYM9s-tB>QMu$
zLKC|JZ$zE4_WhG)rOF;2@J60jWi<wR8-=33cXqaiqsPD2lem3J8Y{Kn|0h)c?*p{E
zI0&jfZ}O>1pVL!tsD0=;7^b)wynUx#5^pIi8l0jK5r>ZjhNjq>Y}y-j?>!h+ry^=W
z_sd9rd^PFc^*A+4;CeB|zwwqLKNM<z?1k=ehfoO5Q~X^c5z|ex`f_SdF4E2i@=Dz1
z2*B?e^S_CnN-8EM6kU8K)EIWl?~_`A!Z4QK%yImrvt$!K7jTj&$;%f5O9CBnXM3|3
zQziO9&Tri_6<+U)VX4+S<zp7t?+9$J;O9@u4{Q3=4zK*7NQdvGeiqf6Hz@#%@%nRr
z+5CKYsK>7>M1&7GJJd<-RMS=;k#$P<-gu|xg{s`RA|)cTmkW~4BrG6_UR7HWoRQ(1
zbbld@N73NTptG`;m!qLG;nZLG8P<X|QEr+d1K;WKA$_g)yf#o1svK&Q6~YJoZ?hCy
z5bv!X;2J*hpf_q;5Lr}?#JuACkao_&E|XG`V@`rr>w;1s$qdfPB(MiG(-d1hm9Uzd
zVOMG?9McBXt<hQ=fqJV+a6MExMvv8p?N5E1&jpyquu#81{4gTDf(<=2JQih_i%aj+
zT~>F9Dd@_Gh`AenPO<PnTfo$(Dd->Yr(M7<tScOyZ1L#jg4qUhcRKAs(Dn^!@1uAv
zm5xfHq@Yj&CagiPmAZz;LZcfV);;JhspbpnTHV;-FdKu5FgG;?bGM@oAVcaZ>uOY*
zDk&<yr%U2<VPC*o$u(U5?rmyq9fBm>%v=v_yuG7CvI%YAu|4WBo0-GX9wwCsv%hv5
zoT8vJamuQ!1uU2RD?@T-N{$O+q9m54IA+4P9r|z*2Tr@}lzZ9^=btR(X8K|-Z45Dv
zN%Yfavu*;cL=+{od0hM0x*Z8Wg#NpXWrj+GyzHzf6gU%HWZC!BRoHJZ`Cud4QMWxg
zkl41jaw|I=+bv_jzpmnrL$7fI?b=Oc;HHl0>f&Zi2Q(0h*u)Ut%Ur<rpd#t{<M?11
z!IGr0(opVq;fmU=2Br5Pq;ci@rT>eej18AISdG5@s1lA2lOKm%4yhe1BAw4XvrPi6
zVILo*R~@rd6a7C?76>EJb%o&J?D&J1+cP1Gxb5gyNNV9R_<N-rGq))QYJvvntRSeP
z;EYuR^Uii%>xmeftneje7WY9SXi))eCS%9R$;3+)Splys(O_X=wJ?y8iI1K$zUAh2
z1~UuF^p8jq%=2{D64`^G9#OrIDudJ9I$McuBovEdrY$h$GeOML=6pRBse555PSY7D
zE@z<l)EkE#-Q5*%LYd?SKS#a1HP1||9aLd$=NM*pme12*IP`XJ*MB5H#;v`VMdI-U
z;adkaLPNGpG7%-Hb6PjJ;S*saSE1=lfUO2BvPAvXz20zOMrwu1Ej(ZXy0<Zb!ZEA~
z_Z&3Tda=v33d}ETe8ub3vd?5AlkkJxaCEKpp$^sixT^<Q(wzG>U&+CZUvTc%`A*9P
zv_NV^7?6<e3@tPaS~v|5eq~SR$X~v><wiZo1=#%PGd}YFGYy6(MUnw9m|&=0CoGx>
zV)p|2+EvlDZuJQ@73_=0c>RZxkN63;cJ_FnVd0#{A*PVM;W2u+AdcQ1b9tyoIC64w
z=Lu#5oyR?n`B7E8%dT?3DbakTEpdR{;42N9Zb8qK!qVZ=>5VBlGEO>ZWZB-?Nt<a8
zz-n^8t4&XDYi&(4?2QDa*}%Z*6~+kY&^cPbUgQ`00tf}rgcMZ*W`E3n|MGm^6G*_L
z+TzYh1vqJ&1d3P(x}Ep;>Gt+c=PR19gj;c_jJq{e5wp4tFtHp%Bg-kM+Z5n_ZCsSe
zYg;|!UPcLSEo_BFsHo0*X#G_TZbt3moI>Rd<;?|0-ePKuLuv>H)tBk%0Q~m2EpN>e
zt224JZN`Gzo1(bTX9{*oQ>*MNFfbZ190G;QB%P3<3Cv0l0#50QoOImBj<5A}o5UB1
zH;hT~y;H;>pv1s}Vm*krmY-%x&(KuC)ZJ>kRTK2vbc1dN>gJ$UB-&;=H~dDhFOrf+
zBA{`P%^I$-7QFZ2c)%sKPqP%3Vm0c#l>6<$_(11(D)os#@J8l%UVeY2?L!?(-E3@2
zpkQ;b)HkSN-9YTxiwty<(eLv=>ai3d(;wUEhZIFk98grB4WoYv9QjzE9vN62o5}1S
zC68_$tycV?-w9B7t5Y@p0OQG3!vcejbE4fqJWezu5z@%Ap{#Qiyd5kj!0mR*139^k
zCN}-?jD174D6gR!a&LY-Cw^xa+f6A|#OJsUD>j~8citMpg7>MPAoKOkf!SVDhks^<
zhs)3sVvQKIt2}dJ%Mo`rNR^|4(z3#;e8$jO5fw-6H<vb-p<3sK2aWx{)Wb}kySck`
z@|g4acdu^P;Rsy`gIs)Re%h<8l^)918f!mf@NV;+$wg@eDy@BvZ`!MWm>E63iGDF|
zrgXQc2Iv8Q4x@&tAv5D(Le?OTb+N_C_}Sc?IU$l!MR5ZSN+<*@E6Df(^c{yC9ytTq
z1n#q6&tCK19=0|%HgYUDt;lf9k5!mYR##UO@Zg7pgOwN=4caby70ZiAlU$Dbb9@(b
zc7Sm>vbOm-$ZsyNrlO|CVbq6hDln<*(U7Gr?=38fW+ulvJ<u>#bd7~_?|koJC$V!3
zyQdLVk3-Bh?`&6>M((z{=F_xEu<Mbwm*@ex8T{sIeRw}!F|ynLP5H)!gMFLcv|o4L
zX0Ib&V1`}h;Yh#X!oKeQm+|Xq%Zjq($)baL)y4?-1}nNA1oS-1SR(v~)gtSIQ<fU4
z(u2LC;G4T9<1f}NE5rcFmw$@9Ll4Wdx$&bIjOm#EdA2M!3g?Ea(RsUVLb=$!!27@k
z=}!>hIzE=ub(*Vn_8Y&Iu<7x=jTXE@<6rhZFVw7DzUmBCv52bF;4ds1JH6_K-ZWCr
z+#aZ@$>LeL{G~e(`_@Ayla^!eS7yX)R6~<RUy}dBRbOJok7GKkYevY<vigWt+tn!L
z>KYQi*vus`5;+z;Nb4<<rKWY=t)lkcafwGG@77Vyo%-T@4!9+yol_o}P0_fCu*!jX
zo1DaO;SLNZSvA?|@#cPVHE+b$I}u9D*)K=C7d1kz8LF_RT^UlxK7zRy7Is6|V|R)>
zDc0;=0db6%C_GV-SPj@mmc#Y_XEbe00dA^$v=}tLM;+~DSp*Ag9dyce+qkGW)kPEW
z+gJw@MX_CQVh&cQe6nR$JJLb7Z-h`|jE<*-jle3<kvLKxKWVU;GGiiA(g2K8QP4@t
zR6IBo$3M2K&ae>(xbwb!YqHt}?KznI@d|6dcBf}{ME7<X3-n7?XfA=q%R$}~X(y-Z
zQ(c>%O}A?d?WzuW;}a9t*E(0OzjKtb!im1(q5B0=j3t_Im*-v_x;t{l3ta6CJ(S6)
ze76h`g7IFYC^|e{NvQHb)lhMT5FC|#X?~V+u}D@W>pat2d;1RZ`i39T<uasdtSLg%
z%8Y|S<lt~BR1~5+xzxk0@BP5Nku<*;w@uT3#k){9suSi$NLJ!{D>%9MCEG*j;%1IG
zY3Dwcs7R96Zufvj^*h_P4d<<hwG9X9%>sQD7-QE~F#0GoDdX_MN?>^P<tS&x#^C<_
zC%uKR`8AH?$oR1`%G{kIbv2iF1H!wmclTdrS^UNm1&nbQC#@2IdDX8U-n;3#Q80A7
zyw@#RAhSM`X~v&D6Trc6z3zH9`;1fDUXXvM&SGA3W!sFQXA)Y&TL5M4(kRV7Z@G#U
zx|uKv42z2Ezzf+e^<C?)F&DUHEqmj}Xg^x-k;qvrwX1Z?+Js5z-E$@<zHB5yGTNiO
z=V<V^A?3O`Y86nDLv3Cp4n-(A*yj|g2I8p7x{OqE2<Y0~+WNR}jZBAiHpcSESW(9?
z9iI+MGDZHZ<h8r?#fIxHXs05TB`ojBy5)9R&feTyn}`@qlXb5WtA&$7gAowxt>S#C
zx{7Rkay!{JV0l2if|<)@51!GQQ!|%6W5Csu5AA{z34@InP??XP?Qu!v@skcX1ZD*`
za*V{KE#9A3ooRw~m}C`?%N5*r4%{zzaw<|&Q{8vw9T({;z=*h#4mJh`IUU#o#N~0{
z#KK6UR(N_`YwPa4k?0~z%jK~9L0vs&%#0Pc{TTF{vTqlH9%U)_ok>u6f=~u}qQ}O@
z@VRUaK!++I)y*ag6F!g>JnrHsnVGe2()JJ-9v)7Wn41=zul-|#8pf$PzjZGVoi8gN
z1Cc#JVNKfT;PqLpeRtdwhn>KX#@;1qsfS`s5%Z|JSssf7miA5uN}^k#$<EIw3|4nB
z)hRCZGY@pfYO|QLNv{_tt|1=iJ6~>Oe1Kh2vS0NV%?Oy*yFnW4#%6Q0rg^yT9fAD~
z9d$TxERNB4U3gD+IOb6Am%X_OA(+u+Nd{8IhPA>mXCVI6^wK<foC$MC`uABm)|xxk
zVAoG}J`U0R*-hOkmOGQ_<g}1VW2<!cBNjeMqu|21+QNIY4fEilLDzQ(N+*fCjjp>%
zRTzDd>oje0CwXaDNEZ*g8E;e;t*(<S>9uESog$}BQnMb2ty-31X5~~=qjewRruJGk
zZ`ue!G^C&_nmXKS$^MFXK+(NeJ<WMT?&B!A+ms?Ew4|NKRo_J1co9`bfziO}WU9qr
zqigH4-FCe`xg76`*lFo6oOzZrc`#*a!J#pqo~S%tw_?Ue<rhfv`KB;FS2u6*vaXfs
z*S<>4CySM0iPNo5LRX(I9Hdu*Qtd~5ia0H_FD5v*93BH(r~XY&w6`la<ab10S0;9q
zybKKpV<jMrMHPuTj0<O8VIrMSLQPQ!<GRo>FeI!4z#_#DY8L{!`223yj|*YALf+l6
zu7apkHSe(2dIA<z6@u-H1yzy-MrG<ci4hUxO(;9VX`Q!MCl7ZA9s<{!X<*R@szAmX
zXlw58?Y(Iwx-T~D#Vqlgsww9MTPIjp?11{R9No?jHt{j2P5}a4M#gKh9*@=JXY0jR
z#fH-nK+PzU*sTO{M23e??Wa^$suoQKWI8TDvY@Xm;}}qJWU5<)(Pmhyl1x6~`lRmq
z3&Y3<gWwNbwoogJng$;2S~;IX#kJ;kHWl*FJ@__(Lw_YcYO?9%Hum(pN>SVY;qEKL
zs@kHpF;P;aOBAHLK~gE{?(UWbX%Ru`?(PQZ?(XiC?(Y2N7Ej##`#sNHe~xDZYtJ>}
z9q))WCkoLwVd9+08@~84XyHo1ppFK=N>hCLSl)Eq&l>pBV7n_x$-xY9Jk}%(=1(d%
zHYaI#TcFmg-R>V2^U86%5OlFrYCd1^ytc8i0hvTJyM56Nc<=y-2Xd~AgL*JBGV;op
zz$<$lNJ!vQ=!@sr+S@aW@C7fzX!pl+JbCiuFnFt=-eoYEZ)lFmbZYsnY!FXugqnlk
z_RKD)H;=<=ls?WU{M~KBm^}nxFW*cA+;{AE*GFI|pUZAH_8b!lX)s^4`jS}c=o@~}
z;A3P_1ET*%98}5~IS3SfTe_5r%TA@69LnhY^Jk+oS$S46q0{I9c`&?ueO?Dy#CiMr
zT4TsM?KeW3)t-bmrw0bUC`@uHbgYJX_UuI-IKaw$0t*?LV0IVm)D!P~ItjKCIr7B*
zv_4mRg>6>z#)ENj4ZPq3jAsHg?WpUohfXD04}OGUf7HHJZftCn($kSABqS8)yjWJX
zo3)v>8MoP;u?GbG#C6c|Y<FiUtsBU<)%p1J6z%(9owH-1B`<GFl?_6j!S-`L{FLWJ
z(E}nNnzS$=@J*t6!6o7VZ%8E1P$@R*Phj-hQ+_e8+}V{-%aBUJth&RK%%1vDXQ54n
zEQmCky`gjUiaD1i!(WN6N`DUd(xdr0qV0sP9;@2y#_~Hkn}|*>;l)Y6WhDdC$fxPe
z!y@>-y4O|M7UY0?g!|d29;YnR3m}&DSw-=BN~x`^Knz>8#6Z%;#Rcq<B_z}ZOI;Hf
z7)S%QO$P@ZVfwX!G76xEV%JZ(uJ?KJ5XYRo0ofB_NQ#<qN%k_t@h$>B$ju5kEw{W_
zrCyj`h6~Q%&QZj7HJje(<|_a+ToxMg<T$7+;K?5`ufFr@DdbRYg+XAhIlt*FHRB{^
zQlTwWANBWJ6T``xIv8D}77%irK5jg|jA6O;?}>_b2CpepDr~OD&!gygVRN`<PuY45
z1q54t-yBPA-BT@YQHoWMrTd`ddho3Im)Tf()9*)9Kt)9b?^A$gY@d9w0!8_ZWjENU
zTWKwmJ?WzkLnAM8bmtCnbYxo+Ni}}ZgHQqxB&}CMjpa0(nz_V$E=<THRpqx9ma(O9
zr^{1b*Oe3M>lweoUYP?AVW->m(b|J|b-R!UT39}+4I5iXhhmaG6<&OMnu?m;HTyCH
z_Y!AT(WqRIN1yU)?AT4gApT-|atWbNuk1EXFxf_5vaDHI{n&b-)4h7sg({>&LTD2d
zr$7%_waQwu$g)7KHeEa+|MZQ$SWt(-S1%%l5CHqMems)4;QL7g@RwlLyGX}#3+5B3
z5_I$gP1f63srPQRegO_BqkLyo;?!-YJhrWHASt+6T*+gj-Wzx7-pgl5njHoHzFf2G
zqlmDtkKZe@Ta9HT`7L5Pctet)&Vc7Ad5bHvpDv<HJ{vZh!RSphvWn=GQ9JG}Jxc%*
z$DI?hKlo61i{43V+fD7_T11l+P*M(5m~r}B<WgQ<5w6r=e~|^*@fon4AE@(!cboEW
z-`flQFHeD<Cv2mPURz6Z$w^#n|Ky<d%#>p2lAF{H*j0(DvA%c|J*B3WN$5H3!O+=H
zVa*aD9Zu(4T4Ay5?X5~_c`?VLB;M7<Gh(an=iXz$8+M|F*3v$*%>a&Hoqp-nvoah6
ze=u9O&C&kdE}j39X!X{Q??joo&qZ)b`BUodj2A&RXM~^*3E|4p5`hc#&Qz%*z-lTr
zlYO6N&>XD=8x@8G2P+*-%+&oahC@Dn5xq2zrW=v!Pj;?60Yc?WQdMjJF^%6|^{?4f
zZ}@~;^DMEgN2xPEKk5v4nskcw#j(B@d`xhJ@C?=UV;;?}r3-G9sW2V(rGL-3RR$H#
zu32ZiX1P;$yWtRm@LJjg<0KIsx>$lj$mv;)rVHv84EDRSY+Q9gVs19aZHb$)JfFGX
zl)F79^qfl-qM)SE5%5k^{zcYq-PJC5GY9Ok*kQsH5gz_&s>Bc!j*9hqv1l~7uTFP>
zmK?pP5y}nz=*VppMJ`hJ1(<T=kNeB$Rtmj*0~)D0<szbRH2M(6u5B<(pWI^>iFXkt
z2*NC+UpMCK)yorK5F+`$zO^J06^$x?-LF+xGL?{X#LNGVHL^(+mqNAjw08}S|A~sA
z&Su;X5iuPx=khB67EH%0=#yoHQGNZHMC8<JnVa_p1p*SiUQfkJxN^Q>MV0kNkxu8!
zynqpS9{@pb=_EdSYdnQJJ)u2k#d%n{#fv=5f}$%AV*kY9(A9Trn?+e#Vve5_T)amS
z1{D<d2qlT4tNfPEoUxLgSOkw=Ya-C?eG($6uZ9B_0e?V3Y!G8PBLN0mA2O!q=<%<<
zGxO7lU#33#sMj}dhQHNsE8Mzld@oR8ga9%3WqY<O?|8hSdB1usGhc4V**CSLH99Z2
zv>M1Rs$fG(POu>=NSyn7ndfIf{}#MmeRXx!-rDMX*hy?-<7A-_28nfEa9C22x*tUk
zeZ0mnn~sI}d3WFa6&ZX(zVO#rDv4@F*MDiY3GAqNSY_#*Ofroh<hCk;>1F6;Q8>uc
zO0A^%c$lNaY&rAXH@7mMKC#LOf2qY|nD{p2Oz&3utRQlvz9^Shse+JsC)=yXt$?fB
z@dNeEbns2T=V?8ac5aD(b8&d-Dn#bVDmB2$!CAFYtZXdwtgkuc3?CD~d#CoNRrDoh
zw*H2o6+<uIRxO}jv!M}`3)5EF3yf=^hp>lOz3S6Rz#1T-;9k%M&i4+S^c+d#aO>_R
zLFQ(*Q12bfma!$i)n@f<`g2z#Y!^m5p`9q{LrX%ho=Rj{w($D9Vb*r1(|pCneIh13
z(eSa!`U7dxgzmR=C@YJQ`1ul3TrM0hIOG)uUQTf>j)*cIN-0aa1xZ&vJ7rLzcA)}Y
z(W?I|uF4GB55LGW-iY8K=Lw4*5+ojQzopCnk{_E)(p3iB5T;CrxIZeKhL%a_nymJi
zBh)vl_R)t{JZ`X5rK%ns%K5Yl^(9$G${<$9nrsjRCN_bsPBD^-=(M=jI~m6w&wEHd
z7+q1`obL(`DA+bS*>bh9q?wQAJpW|nmEEuV64TrA;J1=}1q!2^EQcPo?!BAIJQ}*H
zUin*^zr1|ow2q8O6E|K>$!l^GsJSg^+eCFfSl!m>y?T=AcTWXT_$JZ3;6{EDva#B&
ze)B4nytz8tLPJS5*Pw7&3Q14zQ4SnO)5neu?=ZPq#x=|_3$HOAPh}5cb={os77P#)
zhFNqR=VF{}9FmnX-hH8dVD?=A-&SgW9NL(yTh<v8gF=1KK_dQ9XC~$z0_tZTxH~(;
zx%gqB*qc1o{(c|qqdaBgWzk~feacaTQWmkX2bNQz!&kl=%o!)P2K~!I-wSufv1(pM
zA~k!$!NE9x{54RXL92Cq-g$(!tVn^@dp8$tIQXnt+^a`tMLEyq#%xGV`EY0_dYM;m
z82av~aS=F(I0V|R8+yN$TzoYCBH@HOma}kq5;}E^PaInI6YVr!XH@riv?42^)DFb&
z_$og`AdI)|21O53D&$%#*`|vCgO((iGh*?ndoo)4J=^mAFm|Xa91+!04R_$u!uDFG
zGp8_Oj%y|pnZr`dA#oHX-TwR6)q6gG!Y5hAhwn~AulqKgu#NG}SBq|+ekvSWLc)u~
zJd3V!rI1rQ!<?pXoajdW-Q9>zH&L-kCOtwgh5K+gKI^OzWii@iiWwE%yz2pCujFxd
zg|SGVo*iI*QtVnaBf?xbb!kV0J5{y2eUQDEbGkSGGnvG2pdE=*%WLXC7!Nj$KD0g&
zbPMkmoTkH5K1%;Tsz@&k-;4g^hdZ5r{16q_S~J}3OS&KaZ%%Xn43AdE`r?DbG0?F&
zWUl#9{53e17mFFgRxIZvj~6vouy+-1>uP>^3*L0gJJ&d_e5F>iC%enKY8AiW)frp7
zMn%5DomC`bVs-wUP}6x(J8g2N*BSqY*;zVJ*G`#`p1&*aB&<)^(Bt2VNT($0M(h+p
z_z^Jbfo%ilJwhb9#C|d&tuBFbq74RCf;t!yTD5{6M#r~3^C(#Mq;VE0p<?VTpy^Ju
z7k}*WQAfpXB+Sv#k?W%*3f~RO!3X?D#7I4{hR>0;Q(6idgy(rWF-A`pi{ojg&68Ho
zgUsr0w_AVQ2{Lm!tt`Li(|6`_&33q$;F|UzASXKEDs&ZSt@>au2uL%>YAXn(?1gvJ
zjD`n?hou|Ofhf?ytGo7Q`bN2}7iCwI`=1uG_H5M+Zw_X3x9Hm5`D)ii9~I4d^~pHg
z`^vccTRNrJL59d(hdjR>o8-4+GaE_pJ{}fPH&bwg=aW3yJdxKoxIMNeUMzh_V7$A2
z5CS|#hX^{d*qw1NV*ngYFK>Q4@W9p8HBX(9)H_)vbFRB<<hph8&C95<^8e!OuQv<P
z6Ch-Uc>Wy{Bz9)Av05E3M3@DrGc*OB@t)~wznZLCrzGV;;u6;LRbiQ_Coz;*aUJ_X
z2&y?ab6wdR&Ad6KZ|4Pbw%NMTbJXv)g7xt_{6;8WX_8#5(E~o4eZw8pAt9qo3KCNg
zafotAb7{I?yO2J5dPU}|k@`O9xDrJi8zqa9%dT#{^g+iDZ5dfrSw#~yO0|9hfmS=j
zot&Sr&zLtCwW*;QbT8gSpJ@d`4oe0}j>uNWJtT<!(5N`4K(RdFZJVE0A1)x13WXi*
zcfGX&4-LreJD6g)EWrqB%mwXzZHdQFfdFx+RP|o&$+Z~Q*>PtE&ov6~;e7%FH5|ko
zfnWHK`r;r*qXUbP(dzmb%c<A$^UWjo4|q~6(+#cqgE&+%pflMweoF3*vhsChZ(Q{V
z9bTLVKjn>;t+`RKx=2aii*$YPJrYeyb;1Y+D5tobp4}Gp>+7(^aB|X-tFs9LZ@{T)
zAj^qD^VJx7xuliy-CIEF*N;J9ebIM>iFsC&U;!!*yhM!Y?me}Av&pZDAKv?i1{4&6
zB1O18>>;2;h9=x0(aZ9Yx&t-(){c<V!U2~b*7=9zeqIOT457ZyE6*uLK!G1hEU+Up
zn}X{tF^2?u**%(ZMKmQogBl=7-s|XyW^=e+hIr47|6f6lULV?c&a4U#a*8;}Y9v1i
z+gk4SJQwwWNPL9<{+=Zi_gdwnQFCy8$_I{!MFoI<OK2(MbT&{2^9XCuwY7bdF67ZO
z10_IEprE>fI#`JLX#R>h2JNl-NgxVCnSbR?=Oa>XNnzIbSrE_|@E!@Ofl*dow(Ei4
zZ#{M0>xq-<4-rEsrv%y7Z%JMv^&Ha@c*0|1pj-wvO+LL?Ebd|A<^e)kc<d!ItS5gD
z;Q$p<p7Qb1KtZ8vQqGrJM+w$Ee`ywhg+1`YWxxn#xQ6W$I0zgZRS8Nap+S-N2*>^q
zMjsnIugVLsc$WA0Dc}CEnKC3KUknt%1Yu2}ZgXceAKkgHHG<O8%XZ@OE86?mG!ybL
z1`K;ysvy>ar?%I?FSb1mWgUJQ{i4zQK(T`E{+Zj7imn4=j~mVvR4XLgfjSlpDbR%Z
z$7TkXh|d`KNw%TkLF6+~H44Ui;0Fj1P{q+VUOi#k!$0Wdd^~g}LD#joSZrVgR^I?*
z={1TH9`cIh)i?8$KH<H7a8oG7Li<vb0c+q<G>J$zhEBOu^sEiBxViS!1#f=e)T$eD
zQ;2>6Bwn8FjZ>2F{*n-?JEtgyH}Ls=;{Z{e-1MJK6z&z{z=JO_uCW+N@V`EQ`kcF&
z-34gJ!7eG#y7r@J@Hj=+FM8Xd^1L9<v@$~!^q&H_hL=x&Qye&C{0s7{u#W!{WFcnx
z<uZ4rI-H8$i&=WnGy-A*b))wt;3jY%Trc*ycM72)(uFCV{YPabg-CGd2_DK2>mZ(C
z3!?oG0S^i)vsbCk_%B^){-rBl9)J|P=mU_0&l6QZ8V-USJ|E4Z1ggJ1P(M55dsu$y
zd5I{xeAKh#1_GSPgaFEyG72;wl!0T2sv`bUmAYp5`y&g!qu*c^OHNGpk6W@ZQ9C0N
zreIC`^wUj41%N;VC0266n||+^I8GIkYiVa@BZ)trJ%#!*_SRtz@bHv&@lU|r?-M4c
z5kRS-zGQ<M&SY?0ESptR%S0yF7~cjKMv}$)*J3d@C1k0it1mI{J=5Q|K||dG$(I2_
zUm%i&l9#wgS%<X_$%9#*E3)l@X1}WQiS7rYiY`A^wO>LkLMeRJO9Yt1aJw%Cla7Wq
z0R|N(?8I-OPVwSPL-LF&r(k<LcRY7|D5x268`+g1U7~|@k>iEA2T4L;E16o?3Q6Lp
zG^`tLRM>yy3hD#tzPzMgpF>!j$s_cHC;6a82^DMJ%i9)<8ymfhj+{sTZ37eK0q_H$
z|K%_Izdkm!n_o0zYq5jx{2ATEfY$@j(SSl;U`Hg~%i((njgeq!`&oOQyNZZ3K=j+L
zsDe8{WTX#yw5V<Ki(mDR=T~s}w9=WqpG(am^@BG6xB#>c0nd`>MsNS6rHPD)y6?e~
zkHG??AvPR404ipHfe0d^FlV!WMRLc#up))?hJxBx(0Usf#ApY|D2E>si~!PK)T2KZ
zR|KLbW22z|<X98WJbn5fQhtTp)}WNP%?O6PhwdYiZ^T>zz&t1eq}J6@t&$h4WOa@P
z>K+gLF#KA61Lz2X!EbcR?*t<_jn)u#V=OEde{uNjoU+K6q+c;<CdS(rASeQrnOBu9
zg<w$g=tGz<N6qmqNWn1*@A>s_6GvV7w{|7gc>v5U|F%hxCO}p;$(v5o9r%|<u<u<R
zin0$l&jww{=K|t6R3@fGq!g?iQZO|I^+j&!Z4m8ma@)#}h$?!Inu(|oluM5C^8N2n
z6G|!MpX5(;@t@=mVt+EaF&(*a9J+lakQBww4-#||L2(e=uJE@PiaBWf?S)WNR6ec2
z|I!2FUwRPN<c37QFP2?gbZ>xnd@=rQ!uio13Zk08G?N*Bb1iz3hfq+TvL<SjU<|6h
zpUSczz2z4;D6HyPhO)AyfnZE;>f4|oYR+zxM+-#|iF;jo`Q9ftceuaBo?*`a0Sg7G
z5gZ-M{KvuO-tzl<n*rAid8H7J2k~`oZBPdTPcpFj+epi-p3x4zTlrTcftraabsC3w
zQoLkM^)bRTSvo0DgAjHHH7qD7lwduJwBPn)3X0TFEJ-x3knjWb`fv6g${)8IkAwmR
z)dw;o;IS1CAR=q(ta%T@G03u7yR{4L86hqU_5Cm8f*1r;Lo-0DFUV!cPk{R_GKK*B
zflBObVIjNcnmZ3cpBMbv=ZBE{eel)*(5%>PXXpaLDb#LiRdx}Ooo^|=K&#aT$6vn(
z{`>nXg#h87>$iFR`8T&I#P|MOMg#z`|6CH-5Ru<sU#1{t|L6KmJO2DTk?Hq}?*H%q
z;8%!h0)zQ;eObNgMo#&cAY?%G^{*c$GOc-uH1GtRxNh$K^%<1G*ZDa-B3&wr5H;K2
z4iU$txM*0LfBlq8z(15OB9xh4@L$s9{`XD)Z%Y?~a(}M>4`%2#<sZv_v!8G~bFdl%
zdJdvT?uH&q{a2|A)B&8f?QwSCyic`j92%0vqhLlqrAEHpXILu}XKQm;B+~;+0d?=3
z>Fo$zr1$F1Zt>tac6X*kr`bkh>!XnMse9BbNUnppes@}w{hEh2vmP7dr>U8Lep#9u
z?tC(5x8C6^ufAt#HtuqRXQnK$%NufMjoA#5g*kS9U<VGn<u)EE{Z~g%>#ChH&|aHy
zX~&(txfbxnqMR$*nX}vHn%_OW?KNX`O^n_ixtI!Ab-m80<F}|!bwt1INSKSdnLD6g
zQ{I>J<T^QK6wL!;SL_YREOT7`6HpHvsS7o!1#>4}YNPwrYEWkup5+&|VJVvowx~6J
z#i~wj%m49p!tuQ%5(=Uu{=u1R^V_5O?b)53KG~i#XU+h`-JV@B`?cAai@Dyu`0|Eq
z%AcPnskiw_rBW+yXqp_DmCCfjEJcS?1($wpM3LLY!=REgnoT+!A`T<svc1RE$vUhb
zX)Ql6RZlf2R3FcUeKs>$VlV7sT=qqSt~p~6No#w`?x$Yf6w2dNRk^I9^06&;U#DN2
ztLgJs<GVBEQk|qCewbWu9Q|>&`By0d)v6p)<{KI@>GX6WimLGmR_V)Mv8O-nj3Q??
zbMhglJH-`(nm`hMf&+)&tu?uhYkeJR_FW8eK(3LTtd7a)4DGND?lt{iV3*mx>)lhP
z>PV;G`GGzcht<kvca_;`B+_5CYLG#+Dpbfys+u_z1Bp7>cr+`go<&5iN@Q|B#*V@!
zMUPTxkP+<5wl!up;5rZ}sog$Y5@E0~{A1o?{fFfC>onV~!5r?!58LOdS;M(g&xBnS
z8$a>3R$rZG$?lzyalT>M7OH&;Z=WZfE}~e&+bL)2bOn#YZnC^iHS0K%E6WC63S#Hk
zWw+PwjY7vg>$^UbHXh!|pQ~#RkWk;(;;pqerfVi1Mrx7}IJaGYi%a^xUEHv$>D((M
zE#qu^_N*(fn#hLP3WLF#<w?F%!V0sMTWBPQS!0cdPyKmOY^i*k4E`#;HQH#hSnXvE
zIz@iRgdNEP4FL*NbDAF%K2K7ZpNL#$GPw@Eoa_EubRn(!7=N5t!rd7)YNvijG>i;Y
zwBYg>k3{(;m&>Y?hj-Y-GI<mkUBM!9w(ALF&Ih-z9%PR4zm6pGpD1h^yVz_oo7-5i
znJ?Q-9z5}hIGpD2@(xNXD&0a#X;&)6Roh&|K4&=GKaQob&o))!T;yXAolwl1w%bfj
z_;r!P%uw1N#)fDXOQ*}lOca<!ad#M&v+*v$^&stdA?91l1NZNG9g&4O>f#}oxw8~y
zd(UX|=bc~U68Xh2NV`&bAl=?5(-q+JnaXX;ZZSs}bkIC}Xvg{kU)#U`n`*r-Az#}r
z&vU41ZM}xZ*F`VG{pN=C`pVq!wf%z0UvF1+uKaB}kuCn-B09&$Wwx8c6<&>S1@kw9
zZRNFseCa?0wr2e9J<1i4Fycq;un`4^?(Jf6l(JR66Gp`kWwT`y-Z`h=TIlk5CbsPj
zyC%*~cP;MYhQN{0`DRX3gy&5ko^}S&uZ1bwdF(0VJ3XoTc;m_0gb5{ZNk9ZP!yYhZ
zmW}VI=jqOM6W5LjyIAj}50}yri?3dI?d^-@%eRopY?hbW!O|gNIU#`fle)|Tj{cFq
zJ6y6jVmH4TmD&V%P~oIi``3P<KdStgJ|P+Oi|vEiooPWKJGIRLpAV|MgbonQyKQQN
zLv)gfJOS#PonZVF^#}^t=mOd3l!xYBF1M}C9q5<sqGWW_wmTamrPYw<KshDTk_s9m
zbS8|v{J|_)xJcMiW>k4YgU{!UV(o#JH}yjoN{UXmPbEn!4ZMm|>~gR*s=VSwC=}im
z%$Oq|l)TNWTy|RNqZzKG3xfbJPUY?U;c<GSE<9h`d(U73E{89;g;Cm($mTdYFPwuF
zm>kTes%G`PeS72C(46D|sf1kIbge)+8Tqr5sV4o6uRTn-?y80+F58MD6@cn-trP)_
z1}3+9*L#c8L^^DbDAvhUnzWJ~naMA0ML!LQk<r=Z<8=u(rsMNQlTPV)L2EW&Yz9k@
z{Z1>iVF%$AN1xc4>AWPxqrhNR;qdfc<ucQ8H^yk6oUI$;ev$Z6?S7XocD?Iwz(n<l
z%S>j+^mOaYFS66*qCPYg5W@1c={#PIfmV_TviO#?+!63fL%?JMWA!nP00wUY88EpA
zZfvIM9TVG?+Ku{z_fG`%R(d<LV;?@n;dD1Akdp4QG$$tJ_CWsNIk0Oh%aQUP3WXZ$
zi>^@^%nBWvqxMQUO3xzvqpV~#@U9HB`f?s`AJKl>1pEW#$&H<NwrJf@sWg22Q`FjR
z;xzVhJ5?tnSTnANJyhOs>V0&sVH=BKTmA(dnO+UsjGE!HI@_F~IM;)ZwmtlCn`6Id
zpS?eRu06S4tJ`kIBn?5;!iD0Oe-;`{YAd7T8GzMHqzld!>6Xuv?8vGocWm)lf6D1M
zvC(O*m@8O}mV0f?^r>u46`Uq3sq-Vc)ab}Fnq>t#ul&lU_ua~LR1ai*p10@OtIWBx
z>mOxIVw*_Us8!QVwmn8{C3Vg%$6VhBNn*O?B|%`z`5_P@yvZI!i0lC-1Jn{aB6Hx>
z@B1Y!aQWb(MiswF5_`FIpqj8&Fqg?{!P6jjJ7*iU(Z5HfQa6<&&IJr(b|}rt#)@1q
z`?!O4=@pkNX*U`wcDYGwwoq7Y4uCQ?%l-8O@Dz|}K^$HnVbQpl7VgYyGrUTv7?t&{
zC^c~3I87+xKqSEh#J}De9|yiwS*NBp%IVE9vxV*L9{=34d8ry+K|4gm`BU7Kr8z-*
z=-s*TT)rH3`=d0`nDD8!;_D|$WuK}|$6vY2yBsK$A4i~$ls$n(4JT}^ajySl#hv%g
z21MItdhr<6n9IS|QASs{uMjPk9mUWop+*sc9S-47S|e6u!V^@w6Y=P}!`K2k;jEF+
z(b1pag{N%_!vzf%bf#t1jd*U*>tsshU1k<8Wxdnoy8GZP@Uil4d9vbpNLN@vVBc)q
zWrRbeMTj0y)tD<m@QFhpIi0fI?y5~fy@6K~DQ>m3a1efLEBUxhHY%nlW7y&GN)Dc0
zPqsv%RC-TT)*^};f56pk%cO?3<c}mew2g0aZ8bS^!*)Lx2F!)9fHS>6Q?AuS=86kI
z*K;E0bLULqeAtbGX~b+PR+c9X!bY2TtjKLj(N`nSU~~7akyeHfRAzk&WABFO>~v1v
z$mPu)M`vg_GNoLW*-#jM*OCL%`ZbNf&NnGV4SH?JC%wsarId>ptDn=w7#5x(;)~{I
zGJ3bw-Em9S4wj3Oviyvgmo!(Xu*Ot%t6~x8Q?d3jw%a|r?$^>H7LDUi_>q}ZwAL}0
zWHmmGNH8KZ)&rgMlRVpNZj<Z%207jBw4F@OLD=^UV!st&zW|0*laLtfqoaeTyKU^z
zF|OdWW$TBIy&^PsB(jPPG@$Q|apDyl86EH1Czl~X3lQ85VN4Elf!SOk;LSQO(6IRs
z5ge<x0Xk>T6n*ZC!NFv+IZ}uF&U6m#qkMWN78{}R={CE*xxwc>mq!Ngnu8<p>1{4W
zPUn=foTaM~dZ8(0&kj+^h8PVwGZR`|IeByw4dYntBvU$6>MR@qFJT~FVcl4WeFB?i
zlW|wn!_a9up9PDXWWSqr^k(FBhC8*(jVNWf=;_PwkTojny^A>Te8OR3$vg$KfSd4+
znM^9Y6iwb(NXudNjvyg5wk`DAEhVF~Hg;(bC6(r{wa-y;;h#8B?m6ffR)~LhvT0?;
zZn;>}370F_Y&MrAQ5f9F=p9jYj;!x)Z=(Y%fEdr2@iw}6kHh-g<=0!`qXXY4x=tbE
zBK%lga=A*!&pMsaHrr>&<vUZwT055x@K{B~WOCUQC;U05ujO?NP|(TMzN%(OXckPI
z;lZueU!5C*Aql`AwC-e6Ix-8gd~e>jTdwpifwy+^`P_7Q8pg_Y-VI3f2GK_}z8cB$
z=fioxx2ndw9C?8JIVMC{rT&WhtUod>QZY@*CSC|>Z}moCC@l#@We?#9$(N;_Q=+#Z
z7?7X)c0J3zD#z#1U!j(eSjFCC7L}q8ZNXA;7NJ+PC@jn+eQ10_8207^e7AhQt%U^$
z=la|WRN1h8NVn<4SqEl}24p{@4tSl5GtEq4-+7voyntnCa{m}YzRAEph<)}-$~QT{
z{#pu=Ks}I0hzGeiw$ieA<p5BLO=!P|Phd$uLJe6^o@y`-qlTrFr;AHexsubNgjZYd
z{YurBfpa$f*f1O_{7HRW_VX*n(SnlGh`!6DV9mw`gnKLe341E5ldQFLejTTr)-?wm
z@QT@<N;Fi#MVsy0`N`(a+lwM*cWNcQzTg!g0c)M>md@hUzW$g!dQLv2>du7Kg%j+{
z0h1h?Y1^SaDJ3JH0Afj6lRV2ttw-=zro*e({_>w@8Q1uYP@(|oPsxfcAwalu2~N-F
zsM}f!rvfKU0jpJfBm|3gvP!AkoKy*k_}Axvo1)*;#2Shh*)Mk4V53oArG2`2O5vsC
zzS^N!tZO`$JnCJ#WymyDD9o%DpSYA!BBzprpK^5;p=UUhr{Z$MBvTjH3FpMvzkt;m
zaOA3$Md#&K>nm8c&*vc?LgWX|7a?zwo3mW&>_$Jr1k6s4c(pSoz_oiMo1+62^XqO?
zf%f)QLrR-}#)$LxfTFu-86AdE85nqYd1hw7yiRVFQ`=n?Ys1l{>Y-)Z@cs99X;qfS
z<`-8_o1byUMmct~*ly;Q1QI!e5I-h}f>}jy!Jora2LKe|+04pxU5ZI=mO!tka5R>R
zw1~8{MV_{z^y9HjVKSa?f(Gk_W~4YA_MUi7A%O}xVyd=VL%Lm3v>=W>bk@{kmz<~A
zRGwb+e>32$^`QN2+tVN-`4KF>oASNHz@cPWY!{17Ei(4X0l<eJ+3jI7S%QtKvlL%?
z6alm~3M3oN9`<8;Y=X<Ghr{?p7?<XAW>+)}yF#xu-^zO4XjTRO(s0G|Qm^kB`q=ZQ
z$Ah7qghMY@Ti54~*yuTWbvk#ps$lvFaxTNhZif`PWaubRC;n8APMhkyeW?sxecgCG
z2XlKj=H=>>#LXvAJ&?oXxyC*nW~6%h)s1p>FDTs{${J5JE)oi%QOaGP4X=HE*(;Sc
zUpI|s-E*x(eWUv%Mh0u9<hv;GaAmc4^t;|3?5z1x-&F^8{Z^qc#iil5p-Nt>Uf1pK
zx7j{3)P}|FPA4I=MG*BBy{$V4y=4!&qw^s8sUA*FExj0BYS1d>2)I(|A~<Z&dOB(r
z8X<?2tKEsu6H7(Y?`!=J#VT_Y%$w+U__EnDmue$R)tr}UviZ+?S0*njt?9`T?(+43
zXAJFJ<&L;Z@vWv&aRTVweg~Mqy1u`+U&{4R?P@40W?}SZuo`M(-Hfq0M_>ZU*89A|
zkNTeYRwt(%Xu`w#9o#!a!YdB&>e69!TE_}7)<x=XNY8zV_&mRojdx?H*rlHrWcd?)
z4<36ujx{VIr=mfzHNvNnCp(OV@B7}aKZu@KRmGJH$3d>>s;a7TgG;LfO@Si*85}KR
zo~cFqfRb3@mz1X~?^Dw)zNM_E=u2KNu5`&idp1KTx>DbMuz!3^CS~0^buuJ#NiCd4
z{Ix6E8=hZNek96!PASftobK4|CkXH+T>14mXjr$3w-0M*)9^%}@PD)H#BH%bjTewR
zB+t9ijgc_S`Wrh4Y~^(@KthwoS}<HssoNDgf3kCTyh84tL6YlIBluD0N_cz5k^8R2
z<q>o-GK8#nc&c6@6uC#y=_5R^Ct~v;T5>`zHj80mE-sQ`XKKDQy?bMPR*-(yu^&5R
zDyU;B7WV;jW+*L$un><l&@X{SG2Hsyd2*rPF(B~ayuH}AH{ldEqnCaZCtGGlhItiV
z@SE-0@l(9&qYtfm<aI*lZK~~+Ukis(;iy(Ph2dUbjE7yC=Bq2Mc!R_`<|KEU@qsUS
z`R?JH{qD`uvEBDs_&mNbG+uUow|2o%A;R^avU59rjE1gHRu@PQE%wZ|i=T)u`#280
zLixe>?r83(O7$lW+udlt7+t|_c^Wan>EaIC;*6v?dimYLd2yLN`e<NU)Gwu73*X)_
zlIVs))QkSZkcga$CftS$|K}HKLwKY%g}r(Y)%J28;v1Q=hk``Y)-s6=4-a@vengp=
z`mlL&vp#v~FBA$L6pi?(Nix*j!bVq+xbJCqaOQ6AWL&UIJb|!tme1N7LXZqZL)1zu
zA37_^<p9fqDz5|KS*}l>qx#<2dbu*7Vk5y2+To1L-oj~H)W2M(p8h5z{KWP?7JR|A
zc)`aXajYg5t+HPKirK8L|5*OW+B*l&WNdYN+eD%_iq;XK{@jhAzS!Ln5m!?{2b<06
z91SggV;I{p%0p=43hS}U*_$s8e4Y!!Bt_JvLYROmyt6x_T_W!A2afC)CyXqsDE0B2
zQ9Hz-?y$McD8k}+88iG+xAIVloGP(nKW?idwIEG`gsYv(TcpLufb+3S>|IC?<q3yk
z628+{v!1)>b#@NGAAz!>@Q-K?pUlhi5_#1LegyW-U>9FA<aZ1X^86Jog*ow6`~n6Q
zK3;dfd*UJR#d!Q?`EN#}S;o1$I&7!g5kyZO?+QiHrQLEe!EmMs1`Q&T%BY7gtEOeA
z!;m2ox<ULfH??D3gipgQ8=@u*j$KDv+?(DY>)-=A-6=SFSc(_oEuN8`V`UZE29y~J
z4U%4+YeAMv&jcj-0q<1|K1RpGxqUOR5HofWeVPXyFghahnFfYyDm0~BE+e>3piSe+
zcR0LGy8VFkCW0n~PTY2zFfL!kW=Nbi{g9USknLVG4gM_t_{~4O{jOYwP@*5O3R3X2
z`Cis5ODf!eN{(B|J7o}x<BIn7C2d<KR6Na$S9Rv9Rd?APf;o9SEmz1fuJy872U9!t
z_g)xPwa)PfOxbNN?*3}*13@5`f(OvFHljhZmoF8A8~FmP(3=&EqKXoXf4fr-H_?q)
z?uQFjkZf6lEv%)7$hwM9bB^ctbJFocTqFB}qsvW9w~wEPWE8P46JI-~%54J8q+IFy
z*kq*2`hXh6#le>bRcrI9p2?ve7;j&5(Ja%+MufQP?7jDH3aRwsL0<M(oWf(dxk);E
zcKbU6q~dc<+g(K`90_frSmz8w%k+9=bim+?-{u*`9ZI}3HplFu?RD_>wS0`06P^A-
z6Ykbyf6R1xBZJ9s>}%RBjdpm{6SN=1O&nDFHT3PYCE?N(3z;p;M;|KzHj)q+mzhv>
zE_B(0yh0)SeXobBMbm;~T}fGRlLm@QiJ{+Imzb_p5Uc&}!!sPTeGh?eg~4q8TV$>n
z#eV9fXA!+0()j+Rm}vawilcSGX<OXX;Xv;&vhG6wesDN+zWy1aF>*J!pek}*ZLibb
zAB&!Y9#+{l&Um!ktG_SoFvcB%-y9)6s=`Wld6LU|1mlQ$vDj@18$pH5Y2@2@L27Z)
zX6hlxAWT=Wj)a5NCuN%05!ij;2`XZtRIkY+5;Up#h_){&H-!CTgVoa6L4nR)-9#?)
znJf9HYX3OD(IB&5yP?OcDmkZ#b@<2*J4CM2q3Q*5*DUY~o79uc!oU5*c5^BYcZ18|
z$K^eyp<ud5(```cfSRi~c*k@vqo1~x|I!BoMqtDllp`1~e51VK=3V)7%t#Ok3!my|
z`WlW&>x2^gR2XIOc&1IEUKt&ib7%QD*@Xu$yb9C>R?!uV*^zcFPWst=SUkrpy`SKO
zMLNAAW%g(7hy7ZMI@{UN#p;0-Ze+f~KCgeFm|cpB8MmGys(UQAAwz^31u7fYogd%J
z>bL!%k+W>KP+dXPmOCL2yU{I{Fbw)97Wrp&&;XKQXJPSc*pH`O?4vZQ9M?H)u3xP?
zKVAq}vrO!EkB6`pBNo6UPhU(!Dzjpy5>I^Oqtly_4f-s6R5H26xTboB7!g#=Qzcow
zB0N}@BK#ELsW+)SYxzzgn0g?k52E}^D*vq(r9tq3Q4r{KZJf)7vn87&v!G)=*rom8
zEYQxzdH<LdH%W^HQ|VMvLg$-t*BRyZtl5`)0T-iUQsJ##QE<7Qa2Igi7>(y;gsd|<
z5r=z8yL3d6m&>8{i|~0SXGY%3cN}GSJFVk~tY`)yFNXVWhf9SJdeF_jcZYi7YeL=r
zAObR`zUzm^e#A|^$*cDI@Ba{im-MKZR`KXncQyDaomnDebdOcNS2PKfKJ#LbS1wiL
ztD<0v-spBq7`ptOjv|3{6cAyxy?Q=Y#QB>q+gF0pse&*$cnt75f<<<OBpOmd_91~C
z#GGWzG!cdCYb&2NhBoRWX0jDjf|9CS4|G|(=ndA*H&#bGRfB?ViF|6roG&mz4G3e6
z%F}teq|*9RJgAZh%bR89eoQfoDSGQcnfm#8+-)s#HKW5oBEHrBx`i54IuXd;#m`37
z&m<tiyOixUn+O6m@zJwHZi=C(yp9)hvN?t1*JXDvZqC$rQ+;yCuVU2MLDmlH%p^sq
zBnk${6?^b%#FM0S58?9}(<g<4kd6rbqhBO7%p{-{7CTMbg}$1H<k)q3OL{&i5cB+m
z7Shkw{q8UG%<70dsu{Q`N5#w&OXCj$>`BM|6^GAI6fM4Q*o=kB_^G#V1F>3+hmdEK
z8}2<uYfx|JbR2IE_v@5>@Ti@jND;qt8(ISLyN-)_YG=7hacO_in9@Eu4{RE}z{aVl
zx!yKE{!2p~Reru(_1R>FD`idxyN}li7^I^c@Kp{E%|(rhIm1g6i;G_Ak0UngnLgX>
z?*AEuKMXj&hc^zFRh^Ws+1%2m>o$=4e!W0Q&X%xhO{32d;W21nC{{zmg8~BxffqDz
zwYDJ+y$eFo;Lt@CP^3~WcK4|DCOOkMcu9Bx0pa3r_|2szw)2_<0>aFaSyFlLglAv;
zv^jGFhbi{8g)@i5;ir@~KGBL!1AL}17ssvDNzl{shlJkdag^auYEtofqbMys8I%7t
zF#vJ~xHU=@ldL~xIWvW32bZ^k0?R^1D;-3;W}jsBk*{sRT{efjYc~_G_DO74Dn2sq
znSWl}(F;oUKN4>9_MISBi_80vhGO+M+EPu(2WhGg$@R4n+exSQ2ELM4xLG(9>Ix;@
zFmsOpB#2t#=Zm{dBCa%}6Rf94M}A*W)O3w~0p-uIvXlQDRC4k&7oXmE*D3s0`vq5L
zaBwN$i><_0x+hAaEBcWI9sAW7DlH4J0#b2{Q*l?SHx6BvpiDGi*YG-=6|`Z<)PEFP
z?LC-#2<qIl3LoOvs_Sm@X5HIRq8PE6k1UIxpt)1qNd5MCj@a$%wC+oNOqITuKls|H
z(!7Au0a0Qj^k~m1-y59I88chX0##AX@dCvhCPvBGOL$*Zh|wxxtGrH+%B^Tn2xp9F
z?1niH9($1l8Vp-qbFy_=su>CgFZ039e;Y&gsHB_YIn0$gX)w9Nuf$t+wTen9cQKT@
zNzu$@w9@gl`;I>7EA#PxrrFF*?0Ci!CdjwCTnS6j{H2knl^vvVfNq!qe(StA3KBUb
zrk{WM+S^~9Q8A-gdelpJS!3+NvQ4}#cC{jpS$a9oQTT0zfE`v^u4!fg63=;kC8+4h
z{M;+9$`<kzTusEhZMMG@imv6uHC+d2MMq~G*Pn^Dhu;=8DR@=~B#dQCj|<(fnf=n@
zWOTW{PP5bt@fHDTCWX5Q?y%r$Ml-*EIH8AXeHx9Io*<gM4v2}GYasXTcw~><X&WUW
z?ebK8@pNZEkfIr+I27*H-@o4m{3H^)a7!CATW>}j%4G))!XowK^<cE8KB&}<hXVZd
za?(gu1ZIUG`D9dR0%gf$8IZw)Qrp;WBPcRqI`KS^^(%;F!5kL{i9$2o`c_60W3BlG
z**r!|h6VCX@j2PB{srv5J0Ow7gRUN^m`w%pX0}L{PmLHhE=6QaR4(B#V}nntAn`Ii
zZ`x0lw^1JjpXPh8!Yf!Zx-K3;V|UmVP_abE=!RQ)MzJ8Q#PjVT=p=6Zny8FPxQu^k
z<&t;6yG=y|N$QhIW3?U#5E3N_bol`Ve(eb7U}GD9>g~I)G2+EJg_2zUMLJ4Ghu;rZ
zJnQ&PNH_VviQ`N`3+KVqYZ)b)zjf&`iyFmXI7G4?>$E=coT-?;f_-aqwJrVCFXp|U
z1V`wUmG*Z*yYoViMKf<4PB?8hw~Heupv4nFIa<Ed0lta?_gBQCw+MJdxZS44Z*M%P
zL$G!Bk_a|tW>IA_q}O<a!pS(C#{!1j0m={hPRus**s4?nMRnF8W(5=%(|2DimiB2p
z1yycEPta%)Xs=yrx5HzJWfNBmZ()jXyuMzU43r4R!m(|g*pAT)rSPiaxw9QXpa@%y
zR3eY3m;Yp&U-a8&)r<ijgp*TAy2b(3mp0L8FWmvc&KDE`)^^o^B!F5Cq%0NAJJ%Fq
zASWiUdRx-LEpD^UD|i8mBBLH;T79}-M?=C^G&^EQGMNais-oiSSE^{WY7ucdU)QV=
z52|T}a8J*6U*wPurYgd^Y*`=^bxMtYI@A{g&L-%3nZbyL#9*u7@&sw%?RE9A&hlE(
zNPO*8QR+6UCCRmf<RsAgPbxzsFLZ7!Dlie-;?%q>F*LHEMJrml_-;o#N|O#!49t7;
z!41?erEWnvMl>$1v_b2DR|Asj8ULY2jFC9JM5>Z3-kWdDlIzitvJ$%HT*T7vzS7_W
zW=z3&JS7?yhKlXm6u{)MPPrhX5=2X4D)Fdjs#o!+goBC+FG=XkdlmT$dmWJC!9HYq
z7KR45eIKNt95wCu+DJE)A*njGYM<8jL@}3DIE4w5&OV#jb<F-~{pGvY=4+ts0ifo4
zhET@0Oa2gxjkF4U9wwJXok_n5L><f3E>rVleYj|$>WYfbpB<zQbOw&IL9=sP*n*)y
zcr;FQU7<TNR5Em9rZ-^R7}9r(1?Fzw2kLZitBRn^-Y*TBexU1`uTq`_T9?t!hU>R0
zK+A872|AQq-R&;jYfmk>Z&vlvNG47P5r&TJ%eE~f;S{uX7Tw!T5<YkWK%xnY*|6<U
z0{s}9ML~|qAEYQw&N124;cn|ArEJ|xyT52pue?B)BA&zSJ&1GIOqbyW8Z3mhnzmgf
zm<-JN#g?F!H~Dp9%g!~FM6pY7588l;oX%+kBixN$x@@kMpWMb6w3|vD14kk4`=`<g
ztu00Kc96d2el)e()&$zFSld}4dM4??!iH5H!cyp4WNB)NGB6eJ6cIa!QJE=zBwu^Y
z9u^(?@vauJkvHGFP9tJIL~W`J&!Pm^_voFGq{Bz@W;%k`#AG8VF!B<om@Chm)&`N)
z+5uE{^`)7=UBG^4!z1YI?$gS!;=NT8pc_~9EO#`b#PgZ9xfux4mtR|jnN4TG@qP;(
zdjjI|NlcXvSFikp0IOk%H^9{bkRs%tloi|bpuOjP>JVycGD$1`?J(<&tnsECRbt@y
zMn=(sIR9=&QPD&9K^%FRA9D}I_|kOSRaI0Y3REa-l|W;AFMBi=?nZ=1gO?-%VpzZI
zb>RPF?Z64E=h|Wyl+K}`a6Ftfa9{)^bRxl4sl(evR?J+{t7qr=h_1-Ipn1(uNsnfq
zD&G-AKkny<Xm139i4zf3@vXaO2hkwlky**gRn{Qx#@Aa%tp<$maVE}*fVqO+c|*Fi
zI$H+2C2Ajers#@JwzMQfGh|J^)!n5GD_5nKi1fJNvhio{T*x+il@<E{9)P-YntdSy
zv1r1HD>?64IF+=VoJ(}3RQJHQXgt9frXA3YvlblYF={BN&muIv5a>@<Sdt!9Dm_>c
zbg6Zaj+)%%Kc+_A$cdicm)Z3Afj&qi|0!#@-}Bqpc@+^Ct$zN@tiy41vBodYFL?}%
z=`K+WHcF$Lj1$Wopu;U1Q_fH^TG@GcH%nA#0m3{uVD*=Mhua>PeLCl(@4KzcAJ)d|
zq|rBD*UfT7fgoF-uP9L4thb`GY{=so0QbC}A9`aRjFw2&rsgmhku^oq*pnS$_8)I-
zz<@Mt++?N)4F+W1H=x{RsONL0VLej~!1x^2C@WCL<VG4;;4dA6UN@}Grj;CcCoDm1
z^MQe1C}=&oq~BHI_F(W!L3c<<Ruu-F;qtuf&8D{G_7$RxQr08sNzn23-k_6tN=$$~
ziwugW;w~R%-)>*G;nTzk!?)JucF#u6ggN6zZ+^&PG#*{eI;Nnx2qzbXn5nU!VHYa;
zt8OxapXu2fw~7^3Qb_orLAP^Vk$|kcGur-qv*vU8Fa}wCWS(#3s1IVBSKl7_@4uG1
z)8x+J@Rr(HFIK5{bH8YH#$v8KS)fIP9hp^GvAEnq_xA&>r)6b&NsLtG{ShUbO;11*
zR&_Czpi_R5NBctTHbNB;54BsslQL7v@5&-LhnA@&Xq-LaMq-{C=XDN`u-k3_u3m3l
zgmJr063Hl${f?6h$lOd1+!{4{K!d#GYM;Ri4789A>+!a>#Z_aJflLQ9I6*VmdX+-a
z!28$kFUg6VXQEMlwM)V8=~1da<j>fHIfj`kQ#SW%o;}2O{DPsxqR|YKMf!JThDWvE
z{nz!E=vy|`okfqLLDOluCoD!R>2M(E!Y6!vZz8t=^i40!7(}9P`yMqW{Te|5GfgA&
z#vciX>`d2VOgzfu6I`AaOQvk7UmP4o0N>E-4exDj^?+g3KE<gM0kO)*dUsbgXPm`y
zk)eC0$VS;+Uad?EHLOwNrDNl#pyXD#^!&M>f=Of6ifNu=L+Y8buxMl=R-0hFC|9NY
zz2d$U30L=PXZO515I_cWo=)O|4xQm(HaueiX!?~dSM@&FK3_F;V<5S9I0>&4_0n7M
zfN=x^QfsQEN^?#Ll1mvvVcxN^dMicLOfk7XP!8|F@C_QZ<Pg2{Wr^W18i_(C$e_Rb
z#|7#Ze@3KIJM}5_ta4f^V<7f}WGY>C&bSqh2_K}{Fdg>3bVYal@OKozbL^7O{K)ZP
zR;Ex&*Bfye9E3z_D=CfbI2szQFjBi__Y~GlL{@TDPzH{*rcnL#res;)gIOW#08<-j
zBGT-q{G0uoYu(f7TOJsj!FQoDOci3<BGUO4;I%bKr$vVG<W1Atk)|PCCvC{IS)=I>
z5wr>X171-`BaWQa^Kw`F88nXD`?m|;n<*r!^9!w=tfXP}p(=XYe3XyA!_h|?9dcLz
z<73UwB!ZM$kK9m=$A^FR#t~C9(m7L|<V%G)NZ+-yX~n1CR%%oKX}~?c?4C-J(jWX?
zxA?N$2QYIzrM1TX$iGSB=i9BBivlwYvL0z1XpoH~c*q15I%+~9cl(>`Ra`Kb!2D^l
z?2X12Kz(Ad3r*0*t-XR@AF34q6#4{<fkLVT!*L_V#0<=eobP>+cn<kf<tCIUaN6i5
z^;(n0ooEb==0lXI<tHeRkz($IEjzsB-m}R9<1tcwHe9E{=HcSKFYj%s!EEuwt8c%@
zf+jFX=x8E)5<A|qCH@{JrUUIlp$Jmt%%2g_J;3ZRm^FiEyTOPB)6=Bivma&9-f(b4
z^dxkh2%8k?9gybuEG#6qV0pM*I&Kv23#l!w?Y?^H(z~k*W<jyt?^ingi0|ps5KNRP
zo%VcZ=sOoogRpE{YRK%y!ZQ>~x!8DLFl-n}>*(JoFG>HEry14}6_eH>cNP}>N*VNj
z{%-%UEhZYibd98@zwU92$I!BY*B!#F4tnO{1xi*RW@hw#k7c*h1c~ku8r7$nRK;wW
ztV?*DYoa%UyHCK{rvJ;@ctOYl?eTCh|J0wFE|vhcb14Qr=WR76u+RqoffREv^1;E*
z5$CI%)T9Q6114%Sp`bLVUywoCWEzf%9Ss_E?)V`p6}D@2h4#Y@Z<@E&*dc$r%kFY}
zm_f+Jd|74)YLhb@>{4JiR7%HF(EYnS*{lVfjJgK!i|_s@2JMexz(@aFKUpMnG-#sU
z|1;7J`I&~rpRfIUbQ$t#!y<Q(TcgfXzP|@@A)kI}b;Ez;-U;{D)C}Y^aQXbv4#aT&
zxzPU7^*<khe^DTyU}A{qN&GY54EeDF&i}OFTqCdNd;TIDC8bMinh%b4RM8@p-2a(~
zhWv5EqSHgr5lGW+Pq&+&%hTeH2|nH{G@Ql%@D*;g#_DZDy)5mMhg0EGQyV^U967Hr
zMvu>9Ipxc!U0gWMUGcbJB6d!bMDM^RIZi!RzqT4o4FB`$VwdweoUV_y`%~bJ$k$wN
z<`ytriCjLPoUaCDsotu47^-UZCpr+Xogk{QZY*9#=|}j(MUS}nn32Y7>K7P|+ZCK2
z@qFceU<Q40SC4;0)>SjrWTNw5zC|amLq&n3wO+Q6!+kSOndx+~3R586`p`@^mYkIN
zG>_I+>%)w3{Lyjpn@dd28b?RA4p;sD>}#VdPbMei3kl=r$af!EnDVB_#MD*hswB25
zX&f<syE8;gP*9OPV8RMCT!13sU=VB(B^v5L-`iVcIgDPJEwt8-UBZE8^E7Q|mW&Rb
z@TX(io%8kZ#0HzA9H>=B5)#$0(U#+(>)M{%Z^d&IX`pKO|65QR2%LT~ER7m8k?~*{
zw{iCY%tXX;P6rOgF#BPb;ms1#oA)X__9y4*g~P5}cYdJYKz(&CSw%aw>t=(DJQt(>
zIg$YTKP<KsOP!UKHSbHK#!pw1^Zj2EI$x+J$><=Ge|cG5IMT2i7Ww7n)pC3xdo}ck
zTw2GqO##<wfJqZyl+Tvlj|l8=oPS$tj~gkSCJhGMX+WMr;X`k@0YXp!nLLF*Ixv$p
zaU6%iyAxpu7pTSOL36%?k=H3Jp+>$V>q=hnU=9D5%Y0#lI0nQ`A7Lb<;y1exAg^-C
zzX<@{S_%UxN2;!GX37Rh!A&@W_mKEo@C`T+AN%ckzT)CR_7?OlK*`QL*hIHMiGiXy
z_*Pf@difpOcykK6nlI<u=MfIMXwomtZj7)#-k$68ejM1NcYItzdT}c%GUV2x49&ca
zH>o3BJ;dhlIpNYUaQ8wx{-(LQVt>cy6v6OknsXGcOPDw{78c&o%z`rkn5DSY-iw=;
zonKToGX7pveL2EHTdvOAfwVF-RQpw4Y=$EdLDki`y?-Mh<OrJAGvRW+Jpe|(*1y8$
zHsSyXHyL}-%Mp4Uah8Sc{aKgHG)w;fB$7z{qqXnf9;L+WY^w5*>0mJAd5dg^RI3C%
zH&Df={Z(su8(~MCpv<IaoIyQ7O2<b;wJEs1%F5;_yEN`}N3^PH($i>>caGH77cV=<
z&vm69ykD5JCtox&f9XOw%*lBh^^x&Xm<en0?WNl1#jEr6EBUWPrSumsW7!1CuL&Pr
z*mJ>fhMo&NWL(I4@<_Lw0Ks)$&ayZlf~g0_K>Bb*+#N5TZGYi7eqR4nWAb6Oymu9H
zdb%d=!2hH!!62^ydnAS|XYQ?+)Spynq7-xRdCJmM4XH$U=j)SWFWPQP%MnaxxQe{q
z^Q{hExQ9A8iK)9Zjw6+yBIM&SR4&D(DC1M%R#&*_0c4`s=m|!m4ftfTr}S%~LJoMf
z%V#~a^|ndnsR+$1bUE=LHCF2*99xyMZ6vP9$bFaZ>MN;Bt=g~D#;JFji?2w%=xQo#
z4(<Y6u0O75%1<+wypOz7S)4n-WxtszDh#z=`}n2tp@iF#8YkmyuD|}jKt=-rg8z_s
z4b?`R*l`_o{$^85p(8p=na$<abvy343`+ZL*%M|I0wV^anNJM2s@x5c2A@z;G-MVZ
zUOsstuksZutfB?`lQ|Coe~`h<*TIFC4>0-BCM6}F`E2vF%qEg}W7++HK{yW!m%Fq*
zgKK`>M^m$B?+=~hBC9&ObI|7U$*$hXv$c`Mcz(*#J}Ow!2qQ{jO+1pj@>U?247}~R
z$m<6uYIsoVI`{1~uQ$w>U}mmZ($O%&+GyS!J?FYb;}i7-r6ZQdv`fbZkHgc??Lu=!
zKgRwad2js|_1e9GVu68zMGN8}2nguVNT|q2BLX5w2n;3N4HgZ;kkTm#(v6gWz|bXK
z64D{vaMtL~_kF+T`~hcQ``Y`5y`9e!Yu)Q!@vIf&JKQ=KAY^w5IXBXp-^iU{tLmca
z-!A(q5Zd`Ue_8k(L?zwtml;t{`B;`!aeVNF!t=k)&!a2P(Zt6`PoVH0S$@H)_=KKR
z&k*sjh<SP-KbnmGmW4m6PNW-1s#BAKoM`#Le;qYRK2;ns;Bruj`y^fVl9Yq1%s#eJ
zyib@z+s{j1OQbmR97dXJysL4KyJKwdON^~*sN6Mu#g!0|s>_d+go`3CEXx>(h<vR&
zpST=;^Bxafb>034iiqd;t!Y1-8J+XN(VE|Xr5^Jke0{a_3l`7o99fty+uGMR2HUy`
z8z@$a`e3BEU_`tdhL_*wR>XF9@l6G{LAKJ^LF{p<Ll38isNTlthPar3QP<*tfB})T
z+XfZxmN{Ybv80(>=r%6po2w((xq$5o$JVF)ElY17YK+9zpBb*CP!CH|({>TMbdL9O
zfp^qL%H+-m+!@WU{^ksKtzKg85x*rRBfr~z7O^VB#MDBwtVI-kTpD><0l)Pd5;>o)
z_m-fCEd}{DNI?^=v!<wtu9`f1o{yA7$HU7+O(DlEzVuSg!vfo|7>3N30)l~g^&&j{
z?G_snh>I(&0*CD99UjC;Trg+kRjKdKW54zHbF`1=ZXr*;{&^H}?8avuv>Khz?YFD;
z*|zQBsT5%kZA`LxKgjJq32IazU(cG(F-KfP&S`0*bPgWn`%3mDjIobaxUppUsPRoY
zrkT83vKK*3j%DBNzrOd5us%D#Q-7(eDFM!DWV6*ZG{G~VtY;Vw{?ET+`MzMUo~%0K
zLl`@qh@tgV<8!lryP91de?oy1xhA|3G)~Pfui}RCkt{B~$u==1EWNL(Vq_9sAM0y(
zZ{RX{SWX3vI738xp?~lo(*%w7aB`w|nw;9sd(NcAt=2}F-W#^ZQ=Sl@{^na<M^O0p
zh}A$;MHGD{)jYTr@V8Ih`}MtgA*W`arW#vMuSVNdl$-ox%HVvHg<LQ5?u@Tk|485d
zhYo>-Hzx|z4-qpz8tVe8iWX){6x%ioc<X|rUn-N6{ms<m3CPHg-?N1azNE^DR7Ade
zq-{E<5~6o6*hdlyx-JlCDbw&dJSr}gtkEvjK&-Bri4}QY*601)fKf~v#PYM9+*U$X
zzP*&Vq%6I`%;u!xPeIO+Ak??jH@#mv7I<$U$kE>I$KLr0kfh*Cdb<CI=5=8&CDt78
zT_+4bz{C@k%hzM+?3=ZSuzPlU{<Dp?)u>6yw?sy~7mM8$RZ=jF4)Rp1Lt8wX^`M+Y
zk!YqULy|?!;DXKt=TQ+2Gg&pJC@Ia6h`pP9aiwV-hQt09OM}D<U6cREeWqgSf0_|x
z&4p2J1;)S5IuXJ|5te&<H|*VL*|$#}{6)@U#h6pkyx%z~!V;TV^RBbm5@fz751T(W
z=PDN*GU#{E+WYOwp{$wukCmK9Q?D$mVrOWE_#)~vKYkP)%I0Ws3h=u4e__sxOpKl5
z;kkQxbCvmyGiY!zQT2`Dm(LPe9Xz;<SRG3VRLet-x+|?P_@(Ay9TZ#Jv)wBwZYrB%
zQ)pOh9S;k#Hh-4Ns8fVJbG`QLz1BU4dS`u!l@Qmw>w`aXSCS9s_jhQ-;&KVvGuG4<
z1425+9KihQoLC;YgUKjSY)LK8&uu^SVDfxp)-PRH!8*b3q_VPAIaC!n{xD=9a22I2
zAM?lo6z)$wcsYxQkn2iJP9~z_fgyaR!=-lNYTh~lfq}XDok`F$@qvtt-(FgZHd<R7
zy0*4_`?j{fIpU&f5#U_{BL$#QXDPH?%QR@|Cj$gY`G88AMkDvL8rv^l#I_D*+}UOz
zYP~|0|5dquH%DAzk-aq@i{-!hIWlW_JJ@nMcy#<AExt5$u9RLPmR*z~;*`6P(_Z1%
za&8vlY}(70-6I7P%WPPqF3d!299bwc<MS<u@>|yqp{woUI^1mA`|1yBdX9G;QBfYi
z`2=7&z9PT)nW@{mYk{2^z1r4Onl8*d=cPiVCQGdZBfec4DcxB!^PFD1AfTIDeG0Q}
zk~TZj5%4cD(yPFP3As26+)I)Si+2nP4GlHD<XchUV%44dqKk>oFFINc#?@FQrFzma
zFtDi<;bvE$P^hyM9D02@+J4ly8DQR4bKRIO)Po0>({1+_>iwvAk<gYKdWgD5N&1qJ
z=bX>+=}v#v$yrK*uz<-GEAFXwtCN<}WT428R1AD2o$nKGPP%;KPLQ@e<Tj{yxEG%C
z>f&zVeoorFZ1(gQbGyEw#$>;Y$S<{Sd@lEEqka3I`BT_Qh4>#jOxMR6!A)0~UoX2L
zFm=l=&Yr2^$MwR~<AV%2$FvufRFxf<ltgO07rp9;7CRI_WfPc+8<Pe%#&lHrE$#=8
zmqf2qZ@yptt}Lj;AGNo<KhQ^TFys47;78@U#DvF62ZO9cnw<`uf5Fyd7c=?8rxLLV
zVwIjZ3ci5}jeC%G%MY<SB04!0)%5L(gveaIj{0IA3I0D}@ZPSkk0iZ>wDiGDa!gZm
zbFpSkb+z#Jm-+em@86%pxI1Mb`?cw;tSlHYZDV7@7gt{H?C9vosq0YI2hBOXNm#J`
zQ1H#}ytk#4tJBxtUw(M7JGk0iWTurr#o%CLVL{1b8mr^$6Bu~0ufJree3!!4=hG+V
zKINt1G7Yr32Ko#0ixdnkfDeQH%zI%uA^+Y@`U<yZ$_t14d2$*h57d8(Slm{79A-al
zg-ucK-HW@OQNZAQz$J8hqJ1c0mSt&^24>^DPJ6WmC(qnZSuzh}8iZcwisx~^sGmyY
z29pzCwx$sS%E?fw`bKAsD6*Hqyj|4%KHNpC8d>~<*1nE<9k`|uzR;RyEJ{Qvnco_}
zI&vR;U>}>PO}Qu*p>jWpK%D;l@m4DTEDdW}H7|PxDT#lnZR|eE4W@R~b$5ptkQ_aF
zl<7+F`}ZG{lY4Ts8+(47Ln4v*MAU|QdPUDGU-uwK=Nh2R9ZX!3X=zFn3q-QB7pp=K
zQE0Q9QjUp(A%mT1m^dXEaA3fJ<bXbku9$jP%#qBGWhi_*U%RLB#EY5=rq@!ha;~V%
zn=lX=H6JXvuMIIv@9i0N=!9-9MOCS~wQrWH@{@d=sp+=;msNOy!PGm^YBx1Cy+5Zn
zQtovB{{0UL%K0xuP+fbo9AQC0mv%@pq2rzxiN74oBl*~)iSFp^%&){!@tA(krwn^T
zKvadDsKUNf1d|P-41f|DL!4MT5A<#3TRFMlr{Shl4uGFOF%3`Ny<ZzGih4&???lk^
z^2jl!;G<XWSkq~n=-7py-u>JRM{%hOjYeBS`^X&>iat>P4F)6rA#(_O{KSb$?8K9H
zY~*ME%;k!@yJwFe<mwP?P5@zihOa&_)xfPyiF5U=1#nRR`-^&L+#oUQg@(j0@W?Hg
zXlAf@wGM_S>x3ky_)v1ci}oEVv0-L>R%UOt?n~$iz5Hu^B~49#8gL_NX=!gEi<v0N
zmxctR_-rZ@IQR&CB(KP9yjnkEE#Q}7`Y|tOYf=Hs$*wEu@Rw8$%K!Yda-QCKYHDLN
z`fFm-XREm&!jv;8Hvv8ljjH3<wHX;1@$vDygsAqP`|9`-<}zfqW8b>D!J(!;NAG>O
z2cL|aI*mfPzrv)9pR(D$`6Wa5rD`UqLc<T4%fUls=3IuawBIecaTjwzo$x;>O<V{C
z`ARjk+^s6T3!^TtU+|2lwW`E6g>!mGpLTNEhX>M3DIB+NryPvtRbp>};aTlZ_=<f%
z?V0SNpY;Vhed`v|ia*TZNyRN>>!~O$Ou0i%jaCP6i^YkU()}r#o<mVS$}%C1vfQtg
zJix1!L|l`4cIp4636C6n3F`|-oEweLRO~L6oH}(XUO0#{4CW=aMsT<G_7;I5^QGW?
zF;ZRzZOoFBlO^ddro$Ld3mY45c6LOk7YU4B<hgUF0t^{+A$NoZ)6f+4*vd0xB1~V2
zUxyaL@7}#T-07(Z=g`-D{=BWs!RGPd?otIa@2RTe`1s}~0e5(KB6-Z>#o7I-1ne!l
z)0714*Yx0DZ}89d=Nsr68I_rg2x?q!#kR2ovTzs=7Re<`PdMsmYcujbQYe(1R4=tP
zixG6feqhiICX5ttD5$6qYQ_2|SjZf0{r1zoo`!DpmHcfa6RY5zI}R{T6n?j(rze?3
zO-*g+vlTlB2kx^vI!OVm3CkW7<Z<4wFzt5F(X#;ltFH?rk~g%~P@=s@8<}Ua!_}rE
zfc5^RE2+ZiT*%_crDT6XvRil8FlVo$z^-$`%uAMM1$(ofGU1gx3}@m4VCIWRNzs>=
zzf`#Oz48srrA&+_@t_EUcBME50G*U84t5Nbl_|E<OG@rIRfcD&(FZzh{|E)sNCtgD
zCz~U<jR*Dg^~D%mb{7iw_x4gPb!z=+TFYdHGgR*&<7TT5cSjCko*1rA_4djS=m!if
z<mt{N$wAm?*{c{uPe+#sZLpzb2mJH0fEUHHjpb#17}pBA<m-Eud}Z8E=p6;8xQtAu
zR0J3Q>V1-n(5V_)Y0Dx4F__V%C7FzO{U}sTjk~)$Zb%nc>3PGp&PD;DiNXdpcb#f>
zq?d}M6j_)Y=}P_R-Qb_Y3&I)qrJgU3eXhoSTMi-_xkKu3RbxL?W7SPG`Mm^{&l}xy
z9?F_g1)b?vStgZ4ijzpUP{$AnC(WfV?YAC(ef8v@f0yd*;G15qVn>FBg-uRQ8Y18l
zFP@V{qo<`-tQY!49zL`f2)(IvU)NxyT%fSAx!DN1zS7gv=P7=}wk;L^lz;c&ntpFq
zToxQk^rcLtT(<!p-4-f7p*|g$Mp<={R@B~}uT$(MG=+wXNViObdJIPT>f+Xd;{*h`
zc2CltR;KHs+ziuU-dlHfH%t`8O%fHb=b*f{LtUI|YzMdu7y*+cL8Jz)iHL}-uSVPT
z<r{?R-SGwExu8&u{a9q1!vz+3G5>0b?NpQv6%`dI#QkR^EXs6|>JC*tKIWuvzF}~F
z6$lv4)BSV%@<QR|M^s#Y`VfkNB?pY`Rde1NB^V}_MIbPuAD{GQX}tM)W4@1oot=Hy
zf*VE-B&go64hRV7h`o0Vnf^0M_1w90rS>v1GRS&=T4CGZ3DbGrb1-igBNxj9J)_|;
zJ9~PjRMpR%@dgedlE7s$Ecqgw(}3P_f$~l^7?8;VP;@{eb(j6%0c~w<k&?I8X{pbo
z#l_=^swAWM6p9S3eQhUQJ?Uf-s~|=l!{CCZvI=r>g+#f?$;sItAP@*K-?B0%W##an
z)BP7;p!sZO9@kWe5)a|F3Rs5Q_n%u9T~C;y{G#B|hV?&w{5TmISzDPujjfC>=w`=#
zuLutPYda!-SgfdkfB;{dyE{Rlx1{udE6T0-W!ERg)z8=)#$}T9Fb9n?1=p0E+6l_u
zETdtbN2=M}a8FN9Z|{2>&-L~7D_5?-s4m+Y(2unRG&D*lmd*!h0&Yo3PQF}vnH~8!
zC5U^%oyIv<mESI|Hl#j4R06PLY<%2R`2DldFj5@_h|qM&tf%Uszdd!dw&uFCt$gqf
zsqqm*F3_DMrz=W+9K6<pQ$9XSs3gCXzSzo`Ki*qIPRRo!>usMug7r7-&3ZL-8iwH>
z+cYDJ!)VM-R1*owTgDzahTld3cWUe)Dtgk06x^*uy8bR8YA`Z$xSDfNOEx_rGm}l8
zj&8(p_3S`nw1C5<^haXYIK@o0{gyjRKNr&geWIrxSuYNXm87n<lP}t!ZB@~gd5wdE
zkppU7EP>3T(unPJ7`8l3aY^8e+(i_sY_29TdV0z?k9TkhYrC`|yQnS7w%?fOCa4`h
zuqE+^<A9vvx@0(CIyKyz=}|<27bfNM)E`eZq3PAhH)@V__dB^?eu5_wZWE1c6=<7d
z&>UFHno>m%U5>*zF2^lX5d!6`=SR{<hun$IoJm-SAHir)yAOA%=jupVTNh;cI5j7B
z*xK8tnkzpDjE)wBIl@U&5#|hr;9$XFd<x^Wpf@^PC>)WGQS!trZ*C^5*4EZ`^&xq!
z)(T-ZSiHd_wAza*>_?lj-nsFhNU;UN9_aaMOKwcwb{kuhPJnoGHS!Dg#?imc@uL%}
z!1}@rvHrC4ZZCoT;!gZL0@W4{ee;?4h<DWpPi-P@j*gDz$$rDWb#*lpdLg$|qs7=5
z!ZIG_t=}1aDAY>H`66+rwo6(?W$>eHLXiZLlT*8F!m*<k#twWGOum4q9-i$^ct~XI
zpiszfJjlb>3U5FkoEc8U$S&Du=gNPO?I(`I#Ch0*FLMQWKQ0s@d;D*E6MOnI8ZG+x
z@#E=DLA~i7nYSo6=Lc?I8ydvh<$;hIM!$TC*PStlohY{*3Q13AanQQle7HYyXqMly
zwLGq0GW{&;#WjGhwd>D0pdEI?0<0^MMQDF5a5gtZ1D*Q$2aIKH-|@W2;6+j@hDaD*
z?+`!;kJ|m8%i+^Oi(b2S&Bw=QTHwgBV{g8Exf_Sc&dW<YQOa##R&Cr8__d}cdqdop
z&{v)*KPP9*o;+tBCOLNw!#6rm?QAulFxMqQZ0+nAGc}fuAi#MPG@=r8<S{a{wtlj2
z3L(){<6@V?gS_GXe&xLHQWWIt8KD^z<fm7Ww~)Z|2yp?hgp+?Wy1SPx!S>Jh=X(zk
zF15u~!|;=4cBHhlbY7v^crA>DiVwV%+m<9nX<Xuka;t4^ZEb4u8A>4Ix4mVgOOvy`
zx7r#^s9tRTW4O$LSwMX83}SqG`l&v-61%`6aDTl<m}0Ci0ka{1YJe0fO8h!E?IbT~
z*?mp9eqOB5c<8}EFeOfQZiEHuqQ8}>eKtbh7%p)-Npa{v_>CB%b`Ux9x%Lm5uh1rj
z2xQ*6bf*<->)?=^0b|p>TfV%oO%nFRc)ojgFy_r>>kBy5WY6*9n+Fr2eO<%@b@iCN
zo1b68(Zh@@Fh+SD&6DM~kWhVF-_r6#t(K#%%Jq!CcXBeroy!qiMi;JFO<>PQuRp5B
z%3C!`zV@f$vlM-rhK<fTLzim)9<x24+bSO;S2m|W>O<IO1YuxYR8!<1-hJw$IiIGV
zqt(7XKJqE(kpp51@-WHn56}x(gOD8gz;bJ>#(U<<7R;Y8c6j>q{RKoPaOt$Pn`#bl
z`fy+~H+E<*Jnk$ode+|&F9PprNf%Go`BK(BD+su$l=D_H->?t)LmTY-S}Xu>L1I!8
zmqE7=SAl*f>c&g(xd?z_nGEwABY0UL%BV18ZfbhVVQK(|y}LSSY$OW1pBvox`t?D-
zv7Vmj<b<!enOR&J2?<FRZ+$)qCvsMm4dMd>DG{j27nAL8X>?9BPqUfbqOYJgj&zta
zOiChk)suLRzI5d8=+4Lc>Fh`_f514inSz0Pz`)GQoJcyD<dLij6YK;91*=BO95(68
zHa0d&H^6qvp7-Ca!tz)6?GM>h_0{^23x9^e8d&AQ!NJ}#@16Czz+>AxJ4s1NK*sh!
z(qKqA;tYZ%b)I807=vRd0Yp*r1BHHk=a`w9jdur&E&9Z<Nnn&}KGR+BEH1YD^d$Yl
z#fwS_lJr{Z6LhQZE)WynEqR-H@6&9E`&r7`Sv&C?p{thga#zZhh#;Hj6h>bOkpik@
z48&+*cDStM=cN<)_#J9-#&YBKw@rdWLg@KJxFFE_Xf_;!2@R$<vOZ-)9uh-rc^$#k
zZXx|rRYp7motvcSt|#(84rfi`Ms{{~>Lr>x);`0pNS%7oK-<^4?*Rv?uBs|hl{Y1$
zqy(Q~6_s5W#%a_awE6ZVClaU<e~oa%FpT@Nf35qpsP-P2uCA`a23FOyjFf1dhmY@c
zE0K~l#40}C5EB*sZmq4WyE)h6=P(W9R^Eq)!>AHWzq`>uf!W;n*w|BTM@RmpyD)@0
zNfk&ksbKu$4=jOJ1qMCu)|Hi%;JgEvFPuMr8@%-=>B`v|0Oy~Ji;adp2cM(jHl{M-
z=HS2)b?lC+s%nYV92Xi(y!fm?GBT2cRR!C1qp1_~s;bT<HE#>dZ+E`Sp>s{BH03b}
zx?444sW#u!W-qBat}+?a59WpgKVfBaim%M4Rle|_y7SBj;x=c9hzfIa6ApV1H!BWV
zg2i2z2ri<oVSwV?u;1@_ec}uSm!T-VVRu>|OvorS>>Hbz5i=<zq7jTN*YJUO1p^GX
z{POb1d~`L~4G@Gdn^=?^Zj?572q#aSs>folRs^K1t*v4AN)rOt*=@d@2~+yW9`u%+
zoB|w5m=*O7rn18w0V4z_D|P|&ga3+fV0gIr=eLblleJeTn+Ms6FJBgk*^Gmmr7cPF
z``D&to~p#Aq+o7tZdD*jk7jcmul@S<t<!*OBGBLwr_C!RYHRCerL^c2bkzJEBps+Q
zH8u4FMSD%%*RPIi?GMvJRWiZpFe~%%@@9DG>ElCn$NJ{n^{J7bo>1zKPk<ynnhnAf
z+upndzVo+^_QOtPSxm7_Gi|angn&5nSRa)9C&<5W#b_i6zXw9Dz&F_0DOXRR1d)*>
z>8O5)v*KJZ2!()~2wb3w$kpG6iubYQS%dC0jk$RUEPyQp>o)+$0j#1sQys@7aD%x5
zlIeh93&OCT0;2&g!(RVO&y<uHcdScs0EHf{a-Er(d7NSi&IS4mgd<E~1Q75+T;RfT
zMpH{mZYB@-KbTa*zMSj4MCg;eEKtojcoc-Yo&CAKe%S8p@^uYoaO!3ad108+OveXs
zQry@yU%dDXe*{FXt?g0H9dp?S>U^Xiv6^2!Kw>epfD!{mW(cE^Qag72?7~7_^^Q1=
z96!?0K=EMK;r4b}OWgaF?Ve}=Yw;)~vVEi<k_Y?+)}L!b<N8iqZNNEI|Fn>Bc=w1W
zq<~;Nntg|{v)t5yfCXMjw5QrCN0ARRc{apL^-Pb5e)v!J@mIphst!BI6D3_i8d$0w
zm(>09cOt~cCxifnI{$Z$>Lt@(d7^Ju5OV$$+Z|X#)ilhBpvD;bG#mOeFEQ;a|H;Gt
zOu+<w)<UmbeEkt4)|UivBxUo%qB_FQcx2=(ME_wu|IWeLAp*+{c-ODSzvUyE=o(?;
zmvMVr@>Gl=;)FEPSB2y8<dl-KqfGmrD{tLJ?v}GrTIZ+r?z>SPy!g2!)WX)}HPwA7
z5d~r4YoGCuF5kZjmXa(!RbvZ_EPQFc-TC^W?DB}rnXEaHXROH&P&cV)qco#SwKcXo
zALrXBZhU;d=2k)&D0z#QW8n>i&yk0|r0=MGc65RQE@mA2KaY~n1Wm0{As-Yfj$IzQ
z`vC>v>O5D-I&}+uxO_PgHTFERR9p6*a?_6>kGJdU9ki9~1bbGuL%A(Piutt%G&?Bw
z)0?CA0wE=`sM6qV5hLo3KHNC=sZ`ze71bQ1JMttTBgOJkQeqMpi5!2y`~p3p{h{!1
z4>73yz73a7kbHLma)XlwqT_uZ%T(S7JAWEpq!ucYI44db7ZiHG0Ojm3I$bF&O%t$_
zwJrR~MKzE27SikG-&s)kzms!QzJwAVOjslaW>EHQwGu_XId9Y~<=fW$#EsQS7nsV;
zTsa#gVoD#=l`WD)j&>^sH3paF`)pH0W8xsB{7?^#eaA{AKz3Mmah0vGMF*}TV51Q^
zgJ$m!AYI9|FF_F4m*+<)7W>+;{t%;o{{^c@1UM`iiA<*k_c1;}-xwVffJt9IG&+F`
z(%7>6C4gVBZ7SmD7*y+enkVw%yM3#SrLWdro%eim+e8%7(QGj?p2MvX;+}v!>ya05
z(wZ`zu6?~QlO1t!b;Jx!-SO0M{Irr$zclxD;KLX58S}EY`$*$d)M_blYa}CIh|3@+
zH&;O*5@gOFzegP99TrA@VD!u5Qw2}sclz=KYSNC0=^DwqtExTdQ&q3X1sCUywB$|s
z9Y&FK8)ae%mO12^YLgY1?kW6(H6a4o7wAP&HF+I$hh~uDL)63-?XH2L{i@Zx<@=Fu
z6<wwtQ~UUAJR<OIN{(^fsXcsvbE7$_S4QHu3V)R~TAun-pDy8&<~NTywLa||A#yz?
z<GP^PFkU;u(H)&XBFxt(Y2D_n=z`RvWC__enJDP-$I#L;CznkSSE+GYD=M4Eu&hf?
zQ0G`K`X?~cpPXv{@LO;15g$*9`^^&X*8Qc3uW%WHY<HzoKj6$2%{?H;jV!kG+Ll0!
zPt`A^^CE;}x@kQ!#LI?iie=CXqpa1ewGt374W3sG{SEbSfQfE@L@KWw`}_3V=x{k{
z5zhoqd-hNPCc%pb1`wK?{-D)Tw==nw;nIn!t$gZ<ku|X)%1}QYCEzh<aDVD)q;DVF
z4%xeRCw{>b9FgeRKSHs5__$aLuBzZP1XyO@9k#KyZ`w18{jbDzegpPnSqTHtQj+1b
zDmBjyb35<!CDQ234*q=BUm5|2aqq>A-}u*d`>#@p=V;Lmo6K0%9`kpf1U2dx;$gYi
zk7-T@#p=rKT;OIcJ_T(M>%A2*r~Nv~W$k-^%nM0{Xa64uF#(ke{lQQ!P)|7Jw>1QC
z?BcvrL^oLDMffWr{UiZJ-)<0{rC5xS)v~Dq(BPgdS(a=7(h`SG?S}w7u=`))a_P5m
zm?rxR-)|x5MC?-XB!1_ORn(qPaG797Y>eARqCJ1c3Aa30992lwMN^ZgCY5QNx#?PV
zNJieuTc!2`h4LM`JW^_3H4v;WFB?6YJNtu#O3HHU4Y+Fn8UH?@kC?DGS5K}m0XL|r
zu)k{t9yqC=Sc;S=)Uos^u(F-8qQ<J#Tg@5LyO@fI%)a|zwix(SxI>3jIfk_KjbfvZ
zB*gd+?}+iTBdUG`lZ+&kbudm&n3fI)x)*c|YZ)Y;sbk_sf~G>Y&0mQo@YP<Gx!qPz
zwYqcgB%}gIEwBEMMWKANQ{1nfyoTNJ6?(D9b|Y2^Y$Pw~E~Ub$Q*%+SWx5HQxr*ju
zR6d>7pZBe3r)I_jAQ?z9NhOpsDEK9VR9|Dg{uxQfm}zKhLrh4{#+(bCYYX**k`5Qu
z?RnQ1#SYsX3uhzI;w981mrMDLn+^g7dS+UG<!W6ciORt;HtcazN8fo`f2X<kq1uED
zwdZ14Y6)|>U9dc4%ork>>>96iSlJFI#}jn;{tqR*QEKJBS51z0pGGcMqxzwqBl%s4
zwyhEAyW?Pk12-CT6wlR&=Ur{L;OZo~k3OI*u9?zuQd0E!WOT3;x9v$=C!j-RKKsyF
zo8WWgh4WxthF-H<%1>w-^jlfhyajfE6l#TH1H%@`QTAyeivy%wmCSUnZ9KS1W#%~4
zBvC&)Z>qH0J$Yj3`Oyz~6M}OW><&cIhhz~KdFt07KR3AHWo|bSk7?DGYC2$w-03F)
zNqkWCTNE$;!Bb)bvm6+cStkq}Z{y(^#j=u-+s%x7*Chfoxpf3HUUIO!f2TCZ+>YGh
z8b5L~ZL&;EQf-Isg(qj`&Hu`-a?M)y8XG%dLl0TE=P2e_iWSJf-VH}G4v>U4#^fwv
zi8Y4~)Sd79m!<{fk&YAvlv>@M^HN^fRwp$V4gE<cuWti!WP%qt&d+QrejW`A+zT%<
zGk&Scb}-Ou`QM6<cb6ef{n9mBA-!K~zY8XDH`V(FR4T2^nndzp^6IaDvx`N)7W>(B
z<P@b2>5mT{Eh>;$ep_hJ=5qiWe85U!$7D#l-z%`q*=|u_nbU{)u~FEfz2ExY%d7PJ
z74!;}RA3T<23g0Nx3fN8Z8x9}ljF;0BX{P#{<?rPu?E8H$qSZRegpnch{F1r5m^zR
z8#lHH<wj?pJrDalE|lF^&I6TFecoRy-+yB;j{G!nn<@&zln|2u1oNi=DEyV;XcH*n
ze*B`bj2`c<RKhBNa4N-rp$1;8Lly!0=X*lDmq#G121tqvtNp_);Ne{v|K_@9|N5KP
zfE3<K3IN_e<62;{9}E9kPQ_RIm9g_J3g53D>ahO`KV0PteCS8*OU1jAUO22=W}lzb
z^ZF`w_0PD3>ixAN<oxu<@NT#PxZL~YqrTA-#!VEQ7BXON4q<sVO^5RhS-@e3v86rp
z?}(4v;s4>}Tb^krK<n{YEygeZID*G<^6zlc14>O@-G>Ks({pn$Q<O~&W`?f(_@su0
z?8<y=GQ{+a=*n_+TNfe9Y@`m!fT*u^b?ycJb`UFw>$^gGa)t1Z(Bd?)IJJM=^6)~!
zvdB?9juZd-gptt@y!(Klg<45rA@}a8-Qj_g+7g7`MN1+gDEAiv8Dvm?)I;#>0iy5a
zo+5~okCpxi8@+6ao%p?T9I8TL6htmy<2P^K1jps7p`tNb)X>ln0<Lou>M^L!3KthC
zwz!o`q@*qzeY(|dXIEEOJJ*znS^m1}v!_jlfITG+V!z)e$D0Mb{xeKPY$OT^4oGCN
z7-oLMK0u*bB6)KsNk3*opsp7pex08_@)HnICx$XIF^P8?+{^pJ0^;4}j@?vdK1&^V
zB(~+~-?Gu}MtFr!KCkRC!eVt&ih#hKLM^KHF~m&a`Gv9u8W%Rnz(hw6gNwykM)JV>
z&wQojG05;v2pNz&Nj4X4J}$a6{5fH{>nPqZ|3B9XjR;Wgt7|iz5bneH8dPA4O-Jv8
z3swKvVSV<)O#apW%YR`EA;li!ZeeOI=`p-E{C~(?e<Ys)dU2?P8xoP9%~-gRA|fL2
z6A2Red3pRh6^w_6^Fu@`m#oGPm*w+vq|4Tjxrv`zZgpR4y;P3BzH4Q4on%blIP%u%
zZxeHNFRg<=>MN_#n*~h<r|H^=6MQUIiX)z+p{sIvfX{UOV{A&?jg*gPoQ1eJ3kwg)
zo}zU^dUlr`*V?uVW5R|$MA?-H3%}_<M)$()mq<~2qVQjyr69j=cpouN=u6mc9^QqG
z6m+t$54e8h$Pq~8mNch90uri^&@ihUJkk!fwCtxhmteXhZouPum*R-+xUa0NY;bV!
z$_k7f%q-jJhuN-OAY+uqMWawyB4|17@9oaY!V(2fqOaHtnl*FwcqrhXqvCPev2@>D
z;<K6!dJi)sJ3mN-QVZBa8n@;B?P@e~$f8wX1qpda2x4VcM_iyJbPEw!3uD&}VO3>1
z*K&-A|Eg3UNVCUzg#G&L^(RzFv<J_(vG+xSRb23YwLmQIj?Ln{>Bno_LzZ&6T%!DJ
z9LTmia&AgDZZgU_(c$$ccX*|y&GVbhG91M2JjZ*QfI0H~4a$u;q7BO<j1bPFa`kNj
z*FFXY-NG<eF$raEB*8f<Vw1xNlX9iu79ycN)A`<P5ZZ{HSdi$ht}arSSsvrE^(PU<
zFef>p+U-Mj_H9PS6pN`Ape3qMZ}8+vH%z$wpkTkbSY|c%WX`o_d}nKmkc#Kg9?=fS
z*;poh;LPW_j9hXHC8a*dGp($wBraIabZA$zWe>+zxevp^Le$t{ZJM?@fFca4m8_=Q
zDs^aR?5AmOGyQ07fjPXl*OOHo7V<ktAg3$T#pF%O_N1J13=@ZWHC!LlaC~{{tJ10@
zxlV@n(IUrGgBKPn-d~H=dW(*mR9xMD=>PuXPVfId3z+tsYr8x~E9}<GjfA<+O{a;7
zLJQ@Tm0#Q<^FfV69=-y?i=^s~vIij#4GrgEGBMoDWim2c<x4QLqI8U48H~5@7-W=7
z>J?i1>Upsy4K7_-Cdm1_hlkV^e%nXU)aTFB(m#=xf67)0r^c%Kxz{DLPBUAS(#*OC
z=}IJ^+Y<Tho4kpMNin9rscCIx<@C98&ZDlRpy-GKHk!!kX+2$nJ|zfEu?F9PjQlvY
z_E&n`z0aX)!wc2@-(MH>(bk@lZNoE_lXjLB@6X9HJLUG}xx0{s9vbhg*KTKwJ{q@u
zTHGINX~Pn;BrDml>`nyA%u|vxa&mI6STBO;a~gC%ZB@;B{)*!h1g=1;>sEd=rcZfQ
zh+9C~*C@BIsY&wo{&F2nVVH6DQ-}l^^1Xu?63jd9Yiyi@G1**=Fwv5;5ToBM?7g=d
za~MPqDc^Hsw_>5RF*@I2(|B%)*l4h*{a{q(f#5t&)^eZFEzsh&qU-B(R`|Io+}Lv8
zO@@k(pOc=fYaDAUstU*n%G1u4)4?YsQd|)R6>qyb)4}XZh)3>ANXr$6F>^z?>G3wd
zKtmR9CJl4GcxU$0hhhk@=7w2;^g--j%mN~Xn(T$;<-?7Q65$TB-x%%ewX`5NnEC#F
zV4&8aDHL^dFu}B4jPZ#l%F0Yq+jT+*8bT{NoXBP;J<Q;sioPaHwr*@_Fnj{qnG3Dv
zWD`(*CY>UXL{7Y{4iPJzbkTlcJ#?(iN^;J@t2lhE#HusNkM9Kr6!<WI#G`Y)uAM|^
zF_va`+F@8?q=wS)l8_x-O>;bGXZ23qQQ8ORe0>Bc$m4%-e8Mm?GY5uP_F~uO`{crR
zA=la43i*yq4#gb>B-c!5vL8obYN~kjjmvkT?1<lPC2^n0q<lMxe+9}CJQ*3JBmEET
zyA%}5_BRH&xVHkF%e<{rIAAB!-W4ij6=R_z2(6fhG*0r$v2d%+pSR!l#^#Ob#yBN8
zvUEt_M;t2)6bcsLSnjHDe9HRZdc>QHa{Gv}^vs~oB)9nats9!)K43qoOXpt}$SMO!
zMlneD1u5Qga&$j?I_#=*zef!F@=Q$!5fugb-P|;`GH1DRne}D7N2-<GS2j-!5q(kq
zsYR>s&_LeBfy)!133qu7jg6`tNJyZUd7~x^28?r;R3@+$Z$ISZJdWP#erCcu<7^Zd
z7RH#q^8F1_!$l`YM?g&b(W(==CFAiJ{Yqhz-mH7!V`&(Si2;2Ml*8yI<am-*tT|W%
z6w50rCY!Q!IOFz~Yl1d}P1U5`qrF&4KzUis^74RWBxh@oMpEn%elaU4BWY)he5LK~
z3C6VNgtel}>~xOC_K_)HMrFu;?T3<Fb#G1`o)Us-p~aaNM<_Sx2slR>2Daf|_*@p|
z{3lV1D_lsJGVRUL^BEJyXO;Ti{70}N(@50Hiuv3~@3oFh^$HPh5}5C@>k8Xuv7M%;
zB;Te)T?4gKC{oH!%xi#=<3U7jc`<F+yG!AQ=(L(-Qi&73m1KQ&Ht*w0E^MFhbSI{n
z&B<96XHJzLQO9e(rf^GR`~u;tO7W!^dOn0+(x;`wy_Mb%Ovqe@bvJ^v;r%4zK)%Jc
zL#aC@C9{;J?c6Qh^L__woidFTc_Iy9;S>P&audB#NhgSg&<Ht2MFzj*qN1mxI#8qV
zL4jT>Jkj)WM+WW~4W1yh5H0e=g4|`=C!_#$tfbOsONhR>*_tFJvUo=!*`M1MRh>?9
zdH&JmL21=#oZg<NfEqe}eB80+4Pqsu`IW+pzy|3ocGrL&fkr89BFpU8`*U^u){(3f
zVI=&PfxeQUsQlFK1eF?9RaJ=!P`Y<H<9@#aPSqAUkX)K1lWz1MPKYp&`(VTmS)6_$
zNJ^~i{v*5vISsKe#_C8OgXophwGLVBLe~U&ZgVpA%}=%hJ@}6!S?xE-6uVgGLq4u4
z55@)Z=(*KOa2;n)p7ggd{pxYP+v3lg3hPXkX&ib43-fMPo=S=^4(Pr>pgzi!sOpNs
zx9{=t^>Mm)G4lN^&G`|bTk@mY!!Jii0l?r~#;b0cZU;S)2e7{063$SuA1r_C&3$^0
zm<zJ?lUr+JPxkKJLmblsWW&Rw##wD%Wn=Wjy95B)vDS0$^6Kg?14e}kii+Ms-d1&f
zG$y%sg@uJ39ZU0~m@`W|c1RnF^w8nEZQY-s(vVk0?LYvjp`me(Gz<EBJxo-{D4wMF
zas8aWBF<{6mZNG9J{y>7&c^zm8h+z@n(e4}Hmpjw;TCB~P>ORb-hOIEN^?WY#i=J_
z0zVc=6DNjxK=>-j7@cpvZI$w4zpT<7L&$-AsnZS*AHLt#L!ZQ~LRkeivlWWtO4N!<
zO9w>|o#0z^8=+fbgxFk!aTRaR3JkNAJ^_cIr=KKp*?P04n7w$Gd+=&^j#86m_MqKV
zREITy_H`K(Jw1N|(g+3E4{16@Smhgnfu&fFdUOCuZ;@F-^@;hwm>9P7m6DPD7M+mX
z%oCUm5(@HZ)d)^x_gJTQLO}D_`Dc&X(ArbMPvlda2k}@w!FKA|E-=XbsVCQd>B;B8
z%b?1!2o88TVoM0TaiS?kNZr|~L4DIS1b`K#yi!=PpT0k+4n=w$gNTRkdRxoZyPth1
z-rZa(j2XyO7t&vZWO#b=Gg1_=$%_oqdn=7>bF;Hh2wDK-AvQJ^X0UiLc2ohO)Xa80
zL)vfR0whZ_gzQc88W0L`%0rkCoyZ6YJwDr?&n;+n;uR2`K=yZb?00VyQgTT)IYE|a
zL6;Zmn&0I$108C&|J?iVwpmxILIbx5KPS$DDo~J9nkBvdSv;eOhp&aE8PV`sjGG{(
zL$L}n>u66Y^n6QoCW8UR6Qm!0X{emtiB6$g95?tFIT%@VG%}P;T>h;Up1!`p>M?UY
znKorvD#SSUb@uGp?p)`+WoXq3R@G*qUpp7lI^$M7stnC6tgHnw15mr6zX(Yq!^wkX
zKbO)fJDsNYv}}x=NYxVS{Ot=~HJZ1{2sOLoMZ68UoO@Jl#0KoXzakLanN9%1CbZTj
z3|Q#>#McK4wF8FKqC2l8!Lz#3t<aJK?7=Pq2%01>SUk&%1t8JNBd>h(25<<O*M{vS
z<%kp0zt=7XwztD&fQF2Ui}jwPWm7#{eC)kDvQHoDG~B9_4kN$CuVQy%oaQ`D<a>%>
z!zcSu(x7oPe`y?Msp8NXGFdn_pdJG?BPULr0IAJa@72tnAE`L3B%lc*m6nuDgJQ)!
zMwpi_Au8&x>-zSsyn=#ik?H7|j1B%pA`YW|4!cDyKY;?9CAIBg&4=%(?3Qate-xt<
zvOz6A8xTuCijZgi!B?_0mlCgUSP#p^!R_s-7&d0+PnnsdhfuFz>3;6Y*M#0BMR9Sj
zb$pZ3Wn7U=-uE~*Wlo%-+~@33!BweeLR};zL&u8B);c6W{GeL#^5x6T_V3b`3hAxm
z)GM47Rjq=zEac^bsxwFZt-*>-N8wYFQ{FyfAspDThZx%>aAPW+r~1pdt9*B5U`+pr
z)P8?aw>fa6K*}Y;Fw5)__W1JRJ8`bMWYSLu)=8KnYk+omcz*W~!XA4L&!}{OY;iR{
zeDHur#T*iMXGm_E^31`!`HRfr>eE?%F)=ak?|><Gw@64xFv8CQ=kvB!?Vx3+6rA+T
zP{}6L13_ZZB0`&qnb~EtWTd9H*2^902Ff@34a!POzgB@hu->>~YD+~yNeSDx%I<@L
zF0X|<dsn!{sc{;!`sEl=P;jsa3Iz_1Vp_*1MYh}8kb>N(_>K~TocQhMU+}iE9#<R>
z_cxzqT^UQk!JvKxCcDC!GV*=@>({kG8m!<(?Y}(M0GI(F?P+msS#W*h%;S|8OXGK%
zp8I?=KOu}bHhuChap{=NHbyJkG{W%E&BY-*J3F9M`Nc|9zc}sCUr?ku0Wfb%#Km+|
z?<*_AFOUtzq0-(FgD%c|-ffXFx*LWnud7Z^cBt+;r#`QmN~#MxE$^b(fyIlSSkjmH
zWxfGy-}*mk{b7JUX$Ho0Ix2&beD^iIqw(k7^KWkF_$}QM*|c0abK&7#$H}IKmW0?>
zOy(=zk2^wKeI;%k$7jxb{yIMJeqaX}&D4W(@C^Ci&-OfqQagh<4DH)Idp)!V6Yfmx
z9{*|F1#a-wKlhQhkGpe=2iCWBGCWXSURCbXPVO;3`KOOV0t#*dAq4w&T0Lz4m@x1}
zV2c2~sTH}(qyVbgsp=BYtsotW4~aWwF|0nT)(9iuh0{?4+<zMH{McAAv1hfennej&
ziA_No(vh4p&Ly1t3+a`$K+^bgUT5QsV%U0q5Ydyg$4=1VAhrgK4Q+ZfF!LEBLBrl#
z`E~dEWq}jiBX}|E%{+IBb<mlDe0T0krDw{)`DIwyWi>h?GHU8O+B)DJRRtisVzl*?
z^hFe&SV!}O%BoeaEnnfffqrdE>7}USXrDc6;$Ps%0EZv0?Zo2hSAvH(O#!?mu?vdr
zSj(DCD)z1a`eaS8RUoOOfXfdXar-dq0c@{23CY9fpe{?5DA&-onmCC+=;gitCYPya
zVj`$@nI7%4#Ew1l1CDg_FOHOW+2UYtJL--N1Qi}RCquFM(sX+QAn*$!Ut4F~ULPY6
z`Z=LcPDUD|cJngFpO}C92PxgV#I5=8;3!39*$+Dgct&?YqH3>`9eoo#OwdY3ektlw
zco0eMEyeYoVpJOzPZ<T_zL#~eI3aif>X&I6kRq3yD!@0k4dQ^W#Eq2NKyo0Zyx1>;
z;Wtsb7x;%1NE}VS&mY04->8|~L`X)Rk@b3ph75R*%!%=%xZD5Bq@JXW27%S{cS4NE
z!;1AY-n$n6jn*cp<M{5r68@(ty40y3W&9-tfYSoK(|<#Ce(D*Z8zFzu4cfQ1s5DCQ
znVytmsk@fDx7~exs@2?3wNe0WzrB`U(56T8H?+BQhyMzYltQ>*V$(I?v<?o}Ha!2p
zwb$RS{L2Os-{(hk`!=P%u4ZL@b1eMYCLBY$vmGzbZL2>oc&$dxA1EecwaU}SNju4F
z8%dv&xR~$AL|S_2Rt~U<)~|N`W=+FvqZXaxk|$CtCf44;RU`kp>bJzLfGCzyrF!~k
z))?S{AJMrySu8j7{-F1VZHsn)#59yp;2{e?&+6E1AxFH45B&I?mEgZt3~Ye09MQQk
zCV2urS!2dUhS=RYCujNaX2sRz<DPzBmHO-ec2Zs3dQu|y;(z<sOqtJj2;A^R7+mQ;
z)ZA=RfY(}mfI2Jy6L?>=AZBp?<g(9C2avlxYI0Q%5QV>ZcSXwi-`4hi3(ne_#r?X$
zcfXwg;NE`{@3b9*1l2gqkvyM2EyqdPuk4ybSUd_@6(xA$ifO~@J!q;LY)j-g^w<JF
z`n1H`zsWP7wZ2I3Z$iIkZ@jbn@Qp8yJcl~T=kN-aJzXcgTO#r=_I%$%7sBNf=n*W>
zye1t7vj0-B;W=qZqkH$Xev{mTYo&wWKfy77t$%HM!~vW>UP16<!I_Z1r3^@~rvyIh
zFDb?cV)&;NTY(h+TC2LFy_tND692uw(Vacww=n$Dybh;7(0%^uAL#Bh{10@e9wi1X
zG5huVZq~h6%cbFcXZQ+rD7-6+kdxQ*{f6b}s}a>hTQUfi@c_=-mJ`-u1<R{14aGIG
ziSoCPC0GH1y*nNL`x<`}+Xm+M@aniNF%Oh1RY>m9YY;Y`U&1ErV1SOcpG{#t396&_
z*=k8G%gTNB(BuRfa5LEQ_5}*^GVfnXPUIzxBtryulf0^gAN|U(03VnQSME~J2K4+#
zM5c)CU$8Gks&@p$aeP9i6t}BsW%wW@O7c8Wwbfs5Hl39cH@Jrjili-Mx`USyhHUc8
zovhESPO4a9@8!9HK6=>)VCV>kba{xmdfR>~5ipEAGMu%2Nz#V3E%=DGFx5<TdEj{X
z<&kUf!!JL7_A;`|b%b0F3CiD@OV5jE0?l*8D*g@VPF_x)GHg%w{o}0wHz*$BkWLpq
z0j1bSDZ;F;Gxgn!p8g{iKyq#Da=@?a8_ao)Q)7k-(bVUq`>!uOxdp6}Om}N=jYsa)
zEWz2(%YR)Q2nOtsB^IM!2yVxR*R{`m=Ev_ywqL2<d;Rqc%JD&KLkn~INAr#N0H*i9
z<8mKtq}fsb&@^l;%YUzpT=cj4K6}!Zn8@N_^osEhOUwKo6a%c9{4Lx`0g~Y1wcf?$
zYhq7uAWd)RXc+4~R(@m|P)U}Oh1QQRCpTR4^B|Iz^cCm&RBqFD4tGC)(g|3(GW^-=
zzzFael6D?`-6SGZP^%<_IF6d)FP~Ji%jQV`1JDF2+$}`E!hP=U89j8{QTWf_zkd6D
zbU*wSf2+-y>m@;Mffo7LovU#;u&(_D*5tVF;pM$V;Ts1CeO|qA{|s22|F{eCB8b~B
z{;w@v^d%g`F@XPA!FPHJ@}f8YlI?QF5e8~fyziLP1&fdNUDED)w`0FZ!Ko17t_xyM
zGPvs}&@=Is1h$rW^)2yO@%Hl?rmn-0(|O<o4#Gxl0E7BQIzt9YErt4v7l#}~X>4j}
z<Y@@y>x4v(;qAXL@%6D)F)>*R#VL^rs1xI|uyempsQjS|a~s$P)8Fe&(B9l%`Em08
zNlcXz5#F@o9YgdYxE8wt*WVJyZ#o`qPen%D?a@R3u?<vXA_}$Or~H;!>TNDHUfZ2N
z@TNs7*5)6-2@H^!H*y{C<#79NL^d@(T9$x$6#+j4Hw$P@`xZNzMt}z<#*G*UfEE|0
zul&6;T+sBvIb8uf+^@dHnKxL)(mz&#BUa`-b4T#tS83sV$VUf{+k@6{!$|@X0#y(`
ziK)iz2!0j*ht`u*^ti?TaeM7x)$V&xwb<j~eie5@C?4)tu-HGY!3V52@%;(Y?qhiH
ztHL<^uPQ!;vxmm<m{)i7&|n+fh_AW-|K|uia-X(DsS4@Nd)=tWL>?w>JPpTI8pRmx
z8O*2Zx}n{6y$+4Tg_frmjb0FW`4^n~e>rXssZ?v@-k-`ojz%ukpIYUOT65Ls2iC*L
z6P#4AKDW}y9AE-emKB@NU=UTIL01(*`xzS-SHV7YKF^P0n!TJm$6Ba_;x|mG>dX&R
z4o2u*__v96xia{&ICVQITN`9vZFtk&SLXgNiAmGSek!>4vl?Ck6+xBxSnG?k<B$z#
z3}xHg+~g)BOwVtI+|^BR^xzQ>6osefOX(I<jhn1{99zL6{Pt!QE9{}@vy0l>?U$~A
zWNph(P@X<Kh_AN)aKk8Rf9!0?8HRI<O3>0wh()qDD(0(DWXwu(x`fWs(+&I4_^DML
zp|JJ0_td`dkCx~7Nu4Ea$=`3uzxu<D{7@e)O(o~R^DcL^OZTfr<{k4#(K!n=Y>hJR
zKUB)Rv=E8D)&D7hC~*aat~90Gos9)%b%jSCFuaaiS!A`iFqLnd0)?-D!v-A`sKcAh
zkODg;%CN@U2aQ2_c^vxwSNJS>AUD`w-7fy85}6mUL<QdNp9<V^uW;nnud6ut?A+4p
z(|&9Msco~dd#;CXQ!BmnY31t&yC+y91yrL$HK>IohvhedMw8DiRWvztp3aUGDumTP
z@hMA2*9^Syt1&Cz=b!6Xb~OI#B4uQFN#;kx^a$y}{)ymP5f8rq(pZExq&}BQS6dK%
z{PD(!)j^9^NEIxEJTum>L5)M%z=e_xcpF)Mmg3h=E-m{=yn*=CICNsj+%V0%@P{^g
z1XY)mKRs`^SuRDREW`h#+@gzJIFaH<_4_zdN=m8>1L{K|Rtj>)5DQ)I3SKc*?I(5k
z-<(`xt!(11=q#+#jy-oiXy{BO5*|9DCuB?=e@54(DdI{P_invY#FN4f4}SLlmMl3n
zwG3kf;>Q~zLA`a97St^pwP8hn3D38l9zU|%@erfUEM4*H*J^+C=ZQb=mZG$>ReZI-
zmbzeP?rT2wo!WgaW4rUvBnML*REwG`iFex5MQnaEXV85|gDH=KURf>*RJE_td6l&A
zQ+e<f>h^`@F^8D7jFkRmJLa<u?EfVqwR{j2>bj4Rd-}9@*0&ZS4g+ZzSQmrQ#b3w8
zpM*K(eDgs)zCM~NsnR^|vu#3dF-<LDa0Y%%+VGU=D?f(Ad$I_NnO7|m>T~|{TRrMS
z6gImpn;%3vGzM3mlqzc#nQqkiF$jqw#;>KDdvKNR_^j>vv~)%3eK51r`7cy`nw*_2
zd&XB%Oib((HwC<ce36TqH1~<gllpZmtM5^${-mwMS^Hc_IEZe1`|i^&_$j))FuQWO
zPW6YckEm1)E94*TS2EZc-sNxD4oGacEYGz1HVsJ8Y&2r<8(n=f{VLY@JFq-rpQz#k
z-I<Wpi=}J%IrT*OW4-*y|GGQFj%=-mU6f%R9UV_hI2sxn`u}}EX_#N$tNy*jC9Grc
za#HRINTzs9G&?_L)h<`twx09NbJ8BERcPj$Pmiz=>ONShfv|m@ez36R<W_rrs{p;x
zM6QyIyi0yxc&_@wH)FV?JpI<u&eNfZ|1^l%p3r4u*4Jk+I!`kp8{#uu258p0EwO>$
zcGk}jt+EDYyScetV1OO9i<Tib=5=Z5LpB?s8-#WX2eD~Ri?>}7`dN{gH@E1Petc~5
zh!MK2JlpE1<-FPX_F+yf;oh{%(jn7$Gk)U<-A^&;>Dw^))gXpQ>Aamy_eK2G+*g$9
zH0qd&NlmSUT!pV2jjKqI(0rYa!`v8$14&i09ZJoS8rvs=oHulLrpJgTy;U2dDQ>c4
zE!I4;<JQ&`oAZt2HEYs&l^R8<LgOA^!#>Z=%<e~{lj~^IrIy`L7f4f@H!d@L{ah$n
zL2iV)=fe>Wmv|nYF=EdjLSAS3_a+ll7v_ve1`cbICzlV$P4PN9HP~zHRU?Wuce^cW
z*o!J|IqVN6BT+>DN%AC)J7Rm)%<+2#rI$xcD>p<wIxdAfmIpnomo<0t2;y&0R7#ms
zBMT#6{Ipk057!<+&$eZM(6AJQJI24*4<u*W?;{X}417laJZ>UmG!-tG&@5i<YB8rq
zxUZ-AXbAI$mBhdpVUe?QXFJtgr#C9ZVqJ=*!em!SU3I<5KSuG?W-!+Ztct7WW7Iw>
z*rZ1{!(-**vn?j}4=;9ioFW8Ogt(QJMdoAohxdP~)`u=hKfAp_txvv-nw+m6*US`>
zvYq&uaa3ZTe7R?(^`H!CpS514D@`?!J#RHPdgVq#_L9%n*=LUrZ%%rRJ6bNCBR$*R
z`f&VF&8VvS$vdH~fm|idEl>5mOf6eC4sZ7N-UvDGZqWGb)NX}}qSDX$+MYVGPmW~l
zYrTc)N+t!_mDi%nC9d?X(sJ&WlO&|3^agV~_*9Q<$vsnGrJj2vcF@`?`t~06%igr!
zZz<KgJ!Q8#G>&NSx=2&0ZF3^Hk;&&2y&fuEnPa12y=`W*@p$gojf4K2GPxB;M$eUm
zi5+4_yEN5LhvobG#Z_L|k`KO@0Q>d5&?d&8S1^TvzzL1QOTQa<GERMe6>d)|(eXF^
zn#sfP!URw#%YYmzL|!`}tor547jkx;_mW8ti^X-LD^T+RPoAOvX%4a)Q!P=D1DRxq
zw$+4(oEsAT97qP4=u6Ma$KTz__&l3B0Fz~*+-a&c#^!bwsJd7nz2}e2;>mExYv3YR
zKDfsuEg_@>F5|x3Zs)E*c26doO|u57S&Rn?n#<dXk7bopX~<dkHu-vZWZ7N4?kj0Q
zFp{=h@{T7}Nl6KnAZz#_+#G_-+pAOFEGkaxU4_H;b6}iVRZMsM=tSO83+zC;WyE2r
ztVhjZAy`qZ53;M!YzQ9yp%`>4HFqUKZk{@MvIx@Q3sYR1C#<tY9?HD=#gV0JP&(gL
zZM=y>(rc6uTAht;*c+{3;}3Ccg4}wleP}?1t#1$-f4R8G#wTfp&be+HDXT{BR?3V-
zY!zct()&wh!)PNWWd;jps`u41n4j_}42SRQefYF9d1`atHm|{chkMe-<shRn_Z8QN
zrp5fj!H9_RjcsCG-=;}!2G1ET1J5h<KDT;r-t=RcJM|{lK~ii;+-e!sQ&+i>#bMQx
z!!{yy!J<MnMqeO%YoxwI86bPJj^<pSOy=&vOa6BAjDt`w^q1v*ZLR?m!z?sU#r04X
z6wjWxXkYSSmOlFHVvx&*Nikz!wD{`Q;r2?|jQRC!gZ223g{YOOMyqg#AQhTx*Lf9f
z?i)Ta)TrroD0-+Acz%lh*xti~?dJ7WMdzuim9t%+PM<66C3TR#q}ZL`n@z*^@u0#v
z?{@KT1pmwV=+r`3K$x9|Cg#;K0xuc8WSJNe7tSzA$n!+?o_AO5A-(yuE#E*<NT|YQ
z#K{IKSs>F&;w_fE+A6Ra*<97_D+vWcz(-zKocNxS9Bs~%3a^%-eMU>v9Y3gbSfA|z
ziGT!L&=Xw703M<*DE1YZjZ1M4eS=qPP$rYz&=AZFZ|Uz?PMd<f^>b5b^1}nHA*h?W
z5^@FJv_oCUMfVTdzLIC~M+TSgG=hX5u7p;oxa_ZYFpX__dpc~)FR!oX^R7VsR|{7T
zD2Vz#@YC&P6P?!ReJK56RWEmh_M#oi2L}g`Xz4x!RZ{zXx-kpg=@EG^_YIPY*|I{e
z2pHjt(bhn~k`@q7?>=D6qQHV`jW}D)s82rJu0M<2*i_fg;@MY3vFM@GOr|YA(y7-o
zJsC+O&)Q(~b5Z|)82jq5DBJB_R6r#RkQQqwLAs<A1!+V{X@Q|rIs{Y%X&Aaux{+>>
zbWlRNk?v4Z`mFi%+uz>%ch0$fUKf81&M@!$yz5!(Uh7%wzSWRI4n2oH-J`7EV=aV^
zx|Mbl*A6h4<cMe1QYj~vC##@-2Y+2@Wb-Ny$9!k6k2&7XXHmh*_J;Y#i5lBmHQn|X
z*7qAG^YrDM6t9Xzx~$*Gql)%rzV4?=rR7%;&0^m(xk>A)Bf5W#vMa3)|FOBN(0|<H
zrogh~MSOM_&XFPfXUYQf0&8n!^|xd7@(u9zvHO1glE~4<7>)E%H0g?FQ3C6L@!C`C
zIuuI#?XtGLvx&%zw@v7W`WHMZbwX`S*TbsTVl_BtuiB;u>>n;uIt()mXkYyK{6eDC
zyPAJXxTCL8MSFXnV>gKXx`JtN>rVAM!r26;<n8I<;gMT6a_eNt2?fo0oz6}bsLNE<
z(6*WAI0ch({?N1}`sh(>wzuj|atyGfM3vYs$}jt{)@+3oOXX|TT)1|a@7`3R|1|<U
z5}X3PX!a%P?>X7dcW=Fj{BATeJIfbKyRkE3DQy!wg64Ag!F;XG71|7Xy1TErZap<7
zd2!fWga|x@;(H{kb}cl2874T1jX!_z;PnwH>78dc48~q=wquThF=O~IjdR_O_LY>C
zzw~^EqwHosvWqbK8)JGBW89CwqU#r!U;t%BEa!nd%4mzRD(we|9>rSN-L8oYs^cr{
zJ9W(>Quvh7K__NmWf~<ya;=PH9mfp39P}5J2HVEUhVBIQI6IH8(6hSM-Po{r=YjD8
zTR7O0RfJ6DbNz}d)`RF?<<;=C$a($Yd|h|XI>;BgHvoQE-uFs&aqw2vzaAU6RuNim
zf~2HMRj=eb9B=QZEt*v~mZTQf^PuHidAuk*bVuW`{-yC`exbbi^(OtXC+gd|*<y2F
z?V!m8AkLoWPwia>8@=_IgO_~L_<<onx%JYr5|Y>cLgwP^;&)<cLb0{BWdi2CZ@9zP
zik~K0-0z=a{L=h*=wAroCO1dfK~VMPG5gn|vMPmoQ{NEn=~UNBQzRTM_f^;!va+%s
zRK06%ZXVKL(}u3*!76AsJ^9gikp~~x9NN1p4xvXfC;e+c8!#~OnElGvZsIfPWiajs
zb|mxC2xt{byMO#^nx}`E)bFq+eBnfI-Rx6R2I;*#Wwo;2bc?cVA(FDnpU<A`$JqU(
zw9O~Ym(+JW(~EzeEG&iVUIRyOZ+7_grfjdBQ@;Xkn&i<icqe)@d??4mPntNZeVS?E
z)<(O_*2IgLcuhkGTL*)^lf>`SXXfLgg=`E+9p=hxS_UG+2m1TFsU)FqWvdJ|rt`s)
z3mP8@lu$s@5ZxbxM9QpIhaFQ%>FC_lOaWLb;zht`HGx&u-`MEU?*`}n)i~hv#2kh?
z@1K~Z&_iN9D@1MtbD71y4sB7kuk50@ovr`k|73_~(M9fWtx6x4G3IvLd!R0(`sdQV
z>Nn%u&JGa~vrZ*g*SU4afIQkfRMSHCakJ}|&*Vqb?R^IZ+tDq`x70lru3vN`rySa+
z-23%)E}k0NYbMuH(lkavQ8O!FFLH>xmC4CQa<NXir|)a*eeyeZvq@U(^*<nm-ds%P
zy@n8*OOtl4-CH7oJsaYt>!BU(9z{Brvu6^z3rcASrvN##y};78v5!$IcD()s=WUSt
z4$RSIc4=#@2zI-$6e@~tp-Qj9>9ZE!E5E|5H3RC$_03YG^0&p6=g3#0@N*>t^>bga
zYssB_{f-}RV!^&-ne7!r<lrhZ^wQ2KL2U4Msjn_6*{+yRSf;b3NJK)W2o1?DGbnLK
zm5pfB)9k+LM?g$sOtQY=0#w-Xx=h^MN++H$6T2V}>3p^GHX}2Q<}KRYF$ZmZ{nX=I
zGC2!WgmlW-`e(VHjLEQb7}#n_oe(}s@9BC}35WWMicQ&LA9c>;QnTw~=Pw2Dj^CKQ
zc<<M9h0ssCG=6@5t$2%t&=)$Zj7s*5*rf6Mbtb2Av@6sGu9~ALC~!AgnE&Nj$;Tti
zUfqnjeZ1GPu(k?5qAFZQ`<p)+uyN3()Ep_Q3A)TlPt|D?RpVNCp>!0IE^k>nviDNJ
zf_L)~=E|O@YQ~eZ@JaJLKzI=3hL3$J?Ar94!=*0swUzmXwzU$x@Y?Knag1t2EkLOL
z$3i6zuggXiaR8krGlkYqAk?~yNFJ({KVhMNAa&u`#OR2Gp0hP(DB(+hc9W@9q(4E;
zMbGCMdIo0knFrD@{ewb85hDDO<F;E9W);fr;{0YK?9-zum9>w)3|L$w>6fZl)jb;L
zxXtT&DaS1ufAn@nBup+4!H@ha6pGWAlcS`hWb5VO@xi)wshO(!$a*dDc98p_$5C8F
zgqS)^`ub|$-{s?|&N@2S?a9(<^m_4ivE&Har!{I{s=9lAcROIN@)GVBmNGFhx#l==
zYGXM&zm{?EEP}UT^aVoCWS(~C`x7-awS`+-@K=CQvTGRHdab@j3r(&$Xo>lgKNx|&
z`Y`ppE+gy!f6N2P=?Zm%2a+S89{4=r<(J1lF85ii`;$(;y5Wq4MO2`JXtZI^q44+5
zyggnQuG!AgK9u|YlOm$WD$_UDs^{#9-h%SEBXUlTdHk(_wqtaV(PX_ou0Tsd&w~sm
zq2}C5`4|1cs0t0OB)pdaE<d%mKiJfFaC?=`s>Sc54LaB6ZE-QB^V%xL^D`Q3I<e~_
zt(7~gEKarf92vFC+IT$PG&SoEn-pmH&KF*JJh+b&!Nb9uaG=+Ojw*n?lRqLh!yw7U
z(U%_NaIJ3=U<h~8CmZ5y#EWa>w|RVTbcuPML>X>pzjWAnbCW`zoZ`l1_`xBk;7h)X
z=&7%08DI6%epX_lB<=UfAqfUT9*<n*r#WnsttV&as5{!B%AIn04Xw<+)rwUwrnf5s
zCEqRPLqp6PSGFY0wQ>}D$tKZ(CBy3Ks_o?mh-Bq#<5g%{ET6@Y?J)0dZE+RP0c7vt
z*@bdbI8#|Ij+&yP=+|xfTf|O(`7Vy2sSanP5pwN0xUR2u{2D%+t}<6c??w-a=hh;y
zur})H0{q|m>9jvbwxI)mH~^ieLgw<esVp$)7OaHTXdj8c<duOJH#y$A;-4YR4b|MD
zGxC$O%JwIx2x(!8<fy~fQ!uj|e)p}+QSIHVL|e{dp<=F-qKq0L<1atLK9-4^RSNr)
zM~i$-?_#WXi6KbME!mstCy8~{9^cz@A>$q@4jwb4xsK2q?~ksmBWO-}8e)Xx8kt_0
z5_gL+Zkf_#^A+GdrJV9{D(ebWc$@<BxDZ91;xEl^)xw9vOn)!92~x|+Vd;L&Jd30t
z<I^g2YI2I~^o$H|Asnwc0J&-fx;UL5zReefg2no1S6oIk&Q-w5%iTW;yRx&?Cp{zm
z>)@bg`>hvV;*451I%?R-=ZKf6()&FM(+M?!c5!m*+Bi>4N3O=~qD3I-=5~+izUw|0
zb1yH)t&1No6p6{rJsnk*GuU~W2c7Pzuf<5-t-kZe+I5Q~j4h=}U+rLGEZt8yjrELx
zijzZ3gTNFQp(pm(;wV9jf@QqyQC+_x!p5qVekJ7B;uzga{ANW&k!8K88rrp-fK}$|
zB}x{}8D$Dyj`)0+$3>PpH+K0?Nb6KsBICO(Rkbz6G|%kqN;UJz%jb36s3Tp`Kp2c{
zg9Wo4DA=3jPZXXW%g%g1&TP2-axGLDjG=9ipY-Q}G+SCAc$=d892uLgW$xJ&eh&2v
zYopk<mkBDO27B@6O9DnC*f@l5!4F<9`iVSe;IpHk`S|@2e1u+eZ*T9fy}bhQx8JIv
zC2CZ94Kjw5S;law!<Yc_Xy_SHYu-E!lOV+!R!fXW)zHdUd<3Wghw9nCt*)e)*F8}$
zWE=T$sJd!lQPI$4>#c=c3L3DN3op(-d#9RAziB&Llu(_di;@fOGOVAnbRtFt>_&fz
zEy<DlUW+T_oBGh@VPdt&zKnq7HtH<asH#~D8r=Jn%6fEE_~wxD))y-!oj|u-NwKur
zv^Y&`6`b1s{^2{dRB4paaebRZ+dujZUK^gX*Kn74dCk1YZt96vI5|Q@!FB&v2W!jd
zPD^6jf}F7jm@i6umv!GNbXR!1(X{EaH%jMXOs6-|@h@2$M&??MlXH6?X4Q`J6~u#g
zstkgrODaahymp_*U$`vR(wo~8gg$Eaqe!OdLTAtsCi{hobon<|J+IuTI3LWZt3d$M
zXc9m`A#n=EniycrFQab>8n>)iWA8)Lo~Ng0F$S-YcElMsXifsefX0#T4d`0&b@G{P
zeAlxx-SYGPVb6cA>>SJ*B+phm&0q_N$ZKhJd<meUpq&1*s1rG#Ly!j7XQQc)#M>6`
z2hHt_H*Zz{>K4v2{&9l}%M2@)o#WQYRXYz9JJZ(w9@rXoVwFw^N~hEQX53KKP>Mwq
zw3A!!77P%*Jbj_89TlZo%_t%!=J4}j|8C-@O7D0CHE-4C#=%|j`qz~j$};O6H)5aI
zEivrzE*~u3DpkJ=tlZ75BJp~&d#CqQt$qTON(nlCp`<=kA5ASCS{*XbW`?ja-AW@E
z$gj~ylDeX^D|1oocFS$!+SO6)Qu6+7C6$zwD|G9xsaIFiSjM?MY>02jHI<XYC|NL!
z&;_6N_m6v8jQRjPMA!Ij8LEnZ?To~CN)lYSkVXq&uNu`1nR7?Y6H71D-u`mF@g9^=
z0;<1(N3fmvbZWx`D2vQtT7-2lL=yP-j`uR5L_9mIZ}cGHk5i_xy&UQ<<LG#o*&Vto
zp<fP1<?GHc5YKfmlj$Li2&kV^8w4b@c@$m4d8(G$r^2@~e)TqSxzJu%eAIgC8LSzf
zc5(`oEoO;_Uf2)`;$D^0xb-ZSOTSzKq43d$P=V%bByQ|5p+W$G>l5jD*Gt+5_<5Ui
zZ_PRf!byK-u|!6ab8x7iiQ8IVGqyB6+9p=1^!^~A9Thx0wJY^zJk(U?%c4-MMIiEA
zzVBARnAQWWjI0UX<A&AMcM<Q>JNI~xn5R$Dx?AYoRXa@48jW3c_*=@|Ep6UzyD_#U
z@ek__gKMAMEpVd99pg+qdEYuhNuf~o_{;HBOWacqe$f`2r=jK&#uSyiXY0X=>Hph`
zJJ3LaY2Te9S=~@t$}^`8Io%`uFD(I7LpUwW>ey*$y4%dmv#Mt}{o9~EV7w=^J1XKv
z`tk!5BVbITG$$v=V-qCNiRrOBh6#p3F9eETD!$o<t_G&yTLZ>_;Sh<1TXYoBFxAr^
zIx~Z;?<?d{NRye2pyJ?|`s0)9RfAR^80sC|qYGT=e}6mCyRIt@{01iajk{yL%7oE#
zu7Rau?D{MK^0C!t(_Sta7)`PErPTScbMRWQFT8a7Tx)q+jfzTUNk`puQ#KayV>MHH
zo~w9gKu)7#HkGexNIu-@^hLjvARN9!k+*J2tDX-lS4wY?SyZe|;mAJ=E>v?zc}R=o
zm7OfuiKy4pNg`H$nH7I9(3Ov>EVW@H`*Ghd<jP7pZ5@v@Uoc?~3xB_L?(y)fbn54w
zwDv8UGY_RK>;=kgw$f^c`mIe_BeOXbU+3G6i(EO-|4i6vy||LA>O_2U)ZK1e(qUJ-
z*1^5a9sgZxWJMrl6PLE*jP|gax;5{eah@UC`cH8ND>vA!-$oVN$?puTQ^j&hwY)=1
z434Q+?`@&If*Wj9Z|m-l_PxPuB}fev2g<yS&m+#zoJO2U!9pMm!;FwXA2l%AcJf%!
z)N~o2wOW{!MytZwGi<@OvX=My=#O91LCe}imL?{YKpX?iQ5BwmOfG7QR-<mlJ5Bk}
z8}(g3`Sls-tTsr^EG^;W5>v5U$lu%BGrN`3l`Ot&iQuG!J;;?WmEG5!1(s2^?8>(a
z7&NH_WOS<;1H=$Ul91Fj9aH&Gh<R<b1DU3B%|YIeGg@TKYN94E-}XT4l{%yjNHp48
z@DqO<rBL))Lm^Gync^`j9`g@2$>;CQ%{52js?~&IVJW;uJ&<60d^LraZY730;;=2Z
zn>29{7K?U;ng3LnTg=k(Ag!&4*ql`JkSjhNJ7)+r@4Ev<Ta-@G^UG+z4c<v4UGDJ8
z0mpivzuq$0H^#mcoyf1g6-rK7*E1-L%`4MRd?Ba@7$;lUMQKgS9qvjCHusG8$6V#u
z*l#|GH}y9|C+xY|mONTKf2&NB?VWqv3x!Mmm&`u8KfOW4?h232fLSgz&u~`YzI*<!
zS6PlH{Q%0|5zy^L)_fUiTSexu#8++K1olW~HISCy9LlWt>DN8)STrp1wQ<Ot<1?>V
z_*{x<Y5wT7!#bC`9ag?yOfJn3`Q(lZ<M6_$-YFPXd}?PkFpqCKqJ0tOe-VZHc=qR|
zj_Z=NwCyVS-=h0>n4=gBVoF%Z<6^N=fpY5KtwG`)G#YPbW4LzZx7#>7KZXny85T+V
zVZ11?nPz`!Z$E-V&*MS8Amfs<TA{)5@s43$?w@sBj#~>))wUb(5372i;?qHjp`eGF
zH&p#};vcJMYS!p|zCcD63H69jqW)u;3LrD>RCq`oSLJf;<&XM^2vV(8IB0!n+8U#J
z)0oF*PDVhWs$)5Wb;FS7p4(yh`&VqqfSg&Q=H?74&p9z4sg<ai8_k4<h7P@f?7l~P
zXEsust^}FvI1By6E1xDJV*gI|JDe*yoE|lUt(=e9I6u%a!9#&{<*YwJcfHfC^R%-H
zWjX8V^N@G%y5E<$dNMV6-SAw;VsHQO%SE%}Jl4;JI7*?%k4f)HsM@VPu~uEHojQ|6
ztX?Y}yI9e+iL<1;!@3{rBBcEiAYGexOoP_KkBgTHOZv8wxb{?3oZJRhUX<mvoLqI%
z9I~qRckU?+(4)>jh`ES+U9)WBrM-!y7*S119bh)71dM-0+4ZZ0r*%?v_V0~+sgSKy
zt9<P2nK&MgD%x4D>`v;RquW?NZVqkb!E0OFK2Rh%w6CEj#*KB3yd-VfQ_|zYgVJoQ
zbTB(QeWV_TzM%W~?b)HLYrl$lxYOR&G@BM}ZtkKZ5;`C2(fUkZbt*HMp>MV#8{&Uq
z<hq?}S%P98rJQI|H=lF(V4lmo!pHHp*V`S12=>1@6m&MybDaM|qnHvIHK(m%#IM}Y
z@;aKpke@d>h}Kd}QWCQ+#xDZ957*$gs2g{APf0;R;@<(#;p9d42~9Ukb8|aeTN))M
z<(%i9%*-(Tz%rWu5>s*lGQUp2O@bOXpwF^jlVW6KeD?K=)bTE^!}_>v_BFR(I!6Zz
z<slF6Lr=sEPhMW0+eRzB>S`3b-i7P;MnNQDt}PlkrG<6|^>R!a-kygx3OOk$-LLzQ
z1z@~RXEf(Kvxe?eY3i$2$6a?d*F?>MbQAjRpR>G*_5;a;;*y_rO-AyN4r{ZSU0@FZ
zVn{9KBL)kjnYv$7_-Y4D#By=`cE0K*jz4wfkH0YG2iN|L&@GA<g7J&dQ|yaaoR(WN
zSE0#hEWpKZ@RW>Osk1v479EVwe=`bESv%)$xOvut*SS{sSayHmdeyuvT6#aTHcRp2
z5!4z2Mn4GW-?ZFu<y)L~UpqXk$stK3S^qw4wV34O`ZYkC<`>b{haYv1T}3{Y(&$k+
z(AuRws>|J9-<dd}Rgb*efiinO=b!_**PDFDiIE_j0m;(J=@ydXl-rFLnG+-Ur;nhB
z#p85wbYb>~-^uqK)&#S|(hD(jxMM8^f#SLb(Y%x^DB3qyQgWtB^4q?C@s7!g;qtF$
zSe&W#d<_MTZIzLf?DqZRP399P?iYuCtv3;^d4>(9CrQGE0mhs{*?2a;{7T9a5B2#}
zG2jxN-8-Q8V<}%TZshvjW!2TRi^uPBCd~}yTYD=S1$g%e_zy*}Ic*^!yj}hX3G^-^
zZT*s?+VbLy_994{;VELc)lfp5A>SSsBH1M?75om_SKa?!7r_I<L?}9a#|&V0839&n
ztpjp680`nfZvOn5$LTgUHa&HZ$IC3g19MxUL2hm?EiLU9-8o8aZS4)UEz%&sF!Bd2
zRF)ryq1-&scVuDj39e;M%JDoQVc+gKq^B9_9SbejZKWMsA|hZDfO8TaS<==Dn|X+s
zpVfW0B5cEj#J!NflMJN52oDi{pp)HmKi*8t@%8V9zga=q(Ay!sSmy>)nixV#(r7^^
zYkhs%c<#VMbqx(c;Hy<W0H+1i&l{Co@L?Ftu&*jmZ|w&TLgt${U;7ft$#sGq+faqt
zg|J_RikSmdi5jzJdgj?Er)2t(iJY?wK-=)-F7OoSPJWxO>(yc?wimyIlTwpglDB1B
z!9TtK)+McTdjJCf-rN3N6?~4I@h1vsgQ0q|OP7AY1Rt<9W2_h)_Il#Uq2a}gL2WXx
z^WSxufusY>`n&J<Bm&sex3s)YN~)}NT=kh1=JG{;`0m5$p|}Z#gdMMc4X9CfAA%>Q
zy6<*KEGO6nCzjpWkn;owp}d^?dV8;skZc1X;tm7=xx{<0-N6SKnVT~+FqDHFL`xtw
z(_KQcRyg3SuCBi3vcg^%=I1A^t2^(IU1~8VrOfTLfh<vLr*!`{`R+|Tee9joP5J%K
zfDb63d)$>=g>p8wsr&Js;kYvwCE-vmFcAW+CaudJoH1>0Zhl3hzqvo^X0Nx)Q?ol_
z@}8r9Zs4CF2vGjL2kMmYB)~>*&mzQOd@sed{xzI8gU{hKI1I|f|70F<z=tujhGQrs
zJWvuKV;tVU8s9bJE{1hS@>`hP+Jp`PFi}LvY%flqzjnrV_e6I!@P;=b?mV6FB9Mys
zz|ZYZXms=dh6<&E0^Ee=01wBgx%egNxeK+J-!_E|9h98(`-qIF{Xp7dS8M_Zf5QKF
zy3WR7jf<f_A%#y;`?m$mE2$pTc-wvmo+cC%!(-#{rd4qHz-HQPh`}3^Wty_vb`?~&
zhy`(BOBdSLU25Wibm~7$;4d=pJJ99AkC8ZODtwJXL6)W4O7qTN<_GyF{eeimpD*D$
zr2jwH0e?ex=o1>_=nIhY4d3UMqF@2xyZhI!ycsn6?==KycIho<1<xYRAUvM&uyH`0
z^TC~HfZqb?RrG=GKL;``O-etPl$EGeiJ#N``oH_?2bY?Z|95xy=7x9fe?JnqdF%(z
zptaJL4aiBn_0J#>Q1JtvFm=NJb~>=n-hBMM>)?TX#T65SQVOtsl~VpUlEOS?oqE-a
z-|8_G<Pn7a{!otx4jeP?|Bj6quB?VPf7u~sEkHy9Yz64E22K8Vt9J>zpl@Onn*r)$
z|Eg5N;sV`!hx4loVqi1{X3voT@!(gw+5d|Led|B|>Ghdi41*U>WJU%9+}fjm2Z?{(
z+7SHaIr8sC7=t^z6x;Ep`McWD2_;q=@T&bQ_CG;v@&mKZaz1DoeWdc2i3DJPu@<<d
zLY*Gcjqjw;;~b04kIMiYK>xY}sA<N9*Dk^Hk|~0BWuF+I#_)Om{vdn+3(E|sQ2%~A
zycCArg;{B^NX-Afl7$TC53W4}uNk0#OHRSr-8)=69cCZ1)Y~f>P;!t24DpzEV-3>$
zv(Mn=D@^G<7u9;ZisL5Tz`6R^_TrmlOO$nE4r|G81vw*xv<H42edvD0O>cDlIZ}r3
z|NWgqloTPQJAvI_1N?U4W__a;B_AzGjrAGG8(aI943MteTc&bx6$<5z(nFTcC}nPB
zH@e+^g<@y@!*$7X{$Cylb6fAPfa^9x8VwH%(B-XG(>J@9RDP&v_YaTj=V_B>iY{KG
z;S`voposC9UxM=QA4Q4(+gj{VQG^(0DfGU2Sy}R}nmevbE8EzJ?tV^3X0QbkFPrNB
z_vu(J44R%Q)4VK3r+Z**B8<LBq|%J^y@F7%UZnK*cY-*LS=5h`{{$S&>(u9=Vsn1c
z+V}Rt#&+3X=$67CFfg!AN8P+Hp`qlNCs~Q&Q-?~V0mL-SzyI-s2eVKg)RCiKZDkP(
zt|hAQc9oHH2tAB?+u!ehj$8k;;vS08x{cr(HTquV2;mD_TCL3~4_(gOaS_b=Vcwbg
z97=hZvO!8pu@qDJs%AU7m+KSy8OY!fL5k37%90O8^HFkioFez+H6d0i7UGQP6mh0C
zzUCAc&!B}j(?Pj~_wj|stC}^L@6QKzmN?J9Hv4_sVGmW<IJ~N52B7O3j--c8nN5R~
z-&!(nY3J^!I|&+zj8_*=jTxGZ5Ej=BkCt*&#pU#{t~q{+A?^wFRX$QiX*?s|k$yeI
zJPZ6~J_~gv7|us^yuD(B?GsZhwu|Hx&(EDKaP4}Nr$Z}4$6A>ZTT21e6u+bG4ZfC_
zx0Eh%z54y!-(RT=M17dVaK0C*+-gCu+)drq&P^y@Yl?~U&Do4}h_B^x`AkvS%hi{O
zr%9aex>`b7SUS-jqc^KUsXmO(Z}sFR9!R*cnr_y{o1aR6@rxYJFgIJ-yi4`w#yRT&
zAgThkzxPJ4s0aq09-&(;&Abg=ed;MDFKq2yOh-lYC2uCvzntOf7MYQUOMR^8G4?R>
zmuOZ_ZQrNL-&;@WwJDPH&T&K>&iBhL2QuXfn<}FL`P%ZVp})TJkW*}0t0Ok0wMblj
zzU+UGjtp4xv#ozm=p=Z1Q`Y(X?RgT?KCWNQ)bmcHKbiH(|8bpQcz_lczhE~TIJf)J
zSB-b|Rn;}JJ$0xeu)g`3U@t5o=0%E(db+zPY9k}c?vzfESKtN_tC7Y_#3(}dFu5n^
z9^I#gf3C1n4U?l<uyGhPm!ob&Ni{&=v57-sW?%l1r3t&{PLX-~utkTqeMv>Yorgxo
z({je8ccujgio7jvcS3kfp~8?sdIy020zVzi^2q9Co?MFW%5r`3nMaJ|r<K@zZZGc9
zrqWSs>JL};`Xi94ojx}MJ1lh9t6prUy`h8ET|(flLBIs-eI>t8C^pxczVfg!{jYj%
zO8~*@s=IP#>~_j|$-Qv8$~!kp7KoVdRM-YYq+IF$F-+FSWhLbID`JBH;t<r)G2sQC
z8fJ|OoWN!*K1kyRT_~oSV$`aC+o44s`~Hi(>DolSa$2smj3j;HkJkNF+zRhSN%wa;
zfqCOI6I(H47a!<_{#l?07Gjvq)J@Otf;lMBnu2Sq4f5%k1ckNFXg0l#=6u{Le!T-@
z`&a_zF7A?UqPp<U?ycEV(O1{9n##gJuaKsj5QA=-K&a&mQo_y^{{d2uoMNQ$K7Tv2
ztxwP3a~DGg6rRBXL;kCes!!RM?>I>c)}?T<(+b({JhZ<w?$%L!GtWc(Y=~YTy*$a)
zMAPjLxNvtEKNNO)osVNiWs@PdTg_D+n^)>vvsEmdB><7WLE}#;wQW0RpAXFg^Y*1b
z1KMkbN5$}?(>`R6y^tDVwDk*+{BOG=XcfxFKVi3&Ht-~8J%i9Y^GiIvLcFQ`m)PA(
zXTNxgwxM<#U2jhk{}+Y5F@xCQnQvN*%|5Cx5G6{4_wAm>Y+$%3;oDZT&D7OWcM=11
z%f^F=UncOpL9x5giut$kF47j8{2aIOBE4^W%%Q+Y8=2SDfNvL&BApWl1yf6Pw~|`3
z%ira_egn3@AdMI<I(W2*-&yjBKb#&vz4f3V8k}A$3vvGQ72FEP#{@SM^AtD(!;F`6
z+``65yF33e`icP9nTp@wE>vBwp8}y6xH>GU`Z>%Y;v^5$vJqnb1j|d+#Y7^0rdfF$
z(bid&ufqqpOJ7#4UsWl&^pJ;)%~VfJx&r4PGz5`ZBnowTS|8}+9{jd-#wc)?qKxE)
zHm#o*o5HpZL*%osM-WN2-+frx3=5WA6gznEN~1>euN`>9Xy$hye}Bbn5KaDqeH0G&
z4Vt_`9MFV4ci+}@(wvN5QQLg3FQH2-Hd47ki8-`C{;u{wM%}fa%(KAu_&10GD<_zw
zpqSPIuvYp6httva{n#ZH)P*-I?cav*^@tT!O#=FY8mq*kO9SyJ3v28>G@ATne%m=x
zux76LY_oxy>-=e;bA@$*%jrhLF$?#aZuy7>gfkNx^<KA0$;P!()ZU^MMec^WQ+1}2
z<F$gM7~J8x#$TacS`QWMw!uF{nM4;AJ#&`k!~SQT@=$#F=50dRXFe^_%quQaQHWqJ
z^B#D}(T9<D0|-kU?8!2pgKNS&7puRC*K!wp1bB)xyclpCt?Y6wf_Lu?ELGJq!LEU;
zW59vXOlMvtOBoH0as=N5I~%U6m8GU(MxuZ3>l?o!p02a3-$L*-dqU34JGBFhP`=8j
z@AmE<o!Cohr+<U9d8nZfRh<QnyI6&9mf<Ppc)Q!ixQP1uS-y3|nO;~Sy~M-BXb?}R
zQB^CA%;YXOp9PCKRN#6*G455)@j!1oIVt<ppKAhUNAhUz)p#+3mYor*XeQZQcvQVS
zKMc<#XI2;^{YMk6N8)m84AAIe2V0q;C^^&lG`#P5g-=~kz56Y5A={V>R+!iiWp-xt
zyz;V8Qe*)9Y<tRYZ}taO1AMD^dlFSP|CUs&CFHl;cla4#WxGw9|6GvXD_E{MlS2GU
zv7PeCv+TRDGymBDd~^Q_O~T)v0S`E=BnC|~G@xY^>^i<Ya$_4WEXj4XF!^aXLco)q
zW0{nVr(s!(;>%r)vp4(F^73dH!YNc{D`xqRaCPdg1ZU|}lv&tie$!m<>pwI(YVq4m
zpjEm~KNCAFpYR{MmLZHH8*eA>+mcmKn<gDROj9jE3UzUnb<AcN%V$S>u3Xt*zYA`z
zE~R%VQP12Tf&;iexTCxLRX*XmdE+d#m)9<{A9oom-%m38+qpod`5j$c;vl2p@&+#u
za4Mxb!8n$6L9OvlAZN;u*L1ko@D*i;FIVdBZW`|Db^wr6v!Z<={$*&?2!L*d(iU#?
zk6h2u^tLCR8;MFL3H3LbE{?xE>WDPwCGr0gNd7Fn1|rl&xs0~xG*&iotn&kZze!g{
z4^#yL`}6$1XcqeUg=^GCyiH0o-0p>B4-Hx0zA0e)9pq;G8x<dj?Bowr|9U?tlYQNi
z2kXx!`wQg`59fDY-090Bvj2L1GE_EZHkwS?ATqxr^I51u!G4+VAYxYfLfpyJK#v<!
zP!A8d$;JHK%#!#V^NtSP(yZ%+Ul^BTw+yj3$5Z{bWz9&rPdjZ(55#N_6x=K8W?lP5
z=$Argejy=y#5K+SIKX_5zveA(0r~cwfpiW(fonG9aa*A~5f%cfqyGP4X&Q(#uVm0d
z*~HVjz(?sY6H*Ai9iZ40N!z!lS|5tYTIezgdnvQ8v%Lz@mXC=FdHh%y9ot|L>=$)3
z_+5vbm<tf9^8G8}kZk|uQJy1_rbz2Ac~+179kC6G6jYW7N_GPTSZY<IIXm$4kou*s
z@c7hSR^R0m%JCSV?$0<<zIJl==E;cbyoB7Ay7EGdL|O7q;(=f$!@|7aNuNn~_NQ(7
zx?v^j!ou<D|G0M8C0+|u6c5ON9F_gdzoW=05*gva9M}{*R_zsj!P@eavG4KXYl3UU
zE$&Z`UK+so8}(Ltd7K9b$h-dR5OhEK1`oi?|M0bx6bKft(+GqYJW~LvWZ%z;t5_D;
zsPk1hVZxBX-(g4*%jdV}fPgL{?&KqgyAnSZ--27^$@0@jQT2#Cg82{Hd4<9x#=pNO
zDspaa4i2mYK(&USU#*HY;<1QGBk(9fC!mnSnuh5v&YmVZPqoCP@B8si*{L(Q&#G9J
z&jrdHL13eJ7|>>}2}ko{!EkZ;PCt<{0N@V<2#D?6n{JSCL_J~C#4e0X*6ojG{5L4*
z_Vz32H~+ck-#Mng=!`K^S68=^LKY@sq4{}lAvp-6F$*=wuC6X<aE@4{a{2&ACm4Z|
zofLdZic$x(76WVKL2_E91Rg%V$is)2ns%jWWg4m;m3bW#GVz%5d|$8wYqr$ym`Ttu
z(e?Tk3BhZ|s6P>N-QhViI5BW#Xw(EBdsEkt?24vh-nim7aCvG+zsjH<0T+tNU;kb+
zbPqbXxVRXU5U1LCza!nB0;TFzV&c%SuwrPm3kwT_PGHcTymz$KQ(OweSs;%269^jD
z>_0a&HbUjwukLV`RkI%rf=gZI>u{h4ZGI(H9vYocN{4fvV;CZE&_{S66z+p{E!yfM
zrvJ*5IDQA(pLle(I}L-_N)c*$+pcG4B%@1N*lFYAKt{0{vn8gp0G5M+Y+M6K`MnSO
zcPfp^)4;n(V<J)^HwPC`En;Lv+@HF-x^7by1Dz>!w8EI(eREEuZ||ieSybQaQSq1o
zEKVyZ_z9{TYdz_mFiM#_MmFOhDJ8Y^MGRLw%?*Qi7v;rT{$HTdY(kCwBi`>A3mds`
zcX-wC)9=*ENg90Fn`xWe<Cco-KNtzr3H}r^ZrLD2x`7H8A?9TO6;P-`r?-O)jVt~Z
z(1K)trKAuoZEbB`gsbSf=uhy3ztkKFIR<1JkZ*tmHh0j@uFUbVW8>kD(*9pi<97vz
ztN+sgEs-m$tCprn2|+=@!SCZxHLV(BDp4auc%Yf#-0dyuK{)_msn0F`GEdCX2C5wk
znWsqn$8L~8YB8f{Xwrh>Fpq~q_kw(IG7F=DChY$Y$<?CQ3jT!$kErRPs`TH6Z;8Yu
zAivRq&e5!jI9k;#H5(dRQ-by>e}Y?OMqE(Dc?&hH_qJgcNLVk|TS|)cTMp!O^7X;F
zbcvkA3f-I1Qn|JGSkF@ouHF<+n{T=#Zpd5UU{~Q>^-HW=yoo{a+y7W!T~0BvRQZCs
z8k@Pcijf-K$XXrbrM;B*@3F*iC;v*SXelYOp8v^VHnO;f+WrA|pRZ!E1Mtmwe^Wdk
z?w<X#)1NG6Y?7Px9wxK0RQl^KKSlhrYX<#Ms-PryFhWH}=1}I+%;MUJzOez-7>qdm
z6MkPd4k$8|eWQhxb~S$N5n~iA-RTB{N6k4NZ%&myaFaCFm8#5qWAI}^c%z?g{r$u*
zjqEy<&R^9BOf?OvpHOThDj-DkjE=Tl<He9L0TtGH9>gqgow@?;;Wg)l4_Y<OLfgFx
zYinyjwFw=AV*Ec&R^rb-yCOc_atep!ih{r9UQ!~t2X0-{jhKtaK6EWcL;@yBZ)#fJ
zbTv`Rk_EJ|mWr~G_TI>N{-9awNf?78*C}K9RO(0j^+)vn(K#*-U&j)x?$oYtx;P?-
z9}fvx%-b1kY;FRf^>;Wp0s7XiwzeE_t=TI1$D5t^m8dD83m8EHogFr#-Jomdcpivc
znMcon!vn6aJs_Esk&!X-4hjki2#^I{HoC`g&y<y0q_0y@@PWpbyc`IY+SqKuedTIY
zaPab~DD1(V;ghkfbS0mal=(t)@(XwsLBRWcrn;}MFR;M%!BmnvNRXcH>(4A}J?HG=
za*>kDdyB2=3oznJ?1r3L?N0rQVYa@1PUEpO;q`kOcK+Vz@{e4RESgiz<5doFqCFC2
z*)^opKY?NHM}wsIFu&>j_xpD_fT1kqSqO^YRE04d34$Wd?BwK6IR1DSn^Ruy2yPv@
z1--N8l-4jm1DvZLK@%v&83a}Z>{p&JTrpvNKzYxgjl5ekrAJ~aQrpdGwEP9kwlt7P
z%gSojzH|azYvAlNHZd87-mah1pU>jrW~E&z=iIKVw{?|S@|i$mWh&gKj?Pr?416}Z
zwov%c1wSpl@rg-2*DFa)EE5_D(%H$``O206p$6{tfdRuyoJIu4{*8a-v@~b0QrzWu
zqKOyipTLDBo(AM-LqpN7KY%9usd*vzpE9Gasnkd<wJQ7Ey1J9&ql0$@6c39AM@Hyr
zXfoREfOH&K_F|%<g0m0y_d%n`5l&2@PX`E7)14{;WyHi<K(V^qW(68ovwEt5raI*k
zk*U5u$(1WKY{@S02SHxA5RR#Ggo(=azml%eqVw+v-8O+C5l|rmnXp?W+dDf3xT2sC
z_r}9RiM0SC3-H^4de$0rWr5;v%5|eL$E2QhFtY@1(}>hmTEZ(<$l~?YsHi9iEO6u#
z)52ph8ig$;E<Q3i_+yFf#gwRQWRDhzxx+kwNnaKT(+*6|-zdApswyej3dDC3D{u$|
z&FanEvLBv^iiz>_@fDbYU=zcwTW?xI>MPxj_zAD`n311XsUfzde$o{vqpVEY3Vdhz
zFi#b75PG--2GD@dY9-DmDEJc2@?gB`>TZOUjSV8`jAaTOjtR-1D|^{ZGIRdcQ_OT9
z^y^8ONl2ifTyE@a9cr3IUQyHD-VXcaXgR+wfzQSO#sE|9aSepfN<ZUxEwGt6`kY^g
zZd=L<`KmXQKCta-(6U~g(8NMv()|1L!|H60d&p4vey0X3Qs-tUs_9R$-FQ%#A-k5)
zLz=(Fi_D3T3odCqcklxLU8Mz2&Vw`JKq`V2jAw<MKnvU5zRTgPOG7Pg07{b}u?5x*
zn-b6S7Avb9UFhmy0Sqi&<9eN$xfpc+IHUB^lCCWf`rf|nLshHOh#OM|UI+9#t2JJ~
zehml<y2^oYVDtwZp%rvG^=W=hlZk$q48pSVs;Zf#_u%C2#(9u8e><`U(p8g_^>l|m
z65;%|3v3{N3tOCWYHqHj8@PXC->!_;#Hycj74rsd@$k5~wg7(TEr$5V7xD0PUn?mo
zfzDNus+qa@fZ{T&B8>?Qn!zAbaZ!;%{O+7o`ZyhJRaKSykNUG_4T2mTg&+>B*Wy1^
z=UxX2rOR?jz_N(H8x>J$J&VW0Nkb!Q9s@^-fwmhU1^I|Kz;|mN=<x<;VU*-qFPz&-
zpkeBbYmC0~LWYsJSMTeYslAXCLy$lpWN_bX=Z)pHEG;b5U26o{bF?ENHUAdKj2;5p
za}1C9mzdT~Io0@z9c4+$^LA9NvJwgUeQLRCCB97|8x;{06+(kbZU?KplnhpeOYR<v
z`%%42_0Yoq`AJ|~!<i58l!aM_ZN9y==dm>2zJKT<5HcLH{%$<IL!2&!ym}ccqYPW4
zs-$(S$9O*};etp>$=$QER<^3_x}FEUxrUQyZ`?r07GB{J{Il=LB|oDs_rnP+un1(A
zz6}lzhE}{#E`V?EOzx=LeglHdH4AC$ia3_Hs@0AK!YoSSbgZmF%=P8Aiy^~|L&L)+
z_YD)^k;E8VTFQ~Bg3>@$d3hT=G-fd%W4I*w11PIE3q!>+H4Q~MFuHfx8>OkYKzxx@
z*3!b_aZID15Oh*6Rr7|Hj|T<@rcHta0X4h|m<iz>fW8c)6=)apJdkoBBxidSQQ>yv
zTyh@H)BAAnNv%YhxC1gie*6eD1ZLRAch1vL50+VK_qonzz|Ks4CZ6{G{iojEFfLiU
z+;6b89h0kJc*OZU6;&(@HUz7Cx@^P1Q-RAiFm2!QWhuL<G8ul~xSJ-%5e%rB0gm{I
z#=(KDte9zCsrmwL0!>I(&iUy3^Q7yc&bRJz2ru<UBZ__qFEk|{qLs9+^YmnO-e5NL
zk=)i8D0gz)tABG!_-ZDf6Kq8k8IvX<<wkMf(atB>ui&H7U-NMcc&r}|{5*k2!m*XC
zb32+3_r&NlE_NV1p9~fpaWOgnlxg(V`u7u<e*-5_1zhPcYB@SKM#ieuR?3#t^AUo>
z(=pl=Xknl&0J%Jq^xEm^>7i0-z>j*9up3it(4)j+QPA8XQ<TBa@nvb2gv%ZXb+ZZ{
zJa|B}G@t0+UwBr+?-^BvOV|t8Nsk<K<g3;UfVh#X3Iw8Ttbl0TXab}~6^p*98r_1%
zS1m;e3GXx~%hbVfmOFQl#B*64!$1#fyaGF~k4O70npyNKW`uBwh{QSw71PwzWOb&Q
zJ75Vk!h73qs(~8C`DiwsH%qt3=q{hhy0VP^s6-F3MS#`UpW8AFcDpn&CSQOF(CRJF
z3f7Njv!0Ql%y_~8GVh{QfsqM$Z*K0-#afn6(%^4@>~*#RM+qTh6`Bq|6}4cm$#n5X
zEqqQ%I2T9|%wMvEmN{&-%vd%6e^*lWe4mg)nmOu*YFNF%*;iiT$cMhWnVU00eu4+e
zHPj_ej3YzU%>*%?XCF?Y&wcxJHTs1AI1zJlBJQ~C)bbhuzA{U?9@RyRSiH*-(}B_U
zH59l_p=6l`${PsMuLiL=fsW7TRkt8r^FkUL@`6O@+rgos%y!Utd{C5OhlHS`2Jt*a
zM}W@I&``d7ICO~Hr`_6=pEJJWQEXJy%=~<I3&da4HjHhk7)>noWgDYm-deHf<Gl|%
zr&M5BxTNgH)io@c`0|K_<~-<^bS`f%_J}JKR05S6X6mW<85pkC3ivGCi`Uu+FOPAh
z0mXm!AOubrpN%8vhkTNogjQELT$h@nLZgFm2OQLo!`oUeiv)GT_dg!hqY<Bc)hca`
zL2ps1?iz5*T;%$hrIcMh*|yu&x_Ie7w}7CgxWQ6?5kk3qBuL$8l??!h!SGaH?5Ggc
zC*&Qh5Cq4~=Ekp`b^%le3>i)*pYD0UMhShX3(rN)-2*pJ#?_0T!|ON;=fRVjOXJSM
zH?LAaN2%ew-z7zrB=O2d)a3xw7Bz+bcEyZ_f&l=GJ4>IhyQjQ{){mINB3kAEN^V95
zh9}C2<Bvt?<Pvgt@MHItmw{zpz0{wM9Hbn11$lYPpr`A!`BSFKCgF+HX+VOBD0>Jr
zm)aX5=wCtySQbuUvYaCCwr@Z{AJ;l_URijb^h0dAHhm6?07%5j(sUmg!B~Frpu@P+
zJ1TH@GFdM+W+W}%FQl>GzJdpEmgiEn(oI1h@+Ya+=Fx11ujgoY>z33wvmmYPLdIOB
zD9w%T3QceMA$qT-RpA>b%*I%QQsd~eonL_XL=KeCjYcO~vULSX3UxiIj>YmxPDgNl
zp5FH+Sqo%ga=sAre$_(af=GNC>n-ig(|Kc3?X8@S7qPI&9~3fNWUVvyQ`x2BQ2t6k
zJgoEfsN7RTlUMGr^&-ZRoO?r=^`rj`-XvN$_$Icv7?&7f1L~<Yoo^=aCQCPBNOVQw
z;^Vnz^*_sjDz;ciFlLHC8}vr|3^Kbgng}V`W`M^fbV(np@LC0(8xS*VWRg9P;fp02
zNs%O8{W@A?c#&CQ`xQYF#Jui73_?y?`(7uPtT<oOyv;*G_*6g#N*a&#67vwi8{K*J
zHAqR4*1`h7RPaWG??Mk3UAG;`qo$t+O>mYcyx<sm8&N=kJbUew2*g}Nd67PDRc?L`
zvk!PrL0Ffy^!@t@@LeFF=>~nm5HibVzee_i35pxQYI13$(?6Q%Q1q>-7rjrnE$Ya7
zN?u$=+#vAtcER^+H+)<!#C+?RW6nJmceNC^CfTUzIOuhH#)c)~{mQhAr^02)<lEiK
z9P)w&SFmd<WXc81B1U1>FwtDx>!U@Z*v2`mCy~KbYq!T6t&mHx#kR<>KJkL0pzm<5
z8H3whbl?AOx>^R~-MdlxAUY8kQnc9(b%ejx^X4rlV@mtekCc?cVImLu$*e>am68#h
zz*!t4yh^8H@<?9Z;nU#YT>w>NBdOA4M13IlIW?uL751sRtb9C;h*u{s{ocU}r`iU{
ztQtwcP(Hw80J&)COu3$N-FN8N*jRCKaZ^UArdN#HR&Z$o+p-CQ(W^n?+t8);Gbfdj
zn!;Y!uPgMPijz}CZ0rc+WSB$^d`FjX>4x__n&6sXl-I%Dp7Hm$!&b<DEn>QuxWVM>
zZ$9_;P=f>S+Izd-++@S2gZ({Wj*Yb-XYyF<Q;GUag?&tI2BaY715Jc6!h|t=*9Ioh
z5*O}rT*79b%sqSM-X?$$`LnR`Lw5q(AhZmAW|hXYu~FW#!ag7SnNCVt8gcUsF2Vac
z5Z%za>-P$=IZ=NGSn&DJUnL_Ge+YEumWn-7=Ss4=P&#YSk!e`WY7fnGTmv8gQZZSa
zAPP*(SryoG7~=~sKW`&MLQE_o<P8-1NCShAV3=@JfA$PLYpo^a8q;D6oyEk&#Omtm
zF>_Gb>MiDrDnHft8TBEg6$!ITGy5EX&!+gr!TKOR-f@wV!Xu<P0gW172{a(`a=#!h
zK907ssd!dI4lGufE(ROG4$e&OM@9b%{r+U2J^x~?ai3xE4ype#B`@q&O#Gq3!OAd~
zx7nzci>F^Vof#@yTFq!r7Zr#tgUv2{&!ol8<rM`NSQn=i5?XE=)Gso;g_?2GYlOo0
zlo~|Q^ZL^}KLtSWhO!*lN<`TWF`~O$zJvlO>5XH65UJ-92=Y?unw#fh9Aarf!R3r@
zjJ*_sCq!ujiU-{$n?7PO6UBf)0)d;FtHxw9fSEC)xV%k}et_;cGLF|$3v_i~y!Z*z
zMB2`av<eD65Nb1-MW16Lkb+MF+kk@E^lj&l(vZu6kV<BZOk&zoAdiMY&^*;5It0RF
z>*L4i8y3Rb4#*5h;SQkZF(D1|;NVAn3ux%&>%xZB&C1G>SI&=F9Ip{LPr>#ai?x6b
z46VVFs~WEJ^tMK+8O6X}7OlA0?Ire1`i83W!Tc-gs+|vrS3WZr+mnAt#p$s`4}uT0
zwB9wt3eCX667rFe_<7H6920dUGM(->{)sv(pHP`)pHQ!Fc%QcvdVdKFk?3zj1W}Hr
zdv5M~vt*Ry(9hUuSXfs#?0=^nT0_M|21VJQf@-)4DRX+Qo~|y&9df{8ynlaVLt)j-
zJpf~gn==rKZk;H{ra_?;_EKhM=IEz4Z{9%KTn9ao`L+956%`epo{yj?0C{Y?(192I
zYbVPWHL|IlZ!7Nc+sqL#R|87|(jR>v$q_AF@`x)Sqr7~uzklaw_j@{%=;7hvpd$_x
z07z*qgq!&|AkzY6<q4b+XAX7PMszE}6w2(d^Gi|-Y>;traYkssTuySdp>*PNz`>DI
z7Ic*_Y3CuNYl8QScdk-ghG27fT=rFOm)m|ZY}oWgKlDHmE}JPHR)*3W<kM?fZv20l
z;LZPOf=F|7+51mDsiQ%a26|*;y4%|B2noe|^(3ns{et+M!?O<kt&%xDK9Y)xR|tVC
z0x7^s2xR))V2v1&VbA5x$8RnBvcgew(~sVQ2L!UQap0_qnQJD~R*avaKVk!>9AGMm
zq+*Etda$)9GO%uxKREbIc5{5nIaVqWwsrI48vuET2K}XNd&2s%Q!&C%@$u~sG2^t-
z%NPt=c>BefFQb<<siVP(hA?e3kl^7gQz(nfp^b)X+F^E%uKeX52jL!+i;U5blQ{YS
zpa-RQ3rCP{0LmR>MF7%HJV}N7<rrF(!s(<8K$xl5c%#ZO5ObYQc<EeQq}sMUl2pdz
zaYB7I9#$~J3@0+QYQDHR`+$%FUtH|r-}csd7ZAR{s6$sBz7-PQhY20SkgveKD=Be<
znAgtG*VwV`-Q9Fd4rCS)#pK-3yfRc&@j6*7Ko@6&&MiFdq|s7G=ffhyCC1WF5>$M=
zW;>xWedRZ=3=sKIh=I*nfX@x}4z784^e`U?!2zb(?4eh_o&M(N9bh@Q>C@QQXqP1;
zDvDj>tHvv(VF?6XMH*SENPs$!R;g4>Lh=qn`JnSnL2(2FoAGM(ec(`32ljsbGLoXp
zGybwv(~S0pK<hyz;WdXD!C(${5I!I7eG25}^Ip+@kOa%5`3ej%O34dDGQd2jbF&TQ
zv{o_SyEeW9dNQa%7}kH~8QkA>2MY2GOiX%{Kw-4rn@Jp?2IbQ8H$5a`g!Kk;R2yUD
z*0@5V^l3U3!NEY57atnhSX2Bd{`MMfGSO~SFY)9Uwghz^La%QGbtqN)$|;fmdUUYv
zkIGY6lr)&MtCIq(+L@og6QPg@`2E2p;&JDM2Zx<ngOjy>1pD8DyeNcj!-H1fup!VA
z!Fu%=9E8f3jt&EZw{+rk^z`pcKl_ujzj3j>+XqRau^GT6*#G5E;hcai>z-asd3h97
zLU?#{nxBst$P9pDTnHTtiv@7-W0bz1c64-LY7sDalBWDIR0=AQv`NG>)_hg{aQcKJ
zZ>>qA^fT~|jxx%_qY+AJK&M1`>5>%yL+~uoG7-VSPo6!?*kLrY%!B8dZC@g<XjycW
z<}KmkgwwsQA`0`0f<iHGm!SRIpu+3ob(U|O4KVU>i?#=^XtknO3W}DIG11XaKfk62
zN)Kgs4(zP+wv<n3KXVoq7b^;InLMJVfZdzWE?SucE{Vnjto=?fABUKGVs#^l=8Yb}
zbMG!*FZ={0%mDqxk+OBj^q>RtN0<0@)ICE1Li_dcv5}G3t!pD3c}YnPU0tY=aj+Hm
z=djq_oH3ll@3cX)zI56lt(;0G^Ru43+*=3Y8+MQ$Uly!B1RsN&Pk=R{ir}UXqU4$(
zEI9QNuO7T}#s@r0A;NI)LC2FKvZy_d`68T|#_t>JM_gP!G(3*&r?4tdi>D=IR96df
zafJ!cj=*)Q78#fi6*hnVOn>9X1XKs1z#7G69OCJTBl``4Bb-lxl;RO^J0}}rB&n>2
z3iaV(Zf<NOagIYS0h4A~szgLYgL$$V(8&H`KGodJ4C;qjl|Qd2Jb(WF>2}QN*CZ?^
zN>86&zJ7guWu+~z57bql+GApZSS-d+wjYJgEG>PBHG$ES(jVIF$xBWfQ0)y53;T8l
zDrmQF8&7<CJ*sPCZS6?AJUn7-Yz#^*hWZ@|LIMyc`4Kbpi?89N@#7#w3MrPz9wrE)
z)}~a{)VT_~)+G?+<xgkY2iz{Pq@<velas4F=%1+f7<%li2s|w?0++K9K*g&C>Gc5m
z$r8tVP&>JJP0(>Z-r?D4d0zP#o>~Gsq<@y2!2XmUYf@5@M0;x|wAGVSQEe*66$sQp
zO>CwsmR(Qm*)wuu{0esTw_xSq&#wjVj^3F$E+{FP?W?o=UQ0va<f|$Hm03semMf&B
z9K4}S-KijYF&ft3khVZ2g9*?o5TK!=hz0)b#yOLW2KAsT71v(At7Km0F{9BFSE&l_
zw11f1q0C+D1xzM>M_+!sLJufwm>#BZv$Grb=codl({@?qrl(JbyGlz*IY&lCLV}r@
zf!YnIX7U(!r$C4W1<G3c)fnLj2m?T>3D2#>sH*{p(8kBzz?AVkr(k3UO36UCp-|;Q
zA_wJ60|SH2`Obk{vam0(TQkjFzzA@xQH-vT1=0{!7y*`sjVxK4m?2vMv_+6K_Yd}A
z)D;DaP&-+&A3+JFuc2gR6|(azW*bQRX?B41>?zd&1U-gqGVSUg4frsMvGp+lN~KG~
zjTlNuD9l7hM-S8V7aC`ku{)gV7_qF&Ej5N3^i+6q`>T!^ZfSGl6c<yo$8Z>2W-$g|
zx&y;;17E&m!8O7H(2-Gc8u%~8+?hiM?}xfYvFSVneG-XgsD?gzi0=8|d@Z2RS)dRo
zn}XBBM=e3e>6RQ5r>Ix}<)gfXg$49TUqI{U&xSuH>VcVwXwihDKV=>$mK=>Cn7LZ8
zF=_&4r3K6i|6ZLI$9?l2G)cC5&qg&iyvJ8(=rx(Vw!Kj-`UR#4Pg^s~pYm`xjU^1&
z^nH3BkiP`G1$uk4**^N^>*oigbqAmggDE#L?g456I28%Y4bwFHpa9N~L<g&AIW-Rs
zMk%^MrN=8TCwKw12B#>&5mQhE$djg)><&6VE{nMkVT`+eB0@6mEQ&BoezKsYBbZQG
zkQCrtV<63P)GBHypY3r^{Fd%C;rMH;SLrxcDHbx9feFS6@<UtZUs6(1U=$l$TUQ>F
z5)tVd8-vhhLuF?8T@ZKa9ULrgc_a@?ZT(j%GVjwtf{6J>oInEX2ZTx?F3UdN0rM5t
z1P6~bli0oWHWWpd<(yyH<81rLLzuW{g7r!b-n(T>a`mbVKd7P}dF7#r89{=lqy$4m
z#^XaxO%1{@%fopg_bAz(zk=z)52in57NV_@l%Mo&RX7l1zOI+(_XY_WRS!BD!uQ$`
zAfT<R_QW{5Uh^RcPn0Z!R#2`L$91mUhxyqNfC|dW1`ED|AHXn;`L-a+Le3oI>zj2X
z<<mP{VhZ>@E+N4J-a(x7-7XQ;8G!pEN@8sc8}oG~*uQH5G`sx>I5AZ)WEx+hsx&4i
zGst24n1X}#o(V3;^~&YT?3n7S&3wY$7Y|t?fx|0i>j&&O0!`LhXPBX7G%z!(elacf
zdKwKt0`gatoM%rX?RvDJgr8Ejy0St<OiZX5Y4`}@Nvd`mumv)U45aq=VO=9`t@+Es
z-bcJbobge9q~BL@P$7M!OL}eY<ZKGZqdt}NskAlJY89g9<Ocn>ynTe^u2sCGwWUy2
zp#Q>H>)@K!ttWQ!AC+;io-@E%#eN2q2dnSr0JrXA=qC8E+#H#m&6KSJg;KD%IvDr^
zdLpPD&qF|hUwj7y2&`3<ycX3)T}fG4j3NUn0ax&V;_OVOz0k@yZVo8?d>fgAjaT+z
zS$^3BE68W&w<$i61_&q0P*whNeRV7PpfP?-<a|~~bF0e`mv69=qD8Qj?~{PltotEG
z7Y7QUPJo3;ZwXR@v_L2)k9vAy;zs0GLe1OUSvff%-*g=Y7IBo*A!BXt?Cku;?YZO!
zqg~L+QJ#<-8><cu>WqC8*vXsUX7U7X^;Yqw!BU>OvCWD1w;7E%U$+&qa>uuGSEO55
zGYT}f#ferY`ANrtHb-~~u5=3}LswK*W(i|g;9__z$8(-A5NIN9&d$w&QRc&`#yS`a
zoU6*n%8nEp>zSGg0Nm5o{*Eiyb73(H1b<~CnxML3u`t}1M{^W4@!No|z&16C4+=H4
zX8CeO$+WXcZVWMUJw_+EauM;`zVEtZ<-27WM<6f9%7i*8w04`2|63&ZKkU7ASXAHo
z2Rev|pop|I(mgOVlG5E>5<_>VfJk>sD-zO5HzLyA4bt76b2sSsobUOabMAA0_qor#
z|J;E+yH>ol-uJWCUbFUKi0k>6h_q0Ox6Dqq@wGNM%2Gg|76cL}yc-x0ly7<d&t@O2
zsx2#_!;RxB;TG%Nq=kr+zvL`fK~;YOxTleO6!(zrDS-U$%o;O~6Ee6Vq7g}JE)C4i
zV2qE_a_JLcu2E|s+iHu>>jp9czU;qb6f4dJUogD8#yo}o>KMsJUvXO2rlu`u<i(d`
zROX0~POEs5Zv~85fIz<ofk?jS1DOA=+K=KL(VtHK<cnF(cKW4_nLsm^9-NDlk1v*E
zJ%upp`wvS129QUX0N`_%QJ#FQ<M9;)<$E-Zl5$!^=f$FW&mqQ{SrC1L?M~7;ZaT0U
zXLrJRD?w;ie&OPpqOz)vM2D6GcV8=6T!3~&B6vSIxmK+5*1(`fGT{A8Op$NrQ3IJo
z(tH2F&u>v>>8O=V^HmhwR$flFWWQw|KJ%BEb4oOtIWoCyC^V4i6EPE6$y>J^3h93)
zg3QWZ@Wpc+7&jq1%h`#q*{*$c_2y^olVE*ZfBNS5hZlwL3&*rP%HVv9&%)(Wv9z=W
zjIG^j86jl;Ux`R4V$msKZ(Y*WmglB7kVo~ev<_oJyj+G7&qZSoNH^99!p=`Eul+e|
z+McqVFqz?)U3u<ND`uE^v?LHl&fb{RdOMyjem)yXpv>GNfBBzP6wD1t_QaKV1wpEz
zm@cN0o5mO20d01;&U$?@7cD$&($iFsaL8Uxbf1w-seutszk5g&-m#oR)l^!bOcURP
zm<#Y!x1hLK@v~m);Lzp)QHj7ye_<$go+>?Sk?m>oW1Pr=3`VMGdNuUrEih%waSz=B
zH_o6&E3EGH%_b3UIN+kii*mLTYKUrFcxbm&?QelbPtM$1ab!#@6H5(#sn(RHx$hd@
zQ(U*KhFq0TikEFs$4bLh5;x);u(m|cI2zI8S3iWnr%<mz=3l_D;3)lUqZm-KhfOXb
zZwJ2%zf121XEHQTPug7{7wi?zOApTbutz9DOB|yru#0h607&WBOWzzLbc;Lhu?;T>
zMB)TE1fa#B2>l6L{x$gXgkdN$^o3Qf56KI-A^DAAS%kR#QDH3$Ne;I`Zz6hHU2D3F
z>wqTfHY2I!^3v{b&t>xXRxUBtw7BeB&F$c%<+F_E2@0HB0_WgyBI(H+*P*$sT_I;I
zblKOk?aQ{{hBaqy8Lk!<`lCS%Y!FR&+|nYNJC|-B3TjZVN}YBq7_zn8unn~B9}zvf
z{IFV5Gff1O74g^BNOVU(8|blRV;=8m=KMCJzS(|?9q5f*TWGmKfXVsU8oKxhprG)?
zSash8+(_5SJQwz>H^ObPY&_&1+dq4`UNo~n&9@hb`>CRAb&Dsq7dz{A^C__hozT&L
z`#c`Mh_LwhQMEfi;ul+1#`4E)ch~`gw<k~n#AgY`WI|4>Hezpl<uHBg#9vD2a$mi9
zm@<bdb7kUE(jx|?_XJb`kc%WuM}4(nKLm*3D*_wN(h7O=INU*fsH!T>nupjyJrVQ-
zm(j7gqeL(-Uj-UUqns9sk9vqekYo|;Ms7Z(tD$R#01jaTnzA=7&34E76t&k>wgl>-
zqz24;7oR~2yxjKIo6%R)3Q2l3pxqd!Zu_zuTKAj^=Sv$-yMZzX&+tf#$Vz;c^lN`f
zB73TR^w4!~Go-txw&H0fz0m*=X9GOzJG|m&mjuk`^LTeI<ps9iD;l}Pnv{3196ic*
zsorK}-b%aG3ylMY6MTrSTS(s!)q4%R@733zj<#TASD{TMUQwcI2w9d!K77`W%zx);
z8$l|AZ>Qs|n5<omc>*1hJ>V%Uoq=JvoKi^%7pkR63}q7o2a{b?Dyy|*%m)!+L82SO
zj54&0B}O+HscK!Jyu@eK-{YzF99q;a=Ba!EAEu0Ysi2B)UfAO^p{-k(Lu9-aCM#l6
zV7x$yJ?=DwP5{gZi{kE2zMtNG$Hhl|LtE4pn$iW^nXAvaxDuY>(&CzM(-qQI4=M%6
z${QfN!h9(W<BmQ&X)P`Xj~QsySekb+PWye?TnZ9hm*Z6jyoLb3`Gp}$=C*(v2Vh(#
z;%QkbBX5n)2Oj|Y=-_9Y)UgJ085|Sfrjru!8FZT{mt>lK3|39A?@#F5&tyHZ)lYhN
zGCCL}J=goaAt9(ZHWip&^E>3+Q3!lKzO-9*uO*+b<&>QWMUtVXk2~jrD1S2Z|ABF0
z!ikucg>~)F78u&TBp^dUoX1xxjL)d*L6K`{zg~jRwyhOU4jx_BCWkeICgzhZ+0liI
z`tvUn=VwX)5-koND$_|;KB*$h8H*0?cH2&`CaP(FfI8Rv(U(6}Za{hacA(u%*$cy$
z$e8b$exnPfS;^}(DJsBwQgH&)HShewZz6$jr~c@zjUT#CS2S#mCKew*IE%w|Ck+M$
z(zWFCHI;;7*>&7J$!{X?Y0NPmbK=lk;N3iM-{pC^XvPVtWtYs;t9wZPtK;hmleSpE
zNlSnf9;&&1VC(bsNdf5xys4>YKi!1hb0LyNh!`JO;-cwLaRHMQeuw?PDEQ)`Oo*F~
zBjkonfV&$_@oln|zFt%;fD6ssB~KA+n#gL}i4C!lx|JKEm*%c*`NklfeN-Nq`>)!w
z3dn>|qwU@v@{puwLP96h+l{asK0kC<*JxmwZtIq)&t?pLHor8*O<F5!|L)F)?hw~s
zmCK*m65>|;=02ZOtj!4P#(~KzJff#^tCO|4XxY=symWD|w`5F5TscJ`mF(!Irrkow
z8J1Km!xs1MP8Euq<BV_{n;o7Q&-V)LtHW}V_uDizV#$QC5|s{jCLb2lu(Ysj3+mE@
zV+}gRVEi^_U{LSR$EP-s8}i^c0g`mUOK-Vd@bmEqli<CHB{MsNKXSW)wN$-gu28*g
z3bM`mGA<CFcskk|RnChK#)Z;FYMJ%;J-0P-w2DjLL60hq=;=$zD^!3Yv#r%XZ#Bjl
zW-WX6{wTBD&gcsun?_FT7U`r4lSQ-Y|75YC5nhhW6~Gj-UvfdZWq;<&eD8~1NP7&7
zI<`nUA3UbG8?csEVC$!g(R{hR?)KVKHrU)?RlDbGtU0>o1=yrjS3<tY4mS@pj)L|#
znxt(d;R-|t;7<ya)su^SuTjQ;_T#QUxmtkg8Z3bbAqzWx$ty1^JT4)7Czm^5df&;q
zlh7suN@w(7c~ZQ$BfHG^6p@m_=)5n528fMv^ElWE%^B}Y^7*uk;$ce_Ts>kWfDTGD
zNgNng-9`2@V~$w4A+0O%!>3&nL#U&Zw})N%fZN%tJL*q2?uoWL_*Er4F1j&!uf3>{
z=)O-2dIbfK247AJO>GKu&2tTnv~2i8S<V90ln-%cqkMqS+?VQSDD0TF{P1oj)E&_K
zON}4rhN1|P0K_t4ZG5uwO&z^%)sX-^)4KGnTVC_i!AIw&4titJ>h0RA?h3?cRAQ_|
zwJs@IYsxk7s{^%8^7q-W-5LJeC|d?}+R7<tmvz#j#1kL4=jJlu4OPP$-^iPHMjl{e
zrRZF@ew)BeU%1G|0tQ>qS-s3}mki~B#s%f0#TdK~AXxD#g#x1!)v4_b&C`z<)&=>x
zvz_C+0Q@A5X%=lBmqR2ArYG}ljMm84VAqx;S2(J_wX+47D*soeNVrN5i5t#tf2}fT
z7G9fMg$FlyMWWuAPDmALaRB4S*96SwJvH6q!dc9&LO!y~*zxY$C*@lfuzVT08!0lf
zlY;g=o2iqE-P@4nlt<}ocrj!``%gQ6wGJUb0Ha(>;Q3ab^;Npr-rwZ0%qNH5$12XS
zi}$=Q!Fg}GsUOd$%LI9zf00&*Pu8midNfb_ZLupB6l3&^RNo9O>2ypuYbl*|DD$F-
zK(W$xOro$EPC}E`uq29n@oDQ^8Mr<BFZPz4I#d9hyHq?okf(47pwbB<gNG38`iZFW
z2dkwA+=c+2#S__#1%gs|<`biYbI}5}Rn`+GCnEsH9ncfS7%wa|jdc)s3>6MmY-LgW
zi|h26T2hOg4}S<XHXi#iRO%HN0WeF*HMOy|_m<OR!(1@wb@fD1({9#eczC#BtRgE#
zHzPgKt6Qb5-ID6PNtg96^ZU`CC!LbuGi8#2g_Z(dgc#;Nn8Q8aJpyK@5SBKc-Z(N)
z(eRcf)zoaRi_OV>2{)hjj;i$>RkNC^%vKv5jz$Wu=CEnbTub*(Pk1EuXoMGNDoG2t
ze9$V(qfx{IE(&N!ewY~4!#cMPz_#D1XdAwUWveNxW@E)!Rmc4_9@L4P`F#6s>JSL^
z>^c&PuWlNbL_^Gp>=`Pju@V7<rhOEYb#Sv)AKleOOOdzP6?<0Gx|*oEFUiPQqU}K`
zTQOng0Ubvlogn7ZPvSvbTlnnFEjTV3Y?}BzDtg@s2VEmUub8*vWp6<v?S+J>2|_MM
z2MIPG7yFeE3#Hl~r`Xi49UTJ`&ED(OR6GhGa>+N8+oCFo1>kAzatx!dc+c`Iubqt|
z#2mV12d{G@{RTHcFYvzAeN>IcNko`O@rL=mv?OW--b>D_TY<~sd|i(a$_Fz@>K@tH
zd+J#QJnf<v42(#dc{>$%^hH=HY1+koAP$&sXxC|$9OK1wEM#8<j2>ejPQF%C4J>>|
z$}l`aq{;sCYn1N`etq<D2`+C6`CN3>qKo7dueR-)1!g<8OY2OHYS+#faH31)<+zfa
z(U{Ft{YGLD%<Tq+6Y^c2m0ZmDBKRu!5N5O2_t}E`j6b5glfZx|b|AUEGUUZ(eqNPG
z4~xkAk7f$_$|y1g)hb1w0<bBJqov|A6tm0CpS=twf5(a`7$lwZ#bbO=Bi$pTVvo<y
z;Rlyp?cDr{rt0C-E1v#2GuT8%MSmlQ$Gm^lSjICAv{#;1^`?^vu0m%aQ6h-or1~_X
z%I2Y0@;`HXraT10(8P$o?H(V`=1~pX_n^GpFY&eskt1$8*&a}_`|-ZWq!sd$meF{Q
zxSsU1EJLXx(lAI*rg*Kr47W|}_T;Q*KaIw~;w9R*&zb0n1T_Y`6o&@=(ci{f-vt&0
z<Ir(%_2kG*)`A_d(9(?Un^?}WLgofxtZ0{yJ(M67h-f&P+c&c^5}yi|e&)TXVYcfl
z!C4B^-6rim#BEGYq3T<nxxt~jov*1P!}!_wh@TtZfESx6z4T;+ODehp<Woj=7ZiNp
zwPpL6UOYWaKTYv_gpXRCo^*6ly7KlGZnN<w6g(O!$7geB%HgnAiMqM2Rc?A-zOl+@
zw-FM*NOGcQ{n4tY(}UA}yLypA^8Nc~RrX;z8fDQYf{I-TR#`)NQXE@r#xsq%L~3X%
z4Wcsbg<J+Zm$@+=;tE=o(H$Q@Ubmtrz0-Xn!sYTef((`SkE)_N#5qcB&bDXOW*fkI
zpMGqV(Og_r2ZTRpMVoSZ?EZr}svMOW_i2DqaHp!Qaz9+BIq^s*^OHH3U;9eZM$ZU%
zWl8<sy(Se>h@(Zv1=%{2-&weAnE9v$4|{Z{*Ktr+ls?%A%C%<2*78z8OP@~t_TnOG
z&e{6tnnarbjYhumn<YwM(*9xX`-sP7_6BLSCz6RYN&pjFgZd^{rqbI$HJNg)u>coQ
z9rBpjA;OP9sf59IFo@u@Xs_lNIpjyttFx71y3#h852jltH{~`>sndJCzH*R9l6gbf
zb<TT}RWOj-P<?%a643a!3-`Rw73MhI53(=q4f^N22+Mvg*&)d>M{}WNhy&al;)ol`
z=B!+E0Ba@;SbU;hWBeOuPq#2|7vvqoB#r95JT1MpZfd{C*jvMVn}OOwq>akQTa%{+
zOB!wi5);)n<1HfOjFbZomC=(v3`>whsHweug;&p4VB`>ts^|{+5ezJV5zbdY`5+Ky
zfJ|`MK<$pj<|?c`qwniRS))$H>bDGN?+a>3G#Y=o<*aBr$DlpgRi!^oyKGyq{>x7-
zt^M~kye5S=IRnT(O>LsUNK#M;MrgnevEL;H49`T2^zQer?O)lVHR_MG;ydbRK7OjC
z<E1;>f_A5{TB&B6vsqmQRf%Ck^Z;WV3p+SG9I9O3UE6Y(S$(3$Qp)#%Rb+d-rRB5w
z!D7jEQR~l6rB-L%d|0V<R!z8Rwr&kO#dR!TFi&u37B@C<<-aNBGR~?NMFIwEYJUx=
zW?Wdb;^t~=ad<ShCzVah@@Mz-)kT*Ir3^}ceTvD}ic)Y-PSNz}=^BH!rGD4<W+82D
zZ39^<U<vDviCPVs9plxoem+#onJl9`MT7BCi;-*FF0m%U;J22f4m^y4*StXoeEv^7
zuFgk_&fZnqCKk`L5=oHyJy;$I?#X~%p4Ftdv|Gx?1c!ZPK6PK^9n>D6`%Jw?|97Qo
zVI;*OnT(`)tEr9SbGZmF`F?H>eCFL<WvVb)`~+eU+qXMYCih`~!HDbEo=tfy!;iW7
zXBsS&TXTNr%SXQ9DIe-Hyjjk6ZNxH1b;FMQpO}q{*vz_vw<j7nc<I!xYh5?0C?pd`
zipCB_<0!hxLe4J}7?q3<#fWX@PFoH~E#^(<1!c`<vdn(e%vboyQBX#DdB097g|2`&
zI5^z!EXMN+o}?chY!bwGEHCpGs?<gB$c~zimhHjJ>^-}(S%Jl`P6Y{9N`ThDZl!v0
zu%>r?4gd6!-+5igOg&U*B&YEPvAViCuH=`=%V@N^=iVTo&D}hDie5G*Eb6p9qY?qu
zDxSG*X?I+bFp~F4<%q6HPRV`uA}w3281s0Ox{D_CZ|$ySOqlY_n^>F+laNevl~KKX
zKP5Z-0lwBVNcy!_cjexc;lkTnoTElaA~*<pus9U;=OQl;PkBLwF_~VU9!c#rJS}Cm
z_CRGnU=DrAU({(1JoC-<z8TMEQgrLujO$WB+LzF~;uxlj#7b5Q#zn2?Ah!F@o%p=x
zya#f+bJ1}HJwv$r0%Ycs2>9&|l?Uq_`uQ%l3^8!EIOujRPJF5&Bb(o-*BxX`okHo<
z3b2yv-+dzcAv_+lf;QDAq13X1`E7D}guh$|Xp#bbDS%r!8TmVZWW>SJ2>jkodR|w>
zy!kp_b_qA=N$V5C*t=^bp;JZ>z3SxX6+nQitIk_}UbAufHVszwnoscW<Pkwut6Dt$
zrXF-6AQqd`Te{0T;GAa?3fMFrtL5=<<IU8ZHLrW;+=BkzgyThda8K_{9ke?>w%u%M
z8*8Z<aK@nrQ+vr}sU6EUWr@Qq%YICs+at=Dy3dh|=e{bGyR1)Wj59v7-Ytm}euNMt
z9FR$^0yy1Y+v0Z}vtKVR#EnL(41=f2o$Ac_F$oR#gxd~17RWekn3KQ+ky24R^PY9D
zIYbI4jkXP}$%>{MDW|-Uar&yg`d#62`*Q#z>8R;eEgo~$sf#Z{PnyUos|`l881J4_
zwVIl`BRGtS;u^!rER47+scGF}u+gBGd(?m{x^vz*FtCA46L`R>#F&lSub7v#i@dtb
z8>hEa>D11+q(L@MNN3{a>Y8P;f(PS`>iB*jZbTiTC<3^rz6ij$K%j`|NZ}lV?6naq
z?+fe~8^WH8Wl0@C)Qs8zrn>3%TuGiq#A7pPZA2JalhIBxr)QR%535F)4V5QV@*Y{I
zTPE&B4^F!L`nqj3@HJW{Q7*AVyFPh$XNe4Tw8p|^rz5pK5bz$ys9&g6Gw~S6As5qy
z>OP58Ri<W`6BEmr4su+bm_2WpvSxc&(@!S1!J`>7#SKmWXT%1Tl+^MTd(8-4Ut3Z_
zYLd4F<{I=$d0yj7uYZ{CD943G?Et@cW#{1VoO0x98y!6{7fmql2OQ(eXf8YqIuAY1
zyvBP%mv7Hh=pnP5@gEAOK37^|gtM6Rjy<c4x-){spIr%X%`Yi)Kh(V|Tk8WG=@iX<
z9XvNBhuHF<<A!7^|2?KpBzL63mO^SdcD2gCoFb6gne_3N1xs5FX~29!rtq)eu;_ZV
zs@2abak}w01`8rB_xPD6W-doxnF<p|N)0%7ipB;@ALqGFUM&ar<oz`AYbJ@r<|dV{
zs4|_D)CSLDP-CMfU0XTJcgSCCePP~9N;TL;ZoHbi${Yk<Xp^%8#Ay%^+5R<71O7s^
z@vV;P)yIgbViUktypH`f;ok|36v!EkdMt?OSR3e*9X8PVhc!Nzwty`dZvH}yOdD|K
zqKrm+$L$m?c64~!80D-}9u)sUT7v#gbK6@xQ{8$kq7m#|tut6piO^}MW!_pOaox3O
zatNkK-v5Yw8`IuCwMb1yZtp9%aOI4$J~Mwyt8*S-g=9-6@+H}}C5Jl#Ua(Tjbvjh@
z(-#LEutgm~LdP|LB{co&UybC>rz|?eDl)E%YKj{jR!t@pO?T*doNSo5h3p~+gcC4K
zH^COfwyNZSxFh^O(a_Tr^d!LD04-z170^P}?#%cp->YEhNL?>_8zX>SnPhe)vv={R
zS7Epux|JSo9`ij77Ka^ykr{D@U26NoPF(cSXm|+hs&2!8O6*3Zfmi6=X`#}if37rJ
z^8}AF_Y0kRove=471gjw*Xf+smDa@G51WKrfc}F)aid?Z|1J}nXoWp&v<k}=bDa`+
zblej(N}KZnVNo1K@^m>AB*J3+R4-V+i)du@5-%@rk=x6@F~Yi$Qx2P}=H<)L4^?xx
z*<1ETq-Ew_S(^D1{^u?LA8jqabpsf_d|5<V7)!bVqtIlHNrt71lM@zy%5`;0jSHPx
zUcT315a7JTDIp7(A_~B7qMDoUT-eAT7dDzd{uL6PRAu|Ec5)5?a&7QH0M?W>N)Ro)
z>_3L{>+uR%VfNOpd+8B+ehIjkk(0OA8&4;vi-n>pc@I3zYYtbO<c=R}oDq%W>xjwL
zBuAf2ga^qf=N1UH_fd}XFO~I+lr+ELM9cWHRVqfRlxZXpyXS_cRd76WwCT=fbCBP;
zaiZ(0@N9jqtg4vKJB61h@na(z;}<2IB^p{Gx7~}bDQ7z;Ai^4{bt-0`Ub}yH<@I|V
zP-%UYT&dv682l8uYx?sXgRlZ-b{<MfBi%lX4Gwe|_}j@TH23SxiGEG(@Z>W|Z+;U6
z%!+Z6=<i}9!XWg}d^Rv^skREf)O@x+lATh6>c0H}okFS$POEZHIXubbh*B1Ko5PSp
zX@PYEPPr0#e{M({mmUKvpJo%BpYZrme1UpRbno=rC>>z2SpM7V&;vL<>jC-WfQZM#
z74x+YdNQo(POOxwr(^j#?K$0RD=R1Nb*DMt$wY@y-pufMFB^O56<U=Q(Ck?yZ>Mpc
z<JKoW7s+#~TABh5%QzhXW`!2y>?GH{iB4Vze);z7s=JwN+#0GUUdLGMbc9_J0u936
zi6f(u?L1h0E%3eAd3){&Xz0`WhX=3>bP&n>Zv}&Zu%_H+z!)9;%Ar^((R#D*y2GX2
zcpf&qEPg_$>K}^9%w_C4@ycG{1dpwkVdI<OBmbxI@BnC%y<K0D(W>fCf7RriR!hk3
ztm<+l5{}DpJDil!iGL1>J~%xXY(>7)e4dt^BjD*KF2lnzI3-RmFA!5DrN^9m0mxC#
zVTtJqz~}=rWebd4o$)%uGu=s*dSRp`;B~RlSNYo6`JHnXRi&X=VBPgWj`}1XV|Uf!
zz`($3ZHE%C&URyPmd+XKmoMzoKv=QE`uPFCfQIuK@;R0oqodw$_Gb`6Gb=5X94gEr
z>||1TRH}I5aWAkL%y;|IfhY%}|J@(dnXPqzbp$JwRGJrAyQu9S5koA0CLZAwh61s>
zy*Q|1i$964%q<jH4q69_JzU`-7i;sV(+kszJX_mzG^dDr3UI$Sb~^#c53G&mb@9Fd
z&)20;Uhvx~0{xCDKXn$-=fujzo^G=ZmmG*Fef251b7W-slXSgJIu)XgF}jpEnSe){
z$A>isp9>E%-tLTjPMjFOBK9=B5zX2+8-^w*l&do{^O|>$scex!lLs11_R3R|YS}XC
zTx--a-fn&XFqI->b|=SpkhffhjM-#u_+}a_+qJ!Wz=}5I(rJTVyDY2Olr)1P{j_V>
z#2FPPq$|aqm{-w}hw+n_yx0yG0gLr@(mWWC_~%$JGSb>xH9f@KB2nM)(JKSt(PAZ+
zKD8$m^FY^{US%<d?wi=oROud!Mz!Du9^2yT=1|4{ehjnn&eMZ@Zs;|L^bd2YXn}RJ
zfljh=o+iUryVz!-z#{cB*(+`*Ltc+FHaCI;!5Y|ArOVQ#{p|y~cIt!9HIL&>B|vlY
znIb7wu1+#Wah8!Y_1QrsHtRuOfhaL`5WJ>Tpjtr0Ltd_N)TB~)ti3=gGBY#Nl|VNR
z;M0sV;_|m-npBGzVv&Sub+WUWts!pifW0U>3D|b*>b+&*<06($HaPsCQ+I~hz-B%M
znXI#)edJ)`F&G0~l~tZea+r!X0c+FheR{Wxo4_#bqRBa$`E#S|%s4)5`pnpiIalG0
zp`FN2sQFCRM!kP%*)DGoSK9LKE@NL=XNh2}u-YE*n$Bx@U~Pk+(9M~OtB6?-C&K`s
z`?9>)I?1>XYrL~MPMrH*=G`F(<Z*9;zmLCi{Qo!pKNm?E<ev`?zQ;b<<OsVD7WW$Z
zUVQI1e`{;I*Pc|V$w@x(m;Ubyz;{Pr7m8+A4~69qF#r~rIjqj#umH0;{(LS5ifw{=
zw2k{<0e>r#=<j8Q|Lw+H4+R8<I@QG<jDifcHga&Zhw5A1AKDn0qabs!k}#3nAMx;j
zmECNiU{M=uN3fEUf#cnYvb_`Z{;I6L6>y6a{94}udY|I8kg&9f2%WN-71Ti%>Y`|4
zrEe{-B%=%#u{N|ZGP5=TtC?8~T05Bi<NPk0Fx0`&-ptm~#-4<W4X9ZWENW)&;3#CO
zZ%@Jk0ZZ%uIc8>N2l5*^nmUkhu(IDJ<m3Ar(RW$@jp%=teb2)^#s8H`QA>Rjhr22z
z9Q7^D3<a%CETJS!U_l2%sI?;rCl@pLuDH8n1_(flkiM-r)Xc=xk%XO#1uW=na!(uy
zD-$zVM&I@K351=Q^}c4c-ziy{n850PazWUbz|v6tJGqdsLcmInP%Bj+_kHr$uWekl
zNEq1IIq&NtfiM9a1J&q|+}{>8vxGtbDcmXQU!`y-mw%ChyuJw(EU#~WM>gaxIam?u
zVB=(O2z3CoE8^-XrsSya2nCMCl-Njq%U?{1i-hZU(Lh#5N2t9ua9^I8iH+oLyRY+}
z4&bUN3kl?&46rCG3FKZaU{MH=?!GQjHWHTmBR)Q`x}1SI)X)*gBVh#)1>qzC{_hC7
zKV@cO2QvN6|K|b+aDn6RDL8=(_gR3_<n3(?m7tDVU|?wjurk!u@pmz=fA55T?<90c
zfTS`|BQyPbf!>kA0bv59%mzqO2rxcCEDj`GziSb*w{fxs$h$Z0`xCGdVD0+W4z_pl
zGjszBDFG(nY-R{m6nhO6tgLU~03=azw0AOe{4HY#683+w%Da;P(g&b5K$Kv>%p9S@
zP@qLX0sUK>IGWz^$$XdXua@YVTA^-bGtK_|*7`eaflpFI=17G+kmS1}hhTtlhXKyj
zz%Kk+M=?i}V`=T(+@(0R?uw^%&IAIOLWhq1ye>RMvo|%=RG*pX7dCfMu7|DdeuXV%
z;IvVHjqzK36jDEXd4tt6U9>AR|J_xCAr2jXfdX@>B5P5!8l`ivz<I06`N>>n$Zq8i
z**FYE`{|GJ0!vl#%sm@Ie8d#l@=2DEHNH5gZjXNT``w1`H7mBVie1zR7t{+Mm|yQI
zR1|SOz@Cvrtrt8R1y$(L?K8cIj(?2#ka{TD_iK*>ir-|2iRaT2Q&Gh83V2lvRF0jo
zz^n{s<rwggY=Wo6EAFHmjR$%1oh2`KCbF_Nkl3Cir?ofRSj_bgM-=A3WDZymP7-2c
z68LhxzyC^f-r6Ba_WtTrJIUSs^Vw3RRjq`R;`??}B@%k5c?^N#M(pi#ySE{$qJBA?
zoB6wsWnv`2anyug!TGHitn3+vCLe4xF$t##1aOzej7XB$rrs&{7~^FbCzY~!EV-7)
z<h)^6X4VZfH9K?0ZmV$|(E2VV_FPDIi&e||Mc3ob_k3|C9xq;sy&IV(VF}-Cjr8>l
z-0g9&{DN_t<%3|UO*)uMK<u8PlORw9<LX&h%=9>0twgN9V(e-6fg5z2mji<r^3UM0
zC8AC}V&DG=`y!~}E*5KWi2l4;4@-Z*=0_!xR6QrX&4>2y({N8XB-B@hp*6&*JV>c<
zGr&A}h+za7WQK)Nt5v{}=VjfmA6wlGQ6s^gNJqSSA1+)zlXA#1At+#fnuzTLdFUC3
zK7ct$@aL9ur7NI^=xE|#*ici2auqjgk32c|HMhmZA^cvio5V%SJ&xM`VX~wCIAW3k
z<JHx|2c-p5QL*prTQWiGj}zy<zd3)Yz&jDi_==6PazLZEe6DpI-RruBJ9Yg%Qh8F&
zmBJLxn*3uq5h=1jWhhcAIr<)v^;wI<S}<qN>(viIM<$`wqQb-&2jpfp=CV7nBgcz!
z?{wSbub8u^@&sH`mMzSg2|MVMqK^z8#<f0NT0}Zzx^@(pT3h?1^(vqS_PEiX6Mv!i
zgEcZKMDeU%*f>x>xqS#_L(3}PEnXq7i}#H;k{glax3Q&mh5SBIGnSmXR>Loh5AZDX
zq~-cu_pcwqAK+!c@27jNTbfWmEc{7B(0JWR_EcBAhf!K+S+X%;gkpS{iH5{8v{_8t
z%c^OZW}m~N{MTC)el0Rpb@vza6A+<kL9X_Cc&Zok)f$CuNSo%?O4eRA&v^>w8QDjs
zk~MRaeoXtC{Gf>vWvcI|)@bS(P;Y%EV~IR+GMRR%-OF51=RsKwL5YaoKk?R+kB@wI
z@kFyr>Kz3{RD_JeR1iht#D!^VoEBlNN26o^_7@T-cH=QzH~U-lF9C*6f9A>L)u!QB
zRl-1hgCCCma}>Yxl>cx&PS%EwW;WJ$u8o8REDS_uGixm%0W;tS@1rqPi-h^^{NFbq
z#cLZU044zG8G-wZEI=G#1i}C#5S^G9x$a`ppW=ZeT=#(k@M!mLAK2fOs0i%ugAcH0
z`E!0BH17QK-(vV(-v3B4<bRMX#C%5#5G{Uhcg~#Y?g}Rd6B|&-zwUv%vLyHZ6#zT;
z#s8-6e^JwaEBAM;TpaAIfTL&pm;HaL^-dQ5i(3DsyuZr-2e1F_?!WPRC;9*PYrWI+
z|6{fOH~Ie?uYZTGe_7?d(f?-)`fug_*6ZKx>)&enJKp>QlKxbrWO@(k03;z~V`*cr
zWUFro{U>e!|GE7qv5<|8{aw>yVP_&?VP#`vX8C={4q=4cffN9t0amL;0$>d2{TFcU
zFUCl?{-B}1H=^Gl>MzOP)$$ke`Uir)$5+3xGzl|=9RmIfk+QS=XOdr=IXcKg?S*Wt
zY;COX!7zZ)@4<|)kn$fWBLRRZds~}3aC1-RU)Y8fcpVGN-@y%R0`?2^TvAj*6a>6J
z6Z8i71Hl$Rnj)@d#vqW442Tx!)jS3vz{7zMfTO#~R+8}W_xrnJkizrd$9E?RXrPC{
z4mf<)qJaL-<GXYqxPXr!(6>m~G)M^a_|YT8M+lD*5fPD)9wR@+M0xt;$x~c(3{*@)
zJYph3JOTm|az-i=QhG810%{H#dM0L8R#svvE?!Ou4<idJ<gO4nU~RUiPoBO&L3sgr
zN$?U_@9ls2fqe&|J%&SkxC;+Q3VMJB2ag5^`w0SG+6;#PNa9Wye?D*z;2$D9LVS#b
z`~*l)@f`F34j%r&LwE#)hXA{9Uci0OLo@{Rm&}5XFckC=N$oHpzR{l^lfC}djHNiZ
zPtIas{|*Tm8|MWs9t9;8H4QB*8#@Ol7q^hGh^Uyjgrt(PimIBrhNhtr)Y!z-%-q4z
z$=Su#&E4<4e?VYRa7avSTzo>}hot1p&so_yxnJ_~OUueDDyyn%YJar+Y;9}r=<FIA
z9vK}QpO~ClSX^3OSzTM-*gQBqIzBl)JHNQR;|mT1|C_CUaP~j=LIe1E@bDr0L&Q72
z;2yXD8$8-WgqO^Z&;=C`_3bc7A-<0>Uq^rX){I2PqPUM`U_XeAP0qSNad5}lJ!k*d
z7<>1Bi?e?)_D{a1Ku-a~e1Haz2I2>K$)Q_mr{^T08!aDW@w%k`P*hB&hNGb|{rG55
zN<sjEf`I}=fk{LgewvlgR2Sw}mLBxV%k;qxg+n-FE}e}<OyE_%Yo6h#4a18k+32mq
zwwp8)j5dl&`pI028irO~vna|VY}DJzj2vCF&%HGTbh3)QirMHQKlG3ye$=HIn48!9
z%0kS?c$=B#?;vRnH4pJmf8}Eo?)&goxq=KmCLzWL_zD48QE<oj;F)G;eSDa8|CNg;
z`RAe;XD*%<2s1DQ)m+va55yud9)c>#yd{9L(@TZ9DZS`};X4$9f(e=%QA|gmAi-cw
z+5$LxY)ToyBAD~Q%ZyubNd5tugp`E%!`0Ym*(_ENu##$;DI8bU<xq&^qnBCVvsnXt
zFg8St^en>7+$gFm<}ipklERkZH}+IupapW9iz67QBjd(A<s(NP_ZYqA@i=NJ{)3$z
zD!IINtP<m@Doac)(%6lcoHsOFtq6e%L$&zcNV<|CL*mia8+BnfuQIxl=de;G$&P>@
zqCKGjC3h~aQ&}6S(QP|iWc4~Koxf5JAtP;Fg&Xg_@VQ(ff2-0rGqoATrBWJV!0%7h
z@>UwIz~9{AT<7PoR*-|ZoyJ~X_rpe&-jrslGb3_u$qLL{(+&j~$V>H`_B3u+mSGJ8
z1sCSGHTZ)9yNO`L%bB-Wnb{nrEUw8?&Wc%Vxi@ivx4QfX^S2G))7J;L(J)Yi_sPZ;
za$4}^ED;Qp6emMr-WY)yMTG(b!F_>&N>gs#-^O_$r}=F;dfXbj5N=dsePK<loPTvN
zt+u#d+mX^Z8F^&_1C3{`_d4RWDtfS_v_7ik*#Gg7f8+D*4GeU$nU#!}dtJYF3xAjD
zOYbpaNLH0Gb5;@6(5UxHHUB=I$*DaI)NeDC@h#)c<tQUS#_N!w+pZ-(m)loTH?c5~
zOp_#U*cIZ~`q_0)7oCQq&MYMi)Nby2%~ij&!wCcVc%Q7FvTQ%W5jnZ;p{2`}dyEJJ
zCC*<50yXCGFFfhLZbH2}15UEHS0+kq<Z8MUbQJdWi4QONfn~oyx*z7y=wKl5=i74_
zXloEi!`r~O#w=nZwNOSZ_0jtm|Bd?>#{k3|)9bt|+*7J8Wi`E?p3Dlvfwj0r-d;db
z1yT!cpq?`YVcrW?58P9{pF>{WEDu5^QpC#xLFNekSIpigTa+S5WYk=sU&NY8uN7vh
zRo>5LwhfA4J@OU7l!yCYZU`x4S4X!yrm9w-TDz{s;+YXJ6$JjifyfCWGAlksNugK0
zJ&9E<%$|Pv+SO)F8U>j;)93$mE3$w4hL^TiF}g}z1_TMA&O+42r1ceHz!IRKV88@T
z(U?(#0KZ#DOJn>dpF75u8dMlAq~aJit>H5iS$~s|^u^+vXgFcs+*<gJzoC|{2&PO%
z@@t2aocFQ=bJEk<HI^_?z4=f1LXuxG?R(6d=7Fd0Cp(@*N-J~V3_%uB;0Ha1YhRBH
zc&w|OE@vnr;9$HS>pk*|{$A{rYwyZQG;&f+;@LtqmdLbiv(6z(V-y_Oq2@9W@_FrI
z{MNC(z$ASXC6=|g=Mm;p<AVA&4(hFUC^9F6<s9U70nBNWeDBgwemX?P9j#lWnoXoX
zL*S!JP<@#0C8EYuXf0*d5GuNYw23GrE$CtGLU$3uoyXOys`J{aL8YN1k96b($DRP)
z<}0NEHa3Q0)m7f_`hib#D`eC~PZW>4kscC#9;<lM5a5Ym$#Etl-nl_D66a%uSsr)i
zckaLB;mv?+peacw3J~jYPVy=L!*g%(#(n?r*0B^T*8Wv%moRiYGPwQ$<T*Z!{fmd*
zhLa93khH(iX4bZ*pGkkidcoyb`jl*$%bSZHNc_Vy-S&eQY#EwSl$^a-Tl}c?bh-Tt
z{gs>SOfwP=FHNsU5Y3-GV%bIA(9ydleG4@4k8FuI7g(|RvER&5KCcy<(JB3uZ?#&~
zk$qvPz8JA7BCO!^k@-8;Cf}p|(@3vhTTfCNAY*3pJ+owONSx6=YwHNgxn0E+_;o}O
z)8hF>>lA2?g=)0=RI3fXmNhi^^*a}|<V8?Fd=Y0|L2$t<B#AxUlm+hqV$*<Ey-r!-
zT2Cv3Y$sqK%hSteURzD&`;C6k7vij;Oo+}wEN@LHGgTC61k2WFB`;-dP}j$ig{Ndf
z*NUyi`Mc`17N5mKKcA;go9#b-$@F>qVRUTjNiJT9Y%}%53U`R)&|taNtcQXW-Q%jR
znHX1Py*=h_y76+k0B*`O^om2m*K2uZkmtlu;&Ij$Ho~8olMBc3$F-)K!e*tsw5@6{
zeoTLSab56AZ9gM$x6s6lHB?{=pUHFytx{kqzWK{(_cz_%3?8C-Ma5L5&*7`_q~TKH
z+gQp@<ogKbf`nMd>}kKk9}UY?R%J+)r<*RS#Ha<K??lMHt?*x(M>MlokV+}ykgck!
zt|T?k52G<6VB*~I%lJ{IqT~#YPN$OqBG;ZKZt#wTo&*f^gLt9o@_{-GG#{;txLDw7
z<*Aad)r{Br)P?a&2oom!fU>B$NEbWv`sboYg|Ts?P0I{iFpvujB#2oHtnyp(w(@Ia
z9Lf+Av6DH2)#>Ii4D{Xm7){3|Acjs3#rWlIth1o=@xv&@WfTXs`~~c>g%`S-$(nBi
zAFF5jKEgfC;SajJx{-0}MyL!&6Mv7NxYtcRn{cc!bCujW#Mn7P8pDL8;90Jl%Sq+C
z<wGZ=5+T8!rGW8FYOj5cuPNMhbW^4Rmlt>VanV4`=Eu%@F2=0{CxV1qzp4CA7hBOu
z?WfNr)_hNPG{)aaX?e57`Lz?p3yZkCDyz?^r4={ke3Zt+ZN6qjz>{CF?NV8rX`Sd<
z+`IBiD1v;cW^YTlHX+H|G-`HAkc5gD@+ROiLTo|%A=+GGmt0jh?rgoC=9p}vk-%B>
zMU!jvwb+2iNcWS@J&|n{ZTdK^*=M7Uj#5H)mONX}r*(11?cQG!8%~7$s-QE>nl1bq
zd&(8rh18!gI|2jAnIGS<&f`)<ko=VJkH%l^I_oIb`spt8b1KcRP4XsnM(fIlgN8Uj
zY)v=)2^wwU`}6`mW!IC9HomaUYl{8D_Mf^$<s%s?)xm3uZLHzqy5f7s#ld_SlO*5A
z82fKZH9zgYyY5^F<z)GhcWYdC<Dy-<zx!dz|01zNO_@*A8o>9(c-7Yig$uGej)T}=
z%elbh_ImpT3=q`Q*7=)D%{Wiy@;3g8;-<Z!k?3pH{hpes@fr8&yO^uqNabp-HoZDa
za(g6TcfF-&lYDX0eI1rYkE7+a3T^*!*33TD)H@-CdXBeT(SRere;kr{z}S8j^Qnt}
zX=_J!)WpN;i>8>92}I_hAMLs;Tl^9V8=D-m`E$s-PP7D;8OR~?brW78rCm@sRJJm&
z&dNBrByli~AaA|@dB}>U?5O8_DJZGccw&O(?Lk>p$yj!+qS4Hxw!7L$!L(9~bEAJ{
zQWmc{uJT%}yht4v>ZNk?SqHi(JnzO#in-DU!Wm_)puF8K(P@{k8*{7)SK>6|Gd3+$
z34&DBhztwDuD&ByU&ANiA3DjeM%B@fiaaooh3xPpqsw;MwtxLfJuW;T8dQkQZ4xSD
zP^JpYeJb{SuF6^UgdpJ*XkYwd2YUN?%9?a0I8@UB`0!_WzD8_O)j!(wBe5I!6(WzK
zCGJa!K5ln4KYCexEH~Y`jD6Grvz1gXMf2w?R@-p^Y9QCv>Q;V%CI^5o@VUIr8(!>R
z-rp0iY5m0yvg}!H93GUWqQy2}7jV1QL*;y{4)vBD7a^9BPZO&oIi?ORPN+EK0r&;x
zKN|w-hnP|xsbADFoVRq2EW`UCD)ow#lC@8LyzW0&u8IM8H2QuRC;H%cDP+>{n<Ivk
z>XnZALV{qNVfKp!@**5J`>x$t?A@v83um9}9Xs&wWBwvuqjI@ABbH4gau|r#$aYUO
zdWPKGRz2)!9noFhvVAV**Hu3Fd{DMhU&rC~>BY-MCQmM(#M0tKj+9t${~r?Q@<MKd
zB{wWl-U`alVX{QZS2b8yNP)y3U?Al3BXyeQnizh!=Z43W@dIt!;Uh$&Y|Nb7uXKkG
zzPWmqko0_}2+%C<lPCe;8(BtjpKA`Y1U@2RAb+@_G;+_T=wkyEXYSNPv7A=YJmUzN
zR07W|qqiS#o!TW^bDZ_xNsz1M;dNEK7}1f7Eb>i2#8rAWyHj@P{wYpDMEpm{DKVa_
zw^-@9nxVHv&Qy!@hNY%a*ds25bg!-UJW(}#Rpc0IinJ~vjE?UnkF!$U4;02fT|*9X
zq89|eh~>3VTZ9+J3a3QkUdXK_<C~d2KI)>A`ib`*uPO+9^2C%U$ePj&osaY3S^HZU
zDBO7Xxq=8LNF{}k`%39Ui@u4S3vKaXl<*Lfwr?ujaP-Bf`EkZO??bLR?+IQQ=-9|H
zYrB}Wxc^m6MRjSlhsPI+caEH{E3XTi<lV5aob=GWmp@q$pA32*BNVG9yQpCxQ|Gqg
z%Rq$v7`L&+YCb+v(BTiRN69;v{V}wrm@x0vT^f{W&}s{}yhM0qToZyJ_ogCra6P%O
z)@_7r7_8etV`yKwZTOv&%9+_m@H#)-BOL&~073@exXpU6cha@c8q&>JsZ(c$eEK^?
zL>I8*CUze!bmVm63#jBC24#j&-*bA$?cJvMPWc@WSE=E3X&!!~i?}g6dEKVX6?V)M
z(j@&Lg!<!u*){~wdalU$a($IIC5(=Uc>^UQ?ht1bp%-R(ya2^L9J=e6p@V?XX~%t9
zK+zoa84W#^6b8~<Kk4o-o4gSB_5g#PRqttA9WydaN=AJ*c@+2+30Z<IKSHCUSJ90;
z`0GJDQAJs%YyeX(E*WvSRG!wS7ONN5D0Dj7;Jm1^8eM$Sf^M!^|BDa9#7=b(zvhh{
zRr;bs&#`M-C-<l6_|CQ*@UasrWk#Dxhpq*xih`JyyWjbb^Yj$L;J-=mon(rq#M8Hp
zz2QVmRi-4PZ(p8S9vM4CFrIC5%f!#1b&j<ba>Al9=lJ%tlD;4z$aj;DZ^h{SgU@s`
z<D!WJRfu#a1gc(B!iTjv$i$LXjPIm-LhRLO?KWvB0QCAZcAXYUp(tl5Th0vi!>!?=
z`-slA{FQRDNq?LG!D@ybON#?G(v*A1y48}Fhpj9kuA%nMQSja%rZ=`+g_7!P+3of6
ztukt+4*Ty$#0qL#89tp)vKC}Vc`dvMjRZFBA9v)yi7-%*{Ii3NFCqJ<gN<r??hONx
zil)IpR^G>ks;$`*DGDPDQqW(~&sYXE;E2DNxBqJjxzmyfJ_aevouCqiA+q$8cPny7
z#NqaAZY+-5(S8SNQio1bIx;vb)p86Sw};2q&BSNDwyKpwy1IfE&wI)^!x8hS9yD~f
zYwDeryx$#FgM8;XzbzO)?sc1Q;RWx^QC?{k8<q-Ddf4!QPx$?<kUY}gOY$9Tx-g&y
zdV*huEz;3xQvt6byq0KR(+on+%;O?4q-wBj!enE%`8;iwKhL)OvATbs5%q}duz2^Y
zg;X+CQDoY+65x4^i(nu<z`?JJHux>fUk4@I+*H6ogC)BBiy0SqCpMQ?i7?P>wrze?
z{$-J2@12s{aQCU)?_moEEP?01uQpy@5nrCbK$BA+&ka9PW*2sTnISQy_n|K2gb)Sp
zH}h*N!$8<D&@w+1=teeOy;4)!FU5-dAwvT1n8+w`3F6PD+>vH*OB4N;KfRu;eCa>P
zr!1BFtk50A+gb8_%+slO^X%!oRl?YVx9V0iAK_;r<d$^WTPJ5(z4&G@N+l-yuMu2f
zpn-ypyrGbaANFHyX;r~a-5e;7irlNY+EZKQMlk#owlqgq8{DfBd%`)xL^W`%1^UU|
zN8vA(E2G`cz6!fbkmg;>m_cjs(${{WqUHoIR2w-GThuAA_C^Ts<llmBjYj#2TxaXq
zgP{gk6-a8%tGH<3;#aW~YUUsnVASsyoF4D_;=-<{S$<jUVed-VB{edCygoudU6}@-
z$|uRKpUJZ`Z5k&QZ3YfMdq9^UTYlsk!zJ(M6LcR(pw+=61!DGX0)m%^{P5Zr9a(!?
zlejv;I^HchWLHY9`(0ZfEXVtSPNA!9Qy$`e@#}`F1=Z+N;-?MAp}{s;GyF5UI43jS
zc*zliw6xn(2gOx4z6<Sjs-#NvW-kc)e#zZ(;{nf_?7X2<Qf^Dvxa!`%B~<54`4`Fl
z5eeakKLX4UuVlVo`|zKQI`gBP=<V=7nx<oimQ&8&hMsvJ4Dr)8w+%`U@>;sSEz4<)
zE}SzvG;ylWhU0BFJP#vo`(oTcwRjqR>ZLobB67iKc(_b<wKaV7(0iV3wXN1*x7cVj
z=>+OIy+nLLP!V}~6?-v0q;g~v=Dln`#=o^IcZ2-xbBz-_(*}(hodZkM7@1i`C#m+M
z=x9+d@dvfjE?RCzr2+!)C=Vm^RNDFbYIvbN?meY6Ei|f#GjLkjD-9#`yH<m7y}?0*
zn8fvbX%<MWQnn4@baa`Q=K3&@hZRldx~RaE_iMV`VW;xia0Y8|!jRZYK~Ep=Uph^|
zNQC1TtB(m5i4SK9GGQPmI8OdFAuozXJf1v~U&_iY!$m}@jYfHW=(Q&K>Kc;O5gUbT
zK6#*epEAR(@%>^sjRC9T38~XBMc>J-LgNrOROqjzTg1u(Y^8*^-^ndFZ3PgA>v6Bf
z6=pucgx->Fwb!&+w5v<Y;O?9ej@a3mZ%j0Y3P#!~xc9AvwWX>Y?;or7m5JFn(A$;J
zD@Ue`Kd5D`Ess<Yeq!&t|NZBK;>R6lxs2a()#rK<eyV}{AZ0}9KgtqF3kEivv?2=i
zkf&y}yIE;4+VP8ZMfsUPH-j2YSTEGIW^@3E&I+_w=JuX#0b-wCchS^SY)zrAbW1zY
z9Em5(UtjdPR<-tOU1?hJvCV|qz5MWU>zjZ||5EZaeORulisB%h=j5tEHujQSfy)J%
z#{$UoI0#iRa!V)ir>aK%OHvN9{>aO5-u+92(t)CH?VUF=j<ppPjb*tN@!hp;J^0ja
z9a-6UuA^L*N!h|{B5kkA-5!`N8K1Z7xql&JdIi;p{J5k0p%Hm#<c*Hb((oPV<O4r4
zdn{pAoWUCkK35yy8Ah7lH;MSl88IKs@gpw_4`to+{gwCxs!d|XhEDcP7h{TJe^-!p
zMh=R>##haaggg^N?2cygr^_)OvD#~4JWv$>tSP6eJ?m7f*dgW{8COPB+_1Hr^4hED
zO{#6C)7X(vfdM7;Z*FAO;yjheDzQ}L#yO)rFDfgI^|@!boJ7LONWM|Q6O7sfRM`{a
zU1VFv+VdadZD)-bzQd(R5D_1gdPTedA7fF6kz_z)kV>{+9LP&RNHZv_mD*6%QhEUI
z;7(!)1JN()Ok5^+jy2-xHRI20Tn0}4czS`r&PCMz=~Cs>ftH-bAo|2CzSjf`I$V44
zyyTAmItipw5ag$!(r&mRA(_*klK=xjHWqA4WrQ4BwjC{Yi~-R7k%7e<RT^$2#$C-J
z=bAB^?9rJ*7}w^vyf^$rbUDM@`d>#X@H?U&)kSOh4p=>m>={iHQj3)NntW(Gr!7SO
zEbAElsE8Ri-b`7(rD*4@)a=bh+cOnU7ziGv5r4|+W-gnQ_;QuMEw6Wfb(7W5CQpd&
zRO5N>R<B9-0rycapGtUR|LY#PH66J?Px;!DxY!q-!3I{vDWzQNg1M@dCa?L1tk@1I
zF~6$l(W^GG^n;J<W+~U3J&+=VI(UUp<vm7{-SDlww*UY=;P1i*4hMVkzA}VpEh_W2
z8}QTqDnHXJoB67rL2m~GJ;0hJP#d#lNl|q+i%%Rl#Vx|DT-8f5aw67zcO!Nh-hbn0
zvnEb{5ja)^_^WPju7^HGR42uji4wB6VuJ_eH5-FfZg2hRyfRk_mWT}2jta(!501cB
zYZrsh5($i0!;pZNctsuV>9k+gymchYLDlORT~n|RBd^tNd6(Z7FZnfLhw4o4`e!l?
z^jm0W-^1|gt6zqfqoQRt7Z%_xB|$HW>Nj;7T6=V1eo_=85Z}g8mYIUD{aaE9ABM6;
zl#<=C(rkCBu)@+wLuI5sJ9}tVZOX^s8|_rz&@PM=;>y?c6{9Kpz993Bs0++y9_tdk
z%>+i=o%If|G-de4<ejy<aruu1;qH4IQ<Zdx!ICWA$(*)s)(#0Wqn`*hE5mttU-Zf;
zRd`^a)b%!`GBf%L!1JxvBybscSty>##S96MBdY``jI5}8;C*s{pO!VW^Ydw07a%((
ztx#T&O%CUcz+JRhv_sDeIzM&SvLVHz&de&Cx%L8jb`C(NuGqQh@^Q)25{HAc>*!wo
z+nOl=MUd7*GSge-Y>Q)myhR$RH$3g%zStqXVg$y3O`WxOXT$X6&SV^%w)r14t@6V_
z)-ceOYU*tKKxT12MMH%L{#K}jWD-eyra=Rel3%1XZCiBaCf>>Aq*-N&-75$G_0J>e
zJ|4;5K~2lXquyJGHaAbl%g>$h<pMLF;;Sc6a8x}n+)FRsxh>-JBjevE6V5hbE$&l9
zeaGGMy@)Rtiuv9?1lQ-<mJVM2++-1jL!)eXRUKJZVnxKUP+F^o(=g1>cjID=FMF}U
zL$%(bph0oHh6_0cQ~6Yd0Qnfz|CXz<c8qj@EqLk>*Oz@r+J(oui3uT++}18V@I{_y
zr}o>efdw9=g;y}p<9Q)7Z-(+18Jy&t*(4uavQ*!GtkG)>7$~~$=(c$e2Kr2v*T?}p
zJ?YK$9?!V#8%1sAzaZG--v-7YFCpCc^^Dl%L7oZ=URtfh7$+%SkomWr&pYYT@?K0d
z$Q^H~MK+`8b#{zeN>^WH@y*=W-O$27t*9^LKAS^Klm8FazA~uIuiG~TT1v4((c)Gd
ziWf~O#f!TaDHhy4#UXfbclY96++B;iTW|^V=Ks!lXU=)&o)35Cet0I&hn-}Sy`Qz$
z`pMcfsr+<3xAQo5rz5WbHn}^<w4T`7mnZM~w0A>G#N7#U9VxMssS}ikDNkPm$IH=^
z%U>)$#MO<WDxII^&ApF%#rSjiHNS>_uY$cEUw{vp<ug@n{TwTsmGX*Nr<PtfJkRWB
zo=@Eh{Ci!)Oe?WrnMAIaLoN+@yKdO@<<h+dbU)>^(%2CMLL{F$OUa2G;&%IKF&Qs<
zxtDXZQlv9>U=*E;!Qm{&RMWe4q2=4Aj&>tiet@kv-pswN8tbEyofnJ!MRFj<CUuiX
zK`vt(uC$`-N|n3863h4c>G`~wU}y7j=3w4S?jJ#@x;QB?of8eESz;2!j^-8)zqiZZ
zrmV+xP12MLJ+WMxPTo_%Q{f%gd(MbIowfqII};so_g0&314m1HfHTTIeN6a^Z<@Eg
ze)c(Bn<Bu)^a=ljfH^2x(*zH9Kwfe}BkC@;-(x<6hnVis>SiuABxsTN4HKK&0GUEl
zKFLZRABZ`%xubK%Q@+MCRqN#J2BTK#hY8IBj{nt|cj?#wWwgZWL+1H*9Jg+z=GnZ7
zYxH6pBe#)g?((LX#G=7H6d%po3NZv!Opv;@B1)xLJz8&<Tm2m!y!8hdp{motOqUPJ
zO{lADY-^5OoFhze$C`=c8Rodb-`BS7jPN;KmUVZuFp(OF2z8~<wF7eL@pWU8_jhMt
zH^TI@P*YiK9$~Sh%o(UHIGH}oqe7xv5w1Oi7YeNK^DfPMm-7Low=P78pRyZPf9yBN
zDxau)$5FYclWcWr%`Ow36c^`sHI90Tzn4gGsjVw*>-esh+e&TA-Y?IEx;gU=Ty(A#
zSo~#Lf86=ICd<UPoxYI^4SN45g}#=`a1XNb)hM$~ozMB5n%js!Jt3xQs9_y^#q`eN
zPhhNuWa1sD+ckr6wxg!Z#E-wVKGx~@%ewKzyS1qt*sDP2jW$bfrVA;PjJNqIxH9QO
zMBk&a#SZjrJIC+I`=2}ppO`XxZSNVH6CbO=`*nq`qu=WjKQ*7xpR4DCHW*sPX>Woi
zHzPkoPIe6kkRuw;j&#X9+e;AvjBWh6jkV*7Hb<@$jd}UvEvKEpylozwZN83yz<q?5
z;rH8`*w(1q&s(t0k`B!B^3zspb2fZU>ei4lPV^W3JM-t|9T4sI8r#bJoMi=D!yV?Y
zG)ARVr_y9nz(n#|8F_dSu6hfzB@pe}lbN55s;xFnB0t&Z$0uYV6QjmAb2TuL2E$g-
zVqGg|hRK#hWo&&cNk`SIC1M_7`MEwe+QxWe)M<L+f{KQB4Ps)S&+AJ!wUV9gUPNG#
zTVTx?6kp#ES2VR_Tl(m0sMNAY25Mp;0su3(PhioFu=_@vHzbE~TN(cxO`J^gp_=}F
zKAt{=C+ReB=&+&Pj(^q6NMSz1QY8-2+dU!Nr)gtFp}WCoP08Q%R9T!~lDz-Hxvb=-
zgWK(LBcMh%Cv_nva-HLGhIrs=0I@SixaF(TdH2$N&w((7;%k<^B9r8)BnhQaC+kJ@
zzb5wR^T|mfG|zzm5OB(WTq$EkoP9sZ)Cy8vhuQ>FtYQh*?dtFWcuL}w|1fTXZ)CKY
ze#&6tDI%#7x+*PxHt|sq*FW}Va_s-mM10NDHWOZEkNd-zJmczxP^8}C?t0Y6>no*w
zL^Pkl8}O`NXU{4XsLvu0l5jgY1$~&qys-6=-rfxr3Bf%a<i0B;eA6kVexmab{17;<
zJLBQ>IUzc#<#*m}TA2*0z8rU1z_l4a{h7_u#QO<p9BR_b1FZ>g8CbhoD<|omP3o{y
zT!IdJ^73wqE)AM~VSgeI?MIJakCNfRM9WF4#2hFP3g4q$S52`3nscH_E^i&FnFL8T
zxq$<*ummUVsy6KKVDv!kYO?;@Ci0w|M*wr=OI~#8=ZPw&(^b31>+E(l7lQT>=mE-t
z>U~lq0AD2D*p<4S(h5lcruQ6?tz)5xkuzx5y%!xdq3-M#)Y5@`8Xj{pj(>S39Dc|n
zBfwT!IdDy&y@fw^ZE9L*Ymb?MVO1EJ3)#UN1_6_q%v@FUn~TY2Q_70Si0woxNOUHj
z2WcT$wfyTDv(t&YM}&MbIXfwj$&t)R`{MF!FcWR-O({Pizrq4=yn)0cGkBUnDC#ux
zyh4HxB;AeV@@~{=QeT{|;fy5B@l1b4Z#H?a&RC^rAg89brERA?=^~f;s{Fm!e}h;M
zhzkJ0k<tM?!QFoW_qg0=j~0lK{J!;e6XQbr?xcds`d3f20L2#obsPF`iVIqcwHpLA
zBf;a|-;;SyT8KzG5LEs?PXI7v^v8Mbg45$bogCXL48XDcm4eJZX5cwKa6KKxT%B)7
z1Cw>xSNc#%TJ8DkGf(1qgW98dut>(<Z36U%=<}AzUx4shQbmK#jE2;tE&CEhW5_T6
zm1YLG{7chmxt{!r!pOT@)#qtgT&E6)c8pDj?|lbs6SNH4wL763yEPgaZwftiH29`A
zt1kTx)iYDbt?*f*&EUCx=btCU<1e7Q;~vLp?Gc3ddHSvXy}%2eLj>(`&;A?$ntvuk
z?D}B^e*sGdPw%hQL{J#KB;UbopT%x~*GGQ=H9SrfXlDerSj&sxVJS9roaf;eQ8VxN
zo>>uZ^XY$X1K*fdIChcKfH6T94x*kGsEGnt`QDO^e*rkFk@}V{=!=p?aH&bUSvb$`
zQnvf?+UT2CvTPrJ`vjj?V(z;i8OUkrFIn=&tZ0qIic$_WlVc+=pv9&(gN-@gQeELr
zh>lj;PDIU1l!tXcAd6~OAzOH{#6xZu<N1(b2ERthN5ebP`C4r_(>WZgl516WeY*?_
zD15#LJlA!4NmSQPx5~4YooxBx*MFl0`L11;<ISYUq=w1tHEJ(Qx>{h4NOFvXQD+))
zGWUvn*oPjhet_OlJ+hvSRiM6r1);8mKl4m_@xsE9X1Q4<)FLJ}=Ol$b(x1LNI=J`t
zMu15QwX-a%Qx7Zjz7M%k^9zr2Ojq0HPupI-QlQ1cJJ~F0w!2gI9NmjB>X<023UIn^
z!=2~G3iZ^cNgXIz{R7i2Ylkga5a5Z~0EEj=yva+Td?*mu7Kh9^PTkkulV;kUR8tG-
z>qD1%MeWBEfm%g`zt!=EJF4Cdq9;Dj@kjC^_UUHbC$&T73W04M7K)#@JssBnQ5UYp
znfLU{y`TpMifg!Q7MWM5&A`#JK<@(@=If^h?b-PF(z>$%cGy{^%@jek<5;wV1wF8`
z9zskSUa2vGt0R5HJfML?5G$Mykz|7-bf>&*E3}UYVpl919Cc_*enjlha++m`#I5ja
z&to|+T(40aP%M#Sw?=3<O<>nbUh8+HoKi%1U4afO<ELwwv5TnNK`;rZlLGFVrZG-G
z6~|vdqr1TbgrVJ+XXZh@-5M4Jj}+*C@*adA9WdQD^Z_sU5y1N`{bjzSU2%1=w5$}!
zkt;Q2(3zbeKefFleufLH+x$bbT3daPG;8+77AIxoy9MK%XP(|8Xsh;iC21g;Og!ZU
z)};1AszI%vrwN6HSE!vGew@SV@9_7Xi(56-g=VgFTXKbCjZj((Dw-nX1cM`m154=J
zmbhp~^)h8N)}3@}L$`~2xsQhulW87UO+{WUN_JuNDU<#KubLTn9gJ{!b5ka-UJD(m
zIU6L02+B*-JiHlKuZkzid)M1&NHiR8;CQ4_N#CHyZ{)YYbY2qpOAFivNtu>;)GHd^
zB9kTq<&?#-*c%o%r_EV=je7D=kx>&>6o4j)2i!VQ>~CGM*0g=$N+>mT2a{A<G?Yaa
zIx(lhpr|>X)V2?@-qc+;<|G)~eYv!;{lMTO4dWTB)S(u4%#rf9Twm{rzVk18V9&MQ
zZ=BcBoi9CH2r_z`LT!|r7FOKm&NQdVYz)H`rU}np8tEI)6ASyq?+C1xA|iL)5}R@8
zSNj4tHL{mlvvr;k@;)fvjjfdW8UdbI-D}0=a;V)7oUdZIT4Gz;Kr(5#st3&C$gS^+
zg-ymkpZD&;RqAo^7xXFZ?^33kXT|e#$CUCGKQES{8aQf>hP}2f{|TUF{{4{R<WSyQ
z*9D}p%$#e@m*DEX7*=!qrD7S(?OOc<uJlWIx68Uc2a%|~DG{<o6SBCaxV-`gDnn@@
zE3-6X;Et)L$568{7h^?XL}6+0r_o>sKF)qrH<u`ktT3N7lj9m76+!%BO4N^Q;t>+8
zUE{A??MC~CzW?;cY57v7*nw_gFdNq6Al9+FC8xJ6_&UNme`B@v7Jti#k%gD$R+~H>
zt$C)yO>*6ZH$`}g<33`XW+JfM+GfLa!d5BigyX0CNsOioXNWqS9k2$x$e$^+s0GcC
zxnMuNRvTm7Qf-~zmzl<xkL52~|0x}P6!lHRnF3;!<44QPH{|PvOp0A@hr^yA`eU;k
z&5$Myatks%(2!QuAr}^yWfbg8?`d$btZeVw)r?VmMR((SYOlICxq1p@JJM{7LEFK@
z1*$wTwUvc}3{j-vGPI6|{qUFH=KYwB?e<Sw>Y0lB<hcfqnTOIn+NT!`XQ9)l*TM{w
z!Sr?I^U8VcIJGoy*9J1r`mir}z}+6+V-WVQz3&;{SjAw8Im082M>k{M#XsWW9FL|n
zS(J%`ElvGBtgU}Z^b0E3ctX;?^p?o;SWS~*mkwD>Po_5K&nM>^6nHGPx!)vcvQE-R
zF@X2KG$*t9)-_}LwTo?2<U8CXzCK?;Q8ieJYI#1-MI$j}Vm~r2z^xYvyEI5PG(lh^
zwLj)^-Us$5i?|GQM=sCd4qmbg_nKP94Dp2ZVTC>J340%2thp!)lhbC_1q!=#?7K1x
zDOYUANIlFz_Em<B@nZG_Nn-9iGzjxYHg8beapLpTXoN+1>KD7kX|ihSNHn9G!F_FS
zt`9`%l=S!feLFXq<%R;F_D3!erq$IxpA&#4(G<H(?+SGS2b<%;CrQA$-1iYJ+MV2C
zDDscK$Up7C4_CqJzmRuRC=iMRnW0ZYJW|C)r!ESgMyf8I@|92A=^ya(399+kTbcLu
zX`-fi@RCD+vXl|osH;SG&nUSUEez#<i{5taM6Bsbme3n?;mGpt3t>L{Cr~W0U%ou)
zgz&dq!CsB><0H%Rwla}}))lrLw~@~>O(%XY;GJdG@+a)JD3hg3_Z(Chf@PM4*RSeX
zVC;)Z)wRF>jL&EYa|Wi*7{3VFEJXtwS2|+M_HL0ToFZy*2(dVx`X1V;kH}vDlpm41
z%nKSkp=bnYOo%W}v0rxt3OHw^+RPKaA~AiPhx{{FSk7G%&TLDFNFZ<x9`7>YYG!hI
zVNh3o?HdYTgG-9PfClhWslIp79Ys%y9AZydRE=H|M>NCw@=6Lh`<l89bzKAA{}*tA
zpx!N~Q{~yZBKMNq!k#6pHJ&>VWaq@FC$RTq5-waT1)hB>p!@WE+Sh;h(yjl$gDDLM
ztQIhejh|KSaeDo@y;MR;yP_A>H=o8QVq`qDB88PMubtMg)iV1RKPtuU?VST+6%WZ#
zVQi^xb5r~C&?@Va^Dk30Pp|R!4KU&O?7`QsdB)$q0C7ibYw0xI4_8q_1<9JWrfIkE
zm7!HoS1ytN9RNw)0>i@>U7y_l0*+4<{{uKds27bNB?x=!Sz*{3EYgA#K1e(j^39c@
zzzanL$*_+Q2hB5Ukj1b&E_*ph-?0|pI(VfjJOMPq4%RN-)gltf!M}j)H#cC?zkopk
zguvh^qVV}Ab1&=81zS!k@x?N)u$`ldg5j{^A0K}30GA}<9aCTgyrtfDx!D)yzDa?(
zpl6}jwinBu{Ohe&GO^D&a-~g1-NtRRdUin`yY~-!%Mas;dlHKE9EfAR2XazxC56Y$
zCp0U6qsc(A7b^P6FQoMe_4l$7-`_Ee;Kp1H9yn#rhp1Zcn#l^g{3Lty1T#w6pTjI`
zVE)gNf51npz#X-AJj8GW4Y!i`7a(4zb%$8gG{+s+XA~E?C&7ONM^*XY44Tj5^N%7J
zPkbNFrg_$9Pu9@uO3A=oj&~}Gl*70JuNN+9Su--$W3M(_FMEL4UZU9s(zpu>uGWqB
z8U(OJjJ98|WHOB(ru=IxBDM3zl-u@tQKEc5hDBWm^^IWbo<_D_cCBGLkJ>5+pVV_L
zEHJ-HkqzKT(7nJTmlkPbF1+j9)G?@8ax?0T4$&0;Eg$L|s4!3KvKcBkNQ?gUkN>%o
z?^gA=jutRq^{(ReU1$+hAnz-9t*#^!>)O25megXN5DOR87$e*b@QL!>36#0zu4X-5
zR%s@8?y6Lat>$f{TMkJY&ZOTp4JxUJ;#Ed=s{%=-+iYCr@8ORU*ne0Ro`vFf^(pSW
zE00nF?AkN!%|L0x_V&aPYMxs(u?WQ*-1bKFyjiWv?#6-?4IMdJQNVY6LW#E`THJh`
zpt`Mb+BUnjHo9Mkg^b9B=P-c+#|itbR(Vutneqo=tyr(G4^f*c@HWsGHzv|qkL#4y
zA8sS((0nAZp9?hM50~;y=k*<CRMNGc6QX_U1mArEOx`y!v<=GTw#+{hYb`JE7H)*a
zFyXUNuI(~M-H^;L>>cT5cAuk0UR7=7cA(B18(&w7-B}b`3Sb3-MXJgztVU?RrkyYV
z1e5%49s3PrE*h_Y)W9@u>Poy~R*AR7`ZQeZ@9mWcRyO&5n0jJ|3yE%&Qz-kf+KThl
z2nj+Be)WDJqz|t~t^9Ob;raXy3;<75{sqL{0q;uwz@mb;U}OGf#rqeuiLYC7hQCV%
zmt;x|oUKu}rWjxxRmtv1=Nbf$0SJyAuYT{w(Gb)z$rX608ocvjw)nwX7a8knP2ztc
zZi~w%)hC60RY_S9dDJP;mzkwddWeIrnae~#4;0+?op3r@%e35MmddNAmTT*RFqF_T
z4(#$_2>8q8{Nz>hXE2-ypPH2=<_zL=CxtZphkU|#sCZ`kLCq{abHU>cAVv0PD?9yM
zDHj1h{Rb%IGXg*O2SfPR<-#3TtM)GqE{=hsQHt|0yV5(HdyRB4wh{k{d2sO|fs6Wa
zgTn1hMA$GzSL<?ZOC+B4I9@7)P7_K^AL<Ie=`wuVQ%yQLn#dXNt^h+66+=*UO>y65
zVVPDmX6(iG`^utkzXUnciiq$Pbuqkt?=9x0Sa(l~>m>M9e|tXM5{}UG<>OTlRn0Nr
z7>T%3Pct-*D!Ly1Dlv-w!55kMHTIH7JlV+%=dY9B{f?_OCP@wGzl%!l1C6#>Xp-|H
z`bQe6lZhgQb~=p*Ka^tJ<m8z?wXL*##ra7@w3l7%ly7ii%p%4}F@kN`Sr;UmT9g$m
zxcxn+4f&B$bE%~B5+Gu{oT+_TL+Z?=bi=tSsZNN^-(f)(<8pIveYIsLP}LRXsMRc-
z3pQ2Yc$~U=c++GR=4y3Q)1L(Eyejd~X$#6{OJ^M7WAHxOa`%R&(KNeWOWoc#Et}kU
zcn#lZDCLpRTD^69P$PjhD1Z7f)IoGpT4T|hR{ISg9A)X<_A|75Kn!OBE54O^<A6__
zU1MTM^>CEQ+B}MHo_lY)Vz<HP^L_LKBxrFR-3c?zS5G6#TDH@|!9Tr^S7b%pe!(Oh
z1rT#&pp&qfL67;q(x<%$#^I#)z5?A?R>r|ubG!l^?{qzALC9?FLEA*6`yHPQyUp4B
zh#FM_qc9tPHB!lf(VY9Y^(%yX;_cKwDjH?w$<Ji3Qn$R#B&Ut4Yu&zbn3<b*3PBZs
z${-Sr*^28n(Mnqx;^Z&sVZOh&ASHhRPBY*LHr4cg7xVq*lV@MITXnXQ(?BB5Qxp^O
z%s~A)AyC;-0_G2Q7JcdOJ+z(<k}`XL0qIn^@%kGsfD_8dz835|t#+EffQ<UJ+ieX}
z^V{saTa?9xl$%qXqd#Z#l*8OXl8Ybt-o@%PYed$yk;p;1M%Ukpr)TG&T=Zo4<jwO*
zi%FlN)hOt}Ufzb-<TlQ{=Y?`Fv=Ey2Nk7-c%UCeT{5C%jcrMN!GMTQV4R&3XFIiI)
zdViRGS5rY9m@wC;i``c33)JOh4#>;f%vevAw6%w6^l;IzDE-md%az1o0iNYZ9_R+;
z7VBHiTRVg8F2^MJ8VU_h)S8vO8I)wdTURp|PWe!}1xrO3Mjq>a>2MK@UyHIjUx0&>
z@(QLr{al0raQ(T~R&+8TxMIG4r{!Tzy%0vx<B=wZ(-gVLi^93o-#~Nd0M_0SP*yDx
zccS3pyJ9D{Y$vVp)D!w$bhgXk=8tQKYS5La3#+KvB%!NmPoQ}-K-*(~GKer;2<$~r
z6%JL}>xD`=F2+r%AXnDiC2z_KN?GToAVuf&<>#QRV_;-l2UZ--w^O(4Aofs(ks_KA
ziv~(uclfBOq6I^K3E3Y6Db+O#69LX4Zq|Ht1D}o;6SD0+?L<>tIr)`*W5;Fupn-Vx
zua*!nRrEAY?~&1m`FvcPH(JOz3*R*~Fw+PG3jeT#N*PPPq2gy-vLC#CjvqYY4Dk}D
z4fmbZBg)=iK!zN%v(fyTGd3!GF`i=o$Co0D2clseDE%ofQCUZ8L_lrCa$}LPYIUeP
z%@dai$YptkVhC>Ow=DgEHwl7i7hq^$SGSaAZ-Yb1HMu9I=!YjV_jLMXZTeZV=jN?a
z##TLxjDvVIA8crf$#<I{NjPkg`kJV#dv_{gUu#KUDo}IpL~Sfq)twRR>nscy#k0C%
zR`=tqP|?q}sPiBScuPDy!c<N#dDx2EqMNme8c1rwj3$w^VJ9V<=Wt|)i*)+kt)dYR
zaIPsNu7$usD`^|*{-Lf6-nd}0bs&>w+oYx~E0#0G@{QAdJjlpF`b;lAquQEORI4i^
zO@rCc9Y?XmU!<UJG_Y)fz(SE9|3D?c-AO1Vo__R<%Rs3`Yd1(U)q$Hy^!n&qZO0>*
zbTd1Lo}I+8aSChM26Sr0Ymjg_oT|%&^xe3DN8+Q%5(Vt$FW?q|gjMPbozKo{F!UE6
zercmeN!{na*3|v}2O%d?dB&GeG2_CWLlC{Zzzy}qu35Yqy+-7YctoCEBBA$5WYU?7
zJo)WEQs0w^OR84(=u)PBibM=qTr*mB+Dr$JnS=|1WLZ*b_^~%&dzudPNwpdj$}#<#
zpk{kE@+;^q1j*++maMnJRdM8-vOSL!EX`)6h~h(ohU83n&UmIDO|CSDF9pY$jon<%
zdc>mc-WKPy)pXQG(#L0;FT~VwVp+PqpBwz)wri0*ZLS`LTsc>tL7v-lnV&xy2bzB9
zVD|Wvbbz8A&cu4t%K+pYjLFO{?A-|K0}5699y@B}&E_oyguaauohuc|^#IR8MdA-H
z>fK9}#E17yY90k*qm7X5_NCTmNosiJGH=`pG`F>cdROhr`(D%6(Jxhg6z}n|(v!*H
zzecUjW@GuG8=4<-s}bdTi0O`RH(RWB6E{1qoIxEg;}z?|xh6Zi;*wnE_xO!jrfcTl
zMw!x{CGz($H4?rz;K^cC7=m$sAq8%ZIRO@z<o56$*657=5`Dp>q~wY#;cm4>pTfi<
zC&fc&B4CS+=2@wH@8ap1PxcKPUYa(owiS8i3csLucv}uU8Rel)9fms6NDO4>0!+f4
zN6nV>76X#$W8$QK+K7)~i5|!Y5;JjzX(yiKRKJbC`doQgIiqGRFLZdDq`PbS?9AUd
zHR=1P)``e_Gicx-&YNT1H3cD)K&ZrbY~6PX=<<rT$z=SI)c9NElAZ)cl9C#lAxxqu
zF4T4G13C@%w{%aN-q^q=+pa2_Cg$1kb<h_~C3Pwc*gXt68m*iCv9Mt}9~6>*VpQJD
zUqw_Hl)C#T5qFadJ61?Ss@q1nJV<2?FT5Vbs{^Eay`ykdG^fWa4_y{N9w51Y@6d0u
zcJUVwRfY-&rEv6!I`mvIDIja>Ue-OEON1#1#W?K=qZd&OrL$~)$yTMN4*W`nr!1f)
zIoRq#ar_0`SENw7pc4gM+%=`RoCq+O{!n072@G%Msd948?-;4}EC!oesC>hsO$#x;
zCDXab9;?}m9(^MtFI!|gR_!b&ea+}Ug7=a{3C~c`<AwH3CTtJ&$<bg(M>&ecU0!=u
zxLi^(1;S8{F}6Ct*#TiWUat$%QNrUWvq*aJ2^slbb?v-SNDBdrmjlm!SG<AfRv(L)
zIxixC_qRvQ-yW=w6LuHvkOKl3*Zue(e6^n6!-C2;6zae~T4it2Gt837&sx%qpC5Sq
z#Ys!%(0_qilG)kmo0I%mrnsYgeW4EEbe&0O1krTcbtc>F>24p?Pt~7`+=-vApS?kW
za%bF|fnTRm6sBh_dtXYLRZTL67Jjo7DZvmcZHdNLjRXh5i2My}rnME$lGg5RkL1p6
zD$Cu*H`S9Ji*<#LK<#=ZNO>}NTL3!|qg-JR^=3GF;&?acV8<g&rSX~MFQD9}mpz~z
z@4&IlmBMmnTagSoC_z~O3H5aof)1Rs?ms@&%Tx)y8+VDln__socYG5PDNIzucfqV1
z73?Kl<Yu4<y_GYcGYjeJ`?|~v#a1YR6qj1Mz8@=fELy3uIHhG<*cS%eAOpOa(#_-J
zlQEARPAf}RKptfe@f#}k{4a;tPytw(fyy}$BLQLAzK5~WjeRG<!hk<tii!w?c;6bj
zS2#czWR_lzjd!oTgNtq~5gi-z-l|u_-s@)5p=MF~M^%9MdVM%%ybK&s76V*hA@~X0
zzES{EGk)3(zotV*htg$NPcpiONas=o^oC#~H*n2}EwKs_YcOqwPZ=mR2V$YA!Obma
zdfEL@s~sOk*%<Os`KZz1wU9R^P=CI%RkFA~yNHvg^K(GX2@%<+vLS~026X7s^0=8j
zS#Rw)#$^3Al$UA9TNHow;4gp(E@$?pS3qm^yRp~g+dY^v>VdweI6dC&duy_}a+q!J
z`!rSz<)wvOjCYm+yuei>sfTb4jFGY4<S-6?+BB#ILpCj9<9&0=tpHi2oh(<L%|Kr;
zexY*0Aj?43fP@Bj)D^~CRIropx2S|G!N@yIZ>(tvT&6H95=vAXuUMpk30$fsu(=xh
zjfGk?{da|ZAwOTAFDSp>JI5cHl#Qos5%8wbYM*3JxRuWwO0`*85<@C1qI_ch6}5TV
zT7BXvC%i<;5z)WP2M%;EPg_bHrjMCkRx7b#Jf&R}Aam_o+zT=KxT+gaFZ?~wCHvtP
z7+T>0Va!YTy)s&k-s{m3Ox#g(THn*piXJi0KTj6b*y}V3^VB#0ts^XK&|(Fiu&D&*
z#6mo?l+Bmsg|y1<0!}(wjj<dk!>z-8kSk|)s~K`q)E7km%i4&%5jzxbFkKGvdp%I5
zJyjFyC8(gbnMCj}9<`vR1UqRn%hS(IG|B^zp04QCSK8Liho~K-X}8l-7!cZY#(!zj
z|E*0&yuMgK_?T6ZRQXiGKw4%(0m}9&GlbD3Vf=<<dQueHfXhOt&QW59DP7Wb=7C1}
z)xd6;`zP{Q37w+yl^m;JF~7!ri<rZT@(`u_V@%qqWggZQi*||ER{j%ny0DiRQr*}x
z^UzJ{I9p*sHIiub3jqjiG<ECOq?rb71m2`N^Bj)>B@ofZ?18CF80RomRY>6lZJ>Tj
zX)?<h{DjGDt=+*Ei&?8PfgGhIC%(WI&R%PBo?PrZfQqOo7UYu*^7&bwCwi#YBkyzL
zA=~OWp&zWS-dI)>83|*WYxmqVu>HdDsw3saT~3H4H4PI)=m!bTjy*<O+m)jI?1W)@
zId1V>p{&W^6`o$|5L%2}Q1O^Irn9<oMK+29zHarR^g+tGV>kwrv|1*JJOTL~1O4mN
zw;%$pb+j)W$6NT4D!=E-Ov1P8`N<BoT7k%wLknQT-XGG|js?1TjjJ^LfcY_{T<O7~
zxXRp#>q2D<I&SB}a(v}(#h_=_0lTd;szas~_g`UQI|+N*P-yeVR!v+vq8^3OHccP=
zq3%3pN;4z06Ft<>ZKlZfyf?e_Gi+1=#w@6$E|mx?Wr-V)PxG_nsHw_s1w3d)i-o<4
z+&<b;@BPk33&FAc;HUoVvW?=>dK2~WO6}<>8NF51s2GY7c>#?|r<+ea0&o6tQ1U{w
zWdJfV+6*sh;|^s2#!u5qYDwz}yW&BwGqiL!lnCjfsw#eUZ$vgZ6G1mlPF_3{6V>>L
zMa{)Rbs|38e2P6<jh+YFpBJi24I_v~W-OQVA%z27=ZL7x3LQ<|A7aaSoK`e6to{%g
z>0?Z8CH}mM#J*MX+Vj;K%=EO_R$H52v0O+FTEX8xt!wbWiwm9YEW4Bu3LZvmZKHPw
zqFGbUSxC@?K;?c*KZPuWcUB>v{VH!a`b9yCu2L_8D@L4#=Nl?5yBhsCf%MPHM%YC6
z4WaH(qUcuWM^&ZS&`(G|Y4UguD%UgL+}X@6yX9&m+nVX#+wECCzmVRVS*MV>9F-B&
ztP^ZJ&g0ak9joF$93VOYCNMJ6S1nd6PQ7;)L3gjHz(se<u*efs=2jl*pLp?2oZqV4
zG<uM(maV^(6G={*)`Hn4UTukJ7-K%mVhznp)cyUS)6LxHs3w{O@SrF~xn}bne_1~<
z$n%jg3A@>q3Mh{$DI_^G&Wn?S#{}W^9>H$8x++Dw*pa4o7ZsLrRIhy!(sc`(mTOwh
zk<Igy3o!Z7l|~+4vvr24b*H1}*Vm`D4Uzqkl)OMNXiD~TYX|SDEzqWrL6v~kH{r2=
zF56Sk0)TD%?!~~%7scZT(kJo*Z0ZEOS@zbiA1L!5>8kdQVc<2SLjJr06eFU<(?0+G
zoD4MmV4|zsG!2_}tm&w(IhbU+^Ub>b&Zz<WlU-E7^40B;SbX3>rdY(|kGYR*bO3O@
z1?J8?94?D+aHY(iM@HFJ-W3eXwzw-355F=G!T1=yY=Sf6q0;&zXUPrRtI@2NJ9I9(
zb8I2S;sELY1aPLWBR#!obYRx+6VzL->THaQ`m&pQX@Y9!f_fweET_6D9C-|b3WrAQ
zjJ<iv(IsDsWO`+4+baDqc_dC~I7t4REH>UqBq;Ny_)(7j>B>?-hCkD8JIY9*P63gq
z<oyM-M<88n+p*D6oO3BDD~&t`(|W%1Za&LyN~AbT2^prMO|*Kl=oo?~a$!l`;I(9w
z$x+b-5p8=;DTyyQ^jEEfKFz|5T`F%ScVxv0$)cy<lNWWwldFO+N5NF`IX{09?;wC*
zq3P#)yc}*a959nJ-plBpFD9^u5+DpMSZWJga^{om7mN=OXFOegC7ikj;qPJ3cleYq
zRGEGvTmg#-5V8NXlCyZXUk+b9&jS;m);?}slwAE!Tl#<ZAN}Y38#HwkQXFkXU6yXt
z;8SMKpHlp&uaZc()jrvRgA5+`)#`rO$5TdGqOkrCue%DlszU2;MdU+IEup49558A5
z`uCEr^Bz;mZaD{MtAt+yoNbxtRmQW5$Ty<Uf0rao-Ouem!Thg_Eq=a>jTOgJ{>S+A
zf4h)><rONc0pi0dEh8*5O?|7mA~l_ro_T(a;LDQ#G?^!P_f-D?D(1ic#r2>_BlyS?
zJyk0nx}CuPA*rbNRuwy|T(D&{pfBAsQ>F)MB>l#Y!R2$s@K@iN&Ek>HJX)usk+7Bi
zJ&AP*Q54mBwUFKDaO$Tm?G*hCz8`%G>cFWQtJu*-3laqXBnINPTjHSK9hbk_tSo)U
zqN^(jq7X@AC=*OQu+d5Ax<P`TZWKgAed@#u#cxGsWIo5R$vb9PK1H0xejtfPxHtEz
zw<YXlYD!7TF=gU|jXf^hc9&}S$>e0%Hz5lzQSFF&jeBX!FN&7!Qy^NQ#E!lP8Y9fO
zvRpDfHy@hMUJk`-Eo(QT<emW@uPaDSh(D=*s1pK~@urqaoRC<>Zab58R&sIY9Bup{
zI%~csa}$81n>SP+D0AsltfNZ^L>|eqSh%91kcJl*om8IKifZPe+=_J;%|^sM5<Txc
zP{`bwODI@>sJs3P@W0QL{E+a_o5You;yuyizr$qL)eMr2Yps`_&c%|<zA792EXi_;
z7o<+QHCMW?EL?Y0ZwuFrDymr4<57l|f&@vg=m+_*0&NL5-mgXT#Xe|o>F9rI3}0UC
zZ<C*;4Zo_;GI>6FuDjOa0wK%)1<VzOCFvaDR=Zm(yDn0z8=olm+I~p=n83q$<ln8z
zo5&JL<h}7QH=<0pOi!}_xrTn*v`+k<Y(u3*n0+YBc%59(5%0*_B;<@=tjvKpfd0Rl
ztW2qt$h!5+D}D743<QQ|VT(^%p9sC%U_Ut;_?i+p6vc?Gl-Zm+O@lB<&Q&0*!>~%r
zb-5c7p_~~p%=qHuz8+xhQxWC?C8rjXdt)`1-v@9?AA`BpkvTh!+hm97FDn_v;6kZi
zTB{1kSb?Oz_pZfPk``V{h0-)Ou5F=7575mT`;0cGXJ7wKVfN?tO$0^OtD}t@S-9B&
zkKpaN-p+g=nWrO&C>k#qEb6tO(dtjX@Vy9ku4wpY0KwY72FyhUBkBjR>R*6|!c#(x
z{De5d^)=EmyYFNzu_qs^7&IkW6DGKeyrb*<>@3;lVHOPBjsa1eA!2G)xo*CZa?`H9
zf1Ays8*`6q6`J9c>4Jo4beTJa1m#?1H#0M6oWOKP4Ic7db2iAwP8IpiB*hids<sI-
zz}5deAG=7aTm@lxR;mFq-VW@2h>dNFo>Fh&4_kSsGJR%T)AB2UIaCTK7rR}$A2oZo
zjlI){qGKA)T;A>a9i}%DS^Y|>$@d!oAy+t4hOrfwDwsLCfAS1dxK<8$(Y$R|-ANvQ
zAc8r6EhpQhJ-f5BBx`R&+OzX{Dda)`TRVBRJEFopA$w!gSf-wkaM5e-JORC}pIEp<
zCfn9pc&AI$8hw;2jY%Ru6#mk}(uZNLE=<X~2yrz1Dc0MPNJ$^mriwG!w{Gu#OP9Y^
zmeWvtL&L`Pvztl8S3;q9YpQrKzhYCGE6VnTTedZPD^IpzLI9!tX?M(=YWjnd6O$W5
zOooP0_ED59qykDo;g-NW>EO&y%Rd+~gGV!MYunWf45?38%<#>3%UR_hO?;vJ1G!>{
z)@=E$<fbsAUZE+0f^k6IBoFfu+S!TKcETKHpO&g!BQZam$rFfv<PjL!RyS=5T|n|0
z0lX%&&*%vBVDFat-gwSd`EnS)0cxQvK^E7O^_*NGt9%z)zN{;t`7Oca4X?O0Dd=Z9
zfS(-E6o)_6n(B}C@zy9+KKFMhfOuTymvq^&O&z+gt(i4zPk+wWz^DVA!i`%<YbM_#
zwOo6Kkqx}Tv?cX1q@DQg%N+WrTx<Q?CzHsgZH($$^k~5Cc)4B7-E9wSIrJ}}Zr5~R
zsOqHx9F?d@Id&G`q~Rl*wGGSZ`_)}ys$?u;TIDhzvL4VE4lLF?dQi_L3;(6r?@%`V
z;v%9Dm|aoRLtzFR@2;*4fwF$-7creYxJ?vEq6#kD)9vsT5-ZgKHJip`ZqbD&(s)s6
zpbES9neW=Lu$tO<Nt~}XmEILsu+-k4Qof~T&LZ04rzm$1C8ASM>h%cjVJ84J10fmH
z3N!kzQ5cqxink*@AQu;|xSkocUv6gTK%po4bsLX5>;O0BXI#O3l$7+vC_0j&WzHgO
zt4M{N#nzHJD&ggi8Bqvdo;>q(JEx4|{#*!ePbrNx8885QICCuYaOR63ubn`D=yin4
z&Q5z<eB4#2W|^fhQY5fkwR-lNl~WDc_prH=SeQtKd=MF+CLmMF8roEvHE`+HBAX!A
zwEFzP`0$%Q&mS+X+QxQRTSQwPt^(IWmgbi72-uz8P}czQq*_b?k8UWQFf_`%q+yKb
zvm>kpeW9g#ojY~IY+IyEJwKbaXshAvNugdcy{u6@jpu|AYwL)fW}#<Il=Se5&lCs?
z*n#e!%{J7|4B*^!PrXY@;Blf$gF_k1ul0BqyVt^1Qa8OScliZk1$R>tzP$O%vUZ;j
z8lfbY%$a>DHaFj=>L!OR7A0~^M+~RS_L}Smai~5$WXx6q5dunl9nn4MTzN$#9|;X0
z6bhD!m4>aBX5G0y(tpGGJ<26>i}K3mGZEliz<KYyoE_cmsHfvRM*Du=m2d7srkML%
z%THxHsoz`384NahFElq^Q?C2H7V=+g(*tg=ZX>L72!lqT5nOlLuo~u|``P#9Hj);{
zN+)IJ0Crz}?6cgd!fnURzH)5yHw(mBXLq2#06KK(e+5E<t24v`y-?yYctqHpa-gZ_
zOV2U{@y}P853I#&2oJ<|j%CM9KZ3-j12$X`{^8dF>4YuC)z|q+?>YuV^04&pzjTs2
z-|H>4-AgC1&hVXRc)Cn`j$b;UUfWpfj+_6A_Mx))B@Q9+A`x!x{0oV#L$|96CW??}
zrXaXpy1F#h`ZxKkKFNY*JCUTElVbu|$HWA_APtDBi4!enRj10nxPI+~v+R?=mSXL%
z1dy!vs#A%G>LMTMGwFP`mwZj#Y%5>xUHu{zUZPCQNS1>uqhai_-|O+lt8#-tMi71X
zi3Pia^?UcP-LpdZD-WiznJsoZbN$H{B3Ll2Ce+MA3r9J$btx%v;FlicJ2f&Lv6(CW
zIWqYn9Sfo#Vv$6i<U!43Dw~oaY-eE$tGt<J#61La>r6RVKObOs8csaOTWbOD1N*Wd
z^K(>#F`tRuW<Grv{2`CMB^H;kVR9p+Tz{2`>K3dUAXjLiGKzM<*hTfg^%hg-oG>`H
zCxfNFCJEV^h-(ZRMjg|?DMb<C>?3>^RjhNwWxj~lH8eHan&1}q%7yeTu=&Nh@l>kK
z&1Y!cpKb#;S%Ya?>*Ld_(vaOoFdNQKvFc+YJB2M(9$}_O$(Bd9To7IDqnNwxJZg5O
z<T+|o9r}sOyu`f8qjOtN*!Cd9%gs24Lg7A1kKwysgf?Lkf#GW+us1q-&yN<``y1BQ
zZ0r@vi&JRy>0`K`W@<QYkeVg#Y&Wm#FULZ8N6cOz=J|pInD6@jQ^TG8`JyoR_~1iL
zap1ounn3BOmSNdjfjqva6UN*deTZ~?I3?w3(sPJm;$hv7eBdv>w7`<E$4u)W#@x%q
zDHTvkgMm*|L|vw7G2fg&wWO&%>d3JlLuGuum33SB?JoBRsPqmb0Sl~^#cPm>8T%j8
z%#P057kX~#tt0Adpq5lKuqiyk@nJRnrih+QAcg;`(c#bz7tROsQgk29`KCeK)yxH6
zqujfyFr3VFkyNfI{AX*r;+fWli^G284<tJ$Z}SgI+Z}pWNnTXgY`QcxCAb2FD)g$S
zX?Keaiqe~LCdKi3{>@_le|g<;5sNp%<b?og6URO|W1gw79!pki+^Kux{hoeQ1+L}s
z*+p&A?=vGtwA<#A;YUc!kNF(FxGsrPM5(lAzIMD*RaKJur-uP18JoO5&037MS|Era
zXhZjEALLpzP(b;=lm-8k3dl8iMcpxH^0-)VwHG?7U}o7S`x&ZUdmP=iDx<vb*P|}l
zlPkG;*4qJN%zi22A8|1Wt8m@@te4X=>cq?0X!&gjC*DKn6nn^%H=7wUR?6oEKcZ4j
z$tJNICqEJhl)ev=`ey6^6&APR*E?w_a4WgBXh)9Bi4B>is-pZL43hXn8?TwOhaY9x
z!;Tsa(nd&p2FL9EXWrWznR;r=vIKit>t3Cy)6@mAg`i#Y@boS_$mn92Rh3k70g-(r
zLU`yoC0b2Xxpc(snsi@X<6|;at3+Gq{&-?Uv2XtPv@eutS?p{@-)@x2VqDpDQ|j`|
z#=5&=3R5FRu7etOB-81Q${zcA@gq}jZl6sKy#+}Sh&K+mka8VDS7J^Oxo3I9!DaJh
zGNTtHLm}eHfdNG;!0bdOBEL@0S?(`c4AcTNhZ}C$Ja5(;UY;lxH7xhv1$vpXO(e1}
zh5b3Wzq%5BAcS`O`SB_u{st##j6?BoviZ>Ez(!_lGLV*3y|=B@J;>tT!2vbj(BSZ`
z_$IPY<%Rff<YJnsUpEzPYHuu0=o{2628z2=wDq1}QG{OQ)i*cjov;)LbySZUk`Mr$
z-g+OIUL2g+WVjyo`|dN|h-f5vjW(U6Jd%>3DQ6SOt(1==wTTNvDlH7v2br(krUTY@
zD@5H4;{t>-_z%>tiK*Z4ZrL4*8=?Wy*nR4z&y#JewFZ3FNMZ+VKt4^wTpfyjJp87X
zM<_DF2fo@7<@=VafPF>=q-4~_ywb)|jvVz9{}ZV<aYeiQ`13)^uOFsYIor49a<j0%
z!JZm!ZUe2HTX<HhF;5AhIMta0z7O;98WtNs;(EV`AD$!%h5Tcvti-~02w!Q`g|iJe
zHqsH6jn#G0F&}Bt2m~SmxudyPm%cC`2BoUH7aH`{I$_@vU3D1LAp%!6<b?3^IeDDd
z(Wv>FHNv)}G(g0+?hh=tPjT*T)u*p*9Dw{`$NiSUn`MVENAS$~)kS7*wu3hq>ktu-
z4pB+0)_Msix8qpur~M*!^?9LluTIh9EkCRV!)(1FWE7IMEYcEqwYo<UgU|O9u40i#
zQqHBi*>`kT9bG1Y<sI@H&ct&6_Uw#Q+`~0^%j8b9qy~#(^vA<@?0w7h{(x3eXx#+t
zo2)t37E-g|*2MR{E1rBHq*gE!*;1XLlB}eJo45X0{^s*5v|D;^8Qr-mI_q)H>h`8-
zTceiR_?*U&9WqaiEG_uwsk%n!c&~seOJP(k^)|Mbvq2Hhu$%JQmzK81hWWyeUtvbX
zVmNgd{NwV;N4-w;oj&-X@IOL^Rufn<G<^mX{uDd%UJWxeK0#)Yt#qitrVY(i(QScq
zE=UfF*L|Mh<jkHMw<51#*pI)!`z775aY#8&<u8jxqs%I;aGEO0ran~EXux$T5UW+Z
z@F2pw;1J59e*4o%;xTL854%A0=tO#@TdCBMl+4q#a+9AMh-|(XPN2A)7t<AJvY#9U
zoW}p{mfD>{5Nmi7A!q=3P>*}*OGZ?336+UGInd6)f$1lH2&4~!OJ@G!{i<9q1xP#j
zwEg`8`=S^37HSlkf(mEVi9)mzdp4~R65o2ve7lb*+Z6mzRx5~!pd8x2?&!O|7;tzQ
z+E5<Vj6`HEMT7GleHi}&0$d2|>dx<>YJocAjdoy<exjo*VE`T-$-eKUj!M-z7v7!m
zCZCF5&bmLMnVYz~Vda<ejJkpaxbS?LjV%r2&>k57j@*C)z;OrD_pr=udG`lxF2Bwh
za!O2b%IvrH*-~UCsRZW$!L#JJ#7Z|2o6AW3jQj~NyrvjxZEj;1h1rCa-`NWZZbB6$
zka`^)BSj9z&#Oqyvd_+l&L#af;zrrStyh=7-&?SF4m|jQ<IM<%%7!dY_gmH{Z)^h=
z;nOg@44>Z~6f1#GI#FGX;Fkz{$w3}?erWDC+{@6|IbUpJ4O_x^DyMIcGKve865!tF
zKDeDZpL$+C!mH0T&1PewnGJC^bJ@?G3rJ5r#XO8ygLGU7B2qwInrF41c`X>BQq8QF
z7Ts(_m%@ucJOQTx(s}yD5{fr?4Fr7Vd0K=)%0%Ac9(z4uqMHh@E!wH74LA(-{&}8%
z4<edYMsppEx?{SuzlI&T+3P5DL{xA$p$ST|;vgPs6!I;2JognSYx99CB(GAf`FyQ=
z#eQ@)vW+0Ubti|jgyDgCJ!(g>GFXIczkj@aek{A;S+kend;bB^_z{GE!Xb?Uk>H#E
zJHa=Ag=gN$TEB98e1mY8Thms^erv1`?oDvj!qLvZkV0FhMtiZh&zyCaU8)Hp|4`pJ
zU6I=qXf=zTz5dbl6FdaI55~m`=E?#O^cR5Vmkl0>DyFyC5jcXu^>zmWN5ILIC4Ik}
z$z6f_>D1W8cmnV{R!^gsRYzgYOUyzf-Ic6I+7c%j8*pYEHKX`Ui*D>f1{6ET4zO~z
z7W)>ZJj?#&S6LSR7l~sGzgMp~?GeeodIx^p(u(}l(A-$pDx;}XSDux^$ZywAX9wDf
zmaFe!;{hyJqsGc|e2TSdNYX?M10=3UZj_O^{AEWV@M|~fQ<8-`^Xqb1ySJ|gF0uzZ
zT^6O*J)g;XEM_e=3OZqh&ti+VPo5M{uV)c**;vn5idBSRDO+<5-#J}Sdyd6)+Lop%
zQqZAnG9%djQz>)Bf$@6k0flwg<L3r~`}>Zqb-s(;^h_o~_3v;VgaX<>AKXTdkh8k>
zAe>(e9+gkkh&D6QYF=pGpn|)3`IgpX!-j~o)~HEG66{@Z_8#;{J%zr9h@H3t;nocG
z!FRbvJgC>NN>AW{Hwx-=txEL4&lLL}`E3Dw_QQA#!|Y#2+)<+nm?IHwBf1@cy9QN=
z&R|OG9YhDXZY~DsZn;1stB?Y#_&4${X$~LVQSXh?`F^$%&|PZu&2_CkyWdxHJvxv)
zsM01t#r4|4!SCRb%`n~|rzWQ*Pd9k4-DEYb@nW+A0CpbR`{}42A^7_GX0ugahyx|T
zC4+=>sEQdqESc+Z?o+?PH}aov4unW5EnFxtg~SV2R(tVxy2hwOqu86@=2EEi^0uv@
zWHe`3=r^1nJxhZj;FV0or$H`ct}2m_7S+?wAA5ND5+}u+?S9=WaqL_YyaJLl6;|ao
zX1*ln*i7999=#uk7wMb_PBnsY#aKH+uQdx$M|7H8aRuQ0nf>L0-5w|XLDxrm^2;_H
zykuMv`))trl1KjfndN`n2>Z*LYdzosYL`X0_oqcV$jA=W#56<$+?g%RL5KYrZdk<h
zu(|M%ycmZHJ3)5(EBq!{*s~-8gVRS$nD)LG?<&E0VF*i^lTJ;U<$MhbyJOT!Hx7-<
zvYj+?HE`mxgUHUy$^8IfBg#wP71XBB2Kg{1c6VTNvnsE4p*QY8UjLy(#BEC0Rj+;K
zd6NxzUms!d<9bFM_|}&q)J~e%)y_1&kV;LQO7|r;y^u7VT=ZG9Q11LMpp%(BrJcV{
z$EMvN^__^I1A^55kq6c07Hp`F+KdWy)u*2ri)Tx=wxvj_c+X24?x}7Y0Cz5RlaSQ;
z600q>lvkg48hiM9n-qNAncGLxbv|fjwvmwV!&g&RcL4h=X$!@S?4t*O+4bxBo*&N{
zmKOH|+(W+vB0d29T+(mf*}d-6zrv$`sPnFTEM*W*43m;Y)+FQ7UNCR~5IsaoAYboM
zQtauAmmb8LFSf0w+KDDCs*vFC+b?^P*B!p+Y48^zUlFNTrIpjaHZ}=j*2^=A^f{m;
z|3ixf8gXt@*R$1bo!L`_>JI55Sb+r}WTX(=f=*Tlq8QMk`hRSH`~TW5_g~|+7rpzF
zDdPHf^)ohqS-_+wr7GIQKMK5IjFG94sA$7JP{{f>SN)%#kE$iD@PnqPVD<&(utdUC
z?ZSm$`k$*xZd{r{O2sdJcG$Wu6&<~<rh=N~aq9yPYofZ}HxJr+zXb(^-z($U1ZS6i
z(dgqNRs9Cp!a#(K0HiYejJSHFZQA?Nas4`P>uu-<XWgPBt%-)Qpa!RdSAF5vsMHnI
zW_&_&wj4hw81o2UOHxAC+J6nQmBtI(FaAmycsB{gQQn;E{#Ih6QS~AAgkGo;-`=VV
zA@B6Ti!#?V_Lmu93-Luk5_qp2W1R+Q{7rs!X=Q)gRQR2KTCt{uqWKk5UWu}q`XQ#J
zCfkzIq4cO)TbTD6SwDAkMEM)lL0X*1<PT-3jtjfFAa$3Q!3rKOMwE~nO*$>(A%V0Y
z6+h;zde|tOfj}|V^Vj8rx?<fH6v*VHt+Om$PmAFbKOE9ESyrjdxYSg~G|SkrQXj!w
zNqVS~9f<;A3AaHXcu*q~7&?8he$=<fY_mRiCh=n-o*#{UEWMSyo7XcoO^Er<pe*@`
z3^m_v7Pg?<&VG2VyEZ>^Rv26k0WA)T#YA;CV15j8C!luQf-;K=;alZC1L;-{QSpmg
z#%Fx>Q#<0>8eDnGOWZN*R;pv>@jD?SuwQ93#18e5DW8Odj6cjy@srthHwc(WfcZ@W
zd@z7-b~j$8=LtU`_J{1;s)P%(^pLSBw+g-<7o`QzJIS}`B!ELytD8y{rjb=@{l45*
zXxoW#6p6oTVlCv?F`{Ri*i)HW|7dN1aE)Gsdv9*7TZ}vruV|%MU7GG<@5g7JS8e+l
zAX-G1^OCo5$|*8{FH8uX20t0oD9_LDqFHY^y}K2`Ow|lsF=~%b7J#&rZ_lcYd1q5U
z-)=@jbRU(Xv;p$>d3}r)z$aBeQ1qoNMC>z~_^arN7l;<;Q_*Nr78aNQda#GQUjo!O
zkd9qj41!?CRiqJvSg8&^qpfkrwf)0RU@~qZ)WiRWySI*tqucgHu|Na~?vmgTB)E4-
zf(Lg`fZ!5>Ya_wkg9mqacXxMp8fn}+x5z$w@9#U~zWdG{<DN0zeSdXV_pDiS&6;b~
zswuxYCPHfp$3Jc(X@VgMCqs|Hkqt0<Fu~XpLH2kVD4Rw9h!=M|ehz%xUS>oIpZ~f5
zegOUB_pHprcSJ;ZVO%XmnkKQQRNoh|Ff)fo-$xA##Z);!XSx6v&2Z#t@TvfFVa~<I
zN!Rz20V=G*2!65GNd>RkP-bU7pmbv3E_|d6)2d@YMv3<WB_fz=-=b;eEnZ`{s*;g3
z3Q?V~ix}aivx^b;K2s3oeoJCYi&!k}G!s<SaxH!@@0OR#h*JU}?_s`EzW>I4J;_(%
zUlsB#?2t)veM`7k3>-}^m*IJ%qD_^2)YkOo$FK>#3B!;7Bd&+PB;@~B_-U5aof^#c
zY+8*@ciF_`<t$l|Nu~aDn}B?JaV{@{@Sj7df3O1dy;!Z~1@!Y%+Z#&C7XWzlM%}B{
zjDzamaA(K(4^Vml!5X@PQ)i(>zw3{3QEUtD8M{@rI6+vX4YYj!OPS8+9Raz0ZQP9|
zZUYhdw~v#3akgR$32r#>Buw>h_j!u=o%&-UWC!`CKOiC{`Ymx9@>-w0O5;Zqlfqq8
zD3*F6*p|;-LOh7-$U_pLzP_m+!<#K|(1GZwvMKG?Cxnil>WsBPi~9ChnMnCPJjZQf
zjJ_z<nSl}tahOIOS8CY{(Judew66=2!iWN)A{IplqD+Ic=HIJK2V<41Kn;|mljb3U
zV<M38*x7BdtL<bnwyy78*C;Jnk~$~SyFzC0B-9;|VzivXi?27QeGbRVovf>c@4A0!
zzffxQJH*r|3l58;MG6d@${SZrioDRzk+HE4tW#JRYG?>trY6q(oZet{FqR{l>rMZ#
z%9rZARf#7J(o$&@QzWYCRZW`t)y08dY8m34fiB`YZ4;I!S*cg$Q+$8qJm*e?oxO1F
zZ=U@_Gyh2W%M8ar!_>UK|Bj$W0LPR-_ofSxn(b;)yrx5G39bMwfBxo5#O{0xONHd}
zmz-85LN&WJGmxTHjTzo$#3?1O6-3oeP=WtxV=Z(d!bkodq!A`sYK&QIG?L09aDh-^
zM{I>DRR20!Ifm|jNGAzBru}+Zus#~&VOIvscT^B?vhgH#^6GTttT;GeftXzEhR{&*
z+ToO!7<{1{VqgDaM^Z$;Kaw$|O1eK|CQ4@o+TOlw^+KRxzT*`)I&LyN55YxALkmOQ
znSBGVR+zj*1c1{5l;E3#cDff}4=mU64~buX!wGS%T<w9|<Epg%dL3b6xtYBexCfxQ
zYVxxUz&qd`cTWc!Ed33aN^Es^-m)fErR^PlatKr(0^DZ76>t8{EItRY)R3BoTU}_z
zd~sW^V{$Jc&zF%VmARR1-QlLw8L0^k?UI~%l2G>NKu#8@%(mnf8OX|9@d3&g<J6hG
zFO7z~1PN^}G8w>)Ey-@U4?$VrQMM7-{iCRV`F<Gp86eF9&+85+$o+<M0`uLn-{DIF
zQOB=w57)re|8}sz+;%XMRBdw=N3Owz`ml8m*<Cr%W`udh>RzpZ+rti||9rnj2L@NI
z?LC*u6Mb%XzsCix1Y{t82mn$sM%e}cod217dQcfh>g|&`@G2kJhiuLs!_akI^&#ti
z@A+SenIDNK0JU`jRdx7ZftS5K7>>)=S;0ghs|DCN%r+4ONZuUrxBtO<CZO{d<p5T<
zEuam-@&mHLn;N4#she>C9MJ?Aq3j(7@bdXfI}V1J1E|CAQLKMn>2jddg?+o-yMkp$
zq5_ZyvL|~WNDK3{*S$thT@$+`i}(%qu7TwqMHZUzM;!^(j{?)QJ!)gm>OhbZ=BWbu
z3vB`UBW>2BqRnr9{eqpsY<<8BfY3i-9pj)~Tp;lAI4JNJ6Y}%tz~1>^LJy^~14aYL
z%N2IavZMRy?-ZcY%COrffYbb6`WuN4{0&$AFHuSknEoOPUKk+uKScQ>_S_lG%H_;i
z=A#nr&UY^V`u`n@7@!^Hus_=QhumStk8&qInn`mqtZfXy9>D&m;GcH^tlnpTmz?#0
zd|7um{Bg8)=fB}D(H}3L_(u~N|6nf-u&2G2{|9^UANCfH=QRKN5B9)x^5_Hf@_BdV
z{24H5S`Gf=W)7t?AO1xz^5D<Lf1!{)KqH2Ea*<!70(a}t1}nD4GwvWa;}+yA0QU@7
z>sZS!_P>||r#}4)bC98Hwdz9^V6uNy=_d8ynE@Vs-2qr(6kvsq=VJcHxsO&De>(iA
z#f;CeTbSJi_dVq;_XENk5R?$q9(FBuu6v8523}?Rr+t@IIslX5IlwtJ*-ajR{(=Po
zS_#6qM+R&OhWUUG^ThkN+^LMXj}k{2|KY6)@Mig6@Xo6QyZy10k{w-Ta61Tqs2n8+
zvY-BF4YWo!2HwR0)|7t>^N$mxGeB<E7bOLMryz%h8K<730PkCWMq=84QX!-;2b*`d
zO%%JDIV&k>0T_b*#qfVdE<X=y22646;o=^${~PXFtWx$C5pYudxh?mU^|TN0fL;6<
zIk3JRj6}0O(vv(6;<g2bxr7b#xCO%yfawY7=_mlulLZL435xf0mj%EC-GqUFWsOkx
zabl=}+xgSMqr{J+RRU}Tv_=XDhxRyHhJUI)HRg}~#@!9v%uIuzp}XL7<j3LRg4&~i
zt6{)A<{kcA?NAk15Bu&O3CEhijUc|;4jAUpzyAlM+4g&shX1e996}z}{z>2S{|)-B
z``zW1W%6V<V4D72WWXnB^8uLPT*<%;%LA5E<wrxdzj)M;mF_Xda5FHM|4ssK=S>3)
ziWo@p`J-aN{KqwwAHc71bpQY3IgeHa6ombK(4aMvQDEIe%K`tftEtCFR`MSk;V!xI
z`@jE;mSp0YyN<JxLh#=PN)8P*ew-~WkCP?r&sre!$Flx!q)r89#DACE|DC)aXY&7!
z+`7Zj;y7L;2z)k=%j5sjj{j`ECXAfp-maDhpvWgw)H(;=a$MVdY(o?;RS;9Ieg}^E
zx@gSj)|e=hY%IL%8nbhrou5sM*jB@moALcNY`AIUj#e62Q0#IZZXS1xe>up%^x6_&
ze<TVS*az`lfew<nVV={eS6h#}#lL(vCdu)&B+EGae`A<2k3zqe7&qx|YpE-LGXoUm
z80@v2qgVchYheU73x7RudG}wvbOr!J7m;Fh!Zk3^P(BHFW1xslZrwZNlr_mqh0Y&%
zi>N!cF=MM4gwkq67!n4p-8Ji=@se_hH!q$rfR?GCOsYgUs8H|aNg_GGYkDu|n7J}5
zZBE@W@@%g35#;lQCk)$B+&2k^C>It>AN3zJeZXk{#ad6&4SwE4lE)X)Y5+vnVAx?r
zj9LzKR;k%zH}cg+cSD}4J{4C2ZVVzm_|>va%yM-p6mmc8XzMb4dIv2gRig9i+2D4n
zj0VV#e|~HCEySlr%0sflHK9=<Ng^7NsGGm%l#*sGD==K!bZhkBo3fAiP)dtL^3rrE
zn1<0cwQ(^aC-=RA6)Q$eOETj=S0&4?JtP<MvQ<=HH~FEp<O3B$q_{F76rpjY!{KD_
zwq1hVB4jdy{L_pob=P#&E8iszMmcvQLCB`ftsR4&Dp!OQcx2lp-XM*Lc?J8cL)e|F
zuJ4ULGO_)vj1W|)%6aGGV74ZGFtd#0PGD0xlTgn<@Mn0>ed(`-p+$9S-jIaV45FB+
z)q{-mGd>2M3H!(f=TeLk&2R6bos)=A>koTpDb+mp-ysxr<@Pp;H$3!cBv;Y3{)SVz
zxyp@U7z1}m#}H1KZG1ID!)00-uC?N`Y&=J<G)#V_Fzj@|pD^OGw`EZlQn!nuQIK0(
z;g3usmB+{Ie%wUs8=al(P_Wxlgj_(4m&X$CE+}Q|!3UR9E6C_`;x^VY^T`x!W7z<I
zb$mM`qqH!46w?<S%#tTm)hqq{*<w@!(&kQ)8=kU9cD|b)!xE^Z_q41wzV?nD#72N4
zMrd=bI=YNLp(zQ?@@XiZLv_~=v){SVYYb|2CkA7g^-<+^$X0$n<>E0U#cP6uw!+V%
zPaRIieRLYreji9zIPg!W1}U+5gy!gUk%3AQS~ITD6dOHBSkS%tx-42#=Oq~iY-0Sd
zXz)xYS%!{l6|3XRlTf&O)0CKv5-cq>p@ITO#yfT~DcZ)nFH`!_G0&PrTN~;g7-qU8
zw62NV`0yBo@~n|l3CYUE;emc|4vIoF1tQQ}?bp5j+mlq@(jHHG$@KFh!lWDWMeX=F
z&+AhH^udBPb6IsY5(#K`_vQWQi>pS2=LJ(+er*Z5e(tZXW$(B&tEZ^2`&^s^CqvKL
zBQ{W<EqDzk!NGM|r%)TaO6cc14uetq9J05DQqpqGx>=}L`jO<ikyqj!6(eQcjS+WE
z-@ikO^59E2l{jx0!IpsDy=!Wz7xR|p3%MVYRMGdJX63#>iu)|9)3g^pX99a><*%^&
zjJnAuIat1siN81dFyd!KUBJl5OUOI2kTq>E3=53WQ4nNR(Zk<MDa-DztQ`$x2Ko#O
zLJS+d+iu;4x+}7;t-9>r&#y?0|Eiy;+;Te^?C^`#LgfgYy>WU`dm%8b8o&`kt_&MJ
znHnX6`SJS(UM}BiYBq6-;`ncvQ(90`yhNA%@Q$oyUC<r_ZC8~j^F)HJQ(1-Xq;Bpf
zgJ-HeZa%wbgtv~>Mk@N=@+-f#6!Hd>V-=|?{Wqtq5o@8Zht)z_*uMz#j0{=__D{Gn
zt{vkqL5kK12`h`9ukKZAm^3)1SNKvR4w|!4)zI2t_w2$$Dphfz)lS(^L+Q>dF8QSq
zvQ&=ExX39j|32E1_b8>f-Lu9*Bqw1$R<2eX#5Z4)H{-{er^kvD^1Bo`<ldpeDfaTx
zu7qP4r8nk?)=qL(U)z1nZ)h6i8aJI0)ooP9HuyAm^vNS?pMgnJ$M@*Cr<tle#xO``
zB1)n%LPj~uJa%48v1!yD2OeUpO1(OQ2)xXeSJzo_xv8m(SqM%>vSS6J^cEyBc=5m)
z&Cn*Cg<+Wx2^Bn~Z4~_Cd_Z?V!#!*|udZ=64W`HJo)w)JeMdo(;_D$e9(bB}Zo%J0
zju|J`JY|_vK7LNH9*JN3cEDkqORa%<Ty*OFY_|mAMqr!Q`R+VdRmQc0V(d)EmBvML
z{8yJ9e7yX9RG4O@BhpKB=cFBhQIxtd9chqHCFsfZM#sx*9V!1Rmx0KdG2)!KL%cMC
z#z-&OZ%=s;L6T`GK9yE4cXi&JfCGruw}&+w3W8khY8}^={OHQRh%a{1Dn<l<mP4tO
z*O73`Xz>?dTnl?Pkk`XiVxf<FI34fuW-RN##XfvIM}W&Z_?(cS5RH!*xxNw_nkba@
zOT&~XI`%f^XUkc}j}8JG(#G16W2s`N5v0$y6l517PMpQ5>5drd_;aqT$4`=+wY#Rv
z%r5<6dD`LhZR$d2XhD9{xh6We6K@*fvrYNBi#S7D3PquFy1Y>qg>n3;dbvDWUf+9C
z8bjgWE&~~R8;RFKxI32lf<7fm$4N{zcb~DF+(t*u?_)kpv%N{>d#OobJ?z9&qA-3w
zAV~bFW}@=iHBrj;8IDVk1-XI*kZ}LP_J7k&`(JV8{<qNd=}BbJZG>&frhBmNc&@q?
zO_W{a7C5WLD$#9FOp6Q^EYt|3?M|gTP^SaSYW%Ce1k0WPsO5#i;k<$<8aKyX_~ZLJ
zRL#vOgweT(V~*tg%|BW{E1e6dj%Z9}%t9Qh$LUd{xKT!v!p8Goe{~TQj`mJsg{T-=
zRe2fkE;7={Z}n2?_~jN{4%I!q&!5=sHU8CqS^o07DrcIRW8nTUBK)U1-JfKgDz4U6
zNWK)_Qbxb7_!vVJ@ch$RmI;KWqNsO=;k#7u20PJq5+V-T(QQVYEvoTEa*c-Jc~R2x
zsDAMFu1+gdyt@`;lv%cmu}+G3?=VXA+Dt+Al{D{{2m)5P)g9cT%LdyZ(~e>Oz+say
z*^==@aaF@;>}MHE!DK(2l{NRI!d~H!ZKg~@Xi<kMPXbHc^p}%d&{j!mL@)d3oA>0B
zxXjq4LkgI@-k*3{6SN_Eh!uTTnZe9V^hXs$Q;$7j?W1}Q-~f+EZgsEH>+PM6*p*e2
zWAkA(Iwxxi)Y!nuNbK@Iui{rz6D{~Ay0K7K_<|f)UCeULcVXCryx#3h#hhc#P91G&
zq9%6Hd#O2P>eR8`CW~2p88d8S*Tt5au%Kr->K=^%@0o$R1#Jk_rY!Vn!S}cXHs2Mb
zlRE#_!nm=6WsB-7EYanh2xaD?qQahAnNm2|eq_(-(C77iY14(hjFz>!tQ);M=K^<{
zq^oZxCO=-AWv@Qj!3_``t&S;XR+F+{qJ-_#o!_c()XxPTDVIJ`CBUk^Y0el=3og?h
zlrY?knoPJVRsWRKl;z$6WI9uCI@Jc7O2wvLz`n*`@D8t^8q^5ysZA_47L|b{?KU&9
zy+?#U1qucoInT7DYZd4Ztf)A*w1o(daiUT8GIs7=?1HnLyPWXb1^^iHNtXTyBGK0<
zlPR+2CEA5Xb(!{nWoCZ)r)8=ZF1*FuH4V`y2eF5lkGr6eTxJ-plWItDH$#{G-j>&D
z{$wwfK@f+BFJ&sVj+)_s-05PPMyfaM_;K)R46j*!{tXwfxMAS&k{Dqj3Bh?&Rb50_
zjK;$C<NWykPDcV^i>8e^;3>5j>HO-A+cQ@-iI`**xV3YwZ(;+sapCrG<(gPLI>Wd;
z?A1-od(L#3cLj$zshTgJ0Jm{?{@^~NNE9qYJlZu|rTO;NjODY_=uKv{YQChMyo>w^
zW1^z2tc${C+|d-`y9hhm9G$v{gI{?_sTDhjdQhH?EC-Hwl(b*yqWe9%O;hG}68D^G
zRfnM`R;)9$3ZC5r*5>d?_Pn?w1JG^4i;9B2RTk8$H85-HVMB;d(<iXeESTZ^mJZQx
z`kYj?Q*l-{|5{PFE>1RXxLUthizW631;kX^yZY@rPvL`6YtcHIww<<OD~zPJiD1xA
zvsU|hua>D6ZSM@R2X_xg4uu!BnW5T?hXQ)(iSsu1V+g0y`+Q&x)qpa_)n2>DX+X>r
zuQ58<boq}BElfA`x%0vf7M>j^*_BA8d+?BBI%-^Xg_z&&c;5>;PG|o735k2EDk!Ql
zYhXRPsnD|)v};>e+3QNawp*B8r&;60WVwdZpCvA!`3+v=MvO$Cp!hNnZU;8EfpH6O
z48G<~1n~dO!sFHk*&#-%QLQ?JX6>*qpalow7tTLnBc(9qy>mH~$Qn;O-Iv;C+(J}P
z_W=7VRhYS`g`QeGrIM0d5eZeyE1NzAm)tJfa^v~J6f#(=70R{o9zT~`wCVT{MBY`m
zIPVmj8G(!9=|CVDhJGyxX?Hgny(eB<5R0~OLazCCB$j<y%@m44*rh>4RxH2{c#>A3
z)xI3bDY|prP1gN8JWH2uOU`FQ_JA<(>nzWgugeK2r|(JE7Sd2Kxi6R6*)ZM>n4=3;
z1p3_LQeIoQ3M`{QM=mvLe!e0aT=m;)0MFG}_7GPjStp!p&>@ag9zR_&;UirR`La5K
zp*g$!Ds&B7t9FSYT$B(tR11}LXV)MKO)T1<e>=`{&$zY_BJfN0wX~id94V{&K)6`$
zb^{R|;@}$?Nza#ToxRIuWazp5RX*MZjP^=oNf;I1sb&#bi39-!cX!^Yp6jh!_44xa
zDgqIsQ*iDtGS)7ol(+Yk6Dd`O{~3_{zj~Zz)?lDlWc~8XBtD58(xei(1@K~;nhQQ&
z%{Z)1J-fhoxW|DP#6?-K0PXCP-(Az)S>)V50M0n@6F6Cs_i^^)tV*>3KhNRYXyOi4
zU{*0ZUE4;)+`&70@6>=&(*LJMasLN?4<32S!`A&2XuqLQSXS3AUg!otgjjk@m+twe
zx|EXcD?{r`7b4&$F>kj;vEZq@@dsvUqp73}^WNMQ?K4C-M2>}t$Z}?uUTBr%<}y5{
zsQ(Gb(gB^B^`5T@fY)dzNB66rCFagtm4)3HXVhNdE{(mn$2?OCsv{KNj_uZR&Dqm*
zXYpdR!rPaBpQevZLPvm5H$BL9L~c8sVvwL%tSA(dj4{BNv!S{g^Hn-?Cot=qFQNB@
zTN~bM3h~UTYPo&kd^j(7QZ!9pxegP^{azYdVZ?<BE_@Be7BOz_NdW*C6?D)6atV~7
ztdmFbK5pKR(g)j4b%n@%0}&B@ls{t&>rVFhuFc6>+*53=%$&h!Lm?+VY$iGp%`CLH
zC#S_4s7LBCLMgbfvLzUN3N$uIvyG`e<0)bl-WARkc)EBlG&xVR{arJcS3gRFD-Qqd
zZ@7Y#kHWKWvxB~2&~nnji~Yh$!d8hSXV*keSXU%m0?KzPMzo@$_D$8ZmcQ{mFJeFZ
zgq|IO*H*A-QM<y(o!45#^_pYkSN`<3L+zd_F}v@)a1T#7)6wP-$+>n&eawvK1(zH0
zMRMk7_6!74`uU>{)xTv=EjlM$vUsbEK6oqDFWmix<J4P%4YP+0r!_E!Fjmw-kGw6`
zhKNY{W{(!mBwmp;kY!F6;i%Af{`0x>_^DFZGR-}nKQ-{`wC*bNl86bolTS$E!Z_z_
zLtc$f>oV_6Y?>RPP#}g4=Rj4OIh&ZgBX&AMI%WYdr8eB{6)eu!`V*$Ea;!dhwUZ-c
z*?nnwD7Sy+n1I-efd~s8)?SPj1~3B3wpr5`%`Eq7%4D6M=4qV(dzSQuWEsUnh1U-_
z8~0{HvUu-tt34Y~$TfEE=}VYn))J*mz8UgwhNm11O2F)fa|dGf@>C}mPH6o>6|T~2
zL%h1kM{B{}56M|#FoBNK;_OD2uKL2;oS^kdMgBMUWn`c^{xC?w72VR;5Cg@S&jfC0
zeCJI``rC93)qJ(T;jmoA$L@KHMACtVJIYg;vaE9^_RF^ZwZr1Qr2Gi7u6hm6Pgn4-
zUntd`XwL-Q<F6AGa?WPFT>8K@6H*$~M)fksaKbB-Jo;q1uAke|oyH_q2)*`%=_smj
zUfAk8Q)X3djoJ}zD62hwD><v6835ka?6cN{jHa*Ba3_F|>F4#jAuBp&cuIcqRM6My
zb)!w^GH?CKPOG%&?d>?fOM}~cI%?LPf$z^t86&MbP=iyai?0LK-ZV4b3p7s8VT)<=
zy~T+F7f4iBKNIxV$L!l{`-SmgT!>0Ie}CH9<yyD%)?t1&(}DZeF_7et$!j4@E(dph
z<|jnw?Ma*;jAFN5<ecxto<<i$#B%gax1B#LIqw@BE&^L5BhGQIruDXUa4E)<9f`Ac
zunB-5D*`LIgQ;-4lNuv_kUOEmOYoeqcmn_$3oA)bS4C#)eMn-jC{N!ZL6{Ttgj-*Z
z$sNk=VwcTY-WwO8>A;;0V><P&`()WK#siteoUa=}zy&fhyX?=Z?Wm`zBfqUUcDH?^
zov|E*Kj)+k+fF=4VDgAxt(@dkv#_EPqx&RgO<(&tq)XXR@MPJ8Z=cb-aU2LMp<Y8e
zDBqRWl)^@xRvW8<T5_5IJ!aB~cSr^LXbaY)kZ=p+R#(c&i^?F&612m?>X{|Ony(F?
zku0Y0z3jc<sl#mEk<ExWozDyU`8HEH%9-HpnObGk`<_G{YxxB8n8>PHbnqwAMvZXB
ztN~E@d|@4jGIt7r^faN{53xr&IwR)eFZJ{X9lMX?NRmcT910yHY?D<6J_SiNPck1<
zMyhR8y+viPSd@tTNZ!dHP29;fKR9EbzPvqrI~1)ox5N-YxfGehpsx1v*%P&}PE0MP
zB3O-G{`K<<rlKP|-gAlNul32l6vRCu)#ZoX%2uk3b!W5Vskd|1HF>I-J^*c$=_&`f
z2Gyj8NWNw=khpCdeum$xDT6yl{v4=wj!08i5JspO_H1vNqN*}djb*zglxOR6aJo7!
zn{};$<uLz7(f3=LI1(ZD84jr?e2MbXREfZKZMI=qq1l$71C1G?9&ewspPp5cq|9L&
z&%5d(onu>=;AYm$5IM}#Rte)ok_#fQL%eGZRxfbi4=PokafN+ZJNAc>S&)stz-jT?
zXRP!@cy^gz4**%P^4^5X(k2}3{sTFnuGUVxiZMI%)`a3mQV-s9WW@<<$hGb5R6z;D
zyk5N~Ykl`8Ow?}h3MoFM?RJ_if9k0E4z^)DE;H@&u;NrDVskko4c@BG9YT;~ClR+M
ze$cGD<heJVWG*_dv;OiHJ8G!>ek?T?hJC&>J9%ekX=ZZ$&|&-LV7bL_*%n6$&*Ama
zIHL~|iq`679wd$b=YK|5_@5mU2yrx}%0k^UY|OWLV_9)~<YVC?u8D4LnhoEqu#;t;
zO1q47lHXuffv!}4!+p>=C4X-GVqM$lPVh)$(Mg)-LVD?KH;4$XW4>jHJcd~HF5|RF
z5G<0~aoc(8)-zPPMNWX8lUBqe)!vu0OC*%dJU5))_US7tW$%dA**lWyDcJ6P?w@n*
zRhv;lC%RSm;3i39w?V8!TX3o+1S3^83BxHI$);zrScD>t*{qxT4$X83Rau?Y+~G?L
z)qQE-c00;&$6eo*1}T=6ofQAfe)|o{K~D(vdno+I>3x1<GokoBCceM>WLIX~z$w=^
zUP|7PDqSd7nicz!qnW~XhvNI7)YT{w%nR6Ox#2=Q>a_xfAj@Vy7wAeCZKpelQeBkY
zL(1z!K{fCTcghRo28Nr3_s#X!P8&O32N77Hi0}LYAE6uPoacOeeMG&%gainMd@oHX
zGuS&AIGxs@14`eU-=271D_lt;&x};p8=dv<*c#Ukz^@RFtSQbq7V|ghs|N&w<}UG0
zw5Z#;918OEz>-x3#)!%@9Ld62@}yyj%`~3^5OcNMV}`__69)h?LUL@TUd1HfR?pVp
zi3u=35j+d~BAPjITbKtlAF%>$-;XmnI&|~p1iY~tCw|IQMOQswg>%qm-~)rCz*@2t
zt50(Li93jj>#X&23h~_a7&6KXBh=_eYBZ)wq14#{C3E}EBAug!)|K3tQj}8LX(VtG
zH{V3v=<(Yun^7mvF{(pmDkLZpjh2=d%7V{Cv+CA(o2D|%n#LgyP9e1?WL3FlSbNS-
zk~1XCP`_(S+aA1R5B?#?JIp_AaoiacBuzF9-2?9-+#DQRcw_bUvMoG?>@(_|wa1;9
zhUD!gw9Hy&`@E{dcp5=H&q|DvhKnPF6gtmDt>5HCRoTovgVkSW=J&;SB#S7Jt(~ea
z^4_YV3Fe}CKcL|dL^;_g#*~CiGtr$Amj~=!tSP+DyA+`{AuFBuSC?lRFTUj(mD7S+
z{H(QCem<C%+%u+@Ynv&UIQz}%P5wGn0Yb{~=A}B$WY&8L6;{yG0jWt_$RXLfnUlr$
z$Y%y7NM#XTzpzhlPejwGy|*G|Rbm<|A^Ye1=GThqiMr6k<}3jToX83<e+fjhnB()J
z{4D{NwI-vEW#t0MTiI-p#vp5R-15%UrlbqbsR^Ew=g%`PEg?X&lw2i{Y0cRuQR7bU
zd_2PYeABaIZ0nL?F)bFDx5jwRA+`QU*Sb&%x{KC1YG9FD-mFPPVcb=?Mh4fEN#@gM
z=Y7Sh9rNd9i_|d-aRD&3r7#8;j4btQ&$T=O=`|+{3y(W3W*QH&;Ewz|11!22A&Jvo
zdOc-R8QbZm5Jv&2G_B!q;X<_m4nNb^TJ(lO9*7z-XDmOMwyLf@Gli|OKX5n%5^-y>
zrCRHLN{NOjD9uZRp=nPCS*ZEGSkTO)30+>N*FibmQu_SKVt01e)I4zg9s}sdxk3P*
zRc|@RRBco&HpRoQ*NMp#Lp0?J4$h<Qw*MH&Hu!Ec)zlBeC5DJ0Wrv;5JdlkspmOE{
zLXL+<uFl2wzTP#?(9Gbot_fnAfWt;Yki=Bu*P=O1h)RUX`|h<f(x@XWf{Ku?TjU;|
zE|RcsmwCGpOPlfuJGLRF`*&SB6cf~HokUK4eGIkwO%l97ZZC&`Rk6gH1vBg^6IFBL
zl*WKQy%J-c$8p<@7ze|5XT&0$k_TgFQ4==C;If&jCjQP-6QVP9nG(I3GAZNWhWANq
z;Sxh%Q?ag_!7mmavysLld-|NxD<iaeDG599P>@cN6fezn=4mtKl`38GSSmG%>+El$
z7J3*D2?)O;ZJZTU#pgSy@|F-dLd2kgO@;F<^ITdLZ`?m>4l5b#q&hFcMyp{1u<<jo
zIe}Er;9-U30Ze)4Hyn21=P3c%)rJ=_OK3w&oy>v>SfHZsE6{LlAmZ=GM|OOj<W>RX
zgw*Z0KLESI7F*P#%HzOg;wb^%d<c*>YJNu+UvWsh+Tr@Z6?~?hiA~+rsMwPnNntkv
zG?;^uzHcXGY}du*xU=izDlvMF@l~08ylDl<s0$YidvNIi;-rCaHGkb_|K&TfPRlwe
ze}~4mmMLoEq5=Oa20Z>y>(*PMZP3~`M|PMOHh2|y3-__BuTK1$Z*u(PA(LA!EJ=(F
znb?DehZBI6^t50)cD2b|&=w{cmEchg73nW+TMgVPP>_$5%7c7njb<EA%5mw@W`gO6
zrgj#g4~S169vv_nrZ6dByT!jFSngc4o*=85y63id(Y8z$)HR=V(P`yuP;uJnGWn%q
z)(AwcaJ_}|&FuCQYkH>qtj|Yzk6-L?PpqzGWh+{)@xNO!eMxE~T`Y6*C)W3v{j%!@
zA?Tjct|yDpXq+Xow3wHsU7k-Szh}lk29LhCx<>{>i`S?mUa2eH2d8-o#hlT$FA0q(
zo*X6@T~w-a9Yn^4Dx>B+6<?j1P+-WaVDP|s1&7{&J0|))p+O*N<b@O(r%dv(*}fq;
zuA{37#N93t)A00$kvG{MA9iFDe5c%ZA@xFYz)a$pB6J-h^zqXwM=hk%p(GJ&Q1_l<
z{7ntgomfB$Q>PKJC*gycmFP6{v4Hhz5xz1irv>`{`tXLsNv>{0#SzM0%0=0B%4o`4
z=wzJX80R6!A~OBfLl(Cs3PHE?)Xjyq(2QJ7Y0Bl(qa$I~)+5Be&;~K*6O5;hW+F2J
zlpA?=`~*}9#0ycl9TY2gIHetSBxiDtrl%WnGtGF5+6@xibH&&$Ns;-31-J0;$RI*x
z+4KAMx8_&VCo5VAqYI550v-;CEgwG4cPCoqf5?^wTalg*)HB+Lqgann*a>26=?Yy>
zXnF1QXw(F3Oumvzeh!;o9-i$*?Dv;VP_m<>f>q_6u)P>ETNjOYNb7Z<Ll5SBq0pqO
zj}PZ20_XBWBRTl?$E~aCx2DbKL~6KgwkT<o)ux5q2|_t;c1C7SR!mEIohKp`Oqh=?
zI!oxGpdbrQ781vTE~=+%upTcbAoc3S>-&KlZ#AGfw7BgEsquV}DeDl(e<Rr{T>_DV
zE&hclfjw>5;3!JHRE}gk9RMGfU(QCt7Vg$%H4dy2Y+iz03gLw|=PHc}t}LTKj8na`
z2W9J<Sd%In&0t4*AmjV@f%MAivkYDmtALI^oKT7+Ola{%rg6=@!)kS#IYRqZk@V5+
zx};Fl$H*qT&jgI1Mb&{t860?;O6JNqey)qIV>)kML;Q=Ooz^_R*xeLYq?AW+gr-K8
z-WzrEqM+g<%_pKfRrwkU&in(z!Ip%%ex4iCub)MgrVDRv1YZ3_Ml#sQEsTGY>3`(E
z=`1(#p=rSS)6)(Tq6n}07+UWh<nsGHY(Gh%ad5-!*dQLWTL5^c@aOr`r#n3KZv?kd
zGL?}M&-%lEITUGrvD)H|BW^HEkO(})ob4Yh!bx><c4NqzbIDWFf1B4@z-lcdb|uY}
z8nexk2(%;|kJJchLftAO+zJf4l$IjR?_LWIo!yI4*_qn`#a+3me5}D@4BvOY9X;?V
z;0U7I7g34TzQ<^|-;bM@fMHBWJDrg*gwxxgcScI)3d&~TT^_REgpKD^PXzHSos}JG
zuxoHOno5wIc+MvTIhWR{=th!YWEqNkajTMeISm<Wal;!=9LVJ4tIxrSC)mD6(ZU|t
zOQ3pMqH_aXc^~%?TA%oX-r62!leeEifOwiY=1yeHN4;9NK)sl2LzuLsIuWLEz(^>0
z`+B@o9&jZ5Z5wSoUp$M_!kLn<?iIRSrSwD&MOg85cx7LfI7OrhIu?NQF^ihuX4A~q
z3>$$OW>Hv_P`OLGX&u6{{R{eDrMyMLHpAe`qT?^lb^@~}w3w~sm27LOiBd=Xpe;kB
zyiv8zxYlaR()g$t6JXQxoy{z5yi(ZAIV!rl)%v2FaXf{d>gLlV2DY1?0l)T7*3~d)
zJ=1b2hL0t__Ro>sq+Pzwc(}4V<@UJ=#fH4`V@51b6KW9dHgyR@-eun_pUqBwQZ9~i
z%rr_BDBSz1Ea^n?VpiRy-jA}bqi3ypSCzJ|L~{HJjbTdj_{|sDwVv~w9wSuaf*NZ$
z0GFIESKfyJw@0NSw~C4Jg;r<-w4-gAdMay*(}F}xryob4#UzINGaQd9;xE#I;$l!|
zDi#MA-Avv%eL`W%&IEgqOGCRR`86|pBpZrMYt#xsp{Q<Z>oQ?2wEf!R{AZlFLeZu1
zTt=VHkMi)&lRjG7X4CDQwIX8mu{iO`%3aGsc?2qj;Dgrr=QYX{*0m&eGa#E2_D^L{
zyPg0m+hoJ>7cLPN)J0wOJ>Lqgy-S<nZag6x&c`9=ENHu|W<*shbE~z%0+_f<Z!(MR
z^FwnY4%qu};6I37tF9IyDh4S`*wSq@{MhCtGx<8}_RgMy+|@u;UrQz8<TaYAO2x!V
zJB)UsdAf_Tu4STAZpXwNZ_(L@r+C_f%?^aR$5*xKP{Vafy=k&lG*#3Ydu9&Xi{w&a
zOtFs&J_|H(#~quHYMd~rj@_O<saa&&8mvJ*^nhQYWJi6X_4&j@6pbcrU7U#%F?(Kp
zY2Vo^H&V)s`ctPLkCD+=kzt-E<t_;DUHr#*L;9Hy)o$pQ)572<@m_};3a&FE%SFU|
zrNj@;UOWJ-1$Re+VT|pJ%E<`z0BzjP(vp<T>uj4U4HdnDBrJH~$xaViBOFKf<_fr!
zy^i}deEMM<M4i;&Iydr00-`Y~_k=N-rvF&JtR)5QN`-%@;rL61^PwetTkplt_j>$}
zWWEJSV@~6b6B2eD6IA$$Z?JvilLdO)l*4@((6<EN_97HRbl71%shyZ^-ovoo2?e0k
zz<!0>I%@MS$LhAZ@nWyb7TUKXOj6r@ffn{D5Uz+O<|}iSS#Blbp-Ne!T&*4#<>4YL
zI`4dwn5kt!rW(XvZyJV8W%jz?hst<~R7N>w(pN!H7`a6L9l@=xPo*0fO!JP>NV6{;
zQ_A|ysT5WIGfEqNtWG0BULcAO_dFDYvKyfxm_9#nsZuKy0bd{89Z7kt5UBZ-+otLr
zN^Ma;9YR)jypdh*+{zF-q(Bh%;fT`k#CMg1BuoR?{X9a@!uxG$=sLQhC_7oKwjG?5
z>ZD|3X;<8F7ROtH>@X67LcMJ;ZxbvFzMxJ|40DU{b*?l9uSR&cU65;xOmL}nA=L+e
z?Cot}?pyT01oD9N@6R-N@)+akC7HXklH`J@OC!=_(%+TS>MX~|N>o==mU0K@21{*=
z!d?;tW5`59rhQK>Vk0<eOnOp@Eim0QTP701L?))Cp3>IaSkqE}aO>g~@O&_fr?!op
z%~Oibvbz3ka&&M{%G8xaBUO8-y&fD|<RymN<49vLwNu`xOQ<WA8<uh=8}B<UIQeQ9
z{)ln(;u-VW#7<;8Qp%p(&vg5F!lq}3I$-A1Jjb|BXTdRO$L^W>;<Mrs(n);8{NiOz
z+Pb3EBH|}S2DTbuA%`3TbqX6YlRlYj{#i|TLa#&x>}aWMCQ+3Va)K>54&Q6inU`2)
zd0`5+RRy#9U{FMg9}YjUcBh!QmoHK`<BC(!NSdM1vzX?UXL1d1Ek;o>NB3(Y%4)h;
z2`s^usY?0k+fM9qUe%y!ukU4FXTB54vj<U0_c}Ph2(_KaQrGFQa$(f1lal%xbXB3B
zhbSo6m%Zp1%Ou<3oEf`fQr=K`LlgL=TwJ0D6h{%bWmK;1A`Lxi$S~o?(AO*1l*sAf
zj(c75DFcDsOVFt(NRX=9vq-{X?QVeZNLh)g6J}oFb3w?{BU4!zel+A8%kKi=oZytO
z+;s99fKQSXXm)seR0~P%Xfk%IpB{IUF|nk5jVJuNh2ckTtMGBe^%Qa8J~C3p_z}~N
zx_t8(l^%%c1H$4CEhQz{d5wnKWQA#@7`o=6oGez9Cv(?(QSd62Qt+6!l7waCvZm8v
zW;kB3WX29+@l)DV{UKG^mkR>VzL#pbg;#gi()%bTH=ZRK)e>YkNavfXpjJn<;a0?F
zIz`jdn#g6OIG-&97QV4;S$pOJqHZ45|4ezD+&N}|oH?H$W=-{0bL86z+}G3Sm+^;v
zggiTl30XEphm*FGHK9&LNBRylJZH6VZ}0a397eOe<)*Hf#XW0^SYD>3mU?&vL{wYI
zvBD61MeR6kP1#S))^bJ>g2ldCRHIZm<8nmdDl>zM#yX2N3&L3Cg);-VZz!&t`Vbku
z<WebaRPlyw0rUk2EH^lWik#9~Hz&!D?mgeJ)cYv69B&Z>VY+wQY1QMFR4J#+25;L?
z6@<-R@%DyqrnY`DE=RaE3+D+`eA2Gb-=<${+x}J%L(9Xljgn-drCQHyj)|hCHbp%1
z8NCQW7{yBo+Lap4WWJ?k<A^=-FA2PXnF`{qPR2OFrMx?ytwp44%W77f+>vbQn#sGe
zCuo7}xAMPm3+*tGR7D*kRn7MFeBYk2+q5E%y1y^3&K8;ibSBFk{l5Ri^9Pj?>32uU
zZ0Bi_;zYYIE_V4Mhyo!tbdGA9jUwfQf*7t2_xWz3c--Hm^(~yxZWgN5u0v&bR)(LF
zp0e+WgcDE7i-->!tz+E#&!^rM01z!jPBZPtHzc6Lx!-U<OGK`{f5W}z%7Fnz3A<)G
z7SQqa7gl&qaoA#5Zpqzy1a!k<Ht&duev)yIvb=+@DtfJsjH0f$2>x~7(<HVIy1^89
z^&3tb`$m-|2NJof!yG5?aU^U_5h1)TK0p!%_%%-+VDWaD#=3J8tz$33*D$-~#YZTs
z;B=<Jn<cv~7f0Yr=d5B!O39WPtK#hwk)p;ZN%b^~_z%0^+q0&Q4_4Nj!@k9R<9K2D
zL7eJ|@J)TvdM!C#qoQMjax9kwX7Mu>g{PFEz8?Ik*n6cWpci@-)lE{fk$d9tZ+!=_
zXA@2@>_eu;0_(&3J8~(T9Pqihc2*y%N>f+_v=Q{sx#4RsA!<T6qlukF=UkbXmZ^=k
zRv$^hLecO=Cs<D}bf4{}P}Ry?<@To&QVJSsk^gjio0?YKv&LNFkmo4XS1-nBL>q<E
zb3H?d2;I9;O}K7QO6+cC_mi1Gh8X|Um`i_Hv}HQqI7Mh$ct3V{PKq}pT}|HLi4gU$
zeIz?Mu9>pUe#Jv%YidSYk`8MrLdC$-S&>~QCGWVbInEJrrHa(dJ!$kIx0cwI7A{y`
zbYy8KijFk<zR!+-T14d5ux>~I7coOpGphQb*!Who4u{J*UGzZHme8%3z>ZFg_g2NC
z#K)D%%*@IftJC5zn>|3st$$qK66m)aF_98YqO?E085p@>_qDj`3aI@f$I}JC<8=M^
zvo{ncgbp{Q2mL#7KZD<1D@`}QnKGOf;Lli*q9Tgsre>j(ZLlHylo2KePPTI{_E}2|
zs&k*egi1SET{;N!gbjV#v`%q4R?wMqG}PAUzuaFRvjRfDu{7N9>W;gjPWQ%Km*=Fv
z;T)Ql7r2V9baps@i4-^{v|jt_;%LoD&b+~!7i|6{SrihSlSjGIq-55lan;S&RNHWZ
zEo|i&(amv29zzt4G18Fg&aW!ru%?UBoJEuNt$G(`Y@&F`ug_-pB#vjq*&Idy&CbY`
z&2=len&?OMC0<WUEp6o)A*CbgRA{gtD-xW_UIFKIJNk91PS+o`5J8+PxmmTS>|eg~
zOfzI)Ef#O>yTXOo8`_t77DqF+g?##Dixvvj=xiKy<ry;#*CBYn938tWA@r2e$FhY*
z2F*lcWiLX*e|^$ondc=!_nMmLD3;CbsLeXV`@+|0#F_(r?%6)LI?J%(lX#2b)uh~k
zr<rJB#SC5S0oj#bblld#Hy9)5x|@unW&^Hv_G4xhB7_}$c~tfUMf6i4jKkU(ESxZe
z3%UWYmE~gJ8U08<d*Jz<w8pe;7@LK?ocothYaRZ~y~A8xs?X$1;wHqS;DF*g@)*}<
z>rOl4so>91M<L}TlzGpFHE7Ewhq7lEmqhG1vl`<tU+TwXJek>bo~lrD=7iwiafC!n
zJ%cINI;)m>^n4%|R`y$(O9_#biB3}Ai4!6%ubH-E46-7X&X#caHX&_=bHY4*PI0C>
z(>@9zQm)m-T3lY9TjZ#|O%M;E`t+{<q>bRf%`7C~T<u<d9egPJLK}ibD$*~2mtrg&
zRxhi+-0;QPYOP5B-8e@G*Yfy?vXhR%Q{`y?HWH(fckVI!Qt@QAB-%%nH&-+hjU*O!
z$A^haIuQAKt|9YvJGm^05jb>|Di!b6?;204s!q^zkRu}l@VW@l8h4DrjrfB3E+g2>
zn)|TVm1>{qvrQm&m=s$-^LQ+st>?=ox&d683EL1Gx$+5Bspd$!1iJX(e5zF;SyC6J
zEH2|E>Z}l8;Y4ZbuCvLTGviLMZWNKKU48yBO%xs}EP5k2u;9yTlOTs1zijp7vcUu=
zwmqqR8S^u0Vp5^2=~SVHl6@V@mI+&eH+X0~`6HZbR?FfY{7Nh`{F7j`!LS@N76B=o
zp#m8L5py3)Huhz|!3-BKx$ouLLjz$L9X9KZb~Q0IJ;s+QqR7)Kyj0VOu6$o|7ER=?
z{p9_`qq!ESpT9RsvZUm<nQus7<m9DPm1MD1nK;Ckov<*E2`XxodZ|C@17~zzz#*?-
z9208QsY95kd4l14L3czMz>yf2JQf{A@pZv-Piy6#k?=R%8c<f+{^21rb&lK|HVPY=
zwYdyqu{=133_0`iAl<4sENq^Yc?NIbB$ly7hVGE&-SalIl$})7@4tL?f?6hlNe8y6
zAY6hZ*%#iKjo^z}lf__!xxTj4mnUj{HJD@_Y&C4Ax!#@QV6mntDAc`nm_5z1Ix)D{
zAsR{k(|dMR|D-^b{P{gOi(jI6kVw^!r<Xn7Bf@xzNrYfo4Hwz+)9Os)Y71Eh5d*em
zxe4MkrWG5JC|^>h1f33-8w#%_XWm0Y0*hPLdDko|WR?})Nc@Ilq7q2?Np{Uile*z%
zI-*uP+6Z*{o!<xRN1l#rN=UVR&t>ap@1?xI^Kh6{jp#Yy?>bx~5YSsDC?k3aH|(|7
zcu8CxCmj4_q*&VI!^R5std{Xr^Q+N93tMR&$5o%zTBC%eOhQ>!Oz)KIy?10WB2JSq
zjuXSQ^&%Hx=#gK};Jji_{r1sX1fe?OlU8!aq>Uu&AwxA+@spIA^d*Rfu1}%DfXrfC
z#Ntva=caHxk|V*!vWK8v;Mv`=e+_<_u}U-E1m2y}w1xW&i365-=<=(nOv)8X#ECrx
z6CJK;ifKz*4D3CX>4f~brme!*@;I+8{9M9K%w1!|#`MNind^WJH_i5e^+eehYG8vd
zVhIi_+jbLWiRCe%=)ij>%3C9$rqd;Ud(^y<-N=M<y3FKqc5Bk`G(8mbAymqU5^>x^
z5G!!2k#?QbOtGP5!tQ}UZAJay3kRys@|xN=eLa5ti23>ip#RwQRop|zeZf|VANW)b
zsQA1g@12>PK=>REAE~tJj(f@#lsB|if~9GSykI;19Y~st*)(|R61dgLFZt%G+_6rf
z2)98uL-55$%YAUY(LcJFYO{EOHhoOVFOHq=6B-M;m)R4$6GECcFRd3iFr9?u2aF<Z
zMy4xmtpd3DKwde}d6UsToU(pj!*DNwR;Qk7Gin8)v4OND0a8`(90?XDPH-o<4S-Xk
z#{*pqTRp%R?1^db4kQ`qHCtWtqL#aUsEVL86km^SfDiwb2A3)W0D-!_qP@+zC0ap)
zlIb|XPMh$zNBFC+HFMh55=rUp;uDCO6z{~P%iIlNYX?tE_sNhZI5EigV2q)MX-zfq
z%{b3hToK>}uTcoeyc!oeHi1U>{Ej(Mzv0O7Ngt-mF8ZqYnZWJy)sZh2_fklz8x2s7
zyBm3~R+H{jg*Q^XTAo^j@4w^@vg0|2^v%08@TgO&zOnHIWm)yD3ckHKM1XZ4%ohhB
zidb0?pk}Fk#!hkBS#B(ln>?&4U<sH{PK>RoudIk=Px06Y%N20P9i7+jSmuC|Rrh=?
z&dVtB>A>#32xg1AbytF)RCi^A9;W(~*qY_|?ys-*8%)xK2y7YcP+azy0&Xqpl$fGD
zqP*$N`Z&pn5HH{7h}klP{=1^q*Q!xw-<o#X52ZRQ3J5t%UKaNA|BNorB`>mdsAu*z
zgZ6YeheoX>%YLloN=L@O^A>N2DgRZrA-Ic}ZS|vGvBtkUxj@<iuQESU46ER&s{7@O
z0~vRZsR#YF5x<hv@q>d*?@!I83B2nG%N$tEZTBDYMl{YqPrI_D)=Qq2c;j7gKHb1F
z`w@rf<RW_D%frjN*Yi5VjaYiDN^=2BuCX7h5z=*&Fw<Fm%FfJffY#x&u@HRO1?Prt
z7$Tt(0WTl<3=wC{&Qb>H1RHi;LR~ZQ#%?7<2f)N;x6`*tZ!Nj&xa)eDL!l&f-c$j5
zx?-$BQvH|dYhpVqHAEIg`&?%pRumzC?|BH$#k7z3m7N^}$e)ZJ@FcLYeieMmi@RDs
zR9&?8V5EBOkX;a@i>Z}4&F0;S$@>7khwE88)>+Hio4yr|G0V}Q8E`R<rRymr_=Rp6
zNVoruZ$>yAPNJ2NrlxWU1i)fPyPt=mT$$9{CvWMQ_>J5%KFm)PZZZ4U{>rb9`Js^=
zZ==tH%;-PQg)$mgC1NOls#20Io4$XgVb>qQ)aZjQCO-?KOvsNU7$Fs8TydwqetI1`
zX{zxZ<B$jciL+DR-o<$dLM%N+{8|E6s1lEXwO7t);Qo)gh#xUQWBYH)Zrne^LE*FU
z<R=4%Be=8qFLO8I$SI*>&dqw}rmms<oZNk3#be4bfh69Nrxj2~vdJU7H@+dr+;rJZ
zgW_yj<XH-iu`+d;YM3(LWT#_h4~+N9iGu>j(|uZTm6?CARVtjL!25krIq!Oa)O>!c
zlpSnk@Fj^vVR~a%6I-<7)y&zi!tc&R!*_f}P2#^O;H(23Xu8I5^wXbf_w)4P>W;Y8
zZb0NJo)GHGtm%u;o&s<q$ovVR>o-}vwAP2aj4U(`=fWz0@3%ctq7U*I=L&gNQgu1?
zHSB?G&^7Za{QTb6EEam~$I>X3$CQ=u^4GSy_>I0U&^_;*CLm*em+upwUBWs~&l~3k
z2ioO|q{2ca`81$ywTRE(I!GvZNWj@~!0QbQ)Y**M7EJbLV0Po5<xH}TDyia)YPUkQ
z+9TUHP($lbpe0D7u-A}z+5kIkO|41VMkwI+>rOcde^z517aWZ4XE{HAZL&^|t9vin
zP=11(MHRsHlVy5z^$nRmCBp_`uS(B`IL22vbZ?xh9kM4gcG6PovLWYp1A9i5WhtE1
zKK`BWVuwL2<8{&~nhhsfuvZNy<e?=3{C4`^PrAM!^&zwdjm4{dz1e_X;lLj$sbW7g
zIaxrnA!_3nZ%dZi%qTk?nJ%5WzO%_WyVn-ui%f3RL*4xRk710<>ideUCK*Pd;t>Jc
zAWC2Z!3#VB*F>y;!@U&H5};mHzo6U1()|>K_H)7Ow070!a;xl6YsBN?!A0yObNS_o
zVFvZq)KrpcwT&bk@4{X0HN=Y;b}dv1zdyCr%mT&BvsrH_!M|qde5+<wCi<%0*P?{@
z3%-~5E1993C*mObjUp{+2SpIs))?A(%bd3)_6tj{<KCMBeeLTntH)UT<NFEKq0B5_
zpOfiU_9vFYkuZ9WqEEtak<pwC&4dXdm~zidM=UHY>|+Yk`w3}=D|>21g%TJTz^WWN
z723X@=&o&TSNA6H?Tz_oCO&9LSCvW}d>{ywl|gfEgdszRdVKLatTH?6-^W;0hz^lS
zo>NEr(uM*t;WJfE5SsD)aB#h7R?Y`IJ%7yC(ME>IqssMph#{>z%N{%yX5B(;(BE*K
zr1;{^e!^~yYE8yP^G|*bmmwqWP<#1ypnC*w*Ne_HstL$m!ml>&2|3Chf)2%7rhpXL
z{_f+BBH;{IMyQHsURRz;J&yHKyA4SgNBM!!FaDPp&1{TiztFi`ee_#Sv>OkTCF}wW
zlDL=OmMT8GSg3$=LnFujf-i*ai|q7LB5&@MaZ(1e<x2zBO>%3^gj8SKWQ|k)g+wQ;
zQ;yT={pocnr)scmA2B2D2){(PQ<rE8_SksW!8~z5y35a_*+m^`_ISBGQRXAfkf3hs
z9!a>JVvZ0{M=EBcz^zCj9_C7$9q#%&Poy<mZS|9@pCtD^v{6GgWE7FpXYa$hoN4{c
z3*}2zV@dTiJ>te=la+ry5q%R(P)zjBIyD;ZiF4DxeW!_k^PP5r{a}PKpT4V!LvNbD
zYgR3G6QsKT5AxnJD30iBA0%i(@F2n6EqKrg5Fj`NcXtMYI};KD!Gk+PaCg@P_rcxW
z-DTL$`|kVO{j^`U{<T}XRnRmuQ$5}H9(m4l?mg#ethe3X3bcguO-4c-Vbj+R@Uts8
zphLaycQDP`_||C=r~KXFJZ)Rech>7iY*(-ILW%X;dfZjXzDZ;<Aw(rQiGJ#}^I2<U
zRE<^fDOTpHeQi<GBxV-HP2K$Z@5V_zEm{g62+PL1p;q9F?6&TkYp=|VPZnXc$ZXM7
z707PHe>X&#kVeW{_<U@9)|Lb`<UQH)W`JG^BLjO&RDlbET&PUJHpVMVZ#&KM3H+bX
zzCX&W)16+m@c*L3YQ5JLf2;kYgNNzM_weYiUj*k_YlAz5)Inx6uM7@%?2^n=)@3P&
zSX8QoxvYd`DY@Riy?k5Ak!bBk%%@SO!#P}8hcWWO`l8u}T<E698fnN^YAph_b1gn7
z7hjcJPL?glPfGcuY-u|pt?$VkL)M;O2I2bcXmedF(1jKoM^As(TdCYJpT0%&dOmkU
zhD>g+p|A3eo7qy3^!B}qsB?b{SyPLqw_p1YPS>A=+rlBbH!8B3^%}o~*<0jU11$wr
zBr?(UplhN%xoZ)unr)Hy>&zQ1pN1sX$M^MY(BGt&jBzLEhp%p8Em<Q)Gy+8tH%_-B
z2$pgq8+iAucx>JnREDlIF!mc9C%K0kWA48=`1OHr>Q?ANw(WHDvvnQ2iDk?n<s32M
zTXJ*bgBm=v;fxo(x$?k9w&tJg)HK)hwVIz&mf@a87~<feNaB7fJna}|QAMMPngdaW
z9#+<uc(1L42#6LpW4vQz>0<loVuulQfy&#6!as+m$Gfmz|5j195ABON=^wYKY7Xxd
zTBzdil6}|Su`*E#o}dq6-ryQ00h)a`eFM>I5P|#S94o_eR%9G={-S5gfAPS@H)A}Z
zP|m#J%g@(qErTL+n|YTHe~-?n<<&g>^HvhyDM(Nl9|PKvQ!;+c)Lq&EwDe|UCH;Jw
z7hG)cRP>+}s?+x(XG<Pi@*B_X^e{f7F-=|Fw~={aAL|$<GlN1B3>1ov+^qlW75Ll!
zR6tSMh{9)truKW1neRB*swwxT*CUt`19tLBUB47^$(5b-@u4p-CurYZvDQd<>NEej
zA4QA#Y>d^>Y6mAE1WId)>$N|uSp%ypr1F7U7n&#VTU~^6eGD@s{8rX!$eJp!+Vzs4
z8E7p+7<H?z3wc6uct$w+k%>+J2krfKGg#X3Rzi$2%uSdt-P)qP6(1-dffQ9J7C){1
z$TGpsnb{C?zwQ>&nUt=M+G}>C2IwdzyNGOgU4DVBhkXtT4i(TH_f|Y-q~%RE$$&;i
zn2}oAt?*hz^x!&c(w(tsEwVeD0S-;~UJ|#mLHUu!pHTU8^#>vi)>rh56h@+u1cvGY
zxM48mx;~`CXAS$N404H5&dFM^XM}Z}t2jfVR5TL9`S_qv^`-ME0f|!gW<7MMM4iwu
zFj9&!mMC%S&ZhdISsKKmDa2&<6lX@EH^1`T@#+zq^2~1UT8z56mD9xM%k(Mh-put;
z<0-00F!_^{-h5eDuEFg?rzMzWPHL;=T>s<Q_r`CC0^ZSvfumiAC1bSQPvh!%VsW|n
z4bGVryH0&sT~A)~{7QR#?QV{pCl?~e3(8e(&1Z{Rnia!jYOFm~RMgtye<=brKTVX{
zw-I|fs(l;Q8f>a-Y>8oln`VDun>`4hDSbIgNR99Cu;_L_ii>Ff648wG!v?-Kp<r2x
zf!kr}Z5m<hQ|fv<nVnX2H#t1Ue)VhLUeZ!0;UAd3lYW=3TQ~^yO1R<3L_Rn<+e-qW
z`{M=zRNSyHI!C^aSKUbJEq;cAp-$AGV(_hdxWBqKZRD0+ymcB-?S%6#O{R0tbtmQs
zY%#hFVHbc{e(d`TS%4YZH5F_GF@<n-{Z#&3gd(f3U9PI0nu5N?2SFAuu{}3}dC>%;
zi`1(2pbE`sNhz%6HJUq5=AwsAXbC<Tp)bt+q4_59Q|N1}AFLE8rSS3|DE|dSY;AV7
zp%?Tq#+G-vuiLfYRtl_>-&Dh@q!mb*zOB)}iLKZnwbR0{(Xu2$lX*=<!c}EHl9PAB
zV7i-vFjnqnm)l^Yl<n#u7vIw%{Z7=D63-KS)$jUHtar!8Swd$+m9FxhH0UjXs5rr>
zs)6sw4cBHAC#Pgo&8b2~f61_0iN3+2ZC$%nf-DV^$zNiX_q`(M)n04J&8Ky#zf*hF
zE3Mzk(IU6U$*5-VgzoMB9#N2sBa)13J<T1P;ovi%)Jau{Ib>>zFwr5Dc#rX7*GbEl
zXKie)nKjkKi*GJt>zG7Vnv^|!huqt(bf3u?tZkd7*7QcK_dHvyCm}QjuoRhy#ePAY
zJRa{ak2I~bI&4HowARsrI7HOe+-ob6O(wy4^38&j#~~`4NQYjO5hlYMl%$I`5hjkS
zdVPHLiW$DKGS}J*d8c!mSPkXUSpgYFK5Nei__sb9rzauVT4w}L>9KRRPQ@&;EY*%n
z6ko7j^$7h!Yo#=vc{EwCTqhmI^J5rPnkb{H{<bv<6R9dh{)Iij)eC}`t7Lr@sr3~o
z!Ja#S11n-GWS>f}X({KN1spDTA{+Kjbj1WlYZc}HE|XSQSI5|e**!2@MtmAz!A?rc
znA+u4K_gldrGfYM8uGR^)q0jXma#9-OGwkbrg%jusRPFtbyF1nV@OlkTouDwoZ37S
z#6ZhYCiYqI#4tn(!E-ul4Tg8Ci~Z_0bU`}idL1SY7FL~@nzB^q(C0Q^{pACRu0#oH
zUxyJ1{5+|#sYzwU6!NiSwF^=cIsf!d`>Vfd<U6|w>rAXS9V8sN`Hv*uWtwU{YY8ec
zXpwN6U5Nf1L^^|nHetcFVahK(bzz#P=0e-aU~{wP>KFbqV18MQhz4`quOIjhfqh>o
zzI>y#<N^j$$Lr&cyvo?aWOjCWON(9++t@9u=7;2!&@ID6Amgh=zOEc(NPZ@k`}Es`
z>~yheZGNu9E8|B18sl;^39}rF+|YQu&Uhl9fTeH$8D~e6R^U~bydsNDZsaF{_Eefi
z%!cqUs6tw*rw?oS!7Zw5)^4v4!YbX5*J6m{5Ri<&sHaR!wxRL=E;0ZrP&)=be1vhp
zs;nHg8Hra;^x#OhmcB$K%KmPu9@~FRW~t`FtI~Fy;788Vu{+I?!R>nQ@~<@BYWN<M
zX0*vDu0hdl4DlxBClo@yx-ZdH;lQYmVgn{BN~^fvS!Xqk%j!^y3Jy3s$2MEVD4ynb
zy_?+^LQP$l_w*Gg&?n9bk_S&v(cu*>31bg5`9Ki;5_{@&fsGvVeq}MdWA?l9@5uZ2
z!nJIqxRn{}ZbkW_xwq9y)sw&Vz$U8flF}}S#mAlASG@#{>rufMx8MJeSbirsR58+i
z>DwHW@1W|QfhH0@<0S<&F{ZioU?@C2kUy>cvB`gAdfJ{&gIA(Lb}A}6EakPvNphfz
zTAX8NNCU`lOYM2qdC8Cu<rm=)7%f7$&&=<!4`did*vE&p?HK#lM5o<ud2^)^r%X<6
zIDoQC&JbS%J>rztX2Ki~IW7s$>^(>@7n>KC^^!TF<sIfJ-;2x?09ViqUey{C@p3Bb
zb&s&Tch%(jpnhTyAT7J=e{!Jd%eO`vf!*CkH*L`rs(ob>^Jznast47Rv0e9oDHBUx
z>olS#_Y7O|jK!eq7eHf?cBm&f&uvnCFLkR?{XiC4ER3QeD~eFM1X9F#!S;t(82flC
zOz$mNrjL7R_NtiAIsKacBhdNw+qZw#qR|Nd7D?(%g?mxjxTObWNJQZArAJv&s7-AJ
zpUY0E;~L}7nyM4)kb)0_n7ExAOUjVkFZBs;^b|~*>l0oDYeb`0JYxDmfHpQniqll@
z_W4y7sgGz7Wk#n%i}xji%{EQ&i#z&zTLn#p>xCU?TEn_QsL3Asx6b;JdDE5vYS4SM
z8+5EGl~ic3<}Atorm<fZ1n7_%e+u*XSDkMKY+b7_CQ~xzdycOyf~Kc!avVZ`%9d-K
z3Uqq0ahOfH)fx$kS%aI7#7a+&6#J><wpK77xh9TWZ!IR5b;`_DPDs*=a;|U6%+|HO
zQ;jJIm8BBP%f5`%?)~V{dsc2;G#>;77tJb~dA*H!m1xHdH(Rdy?9m9e_HF+3!B~9P
zuHp>TxuhqaA<O>TXAon{BHSVlQX{NIXZDNi?INXOUY#P3Fu`LZQJ}x*?zuc!-(6YM
z$5a3M@`0@J;c2o9k!)M+vvRG|n@)C8S*e6O)ZcE4eIR%2s>!-&S$@yu#7x_iX5@(y
z1|WUPvL-WmS@w(|aVsU<G+1W9=5%#VC1`f2SHG5WRWL^LAv!B7;48kdS`pcbctKV|
zV$SUDw7gCJLJm&kfFR>!#+7?03;O?5Kl#t!MjPzM8k5U{(pBy{<weR;>lXn5pXX$O
z)nuQ!L&jY5g0z*AElraj`&Irl8??7{l0jY6=p(WW`POy+)GIWTn7qZ(8m-s=^1c{f
z1;x`I%wERO@NN2KUedM>-n&vDw`(1#yrIGAE1YGkg}DBVATx2CPfnLHN8ZlOqwA`I
zWEMdaVHQDvD@~wo{9+fyk~7E^zxBa(H^p|z$|HkaMa<8`Ot4g!39)tRNBUX#(!BT=
zQ}EbbW$>(@v^NQTMRmq8Yhx)@%r*yY@&?Mp?rh(`8-Y~J`_&({TYNvVaQA|1uhdU?
z=xh8h-S7Foaxdxs&1+|8yga7i8VKF@c{hx&g8EsUywM_(SIKKWQ>`GYhU#CNYZivO
zdXMGNk9rT?sMc9u>5oF0vcCRDM*h#|DK9bd83C3`Hi88G_Xh-HbX}$brhk|C|KsoT
z945MjgSm^VvxN!xzfK)ZZ86cg-U(2>qx!E?5fM&xuP+vy+RCQ37G|!TYHp^k|NfT+
zn^;+JYT1~(T2l$~zvKL5VPj?OO7;FdKc}Rly`!_*7ZWoJ&W{%EHf9!|Wh6KyY+PMb
zESx1B!CxF5EF6GOa>_cmS~!1kv^R0Jpt3Zvcd_7<wy_6Z;*|dP(nkw3M{^5K1q%l&
z;48S^aSL(_h>2mM|IfX8rX8kxXsgL~)(3p}sv`BG(6lqi^rW?%K;`c@6w<O*4wj~$
z1g~ca2uQPNOkWT*GtqpzT$WU;xba(owC#Ac>r@X0CDWO6rDQ+)&-fi}1qr%XGJJ7{
z#tOQ?Po8m*#s8oCS(AfbtUQbPT(lz~AjI`OKfoXsWChPVjW`Ghe{N)oK+ikv^qkLV
z2nb*CqK{xu=r-`VTlgR73xu!L|8@NS|K{^A!_W(88ze4?JpI?z)i3W*qsQNiu(p;e
z5B1{p&#ymGwqL#dEbNz-CZn6R_!|LX5@GB9BEQPs#MyOwLs+9xFM)y;LccZlN~hAJ
zP%f}P3V(JaH0@&%e0?%0DpzOSVi`YA)#Ch3)-qx4jA#By7*C=9RyW9#wZ!WO`bP<?
zH|<5$2ngt9cP_i}!r=KjNASw=fsNTphG(`j=phpI$LCU#_nxy`KOa_H^YbOkRfUhn
ziXBJqiU-Ki5k6q5lz|fSdYcrT9cJ?4-ybc^vb%ct7{xGK(J!+mx?3-#6qyGd+=iwh
zOVzrh)-$TLzgD$A<F<-A&K?MV%YAC{(K^C|#N~pU82t?W;rfuo@2SJ%;nqJo(f3*I
zXBTBUW~Q*PGt=JTjU6chNL2q~=d0R|27Y2+68@I|_1&S!2fqG}ah7+t=K@Q6%bfv}
zN(Yl%^FF(Q&cTQXDoAk$FiQDqhDbLRHMMLk=4*F$yc3x|HA&ThY{MlXwbYBr$vkD}
zJN1Op0(Xui(TI5|SXufx9>veZxmpWyjXV>iV!IusNzZ%%3FR>vU8JXj9i;Y7Gy&qK
z3LcqMeuqz8+NJ>psjUTdd{ve3lo3P(rd8Mq{w)u}s-4RH<Bl4d$KFe1pkZ4<<XZHS
z-WKlaU_Md_JA1(}%KZF}@-1scbZswmg)kS?<~9e8sg4|hI))m&)(h!gg(3Lb{U6tA
z0wv~%GGxe-e^%Gf*lB1KgDC<{0h-st-B=S(??@)M6-npfSRKxLcIMY$oA&fv=P5S>
z7Uq{MH!mTmA9Vge+nG<T5dO?$x<VL+pnMUcP44y!40nIs=12+YM9BnQshW=l=r8aW
zI_6hlj_7LX<kF{}B4?r<&=)F%z^J<<IwYker%sl`Os&nkD1v3_2pN*51)}8pwT`V;
zrVY>vr**Y;(NKf^Cen|EJIJOE-X00Ner}X}fl%;5|8%FT&+aflD;Cy{<EeceG&aMT
zl$@eMT6Ezls7yn!AmR1sRUajwvE6&Vljzk?K3|u2%`^o~jzw9A;ZifUcRIdCs6{Tj
ztHwAMQLtamB5+f%Uilq1PRj6oaa=&)7=*h6->7M+ss}_g$KN*JBRkeb&XCoQ7Xjg!
zkmHu;w|uXAi3H5aCB729H^1s|DZD!xV}C(yZ8)|UYP6l-wS@kL@oH^Y;D-(0GIQcn
z@{#@gj2j`s2i4Rk9qlvb>{Bmqag;lm+8<#VkP#05_>_lm`xve3W8rc(18sQpp#b{{
zG4A<EogFt@at9IQxMoxF>;lGDxY<pKqP${-ul`G}?W$-7;W6$@7xd=Vv$&|}bMQ(K
zX30Qxu96vCJB6>o$kFyqMvCsA3^2F*$G{dE#!5EYV=ssG=B!I)3uiuM3P>3jpu8;3
zco~*Gtt_8#+bybk;0i2W21#=Fx4akq#gB2=^g8Ff8%B2*S`y36L|(E+%RlJuh*;v#
z&NIXYy(NT{%nbuxS0J1|-8DW5KJPX25Yb>Erxnqi>T7%Z3*L<-kJ`BqmA>5&>9<)#
zuGevyaXbqPmt{BocyFjMpxKTjFFHN)1$x5mv~SJ0!!`1TQ88T>ej}iK^2UmT-W<Ol
zXTD()@vQZYQd(<VoB@j=16TEJU%$(`MP!B$=e_FRYV0)u$})o!9`JT~P9c^O#~Vgv
zqXzq_=veBmPq{~Nydm>8e+n;@$I-h7bGF6kBZxR9=c(>h*Q&8+PP)q$YC6HwEtMQ)
z=J{{*m$un;olbXc#wTMEo#%wdwp|h(%dY1|x`)%l-xZpl*UXuBNpv`bd`#h8H9xgD
zLrp_}MWpGgeqT<EkoJuho`&bv*-;|qc1tf!?mcca&){{2ed+2}ph&Wyoeye~iuYwz
zXeJ^&Y&JO>=ym~APKjh^_wZfE)SS)qWJuWd&}`fC#q^I%BJLB7xL&Bs#t9n5BK^o#
zCT;FGC9<T#3498t>V{L%`HMT*8h?Ls&8fsHwdq&rb1R65L56Z==b+VjZ{5gB!NZN(
z&((=c5WL=Cl&^fPeaU*Kdj9?3B$Rs=v;QEcHrV5i*WOt1D7u9?ehC-CN$1A@LE1_x
z&Z;|@47A#6%S;So-slY{74@wOd$R`*ni}j>Lc`DP>RT4sk)^AhNG1CA5#+6wBq!bD
zGgURG#u({AA|)i|a_-3))oqd=)rPxX^!}FmpGXKc*Vx?{x*ZT}nXc4z)(UeS@Ph?-
z>eaQ%TUhlqw6w}~E;IpcD_p$xHYYpV>`}D8!o=Xa^}U`C%gal!@vG(sFE}o^sia7p
z2)cguJqer!&!ZT%a2PVYdq0$CVs^&8O_-3=u^Y%{I*4gc@(Ni)>^m<nZ@8jD^UXe~
z^Ibw$I-XvhyPUUbVK0qD`C_Hrgk}pyVeBEVn8Evl8L0>ofvK@Ef)(bde8ch#r=2p<
zNECyD>+~kC=YFPMC&C2H`FTX!E;hDO@?ND+eK>|~{!ol`Po(+TdKia)?xhhA!g~~%
z$2%R($F9n3jU)`xNnql|42x&ayGU7!46E9luai1YsWD5~+}zwIr=|+eH~$nD(^k~+
zo-8*aIkDS3#PmXu+#Pw!?C6ww>%b01LbR0~C9}=P4rLKwlHGlE!Jjq-#i_|D;bcPZ
zt~HY$;reML)qh>Q^4+i9BKECqY^x4zVk2jGiY7Yh`XxLBvgRhY4>URGOX(M?Haa)X
zSehsLdYaCo!(`kYbA{xu{bl5=UUh6*)6|n69_^>TjV1gH^_bVy-M&)n{}p3vw3Yv<
zIB;q27u>J!pt8O*2=my{!lW!Wm)VegF1j&!Z#2M<(xClz$v^gm3O=YF#Ar#l!0*Ux
zxIA9;H*U}6HYJ5c!JdS_kszLG{@tUcdNm>Y9QQc)e7}x1$J%_M$blo7n3Iyx%7>Ql
zAk=3|bNxY0u=z<GSs)U;?K68M@P1n3mx!7`7Q)oDncr$NwjNC~_n#@c{z!H>;2I<v
zgygz?$JFy>FD=rSr#|Xu@h<$&EU4*R|4!GtCApMy{a6#o90ToIYOygZs`#MyFxy7H
z?nbe)GiYZ1S=ZIgwa&XVD`S9NqVmAeynC6pj=7l&5nRg0O8A*m?$U}Tt!zZe`8qns
zkF@!`geCv&;P!BWl_a-VyqY+C>Ynsf^i1EE%9)>ZmXo6a<J+p$v<f3C5|QLJyaKTX
zjH(C1i7=L#9|*Vgll%~d;Fq;U2hZwlVh)d+lZ*N0#m!MM&F&}ku;?7_loZ`!{}t^n
z1-!CkP%M94r6VWIUvGY{m)kCUauzD=cFX}&QdAhG=&_O`J1wryPUQt_f-%i&P=uON
zG+?HHQ?CFVz$<K8jv9x#v?rJArG^Cd$K4+KI&-v#X<+_7qKL(rqf#!xMqWjeliCdy
z|LvXgsz$%uX}Nv4E`@h@zd5UbD{OjQPli0#W#E2CYvF`j(ew&j?XdpsP35u@s+w3{
zj%<lKZ2l8j(UxKOrgEw%_lGY{II&;}8?DlwH1gL+;3*Rb4Gdb@g|9=9Jx3mMoyk8I
z<>^H&pY2H$z2LjBy35XYtS~v?7+A17ggFP57dB3su?uU7h_<(ALE(JGYAA4wuXGI?
zFap6>v2>z?J|CbN<*z>%cC@oU#<TQnoaHH!-Ef0+)TxAzDSSMBi=1hT+_+J<3QOJb
z>}@2ecG`}zsA@yRy#AD<Ug1D}eGi;1=vPLdY81=Xb0s#IDFT@uQJYNOSB~z1TT!`<
zI<tESi>fdEsa9cGHP$vBM@krTbnGX|A<TZVU4KTugb(H@F1R}`c%1^Sh@$nrl9C3K
zl+T2b4w4Ic?dnR_(@<j2?lxAXk=}GYI=ZTc(&uDf10aMEwKMegacFHViezvUgRsH&
zneZMnU$9PGhTpxK039Cx{h@jAkFLdzaku#rzGblsqUY%{Nvn*UgJ%{1P0{@&hcFy2
z)QKA@N8JzB*421^xoYvOw9+{<NWMD8z`SJDTBb^IJ)V7m<k!T)qZa$4dg<E1puM-(
zXDY_aot@4%nXkaHJf@~sy%T>6CLZiwjm`Qz?upckD=AQ_bM_D_mGc5`u;3kBL5a}Y
z?LZ-Z?SjwS<D|;u${EtmuGgxiOtPagQ-Gy6mB-hvF<tF{#m((~-_&%dPvqQ355gNO
z_zSv#Zk_F%9|jtsUdGSjl9P+0`81n+zmKl}no`3Snvk~**dW_00%Ev=KNx_*bNjWn
zV`+QW?m$vXRz{OE{W(aG2k~q5q%Oo^dEDVSS}l?G*ay7mV6O}5+T}ul!S#tur{*Ir
zDcb_pwvT+O!;A<td|ABVjTMh(?qjT<GgvI`O}%e#b&6gXZNL%|rhAo*`k1}gOK!DK
z4<)_*z1jmLXH5;7Zki}l84TBEZh7{)N1k4Ene{8ZhWhm}6}%N**3gI-v#z(Fl6GxU
zm38P(Mdin9L$)_t5d-X%;hdg{;9mCwpsHnRR#n~VfR26orUp;LbxHM0sQ-}J>Qx%-
zSqbP7`_Nu7j(O*R@P;8#UXzgUXOt4QAu|U<Z&n3byxZTr2L`I%Hr{LpYhAlQv{Vqp
z+yz~EIguufVUho0Mu1PO%I6?#G3Tb6_#~8Ppn7AbR6nb<w40)f3DWk~v%%2dzS5|x
zQ?~t+2E@IZ?!rxcKN8b!wmK#`mQj6LGU&j@Qs3|qX<9^YmY)HHC$6Li-zy=5`}R48
zefZ4T<Z+DS_hc%iv_9Cf8G0BPmXDP>lTZ0A`+O_ZX*1rXN=7>_VK}8{rCHAVtc@<V
zr9I~XkJCE}@Z>$3d!LcNrcEkA>Z%-Ypm5lhnyJStVyP_18+QH50lV2xy_W2{$$HvT
zJp4{%!s5J~x@pa0vg+xmL2lQ#l=-1kvrtS7%=&xs%!Dh?7)@hy^+Xn+(f3hA-}^ID
zzH2LXFc=$lHEEB-9kjijpxz%8kpgVptG4}ti%9NW%ECP&POUY2)9ti~e61J&Li*Fe
z3}JXL9B%yVnJx|gEa(+)y*5t}R+HpYli%E=t|`&N^*B^%abbzQzfkPdiMnl>$Ix^2
z$Y18CrEhUnr))8Y=5C+0Us+jaPH9wt1Bq-VcUUPx(FlU=oM^WPkq0;O<W+2ZxN~to
zY&A%oQ6vY|nKknR9$O*eU`l|?GVz4<Q@HJP4z9-3``|V8O32OQ-~Gos4e@~jBUDw!
z*S1APlJ__L5$(!T(x@#OF0VX(aHfLveE*7u0D2ZS>%$UzEK{)}_RA>9R%;gNtMH@*
zgh3{J2`x&axfzJcu}vp_T8R~SyVU~{uJHLdgomim76maK+>G@(9&iFj3vR|%YHy^4
z!gJ;L?UwiVk7U*@Y!6|C3DaD%f`>}I$|=(EHC1(je()mCWWHs+KC}o=Vm>~;-pY;Y
zFgi9_BBvWJK`s*+R5%6#%FN^4dLb=~LJFS^88Lh&_mhe9W#8}b1&Ev0RN&nASw8@u
z@zXS3-7R0Bqb3Ip*N{TKpeX!cYB;o2ULy+!A_jS+@&RQVgjwR{a?ozqsgg?;Rn%M^
z^xaALn@$eD7WXJF;}%!K`LtEfnPFMJkG;2&O3s9Gf6-cDtT~T~d6`=}hU?v7l&CY@
z-0EjH&O%J9!0lA>{r1P%fIvOO@!tJvc}a+fhsOnBm92IZs;F?lsO9%fw)Q8Xw3yG?
zJt{O^&1z_GDj&oOP0`iWb3e@GFWI{%E$Wnsh;GQcY>{FuKnJ`ep+ApYi$cUxqst5h
zp2vQr<@X%Jz>vh`_g+mYy73{yfaevyCQEpqL5xDs9{$DnAP|J~;V&P=KN&hC)-4$e
zTWJPA1#@B-(T09fk6;;Xvbd<1+MCjZSMstxjU8PuJ^~h%tJ^|hM(HRb`3@WP-kX@q
zqX3g&9p+P4M)b0#kUL|LNY3u)pUjweWq@uA4`Cs_*k$RUmO-nzmO0vBEPIEAkKc9P
zRwzf6UG}j~LZc3sb$frcIq*1KR@`*sQLLWd!32+#r7?&<#m|h74~4ceng!nZM$`@x
zRbfZ_YQ=HckckH5+x1>c6;6p{p&%Tqynbr*fr&NJJ8sUIja|TYIvqH#v=^FB5K^Th
zA`}(e+^%w#;CWJya~_l$lEaTWii+UntS!9M%|?gU8YUZgbtVG%8?ZNw&9aMyQ78U&
zM2s-aJ(M$({o)S+AA(ahR9_K4>$G#2{?BkRcyj3dp+E>xz+<OFZ|&_(75(I3bi$n7
z5t`QZYXgmN$o*h)xh){xqP0mH5e&8&#3d!!h0sEltERuVigVuXirEaqpX$5SO^D%-
zg>1sq=P4^awPw<ob|wp5i$ig)fd<OW+<rdp*|sVrAV-7x(D>`81%Xcohk7RW^8}aT
zshiW?RDcZKdm8sL)bX3{wJSjt2#&{p8$`#|cD-1o0%S-=xN$SSHMUaYEDRJ=CD+35
zP+#RhbvMeutUEt1=KoMw1gKNHm$$rq>W!sQkp#+p^+QWHAT{-zUypt?fQS+x#wXy(
zv8m=a>2#GH9afZ@K48oL9s+U5h!<XAfBp<2cbQ$>8zNbSS({CjeVKdjrbC`9F-Y>%
zF9IKr<&r|T<PaQgQ*-O2JMA+#TT0#VF~@cfz0JlK&l%04ijNR~gJ0gvyPr;u4a{e`
zQ{k;XDpyRS(2zE7wv54J^?9;Xn9<|PN|cy&Eb{$H99PFx_&yncXQ!n}Dn|W;G~U87
z{Ml@OgOt;`@>lnKi1l#EkLH@8H3vgk);42C7dEev7u;Da;XG$MD*_I&vvmN#Q6pd(
zg%Xu(vpePcJVsTw;@s>!R(bfLVdM?$?(Q$+`%pB!@2a2ioFE!s`v){fp+-b`Smd)>
zKl=ZYr~aHvc{`JBNGWVPTa0-B8`j05e0T$1tVprLFDZCKKj>R>dk5><)nrfZ<)=DU
zYN&B~DJV_3tOMcr<o(`f|KqiT+?lntbp<N~YkwhZJ{|EXIj!-ZI2o=%oNq$IZ=N>)
zrtj}x_O6Ss40B5-J#wcBwgi{%`#EjLG+s(5oLZflslI;l8hNBgfr))u*p$^Z>E(!_
z?39V*2ckmJhfC->|AJ37r(4&9^K&LhLr?K3Sw=+1^nxG!90WfEOhQ+B40z7)=YtC%
z7}A1~vF{)oF>!aKxsHoF?EjfxfJ~{iI`DSeMwcviNXNb%AGE-EAy(t^`f3z>-k$h9
z@>8r;RS0+^s1x<dXjjbdIJ|!{V2gAK(0H~5pRLcD%#J~*vZTS^6|9&c{t0*H`^fi^
zCj)PZNnH0v-Phj-1B{!tPLdkvDene5RA^An=VPC2vSX9LLYRxp&EVU$`@Ei=C`xm?
z@1XC{ue!Uiu!K?$wr3`A?3@cxb?~Tn4?}F;h=}XsArM~!9zmxgQ3?i9>vnZ@^*9v!
zIHf5G**>c~%*_8BcAE!pnYWAWO4mGq8TEjpo0^(Di51yP4bbSyU1C&k(+oZ#15V;K
z+SA<y6n2gU?wI8~Gh}_uv)`>b3^sEZSEDlAg6N1S{)kjWVvUk&HxzMuf2`!`FEM%W
zZ4!V)g|wxGvaOCa@E-n?DH4)>_2i8QdIb>9U!{P|N5RuoCHDv{cE#bpPd+D0b#O!N
zFZ-RTxx|n%lwpoT5qo2htw(73vjY0dnmtF=ejM3@LsFBJu7iv9s1XyH*`Z0g%T4M!
z{#%~gR|P-dEt6)YypRz#-@jIE*3~wXw}(G|W`!!R+o^5hw`Ya!?eE_m=O(5Wh{7Kq
zg22B1@xRs7)Lh;Q8){RV6rA+t?U9bw`*%WL7_a34TqwxhiL8)DhN1a=C3bh;&&JLX
z<lTj8J11KVx7?IjB8_k+ox0f+Y#RJ^Sg^Z;2@yTRQXKzFq=(B)^st8uJ(Kwyr%Qt=
zIzx)El|=JUP(eyTp@ICpY#412My5OC7!*Kh(X(~7Pj^5hdgy|UcDct~T5Un>gycQ(
zM2~8iLIIfeHP|X+9^w5(sD|PA<DIlQnOlRJmKIdZN36MMp}Y8|z}C(6YIbYhHVTA>
z9AwP{s{xNzr~=ISB$7^5ASZe6`W-VuABw<Ja)ntZiA_{x8OGRH>jE9y-j}r;kj7_a
zt>C9DXv3xE$&`BkJwuPqN)OaAFvFCyXoicoFEZPMk!7f<60mm+GJx9-W-+!fVQ?6s
z8P9U8RMQE<K2|31iHCOFR|B$+w~`J#%ik%S^<{NpFnj50_QNb8p3>os*^UoRHX(El
z3xZl33J7bvSJ@l6vz=<9@(Fm!Z!VV+36pNdGt;^5a%tg+>?je$ztibx1?YBBAV#Z1
zey}vCDJ;KiIsbWe6zKQNNY%|LwgO4p7IUl&LiJPrh5ks4Ap2z+N+}&!g}s_No~1Jp
z=%Y0Uo=ffwLcQOKB+ncwr8{!E86hu-acdub`X1PFi)PMR6y78R1IO@OeQJv<AZ=~-
zFnM4-h5`P1mb(j9=vJ{pko(}EKWKPEF&o?lIW2uxswV7HloV0B^}3QV>2YEXz<U;k
zRTF(ilaoch%h6FF?$w(PSV%eIQyJ67`5wY`>$Hm2&6zH<?xo)R%TDNDWd-(2D{N&)
z8@PhI_#MyP%JlV7`J%VB*DE3BV)M1}`1zqBE-@%<`BKa0F}cF&fP;X+*4B1dw}wPu
z*{vTP*6&a)?0P)FA*8Fr2s||U$o<7ZhkCHd_uSI)lJl)q!DXAc+lzG8S0I--QI&@G
zk!*vHR@@)%vlm9NXbYNI)Z6q6M}9UcY5<6rfu+GTKv)<>S4QUDT=$;zZlM+mGv|2>
zN4gNV>jz5AJQEg;r$#2e?Zkkm0z`9GNc%7)JR@6TQ*acb<+8wV`0n~}JbHg4IEKwM
zNu7&k`C~ifN)wr@^-WRk1q--N7Xp9W(HXL2XZLQSA^zgiOt>)PBNpE9L5b-&DBYme
zJR^ed$wYj-&W+X93GbGtK;sXzo0;u7vs4j6+rM#^GRuE6-ypjCIK_A1H(z^_!)(Jv
z<BXtx^&o9qCQ*96(=uMKsTt!~prxU<alvQ7`3lHEeEm@kS9rf$pM13di8Qmd{kTu(
zz@!kleX)08-4-N06L}PQw1DgUxH|4t{d4i$^D^*7Jg1DVyQ1KaTOMj4ok8tl7c`Ja
zzrFvKL_2A!p10gFvz)W?-%#9{*(5A{^wyw>=R7!RR8Dn;2SNZE9YAc8GZXQiSmQ;F
z><?W%0e(J}*}rLVCi#l8^YiVjGdUj@KtON|Bpd+1V&<W^k8f=^Q)*^yst@ZM1rkOb
zGe0)dOgB;pCi`(8i6>cBVfdX%JG=c*;_4kMqE@`}Qq4Oala=4GLQPFmSop*yV$~(A
zz~mEhJh(W`o6*FM!-qn?whkq}tNnT^B2Xv~5MxD;uS^s0HRC`KICr_;yodQzkS?Ut
zapJ_gNr<mFbeBU|(c*ZBnVWxXdfMCf;M^!Zz7zVYORJyiaRFO|B5G&-eH9I3bP*;T
z!<OLGDs*;JWWn;10nbdk_osKRL9i0`22ZzLF&Lhv_INJ<yOMU>Lt|300k34F<*RV&
z>goU%G;_5I^d>Jnv8nBh=7xB@?p`~Ez}K~1$M4M?8hSh@k^fvXe!4V9E{l352*T8#
z!sYd?Oo^{vI%byN#$3O=?3OhSlxWcvac`kgdsX1uC?D-dZw%uv0@rh7Bsh7=sHt6i
z9*u4nVuga<l%YU(N9$@IVbZ|h3JQm3G6c$#ngA{#Bmh*6jGW<Ux}e`<A}24u02H^I
z=>hBN>aLgD+1WwgyU4Z_&2Pg3fxuqR_lD0wj<fgj2Io~8qe3IT6ZA0bUNyT%v9S0C
z0U`3iWwKU<qMGgd0JNCwA~xHhZ?-~2*yL&7;wpT%7-yYpbVg6aDyX=cmQTLxgEA+G
z3jmcU_dLIOMcum*J>0$O2du3NqTX^-9S)5dE)^|0_K88>o%w%U$ylPpie}2O6K!(6
zyjdtI*V;-Iric<)n)=c7^;tTJKL_Gyqk)Siw%fc{J~D6wqm<(Y7=@Iz{m)0tNOuZ!
z@g*19=WGWdT{rmuCJgiowBi#tUc~2j<5W-4YGEG}r_te1)#LNJHUm}aZ}knX2j!b#
zv-WP<a@WK0rRP~4(+~h6Tp#WFbS9Q)(;7~dA@vp@YEJV5iP(@t>H)B^F_U2snc!1W
zZjRLLv7my+Y>C$(Ks<I2l8NCe#B*^M9Y8L_J(Dli>Pt)Z8V8nB{pgP`ft-vz=eH4~
zXrZslE1;k9H)l&T`Oj4H#i+hw&@>(1S}?!=_@vrS$HaL?H174OzVejpa^ayVkk`~$
zZ2-M_uyu6I@5phCa==Rzf0Iwcc;@}dIEv#khKNoG5!|~bNmCvu0#n4mnw8-VXTfo8
z7#P~$x>zbieDZe7CVLoFqAXH~4dMrqs<L(qcW=<<w%$)pPTG?SELNIe7Czc+zp<4y
z-)NE+;2edq4te42ZPX3`xtaw<c^94&4S62Z<6kCZ!rpI1gG8HSC{=3-=)}?lrOvmU
z0LByuV?K62KGms!!1H}U;MAYW$}T|0EE)<IGOOxxFY@JDL#bZMDmU^q_{DOBd9@;Y
z5;rM5!c7OCFEP&$n*b8N>yvMDpXHFK7=ihMK-MRIblqr{FNnA`J*3^q&paHq5XEL)
zYC0u!oMR)KboXZMK<5amyZbqP>OBQpOYHJFmi}cQTZs_9phrGbOMLQiJs65^ElwPP
z9`CPL?#^WU)W43+j$dZM-HUQ~aJcsa7`NjI28UC9f4<vWJ!(Has7Ra@<HsQA!p4TE
z<F<NMVNTIP;)uFWD0KutF9Ipi885bEvHrXIA1>|=*EE*Jzu;{{%56mc$C)dZpV<q2
z=N{)a%>!y$dK6JstpK_fU&7n}E|a?R0rM@l$@!%Pg`LOYHHZMupPA-dR+su<97tQ5
zr7^0)Ma*Xl0Q)U>@)98}3&3s&2#;U>Km#QDSiE&$4Fet}MMXuZK|wA=>@d7>4-{0?
z;N)aI6?ltrp^V8LTCN8&g3=2JDgpu)A_Nuv_iZ<&LGy%NY)q1#t`~SQBjq6upUL-n
zO;w^UDyR9im&h`lHlWJN4?*QMKpi0<paDmzO6xh65P=M9>r*#ZSIqs%F@bmZFI{<)
z;-RvKx(h3zp<3QV2&*Ij1B<Xqb!wH#L_xo;y}%n+Fj|#eSlDR1H=wWJkNWim0F4pI
z0Dz3&*S|WL@R?$bogMJmoic(_jONU(X9!<#{ypBmqYBlX#R(QN@?8BvX3s`HElk?&
zU|6r=QQW?1Tn`ID;6L}){aG8%#X%352{1zn#wgc=X|J1+O-=C~&es^U_+A|q1TMx1
z$T{*Lqyc08-{0Ow4#4}~e4?NwPaJ&btJ9EG+5qYuj+GYNyFi06ECedt|G5j`UI_m`
zcm9iV5E{lJW`QpPNlWu!WuZQlo}5~UIu@1lkI|$mxy`w(o6-f)FgH3hj0p&nb~J(E
z+KFq?oF+{)xGbs~zEfUd^}~4a3-#vd&dtOYSoY#P8By?aw2yUX{W#D{tnGpYB(;ZG
zobJoi?;D-?SjpRLncB|TU(zSb22-K}%n)5|fAtiQ^#@RtW1x-Pi5S941Z6k$bw(>T
z23D$|)n7m_ny+>z^1#8C!+LerQ%wfFBceVxsqZa<PL^q9=jgd@-@9@6*2MCdv=r+(
z?CFh#Md7*RH4HOw3WYnpdhgZ-$hq9fUX+xp@ARsX^e+Laz~@Sys=;2mEr+WG5ai5p
zqo)~*oZq%4HtgY%p48Xfp!p4L+}?7|*OQFSsj7$7Guwr<IF0r9?!FeOt^5vJ_wNv?
zFaZ!5JYI6!ti@ldv-aM!?<>%<-PPV|CQB#GQgLBn;p%X{-|1H6Bwqf+=oNK$&eShL
zKRbAd%?6pY!dssGxaYEgnL+OLPqmwFR>A+MLGwuj7TJ)dO0&)sdKW9Dw>;jPqX8S~
z((KTaHjjh*O|+RUk3C?~5D^kKHl>L1>?%Ak#EhJ#6V}i4T*gHEcvyL~oZ*t!D1<?J
z_|CU)@|D>Wf0?3}=LYRE-)VKHAB3am_G3$dPMBtp+kSzab-^d}sNRXG3A^Q@t7XZ6
zak5#LB|=pba`N<{4x%hzo1_>oS1G5r-yKUiU_|4WS17}9v(vj)UUCPG%UKe%>bG~p
z>b&I14GhGf<wZsJ7OyohF);%^^bU16pPf&6L7+1g;ciY-Q&Yu+qM}c>3x|8P$>oJQ
zbC$te8}VY{2%o;E!WEE1ZcDxV)WGcQA-(43JEgg~HV&3Ty<lg5iMTGgV|}rOz8+my
z1jZ_@JMWR$zQ0f2Vf%Yir99ERxeyiSWWXE1EMD7}u)878Z+>ABs)>0%=i_yCL*}2>
zTxb#9s+lA5>fiiWaIVN(sC9F>COVG3!zkUTNTxMY5cxq*FTM8-I4du&h!*Epz1vRS
z3MhW+l;k}E2wDHy%CtjPhyM72(9Z64SJcq{{yu<z<`Dx3@2WNOBfPiMegN!%J-E1)
zKMegP{BmdK-<U~jTv-hvMJINgN4*H3o?Ml`_(ubA#OBF8rV%K=S=c_akqBvj1@O&N
z4jiH3{6wH<GN&7-;_2)4E%KfQCck%pG6B4D>AR=t?g>m%^*bMRHMNP&ix3&yK2YCt
z=yQ#HuzPaUNtMP@o3FGwj0lopa3^%OBmU$aQg<|0JF$@E`fp?szU2gWqG1<Y7kmn;
zq+`!Z@u*{3^4K6bD>c8p==}Mbe4*~{f&1rXgL3L6kPY0EME)c2Z4jRJOO+Q$FK?XK
zjcg{_Tbj&OD?jfYUx?0t=UU88niLnTh7Bg0Pd>Q39vlFf2Yj$PLx&x%YpH|rp6&oL
z-EuoY#B_6?gu-rm<Gwl#U<!p@AK8G7e^1GM|3dLenWCC-XR<_KiB1tUSGM-Zu!i~l
z#g$K`-#w<fM(JmQN8&HUN_Q6kRdEtjD<t%+KRP-+y+1qR|Cpp|m%zE$IA<3XC2$v9
z-T8BxJ@o?K#vluXLdA=>59;9Y$K<$23l)%?o3ruu)xjL*3^ezwh{|F)oVki#6TdB}
zN8)CWKrm#yhy|uJTjx+4)YNpV39tjeii1CGqFaI_oXNs(c-H>H(5;>t0juG%&AIh-
zQTi)oWA?1~n?!7tTmdl&v!Deqvw(xyxOl$*+!mmiT}@P`)=hWMGw0r$A$j2E?r=Ki
zbu>ad1W5=^i<u#<KenChN)UCBzuW=h@V=xlB3ef~wT1202doOaiG-q$%E~Lf9w3d*
zy$h|ayKo#S)71Xtu@IibrmMqF@Av;bA}-de)ED;*@etvoBfSUkz385J*Fup%ByM?F
zoBx>0+&6Ex2$t8d6z+hg7`9=k(I$Ox1^|j1622VI`VZyA_jLd11ZW#a<61b#$BLY=
z`vT24R~*NNIZWD~>qCw_GOj8vQe_1xfhs<MIu42yu6+d(scMd7Q?umZpWCPBhd&g9
z6&_@0-`M=Epf_iKmy+jT!^^|1!UDpbFpzsqPzW4@9g|N^GM|d^fr>ZDLNM2+vMg0U
z=qFa5`CcCsyYc9QV`M`Es>B*ko{gG+{8vTyg3H$8orjjJcS*}%>)^g?^9G-r{-nD%
z`fSHddTiDk%k=By_E;taS_IGvmg?*2KA<RejzhT&=*4P{6RsAiCA+knSmhyx?G}vP
zx>}`6WU(=+30ld&40re3zM<`1er0Vi9;-SJI<bEBAR6@>UXuCVH=&kYUVGq+rYiel
zSU*NQ`5(ECN|4zuhTmi3yRF(r)dli`=nN%^E<b+fhgu=f?WC$xCG|&duesmlluHtC
zTsL8Fv2?7hBKR#@T0|(C>o)1z*kIwo5Ceg7uw$b>+Ks(kQSM0o8Ovi++c7USwfJ^j
zbS^-Nb^i?gnc1JGKz4IKHXKtkJ2bm<v7`MvIHf&|Xs(^LfA^xP!`Pe5``$(MDj%0N
zz7Li;ym*(k>7Aa`J=iN_u1dyFR`N=8J{t5rHr7?$`C<&a{qK13$}{3>l#+K*@<1Ty
z*I|27b-nVa&K*Wd^#_}eW!vFon_HV#Rdjqn1)?9XL7;=)A3r1Jpo9BuO38U)d1EBP
z6ryfZ%~1CX@-gK5puECH0&D7PEcsDnJ%c5|@Wyp;Y<@j7tpzbP>Awa^@$pW^!0FG}
z_f0d?PJ&J7V#B0O*-dV;T^d<Ps*%6H*bRB=Xtw?1otEUQSE=V~OfMg!nrIN<yS!Yv
ze-c_gUYjP&E#OTOxy)iNli<-7aiC1+q8*FHS;))FW+~1oq}y1(ik}PFG)wBLTXga_
zb4ne^Yw`Ug`(aM|{^I+nv0N3=jl>wM82J3-<?ig*{+F!w3yyAMIK$=T&MIp?8a5%l
zm>+F?9Ie^J`=JKUxq%nM^=rPL$GM|uH^wB{o&<{<Mm8#7kxLIA$7e4g6rCk;#yMv;
zDE?Lwnk93_#`bw7MPlxcADdAkeXfDc-52lkYko(@{}A&e#I!)vatQoBElqFUcmy&v
zo=o%GWZJS3@;(qa^1P)f%gRk8K*k$-{)21uDKo36EGy5%-nP-H_#pddL*#b$SGEvh
zsQ~KVgIc8%$-m3X3UlAtzmF4fI-iu1*Sft$6Axg^=Nn-vU_e9~F=$!N4ReN{D>^y7
zsxjGg0BJiQutNOff4$)A>Frf^r}{pc^~x3mzrGfYAmsL6-_$xIHvd+2?--k}AoE5a
ztGU-?$jib}hPg1)tFfWs^wvrN^o;?&sFGx9W;1ZR>f!>VeyR!+vQ7Heep82@SN)QA
zt>`Pt#iIO0-9UcFleAXr!gzDE*aF$vme2Ro3ANL3UoVvp5aurlDBzup<C|2sFCAK{
z12<5RsiaW!vSn=%JE3_ve&<%EyeJuxD;Sn<^jZtw@LdSmsFB9fN_(ei2PZ2Y3}7sB
ziG3AS+OO!KWDK%e*JjVf?aWtBu5tZsp#25m@MJf>ja@FPfz;c0W?CmND7Gg;l|m3v
zZdzTdmR}$!9oM5=9C2G8%HrHH@*rvaH43Pj)0IeO7`@wDZyqdn!Kd8t*Lt7~C0*i;
zZ&}|`n?J$)v`*67uj;X{>PigyC)zM~%pa>~;0D$ys<c6>4zk}KTgIG`Z|@upFdnB)
zZ%R<V-wBV7LRm>6Y<g=FzGZ7h*ApqKp2Ue_y$*lt3?4pQ|4AQ}TuOpuSDZg_wO6a<
z!&dE%rlkc;OroXa)1XrHs)R3Iyjp4LES(xA`%vAhnDo(!f3}N0Cz9+2#%hQ0vZ*AG
zE5#XqoRzsa{pCwb!)Utl7F-PCEK{tI#YsMCN@1_Mn&a2>KwoE7a}qz=Y7PUUP*7MX
zmXr4SP<MAkM&cZ_I@ERz<#^e{dMS@wK20;Ar0u7o)SF4)Tfg01*&3V%+ON$>+~SL=
zA{uUr=S=!xiD$&oPBtDCtu4=&Zl>P2sVFK;a14@iM{7AQAXE2NNb(ItDav0C%Q7K4
z0a0e_9qmiThO_(l!I4`sIj=jM+T-Kl_7@$m#4Lpe?Qx2!(Y`>j5?+qO8>^Pg>|iH`
zTaGq8Q*l7wrj2ITH4zF22RTD(ad8<RJ(b<GTcWV-VO&CrkFtMydk<>*#6Wul7lXb8
z{+(WDh&W1`M@|<E^vspEb2O*!ay<hjA5XzF!zwn+{1@G-Uv-829!CR~7FG={yVp@x
zpSugWj#!FBtf~jmc~Q}XQ}4g2bHC_@t{swU3jQ`b(!Q^r$hxym&O`N%Lyo@b%w*yn
zu0-;A-?GqQe2>d}xf6IPaZdF7yUIDSd}T+(@hEsjFc*_etM;G>^u3`>yV|5;7e{U*
z9II<<YisJQ=(fA3dGlKwNYL$A08^ZzrqW-hJ#>`rHF8lh#`1S;%-L_*TdFE;VDL`U
zb-bgFua@bFwzsr0%q!kF;)Q`3=T?GVQooiLtLoz}s*;}@z5f24=WNWyj1xw@Jt>*1
zv!?!nrv7<?-%oCKDSsCM0|=)tF?efxB)Y%7ih>q^%}t$Fe=3V_`E18#!7)}|vl{UC
z<P#+6&1dF!!oz>_@Sa(<(?0Y^4IwVfrCB{rshrm_MQjbYE}iaBUUgGNd<^Dq6gT^+
zOZh0Oue!YQ@F#IVgqwB`*6}?sP9|dY$e8(sAw@>*NLa*`)w>10%2zK>Pv)8e&Yca#
z8p^nB=}3?Q>Hff!y^fLQ%MA3URat&7z!x<c-J~c7tL!dTRD|{ic{Z%}6{L`6tKCHJ
za~StvI7c~WX2*uFiUwpQB2sceoHcec{<<k7pYc9|n)npNDHN;c`+-(D-~T=$0czF>
z?JX!MdIN2iomUh0^v;<ryT1sT_}kF|R+fEBl4^wBe8Z40!Xh>SSnN#kj!kL;zKEh8
zN^|K+Xh=j<6l}6%f4($0+G#IinGruYf@(qA!GWS9FhYS{vTV7{>-i4Q*wMSPbj^m$
zavJK=53YSU=LMxp-%fYN1nIA+LZd5f>;xQ$zy$u*O*17c5_?V0E|BdVBJE0VKYd%V
z-+$8{Tx3MT<fPjNJv}|wG%{aB-l4>N_Y#`FML{mH-~K!G8(-;`C(hBq29uEnbx6Bz
z#_fOCCwIe^uaA9;jlN|v1BXGNUXixa!s+XWvDq;aE59iGd}*TA5Uev0ziun|GZjkX
zZ(=T3Uw95EHuX?tZv$-!kM<bU=G6a1df_D(&Zn{j3H3PXdkW`Z9*O=YHAa{Efm2U*
zMb{6U4aETtde>Yj`&x)k13Yg?&?l&hMyVjV(VhL--w+Ws=47qu9~(g)U016J6ci4Z
zlL2DEqsW1v!B4WDCo3As@u$^yN{p>)5Y$&P#kd?V=wB=<_6>Z>w%H!yepX3%nprM>
zJG2rI1ou6<rmbop>+8xYxHpT!U!aGej?rt)y`^JoahQngxK?xTYVD^4sixW&*B^eN
z6>epQV@Nbuhq#D$3v)Z3#_Mg$qValIza$fKM`stgyr!k5?okdl>s9`UUFtgb>Cka(
zqcm>jWj+>0BLl}@&w@a1oUAenxuc`zYuvPEoatP2U%1d#=1s`uBa7<}^p@u%X>1xT
z>q1%n|3=+gcEuGf(V{rPgS(SJ&;<9!U4jR9cXtV{!QI^<xVvlP65QS0-#+)8@$PT9
zAA9tN-eY&IsyS!PswE_(c--5-W0aPL*2Q77GO#A7``MU~Ltjx&651m??jQJtH=5rA
zJ^G#M_Z$9fbWRv39q%xfG*z)EHKR>j?F~P2F1O((K0V#CvE_#`ji|dxdsGm@GCfm$
z-TOMXmESfP(V?~lA=YRx`)*)g?0HwgOdB{cIY9^1U1?PlZuD|u8rLJ0W4pPYYGBMh
z?9Fy1YWd;th@AFuG-LIBG3)4~OprSk-*YJi$8CGp%OBb9D`=aRu-GoHaz6G~3mVA2
z<XEc*r`*<X4C8mq&!<Z3@MOFWXw>gsdljS_^rWP^`lFWhU$<ZtTOVXmOxl@7UM(+F
zkoPYHgnju5F=+pB^Q9$55s&rY<>=tU&TooF>-3}Z!ch4A!uYAhigfUf97KADM1_n)
zpg2`Io=32cm(?j17q3QjEhRZ7^2H>!Cik6J($hl7gFNj=b-R7sJu7pMmM8ouA5^=M
z+gAT{PhQ?(8RHJM%-4l1RRxu5x0g(=vf9?_oLCVZyNknuQ=N`qYxkyZ3gCPt8YRbW
z6MZ&#?U$mQ?YH)z2>9>TQ~XGu{^Gqg8%)6Mqwiq-1sMX__{!8-#d!M$s_LaO?atqC
z!xGUTdsVO*rJvUZy5QW~JYC#*KAXibTRN1blOXupu<1~j-k;nWPQ&_E+Wf@LEq836
zfTif?x`M#RHM%**+o>F-0yk;N(2x-A94d%;Dx2M0qVMt!A2k6s7zPsxVV{t)jT+3@
zP7!!6<UL9k`4@FM6hX%kVOLaVgfdxC<r$sIdo|#(Fpz0KZ~pBrleQVKeB)Oh-#2Ej
z(f+Fh6Lr^PBd2vV<oyjl!0h1qH4v{GqH>vtXTe74?Y!8w+Z`yjD$Xb>Y;d$xWyjq@
z;?)0n^;#Ma#uLvB{gXX;!v2hFdicWj69;T3O%4t9B|nQkTE_Z)!a=0m9&ubuzbK|v
z17NZcI>k~SpjRFGaMWsHoW5V~5u(Xub~J-;%!cQAh-C%fgD~;g8U{P1hEL}s=J|Ys
zhXK%WGRa!N`^KlN`fjYnoyPnl|5IG9`R*>Z7Q59A5An9|{ft4e${}7l<V1Ncf=&zi
z<ooGT_K&wuX^$*VB6y@sw&LahFk_onhCop<Mm(mK^80;We>h~VouhqsK!_{XB)d~*
z>M35=q0Q%t;S?*;Xu1MtKz$5$q@PGB?uFRGwb53yzkaOIEiZv?B;9BiX;eFq_xP~l
z<9VcNZu*!G=nPp!ip&2MjAeg8z=hTra+0*(CPM~}w$&m4nXvAqrU5A|WBjxy4CGt=
zP3wH=99;+?PK464edjz6HX9{;_Cz-XDtdEK1N8eOJ$lb<KsE2)e>W9VGUOi%-aAE9
zx$tRrC&KS`NoTf(OAB)Jl>=9^GB=R9jDDk0+`kgwBE%4@pz0kO>T-;RuZiCVGZ`qR
zvM_3F%hcO43me-q^X<XEw~wvG)93z;YyBq;P@)7I_hG0q_w(($FLLEng?NTwR@R{$
z%$_^AwVQ*$$UKDj`j(cJk-C_v96An?pEigNQUsfD{r?txZd2@gDJatNhXU8!XWE(N
z#$M+|RY+Ygctl#x9S^wvf_U!#3&<;qdfiY72&(=h{bmG*nr?g@LrL^;>!{RiDt~Xc
zZ@^Sn7BvwP&lPgHerj0W|JpA%mo0dcl5HyU2buIAw?*<EG;nLJZTxI)<{h7q`!6)7
zXHQwkvfE2nZNcMm_(inSf85%h7)ON(l7BrvbMBO1654$T{rH8hzqJ@`W6vBwME(BS
zwkL)_Jja)_7otHf2<hX4gQ?O}#NmyoBLsVMAt>zI>2i{rK?K}RAOju0s)+N9_}&h_
z!6Pk?dL4IJ$p!|w5)$ycAj&NN=wlvFynwMoqRBMSYTMGc!Kp#?gFf^+jd7QzDr_jB
zza8rD`)oPTm}+5E-(M-aUf0&dnI1?kYty?VEKl8%0`Sca!9TeqQwIRcQC=V-?!d|e
z>sSr9na(~;VwCLeO-g?en~M<J3OUjLg14^5XC*LIy#}ma1X;Ydvtt}Q*8;jV{rl6P
z_>zpAJ$GTZFh<%flBh_6li?T)#Gvn-Lh_ch&(lvi)nJLu){&r=rAO0;rAL;GJwtcY
zIho7S{1a9BA5-a!bd(dTm^?X)ulO+hY{xbJ8T5Njc>$s%T814~94ti7jyItRn-H9%
z4*N82^9j_d<59QM_B?pwU63e%kE0IHScPrdk;CP+V6k#1TtT2Pt4q!v<Tm$~!vQNu
zK4@#Vc7IUPU!mWJA24(qC9@p(ylb4-T7(ljGuK;ITUeg44NfrbN09Jcn40szNtE$+
zCVpYTBFR?$JGSPS;Ls)+ITDpIWeD;1ihughQ)h<Fh9WhN5f|sl*B)-k@5}^uFn^W>
zPs%05^`$LA*5eeM;sYc5>i$IGc}?kfRgX5ts#|v5{%~>suPLH8uqJW~#`h46Imk?)
zxfk09JWV6b#J}D^2`&kVLnYChaq|)o#|qQz653ogzzWB;=m~v&bkkt7%#BUxsfP1N
z4F$$BNOm_mw!pFtK|gD1na@nLgSkno$bhx2OAiDKJM-!)#Nt4#8PBv+b1)H3rtph6
zeSeDTW3$T%Y+j;QqC7*`nUXlIU;u4DaeV(E0*-foWZt(~{OH86Flm_JK$mYkT-m}+
zZ(QtO)GD2OP__A>*$+H!DdZaF;U+4rLa7&N-XrC##b>O2;aFIie-y8-^}Tf#B(Sbp
z{#stLc6O#VMa@kFEdnIz<LKlHv!S$$0hQ$R=<I>IuA<u-OCzV<e|uI<(rU)gckq_d
zY#UtMjut}-Y?05*zSI6?{%_eH@~ZSh*mq&knAZhGV|d>QCCEJKYh=h*kwAEK7LxpT
zBClh-o24V07l1y$XQqF%v_i2`aAn(57<^{YN@7w!X%}((Y$#Po@^Db}FAr!J(ngta
z<m9y^VeK(xUY*VKk7c~D2-<9q#rW;KxL7%87@-MtXrtar|3Pm0z93Gy{C5-~GlDSS
zi6bW$q}K7WRqw#SRsABB95pbneVF<R<h)7R%%VQC4wZQ}9uFC`PaQYEI*k!gA6@>s
z{t`MZcw`x?fmypTrCFY8md$E$cN=H0UPW`$ZMoV!D(_$7a90;?NVH8KH&w?(m}6_u
znHkI`Zy!-ARvPyWO?iA8<KR-)#kl3k&*5gMj&uJ6zsj6rj7s=}`RkWtS%v={Nk@}Y
z$I^v6ctD5|ELJ^F09cT?=ODG3#81DdZncB2@&p{g5~Vau(x<0<zNoicGLwkCY<8QR
z3=?OBssYVnK^rpBJ{oa2FS+z|+vjXo3n3o&HW!k<!MGUiAS<Bu7wQU0DZWleMQr?H
z(CiDYdS($+(ndFe303$}st}!vF{LNx;r<;h1t=0cthc6{*LN?w+++Yj+X5SKf?p2a
zGqkl#`I4^;3)}q-NXj9XV1D;3h8FVWOsF5#hAl07z7u7q$-gUprC@&Ce>)(HANP=#
zLoBZPk;a@U8r_!*5{_X(k)a9L>!+gXJLlDR^n{x9+B11X_U_xF6u-K<5&i<6;>eSM
z`?NH!?T3TZ#CwSBHm>)-pwbh+sbIiQ8Xjmc%R(9Z0;@iw1UM%x>7HsBz>yb+PJn~0
z070Gvl(e)g{r*}-HLFizC}qKU-0tRMt%FFEm+<lZ6w2MDY2lloM_}2h$o|u|k#RxY
z4kf3%Ud$7%;~^M0*AAGJz)0C=yIZn7_Zjg;rnt;A=(Gb7jAqw6FQyKLA1VBSS?^lP
zZ}z>r-*(~cdPU9>DiqoCL?IB?cN3(gm<RowSF(FLRV&v-PGMy4cpQK4l?qzo@>E(_
zQw08bhK<v|jRW<48_xX9Fjlg?z8gyy93Uw+Saf%sxZR&!PT*Uy(H=k3D=i`Mlz~WF
z`KI{P7$yW1)P8Ab-+Pc}SV*xot<Se_M=_SJQwTMNagL8quv2kSvJA^Zk^h6}A0>3$
zzQ;62q8Qr-)E&J1YEJy@o7$4W9TOEKR|kJ@-gk#whJx7=s2?RJi7eCOP5Wr64NRGy
z9935xuhm6m*%HU`aO72esu_5jJqht=su(OikMq3B8GoHGRXykljEyh-9I<nS7XQZT
zynrws22wkqLQ9O@7He&*&bp(F_Lr`<cM#e6t(kIrSnPWi72fm8c6bDKW}uTD8?NV*
z;x#Ut46=0PK30QS8>KJfa=mK(lO?E`bpFh8m>TW-dAbHUsod5mElzvTYeo}|qM!i(
zjNw)AKH@$LWcv@OL<j~WT2|_7YKtB4BMk$I2`ZF%8@I>C!27%4_Zi^j8~(dR33wXX
zoey+OHrdI`OB(N>zWh<Ncqk~iXI&$F$PO@s1|ZBx4{bl3?(8MI9x2QJFa@%*(HG98
z<rq}plpSof7Si(6<llmh!wpF`)|>x@5v^Z_yl1>Wf8**3j7%_^iGUxc4r<TW7!8aH
zN#{8d<*eySe&yPF;&b4;8rd0|+b{lZIv(aL$an;`0$d^Bc^tml=8?r5#BVhLEwNDT
zveNiFRoc`G<n;Z44AS%lgd4*qV+C+jYH|KotyH8$Upp}SbdR;;bzh>hdgr4dmyg@$
z8#EzJUpS<@J2Z^XW+xg%v~(P{TiYx>i&OY#7V95SVb`3ehE^uu$d}KL43!+|>bn^$
zH!_pdN&&@+=A2AF7B(WOa9=g2P`d-4-(*$fS}Vry6zh!7mml8^(%U)l%yf{^_R*)G
zTA<TeQe+7$1f`f)GC4uLeQG+)Z?Bh8yHLqDxWo!TVj24-`LY1`P<*MAK0a7t5UA_h
z0Mz52-wYM|kqZRk>NKkI8zmL*bT9vrzn{@>0lW7^JQYY;$tK-PQR3&6fNNUu${&tu
zlGu1&zcf{&uUcrRb}m{uxn<)+i}wh`KV-;(G*(*=C>zD$M2i!a2w{8s`^+=5VLnbs
zXF+i3qH3cHi<x4mZD(J6CuKteKZRx1GS6@@!6{0~g*I1L^I%)sEpRf3VYV|(a3Af}
zjQ5Z%oz_BG#PwN1HVC?SJ2vBk=NQ0;cB^Bye}-(kRRkgc^$MRy!!RioT{$R6uT2*k
zd(U{9?oZFF^wdK3iN*rt>Y0zkFQe~I`hg}cF@OIi)JN8a1M&O&o;6dSQY<`7`qL-G
zyl_$a*367~8;m-}Cl=1j0ZZ4BhBJYgIoA5d)>e4!R{PgEF3*s{UBsQgQHqL!i@%QM
zufV&)6jP(-^0OT-FVpF#wSo1$-7!DcQiFwtOIVir`>D=B-cd=fn@5&a3lmfQ+u&-2
z;`i50_3=;zsZ7?Bv!-doL6Gg8Y00>!eBSU8%(AU;aHu`=I^hdT5vd@Z@4LD#)F`)1
z=NeTX)axsTfp9S0=!uy;>q2&JVJ^F}hLp%JJS<$<uasZh0q)Fg(OM1+Lh8s74W$hH
zNsq8>&g<1U_@dwQo;>+zqt)Rx7pkwN70CqkcX_GUbvUwv<9&HLt0u+j#>+nZdi}-K
zbVyX$<cruknxnlAl>Sc-v{Mt!oB>r6YK>HUOzYa!pQ-7%lXh+l=h}Qs6suJ|7fvk4
z<i3oO5x<2qGNzs5rIi?b96sOJ3Yq#=S*jPmtOlJ(jg}?Y4E|x~2Tr*@<-Ld74{*rB
z7vSq3O(>dLf+I_}nL<kW6?tOn5QPsw-*4?iGL;5lEl0-gJyvRTH;Z!UZF;(QGjEq1
z(1t->0-4V&t2W3s-dG8JD0ik%x*uc;)8tl+{Q``P&E?##R~~0W8<~UuO{<9@%sJeG
zf9jfuXc*hOu3zj7YsmA8f?1H1bd*Mqf<XZf!H=Zy)+Q~i2>EPyoS#K*E5PWih8GiE
zSKB%?H>>uCgMED{R}!H=TD8Q=%T>wHK>p@nX5;Zj_y^*QaaX~=fUh653umOV_!Zyc
zV#ZvzS=7LhkzB9`TN-EE9KdzoSszUOhnHBBQ8bH>4ke{(bfyvbuWKEym5NSmmRmUP
zWl6?_?%hc!F#_~)RDPU3AC*%DgV2Z$TOY{E(qM9%NC6IpZd`k~Uloj=zt2M-6)Bm|
zI|To+w!ocFATl<Soy@2!i&etqF@Cf7Eg_%m_in~uOk^YuP32py$C?Qzn=u8a#MiH1
ztMX0lww>(61REqQi#uvkjotfPnLQut%$TL4Z$URm^)C%J$!*jgSjUdD_iI{b!e>si
z1U%$<Z+QoH!uDJLh?S7&>=8yFWptNw7Chhvd(><aIh^>>Nc4;iLv&=@p8j~hqYmkD
z`v(J{SG2yH+`4jHB~i@9$Q~dOgJxw+>fbqdPobov!+pFw=R!?<_qX<4*6Nd1b(2U;
z7r2oPizeqT*n$!wM`E|`+3So7s8Z7f)$z?84c}hWz)G!rJq`%I!K`|l<Bm!N?OrrY
zLx<8=OASF>|N2AW+uQpq%AhBo++*KmXx^O8B>m3_g(bey&;VJf-Q;OIGR1U-qvC5G
z?6(dQA^{xD#7rY&6A>ePT1xK|12XD6v^y^Fzgp>?Z9SIh>>iVKjP2TMay2ClscTjE
zx2>oF{2{c~sM(oalV>qF$1GNxaX`=dmG$F;{jE0}A+Ame5MNzZe2wN?CtQ$-dXRy_
zZhgSuq|xH?k!z@C%P%udV-U~B%=k7n9p7COZ%RjqPZ{DUbu149(>K{SB**8OInJ98
zdn!3ei-3&a4B9-a?;Tj3`RCAhhEc52_CVZ;{Z3I`5xdtSFc#)W%tg}ekQC_|oxd3w
zvuPzk)8FkiGrXjn7uYxS_KohZpU{TaXUHN%3>!d|+ExdqRED;84j6Sp2*_+8q5P{p
zFN0xp%Y|V0%S)1$(6n0c-w$y2N_5H8$pqZBVt=u~%621M3OPs<rhpT*wQ!w|g3DKa
z7fxD^(^sO%j&B`TuQd}_$q9Mr7thwk1g?2IloVbg<S@TnYDe|_4as_!fxx(<eSMZ>
zLIZ5^5x7gg(g;q#5T!cjoW9`3J2fbIKgZ6C$KMUd8M_h1-mia7L3`e0sQ8vwHN1o{
zc=ymvOqs@9;K8<SWX|1_qfYavx(RxvpFgSLUpiJe);qlZ6jG>~e0e72`vfag&vnI=
zf;qR>g>VHD%ShOZ4~v1=w)&>;>!Jly2+spLQUMybnp&##DV8WuUE})eotfzYuk8j|
zt2SlU_a7Wgq_9Lcfv2W_NTodA0>>X)m^pY^1353A;>9CUJdV_%EId94^20d~*5`Ag
z_qeRW-&8Y6rxc}O1bx<nY`G&3JY@y9Bj9yjw6rsD!I{?jHmr`5^mX%NIIzz|a#0?y
z9Z4RGfc9^AOX>R+f{?!SEkkWRMg14_Zc+zB+52#9bdzbR&j*qm%sT>(+ONCZvIp=m
zNi!@>*)v2B!~k1%is$5q3N4{8mI|A0Xk@QN^K+qdCND#1&#mR*DHsNHA`Pf`zo!;<
zi-5d6w_oU|=E%=;o1NMEsN^mutAkkLW@!Js!S$_8kJjNBNwK^(9$32MTmJ2FeldBU
zX-Zl1HkY-W9Ry$|bjJRdxN*ix*{~kqQj|=P;di`oU-;)27C7Mf;d|aAd-T57!~UO-
zQ=b9N9~n+q6A(7P;~}H`KlkO+#ZT3b%Jz3n4(0`~M{UI$pzE*nFsb?WZYp_ZN>c!~
zgqXePevf=ods5eZt>HbeboObI_G^#&f*&<7;YAM)hI)Aw)qm%in$70xcm?=lx;pdU
z#)fL2hxopBU_}Wv%VdqF-NRKm30n2@TXjX=KmHGcFQx7ubZyO5;J(mMA7SOTp!tm5
zia7r0ker-r@2NsU!lpAo85*8iT3s5S(GRODU+F>0bsanIss(Fa-Fq&rQD6Q^Gqwg$
zwuP0&QN2TDannXb%AA~W^Zm|;PJfX*(Y<=7N5`I(CNyQ8`hufcs}--qDhMbsL}xc?
zJ-0{z$5&YnYgFB@n}|Z#|CP4(iR-OAH!!|k4g5TjYAm+TXYZyP9ReTgMKkuSG4G9e
za^)s)z^}G{?@k3fq7ZSgBGjO%nX=mHl+-7^P!5m_H<yFsNm)k6%5hngJNn2wH73W~
ztJU<&S08|)9_$I+2{GoUwu-Sh`>9A1U&rg*J(c&py5<yvzzwc=epb-%sO>%%_|*6}
zql8aSu)~qPy%avh!XE662^APXNvX3weSVEdBx9&Hm{v6X+|D1pYjDU{yPw@9cXZ?C
zH?<N?Et5t;#TuHN?7AtyNWsp?Xv5FXj~?4&7k$tDtu4PDXm}|(-)=JW&t|xi#U`hx
zr|Sv`XlXfQA1JURYHQt?SBE5Lv{SSbwzRm1o?bGcp+)=ob%(}E4asK(wf*I<CPjYB
z4Fer~>D=Ak7E0e6xH(wBEpkq~k~YptOhtb}@So7Ha*hsh?fGcFl38mub$xlIOC)0*
z-jI-|-nKEz7RPwFtttrQRJaMbKR>-8`8S?oY7)#M8W`m6?mp$B;+L%}Bs1RUOm~3(
zRXn(yHkxWf&5(62PKWpK265>BysMSQ2h$~!mGPZ|F`JLv9W%z5Y|P>SPg>f`!kh>W
z0iPTrZ{c_#zF--3yMR!GyE;6^zjgnPD||ump0V20qug%oaKp)*Qzxroa#(qZo6v5T
zYYU20R!}j`UR;`E{?ZotG`EMcafZuybUP<Ur`5_w26MCVoButM`@0oE*(e}0@JxJq
zFRChwG|}R8b&-clx}BN(i5C7Ju>U+=$^Rk6AQM*G$QtB^wQH2U+CF!1)z9AnG}+&m
ze`4J1-g7d@-lOcutd;%@$xK@Kl~J?MqRp~$3PuMs6yNCgZE!$f5DQY-@<Ex|V^vjY
z(EhQyFC5cMYOZ{O45n}J_fWUPO)yYMcg>gG07=ha{Z^j}(7%D!0k?=ug;H3MtmEy{
zZiOPNr-jWEvUk25%k|Y2PqmKjuP2fTBv*I938mn1{L3b0a0v7XEqw5ANaOqXSf3*R
znP09T^eh3w9SDz*i|@IRz2<5~5ndPuBDHtj+u0))AoK-D2@@UV+6?^-&_G-ZdDlw*
z7@X<<I~yDu65^TK*ACYRvYy*5w9@Lx2uUZL2z|r9Nj_qFN3OM}$ic)!^(BF>gh@ks
zx!IuNtDYJ9;DvC`Qd?gNiKQySJvuy6>O9}W4#vkzmz|v^<o%U!eV%67^=aaq@bmZ(
zc!R$>uXNxv74GZn*~8fANXVLZ`2GZK88$Ytf6?!}NE!MJ)e5#B=>zkfJ&SV$=~=q-
z2e_!hre-JLzg+)T*ynL-M^toUjyX4y;{NjlFpNEk-I#(tIzah4nwEdDdv#oL_x*i;
zR=oNXUm6oy37T)z$Nbv}&;Y5UhuM~Qf0~;1L3Mp;bb`1y9oH@HdStP}QFUBG!`H6U
zrz;E^dVk&69gVU}&8<D;>6sa2XKaAjWvA>qc364?oNCkg0GYv7^&zVZFv%AmipqAn
zkW_ZD*EZ3QO1>~B&lTJyck4O>Ikh9YH)G4m9(lQd+^{A!wcZe<IK57>JU;oi-+ZqH
zmm>+A0>v(F+u%Z)WqB8<)FSS4pQIR1!5^{}<B`)%zk}08Z+O%!9pcL!;~3!{KcQJH
zR<&BMJXW^)DfTuUzXCX;o^{U6t>l^);)P|uTG-JyDK1HYXwb|@Wu7$;qn3L#PIKYA
zO|Ox};Mf>7BctSMM(xAFzvvI>8%R1vK+sWqDQw>&d`;e?v7|7zpC~?RWeL+WA;9Y)
z@hhAGG*%TLK?C9jO=H2nqvY8^?Q=CC;Ag)7LPMb4U{9R)4T$l(ezr}F0;M<#^3F4u
zF0$y=OK`K8)J7e+!eT`XDm`G!^(*j3$?h>+2{4NB60Z(?ZV;IgWD_z64bP0GNn0BD
z%ELg$7&~yzL0Z5iV82y`G)<L1BC}4cL$90l(a})d9*XkXm*j1|0LB3$7dNC#e&59L
z6osGG{9|wvZB9?edB@R4{+TxL(@J#ZW%&m|<=^$=*xj8j^%Sy|4qD%S^t|E%IxFGC
z9QjA5$Gwrbx({1|4)=~UReXHx*B89ymm9#?_PH3G*wX)sy<vgq<Me$O;+|t3=NvFm
z(6|6xa}0h_AFX?J4AHZ55zD7^{6#qy`U~{s&|=vuDUr-01gy}gJa#3y$^pkb_K)ms
zQ)%9iRhQ52^h`Xgyz~Y6Zaz(s@WE>EuoUEbK9#{S-1r89{Wqzh#d&>h!?^3pnj(Qc
z4wqt%6LUB|r3P~{V@ULm(^`7s$qj)tD4PZMpEeW2kufMX*a4g3ft*{c_z99C?qq}5
zii-FP*I!JAt)t2@%%KLt>@YxdYnock22`j@>w|#4?u|U*ey`U6ZjbQG(I|Lf<2|_g
z*8%`PhqM`!!4vvU(pDg}ICz$qw<Shpwho$Mt2iUkr2_--@WFvadn$?;)FxOh?nLTc
z?(p{ENp&<<|2hdd@!in15@Znm5uv~BU)CrAenV<Yc_l@Kc(tEIr3K8!t>=oSxLqu`
z1{=F79WOmZXf&=~7Cj@~NPxp>g7y1vHwa3lu|!6~4lgJY-UzXw<szfxbC47ABqMdT
zKtClVop}Ea;b$%7JM^$(U_5VP<)OZ#518_r6+GA_%TgHqr{s*oQ+bWTNt&4fEjLDl
zHs@&Ip&&4|XH2Jciz2(VFRCs&0e}tnI}bPKhb)o3<gR?Jh`?J4ieO+5lL^kkPehqX
zT0TOI{dNmtth}NsE-6XaaO*78$+3(3zTwpwxP3aLUat##EQPOjA0w7>>%6du7vv^_
zg5<TNZdD!73DHIF`02sy?XRy;0RJ#Gs&?yg*<p7>62vaGG>ghEiJDYNQM*sNw%W}5
zG8v>|?Y-Ex%BSGFFr68e)l_x`pCChmje~x5eRa`?MiBfC_-8oUI4#Xf(0ze^%6lEO
zBeZ5+12c78i}eZ_6;1bgwjgXi$vOZ)+6+d7GQY)(p^^f9y-|x|Q6Ry`=>&dccd$g4
zn|pq6P6Ulk5uuYu3DJ`0N+=Wjq!Kok$PXMh3Rj-VnGqOP4vtZjOgnHVkqEYIe|`Vp
z%k<W$yk9>!T)*fb+PKiE29HU5qP(bEUerx`R@N=lNhl)>yE1$o2OFN8rp|^Lh$EB2
zO~I<6o?drzu=@j`kxLR+cukuf5C7OwCY{?SV>{5^aK2c!L8tRJrOQ@K3S%{Gp`hy6
zzkb&Z1h&R8`8vxsKlE+%<Ye+!lf~ZF+WJUx?+H@fyVpn+&(FPyM~UXP+;#fA1N^UF
zP3n4)s~%rY$?S<Ctew{}@wA$`xde#ki{wa(OCauoG%IR!(B1k$4_gcn8fARd77;VH
z+UqB0a40;&n!=I_g0@9}0xIWww6mrN8lF;>g=~O@3KD20r}wK#sUC@e%fyLKrIJ5*
z*0hqD)C&svWz8#~mM-cC?@K-=H4zkKzi`ckAi)Zm2j&UOIqG%?%qi(^*R8>h2U8<4
zMIY}<R_=inPSXdO%@Esl8yZ+IDkg`dzyHV*hccOQkPXhpe>1w`2a$52i!`_6jSj&N
zhk)<M8(e%LJ29}Z{EhthIz7=Gw@f$dH6K_e7_2Tyw-x-_QW%<`^7=}dw=~KU>XF&(
znaJ=b#jTPwwN7?iA*_mI5q?K!>#Etxo$jS88+F7d@o8>+cI{lWotet*9)WNk9=U1Y
ziN+eDonANCw|esrH=$7XQP1D#q=YEXIQ^Z&;^-nGgE5aT@^98Fphb*=BCq~_cz6^N
z7~^l;RBwLV7#^p~?$Sg5&QOVBSWvKFZTEYD611?W+wS$Iz+A2U0(Wy=-~NmojYhWN
zdO^N;rgMM79xOK4*9<4`$i#=sA)u_x%Ed-3KXtXQDcUEE=dmg|Mxi32<O#Qqb=kl5
zhXyjT>++6JYKMBR{x)Z+Y=5y?GkV&vAZOZV3qA_{A_D%&{QwnL_kp4RBPb})LT8F+
z&ANP;@Y{0RB7{UO>j|W*4_rUr=1DG@`0PZl_wVLOl9i3wsV>!BZ)sTbK@3=kqtLq3
zmWgU@=VZ!&WJV3LuVS-_==v4@?Td{QYj*=w@fxEJ+6ybvQlYc^{^99(VEGmd;qe}*
zb)KrmT={SGSWZ?T+Iv|;^T$+(Zj*napF~nP1&@~aH=1OPoB7VL-FdV!#3bXIY(EQ1
zNSO6#1rXY!7jWq_zH<<S*PdC^f9V&W!l9v{Z#OvF9%^EYPvJc|cTN~I`$JNk3tyR2
zlFSw)n);xJ<)>TC<W&ZehQ~&dUo{*PbL)2_zyQfBl5~0OA$ryNZ<&gL)Z4)TJOpW(
z8Hl}>@_C!=JEnahf(XBf!o@WAav|mPU`{~-!NZBu$Sd}MTmubtZmB^a5LKw0#JSs;
zjz)}KCJP8`eB)^EoPKufOtp!HgO2|BbANSr$L+zmr;cJsdV=;(HWlY?CM6-EUV-%H
z*lst001*$ySKjjIY{<q%F0S~&PkPZgWXoCjb+^oLjmLF)Y39coZp?z@<TQvaKZD6A
zC~AA$39}+FK?jKomM7!A(wLvIun5_S4rKK8x7aK>;%FW&u2>jp{x;q*yhB6go{{j_
zdv5Z5tM+~Bn?a;YSNm7>yGQ`vcyD(n?tGKwpwO8Z=zr5d&Y=gx^;_}2TAFr|Y}_-8
zs&wp%>lov`MgJ#AnhqN03h?^1sZmdhCZMJysJ>@eURJgEBY`*)cOlARSDikgW@uxP
z{#fl<sBHr?^t~jFs?fn#6bR|}Uv9;kMq`d__XYqRx2r!`CD>ER^+}o%65n@s^;Zkd
zTMb;3QSvC>FbQ70yfC)6o5AppPAA7^2SsboliLS(mR9CxM|%f;&?UP{ArF5@*f2N*
zB!2$HJ5dRRFwu=b5N`2neOqlp?MwS~ijGVAdj;CqqEvoNq9t4{5EJC8id~^=&9WI$
z-Q-VsiHi7cYgD3}6d#QHn0?7osWFx%JiEm}#%AIq^vE@%%(ZP=@)bGsnq;j7pL8VJ
zJM8=G_#t(~!_(8k+qjY-?lU*Vmb99U!Yp-hQ8WQxutdDl&xxRH76<8h*5{F*Dzea=
zEoG%`c~|p=1uJR)-h}lS&hY2GSwf;RNlEw_n%y~s8^EBfv|y9teAArOynpu`8{F$Y
zXkpD%tBcE05)x3kkU{KSugV)&@9*)vbm{Yc{Uu6>Z7HF`ix%+Ib1Xo{^0=;OekeFn
z%XUDtJYA>|{|foMpZwsV0ZrzWp$+4zwmA1SuSiab`t<j4)16mP?{5=Pk>&3ot0lYS
z%5(LnL|G=!WC3Aq)#YmO8ZWO`>Hx;g@-z-^TVW1utJYI}6MlVp8~Zg4YEvv&;ehs;
zcSks`s~gm6?N5mZ_zW_VV_&5_6&9M^BY0HP1U%$^y#IZ7^eLK;sD-<2{48i3PMds-
zlIcsti^Hj;rmHN2Z0dst%W1tX6?9`*EOgmR9?=}w-+YA`j6%xqw7Jmh*FB~C_|5QE
zGZgn}*-{fIJi>hUIc}4usr5sHrS~n~sJz=B4ds83K%}=fLP5~A9?dot!``o*EqRwC
z+85T7X2*EloQHUXB9K)C5w*1!UwCex9v}`8x!gZh@%p-*uvs!csaQ`-Sbeu-75~_Z
z>dy+EL9<qo9pJ)+ivBh{lDD+XQnbTAI}^7v^o{nj!VcD6zQ0_18N=(6DvB1d&sT`5
z9Qmd$@E`dK%#w6z$IWxX#VU#%EV8HFo;AC@<p9Ex-N`bqEA+RUjwF+!<p<k#Yub+~
z4VCn4cef@E18uX<o`hqrvU?(8G~({Oo|7V~8pp3AeVOyHdT7ej_g<U*oiu-kfRsdo
zq5=f0ZWv->tbuEQlFHYwVi_mqG{2#8m%1rm3Wg%T=0W^~e81v4^9|4bFstPCJ^s|r
z+ZTd%sJ8WDUs*Zdw(p?mv1w`sm^9)sDgDDrkLKupAcI(*gPs{4Tk0F3d)CEpIc5w3
z8nZyIXM8gKP`lmOa<9m@&3$9?p=^_|k2Sx{)s)%vV4)!D63+-&sUARXM8#S_vOWH{
zk;-WNk@9I7Qe2YF-@)aAC!GJtui9*b2&<e|GN`%JYC!5(a@8j5?G=G6MBM(mYx?>5
z1dxB{h>7`qk}S}$<~O1=tNnjKq0e6*vuNb~oiZ}t>J}x)$!tTT@VqDD46d5JX5po;
zA5qw_<B$9%QCQRdrOuR>*Zb{?1~0LIfAs1GBy)JaWBPGbe&;KyQh8Z$c~0<b)a5PS
zyzBpCti97k3{p&(&iagwKxf+I>T+3F$Vm~8^2^!Y3m6$5@69!W;|c%M#F{bm6&F+r
zOe<v}eCQVp=zNo+`^iv;?Sr(}$XJY1HUtOe88lm}&6;c;3qQ0W+Q07ediUeutKx1@
zz>AP0{wFN*JF~nZNmzl3@@7_P%AQ1Etb4rw4y4NtL4~Oc(|yx~K_Q8+R3!pUf{72*
z#HnD0y}fhaKF>B5X7iP=;ubu@BZe=VUcrrG3WFUN6+OLs!+N)~Tk|P51zP4K2#D9G
zHQoL#aL%yu3Rnyv-?K8%?t!cfo3wNkc>~ws@xYCl(9(j#a{AzVA{DX9Q}<I~cNtTr
z?^eIH7J!n}=(S~z)L<FLWw>c|f6l^_f>J))qk=FpffCoH+64>h!>+?t?N5}XRVvej
zd&H@_a03}8k)TUAOKtXpkGKZR>}$)?#608>?iMPmyT%u?r3>)6ELh)fxN&`|ti0IV
z!)d3xAp4rSp5iqeyk9(75x=Wj{K?4TV=5Jo5I$YBuE2LA5y!Q3M%=uz@)lOA!~Wfa
z?uJF}V*?CFi$(qV%&%>VQ^|)y?3)8SB8DW}&3k_~N*D%vAneiN;VcU6t`DvCBg3?!
zoMXu7=-O|V{E(Xg1YK=mR>Sc~&#o=z?7I4TJj<IORA)j+uVdgtvpL@NtkW~Ab+SuO
z%dHnRp{(NU6~?S`1eUsF1B*cLqPC^{k~1d~QfH#YrDrDY<)UY18yl^DRihg_>=JGt
z0o%dqiG{OCbIHh@KE7)g=&p1$)d)^|jpQZOu3lt3xj$jJP_uh2{XL9Pn$v_ng`|3z
zuJ~?;)oB`q1gfrS#4_|=f;6r>*C|eVZK+$BaOeAh7?T*=O5*vu`Msvnqq>Pu;q?0t
zh2xq{h$YK2)ycFf<66BeunVq$6xIOXlQNz7jDGY+u4udslhGQxxlrVdgk2=7I?}S?
zX<1_1-KkyjKsHF(oO1;rDe&BamKE!w;aE{o91)i27}z%b`QfAoqIgr1w!weDD8k9P
zUN-9B;_9DVCd#|`It{e8Ll?J4fsvJ&o~P)#o|N8W>U8z|MK5^qoR_MqJBI6MADZ_n
zAQHE~kw=}&9V(FX>ZXvcX}UkZ4NM|e#XG4TDoQ%iT)=Y({Qovb?{O(!9H$1oJ%+zI
zxZ1)}N_vZ2DGf3E!FzYw!|FAWkr@Y*9aO_G1@H@_-zpf(FQ_Iu9JdetG;J_aZ$NiL
zQ^?9m9vDTBVyPq$TaQ<mc@?g5i~^^$W5S#IUyMY_+eFi;KkZL-ZR$?vKS|l;>AIBB
z*565%+l|F-rv1GAFR>gD%j$0Eq%C>;3O_oM5eJbP%NJvD0;T^DVP{?j5PLO|@)8%b
z>VNtou)}HN#85&;y7ksxVAFYJpzz~)B2MHpI{I(>g*R&ON5?bs^TlWIwWD^Ak~K;L
zd4;Z>m1@e?`e17rk{^TmMa!iih{?bio&nTlNh@v{c>tdFR2TL=*0L7d|J#X%<wwz3
z15_r{jV^nJuk`enX0N~|T<6`h-+<faf(kh%iNKMIIi>lrB_HQ|^95lD*lFvve)loD
zLX<$_E=c{0`~?U0LAy15-w^8R)03cs*UZ3F3}%SmeCc}>g0<aW%Fg1i@)-hihn8x@
zMuHt7S6Tr84D*T|d{{?GSsswT=<Bj2kiH`Jt$%&ZtsHe6`?{U?HzsDDSxL_!jyft0
z7r#&)EloPBhvk_=PJ2XN{<k1ZgDnxCMZt~*p<KG6J+QI#aPipnKfLG)Ga)Qu!R6k~
zK!Dk$>2y=1`BTxJX(+b`#V79tA<7+Ti+|?WzHFie)zTZTk5sMGXkdAABC_~ZtR^SG
zH2jU9#LcI)<%<RsAJ`0i+f<@!cn+DGPF?*e=&K1eI2c={!Oq`H2*eEnu6qV34gi_t
zVS~~&f8{IP1L#;-=Yo5~TUXyW<n(t<Sbb#1v{1ZZvH#5&LCccx7&vBK|IOqU&|nkd
zl7Q5JtcOPSHY-<MW$BA1L-*b8q97(FCgj1U-YzJf#28y+xkCM9_q2W9Ff9$PG!P}d
zX$uuvA1|-0NKk{6f`N`hNezt)BqFFJP0iDFd{7UOzoE=K1{N@oyKnTWh$xGvMrSDu
zwD}@3IwFB~bT?8Fv(Uuumy|7COOiuuM|gT>al@iHmaR0em_)}k_8vQFRr6!=7{m*L
zJw3fM;oX!Hc08(EK~PxXsLtjT3@b^VOPLNsKlqsjaF<U!Pft(cQ3-R?$`5N>Y0eMa
z)aPGVQ*M+xUFPH4jr=5?VN=!egxI+&;*8mdVX*hE%vkg^9Q-5evFwRklVjsB{vFS@
zH`+`#$BRID#+g0gyKzz+Hj89iqC-ML=BVObpWeB6zF*=Xllt@$s$8AMc0_%d1JcFd
zQN?dOM1Oz(lf@;cc`Q4o-~X)#NFdkFx6YBoHTm%Hu-p+vJq?50Z?_OX!Z^Yr>R(si
z_X%nBFME8}#Ej<k9$RWog!M%ol~>s~s<n%xlj-EsWUjjeVKTDYezg%K4$6~dYKq^k
z{F^mB=lTqRaLlu=w4BHT5`HeKiWW^|m`31O^}eUqHu+(Gt*qf`=W~9n?ep`8jyuIX
z3BQjIO63v2(tAQ7NFYZ@<X+&ju@_{r@<zqEY_O{!EIgi{L8G)K)}Aj+HLQ5*T5G}4
zKSLq3>(7oGsJf;=%I@CDceL<6@9(lFed3YbxF)20*fRf+cA9${fV)WkRvwhMJ=na$
z*x|{ZUf%Ec_#@|k*k93u0^*p0Fe4UVU|}i1nTML1ng?}jL6=!S)N~)LH&8h>e1b7b
z;D0!xX&ldLs3Qq|dW*^Ka$0av#fC7=;%zpq%uGB-ww6S&KUNQ?^6^p8JZ!gNJNFIv
z)UhsNY|PHRRQP4cRR#BF2Ps-`_V4Z$Ivw6-5?7;Wa9QG5v1J)b{!0tu!NKC)mvctK
zk`a%NFsUeeEn{zvZ;f@dA1nj^c?~Oc*QfMJ2ncaqs5t^(M1vRz7kVr8Glbe$Zb@<H
zZ0dFhR`G`~^$i}Xw70V&{{MfqCgXelMt?w`VOT`y@*G?Jw7;T;51h_GbQPXa3X3gd
zZ8#68$;G$jhHT9IMs*}M-z=i{Ei3jBKjEp}YiqlZQf%slAn1g>oM?vmMv`N%7i?YK
zwJr+|BJVT%X71tnH!Hp$tX-k9-2JhQ5jV$_Mou3>f<t#UmFit!`Xg}rqoujbdO19C
zin+cpEHoTyWuyINmsH%$@4!@)Z9j>fs@j|0O-D^gJ(3R(l0iO=7naF7zMK$!e(Gvy
z^=Z&8Vav;KwFZIL$#Y274GLPA((E|<1KsepEj0P5r8rl)6C4y2Q2C{1tfKR~<+N8H
zIXwo??m>`L&fWow_`Sl=(P}_sy|TCd$2;fw>#D*N#bxLRJuyQZ2QNi;C3;8v;}g$V
z(3%_vuBf{@f2$6XfAGJ#5=Uykw@WPmZvnY;ioPsf#f%R1KIeoJRHxC_zcFN8;m=!)
zlU~OB<MdWxHDcJc>OC7W`gi;u-19?UK-C`?fJMd;9{Wnd$}2X&PYT&;w1s=({70SA
zcoTa?CM#fZWViReiZdp*?Sdpm*ZI1+bJ5&`Ei2`?Z5m(r_I__V!9F$rQnSF#MOof_
z$>O&$zhYiyK~V7L6SpwzfV~HtqZ$t<0H|@^{N?vdOQe0=cT!_$Hp|VA)8+z{bBwGr
zQ?JyNF10cq@uk|{HcN{dQA>-1s*&7bMYTJ)T<-tCN5hWZ5$j6q9WX8|bUEq+H(@lF
zSjyq%ric7KdAuUkdc;>&UBo9qhu=Y}JYp#wsueI-DlXLx?R*7yO4!ZrB{d2`dzT|d
zgf`ne6r4v86{P9<Ff+6FAhDhj8W|s`#mSY2g|~@~X1TpFzT2r3mBa;;N8)X58~ean
zyPEJU5tWlI{>pgkc*xk}G$@4BGg5cyI?>bc;dgdpI?n$pugE@?sh`6W-zF1vup_=`
z57eeUwgozg;yFPEF(7X)q?qWN)DAhe?J+Bn=jyB$Ub7quTh5>Lm+<MV;7)Q7%^ui6
zqs<tK37=Y8*|PxxRu+e*l}wsL2#%kFiG}6AU-Uf9W3xYXlg%tJK)x4i+P`3@n!%<a
zBmV9&L8Bc%*|N_VGnahC7++sA_e2<<6q_$l7oQw&&E&%sV&eIZyW;B?o;Rk3?k&=>
zp?$t&uy}rbp#KlcYIu$U668(aSGD^rvw<2Z0B$-&SR@WUFoq`)aWNlyv1-V5o4*ah
zjaOcE+-hyklOlpb($YVoifo)wtZzlXTim9opq|KQB6Z!6(Xmvw=j4?Z6~f~YJ~gCR
z*2naN1GzVRs?ELQHMKl%bb<ky@j3Vg7QU8r6X)C~u<j#D-mi3+#2!I%Ma5D+10`T^
z+)F1(cU^hym4HCRonpl{D+>|@<XXeo-gtI)v(24z)d3HUd_ahVCZg*;P@^KdCSq{@
zDw6<bn9)A-JSWZt|0j!C!;FI6ic=@8KGI5bXQN-EOry1no~?;2BPNIJEBpA6tuMpC
z$cSHLHGdyvv(Lfw@>B$_!LS5c!*_M&&CR;7c;(O%9&`16q_E!n3L(Y&gplQCOCB!w
zpaM<vuFJZ?(4m2ySFN;6F|C6gLIU5d)8apA0X{k+U=}r9T#!?GQg-z~;MGlCTj^Wp
zdBYPvim3YPymDN?i_A_hc=C_s)V2i#!;ZV-pYt_$9`Z6WzJ`5KG0(jSfZ~U^dPe+B
zQohSt7ThuwB3&s<m(IrKm5f5+vbHru(2EQx4}AQz`*_Zy=JS4E*5lN}3j@|mZ#n8!
z3b_Zox}`KbI@W?Frp}P8IXf1Xd63i!kEv6|_s0M!=#6Z%*Ee0LtR>-4g={kIztkT|
zX3Qb>U42TO3RxH}+Rk{rO&G2lvSvsck&@nm{gaT8!+rQj_&lehl%D-`XtF)9sq`ba
zSh7a%QbUvOwG(rQ&YmgezquP76ae5Yu|LOmDXd<Bk0UJlzk6c0w>$m_&$HHFV%~C|
zk@8|qU0gjnT=U7f!V6gV-cX+?C)ftuegM;^!pgPv^)enV=+-rTFT4kYlPo~uS?pnN
zCSFZw?QJP*F->nnORIr-ZS&gU58vwtKaf0R%kz2EV>xfZ+i#$6pCW`TKL<No+m77d
zPAw^|T@Q{elFE_^gM5!httrB1-vt>9Au}$PeX&4`JQVlGplie)*+pwReCF?=q2cb2
zr`8@L6JzjxeEjBU&@eLy-@4Zi*~ofV-3LXm{?^O{LcYrIWxaTeQhM=w`Ck`kK4iaB
zs+ipBej_<BjrsHSMY+`YD^lA@IAnSlXr~XnJ!1CMLbJmtq{U%Mz1qeksoQqk=``Qd
zMFq9n4`wKAp&hk)K<(@A_lLTl&D)XVP=hin6pIe;`W#q=*@g<#YDGs#XWc0NXi~QG
zlTO$Hhb@5VBhNKKI79EvEI2*I4TvboXXM=O%FPIP9(O99LS}U0la})O5Y-M%3?Q7z
z13#lh`zS7LeEK9H8TRg|1AzNiw2f>>Ncbyt*DLhC4K_5+tQT2B{A;7(bG19?mgJF3
zmVDjvjfm{JYq~H>sF3R`hNgOTedDnqL_nXoI?_UYKY3@mzm}czhSb{XT(tYL=9N>G
z%Vyd5dq9fEOH_~v1ZJbC1+!>`nlA6J`;s_~@1@rd6crffvqI<%Z}#tcE|e41JDXUT
z9ZkuJvb*@$t({96!Pudob_M25mG1o{%Nm$<C2dt(8W7R&JTC-^o9EOvc)xn_?bT$y
zd1R)xmRY3LB%8~5S{<E(a6bRLQU1wkpP2+)RkFXtW0O`|&S74A{8R#CVGhyy5}hG}
z|ACG><#!@@kUxEx!-TATva7YcgaVGGa>L}_s7cn*?X*V&Lknk34ZGD3RPRhQl{}s}
zTqC*>fHOVAmQMZ4zA3@eZ)w(!L%EK^3sZ5R>GJ-rS|=L5v-JuEq?yo%C;ZLuW=2{H
zhYyd8l24x857ECt&$HJzD}&7S;<l|t8o~-VXKOrV*C=-;=4GX63_5mt$>Q?D-@^Yb
z4uPc%u~~lj$hBZZ5veX|r0qx(JV#V|>6;JgM8L+le>+9Ce{=S}vH@Eo9ZV<?thB}d
zS)h8w;~!F4IP?JcH4KPucx0V;7(7j0tU%~t>j;NhjH8*J9X3em*MHPh?PUtdfQ*TQ
zk1vN;-0ahUQppFNv7zuhtr(vk6w~mr`8Ea%9f^F0wP)<wVq3^?c~v=9(p^b-M5uo$
z)fVgb;0fSvbkk_cVfb>0U}~DEzF9Y$wf+v7{~`mO*VX6e@-+f+|KH-#u}~=b<i=)3
zL|K{f9|uERqK5?_bMxiL@^5qa)2)rJpQOkG#cq?4bJ%<#c`OF$`7{E^c_+$q7D~#V
z*rxcUSftguDi7$;oll)UZ6*AYkoeSGfAr;v_b}-k4D6U+-uP)?fV&f~Fh_xT@BgFQ
zmKUiWqjm9(+FjYxLc`<q^kr7SrM_Ux>QEE`K{77_+Q#<qF(3hV_@jGzRz%DVClqY{
z#6`g##z`EUtqUR03bYwYS>0evh4Oxi&A6ZV%7eo#ZjtxnaNv8hr|30T+e;m8R_|!i
zJErn#PImCDhKw(mZBZ&qnoqD%n6}6VzpSNJ+J+`0R|fiwnH|1D`?~*X*nhYIDJswZ
zYjY_)Bc5{_?>@`e$5~jFx7EJ|F|HpRtk!EDe&?@;9UoawQidwNQ5u)U%>}1DxR@S0
z9S+?e_j$I%!Y+@c5L+>L5<EAZ60e`hY9%KpLhcGL8iL{f5PXvqX|b_$1KBm+@Qj6N
z%?y0r#X-Z+Mdp_jX;9fc0?&|FMXSUV7kZd|zWuiTCzHzU`BKL~u%Uh~WhG3(Ey?h`
zvjl}4f8KRysFAk$&jLAg2PQRiubX}rq~`^gMp9DGs*I0~i8+Q35Q~MRwSW}1y-aD1
zaxaI%z&5`>V>$;l=J4#Re0P&$dfIamy&$#RxrK~gR3;<y3ACv5l-E}ExIQoaU8BeM
z5%crsW+Lp~{~IjGh!Ij$XZ-Q8iIfa!du~3ii{fb6e7zM%r~rooFdIg7@iyk-{6ux7
z8dnZ>UA%J9=ItjUON&9BM_Tf4sX!~D$q_7QgyzG}kvsG6enNBgDxL>oM2}vee;-Sk
zyh!mU5pHITj75~^QOjWtCc;SUBl^;Q(n7zpJwlj_6>P-E7UrRjD!Zk`Jp`i7!{tD+
zX>nN@dAO&JRxxYVTU&8PyO5a??+AmKm`&+1Ya^jij`$pE{cDEqI!xjhMktAF#8y9A
zxkFL&j$oXZa>QqOFJe^SmQc4ogF@omU#OTbRL<>BF5H|@ByTZ-@Z|biIY>WBV2UR+
zuYUcNlG8!|i=X0k%2}8z3~x2Us&1j9x)F9w0&>i%H^t0)saS?X!ewGhu@t$UU>4Ha
zcGEkQcl4h)bj7;3akPB8`GN#rVTOT$MU}hbI{AwH1=<2bVVg4e;(95(&Gmgw!STq8
zEQTspub@Cg$nEpw5=4ebLg?SiRLYmn3H<G`p-aD{y*2a)wd43WwtI_ZQv<D%K9kzN
z&!JNs12B{kXr(&iK~?Kouc`8HinOaWh_d61g~CkHPm{luy9>*c;NYCU!J*?|aM!QA
z7;1Jo?iWPldU>6)XK;u|?{47HV*Bjll^uOv2=z<pd0$nC2uApPN<SglkMyb4W?sq&
z+_D3&fH{4AlO8-Hd$OckOF_|VGkMbXmmS_&buayvIN%fPx?Fo`-Z5g9KjeROs8<m#
z?96yTG9a0%MkS`DrnJuCG0b{DMUdM0Irdq9+DljtBu4gMC8%lV;5doI>jfJIAYBEg
z8ILUyu;quG=7%`_17A)TIN^dtbRy-~Jue>%MwFf&mn`f3@+2oQAzuBWUS2d2Mi4Mn
zk~IG*vpH|HKfowpB4@}>s8SUd;i@!1p^zYQvN)%4kDSz2v<sbB-b?ytUcB2DX7Yt&
zU}I|<nAkJ&vTtcv^cNOHO|JZ|o#Md7^8=^y*qrKaBRQvjw~tG0`Lg=3HS1-BcKaa~
zAyd8X^Xu1gT&fz09k;TqaKtY7|6}jHgQEJHwNY~h6ck00isU3oG9y`uk|j%)EDV`p
zP!P$WAUO;<2PJ0=1c^h=FcJrb%rL-2?uOs{o;RF(>wdTD)cJ0Gb^c-(vuCYdy}Eln
z-B0h?ss_4c70$E|zO;Jy`72(|j+1BU3HWJFSvDKl<TmPEEWS136clG{!$@*5INm_5
z2vx!F-D|o>lahM1`AV)d+G%$FJkvSt`&G2$u>*2*jR$0#*XN-4%jmr?!c9hYPNGZY
zA55U+>p%cnn4)R$3G5Ov@m|1uQ9PouT_a_c3fO--6f$Z0fCHarQmFHS!9bp443mq2
z;@&A`g}SPupeQO(pc>tdtaqk;a%QfJWjV!wwfT~0${p)pZE~T8zgjutxmYqCR90=>
zES~+is$@|;ns0nf{Crnl(bwI3V!yg#zX0J&7b#iD>y=D**U8!(D0qL*|5On2{JWH?
z_Qq_^t01Xy$hmWb`@?wwFFbh^5n<66?5js~2JK9Mz*E%Z_GQ=A@`=IcM_gPbf@&JZ
z?$_S&$qC)I&WyUNGNi55%s{J~0K2NoESH*63^Kd`CI@{8+W+*9?J3hThm3t)HOeyX
ze4~UjtK9o@(Hm)nrSE4VR8q1ucXIr{KJ$kvd0IfcK4~M|rKR7~Ox@2X-&C!q^b_D&
z_|34*9{XibIArU_W<Lr#0&hr(z%5+_lhtwQvldo!c|s9fVuw$3rAfLnBNP;pmZvpp
z=T3OWC^_-wb;+wxpNkK92sh3-Xr+t1c-K$QcFU*UK*s#|bL<tDt7oU^DQ?_hSnYC*
z_~Irl54J#Feyt`Q#_MxCgEt0f&}&yKSN2YfRB~D+JemIf2`_r?<q=o2=$knH%s<!p
zawfg*8J(%B#SrSd+9Q`511;gJe441_sa2(+K9XbTOM0MpgOHkXCE#5~Mt|?5P$JWH
zHIi_0gA~sz%kJDC!Cx=YCr=54+5f(rkR3F3v&mzVwzuc}zPPkEWrDB9`B2#V;KlO1
z0u>GUKz6%#36?<U%FYic%!g?UHII!7=ipYEW;MJQED8A+6e8ZV;|VvERKw1$NHa!m
zri^Q)T>{S|Yr>j@#;AcX1>TXJq0R6ef#qsp34H9%`u)E?Q&M^<NuNFIIZk%-j{n1u
z10%}Y52&BrI!Zkn{xL=gyytpI-`4@gTdFMFMD<epQ}}i9Kt`5VSP5ed6OVVx5C|>Z
zM#x0QyYnLg`a9>|C!KwFs}TqE0Y%*<4C&mx{ncMEjY3l5ch_^%D-s{eZ_HQu?`SDT
zZj<ZrX*kW^PRx{m&ZM=4`?qW;>O<yh2kDwY2A6u^2op?M;m6M*XOPZfZ_eJUp_{I?
ze}b$z;nT;%gt6X$-}|1g=>9TIQStoSvylQ{jkG*zA{IZfc5@-e`VTVW>l-^P{8}+R
zoiICFR5n8gD;JmfYZ1^Nw=0`P+Giwy_yn{q5MiEDEuCD>w4y<y2T`<EmdnpCo(tk&
zixR5++Tw$M`03!Q*@g~X@wnJLGl;j$(5ltMC6#iP{lmG%IM%Z1Kj&<pA7~4<O#Nwc
zUn(GhU;F_HZ%eJAj70`l>jrhyq(4{PNCaAd)J)6@>#crbMo{tTX1^vGmP$71c4ESd
z3^vCdweOd!<obMlJ9BjPW(vmqyV5q$AizWmu>JR5+x$}QB;)C((kW1x!_y0TqylXr
z(G}NT^b8uB`paK$eR#AX^&~L?2~}W7cz@pUCN*`3r&Yj|FX_X0uQf)@mPX(8e8oqG
z9&X16XY+@9(VWQ=o?D{>scNOXPeiMVdOYAiyH`w)PK#oSNA+U7^zIkWxaUj&0HXs;
z7BZm`6XSBXUsL%WU+OGo($Mgw1&~+4FVdx!Wdh_fi`dz-(2SYB^|3G3k{Q+9VoSGQ
zGdz~nci@UY>jCv0eY@~|i^kyo&-3CR=4fJW{lZwIE^HulOpk7JbB}4Kgo)ev4r_b#
zBJ}hWQv|tHK75Xe0nqh}5D~@MONlob1Z{tMK{~#-c`<zZbpF|G1Bxq2s5qaToVI{^
z+_P40tNJ%6T8H)3Y3xy1Jz?L>|DN0cWnIpP3y(kMJy5*@@_iedX!FtVnfp#IuOu0>
zZ}W_s;ArSY)|@jXEpCGWCv8CJZt&J2Joesk92U9F#mXUi7%Mz6-QCmu2|t=CJ0Atm
z@c@xac)OA`07S+nn)%&kF9<T<@6kQJ1yyK^JFmc#$NW!3_&K%=y#4O7Q-4LNtjdR6
zWX{jis<c_wOP55XJUEhZ;0Y4FI$GVT+z@xk_8s^P{d?B&GQ^E4k1d(r%B=f5%9__X
z4Zm~nnjZH7Zvoqxa%cOa@<LVomd-3eYzviUObR}0lu-KWYW5ibrM>)|V@Pr4!Tahd
zpB9sl7L`|OVpnsE$N8qF?L8UjMQ<%NjnaGh?oy)f=z6@oZB*~Qebsh_=IRzh+NX`p
zTK~#*a$$8uVCOk>PfAEZ@|bXFmnZP^C=Jlzcq`75Qit(6-)$*+Iu~q5b47z%0P&|q
z+0BDC=GljFMhFr^^5I0`>v2a0p!oV4?g|L)Rp9E{y=d2cbna6M8%pJkwhr1}(?M&z
z6G+N0UHItaf;Mony`C$bWS^EwK_}^l@wmS!I<<5AwS&x;t3Ii+i+4U14NZ|REYmdg
zr$<9gWYFm&)y?6p@#iq^lPhN_fS<m<C~@D5?puPhjuyvrP31$Idvd7~zGtUqJo*}#
zd$>v*be#_;u5zC#of>#C7uOYXtJLcQ3?UdVpC*3G<>{FZyc7>EXdkRvo3~9a-t=YW
zxBx<2U_XF7_)L$rWLmS=^P9^S-@V(<fY?T&mc9Z#U_h9hn@$akqW`T@uaqTX^bUA_
zMnLh<+i7xXt6}~(18gKG5nke6e^V#XMR=+0VR>nao<4W&6M@N=Nxq#B&^>*i7FCyT
ztv_df@B3Szkn|p7$JXWZmhWiEWSgGcL$^mI;ztqClytD^)_UJB-Gh<QWq5yDFW?;c
zchw*1zZ#pJI}|hAf5N7UE4P^g$$<8?cj(dtUd%+r?#%A_af@GeCYCoL*GH5RzuczB
zwd9`TttU)oCk_n1{@$R&*{6D!$F$<NOEt!XQMf?Eox!`W*K_P4KTvJb&!y*u`oKQq
zy_CMkFL#L^u~|=MK+ADOb;;7%7kHfjR#U{~0eSVapHFYz+!$|#em|V<r~n#3+Ry<g
z**i0-<FbK|O&gxAvjoK&D1o#4)8smy9VAo-fi|grgxqr8L#tCgu|yZczb65ez-J@h
zB~q<#zX&jT_pFZ>q~YJ<>_@!#o9YZ@m?IQp`|08%>Gwb)@j(26l{;A7uhSRBU2v}F
zM|xfY<Jt!HTG0N~@X+~7$2z`Z-_y$|Mjd=jWJfPqP;?H>L7g{nA#n#~X<KwO>6*t>
zwy2NCgj|%Y-#q=h&$pUh50G}=CC&y!kYWH!@Xu>;8*6doxUA-DY`UkYy!_xQS<G3x
zfOn}czFg6Gspe_Sw*$1j?6rnLUQPiO*(Z6$d1p#cwrtE}+JpgyI`&e@60xyn=dv@g
zCZp8S_YDvuGn;F+XMC?s9#rX2xc0|uy=R;%^5J}Cd`5LYFVk(O@!WAuOG`;`m@Fpq
z+IBK0=~A$O`Mx~{)DMri$7kNwgJWE>Q2;I%`~b)ta*Ihpf)mK%flop%x9<or0Uzy_
z+=E)T0Ut5k{`J?FD$S3QfIB0+UIKMu;Lcm{Frd_TGKlnl^&miVGM+p-%i!J<wR7)3
zJRTXi$R1!WEuH)>Aj{;dG1qu8RJTd@=2_cE51NDKeDBI^bGuPKc=m3?DMoeLQuylo
z2hT$Z$B_!Gi~Wx$9~<_o%y~oMeod3Jm8_NP^X^G$XV07+gr9l9g`+uhK@N2O-Pcu{
zGdFJ%Z=Cs=1v~rf*}lM;E8);f|8vv-X(}jTA}Xjr5B&q*KXRDx9zi;5*WDs=wZIgJ
z1T#eW`>gi#uzjrW9Va=^`twF4e#B{zobBh5cd;KyA&7PmzTiuW^MsIirP@5-96p7$
z>dh<cS4IuM*{F#QPxdR1R&DA6pil4N^c7caZik;<LG}fVq(QVdh$T$>0%7teS6os!
znz9TS*+=Nut$e5y9lhsL1kLiyz;0#S_BGt(>s6awQ4D(bd?Rmq*aayPffhIUebt8E
z1%-@QZ^c8DcItQ9X;*E|XT#c&BXGUa1c(%K$m{PJnfsOR2jOBeAlXRh)2BFnU~0iZ
zc!vUL{R;7h5N>h;7<dpqAP1tQJ(Xr;)y4uCLlGoPMRagDUC7I9SY#xWnvr-zb-(i4
z{|xrOUuO$JP_^DV7eUm}dkdrza;NLM%ZtgqH$ykz#xLTXC4IY+q1^CIa1C-Ux*;(m
zHIN-^7pm~QufcD}>xZ*hH9{Mqh`lWz`%%ojpN<yxd8v-QuW$Io%ucMqI}_%dV-hO)
z?YK8HEf>t=nlS68%dlx;ZQk9kwO6`p6Fukh*vlOLI8~ejq}=;?mK<GADM_f2ByG$G
zARY-XSKy>V`8vNGvIn@;)(r<cBxHlN%9gD3n|C@gPAqBc7Vkc~LMX+4q(Hy0%JA*m
z-xN#=J=w-pIfu~FXvjKR(o_n}-n9u<>u<um9$wk2yl_#6{)v}d=uh`ioMSh_e9Ihp
zA?_T$ux3-ag7w^9?Ub22iFnXB^v>mlK7)_my=qvl-i~>VyEE>+J9{xT^GCSay;>=&
zIBd}`Bo&$;@B))nq$I80XqkTY2q*aE>pv94mM@C22Dnz|79Ir7C_!yy^;i0Y0ELh}
zu*TP~<F;D}RdVwUyyn$=wXgW?!-nada#I>TH;n?^vC+BM=uy!U-M(7HYO_QCNJO1L
zy068(3P6Y_LW@h^Mj{PMuE(>gq&m~ko26OQ!Z7>jYaxHBVQ4f(Mn;D7i$}3$Za463
zSy#64lWCJJVgqCyEa{V8Z;5G3P0Jh1H9Rc1ezh$6tNAST@-G`+qcykSFD28_wcbS|
zjq+0lHFlDoyBx(tz1m}3$GVN-R@!2eK;>d*R3u*47Bb^GJzO*C>_uSm%N&3&pk+YY
zBrB`xfuZtVO<{7vivQjlO;9U(TW`s*(ZkL;%+((RytcX5_I_GA6t!0v2+Zp_%(49I
zZeQY(+vP%7qx;rN)a)Zi?i68Te)~O)Wu$sdPycHogq4#N;|ROmTNGD+yp@ydJvn#u
z+?4G!h}f--Z@EMAiiZX5Mhfb^+AobCM&HHhPn|n=?h-kD(nj6#oaF1T&>G-R;w{nH
zz3gF1ZkoH}{`Piq3QkzD4cJKlD)AWqJdkZ~lW(hCPTk;kFyfmO%0mu#Lwrs5*|V}Z
ze|IKK*tDq-69zq7@?o{gQGokbweAX3JcfF<#{**}bOVUOO$fLqMX+x1R)d)PdgOg!
zdEfz9ViPNWk!G$Eig}kv0aAVrcX#;|a{zw*@?~egF>GzhSSwh~IQgmlEjqddBoZlD
zT%;4os*<*ODznt5TcV>S-{h37^*fAy^ZYR7ymuam=i7(ixbC=IYIoyTn+6sip}f&6
zLC`3vDp`~!rkgNl=xo^z^E{pA<?Gi-^3C-1p%=pf^i$Pu#smc)ziQ@J_8(HcFFCVW
zp8Odn%HeKU=UuSUEio)1@o=#KZnNqK=+&zp78p=Oj<=nW4==s2FkDJ=F__IO69ys@
z)&ok&ZhSee_jr}I8V+bla|r5$ck4vwX@k3URP&aK6R7Bss<y>X9h`>`Z1aO~g&y=-
z7RG6boDn1Ne12XPL9!8}EY`$J9hUkV!rkM+aB`eJMbixYy-v}MV^T_$vIg^8QIwDC
zkCLVV`1w|nQJJ>wPuGgEG95Hwu=%eEr&nCSZk8TDy;U`9Z4&$Q+S2Fhq4gs}YMkio
z=qmnt{DZf@_Q6KD%olmvLV7Eqd1tS#DQ?BJ1`0#-VeaicL_%k9?(oVND?#<c%8>$Y
z2D>;=U2E&{pZV<El?0g6QNb=xl-3Y*Yofk5T#<hoyd9l%&m>n#dP1+<!~(Y=?<Bz+
zY=*;XdURXH9w2FldzpBw8;^f`8)pFL<T8}}+6|zTG*A(-vIdQJC3bZ(#_6p*B(|35
zRDLA5F<r)hT~Yx%2=-8~_X>+{;#yB-w>W>k_;)nf)vM`zW?y{aJ!TQmn#pYI#9-U9
z-r+u3%ARFbI2JgWGht~T;Kq-Z-;n;OGC0%-^Eb#M11vIAp4xv-g#L<ikr;<l+LALu
z2;S{scb0?w587+Hl3ME>S?^`RsLG6uozg%&WiX%6bTb<IIPXD?{+6d~rw6mRe&t(h
zz@)H&!cHSQI|pRE2dlbY`Gvjz7-*JoE_#*0j=f6y2&#zaFPv!B?dHX~><qF2_&C3X
z@Z<gwqa>T8;LfRoc({5x`t&U4Y|l6L#m^q24-estgj|#IG~wJn!wQA^xqkhs?zl{e
zDR_)fipR&G(a(&e?b^9!^FFt6=#yul{N=ehv*}Flr4DWM*Ix$~H8YJ;u$*!r!eqnZ
z%9xFD3yX)Gq-OrZjX7?<l?fa(_>28UOj4{dO?NhtOSI12nAmWU6TRMSQe_<Fx>%fd
z2yIEb<Nw%-XxSGxDR`u9*S5(O4rg7HJ&hnrjTC84n78YM&$(e+ptrB4MjKxoq`0q-
zCct(JJvMV0sl~tK<Gm4|R}~rjjG$i8>AbM!)QvLY>vvG81gqpC%wgc-BF?$iQN5>m
z5lw@;c>=oh*JeJ?Ix=v+N@^t%77_eht$7_nC8m1Nj!p(AcH-BGOo>_e-Kb5+>6LE*
zpiGhog+~spsZjGFIE*M)=N+>0nbtQZff#Rf06bP74g__aems=hd1&8zFBo3^3)O4d
z@bs8vS@@c)Y_a1+Mft=aFdncHJM0eA-Y?(QG1jM2zl?d4=J1oy#Y6ef`DEq&thTka
z`a_76EF$X!iS=w*vGBmeRY^Pb))-XyF{!}&yPn^&O^H$c-a)l;IDOwR5yIm-P8CgE
z(%bCgq2A&(6j`2Jh8N|XZg^xCbI|QT<IBo9hZNWsJw>5O03LuR`1UY?CToamt-Og9
z-s5Ii!w+{jbqPDeyh9b?*fYbx7Uhy6yp?m)*g;L(cZo3qvmVrMj1Mb`9mM9E^MIHe
zN8dunT>&v+r*_%ns#Vss+ugJGJ6dXne``OJkfOFZecwok?HEadLK&Nek+u8gQs4LA
zfRqbcopv`q{RxP}#vY22u)LZQFqOywqa>t(!iuHIKK6$EvlD>IHBC!HZ%~ReRsPm`
zY-0*x+K#-wQpGdd(4&q!qEe?9^Uhoy1njdji`R2?L>}r8$MGH%nYQtVK#S$o^;md}
zejQ2W^y;IcRR<2@^9b;HK%3_38|50!2GXBf4*O?Y%j08gL%qNG{;0jeXgm1`^^8w)
zZ4g?MD!K=NfISP&%NHW(2=qwA?}Qi^*W|I!w1AJBJC>zG2X;=K+{)BX{Ojx4Voj@_
zt>@UhkqvQK#i-a;9d05uRem40kEwS7OrUJJG;(6JP+1r&LS<{e`?6RvSiNzVdDt1m
zYI^J*<lZu721humPdjlUf+c%S5qX8#`D^5`*c#$3`=ykviN*YATK%={I8ipXw|lE*
zp7dAcBUUn@@i9<%o{nULM^VA6q!fvP)z$vUEx%E>DR`@Ohoeert`xH4sE!0!D1zhg
zbZGirR}dbHt@f#NFV{o7m50?Lv`j>qVZk_2=Jne9LiXLifLACd4wp^TIr=ZMs4k1&
z_HA-`f!CknZX{2afb0cKwJ_N{bp(BVJ=s7yA1pa}U|TpBH{K9a1YM1`n8p8GtAcF`
zZ!xMwr&`|6*+RC5&acn~!)@5EoWQOv8(V4H$)-u#W>eSpM(371H6;p6rFLcY9ZoR%
zGA)9L%x%bh=2zhTqnl8kE*Zmf+x=wrdYy3V%e7_Uq7};AxIYKPl<^W%ew()M&f+z`
z9FHfk93M2XjZia}yKCQI=!)3>JV#hoJ%49(wRk@c@^K*p2E3qreA~khh;hQqHsX<h
z>d0Y@+Srj;n_ka`Uw7<R!jFA)Oc?A(N~$f6cOPBivIQkLT`w}IR<7M*#w}zj`^v(I
zkmVvr=>=^JrR1c<QX<n7a+vD!$w6=0I*m~^NTRZc5<MB&L=<F%9T8xO@Mw_Lh4hL$
zH$5uv6)M(?y;dJ`R(T4ZJ6&|?qK6Y=%V*E3w$;vO!zOrfX758ND$YA*4PR+14v~@s
za2k-aDaQynvs18=7i*2j#ALTj|Aw(%ym+z86OWpHFCVc6oLL@f=wJhK8a|dE@#_Z(
zD$&H$bYExRsTvm-;%M<cYAJUa>j1Yq%|g3WQwsAJc2>m9ss*_DP>p@5B4UC$e>i<j
z{SW5peKT!bcicdDJXXkg%5o_Ksz)WU7bG%^K{s!-{r*zD2;eC9(D`M;>2N@L8`Iaw
zR7~{ileWbJF+*_MLlC9ej3ZlW9CaRG`{B|2l}~aUo|!o+=IMQ~P6}l5pi}8<*$njz
zV!(^?u=*uieE`lja|Rz~)>%N0*G^-xtJ`><Pni8@2w`qI8PVS8ipuy>vNwBwq&Nl2
z-0;_k4<8OWOa3Wt8Pb|j5%6A(%xr}Ad!)k57>c~Bzqt8k@@mz~gVsY5@cIT`jZ)Lu
z$KTD$j?CKbVMHfq=HLf2c)wPZX`yB=;cQxqXu%g3?(#!>ziaWzpw?Y2o4ZdPUf?I;
z@ba!eRp6vfFL#N}b4QKF+AuqtxubXtsa)6xfpp2%6LD2Ib@dx;JilZ}LSTrrFS~po
zHXD?A3M%4tS9s`Q412TVW%q<EzmLixDfDLN;F=s1^gBAs-Dmwvd(WPo9_JozLN@od
z9U@4cKMV{qe}3U(x<*@*L*y#$rFiNzk~JBKO@8W~0c{pT#nr2A8rW0!t3-=~0fg(T
zUN<l2No?Y_&0Oh*2wKUSh_<MCngQ`>o<OT{cLBCFpkHs$ay)25yi7K{-v$ElIZYI}
z*ga$o@aVw`n^SEBek47X4BxY7e=n_r<T9`V&)*NpWdUBlv$VpzWHg)BVxzMcqguEQ
zw`$K_riL6qv35l(l+6!Z?CiFjrC?2OfEeXMBD3$^WtdIZDC=b;<iiKfn%dhTYWhIB
zE0-|W#BWs@_P0JHE+Oa7r#yvzN3_1SBENRch~K{3@ow4sSJYXjmSrCmuBa(Yh!L^(
z9g+d#9xitYIvl*&EY59<?(1_q{xN?EI0&ALZrk{75xteiMvCZlGqqEya(MOYHNcj_
z>8)G8*CuQl54Bm`-n^-17r6U)(%EO#_@MzP->B970h4P3R?!<v4<r&gd*<vZKwz47
z-7Zlv$-q?qV2l#)pTO?u2SKS#MYoXvy332Zjv8ZM?&fkVzS}!OHyMd6Z-neU+rI02
z#Hnj-E8Gvb%=mdz+Ami}f$dbxbxgD35qawbF*<f1+o_5e_MRM@*56Gj0SoF{4}Y(;
zozC4knC}e&>=9T3(rX$MEkzt!%aLWC@@XAYME~^Pk~{{h7%3gzHPIxWSbg7dJ)yjI
z*=tGbO(srsQDII4U&wBtIKcfpl`_^!YEy(aw_;dzaRFEQ!o<3Hg~Y3O{|r#5a-D<M
zv+G@gVD<HNR}#|w)&4fH(njGNuT(kRS7~BH&x#7M&jKbBJ`C4<Xj-+IRObHTOHe#t
z{Qj@J{lNJm!`wJ;zl8|<q;D<BHGcq0Vn-4ogx)x6ifRrFCAZ@Vg!S9z!~Aw?g?6X^
z%4c*UCYnEQ9gCK9A#B`HZ{qxbLXNE0jy>ibB`80$Wlv9eVx~s$!q{T-lY(Jxz?0II
zEI|BUZi1EabsB<ZD!4eozi=3G2mNA($<1-~P|=FUK8w;giy9})K+-R9$^DL-`LJ!v
zc4xI@y@_rK1iw<z8%(#vsNhkP<6T9myFi`nGqCW=72bs4YgewUq7Qt5REBpdMkmE*
zAEJ*fcrWX(xh-6KSh;_D7|BIWR+>jwGtTMkwP5^E`@}fvn?C6@TnVSLo+<-ZOFIG~
zvfCTv{EA<Xp~$>F%+G{4J|?6bM*Yohu$Z^`0q7(<MEDcyaeK^8+elyivC-=`9OX|)
zo!ta;ids|P1Qj7d#Cs}uTogj}tb1Pd>+4+rz*aXHq}=LIyLEBhar3KKP@1=F7nFDi
zypi}4huVeN<w*vOyOgP0E1~P2DO%t;9Q^X0RU5jtYaTsIDgulQfPn`&+qBO2U&iP(
zAT$)eP$0QPPV`t0%YEdB#Uht7Wn=>m<x6-DG`#i4xQp-V=#=!PiB|z;`)+tv!iBg+
zx8B~u*h0Glk9F-$e9}#Im0Jyj=uwkMy3hAx-#8S9b(r!nReg2$WY6J5$dVfXqI8xr
zr#vAnlbJ4$E2jnvlpSGPY@n|O7?iS}qDY!*KNSGq-)<OHQd6)i0*8~Qb54q(MgcTF
z5lq)p3KlAl%(C_PStqF0sPQ*pEz~zCv`cy&8JtVKA6F)eidER>2kZmy1=Z){onbeZ
z9E8W96%QrmaAwQDg^KjVTUdVrX|D*8s~%PNRm$b#O0|YyUo{~|Li{pNpga+j;Lqq^
zX0+9znzXiXK32dPL#%-9f&UyscS5y%eG6c@O0dc|ajmVbgl8a7s~*Bj*)KDx;%4ay
zHZkq3>5pPWE^zq$bQ!CqqNW3!Xm19NfRaAD?xspu*M(CwSqV@Ow>X!(k`TUV4Ba|N
zO=mL`+TT-AW+-S_&#!C<fD<EchtMmdaKPyW@oVzCpvF4>&bQTD3&7E>>4e9+X32W<
zZE@(F<1W-Q_CMOIPFWu-<^?qYYDB6Qc8hxzz?f@Nef{Q0<*qw+qebdT-@ZpCXx%D>
z(fhOPV>dqRy0kO4^9h@oE0p089SSm_@ba01%2^0ci3hiL>UWMffkk*DX(w$x0Z0VD
zzIMDp*6fP<v`5Z>vgxAt{TY1httIO9{OwNFH>Pc?26UsV3<kaJ&*G-k5qg!Uq!-Pd
z?Z5a7c_MDsAr!j*?Ww>Dw_&SydAJI@zn_}88OmX~>+^|CGADmz7d7<05?n>bAf@1a
z8IGxZj53!2&|%zStvzDx`3SRTv*o!1a0^vbC6y}T?l-yU@O4JpaS{4!Ux1Thtr-Sj
zYjd-s53{W$Z$JN{j0N=v>|BlG)Qj)*Mn>u>2}W*wxDKFL-Lgp8%aGEhKqOAzypeKN
z#)a9%)*i^bR%ONaW)6!&$Kcqq9L7ZL-tL)gOS=pCvp|u(gvn<c8RJw*UGnMDi|25^
z8bx(%Fz~=wN)031n##wBd)ajLX4xI_BXn5Z=mYT7sQ6-j?=>tQ-g3>~2wJ-^PP~*`
zzn1mk=K;7Q5>cDynEvv}^D43}S{Nw;ky8HwRQQK|K?!b#A{JSK&z<&`$9<dG{3{-o
z_8N|J@vNz^L^9|WYX11)9(jKRuEutBVLF=#8UOtC3B(*@*Bv)`oYKNr??tHLY}>Hu
zdjDmV>bhwXQePo=I@?d`1yFkz)11S3uQXW=J)o^+e6#<|0voI@G5NG?sgnXG=3H2E
z6cb(*1lQ+6pa!8H1Q+?Z5q}KS*t^a+GP(Mx!z?mBL1h9O--fj0uI=aE_=R+|HFT^`
zttq>A64DGoaF~?z{l~7Iyc9ydFG*%8ii|Jny^3-KLP5SGYYwj9Ji_V7@e@3XFYxn>
zoM4PyiXOMycrl9u$h0TI*!1_>Mn^klI<D%2GkHV6N!Xo$19p4<QuLK8S3dcRyj@i<
zoMevT@!i@kZ<<pSJsPGL%eN%CPhAFTHj;;=hLBkdO8BLeGJsT*wOqSNF`+Z?1KPuE
ze%_CU3PpNrNxK&26<kuVOn@rcjr#>{S$}@-c#)zzQ23G`y46%`QAsQoIGlZ*n=|PM
zyJHjx6@g_03IM<9e#a|909ML@#8ay>_HuM-%*fxY0TO`H_=w+=@r<LKXfE_yWuG<m
zOD1K2nFEDFlS&txUkZ_dvNDeWp;ATt_4O4R9r_?Z{iBDm^mcq{WQcM%^pF_61QWEI
zu^f&H%uVSn3uKK8to14w{k96gC&xMO=ibA6)rP4IDIOT71RLMqo?i9i_AjDRnA4si
zcyXe{fJ$mEUc54kD@dHAvf8|cXGyxG{)Fsk`cSLy7l1mBAWjX>K;iJ~M9ACq1{Mmz
zZc|azC|Qam7FiL&f-yr-92-sriFy%-MD?h6p}Sp|_tY8QOuU{>X3kAf8F}y6{UbNC
zpK&(Uf4HmJkc4dlr!n|WGaRFoc|VZ+g8iF!jM#?`gQ2D$KYn0?y^%h;P}^kp^Wen$
z_q#?^wNwLHlUq`D@_~ArmP%lMt~bgqqC*xy&3AD$4NP7jq`>k-b|nw2zv*V2JMg1(
zGTrV8{j+43i6xg!R&7>4|0bsL4&Sm+1G$O%wXzd4gcc|IdMS^|WW4{zD;e(#DE*Bx
zS~wKw2CU{5js{3W0QLwd4rPX(Qc(aOR&Yv1g-1g_vYe6+K-DZ$;e?7hp~I3-sVMgk
z(2w^|sVKla-emnnApFl@|0Oz0#d-g()0gdpD~KLWT{aztUs3?9vJe)0EMnISUwpUK
zJeLI{ivV^nA9@Kmcmq!ooDujtV6y;164)#auD__sLEv`=PT9`?W!{t4lhO2>E#u1%
z!{553{Ih>X0=^H_zPZhb_cPvj^mzZpqyG0$?j=k;!dq$84pc8cWFxu$E{)@K9ds=#
z=-J><St{ngng3+~*<(LUa=z?u9`?-GGd|^txuo);>4LU<y)**v66TJ0i4L!Ux~K>S
z+<`&(NiiQ89W9WZlD!LH6tZ|sSqere_vH1!%fE=?e{rntA$VTFuS{k{I`DH;d@Ekx
z0u$UOK7c#J7@kOO^%|D9F5upc7VDXc`hSF*X7Fdh{@n?$Pp{f|(flice79y0zQg~o
zp2_2o4V$;x7x)LO9NUPiHw(Pvffm)XWHqWwNqV*MhbM}K)1w#Qazw`_C?maltoN|j
zwf`c~|A~9vLLn8%XmJG3_xJzXnQHK@p~ZeKn6M-wel*-q3Z@BEl>RL)g}*i5Ub%c<
zsCT@1T|f$^&VE|K`4`q*UZet?O}Jj;TM-FIYF#_6YX8#$_rbRSty2A)R^LNE3J=2n
z={Bo1WL1yGcIzywDS`rl#%?co><cu7{&SS%PzE5rO7FLhmBx6>@1D1bH=MKnIj{oz
z|4|Y2E9GAUaQ&5kA14bI{_Ow$wyx(yNP9BPx5YoahKKQKf@-*G3B7c|E*J1-p7CN{
zZ%kWT?h%BwE=c2~uH8cH*p7(+9qZv*rC>4Y07D?439@O2f$FSg(^ZSRot)cHjZt!X
z>HG10RW^&=G*k-8CCF$zFu--9(FGjMoMj>uV6$P(Z{;rvJHCJao)w;C>^*m9y$0Zr
zIgd5K)a83v=-caon948R@u|JBQQ+yJni3TQL0+@H8$AW}SE<Nz6mbwIDQO_UJt3wh
zq*d9rpYts39rD1ooz+YCgm>teRoktAEO&dmq!1KaGN8MEX+Ak6oCx9Q*t48I*vnqE
z;Y;gp`ufCW%4^uuVzEUh*@5(sh%<(%!|^}>8-xcrmhD$A_kNDrOpjrd0VPnKs&s6|
zXbl<5mzOW@c7R65GRlLluZ9ra>XupaUHgS+`5!Tj=VrUz_;|u`8cparfKsxVU;rIh
z&p!-bz5whVzY0j)b(`0aUZd882(aCP7j?*V%L;}-<7M=^6KS-P14~Wp)gk%e4T%FS
zivnub#fe<Wi&2%aeV{iT=nKQ&6HB~c@BvmpMw&brbECihWQNHsPze;es8?EKkYs<y
z!{)^hmP%eCL1^U&4foI>e-27u1cdJrAsVFyc3PlDo!QC@j`7y-Bx?mt87r4(W}DV`
zv^K>=xCi$uUv3C*!QfFadXSnxdVX>+y{8<SD2>y9rGP?a8H?1^dJnVgap0C&842-D
z?Cl~z{kqe`5jC`Hi|JMY`SwMA;p{=G(TYO`a;fm!6N};XU{svp(@I*HByND5Jq6v2
zHE@P)NB-q^Jf9QmMnZ>I1f7Po(Y(}aHZB1AE&tljNIR~+_>~MD9V`$v?b@^W`GklA
zHibFnzON4ef|f=0E0afx8->eyYyEo+)K#392|IuOfJid)yJMv_<%T}uvf`Qks*$ly
z^!t6s9npjzt_LCT!y=K>!_N4&bEA~!F6yqyhEBEJ*A+@t&~+>Osg>QlvnVeHXWZ6n
zg&gj~D1e#;2rB@r+%;BjsRDQoJyWQ3cg;^-Yn-Sl&=1L-EZlRbozf}E*Gw{nXac3k
zYyJ$u=%vL{i>M7<1kyU$b}WaFY<;|Vr&BMoD1vUrwA&W8{Yv^Ypfo=E5s&Zl1ZkIR
z{91FqB<`_=+TK<Ng|Fl=S!da10Cd!}<8(Lrl!FA8UiPWM9w_Dt*=rvjBfAMIc#rP5
zBxt`)m>3r$!%TshETDvuPiJ*F^!gE-BocJH1=}!8za>Jc+fU*Avb>BC;J!ZckZ&as
z?pgBSFD=|k_=53)c`kJ--Jz$W+nc<L$V1Ltze<)13yOKmttgP**^V(xX_^75x@S*t
z+~ge~tW%JNkw9)SnsMy6KUeAot{%9Fn+&~+^IlJ8&U|>$*JO&^(U=vHoM0vS)ylY8
z%*?;p<PL|#?kZE`(~?gny#H9g%p_bVFtAbTkPxFot5xZw+!zj}Rx;SyBx5hGlJefF
zWpnW%RCB@LFE5<#f*u=Ql<mpRq>Wgai3obU3PgS>12zg!)`bab<Hd%Glkl{3r<4>|
z{f0Fs>xGt((MZ}m1a>3gl7r+R0lyaIAej>;Y9ovQCBJbEHB^fKU<>YVGe9#cgRZwj
z7fCMoq{0<56cpfT?*Ke4^w(GCZAt(qDS{dwwZhJyKM!<E+>|1*_9QWR>-*2jfr^0!
z8+1j<kbgTHzTWlOcix_rS@*|I2H$pmbVM$}^h#d2bSKW|ijRl=)wtF8NEAH=R1Xr$
z<{rmYy51dx%Y0k1<f-o0g@G*s*6?XHX><o%rxaYHOal_AT8(ihQTZSIZtj&^20FHq
zfi{ah^v?TkC=h#mmvfN`eMwUl-U7>aL*_Qo_f2WFvri5)kIyS<fG1flt!%1;MAX(l
zxT6>Hb5=+zXqvFVlfbl*)x=dyNUEeHmaL>frJDT>gZ!N|pK`YLvpWK*Z*A&)mHYiu
z6X^+nQg_A`uMKlEjqFNH5`MK8>%!F4fku#{RV08WMw^D=o*P$zc&p0>DZYPO&~N7V
zG!0#S=KGU8p`L5iMtTCM(mppRaPN;K`3@iAAwF5BDowa`I(d8()}Z|7!XyIGst0Gd
ziWD(R16tb@SCJJNu#2~BK|Hw?4Y0e0HY}#cqktKbotR;L#Hx)@IR@QSxOi<H34}f-
zU7n@{D-1UMHq{B90P7FHA3bkK7Z)elxJ#(>lS!?Gd_%CU7duG&L+&y7H`W6^@+$)y
z$Z{!S>$JyaxX+>|PG9eI>gJAkUtuh+Z)v@C%5|G9_7x84w|1E&xkb;E+sb%vZ)sf^
zA_YEsngjKOKy}eTmD)sXwn$#UU82@ukgN0ldOcF|9~i;3<ZtorI&Shhzbl%@lGQ<d
zx~5Ic{<FJ}916GEJ5kjN&o}FL;ZjO1Bkg!MSXsKQSi!bm@cr`~&PGxowkHGZ>6|$H
zHlnE`TO4^j*|_~RZ;N!icyXlrt^N8Oz3%4M(waH?+U7=k=f;ugqSBhFhg%x<rPm7n
zFx0I-({3v*cyKQ43fluF@lW(W!>-@K<M9dc&K65_=VEqgnp`EzOq&0Eg1A0M`1weP
zN`nH0GhxQ>p$7qvwNO6t1;5t%eiScVS6hu<<Jg)Q!llkPt>(Kkj%;;agxLo|^B8fH
z@~buvxA(yYgu#|(pKBCSmoHw_y}j(~hRlHdNEv{aJp=g~KntH5JylHX?8y;0u=U{V
zQF_X$7m|wQN-1UIWp1G@4Em3ec=s#0nAU>M!mln)de|=@^|;`BtNs6wt4a$eV#Y9C
z8plgX^hqV%*!6oZurELyfb+<Ju6OarrmqfU8X)GHXIh?CZlHE3NQ>(`4+DjqE|Vr!
z2h#$fmz-rljpv9PwpTmQYB>E7j!EZ+Fr@|jC+k%k9I*5c(1Qw1o&H`}i}M^|cWPnM
z1Hb>vQ_^QyY+t~O3J8j;HtJem5=uTFlK?2C<R5lKo<oOAf&#}zCajrjqgJ%ku-c6h
zUSs(3^)p@9ROHyZ|CsZl?Mlhp$r+yxv_X3oL6duz{<-MiW%nIk(J^y0s4ddyQB4ut
zy_MR-=dpzJ>{FuPK2N+c2l)9#z?dS&io7h$6D@uGaM+Nix;E`ywC>M9!xY4gNBfoI
zbcjR6O6xv5H`nltjJSu_SKvqW9pjB|s6@9%9kQJNP$Ic%d#P-4;uBl?tHeVg7&>HI
z5?K3MFx2A~Fv-@PV%{{-Jr%^9(4%_y;&|n|{mqV_^o1_MrDn%>4ONqmh)>Km$kN)V
z*8ef&eR=OLMiiOaf~YoSQ~ulVyakrs+#w303TVGFd;o5e(d09~csRF<!MJlReSi0^
zy|uTlFjm)@%JXgQ;`UFE_31|Bq^w4|7t=!=P19B41^}~#m*eboyI01?;DFu4swRZ+
z#$5f6U8-udUhW9_v_j$E|N33)aRmY?%3x9^$PP^t&6R(d;YVxetWGCh)HFckwf&E+
z+r|kr9~t}(q<B<<?Uz<<=N{s;JpcFIlkB7JP+oz%MIiH3Q%FyJi*&TmvX^>B>gU6u
zE|9?aRXB)S%M^@{8G!#$jm(EpX;q4g?W3321!jie+y+v0=I{>3FK&6oZ#I#~HccjX
ziY0GyJYTgMqIn^5WaJD;E|d;!@tEI`B}$l;7~<-6dM+zIttpoU`O0mzSIS;)-B@4O
zqK8v;Mses`j@In%`iVEVp;p(|r%RRd3%KpK6K^}2QUK~H+}#J)Xp37D+9*IY8hzZQ
z_MAJPEjp&R(c0$sck|b%M-D0@vr3jSSbQq^&9hyT`dF;m$T2u{$Mv#f+k#!dWXgB<
zD|vW{omVZ6@q@mjHRaKDi&5(-egZgUykA~w7&A$5HmC?%BYLN@*mu0YMM^4Liq<4C
z4F$0<boR#})N9JgTDOzbHBL+bSl35fc0J6|ZA{5FiM>@jMXx9>o+g~bDY1+xo$)N7
zVL)H=p9jX^!tO(iEySsV{J1Rp1XLx_-SPd-1q#m>>LOBK{afu|&?AOjQ{D3!Fq#19
zrH9>k8lw*<v4KXeeh6NBqoVjle$ld#$YL4u8d`uW%wfT|W96f6j3;|$@`BQ}U5wxv
zT++i;o0~EqG}(#m352-j!gAM;oB6_SYcokl;Ij|+1!M={4?aLwXiqo(X3*)?zkccT
z>%BMk1+HYmupj>t;PlG+3XbN(030HF;&eB%POk(2q%X5-({k!Tmo!eVPNm-$Kn%e5
zWkG>#r&l)sRo%oToftwUO#1fe)yE8&^c7;S@M#!$aP9Q!e?>fT7cJH}8x}pz-g-HR
zB-uOYe-cd1GCvjSJYhJ>5=F22|8UD=n8X|ZfIt(k^dldDlKf8#PJ4EG9{h`h8$enI
z_~$>bxNIM6)}LET!WdLXBm9Gpz%Jsa9{10h0urg|OWsa)km`UQq+D&Nc%AReD(|{;
zC+?208znN@cXMG<u>sDEwi(A7Re2}mtKe!nD4_du;;S}0_y1)h087ApbHXlAN4uu%
z5rwT#M##gYw~uk3x6o;|hyrjL))wh=xG}$^1~{tizZioINKY1Y-sS)Ge|v5q8|F^O
zkSm3)Z5DWE+NJ*O%GIe{K)?y(lyxJUP<|UgaJ4PV?J@T@fBpZgf1p|H8TG%L;$K$v
z={^x6_>g#1f8)(k9#6<K5cmdBr4{%mYUjt20P7irXc%Ce_kb@8;b@)##z6xZhuyxw
zJHU!;2H=1d$)*j!Q>9NW5c3|oa^rNK0xDdRz^Q3vN}YauVwUbQAn>JAKNz`u`tiqJ
z(;And4dydybfK*(grI9qfP0zBwS^T5teg>H?P_{Ps6P4Mr861S!1FZk0J}bO#x4_f
z_6*zqo_~JiikR3NZpsA&7Ee(7o4^Q^PO)`=62+g?{cib%a-Fxp;~27+$nYh`FzGXD
z9~2dC`DX^u0{$qxu~;5*989sV93igo*UC;D1qCP=&>C=P7f}3vRBDa_s54zn5^_K(
z=&H5C>j}}xfQbS?$TSiv<?+`4XIJ(arcS@;zc%pS><kD=y}<4}l+>w1UObyYIbI%s
z(*cpBTmh6&gbv}o`sRt>n8dz7=jks0m&J;cY5#v5%Ki@$??XDY|F=%5Q7tpXJ!jqF
zQ0q;L4!l#fHP7fC<t}=^9(<RpKE_mzAS`n}3a@X|oL%;nqgy%0isePpj|1LI>yit_
zd;Ea67od>6ZB!|`9~~pExXiw%r*&Sl!g#-Q_dtbxx>y;=%A0tnq%K(o80V@9zJBOY
zEj2PB8Qi;4%mTI!6~#x%OG9+#f`K!EndneKqN$fe@d?i+3)#o}$Q!^qb!f`elcWxJ
zSNa@5qj`>v=M~|U$B_f?dZ4-qdPaga41*n895Zx<H%z%r?>$VZ)vsTuz~0}j_ND<`
zW`7^uY(6?;3u>?z0pYa~KQ~6i823NwR=n*O^gnK80UpZu4&auMV+FP!rl9ExQ9Xa0
z%6z|H(dhK;%=M+#vM+s;MOV6pVGmoLZp(rMay}oTYi9Y>JvOw7TB|nVB%Hp631#*q
zEl&#7qof5kZA!>1T8k>(Tc(*nX0kT^8;@O`+cXw)FCpF9uVm`S)i;S8TF%$p8Jhgr
z1yN~UE0cc#2{QQzwSI1~g|x7VnXa*SK4#yqq_-g&X+xQc-uNcE8ART4q+&^fD*4A8
zU28rGza2C1yK3~PftEC);s89#3xzanuXu&&$jyq?=f^k<@+@%fO94TKZOtYnG26z}
zYc1|9yp^p;5k9AyEci`*gqqvnfRU{ww=YBQ5|ZPF8iTTZ;f(*08+wHl6-ZBX=+pwO
zmwFyhZ{F@lwq88WoO|`kiMi!DFW~;s>Cm!@0w%qWWIkqp!q4NwQDeDi*}y9I;;EV{
z?A~y6*s^}L<d|dN#>RZJqd~Q66HupOa>CUQWr>u`Jr+xnW&R<4r>7^JcG1^*5Pl;w
zAC{)9(ziBJU+k9!JzN~Q#4BWKWpa;lkQB;lPj^omH0?4BZwTwoZunIlv05lT`nh{g
zb0Pd>fyC7{q!@Erhznf~0Q>DHh!YJy?h`V2TiR6{4ah#)F`Qteq3yU!musY9*Z;8P
z`6D1!**XA+m(f&0c1MlFB#Q3`8L(VM5N+upRU|ur+~pTV8{Xs>HANgf3Ozxd-!NcY
z{UU;}-Sf0mo<#sRdFwLKNGGcyOPt|h8uNWPUr{ylio-(q<BLGJ>==gYQ}*S+@;o=k
ze(g%!+^=Ml!ig5kwV2%E@CmWV7t_?WW@(Yp1b~*k1hJ&AQ9|6y6~1MhGRB`Y2#*3P
zfRXI(;pX2~E$2#a2qy`ImVbHx?Bo3xAk%)08{Wjo`xZdop!4tb<2?)pBLkY&E#_@3
zuGTi;9!M4OotIH)nevj=^BI4sw%8slx@zNI@2)=obnsTQ35`#wwkjm(!iiNr$LXiJ
z@6OiBv!JJCO)J%{qYu!M;SEDMbylD57&m@#-Fw|1B(NOh9jAb$7s%CvzHWv&-&Q6s
z6b@6j#-6L*J-2%&CJ3-urZwc!ep7-8gTwKzbsxj_xc{Hz-}L$Ld?0Q2t%5nG(ZO7;
zG*-hBO#QeYJX!tL2`OSM^9CQAG~=PT$=`_<yD>81T{za=G>1-U5KMq2_=Viu82!VP
z6uTBBh_~h6gpEl*>aZ^tQ(4J_OX5fJ;>^4Ych8<BF>Klt=l$u%#um%oC8s9<Y2-6T
zM!Y4o$rH{1qd0!TW1aBAC$~F`r6rjD+Z<fao%QEqt)N)%dN+j{((T>lTR_ZtZn17N
z{b^&Wv~vlT&uZFV?MGB`{Rq6K%CIO+)Y&YdY_^?buy^d_I9<a?#MHh>0I$7xGltjK
zwKicgW%HRr+Y)Vu-IYw2OioT!rVS)dnmCC-^fDZtiV-^23{b@DC%{@PNxdeL4WdcC
zfIrZQ?^OcfQkz+DnCQS~Q<`whA(jNU&)SYF2r2(0a<bbxpjx|GA3_^rRHvyn>UcZG
zieVEOVT1|cRk(cNk{He4sBkNbDv1cA$hmJ&cJYTBh_*yWe1ujG=u3KMT|Va%d~_}J
z<fPWXfQ#d~S)q~~Yi;i}N2^igz^+u++txgBx}_c6wSg4g=*z5`U}=fnoY+J*QZGx5
z-XH9OylVb7^oX@Z=01?u3_Zt9QupF1I;~z*O~nN7b%BW`oXz4^I66~PP^T@yvQv!_
z%5P5k`L+If{0-=)DbUec?cXy#ewaPa#3p*7&ke+ytSQ6%<L52<LZdvk+MA{fz;hz|
z_K%518ejZbo+^1`emVyB?yje=BRRrxwUfgRW`qGtXclpYVvY>!Ueh-%8!Tu)*6YIW
zzef_^rwg#Ig_W;Hg+sNwS=zG{Na@l5#s5(-23i)0#EEVh89e(QxJb)wP(|<|cus1j
z`JFd>?L5<vubNU5WkJ~Kl3Qq;>U9MvD@D@Yeh5g#la^wx)R{y(`ZuX);TW)otI_M%
z@BFHz-@ms|gywZOyg_6P<xJE%{-UCv5|=6z_~sk3jkJq60Dr@xba%TXIAuL=*f>pO
zo+N??a-iw41JtL?s724M0Oo^O1v<y6Tbo{ds6mU?-1(b=*~F64I=@%GPgKO3J3`1~
z)x<MGIg09}AwggMDvyLhTl7kN(3l@l#l}wr>Na4&5bcCplMOn`c<XE_{D&CX0j>ie
zyh*gYiQf4GE_i(O$tSQ{1a@?7?fpq|HAzKG^^r$;#jTOfo4j1*kgFB^GK=4@EZ;CZ
zSi`%;%PB*gD<|N2>;Bfak)<)bHyIXQaUV;Te<7IU0?-?Pn(F(Ng2bjpoxIEYzIobQ
z>D$MC$(@Pi3KO*(L-pB<e0Bk^Hf-YftV$s*Z~4n(`bHCnLU^M&fSt&I0gZO8!3q-z
zsKv#~?YBmow8!-VxaUY7<KP>9;F%9yx9o*`*u3sO3@4UU>TdDOJi@Jf8U9#V(*T4<
zpNx~r4xqwv!<yeN{GD4<OO}g(x`EPyh}&;YL^N%)6eLvNBkriI1^ya@;MV9U7!Olh
zK~Jp~QF26~hR>hC$4XWlcJ>fd!&_~$EhRKp^&CWv$A;xT+jk6Hzms{1Gm=qL7D#SS
z*NOgp3pO!6S*%z~X+^%<d{~gUj+9xHAM9$<XxWJh8lotC2&6SP_rZQReTzZ~_pPBV
zVrQyykM6|Nr~1c`-CVs5WqMr4bi+_%o97><%t~0~7>9Q~bGbGXyTBVxCsC-fP3cTd
z*v&?k;|<Ps$_37RvXiNixP5T026S!XbGF8GYOIl3)A1oY%`tQlb2-*~r)N2+zU^=>
zJL@Y9hzv?$V7I&;1@MY$c-yXFE%)AL_mgk6Wc^=OsY1$AfwTwE`uZ?jl%GiJOoql_
zz)pbS)kKE(*5afv5q^cRXu51(i;W}JAff{&au_EtJ-R-|DI;9`$Xrl^CSC%sPxB%#
zJj)++FFSP1kOu!N{O;*Kl&OR6_EgR>9_Z48C&xWW5imzu#K;R%=&m<jto0sg&|UTH
ziOSxrBQ?SO@~XWII&wLLL&vaBOsMCf4>M7)ZIe-0$#MYvb1fG?yfn=C@1jm>Zhjg^
zc$Jh*N7P?;pf!!9^*KOU94}D(5FlIDIaOpSEZ3@IHpnnu$ws8b#WxP}Lti#v&>)wG
z7xBO&#3If7g-2-2QGd?qMgrQb)c0^t{Pl2o%Bd1cnydLH9fO4&+iaQ=Toxu$eArLq
z1fVJ<*dxMcb}yI}eSfmavX9hAtt0O;=OJJ4Thx8;;x!A(eh4*iwgEmgOeD-wXaVfj
zlULj61N2mqA>UNQ1m-@yoq9XQS9(mds1>~)HHAw4y9j{Kpq~XpNl?PE6KViX=2<Qm
zS2D>emXx5B&wOXIF(=ZPhd(KB@mei{`$rNSXo&=}<MDhLIIiQCX#{F6I|2N#XMEzR
zE=}3t8omnHHNxTb)YkcHs_lJgK`C<iZWx;Wp4>_~3Mo@g4Jt&eo6Jd8Z3KhzXskep
zqc+VxZeXWLgmQ)5-sR>e-&^|U8Jq0!*G?7=041exNUH$>U3Z+`EATT{Ajs5|6EE+W
zv!L`27zaqdfpNwXW>uRxB;9%X>Lqf!2;;%|+LFS5e&~CYlhdFG#6XTk_vyv0kT6LI
z$1Y?_D5)mmXfCe^Ry{V27izRm8LAFtp9&9zSD$A4z<4e8x=O?F{B)^}R4EXVgkVhP
zciRc{^k@}Z4dSn^JfxT#Q0iw03&S$;&p&qq>d*3@<so{A{6c_;yWMG5Z&^SUtWCSM
zP(fP_fbR>$jnMURGRECpze1pQQN#8oc1c>?P~dR=7!pjbzP~YQGr%ePsCJvVrM3`d
zGmOC(u5fZ%wvPDA1uF%BmBT}}6@cQvNqA|)tan6vB=*uzt?vn>I&k$o_i=fB%sN%O
zK3j?B<dtf5bQIY&_Jid?%Q!TYZ(J?51T)(5+eZ|(9X{+k-8bJXm~4FcH{Q|CPF61S
z=GaJ2xpo{5r|UdZ<`=vwt0v{pu3Uh!p>4+WYivH8zYHF?_8ioOybhx}t$F|<l2-47
zh}z0>_=@<X2mn?5_yKZeVIaM<%@gC7jKh);!5LIS8a*h<r0mh+of>K6-{<AErWUk6
zsPn6?;fSY>3vtqa(}JDfxeTMkj|dQ#rd$_%Q)-}3kNv3kc`)%ezpd|#f7KQ<Hn4l#
z@;dpi<;#JTd03_9N|O8vH>Yd(dfGucJMq#7qal|W14o<fZLZTZ*2w69j;ph|hF)PV
z4XQsc6is3tc2j^Dc+&q}3`Y_k`+u?bo>5JG-`eQU1{MU7CPh%HQUwI0#)hag6#?lY
zB`TdrheSa@0i~DFLPzNkAk-ke6M7B3gc53~2?_58&;Q&z#yR)HeaE=xymyTA6|(nU
zYpyxxn)7+)n(JxvQDzWg{}E9ty_MMg;AJ{`2G!W^MUibVtyGU!@ZHao)*8XUR`a%c
zfdYWZymu(nd+N>fSF|Tcy8v;-vLl*@LzlD~lh3>KT|3I3HXw>+A@`!*)XE*WfTv>E
zJ~Z!%0Wgrt0EmlF{YrcXN!!YP0Rliufb9SU*chXZ<<V+?ui9YH1YoU7BWT>)CwZeU
zjMKYyIlim1?X9LIB{A>XxNqYV!V*<Hb>lv)g4v(_hWfmk^G<OFtVu%THU81R$>jxz
zB`slPAT`dbq%&693pQ-fGd9>R8NMDHS?Z`E>$U*^&;}^$gSQecE3pZVvB#q`1&uMr
zA*F?w<r6w23EN<YttbCJI-Ag{cdJpKKmS9DB_AG+mRL<9brDc9wbBld;mJGM_NIL&
zSBmJDDtwxWA9>!WR|z2DOGHF;hi;t(<YQc~`~bW5{G8_J<iDH^-3}yDxmw6k`|VVH
zc)3m7gkmL~>)HrZb8ofxw^?6?I+xl)QqpYCShn!o9z;rxH1g!#J=8<}74wO78ZmJ&
zg9$JBUxKp<2(arUiKHpF>jcYIAMEe%lc>Kc=cMAybbbL6N+{VXkd}Tep}pOKJ6rEo
z1u4+@tGE3aY1DQRlPc7vuYxnY+>MZ4(+Th`43n+S{kxEK<<J+!D%+12)5AX8jwSC2
z15^Tl7C(Bkgi9;7N~ly=#61IPy0pK-@Pnz&Qt3>B$4XaeIW_Iqi%R%h2CcE;i;*`)
z3`$DX&8EMVDiY)DYLGmFI%ehSo&sY%Sd8>J3!)7y;XJuJsg!9audcI5f?;#M=a{(h
zBOW|7%$ZfYWP-=1Fz-YloJAKAt=6RIaBAlYmrm4=t?|%oe_Cj|dbZ9?_{ii@Zt|lP
zR9;PT<dw<#pT~1X9z2f_<St$?r~UaV?@p}bcd=lA!0{{S#e_X<53|U&ByV|?_%zR|
zuiYD%j;-5RNwFB%E5E~g_#CYw$`;QzkT+UjGc$Ml$n9uW*PrJ4osGuY{F==ru!%a+
zVYD}ry@*$TrV>TfMjGL-e9W0Wf~vG9bk#jIkX{!Vb$IAfmGoNE(^L%z0fUpFgY!nl
z_rS46`iC!v){$=#OFm6wm*r`U?ijf3cnj#{byin-*|=_`*@2+z3CTVtP}4t<QW$h|
zNu@R%tyj72;w1e;S1<!$IRb)yXI@0D>*h{m>z8Ym$mvuoCBH{ae1h-Sy*UmRg~%Op
z2CFQwww9_TA4aw!f)3kd13V-3wA6aU!7AH($RDLMrCC*mdd8L{uxX={=jdB9;6|EX
zfRJl`Iw`H{;TF8}8BJr94-w`Q-91hnBlPoRYseUP_N^`vA+(Nhyhrstt|6=|@5$c(
z;2TgxM)hxu#Ns;G{@i~KJpT^{_`l1x{8vGg{fju{@A3;D+`3yjjf<$r9pWd@KcshQ
z7JmD}KV|}yZ~q@M1N&YB!wBdp`<b^`<r%Tp$Dlen>g!kaLsP-e5Vxh)29xZmC;#1i
z!2iWYSUw%DFhVlt0|S2DtNpJ5OWO=@jMX*%({;N@lpdCQ%mT1VydMUy5@}A`b1O&$
z>|bKSo(B*=Q!beDA;>ey<Q-Q0Ulc+&=7qos9{`YckGxKR{r?wqffUgVGySz%g&{dK
zPC>q5Q~NJB0|owmlxWm!ws*!;$?`N2ZU$KGabazv*Np1v867+M&nJrt{I2|Uf(pP4
z_$(n8k`b~1hWUR8+xTCl|Non0=>KWM!)_y6iQv!!o#dqq_87atgEymL^3SWa^Mr;N
zCoNqv2LQ`HW_-(*j`0~k4M!t`DQ4{`InI^R2gb%Rx>*&@S_0L&Ioy=(aoWF>aM=?0
z!^_r)B?ycraj62~4W7>q@TD^q&VODJUnxo4$l4iOpj5cxQQ=Q0c85VDHBWLzig^U3
zMK_Dkc+O4o6tm$jGA{)HOS^8r1nY101t28@Almcu4Z6x_f!rAwvUWJgWtmPAMx3||
zBzrGHZhL;KwA8l!c$hqHP@1QQo{NTuqgf?w`&nw#jy{+@e%jI1(%<JVq<(RRRBN=q
zf*WIYD?IoZx*3MGSDL&cCKWFvX~KgR1fW@(=?sLJMJp#{QbcL~`418%bO~l~mC`HZ
zV}SawGL-gxwh0RKiPuL#7e;X2;0{&H%d3-2d9yi)%Y&6HovfrlXB+KY<NWhRw~wR@
zrNO@V=*5%-%N0aJG|mf%qK@J0!%s3A3&ltkD{M{j4Ks@t?;(@x{iW(F{8IeLe7hAL
z5t8D#%Yx?Qvpx*NB$I8e)-lpCzl`B{sj~K3G%(@_tuHP1P|hmSdhNjuQ7|xee+%Gd
zffPyDJ-Y`34wEo;ceB5LKZ$_&xVt}#sk!%Fcbk5Y?lEK#Bd{Z2UCSYOfi_glfsr4c
zkz1zj=?3sGK;44gwoWPnA{y_uIsi1FKBc6j)Wr|!%fRfdqwL0r&r{>~X&vY_$?<99
zsE2X}))Jc4+QNKjg=175kXc{RJQ@v@UX=WnHS(fV3=|dS+tvAI?|SI4+ikCax;Mc<
z5F9JBM@LgMu77uLFW=!_g_pm;&jgK|=tqXN>HvzlM{7klaIMQZv<+D5r~kIp=o{mx
z-5y~%ctXc!`zD>^W<|ynP;#Ys7w+Xnj+3FgNMlXUxOiN1HodrOGuF_E=Lg=`paPmK
zyfquR+b<1A-C|}ixp^D#WcGcS13^NI_@t+}W6-ZXC60+aFKW^aubI}L0F0SZ`lIIB
zj#D1?g%|3%SEFufvn=iO&jh(6JM7m^7+yXuA~Ytp+DjAv^%=-zWukr)b{4bkq4Dbd
zO|&LkP%v~*Pe^lF)Aw2CB^-dsxrR$TH0F1V@-X`h2v>$|nAgP)A_j_^{JgTW<iD<T
z(BRXGP4=n;vUG#7Ch1XZ@p5JEtqzhc_I-2fp}GI=>=Q4}5;t|ARxM|)450EeH4?kC
zRa^|J#!zm%zm@oxv}Z^SOdQ8Nhk`QXZTTlZfHx2{d3Wb~{QSgI0KBbJ2g$qe)hwFW
z(IPc__CUn#QIIbl+m!18&})`QwLmJe7Nlf2d-}Taf<ce}OycU$q(cnGsNm~9()JE5
zaO$Qy+Pb3nm9&ZH<7^#V#4n(xiSfi0fN{m;(IEG|T65_7q+J$LY!FAiXE%9;aeL}(
z1uoHT`OnWELsI8{j$!+sVH(!lmJa;+35Rg4Zy+n(f(gvt^VhP(GN~ey#{vWZdTEP!
zrQQ2f6N(OoS_1vyE`;x307RsS>kUv08}L(6d#p)CgYSU)>@Zh~L2zR1I~`2!d=i52
z{O$_V&6!M_TTx>(BcBp5(eA)5cw85j(w36AzjLbwMk`EBb(RCmsyEiuDnLgCPzbcE
zqvQS0zJWHW%&HNc8A5S$feix4Lzj|GZ;^uUVqwR0OE*Siu4P-ST49)?HH%KjkuY{C
zBJY0(4a?A;Kd;~-9=Ok7ca4_lrZP<hP%E$Hcq+g8TcwqqK1pgB{JcA0M=K=&h}Vzf
zYvT|Y(@apPMHdDKYc`e7HXvMm04~B~K*&mdRnJ0=AFc2g2a<PKG=I03SdG_`@!a7z
zQ;rN@)wLV8g?(F~P{f;OzJoUXOe+vShSm|?2*w1=bdfAe;4wV}5C%X_r(t-HiKI5E
zM8{7^IRMJ=1jg3KE4m61n)u0Qhu>wcRgyhVz$h^}I8Pd~Xj=eylIkQJPq^nQ3>~iI
zQXpYJ=Z#m4J#ORxt~1QI-a|($FF%?X4hKeDe`g#6k2>A6M<W{ErC|1UNAvF5-x|p8
zX$<0AH8R%sYryWGR(f+)ab9DC@{^RO{nE`)rD6}EO@WEG_+4ag42NjK?f8!Nbjh&0
zBmgMB`uF26*m9!t+GeyQf)h2AJS7V68#T|fWpu^b!OkCj{#@2@cSf_yba)l4?KVQY
zBiG}S(eOScK+yJz$<1R9;SosQRHQ9BO~5i<-t^MaR~{10Io2+e$z3}?Gf-x{!qela
zrvf}fJWH-VUsXSHRmi`eJ-5*lUB!M<KZLAYR9)X)9v`Ayc_5;{klk@I*-;Gt3K_si
zRU1VEi}gxUj?=P#A@ib<{EdBH?qgiQ?XRe=i44}jV=_>qkMnBv+i~W`cL_@pn@qib
zF=&I~-LXU8<X~AWg)pJgfN=5=AH|fX`?5!vrerE+#=Id{{-gO9@Mqo&7@!A`AdLl-
zANt9mUZr;<R60fi0C>J7mWL*1AAG_805AZc><jfxRgH9nA9pci1kW^YYP~K%HN+TO
zfB(*(F+RWMc)RKSyQ&yf5J9zCwXa|6zI%S}*~g5GFS0)0sCEfK7pNM&X?tZT^zGni
zZ;HRZOtO+&kkGM_FhtsUUZ*5a!i(m6dBt>eL2yMD9h4vHYISDY#!Ek;@9;Q{&KIve
z&aOvm{Y3dYeQc;sA?BXwz(Lfl)~BvhXpd$k_>Ui&U(a2j<k<Nw65Za6$79(#NUa>>
zGcsv9=an91B_Wj0l06AqZObO^P7EHA**5D{nQV*>1>Xx1f$l;H?9h4WW0I7t^kv%f
zN{YbG0b549{YW1%kg<z5HE+NlA?&Q#BwW*JiQn@O?638zOxU+|-|X$NRV+{Se}yvB
zZxhtBKH{;ZWW`-J-|Rt!D7`m)`&ww$loed5ByM-pD10(`x>=$`ZDXnDq|%6>zWgC)
zf4>^_<A)xiLx@xK>35(mf0EZ>hal@z9qcJK>9h~~rJH_9N%3Ox$?zp3yrN5OU<fbA
z@fkkVL!bHvibOyz0v5RPZJK=K(>xmd>7$=-BB#@df=&hn8f^tndc~1jtKLt0pCQGI
z?FB$><Q;Y(?j(9eu<gF_3mIUte_yU3J{~)HQIO5p&4wVw)f31Zrzcyy8_#lhFaeAR
zVg&P7NtNB>aYtaYUgci#bZ0yPfEC1fmZO?_@iuWLW-tj1LIyK4WE8tASBqHhWOxa3
z*^4;Xy6(CA@oso_(OCp1fKG9HLlgfQn3^w}!`D#A7P~6j@#PQ^8zcRSbbDXZQDV6P
zL|G|GbSYP4EjNd2@kJq;X81SjPpv2NcMty@9QlBnTpFC-i(a6-dj%vXpl!lgn!UU8
ze9a*V1vNh;T)M2;`w*DoXEzh`UkqY+yhOYdwJ&FN$Z5xBRemK~+}~PXT2+a^r>ZC0
z27UTb(e=Ujt$m?q0^7)MNeH`$kM?sdoBPe2Cg295ep^bTDz@s(n7i0{ikjVpEMmEq
zWJFfmd27eL5QDPz&OI5~#KmtmoE-fV`U6Aw)B+sm%neY(ImFHh2JM;pq+g`<WwK_j
zhi6j|tC>K*h~d<{xmVsJ{(Z@6Z2%QFb<zrzSO}@>$?oYuI%s~z%9V?h)!_E>&pLl%
zp%%0*Lv&31SV$P>^R5Z%2bv?CCmuYxu5h*F?R4FTz6^1XO327o63@$oGu-GYKiVv!
zY>}cNZlkE$TK7dDcx>uGqtpJK%4rvMp_%1Z<24s3&wDSNNbJY+^K`~li=&zg>8cNR
zJq(w_E>Z5<-E=1&n2$Z69uZAwJnO?m>!T>en+q8Q4okz4WA!RT{j)Gup#1z;(f4o5
zQRQC8W+G|j!)@lXr2OmN@$Odi^kFQP<tFkGhShfxAZ~VKrE(_BO<G^|Ov|Sm(V_Y$
zAYQXwLAoU(H8Qm4)2;v^z@L*weo9K1B1W^El!jn3k|ip^$@7CEMs8Kn;~O?uk7pvk
zL10_ill5u-omEI!S%)PmdPpO~0TC#Y4oy;K`YNPe$#*@|1PM6tuaF}6v25z)pyKFN
z!R6nKhf-<BwK$77hgSp9Yn17@1HwWP2#y%rFz?;0=lOVn6PK0-uppsSN^nIs7lN0>
zdd{*M^Wiz5R9)|JH)ghHc;cuLR)dhYQ$kYnq_Wx_t@u<Gxz{2_SMLJ^)pi8?e#8P(
z_gr4xM<1#agqt&1(xwCiBh7ms;598%R}EwkU7^;QFMBa13BP5CtsH3u^+Qzom&n50
zi@42w3<?w*6LZG}A=SQ*%tz;bjKB}|EiQp0E$=0bko}a>3GM;sV5e@M1j%j+6cnKb
zIfCer1VbsC9=Y1*yg~V6{$WtgS3<N>vJ>3`kl$NpyFAgNClb5NBla!apNmv1XT}hY
zIU*P#pY!a8S9k&MPjN_U4qW%Z9#SLX#^-!K)|%XJI~l(;-q8+{l*vE}KlrFn1L}Tt
z{6sEL&hxGSHL7VV+oid1-`B=P6F!yC32gs>JxdGA-Snayp6j>{?utK{T!)4=DJNb|
z%0g6PjBl^yE|3$GX#M@TG@TObTG@yG?%oACUuRt=3L|z}6=vfwF7MMEL&0YNn+QcS
zyXi*dV`qqnjv}Ka8<k;Qe=4uPpGhkHO^O_nMv8sg9$m4EdMz~|y<@0r1j%>Jf3Knq
z+A}ob<wFV$`L4^~wnkqh#iE&OP5!N1UplF>@EvKe6R-SAx_T?6>Tac~jx=X<g))aD
zP^T5ygS;2pCV$nnM#QyK0O@I`s+%|^mT>E2)3E+*(%r+Sy8iY^tgdps++~$vrFj;(
z07enYt-K+iBwoBD*^^w(n5$qr#WQMLDpaIMLyc%Ya}to9>bXg2j~;jH6d-I_tZ>0T
zd2*_LJI{RhUGthYS<UcOyVmGK8mPp2knJW<y2!q&z{N!{{R7ZSUydB96?}Bi_gKcv
z%<O3DPAy`;;l(gUQMX2QF|}Y|@AfKo!wRk+8~vz!{>0g@Ky>tuW`|7F_QTyqi$ByV
z*?NOEFQn!o@(mR)u8Iwjn3$UVxXiO?Y6WgH|NRI=e~_}AA+iW|C&EK^vaxKPZlH7@
z{oUVpdAu<%82`RDH7v54ys&ceqs3L()sX`Kz3_viI_NcElEASeZml$gW_yGfx;XUU
zyw_~*s#Jhzy-TaeZzB}69ypuT`Xyu*7|HkK(qnCCU2JonkGDnDwEoV@f3~so9ROgV
zz#HV3pO>O3f`r0yw^`L(ZC}6jlv^0LwIu<&48$X{;yKt1!xJ!w!Q%Sn+K;2Y1M|T=
z<y-sM$3V(?{Qa>37KXlywEoQ>6WRn{?ixvWvMA%=zrKE25S0<!esg#>>U`$e60+JY
zjG-|8HmHZ5eS}k^!Rc|Pi^af>&^bg5;QN<kl!*u4S%r7_MEURD&z^dXKln*i@6uB|
zl-}$s=~C|SWmQV~M5Lt%u`Ko#IW4WRhcgCezH8a|UyST&v!$=;_Xav%v@kA#%WmG?
zgJ2e4E>6CVZBK|b^*&VH(>v^)xCX?_8Bam=KT=A8WvNaXFR1wNG`B2k$<YKkrFHfC
ziOGHm+=BYW{VnBp1!xC*!in-z0R~-sVxLsTi{KBBj&}prvTjW8-<o@%BX0WfYAk}M
zfTntX>-DFAuEElO-w8Myn-ll@<+vm9|Ge<OR)%t9Jmg-J7%y{1nI>9B1*8g;tV4l6
zuH)FRa-1#L8Na>=z5uM~Hx1Ak`(y;)ademfr8-_epv4WKoYNKEn5!pe$XJ_z#>?U-
z_FM1tZ$I6Z459|Q37xE}-wLq&e+#y+(QP9e01wuHvkyuby7|wU-oTINj+A{3jxpVW
zkVlD}od5Wh3`U%%R9-|~IWA!bcIbcp0iwZE|NQbju517A`J?h&J0b@KJrwzTN@Z5!
z?7v1_W~SnO==Bec)?nm?za!RroCvib^)hLmxi(dF^lPf-m;EKcLU5}~rR?mbD>gso
z-Mo+T>O@Y0b4f!$^8pzFiJKWa#_vCo?3gQHEXM)^P6Gg&6d?NJ>~^>bc_L<EZaK1{
z{*bZg%IDi%y;4ADU7#BwacLW(;svkx&35C^{t8jA4p@VfK8z7K@4OP<m3VNs!#46O
zFr>^r3{H;yEaGbS4Sog2lSctBf99_(0wX#DoaF<I`}oU-C&qnzWCl3E3m7*LZb<?<
z0T}o38J&$2<30{d{+t;1@d*F?HSYg;;eV+Nk)KE1Mf;_ltfZViR!G%cP~sE$mxE^R
zPPg^Vs5V~l&T@Dxau{*#U;bL}{=_MNl(m-uQ2Hj_hF0~~eD?2-`P}Q!Xy>`N_ZPKq
z3vi1$x1K{>1uPwK+Q7PAT5Qj+IqKsYn>6+;FCkS;Y4!fQlRw4R$T5Zl2k%Di?TxtP
z`J}KQDA2V|Roh^BiL@LRPyTl&>W};koa6$!UjX`Y`XNa34R~vFct?m^cOrq@BdxBU
zJQ2DaH7)7}b~LxM?VV5Lugz2k+y~%#&H;y69-L&u%vXqRb%HiWEY%C>7N+)R4{@-a
z9$CHHE*(!bwKSi2wneeD7L;Y`wD~B6cGLW)59h+Z_wEt`=I2a4jte_c1#L_3nKxzH
zctoBpb$+u3UQe{U#o)OB6e0qfo8EokOvo_`y9hp&j1b6~e}BJ&fzGNv=H6yU?3VzJ
z-OsgK@j))e!TMhL&iCs~1_wy@j6dcw{XaZ!U7$c}=*xyf`L~V;^pKSo9yV=*(QqD2
zPw<4vY!!=uT>)T#Ul*WjN)YAW=>9>E2U4yh8!cU5+Rfjn9Bnj+CabLt$lk$DcsP6#
z@OotA!^03?upnbG**yTK`GJdk4APW{TyP}82_imVMjx-OFB2KH6phY;DP*iC;3|Nv
z97vUu6EjrD1KO)Q{ZztEJsJ;~PF$hsJGwur7^F04ui0ne)czF*6jCI#w%l_D66J!>
zi3P1V<F|W|Z8N&<1%9KKsP`W)YN4tnocjhC(){Jky)kg0@R6Rr3X#nw&+v+%5#M%G
zqdj8upS+0M!B6<_m6OOt&5k&XM-JYw`V4KRd31=J#U#qeOI!qn-TOnYY7m_OS`|lr
z+8Bj>!xld2kxKNp)%nmePUqWr&tq-UYIVC_%+g$-veU*UL1B-JMpdmn-%i^^rls|>
zUn#nw|IkMz1zUC6C2lO_!g^r=#D0hN;&-*q+kC+(>*(1f@W9klotKfjJ3Vrd+Mbn@
zj7vMnSCB$OBSb%(agdo~7{8QW;1C^ma0@R{xpfvXHV4r2nj281KAoXgM*Xk#h7I+G
zhYTehcl>>9oOaS>eU}8ZW>cS<Tk<tWS|UE?-sY^>y~2Hf`AIC=!uI3m%I1d6mcEPg
z>18elAX~?q3EeD}b8W<;*p!^QptO#(GA)PZR<m@z$GG?a0g`o|V1MyYaF7{jPT49~
zG*7VQR-4*$%^;1RiW-(-(XlV!hzT7nM>O)~<n`u0Oj^D6Wv00u(-1F>I?_Wo5X1aJ
z<uI9}WH~X+q-L#Cy^FFp))5Nkp8o-hum6DI_`5RWRi`<PNDcKuI!Z<FBEu`~4Azx3
zYkb7eRSqLY?U|a0uDSHW@2zH5i2dz$J3;n{QTajXJ<pxayyhWAS&y#xLer+Exu5JZ
z)%47f!#(+X-R~enBY@l|-AP7{dyT45MO;rSJK^#!1IsEXG+FFbqn}B5_w43aX${J8
zAl5gKnVprja97->wociK%j9T{TAuq|n462^IFx@c;F~&ya0w=mPd7VeR5P(Aqx_DH
zO)ihX!tu9J1<|X;;a(W#VSHY%A@0cbDIH*u-g}S_8f{80y1D`?x?q*w*60c*3)$b)
zWUtj#>zGpBxN<n$KO6FCc$yN$wt5{SXmR1?v@xeGvqvY~)Y8(@S$c48twdR`o*1kj
z?#MsF;e4;9y?&r+G=O}6)F!{YJPv8LSo^u}V2Nt?QOk2k3lwV<pOY)Bo<AO(p6;6&
zr~0_1b~Fs%UajDtnm>l&O`K;s_yRSaYh?BMo)-{I6Zdt`mjcz{|B58JZ3E2~xYWI7
zl@~mGQ=`X+3o3YSKHT^XfJegI-g^YjOY*%<qke}(65@yqGwCuZn9_7FWtFt7+8lHa
z>(=ufutK~6K^BB3Oo1Y&%5@4g+56{)(mOBJb)1P8%DnZJgxchp4>G+2fy`p!NXKrO
zgOG0ws!<cQopuQcA>x)%yV$;mG<y)#0xEpz++6UbU5bp&h`#ZdJ75gAcO>0dXXV}{
zut5iJgL6Z4j@FRH!f{F;fIx+z3IWkbczgPuH22-LVR|do_HlRVS8=tcpkAE=Mwgk;
ziEEaY8N?@Y!z0WV`~)TL*8YZH_6A{{#I+9hd#@TlZ1xcOO-#bf+M_4s<ZpgYbmR{6
zU~Y3F>FMc~V(KHSVOyCGf~y>_POuP3Zcp{N$irvkF65@ZTc>+;58^`y;2%H{;j1o5
zaF=-zWH3i>j=#4f?Hl~_UAQ8tW|CYyJ%WQHuCcPRva@4i#I*zrP2A^8ibk0e{H<2X
zHx6KEvuMc!7sowW$U;|w46G|&78&%T+$h25buN<Ym4!eU5<c>A&+Og1hr1GXpFj|&
zQD{#JaD%G1Qx}zZH^$t2ddV*`d$2$byEve3@H{I{U|Ky?4dn9S$&CDrTRVK?eenyL
z?F&k+t;g7iHre_^;ZR3MP^(5*^TL8<M#j+NqH?&N;p6v72o?fEpQ1+#os(av$e*FW
zxLDqInom>=$55#xUdWALw2PSAeMwtdmwrP0Ki#jiAFnxdVmn%)yJ6&z>ZM}E1JeiY
zDP7Nc{7B_B>4W0~@bPKtUD7Ab=L#nDk3bBp>vrN-Ft_avQ}b^qZ#)A~Hev49JCaC|
z8d9PRs@lwQerj%7^`qN3@iP9Eg>-KPd!ic!z<Np??2nv>@?l<^^MHv;qS)Bu_OIVd
zAp`-&sjg;;L^}6Hp>nQcX?+_X^QCc}m$fxrc_`tu8J3QxCcbaRB@88AekG!-2vqcS
zd<GB-R~v#33x;g^Qot;{$h-a>bi(Xlzd``q#g@JJBfsY5NO$eTcQkZu(C}H8uzm^g
zrE|R9Xq8lrnzXE4z+IwVdU^av<jU{{11f&iLY^FFxI&UYhe*2tC}V0C0^Zp*D0dfV
zk0G9MLquF$4%^(<85G`Z&I8)s{1mLn{M<Q=k-^~)rn#x@V~%BK$HhL)<@kHa_l)kd
z!6QqU{ZEsN!e+8ANwNuTWnGjg3fJUNDwsU6`DuTjPhB(j`i!|2<p(zgb)w8c=A}D)
zvU4}H0*iVXQd+Ir4`uI{-UqFt7ua?+ws$03@w=n1=yVjqMOr3x2VRJOSP#5KEN|+3
zZlchf=ka)9_MW$u?cx_8dcH`4e=^(Xd9RFPUHzO_8>*$(h*8*M*?qZ~-E5W#JDNS(
zYF~b4V|@L<z(#9tX!58*={hmn4*C{xZrp33%@3StW}6^x<`-FQ^(bzQ{;6K}=-AQ5
zRh@^Rs&^JI)8iEv>|OCi7VRadE=0|r{@!FgrzpiF1A>9R@j-h&TFI+|o}hB-*{9uW
z6HtDaAX)fk1E2-26rB7zi|%dyRrS#OAQuEVHwQJLYFqCz=Z5=YiCIQl0x-=n+j$EQ
z)&(~Mp0HzR?WvmJ%JY%JHtP#7O?hv_eJa3_MRUWmQOst|U(M<si8|emY3Al3S1H68
zsH>|>YBYs;EUA6}WUXw=CZBvAZ(T8;Ibyb~3gK8?Ek^0T!K=&CHv-Vya~<gZXOLD4
z;FX;kNARbr$ZGr}uk{w&V+BTjo~734`oK=}avP$#@coze-o`FuE%T`22BHci^BA}q
zfV+5y5HAY}lMHmqctGhJt@Y}{mgAvNuXY2u*5@H#ExDMX04TJ&9N3X7+bH!U73_0}
zt|ry~9OR^^HaXGKz>oy6<=oc%UPy)oT>)f5#$`=mH8(ilLjsu;R>>f-n$+D=88w-E
z-O}8AZq4g6xxb98HX|!8vi|!rMgpf04m;RCLf^nxNlY?c*oO4IJxXQ<t+7yDu=BO?
zttcc3D@T8fH$RTQ1~DF;ov|7$i}ASo6yzOpT4ee;k1RkfMSpz?*X|R&v@z1mS>H99
z*VLDhVI~0ndR^{Mp4IitnV*t8HaWr;>E(EXK*@0Ufoqu@!I;5Et+Mo9T&s0hTlwux
zJTl3RR9;=$k7*A-5ad2+F6^HA^2}ua5hK}CLnmy1rcZ$yfiRSP5ih#G{r)(s8zmkP
zjy&7tgC6e~wXd_b<I5r0YzId;64(S<PvhZOFNqHiuu0e}yi}ExQv0XjA@@v7sEcT&
zkg#B=SGI3%U@Ly4<IDVhgyBR&|E<?)(?^?IO=2|@OAAadUX3>>or@jAqz|YAn^&{k
zmIAq&4is9FC2(-8ZUTz{j8{0_>^<RFn<*d+Xc=&X;#Wa^m9B@*U}VKTxjXg39i2PZ
zgVlpBf>Zk2$0NO5%kRwcl3E2<Xp9Ecj9>l<DlRR~5$!Oa)DMYjQ5)+T%t#iFb`gl(
zC4LJ@0_>EbIXDb-swI8|#%51)E6D8{Gs9oMiSj+ib+1ncbWn3GWf82dZQMa*!a{X8
zcIiEBBaRU*ZL)$Fal3xnp{oArn+caAAGJ5={jDcnfmt#7qmuQU?yky^p<(flTX;cs
zxf#uQcj~WYc!G?*4cJl3RjoH(wuWmHJ=<k*lU`u>jOS@uG2lLTx1<uy91t7)&CAJ&
zK}a$fYgJZ2I>?8?WJNNq$>ExkAh@{MLM0czdk<V^!?z2~!&^DN-w%~Z)O!8FTT{~>
z(4Ch97+(H%h?YvfLOn4yd)z2K#*J#<-3TAHGGeO0kgoA_H|;Gv?r9}$@t~A0Q`CG~
zSgcgl#6?!i&F0@Xbbw0vgCPz~P6ws{S!lU1ySA)$y0Rv{>CY(}QD@Nl-o(%=ZG0uh
zOE_^4FU!W!Vwi6i)-#D=i4{aDxd*aidaSrO*e1{Y$&>)Hsuu&8z-ZxlMSAa$lAX|N
zmZMv<n&=lxdVJkKok9+3{kcr{kfsUb`T<8K_@B_0v+M%FxnmRXoo3Wg)Pw^rx!~IF
zRM25xaL$`XrC3FmwJH`9_nk_k8sgcn@oj>(#MwieGsEP=sYi66irovl!2L~&^bcmr
zRp!W8g?o$H_EV&;sk~pi1)`m2S)wDZ0@?#a306Yw3t8L~rZ_n^g>j=8w<ljYs`@)k
zvUr1bc95pg9&woB0jIXM6;;QrS6A!ksKqBKJY-Kfu^)<W23|}rE!07mT9D_r2^vAP
zTt>_375fS}>{UQE^0LEA_>k>``|ak4(%Pgd#50BH!)>N@I3^7tR&tS`;t!;#%3rgf
z(EZTJgZabgm=GaqE_d@B#1Ysw)h<h84Lys;Iq#$gRRx^6*(|bZs07?B(rIVyF>QC`
z>Ny_nG6`9y8T`;&MaK4k4p;L`jv7XQo;IZb#WoO}=VdXwU<8We3|1AtbIKV6LCHsp
zXtv$DI){1)Jdj>~H8W^h5yDf}sG`&@v`_QlxYkCSjK{yv&k>rx?u<q{AS#6<F+fH_
z{K?4z<_|_9O4?a;7J`i-vkjZ6Hy=<=p^c5#@@RKU<#g-WS8<0{M7J`O?EGV1lJzo(
zmMxgp*~LNb+Eg5)hnh~TeAf<jIr~U3x`v<sR3}*>EhoQd%w}o^W3=Sptr_MBWE;s@
zh#@7kF;0SoCIMOs+3I)MZFD7;Cs$84)soR9F2l0-o7i$f&=2*XAj`vNM@_5Xl=KyB
zhKL<&*-!^u5{EG<aNJ1+tryNI`j6Oy!WkzIn?UT5XTpyRZcac&wT9f<L|jF8geMJp
z4J6M+Kc7CPbAUOo&0uc};jU-&VORysTs6bd5@@K><VXl2c4}tvt&NoX0C=KZg?$Yt
zq{||%aFyfzl(BiH`MPb_&sK+KsrYP9!*XL#xXAk&j4WDS<Mc<8TUzEbdOQ=#H1dlO
zD0=lkW5W$Nx^Wy>5r9ouEce60^^7r07Xu#GMVbEmh@MVizu=2~f|3`p%8kGxqTeB$
zRyPI^CYs^=l5T)@m{&~?(5!n*B?BAEPs+{R@p5vGy6wVqF9M6O{U^c_M-2X4hVm#T
z*C)1M&<E@4974X58I<6?@@eH5c?G68KRr6&8tW3H0Rn3jBUJiDq<rCd8b7JBBC+Fj
z@5G*2S%Rv_IoctKi_7?@awGN^#>Pfo&t+UT@sNFI3knYcn^=A=_5w~CO%sak7(SBp
zB01n2b#nFXw27|nmo=vE0C%KvYh@u?jcC7e0Nkz-mZv&R|JeQ{z5wA{`D|alf~PmY
zNW+B|mR7*4N<zzH3SK@F<c)AJ0%i&vI$Vq=)mrn8J_>F|>TV7$;q;oC5YWw%1Oi%q
ztt|XtdQ@OAb0I=~D~`Jfx+dqrS6TU|d|KBQL@iw@M3p%s=R*XL9u+-M<@g|!^9L?P
zSl)o$b~5ZAXXoO*5ghbZx0swl+AW^n3Nn#;#@+Wp!P_N($BVK=$U%8sYfza!2_oyS
z#LOYZsBFQOZWpp#5gZOzzOMK>!l8G<86WqZGp|eJVSM8QFesZ49xhGM%ZySHJ5{}9
zo4_?#rf*!nEBNgXusep{9st_VNj{q_3g1ceSce^@9wCb%n{>2+&pKLw9!~F9`q1Zl
zzOyzp@vDKTx#riwPgIWVA1FV1^E%~6YI7ym+MDVH@wKp-d>d%E0{wz09-0WGF=81y
z0Y4u|e4%skRPrhLc_6>;rO@i1)HsYu3-OBMaHCa{Iee3q$KdJy8es74p6@`kEN(6t
z>=H^)C9=$H@sW(Wp>cxwExJ;#<F(t0qhqY{Ma02gumbMj8U(Akl43v`u{RzY;o94q
z=23q0`rJCiPl^lh@KQ3GKNSIY@a3Dl^VjO12R&1N0%aWaf(^Xuplf2!RY$g5#_cXC
z?cSe8A?Bb&u>=Idebu)%fTI?t2EdBn)Doclnl`fwG9yxzyPG?|Wz&DOotjXc4!z1L
z4>Mc3UaOMYd=S(!!8S(S!f+v2!yQNj0N!wc9*jveG9GISGT$ReV#w*qxh5n+PRcTg
z^IGK_t`VO~%$HVAt%hNYc-vEX^uR0bNO~hJ*WZi)!$ee1fO5V>`2j%?v}n{xv&-r3
z##%sww4`G(0-^<cO?0m`glf$y&-NDPlH#?3Ay4ZIb`XeUq_y=+1!oY+wSumc0v(Xr
z;x3bw7vJL+NkGKjX<ff$L-s7qyGNR}sZdEA!s=G71QIR-H`g7;2<R>w`lb#xI-d1U
z+BqiD`j>EhML$Vvu+Qa7T^8*T%c?}n0jYcMOlLc2jElB^0{BOht)ir({vh<OM5r1i
z^*Vw}HZjQ#lsk??)$Gxe`rq$97phuuv6A&z14rH99m~;c^3(MwFL^wKxqQZ`O7*gP
zi-<l|KI~@gZ-8>C`Q3*BASS%f(=mMm2i-Nxc1>0DJ&4=d@Fw_`1zAK20H5FjW<0^1
zS%7k*!T}s_v7kO9C;YB?PNa_O5Ldro>vQx%Kdn|@p5HUz?t(0Ulph2<m(s_b>Y;5y
zTP0G7f_p()hkd$BRk0G^7`lu3afZ)*GWPbdU(?D&NN#B&b?<mW#^k96HA97{jrYe?
zJEj15hl(TMt!<}RwBjDF=A#|A&EXP@Nw8>K3Rw4)f87Z9F;}2$Bi1|#p?xMAz!r!C
zgS(>(x#6W(Zu*k3Ub`1>PZOa`&*p(Dq+#9O7av6ZOU@B;qqekS1<$Z}E&z2rcFWty
ztmL}J57y`EL(WhhjjIzal`CXgCjD8hKbXFZ@(L)|9)Ep(5!Z=z7rs0E=YVOzmlc1G
zxfLcM!@3CG!awvm9I;bRLIh`cZRIsS&;*6o=!~Y)Hru@M>I}sV>@9c}k>9CGNm{=d
zh#|M`E=uhcHoul%z0TR}Tb6m_uZDLCWdr%L2RyD6K*%RKU;9M2<_z~#zTbR@`18W@
zeyZctIGytyl}IiRPa{W5`t^wxy3QPqprD{)o*zM$bCEw20<Dq|B@D)ej#83uDc<L#
zY1gQJ!Y3aV??_%)h|jA_&3%BrsB&Z(ZDy0~Tg{|)E&BJ05xWb7bnq4_Bf8&l>8}nb
zZavq<6}H=Q$Q1x>2ErAA_Ev@Y@4+7mQMY)$v)VrxxW>R)$8jClm_KF-Pbq4@eXLl)
z$qnHw`Ms1_I(l_M=M34?7dgl67AbMnjOOl9uCgY`WKE~POn=$a^v*hA4>QQL62x(<
zUGUISrDK*(oQHrWUSXL5=sxC9KnHCUS#<LB>#@c5(<w3Ga8J6s8vp{!t25FJ#fiOH
zTxR+cbdzp%YDUp+!ZRblWO9qyCHKCBuo7Q%zZ4RoZN-^wvt~+lZ4E$PzqKpDEH670
z<w8H%I9hJ<R^5KR1My#Y7bLMY`fy@NR3>=+S~YLNR)_Dm(kmW7;g2i-EVA;F{eq+z
z5Z7gBfJ~Nk3J^Jw>ApD;IFbd69DCo@lo6KDzEUgB6&mwbjg5M(I#E$Ix|@lL)Mq>g
z)CaKDB|6cr9u<=5>ZcQ5i$k)fq5N%*uVSn|2ThE(zI3Q~>o~yz)EgX{rpPfxZucV1
zGHpByN@{o$a9eE4QG}vieHqo&Uco}mykC1o&6{Ar716`x$ZA#PtJj^GFbOh~6ODng
zI}b->7#Z0Pwk0u=L+H1qBanqR!*WP2zRI!%`oY@irM^rt{i6Q1iQM9hbR_JJlzn+x
z#S)8FwtUohqtbyJs$9g4(Ne&V1pj5T3|s&M6E|zgqwG+cXH7yq>wuHm-iZ<ukhoU5
z)ChXM>H*-QUtM;5H_iB3`FEfj9Tc$D@87z8<h0`xI9!&3wZvLztpbalHpjx3jAiPv
z9l}yd0pwfbKQc!MH-q2XSis?oW7)sh);?5V#~wP$@rXA~490qW!2yBcqa2H70Kx=P
zd9$IegZCS!)(XbHBtrS0xW_Tol8UV|c&oPzO&$b%vFZ!0O?cKko2YN}fv{qs7+7@%
zK<F}FgK$y(i-w1A#w8%`(aqB*Yy@a`3?!DIg!QXjr|XnE3h0i=@UCZFr)ueShva(3
zExY0y;|pr7=LBG6l4?0$30~0<h4^k(n7Ia<y15JWJpIUh8&U413S=6gtJ7604p#Z<
zxWV^raf8Z=k*B-hK%7Zb{|)8uX|LXwbo(R&J_dm=K_I)BTw#%?SC0~#D_#7r%Oyd=
zAljyWKS~B{yQC|kZel7XJa#=iockjS_v``SnKARyaArcx+K5&}=-m|b>iVVQl9-R#
zts|Sxy`aRGIKE);VdKK`rHhnFKH!3R=13qa_<EGQ0c;6>HuS$pf;-(QVlV>j`2&2m
z9X#-Dj)&tT%UY>t8AhHV4Y||RMn5K4^R2NR=q<lNAO=o_xekeC;Qik~_Y~phGVNC4
zfMdo2&iu<OV6R=7^0y@q&HzAH+$*xD#rBvdyzA#KfID7zYa&Khgb44U@5ocYJ`r<n
zo*4L6!3Pov#dmgB_`n{oe<3Sv1$ZX>{+WsJ_lP^TkLa*cWQlFQNcH&fcBl3!y=j}p
z2q%cW0~}*LCTjLZ@4;g_2BW4SFp8C%k<fYp|G)@S4l+7H@o;+wS?O{^(i#Br4S*tQ
zJ3d)9`tU09INb=2Niwdmuoa1^nVMQoFcIY;P2yOp?0ep=s$XIjrIB@hh%1yM`KMm(
ztsz<t8M%yi8g%jV^J^-Sg<J76&t+ZeCh>=G)`MQ5IDHR59JpmY4ow@^sA_{VCeSKA
zK<6u<>6zHACqy>^W;Jo3)f1=|xL;N3i)`bN3&I0c73MOm9Elw>v(A!j71-W^=xJPp
zRNrF}piY3Ib8J7{!pJkT0T8T<3|LY|k}!@7jk`KI@Z!PN{wJs$<SRS~Cz||lM#n)8
zBra$O)??nKZ54`7I6T59xV@nr<NEW#<d)TL0(WGwAn&WF7v`O!kFOS4<hO+7jD)9u
zyIREmP%x#sczq<=qkL_LbhJ0Hq5<yeAnjJjOe|GT5<C-0hMjOA#*>vK$l??s3$Jzv
zU@QwwzDk1G;lak2>KT5^KestXxgPU-2)iC(IK7#ZKs-C=iI6i77bx+%yK4?lOdZ`%
zh3r<PJnZn_z0ZP}kNBQ)>VRpzk?+lmgD>ByD(w}K%j3HD7IXInYMZ-q-c;9GY<=op
za=R35JyJ^2Ih^gNW9ew7T+D{5whA)yor4R{v2*!t2WCN4pLbpgu0VUa6!;Gbf}?-W
z3TPM=47b~n5?5TVN?y!r^isTonv)72*gkxI;W^F4&IZPWA<bOI2M3UOQq7FuIsqCJ
z7tP~axmog{H3@Wy3r-@eGhs}a(rSdCo|#|<2O!^tU{K2Nl=pwuXG|`VUSGHX4c^VT
z<ztn*!7^Meyt3IxLfJk+P)93y%shR)f4cxX6T}-bAO^!tL$oJW>S$iR3gJ)d@Ob=f
zmm4I4bMSm<t)^z~I^1e@fBQgkQ%002oe#}(HSLO*rE&zNaz^+97s=HmwlkiDA_#Wu
zkL-9;geQ|gGB?cEFyH>6j{xby47_K9RntQaAXUo|;2LX;zjm;N@#oXjWWD_sEP~$j
zUr%%+=tUnAz)6eaB&709CpsmTvkTQ;d<5)8j&1O?4<%^h|NhI<+u$4Po!L{~*RRgG
zS^XUP5x~B`@HXLCDBIw#EZg7%DsuU$FO(pA`V5fnP?c(#kW218*hDlgZa3@x0-RB4
z5mQG^W}QAq)<Zvl+1>}#!0_w}Mw^S`g4#+J!&&*HNFnM2G$pupp74IA2zvtKGww#r
zvv1?5&Jrc-lnW>{jA$<w9gZ0UGD316zlYaO>_86PxDR6n-hEzd5wmp}P^;6xH@MAV
zR7J+o%8|zR^geC4PNNxoNsj_)O>SM#bb|{Kf}|L`5x#W5OD_?To<0=p=lifHW#_So
z?3&#7d$LEXaaE}N&vhuf_DNW%TNK-s|77T2*D)<MrvRA(^QcM!waYAGE}sV^@%N4Y
zVf24z{T~MUSHk~6gb!d*cK^u;|9<8_Nby8S|8d1m<bm9H0DI#4_kHZZJkI}JssDcs
zI|Ag9GhW&t@XtZ;?46#=WWApcfHjW*ncVY`SjK)hdjU%iC`|LQ8Qg+BxgxrVaik(5
zH<Axo&rnSO{{bKWryCI}2gpJ4uUx4<oF*dUzx-YL<+EhHuD@z@E$za|(<gfLn_(L)
zd*)<~%|iGA>9Ych{4Lu>i#t#8{*?=9a}n)T$=rZ5LvGww^N5J_q<g?iNES9&uwnYG
z0;uoV2WKx4CB1Ka`v+KrD`)A20r`91IDMAP`ryjr0zh%jd!N6u4L<weO072~=*D^O
z`~UOMf0G~r{~;f!I#IF@`?qrD>cDhci4D<didk)LTZO=S21}lO5Mt6KM!;o`q${zb
z@<yii9&yMsz1&>TgoEm?0CyYchVIPQn27<m+`kfaJ8)Zl0+ZOJ)+~bOF1bZo$@JTf
z7vf%w5-vJC)2<>5WpA9@Y0la^s$@NiQJZUNg#g=~g>l;D>%YoEkfG*{%9G#nJ@>R1
zSgn%IHzSTbUxpyA!o;^X76#hE>P`-9t9Lq@+zTQbpURK5c9}(q#Jb$OH8uUCMUx3*
zK{0&vIq2&cT1n{ILt4Ch0z!~rc$3#&oevwtoqd~|ci-!oWi>DDbpsHW9shiNCF%hp
zdW5K=O(~zHZh6#fe32o#AJ>M990`W$6?(e8w4a^%V8Ts^uK)Rn)7;pEv8Dqt%{p4<
zF#7~_4X+}{ta~I%P`Sqa6WCE(i66)E$Ui(<cfuT<H-)ll&vJnbJg{ZFj#_((6}ol;
z8yVp*UtGS3lY?;XxP*M1T=i&qY`yv<%{;QD>M6UNY-9fQX7wAF2HVG|J!+4xec2~e
z&UB1Z*s1knORQ2;lal^;R1SU|O+(pYe33?oiNI6hAqRb{psuAy56oBJ#IoUd8(S50
z&$r^*s%+SGLI>_T&utV1HMl=hSlg17u<)uj{*Fq4=W;zqs$53#@d!f@&2bu|K5>K$
z&CT7IqxOJ#E#<WtH)KndI^r(H%Ypn!XgSnm2~&kKY&G5g)}uU7(|Snn_l;-4`Lv#i
z&1mTvWNvf>@}kL$AJcz&`Z@-!TsTAHD)ttg<_FK%a=#RJpnB5OCMZbd*VLb3N2#)r
zfwzR1(%#l<lo0Fn({Kh(pqMp>pIa$^r!Gq*fRxWu4wLU%^=~9+2@3WGeYqtl=)BZ_
zbHVUi2g3Hq;UOJW*v>oCUNQO2pB)h25YIf%wHhd&p1RqS;mE;o&}2EIMg~?Te<(FB
zItG|z(UZ}UF#0Ibc-8ccLSYY+KbB?j(<xMiM;47+9&T$X<&v#c<rVj;AHgWKfSa@j
zw&T_h=fX3(%sKa{{K&tydl0azi<q6j4sy9LPfr!BhO64iDl6NcV|Yge*nANVM|yxe
z4Gtm8%I>y>d^r=7yNEF~U&JggFOQCP^3Kjlq|Y@9oLERp8zR5CaN);ikV!Q;1baPR
zU0K=p`=52pshmj}n^g{bN>dFrxf~jr3#t)$zGokoo|E&~D5zV~X7n)<Xz%VvtR6!R
z+{~BafB!4nM8WO6fMd%=AWq>?g^8w<M*<aL^}RWu@||cR0d>G`4NII0BFMxfWo0ZZ
zthpU-pKEPs;Gc?9dH6(!60i57+G1?2)4=8XFgwS?YSdz(bxF}<Sf2Y)jXWK~W`eDS
z*HJ<^t6(d&Bd|ExhhkXM(v7I8x^*5@T4Th1effERfB%Lfg5CMkWWwS`gF%lj#`i2W
zkq*iFD=qD5<6TK<clY(!4lc-%`muOu$Opm8hrhH`oNYHvzty;Z!H#3U?qv7nZ-W;<
z)mK{#MLDTW<eA*t(Q^e^wzeVS?tXZVruDn~!h9YxAa$mNCerRG#W}wZk!{5o8+m@W
zfBDQdj;QQMiQnUeCZ)4V;mQr7HDtsRDKEm571!G&!*zudHLLZ1Wn~q8*pYC3)qePF
zj|<r2_K&AAXjp2JMPQ}4>piuNOa{T)$J!rC*ul`Z4o6fDsqd=6C?+p=*6BFGxsz9?
z?wSUChbxO!D@Ez}@K<)pIc{;d>K?3!Rmpqv7zL|h89cqGbv&P+Q>o6IRDZ{fS7daY
z*I8}&+^dV2ejJnAtf8ES(_V<EnqcRU=<R9!)8aa}=<t9#7#BTMNTcuQUW<Mt0RLnQ
zdU`6lUC6_Qe`dMiwj^;!rf($`^lATFr@<M?EkR{tYWj=WN`5NFE^z!ubL$klx9`Cq
z$4;uFwBo~xxw8Q)C{inYMcNJ$duODfIHN3wTHPVROL+SbHRE1yVo1X$1{x27RaqEJ
zUCX@SUcCc28Q@IsKNq5^iI3P|q8{XP8a7AM;mCz@Ixcbj$v@{M-dfn4S#ovDuXH-P
z5YG~Sy(HQk1~n#HPEL=WR$yh0G}zKFURuue--y2XXqAc4N~QkOr_q3SoU|AG1sNj2
zb$y1vO!z}vvRwj9x&y0US0BY1Y%Sn@uMNQo!c;W)-(zFL4OJOuX*0|tBhbW0hRF|z
zFKk^H`UdT2W5hiuZw=({S-rpM<4+SO4Vj?SC&Lf4E#9$xOn6;ImaLiadjBS3EG0d)
z=^p!@j872UK>q>3pv-l|R|u7S&av`Lw?<M@3_<ae0?o{Ql4hsK7$>8`vcZ`(@H<Oc
zyXmlcfjWt;Y~Es_VR0#x!kl#)DU9h%4mCWM?#*K1VaI{ci%TyYj(QQvZ>CO<&@<u}
z)?_`u^4yJ4^17T$HF|$#XAF&-4!3TFRNSxwT()LHC5;Fc+spPmqvm(xHG${^4_d`e
zQ4tT3(l)fotQe?S$#t2%0Gva3JU%gLD{Q-_YS+N`z@~1ndqY+2RNXj#{^2+%`@vFZ
zGDzj?S*<N>hEsMwNro3cvnq}lZ5-{5=r09jZHfAE%AxHtze%d>25?63Ed|r!x7lUd
z(1<T+-`|D)V>w8bJ#BLV)tCpDJ+v1TF34Bdg@yCrdsR@Gk>#!CIPqj^muN6Q+2{Ar
zdZ1+f*MzrQ&PyFH_f_>Qdnat-{OMTxYY?>!4yj^a&%$nz(Y;dpREMycTvc`*(FEz*
z58eM<dchbD_2coo3|F+7*(A#c_FXL-`AqO)hLIa6>ngHtckaj>i1O~_-I{f8_$`|k
zF04-4>sgg@sw*Rpq0+`{HVyQCepRRu>MC>DsXCHd@7b+SI%_B(%QcDO0nIBh{*0i|
zT-`zQGIV@gEo$ug$)grTLql8~rDZF4J6N2JduY4>B5`Msv?2*}b*-aWL6sbo-!liz
zMfk6A(NP@;8w=B{u~ZV^i3eRpG+_3suW9XL?d2}Wq%e2HTUuE5qEKtywSz$4<S$Ql
zJe1eyw3*H2l@?Azwl`*}+mK;Oy7Q4~0=ARIgV0|;9&jlVDCCSFu1nxeG_NYJe|Ka%
z>?&7*!;k9KsflX$H!-^iS+}}@jVrB?Gw7;YpB>R2^M_l?wn?E)R9=^NJm`>xyYV2B
zbN|TDxerbnuiPUM?vh(~iy7WOed0yxNKgW9vr|lGeO42Vdp?T^IS5W430^lGsj`G;
z2NK>2sRFw-`3KDtni9CIMO~=kDdpdEAl+UO^&*f@Xp`|p`AwHor&ACCN!Od=FMWYI
zzaNUiUjKP3fGU!<2hbHI4ZMed+@Y$J=FKY6r<dhR5#OB!@1588NQk_Ii{tQsmp}RJ
zFT?%Y_FhE!uXA}@pIlgFMmvVjj~@L4VNjv2+?h!1mY#Y{@jQU!R@NRsTH7(9;x2z!
zV?Tb7nYpwo|H-g=f+exWkk7~e`kO1PZe53sSOi``(@Z4(w5ClrQ%^=Qj%lj3L}xH#
zmH)^nN&7WtS$6u%&oIHO&N7g-haoZs^3=+F?uj?Aec0H%)8b~XhW%4pT7|20!YTFC
zC*+qY8C9`ea<d(M9f&HE`?Or;oO(1H^w8EpduaRKOt{3Ky{9ZpzLuQ^Ir^w`o<VIQ
z^OzU!guJ9(7Kh<`i$6Z{zw$OPu5!-|T1`3HC)bMO8~c8Br~Mu*|HVGgTMartp3VT~
zsZC;(&7^x!sRyCU*A7lxa8zfaxx+7Qy1Xs>ahYw#vA4)#g~>`fz?|a$4`iZb{i#Vk
z^eXeCe{}yBZC}||N3?W_1PC62J4vwM?r<PTa0%`j+}+*X9S-j9?!n#N-Q8vS-goAC
zW`4kY@9wkD?p;-Dt*Wj9xnA?kH83NVv^t)2_xbcEYQCDjoa<ai)i0T?Ewht+e*U?N
z#P4)E8B)dP5z&O}tpP*K&20WCdL0GYod_v*8Bwb6Q=&be%_0L@y~E{Pouk|%s+k$`
zc{dJcjV8Ii@F&GJ)(i6TImaSDd=eIZW}2OxP2Xvt<ZS;^fxRD9?y$UCRyc=8Mk45y
z$?k!m`gvGfd<45yOe-BLbBDLp;oHOX{B#Qz+OQDB&e?i>15yG8ruIHBcU5oWZ{>Nd
zoUECWztJE4$}l{oSbh@(>+Q${Vgf#v{*yGVj^{X4HmuFd<t%xiwz-WW`Z(zaOU-eD
z^_kM&>`D@ovT(fU|2mymq0E6f$K+Om0h0|8k(E(S4weWm7_tR(T4Bi?2od-f*P2c{
zJtk&|u0_O9q$ot{@u^*7v^XZyXCpfU;k$Gciz0;%)|>MHz#Rl#KQpT=J)y%{z^N9O
z7TesQ{=~mK^RO=52s@Bz#%;3MQb>}nUpV^Q`mOQDgztE95vBZ5o$<Q)fFZb17<Y4s
znOtU*2vOQ@DaU0}F{Y!&lX}7i&qCX6xLlnOVYS6#=U9n0RX(J&q=<ij;zW){lqO!R
z5aq1+^G2qC5>`lK?#K2Ft+B~BI7fj6$poud5Q~n4ynZmml=BU|8sau`OGQ`lYUJ69
z@Aryp4bk_Q1u*eixGmn<WStngC9IG@p+hNhX<HY?Q2YDRiytn0qtpv-D7S2`Re32U
z2F9(*5J*iu`MM$!$Km<23w2<k_5I*F7j@(K16(P%kYKqTXrS)X6hxwsm@&o=Pp!6q
zh&%$m8D6)9UtFtbUo<MEEGjBNA>P7CiWa}n+IUBjnFD&XP*>+;Q`tYf>&MFYUmtY9
zie$7_olhGW&#4*B^Ej=oKX0ds1LLNm0z|B%pss6&yo^>HZ_j)YN=iylehhe1$hZ3G
zYczc|vos+)ZvtStA(qmvHl-J=uASY(>@0@cTo_c|Mq#C{<mr!TQ$S(ctL!wtT$^eW
zmVht*NllGfOZ>EHgQaL6%`2Gu?m%~+Us1sb1X$uf#>Syp^*WZZ@|FrS%`0A{@fga(
zV0>!IQO(WliV8Y97><W5KZ>Gt$oRB_Yu0Thh(aU_2o$MzIajao!O9vtoVg~(kLw--
z@A3WiK6_KA!-g`|3pieGc<x`h4mhk>7^!^EFCX2!!>*HGY{Kw+i<giH*A+?|h^c_B
zi5j%j<ccB7TFG5mqdp+LxGTPHzF5W#J!Sf2qTA2#(+jrv62R9L<3Fr&CToOmc3vE1
zcig~Kw%xn+^{L`T#;mM!M{(&tof44tCdTUOfi}$iWGg_UBI+#)5+<J1m&5z`XrBv;
zqAUild6w&)@OY45YYibwmfi1XeuLy=rgQX{9W<{Fnop=P(U6yLxg8#Zoz`#7-??4#
z2cTr}h6=Nav-!+QBy5faOPVOX&X=3xv}7_fn<y!%seu`mc84Tp>yZ|@sgXndidw{E
zC=GG4??|oqM-P_E{+;yD$hM=wTa(1Q9#e`KT@baLuqvrn##8CIlV3@((H=hlS~JXK
zauMLz>eWiGDz0E$Qe3=J%l(910qPZAXl$(5MPey2F<~xjai=M_FagHce^blaaJ`0H
zXi*#)ln-j2`$QYGie09jDkc>B_9Ugtb=!dPZ?$L$V$YHKTC#lu9~S`TrlzKRX$|WV
z!(V>?5~mv=ct_xJCNQWO|7E5ylPD=k%Y;}ugmJtEc7YaUeK2#vhzCiaOo|Ikk&UP{
zp7R!mP(~kUzC_lUFNvoL^4p}Os{UCDj3eCH{Vq-641F}QsrmcYL<s)FzM;-hX#H*<
ze#(p8;y<%#W2)KQ-Xg|$1EA$Zya?1cV-t42$h=$mN@8BO^%_MplRts5&=#z$jp<8C
z*9Ri3Og}kcUeF?CY9)q{(z(1;6&1_QE@6+}a9z>=G_W^)&yp7#1H&VC=B<Sjd2GWL
z)lBJU6f*_&q%bTgK9|<Sw7mcn9I1rvGzf(*Nzeb%cF|_o{Gdr1Y6Z<A{}tCal!;eE
z4A$5`5~VkEMg_X++ly0iiMVM?PF7wXhtV?Wk&*PD_fdSw2xM*Sy!KHqLsN4FBfwp#
zOHlI_+>g-!Rx!sSI@IWN*_ei{iU$$P+%>uz#3v^5#C$8zmP8{fub5z{J(xXX498)^
zgc=W8z}-wuaCZQDQx%Zxb;KtKKh?4nW|3YUD=Ew0-CU6eWo><#w9jv&;AaVbZ3t#X
z$Z)1keF(yY_qje$hAH-gO_pe;pWV+mv^LxTA5RQbCk?HW&7R^_{xbl{DR^(rNfH=C
zoZA&jFjtG$fP!EzEa(1AlfdI<EqvTyoku5t!Vom%^s+qlVT$MHPI_s<mb|X2$ONO@
z(j0nq=qWvwR|g^ThO?@8ZSQ3zC8n&ChI`9nkvrtUt&I1F#jOTQ$5-fuwr~#m#Cg#{
zO>viHg%w2X0q499a4M+#gt>k@Os#@jLFo~Hc95h|2+Ui7pue5JzSeSs#2Wpsq4Cz*
zTU5QP<bWI@z}Cz51ZTO{?A3QzU2>}4*c^b_JpTZ?H%c0+Xv<${B4@Fh`o4XAcHHv%
z<Z^^R$deFR9kP^)?3p>TV1Kw6BJQ%V-YdCY6Q&iLfT;P8(=yC*%`IK^a$8sY{=3Wt
zxs-|{BrVm!#n6fa)U@b%X<nYWfq9MIUH_Rtkl|=%<<R?2T(yyi)-o`(bD^pI@#^29
z>$y_)$2<0HtJ$$s)~N6h{Xdjt&tp~EUR03{j?4?HH(A^~<1XK=h9r(Ap7t)(YTX7L
z?{hZ~O`0*Im=T;l5$15Aw6x#p>k%`y9$<9hnVbD4T%KM1npz6vZ;VrRH@<=0d}X44
z2c5?T29jeA^gBMbj6qH=w?1s*9>!UDjZ25-jyuK1<+5z-cQ~=;m>DH1MG!Kk<6`YR
z*HU_il&c+g{5?Zpj!a^+Dj@u=BH&`k#47x$hat^JoxMzr$*ZA&+^ipC)qw6AIqd3t
z1n8yg@=@)_<*ckxD;w91k<?0O?}AbD@3i?cImAPf$EPyVHScR9Iavh->x{!XAjsO}
zo)#BEzuI-vD3b>Oq@~e9<=>f;x9d~6db9C4(jT>C{-+xt%&#0z^4d64;vb)=CgYeH
zPda%jVq$_ZB?h;*?~Fvdv!aNSpUO<o|1H@DHCO->Z(Zq<XA*1cB!on(;uFZm!Righ
zd`{21ikauUtv4Teh?oaF%iT_A2)!Nc#Zr?!0MOA#v7wv<zu*UjhT3}dc96dzH3=?7
z+fL3)jh<Uq)1)$IykvnnEbBg666`c7%xn>w(FVX1L`%Ql_#w*8)^C4KPS2OC{F0Ij
zgwpQpiCcOAKQXh!LK?n;GD9=7Gq@eSjp;K%0sG>XgvPo`spEK@onO96ig@?<kJqy#
zP<MlM(P!yXc=ZK6jM)Mvi1?c|k$6W5OF=D0FETA+!;|0>q&!gXN2S(<gk?ZSI3P1f
zo<3Bv`Qf{j-%d?iiu6ND@DO{%iS1H6*`3T=D3!0+KJCAu8`1gNYj)lgjZber*Cr;i
z;rJfLQ*XSp>W{l0Qw|RgBl0GOI0n5s)2IvAU-}J<GaUI5xK^(9(b4^t4})ai?f{(w
zu)=pv)~}aVy`e>RCvlDyz0e{M#b{5q6?akpYNOJUEPsE6Gdzb$?AgxoN}`?bnag6?
zm#)X5e68&1eyw|ygfkDdFjtZXs>Sg_m4EhM=c}i9#)*~a<w1#)wqF$3MBsK%K|ukV
zUUMj1g}e|uIR%yEVUGQ9pl#NX!{1>6P)T`N@Cz8R72Gc`)s}MpQd3e7I(EJnY_@a$
z`}1V?lt%A?{8DeR0I3#sNCD68BxAVFVQ{foJ(Jo;!1<-E$jm8a&E;*WZ<T55=m7{_
z)a$L1hHICA=POAWy;%2(H)WnAY;~lF;rt|IxsdrzrY7zcs4f&&P*$j@XG+8=?tZH9
zDP68~o=daEG04{YoXGA;u&(RvcYPYG{ymJ1T7xZbEjQ_3<31)Y`5Q<;KM89{Y=37O
z&15ygPo+*~zTq>mb=@jrOh?qNczcp&DaECxqNGXZ)l4M>ueZbc3Y>E<4*RCCj-5<g
zwv9_aS2$WB&TKVHvi@?Wc~`8x1Eh9WD;e8a1x+bh;}a3XXwJ88sdg>iL65?4;+y6g
z%d-!vesHVfanp#?TJK2=(8mphNGH?vqdy57W}WCpfKVN<;F9OFv~a(2TD~uxX}z2c
zQ^ktLWWvJM!qdo2??{nC<8!NLoj%jdzu~wI-zrvjIpp={KW9syC=EQE={R~hUbIyE
z@Hu>*1`Bu=G*>M%rw8{kr1=(Z1bLO(f}5mm=Vx^sMELQ*|Fn%Fd+v<L$b_vK3_r8$
z%-~U>%9d|d01|oU{HVoe<aMdy_e9o_d~>jdS76~Nz_8~gIwuyJF<lDuUmQ<&ywYrG
zQax|#4N++8`~1&HN)KetLmCqE$ii{h{8vi~3J^5Yw(EZ9eBM(X0+_+9jts9a(r$iV
z*!R__X8y4;@7&*D-v0ES+%!2?UKKRF>Nng_<6d!@!-!vn5X(GuEM|x^>;XrT2#U==
zcduhNGCwmH%xm?0oWJ~xUD)teLqS;;mwpeTzUr<QW$F>Vf9SrubCo-}4;J(V`B9(}
zGX<~Ob5ME{%#1rbBer15>H)*6CNI_yE%}1{8pM{0JmUkmCrBvTU#iZ{S(y#d2RU?v
zfGkL?5_}}%iUrz^Ij9%B6<B3Aq5B=x>$)RVsQb?7GEq-Y(I-q<PjRugRtef7b1$9|
zji01=-GhHqz>r!rRm*Dr4c#4jG?A)e?&NLpU;b&sq`ZPgG}Mzk2Z7n@qZo<KuIj2s
zd_gx6EX5n{w9n_lan1XUYNzahYapWngop1@9X}{l(he`Yv3)uj(E!~!K#>@m<~j!f
z{9K2SPR?nEQ<gB)m4c`|<S{opOCg<7{UAG7-y{@6L6N^l%;SoevnDhCnvGeQ{Y+G_
z>;HRgvH2hxz~g`rSh$}Uqn@j`-QrM5_|u{!--GWN3*F~rBKizbS#L;CNN6MHle3KV
z8OC8VBF~^>X+?$2)iI&%!*mijo4&Y@2sdsT+$}t`^zTcT$(DFX8&*=PI*Jk8*v{io
z*uaEFn+J$2=}k}H^U3WnF#2VAaanHc?)dCe5h6L1y>7*T2fNp$!8JCn@tJ-G#h+F|
z`dk<{ws`lKo4wkBp*Te)CAI2BsZ>H{;(ux>X|XT9r;Jw0HgC8@-Ho~9u|K_&9gGK3
zxg7L9q^C-6v$TE$E=8wl^hdm(kWoV)2%^MRy5b%`1pY2z;XHU|{`@T-8r_F+@p?4R
zO?kra-9oE&wbx?a&kTFWo-ffbF3Q<hy0%d2zmd**AwzvW<|jXUceur1uLWE??K7P;
zJbUiol+|Cd{De%zjoLBNEs=QGmj%ITaGd3Dr}IHqdf45RRL7U!4)4B8GlOuS4q1vm
z=MHFi5t&<vb?gE<6dh~iTCKWrkr{bF`fh7~VA;W}?a}h0GM`t)lj*CN;SkIq$Y~$i
zz9YH#ak5nHqCI}q^sjE})8YLB-a{)M(XWV!)^L1b52y<%x#xKJGVss(x>OM`CplMl
zRqo4R$lK&Mgxd}NvE87-SqwbW*XHlkoP7H|#exDrmMkm(pC;e$aR#46ww?;5>u(fs
z=>#d|pgFKl;X}op{zSF*-qE-@%v8Qs`JHC#ui@c@&58V@klaNzl!Y=Ncuvv|ivjb~
zzM-%{Q!*ZQ#{VV1>Ofc1X2J04NJ^IGYf_w>O?0iK^|(vnLg3LwMv52`f?Yz=FYtax
z@`AN<d)^@~-+5-{l-AO`@!&mPFa=Ak4DO9j-%w#<sQfg)T^TZuXb1nYgt1WRaD{fN
zX01Z;fSzY%GD!At)q*j1P1h1#$Ktn#ejv_ofxVny97i8o`&a&f#3&34lkY+OE;iYX
zzUk?K96oq;KrWRQd-6Us3ErC2UplBaSTZ#oW8wI5*v*Khpg7lX@xJZ_DORwLDJW)Y
zVs>(O#BET-Yk!DB#0||xCk2v#s%kCb^{(*^B1VCCdeG2i)X9Ri8S!;RZFP=V3OJeg
z0v;C8_*D{94UcQ|d-7WhND#iOp5L;D$b3Ze{c8HoZB{N1NO{!M1GQ}>1r;c~P^CVC
z<kA^vRWBr~aL(9o5^h@O45moUJ5*p=1-~F>GVX*_snlQG7#6Onn&RWia1k3o7L~Qo
zcYoB#Bnq&GyI|RC158ZJ)dNx1_g7N70%EsIJ0K&%3=?xQLBcu9jFLG*E&^+izS&j&
z{R#rTfq{{n-KXylE~S{45K#``oRxc|MdR!MW*G7+r#P@Z=k?ldZE5nHn{4;3gKu32
zZ@22Wk<t2qF%Mt%eKl7d`&>fNPJ<%GCJ?2;umH<@I*#4wvi}5ITCVluDd$UknxxN_
z(I71b9_Q;cz^vuF1y=6{V)g0e6Y34#&HKMKzq{)c5+^|2L6Hw`sp~!7ItZlJ=i}lE
z471%QOn?PSvk(S3(EDpV6?|nS%M74s5n7;km1^Hba`teubL;eP)NKESJ6hOR2>bs*
z#(Z}<bEjktfucbhB|xyV>N2cJy>)xo_LMmMcT=deU-nY&e+Nz>OBF!eYI;9W=8AA6
zp+ID<Sjq_bsC~nI{#{$p(v{&8GLvhDUEub$Z3LWdT~Se>)aL@DNbvRbN&er>6LX<l
zSc<n)_g_nsW#VTJ&y%(bO-}kf{)bK>rT0}8f$qgVcfeO~Tc@0gpL_1^a@s!Z)2aog
zN9^)Ww!ugYCI`MfZ#=nuL;3mUGR>m&Pwf66CUVF2Y5p-5X#qU+&;}7LX^Zpa_OxqC
z-oQVnWTS|6kIj4uFgU><?DeK|Tr51P>=d;J)#tnY<c2wak$Zkr#(7z-R6w&%Q|d9R
z3;cUxj!)aT=ap5;XtB3gZ-e0=a?0-@m-649xYWLZoZl_DC*=BNcV=ZO@T&l|klAbM
z54nx0>Q^Qpw`#&>GF?(1dB*TEgQlEPaF@G_(YcIIu+8M+8l~k5%;@l)mfs%@QjnKN
zY{lt41uy^r#i*8oWNeG>aI&>!pU%@r1(Pg_GN7AH&Y@5(={LLM%<UwUj>JCRN%js5
zya(D_0%6mOi<|c<+J6`QO-Ik%$4jf}Pvvqv?vejHT(4M=lLO^pJ#~&Y)i~%O5I&*Z
zGBmcHjjpS}!g+rHBo-AgC2egnkS|2{&l7HDO~2-z1$k1wy>kF`=HQi60U;$yubfup
z$0D55QwY#7GU2nS0Owlf+NOXE)U-rJNZwrCP<B`ObUG8T*Qx_;uS$t_%-5rT9-0AR
z-CnJJcHR^yarl8sY6_%38wd>)d$2${OM2`~<C6kPqu*^`8mzvtr`$7>7Ai8(XpaIp
z$J+y`(xjNQ@aKE6(DqTOY#!<*n@=YhPzQ`Zs6VtwBTgoEN6fyvqEq`*Qc-$8MI#^x
zGa8VXeP0D!f8KxaQ6{4c&&;ThB)Cs4xI;K>e|PGmM77sHT*uHnSxr7R8s!#aP-+rx
zR}+l<&H-S^aCky6kQj(=XFXMGAU5sA9DE>CWiG5zD*AFTA*&?Y257zl4kvZ*^V34i
zTzJ)fxxJM)MkLz?44gx7*Xa0f7I<G(Sb%oF2Q0%{&)GUd7pUhO=ZM%2KRO_O`|Fvk
zOR85IMX_pyc^M1$d3l$;KK)*(T<N*0Gt(=;m1lH2x@&fQd4ZE{dgocOkQ3O0qk^&i
z$`onfmC5JE#~#47^~{`q)kvJ@4Z)zE#x}===b9NV`~Do3<LtG5*yy+V>6zlZ)*Ihl
zmGSQR7W1v>tW%%(=>gC#wL3@RdP10E6xyTVpnkV|fd1L6elpS0ou8h@3I>5BhmNKb
zG84%;z1O51GsZ|!*1(zVx5pa3f?{GW@ww<t8&ToH9~WYVQ|=G>%csTfZ33E_KWMJG
zYUvli?F;64(=zUfMy$cCCs9{MU-aeg%d9FB_*T<FV|0Y3?7rdT?I3`DFx1w?7S!KP
zpV=KPYwowkRikFcE`+f_axvK*xCJ|Iw3vaKi!EC373`kZkM&f`r90l5InL1T>>C;r
zOy437nFs0?5$AmSMOD1?cc|#80#<ih+2#`s2>QXf(!^6+Zk*D;WaZ_NZ`J9?e%ie)
zEexn@>_+`v`u*_f_H?~|T$lj^k3I94DbgN#82oz)b&pQ5-LV-CH|W;rf(&s`YCCZq
z`Bqa#1gIsQzzzsoQ|YYsh2-;ck4oz~@vU=VRx$HlSwb@CcW!=veH_Q6f)U()@c2tc
zM*rZlt5j8%?WgK-<^|-I6iqFHnrBqst}HXS<wAtgd4xoCUY{)FT26$FY-gI}?%SA@
zViHbSQdvtNKHRm_;eX0XG<I(^Crw=JaZ)eU58W9Z<^~WU&7Li_k0-*Sk$^`dJG;Q&
zV$L;Hl9=OHv{{)i0Zh&41<*e472kN)mwGy{z>=UArG2Ds8$n<oW7eVPdQc~RD9CDU
zXqq#F%>tLY;BuuSD11qxk^KfzpaL|J-WqMzxs$F6N*B8~dLSDKD?N&upMzfj7thso
zfw>(j?tB2>>hCWcgGTyNYsLSixfqZTcg0~y(_Woo7`ws3m40$C>&14;47%s%g%jH3
zxpF598BrZe8?(M{bEU<_PUq9*W)DQW(I$W@4#V`p$;TO|R*^jpad5cH6d=iSy3MN2
z9mJKS{%IhSVJIOTmoDWo56ReP6?-KiK`|@oQ+b9)vap~$Gmm&I$ckkDHCR-f8pnV+
zK3wp*HU(w`tX#1+dUr6pYRm05AC?cgeT&bla|a4<C)bbbjC(!FCuPw7ZuYkum|)?P
zLdR#HlwE9aF85y@9O6GTyM#4KqRNj{gS?9Aj0d!c1R8%gHjOxxe`v(Cnj~O53|XyI
zg{HuC-yIO9E&K`D<!v_N4!%P~*_k+qBrdXJ)S~K=GF%Cq`>(e{J-ytEfd5|SxTJUP
zYCLJY6=PAfp&s<8_@*wAb|};Wxi<l%SBvPiAtuX7cB0?gY~fGB@Bwx3fOo5UgLg%x
zGJ4_pX5<$Gne9v$q#@@J5A1sZgU9=*iNUBY8?;2<i=APQ1r>}fvMDBJ0^mLtEz0=b
zmORGoP5tfYUZDA5J#LGHfU5%IdwT$)IKW+F7~)OQ`LaKOTU$^=4k?YpEq=RA3eN&-
znw*;Xk^bhqQ<5)Ep#15F#ZnGux*Wb<ESrYYcrIbGhnEisde#bevOR!@7#_j6K=UPl
z+xiG$yjdb^{Gzby!>wuT8RTX}s3bhoubXhLAbTqPsTxzbPe?!*28l*iHZNaDTnJ6>
z6Cws8gTODLKP~E9q-PZu9~YblZFIG@LA?SB{7w}ccUm3kLq>*97fTr~hSz;p1R4Xi
zP#SqaUCGg{_aInad+VUftZ{0WiK%A6O1LVb*{Kr-&#olLRzsZVgn|lmVznW~YliRk
zY`1U3O#Ko${=P#(1=2@j@JvjMK}P6^64@M`z^;g<BXFL~J<*XnTQUmmPp>G8jaGv2
zLTx7YdvDLZ!#^Jo+vt?_q$@K{<q6mg6<1L)IDZxHFHkBRhh6pjg2+s0FPIo#CrQ6`
zaQhk>5+^o7uZxw{>Hr6KWU;b$=7u&#+(cK{bblSu3Rix*Pp^SgesXx`lNJ3%yqQ2@
z=GF7uU2!^5hOeVIRGb@G%G=gzHKfh{ZoW_Kj1?OVXs6R^7&1IznORu<o#WC@_b>Ck
zUdq{X#B6#E=Zz>LeXFadlj>U7WYuq!aiS3=)1EvC^@FH}wblNZ`V%(B_AmP=hn2Ml
zbepSxpI4mF%yN7$c!g$qs*u_|gVY$!E#IAPZW<f#Ho0QEU0hJl*OsO@&1e$-_UT{b
z3JLr7T7CMW{#x|vKD_fe5gyJn5&^j<ovLhFb|t$p#_!&ICQJl{e;3$Q?fbhN5&cu{
zS{6(U(()Oc@AQS0y_bAWCpFspJD132%f#QiCRW(We=kAo7!j7w&Dv>kTr$h@KFj|;
zVXyK#o4muXa7;Xu84i`V`})0@6XHfzyW;B?(LJMMD$95<F3}~;eQxKwnkU<OJ#v^I
zFd4i!uKF|K)lKX3ADrVunK0~8S|fI`pA$U;B|h){jiYz?486ZTI?{nwE8Z&R+1$eu
z;8f~Wf-**W%4=Icd-gKkV`FqYXEt6o^P6W!it0;al}<ylm?!YoLF%i5Jv;U7^%A;e
zAZ#AlaN-Lx<HBV6+)t2~(K)whMDu~$s>4XX1A2dCwlrffgH@uqz8LmlhT9#cXQb4y
zKpY|h!V*_Y&D(Lg(7kRezhZ>7diSOg^jA>j%oKH{SHPaPOVi%ygi?d_tVMXnvD1y6
zf(F-fTy!+U<Y#p|NQPWS>|zrP24%g2#QE%Wa&kK9(<s3JS{30Z+-1>aAIhJ5yN=%*
z>oTpc-LH;m?W0IvUmmWQqj{9<P+pOsVQ)y?yI#L^8M(ZTMBas7It0QOC`aqMn|640
z-C1x)^hbb&JlxDJE=I?y;1N)INeBixYpd{1`;S)#WNZs1%{q@5QNje~ul_tOy*=WV
zeMWiJ_;{^tKsj<@vAgFv&O(S16Y=oAJ>deO|7a_!;p8mcRWp9{WKP3nK9G%?o1b0o
zx{i%eRe@nfyrsZ*ONvcp8`l&$g&(6T5tzBYxglm~>~0YGg|1WLEArC%gloUP0*!Cw
zXYiLvua|>#snL!)QApTCtve3qJ5&PLqorp80wURKfyC?LAmNGNHmAfJzj1D3>56%!
zHZbguel>5n?8^4+;(REN-kbM3EbX>(6A+~BW!btOU2+{49yytI5uW3Ebe;yIM`m61
zBE@ArJ9c-(@P@*Bcj#p80O}Q9&c?pfo{`M|_U{!*p417>Y;-U=2{ZBQ4@=$Bw<rQ8
zzuoXSxS<(>DpH47s2w{<7dW!viN7iEi`%Ai&9w-z#66-U^Q1WDmLPg0y7maQ(vVRv
zT9DayZgjcsS?Amaax~;BT)G_GnHBAwK~ae{pt#Fl!lrkk?~7^apj&E6G!7bqT`1;p
znD7wM=C^P>##1qtw2qUGX9N&?+oP`sf^rHk{m)vQ2<Rx=LG*~;XCVomZL`#k3#RY1
zRPy;8&BJsR-E7|0`$orXUY575Pt7e)?Wg>)FOf@j*gQjY|3>$muJG{Sm{=Culm1p5
z7dTDt{xD%PpRP0|Eq4$DF33!v1X)f0i112I$O`oRqroGcGai~9(HRYvqCJy^D?ulh
z?xz{`0gsr1trpv0-b?}tupEINzO@U~%4TPa`tHN@Bz&~Qn<)Y6Qk9*cAu3KU&c)dt
zCH*JU0NviGCUDnqyX_5;Bh(0&!5JK!>I!FWan~bG-G<90&YB|?75P2l6Rv@=<a6w2
zOwpGu?o_hFZ9$a0F{!tv9i#q}=jj%+Y*cf?wYqr=0(RZ)ao&EF^my~vF*XQyM>#3P
zn@uaJ>*hGK1*;zE{9<G0dU1LDbBMwTP3cie(w4G-BehTJG8HWP<X3B<bLB?kaE$Ct
zjd0=M6luebu7IJhNfk8Qim2CiV#_-waaYXbyKu;#3`rc9KAZ!UI!k^+Iro3RxxQ2(
zUVJGo_IT4}e*)@HiB{A;3kC6}N-%FPm{c&ZuY0_;p?)Fs#!Aw*Dt;xfJ)&V%J8j1|
zM&G0ob^apmS)iQ#+Qp%w>I6LuyS969`&)P0K;~$?VV@XZOr5)S=SNi<^KmDN^)KI3
z>OWR~N@l`zslCd>Y=RIvA93eG{+-ESfyT=8-qlKhcj7@2&jndi(>nBsAEcC`G`Gm`
z9AO#Jwohf)Y#2WTD|OcVukCDi&)|dMOoDL*GATVw%2V@s2qUs;9Wk%W*q>bJmKX;^
z^TW80!-5(sZB)|Mrb^vVw!0=6c7hQR@A;1F>_!Sw#6u@PnO}XRwZsxW8#L-b|7`{(
z6-pdazamZ0kIH`{CjJm<x40^Pnj5>=JVG+JW~9W>Hy0BtrXH-(b#8&2%R&<p5~9~Y
zF)`p<%?_SjE!?5lwmjgxVV?X&ziY%W*pQK@3Ge>;+M@1AnupCac9&mNve%RFWfV;N
zdNCLMv1&hC@6j_~9V{Wr!QqCX+z5f`{m;8!xorOUHwq<4p9lI=+QSa1@!&UdM#Pst
zB;@4ph!3c0iyS#9b&w8O7dn5soFUgoogv44z}>DDnT7uT%Z1ep`PY}%WcSWc2WiP_
z*(PN9@9J4vSEuzDPv*aI<Q7r7cSpB>%8OB}%We=7*msS>O2IUiQK+8TeJ61rJ<H6J
zrFcJS8J;j2*Tlk3;`butHKe)aK-}rBO0rtbKi}C21e>XYDzsXOUUbkhqJ7R0oz))x
zSe;0-7&$~F!0d%jkTG;N*x6!#G1o?f`?%-Bhse!Hz<T~W!53S(AYgmFVD)j3{;B_F
zoR{4A=<NknrN;hWMsU+^S*(8@6b|YaVl(XFiOJ5GsF``=lddWwco7n$=8ARPqGJJ1
z&kfmq_rYJHU$k{8DJgY~EWctCAyq-5R)#NHdm~5uLoAk<r;x!3YOg;b2SHckST5}B
zS<s8Bh<+jQf8<(KN(pk&Oy)9i7r)iCTpkGoLoZ(1DI8+nCj3CcpA90yIV<|%nQ?5X
z5>_Js7y%EpO=RfT=aVcSCfT5*xd4WQ$3+E{%WyqR>a14{<>bH&i*KZ)pRLtX;*b(Q
zVZixwU!3{SvMQ%Dp*`be7j&k8i=}mh`~!HS78MWj`KO386Xvk;T$?#`B1y1DQ^at-
z?QN}z?TBe_2O=c~bwd+8mParmkwD#5@x+56F{)F5N{!zkOWzfC{KI%tu`G2CslqI;
zneUI)O_p~PuL-)8x1$MvA^*c&^x+fQ1SWwmTg?`ui%Jq+F|U52%e>z`u-afFqan55
zPey$9fj4*aIZd=q?%7P{UT9H(q?=QIYz{cm7tcH;!U~M587?Dq3r9^Ro>uFG&lUKb
zy3BPLGkF<q_i!uF{JAGMCU>-)(Bl<Klc((<ju*A+m-eoavB~9{@o~yOX2bEN<^ku<
z6lCv2jJ?lR<;ZCv?a|SKUG?0_V!C`ap1GOjxutvAy|o9lSzMBWPfMxp5II#<!SDBL
z58u)C;_R|cAf>CGZ?3Ns=j*;o9g>4qxCw<(M-B&g$ngV1`>*H9-ty8XE~JMkdoGEr
zEgPpGHxQNEC$yHU-H09MZ}Ns_LaEYJO(LYk%n)5%p0Mau9hX0B7khVu7iYUpob2rz
zbLiKzw+^ZPqFi60_L)OLxV$-j3QsKU)=j%Zt|Ed&%(WzHotOw`uE8$VckqVyjR|Rf
zi^**bzpq`RAKH74koSaKbh%<Uu)Vp3432P5$Fu#|5}@X`7e&QkfO!8^*6sXCihVb@
z7R6cY6Ed`fs4SD&+~iS+1_B+meqH67w33lUNQlnn5YER%CztAONyB9D?C0|=9!*V4
zSDhYPF_eh9zHx#bCNI}2kk+*6vo@h4N09k&sWpU<{4>FHKCnyXysB?i(mFIjQ-mYL
zN8*-M79MfbQF`D$PUN&!QR?UMUA3?ois*TjB~ik2Jlriyl<ygr0sa!R=<cdL4J8q-
zDyjD^Sysqd`J4{~y+P#EfK5hBf+DimuDCF_emyC$V<J!fX9F(h^|iQ|U^J3YnpcP4
zD{Bclkeep_fj^-7rW@uH&TW|Q@W$9g^oQ<=-{O_8PR?Hm1s~?I`!lG=KDOu{Oq=Pk
z7!_4WZ&S-Qb#HeQq(uBWrvWT>H|mkMKy%cZ-%q$y%<?I>1%F=`r+hjnbn?0tA$|yn
zh$Bd#+>=%`Szg~9@8zktXL);PMke}Hj5Dz+nrZ`Xz)hpIZ8a-6np(5{%-Z=Q9Y%RW
zVLF=@!EZ0iJ~^2uq~qrCeT;b^eF|Olg|S63rp4|isR;GT&f$hTOfa&i=W#nRvM0Vc
zG$lY$h5Rx;uZ3JcosRY!JZJ42>=gDBuEN|c)*)0HjrC`F%mkpEXo|pF$0xCvZA%xn
zAtmdT04u|v_6Yk6#-|-<%c0?#2XTRPxP@s8PuR(SD7E@9#wbWU{nYl6uSji1f1QN*
zAzEEfyv2f={7D^iy+3%pIFl(`s&Y*FD5r5wCL}pbZx?}2QT##}YH4H@e=Mfe{;3(E
z^dIFG(xpsR@MQ2bWuqqG-I6H#bI1qsI~OLCbt5ht)_s9ZdVQ^XC)?*}A)W~syhO|G
z4<`iKFMdXv)}dK@wpryl3H5ei8C9C(fQG#k6&nc-k=WGi??tyhV=KOGNrG*CkSp{j
z=;`d5*jV25!So^gIv@pMZtYZ8V2O7O9132&j(^XL9=SuJ5+R|%IuF#ky4gXE%^r7s
zWFY7hC}$lQfulHzaC}xhoVgEXf8m?`FSjcmo`$KE@NV402-X%g|B^ioJm1Id2_W~b
z?Z2epC6?0ntX96Qy}o8VC2Dz~dH&x1V!u#nLH?lcdEGnpBT#qhnR$T~`B0}DdU(PD
z)8%BLtWPAhE}k@;r75_GI51gCmejr3J`T*t9wOwO+F3?LvPPCn{1+o|^MaUUG^IE^
zGr2dP<MQSukV%~x-TZ-$$0HDxkjZkCvI#<%GT3=^=$<{}*J98ku7cW&du<0K`?)TK
zOiE&^yvSJg*1v->btJBq-dp{3uNM1voyt9<z-H5BHuLqK>}sK8CJR>)Pni15Eyf}J
zeyPo9S=~bh+)sgWo)s2v5C!|uSX6;)xZ#qcAvu6uy5!$2a?T1_UtL<dKP51O{7lu>
zE45lPEl~25E-wjeKt5l|KT`o!xr5R(zaWY_@Np;heyIi<>PJ=5i9j#bf|^@d>)B>s
z9}6xe9&wL#{$B88?U=~|oIJRP0Yj*H1N8ZaV``4=Z_Zpu@<7W>zT}19$$I)>k&CcR
z);w+Q)pfMtc6P`6bh2m0--x4|1CWQZ>Egh(pKD(efc<Z7Xf9_$<sOZ+i;n+-dj0Ue
z+OwZ)@$mG-J;QrJkT5;~FDq{CP5)3)+Vmgv(r$)L#?dk*i2C<rfJc{sk0)8j@BD^&
zc1wEF<rIbqEAS^$;xDR@s~e}lunUFL%hpKW<F=)8kTi(&cHxG(h>0SlrO(%5QdZtW
zx<Fis9VVUAhmO4mYBek?P4)wm64ZOj`hAzlDChtO7D9yY)duG;5OXO5b}P`4Ij_)E
zB=AF-C*%4;HurVj&>slKU*NWgpShN2YZhTW2+JqD0zQmxyGg0Y_lccvD6UOh@5W|U
zymk*6^|rSam<*aW2slK|-U=`rFGv5p&lus2D2Ihe=%9*wLi)(%4y=WVH@Q$CyuEM{
zYWji^sZ*88RfFDL2dC<@PhOEEeNo(s;IX!@;xsV23=IDI{St-y3>B^O`;oJm$jFRN
z$03WHk@0>X3U+cmD=krZT~1NnE^}PFNkvE~zKyF6w5Y9UVPdF@*`lW*?K#-%e^015
zq)H}{Si#Tx^~1>2^Sye9!u3UK`LF2m_)li*FE$kfSs01K7t;>k4-TDU{m?dzN<h{U
zE4{O1ynrP`zz8HUKBk<KsayQKWIm$2Z)u2fdq{&0(g(k*3h?vm^bJliSQKY-KmaYS
zFz=1mp%hJU?=4R@qy9`YA!3(qcwRQ8K-m%~EZm#qp0oY@X>w|fmFzu&N@P1Zd>-7I
zlxOhro7V<jOz+@_q!@!IpKA~H2r-xaH<y6GgP&eVth)DGJJ=k0v&N?uw6g-bht?~-
zmO8rMcF7=SL)|{`oAd9?>K%%#j^JCn%k{4n{>gj#NX6&&swbZmIpGsR$X98~kx){8
zZbUhJh}>_}$&q@76~qYa$qG0@4Bgq;fpeoO9&K@9KNgT=0>a^{&b)30t)yqaG=)M6
zFLu9|@&%H@9S5lPwxHtCKdgOwH>9hdP-+MU=F@+e41{Q8+gku3k6$Wy`_;XF1}02C
zCc|Ghp)-j)u$y+TdBuT!5IH`dC>H`dJ-F(!&qlu@<KgHJei+WHq^$-n-r)cnxphoh
zdqU3_7o><Trr7Q6QkGfoL^%vN`&(Ow$6<H)`|PXYa?TJiyNMZ~8>F;3p(6R7UA&BQ
z9P)gPmI2^p`w#<PoS1l-W-%Hz7LTnn_r|!A9MxEGukGWihRfnHiB8q$)``kjcXz=c
zr1j1L2yz;QAAPIMf%~XK#joL+yC@>oRXpDB&-wWXZr!a`Mig*-U!gp;3dOF=jcVMu
zv5X$rcv*a>>wc>Eui$__I6BibCgl-#czZDds~lKqNV-6JMZOV7d?+(@d4uZKTRH<U
zJxCHtB6$6mV-g+Lwr6Q?3l)?6Z=haL?sIdveEkQ({@->s)q4_H`9d243(ovW&5Np4
zROrMaT=gzYmp4EB!6!DrD`<qYSSq3=4+V8(lW(y@7me}g>C=h6KSJ?prDV+Jn?;i~
ziDa=)l{J`aJiuOcLVCAvOhN>Lt>NCm+1mY)MDROj4}_W5Zvg#t_563Q>DGA8U@$cK
zs3ZAjUgiK+=4prTz`pTRC8t_%|I{T^{IW_dt6J|a)1yG3Tel2Zk3eDNdaLi~2A|C$
zrO}!(FMuGie3r-91a5Y@C$9rgd{pjM8+yOUW<QO2y~-{<_MhCs<X~oPx}oAP@eXjF
z2#li7;&D?}zydSz#>xqUKLg?>{1J@tC;rroKyqWLa~|haIU8J<1b%_EMjVK-@B7pK
zT1{AZk#uNArNc&68a!=xjc00Y2Do_{+}aKqkwlA~W%ry4(1u@x2=CLDcHb9C2mb)d
z<!0!8;8;mm5>@GFYGp}-WiAm5EfpQ%bU*n)mJ|e9P&RaY4mlLB_&9kD%;eg8ZWMoF
z5Q-6|qW+vA-Bw3=ZmUB~YQOzn%H-w0<B<lu(`HN}L`~TIjfn7#Km*&^ktA0PZpjQH
z9#<&LKo|7UA6f9jRJGW}w3@m3(D67)u>;u;)OcE&NC*Mn>RivB1HxvrQN)=InWjxo
z_?r*t;pDs1@D*9x?>g;0fFK9~Qh;sw%Cy?LvE6?*Sg^3cbWQyqEu<BVer~IUq0vUj
z3n8jo1|BHcy1DJ%mer1eM%{=KU$nA>7M@6Lg(%4`=vbXxJe3wAz`1vHK0R-T3nr)t
z;LGYb-OFm3k8=94eNR~B9a5G0TmvX)lFCXxF}33I>HtwpMupn56<xpF9(g+yYD!l&
zH5EvzmcG)e6M`-=wYWg)8+q~#DCr^i0(-_MFX6ZLf}*zcqSchDJP#q_?U>zY{M3!0
zb4CpR0k?xrC>^93W!Fp-RO<>4VkX`TXYWmtCT3r+m$GYa0m2b;0tRIC{<s1x0@P>w
z8pbfB=Ie&cxLjpt(-rm8jatnp**|@>G}2}+nT;>Hf6@n6X6dV}PQ<_eqEk;kRc$wi
zFJ-b*h~EOYoGd`u#v4(1o^AhL>7S+nM6|lq)jmp;D|zB|=m!6%o;Fn~+2`ZEV-fBK
z4X(4NifoK<h!6hS7KbJ99p%UBAD543;aiGec#CePC<NqRJKh3|u9M}|lAvSbT8kUl
z`hXilN@>kDoeG~!>FCaf!h+C(8uQuNfpiqPpBakaw!Xui@%yxe$|;q$FAVt0HP+`e
zB-ZN8JN|Ilgf1V@Y7NZn->x@W^(D~vFmRj(9Nl`V;5%_eiR9+^erHS?!fIdd)ztE_
zE2lr)lYGUqTqyLU0X}nKNqv$VX5`|8mlJ_~7Wx#`8?;Eo^cia~7}>8nyVh}TS{TuA
zA{k{6LC`W|KeTrqZiaa6;586ERW<e=hg=AV$I;@#CU~QP_vZRY@W}#QdHu`w^m1?@
zdiG=ApwR(TNo1!A)oezuhERm?y1x=x?USzy^}^n%%CFKnV{M*Ww*%xNNeM<|c;Cyp
zZ3o-PE7u@d!@p?DV6%K1C9ks8R>FRE=CGQbSI7Vv_1_jF?vHA;ORkw;&@~)r!lrXZ
zT5trk3MJ9;|6+JSLE$h9o;|x(r627@Qo$r|oBRp!RLYX*{nt<u+@Lc~GxqlwVr7!v
zMQlpjbE5jE|2yDFQ54xbgAVXEEj3I%#idnsh0z~Sa!JT_bX`5k0AE9LCeu`Ore0#{
z`7pB&HP`3?Wg1Xt(!95aF@8Ebt18S2e-yxf?JwaE`jJe%hq2IKh{@k;Cvn1cJ-w=m
z-jOt4C$N_dpLM#ueEqDf!SwCR<4g-S&~0Bf2oO7zmchw(y5ul8<J9Fcqm=#?{zzUS
zrFGyUSd5HV&BSD_Pd5dYYzXMdjn_+Nhc$WOIghjM41D&GS1!J3yWUF-7C|9KdLnWt
zGpB^@$3dywZs{ycjNGT&M{azSx1N%JL>L{kT#n5lp6z9ExClyaTO(H89#bY4=1clx
zM+;NnFa3u@!@LVs6fK!Xo&=*5_Ei}b$cd=rldp>!NbVG+rie?23Jb0arpvZxRVWF~
zuhq33Z6;fvr-#$?()~q~H>(`tMk2D{%&z4Tz<s-nYb7j6e;-7H?$vx)GnMz14|sp-
zq<-%AGIOy^k_3WxvdtW+i1I$U(-BY1h2&}g5hO}bj&4^j-#4$)js~UPE^pJ`Ln^T-
zKY{W4nd#Eh9!+(2efpu01k%oWc^z?-TB0hXQAdds*3%LTFEx);u*p=8aOsPw^Odls
zKFsQCfWLoV-@p9)zp$us9DCTo<DY>+pHd{;jS7EK=JFqoL4{g_6LrSGO`l2F_Q&sH
zcK=<X4@5xLoL#lNDNVgfhGCU^X><*}phUN!-0PnUptL_!HI?L7<s4}#__p+m)A*t}
zX|=ZgOa*_0^RXtdCmK#2lRckIl~$g}%L|*1zQJ|-h@mw~w{W4zcX=QZH}DO<&9CuM
z{dc8zQKe5~A9R}SWK_fbX+Bw5Bp~i6gYE6F@E|tSci`t=jp8p!;+IG44~sXC%olb9
zeX-zpsmCYP>dSJ^@p<()TxZ8O{^wc3rA0NWP9rD{THHkd0PGqcdYe!P-#-BTNCmqo
zW<510ow)qvbChI56A~6qH%EED<$C2K{ofk~tYZ3R*n!RJXtw>eYU@X2lba%2$J3gk
zUdaFRgtuBIfV9s>`n?=vp1F=;$k2G@8+#TAhhfqFLe+}}!eT6O4uf}4A?PbC5)Omo
zs}LgoK+bUDh9;Z>3+MwE67*0gSP4I1;(5OIJg;OoJr@IxiaKP1^z@u_sr@17_fFVI
z%L1Co0iLLMX92z*0h1MA_ZRN|#ZpjYX*-WGo&4`BSuc)?*kgVA$q66B>hhED3H~EY
z_=cHQt<LyP?Y}RLR;^w3f1aaMi8=P)H#~><|L>9q2iEp?t+m&RoGsSuK%lX7n^K*t
zczrqXR5!#&*!%M9zSm)8mJxmDd#o+KwnwS^J?t2+h+&egcZ)9P<d5e3i_4pZhGS(`
zVfs?U&mNW)hgWKE|Mc+MqRlUd>Gm$m%b(wBYidTFz380)N{c_^ku>4@ci?xO;{KG!
zf`^AW&2P5@3y+~ryQ)FZ_f+sp3!qmRjb#QAWx4FT%SJ7PDn%8&KOF$zXtF!2)5A$q
zo%<K4+-s@R%ZsI_xht(R?=W}~10G(C<p2<a>GmKlX=s0h?OTtoR1Qcj?*Iz8M#h7X
zmL<uuhm+s<gLC=2p5i{KgqV01ENZJ~%*(E}TIsajxfz}E!Ol)^qz)D)T8GCT0kYfL
zR8pe2l@rCSVJna|)D%#QQVyS|#~7RN&^&s=q^jM|aROb`CjEEAguI8<ksNdVbOAK-
zjL?!-fb6F9tkv`^5!ykVK`Fg|&=lWBw<oLh@kd-7Z>)Q4rttr)EhdAKJ-Bb8JIURx
zP06b|)39Iv)%~Of^e}W2n{zPnr`vF*jD&{t77&DptaQNh2m8b5bSOoNNu~59Lo#Pq
zp;ONRIZw@%Vf#jE{>Gl~FG9d+YB)v#d6|{+?VXbXDTe@~&pRSf!xG`=SFracfN9bP
z?;mu~0S1JY^YnCq4ji(@5^g}+FEGA{q+KlM7gABtba>wl*;y#pl8XaR?;xMf09R^%
z`Okj`&gS>o%kA3w_M5&Td3e=jt~t<$q!3`H6CuDF%;yL_Eomp_7N$gI=;SiAl|?JZ
z-~RsJcmIN^)0lf~D7iwGK>p0rIQhB?_n+hapEa{58dNN3_!QLhRXu15nnbZ5SE7wg
zeE7Z-deTu^g@;N|0J2A0z*Wb&#O{v5g8n)j?HK^5x^KA18S(F0?@srWgOR*8tPJ%H
z|6XkE;bSH#$ryeuF-_gr2nz-6VQ(JVB7G&Q;@GwB>z!Glf7D{(hdQW}InnYD<b^8X
zJUuqiGqeB-SOMxSwh<OnUF=|>Z9wBNJRxJIorMesxv0SB=C>!WHiPAUv2|ppOpwkS
zE>+%zcZo{;F~S&<60zoJ1B5zfy32wU8)7^B0gr;j`TKFp0cp*qud%8s|JeBIZUG{m
z(<4(Ou7>lDt0jnRL7+i`ZnYs{;<7hp^&XV(dP({3?a{;zA1=;sz9F(gC$uj-$7s#b
z57zI%)cDZ`Y!^+jYi@bAx$C$+yX1ZNc&jE}NScS4H4g~N$CN9#Z3rxerswiy!X(Bo
zPb1yu6w>MhTu5(v7EXP4i$592>z`;Pv8_4D>Sd|F%RFkc;61#iM<-qoTlbFLV6b4d
zjP|d?RQQGoWH0@zH{Yq<Gor-npKBj<p2xHwUAeFna<_4yULj~YI7m%Ib^5-o!Hbhb
ztMTJr`rL?K3tFG8<9U88@;u<!;`OzpfRc7!E9(LtPGTlE2sL00sc1;%X<6-G3Rrsq
zBfo$3$ETRidc_5-ya0`B7+brB`p?L>W-h6MwC7TqS35846B#_m2Ktt|H3x&=m>-?g
zM&D!zxmEnq@k6IWYM6+F*gpn2f3Tg_4WI$w@kh|8Ak;WkVZW!j`!HLOt1XJq+B9*%
zlQTap4kv$x#57Uun9m<@cyw3odF1@$C%_xgD+dKfkI<JOG%-8t9{09F8bxxe5M5ex
z-I8!r0jR;u>~7-9PE!}Jj`G#8%a@aRs*d}Xot*D;0ND2HcL2}*LH9JJl8J2b*qUX;
z=9aMASqmyheI{~}3U9i5SH&$%{gI)Sk@i|&-a>UT6oFV+j)K#?tiBG<_0Y!DDbb7C
zgyDL&Z6Z_EnIDp!n{afbKQ1O45r|UigV4e>xUn)ahvo_y+q$UM<J?4Of37NKSZI>n
zS+o6JXOY06u9~yyB00_?8vGD<$ZdW=NbuOqM1f03{QJa@t>9(c&COe}`}4+fi<|B*
za@^`IcDhWP+D6}F7ozG`*K-V{1=I;HT(GJQYQ-~{>uBHDY`i7}Cj3%e`4vBOhLgfw
zT|4#ukWXWO{ao5{pWOOe>muW{$VSVuM&erWM<RW>;@(ooBq2jimWEpxbYUbHZ-*0f
z%~`?E_hi6a*=lRz5@WvE_Z$#xh8E1j;x768`qkt98csq=1Ni6CRtF<72Yk|>CFWY~
zh8cBrNUs@DQE0-+xB_Nx`z0djAXO+G4v+WHnqhOXpC)K>asmS1_(V3bF)%Q&uwtU)
z@z`}^3md&GM&L}Bm*-JElPY8~ODKQ-B%`9b*H-fsQA0zM`)PtJOqKv-OsW;qpPEaK
z&U*eG8}|y>9h3sdHp?awd`-;hN~0j-FIPcQS{-gHES%lWtgsK!^zJ{_jTaSLTkl~1
z7Y^ksCORDRuZHMmZ*Pc24M|D&O_FBTWiFR|WR;%ezOpNH@@&isW9RY~6+%-=5|Y6Q
zdI(rtR#$UbYaD7mNtYfXa#~qMeTVs|KRAwl-|H5vq(^8!nsw#pZpVk$T}~fEAcA_n
ze~&)!>PP$PJKD`ryFcpn<>r$VlcpF<{e9lg8E>^ELq1Ix@~`ray#=%m7T{oQpI7!H
ztE4z$ntSlkC(#0Y%0~COWdf$LdC-S}fgXw~+R`}_m-ln!Njx}v1Q@r?TJNygqGO-(
zr8l`vsCj$a4|d3EF%SGvhh~PD0M>=Ie{<{u@=lv-I+_C3@LiplfhcgR`D%Xno|f)+
zcc!9`EW&?9GJk=%)MSAc3Y<^hRvd8>9CeO6O11t?z;AtH#wZ*X?L`(B^0o>40w}V?
zjOImYehylHSl~Q6yxzX%eXo;KXXfJ(GFis3VU(LM!oo(ES4y7_C%GLcu(f*V9iAx=
zl@JdN{*3sUU;C!VYipxs`VZAiVc=TR|6=YfqvGnCb<u7#NFc#og1fskPH?y2?k+(a
z4IUuD8n@u??iSn~0t9z=yZwIOzI)$s{_L~QI6rRFb2JTWt+8sZnl-DQdS=bO`7O2&
z!~kZM`APE2fYtJyW?N0hZy`NBVyre~q_isF$*#2(w@sbc9e@O!Z}>JVUeiYx?Rq;T
z3Pf^paK|kXYU5FTLM^#}Qh>oKahWu#TEK53W-FKkW=hK6HH2LQVKEPwSUzV1*L3lC
z2)G}O1#{r=k)*%ld8{rm^ROM#+%LIwl5Zm*Jq$S<Wy`XwMh_^avN9h_0aD=#=#v$F
z#xryJ%0jEM0|2BfEY}_3RdIC*+rKVMD>LpMX8SDoVNpXpTA-1z*?wYiJarX6@#T*X
zxM{_|eg}53L0#h2T7AdxXEK$6atI#@9=bU>KAp^OL~3~A=5mCjsAeC(ho6!)?9(Q>
zxs%$v8Ns-RfOe$0xMadQ|5#CuR(zrnm~LQ4q#CuDOOfC;#jntg51812od7(wwZy8O
zz3FRS2wXSBSYJ2}3Du$sbrEhC3*@H(wqr!+bv5}5trei56i}48FQQ|OcT0zUR#T6p
z?vofwFhbp9XUFaYbqEv^N`t8&y%h!xW@NPz1K8c4M+Rk4@@#t+rv_sdVaUI#@A0@(
zu_lMuV%q)2Q;9wbBAu3%lNAw%P?S6uzP}v2hPsGQ2yEZzPg}oHPv6kX;x`~HLfLwe
z|CZu*T#opXQ~;}2q=|X2ZCN-JZGm#{_+^8(CtN3|(F6!{p?g5wW=JV>?$vcUk{DP{
z;ch_fT+PMzKMfh$nO*T(4u5EV?t$#JTOCZ_UNaIHC@7A;UM$xp>wEq{p~NAH7wmwg
z$OC?0xRpVBOy&H<jm`T#oYi7AgzpN+q&O6f@S``V;G37rV9J#i>q577fFZD|^%0DL
z+9ak{I`dB!SHBHI*0;fl&l}?fTRWMFIaHD=cM1Tbq8F_&o>EdYYdu9F6%zsVP2?BG
zUV<k>bT_N>24l|Z^r&`ppb@oIvdV*3@+KH!UZsH6C`*CKG0&5IE0@gyuCNB5ZTO-d
z59OEyZ*QAoaHJW$39;n*Ac6ete7&dk7LA%kj|CE9m0X+o1;0&!a-WGXJaJxRB%v8|
zq^&CX-SN~Z%^!$AIvX?-%?Dsloc8y@^4qnXY`;3{5gzN8*H7VzGsi7_zLqN+_RVV%
zhIrL#4A){z5&fA@*2X&%bq#paFKDRBBifxv(fN@fu@isTh6TlL7O9QlNk~XC>Prui
zRf}1U0_a~9n}*(-`Rl?yPJgLKWej-zK?vr~wIl3<kq-*@&DMm*GrBzGdpkQjac~=m
ze`i&{0I@=()KpZ7Kpvb)n(nXZ=o#%@mUEP#ht2&Ty->i>^R+1NW~Bv#@nkP)I1P6K
z;O^?c>vlSvZ<EM3r0s$iFX9a;uI}pP*qA=pqP<jk(9v<Gm&X$z5E~^D^zYg85O+pU
zW=2&RpELbH$KHHDt%}r+irK4ZU*mQWbXf-XQz(sL!iRKcXLB<h2Sz=5nRrqPA}x={
z6Dlrd74y)cU)Cz>I1d_`NQWK$6%rC6KyX-QWMdq4DZW)&nfV8d@l$nmdxC`w5FW01
z7T$_$gvVpOgUA49&^hzG3;J>to)!2~c9P!T*FR((c8|C!GQw3`v*+tb*;B-3dIrsL
zE}**o${n^aPh48vMh)q-4>Rtsy?uB$FkvV5_G%-SjH}5hx-EeIx38aySJDDuqk^!J
zqPfyve}yJM$>DH4mXASZq|}JY_sbo|E3P&11a<pKefcjIv`IhPI__*fR1$X1)~3)K
zTlX1g0UjJDHD-uIMvQ^ZNffF3DGtaIcTPS{I5^E^v)R$gPIK-$>9gF9<v7qHD5*hl
zKMbPyfL}uY>C>`Gs01FDH|niqwo+;Gx$A5>nKK*P=~E-AG`Ii(Z|Gl)r;*Kz)MTu3
zen*c&7EH49i%SCh{Z4XF9vk>{(3i-yW8^!LFqVE=<i%Ai5ihV8@`J)=s8lyiwwAkm
zX8Qn@Bilsan!6!s&jg{<|K$@m=>g}n3mCx@LH4W;DH@tfw3{{fej?I$pF)rw)via}
z>G7QOQ`z|oqot<>yr?LUMGG-nY=&fQPu}*Vd}Bw*qYtNlgU7w!R?EgI9tQ&akm|W5
zyR)Yl9MYt%wU*gd!Sto#&k*NT=V*VQgPx!`gkL~7h(FMru#1`*%3CF(IhKM><e=p5
zHEe5$SGaZKtl}vGYRxkttm1VXJP*^;4~zSvFI&&3=oiH^37nc%F?f8D_B1{4!Y;EA
zDmkrak|^|si1@->(x}=<8ypsLeJe3BF|`7^@~L^94x5w7O^T|)Zsy(>iRIMYI~SJ|
z4$+NUeEQnERLBZ{i_Ri%35#w-$6&CbyIUiMWbMu#6t5O)e^H4%8l>brmZWt`Ewg)~
z(Q1Mn&8W1ovoZg=_>!Ht2BX0RG}A=6;bCX1wDy|F?_N_%m!cta!qmj$_l+1Bu0P@#
zOBagG%5nqJkKIEl-6RV$m%>h3PaU>Ov=T!dk{=^J<rXC*Bq-0D6*J0E)hYiHlgU?j
z%2kM}vt?O6Nj@^r>wNQ9%IdWqPglwk-kyH>vwPFlcFD)#x+}=Bmwq?uwd=Ku%^IDK
zg#R-O=@ACc!SfPExA0pjq@tqO(Fo46+pWWB>gRL`4)=;b50#d@9t;y&{0}b_oXRz$
z8u_n@WQkOso?d<|-{Q>eJx--!d+<h>eM2-2^p+^;rBZBkmKNisS>Qr*OM*+|65|)9
z^y?jh>GWXG-HVNv-M&Y+g-LW9UtUG1{n1XQqyKK0(}>Vwcf0zaH_HsGk)VlA>&gly
zp<&9a)pN}6==L=CJ+S^cyj>OqtgqVLYnd0GqqEspfozVaayZ^BzlT<$&*x5?`Mp@z
zg4D?RkUac(!;);e|7y=@jfRRYr`4@`vjiJjkMH0`c$e@&N7Gx+`}q3=>XVJFD<(6+
zscd+JVkW03BsAfilF!45W&TTa)(|BNgAlX~#4wzBG?MZ2@z*+v&exd-{9;ikPoL9R
zP!o3b7vkM9rS1LV0tShvfmpgrZ1BLfyF04p0X^OCJPOUv%7Doz7bM~vw}H5%pL#jv
z@W_aomdo58_e)FNNm-5S0H7{$(CyX5Wmitb7B*(b0GnXbN=zD|;oL{;kC`PUpQwZU
z5rPma^G>+0^~5i0bKC5UzpTdDhn_ChuVJJCziX+pPR`8D&BGi^_#<ZE{z98Y>I;95
zU`2xp$o{KvG(W1W)ho4ph&^IZ|9&wa@gWmmASsvDftlI8o{s+u%Dy0a=K{7RCOqPn
zrLq8)UbEfMY6=G{@;C9qSmMV5LQHBD?W=5COL?uCsW0yyyA|6#U%Rw^QalmS@o#R2
zh}LWR_ft~@rx46EHPP}<={6*!ufHs<HCgPqT+^<i+^#UK>fbgKdvNpUurNwAi*Ih8
z?}L${6?65!lQ2KsKhUjH?U<Qok86z7Zz72L<6?gt3U9q}C2?M-c{2wm#l>A-BLQBI
z*NJg5e&zCi6J8!;Y{X4k(Bkq~W2wl@<t7$_<Aiv0UfaIBece*TXjx>Sr#?N{GE`kc
zJ$p&r&73_pIj-fMBwpec;kvdS7#h;JuOi8Ad73MX!6h9o(QVYWqu}UrB@B2Fm84Kf
zq64i*%Ou79TKv@G@5c;pDnOd|SCy_;+W^>fDzpA{y5JA;d({G4pOkRf{?K%0o|-bB
z>8LxJllGDv-hzj4v{u1zJ~W-+J0;HmwXVJ16)FyqXvbL&PXt)*YI0lp!HzaKBqwXh
z8#8W@3LgfxzXM!GF{Ijj@ivy)+Vmo!jS_?-?z8x81qskzIr%H{r6{||jit7zt)ji+
zL`TEa(#@A29m5Y$(LZ(YL5Ii)gNtKh50{tJ30wcsL*V2)*a2^(Tjk#Uaq{Lc{;#L`
zb>uF~EX+8ix|YGp>7hp`X0<+?$oE&93}GU0D08p@nLlEAPaDggDE2C`lK~!Hwsxdg
z!ayDg_x>{~mzC9ciyu#YO%5i#%m`E9Ca1?Sz`cuskea~kec8j(@;vawYF{&XtI&vS
zuHm|;)91E1dj=W8*TZx^lEnv-kb(grrV9*pi>5@DC}=kOP~2O)J8S9dSZiWrzkBgH
zjeABtn-}*v37+t&&}klZN@T9SPr||MR^oaB8U<@z<*g<RQY}5uu@w-`n~)d6-#?J<
zQpWF@3{S9nGV%~=VgG6rr1#WIKWXvj;cgBc*e>k**(J4SS=4PaLY<4yMM?IdEOdw-
z6Q%Q~uBHCL&u>?gc>z>IjlKLfaxI6luJO6`ILg$1wVVZc$em8Ra3xN^`AnD9yOs;N
zUXWSF!+E}xOKb7!J|UJyiE3q=W%Q#=)_AOplKvX(C2taeA!E3jfrE!*6*~J<UCv99
z(?N;2Ts1PU8h|Y3>)QrT$F|)Jx#Wl@TJURSc}h3PsqHgs2kMQ-*()|<3LB`9l*38M
zxr-uLVK5_B5bSF1PrVO)G+SyUzOmBwf}q(ovYxO!HI+5yv=y-S+U0#*j*Zu#{-HTJ
zIYKZ)s}3MI2Ys!{IR^X81-BsT2sG*#Ne8Ax%$;@L<MY;5@S;3cKd8J$h>tI-ykPSm
zuP0854$ESF#=*frz7<%xuj=%wmrO}Yqs!uz4Ebi9iZJ$6TZqKfcgWLeyh;$FXhUlD
z#eOLq4Kx>lX|xKSlbKgPGy(6f{a!fX^1tDJo11}y5PP0t@mCplcx?^C*NmN)DQC&5
z&}D;VEIzHWI7Mz39f@r1t28Nq$0<&qwxMnrKyby5_;cMp_+`i;8Iv1AQ>-X@XW(Vn
z6it$t^dm}U_)~0;kKq0?(@KVq#aIdr8JU%h!`%5b)iU5|?P=;SCUx=8u!^;CQFI`A
zZ}YOxI&ctUGErZ>*?lFZ{V8@eT(QjhK(sXfs&?l#V|6uwN_L}>f=2P{)T)(U3r45~
z=11&cIkNi{UKWul3)j*YW}1MY4r@!zPAYqof{|)9hMV{K14HMRm%GA6VjPr~IEs)W
zu}p#j!i!?BZIy!OV^=oFDBuN#DI{Wu@~{j}Junl#qJMaRWX#CUWd8FBZUIc<UgE5G
z20A-et!joKHkKzjYeMSYTT&&F69692Hn$ujh}_iVk_hzvqoCXHbI<<7t2AFBrH5rf
z|62(&GvCVGnA&>RXDQ8?<YX3(P9h;LWd<7!v{~8HJR&quuUFpI3|h1I{$aU=9SPPj
zL%HY#VOm0d(Jy%%tmHWP2?mPT=?8p%;@Z4`qzQ;jHF}^7tycm(2;}_?_heMP>?HJs
zBRYgD6wANC=Z?7B&f+zn9pm2X)lR`?;*wIzK%4_0H<|?n^uLrn;Co`T{^Wf;&|!F(
z@`atNObxG64!EPc<~R-evukfbSiKC0{EaIA;8j}8)<7|I!nzYU(cA0yxY#{mm>q=#
z(zX9{|KpMB@xEI~`@Q9wUbuKR>2(~W=yRVrsLpO+O*eOzN{>wSvS`<BxDbb&yxV8W
z*vdIOLx+R*h|jd6-=A3d=#i^*vWA5DWq`wq#P$jrvmU)=b`Y*rAuT&OdvqmzyGgQ*
z$KQW0tKe?t<uJ~kLHwE<bX!mhyOV+tK6e>zVh<i$CmbWABLAbDD$dQmInTA=7c)|i
z8{DHoqhLj!r+p<xK}Cf?A^=EQyFrf--%}#R#lq7D18Gq&g@Tj(<}_=a{@8U%k~w+W
z2Ga)!Ml2z!fw`fPy?tFeDD5&4=#aafHDA3HL4(G3L8c@bFdt4fjOU~JV$@K;za+@4
zs7Uj4q71SUrlY^!4_sq#t1~i#3E@fD&4tgaM3jjoWNk~vr;%KyPBf!3K{->r<z~?>
z6`jb!!wdtp5~80}H?BK;Byl78Oj4g8D6nvcB}s$qQUhOHJ*$RjVo|H{K*58x>z|Yr
zDF1UBWfE0-O~}iW)(6f(iY5oY2AQ6g=YaPh?JK0x*}da=vD@j_<ilTadt1ZJ-pBor
zXpPc^+<u_S?`Lza^8FT~Ed#vL?4mmBsQk*eIUgNKz)<;`W6JXB^g~K!0J+K_+Al%R
z*>I+uE`OlySh*dGKAPMRb7*M&U5#xO8w3FPczWq=@U}Ml&{9^w#MIb#(}zE6_ANGx
zG$VTQK}XRbC)fk&;BExaHE9RkYo%IH#ku&?yM?ig&Z?@m*R*AH8$`Gh<6|Mav!^`h
ze6tH<{NsWkeC?fJPPD?<&?i<PpVvIPes<`eQHF=0yvfg45`4`~73-0N^3c*WeWYXh
zjwJI9mfMM5=@*i?kzNn?Mz50FZz>mvPp#3;22CHjzThFS;-Qf^$0rbI*Kgo{5CP#s
z@5`~CoZF<;&~JHjBZN7#S0F4sw`OB+aof&rh>fWc%eHpmK?Jgkny$a@#M(&n#thxt
z6jhr_+`X*YY*R0q5+r0R(6#_{J?IL`+JtJYwGeUEBLQg+Y5h|pi?!_y_g@kKzm4pG
z3eWu5EJP7(QgFoqP}TIM)Et^gHL=rhJsUp>0U~5%0f$^L98J0EG_dLDlx-FpZ4p&^
z`5Gzq&rtTp5g?)?Fj|bM(kXH<-Czw*iqLt6I@}p-wAG;MBlPV(TqX^%poD(Q`XPGa
zde4Bw%*U{JYTCRd%D(=V&c~qM`;_=#*s93l5iFeP73B^u*qyoN^(i!0XM|yi#vUXj
zWGQ{~zdl3>QjlSB!b~15?xeMeR$SQ65vfF7>-z5b5*dgdD>+fm@d=3rjSIQhFsNbo
z8Ea-{09q7^wg%ex>jJSoOE$Ktwr#vJnN~Z-DM44uaif_WQqGCxRX9ICC6$zL9hA<a
zqE4rQ_jP;DW`^?jiH|>tS}G+&6(1vB#0@(YQ%QDr3j2aWr#{j$(k^~c`Rp;s_}=#~
zf-J|R?&%Jg^6IoaHCMg~Gi8}c+{fcA+m0?M4A3C+p~AMuQ<8JS{4m{IVhB1C5^3a1
zY>ve^(l1-OVtp(A?rwJLt--Wsa{=T+))kGsE0&Y~G;P}%zQsW&jItG@Twzmc(2Y1A
zrHYCQJIH8&(qf)M!UVF$T+`rw-r>0emU(}e*%B)RhWb`xbs5UoYxIfoPy%OEU7e^S
zIB+3yn6T=qhNUYV-wQps*P*o~gRQYfr%gio3&Q&C$jC@kE_4&gM)KY6Um=qHa;jtM
z0T+UPFv@|qdC#aD$X7ocZTuHXm9n?f*oFdJ1N@;g5KW2vz%KE5u3@J$#~0+?cSYUk
zX1s%}#DR2kqFP_wxUfSJxv^xtE|2{*H}gtAevR=;Uu&@+#5mbuuSvMOfxK$4ko*>b
zs{Zrp{FC(K%QVA6UQ9Z#eS^CIDlFad9za-FSUuhiEPUplXy7_ImnJqlmbo2<g!AnC
z2L$(-PzbJ8zbsFoLHqIwoe@VNlfOLo^?;R>fI<|^2Wr&BG6GMJXFBCrMn%2D$vM@u
z=$-%!0sj>4CvH<q<}LB^@&d#l4NxYyCg+}nIhBiX{P@Ij6s{2ow=hRTs4?69C^!B`
zM^}E(krh+lyda1}zP=H!QoI&gqmuf5aKMD)QK0Jk#86&xo1k)vsJ<DuUcQ|Aie6@{
zPL+sG3qr0qEPNagv>SHmYOVPRVUHDL@?~!7C83=pC<q3i(nlVh6<8zo;@4q%Z>^@F
z5Iw)If9}?s5OK*Nh@sR%lC5>KD`RhLqbCuU^<->0GByykJN5%>@a_o(`TQ&UJ^ldT
zn%dhceu}_D@7o8cS}VBMr{<zE5(e<3T0(95TG}VIKwF@h<j5C?KlkM==Cf*A5D%We
zHCe4A!lh$`xD#1X6$6+^q4?J@KS=NA?Y6Roi$$&uX9~0-cIUTF?ODEvX=I%?rQO`7
z%=(;f&j(xDS|-oOR<<|93pcL+I7##+YRg3orO-fOQo$ICSj83xk{lKvcPC_xl<0a0
zkQ!834SDV$1qF#Q3VK2pX63?HuD3+#FwS7A^Xi*YlYKhp(}^PnXYwl$uVsWGi;Tw!
zivu<+$20f91BK2-8!V7IkIwg~&DG0Y?~D*CV)}7Bg*(XSvnDdpvlq@}PHNat(V(E8
zIQ-KoI-`PNo(1_qb3ULVwRU%Sn9eARW)hCr-p(Wx2kv^Ut{>y2jMWm{X^$j7T=&DT
zK>0ji1nF@hkjC$<GE|z70AEQX4UOV^ysh#&qf<8sTJyE}YOFu|$h&l*<o0Pj$!yB}
z`B}USBt4Knd4n`O=wWVv_WcuD^@b%R%mfpsELoKGj_c>ot;v(`ZwCLE-ZN1<C1uY^
zFK=!#%xEd?bB=T4%afyP*`DC%Njtst^dS}bnn1k^fvDYt<an9y5oBmn4_<3F?c-kr
z@%o-BUeU*_8WKm6Fplipzy^+0k=jWTJF(-TFEI&rk1hNm*gO``qf^jLA4qE!S48#g
z*;VX4#IRUc`T^)72^E2DK5W{Z&m!QMPA}{gGA{;5bMRltIfE7V{*tbtKECNQBRTUe
z@k+N1G6!qrS3T6d0Z2AJFCQP#SLJM!?Cab@!fO_FDrV~8p1kn5II`{uVlFCMO=fX)
z(G2-DJq~Ps5G(_C;#NHG1ZLQjV68>boo_vfF%xTA@28C>-m^#QerT0ksMqf@=W;?P
zeh8=I|GIkCN&)}mB(BsZC<sM1jKPohN1?+L(Uo!>b9AqEn=d^r41=r=?T*A8Acfc3
zXF;#XFX(fGRmp}QTK41&RFG=#?R_sX$^*wIyr{SUU+h|fga2ABRV0&6AnIY`;4h;S
z^6<pw&Ezy>ew7gQxKD|STA4Z^n_&$K@Tzdgyxwv4hEa4CIGK5H315u*<<{}+xnG2+
zjAP3aAQ6PnM{W7T`~bQ%B?2)9$mi7eJuQD-&KpKKHd4;~3L_VDT2iuq(Rm%tt<kv_
zUX!Q_r{CwdnzANtmN)6H=yt5jB{I}i*e^uTKzaABCXc@<*OtdLLbZ3>5)W0R#P?a;
z(}D75K(>*r2b)gP_{30(eERSFouq6jvbgaFed>So2$O@sBQ=D2(${(CE0p7+ayjzA
z$HtDeBAwJ1Z5`;!nud(?jEaquKRCmH)V%-AV(ahU(Uy_)CJYAyN!MZ7SlDffe^h9z
zg=>wI@u9TvAmIqenKs|!b%WZf!w*U;<N1a7^S-uq>V?%DWf4k7B*5RTUmRfe`1vze
zP{n@ryU{3zxU3tmKCH%rH6&#aI$aAa1Xt}DJb(XCpn?6XFLx=xzd~P7nyBX<KU7-B
zgFTIjGM`aECbHqvFn@QvM{B=D4MG?9N_ko}-H?%tgmyTDmMdYGSx4fc^YGYcB&M62
z+zpK2?O58kHsh5g7`bblmBnzW$_9In8J51da2S;&>th@BDnlf|{}IDR{Qlu=&;C7C
z^JNVq@hCfnTVI<_s#0ku<i!odOgN3gBXq!|`&e$eTlmjxS}30pYTk9P<JCjS*a2$`
z?l4s4uq4^xz@))V{`G6NPo{+7dY<TtH;NAvTGOGQVDf3#b>O9@eBQ&!hRqqtZWBuZ
zKp)^CL09k$T?Dxi_9<+5yEx+DL7u6|=Z`PK{ZgIz5!9CDn*7xZW^orQo$UI?`0MFr
zy)_*z_kBR8X!|8Hv|jf_ZT<R|%QKc9wHKBVBR5|fqvGD4wwZe>7W%{$MBE4;pHGL+
z3n%xNAW&$^2qqF4DHbOOh8!Io{d#%U`Vd+tN*K42ab}JJy*s$aEQ(4KY1ElD*;at~
zj#%(-=%IxF82`1c7-_Gp@A~Ez<PgxjgN?;yIad9r8fD<;;dY>naVUoORn)_^(>;F6
z=)5t@E|1b@Z3$5o>)Z4P0pg6D9pp&Zv^@B4A!@GL7aYy>F~cYKZgwwxr?y|KHY|Mx
z{T3fNjtme~GI_15_Rs(AL;`}F-=W;x6<D@r`AP?!+*p!}1`Z2SuLPM;&_<=Cr>8rA
z?3UMcpPC^aE2d?UPJIt`Rx3&Mk*f(__iYyUjDjt6OFM(kIYjm>+3(ru&-f4TPV1y<
z@yn+uG+Fk>Qv+YeM|xq~y1#tJC8EMdTBIkMfqy#=p&b>yhU#E+QyRoS_6R5}U)tjJ
z4Q2@D*|+PDAsRFzBlV@Ef%Zx_%+MUX9CDGMz|3lER4xP0r$ggIn0uckq@b(tz4(=B
zFfuYcT1#EK&FFNq{T7OVN-*K{Nl3m+COfZ$f~@;qO!D)lx{M%>Amv`pNRqn{$!7<3
z-;U%gV1Kge#UF<>KB1Mt4xFAs{@c9zW+X`((?NC^lVO%r9zraa#UWq@N-z22$;?gD
z=$iSuox*!-I+@2oEOuvC=k9We{qn{fz3+!^1evhne{i7%-PZXnz^T!f9CW>#d*jKZ
zu`OP?m7Zik4!N}7i&#HnVzPcQmjC`egg!qzXg?w5IB>e=-)EG;MuwRlF3gMG+=9tE
znvhjrm+WKM#8}8`Bz?W&Xxg~MpABT$SDjn*+VP&PXh*zxA3bY13OAmUwQ%H1Ca^rt
zT-JrExi@b3PJy1blp+XVfi0X`!B$mFYQ%VIfToi^J|X`-sWL>$m=y;*IThR){cRgd
z14H0aTTa0>7A<Jd6mJs^y(#_kI3wj&8zfcdhjh-7!dT<f(FN~TlzYpv+xyGTXBe2%
z%nt1zZ9fT)F$bbkYK8n@je?~5${6V@vxL5G3?Xs6V=sAfy61zU1t;wLkML1R_m5#+
z2k&O?5{~~4sUdW90KY)1SH^KcQET^o>EUl(w)>X?>G`{*c@|KgZ}<qNgCNN}%Uya;
zxTvR%>s4+i`HG|MhLzRBqYCQfH60IvpGC>ZMX!6A>GE|Vthhxo;-zmn?6ECc%J@~9
zKd~h8O6*xeq2$Cm{b$F_S$p;`!4|MZp1q4?P$cfVwOT2P^VXb}J=cJ(R^E~wQ=>?|
z)9-|q9sBNd9r$8rCwImW^>-f<0s#G050eHyEeI9*m#;DR!6B$a6Latfi)z281|qH9
z1f>sSSod|N^I!%hYS_03iaS^qe-wmyp%hi<rxV*nas1V>!m#i)qfCApn%d6`PD|4@
z)S`K=F?97Japsdz$=O#e34v(in!at3X1re+m7ly~*5O45OROMpbroIl*03y|LF*aB
z_0%;bMZ)xqjey1`SS1z?)$Ag)tZa?Fvb`j&ziy(ncuz5G&e!K&duE@aw~tLKs?+G{
zrL%UUqFht0ng8mS$R;%H@0U1W*b8ZMsQgwue~^c`37-AJs-+?tLvs7s@j<nx=s;3#
zPL^1fXp@O>0Xu))+|O{5LM(;|f5%caw0GhqF!MdF)Q!|YTJ_0W!!4Gjbj>Al-`R_(
zNM;A1BC09FbDpPb>S6NxzQo_Vfx=1v?8aB4W#?(E%+)Gul#fAi;}ddjZc)(fWn2C%
z@*j(ySOB@EH1vu{MTm>&7%!OvB6~Ao*-(3p?%O+F_|BP>Q|eEcNBNgso)kUmPQc5E
zOz@C|z<YjJ8Tkq6MrO33i33i56hG9?CH5K@tA@C9D^6HFIb$y&Cm&CB7%PnT&cD9|
z#B0zfN!s3C_R^rqxw{SJolh>>bwd~Ee0l1lLg2F1A^_!_lb@o(S<pfbl{vPA2Ug*3
zH2zFX`~U@H=_{p+`tSYyJDxk>+Z-Pk=q>OO=cA12(1epaq_hj_c<d%|)8?a~M8!R+
z>YQ_+svO%Zg8j+i=w-&}ZkzFZ8L07ht?U{1BK~DrpD%P6b`!8)pF$7AH|`4ESD56h
zvE^`{85t>mW91vvYnpPmpQ}3Lw~GKn`-_?Vp)UK??ocu{TLkF$2%+yE%qUkxqtGDC
z)t<21W6I0Phm_dlyF+*uRI(#Emu>T@&v~Y?yaE_pU8GhQd>mY?xW@iBoQAEQ%(LqP
zG#4Ja=9`F|4VSf=$6NPxbS2w!A7S3OcyuO2%@av~L#qa;VkG1v?O!h=>SzmP14m&a
zg3^yi=2<SJp=FWce-xZ`6RGms%0cyRKJcWRi4}x*)=B_7p3#Qwf@Fkxk9k5pDxxnV
zS`owVjDnsU(j3@{1f_?1wMNCm66nJhAnY{X6=f`LpZ-OSwesYbk%=r-(5zl+NzpEO
zDSW`Ma1PtSnU$*zuljIM9~;-GM0#f0^cq}>M?-~__uDy4<8F%`5|cxdh)<&-{=)ph
zeU9|KdC=w6nbM(1pmjBm7_%oasOj#o-?gvGYefY;m|W;|7hZ|yJy#@02s8l{@qu31
zBMrZwF1LEqW?C18Tsf^X8Bu=VO3g&TMy`V*`(YwM1x#(u`@Fh%fgHS;(VDK}vjw$n
zj9_Hzx7F1Li`!NBu0SZ`9408}W%G)YQ@}u(J3(Xq>po*4V_1cLXZ78CS1@&Qcwj5*
z-EXH)7+L}L0ZzZnw*yK8yLtJVlghOv*&JVA-el?1X1OeKx$Kr~#$p;yik3rp<ODE^
z9}a3k)%SGb%b<AjTjgTw({5DC1R6yf(WK-oK_ADx4N6*CxCL4Y29WX3;ej!nV>^bZ
zNVy;J(pqQ!QA+Kx76Hu&hnX?OFC66D0(4i@oc7#}hhA4aN$U?}I}yv3{APK!cSlMu
z7iw|sE}-GpB^!YlA}gnp`OHS>-#%OuPrs%F2lvi{S(ZE@BNBs!y>a4Kqv=A)Wr6i?
zmIlJZk*jU+QIjv7^-S%od01E&7|C%7iE5Bv{Qcg8%pOF3vA+L=K_+#nm*-GG(7kgj
z%W#dZSAdz09&4+JP5i=jMtoQ9$Koj%3>L;M)URBz{f&CAnLo%V>|w-{5?K6Zc~cxe
zwefp$H)9@W|Bg!(7>JU2YS<N=^7fAnxAL_q>K-3PMWNXDKc#f$yqM>5BUcQ_`X^Ae
zBHKC+S#hbE?h*uWXY&`WzNI62xz$&odE1aUIPl<FPWy9_dF%CId-1ITZU!Iyq4S3)
zFA$-Yo9Ez(KzEy?w_?%eN{X=xO@ufF8gKj}M7-V6mwxHcZz#$fl#w;^nB5cwPx_gB
z`ouo1IxLF}4b7>)780nfML-dxd+CKP0v9E5J{*vwgal-jmSEclAxZ}w_KXRGk)%jf
zC*m(-k8k|c$UGR4@r@G{?AhFG<RL7D;C=RC0*vit9eIhRr4`el`I4nSuVR7;bzVf=
zrx(<xNI~X|-$o;3x}Q!5D8^fjBH`TLH?pFm^PsMw9ggcgHU#0%6k=?C$9v79D~K@3
z{e;w5krPKmNP?<QNB@aYND-N!ptu4DzsGm$@N?Wh+P!!*fh8@aHdjDni6!j!ic6;8
zwT9VyLs)%AAQJtHtL6t=|I(DB>7YZStMBS9>N&bH<|Gy4Wd#hzIJ#8!6aISlG0!th
z<q!LY^>*~9bH90>IKj&KY4d$@vTA6~f*`Xk;0ngATT)b`Nb3Wyqlgonz_LeP1xS3^
z|K3ygtTkrOzy78Tr5%4n+I<qPHgx5}+6=ngx8*L!rsaoZ)50oDrl~0=+S1?wSm6f)
z<lO>K$eVp$u@`K;ZVIyBi`Nc)a8kv3&I{|u-*hDjN%?}w*%smyrUe>1I9eB%s?15B
zSxGRaFApB7p<5H6`Jnl#=${EpVvx>RW7FU+Bpw;+X`?#6JaE=~5lJhiBRo>IFtTnV
zu-xd3^^avFux*HDSU)=$2n);Ewg!c{zgIHs!INq?h&2Y>=8qUd6N_8u%Zn<jX22f&
zwA5bpqWUL$zo#ql3X%lR7}LSirM;lbw@xi+Y7sQr9A+Q&0Rv00YNeu>@yyGW%IGIi
z4XRwh+1B1@>ksYwjcQZ>l_m#;-8J2FjM$b#kWVPTg%(16PTCVf)H)LtIWQgF_p*s{
zu<tiOprly%`?WKV_Ok;S51=+PG%Pl%mZw}dnD|@hkBc7yN3v{J>U2mKrOD&%Hl?A>
zB3`FLjIU%gh|;3NB4iOYWt8$rU97CJ0gs}a3$VWCzfz8SAFH8NSkk}4pPhakEYC}H
zI-)1TG@3xRXFASD*q%`l*vQ`wJLmXpBM-NG4kT-4WkTb8sHx`{x<$Ah6NAzd))w+z
zj2z{^C;LqR%sS3&LTdI_^}ULoqCLGl`E5D?<fz!*aj6frTr(yPUCQz?Eirruz(urc
zK81uAq~}!~-N<k3foRiHUm)0|NNWA}Y2@1JUy+>Idboyjgc4d|y~ox^wwT0eDwl1Z
z$I~}*PKWn)HmMOkpm`oU#hi9e;ZcUoq=&p(;H#giep}q(qHp<YWJP7vsQHr2;q08X
z*AjSwuX4p^W7g}=gmurI)mFUMKbG@99GNaWMZ{PWCHHLjkrM%E%0NTCkhQ*o=EcWs
zH#ur@{xo2n+m3z3#JIfO?i*mPN?B7;_~jMX_1*llp212?`Tfuc15P5(2kA3&?3`r5
z&_JpgE>0f%hDEz-`^~@kcqMdnN=2TdNlN<JbjMT3P8Fq%1iSEZ_3kM)6#It?7{^O{
zP>P5FAK$R%*CIVVJs0TeT}W0KTeMKxuVPkPU^~<Fba$lE0;GA(;jZO9HDrqGM-sEH
zD8FP5YFdoeqwW0EmF;+H?`*z@>^!WzJ1hL8K?jC<VXa<l@u8sKGg9g0=DF)?JJE3)
z3`~&sW2losIn@Xx6W2YmCm}1Mue&>9Suh?6f%`Bwg$o3wE69G&6CvP4;>SYh45tMf
zG<}75zuogdNh*62(Q}_4@c;vuzl1+cUi&>i7qrjMyP!xpFsk}nfevq5y6;o)IJ)H0
z)0?D;?_Z!ml1{JD^Js49>rnPW7tCeY9}BYgqacoLm9#RL=#V9*yNRQP^6u-Z;Fcx@
z%6a6s>St6J&q|>*2ZjOG$Jslqex1A6zk57NHjQ?A(mwPqV~~mherFhqC3xB32m~GO
zz(;Z;)LsH$UHiy@%jW8mJgF_KE@jkuGf5xEy%d;{DHBxKLAiGXxF3AL-ySdrai3u+
z2X$0%8_W5!@T1N|6opq+=Lqk^F$Weaes<)W48|i7|A~dG>sV!zwnTleC8mcZ)HgBs
zb@$%b<s1H~LQ`!OcklMcZ13gSA;<)r71ApF+jYD>FZ=MsFVwIs;Im15jdB>q^)=yG
zg|MJa_We*TSp3nS`e#-6@69|K-4=hE;hJ5`IOqz8{jr{*A@f?-uRiOfeO~A-Em3GY
zZz-QA1(mV@qg7_CKqv_(ta3uRuKV*oivUr2_=~gOdd~z|w{hbEP-h>>z~Bju)X{hX
zuH~vfEbr9P>j6?mh^_}yFJ7pS^+Wu-isHMt^0WL8cJ0I!8G4)pzuNE6N0Ty(PbWzk
z%j_aDa-c!z{AnZP`-j>Y)$!Jq2UflvXinaNict|c3F@jahe>&Kl2kdHj7^ZSDtt(e
zBPtVPj|h*<xb2XLK7Rw^lRrN<QTpK6Z{jLI<8p{V{n?gfTx#y<97D1(N|k(kLdgAr
zX&5Vo49>Hl_VmJ>=vGJd#$NX{LHkZ$n2+ig24sZ=!Wt(_2}<RIXTDTu&<4RML7wKE
zo71M#3wX=AGL-%-Z6C2DMPZOCXW)(T5$DgD)m=$B0@G{WJ^nA+mx{+aQSs;>*2j*W
z_?2_g3N~4d>OjmIS}~uJ?-#ecvN&(0g+qUqgOcB~6thonO`ClBopbMckDrP-8|fur
zmP)OFmz8!+6|j5MKrqU+9>;$n2!_z2;J#D)c@cTsV#)a>FYGbqb#$?<Dq&}7h<l&O
z@G+#I`Xg1|v-~jY%Yu;C?Q37LtJv&2Ju?i<WB^I8T?7x=N1p7aiGGF}7x5-4L{DM2
zSy*>@AxaR)@0Nz3$`sYqbRi>zMK~-?z>xIr{3jl=96&knJWXI}%^y+qy2!Ytg?eV0
zmzOWF0thX4X|K$CfWMV?-+VMzIFi#yEw64o1$H3GG$k=upM4E2WLdCh#V-@{f~RVn
z5iSDpbV<rz{W8PVgfe2pqLfrs%#Aa2mnMt(q8>O%v-RbCn)B<44|TqXS_^*cc7nWS
z1-=!QY_2mzz~NObJGECw@DWH@<VMmrhr~cjUl|4r29Mbqr5i>y1$zZUnRn!G_-Aru
zd$Q*hcO6dO4#EQbi=#h)Undt-Avld<(ow|%ZpP#}c~4(Wo&R>EWr2ik?Y$w5U-vxb
z6*~tRej`3*^n0*_Ee0meJLdIUAgB07s9QcSBm%axoR1t=_rWMhEIOlgLdwDMFCL77
zJfZ|n*t==-wN1#poQ_GmFWUm7kLN}6q~2*)r~O@CDcpp=mrklUq(VXISs)=tpD+5p
zf8%PqHlqHRKKf~fdp8r4ZxzO0!am?9=p<!COXhcD(9qCW5$4r*8rBsKuJs)g{e}*1
zlxOQI8@^@fnYn^&4)#yu;Xi|jW(dMLHQZH)Go0Ll4<|PFCQu89p>vvf%F<PwjK00B
zg)ilDTYBFL6w6Mr*xlM$vuQD2OHJJGO5>JHoJE>dgLvRle`Or!4gF|3YZw2~`aaOZ
zTtgJ0iY@9XDA=?t@XuvQ(DfN16BO+hcy3!c_+TzE2Ki=w-i{Sbpi-om8Hqp4E#&tN
zOi#e0_^5d-#3_Du_LdAuid~WeFpTRq@zUi1KKEuEj@d;nUq8}JO#S6jSW`sQPe~nd
zadvjBUesK;MZMaD&QQXR-6outWK5r2T`b5OUYl*tM5p<&=*v8xXXn8@Gxy$tf`SbK
zUwhSqiqEX(RcBUf-P4F0e1TUn6oX1%+wS8anJkqeY#zgOC6ve(+d_S;bd|r7`XjRK
zcic}J1nwqJ;5KqmzjNaV`aTn%gfU;C$8;Twz`?<NR~QYbAk6B3$&^6nJLUtx$yF9!
z^&>J6`dmA2yLHt^>F^S&ifnqcrbEef&J0GWTfY0+gHfH+E^^fYX91PUCBe?0uA9xO
z?GcxM6^t+7y}_voC8^JUg<??7u4?nTzXoJlGcHekAIQ1+bX9-mzK3*tgjcJHQW1#M
zaor8>i5fn-(Im{Rzc{~Sp3~{h_G=UP(y}fqt0LU(X^2|z)ortPkWhhX|L$F90AQ53
zB(0@0FHgD#s?yZk=5jaV_Bdj*PW0y<icRh(riMq<B`VH63jx$w_*qj^;$16BB@mxJ
zvGv+SuVN_J$mTQh`nN|6jXMbtDBrjTMX{=otNJEp-uI(6Dd54K-gKa#56kcEl~WUX
zACCAbktQb=93!e|syBIG5Va$y0z_v_9T?LEqM~rL<YZO<{?)nrhqIyF6SYfE7&Qjz
z;hHO4i$X*EW;zuYPa(c^EIo-}(doClNbchQsh#3b&c(yNvClU%+88pPnpTF$Xtw;Z
z+BkV?^s<Kim-WRR(zRUn9UY*vn)7;kpEN}4jfk>?yKdcR$Hk7C-9UwWJ7~eMeGH1`
zF6M9_ZiJLFOtd#@`ojzHXZ~v9EsAmD6B{1vNQ%ll-Os}d_^{?L8{WAfQ`Z267WQZ2
z$n)2g^dI#hE7J-JMqP%yt}ts?pJh##{PO0!7mq|#6uE!zq3**uXqPo;mY`VSH5vmu
zCUM<R5gxy#C^}0C?p`3wOq6d#P1-KLs!4VJUT<>Qj2-vB<vK6j?(CfaDYO{A>FYuF
z^i;g;A6eK^JN366J?a2EPLl%UpffJBK2qxkEN>dN+NNA2eaYvr?V;K`bP_IT;A;2|
zwcV2s3Un%3yC1P;?B8}g=&el0LkvEAz;J%i)YEZiP|I5<rOIweI7W2Jy#g)-paKsA
zHp9FI8ZsOCB(cdirzUYz9(F)utl8_`Mz)4779WdFm%l^)4)2~WG}XrVZ?`!L_X;wT
zbFq-|eqy92p|<KJSEYT*&Shg{|HHzH6$-Te$|-Ms<cH|0svK46|L!>r+*#J~^6-Yo
zhsmCMpW=I`v7O%|z<gQaZ>>39X!%waOGg%ocyKY56R7np=op3|{JAr#d9y!+4uq)<
z!{kUi_PyoMpN6q$N9EOs$+BmnfH0Fw16)py$Sg>G>@t!E!(B-%l3QNGDs(Fa<uMsG
z_%!y~M6s`<!}z#8OhVJtX)xw_74Qk>G;cG=r1DU@M1nb0f2d*eu@5zSALz^gx?1PL
zbz9s}PDAvIs`~y7AnUyAO6TIo*v`W>ystN|U@<t(iwC2Kh3?QBkttKa=VcL;2>8=M
zvXMsOQ=7wxd!u(T0$-r+j$UJA77G7p@uz)GJ#)}II#3aCQ2RHJ%-_U}miTPx0EJc~
z4ucTi1&R7+#E%8~tC{`Uh;G7FXs{zd#m~ri?pxG}zDnlg12z))y~UEO0wzgR!H2%9
z*<l{MLQqh^7HZn(S4GjoMbXIQdC!9MdF;@)iFra+r-synm%5a>Iyfl$^4~`chbO9l
zUp4V}_TKOxuFEtm(r5kEaK^H(Ms7g#Wzch^&Hp(;stkHU<1j7gZQ|Ye739rvFGhzh
z8ip6mA7_YcCyMR26eIhvRyAEPdviWxqXT0CEoKypQESQ{@2Ee9r&mWQ6HcKs{`{rO
z1CPtwcN45n_+%qsk`eGL&#~q3v1I_`lULcq-owMe#qIp=boZa}qO4ECnY)%W(jX<?
z)pbAr^o`00Y)g=u1qjw`X~NX6UAT?u6sPtmKl#CKgJHRoH*np>$GtlB)RCE{(>HW!
z0-$rtArkk38fOlIh5P^K*Y|CsKPhGY_uh2VJi?`6%kPoR9n4(aT+EH_|Mjb*iOqXt
zHdbCTR<eKnDk#XJ?&W08qOD|NV{YokqULVm_Rn`HbHdV`Ma$aE&5DeNm5t@IxwWO0
z8yP1bAB(u7ouiAIld-8ei-fs{wW+zPv>1z+wVSK5xr?}?y_2JZxq}-SFN>75otwD}
zi-fqkgt@7s8T6VC(C<!;cE)bzWNt3*<}C8&4wlgCb8zvnu?q{oNB)2B^KJ2M1Mops
zN=6C*1Ofp@&>z6t3P2q24gmoX0sb8#A|evfJ7iR>_oyf+sQ4I|=vc%Aq$I=yL_}ni
z%rs=57%7N|XnE)uS=l%_IZ0{wgm~EnnK?Mw|G5Yd2?+@m1r_i8dpve>B69Zs@$0P%
z@bMk+3Z@+v_z3{>5eWMc_|^v?1pt8X(7XNThX3OQgn@;FM?idsgp2|`p#B2@1_%oa
z0|yH&6$S@A+7J4F0Nh7-401M61WXlU#81vx?14XW-cg8EcVnwgol|m{xC9{~<KW`q
z6Hrmp(9+R!a&hzU^6`sHNJ>e6mXTFc*U;3`*7;&;W^Mtke`@3E=I-&;)5|+J<XdQ1
zctqq+D2*#A8IqEkoA)cfps=X8q^7p6zM-+HxuvJKuYX|h_t5b4%<SC!!s62M=HIRD
zo!!0tgTsr<tLvNFyZeX7f8+uJVE--Fe@gbh$n_B_7YrO6EF9uLasgq!LL2NyICye4
z1PoCXL}O>nPwav3u*81kRCgm$aHyVRo48CN<4|&LQeFHb+P@_G-xDn8|46d`6zsp`
zS_YuP0-+BN_9FlUxI?DR4fwy`juhVw=4K5C5K4X~k9z~W=hf|Wfd`07Dl)a}UKgUc
z-%tlIsA%@F#msIJpl@OUWz#=SNP-ge2WYdAgYePMr}6as(ZGo=k$`MbVkz3v`!UwO
zuzN{>T5egIyATpquS0JRLI8Pgrit^M0&5WeBv2Ad`G4FL9ipyZ3)24m0lnA%gG)#f
zd#eTU?H68VBTT8jGXwQ1;+(ApihP(-i*nR_q3c-__-O@Gk8LN8_e$U-L5!sg&-m}-
zOPgRK0Y(4SIXbxk4F7S8PrRCn$j5L~(%#3J5G#J#=i*sqVB~-IoZcZ8<wgRGzoDPM
zs`omyM__Yd0kv1E_C*Wu(%sdxpdE{)@fUaqEWlHK1E2{f097Na?87W5j^w#d`Hd#w
z)YYOL_=^kv`=0#Y=`~{OZlY_o2W42RVmNk^*-eMF-&nx+*t`ulj??K(GtK|;_+vK<
z;Z&xicSNwN{(XYRs>y%!3i{E%dQ#IW`yG5^DL5e)+LJamv@mR_P|8~mCovXS{g4)r
z8~J~~{eo_D`JM?2h#!b<=SLU1=;1hCm>WQ!_LZ2``Wq3nE3N+3l_lN*)}e4h)z{g`
z2^zun`|BC3U0Vh30g{ruc`O>A(nR2~RQ{{+wXn=QrrQ7e17MG>tDLe?m8zX)TlAY3
zk?3i@3aT4mb)X#FZ6TcNl!AUg7EoE79I7*o;BDkuNr1>d11BHnVD&$BL-Ad3ZtQS|
zB!HL}`rQ9#ExW0`z7E;J!8n9RiCv`mc^}4`fnMpO^}dLf7dz-1`c2tUC8okott63C
z;oT0OkdTl+Kj0vuYpCQaLHYN}$CZQ$M4RWqOYAO#qn|R=dtMd}DIUvg$B6gpPH9K|
zB(nK+()Zybqy<*6t#5#kFnQ9;wa&8{|8Hz<O225<TbG5ta09@+6+FX1vzz&uUVf-y
zutzcU@hd6od!#Jp#P9$QTmuG-Bz->YNg}UNPml-*t^Nz>qs`|fe`?9s(^W0bBm|aB
z1gn)fTUDgIpA@?%GFZO73#-bAGM0aBbreVPp9J0jp6NNyK44IWeGneZJF+|MUDdKX
zxNtd%_6T6@L*#ez*^TAglT5ZLyma)!e1LZpf9|OVvXD%_G<+WqT?dk?<c(VDRXpWr
zwj5S8{}x#KNONP>q(OD^^X+Be0<r%<fDlZ8C_W<B&fOoXIY;|ZSH&R!q1ZqxfvQg}
z>>wBcL{Mz$_tj2a7ePs(h82`)1v|fAfiThk*j`)JTBi%CVigDUUJ|_;es}XQpr2tL
zE`?s1=zTmn(Tle-!WuT5Rzq#`X9wdSVah*Y{AIJq{`<lCzuGVb+VVNkLZ~^x`*8aS
zV&6`9RSLC1;;=DYFLd9u-QhG2X<@!$yX7`&Ky7MRfh0hqkVo>-UroGXkl{ew&9Lx>
z6DY0u28h!ieCnLlTr;s*zeH(z1AKjqT*-!sr2Ri^48m^!?S^V$1NrzLeYPt{`?Rnk
zaX`uq<e~P3lk$kq(pfCISqXkh_nK5=YxCLLW8~^57(}#h0Mdb<0+Pf<K#N&-!qrg7
zK-j(MgTFyNk){=reV*6h;`Us71gZkXc?r8xy@?E0|5bYPXV)GUe3IAB;Q*@EwjeUo
zV7pp3fE&s79Pdf0_^y0^Z1Kr;u_|2j25`_=?Q?4`*|`ucOnn{B<r-&2>%4Fzg3BD&
z?@S{x@QEMR&9lu-Vrnl;DBh{-ktQ|wd4<^z+~`a1Nd)Zd?|EdXtcUIWiWDQLXi7Hu
zLVXzepY6A|NxFg`XAfK{9m4X^po<oPt~=ksc3>LpC?Afs&+wJ!=?{>t^K!!AJrisi
z#AM-)*?T4cUO&L1+fgteDM?}NyiX%BbVmzCAn?>xEBZaAqsoNyk(TK<Gf5}jPXVlE
zF<<e#e(_%OzG|cvHF*#w@6kG~Ie9N=a8-OY(u7(0@H1(j#{UJmoN9PIJ91#!8p|H?
zz0-~Lcj>(IH@M&2ol0L)IpKM8%!7m}leml*8$pR8BUV4E4(Wb=8F+D43jCXZ@O>_<
zx>Qv33!oTPr8Io6mu`QsUi*nGq`EO~KsR`&V;Z{Cxs|9|Y9#>@P)ykT#R}#PaGO~O
zTl}OK!eRl4oSlCDY!)Z#SBJx^qizJIsCT)dyohF~R(YmOE#}wbl#=t3p=UpDLZs+G
zcNQTjmvrGrjKWZ0Gi{3$=LC_P0Zxfy4duaPbMqH^U<-_Cv5x!zPT=H}I_Jxm71ghw
z#Y@SC(3<{M{?Eq;biBaxb<WpQ{7cnq{mX5n_B)jhtzZ$=YuZ8wq#9?p*-#xrciG66
ze#Dh=S2>FH2Mj$Z;T;}f{(nXV082&kf7m{c7O{^4pneceslob+^|@CgJDfp)JEI?<
zgdi)TuAS~((uNF=fN|8WiL?VRvCrSOKU>Nj7u3aEV<U-;Y7Mj+9U3H=Z-5tXz@a-S
zu97gu)bt$35WSfT2nOc0KD_Zg;u*ooB4*Os2SC=Ia(TvZ!QwLIwE*bxdgAyf`_+6p
z8TNjV;tfzPg8K$&3<otet|DIWh%4B8TvxJfE$Na@|D=tt>MDkV1v`siFYl?e!cMqU
z2WQc8OE>u0+_d6|6bAnZz08RoB1g{FG;8G0<2D^-d-B`5pG279R1Pq6H<VA;^qVgx
z@sOyb_S`Vi@HEmOVgbq4G<@su&@;^k=n$S6`a(^~y;@Z<$r`p2K>cHLROs`56Qspn
zd?*`r=qDH2NjD<pqdNEFx1Qz?0b2!z*`;dB#fy3l4xU_$1kSW1?64%Vx7jd%<mv>f
z!h}Q(Uh6#s4&nmN8C-{?21&(VxtQw23AK&Glx<WTpL*Dq13tQ=cp(r8TE67GNPekK
z;zJoEEcO2YxP8>HsDpDW;m~2;OvD0qNV#gOX3OaoIs{pRuIWEgi?z}u*zfbCgZ1g_
zq>g)?3RFJTO<cCbw3f`=k^Yu2u}u0%Boa!o>uX9X`~Q&k)<IFf|K9LtSxP{V(xqdO
zSVCHwMWnkM>Fy4J1tg`Gl4g-EkxrHFl5SBN1eBEY-g9Q2-+j+B-`_dsxt|$!2L9O@
z;Ir5J^?udm72u{bzGiRM_EOY3rVv_Ao%p*_a_R8`p!h&M>eVpHn$2l}W>B$Zx2LS~
zA|Ok=$wfjzJIxJ;f@WYYk6L8YOW?(1`79U8J*iXV#*APREA!mZ?9IcUL4rRjJ`~63
z%73>2?Qo96I+6xF13wa_dJhyUT(C5YzIz6_?E@UE<}Rf#og?oL2q-H#n8MVPWZh)+
zmf|uoE%G1kcfPKSS}8sHVjN@#%A@NF984r(qoD0!Y_LHxJ`Fx4RIFjS{jKkAQk-7s
z>xGf@XF+QHcU%b6=n>4&z7opgYfU)1l5QbxhXHAvnexWv5^H86q2xPBF8wm;eantg
zBR77><+Nd!fl={6iY`I;Ho{#m{Sc<Wu&s4!`)#U$pQ^#pJ8WYM=2IB*fNt^>PSaQu
z<MiPvu~0IO&C+MH^?Sx9$Her$r>cmlMTh5<(p4<HMk$E6;A6V(Kq3d=V7H9ZmEqGH
z1^i84nwe{2t^ZG-_<#JQV<c4n1?bxoFGI?z+j?ieY(8<nKRzdK{@>W`|HN_s_WOD7
zc>H!OjhPe#d+xlLBIi$LO22PhtM9eC%f+k$^6paszo<TUdr?%g#?wwAr!!N&KY224
zQ;zeb^3$eAUSh~EERw2uX|!o2EBmB8$jpxm_S8Gz>y)_<-*%h6s*u5o7aop|9d}-w
zqX^1!U0sS=%o`I|)sAXht4*e+Eft*Ocx$1nYSP+A)iNFVtwE(?U|q9hIege{U9W`s
zzUU;56dsmorfcx5wTIlqm9`-1(_RB=?)}f``V}ldvn>LuCV(wW0WN+qEk|vgvkpqp
zwM`?zKE=h83+N-RUO|!c3yA*(q9?&hLqU=mozVlUuhi~88R{eUVLgn!q1pgYMrpi(
zf#5LOC^p$}wt5gt{o{k22a`IEA)pk6M?`L5r4c^aXdLMC4)84<vSD0qaGyf(6}vN`
zfZ$hi9~R}<V)QL>geK;4=9c^QKv4&}Rp^SIk6oHF02T$V5B-HuF-fKJ`*eTop4d^m
z<>pG_1@tui42SV(yUgRt@55L>Sa+I~?yHgQ^&Zo{i2Mt9O=9UEX=D&?7uCbR4u|a3
z{{=+e7{I<aTW3m>-c7G)r;FZZ!k6=f2UymsUvuDHy0NdoV7H@gYK_k>lvy%HWP=hs
zwbF>?$B!rysR`4>JU*=Cr2)2$9T%(ALbnz1m8~;zu~EDjt>TfA*R%D0H`N(6)b3d$
zp}5}GLbyeL0Y!j+2I4!eUprLI*(Iw`wJg2|xq?qHmog2yC##`QxJS?g+bdtT<N8l<
zCzQ3{CJxi<s!<ukXflf`Zco)V+Z#Si%-W4uVC%>^jys<%gJxkdC+<d1%Gtx^kY1B#
ztBgQ=fNr?XKrOR13Xe}YY+nWutw){m=?e<=uriYz>hcDm!j(zw>U7XWai11h4VC_^
z%c17M?y8VQ)@WgL{2cH;Lp^NA?{dQAv+B!X5Ai)X)Y>2XVoQ?VUC5p+4yr2PlL+w<
zy!NfoGG1Zk{H)#Vi&a0q<}axT?2;H%GoMyxuqO}|&_=e2^Xa@*Pqe>V?M}>N4AIzU
z&odEwvvR_B+Rn5doc6+!ylVgJIC8YQRbFfxjhthLzIwlzu{hacaM89#?Dov)HvW%C
zs8;r!qvn6&RHf(Fb4CBT#v{0o51*M$+x!J$a#9AmTpM0Q)FL}s$%uL>_Z|N)hBbOX
z7iWf*?JIPW0gVwK{A$!D`-iB;TcFW}oA(lF$RE!jP9e2>YHh0c$|6Bn9_owy&5TBI
z!#@?CmSJqozx6hGMTJshQ@d`^YL?g$o2B}sx^JbE$E2f$Y?R+_u!()C&bWZh6ZYjU
zpupp)UF#d-Jk{g9SadP6cOb~_>tHtvQA(2kgsP*&v9^ILbMmvC_SY?E)OZ8&X-o{f
zReno>^KvD73JprUE|!zTQwX*`#~nIBO?=w>)IEA0)v69^-5dLl@}eaHhgITm74rE7
z^i=_T+X3L|-zeOTj>q-v^vp71WoMU_DaIzoCssT))~b@GjW6M~&%}e&0@|GnWflri
zD7=!vL4S+D&8%9ignoF{9~%Dy-Iq_Lm^Q)VFthzaiBzBmFAjs~v@DM}hxM{l;72v_
zTJP~p;PoVyKf_>o!;R+fSf136q9pe4hPaDLAH2tp)I76*S`71O^YGZ$zik-h_-kNg
zW4exGsE96!GobHEM%$=rh#N;p4A0;wopoZWt^hfjz(|c8A%;J(ZNtUh?pNVEM7@6#
zGcjXHmShmWypry!rT4l)wHv*qpCDX92g<P<7<0tTzlP!zoL6YUk^*#u)|5PNKI@6q
zSi-4@>CRdqR)j*gupAN^^o_iy`Bzh%JEPWbNLsse56;Roy^IXDC4I*{L(eN;fv<3r
znzLD7ec$gd<zakA@GYj|k-}!@v|7@tsw5o7IK8&svPgG{b6y5HxKnujF^M6Cg{qBc
zydcaV{mC<;(T(`(rIJAP7v=ZADiskaeSpsNjscz3mv)!o0g7wg8Mg|eG<a2ilkT7y
zw_WV_!^AWwpRAuQ%h6p>8OHp(ZTWE=iVBaRv2L~zdi=QSxlHRwjKGBOwU0I-bz`5d
zbcZYxN}Eqnc*k^^Y;OoqHv@A;-1JSGp5s`L;|vs_DMJFH<*-<hW()+|+ERx+vFw(4
ztNdA|h|c>Az0hrMn+bt{K#!M}VG8*94mHpan^(}#j9j-)pMd~il7K#CB*}8}LIA5%
zmnn`K&wFLh^KqY8f)sh0Bgd4ydK0%ibPrB>(X6Fr_m;nEgl@QU!4i!q72H8cH{|BV
z$LfdA)a0uQ(q`U;<Q!e@UF&AVLr!p}5JI0Wm$ZM6NB>R}P368)+c1ql{fV77j(Tv@
zo~mvY(U$1G&n}`rdr}3$sr?Xeqn5AKo~SjoG5~wdwy+Zy7(Pt~+SAtcd0W|2wYTR^
zH$s199d{nz9&X4FXvmQLcQ25t>v^^O>XW@Y6^?HQhRFY<W`M%VkU_4EyKnpKF0}%#
zS6C|{?RPTH{}X(H9{m)nVHI)70U-FpEW5++_BL+FvLVZ*7S51YDO#G)Z!~eX5jfbo
zhxx7!wN>9#DwUy<928fN0s`_q4GiNenRpKA7@KehHtjItBR;P)yQjNm%q7?1sA78>
zB%5ZtZ!w0m^-l(X4h)lwuB`Z@JZVc198B1@9C9?}^-G9N@+*DaR6G=cS8+?65QKse
zA#^7@V#e_VBHpgM9pPnDtG`;$Bxf&C*t~aJL3OSj*G^my5w3b`l>Igkak1M%ZQXSh
zHK~r{QX1RcjInRPl7!9GyR+Q97h6kP${Mc(FWRUlzGG)moxzkZ=XIvZ@Q|bgzGf|e
zx?NZb`gr6Pe{2G)`|Fvs+&ZC{R>V3sS7=BfvE=rZB&5T>ggzv0W2&IJnSuHw*r6`E
z*baj~6ttIetYK>SfRQVosf@R(6jP^9IuA?#ku{<JgdwoAk+uFly$3YZ*(FfwTqeV4
zuApmLvFgyh_c7ylSZSVARp!}ZLC*JZLR5G-oRY|GA|;1)Uqv&p5K7i04fR?J-u(+u
zCKv@CG~vB)(#S20`wPSyHz!lsA(A4@`&T?d@PHVO%_NX$`tc-Q<_~#Nd<0GarT>m(
zyA(;lIm<1qei1t)H?wtU#S>22=T2=ryy$ww!W5(;&t;wzq0i(i4idU=Bs$tK)hkwV
z<Z#S<Ren>rgkUr76LXS2lk~-jO~$=94T7yM+HaT&emKe<lVw~=1-%@*@E_{mw2Y+K
zIM8u6nrJy$2z`{fOxv;j!GV~yaS(V6sbcVv{Z5&@_&Q%YtLSbHduEDrwo|^&BJRAO
zcQ%-{Q=Lcu!s$07JHy_P_(Z_BCfL`_^Ky&_+BNinTGcbWvoXWbr=g+R&cz^u`YsEc
zEA%n^K31dIEO1uA-2S;ca;jwfqTEe!9S^7^3oS%H7mPWU%X6q%&Yl+!_El8G%O%CE
zqNu}F9E)0GEZiQed&t#HA$Sirmm+yF>VEm{i#zIeONv;tyyps`okf<FCU;xi)8d|!
zSC`0>2^}L?F=hrFKZsHmc)LDbsvXp!d2r;YkfvW|rmF8Il4VoS2#=Q=_3Qk}3dWoJ
zWk1&Tas}w0`x#XJYbB`UYJSje1NqrUndEWhD|Y^9nr5u0dJDRrlWMDLAy{4XDc8ZN
zpbqbBa=dJ&7@+e?2t18Zn&7t`r$iQ-bjDa))a0*SC#z3*9_|?^9x>wobXM-VGwN6!
z$C12O(^x*Vf46f*oKM}vq>t?p+_#<EWZ_CSB-i7SDOs8e^E=oMj;A;I)Sjo|5Nk+-
zbPG*cd%uFj>Mn*iQOP&X!?vV9%k((37T0vIEKMS+M}5=3uDn!`qoCLqkN2#Xr69ET
zV()T%?UZwWWT6~9wh|1z3Y^4I`+w7M|M2raE+t?p&8D`usu|U%VrB$IwPEsu6Hpw1
zX<g?3V^{faINz)zr0kR^mJ9`lDnJJ8Z+VZ&8&6#LotVeBf?AMYIz#eiUm5;pYF3^L
z@Mj~2_`6BdneQ1GJk~`;&|1DOD=n8?XT?x(Eoihju~A5rpKtzyR~lH6S*<K-4LhMr
z+1X3*jPok7U_CLqtWQZFsQE-}l%zcMdW!ZwUlAyCo?VwPvkopojt4m^PTBg&<td;$
zWr=oFBNvZG+#^&6TF)xe?b_d1d8V{b0`j&uV#9Tx&CPsT_VT3-N5T*_^w+vx7YrI5
z^#dlQNCQb1KkJj~97GRc&!6DDGZ5#_wf6=raaDGKc4sbKiJMGN=m;h+*pC9mW+;Ea
zyPm{!07bhTXiL_gLU6wc3Qs70E^lIHIv2tyjpb=Va3{|lJ;o=CCChJ9l`(k>TX0b^
z@zOEtk7h6+y!A!l$aS9`D0?m7*Fam2_2O$N$PKL*h@8~H&ZUGqIE)S2@r`40o;07n
z+Iqiq+F+rc-I^_U&>jrhRin5LxF-XZ7~#a0`r(&Z-5gARTPjeIrI7C}<6CGV+EUBf
zhP)$@M_tgUZNZ7Ff4WI;3keUb(v0bbVdwd_q{(IMuq;molN*I(`?)E^tID-j2fFD4
zte|QN@Yebi`BKMhiF&SzP7Bfv5X%Elc+($U-!Y@2aWf)1$<3X8>6x|KTl!=;n|g9m
z^6vTo<x4m(gYbrca4MwoY0KN4F0)|Ghw{fPX<=@kTTxhTNh)~#rO2Fo#r~$|ZM)w?
zNugJB`!6a9<%(@y;&u>JH@@!ITs+PS0v3}ov#_jSl#<`Y+*fq`cO1Pjcro%e6JQ~1
z+ToYk<;=<ZxQ46YzK|j;?-_!0f6Fjghr01@$b8NQ%YingkadK?AQQp!fUND|AC8TW
z?vH68O?+^w$GrY!55(DJeDcnt2vUst8U0Q=Pvmd}3hH(rb+Um!icMk-n3Sp5tSoWF
zf(e7{FW+f#=-Wjzty|_~g^A8tW6f;P>|&&K();sK=YMy7b^l3JDch1;m@C-)2qwCy
z>84jm7+E@%B@i{S1h$GqFD;9*cA#|SC+2TVHx4v_zGk`(RZj)dOg^SuHK|>;<3!F3
zx^_6^(#If~JU)iH-uA%op1LXGXj|b)@GJqZ-@)FW`!upHDZpbeRR3n?o+=I6-j1v@
zvQx*ILp<AmHw3cN{sAwv8=##XdL<uA!JvM^bCusLP*C3f%0aWmgHbC|sB;2UflZXd
z1EHGL4u<lB4606wi#D?+MeG)n3XA2xy0Am`2gpkqUcSmipa>2emp_R)%RPJa7f5&U
zz?q(^*)!}E8Rg4pmV<CC2b9q#<j32te02J$85~xzU`)l#=j>+uy^ESS3pjrp80}I(
z(naqjj$7^+T{}zJE#>KYGspzg`IzsEA2i0#>Ce3smIb9=hr<c~8!ZhZ=U-UK;9rF4
z|BW^P6W)nk!pX9IKuM5n2eQc$Vzjq5D=TEm`rdfwH+}<W`U{M(g7)sGL-K!@I0n<_
zYa`8?XthnW;%X0Dt_fQzh!<s?@l|++Zbk=^)zJEcq3E<z_GY})QD6PFMFyqOqEZw?
zNQWBB-Y!fyXJw^kq)`sdRd^m7J#PA1WtpdD2yp(?(I(KTbqs5jRI7#!e@yxi99fiX
zZI%ro%>u%<#F`9rngn4y!VkCWayH7!T?~b*-vKdX$wV~*Z+%5u=xNFsx<jYe5S*XR
z-u3n;=+e8%i#7iR3=2+lAhLxNrAtx~kiFP+Z8mzLTRRN9A5{Ja8XeZJ*bol-iPTxL
zEJA$%i)gBC=+O0Sq+3khxzZR@4jU7-=Izw;@lGN|Kz&T~QR}5Zu}eFdj@L<{@{!I!
zc_p@v&LBU?NuDQe&Zj`ko-|pO%qlDL$0?p-Ualj_#l~1%!BuLG^ErCEllzoE>V4C*
zMH~JXs4-NqAy!J|2*}%<=FiC7@-ZKv-uM!*rmyV@r#?ec%vAJ!^b(EYA9Ri=U%D+g
z3e?@K4R_Di7?a~y)DA;aLp)_YT#|UruFpo2M0Ab((>-XbL5FwW-1VGGUN&QLLqEu&
zR|Py5DhR!=0NtWR%t+k30fK>I{YKXnu7GCd`bKwFyCy5b_9#m@ULCZ_pp{zdn~l(p
zK=xyUoUrG%qH2ck{Eb~Sad!jN!D};OF<qM{4)z?ax;le2?K8E?9}uKrxRtea=&3R;
zsCh{LU2o;Yk&}OsDGXkHY4e(!?ody?lLt7fXez5F=qWwk{0mH0%y;+Nbn$52j2Bi5
z?th1G<ownv)gYqBwwx+!04?wrvNV%}*<Ms$%sLSzJ>I(ya_Fn$9Ge+m5Mq!d{tF!3
z!7!8LLfkum7*c8<>S~i|bDmBxb?a5krPw5fsZmB6lPrDswPy8d_xV!Kbg@aB2GdhD
z_c4u^gABDuIAL1vcZe0aSh%uen<fsPtXw0ji<^uLQ|yU17HMKX0+x1)ufTY|i@OUe
zQrjH;&B{Qvqj4lM_cnfUOj|Zg>1zz>re;85={f455^HgraU@=0`F;rO6Tc%(t<0T6
zZt)6tuL4q5g|lxc^-XkM022NXMlAS@bLCn`-&Fn?Q}dfc-kQ<7dD^ssh*<gxK4@^>
zIi^pisZ4vaA>wJm0wx3^LmfCP?0M7i>^A|CsiVsd{sZt-8Sa-UQn%d)DBM*FDi6Bs
zn{VHhBYB`F9)T&ECdw5&S*3P1_P;7#<n6=)^Cyp<NooH?vc&o=1kEvppvR~hR7?^4
z$l&uX&Uppfv&9<XL3-kbowGYTP{Fcd<~x^N>dl%jf99RPzy0)e$ZjkNrSLeg{NRdi
z$JnKmQGs|v%(GA7`=Pb-F{hzZDCtkz&D$d4<t*TApRIN<xxY@f3mGX5PLdJ%FWo9^
zWA}~aRPXO3%ZvX-5y2rMxgUzj-((B$r_&OIZO`x_k==D<`27Ku=tbavDCEfTe_ifA
zRqWD`RS@WPxbDp=o=E+|f#+xZ7my6unreKl7BJ0gpZ*g}m#4S?WHKo59;3d>E6^_G
zVe1f$LO&~*ag}UkaB*A}=Ofwjkl0>dTYj3qYqg+rlPjy9XgImLn@5m6=Z4N(cVU)t
zaRn51kwZNeR7jxQ(uK*^#s^Obu&kDGC6Jw6O#HShe<Z%Y+I)lpJP+WQA0gN$;IbVb
zksyc0vEeJD_Zs=;Q;lY+q(fB)?JpA!v!0*Krrv&vIYSX#@<C-)m6fp{@?P-2VhSH(
zI=pYJriNX-r+^DV2JhHm>nR>jj+x+yjn?Da2*9uj$BuqxD(-_C(=_Ms)oaSeEo;!g
zSS&EEMep2tO&R#$4{K-s3qHLyOeA@x+p339xjC+01O{4@O#juY<un|a<j2pFH?R$<
zZcQR*uHYvm*9YVbfeIWtZsz*c%%@ffdd6N|8z<Ei_&HUM?ekM*Lry=`2a*Fqmk{*H
z_KR-4)FF>0wG`~>GKR-}K~o?4oSBXommAfLXOitnJ6|u`3>|%L02N!1^8%gY4m0y?
zkNSfx*m;SBx}VyO+KOQxt0phz@{TI7u<(Pdnlp*L_gQ1a`Z)ZB`$U$2+R%<9S^q=L
z0h0dF_XyS%sSa{>gGf@l8w5x0w9suO3h9}HSL*%<W{1&Nb-*FNmsI3N<nXc2#OLFn
zcqPe_xm_PM&6`c)rJ9cYUBR4`Jvhbm5_W2wEe%nC9rAEcL3KBgLR`0|2xpW~3W%-t
z`<kr&a5b?Qcv+j44eMo~_|YKss1FI{qn&-zFVFqE-aO<@Eq$V5%R5ErsR2USmI=+w
z+T}x2)Qv=OS*(Q_{e^BF`EGPJottX;NN<(7VWL|8OdgfS<gWxSaR*dY(ahZE$Bbvq
z=J}p=Wz-J`$aQzr&5EQ>0p2M~hTy!C%9=Es3Hoo`G-{)Nfz0iQ^dE9-7!?U{r~sI(
z;H{I&eVrb*R?a%1d)^(7-r;9k&i1Vdj5?DK(#KBR6nlO8_3a!#1aMg95Q5*R_~bcH
za3JFLNyfMfk7F|L{$SJy%shOo|2uA@1w|NQLgea_r@*M^<i597U(m)$*;_~`QeGVj
zYRyw%vXB=7^1>;8)SJ@3%L_;#yEtOSaAm67)<grUuZI0Gt&>*Q32DsI2>9T=H%HU-
zsDv?$?gz`YIu?I(f<fEs%Du_ig`{**G9onxeO(TJc{g=(r}K$wgSSrh%dvn}Y;9l5
zX6xRVhW(-|18|yiPwrsTY3ms~&caZ#tG2@O5q+>amcQ;q(LUI&@ZTzS&ga>St>B{c
zzFQnl5ZYIHRolRtiJu%WiBVh^`dL*4AOC^Q;8N?luYg-W#!*VyZbMxOybrLQV95@6
zK@f4Jt*!T!Bs4p@W4fvbKg#0)kfeVpT>ily8^IA2%M!Jn7)i0+dhpbU)7c{X&h#fq
zP#fhzQ<d%@4#3)3rDED;BL#a@k6w6gD1kk%%DL&|xh1*3fNaLP?fYie&Ur0UYqWE}
zUD}aZ8a3=WE-s08M#FV*7~n)odYT66|M@)%8^QHo&|d%l=`M{GWzNImoUoU6#-TIo
zHboTy^}Kt}WdN2D;i1Eodjk~V<G_oAR}wEtR+Rf<!x56#s}WMZm9M1166eZsSUUG5
z<wnCyt;uSim8glQX}%~X{2uGu?v&6U*tf$FMPEwyly7BF^)#iy5~GdEDA26n#WUKD
zxuBMAtbGyP+*F4$DfDm1!B+2pcrvNYMz{FQF{A>mMYZc<I8$h;_>(80(Q71=V_!^6
z4|sBhGKXaO=O97xN63`D>tBEr(vyNvtU0M%n(g)3=DcFUc#-&n<u5Q}2hMt@!Sxrg
zjpp~D>rhh{uh@L_EN!|`OX(S}0kaKg(_$cTsz%C{KZtqo49X9D<J726kP-jD-9c+F
zjg2f!!V+G;5P0#kKS5g_KY-8<tjYG==t%rV>%G>kWSW&!0J2Oj6>B1x-H=7SeM#oz
zcg{9~j!`rT^}2O)M$a{jQ4{>UU>8PF!_=br)!N)NPHyd*NU;)=cY+6b{TF}%UkDVM
z9nLDo>?CHuBu}zF;bBrwbJ0jmyx@c5)3m)<?n3V??v!#JAD0i*DY|rY_G0^x>Gp9x
zo%tC@o|8eWV4V*dMQcYD;Wq<h;&&x(RxB%QeVPe<U{^DP30=!%2LpOq4Jr>b?0Wy1
zL}Gz(UG%Ka6sswUg@tU;nPdq-VT8P;857t7hxP%_mdPhdM5aw<-_dWE%YU{W#J`MY
zp=}xXGs9!-(&BgRy#Ej|Ocn^Jq_}yy^yL>V)vd!XQ*2oEZ=JZE53Csdg<fB;G}x~4
zB@tyh@WiaLgQtDhBwf)lQeAtxS-iZZn3rfD-4oUEIL1!EV+U1My^$a^GdRY#A+(P(
z-DuF-%<*P3CE<1&iL`hb%e+E{%@UfU4i=jOGw8tg@*D#3*^VqS#yF5rMf7u#xOF@p
zIK`gPuhO!5fHt62>v?4_6d5quFlw>qaQFLHzQV^~^LiP}_w@6TFzbLYonZt*p0Cd<
zx-+ZN-5QcQ3M@%sg;c{S9zI%}D*Np!Dk&A0EZ%zOp4eYwBGk;xy9h1)Bu&$rC3C7e
zdP0_<_8m_7HaFt;fMT`8>lw^AZvpRA&K8{Viup%Z6)n`^iPeC1R>-qW%&h@e#Rvpc
z92?dZ&B}oz(U8Q3{A}KNWQV~i!Z_`(nFG5W6qj*vlYMBp!d^faMc7eZ?PzQ8gX9?y
zw?2ujT1vV!+U)KYz<vH+T$N3mT*&+~^_<+{jH0;&j>~tDvQ+y@4QZ@IF&aK!fWm*0
zBHq6cibuV{;kS{$`B0A}J5wwpo8H?@2)vZb^g@14&KvNC2hhIJ8IxGF?sY5I88;}y
zFjAr~b%Gz#s1-Y)7Py7Lv9)9vkQdKZ$xUq*7U|*vL7#3?&8`@Xok?Bqv3@;r&eTx`
zoN{O^@LD~3GfQmKMQoPY&0xdTw9%RRC@Cc++0%fC!7&~00mljmPIWpIjlR8={Do#6
z!Dl7kF*Uvq!uRBNj{L@LD=wx{;pm5;KBUl8PQ=RDyq#vi{Yua1d*!rqm5L=4kwT9o
zLwow%-0wp0w!E3y>9XSz;$T}V%e2GSvzY|E&S)!%#FwPoUGQ{SnKPH>(G+|1#0X>f
zgvQUHeTRR`mX-cXK}`RbEC5(IJih$3R~P;|rI!r6-3|UYIK)vza7PwmxX=FIYKi27
zj&0K>MU%_9O}m8KV*YP@u5C{%@1e6+%{sM>X7$PvzTz4|pRVWmM2eJXDhL2DH+7B=
zir_}nCIXx6=XnW-68TJps_GB5jfwN>w+62>etrD{4%e>Xq*R|5yzzauMzPNd`Wm{_
zK##c|9)4)NRs1XY1!&#3dXOs>GzQoUUcjp0IOSD0<*|w+B*uBY+K11V@MH-IJ&}jH
zrSA?ISy(O+<nCw3a0>2v89wpuHn~SDsv_5*gE4ed?{jSX7>oY^wzxmpu2bq&le}a=
z6RkV9Y}kSk*a(*8MmHFnYu=*N2~(0U42?P`@!ULk6cvA3WUOETW~-(O-pSt>^8{=D
z%=%g8uvy1^uI_V$r0Lh8hK^&2mHHaxgvIT^4An=O3a}#DOaYq_HTsB_+$EZ>4wQsu
zNUj|}A3Lj#W>FjsIwaPTjM*(_P9;XI4yG39sd5l34GH)}#!iWV<#<FQv%g^!IVOGD
z#hCssV`W;aJN}bkHj`_H!PksTmu4pU)zF(!cks{z%PlMDMA6tE_lly2cyCXS(_BX#
zV_hdO&|BGTb9U3Y1C(zLCru(3B8RxgF9HcN{HuU_ZCwC?j@@(P(XIYoLfN%#1b-2-
zIF5X9a2y7J23<QO4V&Qj2A9gv5p4W~Gmt^XLsjD37CEM19f$F8{6@!2^~|SF>s#Uo
zSH%%kXFi>ukxCab<7?spI;*_bM&)lb-ZLh41gxuLE%I-_yz5E19|?&!TN^a6f{4zC
z;qC@K^ikx~cJX@aznJq**0LoH{Wr^*Hi&a(xEb$FF|6%t^;8k$ct=DnuV_nuv0Z{y
zC~9Db!5kiEQOJ!5&`E!t5SgVhY)h95yda~6A>*oChECds`{aLm>e9a(O)VSu9Jf;@
zPo=xs{Wf0i)Hyl*0c#X={altdvVrPIPqf4}QPT@&)0&0$R!=sc*P@6pP(A*AdovV^
z;+Lv07;|H<Pf{VY#J26-(=9REx{FG^8?ir@X2G_d5|O#YO4cVc2)+o4JviwwZzrLS
zUs)*LZpad~PAZ<ZHWi;&;YZOg6B}@>^ETWpsev+bDKbnffASjM7<0P`Sx8lx)^NC`
zw<#;=9hlfE)`VgxbiRzh0YCHPjqOr)@wjDt=f3eI)^gF^HNuaDWi4kv_6_S$*)g-W
z9cW)_XD*FNPg5?K6-;Yw9`LqVbou;4!W4ai4=C=UDzJsTD6Sde=J{tXt)Z?5_M*=~
zO?pKgJ8mi>E-VmTFRVSte^A6*0E!m>1Z#K#D{rX#-L;K?t@H}L`oUaP@z(Bn0HOGp
zt;vc@O9WX?eL|3bwXf`J(Kyzd%6op0rtXQ^=Iz-0TAi~&)}Q7}DajWhLzQ}UF)T2(
z;lg5rsj9jXS;&fq)EySUk^*IaEX=vLRM8+qRT{A7*NI*C0o&>&%@`mRfZ66%7~kco
z<6_P3$_VZul)F0CEw!M5$`B|4(q7OO$q2oUhD3**<Q;(44qX3B_q{8B53%q9m{7#F
z?6&K}`;O(#=}(E#TeYs|zQm8gbiKlKs?UR8d}98$+w(te&Sc*I5<=(x8|k6`ynO8Q
zJ09b$d$^p_Z#cDYG?&7S%3bTwDfZ|kfCz!B@F}wKTfjZkIP+RB%Uw|qOUv%5*ArF}
zPp{_0L^XMmb)s;nDS^c{Bt!FBFv6F)w&<DLR<VoPuNwR--}1+E{?rOg#k0YiR=;pg
z$4n!s{FzL?Mg;dX*<m0eX|fHT{3`9iIKto@Ag>fs&$eogpTApD!adtriF_V0bpLx`
z2mKGy;L_#p7Wxv<;W9LEpkA6+=U>nCw!P3S?p|Q}A@9P=%6c~((d_byrHFDKj%c-`
z2{lVJNA_&Tbab<pVqnyB(xO3~)%dr;oF;|IY70j%^3Uvr84el9Mo?FiZ>rFPxEUL`
zp7rwC!OmnnqOTo9JrLVs*`m1iDvoRA%)<$uWr%nw4bEFAvE^m?f{kFG5bt%tQ+R{J
z0u``4wCcH8=S%oxx6nRVb+eEM$&y#q2ziqizJ%ZyAjMoyW=k35oX%;8LNVPp98mqS
zR*&zoEGTz*9hT3BnkWbJgR+uFUVkq}rrO4NftMNzrYG5SY&!R{id|Ea5FT%=0(4XP
zA^>#^{AV0nE6CjrE$g-u?IL$f@<5->!R>n2v_*()tB?>1*CHEK;+h%Ly?HLc`95_s
z-}Afz9+^NWui1H@R!LyNlRLnOV5UzS`6bTIYn|ZlBiw6wOmKJXT5Oneg(RVamPl13
z8)5ud<5k+=`nF2@1?~T8EllT(i6aVlr`YVM=TAW)ZAk&a)Ox4X%v1a&xt$`4?k*tG
z&xL=$ZXH<^A<rl*yg`%b0PL+J<tgZc?4ODzHkx+@qx%69u!ZQRif%mB;&Lm|o^hE$
z4ec$g&*t+}NfuH$IYAC=E}BFeLl?p*Ab<6=tHN__u#=H6$6bN*GDAK_srH~BxX!9>
z2-DABqf?y$`IDpjeh5cyEAVZ&P7)QUuBHo8#v0>eKr@HX`L+DgZoJ`v^0Y6xnu~~q
z+=5TKke#i~fVmYthl=Or;2BSY=fseiyt|6wCJW64ATEs10dCV0TCM9X2E|5;3wN1u
zwhoUvU9|d?M4m5Fc&2)9Qab03|FP5zw(IX151|C=T_x1nlc)o<J3U)01+v1Omu-*0
z0|EuexNtbh<37DCVi<AGb4%6Lgoa2}R<Pg+Zb73p1(&L?Z`CRf-J!gyeCkI!4Z|nT
zakrvO7t5TrIhr<v*iY%4vVi8YWnO<(EgYW*Vmq;=R3gIWmwbr@AHvn~=4w*TB+lQW
zIhA_0^UjH~zrgt<hH>s>y)6?#Y(1E7wO7s9>Rv}ZfA!-*;#^6j?kOg+--vwxg~ESV
zr63;)bsy?FRL}OpKgpqte>1^gLlLs2YPRoThXHaL^CgBjziHkx<mEZ>uW(yQYv}^#
ztHNf!TVvI^1)?(z()3y9PwJ^D_ZemM5$Zq{Zq=I6;p7QJT&w9_)Oq!wt!Ahi0gg2M
zVSYr_I~JzUl!dt}lswN;`3S9=A}|_b71$MlGlCU!$A-S5*8!*<?k~E*@GE}H=Ia)+
zKsR1(gt}ohA4=I~MzGa2P&SbCx~=EXp$6~ZHHshGjSe6mp8Oe*7$~62^&cg$nfam4
z*X$GYS!>K&g6I>ejIZ<EkP?fyoOI|pT{Yk}^KU%e|7bqr@fNC?R?B|wHbRSi`0G2`
zc}5?46>cK_U*{!S{{m$4?VJolE90-00n6*yUH0tX@{EDZ0}%K8n{5Oy*W$yt*d07M
zjf17u-0F)Ntz=*&WKDACyt*>Y@omexRwupX%8c(f+e8h);f~%|)K1OooqhK|=#|bu
za1#UPld87U01X?mAr^q#+WF(<<OGv}`1#`F$x1N^%EkNS@UcONe*dnj<$+}HlbJ`=
zv!6b}{9H`A$vdGp-!|Y>7xUfZ57_R$+1wwa^^Jx_-Xh|;R)6B7N8!v1gPYH^{>;r~
zdQJ6gDJ36#jk)PY2pbd%2Bh`AG}qNiH1nq`DAyT{dWv|ZW;j3p)6(Oe{tg3O-siN0
zi(+gI>GE?YVmJxk&%MEt!mGN_1rG;|VMJB9Yi;pchup8+67La$Xr(pRn=NwFGC}HA
zbT&FSc1CMCX0<*K8-&6hxroun<u3e_gSJ`9Oxu{ZZfXW~2^828yFI@+)MHjh=9^~$
zXzS;MsI1(JQXI*6_2k8!Y{F5VN!rF1)ZunP-5cYz;hsN6uF&cKpaVd)dw6989Nj7%
zjj&J%yBp<mQ;*iJOY*NwipWDST1e5!0>;r1#M+#}LsBou(purI!zPleo6VwG+a*VP
z3P2;YFKYD@5IXN6anZR?6--F>vVD;`iE5T?uZq&m@NLf{H_2+WwjnV{RiE6m2Y=W)
zji38l-ohf-$xA>WEu%3y%$G;l23l=GPF8m)D#wg~8dJ@%AUbA^>ZVrtA~-_fLZyS7
zR-vb~ktidCl|Fo<Bxn~bu|2!M_dZEZdCCae>Z$e8L6;uIz@NGGD4!>DS#w{HmdF6{
zGBB-v3jJ95OGv_VqWe!AZVMZnlKDTW5~rGEQkg7AUYVxO*T+%+G`_(D##yb@KlVex
zB{*lLeRk^KyIAJu8lcha?s|ny3b-q1?}nqhtB;$6EES?psdQr`<-V=?wEFE^*89PI
zDN!l2z;J!Rs>!Gc_U-4V6-L}#E38N!btCITpXa}nnuQO$?i==OLHM@NM$Ni(R|#YC
z1$hhHa<KP?-dljcqO~v0`6$B$BIbQtao15d24!!>9f+X)R4H;jn}(}%HSx*;$RD%O
z{JH$(yu7;R@`o@s6^ilW`}A$CDD^EO2Kn1P+qN7Ad<w1QnJi#04yDDMvbiMEOcS81
zlY6?-U)`MJd0shKlsoD%5DVBh>PDAHG^{c+Y%6gNtw?oa?}hdmfoA0~34XUvq0(2O
zao$3Ehj7w0QwA*ZW)f~-Pu+;%fvlV0Cf&CEN9l`?y}5Bj=u4BDxaA@lGS2DIYuv8<
z&GWGt?{W?o5_a{f9?|Tp*&vy8iq|c&M6zdeijk8qW0j+*jB?Jl5zoy*b$`}#Vy(N>
zO9jaEzJ4aRGNug|o923|)wwx|_1H<VC}6g@rC&;1#&*ITBD7YN&>}9;NTS5qJGAw|
za)RlS3^Z%EpTzWzZ=C|64cS5SU9YZnR1RrIUi55@Yv(Iw;oxO&(LZk;*ivpuXE9lh
zJl}GMKS{%03bX4ZXCmc5vQ0QR9fyb6fjN1|_Xyx54Aco5`M)c5rwx^gEBM|MrwKWc
zqF2vsa|7WkebPkLzd*6XcsFB8C9U)h&PV92C!C_rw5!y#z4P8tXc>X_On!LVMWDH3
zFZ_(kDrI7A%=6ux`-|T7AV<CK=1^}(rcMY!oM%E|IGi@|AKWnik>UMEN6i1+rf=bw
zC%)w5VM}X_y7L@3uWqX>TMBCHHhR7O@k*`W5KbFnEfl4mu0TYL?U`~FB4R#W+8%G~
z|CmC_#iTdcBY<wIpeaS9ct-OBoeds$7X(T_J0??{FsVSb&R$7+nEw~h0eCrg`-LCK
znc|vjHYf&c&Wv$36_qhDKllqoBuXD<RUAL9%2VDT6bT?kZ|XlJ0^HS%>?Rz-ls+OW
zSqT*SxJ^b0eFy^7v5K10$&=*LrA{rU8u@R0d5Y>VS~gkl8>N+JIHz_qN7(wGh>IV%
ztevo@d&70!B~g{rHh_!_vRE6kBhzY6wq%8wJb>HEcg?n+K^Z?7CJE17jnj^~T%hA5
z+?G-aQ4p5Xk|p|BOt_FnD4axw%wbChUapKkZ#OZOOoo~4nPk_}dQ!MfvKEG*ED?en
z(Gzy(a24t==28UYrDe@E^9tt~Y?z7@V!f#G=5X;XSNBPH2zZHjcaLCiL60f(j^GR~
z{!5Nv<peFN!+gg|ujPJr6=!CoTBgtE1x293_JPZZH>{QXajM~L<cZrKCJhc+nYUni
z76w&uzA<^4=asAv6NJ$^$42+8;5(<*LoJzSnJz_^$Z>XdP2_hoMIPzZAoLNZH37M-
zc)o-P<M?HgFk^f!kaS|w(wUZ~W_55i!)EVKGuhf(O^?YIta!%<Cz@eMekl0TJkwg(
zGyl(%vuY!SH|~s9Za~$L;l8!#=FqM*3UXV~p~h<*y{RjFQy_o!MFco7vwXg~cc2<*
zGp^A!VbWbss+m5k7nBnwksS)5Gn{Q`Q!2Y;h2}g8n8Z?WF7+UT<+}yw7}>>Ea^wf;
zpYD1Zp$N9iR(heWoN*jE_u{)4;onxI_}DZiUf^h`CG2Hj53;m~X?`QqpQg9U6R-H(
zmrfseqmEr%J?dFQ$DU9TF!K{cK2fOx#vXF=J=T?Mc-pz#k=QwjodgKHib8$xwrWr7
z80(YY#ZhdfMNPpcTkdw06NJ!WMk}=(e(P?9is#THJEn7zFQ=7Ho=@RRQQ-AOQ`Lu~
z)ofgoQ5V4tu7Pv(am-dnB7dl0;j`L$rgorP1kx?6eRb9>-h>|SNerG15`>CP0T+J(
zq1ywy)5kvob+v0F@-z|Vp;$5wa;Gm_(bhfK_AuMiQ$e67sncvuwv;GkOOfxS1!pC2
zUP*#%`(vXZNg@Yr&TlxGlXajyQ=*HMFvX8%hIB!dt=fdu1M(t!dWByKrc~dkb!Q$y
zWoxL82zVc$Bb_ITHBUy8_{JUNqw)~VE)TWdd4HB&Ih}8O`src_&5IC<B)&SmEyM8F
zK%?~N?@<SeZCO0{^sK9QFqRG-*A5CbH@}K0W1~lXsykvpAwQ6e^ki=);FfSTtR%A<
zVJA&0gp^NTs^<<g!V{y%h5d=}#6{IXRC3#|7npoXhbOriA}9SgK;W|qHT;h6J~=h7
z9OkC0G!@bnx~#^@A8eeUCDa=m+s@_)OK?$wut1PmnuI40(<Ql!BU5ZpIRrQxa7c%g
zz_d}24VF)Ub$T+dfqt#HujgN2zcSrJY%jSI*#+6uo-!afvQYadRHEQyhT&+T6J_B6
z37o`2I@4cG3(<8GveiJjS%ledIlC$!*I&it5bv{bnH%Uad5BJ8%dC9=?tZB2)fz6t
zBCf&n$5z195nVlEZ5%-RknbfMX+%<71@GxW{(U2$<{zc`P^)&?yv)!zWQ2a-3Or4}
z8LZPxk1ohj-&m^Ux{yJrKJl%Qg3~t3=zT<PPgfTw9U*_nTE<x=@#+3!u>PM-K;h-m
zmD1LNuFfu;2#q==k7Oh=*~G{mnt})yBNUm})6Z(Y4Lg!ty?@`d`%jU5aLRAF$h@uo
z(h}Au$Wtbh60|q#_8=$nNHp8iS;+d7EHsY@ofl42U8qs35_jzp%<ASNT2Xa--Akhy
zfLytm#L1^9s(tr!I;p1u?E{>4#DDrRa(np@SK`2Bspogm+-u1y``vg?muK`^4Jr>t
z2+jr%4kkWX47gdivh|pqJTKQmI|fhInlnfQoO5OU85<gI`qH>J>7;cIl~sc^(#5>C
zOK%>8j`X9RYc_!1GstavmouDhyz?MYwt5uwi7nmt(O`Jab7k$hOVQ%2r&_0=Q))AB
z6oIEV(<MnqsC4E7*eAZMPY<v>b*!3+_eoxgJYJKCLPN?df4R6#Hnc6Rjf!G{Plwql
z+32W-Il?gtqCdRT?yf#w6=#0)v(v>@%>i)2b}SzGl{siwi@jjWlO*^>GM+eH9V)Z3
z7FH@)Rr`*KqCEf|k+XojZ-<CV-<C9pC0@i^m?U4+oZSB+Ota|!eftJ>rO69IJ#ab1
z^~}>45Xd>2JKSmtN)U-%QPACHOhl~4TkN|lYaKlr_G2ly7z3OQCCnfbT3V?OXRHF}
zd~!wc<>%nipm4p1gBoxmo{I2B9knuYZFdGjA(}gfQS;5KHt>P6ep;3cXF8@kocL#?
zQ=|@yq_nzHNoQv|FX=K%>6T&oXMG8yik&7I{bbyQph<KUMFiINj9~M_Z47RTHsrC!
zviw|gc1XAwosIx=bt&nCRG>;2QjyQM_`m}~Kdxqs)=7~)+In%67=_0>>^g_cV{Lr2
z@^r(c%k?D9Fp+Hp-}Z~&QT+yiIwT3N0+NfojlHXz9?(B!7u+)xRyx<3UqUdDvJ;SK
z-{c=?zWh#yi8s-j$A23L%F*#|v)-kMmh>e9O+C=UX|!EwiMeFku3YACE*0%4rjfe&
zmgXp3yezbeb{yVq{{`@Xp(ktB-4BR{3(KmNe05`@g{hYF`%40q0%i1KSIIze5j&>`
zI>eX#KP5kZ8_$Q+v0+jwVVZ9%x`(+*5}jUaL%qhKrlsX)w2hV2Qj9tj-Ep4s1m{$%
ziLLkE_RM&po`V(E9VOo4F%@clx;|72TJtpMe9`&*OSKM1cDm0_IqPQVSX)M0l)e5h
zp=(`-QG;b_7jYFpg5q^kI@fYD+fTmbQ|yw)8Or%f4oTnqr0YO!A2*IV#ZK3L(2C!&
z0%wObJ-qgYbs7F-S-Vs82UbwFWU<YJKelfl%gBmtv4At#-8su|frl$f5AK#MVGk~^
zCovj2g`WDR9ENLF??N$zx@eY^cTURG{eQc#fZD;TN-CwIs^T($Jn;@yajSNmCB0`>
zBUz^fD{-i0{7$+=r?$qj%xq@jcioQYVK6NSr;Hy|>{>G9N}|GH#^t8faw%X{!K}{>
zQ147t816@soiJbh=*K;~EWr-puTo`-h{8d?yFES0ph*<kI(yrgOI;#-agLLuVs};9
zTn*@DhiVWg(5{?Ffd!a~Bm-jJ4_~5it5^Ec#HG3_nc*YaPmzToYaiq;%ST`-J?`gY
zz|&#>Xuql_{_opyb8=FQ>gvhYV+S4mxoW!NFL*IL6ML>F!Bf8%eqvpob>d{z@O!ST
z$bgwDK77|t8^AoBYcS>RAt)5zH{xb&jisQHu&n4G^?M-?{)HmaKvyC`{#{i9p6CA)
zIk!hA*9>RkzXs>eOk&X(-Ann>9sLC~jJ#w1cQEDOvbPlc3S!U5!dW*k_3{rWtzy{W
z-wpkO{U%IC{sQv@+w7D3k)<VEg(R=<jmtc0;_h*l5JFU5k=p=XqPiTWPR7@--NU}6
zzrOq?vOG{<lNQV@OUo3*k;t)KxTKy96*7mD6-6(-+7#j$8@zy4tAKe|TC|yxMDUdU
zP;M_1-<^x5hA2)nbR-SFsMYl22RUjaSf`67RfqNvFX+70({(F8QByrPHOfw5G=gre
ztOpQPtB`-W@E(ltu!ORzX~7@6qTE4g0gOAC8xphG+gIFSf9#1kf({IQpUcx@R@ia`
z=J=Ftg*|(k#0tdG8Ri*@rVOVzll^o-)`#KK8r{OK+~kU_q%e#gv!@@<BUNscYusz2
zhD9~E>pEh#-Sv<j+ccXS;Rgn$<_$J-S9&##&KQJnb)e#;Gr5Vz1BX80ebr>8^Xs3W
zcn{F{vBsWZifPw{`VL|BfN5I#MX=IQv$t}BdSWUX;HxJ)zLh(rr7ks2kYNe5Lw`AS
z>}FfB*oonJ@f8jwFSm!rjv9@K?#gcC=~bLb-UZY(Z9BR#y3q+^mz)?O{j10RY+`w+
zu{7!?p9r}`Z^f9x(JnH>u}@inqVltPo}uGJ0cDr?Bph{{j}KH-9%>u$SwBnvv59M<
zfP-8OZu~-3iDGW4)J^D@-yYP?O?*`YKKYP5cW4#oEEysCK1I1X5N`s^?1uZnQq_#s
zk<I@g@gjBZZZyHn43RDduv0oS8X$sxu$W2{%9rpsEm=pGJMlmK_<P1jGM!zys$MRf
zhyI*AU4MiYDq-@2K+vC1vg}^_j{tVFj>1fTfG3(ruRvZoCY+O%x2jDQP^Ma=>C5G;
z&_;m?R-Ze66s{BM<aW~<(-kVTk2Wbq-&U1xdTVLDU3eMgvSayG$DcpBXg;QS#BM<S
z72TjZwyvVdi-Ws$NeT9{rq?L)G2<6=yHb*9hiEI<GH(V{*=5*e?VX8w6Lh8jY{yVG
zSY(FM{KpU3n|@s|n%%C)u;WZ#^-0W3Ttib~$wQ6y$t$q!I#*YU1vy%!=YV#onoj#e
zu7y?UgjVs2DqpZ2mO;ifoA7O=4~6sDf##@Bm_NP8c0E_kQB9rTl^Z%XOe=qhB3N{x
zb#J2Y3j)f_!u*+W7vijJS(0EUU5s1-bPlN%A2D+T(LEu*8z0ZbZ^fesi9Tt?f}x(t
zK`O@nqraH&2)Ax}e-V|y`qDl^-OUHg#{gbY4lZ|{5BxpsB=TU$wQkSL9V#oxB@J8T
zqk(PF-&g4RpYZ|fhaE-OHZME^+zs6fd$mM$zN`s|T{yBai(#Lgmla^srOhMZq;l2-
zUz2XM!MEW2@g)1X%9HoPKUGgPyVXZasY2Hg4l7dwl(*%fN37u76jcdNs3`IMuacxm
zM2q#%`hBiL-_PXX#dt;Hwd|6j6cwhES_yff36+Jv0MQPdlBY$#GmCeD`jK!+CZY?s
z)0bjTQ($?pzz3r+9GO+%w#n`c72ZRq9Gpyh0SfxdF&vHXah`T8Xu(fM?9K6y^|=)y
zr!%odHC#R0FZcO|mf<wyvokJ=V6=Dpn<0wgy=dyja-;2l8v=wOJn=q9yWM45>!iqW
ze{=I5g2In4D>IeH^eN}xgZS5_iDoCOK@5izR+WGACx<D8M(oYC&DX^rT58=wKcL~T
zL7J6QY{JtUlfmeZomXGDX3dsvw?J2N#=d1Pq(7(n_$|}K(UT%TBF5`7K$%zmW)rBS
zb8~!A+rh7Q!<kB)mR2XuZu5`F>OTr%khWL{p1o=Rlx5{N4;NwzVZ0)*$MUDz0{*AP
z0pvARoQ+gA&27;l+_!Qq;_)7OB9Q4#e6$aVHHWEUh0Zc(eco9)opmSXeVR0s;1N1S
z^tjC-Pt)gC){!?5lJ~u%mwQ~K?(*`o`e1~up*-d)Z{coHvi;|`4f&VP*<#4J?_>aT
z83GL+vj-O3qiW1qE(GtEO`4skq=P$dCPUvus2|Zr7R30h4kza1XV|VG1hA*1!LbHR
zx{!)}9|BV3!dK{b!byA!1cP7b)B1t365K`EE&6<E4QY<zF9C#I2bxOmP0j@h60K!B
zy6w;<b`zxPX~wT`mJ`YEs!c79ezhKPz`aP;YsSYZTV&}G)+XP>efmVX93$QdTqp4M
zxqa7|u`SkZ3&+*=9C&4D|8-`y;o_2+O<`h~Z#op2mqxy|H5Ln7>5o(B^d7<t*l1Kt
z(ddZQp|%`TFQkpN30l-X6DQizePp~hO5WpiPvVdB`%rhIHU4nUn?6kP%}&0e$39hR
zC45WG#*`ghi12~V#efV)NaBt5bKG~L(jGX<>XUL@%AB>=hx?5W3<_P>WZ^Uej42A?
z3P)V!c3d2~tNNj|R1&NEcCWXlXLU6xB;QVdyS>)3Y>ON8H4il=K2&EXeU?NhXCsl<
z$n_w3uf#4_(+?U`XN4IW=S?wSSa-RtA1=J`p#ST_tk+3YdW1CAd1c9*@T3<y7NDZw
zq@?xgfujCp((X6zo$x_admS0l1NZDlmC=&F9)U@Hi_V5qwx`bHrI{AN`3}!l?Y!Lj
ziXX+wn24sTmzS4U%-%p3+C$=B<*UVNnh-9E2RX#n#(E?psNLMZP0o@zE_YE&<Hojb
zu3O~&QFq8Pt|KJ;zUx47{#0EG;;IO|)r>Dqbb3ALr?DRd>#G_jwN-WxUrL@i%Y#eZ
zgYF!t8cvOsNZ+J<4iEs0ZB;svV{+t!3BnlXyn;9z2CvX*#}T{5>^mg#YG#hz9X8ZN
zPn6MFPfggUWP>%dtrJ%(AZ;mgpT-wG@GE@gp6se5Oc<~HM!B#RBJ&@yOd+eb3?(ij
zI$yLgOB1YP)9%jS0}#Nj@mi1w5$)lOEBQYDog@*$5(FbptR*Z#MbLhcn#r-{dPb2Z
z$HlDo{0zK=;AfPH_w{uNPIFilgpzNzj*G)S>ZboM=H3FTjwWjtKDa~B;1DFZyIXMg
z;O@@B33dn)f)g~jJHb6T1a}Eeu;9Ui<~DiXnLG2%JO8)lyZ^fDvglKNPE}V`SNE3M
zwd>g&!RmXJ5_K$N@aq8?Aid~$8n<$Tb^(m;-1cVfSOStzjzG4#3T5Y{xg!RM=ywFL
zeo}$I<Id|IESz|b6H&rvD~cQ^_PJKs-;$1HU#4Q}=Z0(r9~>wI&E{${=E0=jP9-C`
zAyLA*0gEVd&p@46^jnT^-SA5*wTY(BN?H8LW23z<NvvU{jwYX_BbV;T6Im>-`MDkp
z!2SYdO%>}Kp#+B{YPfG#YaKCZt%s(0_<s`)42S{=>c^Kx@bJ9xxUTSJ;LVFN-&hF+
zh9|^a8AgwLmN)XE!hl_Tu8h>*cmd`>6mvXZzGP&7ta_<P0bRCq&Lg%rH5QNDRX^U&
zy&lDmPI#2pI{cAcSW6gKUn1OX+wu)2l&cJhVOX{d<cweC7JX$Rwu@ids1=7Usk|Ga
zZ-tB9($wW>4U?LL!Mt(hs-`$RP$<&f^7{DwB4zI|MNM!B9|NEeRBwO%GES2gKeaBd
zA%V<LZLzw%<q8-vo6J`a*@9J!m_1z9ZZ((uQoRxNJF@$<+l;uq%*c9w-qXUA|EN?f
z=!pxQf)+W6)4NRG`1#gH4l`1K1J%chY^wf0ur>UT!N_1L%=Y<tX)UWIWu;%Tbp{mW
zR7B9i48^;qz5Ol*M+_+{-YM#Eo?z<s``L0=<&$>y)$)|PV5bsAKDH+~((Kkh@x@fI
z)Iu}wmYLNrkYOOLv@5l!*cUENi+5e+vJ}Y*E%XCyyAXcU`lCX0IFP|vaI{S>94bn&
zy~dYKkUF(r9y#_Q9cSh@8=)`THIN;OrbUsj-^%KP`%|K|jMR8J3-{ipEHzG~eOO6%
z)p(%Y)^5_%OFdmO>~b~_ePt?pW8ekzqi9*)_eGQ;tTED<2Kc-8sGfX!gP6!vzFkE-
zwn&t8jec918H*XIl4OoSMMWIRrp#mvBX*>!1Li_sw3HWL$>LtBcO=xYEp{>=Rq=%(
z-Pd#d&qh8E$j(E#>)E{Iu^dx%Z<3r*;JPlj5}Dp3-S=7ED+>FTP|{BYQmV)rd)>FH
z?ZhkADRbuGZM_HUy=qi#iz|UOi`!EZ#7tb)6bGEf4B1XJcUmKMX3f;zjGz^fpqVO`
z+5#Iwwq96@vIl_^?)Um_mBsT^Wxrd?gRcZ8TX(~wiSF%`1Y)0YI&$A?k>(zfsGvg4
zM}N2z--B8T&P;c^)l9No8KepirlhWoTe{z=&|8tg9!^h>jybq2_hLr*Of@EL+vfPJ
ziV+U<+II`s?TtyAt)<^6%WG*#wY47TN-s;bb`9@$r@^beoJT;S+zz5JddaG9wH6WP
zl6i&T5?jgIv8m_<ov|=jmgXkU)s^_TWws4q#)VyGqo^;ZOuI$&EO>G$Tl9@H_W@3b
z$fvinqgi%YBL{@(G#w=sunkcBD)8i=VBV70TrK)teEO1QYY{VQUqV2WWmsR+ke&W5
z%K=|>9ZT5b(OupW-l8EErX)28<({&@=7Y?9pioJ2va_z})D{~F4(aPVZDyyFHbon%
zhEGI@n{QrS>ky3-Py<oj+xz{(4EcW+p!B1p8FW--Wrm^CkPsSfbBzkWe?-f&y!-B^
zAa&?QGO8V0SM_!SfuJ})&a^>-!IV=4iRAQwF2<8=sQ5t|x-QD=E7<zZKXkIUE5GoF
ziSx)Gq5z`c{W-WbNq8J96)g0QB!v2drT2Ey#wj65j^B9I-p$-cd008Ynry8`?dtZ~
zLuFVo%1&HtS={#Vv@uLgU|nb+`?~bRztbVoe9nDjB=9-Ww%N5bSiz#~Zl{SDtyK8f
zXP(FT_?jT*XTW>hH`FT|#=!`@R2O7XItG~W&rVWB4}v<gQJc7sG>$M+Wsd6|1@=%J
z<FWF12dL6A1we@S{TS0+9!y-wR=#xCUD?P?B?`NkOP>WC@u71w363#k>>&$GjUbnF
ztz4blA;CGub_D!US)YAW7iQM7bPV-hpt(mksK6w+mDU;$q5WQUp$23ux%8upGQ77H
zy^;6kGt<&I5o0|?E~0oz5?({wQWLxth-R-~Ty9Nk?A63fAN40=QVz9X(>?bdagh!7
zp%u_l#-L2P9%x|<k6(W9Ka+ABAc}U;dB5Gcacg!0wHiC>aa4b7Mw;zWmZTMzN+^r+
z<=&mxYEM4ZlP1*@KhvO-37va4;Y1<jhPl*LjKogyn_(FetgmUUQ}5dxBnZdMq?@q0
z<+3bGO7$xwPeJ3&h>*0`m1Z(qmSeKxf`F4afASGy@{6l%i>*j2E=|Ebcl@PMhUSj9
z$jr~rI5$2ganpa%^AurGn0+rD|Lq>FlaELP`wTJwU1k6s!G5^zGdB=ij%A=SNF^su
zmw+k)HNUUCDV2L2eYJ?w*bt5Xg@G^xVIG%>A~(CcSoJ$Roy~`~?!N$i{A())Jab`7
zq>tOzq($o=P(wIx%+0s3*!ee!0qg63=NR!9ZJ?uQM0QomQi4$AhNQt`(%%Dg6N#~U
zI5X)V`fE_0y&2)Zd(#?lrCVZogSsM52#P;fj5wQt7Bxnn17IU<cVP7Q*l)o7{et)9
z@OG6C8)QsYhSVceLm0!+&a^O`c5Ij-_GuyY8DS!9q$y<w1-)H~6?vvUBuxX`n%mQ+
zwUKdl#PMv4r8o8oht|+db3uFz<8y@uCPwMcB;7FguGm80`*8;p`ssTHY^Y4RA?$gD
z&+%_f1NnZQWT&|12{#pa$KiF-oL3NVk}#_#Mt$=itKm5tW8glQvy;Ri4nJr`;M-eB
z1A%35+z}2`{pxXlC<)h5XKtfWb)s)*-_A;yIl1sQMsO#oDZ%^1Y>Pf0oiX>jyx)tj
zQ=%gihwz$d!1KWDfVRkVZEdts&f%Dw9$^x02nn0gK1Z5!myvX)l4wturE4SyIf^ce
zS{zFS@W(l9zVW{ZS~g2db*ShXgo`O7o+&Oqr0tV7gFYZ4X0vc*3j`0;D?3t0lZP6L
zK2ndLRmiu}9vhp#QgJy;cjL;ipo)B}Pfo40vd=a>qxsxJX7WTLyoXHl4fap6)8exi
zf&=7~cbnw?LL$12)hSrvpzD0dqRuvEf8>ZCzuKppWDU{Uy0VO+yn_UVaR1Z5!NF1M
z)jbUR$(ueRFTlqifRmZm>3=S@gvq+~qi>unOpXh~Ge@vV@+ORnF#l6eEiZfe=?sP(
zWQRPKp?zy=89T)k6DsauM<~<}!M5H6_X|`dB|F>JC(b@IC*`+{wrria-UGN&4An%i
zXXBJyTIZ^n$FtEN&t>u`oq1^*mF(!WBD8hW&wIt`med|bFDv35n^Mj2#}rKmOIHSc
z8h~($4IhxudIJNY1OtV9ALva?NVZso4nO;KliWj{Wd=i6^q}y*i3A<8{hV{qEX~MB
z-uZm@3#3Op74=4HJV{7!e$}k2P!evW7LG%J!(@y43gRj{E_Sq3pf66o)$5YJ#R6T2
z3hR1^>j$UZ)DjIi`D-h^ePPE?KYQ$^+C5cZ$E2;@u`0<Hz0EBXlDzDqu{*UM`|dt_
zJdkzZ)875Qm-!m=K`OKq-+~gb9(|x$_I^PNFLu|M*u>{n-G4moL7H~bwY7d^fzyU`
za+1nxRVmDKg|w%-WHj=swB+j2gXV*m#O5BPqe9jRrx8}(rCsA9N=byj#;UO%#vExU
z0K-FM?rm-<|N8*tpRlDGv)Y<8V60m|@hMAd)9_yHs4+2`%!Pq#m+`7i&11*52BTUT
z{K)un{F<N%45QNu`Ovgm6UG8u_2b+#jAkxJd=pmqH+5l?QlNV~+UZ1oCC+O<oe$PZ
ziF8s4o_;!711i;_)L8kaiR*d2y?(AxxD8w@+}?`G9~-0$!(fW<0s2&c;CIJ*Y{DSG
zoRwNBP4A`_Ce86MMTV+~^}F1dKf32#HAEP(FZLF|R_s3bZ4alYah+?Kn~x!(vZG1W
z<|tO2vzRjQpGZ}=LS&dUbZZltCr&%}2E?YP%5|-IV~|P=PGDW-5^s$o@^|)mk4U#m
z=G^sluYD8ha`>fsHSv~t<f}cg4;13Zbs}!1u)04TKP<$_;ty2sD~l-jvn{Wz_QCJe
zMdhPKz5-|Y`}pyd`?VLh)M}mm0)<tF&D~hH%wlWpU3)fO&cta+-Dt#sBkU&!U%Y-6
zulJ)^TQlKW)}_E`Y49a<79)R_t43_zLB#ar$x&rta}b3ICo@d5@Q||zJXwVe|HRuI
zf>u6<2PQO9_J|KJXsm?doRq3xo4#I2P4qj}VlK~Jlo9oBn~rPXX_N!hnbB&2$Ha=b
z*`dgDY)Y&=((a1a%cXcyyr#Z0PPDM*ezwowj~H)Kq>R$ED&BcUKG-HcESn%Ztgfy^
zX`bySxuCLH#E|boXON`F4jF8n!?OkQ!Uxi?Q|XatByC4&g?oEiYsVJ3+-jEP;=UWe
zX?S)MH^_lrgmOW)dy$QDx)drV7)*5*;bS&4ue?FzP`#p`h;NBl)Iprp6L&+nTOluR
zUyM_GywJ2O3~4<Qp0u|&Z8TaZ&QkLhsQ~Hx8_0scUZwmW&m5j?_vixsNf4dvIAAu*
z5xfxVMOIQS#I0fmx~yF5)hgVSw|QnMgOMT}T(zgq)$Nz5*%TGvPO~;3>GaqRa_rT%
zE?g;7wErGg8()-N+JHK5Rf5R^dz&RU+tT!GVK5RS83bV4q=%84RVX`{?dZR=;5&=f
zl&)%51rWvXp<hRH=-%2#I@By|M`KigbQd_j6c@g8cBUT_E2hpy-BWEruczYfbnW50
zESVE_l>{K`pyEn&7QcKCw0A7B>Gvy5dB=pv3aZ%WPn(%}CW9r}e<3u+&aY`F^UbEG
zE@;JvnV0nO0?K1eQ7S~&xnE7#p4e=$Lb@>bq2J=_KuKp38PbV8L3+uS4mB6Z!NMt0
z7cU!SR2Ly55`!zHj6PPDa-8f;);AU)w~%zp$!mi{CT^SG5`p?~e*7%%zLZ3UX_3R-
zref>!c}k1jFafNms&Q7!kSoTfCKDBl;5|lrrmxmj4!{13Xw4zg>^aS~u#=0WpIfe<
z6-tSrm*ZAs2UH&<i4SfS{3ik6XfQYD9ft>a5@FTpJA)5{b#{!sU5xtjoIywrCp{(I
zGh&CObYIOuYUj3P%*Ss*_vgx~SLjv?B~|<d<qAIU*lr28Cg+aN-$XUcQ4T)d`5;fu
zXB-c`su`?@@+R{pX#$~h%wLqZ(&BDJh_FBl{*dOQNn>H|X=}xcUMfkmbbdcQ6Qtf#
z9G)*@qMhI=(Jyos0Ha+O#Z;B=s<h-a*6mw;ERxJs^gNX5u!~i}bC=RJ(yF;Q?W64y
z!Az9YJj29b^rEuRcI=n$d>VU=_cj%TgQUH+uuubcBFS=#k7GJ|!#oNj7~}O`q-*+G
z74`XAr-xA>l5y~55uw}*WK0O}M+ZD7&Qa1lM&_?HmPC9<=jd>Zu?H?|FaU%LayrMC
zJ{WF)`=Gd(gP+<u;KDvi0?LP(u=Fa4OWg1{eWtd<^QvKvNT~Upfdko%O<HQtyynOs
z&he_PlWX9#Ij#yxZt+F8_b=Cp#aI)n{9S^ws#Fyl+ol$m<pmpMopJbNO)Lbj)Oye^
z5pZW@t&`^H?1<U)S4WoK%LIM$bGGgAGVDJlX{@e0!wmHRXZ;wcr$I7QR;I|HPoMJC
zY6#=8E3r}QMQm!8c$g-2xq+r{@4dRll6uj?9<>}5B3hZ#MwhMEUp&X=h(I10`oI=9
z;LkwPM)l(SW$3NBSv*K3AVWhxnbBci-$3T9ugZub^GDosof-5X31C0JUq#fzKF0tl
zHm{?<<?RgCk}a#@z?FMrBD8T`76XS>X5eYWq@wMeLXET?TIJc{jo1fbPyBv_x!T)q
zRdD>I0D+$?J$rhBtN_+~l<<#O-CO8hxcpUQ^khSoUEkUn7m;XEb7EiaVSaN*iO!=k
zi@cp3_8n%@ogWJ<ulBGvTMO3peGR7NDVtkSob_>(dw!kP-m)-w$LIVi3}r7<Q6rTQ
zBz0J2rkl`XMO<LGk^B&Em)SnsfP_WsF!Cx6Q0@f4X%jO^JY-e^NX!F(d$w?5tu;5W
zv6LZY8p>u<trsgV@}jbzS9=g;hpdn<8<_{2@+F$(AltjH%aeTu9~l!8*ERLm^LX7_
z^x5v4(H}r}NSa0G=7P-P4}P|XaowecD_Gn@a;RE_dV-2|cp#4oM9*@s)!ti;Cds<R
zS!`M<McK0YPp!)=t2CJxU$#(x8eUW(#l~cOu*zuP!(rrm;qPR4DT(=IV03YK=CW+E
z_^YNt+&m4zWr2K7qa9g_e;r8hv*Z>##)iw1pc|L#FVMC18H>{9?JSC?tfQ`0|Cbi4
z)_}{B`R5|G0x%{BMgg{<{^qO^ssN-4nefnlYGPnJ*NU$E&QUlt{yNK>TWyH~>*U+S
zDZ21J2Rt5uXHtF$C4gq50^bqFIhVG>XAt1-bdG&M<5Ve<UD=;qR~I_8VjSX}2LLHt
z3SSESTUgP*^yFq`nb(czb`mxa-sc9fMbk7|_j^+^y*xiUyVwU0==^uSEdS!)Qu*>1
zND=Qjh8;PY6SG3V@vWcqPi+T{OA=uwskPyRZTD>;ri3W5QreLnRg&WkvZs`?T4DJ<
zQ%m1XKWl()nIGz$w%$txWm1pxo*1+5Hl|I{$9PcD@R}c@#jbIQ$E%4?Zj3e_EmUb8
z^3$O~n`sk}^J~OUyr3i~y($ER%W`ewGAb+Nb==Ll+{PwlUhb74L>SL?+O|7?ksokA
z8CZaFx=tclzZLN_Ni$Bl=}VcVrsKz$3Bthy%|<1dP$$WqDjNV5km5@urV<R<H=*x!
zt&e0sq;0N^JnymQHe-@_L}H7#*f%LSVTh041d!Wy@#20g9wz&0e$EldyEHEhiPl~_
zL)Qu5*?yEn)3W5AB2QzCyT$>W;J^>`i7l%`5U+0vWZGS$y3=lZ>JQA@?88I7zP?#L
zwdQR~&ySIWRlg&Sl8$;m92yP@2l#UAG_}DB&Ye_CtDc)?a($4w=EZ{PBvG<G>Y}F?
z;*nL9*HhEBmihXgmc%7cx#}0FGFs+}tTxOTvT`gMza5b<MMVu5sxJ432Z-$g;pu00
z;sR_&RQ+=JErmP6fs7ddg}XWZ<a5HH(ala6)6dM{%Q7aLTn^49S?0^vauT08V+oMR
zXkpS0E}4Xd<R&_I?W|~jj!hq^FD>=hAx0q1agv0{DZv9f7S^3OTOaton0qKqNvE6r
z*!wwan%o#R1mR14!1Q^fDRAfcOdZXK>Y<7P0X^&-0^8CJn-7kBd*c!0jcUnhw%Mx2
zV}3rz0Vx*_<k+Y$+dAQ-B@QKqQg6Kyk}lazxFFNFqm}C;Hnx=NgjvmiPb^gwKImZA
zSxcabiba@fL17YRph5iw66{!|ESd`BDtDNgnth?djzBXr>nC@yBvl%r5Kqj~*ses4
zU!F(aKKH>rnpuRj2(Z{{4b6x_)A2dxX993MU%#b<rdvbPVGP-QOsvIucMkU0IwFoG
z;=Ol?_`W4>$DmZt<Rb8yoAJ!ptYl>lz*0uCw@;(6N<VJ$SI)gdPP#p+*hIoZ1<>oF
zV{|jWxz~r}I|gTyj2D<6d8dr*?l>xnqJ@o|U(0xMT)r$H@&ak<kbLWnfN|KTM{<$~
zl+uU*l~7r^O}!Kkiu3`@ud@c!C86@Ph+?(I<fJZr76mnfMKUO$vom1d9ubogksWS?
zH?%AP{qUfWA1!>P-9FxgEw`G^a^-h(e2U_(!mnTT!YQ;Y5XyK&3N(^?pPf+xD6&5o
z=ns0e^%^9x;P+(*%QFZa{U<AYQ+)~jXSEvJ>Y9DE>)#Ka!}Y!=|BmAL<zkoXZ9}p)
z{eI@FxGI)Jx}XIs8k!%ZpJxT_+<oqj&8;^Dk11gv=zrohoW70LTts(irOHy*b<LnJ
zJFq%?@MNQk@a4mer~&zFsi*r|HrUrYgCh1`Crr+qUk=D6u_p7aaLPYFbtI1$&f3(t
zJQ7|-d6hYtYK{tBMa3rkfr?I6C^5?813v1hK4rfEDbZexuDtX0v(#QjD4Mu&iHO}I
z`F4s8vzensr115|6%ldX%3zr`v?6H;f1`3qp%~n7_&9k?aaVED5e!C(i=L^&>cuY*
zqa5oH#*7NQz;b^Nt(5N!6KlWAs2inXy?f8nz$HrXO+>T<#R9b1h!sh5HoASrdPc7w
zVyOe{c`H4~mM3Py_=bT!KaS7)2LR|8nS-jRc>J6uZ7!yp`)aUZao?+x1RWpyK$Mu+
z#jpooXO@5;BQfDtqu(k73NOKx<E&r#ta3^bK}8?2@`cO(SEJLyu8rYlDl|{Gh}3P<
z!7sPzW@p*xR&rA;?!Mm_UWC#vbF_NE#P#_I)z^6>0q*N>jSP!c<LBmxXkl5IXNm6;
z_HF<D&hMv~Ie)qPTlIgV!2ieW9+;9qGrWJzoM6#QM{&zK3cmKoeD`sry4j1YkZ1@0
z_VWJR00=I$<C7Ftx;R9|9Y3OMMCZA?7SZbhz))OxYZA4-0Ff!#eLY~%oFt!lkzlNm
z7<6#`8sO4VAn!Uhjvt4hs{;eA=>0SwT99$9-vRu&=xp6|$6<}^^U^oH9!GPjg?7*c
z{=+Oo<;&Rp)8DOO%92bX-chcKnPgr<4@G)&87t*@^?{Ud=w}7I;;5UDxglNcwGk<b
ztwnP7^OzDyj9t>M)-W6@@seJ7Q7FTVWrW2i2@wYuLPF(?9X_B;_2!3mgpEh8cN=%=
z0VOC8tra=2l$bp{jg@?rEq^SgkIe^cA-S=8tMJ}NOqM@*K8iMq9LMa?!Xz|?<KpA`
z6S*T$fi;;@jL|QPF!8eyVEd%mq3JvMaB(6OL6)-KS7tSq-3dKcw`6i7cKz}LNeHT;
z>oMQHH83;vya|!8s`SPk8!nJF^Zy3R9RFrN+usdps;6XZl9IhY&w5O3YmzjFo`a3(
z{9#k)s^Ph-B*9td7ufioh=nl45FGFPFDo~l*@($nuASacF~<4303LqL>LBr|1`4T_
ztIn8D!eYJn0P;6ZP15dSJ^fO~($~;d382glF14SpaSF~ydgZ}84nl1GHVFoZ2|LF0
z8J9g0epI#v?*xKwXSf^2{k+Q9hKiRveS9>6Y{w4nBq4oq@nTeN71(*=>J(CI%dyCh
zMh`xWM{_eR<S3+`v@k(hBkN?kfZMWq4-uFEh*+rw%Z+{T+`bD!OC}i6ktC$(WY{_s
zjq|2Y24=OcoXM=VCIW}d-CJnzSP^8x5`s<sP^uyTfhtq|X*<OCru!^ufPnIz!6pS6
zB0I}J0Y@`Me#E$z0<2v?z6}lW4&BHp1L#|`Gd5M`XAbJ%0L;S<bv-;EV!Scx#K@^=
zF_S=HNFd)53tU~V!64temI30plci%|m$uV}*VN<Uv2WvtrT9U@Y)wYr%ytI<2vC_{
zk*=-Ejdm<mF}l=o_8sZ$fUS>$F4tL?V}U~f+$QWUf?!M!<$2|Cc1s=P3LED%%rH+#
z?2_~YRDXB92P+&?e^7wai#ragQ;)&Lo`Xy}%ISK8?1$vy$J^bT3D_k_WLo*T;8eJY
z_!$o}oL-FN49!5=X=hQDK#D(1%v?ix`HSipd(k8j?{Tec#EW)8o2xAjr!<M?rL_rP
z<o5p_7y1b|`me!q`ep*^)Rj`?m<kj?V;|#UC^L#Ee~h|azAwr9oa$ViCxE-5VV+f>
zXJDmFgW+6_IirAVd`rBU2I`RH=A%sGil@<*BG6Kgm98#Q`H!O1>D-3CE8Mr0T*-Iz
z<!0?Utw5%QNzUr}5*t$8(Hvg9Vg91)US`zfBVD<pf+zB$?X?FZM$@=cfe*D-do|<{
z250!fq=#i<0s)t3oD)JzX+wOWNl;PzhOrS@q%QybIUj4kGseti#jresFMeCAS2xU{
zknC+*wJ2%??Z!LPchD4(^g)4e!heQYE7h%&@_V@r)wLkADa7}f=mCL(#f-Iv35}Ac
zbwAKDO{qfblV&OyOD2zSX%T);OU(0=%+?=EtPi9w+wUT=OuMdSUO~l&>`L3vG)6FB
zC_NR(87DZV$aqEZjV-u-V*9n41bd@|HfAU-3_nqr^2!ME7k&u;-Auo;@6?)pp96un
z9?Yj>|3nDO=t#%@P_ZD!X*SyP_7pi8g@L`*8d2Gi%2N&uzA+SI_p1t=kJ~qarKkLe
z+kzOY9Zy@)7!(6bU->HWZY9K2Sle8U+Msa0y{TDGeEfb$U+@9qAk!gW1{X7*4NSUb
z^Dztjj-5HWBVtL-JnhkOu1ow!iiH%F3G~%!D?sYj$$U#wNxo~;I8f=QYO?e}ePRwU
zt81co6sP5Z)dNEy04eYx_;sd2xL{l4_A=D1`Rtt^Q~~n*+3p<g7{c!Sjn*X&6b{~x
z#olT$!d&z~jk<pb7x!<&jQn*O9KaL#Pv6n0za!vf!%scKHx87}DaZ|EMNb4`s@pwG
zcsn{Ut&IjP5<szV%ecJ=6))x$421ep!CS$6q`~yL#8p*lRz9{S#Be#5B&L;(=Cd*v
z{UU|Ys24%oplLKk5k3@3taQg%zzg*WSc+Zs_ocdH2qe1fes@6%e(py#ju)*nQO<;~
zag^FFx^vh`m4tVR6FNtM;D$sFRW_aq)dh>v99Xc}!KiIM{p+lIadBU^Ty&v=K^hPY
z7oFqxjFx=321KQwzKDdF7etaR&C>>bQ?)~(4V6;NZwrit+9byq3gq`;KwVtxmDjbM
zt`y$VMy!<*ZbK06cmqD_lf_-FM%2tCV)(?aE2EQRc1Y>aGhKELEG9!$OIk)5SwB1^
z&KL|L6GI&&zRVBPnYPMcn!17loc=rzoNLH_LiM;2>`eu-{C~h{{X2AdDBQj_Z52)q
za<8Rgu91~JPhD@u2+QDf_Q*ui&D(ixNs#k%Y{pXUSed<v>GdXvwZyB9Fy#V|=|#O|
z$V&(x)Il>?zoB`fOwR^A#||C}c%s=qK5qgjqjZ4HF?Lx)&5lP7xyx=R{b79*<{;f)
zJn<DB0k2_(?L%gWyU#{GJYqmw&yI+gDDeW|WRNDClaZb&E1}gW!>8vR1k8dgTb5!_
z7G+6aWPT&SaUNRVy4xb$HBUUU@b!(y)8OTV(wF4}5ZO-k1Nf^5SB7y)t4mF#39LxK
zM`^S5jjc0EOs0&fU9ms%PAkepQ2NM5Jvro*1+ceO0#YC7)<pT1aP)Ygo3&D-^#Hxz
zx}?b(SO{|-^1+RaGtl=)I)%gRA`ICJA=a1)+{z16|6nOV;PSYNeG{i%nNd1abj{Wq
zu$Ld4HOw`wDvF<!hNR=Usz42sOWS$xXJ@0SDP9ah4@B1eOHYx1o^zM2tm!OJv~Mrt
z?GGSN%RIyW&Y_7B?v@S>1T)V+T7kDPSWQ)t@@tuZ&%n)is72ZluxrMyWu$_+S)W!a
z0cUcSK?KOy*?xg)Re%za^Jz<Y^ox5F)v!otJwwl~1h|<`3OBM;RZH#Su^I{MYhLm%
z>UcY1%&tlf7;W53f*dDbL;5Yp7rEqb04%EWa<5;Y4%_B?u=JHhEtot>>+zzdmaI@V
z8!ebq&e>d4+O)eitJjCU46|fOZV2gY!-cXzfSy?=<`Zn%=rrdN8T}R!FVVwGLqpbq
z6VlzZg~7{g<h)*wscnbydcJ~w4<dpWUw(mnRNnuz68^AJx;mW2B9kWQey1>5>HiVs
zv8m*HHlqK0sR#v;OvhI%t9w^aVbU!A?orZS)&|~o;Jv3N;0Hsxvv_*4UZ0>dLx}V(
zc|?+}UxvtsJ=<d6TMh9LJSxYDCK5;6+$_#&+S-=(jbrUY+*j~`zKhmYqg6*^6DoIa
zJRQl{QOCXF>ukhz2$8iGK7~xKScuFFeHDPP|4+d$hfd+kITcCer&?oDiD|i$Zt~qF
z*Im6$I!m9~&L_*_7PODBcum)V1(QYz0_owqBGb3FeJ_mdtW>JQ)M|Wn-iMf$6QC35
z$XJ+UvLhm-M>BA};le^lbUJ}MU-rwX0+Xc}8OIzSeT@oa=Dr#?w1&P)@VUNqRir2#
zBG`;&Q5@=15~`s;60@+v4axVIIHnCQORvQqq`Ia$e~KrCi?%YOrB$P^;2h46o}URK
z-C75X?usf*H5C`hdeM6nG7(o$^3MWJH*d748>&kU7A5f;8bYu1nzQ*B_Xu-D*uKkl
z68oghPE~zWnV$2Ml$B^xqfZa~-lvB!O%cdE31}i3;4ZWo47gslQPf}z;s}D*KF_?k
z08#P|KEckZd>A(+X^g5NDxN_{mp1YJ=hsr`Bbi$zWAI>Iae?n0v$p7+;CFdigSU|`
zN_gu=LUWtR@648>Qy77rW-%HUPzXz0*5UArQybtUi^N=VAGjAmB5ES&b<$PcpgV)a
z%a56I!|<1?`{M-WF#F4%eaey`dH4b|R}Qpgma$c0_lq*kRHinL^kGz2Vl&3<bS>jg
zZ*50WrK~Am<B5J!IED0u4<bwXpaV0&w21C#G~E>!ygIA#Rl5TXSCN&owod}PeD?xh
z^&Z6dl7^m>UG%?+`vvlY5+(egWp%ZT6eLuCNOdvVDjaYUxNz^H{@RYvu+*H;qrl3`
z4B6=Ly!@kt73>iWLD#^<1l;0(7x(the2P1!rsUBxigS*PJ9zMAi8T<`7!mfFl-NE{
z0R6*%#d6TpJCf^BcxTbk-cT2{WZHICZt@X!9m{8(Y^apw9p^i3_xf)u#k@?GQO>1)
zNRiJlgwQ~?lRCk=(nPs6?Qs}VXAM?AOfhz7`4hk1wSbK;nAl@Rp7_Ps{%vKgMef>4
zA6{m>-qIj_Eyox3QoY3PUEYCJT+Z12^Qia(Xb`%DjxIgh)k_KTfeDyczI{aaZQQ^X
z`_VY}vVCodsO+InNB1TgG^gWoUQ>)6+xf}@0Y2Zf62lr^C&V~afb4{3+Z<JQ-@SH+
z>!c{<ZY`)3ZGV>%fJ`!?oDCE27p5Cx?|>;@QBIC=Ye_#K7Ci$6^Amy<b~wkM*xR0q
z7lkQ!elB0ldob+B?o~`V@8oEegvhC5l0;tq3*^ug1%;OGz?RDHZ@8|xpNDWC>eO-Y
zLF7&dq`>U%&BwN|bhXsHJ^tCb>DKHt@A0MLInnottPL4>glQTs(+E(h!%O?HnJT%g
zwwy#)A@=8#KlrYT$t0$CBuIMIR$jTJR8~@Das<bH@St}VrKE&#=yIyS2?)_BPNyyo
zDAm<lf%qVBk)KF70y$($0?L4$sa3@t7caDJst9F3?r8PFsL^lTI6+Sj7WvPEG88tD
zSK<D63;-)mP}7pCt<tk;@yKK35aU`&hX-7imNzFpka6?f+wt0_1-`Zyo;wI|af^GN
zxq{OjLNW}8fIz_wL$#g>j#NQVm9+YPt-cH(?J-tywfDwylpbN!{cu@|^yjNU{tY9?
zkU1kLGpD<I<t1+t`W80cp=Nl8*y1^H&;y3tO6Dq2N~AgfSnN}V8ZprN<4)gDk;+q+
zq`fPAD3oOEvmq<y`B`w#!kuX#7Ky)A$6KM?im1M=236U%lXL6<YL}?|{!ZgtpVQC3
zBh;AmWaFQFi>n;g6?3QFQt=L8O}w^Z%G7W&g-1{w>~s7b1xpm|Z!4k2%!d_K>u1b4
zI%dm6c0R$e0Wj*5qWH01mQ!P_j0H(Zc-zOCU1*{95LPcr1l$4>3(_p}q{&)d{Up!+
zT~pWBo6S^dK)m6nfR@sHDSBTVCP|HphIQ~ulLkPsNH9V_?2x#YF71YOm>jm+AYR#0
zq2i{Nu~zAkki2-l^%&iymI&Fyolv5%#w3}qYUV1V1eBqmA2J8yxXI{{NgF$J=1w-5
zT1g7Y;5Op;uVziv2NP%3&^xKVOy87AdaaYWr&0<qtLc@XV6pcRg=c+A_O4zI@yQSG
zDhn3sm@hRldaZkaKnPQne@vuQP~I5Tj~!PdUTsb!q(5?@m$-8k<bE{Q%9K^3TARS*
z9lUifw9&5Be<($XBU@y8d1h|ig}g><EGoon(g(7BQuH)^+l7Z!zFsW}_-3Sj-R-Sv
z<Q-!Nbo6a4+HBOsTFe79Vjdos7mcWzj`$Ey(h(&Q<Q8r9NdH|GDdFU2@SuQBx%5ZQ
zliIAtj~P9r+R2TlW41n^;<U;#yiNB!%HSwS|B<=yU0WAENj1dx;>6s}!|cI26T>^l
z0`?XrlB_#J67(8!HMiZ+1;&YrJko{@x+poI@FkKjSaPvLEz4N&>e`*ZET1g$=I=<h
z^7XSuYbKX=Hh8`RgF^f<PKtNKi-b*-L=iW(s#J37+oH<^LU_(?JksDj0O0WpWMlYB
z#J7ku(B-4IM!cW%lCuc$1bhK*!Y>dKX$BE)t4(=BOvdrLSDyL+sST-);FVeC*9sKh
zGT)|nJ-hJ2sD8~&=dQ475^HGNzf)X36;!+^=>@1xQ?FgFyPG=c>}qk9+aYHsQ3V^~
z692?TiwD0|;L3SxXW63niPgM27PYs6?I%JYUOy8R4h`g{EzhY$(h&Omu~?qqd;bdn
zb^WtBea|VGoY4`{&bHe&=!|C>Bs~N2zQul$8>(3vPRZOZyFWWE2R8Zx_}^{L><}4g
zvZ1$DRLts~`)!hi`hfSL>LtM^`KhN@qw?xa3>o<b_-vVN5}!!3`qDl675UtRIFPC9
z^g_wbruHIF=5su$!|%Pa(X?mm%{-*r8nx6Rg_^KrW7&^O)2;fULLca;?w}$@CQPH+
zgbHh=LZZrbsKrFwv<j6gK(B6>eU%f;0ys1B@U}UAfuQ|LMX*GK>1k%`>9p}$7$}3s
zmmK4{C>I8HuzZ|FNgqmQaiCNk-3LQ;+z!Oixgpm?VU+=%(f7s=HeFmViSlO}s`n{^
z#2=w*0@Q(^=cqRETVA1KGy6QD1P;tpaXf(pi5s9F?7SZ9q>tSd4RK}^iAgl67_WB|
zw<IpLsK|QNw`#5Cn5r5BKP`<SDY(klN%LKujqhrGhps66XkvVL<-t8S)`L3HT`Y6K
zi)%-94Ol;)ETDN6y2HNN``oc0%gnjgd9dqv=H%c@t~ZyQi5c*XR$#v?nUU6a?y{)n
z(v&Q#$SXkD&RZb*)880Qb~?I`s;WpPjX^F4Jo^2v=LWF3wqcf$1y8D>4WjuznRfk%
zv@pNEt)cz>XniJM-WU(1xn(nBhU1djXF|Vyg}H9p^8A`4EE9|-h=Hh;lc*P<J1tES
z9T6rz9IA|>mFIGaF*0FVs(5nGfv#bwJhIW!p58;drdL)k!eMw-<NYB30l8)}&J~2o
ze$Z>5tz^n?V!vS|qDP<E>z5fEH#teOSlOy+e>gK3snKb#S?b0w{p9@i*zoQ>>+W=<
zwl*i*vKSSIRJ<fbqMJ*iu@3}8RYkUNVbqgkxxrbe*BCZ25AFl%Cc+ZUc{Ym1pQ?nU
zy94e~enTV5X9A6~N}pg2BC$Gm(-VOab6MD<7w5PM$Lm)xRA!%*e9~re0~x-4?K86{
zf6Kkn_s%SSL`jyWnQHbQy&Thjb9?ST5)k)4X8pxEa^n?UvF;5EWxqsu_-S0Tj*=L`
zX7P!viE#tlkl@#sNj9i8!!LOR$g+XWryw8X#y>bZyBRG+0y$3(=TO@er-2tX(3#`V
z4$6H+oR8&*x(=fE(G$WMk0-3onSWm-2x)?iks__Ha4FOqrMhO5Ci$F0#e}>@MQa&}
z*^h+4%gf70>{hDc^+uuq1*5)N!!3>q5#~f|r8(N)RGCJ|I~#>x^L{?S4zf+DS0>w*
z`X*P7X9%BloRYpLdZ4)ixu|FX1yg{t(B_z$ee{J24>t$|H30&V$u$F^SJkj^<*`7-
z@xCh1Ga|r(kJ592j_u^+M$(%xI|4yzs!xWpBnc$(eI%clVt8c(%L7{2!3WW{kIBXD
zd#w<!DS<M+w$w!Ynyu27SQ%nmAS5-6I53Vd1zO3<wP{+3N4e~cI?x;A_lV%z*)&=h
z#x72uTrsBrPh=twdjxcN0%@o&5;Wxw{h;^KE*75nAV&396RmFB1!_Grr({CB2kJzw
z?r_a3KpfOY(tP5_L?Mux&E-ue{h~!2!BArSfu{oT4paNjuG<LBFm-o$KZ7KtZ;LCK
zLZ8UI_Z}zj`89`Vp{o|&ZJ#*dXSKaKWA_gZy1>c$>S5FefBG=*VIiWTgr=7pNDdjA
znjKzR>_UmFt8)pV@-fFS(Y(Y`981l*0hR<7)iiJIq@p!y7;pqW#AOu0n)?uA0m0ea
z|7Jjf2?lJMa2cgKue)rBIH)koZS_h(W@(wt71F(MTz3KjzO7yx(MQHD7*S(71P#$%
z14H0(ef1+QlnqY{!FgyIr%H?9FNkb-6WcOE;4^TR;SAK|pVkp5-7CFf6Zo-7M+>H?
zDB4J<eI<;u6CK%a(#>@ZNyAF<Hq=quUTLBqaf$4}UueiTs}JI|>pwCK-*GhpXe!QQ
zf@d1>r|a8du((v4SqisXS_!hPQO4YAi%XmyqYm5U(nFe?Hkt@gO@K*;i>Z>bwLWwQ
zyM<1LpG-x{Ru?ifCqRVHL@}O>YDc&`fvR!w+ccvs;83_m)Qb2rNDYOd#8L3aCYB>Q
z4%9p^IDNC|&AjA)8mRlv@;gsq!ENe(C)QAP|8+j`7vFJ})n}e7)ZJ;sadbus?ik%z
zW-LdqdtQ|#%BxSC&N@sjdb&8ZaaJ?4vB#`CUCH)KzcDM61Tm)uJQSHT&mlz6m~A`l
z?`4va1nmoK;<}dXrTN%+m`)LpWJ|rI4!E(*-y;77GIt{lj(DONz%r=OI?Z?zZ_yOU
zlYto_js%;7Pf1Y$XnzRZpQPrU9Nb-q6Y7*gR`N0H%z@P!O3KW1m$Zqgi{0uwjoOh~
zgB3u@qL8kZhtq)4rUB6xZ>4qosR+6VdNkB5k1UzKBb4U-o^3vdRurMNtC)5n0Rv|}
z#hcTtW90iuQ+oi}Rmz*Ps=&_Brs2ATe|YEd5ih8k!ORXCV>qLEw~~R}%W-ex;qpS<
zYaN1bh>uKrmd?w_L%bDfj2V>fo{gr+p~}<0PiOKB@c!5u$9L$&DqRl_iq#A%yUZ%s
z8@Hf7>OISE<f*-xs-e@w2OcGs4?X&QdY0`(HuT@PWR0MzyNlIE>);W7d5>u*0+9rN
z3Exqm?6_lnm7<$0rS+Dko3UnMs2D;q<y0Uau7Kl<$^13wUm$Bn@QpUR3>7?{yUOS}
z3q89?fBij+;Pm&CUA{Qe)@BF+{#KhTM=6%?eKvTtk~yxBDfl&WhBf2D)>63kFiq7;
zKmClVG^{L+YPm~p$SYBh3O@)zOcdi!Q3+kqpJC<Bs5a_EB&dd@S`ER6<~W(TEY6-R
zuc#89#c?ElZlO+zBfea%IR+)Nxo`XM_ehpdTJk?lW-1PioRc<4HU<3EYY7Mr{f%7?
z{pj58zPmGUwKT45wX%x$5CxjD3q`XFl`-^rfQ|Nk=;<OQ{TM4G$WsXK2w6zzydh}0
zJksNCnAFh~vx9~WMV#ZEaMM%`6nP<v^<<a$ld}IusrtvKxGROzzmshE|I_OJPrCA-
zUi(Ma0cGgdS2Sb`M{}^7tA&Zf?^7pJTQp=gRz7l8^53U|f-D-|&K4{xCRR@$t`?4N
z<m^DE8jG3**vZ}1%mNG)k@RwtR(CUTvjEPe)w#&IewUC|=OcfrNKIH6xYW(f!qpMT
zS7Br0C4V~p$dGb!bOU~s;vi?|{C$-aCpkO!A7||3Y^;A4d#!A0Yhm_OQq}?J3Og@3
z@c-0}-(T6-_<$;ZxAo@_?5yN$!0~6}u1;p^7H)bhDzBtiG%UQ_{wO5=M<Ve@B5Obn
z^i$Eo+{Q%Q$x9Eol$D$ZsDhJ~%Yd9k0_Y}i?_i*r-?g$xyE?f$1Ks&OI={cLsIzFe
znmB@;pGL{dn?*vM<&}kpjhTg-v^Y?+hKVT{D5CD>>Tc%t$4G+7d4z@kVc36H{kLJ4
zvatt7j77>G7}i%7W=`f7ED9EmR&Lhh+?<?Gm;LRjd8Joq7}zbb;`s6W6j_#THP+E4
zOM)wy;MCc(mTzoY>RAX*=F`b`=NTY6Tsx6tj%w%STp<i+q$0@xAGf#c*-Td^Z|8;#
z&Jx@s!l0+S-0IBeLNJMO<|i~vew-riv&xL`5^8-s&VI(3jXP1^AT!9d1G9BTi@F*D
zH4;v-LaiW(9<pxv_L<3rV+q&r?2CEde9_c*YyvzMOInI(@6qykWRjim>Lp~EbS#A^
ztaS6dLT!^5P^28+!U=Z^(uF@n!J_m{HB;pTl9SlMTc>OzN8AnYP8*#wk9+{NDt2!6
z-<`Am#2MXuB{eINZaz8At2$|rF8M4kvT~T0P4%E*-qFjswch9rKUi<z-S>uoJ~zcp
z%a85{(T~w05~tj#mLZJ+p9Hv$$5@xm9OtLK%X16w_k|Lb<cSW7A+Hy#Ka*#ywHY{l
zkJdVOY@w2;NK0bMkxw)8^is4eOMCg=_WKtax3)m`{-`lg*1SZh7pa3dh<)rfKK>6a
zo_h_(C^Z^tD6VOS%gJSOTdaP8^OfXMY)4^VoktOaAvye<V>c$i9{o65ZS%r#Qlpn=
z@l!gQoK3PfeYlJhvC<INp}mvOK3Ui@yKBRQ(J|9yweRVET-20ni^9b%k8LJkrzjY$
z%LK9p=!wgcs0MK!A28kQgsk@|r`4py*bohMYbRT9)oOV8n&T=qSNd7~Kr`kl{jnpP
zxj@gJM)x6`Nu2=lg;BzXT+j^fjwMg;S~+it=vzY!%;c4kbe5*0PC-~lx_kIRwHg&d
zd9Z!)uMv3q9bJPC8OFgJ&h0PukFT#kFZgi_Hz}l#WwKyD?00{_t+YGD^zap8kkWX7
z5(#Td{A)JC#s<*P-`{6<M>97YC&wp(A?ILu<>X*u<M>2oYyf#lIzGMNdgM<R{KWy3
z5O;D17!^>S8OUem0C*5Hz-5@Z0WQGI2jolrSv^pMjqNvw0#Xj3IyE5u%~^o-H(vtM
z-~0tgd7kp0=>8vx@af{e>@@p->9hpfQ+I&6{y3hv1nbi;ygaO205AII6v$N~XZz=t
zp1SuRwDj-l{i9Vr9&S$HewhC${i{}=2ID_y^`GkdTm65(*MFD$FZcR1^#Au;eR|OU
zzqR`B#{XaL_35?x4_f_q_5Sf(|MC6$mzJJn;NM#MQ^fwJFLoAnYZG%DM=L-=NjTX%
zxvD#xm|6U9dGxpJKMPAZIk`R&7Y8>hIR_^fGe9`MPr2Ed*`K5zAm0HsL601eT^7GT
zfZzT`Z~z%SQ7j<j|M}(q7Zu`95zeCSZtC`=Qv8<OEZR2afJo!v<YAGqu(7gsBj@7b
z{Oc}@+qi*M0PsI22WKb8Cq0ChMHUdsuFg(RB9h$F#2#$H^6#Z4Cp#-I*FVe6!<B1o
zoUfm`e{^g<Y<%`yxm;}><MXt91&b8lTW&@?ZLE|RZ9?p>Au5Wa0ingh!Ni2ZWP2Z;
zV&j?s9&08_o8UY1Z<SwaK#G!c9Xr>l*RS!NuQcc@{~Dv|tBryBWQ+KJ_b-w<F`CV0
zz&OK+Z>TA^(r>c>KY;-W=r24j4$f+1c$+r^dun2?z!^6ux3IE;snxDbfm(3X#r3(<
zz-l`I0WJx+sH)283qM8m&#@I>RW{F-mezstYqQegrngEPI~B#rRcmh~5mjk%klwLa
zd)nqVG&l<SAObB!+RZyDaIoba<$r7yFEK}MUaqW1|2Sg#_4VT7KyN<*@#xaxqWvy&
zy6|-OkfWnI_&nBqh=D#*VdOcJQQx-fz)#}-FBQes{BVotq>u79r)rUzuPDA&`&Px3
zG{=?ny-#Kt$O{7+>TL4gEG!AZb#N>as)H)b&7GRE<c3R0O`4OA<|8B?rRy9qYWuK1
zp4s@Rx!K|if7~Qnzhx3ly1|w!pSpM$13mp13vF>p$xZ(N^U)E>&5VjkjwtQ@zEp0m
z*>@ak0oUapbQn@lme-#j-1>?`^0lizw-5pC8c)PR6=sc#c>Vk?@_0SaeEz&$*^<EC
zoD<4&txKe&J2)(+j@k`}=*}B&&O<snLxmP+%@yy(e2)T?gZ=JERdG4c#xrA}rq9nM
z;PEN2-VqWJHR~T+hq?wsh3bB6b$(Y+(SE!YdlemR^AeBq+b{2ng+^HX#p^?FCu|f3
zyH-A2Twn(j2(%`YC`X+<Y{CB38OfvxWcG}!*kR5%kdBd_ox4`Q{z|+0RXGzQ6T_WN
zXlfV8><3P->0ppVSV=}4Ok|hG%a@&B+$)z5QLnVrtvZcKK%hWbs^q~u$AO<W2>NZr
zueDTjuIXat!PHbAgwar9o<+QZN!zHZ>iBiJ<XI#%?yyqtxw3QQ`|BqM1$|3Vqqfu9
z3z$d+=9utEEwV9@?9Vx>PS1lxe^5Ps?dl0ot-aVCo_9)1OMA|w$0r`x?8-FL+db6x
zfO>A`U|?W=;Xw18hoAYV-geYS<kzqpugEU|RT&5tr}j$G!C@k?Z0Y(?uhn}gCvV_n
z)pufa(}rebVu2*<$Gz)BqNE5*kq2Y>(Rap~BYkAAyJI2ssK<Qt4rR*>zTNsup38!T
z+FvSOuc)jh^uM{FvCfbi6s(u!v|f?q3m^VEw>EcE@btW19~9B3ZOi@ET`;`l@k@y-
zK*Wug|7B7Ucy2_Ni;K$uy{!g(0##b;`-^O&F?VrssTu;j4}~~{DS~6(sVU>Rqx0iN
zPU*Rmsqr8`w~Hpqs6Dlki^+r{qQ+y^G#jz)kyyEp)v4QKD;)=Lk-%8VFdDY|2Nx=q
z)kUJN+$xSTg+&v8bhI4gln+SLeND#aVft2pbtY@2gDkr+lbVX1+U1&TB4#mqvi~B;
z=k?mej~omNHVf{%oA;4crDPnVbsA*v>C4xes5v;^BjLXh4w#(6RX{f7Rt%U)?jF8|
zo;ATj&$z^xj&x_~<5Yi-rq{s3&AoR8zK4z!&uQ{Kd4y08M_TFHj&IXopVxOd=C5TO
zSAXZZyU>9oJ_wFpR-4!=aj+TH`kT4Rm2$^Z7v(Ro=!+MX@L9N%P-353l+CGpdKC%<
z3ZyH@&W?p=C;T+$u&_KoU&U0kF@B!TgJ$v)PmA*k%{tVbkbq7*Mel2+Zms`-lP{0`
z*Zy+)T(RYqo`g{MqvIa96s`_ObNB(b4AAt_Qmf}LvX^O{#wHHCpS+i@41BHixo_}a
zgrc%mI5>cxGgqexHC9w)(0LPPu{uqTb6=gOv|D~%oQ~13y1MU+QruU0p-tP}O_^f3
z$2iAtpIiI7=1rpIMPuQo4}D{WacSev?^|pR(&65hpikq#k=j~~byFt?L-vLcVIh}9
zjcK;*JrT199`_AluJ11on@0+Gy?(N+gt2Vm7#EkMEVPo<BqSc~U!uD)4{KFee#3L;
z<zh1U$iKgTz~pe0T3cuYHJg9SVf9rdvzY*MP$O^n=lPJ4rESa5z5m|rL{%{JJRg}6
zEItU7qSkAAT9asJJ(7Fc*wpk&j~VNoHBR1HhzQI4=iqJGH{JX0=v8ZNM_bNsDO8P&
zjFuk|Mrf61)7%Yb*>6EW1Q3l85=-)i431wLn=~J+-in(jI;^NlCu2g79C)7W<*i*E
zPcvs9pPV?^+qb##1tL-TUXhfSl{s14=SLxF!-a%+fS(r|y)xtY=(8|8t7xUVHsQQJ
zo~J<gwa$-bn{x<$l!?jSgL|8-)ZW7CmoQ(dP0`)v!C5A?pi!;K^!wy0egT0H@yi0Q
zAKy<I6=hOP99L#8_S7QP14}9^tBpRT$g+rOi_dgm=7hKSbq|I#)4e(k?z#CP$Usip
zs{vL&=**!cW={48q(<e>-FIQ=WSIW?FCKr&3W7hNeQa!O^i;`kBt5zO=s6HA;Bg=r
z2m_271@kS!L`SbpD=uyY6C)knEULFwG>t*Q6h@;K4sytkbDmroDz%rp6AQ@~xAhHg
zE}*2Su&O5NjO{JGH>NBc2w&jnrXaGJ2cBM><rr8W&M{zgxW7kY^-1ue6KrbsWyJ`N
z=Hpjqo$#NTm}s)zL*98DT=G0g?M_OX3rXW+l~DRt;Lm;zsrx=;zxr7n+t^aIe>y2f
zJ&+GP7S^0t=dkPaLA-t&=NFzo@ACs`<;~RY1m>dRxBFSwm(%UZxxUkk6zpk_MA^0o
zF~nq*=zwgSQPt45Hxc?6`ehA+7|W0NgM@h1$CUA!PYv=uH$Fd@P2c4gHbnQ|1~R-D
z;Y_qC#EWCAUPR8rV6At*90Akb*2?VSO>~Br#TI9MY&=S_G|kM6E17THfJQ~@1zu8<
z{!^=WEN-4ToUZ-ng3}vdL*bDhLau?6?Kqd0Xl`_nN<+OCS1#<>T(e_qhDhl^Ok_zS
zZYML>%_D(opVsv2D~rqr@62=4N>Mpoer%<3tMSXzRt-%9A>O2k0C*4Zj-5Z|RgvIW
zQc~i9r63?$N<#Zf0ERRDg$tn2SuQROio`)~r==1)ouLiqIxd_0iZZ7roM!-K%yN<|
z@o>$L89fa43Nlit>47hxUefS*hFZ>j|F&J$WMI%jLiD0jLtA@k+~}3`?&Zbzj4@}Z
z(1C;MNd@&Bv9?d2WSpu5JU3GY%kI!0=@Lp#preS5XNCjPK2}+aqx>(*zA7w^CTJG|
zAt6{ofZ&ASzPLkzFYfN{?jC{!cemgU!GmvbTL=)`-QE4n=KIg{U!1G6ciS~R-CbQ>
z_14=xGdgd`l$;Lp^^{QdPrhr-rLDiy=i$!iNxRf={__wZ)TTqs=grnN?6^grp4sAO
zc9b$!1=;QtZ5|muFjtbY3W3gJ#>d@ww;$mJiHnT<{ApTAyqR5(PH-&f9k?wH2M1y=
zQ7(=71Cm+#>3gz+oXEz(k*YB}PJK0vOm&!2CQqGbbTnL3ueOawH%~$5>)`0n;z8$l
z%VgC~0Wq$bWt&r6tx>V^64Z<07xMQwV_RAnbe&#${6_ZM9=@=wY-rFHz24MFu<BR7
zvR&0H3=&pk<LNA);l<E*FCt7|*kmj&1W8|L);R~|g=?A5poPe2r3QkQ;0yEe;&%yp
zB7Sejo$$pr<pXB1f`TZWqs{1dy0+P%(*mtHOqnVityACs*_3lS;pw`kD``1r*KyqV
zGZiYG&zxn^WC^{C8?RwH1h%&zh}4XXsV2PQnd*dc<3C8SefmV=6|?x|{!R^yKx_Z-
z?dRmMavb?i_a#SKk1v$n?!m?D$wP)WQ##x=w7gnutAbHLY1oRoYz=z?C1d!EkdxaN
z_qQmiW-6%~8qF<#LIW3n4J`cN@?40WwF-n&l2<pc(AYQb(091V<Xxy_+`~L{xv5%z
z|6aej#bby)x@$+0vR3iZHS~LgVNyH^&sp-#dEaND91sNq42nKJhz`Fm78Z}*<)_6A
z@0sOpG8@S%T7F%d;#tu}89TxTgmQlxA!^LW1~q<3M(u{UxPi*Hdkbf+YR?MSF*NR7
z-(bAVDxHoT-hp!h1ZgFu_fAHEnx+MXyH}-61tv%G@$UEc_i7c7E=d)SE9n>r&G1|O
zW<#l)j<>xPdsHR~&`vwi-K-aA`(D>1%7UEC+1U6vBi?@(#+z4+{As2=hDdxoxj`u&
z4ASN^MN-VM@WCS{*T=6?CNA3T6&eLDw*lAa97GfSQU@8&@s`zvjHv^Xq!DAnY1VId
zS4cE8NS9QhTs8ER919jTnM}SS1fXi`m^rrFaeGNYeN(-T&{{YQD8-JgA9yU<!2^&8
zV=Gm3<52FJA6Qg$>{j0|UP!CEBfi(yUL1`e)CbUF=_NtRE}>thJ&w2c#utR^o}sV8
zjCmu_A<o8{f^~Fm{`KEo;=^4Z0V8wScK^d|`C+bH_xOj-uJk5ToUZ2Fj8$RZ@Pw}&
zSxDETM-S6pl!j)<#O)sV4Q<hg`#7m-ibn5JTM@(YhJE9~47-Sb(<3Q_P{4I<|HW{Y
z&#M%;_>>DpEnh0x$9u))Ce51i%645ZMh)wR(4c1Tq{WQ#1H!_LzV+KzK_{M`bsl2$
zI7}LqS)xV;Mx)Z+d;9w>uIr}aW<|D{z(Ot(ey)lID=3`)r<oVZsy-<=0H8#dBDiD=
zowi%vQ{{46O24HN42q#TJv}%$DBi)n<7I8wdzm60oy}6!>@w4u2puuS5y!YDPWg3@
zkupG@#8FEm*Ic8hs9ugGZuSAis)ZC`xT1J|!c3Ep$5Gl1TCb|$Fc8f;oHF&mcxz^A
zGWKg!`sMD1Jpe<DM*?J7pEQJAU0u!9Z#pT4{tf{jgUP(&E3%0XKK6Nq!JJ5g(&lj=
zb(fK?_nD|26LXO7O7nE6SzIn_yx@COQ?0={$Cl#GOWZes!)1=t4hm-N&MGz9IXCA!
z%!MWZoIa!rqOk+;V35Z#q08~_yuR64?su^Y()2kQg*vpdW+Kdv%_T1<G0<9KV^hIY
zj!W}u^JP!Imt}U;jN{-6^)|*GNeNA)$-y&>*M>_YW(KV3AtkMVhoczNZ?u`#t&L_H
zZA@+X(O4zdC}iQmeV)r<dW3j><T;4ja(%>E|8pD#gpHjzyk<VP#dw$MkvC^=x3+nH
z8xoxfVyTz+W83ed#SMc;NuoD0eZ7x_8>%cxYL*u2A$_n&DknmlXgk+%zOIH@e4H(x
zH2@tl6JVgH)ogVxxI#fmiS+Oo0&K5EKo9AXo^<SDorTUatk=zKTIo$tZcWXg>Bx&(
zxn-6g7Ttrc_mztYZvflY@#&(6va*<Fs!3VxCqG$sl6?{sBcmGxokfrKSX%AlSm}~k
zU!6jqFQ}Ak?(MvOEnsX5%^J;D^7p1$w2_o)unugCzYi}u9Ytc(6RE$B?qU`24)x!<
z^Rte8BE}*9%6a+%4w3+<TKMim<6*{a!`1ny=RJDm+w2o<r`paWF-|G1`+9=Ljg2jA
z9PD(Bd1zft3m#Ek1x@1Mi05sJ*1SdUmw87hx%wMWdc&c7qoA3uDUMd-=F^k!G^^QU
zqd!};wR!il(~7)|O{SlI%;kYccv7Xg#VzP);@86v&npyy`@y}mRoVQKCXcS9!@UBw
zG=-GhgrgzFG%ZbPrrFVZd-^cK@Z^ft6_R2f&jKf$aR?ph6$%dPmws&`g)30iV5ppX
zwec<Omg1kUQ{2|Fva%J$#Xt3acK7<~ZXC|fi6uxDTnB|Gaa<|Kj}++tDK-1%>E$)e
zWvxmw)aiNNkl8%i!^6W9#oP3!3B=D_Ta(`5UUDqz*XHytyvKl(Os!IP$obnYt4?oV
zH@OW<4kE$z2{VOsSP99V`l#;d(A*BM+vbg?C4lQX&0Dt|n+T&kC%mBLI8{X<!7KEx
zt|~H-SI9jfIs$%RrbIPW6_e^&0Jjd88CsMd9BnZt?y7q-OnhEm8jDR&mTUPtIXSSo
z_a%PxgS<&@W|S1Rx&GqCEtddreyy}LEp2dQz{cEqT+3XzZ*S*dcxb4IP(@R<(5(vm
z;j#%9F;y%qG@?h?Fz2M4Xl9(#hLOi<l-lw46savOEo*B2CJP<sDdc`uR96;ZP*$6!
z7KncD%$S*(;k^gOY5A3J9B9UW!CO&Tk|WhQGE!Mu`3|H0=XYPGdKVYvuca!=%Dy^w
z@NjUSKLmxWluZM{_vGr5!12YfTTXpFJ41YCQX>C&Qi-fX{Gt+&@8cq1hIShaq1S3~
zG&2?79t8#lKVC;qJR2DEbY2Mx;$=JYv(NIs1OgfJpSd|6yVbbabDSyPQ8%hAKB<{z
ztLvymFY%d%!#O8@KEoQXH^428el#>(MGJ?FczO6uFSAl75A|S%{zlV7Km&yTU%zEC
zfQ%p_kt}emkpmDc+*1~{=|v<)leWMt)hpn?pPU|+K42+UmX<qmK(dn(kf7Kk^ikn^
zK0@ih(HppkTBbK36BCn0yJbdNS|NXUe;U!j!?L!Rm=1=($8fz$Vg1Qe@!Z17fg>!q
zh`g~^DM?95Gcz;A#l^pV{o2|xh20ZVnf53>&e;iLDq2><Rrlq*u#+E}B)jX-&yy7s
z6*<>!AS6bxK<t|qisZ7ze6UDJMpx|tE=GU+a%-|kD-+y!ZXP}mgF->uq)4N5vGw=E
z@tb34dFH_qJj5MO#(($lP)1fZ>W(n+;^N{X1P(&3C}~_Eo~LBi?68D|@nrt_(ttKX
zG6##pF`rN1v7qWl4o%vm*38K&S(A|b#29Asm~;AMSEa&h^5)2XPsD&`^vnD-Ip|=C
z$l&4q&lW`r%cES95|b|~T29)YPS%=!;9AK-+$tv|xX3ZYl(n_B`9{0_m$<Y!j=*qn
z(eRfkS_K^3j(0eNBn*FQ_=;*ZZ>Fw&<HR=HeLYFt{PV;8<SR;c@t-W^e6_=ETF0jH
zO#w`mrx8UsEW;zF8lDAd&De*nq{X(=M-rDxP`Oh_%Q}jHy{H5WTlOQlR1$0<@KV+9
zf{<Lx5q@ZjXyi?P>aU{ImT4W&oP@RKuW;-A`u-&VCFFLj`&a&GscrtxAAdLmI3$-M
zHPbnF1zFrjRR=a%3Ql{HJTgYk+YVW_Wy45qnK`H#)5(Cw2l<`1<MAqFhwtT2en?N+
zAIg-5YF(!Gb(&TNPA@-!c#V?8e{a3TlY<=0c$0DW%mgJ@yw@+C(|ES86gW+}jh!3N
zA&Oy{pm?69<4>I&*ZtyW;k0_SPa~LdAX|A8jDiG*go_GdU|^W+%9DGi4+r_tAa0!$
z``#+}e5bJ$`^#S6XEMvyv#m!z!gb0db1AGB$UWGm@t3btg<SkKHquRZm%5&?L<3zi
zQ0Hq!@4Gg=PeVQxlyV0ovMd&m2_)0tn)n+qwf%td<#+s~fu1?v4z$oxBsn2Od}!@Q
zhok&_zivH+5mjy>o40(HM1T%L`&~s1ai&OQ5iCs@Y+lK~&@daf;b+-$$Fasxu*h03
zMi>@aeyC8x?v#^D<5@q~cy_i9hyJyZm!FTdT~~1B6QfW%<|>6dm->8^2Dg|ldgf~|
zk~YL`#YESGbcX?_hX#)XN9mu?XV;cJiy6;xS5fuj_sac_CfRuW^SQg0_Ebo=3PM{D
zS_KA+#gGn3Ozof^Cj{#Vby7w|W_6$(_?YCepx2+gEQZ<*q4q8nQ>2mUm$*UdlCv~c
zq0y~!7iiO{aAJlaNJ2tFK6<o(D6mWi#?v*DEjKieJk{(bqa{mdA6YFo;3gdDf-b))
zP4BZT5>YI)mj?;F)T(5z4d`Hjyr%FV_%=(&IIh>bnh*ck1|Uv{k{E`9YaG^l-{E{B
z1(ub2{AKtVVF2q^#->``UQ#qA#JP>30B!;+t6axM<}N&ae0Y(LpQIFPuPV<b5-gYM
zTf=)ep@Z-1fwG^C^dh3Tn$BtDHusS;RSZs_53qr@wzkmF&@#0u-CJ(joU?bXB0$d_
ziWKVw2f(SWEN(V%9W^Sv7OG23j0+w<8*pT+7n|k3QflY)7RI3o)FxrH(c&ePwu=^t
z(#Y33$UzxfDSt7r$!qqUqBX>1J>e+<^@!Y?;s(`_-eB+R$=Y1c-bhmVpRxk<06#zf
z@87>Y;kWiC%N7?G0qPdilz{00ZMsRoW5l~MlZqCZ&Xg(^cqF$I1`X{PpB|+TN5+yO
zpazkQ4CS&pT|daE@e*%2w=tGDw<Q~{Y3|;A=Dk&2n9H<X|E=qr^GKY2Uz7h}FFkfd
zP{O)+p-NgUt>ZY2)F5W&+`&-BO_FU2e85zyxLgZVTnh&>72^&C=(59`)AhNFwVfTV
z&KH(2-zkA5#e;cvruwSz;e6x%`TWDapqw%Ey!2A)Pdk&^6dC=^rRJ=j+l4M;ks>uW
zSk$9K+w@V8YL0HAw^rb0vPfleuTZ7JZDPVKOZF*xy<}p6R_92H%jb5#lI@0qIopba
zls&j@sE%jou7gUks40mk@?GO8<=z4OlT4&9wjvlj(Co5L-3A=Si~zZPfEn^vNg^Y1
zhPGV%s9!l+OH5A(MD(<Yiu6j7!FSnlmY+%E#u;-pD_h06ZlDaxpZ3?3YU8eS6Set^
zL>K?|ad%~JLHal9T8fj%Y>ruPp7`NzT=nWAc(}Pedcv@gk&*LCA^>OQ=LPULFa42E
zMBxh?8#x69v!4LtfW!TRn37TOv^U!cOxSxArWype8_NW(=J!L*%So$R2ym1sk_B;*
z7-3@ZK92t%k@CbxzC@Yq;pXrX2>k7CU1+Yfkz>=a2>Jj1Zrmv22Sh(P*s(SiHKUzZ
z0yS1z{!I7TS?%Xn)YeLsD9gIK!K$H7Af-&DnJN(2)wBSN7@U3z2ZRkr*{zi+?cfM@
z0d67SI0xkxlm@Wk7s+5H2+qe4W9T*Zlmlf8z*+!XLpw+*Y;@6ARNVjf3IP;QL)NCP
zgL@bvAXmz;^#^Akrq<7>%|WvdWP#4WMTjs#$e*u6E3Y@>7ioY3|5$S%HwN2m3E3r8
zEA^06(^BXTpulP;b3p1i>pu`j=;QI4F`UI-hf}ra0Rp(L@kiRPy%IQ9!K_xzPI};V
z<7-$2ef4Je!m3;f=mFbMf62&m&;J&SF(%=ZcXXz$4(qNJ#ThZP1D8uHcrJ@Ax?iFV
z&2Vp0Z9ml{4IJSE<q=FlinB|<2l@wPz(&a`%HiF4nTT|6U%v?(Qn%2UZ3<do%#tkV
zco3(IZ>(!Dt2tLM(MX-WwIL#GGNeC_+<BR@6mX|}u2jA&8~N3)_k<d_CumYW*FEdv
zXd@NH){M#H29YQ0tlgVEVCaz8gDH%5vi@=@Stw({hu`Og4nJcS&vy3j5hdyb61rmQ
zX6+8_{hcFW5|2BiCQn^w$Lr8gFZ#Ey`%2F};l)VbZa?Aov$!(Oyvx0tc@H#ob3ZUV
z;wEtD#d@c!<wd3UQDStsfc(AtVUoxvJcFuA+bq2m7rE>oxp8lD<dv6a2nq2$_6`aL
z_jn2e08amz>$KeNPx=O1nDD3SUrqJ=2U9wOAi4K?biLF%K;}qWaw(%P<0hNF%UFDj
z8!rsB2vH`(lxk%P%D6;3+Hhv3piSIcTHlr}@_rL088R=$4vM)*6IBRI?480<bapsB
zL5U8FA25~%>-qU9Nu?h-6-+gtUpqZ5JxzPw-}U7`e?ySQXrVN@11D8GNjdMO5WWB!
zDNHL(;}Lhup1GHEF%q-!$vd}LE}jt|G|;u6!wP+P(X}vo@6_TxCseJD`4;m_&>>OA
zD*E*3hnpL{Je~yOUf69TTJ;jRm&0F3pJt{1v(S^qi#wmRbs9TW%_LjWo`?xu_yo2<
zp|KdRdKc&B?2ZFJ3T@g%OB9+H{9yM@_raoHb%wvX6jn}8&`+A(>x&xgr0)c|AgIMi
zgPCj85aSdt$vLmyjej#<&@J%Y&2c)KkR|m%=yUNsx8Ddz$s@H$lr!(xuOF49Aiq{F
z<4pF658Yy5^Q>=FSFtlEBTqhRnh<w9Q{ngBSSb6tabIl)GdB!%OVhk-Jmzk?pZd$O
zgF}lo2(fC^=4ayIXhd&PQvf#s%4osJ-c2;y#!<8y)oY*j`8YZHAt6z%4J_N@eOXx6
z*OTHU7nqx4cYrEhq6`lHy?)685PaY0Fhf&?;2SyZ{M<*Ttk@0gdJIFQDb;|L>aEsp
z{^NlY%azX6-d}+>QLR!u@~!^a8_5j0dsK8#(`^sH&t1q~4}M5i{pES5u;So&-GoCt
z@pruNp>v*zCAGY*EgqRRZ;^*<FY?OraOdD)Z*T8+Fd6}e^-OzmMTg#rD8gn(PWsq&
zhU~rS{_<A^kehy*W-vM^D2*3Xjj?_?IJ?IS5(uqY+NTtcl~@>`(^b`Yyz?nN-u$Dq
z%}7yXO4GjK;)>-SC=~k{)GgLwa!7<Lp!b8}c({y#i%`d&zL397=u<2$mZbH}PlK-D
zL$BHP3%$GNU_%nVynY2m)dH>JR0OBTgdA>AqaiVB7Q(vwVEWmBeTdcZ%FWB5rtB7F
z1r=V(M-oSL)OYMI1?kzcPXpD1yH9XOU4V)n+FCt}bb}JoZfA|v1oZ5C=^*!t-}Qf~
zNE~Jsjq0Jl4*k=9X7aiA&F=2`^j}Q_@U4A_QVG&hs~qp@)+0#3LfWv$9$t(lKKTK!
zhsD)&^fLC$o(xEZR_qFLOOrl{FXSOEcHZ@xUV7;t@ev;3;JhSQ_Wv`Xo802TKPMC_
zf=`IQ>pRj>k>?ahX1ckN&9&*Xy`;3MJ}eKgqw>m@p)zK8+y!YZr!}P=kDmcE0=c{n
z%B}qn(8{R0dU;`qGRdj9&{li?rk)}aTEVpZv#zQlZ{zf!hd@#w^}yRE^Dfv_Z8V&8
zwXq;=o&}&ue*B71j67PUHd^qDbMJ)m?%E8dT=>nvv4S8>Rs|PAc!jHT`(F)~`eD&&
zxukBGkjgC^Sf*`0kBTLA#dV#B2L<6@4dxC=--a<F1u1AFzlr?Yo}SzpF<S6eE*sJu
zOqnV@vgxW9{cC+~RZUl;OS!nXl-~iE6@ZJ0SEr91+VuYVk()hy@8+fUv`^4+BQ7a~
zIxwy^@e94t_5=IxH?hvYaDc{FyhKjdkGjkf=tgc|>b+@S-P_$>_p1O%O|7p?Z|`1O
zA`BPB$SW(ZuD+MB*gl)Im&^WmH&oV0Gq)+4;D;Hvly&;X{<2jfl;R`#TI+IRycwhR
z>tDKB%WSHdB&3j--WW~*q?eOq+!P2i6#*W~|2SE@66mbYDediSIOnbgH-B5U&qS_d
zL1}rVSJp*CNr8-=IC|s0k0s^d<(-bDk8rJS;18mfW-mt+N!?b@XZln2M-62~#<y?!
zer}V!N@>sSWh<LVFu<>D@6;p*#+33&3>{QM{3Yw#9gQOJfvy4(0UCZ-p#8(@=#M8~
zTFRgO)7Me5R)E2l;4TxO+~MHf4}bQ@!`<=K$YOz}UUo#;MAuNYd%+~%@K8bHw!T-n
zDCKw~FA0Pw|F;<7jmhTjD?p!rNwo8I9*~be0@wQsbiteI|6nco*z-5>@*CN<%qgN9
z1|ZP?IfjVq;3c<};RIB1O#bz?`sNAdvi<Ft)8JTtC?^ts&xeb*aMK;^Qu;Rx<GeQ@
z1cXBe>nZ2$gk7p8J4vdV|N0ck0{Ncy%c54cHX^fZfmqh~8n_P}5`l@o!T|Q|e5Hc_
zfbL$=TS>~A5SYMI3{PJ|N}Fe&s!y4<F>4_Wd7l_4hUf#^TDgUcQ_&;zWZP*d$g~tw
zpMdoW$;a~nZj{(W_o&f3;z1AqWM_ewZ-E-0M|TiVOIj`hOg0iYCfYiRO$?i1CJdVS
z0EF|-U@W5#Qyqlqm2K%UOE46Jx*87bfPWm(A%L*B(#~L)zLlaX{I5&x)R5wY!8w#s
zburo9TK?O~!}k-gC<(_8uo)85^NzrcG$u0ViA^x{X)u9u>0l-_&^V*-6dw~1@OGD|
z6SbM)|C`qH0(dbBHPtLn2#cd+<`$*xn#^YmSo;^=7XG9eYN9lSj04Y(2h!BI$45Z9
zG+*MIc=E#Ls@I_0t)uc=T>yh%S31z%7P|t{6#dtves+5A^b-_4PLJ%9s^8z6>1kw}
zw|c=&;r!U9dx2^ZKBE7)lkgx&<>_eISO4Fn_~`h*<FIEr7^#WKq`T^kvifgQ?wjHA
z-b#gE1u6LtXzvtY4fWRlj|Lclh^QRNxm07yRa=;Sz+{C#iVib~o%=z+Q~c2zPp~}b
zWpOO+xU+otA2qAJ!cEkUZTXwc-o<T}+MyUwBmro@ULWsrc^;4{y3xd%9Axk;)^`+p
zWc*K^M#Pk?j5)0LYDuw9N|1Snz5nKZ8B>45o7t=9C7{jKERc6K864!<?06&_UkaV?
z>oe#6V(KpKk9ph1V<VrsuB;TDXd|P8dZMiQHr}k%l$n%S#tW_<8glrx_Tw`DHG1kF
zzrv`F2*U2$gJhkv<;Dx;8#Vn!6jWXyUp$J4iN+dlNvS;kuxs^B#eh2+zDLA0B&$qC
z&k^guu3G%C+<O6xdnJ<@HE(iH!ehS5{oC*2G0RA?_(mk0a3kYLRWla7Yr^tcJQ^Im
z<sJ7?xV)5w+-KSh^iZx+;?u+Kf3;_=8`{3*l@>8P41e+!GmsIUU#;GujAq}z2u?=m
z9DZP_{PD4|*wWf@OHqV5P1*@%h$8W_H9|;N?8_H0bf<fGve?KVWpgreycB6XB3naU
zb@5koyXcF=DuER*B;0UG&Z|sBo&?Ri?Kfq3Wq{)0;0=tNsA7|evg0OaSx?qB^`#8~
zgE%@`B8ajj<Ic}*py5l>(p=;Mv88D#L&G**oiAH310*X-Y|Qg!S|eJ|p47;g?sxx|
z8;);jXeAF^n2eB~T9~_kq9u(K`7mBs6up(d8B5YvwB=pTd%%wnv1qDydyU^0+qx{n
zk&$t0ED3btM1YM)$I}%|NabYZHR@{X<3vRXj#1ODm?o&)<zi#`GA^NI#sntIPgX6X
zE3+hbFfp;2m<3*yA^0_U@$xKD*3Lf4yHFWBVXDT^qbhk?d;L2_4Wg~9r);Qnr9H=c
zseJO`{MTojVXWESz1L_U<2a@dI~eNZUEf=_eOM@f9=Ma7SE*%p=l|v8Z`2g*Sp2PC
z!M^P#JH^r39yDvrQDllOPLWT8C8<VXAy<7Xx_LRmOSK^v2d2jf_v+i7?Kw$F2;M?h
zi%H4S^2W>@U*z3rUKV;9pAZk}bJVLUzu#bO_w#2COUzrA`$1Bxnzvv50XT98>F95~
zt!=Qqo~M*mstwN;b<oVZcGv2jo#l-PlY;^u%c!aYNn;99Ma7q>TD9spSeY?>ipthg
zG~)0hwx%rH2K#$(e{RvUGWJf}$Y|=>bbGe8T@r-E{HeJJapXxzd%(*M#LFIr;mRen
zP{P{+b_bf&T%}O2$EDw6@#eA$9+|U4?WA!$>eUN{S4UEp`6vTaA$_0h)U-8c^?S|+
zt+JXL@QH}RXEGv&9~=uL56ca33#&Ip`8fBL=}$REv^<{S)>F(jL67iOH(e65e@f*{
z0g@T+xL2R8&=ec^3@*)x^3f$rQ@}<{L@P_C>~Xcxa#<{(uBV(asY^6yCV6&wrJcut
zN&Os8^BtJYlY-E#tnTV!0xMlj`_$R%+CM)zv+Wy~tf}}lgf}Eo${SO^jvzO-s&==p
zT*ft>s45F)KqPT;af(h#h_{t3B(fWQJKc442ZoWVZuQAfQ@=6R8*&~|E)JJnwF)98
zjp~ISiqUIok)pVbzYB=E4eFnqsyOjw&=@kw-^GUGBnrxaN14iOFWh&n)ah}8)`pEp
zn|ko{MLvaUA2mg49&!zCrRAzwY(Fa>sEgjw=uxTYEaSd}{Wcqh3W}t^g{hWFt!-<g
zfoKzXD48GA;3j7ISC}DnE?04IwJS7`6lhXfFvr>a2c}neTSN07Rm)tg9g~K0n%Y_#
zs`+`CGwWz*tKzj$x$TTO(&gkR$aCH4N8r|nLB!$Gh4LkA)5*|IU7kRS2kNu$x}HWx
zSg>D$7Q?h1j_(XA5}9D__xL2Y!q%UG<Z2n*NimLXOr&V&A$|F%O%;gT{8m0+*7^#>
z0}i(d!o_+EsW~`{eh%_u#nQ7i{*>-iG#mAwH6%3<bEfr}niK6gWj!AR7yr67BTd$C
zm8VtFqbCJ}qt0e}>yh{IOM@O`<qUWF$`>DWTYm$)$`Czz94T7`7d}oY`ZoKP^&RyK
zavq9~x?c0MhXDVvP&`cKIlT*=Qb51?GY^m7@}w@y2(*~NyU48vnTsgf>#-LShKgdk
zsa~a|c<UZ&B*+re*55qoX!ZCIjNAwZ%Cesvc!(sSkCda9tw;5+aAxmS&Y|VwByQ}z
zw{=zN_4u%MW~PwQn<Q&@`?Zd$Eo?Ab7Z+*?%xSdD$S-HX?GtsZz5!r)X=!V=a7APy
zQiVaw+z3&eR)tuaK5=M#4u~APr%SROQG*Yj0Uzd)RPQ`vRnA{R(P12h`>Xd#=W@B+
znf%`>%Y_tW&d;*|Th?ea0xSh(0JD-qGczkbsc*~J+(mDRo0|_aRykfrew7$AO4JbW
z-7Hy5gqh9wA^9vXZ&f@lS<ic@Vn-09dp-P_+n>!Y4E(v%XYroMlYEEghK_umLxETh
zX^Evv2nAjuA*Nj>fmuRN!_LXxqF!?Mz{i8BV@WVy^M-a|bjxRHQceh+pJ%t#qZ1TC
zcGc}DPGzcN(~YTN>dW2J{;*OR{#$>LrkwnXxra#?wq3vcH{rZh=BP%>TVtwv41@@c
zoXlGlBtWyzjLTcQ?_muSeXKU{c4VX#V6Xb+>0n`F;lj^a#YanO<9Sq1r$9)Y8B^XF
zw#mWf*HoOZTryD;ZWK3uuN!G?edv6JD%N{0MMt2`WC_4>i7nFDV7h)1d}2R#zE#!U
zMz^a>HL4uyujx?Pk)7S5Y0k!hXq(KoSmYACCX=<sh3^}Y7zn7y4Etj;7e-V4oQ7z*
zjqbg`AvieiA;W6aXqjxm_D>&!k~O%P`Hy|OL@LHkSi=qD5%bnrkBH=Y#ye|xIH<l|
zgvkw*TbR4}-I=%=geEKWL#aRd6~2DMwR@|VRM+}7M`4;SzQOG}oNj;M^?@-w9z$%h
z(!?Li*gML$86SN8*ADZoCk}{@@cVT(ThIzXB`Z@gSUHMT=;+bc7*TBai<4AtU>0s3
z?s{#H=$rv#oXoK6t7vk})S#vlt=gX4iTyV0iJoMvm|n~h@8gy4(w}qWJ+<u|WiBJL
zHx(o8U|LVT;Rr#`!|_vl$PxxxxIsj}c8~EU(6?5dx4co}Nx=8Ovf}FrCxW%Edcy&E
zn>&JMppHm{PwLTh@{|V@!)b4x!x?dL6Fj;S5=mC{@YOD_yoRx7{*8b@SZJv8ID8jm
zdDf>LFSPy*9#(_#%*VfaUXHeJr;px1J-^QH_TTW}md0%maTnX2Z_37;U)R5zu<ol}
z1}<N*YJ8_(Zfbfp&FxIh{uu5oM%2p021Cu+zYd-BwXcSdcErn9<qTEAz1s0%)D&bl
z=|zO=VYdhQv8!n#npZ5WtjLxI0`fe+SmxF};JV)@3`hEhKvtw0vGfmA=Q$d?P7V92
zddzi6?yS{;3jXsbAlDXeXJK4?@UzN|LDQd*E*Q96(Di4gtDkt*`5dB%;zw(Uy2bgA
z==_xmRN&}sj$M!xG>!D-)NNt7TI~laoW!)AMy|RwM9zW7n<Spm!hNMfHH*A?SpXSg
ztri+6IKKAun0zef@0p;=94-k4keI`L*;hq~nCvWn5N9Cj#DDrYNI!AsY7YorYc>M5
zaT?CO8WJn4hy1|}dhh+OT(*%&-`Okfl!S7E^sl7|zwQCdJqb9>!}nU&&_7kDpchaI
zY--kV#eD$Ey;m9#P#t2f!+g7QJ1~7YYJCXS0eTkF<=Qu2oLM(tM5bpnk@L5=gLAR>
z2DtvM_0dNNNbJ0m1O@Dm_lz7ehG(}9%PQU~1A-ZwtF{MSO#MI-QJN2~cANhM(np^l
zF=g~e$j@n?@Ju^?J|{84X8-rP?TZNLu(U5z5Ms)#o+&fZ1cMGNXa{sJ0-%;|mzzik
zEkv{o=gRJw7S;gj5j`XWYCRE6bN6olqua?<C2SgguZ+Bi5Un5qx`J-N@Ki8tS+}qh
z1PE4sx75N3mx}_g+F-qU6j^NFP-TMUt{C$=NVv0q2LGsa?u!3)fbLZ2!W2Z@(1Hr0
zEb%;;M|P&JzZgwaeXk0$mldK#6aQ+Bl+9-?nGpGPK?x3GI@?Q4Kyu;_lguAbRm5Ms
zqQ7IN@VUKntpY~ycA;D~U4&LoXI@)lK4xIFvK?cLpl#`AeJ$`HetV0bIZJ94$4~W?
zc%{?9xvjIkyVbGKK91~0<COLIiOoqg7PPUF1Ox9Rwj(x3j9<L)?9TQ3-M3TtEn}tz
zw)c)e)4vDIS?XP{ddUmKyHCNPGC0nz7)#`W*2hOq<LBVy{$I*xMIWmUl<8gU?CE(J
zs4F7F<RZUsXLGKbZ6(L!I=N6|>Ntj#eR{8$>GWMvg08|cLBd$z*58d#zg%o+O)%+C
z&fkqucd^jD1}0#Ey$$UD;OVyfwr6XSt&TXnJ6FlU!i1kLY5v)=+w;%|H&(rK%b2`i
zYnZ9BAKNVN{RH`KzFG2)v!~g&ip;iO3DT~P!S>FQlxhGTPJUzqQv$g{kkGCGRw!FW
z9UsfwpZ-7*N<nNX&$}HF-I2k$LOFqTqF1<UKb^n(c4G#M^oL9zt^6DJ)`l`+Y7A{Z
z&7`Hv2!4fZcA82CWGHE{Rp6Ut?X1KKDfjl}S#w#T+h<(jc%ikJTCoyk_J&q2`<7*)
z&aU&yC#tcGuQ^POxarbcYhgv?Gv(!W&Cyk3=60<ymq>0Tz!}UCGQH>mVRfzQLsvZ?
z*h4g2;*OnVtEW_BG*yA?M~RDVRT9Ivc-dD|3J!%4+oqJKPSS-;%bF4|<eUvXHC3x;
zA8x-fqB`9o$0XLv;H0amJ`SJtbdjcOwJ4mHVAso-p4-^)wBN%Sjp1$irXK^~En(C=
z(#>8J@HH!6+M#9H4I&izhX+KzrkSd2y>WU<EX*&jT;%XYfVoC@s3ISrVrU68#!X$=
zuSl=R<R545sGGu5>%TeF@hMk7x>(3%5{NMa#?s%><9iB6Bs7-uZm?;w<<}>GclZNJ
zhibgsL|(8ikv`J(bEHL%1`jZlvf%PB^S_Ofjr%(`%j#azUQ?NBc+S{E>h^vDMb1zW
zUyCR6OMtY0U@Dhvp#(XlAbuoey=LCTu3bCY9_7PZ{R7v{gOQVIBLEEp@4;+of;AQM
zhX7Uk%NI${c0~_^+K4r2JiP3*ET!DZT|U=?-6C*jjyNUbBxj==H*ti9#`R2icVgHa
z<+y{d#pXHk4jPbqX8rg~sTL76YP^-)M~>X@4WuR7=ZI09s79CS36k}65CHB(+mx%4
z2$mSC?iKoW7YUcl>TS<>H&Or~5Ux&B=aV3>XiSxI$>P2WtqYJ-YPwd7?UO=gtgoVu
zz5+AaSzRL4VYz=m0dHzxF}c3Eg_BIScBAnUyE0SKGLj=Sdk8#4S5htq9@;-5NXy7#
zs<8es!{>O}I0yosU(S*g{S;dC$rTA#v6U@Gq9Cl6HPLR!sx-@YIJct{vx?--+x{OY
z{>Y?R@wBxZhk=TWnQ_rt1`n8}C{?}(3P@ynNNtsw_=U7ppA@D(4*yHsCZW{0ERxKW
zqjql1f+_No8gO#lAkyRqr=RM$Ea-S0kD|-a={O0dvyKvZ>MER2ku6E4H$AC+9QctV
z8%CxBCwnL2V}J|mIlQ`D0oLvTebS20de)dZ=0*}_!PX$Q+0i9tNkq?<3AXYM8d5aT
zs~{tCmkV@sz0xYLDcNcdz)+L-E<KGjT*&tM;(rMS31MQX$;b2$;pp+f%Q@3V$x08=
zl(%keETx|hv4R43rFk#iESz!KpYOfP@WjsmuY7~l1p+96gN=ywc%|z*XlC1JE?0pH
z+S2&q!2>2TEs$^}?>U8+ep*)oH0VWvm%r(Ofy}qbKB^rqE=!&rld#6+B<8vLM<-D;
zzIzl5rJdU6rz&n-tAaB!vbIv@Cz6Z=&Rn{E^l^g4|6lOw(5YGe&$&GhYhhlGCo^ow
zAe}bhC7w9Q`^*%^eg+r6D-}CXw*z5F*Up0Ct1k6>y-1Ot9^{ezOvDf?L10^MVTtHn
zpI4jrYPV*AVkQJS*iU!afVNk0LkD?VRKdEPUP7ZVKdhq*#LN`~t>pz$_`>q7)Xi|=
zZF-!-x>8aAkGehgPg)<ZxZ66+VpB;NZ9|jd&ksjs^Wl58RHhr7v$RMp49wm6Sh7a3
zvA)YPEhIT;%+FA+|K+G_T0Yd!SR}74wXinwj^%v7)gN|*&V)FSLt3Al@S-+=m~w)h
zH>Rwv`XMuggTHZSM#{QY21+l|drD`Su05K~HM$u2ef_$L|AM7)<vU$|s#qR{;2@S6
zcTnOO^6o3~z7Ztjy3>-T8FKja_@?T5E0*-d@ckHF$HcR=!s5Wcp}QVt!Lc-0+(9bF
zl4bi^0=E&qs37t>`e!Y6BC$wcEHf_O{m4~7)hp0yiD7K47;i=8)4KectZqzS)7Y8A
zoP@5t_>&YYRr<!9riprn@8mI4+x&p%aL0Q&d07E|SUABw`qQ6Z1|Lc?7P^~HHUI)Y
z5OPp;sTjt;WO@}?nWUJ?*YYz!o3p66rZ9)$YqCuB?L~i}oNdR<BLVO_t@taNzHlPJ
z2&|5jmUSrrq3~G)M0Zg(BYhbV-tchoRkD|r9Y{3_TCP<*T-K}S17aT^h=<n3ElqTs
zbR8xq%nr49?~hutFrq~OH%>hC!`|A<Y}tqU+iwsDx0dQ8!1FB5j5qn&Z*+CJ15Kh*
zy;<!8@TV1j<v_1KHtfkn+wKndUfWveqYpZfn9LL3B)M8g6F(sEUj_&zUuAOV@=rmc
zl3vM2PkTVAJ;^OU#GFG`tQ}uQ(9}kR!+;5fhZX{t5m573N1k{>&<hF$Wg3eEt|zf!
z*E{)QfH=}^I2%KJyYX+y#kf1%u3<3N->dH!zLz=VmY;_{z&K^GQ=t4GyG-y>$iLbg
zm9^t75UvlF%~<hCT328lC2siq8su8$!djh)Zh+>;E>`P(v2_Iq-Y&fO1erM;SEK91
zAPafoc__BL`G>hb{(33l&n&5;)FDwIToNRRqal;@56=33#Q6x{+p9i3J4)yk+deRL
z=;6!B3G0-SKO(Ns0*jPju0$}mMb^nW8&LZ3<fW|t*fF@TJrRJ#L(hu4eHK9R=M|I;
zIHOvS)U)Q7ke`9i+yP^$4_vUZy|(}QUjKXi78uAT9`!oqTDBi}|CJlVW5Zx<^)cn2
zoN|>Zr55aep2gn`C@-fRdLVhTBX7B8#&@1N)e#TiNq>KAc_39@@5mRXHZadOw4PXY
zf)&GIo5PFM(%}0Nin0ugiG+7-!%!nU?VlGBlg3$(GEqj@yG7dJ_)M<<z~Ku9$Xni7
z{-_VUzJyL3wTH(|u<n0+1A)uaTp+j`e9x0G2R*JgqJvGP_B((0UQ^Rvfha0?(<72I
zTz=U)z%>1bUVtEP)2l0@RPr&5{+{1IWx<XMAZ9>NL1)!yJl#No@%ZrNze%L{il;BF
zIC&$dxbt>uO-~Z<+wAfLtm6>O6qw@GI<0o)Ee}G#%?P0#MZn$RzEXe?P)T<+?GHrX
z<TSio<dA}sf#H+~M*aU1XcHiTM)SkU%jo?Idj|8Qz#k3dnyw?z%5ewfiZd{M7Ee+r
zy}AwrWFO@bZT%hikh`qUbzcip-!a!i4<WAPfW-dI1|0Avc5%hgXU5U$7%UtblI4?Y
zXIPz&i0=NRBZUp(?Kn~)3KM>GpaJ%$ELgU3qqp)3w`H;+eoRKtm7d&29?C)akqR)$
zx9-*)1Wr*8n-x595}!SF-lJ-hX;nOVbgmO4;<jXt;cY)x|EZ3-aHz{auKzd8YskE>
zi6CfWeF)pE4WYBMra54I?u+MzbV)N<JiMpoXPxF{9YqzuE0-8Kc_rs#T_bJuJ$9{+
zWWlpo>w;yRysjrXk^#WEI#x1vidQdoubVvxZF>z0^xQPRu>*`Y^?`ZWKL5Wly_oww
zhAdbMq9v`UsVph2<OcZ7Bpq+sB;Dm9>*|=#0K@RcQIbH5rzs%dJJ(X#s)L#k2seVf
zS%j*P>eOj3op%p^eod+7)A|iR0B{Ae`q<LJe?2fWg(!jdr0kVpxa++@bx;RS<h%n?
zTAcZA+*4m*lHo=rhCil$?T(nerP$^f?Y{6f_O|qZvgQpq%k=FwO#|%XW1VQh)QmMp
zsB)5$I=FXsQP7!VW@Zk+{J@tlz$T94{^TKDdGEj2(jZe2Xtk)lBDV<+rs_|zmK6i=
zjy=G(_x?PYKM%^U63uRqp8s~g^bs&FPCBdfRc4w*7wGT@j3`aq!ty)30nmKQ{gS5!
zy);19SgiZz0gx!MI~u@b+i`?O^>L_1ApAw%DA{3wP+1Gx<Mw82w@;YB;NJ}HIfj=8
z0Hi?L<oYQIIX6z6Z|5Y*-~&JGfn9%LMG-GG61pVbXV0ECUuVOuT54H<+-r;q5zpP}
zX8U4W;)TwPFLSz3E~49@T68|;;AE9-c#bk;$yD(vRk{#>>F*pKpoeV-orC)5KxXZ!
z%uA0N{t?e~^LbLUItEy__V2pX!w2_)P?9{}d37X!41f=`C1TaoEUo;R*1N3{sY?%v
z$=K5psv<*xQwE|g5m>c}?d;})uf*=q9lSSDD;2;EnHhB>Z+lwabbd{N8p>spe)`Yc
zUxbgDOAay0%0d&fX%_bjSYC#{PxaX3X<IVt1K4ALcWeK>Ds?HB4r8w`(MFFc>SnF^
zA1~s~M~2{YydF;nEM9Q(l2u7pjK%po`1pH-Fc@7kLf+0*OoYlc0Zhi-q5I+!0dX-B
z0RJyW=8KQ-pR52LdBPFkktHXCreDXA04bG3!<acgJyPuG!sQt@GUz|xdbk4sR|gny
z-3Y7wTb?^Z{(H5$U&VqYwJeJR*ea;V?91Xr!nKnShf&`B#%3dMPM)+t?nFEvLvMyg
z9dY@~SNq0*d;nDStT=)TeaZqhClQnGiG`M4-Ezon{Kp=riM*}?{M_-uksszEDL^3e
zyciBBg6jfFTZv<Y^2^xh;GmrM$Tx(RG=1k1*xlhH>AkCj@3rO#7cn%G_XzofGi1yn
z`Wbtm+W1Lt2<)FjqOjlN<C~4~2KY&S#;%_;dsVo|>-p^~z>a}OTbvy;|0N5w{S1?P
zd`zTmgm2@hS=jg-O0ojqW9lcVF?GboASMP)(-})+h8dr+s<nyG%E>~97>oIoN^Q2U
zU6;5aF;Ph0PFr!?Z-%x-8P%GPAUn><e*5&MwLyWi5L%A(qt86IN{j%wd7L)BJj;15
z3Jd^#m>`OjV}Kdo0*npOmIE2{=(6t<^8i&-&tz|S`VXDpzCy5kQ?pD-Ze^(9x$8AL
z<0J`jCi$5oV0+<1R+W-xOmZ(Un%;sXBN;L)J7sZzoQ=*hEp5KV*pY-#!iyyPtHZl`
z=?PLSB1wj)lE5+dr(?hkyr$ilZv|ivEvd`4;f8x#>yrjn(UC?=W7R!?p8C0bH7;4N
zB{3y#Qbn0VcO83Dz1p;MHcYS-t)EV25+)B_B#cjhgeEtvNOaRB$d-bKnLhxg4(bX^
zuEd*_+et4Xed`DphUT$qvfZx!Z<2FIMb}`!0U@Gea___r0GSZNMT*yBc-9w8{a#A5
zHW%em<tQ?}8}6f`OUZ(>;_kYO1H)^Eam#wjqT(>@AsfH3ZO@CI9?z#w5^koo+=t%C
zyDK{zL`(F$V{Pc5z|{@w2Q4s=6WoDYEvNQ(1AsyP7N$?^p+vq(4Wg~@)ySJN_rQAz
zr2o^ASeOz#o=AhG1mI$O7i;q))M#*Os-EMMM8H=kE5+wO#bs!Cr9aD~lsVYk+<6tZ
z;@o_ZdN(b%nvUUvW7;(Fp19J`3viy)a)1r7jL(42`S_*%$m>-IkjYKTX-Noh^WY?^
zvd59=A@}KkS9WgL_=1PRHU+GFR!gy@jKL2X0O>Hi5U2i$h$uTs1_#JC_b-vm5F%B@
zSul2cz@zDU+8XX{d%ChBSr{!|z!y;`5nh8-<4wpagMdU&xOg!`m%xch5J2hv7<dtd
zQI)=!f`)P9U`Kr?mKLv&VOvgVV0qpo(8pQcA&D>W+8!UAv%JY&n=p)L&BYwAL7^2b
z!ko4%+Qu1>x0Xe^Wu*ka%}m*%&3BL^Idt&&ju?@VTbloO!7A$9M+@U4<gHk&<5q_W
z*U~vIPDNUu5CBE|$v85;ccWCPZGBLa?HMmyJ`3r~Eto{ZbYYw<W1j<zs3~X@ddT0l
z+DVL;smqkq--;bgH9RwlkxJ?T{*;(**RbLI9)k?voA7u4ob_|gI8$3)kvI;kO8{`m
z{SUE(Kk2((!$nC;H$s~aLWlWov(=<PTwhuB1i=S`z<*gg2A%22ec11;Er<TYFY{+H
zK*-~0BSVb@XuqxG)UDbTesSQy<BEZoK7iD=T)AAy`3pJT&snR<PQwt7*Y=kz1SjM?
zPUs`YGyn!Jad%g0{=+=&*C#Jfb266jd=I$hZ13z|1{;yIGQ&0$UkrXj1#y(wbQsIy
z;(rk|wCQeCml2qEfx(flD_@XsJIntvJXFTOfG~sD0(Z2|Bp|^U5Jm$9TAu1(@FD|N
zdP}qHUV8D*-9yZX?E${8;uW?qIphy%c_Nhy$I!Ps81VUh0RIa2{x&Lq+K<|WaP&Tf
z$0qyoze)(vRsOB*zq<g_H$8NJ5FY?;dP)}c^m*%e7r>7{yw;Ox5CdM3=pS(04@@uv
zoGp-{KiH|L=FKj@2Q=(|eR+ZqVMIFcaQB=rL7D)fKg+jRFcqys0<zoX08+@?JK@<I
zjL{EqzHzx4y#Ga}l{Q4MKWWr{@6OvW^n#=(?U5bn|8Q7q%L+1xqt+zRb5C934s^D&
zTTC=+4(ND6>-T&8*N<Pl*SrXMBJdwQGdxfK{5M|&$elKSKg=AgRo<>f6CHbKKR8v3
zT3!5ui7PsYY5>()A$IP0!Fu;D96jeM{MFBYp_k;PmOm-#tFsch2>_>Z(TlZtZ@|V3
z2hl?5gfnWKrSJ*wBI%1kWd-Pg@ZObUWS|?6dE{O=;ZLYWixGA%tz<L_bJ)&C|Ln;Z
zc?3=vm!fnayC4pZ^$b~yyJP=<Zufc)PMCVzHHb9IjU4(wJ48Nl<HZ9sWUus_Q2#r0
zgM+8Ln#>}7*`gNoPYa(xp6djVZ;ym2bi<O12X>BxDBxxNp^)i+z~rQj)0ex`2Wta9
zn2TT;|EV9SyS0rc7CZi*Iyi;>tC^=3TXlKZ0-pTq6VHI!Kx1Dq0CT6Sb9!ctM$ZZE
zz-J&(Kl^6_D`;=^GZI)^@|MGjx{5<doS=>eu(k8r{52@McJ4|Fi3kNu`qy)qmEe8%
zGZt_wcntX7pfS7;zFHz7ik&gvCz3S$<Y07$K#0xXX(c9-C?BR+<CKByqCE_ge<Ol;
z8Q_-5R!#3-*xh5>v8C1Vv<(<v39twUQ}kG0&GlN=?p`8Q$cl@#th(5>Zxg|9y+}VS
zb`A7Ba2FpR^!uxcru(b}H1Gls{KD&%cs-iUE_cE1EXAeFXtb=X-T0V3+Z8(cHkJyo
zXAYcVh>{ygW5BsaBCj51U{w3dCpAg~9%5vybm!sV_M+K~5vgu#(OX`Kja3ut1vtdL
z-MmRlJpscoI45LehHyVxf#p~%3d_t+FJgGO6@j^hC7BJAJ_nY0y$gZky=Yl!DOo5*
z?ADzwWqe6ZB{v^sL4M8Cra!2tn3gYTewKAb@-=>B!_ll$81(%{Qk?Wz?lm^Brwjxi
z+hZ0V2fAKEgS!whz|p?H<vwJ(^L!QbvVh%qxOL64KytGT<2#&FqOBU=)uBjVRDQNY
z{XFMj8EgWD%O>Lee{UIE+aAx3RM=X_qsHrL8i8=DcBu)|dF06W$p>O|H8U%J0pvzv
zeWZ=qw@WAyW>{9o&ICckNbG1rY3mxTD!rYB$>MDA-xHQML!|`3kq$G0+S&1$vw(<&
z$@@XIS7c-mcl}x-wMd>1aY@f5<CfhpH@!dwxacgAqQ9Ijzs6tOhE-XZJ4Js|OO@UN
zz*})3ThDCr_IP|Z=DR*7+A60_^fRrkHQmMk4tCtZ4tB_wu#2q+<&)z`8O5`*oDE(e
zePa^%^diEHysq3^RXl`?W!Pao1sWCvq;S7T`Wro*yf_DFhsx5AF5_8IU?)3xJzHHC
z76l_DC7_Ju&Q*2l^q~(KHwn`9V*3^AQzOJZTieM~DNr0c8wodHtC*YpT6#BKg&Yyr
z4#>tsN%Dc+lsl;VVPXHjW%BDCN>>a$pv{a7$%TDj+c8VT3E1lPoaba?=9lX^OPgzE
zocxe9E!uthEa-LpI6dX_jPl^uw0?TS5o!Y=;~goFX9G{LaY8M}*_JstE#f)aqI)F_
z?E+^ekPIOlxCp%nC*3J|{4<7jY-3CNghWT%5{$obf5LyAy1(TT<mwY2f0c97dVzNw
ztzEraVejl6meg6!I~}0wS!;zVri4?*S9NoN^Tk~2Bg8t4TqU_D!w9U7YI9RnANc5y
z3_L{v?6GY)H=PYx)|}H#MIn;%w0(^oq^;yLa^vFg*1zVr^>(y%V{%)(4NvB=pz&Cb
zXC)AL6BH6f;HJQ~_xnbx&eGU9AZd`#D~G6$^RQs{Z4+|sYz?qC7cg^@X4585PMVPw
zdSiO<7A-tyXr%hvB8-||8MAg3SM~L<RY)AL*6<>JG;=gH_@WUxY3g^Quw%OHQv2#9
zE{9(0Q5B=xLT&*lB!8WtJ;PHYT}eK5gkFWgiqk4?=#a5WU`WE~5+mAn-|UNpcoD04
z{apw~z2-g!i1wnBK*t{)%P~V%&6zkDIcNoq5=VTS2+WSkfY`=w4}pJ0XwNsY>S}jv
z<x5^Yd!SK{_9DV#A5OT^S&@i_05OFwvyl@8#3jpCuNx_6>blS>W!;W~p*Is!SyIk@
z>}*qyv}ZsZ{puxlqGgxxItHj6wf2;q`a!*-p_t!zy8ECDS)IP^xQsTQvUK?k*NQ(v
zls$LG$PUr98<$Rb++IdNsmp}PB@!7~q>A!s?PB}s+wntUS#QT9{wV#$+oYktZ+5Cf
zcSA@5;*jsk#+5D!f<JZKifSmDenF~Lrc|?iE}ETutH>7piIjXbQ`-EpLc(x3c_Coj
z6;Q7bUer4F<o0-K!BE<(tL|7jWvY0`Qp?en1%e{pET%~9!#!QMr{N~m1aF*Vto;!=
z*-=Up9Mg?NC<DFC6mI5kd3jA?IjFE)dtSDw5-9ZA;g<`}*gU=E$K6}Di52gk=`bS3
zdT^g2ok>SZVl+ETN8QU;-~FPfJ0tusGyPIZdUQBXTxFV5@Wy&+A-6Q)V9n)`n<a?$
zn?_oS0*H&HK|^X7$I1lCttn}{9oYhZjQSPsJO%Xu>`Kob!*-$(Bu>)*&r$#$==&>K
z%1zhByMhCTyROX<t<!-fpKri>%Iyi=ZfTG1o+nIew>Q-CH<RY3BA5rTX%j3Z<tE=-
z2?Z$D57SnB**>nmO%Hk%J~{QrIW#G5ddAVERv>%PA81dalQ+{N2wx)1VEDuGM+f&R
zU{rzBZNF1~(+@F}kMCVPv1(?Lw)_ZCQEcO>{SXv}QZYR!J1qwtt`In}oX)(#qE!2M
zT7)0)k<ktKUQ~-I+-rWb*%BmYjp+(v!}v}n^pN-b+zde;Ti)U9pM&(KfI;3Y;5$9{
z6)0P@d>BU!*u_pk0dz@9pGIY$o1IB@0BEzSoLi8CnV*|oOHm^^C5vf~K+{Ld*pcE5
z?v6Tdu`yfPnN(?fZP?M(*%nSxv+dOns*;k9pL0QmptM2R$vpAuFgYXb|HIl>KgIDp
zf8%b!U4pxX5Znpw?h+ulyIXJw?(PuW-7UB~1UTH?;k(KE^TYE8JXKe<S2s7iGu<;S
zuj%gnFsSWlC-yeNV$<|&<cY(_!A#k9pvF147q+PPnS~esXFE8!DZwxnq-UJtmsG9T
z8@aHrUDUCEVZ9|j)abg$Z6HuAL2xQ|fDR`VcA?U#QR5dEbYi5_LaXyET|-gc9-$OZ
z7#_4F9<&*AJ%LOYW<SO~a^Zeb(SA|^yY#Gsw31`EI;y^oVLn5F*1|WIQLn{>Uu`f%
z^S9$?vQpr-h)^lkWPJTP)6NV@{(NHT^zEGxo)myQf>WiD26oW^<zsK0S6|^QRh?L+
zihxenz)p{1WU79GdA-MWkCh-=(CF^Q$%Xo|SXJ8q2e>3NJ(@w|WPZ~VY}^-l>hut7
z;#XiUfQyF@l|sa_mJZ_23?G=Lw8h#^>b64sYaaUz!GfE@0&5_Bb4xo`RKMhm%8(Q=
z>cN)u(F|M$jjJU$XuG!$*36V5^Og-b|C%PDOhAi_t8Jts1AIg->U#@5r)_%`0Kxbf
zgzcHkO@l#<MaKrY(&ZEJNEi>XVV9rx3jQT&wJmA)$q#MI4;`r?12d1zCK}@3dTjm<
z0*w)9L1@Wq;R_mIt;T&C-aLj&r(Z$_Oee5B2&fd@kM`%^jLLn*;!coY3QvJw{k*||
zl6Qm=+|%g{4AB#wWPP6t5nLSqwhY69Q-KX9?h*uMkcYkbQ&iEjh{gRC@zr>6EZ|$t
zZi8?0p#w9^tWE=5djD-=(Jyc;{zlpQ*d3W61Jlq|COqF-|2NlfXa*2JC=wZkU1T7z
zMUs7H|2XgT*Z>F#7+MW!HxwEI;|$4Bsei!|)a`ud|N3YEeMV~vc1YxhwhwwYL0s4k
z|C?tj&?O_!e>A#KYBkE+msssDufaUQA!NGUGqYmPCvw30xRMH5IO-wV{zhDZ`NM#l
zGA|V+yzHcEFzDXMbsEF}*9RPEkars?T=9JKFDR{dnSrE_1OD$P7ywrH)h@yqw3-c!
zDnD$6*!+KU#Drt<99nDY=1T&WfLIlaeI5JK_}^!IgoR4s15x5wp4aZ+E|IM`?d@Vg
zm|p)SzCW-(<hQW*|KJo?YvAuo<Yn>To$LV(U>_hL#`M_X_W+}^TB8-zA>6cq|6fZi
z!2c}rk>Ik`B_VF?(G<o`IrIyvzp}Co8r+oRkLlOV=K&3d%x9~zP8^sxVfX|*C@{YZ
zC~#9>+yVBCd_XgMUyRblj1U9&3m2nHuWA-q((SPU0yO-r1HtM9B$&D4H^Yc>3_}jC
z2RIE$X%7e^w0fkMPeg8VnahbBN|FF1+z<w0tn(7r+PjNB4<;^4jun*ws*Ou=oR%|m
zlU}e3ks&&IU@G{$8flFdo&O!+CO@zQsfo-2o@LlLe%|aWv;4$lV2X18gPKUwQH{#}
zB*17)B9*hyGDI~p;b?FVgxSF1pc&j@gki8Bs+TaovEB^)wn-6ceP81~9(HwB85$LO
zQn4TikFCXzd}nL8rh1m>?e9uAEad^jD>NL-^H}L8DIaNMipwn7205C5^^Ajn^iyN%
zbOjI5p{4t%ps2J?X^#$%vw{as=($Ic`kpE|%|XylMsP6Fej&eQTTpnTS%M=4o=Hk>
zRXKbJ&k}Mt_CZC~xB41m+$Euvi)>3?nAMf8hDpl;fjQ$4FJ+^RVcj!)zpw>hw!eYd
z3RH#Qz8?m3kqD{<LIbqvIUd{}wXH#=&be=Yk9R1ZxwznH9$-zM?TMDYP(6{<ED6F|
z0P`y`XyU<60@Ynd;Gceu0~11{Yk=Vc;ySo>30^EE;|i9IbTtwtCm4r!=@!KoPhg-@
za_r1y@?5WLzHq-M%B)7xEscjcQM9;~g?NWX$Avw(ibYeN7U68`&8xzn2Rkt&4Myim
zciGw<GdwRV0fI<gFAfy;#ICtpk4XiHaI&cRDW?$&&l~)BlZwjPJwkGhm@}R$DY+O~
zxp+&MO-8ljfxC&h45N4G8%(OY5(|Ux2}o;Td{8L{nrDRH1bPJVOdle&a3xkmC}_)#
z>rmNCZm#q?os}Mkzox(@Kl$iyXInDz$J(B0`_Zz3`Gp1n#nU}}>`m4m`Xhx)J6&E;
z_!z4yYp|$88Hs^dVF0s2pS?b>fd=R>fNnZniey@<WB>l#JYdAgv;H$hacaZf(O$Ar
zZvlZ4@^EaXV61oKutksqIPb6isdxS*3SYu#h>|AF%`RdMgQB#BZ;UH0XeRW}0E6=8
z4v?X&^*QQohnMFwIHk<Stod&j)-c=KE_PrM5jCwUYv`q?DYsWs+-H-)<>g+YGX`_<
zbJYz%vR0z$I_^w#%%3In#Ikx==<N*9zkzmK+IT8mBc6XO?uEYz(K1q=p9H%Q4nfZN
zz_AE{RB)lH_HwifF2>h}4)MTNchZ^1Jz0qgao*@tJ*>P;c*v%YuNX3p_V|;fDypMR
z#z0Qp2$o=i6q*_dvr5-T1gi@huR~O(3SU~>N*(KJh?7TEl~j`x6mwJ<tFr2sPXX8L
za7?K}?pz?S_UGbrH#=@*k&K$V-iUd`tv?4Q4-7a}v&y*9T@Mt)kIzr{bwNZhaS$OC
zQN4IQmkbu-XR(;Fi&H31%@S+nr%4EU6GcV+{j5K_N*g8_MD+2%NZD>2pa8qbm33n!
zo~n+LvMp3q>(uTGr`KGqczTj{oW9nn!>hxjux8=uZ{!3oaq6Pao?HpqDPpS2k61AA
zw4|pYU>G`j#bzhP;wNZP-V}<?6{t4R{ziCu=$XUUu306O5-!(+D^!Vr`kB2bGbOaF
z=b+=Ws0z2OMJO;hPsUSS8V97P$SS@=m$vKhGNrl*XOEp9F=<(u@-mZE*3HdK>^BZU
zG${5D+v9q2g$o7Wuo6`ktg)jS5g}xzmQCk-7agW5)E>kujfF5y_Cz6`1B%Y?2j*V#
z^5G*A*W0QNwx_kJF|<lmmuj3g5Y21T^s0DRjGKWC&2YP~ZC*NdnEKd2Z682W${nAe
zNvdqsF6;t$CRs_Yxu+|wtW>ezex~6%wz<|ZySaKa(`@OSw$@byOB`q!VNk`JaCE!z
zW+7Mk8`L#PC`}Iox_b)QHDI9~;&#?$sKM&&xj8{g<c5AlXA8NCnnkkjxED)SM@|PF
zTe=TridtFAUfQ5j6hOKZ=H~Qs3PIyz{M9Witvl)}*|K9a8VS}q^qsyEQt~FB^^Y`M
zJ8cc1__P<tvyiXD)XL>Y=%mJJljxh&xu7U95-pZVR9x(GtnwGr|17nM6wjb|`3~x@
z*i;4++}s77*YFmfJhx|fQ19T*)T?ERlPvA)w{tSMQBQXDYB}Wz&I<tP8i8-heSm|5
zO42w)V~V~zk<syB=SSAQQ9FC94Tbw2kRJjWS>T(3z|wTaM`&P22o#qTiD~um4D4FR
z7eX_w8~C>JXX^XSTSA_axtG~5R1%lAb;!FE^KW7AN<~%mx=FI7s4kAZakEAQFGbiq
zqJ1dSL+|sTPg?hX#O#h=0Q}T!$zBDSWaC&fyd1r$uLTweJzT;v8kM2~Gq-BlPIuwy
zxGR7ix|S}FuR}=YAXt1M%+8RMFwX3lI`d`;w}Xy*0Id;!_FccF`LKRwm@P9=QO0MB
zI(330kOGOp4T>VX`%t#<DyoDiQ?ta>yO$<oK9H4Hv?AqOqTe*H=%+urmBn~)lbpQO
zPc^Q-Hpm#=;mHNaQ$$Xi`@CT?G}K>w=5?#=DTSJkSPQb)JfoPuLBH~@X3Z9p9LXeW
zkJV5;8vV%*S0bj}?cZ6#MF7k9Xjis8=Nt(tWlCsiy-$Bp;l95(zP?Eu&|Y={=mRpp
zvEF&}as@IsLZ06BP;)!ihJ3l=wGJ?ca9{ViLS&j2nE|*K{1Y2@*vGG)L*ot9yHXs0
z@29=lu8EU0U?E}-6-<~wq9*Ubn>A~OkHL<R1VWwrM39{(Im#%sUrd}qM~H@IARO+j
z)59mODu>af`9}%Xw9u=c-2U8anX&AeE@|W4{_qC@$Jsby{9bNwC#qC6q)Jl^=v8?3
zqBoxez~^(p#bWC-(ACUuS0<9QU6(IuW$x1Er?@-p4Zb@$2U5gQXfx}_$eZTHmoE-u
z9VMLP&6IH$Undh06c_EYY3@p<LPLtofZ6!hBS6S}CzD*(jBF(vWGkwx5`KL8`VqeB
z7mV?c!lBrWX{EQ#<8%x>O>*y-iRRD&1y4pm*DHTURLEJ`2NET_C!E#}iV9iUZ0;VO
zwK2;~Fu7(Nq{ot;JLqYCs?2A7U7X`=tPuZ6nF>Lf-Fu}KpL%rW(z1rb?3^b-o`6)z
zUni6MoAH-XK}|CRyoZBB)IXh4a>>2+mI=l6HSUJbxW`8BJGtboD*XL2rbT&vavei{
zZBBiFUGu)QZCwn7k-gKzR)6YRc@STB+aM%Zx1Qi*LF*aBZ)!0tfo!hDxD3sdI9*=W
z+fi`|>~fB#5(HfZjkoKg9CQRS!p<*bd;%P8xX|hC+p`A&3-r3gF+;xo)88hH5{Ibw
z^T6U$)wAMs6EakrJu)gS9p3kU1O6sP0K<+0Ai55U*6tO<q%Bcg9EGtYEzXb}w@a3~
z1+vVEwU8`~)o$8p-{|KipaDbX4~aZrK(DTNz@Pk3E^3+-`5*!2(re>tz^f?h?WlU%
zvM{O}Q432|lVi}0bDPaS`-LG>!_~*aqq1gK%4B@`>AGUOGq)kN->PQgWYA{a#MTY2
zKTwpEE=$M{UZ`NPO~D@4Gw#RP4|c9a15$I5Cr^xav(iW5_Whaq^%(`4bK7)9R3AO(
zE~znX_tuTeb)e>lHhD*ZKM$=(6xYM9c@;g_CIEJCK>*94SN6_-lK!RKDGGRFAV-W`
zl49zgL@U5U@n#Zpm&D}ulyFB{D6N-%<KkK?R!%zd*s5%GZ70^5XWHhIcUI5quKK<w
zQiTIH4GAF=`cWOJ*L*{Iu50WyJE7&iwoG!VMwU*@7WBA~PEDWYmb);z<-Bv{f$~28
zO#f@>-Pw>N$@weU+EK>%;U7Y|8&~(-PxM|9@Da=4p~S$p!ER-qtZ6+vXI5|{UmAzU
zx@+0(jFIwM?ru^vmWZy(-OThCp!A{7ZfzpYL-o<b`UO82`@0Y@KisgrVM-oXnN4M<
znR4&(Ufo|d#|x-21<-+`{Xj5`-)maZ@6@|<2K_bPt|b(xOMk#55Ar@}QYIq2(fd#x
zYlUu>5lV9(qTZ_58#u*#Gf6u-(qK{@iGm#mwV(GXG5q*Jlz01{JN8lc<H=c%fMQ+D
zm!Y`ZwGjhOG_YzC((a%yfd=6i(uu!FUfR){#BcA0i1THibUS?84FWcVzw{Z{z#F=b
zv+=wVP}^t8Wihu)ep&0WQTakH>3ts51;)lL-dU<&q5T~{#f+qg`x^{@x!rMvwg561
zDTJUOyff&xibb0*sd+9)?qE!9-K=?l>#0xEcYJmFeDa5KE|!DX`uwCXYadBR!6v6*
z)Nbabhutz4%Dx7AB8)MH=~bnBrsWK&<V1n~?R$2uhBFR#8(63tK=ACLZ^>R7GN;}z
zO0DKB_!0A-nOR}Jx;LBY;%|U&jnqUt4gx%!^Y`X4PRbutKE>TMsSzs-t?b14nOYmp
zp=EAk-ej`NbRNyytWrO=H<hlVADGRw20=pR)L;giz}OL6J&jMOOMF{H-jyQK^DK9a
zt3pzkJN@~D#wqX~L|WLbl2e;i1}{knUa#fcCksng`m6UJj2*@-zrLw`rCiczy_hE?
zGBeuQouO!1;}Q(cz<oOCnsDzk{P_W_+8E8CH!wNlcl*hCfCl%hwD`DfI7NFzaqFvq
z+h$1NmY((eu@m##T3_MO`Sg^x3srd^BqFFuJg%t6{n8epWV)~Td!W9`vZ;!;j@;7v
zOxL_JNlNiS+Hs%)HjEz}VDITPC%^zMyanH=<#N(=$pJC7eN}tdD3=u06IxfMEs-++
z6Dt|27@PHE$psWfL~hLvE}(XniaG4E*U=6slUJV~s~+f&C?en6Mk?HICh%2a`xZ<1
zh(ynK+lY5OzywnQz%V`!V~vj+mi1dAS%a7bJWpf0QpDC-yS=%C(_XG+(%jux<Ib+`
zxW>DSvF2<(%q66F@!6sm_5a$rMMEq;vqMpqzF3l{WKdZnvx;dzKo#qMd?A=Sy2X8B
zD0i${g$IMfhGQ}8KfaTpYI+(}#tr{0M6o`#NM33#&~7$cG7ga?KkxXlx)#=^R=%+5
zSzW_1hGJyGh)i$6RKPv5G3?zH+<O8LIFrStJg~%BwQci#w~cQ=i7L=F6cGXqI9e&N
z18|CaNj;(@E9%_%*ZB!HFoZvg#fkSY0RB=L)AAO@sjhi7LA|;}hJp^rF*~=hpg${5
zc8bYE=FWhVZfO{6D8S_pqJB%SM%uxm%<Pn$+Qwlu>z$%S@zs{C1-u!^RUHB?7<dek
z^H%{?1o9;QD_w6<AG0Qj<RIH``RO?Kso&-~B!h5nNYx{~9pg|DN~0SB>jCX*HG5P-
zB$wBiv?mWQ4^mQfF+K>?&)%>fCycv@ey<DjP)D6M;n0}tECdr}0mw9Td=)vw`OJx+
z(uhEAMLNRY#o&<~e8zJ^9wuGvA?#S5@8`&1UbxAr&?i~rv{mJpT!j?0f|B^$ecXeE
z^YE6%c^4a0IwwusSBY@M{f)clczMohqbc<c+B#<iV8sVu$;NsMk-H^6C2Kxtp|suu
zt}XbE3ht9YRZh}AA%G9BN0zbC290y6I|Zf{5J~R?)=~1NM+O$>hrsH#z3{3!?|8lD
zA*Cb>PY8v9V4c{^{TqIJbiVy>Qt69WVkFdHdEihf)uM4_^hOr@$+%BTT?8E;So_Zx
zQeg$k^Pcoj*o)XJMGdjo(5aK!U>>VXA2*?<%Vpg8hAJXnti9K?>TbG@S@mHP{zMUu
zN977S3+BpCcNZsi%A}OE@!|j6hXM<N14#Azc(Bm7;@Y|^y`Xc2<Yeyz+r~cHOM8El
z@jF8&tw&P@h2-^x(R=ak5?R?11J4h##~cN<#fa5jv-4u>S~;51>_zo*g)23G6BX>G
zM>_Foq<sxG)xpQnGTmq(?n4*>D&@i?d+{QB5&-ymcUn+d_?O0)xq8pia+)4&4C}XS
zEfrJXT_OUIIC&D1MYrz}O0Vkd=RT=i5-_-9aL#Pt#m(JD!)N7(PZ-J%N_SPcGQ#?#
zOUGSIfb_0yAzL-(iMAYj!9%G4mZa6n9&D1DvbBsKql~iR^d4qOS=?8o7NYtn?uON7
zW;OabWadRvyLW*_m^>IE$+`uUei^zxsQ6G_G<bY9JYfLfLY)Iw@a(1PQ>lC&jAakq
zwi<7YCBKxlPXU($jvROf{+1o;d(9x{!q4V44osJOjw*s|`VOJ>9OFap_u{!iHkdXs
zGT5dsiZWQ429Jw0hOmcuO`5-@g)J}&S)Q%mA)2_0*;ZZ!KHRsch_U->J0CSh=_tD1
z)uB@7)ib}E^=(j6f_3`<0_E&iycD=9M=+Cn*A$)_xF1Ekbn)4fs~L8era`tMY@1l_
zXns>}TRRA~&`N&^a11EUv47)#Ib%7AvLAL5(O)!zHSdI;VBqs~Ao)yv%gr~_HK&NU
zW`sAo2y^$e3kLQK1|ic-4)0J}gu^3<+#^%i6e7EY8K=@1_JZ{|r8_n!K+^dN{|qyL
z<qrAv$|lx#4b3xr<K8^=g?F){A!wlOYg~aO+v2?<Jc5A=Q0lat!T8GCvc=sl)|VF}
zB0oY7q<D@CMmi?s7X}7syO%{R;8X)l#9i}SrdmtAY3~`z`uW~pETWhH=bufTRnj#0
z^`aldG1E+4-HBcbqOvl#6|3l7syW|3C<IE8MQTy_c!$0V7x|J}y?mSHx5bk6002Oq
zIX4p|@6TQA3?E6r3=q)_J_efGW%=?FdAEHdkp^|7P<gR8oyW&*olnTjGLM!o!Aum0
z<ld*8>fsP0Q0h6m4!JWcL94qn7nq+%3wtboj&nL@2p7-twV_MYMWqhiWLEfeVJA&9
z5pdUfIk0VWR!93r*XH=R7+5P1J7j>BO_Sp?$(v>cnmq9Z@JDbca4O-SAmPW%jd$mn
zCLK&~*5b!=OBMZBva$@geRauz#oA{9#`rfk8>%Xijow*!Y5-WA>DUyJOJc=)FJz#z
z9H1y+k|ymqA#4#INQv;;x+FXqc}A?HJjbD9f`|VKH~+c6VglZS{f^?+A4Djj;(7<m
zDt5fI^JcEz_uiCbCu-$P^iO<*`ghLYKbt0=_O`?k7z=Qqygk+*@L``z&^^Qdu$AV~
zF9^%uocs)HzJzhPr2Oe_8fu#7ai;3Xw2m8-5rWUP_j9?6<@mvJ$>Aq|Ih;B1C~609
z0xn9L)sNQX#Rg<nOporB53dkqFGNpRBspPnAlh_jV&e;Sy5)V2SCYgh0UuyHyEHa9
zf#sfjx-%wMJ^qqJX8Q-~^%WfK1Q#l0STs(L)4%hPyTSB&DXvVFH+_ZdwX*a4)o5#!
zt3AII*p4UUNbe}a_kprV$UJB<L<KI2psp;+H}A_~fg>~eN_uAas1x(&f|ZJz9O@en
zJBQ-J?vuLFIMfL5`{<zHE1LsPc!rx*&9YYNCuicjZ0N<fO`Nh7(dH52geH7%Ihk#)
zt@O@zd=ZkDs{~_Kin~*XGNktXl1sHMdak?2pWo5J3}8vSMX}KD^H%X%V^U!DlWmci
z7S=LlY)M{@>JKQX&L5pHbxW!)<R4*ky}UVK%{#in&tFLdYEUWR^hS=JbH0IATT?U2
zuc)#rUB^CGxg0Yd919$}JLyMRF3-Ii;@!T$9@GWvDmEzY_*@-WUr|LrbU-xP7Al?l
z4oGj%tJEX#ia-0@ea?x23h!JqE`76}rH)@IA;kJPjOC7T?*lzN_xXVa0<8!bCl9k^
zGcR%7IDZu@g6UlH6ZU9Zah!HYS4AQanDlOey}wLh;WN-PuObp9p1b;yC#i=gi^+q&
z>b=h4Fvd)=EOy>#rSijQ=vd)(jNt2)y~A-u-)2S+%G6TGbjL4_8L90M{5Ypl*;?*K
zz}ao|4YwuCf8Svn;OeZh8O!S8x3M3qZ3Pa69vhS7^vVw5OS!n{JF!uGly*0zWIVtA
z1MgiRAdr^)l5afVVwTSo@FHIwk1G8Kj@sgB=3iVZK$#F5j^<16ypMl^4DmTez=Ja&
z52AlExP2}^51graC~Daz2#@gM^YpqQL7v0efUK_}ArNs)l@WC?AFp+^nyyM+s@i@H
zl=ALkR%}GHzb1Y|P;kM2cE#ZNfj_t{5MSfv_?_fv?$l3_W!yVW3yb4IlFJ4qb%PA7
z+c*#mk{D4kE@<)iCg)*GE%kKAVx94DyTvme&ss!ZJwkv6)DhRil@`jkl`gS!UJ{lY
zo@i)~^qHIFt888DTg60v;Hlp|PQ#xSI!l9y4U<P$*`*K+(JI!&ioA}ctJTk?C(qWU
zUEEQizK^4SjD{ziKJq;vU8nh`ZV1m?FM&SzRYQWqoWL9&m-jHr6|a0xRXE_n2&dZk
zv~zV~#?hY4Uc$2%|LJ7J?F3BLArH-)6V=5tA4{^e9{X|93cxSR@);&@SorA_>){~2
zK=zZSptq-;o?8$+<qej8f;vjQ4^_57%Q8^f(UC9a#{Ts8z{tRpwA-&EFd)iVs&pr6
zl-Yr-BM4&cM1y-2yCZA=m{8=BP47jm^D1t*u8kEFof;ISbK<K!9ZEEmVyH)cvZZJw
z<}52bt{YPr^YR+G2wCSY1ocr8{hq98nMeu^$^*0J*r0j`h8G>EtC@#v<II4$@uA~6
zo2b(GAnv{ddXcA`V25?Nc$OK^fH^n4p8T?ElPKxPEoy4*Offg-w{tUZUw*V&k^;v_
zq}?|#JDJ3C)_ntMPkY3XW^4irOa?TKNYX<D+LbDc91ZoZvXe|!*r-4(!uy|4!MS}q
zD;A0<^a!N}uh)f+l^>>aegsYDrAZv%k*sJ2V4-qMQIcvgVR~j8oA_6xx>rIqM``_a
znYR-tsXm|(?jt;-g8=oq+a<RpSt?dk#>;4>(T-r|-B#!75%N$%w&2>FU!Q5~a?ZS@
z*B=*xoYd@h@f!9%G{gWv-+A+ZgfUjq;^ziE6KqvVQ;@OF*2*9Mw_o#zEfA8AhcH?Q
z>;SOwy5G7lw0MIm$$s^F#!-5bqbb|EHHc~STcY$36=b3zP*@qQLt>`%)9<6|XH~CP
z#7bZFyzkqdGBHO{WRl988K89|U&H;kn9&N1-`*B3f{N+9YOc>_S(DY=3rz2@q+TsL
zl}D|Jl6b*!{)REOP$A4mU-61^593yMmbd+1Me=Xvc%4#5tb|Gh^XC&Y%j72*baa${
z=>=w;j<qCZ$2kBY0XblSAAk3O4}Sl&kCpwKsU$=D=Ldde_G_BS1eL5B4amA{hQHh;
zl59<qjVL|~mjgZ0v)38pLi+wvXtZkmlSa`#sv8)vSm*8Re_;In<y?i}zG>PY#JbW?
ztucqTgm_4`r30KG3W7z-Pvm@8++6yBaUbGTI%nTVNyg`EV`yIYb?b|wl4?w)VyW+8
z2_7Ndy;xe~JUb0zq^wK?1*BCL4Q1Q+KA;BEC;aRT&r^?k3tvQ!xrR!c8x<hc8T=8C
ztQ!oBhT?ER8^@<p+FH%oHxT{@=v3JG>w7Eo;@rYaqAQoZN+$P2Z2$7iaS-DPJ;ss8
z&~nWjRg8!)cLxsQ82FMr4ZBX84>f-(7YlFW9j`j+BQ0(5wdtfOY688AWNL8mZiD4l
z_Y5X=EgN&}@-Z9z_b4Dx&aC$LXCxLc#rKhdj5EEnm3Usm%@NRAXKuGjOb}3V^&z;5
z)3mfU#Fy96Ss^Tqz)?u_OQ}U`^Ek)lq-o=MRr6#44KrLx6h$w|6YZefXCu~6E;<2u
zSdDnlTT!H;0BcK^2OJ7LP9K5P4oo@=f;4-t9S+yHH$xApjREP*;|cYJ3~327TGi3x
z3EK|_ra0ID<XLu{mND^ciQb_{r$%Xp#W(`NzJ>&pnZ00X+Gtf-q<7=LehaX)upBmW
zXLtQGEA?KWzh&7{^cIS5ZO?Gz{&jW=mEMhYloi&*P!QL#WgXgt0|0nE0_{)TG1(7F
z-6>U_37GJOfyjyNct_iNyM@fIDVM0E-i(YaU00t7us}AY<6WkklB7Gi%niY~0;!7e
ziK_3NbN;a3b3X!WsoRv-^x13KdYXqp-xlfgZAuBr`=EUGZn*qKJnl-IsiFRb)S9gj
z`FF41g-H{S0|Fh8<hf9h{yB9Fh?AgM&G}Qa1zEM(8Ulfo&J0B0I1Djjiv1|W_(y^l
z-_@xl?Ch~IrjnD^Wo1>*&XR32Ow-2OI<%lgqZ&<yg%H%ti9bMWQ{sYEqm>(SxCIm#
zDP3-r;^#!4#tW)<6N}$WJV~rqO)$k9=qE0x@HdXPzKW9Lr(9_Tr$poz2Apsw!k>I8
zoBl>SU4=j*wT@DoVcTFENH`Q;53a{c{ulM6=!X{<UB%NS=0_&!;N?)%d^I+Sj6-iZ
zksFe~BYezJZtW@klb1L~s_+svk%$mTyES0x0ZtpHy-KK)isU!5_`SbYMX&jyMN(<r
zv)pT1#DCrLeAIe$xJZW@l0#jk%0!y(-m=>R_AKqMPibS8llk&)z$v4md&Oz)VY5%u
znGtZ3)uFT1dR?Jjng77b&EIhLf(>r!;ldxU>eKlcK5paMsxwnFqAb8Uqx|>qjbXvs
zDm*0M=jy3_%L1{@JIR#oa<<akdv?dQj}1c$#`F7DxkIL}y(IOig{eCCp;K!~HFxHb
zQ*j+dn^dDJ-6MtSGQZ}`1Pd%0MJ#xn!36jiS2123m?X=72K+}fQ3{8muI}_6{(x=<
zRuOclz30-(Qo}lu2fh5h;^*6A*UH-7&1x<i7$aB%D4`A4wGCGf@h6HioTl}f<oR7s
zUf#EYo5CTYokfDxwa(;7)|^!!PF*?m1+3~PeZn7V=)#6SU_z+CDuh*Rc}!7~6(MLI
z8sD!Hfsb6bfz>tKP5NG+-60C|OLA%rK3f2Ip*{5`N~OOL8Wfo^&*Hdu%;75b4<l{_
zuM-F%Vu7i2#5C^s(hML{&*5co@2u{dmWXsRPk;0>klz%Bsg{_-NSc+Yo|ZO%tby)@
zrDdREVXXwymfOJ7O%K|~p-t8CkwnIwSk=YH!^DLW`UwVNtjjCjz!9r>ucC`VBoL=~
zLX#Wklh2KTA9%x!8JZQ~ePaj)(sD(kDO_KlMy0D`Wz_gJS(sGt?Z}wAPWJj1bD_jb
zm8ca`oMnkBkLSOZ&nka!X@5a@ubV%IKtKv0l>oD8bKH4X^Tc8F&2ZZ2x_N{p9VQoX
z&y^b8uDO=VO{O+N633u#qDWUR@!^Yw5N7+rt+={mPo?~TYISxHAzjGav0;hQZ(#Qg
z!bp6mxY7nq%3ur|F<l?#qtnOOCvu^ry=1kWS=@lVN}fyPf743Gy$31FEA<%e`F7W2
z9t*sKLAlE`uV>}r^ok;scNR+1DGy`)PJ8<NaJk!G|9ty|k@}M1_NAo4vb&2$%-%=H
zWY4S)F=??;g)Yj?Cro&B9H{G4^$e}MytJ~uw4$w}DPO!>V0zi_IC`<OcSl^~g3oVl
zW6wbu-#139siUW^S0YqWWQtQ7$@>7xKjv)Q!2dU{9KwB+56>HfQy#po^|#moytr!R
z6=r|Llwhb+kC16q&bo)2*WZ@Y2|2WMwLUX5C9+;AiSpaMUj$7sN=caY+HUFl*dt3o
zaP>5Qr(#MxOU!l>SArpcpa>)p`iT;w&v*l!uc5PSu$v^zi;Duaoln}MTTvzccQwI>
zi`Ztpf50I$eX3%=86Aq{CjtqDMTMm3FeR(FpnL=us#JjaCE;($%NJi^eX^z#o7eRT
zh$CiXs5m_%NoDk()B#>28ho{F`bE8LH~>oX`=>M))M)Ia#Vx34xe11aHm_;ifBdFV
zAt%VAmfRQ*@l`wd1hvTxDfBbc4<e8h4jrLsy(M!B4ubrNMsb?o6AL5X0V)hx!2k`|
zS$>BXIxmc6_mnZdvUL)3!1$nq^!)#JyE2_96WvJW<(FJ~^v1A33?z+#`bSRAovCzn
zA58`?e1XULZ&6<^oLS%kME`4>6yx+{xy3o`Erv%F*yf6IeWZX6Ahm`4JH+ArZTr?c
zrPW}0j9`*MX}Uo>4(0$+!hiQ%H2`+qJBIXwV!(rK$)K)gfjlg*?f;F@Cam2@{J}8b
z!n}9S)?y81EgeVbBmBQRGTQTGSiY-?w}TXyy9f_wg?`@u$MRKUZr<!CkFtyWpkNYv
zYn<f26La?!`o|N<$$3My><jwKUeq1bT2DJ|T&OTi8ekHnhFeqVIKD~y-XlBuN*9zd
zaOkxEhS1x$(}Jsm;r(Yl-SxC9d8?5H4*kP_Ed$n$HbWfn+5L!Jgv_?79lg4SXrP{d
z4{}G0d=5`NpAM48FzpnnqpSZnh;zZegK#I{&8n+~KW}7_-*(+jC-?pgceU{!-Ce#Q
zFC@ML^i3mu5pK7+WSJ=rBt6IZH$dAc7aQNBWAok^bZp<W{VAX$H{2ERe;x0d`8;W@
zd#T}ji^GZsjGNPNgou&;k=M1SY-;??j|{lw+p5gYgy((S*N{HLnw4Ll3GPUTAp`$0
zJesBzCrCdMGGzHlq+@k2^yZKkO+4OR=aaJ9dWyEn0CTDN;fVk*Kk~s>K`T<xBP5_o
z%MN_Trs%&3eO+_e|NVYZxrgP=rMO%JaKjyLA`!=ZVE)oP(rlz&fWq{RmG#&ifsZnN
zmgix{mf>?-N41WEilS}PSMeho7k3GU|K@Rq>sm0oDFYj#`5+lBTN-n7xW?=fl3!a1
zeL`7P;9>48Qn(;t5{`qR>|~>ekXYyICQMPMQ2``eW%Saqqz-4XpvUKQlfnh8f!j=e
za8-El2bTBOjJGA95%L2mB^aIv)tm*L)t>Vi10BuHD_2UGeA<Y_cNl;BHa<C$Y#!BZ
z-~~K##q>pvSnxRi=*QDj$;$MoclG75VQw@rULt#fuh!R%FnjRN#prm_pL2hR*_4zF
z>qA0`A_q2HckA^)qK>{yZ*rHRa!<*rbwCYqG~8Yw&~@8swZVM5a;zQ97y;Q0hlyIS
zrHaj72b(yry@UgW6M*CZw5*V33DEREjyQ0-RWkihqZW|E=9koXmn~eyue+C3p_Aoi
zq~?fd>TKC@flcML|B^Y7c)NTO5aeX41d~2NZ+a1)%2kM6?&=;nx(h)qIl5<3hnT+N
zNucdnBEuhax(@kH{p*)CzkKdulaqgIcMT(vcfg(;0wZT^FFs*rcPTCi0UAaK4RmI=
znyKAY4(p%#V3Z+48VxW4XG-%1Im$ny;G2Udte(Wgw?7n|vl)`RBPu9W)iXv7t!p;P
zRS}nNyg7WdDsq~#@#5v===2lJDO{8-VyZCtVWHsuVbX=vX4RfB;>a-M_J2x6#dG%q
zz7P>-@~_Q*jKTVk;Qh_?_nn~2{Ge3Z*%J$q0{Q(;7VTe|9ew7Syi-UBj7>^Ytu1x7
zb-L+Jala>p3?LS|HLP8H%#jmgVY_a>24sX5?W50VmJ_UsM!LdKCF-d#!8IFxdQrkm
z{(W$OhroRuD9W7OhJrwnFOCM)qQ6EgIp2H_EeY(#*_BG8dr7_IQE$4~0BH)vrAg(3
zFih-+!OO!`0U-tdSlR^m!!bIc@?(z}N2iaHzYS2M<j@|);AAwNmAgs!zHs{s^wXDA
zuN!&d`6h=<Di2Zo+q<q2qu;dOBs&3_G!hffEWW6_r?D|IetP-c=?Tac$1i4KO3WG=
z6X1(S??RwsIZkhzRQ?Vu>K_Zl4kf=VC1uPSTv1!SYd=UCVE&GnW3a)d*Z0M%tJ5B4
z{3mW&9P1rD<4Gt3```p*QXZMCcU&80ibh5YF+x^AjXcH<T7w2GYV%El#jIt}*8OoQ
zIS_Tt?I;lOLX*2#Do{{1LIWvwE7#)wih3n8wQi|0opQd9Kh;Bch5t#qGvdj4AMxS?
z^Sp~bxARj9IFf$Zm&5Br+XCkIUw?j1S7BRgo8TueV@vZ45@t)x)HbV#XDu7GAdvmd
z0-sd+emkyf+gX7jWgsV4D2ZpKW)rq-)e<8um<RWd<<H%voccEvgfq{JKDwW;iB>?_
z#SeN~r-%j|`Z8#~JdTh1&~4_{Z2n8tbk6vU#KrV<8)nvQ+H#ZMIFUi3A7w|k6;r(;
z=Gz1!WpdkTS#aoN9jHPw)lI!#yA>6L;m792gmvgTatojL9OZ|fVg4zQ4nQSU>}y52
zsVlKI-op00pnl0iji>5FqxM2)_QFSfm-AwkyD{AEy>D5S95;iAb#d9GTjXC!#Va12
zXJ<i3Vx!ICj4cuZBJaOWDtO`7m37V^)*&{Cr)OmXMhR=`*xNUhkh<DRO0rDav@}nV
zVi7nQ!&E;8I<@ksK4$sf236K)Ym);5_@t@g@tXFP3EA|0X>|crl7$Puv81BJ?@iU~
zc?i|gPm!<9FBp%RkuMbO@PRyJ6daZOzaowojW}j?%jrO7kR!F-+=ZvY{nE_qa#J~&
zgbJ<;{alx2M>Q>I#I_*3E4AeT{yc@fAZw{nVxB4f^pYXnKMP`xk}^L1=R}@;#7Zkn
zu_+F$_~5d;Ne-oo_{MZikc4@Smir@C80OvDcG<Ggw8d>djFXvC%YB8U=33Z@W~Lmf
z76j4S@27<ni4)&XVN0+uI)xpc3idvRH5$w+N3BJ2p+FVXzGV7!7_^wfWidHDJTFb!
zR6<~_y=)&@qo|ZO>~hFrYT2+Q#*d8ITAg;@SPx);l?+du3E7Kz8@33RBfh;=jY^AX
zFM@fkkE9ZQL8hu|_Wd5oQ2#$eSM;KJO)hZ2>z4{*Q}j~c=(`r!(RL!9(K6D*80=+U
zOnkp@e9O)C%le#}`_Qk$ix<|hF~P~^4%eT`8d%A{-LiyzlVm)TgdrOY_7Vi!yXe99
zwKVjki)xii2wn7OgR@+{=xzr*qva@o+BbkIV^9N6*3TNJl_{t$JJMpT+LRR>>jq(|
z;;6#W+~`#2BZ=GS;4ewj^ZPmxbh{4JMMz?*37Mb%URgLA{LH+`<Id4_!-bOH)&A`w
zT)D29oLP*=6IR!o{9-;qjpIQd(tbBy<MKqwMiGmK-YX36hBr~m@j8D_OIOdH<3+K0
zts?WaVLbC`uM&hbF>^Gys-t*w0ts1Mwx}=My?<{THhVbp{U!;mib}}Fh)t7p=%RzJ
zssHruz)6F$uiM7;`={1bHOa}u)vbDc9gBUG2@9&mH`0_>*r$G9uQv<Ry~A?Sr@H>-
zt~}dUIXgA-xvK0h%;YUU=S}N?y!7Y@(;wp0dXe%%sLt0$x!SQ3yUf%@KlI@CvDJnl
z-OJ}2J3KFpyd3PDgg^1QdQb(<?4}o9Zd8*(6&2+Y)!Lh#qjAqp*Nm*cl2bEzw-8xG
zq4dX0Fb2G%v1(N?MI@reJ(@zEoDhLir)FtV_&Bw!5p@I_6f18-`{IYpYA!Ctj*Lxy
zyXr9vKtS?RMy^=DSOu~Tee=PysS>j{6Rmi){QVcMwuK$-FPkThSRv<iaux6cWN*lX
zrjL;k2B1OO1@aW!era$OP%4s`DcVML%Q@y8xEu8<929@pKZhJ@(9)-`J#F41z6W!@
zRya+^i*6IgbNVfL(fT}{v#a?iI*bv`z`6cns~EMo7rlO~HyBo+G-PnW^7z2)K#&tH
zH^~I()j-0yk9+`6b3P)D9_ONbN^56EE1q%~VWLycpf;&?3@;mwFcj1$i;}7+WP22X
z7XO%7XnGk=PjGhbLxLbP<_CenM8jg}4_wYESQhQIiPTcXIa-{5t~b5tUk~_7VtI3D
zK45L8;Ti3YWe>=DlUrZvU*ECuB$AgG^*Qbro~IU-KtS(R`mL-?tspPd_`1nN>#r~Q
zB*|D>oWsx8oSl(8+2C@(c;T}bQ!qxjI_5?Ip;q6ZKj40+?9LQS>drs=;~@+TgFUA^
z2oXU>1GtP5At2#3^_eG{H^jHaZP!#hJhhNix6_+twI`)k^&<*|W<RN|ya-ft`fAhD
z5-g4rtuzdl0I4s)IPAV;J{C6MMe^<jrx7wI(zZ{!BrXQ0VWp;NhRPc|-N(T6tIs!3
zDWy=y)%iUrKtNI}sLduMjq}9+uJYpFxx1{yaUsG?NdbqZ(orzzg#a#Rk_N+qRbUQz
z`dqObGUrr{3~wy1N7@PM&tq=qp9bW;xa4jq%+H=rqBa+aaUx5^7skIzHH}|>aZM*f
z96Nea`xvaMxr%-mA1=k9M$cXxG{}gwRHczWORcoj9uTe&qw>6wC`po?0Uv66N(ff7
zytaWDib~0%mL4_EobMP>&;2^FVW_JN%ie_9Z?42^i}e11V$xX<6)p$)9ucs-u7XI{
z1n$rNBVaRdfku#N=Jo6TdD+az6hH1&ri4;KeDS2_tIGY%mv%79pL)4rdL=g<$}gAp
z24iv=pW!XlRA8s0#Y1VEt&H-kgz9sHqvzASao<AfrJzlcjs})i8xU2RngJ6rYUR3`
ziCW$IgMme@T-M&_!_k%wXgoz`k3o$;QysHf@BV7$XWGc_#b|&Q)Z9sDZn8bCQ>+{R
zA9Y>tjl_;O*WhP(N*1*2aVCR{a2}5zY?_ZI^Rl;$>wx_Mn3ZC(jvB8Cvxh3BpZH1O
zkoNcKgbhfcWWj{W=S6II?O5<-?QGEg2%x?%=CcZpK=~_{JAV*}{|;}_fUPAx+!UHE
zsRDHnS|jxX>*h)NyE;c2oQh<Ak6Y<XDXK~B<v5Ycd#^lVac4U1lUD6T;3V-Bwhz~a
zB?k#*cxM{LlhzDqQHiQ6iyLq(XMGdN-rB}e&%aIjWb@yXFZ9a0Nek>Vzt?`h<utK;
zFLpS7hj-z(=8`zz&O^y?(3gl1y$WBxK1)R0YV<f%XrrIcq`hUk-EG;s5;jvfj#`n<
z-}v4hWWXk*%KkJlWknV|DM#=`TM{?b)-k(FFm8t*g6Dnwu2+}5mP@qh_;pn)#}$?h
z`(>-B`#?b6;3;G<071qq1;WvsMG0>MHo@%?D~_G3<{j0GhvW9tzVrr=b_TwD&19ZU
zbJqxwPo{n|!ssca@T5R7%j-Z$b*cKK3lh+0!3t`vQHgAcIWP!FE*E$6$eS=xSaEW-
zkqa=+9}qB*-NTsyyQ(Z{QTtlsD`CGZZ8!_P-!Sg_eIIrnvR~}C<@qxo<R_^*FTA#;
z?ez*DSvJ~&_9S1*83VEhpGh{~7<exk>)_$4wlRYBr7&Oj3r31dl{fQbe0ii_Vj@#X
z98KS~&t8}jaC~)JZ_ZWe?E@O-_K>xPT=X_=28M;=FPdHNtc=7ycX@X@<a|jIiaunn
zx9d$hP8sPTM61-TE%4m%BpH8xaNh=HQ%}C=?%DT*u3~T4z)$m!f3I8aQR+5?D5_Sm
zV3ZKEF;p!ne%6<h2`p*H%|-~dwK|THHv=RROn(SEAb-AR2qW;RZ)-#D?BB>FCG-FV
z*&M%|862h8!-xYAaE9C>IXtPZR3UzEK6m*LAp`cgSA(8y=tHWg^pr)%_54p;VC|K_
zDPZ*I8Hn!Opg&%_zks*uW|Il@(jC^PS0o{JTxyiA{wy)OOJrWy?T9cNpB+jx4tW&u
z9LqYrX^d+A(XcuH>!s1_GFj|eV1M#0f&Pv4IJ37)(7>ZlfPFx~DEvK)_Ki&W1@gy(
zC(>mT-nM+o^SDuqOv{5UWnI^>=7aUtZxXsi<>RJQqG#yWuZhf*QOdQI?z;!ATJ6Q!
zJRB$VGD=aZ#`Wm}<-Js?9+&E3{n_bnJ`12l<)Zh`#(O-=`??V^zR1@^+fFS~bjP&y
zI>A=~guY%5cMPd&YU^-(R#TAs*y=rYD{Zil+tPFdaWo92Iv>zTqzt-qfZ|ED8jtuD
z(S)e;Wbvt}4$jotNsrJl5TEv9=y)gUJjTEsPbz8=9U0aLBVGWyh1L@CPzOkDrCVIT
zef0s}$tA#(EQwi*(Q+c{(zPW6zKr#vKEwuC2Di|uCn`+IG@uzr2b8{;HxZZgAi%`x
zfVh^tQ_~S?j%QJf8rnHLA%ekLUfQ4>nq*=K>~=zh;{OKv>Odu3<nS=S0pg;;ZC6P3
zq9Z@wiz-%t0P;xES3Kg_G*&%fK@ZY_tnnlJd&$}rjZIzciy9SbnP*C+FIJRDFi5y8
z2Hj|<hE>)USLJ&CsjoqePseP<k0>5vG)C6OJWPoja~sFLXoI^B@M&%6w1B%fc?Nap
z<PoNFwz-L`6FkZ+KLUEXKZkblO7BaU|Fqs-hb?7L>4n-GDCB&D6r0`8#hXN-sG%CL
zV5d(-eB}NyR0WV^%vOIN><8Yb8{83nSZZ4|?!{?&7BV(256yJ^eA}KoVzq^(QZVaq
zhbkYM(h;~bC!?p>(rx7J{0zJ;pvFNuzLfBCE$&?q5V-tN;M+oLrfk|3{i}T=D<W_7
zLER-BD}MSt{?H~3c7KZ~1bL<~CIR~Cp=2QOr#5&Ov|9P=U~LH;Z2X9@7Ax-G<-Bz@
zQ=)(t;YRMurud<I6>FpTXXL*{|K@!APw*CqwZ{simcY>^y6Sd(5Wls3hUXp`z;1HQ
z+(0oryjR^mcRMwC%jQz*nN$7L^=<1E%s7|risoi5dSk1yGq(ODLx$@8Ky+K)jw*4e
zkX2AUZN>Ao#kg)6ew?QCJFXAyZ~Dq;c{7*$n7DnJJ#2NZ7YC-Uj6P=R0v0mXP_&m6
zpt6`=gQu6r;2*4Fv;9x?H<6LDy|!emUUQkh{&<g}J$Lgqp579(H>1il|KVpb+;V6M
zbH1H^^BWAj?0)X<2LpK<Y@o!<QqrdXb!GS3G}$*t?^Z@*T6jt;Pyj*0D5%PpC{Yeo
zVL%J`=K*_6r6((NI&JHEimP#Qqwp!ZH5$ME5TuU12vC{$r-tq)oh&9ATbgy%G!)lV
zaFTv%x`#e_Emtz<_WUth>y}Mr;&@GuoxI53G=e<e3)TPCBEEIik6<|%Fn<2&ZZekU
z*!5E(fB0lq9Ncbe*RtT%^se&E=dTM#RZ=EVMj;b7Kd>-0vSN8)%?x^>|7WNC@9UYU
zf!>D}YtRR%|EKN0zw9yrP|HWy|DN*K()!c(^MBocrU!X&?HFeniueOKREiz&a6}!*
zzn0;a!#;##-45cxEwLkYs3<e=OThn3M)v={et`_^haqLLXizJh$?IN2KL|~fI!_NE
z_x`BZw_6zN2Cea*AzY$lo+I`559#A)p6;$c7vDY#`LjX;#xX7h4X1Y2&>D7&x9Af?
z9{h0S&`U7?XVNN`p<amcZ_Nhah=_So8>g^O?}0Ij<N$j5|M4>&y?UyqR{}yPoIQ-Z
zX%U5WY15?=P%q_pn5oqUAacRz)UgNRE$uBA&uXbo@+!Jg82vkM?NF(3=4bIdM8KCE
zp_d-tF5_5{$d+~@<%%CDW|b<(*JbjOf8B>%w?F?QJzTRKocOoZpl-SO<*NRKut6N>
z&@oIcT7EKTSaq&32Wlg(bE^gjcusKJ`W&W+80!iZ{!^})ged(NdVPW_=jX;-0IG*R
zRYDe@J$O)Yw`Nz_pROqd8-no%4w#C*IjEe+cQ@;t1s66XW6b_77J8icf$i=_W?_W`
ztVL^asiw09<7YW33t%N||Ne0=WI)OZSe3)0BjMb(td__4L8Gp1>!43^#g-=4<$(W$
zUhj(9^Cfz%)4%%$sojq-U01{Bpw8FsPcNA-w`T|$J?YS3kWq;@XS`4nN;JZ#Wg7jl
z7L_PFV6whwqov>yD+^+q*K=>p5!S1p8N8AvRIb1FweHCc_Z8i3^chPEx|8#ZRw^{Q
zq6*{FxQ$wBM<3@wKtPJ2IDCPDf?;|;(13`VZ&7l-Zc-~p{s9wERH<aERv*@X4L%U&
zS5nXrMYN%{#z&J#3qeufda!$q`2;$Jf)Uz)rkMf!AWRP%2dd(NvRtL9v=NYid@fKT
zc$o;rK`K!T(I@Wb1YKnba~T?ik_O;s<Ipt-^gPqDv^{?XkVTcHqzRv&c)q;fPS&;&
zUg!d2)hWLRXZV{|x7j9Sl%L0R3u3^&?J=6F$l15#)HVkH9*&SVmXn!ynq9JT85S9q
zl=E>f%z;KPHo43xz2S`c+e0V-+=?wx8Z#@;K}eVzLq)VmAue9%(eNTXo^7d6H45-o
z0B^tdiu<*&C!16a8io!B3E5yQah%p~u&lHqMCTTc)YQO@mnjik6UsehoXM}2IxUNu
z2MLpr1)Ik7u%7VyQ}ZL1LTe7%iUg6X6<p-J#rnp2NDpQLc5_DJZG|6HF*;sXGdELI
zthNgD$DxVqMs?ua(qI?_Kt{9LlFElC?QC|YG}%*s;^}FpLLdTpF%3u+;D9=Yxmm4i
zdi7d-p8}^0CKxN9;)Pm*x6@=yCdZgXY!T5>-u+t@^kzY}Xd5E_tf`Dst3{@lq-|PN
zh><ETawb<TkGpJ-TsQ5eb%l+&L~*gUzU=}Kf1%9LJ^KffkfCC`nhm3(*}j~I2bCjv
zNOJz+YygCem`8x0_=aFA2mc)@g+2Dq>j&I}%v=Q_bqI2kVT2MJ(mnw3b+@h?x1pZt
zK4{LVXUa@!Yj52xkDCY^Ne%jBDk9y~B{f*qU7~_t9cwo7=|D1<_Y?5mP|TFX+tUIh
zHAOMLdT9lRoLrz%L;!D4-hq7nmoC3c{7h|%*xzKc4A}G_;>hXl@>f0wBse6U5zrV!
zGa(392Qk#fuUim2F!J}eUFJ%wFN7$~F6HwJ>jDU2mIOS*zhSu`LqwQT8L>k{up?8I
zkrGwhFP9~-%?y6RLjFE_>#33Rm5g5F`SL?aOF?a)xfLxwr3WI!KToc|+c%v4m~46E
z5bpzkTk`Usy(<ypH<$Rw+8+R(*w@@0QbZ(>w&y)seGsYtn`OuXTiEXj`pO@z^8G>R
zDY&Do%}GF|?F0K9%-Ed~b>sk8<5nb(O7Ze*66gv_ko@KpZY)7A)ZlfRXJ>Q>BokpP
z7+@y|z<5m33DHaBTy4*T-jAL>e-m^3nk_&;h8&`w6*mHHMr9-nFH>)ZLvz+Uv+}ZS
zp}i=m;151dUuq#S@kbEr`fH{2S$9f#VI2v=Ok&j+<Y>9>EoFAZw>6;F&vJ&ao=~uF
zt{aViSS4@JAneM=Vb<@yHV8;yWk@SBoy6(mD5k<DpvQ6w6{SkscJZtvpmJWoXFCJ8
zH4=aZdGtd+55%fC1zkl<u3mMmzfMkFV`_D8j~u3oReK!hhmr&$Af<32)#s*iq#0Q&
zKD}+RIw4~<i~~D)sy`Nb(5hzUOE4Z%XV;njV*={aB@l0aIYPgSNXG)y8s$Jf1>hvx
zjj9+W(7x36L_Ylumi0L?AQJ#@6ja`+V3`9C90~_D-p~bgcUS!o4+hS^L%tT@yMI7M
zn69bRJ2x>9MbZS8OBjh|JBA0+6s|*BY;yb*UsQzY-=RfHRajdcn}>zbwfrA%Zy6NV
z6FiD85FkN=yAxa%cMYz=6Wm>bJ8VLNYaqD0yL*DWySpz5?z}^O_tn4Zy{db^-Tk!N
zXU<Gd`%KSHw>A@~8)kOok2@g`9rn*UT7gC3ehzG7;n~+M8ipg35zB;$psG(U2D;r0
zcWr55aftyYz{<`9al6J}6pwkD%M}49G4t(KWbE8jt~BirY=(mTtcX;EP%QOHTemL;
zO7=+MT^qy%Dc^_#^3Nj6;l#vYzoto;S+JiQ<vQ;W(Z4@Ew^HT4kk~d=O@fu;91p-s
z8&&+LuJ7L6+6!Xjq$k2?cPwW!>=WizpRWYu04h`6=|QW*KEM1G$th<qt%`s&Xx7B<
zV5Y4p1=g7|MjTFl3-=arf~~Vr1||F6*yk?7$~<@g*NHkj#~Ha~(yFN&Wkd1Hm#>JZ
zR4zv^yJr#0bMS(^$U`<`Y6n1a#V;)Y6P838moWqXLA+yNBO%Y!2~N|5lsL^J!Nm0f
z1924_AO8p7`hcC9&89qje6%3U#LF_hLQ5d2v~A<UOZ@o+{5DpmZHhgbRvjV3K-aG7
z<D(Th*Ug`rZQG6Ml9^9D@Vg2$V}{#iHAvZ2KLX!@;qzR_)9)>+GKaehCZu~MwmIEe
z(mt;58TjCkgFj$PPC0*&1>9H&8oU_v=ZKxdCE_)CaYv7Jz%1sSWyy$=62gicKXZ`Q
z2P1cn5;sUEuU>h1I|kjrW0whAeJ@<KnTze@61}pAz)lJ*kSg39=}gXtz=>^VKKih+
zN!Av_z&d+c_0yLs$XJN;Ttp{0S9JdTs@I*z=5~cfv6k7jvbegoB0{}Z_w6SANrjFf
zT4dWK>$S!k?X9Uqr?t%)px350#O!|7A~pnXaayl0#c>AO$aYWrH`rDC@XZs`O!{l%
zLs|jwHQxuf-#^=U3Q5alwtnbR0XDw7b2wpO;ckH|cwq_Vj~-JC8xl&!VW}|3-b=zY
zwOwJS^i55#-ZOo#Z>@hLBA4q_XoKU*>~>#QfRk^LK#n?3<!B37K3OU$mJjJ<n@Fe0
zOUNjVz+O7s=rK>M-)Xibzo*^y&xSL7oOtBSvpZUA*jMHb&%8n@2|x|HmUdEnF(}-o
z5+Y~7(}GpbfWjV*7#a3D@L%4Zlu>3%+>=;%gu`}AFH+%G+ToCbBx6Xx*3`7EzLhJL
zqs>-+r>*U&m|{>p<$CALB{Tk~Yt#<yX)#=U{C@Q}i<tuH2HM{1Lrq%ilR)5wNmshk
zM%CCErvP9_k}>+NzRKTnctgz~VF|Sfv1_$1!iGm8v&P=`<f<;hLW}1WLMg&SQkqsi
zwGa{>hh?po<@ts&LF?uQ8wN)MP`q!!Umou+?&6pj$eLw30Wr;=)UT7;;@<tRTV+4g
zR=<2!(@_>pAZY`va2B3;^xJ2q(2VkSqe6&r1}zz}mqm#+kpL`i17uay)!z1qLN|s9
z99{C$m3cKScsIwDHd_Y?rB=C%_1otSROS`GH?}7i<-FRTkOgts5jP*Dq;c_kzB9xQ
zjG0UC*y3QiihfE<GYDj34Yz(w0pX)aSuy}w9w1Y=Ai(>z97%f}SkuBLIA(ONH#AnS
zx}VF4buVKl&P1eT!orbUI8PR<cXUEtqed!-c1;T0P@2cLzvWFGeVEk7KNMdm#IA*s
zrCZiDpR5F=il0)I{4Q!@%;@w8>J@{}FQYB&6m0?yeJPA=Lk$e@iHSPDSJ>MIr2P5-
zOXyFBPfps!Dj7M~oIPcbZ}#`=n{Z*~#LmL$<dwGfr=pE?`NgJmUk=Dfky*fxRKS8-
zr~S+h@)od)&gcx_XrL-A&3YI_j<C&r&7K|%=7;>EM|q2#YX+Cu4%otJ{=%1nZ`nv<
z+HRw}SF0=f@Lyl;C@_qf<B4~y8+5tt==h8dGJl~wY-ItDW2zGP0FeV5#s(ff-GGZd
zMQ%m8gLC)n1#+!bj)@wO4?GKq_pW$l-nkYkj!2R)SZHO5&g*043Rir!zTeIDrKm7N
z=MNMgF6c&+MsOQ`ML?;bB(oH4QDGmWUkHBR;Hv{8#XcT653nCXfgaN2D-yl*HD9Tp
z(s-xX!0_9YGmw4!lQDe62sVN1N8P8&6udxdg)G--+ifmLvUCtrSJt@C@1_W{;VTlX
z{*3(&C|OH$G|_6B8^d7;U;G?w*V5vG#a%I2eIPN(uL+46%u17=#jB_=`YJcb7y&1`
z@r{!UB?W_3=cp?X>ynE+xk`NN1S}<RCmy7&b3O9G<>Ry?(U-rG6AXtL1>?Pv#!iCR
zBK=7H=a35^+I-sqY|%f+ydG|}M38!y8Mb<W2d~sYejTY)v4^MiUf|ZrI9af}@_ts;
zFFB5+a<Y@hU}S`~h=QL++z{CML8!prG%7_TdE6Y7x`@=Y&sMdWvT7o?8pq(Qcfra6
ztlS}Mu<m<#yxOGCZL|fd{Zjb8uXLMa1Wz;E8ol0uQ5a&Cvv$5x{*DgnWeKm+Tg$@x
zGum9hffiLE^zIcqO5VGG80k_u68t0&rNJaEtJKlz;@(^o+^V5(28QdM5=b?b3U4z;
zf#p!xDk}e1V1-tBE{7~<6OlhwoT6R!j;4B++pXNkrr+vib8pVDl{<}6jV{Z(*vgVB
zJ(A)TLNNtL0WWh3V)N^yH@GAm8tieM!z|_E&jMg}KVMpl3iN<^$f({eX(Zf2!04Qt
z9&FZI@nj&j*pwDG%(VZ?%;DJsHXX{`i^*UKXA#yiJVuk&T6-N=d$za>n)787hX2GS
z)FKo4O?Lzc&jyb=WM9F;4K&{F$YQ(cJK|t>9Nt}hoHXCYFy_`dOy<wD<#E4&S4a`1
z`tZ*3k(2NK<}#l3Rp#;=?EK88Fw1wZzkE@K{1)NpK-nQ^Hh0tibfI~bl4?+RE^m!I
z1P^cK`UJ```Tag~>VU{g_V_az5@M5>{uRn!J3TmZk0+vc-#XtO5mT$)P7hO>>Iog>
zJKK)0h!AGT8R)jx*CUo=OyhZJ)Hxsl2?Jfiwl?qJ)L!E%OnvFZ8UsV}X&!g6XMOFJ
z*S_P2C!|HexPYG$8<qkNk#A<ri|$yT5JFhsoT3a=;pU+J#dogBB>Roe{@(7J)22Uq
z9UsP4aR_*H2Yp{f%fpD7N#UpA^uS3WNWn|L;-iAa#6%PO_O0tpCVPh;k-otXL%SKM
zQq1(VTAYR_ZPu<`bH?5Q&*PPML&=gGg-++bnj9sZKS~gu)^uWVtw{cm)8G_RVp3CP
z6Hi&&REy@PGL<b|l-jWTY@H=S_raRgrGXiHYatvkRFzY$^JbWwnXY64GDFTK(B$gk
zXQ2$KIlhMvZ`O2phGc)}*~#y2eDtzq=+Y{DyC+oG9b=%GLrNqN>8b={qFs6*6-3yS
zcT)d^`<`m+0B7cN;PJ`HO0%zWo6>i=N$K+WMFEz=MahE>^wwI|71&)P_l%l?Q#@a}
zpSNkF(pJuRKQ@>4UVUk(xk|1vC*Ur%!riK{({yv28zANvh%Zc4OWRt#vqnxV_33DP
zFFkeGELFo}nCxp-TEn4JzS4dw<nj4)=0%nd;R^dT@3#caR7tgw)gk)RuSPrjjD6@W
zF35?CB|NsWHE*@mvp#CO&b0XZ6{oybU^EeasuN*ZUCOncwdF0#vZ`(#toent^xf*p
zQMhD(roYkG3OO;H33XsC^)aeL_`u(frKEC8Ny`g+pa2J)5@YH2sciHyc5nBt4*lrR
z;VGHMo6FGud3|vN?XtPD;B|P<6&FI=b-@xWO-rZ9rMVQKgWQ}AMdZY3G6U7O0`TE?
zedl=Z`Q3k7Z^VAD%Htjwz1HHX^_n|i{+4iop%Dnh(CJj4<(5trK*fQ>xrig2jcS-i
zcnXRm;crm@tB<ZMhtv3u1zkq9%i}N%tq5VIE;x3f^Sy)GLYF94x6IxoQ)x|e>NS4k
z7Dy1jhNqR&?Y^IBdh0uypS7jHcC!6<_lN|#s|6**zwTdt_^Rr5yCXGX^YnyuE25H5
z$5f&5_I&hUclW(@MhSC+?LdK&$IfEZAo8#AiIK#?`i^Dq(m>Y);zs!4^`R{)^Y-N&
z%iGEVRy$k~){0W=(js(^5_VTg4iaz!!n3`1+$V`RjqOz=_kE_~G5SAy*y@YPfBtM!
z>U*17xNCLrakTIXT|EkvLD+_VkAzN%pB5LlTbL6)Kxw5>3;k#6o(_g9mq$(PwZ<4*
zoK9%n9b;M$dfQmas0iA{dz#gk-~17PAHLZpf+zb)=U@@*z*8_F`CctWSlD~nb2?Bj
z%1dM2lSj1D#_%qKOwo3xJ}NbWz`y+-Y==NU`Wp4&9zEls>-LAcE#+I3?*YhbewuT_
zjkj7cYH5`_yC;`XM4>@;B<_k|{m$&va&iU!Dj}>|f56t_KK7H#2q&hkg7CRi%ewlO
zuvhQ)%h3VCSHt5VmUo@(zbWNrZ8uVC_dzj=uiC6xOi31~VbgLuK6@2OnXAY6G+Lvn
z+mmvtdYkuS==|4bQyck{{Pdipv=q4?(C(5ZH>;VS-Fw)%EbyK)ZF@eFKV9dhX>@d!
zh=?#WSVvXkz1-#b+fEX^qc{<kPM5XJ8n2An4!m0A2v0G;185@TTX1kuKcdn%mn8N|
zdN?j1&}mRvf9ajoIsX-{&%#{M7?r25$1$hlgEv%QvC9bxlBRuft<pus5Ak6{o`H3Z
z;2GUZ3?e%ax&y3f-QhQ=*L0Vf+a}jq=pB*(*+s5|)Ds7;0MbF$XWyD4Q7^BIMJi~j
z_>8={+raJdCUms`IZM(3_d{a!5=p-gUFmCskNO4aMPhd@hB9PD_LhI`sgfG8D)LQ|
zNi24;OHLmn_98Cu*rOsCTcWk{Cm!G1%SvfhUGLo~yDu*n3qPE9kkK8O>J^Vi2|Hc;
zHC%fh3AAJ|SSLFnJ+7J+B4&u#_{AmZOeTDmH(C5#ev-hu7MB)mE<bcy0wko}fpp9?
zEQ6ssrPmN<k5S$2%&gm~mhF;&Kxj5_Iubc6ugfK?h7>r3ZD5`LI#o+I&)+pN_DfRZ
zb*q<9ITlY;6<w)%%M91*1(dS{Hvj0a!(9bbvp0T3F{13GDwc>jzkGf*T{Zly!ni$|
zv8B_2ly7CazOi-FcWD#~jUq{tqf6#uR4c(yzx6mTMZ$lF=6o{5L47iEfafIdeUGSG
zB{Tc%?`Ktp`oS94!XiuczIyZKh(i8A6$hZFqrFPrp#_)Z`1zj?^3I&D9A+D>*>ZIu
zhkIEYRjldeZvt^ovV9%8?CqsE>8CPi?5*w=uu+)JZX(5-7v-4q-8fi%cL}5p;qLG6
zd!D>V-2LQc2v?ahpSGAGT+3&noHFtXCA;yKp&PzGas>AercV8zHoM!jbFjWJ?H$pc
zw)b`V%Tm~T?mV6iT2GSA>^TnIX#7EWV0+lyk+F27x4+6tI2>R+)!Kq0soa(s<PjJn
zAq+hS+jWh|aQ=P8#g>H9I+OeHU`gPz2HW=X*X3nIqhPlA++Rz=vZG4CG39KjjUfa{
z_2nxq3AmeZuyeBK<EmZBGRaU*FxQ3OX9^vyub<z~a9vh$F0ng0bMW(%dT`%Z|Jezq
z`F%fNU$1%~K!%b+!FR#$SOGrAHs_$8QzH6dpz5qpT&hS|kxK5o8O}+9PmuP(oiM}4
zd2z0q0S|79!z+q^RqFTIZd;oWhm1%sx@y}L%B1?ddS3{~097k0JJQM_bwQhS0Au;{
z{<1+tcu7zG>TtL5->5&q`~jTMdoEY?M^Bg3rI(75|5rX1%Lbea($}a+X7;8)1sXFW
zyMJFDjIB|T*x143VDf)o1qE5vJwKbVepEKLHZyTyRdY3VdHF4CXJlc<s%2&BVoA=!
z$-^pRW@TaNLe9m*32a%~Iyk9)HZn0|l`wO&GBE?L$YT|^a&i7(=Jejd?z4lvnY{}+
zFRQG*i<#4B2U{Z-GjekyTW2#?DJxq5#47ayk}xxIFg0UUFtfJ+hT!1g;^Pw*Mn(F6
zQT0eYUb)s%*V;P$+p+cZr)|x3j(P0d_s8pCBpH?C<ay&B+z2Vf@F<lWj8{FYI2iKb
zB;B}Np~1{FD4COCh2-G(h(#Q6^a>l$q?|2Y^%&zb-;UW&O!GBk>As_l)gpWrryL_A
z|5rHi{~KgHO@X^PFAeR;r@!TbZlVrd5NE}S1_mS=G&wn`oWbXQcid$8Dg2~A#o)td
zfkS(7;~z=|N0l+n&3ET`tZBk{gt&fUd8&2V(q$96jV|vvAH+3WT^lajb<6}uu!BC|
z+3vR?cDPe~!$%41FBC9#pODc{Z>9ADF43eGd3LJKI47?vTYGB>Cd~UiI9O~Y?2LeV
z<#K+8*5T3AHPKpIJ8g4g+4rR!`aTd37pG2pPf{~Jpk^rXO)MiLW6Ldm^T5J5*zvV7
zu>O?ZN>Q8e)GoU^>X9cwXyirt+~$0$uY3XAkt*ZI=jjD0X_@D?T{K1ofBLjwpZ;DF
zLa@JEWj)f2Chd%2C>8lAK264N?4qLLSmHYR{ks6F)BV7hLB*W>yUi1J_VoPxyq(=$
zzjeEW>gqG~i4R_0?@de+h~A(g;{}n|YuE?~Z^uhOK0U0kJN8N-yPvL|Isv80M1!<Z
z!LFB=Lt5oeeI3Fi!cM~(6V+%$2MUl|`&;zv-!nFIW$!li1lyz=y-+^Y?a~JP`a>b@
zc$|O*w=CEe7%@CJ3U#6a{_X~PVgS1O3Y>MLqbn>eJs$qBY6QV3LnRX$9T{43bCUVw
z=_z8N#kToe=`|Ac3cu>Ko}?vJZ$&5&WoVGH6DYlvV8Nbr;pMBQrl#{Is|++nF_Brf
zfCi`FB^5YD-Y_s_Ze2@gvr)4*CUPWF*ae!}ps?gzJk=`2KB*-$<hLzwhYerdZLQ1c
zTGc+}bpMD5f(Yk2Oa`T+ui;_0KCfr(efs29F*`j?%IBur>a{tYBvseE!2VElct|Sv
zU@|p(V!*7;?+g8!_x!Y>VEM63)vCdv1SVg$Ld2P6_`yx=SLg$5iyk2%p}oYiHo(t`
z(i_jyU#H){OPcg5Thn$D-`+OXl(hJ-n;(n`Ha+Ohdm22WZu<7<#}<sv<GFljU1`sJ
z&&9<x+a?j(KQe^O=j8J1$7%jZT8HuFqP$kQ=G^p0+Tq|T0m`$hU(r-1!Wra?j+gk9
zk-wKmzpMxOKhSGniAV-LhsWEkwx5?c3>O#=RO1wkrj}T;B{WLIg<F0|&FhkmV!g9(
z+V83+)676dt|%|J=ew1j_KEpfcis8xHmn1Ot3$7fo$0|iVnjtAQRCxBk3Zuv-{m{-
zTya_NuZwE3^32Ri*AYNI>fN2SX7@@K3BQb&YFPX2Q!#UDWMDV*0Eq};(<_+L>u_|w
z(lQW0alzS{jAr025VJ?d^S8kR17ctuVLllCDV}>}i&Uh(%;2MXtMLqBpP0RB&E_6R
zD@w!0&w39je1B}yp#x8GB{ckp9Luwp#)H*K;(V@fFe$^?xxuwq8Aj5G_cngPm)#`=
zxRv44cMQXcLZ<_<9`uIOb8}I*#@TUSb4Mo0&Wq-oEGi1A-Vr-aXIwZ}3*kH>G>#@W
zWM*cL*JlXf8KNL`<uP_d67sBCWu{zy6n1t?;&!gTIksJs_o?P_kZksjs!SzQE;1Lf
zNGRekm+tG<ruyWf5PrSyxCv$svMt|QKW6tkQ^#N$0D~Y=a@yL>`2F1wF5g+*pT(ov
zEP)+mOYR{Q!wWNd;l3mBMtb4llv<6A-iqn`TSpG@qscr5+8Wdde><6VdMfQ#+R8v2
zuDiBYpM6ZR4CCZ>Ce5DR>R%yfMNtSl@3DTYl?Wdib)b|0L@&42<!0Xz`aUQjK3?`6
z4;7_!`0etP_qW<PLso~s1%(_g+w)(=oeP2Dv9wBQG08a6Y$FztyrGU*G&)lL=os;v
zSeQLBdIoD?=TWULsR$g`+<b|3j1v?hA*f-$k)t^v%;v*S){u7`3aRXm0ItEyn`|zP
zE)4M%4-;3r^62pR6Y4iT@)b3)Epu@M!kZs^mD4GEm?t*o8>MHbDYWR8TU+yTaLK+D
z(jTG|AJd-=A8_pDT5odt2_Fo7i7%JSsNfaZ+<IGJa}O1I`BSX4WX?o*2B${&C*ud-
zJI*QXa?O$+I+{Ugf8k$uDDgxXF>3kuf;W#FC842GbwGVly{F4!&cQHiN$x1?10M(m
zm5JpIycPKsA@|v$1CK<ym96dYNNk1v-kK7`^G{#KW}ZX5pM{>No|cx@hYz3E4J)x_
z;(Z!)S+utCu!9wmw5$#e-Umjpiv;&N7w#KrXw>l{N%}oqi<HJs_NT%lODbV4WEM8^
zQc$RDlc_BD2481POsERXIFKf|GoD>de{zN{w>%%tl=MoS7d3i4HvEc@9$)Lpsr++N
zw4Vivjg37LvM!C&7mp<7@TZKho;b<ydCUtFr}R@-Z*9-OA$GEb>}1bgGiqw6YlfmX
zR2MUBx=n}vEFoMgefxYO_3eVCW|xJIkrB_e*v6Xl;i)gV?x@(#z7q!YCuW`uo9Rhe
z*Vf{FSLyepB;Y>R^!w&Z82*(bc}9eQtiLO?*_L(u^n1Zor_cbz*|SkZ{5oxpiOH{k
z0c}G>)1C?@7Z1ax05nv`9g5o$T(wmGFuhQt3aLuzX8he1*vULOlF4=SAOWsykIOIs
z?}mj;UdFzd9vob}gz);|FIKLY75fF8to?GSNlnPO+OWfhF@H;Ugs=^{9oCrj<dQr7
z5XFd>?NB~N`1vxC&N)cQd_Q{R3B^?L?@RZspHsDEO-|#(qj^FNwjR<dvnv7aH7AP-
zFHqJK3Jj$<TRPP$TtgY`i+tWZW}lxi>0j|i*M??KSUaC@8<(OBJ1HdRPdY%3n*M~%
zBBh~&J)OS0kjXs^S2n$xUWPJIPmDF4cR=@!&-VXfwY1ma7<P*a0-9?@+=1iT=Y|&}
z#qF>{;p%Zex-$7^#Sc;?go7m6&Wl4K?f+yviA_)b{kuV>=6(ohYQ8X?%e_60Xw0C|
zvSBDv&pX%KU#H<TZd6oZnTkHu1BImBd+kdCA0M+x$c+WqbY5l%aih&~sNQ1CVBTC1
zC``j`oSMw#@CCm~D*RTEmPadYKRJ1K)WDMdHak1}x3*e%hm+*fm-Y7<%E^=n5`8c%
zv$rJ~d@ZTG&ZF8j=P)$%d`%r@v-|TvC{a>uD=@1B85kJ&OUftJ>aA#3wz5%+V`yz{
zEyO&f-c6pKo^I4I_gy|AZ=ht2`8lg-zz3)B5oDtOq^9oDjj7#T^cjEMIPF59b7pHg
zsb)4=2*XfcLp{8%bPUrWIXT|GVF)nm_vlVUyz|Z*n+|!5=9^UWZSEbsW0S}GF!rQ?
zWs+WL(=jk`@Rk5P&e7>1MVe5IfBx<8)8(Qolne$WtV9!gF#i`TzIDU#@(xu+^}`3=
z{uTLfW09wA8xm(!1e1?9-+$=?9(n`HWxtuw{zRmdA;hyWR78epm`+U{JfVplnIWin
z3w1hngB^}Y!C-;vhsCql5WHDL0&8e!03lY$ED(nXj`)~2%+1(z$i6Y1_;H$Bf_3r=
zf9TcX5TMoZ*>j1kH09@OpC7tqGx!W--((fQDR|y87CBIw*9yk>FR4t@rwKeC-31~P
zeW;jQYjSpY%I;wyeV;o^jM%Jno|Izn$4#hZUths?nP-i~`I4AHG1rlBpWE{=eAr@a
ztziiSy7*e4%z@~Q%0V0NdeoCiM(Fly;fJdIN@Vq{tb&qb-SQE}`@No_4$U^t`F`S|
zx?~o_#OYd2E0(_gsxA4UL0j(`O-NN~^dfz)=8^!HrX76Z-5tVdOirG+hVtf?ojV4i
z)8@9-0c_7<-BE7a;u|nvvPk3WlvZ<k1N9m|eA0N=Ons$&yS2Fb20Syvg~im+)twX`
z-?Mn)w5wZKo9fe3OvZJ(iePPP8y*s-(?Kx~xX}Z&>ChRQV|yGc&gAag?U5z7U}Xty
z1tGv;ESe{y0$La%Rd<(?x2xiGaU)Ba#H8J<5N?lEpisloj4_L7A1}3+;m>C!FmMhB
zr(k51<zggJ%M=z-bE2W*`1j8&&EMIX8O3yN=gI{4p)mB3dKmK=fuW>JugBwxVfR4^
zZUI;B>8rQhmF+GIPUpT@3BUT3Q-A0)Ft89@(sZ^~dJblIT&`zzO_~@*W_WlrcyV+F
zn|yjWxt=tFF1D=Ix9^VuCNqSC>Fi^Fj7b@bKkkZ*tm63(TvJqn*+U-RFp`XpenOFf
zR+jq?;?$aIgLa287|_0zac#5D&Kw}Y0FNnipy9VZU-1bVY?qAgnL+aLk2=j3j?jmt
zBhsH58nH|^!N;(}A!-)6Ags5IO^=|Q2}|0s#CM8&H-Bk60(lw)yF1%}KvFY7g|;BK
zxzRi1+RoLh7tox%6ft4v_Ug1PipYuPaq~eGvLiVQ6~8^G5RbCM&_yD~?5SO8Vc~<%
zX2Nb{ljnoc`AjTjq3v?3Myuqrm^~h=UR8WYabfE9Wws{l@a-Io`A%|1Gp=F0&vhHN
zVK#KNCf=aY_NjGPtGqWNff0MwBt%)}vl~|eIw?DoFq<qxMN!ckBtA_w9h(sj!U*%_
zSLaNGTFMSqo3V0vcemRIXId4uM@+g8WgbWVe7qK>g1&3%aG-HyfKKVS0`5<9_UXG;
znCPU!wPlr_eJlt5z@Pq_Ffd}G6Y&XgS^N@uHCsT=j}0l4IrhH0em|q5>6jz!kNCg|
z{%s73G>+%Q&Gx<!CK@T4uupq?r-y90GazT~i`FgCIh>N&`SPavL678Ht7kD)n|riZ
zLLpIjTkV`U+ZvKRoEZy~D#2ahjW|h(i5?H#MH5@SDG4IzgsoObZZ;{#H&6DrNJZ8C
z;vFIn?=SecxD;J(05zJls|Ybr>hRmj=fyGFz%w67V@*S90a3wbT@(_>G)h>kx@%Og
z8r~RP5*C>OVfd^xQdldS$(7(ilXJGWR>gCCVxkM6EZFiAN;NDvc+%9yxmzGDmFJs~
z*r*Ia48&@aIZ~U7!^7dIb__=q;^}VssrQk@VctyEJ&5o@GRk{^Y0&Oc*9RgRSroT(
zJZ6%M-HqGyE@^rVh9#si{nlvGX_}F==yW|+_TJ6BA%w=}CHTbBQd=v>+WEk!TV1bg
zZ5`#!vWg0Wrd`?~nDX5OgbsapD&x*V$E`eq-}Nw3B~u~MQ)3J79yaqd(IpC|Mv^-I
zjA&sdR%8SV#-{#YXiH9yVY>>Gon>W;=04b92G(%pUhV|bW&MRP4)LEt^1E-Kc$E`|
zU{i~Z3u12T@g)a>O3qDao#BLW4AL=dxNeNdyi=5d_MV2W>RCJ4ND;^0ssxpLjM0Sy
zG;C*Qpii5WN^pN9_PIy7Xz{B<yt-z)Fm8;{F01yYY0~U{esWNpkFcv-8-ZiV!kMJH
z{Bv9N^ARgXW$H6eS;(4lDt}k32Mma;j8Us1<arB-*d~X(x9j!1wKao-TkV=YZoHG?
z;bA?ss@Z$@t{~S!kYt<9H1j@s%4lOYnO|?JKCMMhFz{oqSOs3ZY$avqaME*K)1whp
zMmOzzy{?uPUZrQsEoSsid<6dA1fLE{9%WJ&x^9#GU^W8<CKf*-Xqe;e(1CDy`sxB-
zlO2hSk;+7=C-Sho6;n=SuxdTJJUs&ouIGSv_t7<GjLGqhtakv~r&Ij};_uM$7!$zQ
z%8B*&5Ua$?L=A=nw{qD3Ha$=e=1GQf@bL3<Gcpc&PxC6DV(g7(G;kYp61txNR{<U{
zTZfv++Eg7jy_d^#Djw54_ipkU1}=`dYX9izd8=*U@5rR8jL?Kmz;_iUbRTdW353I`
zyuS96aaO84xx!&n_yn$V$qEu*+k8%VV!)CLzIuHo``xSs-}M@E_iTy)a<Z}MYiVj~
zsweWa^bKpzOwWc=@CsA>HM1|jMC&=yTlJNxpd0hMWf!KPU>4HzQZL14EGe<c9o%d?
z*2abN1(xAXa>etKEI{GTU9p<>b}@C_(kVwtc(%$>Pj_Mm+19P01itJdz(+iM6>s^@
zz1cQs&?YA<i^F%gaK7g@muwp%oRHA*!|U>R;aoe#irScUs7U5*5IspIuW3w$wg67x
z^78VH$9YkucTubax$Ic~TjuTUZNk+Etj&-5ZN)M;LLtf?$w>>NZKQvbcDHv!)qX@L
z-mueZO)dVW{M)oWu#^3@F#hMymAX}+xQY&?AhGaW4PZ+2yszr`VRl)wa&xJnvf{cv
z#@<U<&Y?#~>98P(eddg{@2;<qWo5w$ob{7aB#Fphe__{$pe~48{qud&)Ey+kcLk{E
zH;yVNCnpt7%Hz_#-`a6D6GGHL*`Zx~18mDIEda88HdMs%zZ#yMojtV~_e7?(JWZ}~
z6wV!SY1;)68NdPF^8fgObWp|)<Q@@7<y2Lm13~VCP)K6dA%Rz0`~UjcrUg-9_}SFd
zv`Gtt&?((tI7LKQi%?g-Fg>RSGv2kz*eU4$^zd+dPC!5atenVj^zs-S983=%Ox_qk
z<*)cIoX%ZcjWi70kwnoCeIWEKEbShDjV&$rcZL$eLPKAJLBdEhYCES%QM&d0!j}<b
z)f{%e{%$8xR4EaWw%8Yij2#?}!tB$hp@4t>{K@06isDN}NeRTJUqNu0WAtj+QOi6c
zgw=VR4^{0dHj_jViU?*uHV?AZ25CKgR|G&pc^oiOMe0)0(n0O=anrN2n1-MWik$j`
zg~Q7EFGLJ}{)=aM=2J&SV48rcs!VY$HC8{BydAv2fw+U)3WG@^LZ_iHRuL9K))K{j
zE>253BCBFM%jkp@+L=~6O;$OVpT%=6M2d&m41WtOn>Ng>CU&GH@wN`x!xR())A`Lo
zxM^>|${E5!{!guX5R7-(sQ}&_-8bcBDptim)7DU|?Se*TEq)3+Ej_s0S_MeWDRE)w
zVfI4Rh?{j>q^{<DZCZ!Qf5=BkHf8C@ws48p{_z;E_h?9~g5x%25?qK@Ghb1ej&fc2
z7#nB944k8iI%uHa`ICQ9kfoBse*UKhC#z}6L70?@aDilx7=I6wPULf6;a0~NfF0mY
zI_ywJt@3a6yOVkHx3{+d14)Yg;vT!53;N%rtyOCpa32UXMymMn@!k19s=H{;`f=&G
z`u;9lk(~;d@{OG7-8)oArSu3^`1uUfOc^C|;(ng}s9sc7#Mlyl<MAiHwy7`KnlP6n
zOi8Mb_jZnY@l|4iS+)Po&cj8@MS&%)r`+dZ(dX$TY3q#b{Ww(DvCv7p2h8(<yTkob
zbY3G6|G3eM$c$R0c>DzD_C9Y8%M`7>`pGYCO_e4v(E+ZmWW66qSo9?i7An|;WQj3u
zhSqY;aLaw)1#y@wr|KBm{a&J*q^s~au46V?p!aRtb&Z)&cag%~+BYDLVZw1?@1*vr
znoIWl)_n1sw?UD0)_|Sh(<tf%HmNCMCSKfQJl(x%T4{#EUPIg)62!!Zu4_q1QS-gR
z-IOWkAo`jwr^h)}1#?u8ouba?Vbi846Etwnb*`W5^!Uw{(o{*X`hv*ZPkkTbCBygn
zw^U=yV{?Hi$-@F}44pm%se)YdSH>~z<!n}mzjCb*Ba+eX&@+bso$&fnp5MMbN87XE
zn6{>FbxR@~RlYr(v;VBC<Hh%Mxt4Ek=H)jqLu)Lbj*-gA*U#OV%PMG}5{#rQIV5~p
za(kXeB(%H0zIBy<NBq-#-G#XmpOZf(ZL{xWwq%;@^C+mcpbNOsKZ#(8`!53wf~bLt
za7-WntJc?zrVWRErkq0e6t#~aH6L!Ns}P5ofW3MFYetQ~Iqn4`_n(q+mU}=`g^EeO
zyhfXTf927MIYBj}uM$1acOx7|ju&c{J_SoaV8*AC52xvcgUHaAG&3}ebykx=YL|bK
zUW30EWS5nceQz%K?#S|GL8l@KWw#=S`HesJR%1Iq^h6{14zH)$-|em8G`xm#;7R{S
zH*M=zqRRV#wEi3{EG(o~%UAG9pu^ZXIhFy2*;2@_CX!`)oTXJpE+1p5Qc9kgvzo+v
zn)HKAXcqpUfhs3s=F*qP%Y`cjCg_y+@jDbo4#mqcJojT$6wnD?xx(A2jb5z*V&w)y
z<F{;Ib~yk>FnQXzuYNvbe&qU6VG8S|WU<=`m=025pLv!}Vj-{E=H_HT932Si?v_t5
z*d8CrydAvj85G)u*e#|a;ywkLp&ezg2s7*9TXA&;|50;fr5b$)b{(BKvys(6XRm=P
z@8(4`MbxcRYM+Ty7dc!00XA}2`1x(nJBd{@(1J;rK;U!03Ip{5=y}^JNbWbOmWWS!
z3l-(4oylG6mf{R5Ge2_Xl8Y0jgO%06Vx3Ldf!Rv4>j_gPO($qykup9`IzLx|5Bq%V
z(*G%n%<TDA?IAeL^PaM7Y{;(IP9f#?{G5z+UPyP;6s);CB6E9|dF+25+LrENuR+}Y
z?50uIWTiHcCmn@gANmwkSM|tkdjDKp_90J6<VVM8`9<?3uIjH4k@QA~*vGL9<9m2P
z1Cnz3vV*c5#HW<Q2Zr3?;o*a+g0PFtt*xoiWj$CFh_E4hAHH3cYMM^gK*j8w)v6~#
z*nsIYCqZ4=qx8VE?T4domcv##Uptc_+J@jC`NdunrS%F{lPP<(i78$j7B?rvi8O8V
zdTCl&jB2ho4?-X^Xj{CBw6wHxDlZ#5do{<;H(=1ym3CS;awcBuKc`hxRP^GsK*`K7
zGzVei=e9<$DB~?2(Ig<3B%ltx%2)}R@SYAFMBkh}DcLJ9SjyKIr~~%D|EyR1G6l+i
z?FG#tg4Qd_%YKcTPTb!8f8Y7D_DL;Pzif7TH$h8?*>IH$1iF#`P;@9SZxC{#;hCr6
zuzv&vUUC^GGz_glIy@V^Zw3m9fk<i)62I0aNNSPsIO73j`+P2JVHK3_T4V3L6b-I{
zY)>X!J<M*^-qqr_)^;cckQhO<g;PlF)9k6bACZDm!T|fe$#xJ=PE(u3)|@*jJHYJ%
zFA1H#O3czRA$d4T$GLVb00vq-2P{Sys77&jpLWeFtWE}~Uut5G3I^_o8E_UiRq~U?
zLdTzOZinIlW2CGKhcuM3UyJ4rPq3Pciu$_A8eAg-{VF=Z4VM%jb}s{t)b!B@M?_@*
z$PWetsvY3&+&|<R*a|Rve8d!9bC^E3Z?*x3EvrEVOXaAvKi3ZrC|@NR=$We+xC3EI
zrmGu_R1#AZQLW1Ee#yLhLq!WvC3_qwypZECPBVrQ&v*N742kV9`L5ahrJG7*a5nYx
zx*mq1F$tXY6wZsI2SGGzQNXST@N7yjlwI2-!G}kyFSulzLD&jiL$<Dm?(W|yBkhI&
z)kN2+rrA4RpREh9Bd&eiLoE(}Xg?tZW-4EXkl1{@iC=KZE^Tm|CE&vfR64wwuO&28
z;P;)(B~OYq7(DLs`q}K@4KTy7U<O3h-yl6m?7OuLt5Fo%enbLv!U${x71#PK(E3%6
z`QY3`FO04MoVBePJMjtYifW?z0X!o3Gb`pUjL-oDkAWSs<cR!-jMJ1k$|gJY{Qmp$
z0N{nIMHKMJ$e^j@a3?6n=qyU)t!IA4OUBQzAPtnGRTL{l7JYdwpXS*;UU*VM3WT8Z
zR<xa*DGH(M-oU_S&wonf8IN<&ULM%6b_Q#4AZ;%53=sRAUr8o=WCNJq@B$gBi{jpC
zz92KD_5lub$A2<My@(DTp^5czP`|KfuX=<#be6l%{)Z9&>lch}T0icAK8YCcDc1tm
zaFYe0!G*Vr#j;aL%&xKeb=$|b{{&<X)K=OBGHDNU*)M+$1|0sZ2rKmRA1+6{ot!*#
z`P)5c%XH2cBhNcxtnTQ52;_~v!q2Iv_w=e99$>Zds#M82+W9vtAjyY;?F}6yel#Y5
zjiV0*N(KW|w&5o*R6l^<t&>smI}>W~uJHy2Wp-un#GhWwr5B^@q*ms$TxS9Jx331o
zj$b$Tg^~gsQir5=rXKW<{xKWam__9SqLEvm>utoG<Xe4eK<M20$zHg<+33qo+Yy&U
zamVjkaS3EUJC*j}!pYa%b#~W@1PlWh>N4G%5rOuT-thP{HNO*|V5*gUckWL=zYy`_
z=QR=-p^n8BijfrSa5ns2vAB2jfC3QGG~fY1-Oc{T{V|^{1Vo0|bFaLA)Lmr-2*Zel
z!<JE&z<0At<~u&D_<|7LDo^MB5!XQM@LE`$Alp2^?(hEy<GBVhVNXI*dZ0Mk!+U(5
zEyBbRS`!1v;C)JDINXxE)7RwigYO22Qn(13B3^+&UOITN;q6Lkf-_Ed1GL@a{nsxR
zV|DgBAja3L@t5Zjx2C>&nzB)D{}@sZkFTLEmMWh)Ji)_Co-N&i2ogQz_**#}r9TJs
zqaVwES_tsUXjwl;b1g^c6(|$=HHE)}9x`}DDprtJ?o}e7NUjUZl7I-d6^Fs(H+@Pf
z2K%%A$f?-ee@y?633_N?nl=j0V;DfUHX9!iP;Gz%6{4pHP5{$?T{F4IJlJ5}{9}x_
zFm-1kJF+pNT{Pd#K*Jg|3;Lqp7ZfnP<Aw6cxYW*P@=3lQ$Nrnt83l|~zlfK?3%4vy
z>_S^<0d9%TRfgZ4o&Z~ezM3J&27c_)9j$E*DYpdxhF(302>7u|8N5avw=9#}hQ4k2
z{}SwmdvI{!nY><O4{b65+=Z&=Ep7iq&DR4`<gG%?0T$g|Pr)OevEBYBY>s54hT)6(
zar6{`_-qpdCC5dqtiA`BA^<!{R;1}kr+bk@n@si)+C_l+q;shM2$LOra>~jcZhV~v
z{m&GwU>aVaCQmO&n*r9?1g#SHMXYM=-+*ai>7Tn#0n3&61!hoPvcc^|IQ1J|;X4ah
z!B^Q6Ne0FQEwfeqYo7~60t3F|tT6yk6tw_UYdxN;f3oZjk1vMdWH39Xbrt1WeDNE4
z_)jHP@D0_Kk+%f7hlqWh8&>wq|B-k)0!GT9LMhF?yA{`J5GE(E1@IS{-n9?*f9+Ag
z*{-+cOS`9%PTB_7)i(jj04*(b+!636w0}_cQi$&EGp>=$Fs}f%!m`L4E?lKRImz+k
z?sfv$HbG@+ni-HAkY47@VY=Cl#OgiO8MNy-RWJ=I)N7{FRM}YDSlc>dYsj8ttx+uf
zcK}1BLnKa!f!W(kOHuvp_s|E*ma@j&VZ)p8N{WM35EWgd&|4Uq-d_euOwWIS%9x^f
z(+nC}c;)wYi#2wqMpd^6(Du(e=DzRW2MNs1@d%C$zG=BgnK$$Am#yo4g1M(mHEkPS
z!t(KXjBgXTCR=F~vOFI(#4txe2&jwHEI{V>@``wlbR}{Rb1Pk1T{j>kY}xb9Xc78P
zFzw$GNRc{Scj*9gJ(aKE(QWp!dE9MtCzA*hog5$cIKT4k=v<kjf-d%k$8&0*{K_ei
zoc!3}m~i9yXFn+(CcnDaYwXw7(iO2^dRorZL8$y-Wa?|=`Q~RyxAMA3?Xm?jC-Lr-
z1irK1o2o~%XWEJhuHF!~!wcEifMMm@6vyCGK-V&_>5)Puy)c@@b0*~8Z6<Vee6wF9
z(ACyn9gWmZgAQIU8R1osi8yD?0Oo^ht^GR4gML>fyY7!!`Lh2|JC+5j_NnR&4=0rW
zGt++mLHun&i;Fh|<IH_6f$y#tZT`bKb;H&oAOQD?3i;mXCbM?Sm(&z9^?!^ma_V3N
zdd5aMv+3y0P?x9JJ2|<FuMwUx8-)ssn|rkjC#=y)1h5*nL_c|cSI<_Tv0K7&I#zE~
znk(unuGM|FV)Q-zHQ4pjJ2tc<vq--t0S8xkz%;si3JTa-3&$T(4$@m_d=wQ^39<8Z
zi`0FUShJzE)DcE)#n>4a*M@oUZoi*?iT>9niuAHyCCg_msa@ov-0W(vsnLKv7Tu|%
z%N0k3G9YaR_$AJfvo;wlbSJ=ALsoOA4$w&X+5iDr3ZwpA5-PXWxkAx5rhUp-ms19c
zy<{KrKVRk8^XF<OLN+0zEnjM6<i85JurN}Q?VNqYD_<V|i6^KegOPHR$8oyk31qFl
zJ#3+yKF;<u!!u0btjy2=Ug$%PcH4R9gq~PmTrvOBnRcGsx6nrl+9M)<eZ*-^i0qip
zMuXK@p3Rl}N6gqM$;YMZr?_%=v%UUD?vu;4YKN_QI2|{##mbY;MM&EHd!=CWwIioQ
z85+U}sWN-Bl5$NsnEck(Y87H<8`tFI<Z!7eZ@bHdJT|2xvqvB2>s9tcRN(U9`#<(u
z&FAn>39SvR_gA*WpQu5_pQ?9O<K~sP9gj`z`l{Nhwy0M<)R;yqwW@+KHHzdeS>!Yw
z)HmQoE8VNE(Xyi@95_7OWkj^&=ieBDJFlI0Z$5y**R&*WZ${H}aPORmc|Wgrelek!
zZd~E^it8~}TxvZZ0C5Q0XFGn)56*j)SiFe9O@A~1Qv0OG`YKW9n)Z1oBFDr6seIKp
zlXT+1su`bnGQGtkjfQOT&g4<NCg+W*rHlZ9I<BK-omY;dtyqwsjKP(>5sT_Vg_G;b
zdt_few10zE#dWbqo$h}yt6AV^fM)g83IPdZlGHJgQ3aTM`)tJBi|>q4LjKu^G)KE>
z*+u&|9|LQbNDw1aBbEAFl{wF!TRsz8RLxudQYQ6kb1X(QcPhS#4eHw1#vR-`SauAP
z01p=On#lHjH2`+XDycV?;I>?v>-Q8n5X?W8gemne)}8LF_IB3RzTwD6&!AiP<2{!6
zq)QY2MI~XxX>_xCFyc*8pOXL3t?3HibNCmGQQfr??br|4zg+CwHc+?9fqX1P^vt8N
zi*}SYckk*7UHHyJUZ;=8kw7|u8`<i!OiH5_k?-=s-7luxp*dtLNK*25&a%Zon!D5o
zlf7NF&eR7y!(~nB%hU7VuSE}++}9pAZaaXJe1#c-<xVsEg}$uN^Wv|!B%u*W;_b?>
zBu5hb6g$8w$)-4=22K<qlA!HHwn=01>?oDNaWG$YE97zDm9rRsQ#G9Zl4M<KvDTLT
zTB?rUue5n6!2*cTAxW;lX<SU!2;A7HaWc0Pf#h1NrS)jHGYrBv)<-E&VLCtC!r|N`
zSQOW-(SZGLOh5B|7}RTBUHbBNk41dEs&#;?LU_0v6uE>3jO(DnpTiYL%Zq*YV>@!l
z;KJtwitV)qzc<M|7H~C&0dC?gK55jR!Y`a;Bc#(3CM>mZ2V7jXkWYA|3&^qp*<rGS
zkxIkOK5d^FLKBB^+l=B!>rfRrJ8c&{KO0&mK22B>r)b|uI)Kf0po#;_6B9G<)k57Q
zI<ChD8mF0=%Sd9CJ?5w7miWD@GaLM)cgcdLisp!v@pQA$Nd;VgZTuOr2K%9cBePUS
z&JQGhaX+4;oF2sjroK}_O1cl7L`0fHVYc3PVd-juEHb@W^n73%E3mG4J}!0tn*yO>
zKB=JRm8$rirq{UWZ)&refrY<TzsY!Zj?=xE6$K(jPLgzh@|$)G&5R=NY^2Zf?IiTL
zaUx1JDOmINj<YL97OF(t-Tj<)uNtR`y_~l86Dyv+0_dbp>D>>&5E{xE44%#FI(Gn(
z2FRz|x<*5DZpz2$U@<NEuD6edY=RXyzPm`|PKqC%qlWHwc`+ws>vsms_t5FlPz{2S
zwjn&><9&H`(vn-#a$WUW?0uw?Lt$ZYi`)5XPf3FV4sKn&yRci}yE7X|mj4&1@3joI
z&V_$_mFt0zj}tKFYpo|#uzrQuLasKJ!A0!;#*v1w2vDCC^o`aZ5#J_C>D-AhhNJ8N
z&)4r?a~Nl(QnY=}!#AX1Ifpy~fV@8Gk)B@t`VFj=fm3>DV08}X_<sD$X6l5A<EV3h
z%%Y8NL?Hx%s^XORXQvY;Gigt}A}{@?fL`It_f>kCQL0p?70o?5`T@|_JTR}p_BCyr
zB2Kc{z@c~2(mr6t3X;czfV#XJdm{>hzcPEiTj8=!x(AAgf<QG$qL3`FD8Wn^sXBJQ
z)~q>YMF1*Vi1fgDDBF9+ZDxw1(v70ebg6~~z~2hPVi^jW2Oah~N{n*%;TYlrHOfIu
zDu%Eq8s{m3m_6|wF<bR_=45sN__YoUGFVT?0fR3mG?a%2{iXuv1&Rnjy)<GgQ`1sO
zgmpGLGyxzv@hhgzdBlif3(S!41DTf19y;L2BIx5+2t5Ai4*m0tX11YC$NU(d?vo9$
z5(J6scfz8~5c@~cCH|OWCuwHu;0DwLl*JqkV}7G;Hc=fhsNv{kR%1W}1Z9wM+<uVB
z$@q_q?&uwb>qvo*sDKKCaDm3XcJ&F5oGEmPfg&WksrDW9FEpWGiiQbv_a=S1#7702
zIoP@#D*D;I5K;UiIb3+co;R8Zo%U#<W?u@?-QyB2@ZeiH1OdOoy|z`ai85XS9Dq0Y
zu<{RH3_`?Dn9#kGqKV-cq2KdtSW5vI3W>&E2X*l+*O8sjBGsR*oY>clti9B*7nMQ!
z4EcMu3Y_4O=*6y3uCGPEZ+nrY*OFh*03o~bicsyvBoJ^a5C;8gzyHPuQdNtNS0PX1
zl+SUF;N}XD0V4U@2jZx6tkJP5n?0ZGe*K3<8KnL4;4Rh-sLNSbXZ9VX$uui<6Ur{A
z_Jy1jG=E?M37bDkWq-sRARJnPo1%P~6{ZSocu~7n8+S~9VtN-k?EgTg3|YL{V(I1s
zE24~uS%J1pada?&dpCE=XI2n;UoN;M)ff999)f9Dn7(o?OD+F&Dy35)Y#0IXL)7vu
zV`sRg8-rF47QvMN2a-xDVD99c!8nG*R9nBxBUNmt43rT{RbD2JY79sKwEXb_QdzQf
zza->l(YXjP3G$X)6Oy$!RWS}yd+-S@L+>&KS{uShpnw<Ue@cup;8b~OsF5$d@UsYu
zt#ke!wYXp_IQ(SUhcWu(MJT32@C`j03N}x4vkgnuhPL=hEr3-m==_~ABug6;$r3U_
z_k(1y3dHl0O`3dX4<Tq(SE3}?8RX5$=jv2^VWUe3k)guKqXh2zY~#Dg=>KW@cRsbJ
zBL6Ml61I2X^{ZxApoeHWM>rIX`wbY(9?b{JIkRf`_J3f?2{?t*QE!RfLM_^4!h6^?
zexR{T%sz~HX>}}<h3qQ<4{@xU*%#pml|TnyZLp2VTS<)8R_?V5Y6bvcNK*)@A#Y2`
z-L-ef*D^1gg1%5a5&&)p_<j8JtwpEuw*)z!6eGfxC_VrfM9v4mt#d0Q0jK`6%4?EN
z?jMx-*IPqdUxb-viDV=GbNh3A1Ym|B=Qn|%t*Uv(wFN0`+Z8f2C$t2*7dr*{_Ub)k
zLr9myJH9_@a*jQXYJLXTl>_a*MF9kf_Y>3cAzc_r#W8Nli>5_~lD`3)%;*8y-9I$}
zR}j`y{4!=Pr3`HNlqJjkf;pu{a<;N}597<2)D$wXD5qzF%N}15600-zJeb_71_3xo
z_Vo|Y`^()G+9CXzLl5Vr%wRJBD*6XG9b`ght2$^RL(SonqutF8K*?|5l_6EGN1S7M
zn0!<5ZlC@Kx^X;uc<qJ-uX8(eLK**p$S+n8SU|6rI4XX~1U|@f2H63LT#%u;7`9=n
zzMWB;lbP*=8WC!;xg#S0hZy3j!}KneG3#fDZ!7GLrfg~g@&x+DR0tcsbX@4cqcuPC
zcgSZoV&@nD8+wW17`6(o-K_P6q%yBW?-14n>%JJxS0(F6V10+RD@1_z*pB(}FjY1$
zy_4hE!t`ELD&M*=#niw_SC&u#jTKnufzEANeVJqXlY;#DXxx|q=-b?{%$<Ko%bz-;
z-ooqLnaB5SzHI^ah(NEUe&HGxLgCy)!c{61s|~fJ0b9zHzSqM7^Fa3+E?jI%wU$Y{
ziCxLNMCw=@c!3-r-U#AIz`Sjc6Owu|@6{D{^Ma%D>))XF8V@dvDwTC)r(3D^|Dl+P
zl?szjb;*DVPUb4AhY6Uk1KNs?zZnH_I5ng*S;$bAbliUUujBn!7LY18l#*GR=-2t)
z7Fqw=Li|Vg%#H_WfoF~>Cp#U(@0{{C{3DHDyb06q>;e^&r#fojAs}H}JhXsCd5$Vm
zIzD_cRU+7_FoB06c`VwvWq#ZP)8mZ)kwW(?cW0u_{ZA3|0e16sg^>Sn`ut`Qw8fc7
z>+WhBDct4#wGa$+@m6mf0e|$M_<4#V+mO*O{fK*^n+4E)^3o<D(uS0$J}NhgHQ+_S
zQtp*6QU6c@E|ff)4ABE(U(rPN$jG#&&s;QS=g;7x5Q3%hl~$YjVH-cxv2(A0ze|h_
z=V-(ZR;RjhsH#a)Lb3+ag}N9z7wWZ8()b!cy5D{ouziViSX||Q<)s|{7z9?GJ=6}u
zKAWv88;OtKSK1zF$MlpIYa?WbLzViA%P;?Gd4dt~mmp&2PW&OJfNC0P3Im8h90mQi
zM&SrW>{m3D{4iQ{p&am9Ry0M0#Tk!v?$8p<DQ7E4))(4k*zi7R>C>1XVCDQ95}cd=
zn`$d?(;Kq@dhXtF{l_vjbRguWF|%EAzsa?jrRO#e(-6oen?1bR&3Fr|pob5S&w6Sn
z-y&hE@Hea|a)Yg^1Q1k%Y^@@tG}&VYL+-Q{U&kVy$W%HHPKu9Z^>&^?#%fNM5=rMM
zoZH)E0$BD!;OX&OCrXQ-)rk{lX#a%BU=oiTjhGd;1u0R{N^+$4+prB#z@9(4YwItu
zup>03F}xhw&Nue|aCD7<b#zTQYGd1I(AZ|vB#mvSvF*lIW9yA=CpWfj+qS>$cYmL|
zdv<p8JTqq&r4f8!Zb}Wr5jK>v0V(=>5im7@UT%KpY{K&Qb6wjjulufq)yD+<T~?xE
z@9JDSL{<P#%?tu#77qA&MSQvOU7BJ*x{J*PZ{zep?)t|;<ey%4nGRUA&Y#8&b&9p!
znbcVN;p()ygnY#`wtqH^#@6nTAZ9Ep&%8yDLSV9K(yHP-ahfIQ?QI3n99#bUF~Y2X
zsgu*1MMIclO2vJ7b(!IHnYn7bXg$k600<d^j9gbhy-m>0MIpxu$w~7*9XNZe-M2`=
zcW`sxV_S&Gl#GR;kL3d&_BAEUj%Az~Q~wC4<h(mZ4geDf$tDaMU5Mz(6zF1Wdf5qc
z6a65e0hIU06(O;qM2!kA`y8&3^dnL*t$O)Dy#VSU@5(RjYvQZO?NI>VCBKeckbthz
zCf6Zu$EBaf84ixkS3|?X5-o9?CexL}(6+Cm>ds9TDtux3Ul|j_)dBDP1a-O1)1*xm
z8;PQyyA-as?|{^_B-(vPQ+k?*&Qi6}{Az~wpShn&ob3*a$1C(9-_NDLemNQ63gb*?
z!~D%9xGwu0T)@E4Oo{R6Jv~_!s!N?=jYK1FsF;&%D*jyv_!HVpBN31H#dM&4jb_sY
zPfARdW!>^2U+;{kz6shJc(BQRTG;+s>BeR^cXb9Km~VppAKMQ|GD8XS*;q%JcFNeH
z)T|8Qno<8_jf%##^$^8cmz1&1=!T1nV=3uLFShw9Vuavn^FhKOM<6LcuY_si89^t|
zqyr*4iNOW4W&di=+RRN;lX{yo=H<amwxuAiP(ajDK{uOQR;O0}KlJ2w|1hTt@k<Vq
zDQwvpaa?J#rHU?0VgtnanZN$=H1!RSzUKi8tw4=kuFqN3)&2r}5-{d^F`LD>B3v?4
zPo#}Qer-n(6Sl;yWUHf*_SuZS2U5tl%dN1Ky)(eZF-s(GqcaWTYXMjVeC#1!&aP!W
z<ZreiS{-1hA-|mBk&zIPi20qjuflFaWHaQ3*M2~b0g#mPQ=L0{-tgcj8u&mC@nX3g
zDN7;$MeRTD)<x!SHBpM}GTo>6B39@%B~Fanp&>(tvp=F9P$+tKqAzArw?A{=Ggje=
z#%Vpe?Ec@C-^p|qR@R{ut%YKpYbKBD^7hjOgW~7bRBZegwxy}vYP*c=w^Sk(_F1SD
z?jm^pXDA6MoQdtq4|SZcO1H<l8~dpL;)^;$KxxpBVQ9wjPzT3z=%|^(M|!oq*lVHX
z#(yng<QHqJTOyw_XGkIQCXQU0av!gDe71F~MK4B_*@Clorh4zUl4M;|uFg`w!4;Sa
zs!X|pfAv=as)!W{WRQXcxyiZxlLcw`(1k=*&-%z-)xUdHqm3`9>A{rpQT0I^IsS@h
z!^K$=I29)tZ9tl>UVR<XK9UF^l1B2ZL;g*lb*IaADWgaa=iCyTi%f9vLUD>N^)|8a
zk4MhC%uOF@`hOv#nu%|)fjol>PPLuP8`O9>{mHJnT9Gga;|aHCXlkJjRsw~Cw<wh*
zS47=@eHyPmfdnR`X{GZm?Y^WZ^7!%b=7Jc(*eTtL0-xo0&{Jvb_?M>%m<d%P*iZKN
z^8_XrFP&h^wg1OyCR<Pw|2<sPcS()NoTuw$F}^`g(QHb%!n1L!kZy8GKN3+iI3`Hb
zh#&)|GIXL-Zzv$gFd=43cxQ{y^;7l97?f)|kA2W|o~|`BRxbbep|mv$DG>u;NC_aR
z8o{g~4=1Gij{Z*+05*=dz>^Kn`b*Tx^j_3dAw_jYO=8H~w9!QZo<art1*d2t-<=|A
zD7A4+s#y*Wj09FTpyf|*-v)S<sb_g~6{}!a9e0^viu&1UJ72VO+Pf227I_X>ZyO(b
zXPWkeP4-~0D;E&!cb~UMF-MAV$V^{lznlKS8g#vA2D*}WoFy7{y-47pj^pHgb9mya
z(72Uu6k97cN&bnP2|9XS;n7&2{ff?=d0l{uXOuN+{~SPS<o>v=nz+R$LM!>0`Us-q
zdsGPS;nVAP<ErvN%~J3MItt*Q(1xAJbu+y>&5OFVA0)BbMXY~M@^MV!>1)owF*Oo-
zUEflR#!8z$IRA?;zRPy9uDw2lg{dO9!leb)ZhxuT_z<&+Dd8H=G6|BW(Nf@k%m6pz
z!}@jq%Uz^<&Z|1wvHAb8(ZtB&OieJJ<cu)pkGDW3&HFFqo{72Il$uh8PHgBIstT~5
zK3_Cv7-snNUNj4pLhk>9f%#)xZfnNDnRzHwN#Il+XH7~`Ldc<o8F@5fytMJba5&m1
z&>uVNQzGipePdB=p|k-ZjV^<OK~ZZp$+q+O0r-<K+HS%etI9KO&?GxUTIYU)VPkjH
zfXUID+aE)Mp({n}tBNIhpmirhlE-xt6c~)8&Z;dcZk{G)QJdf;H#Wg$%{dPu6X1Iv
ze&h+31;~@MfbHVdC3PnJ+>_qjlr!Mw1yajBwlpc-NfL*~gZOq<dR<O>9fNiUW4bV-
zUvQ^yYD9rwhri%#jI(=t3FDM>7}TvAVgECCH|<SS;n@g9RMtQC1`(}G&<JVs)FR#W
zlsb2E<|sVm*!smA0mw&H!89~B6YlPSU)0Z7SGj%xl*-nmO$?iZH0`i?1^E#^xwy(G
zFPhMVsvpd7uD(=t5Su<?LAMI^pXNUK<&-7f;l&Rvb;O^Dg8po&GkeiL+t(i(WKUWK
z$ddto0RoJ>Xmi_25-`~Wh~ZnFHWBRyv+X^A?wW8#9f)Rh&#hJIri)-mKThxpFe2yE
zLKP}_QmML@ze))pGO2W~ABz%y1C*DkR8yr4Jv7o>t{K$K+APx;?&@d`Rr+oyaM_au
zVPN{<xKV(kUtC$yBu<{^!7<Iu@_#!f`OiJ?WA>-WKxwvw_FGaL-{xC1E4mj~I)GXQ
z=uw2jMIgt9qIe3(g89aABsKdg0q_a6+pylVPK-Mn_s8I&0^hWVUrmBh0f@Zs0hGi^
z(4=Cy0F+?X263bgrCzKzO8^!ru+iIKteu6^fp1uPsxwleou{f|1QA<PQG@+Qg(#3H
z>?;mnM%RDs;_E+d|NK9hs%ntYqNCgH;rQd7;yP}5>6>~*p!b@}l-l}MFb=N?jobt5
zEz&NY?IpvmNG0|NXMhQySD2@Vrc*uRMVLxeuD{Bh&d4og`a}cFQ<{i$VQ{KAdzV}L
zR~h(snH<!1>)9qyJ`FS&4D&?nb!}5^P|45i^IP>l)nYH|UQN|_(ipsUVs&?QKg8o~
ze76ZIS2i#6Y%k-~W-18P<FLVm$U#k?&1pk8Xx#`tZPo3NCOb7e>{ZrijqR&$;jTb6
zM*3$fcrXi%RsxHQ!++SFfvA^&hc+Xs%R9r&c`X1a68LpqeKA1d^L<7qhpcCsk?6kW
z;yhhfHkBV`-|h%?K@9zCbBrzkS+XteT9x%io+i?NM?|{eUM0*yb>4u7*8Lrm`K1ln
z%UKqNA`XdZ&di)wV`2(nHp?BC*9Uwo+t4TjZ~&DM<k60vrGm@foDxA<+gIjlVGUAc
z-m2;<h2Ks~v31U_ph}=|Huft&cTnEf6@F2qS}>~<>g$@V*hY1jT@awPp~UbR+Ly8T
zQ#dp_-Xd;hEgL$j`Xu}k+$i&Di1;{LjsFP~<Kde#P}(|Y8Cb!oxiev~i{SfQ?Gja-
zyW`ooi49zVE6et2*C34<K(8;}EMF#F&)a^I;oQRyFI=D!XA|TzLO~!L+OfaAb0ISI
zpT^7Jz(O)nZLDTFHhhmOc{gJ`aP3lz<a{Vqg&)<Z&u1_P6e2Rwm1e)Ek{J?P5+EXD
zT2Ww_=G@$qErO<CXP=tlpM<`qWX|f(fMO`=eq3p%dFj{iUi?_vUDpPJv<&0Oe$7GB
z4<EMex%3g;Niv6n?M*p{C#w)*xF~?g(vI59K=Y2?vp*1X>H!-ow3R~*Gcxm4s0^Q9
zAACbnC$}67J$Pm_xNen0dNL=4-`KZ#JGIBtAK4glz{TeKk^Z=z@Et}t2UWj#x|HGG
zQ9@*ZLo~a`iQG4}d9Zxn_HgQsj38Ee^<Y%}CoERLA#bfM%xs47nVCl0=L#-Y0Vyio
z^W)L=h|OmMgdzsZKk_b(BP-{JP6LY#$XfzG2O^N^d>n5-Ts`O2xZ!FuJbS-&p!_%X
zml;E1mZbJ}jTExGVWNqae850WZGhHThM?f{3oqBVMc{<~5EPCDsIth-I<6Y~MDK5y
z4!jUkt{xjZF`SeOMwN_>)MwIAV8!D=sht5lJcpfTo^l_?yIbCaWTGDajXEKFHcWg;
znZR0<d*M~FJRB1!#8M&+M*$0yS96&ZP}d*7KL1lF3B^o08){+KoCYSTt6_BCU9?fT
z=#Vg1Do8zBa-;LRX|0<OTdi~9Z>+|;%PbvwSm!p#U2ZlCDGF0}xdr_&)@&e)HA#7?
z`uo(NhA!m5mE6Ys!uEbHUm;QL65SjLq`do6f(u?*(Rx_wG265%JzRF&ymqqazU&Up
zi>E=ubEuIIE)|>jkjF4JV7vRzNh!B?i;@j26UA1TZFs&UEE@m@69}SA8W1E#FEn*)
zUbu_sfskY@Sb4EGfWO_Z;Xdu)6513ms4`v-5rdo}vlhLTrm?PhCN_nfA`llX{(@7r
zxWcOVX2lZA>a4eCcyI0tbclzwksx@)_WxCJk`>@`s8C&!q>G#1D938xi9v%jR}dSB
z6E9@l_0TV@o_4kE)%!amcfT{V_od=4$b2q`{th5*9vh9dbBX6pOwkZVC;?mw0|#UR
z!x8C$_+ma|hi9AT+h(T==2>zDoABfIht*GDW6<Ns_t|i(lZl`Vn{&9en-54+_MW~^
z;$P8=k5J_bFb<?R`bUEf$+8vVC<0}IqmN6|u#g7PyPkhBP+O{ZH-<7>OG5ZLOwR{j
zk8CBE)0c@Ne)d89prNx403wk?BgHcWb{F@L!a{Ek8Ohc24IfVZDB&3RVSrBfGq`XH
z&<QlZ?;oqDz6bIe@IN#KUbiONT05o_rLy+iu6tg@?MqKbbEFX?g{qN=i%Io-F=(kM
z4!mCRo#=Q}CyXDRn0tRld5G|nAjQ=O35T=*<%JcIkmn!Tn!+jge0pMJ>IE6+f)(y(
zKB;)GbCZ++!`kv%_`Sg|aB5N|kuJ%*Qs7Mz2bySWA^p=nX-H$7wFGJ1%{M)G3!@ZV
z>?ly8MOZK?@kV|9{A<l9I7-yuLHUPWlU=r1?*Y|eWb>eP;-xheoR~TV0(K))S!G7L
z@Z)HyTRToKymJ#?ApaMmvmpxU!mmGyzv7H66ft^zQ&K;EY1bj5<uM?UJ|^PBSeObm
zYIWUAJt$2*)ItwjmN;n$Yy5F0jmYY7G$^%!A)sI>us+JI?Z?=~Z86Q4PGjZvILYc~
zOXx2T19;wE=L-;f4U;WG<600?|1F?Pvzrj(FUnG<SM_q!y2Yv^l&Y|8Vench3YR%y
z_FF72dzw+-zW{OKVl?1xT>Cnr!X{oO0gt3O(9So<>OFWSnI%w$ZDW(0-5G=AkNV2j
zM7bJ~2{zH}ZT{I!QZ!Xqt$4}YrGYBhug!chaCkU08_Qyw<i@r(4^N(eU%e*){UXD?
zuahVo5U8>6-9lXf7fUg!X^Z)a^OCiwTY?3ckM9n5CG`WH*XJzqn}+HniYn~uOYs$(
z2ALSBw?4QE*sqx=)B7P2F*K2QVThNg;0smkAOasV0b!4PC^U|TGq?Wt^%x4Lo;Me4
zK#b)}FNkT_L0^_mJBJQU*UyVD^Cg>?X`<Pa8orbRdu-}gxJ|9rl^-rk*EUo|rQOc3
z8HY1C?hh6u=uUBjdHzSDqHix)1JrVjG>}O_6tNx#Urj_uPOk&xQR=|~`22|53*vms
zt_j$~0i-!|jLCi}mqDf1fOD+W_Ot2(gaT7qL&<}8Bsa3_Hi~|r3P=~&7tO{*{}dw(
zy6kz%0k;8l{>kQ9mg59Wdo{hM(i36Hvm4`K*Tmcv|G(f#mrc37<2>9baLxHXw^2&U
zvhA<z3VCW6*k%XAq`b*Y;WbH%$sBMfW$y=@6aDy*&7fL|;<AR{s?D6OR^RVMix8kd
z{?#D>AIAG1uiOwOGEi@-gA|NdXSLqCa~LlH1rP`0_%Rjaq-H@90Lge}j2~!ROzYFW
zp2G<+dA>d^(v78?XJ{sJg2^YTdR2KfAj99L7KJ{YGn@N<Y*!%5v6(ohtRap0G{8c2
zYv9rBkkrPhfPr}b%5_*zaE=B*X`=%&kZ*j-a>g&dA7G}o^j4%);v*LC&*(A^rL^;s
z;?!n?wqx~(_t$gY6u=y}+AEpM{MedIwM(AvIRKcBr%&YVosjNK;6~znNliYQ2QX6O
z^Z~%7e#2!0>3dVs=O6fXN_ro|I`m8?lvX|t)Ai(Mws13DS*<RiUw^AUPpYeTyC!Qh
zmn<piS_ZB25(&?7)kd?%(Jll>a10Q`_ovfLmBdC@`YSIyca3%Sj&W)uj|HR3rmL|8
zzz0Te1HKgWh^G{%e9WG7U5b1~!7)_HJaG=YtJsKBuKw=_{N1BJovE_rPE?xeXCicW
zuw(PU2ya>^kJf`~qlIw>lcV+TbGrGJz_iUvd_JY$r}NOgN6FMZAOr((u-mJ$T@ZYH
zNv|5tmy|Hsn;S%KVX84+ttnVY(lEys{7?J1LTnY0i~@55!m|f~Z_f&B<4lChi|*ua
zF$0dERU=M}rg~#-8c$2d=}yu3;;^+`eHFR>c`pyUt&9NyGnfg5TF~n3(>OJmsifZW
z9*ZUb-$c+=fdWK_a!zD8#b~bQ!}>2Ogg_!Z%{f~1qki+<O<4w~DF`p```4bS&2Y}@
zMQO9#mLT0W*cOUohvQM)o@(poJ})xdGI!F7kFs8`!Ndn}*zzhCz)(`8+cR*>&H%;%
zMW*@7zpCE7-xaFPgUer~5Lp};>buht$5lkMZlErB9D#@Z@e-B{)G0>X9O)m@U<pAX
zjwj=J`4#N9t2eHVe>gitCur3A%(A3P+y!m7%xWssu=#qO4vo$Usl5e)I_7zAZFUve
ztb(-ul>>8b_sNBudspi|JC^3bp5nZw)Ndqt#t$2^&$ZSNlmZHayxTDOG5PHXJjca}
zC)>p6J&ctsNBVD?9EN7C6!t`tM;!{?WE|d;L!jX$>CbyJph9BZtR_zM;FEI`JC~1B
zEi=VnR1C27DaBUQd{U_BVy~scrEIYP^p}0S{;BWKJ&@yn)1TR0!kYNO7U4a}|8@ww
zL6LfTy+swtU9+^V+BSHBF_8JvFsuLj9m{LGccCw0UvM*5lTeU3JCt-Zmz}X-;q%$<
z)4rj>*=^!JH30AOe5hIYQ}=`&wo&(b5&Ek_pn>l-9B-K_Tm-%d6Mx7>gbuu~?bK!7
z4B~PR&V9D6#vPy8MlZWqHl>5Xs=(`2u^c>tA)nx%g4(;Fv777zWv}|Oi^=DxPri8q
zt*IpU0?WnojXco+Y^jE(vip4ddQk6O_2f!U!y|6SioHie|ML2+9^aaEEj~N_xrQ2$
z*J5!*V+}MhiH_!~)HbikJv?SAFMH%WWt+LQ@*?*54(v~_Sk431xdSr^RK<A81krd5
zB(vn|EPwSgKzBh0+bT~>%VIF!!P`^68YAeHqYSu+F}1cWSq@;kdGEQjd#Ly`jURqa
z-k}!KjH>6Ti7?;mN5&D|;1tlQw(zN9;#TR#s)INRjJz}fUECKOE%}&SzyEYDK9(z5
zS+Z?Yw7Dl%MA9o`NvKa*d7*#*mN%a{u9r=%k<CD-&*eTLF4jWRaZvXj@P*<*ATLHs
z*s?gZSD!|wGweeq{lh`>OjC$p8yAQr6+8yy>Hg<KSe`yAz7)G>l_#6~)5y1R&gOr&
zYYH+(W;nEgY)==|MBy_UGHdFucLn+}pX<)7vSeyuE&N^-aw5R5{+``N<6R?qtJ(U}
zLVnL02B65&Vy*%{Rqj}WP_l!znA<(ne(!AmJvJjTZ2pFtBOkAy9~5bPnEdx_^CM4b
zn+J#)b4xD)mhV`^yUk(`7*YOdi;DalMN9L(Fg+YyQ=EUsFCXEEpBVEcey@;p(IJd1
zglT=XvfSyX$QrSBhUvtEYy91d6-~GaFU6v|tN`K=6=ys}gPu&}Yb}Sn+~&s$Q8KN4
z#HS<tX^6P)Ayd~_ZFA%x`kA_u_3p~ssB0_JzJ%e}xP3Wz`jMdbZm+xFwmP~o#-I_e
zODCl|if%5CTG>Ok^f|cU-rV4v_5PXd{@V63z&z?nK4D?Pnl{hjp3Y&tLTNtR1f=v5
zBo7<<)ioN8%)M^<d^G{gztH={Lg|x)U}Wl}^Bte~qx(+vK{#=LV=gA08T6j)2Iyh=
zCm;D9Eml!w`0(l1l9xV>`>hZ+Sqk7{`!ZTQP*M9ydzN2-lmJOoShb6!z6Vto$6FtF
zvK4D=l3J+xC$IJlqg<Qmu*hw*!)S6|k)+SfTscRy^|J-(nBrx^6O1g}?&aY`7NyzY
zK)ly?JVY5c$L8;Cy~Z`XG8}_({gv#Rh1gLu8trz{j>bYNt0jEj8l@S&%KRDSVMZLk
z&a<1(D~UfnfBpi6o<pZ^Mm2vc0sA-5oqL>2&uE1JY>8Y7lGVRA?1)jrrG9}{aczos
zc$4_oS!9-gr|qd4yo~g>TS8ltJmes3OjZpi-nslBAEDgJUAy2m8zEf2z+e*Ks!7*9
zuS~jb$c9lx?A@D!)$}<>H3?z_Z&wj{8>NybQ(EMVyTeVF5lUC>OHf!XrMK&R<+@~C
zHz2J9YhEfkXb!_QF*1Mp(C{WkVd$BdZ+%}7Jm~PAeicxeW2lb$orj8ceCJ8<KN!i{
zOq|*{(f&DIYil|6DD1`r-WqI;Q6?M#S-M^#mKPSqAkWw#LQ7<iO=(PP$&=Ff<%*M(
z0oWhe7)%YM+C0%D2rDIX6l|+T{!V$AP)_5izYS6s#KfRZ*iGpoQg(^tab5c(TBm*P
z*0ae2))bMrBJXUA`-VP$fZT&3C{!P@ZUR~(i|a~Rs7pRy>17@l1k*x0!NxUM&ka@$
zE`A@k$N9`<c+}2UG=0!dt99s{d#Dup{o$(f1mw276|Bwn@P3^@m8|j5%Uv{_o(*#)
zZiYSox#;kQ`Sg7=r-{iLX-iG0i{viOzPsD)u8%EgL7eyJoeWqb@!gGf1x{5w-)I{B
zH;||G$Tr?nAzVmvXX-YKbzy~K@iv=!{$@t^XiMzbM`vQZrkT<XRiTtO8JT)d0IXCk
zZlN{!8yrlEu`4RmvEU>L_&2@`m{_JRm+ls$bC0T+P$Oqhs^|-ezkO@@ybdP)!W8fE
zeFQWvLxj!);$J*rR%%5?m-!?MSO|Ow#1-pL=L8tTan+lD5~d~5=Q~>6_kX`OyTW~O
zCCt|WTZKTh4ei8#Y>UO-xa%iASG>fqrK(ir*V#A$E{e`T%bm0$61a`m!rw_-GcIf(
z-6<Sf`IpntZe}f{fXO2S1GuDozGJ9PNH*u=K&n3KR~WbNGt5`WXFI4w(k#(0dyGl7
zvFDQoyYAa{@w%h7a6I2b9O){CXZ?y#^T<XqloM`v+4$3DLK8qoc5qIGNYp}IYlBn#
z)S=<M5lk7kQxIQa?2=k3`P(6b^YH&X&T)_QW12uIz%H0KMBCI<{6H4C@lS7V@WN=@
zt+0wms0B;;;an6AW0;35{2tHgc<l=Y4K+47Re#oo2ZBLIZJXm9uV)9lA{WcW4;9Dq
z@d0%hj=8!{!vwDpRxXd+o_Dm3a(p=+NFCu=i~1f;zZS2L2Y%m!qZ*X-Ng&&o^1T&y
zo=F%Qg{h2r1f%CCtXVVBn<@K~qMvU+Pg!@t$G0WOO=Sfa;suFs>|)yy&Dh&0MMP+K
zssRJgqKMf|v{;;hUfj+l#6<2Xfey_cJ`ubzE<W;m^+FFG#b6L7>}K+K2Xnj7tB}_(
z6R?Hj;J&|DV?7D^t#VC$?g>k5FGZ%ke7a;-c{e9rkH$ON7a#lzi-Tx&-q!8i&iCcx
zWSM&EMZ0dsYTEi_v0mgO6~D|jf_Xzgjp5Ou!@u!AEvvrVn;&(4g(5+!ka+tuGSXXk
zDOE6z(S&ATrn_}QL2|r4RexeqrBAF|iYbtsZ-Xo3nbHWW&fz4J+o_muU^2S;Pm|C|
z^cRGb?(D1}QM=L3B4%B?LJ^^G?rzR-Az-a^$xSO|k3#S@_aZ>1TxPm4rW;qi=MaQx
z3-ySw4~F+-TnYbK9f9dN`U+V@qParK93QIG_x9Iw`wD`A-!hrBz#+<r_YVDC=psWv
zk>^54nuvy<`o4aBO85EXq^(+ex=Xf3ce8HDAXS{aHxD}~!nlP554yq;t%z~y5yAZ~
zFT04+lm%nj@JR!?miY*L7YD*#?Y-a98M~@c%A>r|xng;Sjh~0veg7phEw~VWyrRxg
z@+HSuERNjs<tsjeURC>)Q!)yD>0*+gaQJUCBkh^>P80kXMBdDeRP`TSd<ZhrKQ|Y4
z$WE&Af2cr{{3pMDw157Wf@~>XgY(_-*PS!YWJ~a=vS2dCj&ms@fBbosiT-^pl}lqT
z;({(jRFI`kbB#I9ufx$fkaeI~87;SS5@vR)iXEEu^mQr~jYU$VoVkNvLYI#4ki?j*
zIPDN)<rd}PK*7P|o#52mycwICGH3Np9m|dE9%k|&`F_3;rj&7Sne*RF@(4+}ytX>@
zT2))_!I3enGBsTin3P|+HWySb)o!Q+`vtOqd-28mKC}FKZSPv>lcBQR=}>jz86d)y
zjy<f~yl~!>{uS@w`pfWY^BOJcKo(60q$MMGsI!HF8&fQdq0F96NEdlXuY$%pN_+zS
z#gR+P)SWEqn~SROLdy%SoGw`Gh*-=Cmx{_^EDwCd$(rH*T7q_gMFCF2=zeLXtw^%P
zeprSqHX6O7q)PYo$W#3rZvWf+se1XsnxS#(bAd(F#P5dnO)HMq-c#;$NT{)I_DADD
zT+8XU*owwvQUnM0PZAtk9i<>sH%wJ~ch^%i#Awe5&n(U0p&{=?I<PO;%hU#9*3$0s
zVv@l*1&RIgP{(F=7e%b#_y5jvF5Za=vqgCF-=yEYTKvh!OG-6uBscEP3@x!p?G+VO
z$0f#e=|y?kUZ)Ez7|RiOkJPS9o{yMjt{oAfm#=Vm$`1Z~YxOsc9yXd7;#IH%!BJyf
zcJgtO=o7)7=T0$+2G&7MQlwE~rEoxl*$uyt%JQcPJ+~Rkuyj&}=T`M@shNg}Cp`r@
zwIR2PedWS>{=N7bM0C%B=IB@ZTz!R9ExPC4bY)?n9_h@J?+G`>=C$i$>7*@YD))Kv
z9{Atx;L_be(jZ5b8Ur1m4*M916$_9i5$vQDB=kjn=)&KQFMg@nbi4|kO5c_B>?RJU
z4%TaCnnrI_C&W3I*Vno<Ok6Vndkskv^rsE{Red-Sq0f${Pmrff$`=!=_C>Jj$|tU0
zyB%g6zE8HX(RygI$pmXPqw?`qam{2!XMEE9?(Zeh6{_YojN((D&##c$Hpk`m7)9@c
zKiU=_XQe?HeM@~9XiSOoh!cZ!1ul=0%di>^*l7!AjNHx~9Mssu_;+<eYwii@Kvkd&
zSt#y!<ev9vEta{(G}=NTz-V%LW%O~;2!sTaGWf$nI!<j(H@}LO8ZD~@j50@OPW|_{
zn)v=z!ZLJbl>f{K6k)QZ`U+N9D}(-KQ8g5POpe;kcwm@bwONE9IcPx;D%zg7(c-Hq
zeRiaoYo+wb#y$6AmMr&cW_sU$h&BXcW@{*Wyx1R$R|LWk+O-8WnfO@$M5iUSbRvj|
zuhgaH`8u39{|#LCw#AW=hvDZ;d+h-$KQDHAhG*l2LtFmiPGY(ly6fE$;)Zt57w}v4
z?;eq>;(;|#Z9D(<Qg_ica$CRRr*b7f%TeKIK-s00uYW4}mmR)0rfBd-JNmrMoDG=Q
zN_6tbh&b4d23OIDH%udQerbdg9rZ32mDGAGQyx@2Vo25~U$|S_#eJqikV1Z2#{)h(
z?qG9K!xF!IGlXv2DhhMiJ&)P9Up6jYe}{~emyLM~e_iqgw}tAW3^iXjzcx1T4#J5+
zuioKeYKTjgH+*tzh>>4cH8VlODIwjjJPy(J3>ASIJ$>A!A$R=X<MG2})&h|TysrAc
z#{U-Ix@^@rYzwSxdGF$2KRTKlO*L4J9|8XlV_@b{SQ-Umh~62g+x%U_#OOVsaDOn(
zUSi|j-h|sw!2si!K*|>HrN*ZA=;2SE$flrq>g*Gl_N;jga?NS=B3<12qkD-?$>R$>
zFv3MIY&{cX_qT)Bi8eNEJSpL9C*%vj2Qf2HT-rkf7baJ&w?-I!m8)0n(r)pFhD{Nm
zejsVnu8dH#@lbW(;qi=hGB*RoCV9RYsaQK{7Yc#mS|e=i?@c1|%aExNox+EWeqbMx
zn~);rDO&&6*511<PoqMVQyYe8j^Ch(?Z4U+nEU=ydqv?!B32VO*A~Jm(zZ0reLBLt
znQ1y*1Af<WEG1`JX8mj+GugFPjK*8|^S%=iFUtFk&d)m3$df}?JyKuq&#UM1dNv{P
z)5)6n0OIqP>2i4vL5?=Ars5w(X7}k#n3=1e@7!eJ0XEYgs*Sm}{;`Yjs|OrGW;l-+
zq53j=oVHdA$YkXlS|;A+VjHl%0Sx$=2GktXR8`Gy_KE6gTA@eH2*OJ>CZ7d`TH-b0
z&}u8*vH6gMsTnF~)VGQbCue<g()fCj6Xz#{ZW$^_A-B@&1$p+kO~k~aZ%i1Xr2G#<
zGI`VQ*^(M;sbkT6!Q>J=Z1xI<B{Cw-uO*HavF4V(D5A@AqI~G%5qr{V^)nd#3Ahja
z1rKWW%IcrVn2{+4No69?@%Dr*IyG-s>T>$$BUt+zWEvtk5lh1dqWz(Ia+e=>eReaV
z{l>9kd=_{rE7(!85$mEn)Y$h>uhYzisWKO3HN3i`wPO}<&v<?nyS2PLn_b9R=e8!}
z3)ka@BPev-;yeVR1w~4=;qz(GaAMThSb4jwQnHyf%fy*0W9Tj<G7h^gr)lq|rwre6
z$g(pu+Gl?^(3i5~RZQ&n=oJrGjvuAeTn|&>EKJWwh)f)(MKIX@`Hlj4Yvg2|2MWbu
zqnS{)5n_h$ZTj!*@5LweK;E_G@xn4<d7B5}io|e-V909Vd6er5e`}X91@!d#y7S(_
zM8w!Gyyo_icDo(lZQ5NMYi1`c2B9qKS%jWOc9?!eG2UHo-E?0aoi4^NH}aC*ii{7z
zq^LKy9}44(SHZERr0inJ^RltBD}-u4mowvo#>GGN?{7SkL@RG@N1+<w+RM!e(b`jX
z7U~H;F4V7qi948&Q;(tMqi=9&<C6<IJB52P<9zZCKREav46Qtz-}eWPp-Zd+<5Qyc
zMou!tGxjjm{wu49{aIGCau!5Y8pi@Rzd|*?1hiC;f-7lwb<>tekmzScj{d&%C_?eV
zZrvF91<5sg07eR(W@>*Y#pOwN9PZ<<mffZOlNWW^{Iy>p(`~Mbv+$?|kCYMNG~7>6
ztrF2A$E;}CM(H>?fA)kIl6BwO5H{*PJK41(=G#j0m>^1siVT<Md^lp+`SMYto(TNI
zU|z`Gg<t~0hn)@CWpJ)0$)xSU<$mk_@4_6<nW`$(tFH?hGJ{o)s(YslNIrsUPr{0?
zWw<9;)U4fx2!7<hXh|JHc3sLhme+*&So-z_c-q0kV(Qm+|ElzB)$>3qGA;)dWywz)
z|90Tv;2zBU?|mtS&8UmN<H<~WAc&UJ+?Jfx-`5?Ejh+z{)P6o`C;ijAg6Jz4o9H7@
zxJ2yl=?xqBaq#&@R~M1{ZTRSvDa~Oy`;k4GdR1?<OF^irpvqMxaA1_0qrgUjBd>Ly
zRmt6!eE+74Q$>duBV12%_cC0R1z{NHQ4<mV6Mx_zuW;5U#o6<1m}lGU$Ou4Btox7E
z>QNp-U7>n1_~9kU4HKYnZVFEBrYn*8cRTEe`6NBX)C_0ohUPf6)7)ZDK6~q*v+w-k
zYRZc`CjF*}NKqS`yTcV7E~gjo5%J<9<!G!fyc)B1LGdXR@11q!9Bo2t=H5)oOVvhn
zT)MdN3EvXk4`E@T#+0UAN3VZsKzW`R!2|h!PLRF0UoYyn<$zD_U(j7w5#&EM^<;Od
zACPafO$40_T{MRLI@Pv9LhUBrg8s^!!r>t<xP%xE|5iGYQ4D(cYt?#pfl}0Qd8;_w
z){>j7^aR}ug2~5>pPJ<vzx$0{jcevXUI;!q4gE#>)$-U&FNynSd2;LM{*7`{+uU3v
zq&9uhZF1$WK~r7Z4o{!TY(4oL$f=1@H6J9mEQPT_%EJSUEWGp-5$xk+q#_gSqQm!$
zDUmp%_x`fO3C@Y}vKWwte93g;GHmK0qh4o~Ma;)@Y`Qn-O}o=+X!gy_LQ}z|Re*=#
z_N~BjqjVe|;-IA23W-g77mXl0Q3%t%%SZlBd`Fbe{&Me#cQCcl<cs@&%-V#$ZHD<?
z&ABoNww=$XCg73IB^S|K?YuT_qeCF7V{U>X!da}Hjj4T!<-b!L==)looa1Y@43`=g
zU>LxipKU4Va}fQCWLnYnnueBv6>!v)+##6R?m5>3ClewOc-GQr8$RtV_Z@Q3@4m{v
zggr=U1tQYpLiS`#yi5LAJbY-l@Gd*;Qjt5vJZ%^L=TkS`DZ_bD-cj3oj1kIP2QNn$
zH7e9323?I-JJpU?wb`iNj`af?!h~Pic(XgeMWj5(HO^eiSH7F2<+}&btEGpx9vT;+
zsI$KUK|K0!R)wZk*(`R-Aa-C#yq)|Ae(Dk8buXcxbyP5C-)654t-g06hw~2A3TAjK
zhRo?Kt0Vm*l&j|R?MP6F=`-^)hTfdHXE9UNw2{r_nY9fm&{azOL664m(f(s;BMM1K
z_{;m-PzqL@)7aQV!%g22lftj2?&>|AK{}X}dwsmjL(!f3@vys7=~ak8sQdx8D`$$o
zqJNSKS~q&@xdnIGR#&3)zx8?u(%(BPlb_o?ycZy>M|9Gvx*yjVgn-!*li&Z^WxVa-
z7KBgvCeiLg(=d6O%7J(#0N_qYQJz*G9|Xkw%*(yk@q6Szy1gC6PwagzOUlR~E&DGC
zyK%ZiGN8x~c>hqYRnSB-XRhfaT2}E2N@N!l2cq$}!xPa=+x;*k3aO1DqRCbZHQW>q
zxGPxT(!)-ov=<TejIx{GOjoc-QfkIe?GF^VvYMekG!66+nz8sIT%o62{hDaSoM%H2
zeWD=ixOS<-*FPkKtuG`JDZsW2GO&2jmhv~GdqG$rUtbNXq+wBA@=8kPEJwuOZ;jZp
z@#9?iOIR8;E(INzbxO?{Rya~GDwrj(T=%tS95YIE#%%kw(H0pzQ41f8BcU*K@mQ5F
z^1z7yYA<l`I$5)jvP0KqeA`3Y8$updz=NOqJ8(Jg)Kt1g1iqq~mOX=C1PdWY@)Hw2
zVrb#NRr1)a5-T}9LJ!(G4?e;Z3YbMj5uupSTwJ@@huSSLHIdpuq=19Ld>85}$4<+l
zwxn>WeTuE|`0?LfJw$YnE{7c5(1)XJwcv(QQhaAJZ<hVMxh}peG076v9e*~#qjCf=
z>qSG6c#6#ZMIHVE6|0}!N!I;+CpgPo?jZI_zSIjm7ntng#s<hX+FW{A5am~3%@kiV
z{TnJNeA@edP!q6g{+S>^o^Qf5AKV%A?=ST_?RR%lPuKwLc<m)~MJH}N3&9K7M$No{
zgv&_AM*Yc$&wqR#+$oz^@p)-1LLSk?E9RwkvK|2mh9V6}ToB{f$+WSS{BjS<#13MM
zZ8-sva3^_&|3~#3f6cqkch(V%Bzu)d=>9AYpUh5bCdBbCvW6J9eZq?&K}3_WXL|FM
z=kknm=JhUK>b?jUub{bz`))IA_EaBy9~acKZDRcYUV&~dIWVZPVEGL6`g8s3hdwhH
zT)@vF`|cHVXl>k@!!p!i^&f~j@NAA~fmrPXFM{S$jXni_i8xHv)AGdSVj{2-N6Ij!
z1BloaUUhD?=}^1zT!AjZW%}E2att0uXPiaqgJ$$ufgf&nHxz;SgSP-zG$2P75`B(9
zcM<9({OE!6X>z@NJuv()UMPjJ&pDSCc3)(h(9C@34GVX&ep+fe=8qbs)2W+tj&0&|
zs?q*OART;9R@b)iSd2Zv)YN?(;H$owV{>8qT9r5CpyHUV#&D!wd-vAS*KUJVWP=OF
z2y~FjeFYv;2zlt;r>Ee@YIHuglOItr3fxlSOUcgaPV%fBbQ4U{me3naTQ`{+f2A7l
zz8$qiP!A**UvOtMZ<hObzr1>tx$^S;kDNAJc+;a0p2HazMMjv{4Bciba4NVOd=S||
z(cks!gbMt9-9o|`cPOwfB{1+hg#&yFkT{aZgK`rBi?x*aF3W17OlF)~9e*nC(a+;g
zt<)UTn-V{CHqld&yz{EUU3Ax4=+DOtnUcqdVL`(zd1uf?BcDG%nvcC_MZ4R7;zf6-
zv+84=DYB)D3@+9fKQHt3^$1*uyp*>-qane3&tYIvqWrba049OxA>{WYV=wOj)RDU$
zBhlsa9h9wWIhEtD8<z`VrN^%eibuw`#kH@yWYK{~A6F?72gw^FHURk^=X%4JYe|k&
zk_<L}<_*VIQ4Ey*t$ssmF{UkzBxaasJ-&+C9HOc@BIk|*UDVjXyM)`fd+^ba6{%g(
zn}m6DTHNa8DI@x8xpk=-%iynLuT(&vE(KRthA?A0QO}PaRXO{im?q12;%rGgK&R&X
z#siH{JRp2xtktx@awh|e)ZcUtR0HN~pUWA-ldv~>Bw5yUR)DV^0e2$??$-UMA!TWp
z(i^wJ<_lxoCkb|Pq2$HYW1ssUu{SUKpEZZp;q=bKIaYte*6_K?v6CahGRrVuoFLdG
zO;`0M@5HOFBMZT*5Z;vSSndb8z(J3<@fa<`f8#Z~L$`!LZI!luv-SMgdB7no2o$FD
zuEM#n==>Z~wU@(LKP6f6Q2Vs=(o_Dp&As0tqU7n=n6vK+H631Sv^9D4vjtq^=tSaO
zc$EI%7_A<{<U$H#Jz_!eyN%hbNp7id;av0Ak!8)p$9_<wb32r>R1K7JSlpk>IP}bC
z&bPn;tlvUiRuC)OafB5=@+z-d`ns&u(J@Gzb1pNEUUx9A5k{3H)oyIEY87_+cg!zV
zr!+r8pX~5D`dR6YJ11AL+cg6NbvYhnnmUuh7XS8e-S}%Ic*UP$`UM|Q;W7-jee>wZ
z@ya{QMRp}SY&|qg#9sFYnS+6GK*6Mp=YYsabGgg}6xWZ9SCX~@3(aqf9oRm|Dy<G=
zW3f6jXeK+2m5%<f&Ei61u4iL-N<KaOQS|>ft-!}^rKGSu{4cp{6P<Rl?b!If|9hjm
z4v+$ikXv45n{qz5Opm6i;`$I@D9_*jKz){<24<)DQb9tPIL6Qf=tEDuB)qGS{`s-^
zUvCb(j9O#3p0w6AZjgO&?x}PyoL=X=OM&-RnIu_ckdgEt{<7xA)dKt2OqcX4t($cS
zHY%&elJv5_(Rn}QMb&j@QEVKxK=xj()mDMQVJxq=nBDguivkC{@IZJ_1*D6&yl8rT
z)yCUhI>lrybtSY+Sc=~sx9E5cp@!kY;N;_?JxF}51uC00Ba~|IS;Y(xTx-8}yw9x=
zSvlgR?3*lVW-T-<lv_u{o1C`5xEFpi;77MUO02rYxJ&v=$@V*B1!=+Pg8sV}G*}lU
z-~r6B(qFG$grGb-G{@@cfA}l#tbQ7EGtvwYVt1qQXS1j=x}?47>_ydbW3WSbLru&b
zod3-nYHe)6Ro&$FTc^EP+A2YJ4RB857{!KqweyL(&Nw~g6J;dYJ@1n#@$mMHoJ;ev
z00i0%)LLYIT14flDaQKZJ{_=~BTTdm+b$hA=Sd{<#p6BvI+IKp>NGHA5=zrmZ4w7e
zq3hwy2-(}y#jwD{Op_J|l~B^*wevT|;nQgMscHV%gpDib`9w5ai8EUsc|+mfrrMZF
zbRyX~SL+f8;D$K}Few!zyqXaM*4I)c3mR4Idx+d-9wn}=x9|S1^E&yTe1>uE^u8{%
zI5h^(L^AWY!}ha7Zyx2j=9*rv@^^}*ZN}uT2g+I~W!T8OC2BQ;)^Zu{f3}Bz%`jMy
zA3%5ry2oY==dQ{J0oo!1XbY`b80k)O^WU<a3}17tMhY_?r6#1-S@ml1BabTa-gpHg
zFnZJFgcpE7<0ph((HozG{gs6#E<Ky($ufOAe9@e|v`nM>-HCqjCfI$OfGmUGN9-<t
z_sH0}EaB1yZ2C52SAGZ&%(nulU(j+Tm+xTDOwBEPiavW_P5RAm4NRFn^}NGotvKbC
zMERvkMI0>G%!$bv{XNp3(Qdf1XFoDa(*-RYUK3nD_KnZO1h8KiBj$HU&Jli)y|wSa
zLQED;NdV1Gmh7)PQ8TzcnlQj2RB+%{|J8VK9q{2biH5|}q0<zOFUvLqzU%BXE5x2H
zQzm%>aqr}N!aB2au4pweHvRVH!N{$R01LjRm#L5&)AVgPh6)HJJTmu~RF%-Bs3Xoo
zZ%Xh6CEV$6pLU=9r2$>jEer@a$Y}+O!lWhA8;`n8z(z@X5-7b|YfaJs^hUP?=!!7r
z7u(!HdBY1DZSTlI0??1hc5%zql<-A`2*pF=eb$!*;!#-`4ycD<2%q$i3oB9mBQVj+
zI(V;!AeEauo?n}r?Pv64Tj^GtgDAkjz#(8#=y%j>oj{3ECiNYRBidV{n$QpKUX#%M
z%>z>8ZSQKh!572Uw@k9rLz=@?j{GxaukU1|dy<eODuzv4@?@%vp1%(#n?i)wM)At)
z@OD18$p&^c7KxC?8^+C6^<)Q6)wZ>FyXerrLV%%I`T8!?VDMx4goWzEB2nRlU`T7B
zFHP0!vXHv-uGWQ4zZNO7oiba`2G>y6d--q&=|Q0BSD9LT`Bs|F4+JRn_Oo!E<~iKl
zRQ5ZELtZY!veWY(_6?zti@vM{I}XM;ey#CU4i8K)-!34NSeV|x?|DD#Q3tpK=~cAm
zDcR%`lS@gDiOQ7j%Q9dL3!B#i{Ll@lY<rO=qYq<p=OW>-BZ;ni0j<b<MEdbA{gGBz
z^bQ^mmCuG0BO{yhEg4sqx@)%6%U3WGav(c)7O#-Fp`55VdjF67yJf#E;mAQsiN)^a
zL1jG@O*7#TY1=7ydv3`N@0)n#AT}jmOtoDLYu_SP($_-!$_3elv9N_#;fcbj@NBuC
zmvKie7>0uXvaTyU68%P`^IS?e8?@~s1HJ_)-TV+cjk9mp*lIH|dg57~Q$z&A{YQ>^
z%_|fh%wI~9`ws+;j*pqtdDcF<{d`nf)-*Ja0*Kxwxyk(dBX-8JD7NS!z`7!Qfd(9p
zYn+K{7*m<Vbwp#To?alc>h{SLy^N&#Eiy|3U#Nl5j^*{*d{9XTmFGhIwH>zh(qfI+
zDcD*z;^F%#kK2Xs;fG){@A$vO$(Iuxd>50zqi!#efGm6+$U==Dl{WXf2Kg$_KF*ot
zMn{2Ub1d(arXSf#4~xOI@tq|NdqSZJLDQ7|w`-K~H2km_?*AVcNHanGZVu?E17zOa
zEOLAWgwo<dvmPHQPvWgbRd-#2WoHpm(yLHU1gjacZr@Aj{n_ty&btI<cjW{l`PFE_
zw&B2h7lGZ-BeW}JNmihTEv7UE8JJ-gfXGn^diWti3D2gVq)=7~$o@la{TziOi|gN<
zq;T2Z*zxjV$P-tO2hzY-Ks;Eo@fC;26lQ%%pzOyXo?BA?QAt^>&4G${vSt5(YgSX3
zP-Ixr?;eFWL@sz@+cI~5;MgpH0P+j?FCH8HWQ*Typz9z5Vz5~rPsj$}n-@PLTp9MY
zlzFL^*MGXgh+d0ONM_N$Z^^jBX^v))z<jH~z;{IvbR2Zu5Kz104%u0);UU%eDJ*(5
z%ufGsPEN6YiA5#_ntWnG2~i(kzkVvUua~_89v!h)tqT%jAVNOB4gf6~Vy%L`n7<|R
zf|1-0J-=iH=5;WR(i={t^D5^YD@;&aZ!5!#0LPQJq=dTMl1pZ=&P2l_r^5zQ8vEs@
zFe=OmF^Hudi!!Q%KXi7d(o;F$^wI9e!HvD3%R9lrwv>QBVeiKHcjCJa;7)pr$Zc!|
zxG2_{x;Ot4!br#Nk4;_+X5j)8<q61J0?y|9A;92V`VX7~MVx7?jc45s!{MHK?v8$A
zioqRx_X{hH?wr79o@)mbm1W=n4HyJ23X`tsfjTYf#Exjc#l<kNsgM|p-L0m1>q!xG
z)U$`fS?3;9J6vEbm^UO;<RbH^%2EN^1fBsq)M<tdk=~I<6PN@xS;qRUM3}kVU-JlH
zq3;v~u+n~+pxuYhk9MU!;e0ABg=26Ocv|#G$u*e}Al>tJ9^j}lUHJ|yC)oqugV)ji
zvW5TB@XmSI{7pzo<3Re9?BXB8kfRpze?2LN$1<{cQRYCp3Fa#g1r^CyElYp%*l)n7
z_$Io7OMXdX>?)19v3<EucHgr}$m>;_=r8?mriw4AP}dX0$i>;~8AjR>ukIvV9K#p}
zzxx?dO}vaqEmRvi;Fog%#}6F&Bq7oN6PT+i6j&2#s(x|+UTD3{a(B}cWkXOKLB6b|
zA@x2hjsI^FN+8d_mFC*id~t!4@7pQ2t6nv~OI$VGzQ>0ChJU0urr4GKH;M#!H2ptc
zM0vh;@<%{L9wjQj`e&It0!>7+tJh*2ii|)Wev9}UDEt3g3cR>YX1#BeF4duSjVC6^
zvyE10hjGqZmx)}5W>i|bb`dVZa}2;j6cj+;36q!;uk^{%-i<fsr&g;61VniTXVUDp
zY`COe?lp=i>R&AXZQ!3eZ2Cd@-ks6_jXW^BJcz15<Rro1_e>ky48T{C+!&#l7b`^k
zH)Fv!8?a%egeWv!jH2^Yv;77JKma%%*C|&*5J+q5>1KFYra)&B@N)x0VG`))s6y{d
z1JDfgheAV?L+n`I?G9uKfY3YB%WMe$^)LZHb$A$@Y++vNV@}ghn53g;^0tw#dhXMn
z9L~o-9t2?w8<i=?J8f)lD*qPyLBrta<&__k^w?uf_WY||yYpZi$Gyu3#21k*^{&A)
z&<@v&{#$tk1H4r{MN5p%(01|m><pNzd4v^FnQW_W2U?)k^en-`l|}*_Vv{2HcTBcv
z`J5>L%=;@;WK|Be=0M4>I3YWdZk~prdTu#k=XUJJ|00cCL9xbJ>iYk74S^TG><~@G
zz^kL?mV2@^%!(MrNl96>IQC6^>Axsyct3sr!bR0@fUuyI1@F-ftaLF<FF>K@T12CY
zoAqWsXsAJfsvude5uo=742liDv_E%dw)|;~*l@P<F#jzi-XkHsTV}W&;Ca3Je^U+b
z?jOo3&&A{ipnfCHy7AQqY=_tmkM50$VM6q!G|mC)H?vlo;^w<OYGpJ201$sV5H>x)
zz=)8K?UYqHN$wS$Zk$}Rl`E!c4if0C{Ubr>YkO-oVbqfbs_FD)S4Q2}H2Fp;>^Lxz
zfY3s=ZP#`4b*>L$ealLA*p^>DRsDYI^(^PXMvao_0<V@#ys{7IF)&Fst8bluO^z(w
z0AGGD=`&*is9M1vUGf;`PPcOfXvw4dc04+(ML1u!pn=OmSLiQgM)ajocZ}B4BLi>&
z{a7vRI}++Z+{Yzu5iV#R42&<RCQL&0kI6-iL&W^U5M<1Z*=`dj+XgV4OJ;<wb;;@U
zreQT|@iOKwrXE_FO<|$wDWmQ<V~G&lVNYVZ<1Kud@z~iKACIwxm;r~DoAB^;?Fncn
z{D$@ZeTJ0W?d_V(&legh(ubsh^7@nuyX4Z=7(5|ediECa90Y^JQhpo7w_1L2Tm%@<
zO)=0_edpgi_vY7aaH0^kK_F{tZY-lI3(HGNOG!=j?enwAbz?G$+}-8V76Y5-{CsRJ
zL$)b=irR?mLKStUVl4B4N&*$`{Xb$g`&pSwLIhZkOPSf%!eh$xuXk1+m+h`3Ke&42
zk&8{7QNfDZOikgeZoJ3>z%gpOddkXeJH|%FMwHE162q3o;_JeU>Xx@8gbmq%k&EFg
z2RA2$;>K_@Ju{_Gi1D+IjQ+bLrFi-})wU|@r!yljS4;W6b}CxGklTI_(IZA$EfGd^
ztF&)EA-_52d+*n*iSnH4oqWpRtc^}}Mor7uyu8@Kkm^Af)9E(3c4t8SqF`%s0Z88N
zc(;S)<#pun$i@6ad>P90bsI+&W~30vMk+2AfuiD_vp3<PKXz{A(qob%-<xOj;I;Ji
zb%Xo9h!&@7@>W*f_59UZ_1w0XV3M^w)w+4tyiw2bi;Z#wTFM07mtSc+E6OVenIsqL
zT?O7QGc$$XH$w>K<^DuXj#36b!39h!Z@y8oX+ox>5~U~TY4SHU|K`{{)!kSZ4D_@A
zAA4^dR#%hl4Ib>^7Tlc>2<~pd-QC^Y-Gh5@3lJPaaCdhnxCIUFK8L*B_x8R0bwA&o
zZ=U&QW<#B-v#a*1wW@0U)~c#~_Qr!SV$xFl5lM<Mbf>%m<MfpP`y5$GW{eI-E0dRj
z!~v(N(3U-Ft#zgyL1C7)&g=M|M2|iPAG*(CmTyPY4Er9><kam2?KRG{?Z$UoovmE0
zKm0X*>M`2ylpPEYhag6blRX0EBDMPGZ>{B)vv&cThge+>V4Ku;(a{wIj6rG;HE#Ah
zMBhGARn)ImIn1*+K#<KI6YvVrQedZMcEdIJH8;11`xQuxF#GjiFc~Hf+|SCr!^Qq-
zzxbPG>bkAt=IHT~_<6JvIkwlfp02&zSvLJ;-tPsu&=p>+7gflY)HQ7Tx6s1;6dGR6
z3llk=WLk6GB}uynD+9BW6n&skvV=xeX-2_^jsj|8X_e(gQ@6C}Foe;rDF<<9p;~Gc
zHxG(sJMvgGTx?FxBfaSt5B*dA%VoOXH*?k|VXN&rlljl%oc3;8w+HN8`F1fuC>OPj
z&C^&(Dvu%5a8g#mB#gR<ql>)=2-2Ul6DVmXXekPQxm+W0_|A6X6XXPoT76CWfW^gk
zw9_OELH2&0W_$6J#U9D%mG)=pZJ!Q#*mkkSk5e_l@qiXT0+SW4^Dagdp=>I=jODy3
z*!T$JpsAls7nb^%UsQczVra{(@6MOGka1GSDposT>Zm`A=<9DEy-}t?8qn0Pxul!c
zgUqtP!dGn)%hr~)Lwr>QyJb4d_<k8ck@m_>vSVRy!(e;-+k-1^?09w3T&94j&)ku1
z@&+k}2XXX#NJCz^m5Wo%k9~8H22{&S-YvcGmaYn`=eQT?1R^eW8t19r$z$Zk+UXPY
zi%74c`k2OR!Wi+)4~AXg)x~v+E<TG6*cIB7?OHOSLAg%L8rC}3&cZ9i7+tAmK?G4H
z9i<k%;vc!?ONWkFMP_>jjN!w*<wsUQsmHth>3WL4h`!C<#6F1^l3}M_Wh#~^Fs6;3
ze`pFoJv?aY)7u@0uYv{f&@_ef?bWUQW?5hld&%zU@rB9#w;hXDRp@BgUPDJg{`UhJ
zCqF7o;7Mga+tOIOM?EgdjukX3RJoMBa-d%-H5*C(x4a5J_ws9-1};wMdUD11*7j;z
z&`-{#(`Q@#?XL$Hle)}#_!nT|Cy#%haOb3>XHCwqdpqO>(N5{;%8ZY?SwQ!dNOW^C
zoKe?B+dzj6h~^*M?bH^0CB%atvAnaVaFl}BhPS1QHuHgZdTl7aUG`todu);x=^R|A
z{*r%qb=lJKIog3^UZrAOVy5FJ<0YqQUTTe-GWf~anr*rJ!qU*QYXqe)a-)$b(9jPi
zCZtZD($t_uJ(&Z);q?8tAkw+6`18Y^-)D_G(G@xit^*Idu7;lNdc~xg$zS3U@X&Gy
zQ7WmaO4!zTGX(YnB*v*m2Wb#vr2@Gl<jLC*LcB_LpYr!$7Xt33CrLTDTkbMlfWAyf
zWzU|+_Pk9l`|G_|FEc4^>zt3GwSKr9*Bw7%*JYQZ`1d;atO*xhmabd<U(?N3e4`-T
z#!TTM+0*XGDbr9O+4;_h@h)ndoQ!z6XVRqQB<+*F)?HWF9?N@)$(gymK5a#qg$vdF
zXlr?`Eb3^haoc&`DYTN&0n<K(mns82oX3WMcPSvgB8&GR-6b&KgQtpB^~y)Osy3gZ
z<NZS|O^pWMwMnduv6y$`0qt^lxY$+NdU(0v_#x{KR&8v@SjaL@e5;&nyLgM(9cy~`
zpwVBQwq&5r|F8}HaM(Zp_)3icv^difIiw^!cvu$A|4GrfIioQ&0cNQP<Lh#5lutD@
z0dx2e6hzjRP4VR9VUBWYW>~i*-%bBh?Xn;*;5@<`xQo=%SDLY^>8-$dmjScqRMv5Q
z`pn$%(t!Ya<m<uxe(7=1coUm#R@BRYySLW*ZC6-DV!NjIZsKI;AOd34=wzq&2Ns4M
z&{ua6UkI{|@7(&F^~^2(FMLByJ6TS-RXI10o#*Qm$2(Rs&{#3_9>AmMafa(hYlDpw
z)8P#IB{frXedAD|C@5-2$Hl&oFxPM0qJbbYoWhCGd~fz5C-|xt(hPifxPB>FKkHDc
zq)K-_T{!+CH$n0v?IR6iblW;qC-~TF#}6^S<IjKSfXMAirKP7K!>IkjQhTmlwb0Lo
zU}4Zb2dUn7t@4MyHWihbLAzvLzb)MT$LPz=DJ8!gmn+j@$8Ub-Vjz^%S}c055ok;~
zf6cb!^kLW|3;0jEARPv2$R1X6PCYl`sFOdipC-1q6Nbau^>XT$?)@YPHm-e$_C14Z
zJ8m9x<6jGOBYkaFIImmTo#z`E&ggr*I<E2VmpPuhv*nN;BXa}daX=sdGLSg9sZ}v@
ze2keh#r%n4Ci<VOMMwJf4+Hf=ZsfG#2fg>S7fd#C!p5GBe%=rM=y}+@!kLc=eQ}Ah
zQgofvSm@3tXru}GJD;MaFX9lFL&HftKyu)2uVGV{gqcutv*YmB1--{7@m9*7u6s+z
z<w1UOq!+Vf=4V;XJE9~dessd?QLRdZngEkUrYpVK<zLULnTmQu$9n}wR@+9HPX`q4
zA|$OK0@)Hw*wS=e*L?g5W(glL;l1>;dA<e+a&d5&zMEFd9oigPn}1o`&1N~aJvlxl
z2)8?5x*fQ03MArp?aEPofRntNk^H#%qlgSQ^*!ho%I3yK9xg6a2wb$cV+Ku}g+UG&
z24A%-Cr9tCJDvLH@a-qZ#vUN}vC#YpNt!aUOW*k2%zYD5$m`bD^q$y-bb_p#NY^in
zr+0xlIeK?*JT|w^65$KjXHCTJq>;D1c;@gj>}XtYn33=r1Z#-Ltu8=cXfxn)!b_;0
zn35>ir+s(Cy;d6$_A#+t{#kZ|!ot7=GDXapqO^a4EpS&%RQ~%rayf**_1Fza2fRR|
z7ew}_5U3>Rf*wNbGy~)}y@)e|2kD1Bh3;RS`_{)EOdLFMwztRCys~eDo)=VHOZ-nA
zfdNwoD@6(|bDrgpLo-33Zd?S>`~BD8FhEA|sPAmXC(Oo<2329elt#iqkZ;`x$#3dI
zjmN+sCiecfQ4%6Hc5qS|O<NUm@RPOsO>*_^C{0|SZG#g2&)f|r;VzTVz>%nojZ9G)
z7?NNVq5qSQL89<3{gq|@+4B#_jwCR?u6dW&0X$eyqG*sEjg41tX=zGkNPb@KTToiB
zT=i(JYwCO`ZPoVnx*FvI{=(O=pI-UAxh-;nAvvsCqG$tybqrmbkkV{VFO>QacNu&1
zmw|!B9KUq3p7yUJ2#?_>_bU!gUgRkGB2$d6wh>qHU~fKl)uZ1}iXJjbvIZW$5-Mi5
zorMj$=ow>t%pJ;#8nM<Y*R&bSEw&V|&v#cNagDq6Zsl_PL@f5A{gm7-k14A2D|~?)
zky2$<rMM&<+QUl^Us^WMm{D3=o5KVBkj%%CJ90jj6pzq-q_40}O-&EFn@0FfL1UFM
ziH1Y`cySnd+^&$8ndXQ3PHlNo65>+gBj(86thI!QkE@`Eh6#jF*qEu)2m}s$y({&R
z?jFa4_%q00WPE2}_`dJam&`<Xrv_2^P)6gkzTvh1yG~$<AZG4b2GTW;hrXhmtiauO
z=;TFEM~Pcj(81%l_iAPAIdbdjiicP`IYC1lLYR=mY>$h@UKetp|NS0%TdBk_)fv1}
zfAI|Hk#dDUZEef#DL=0b7nBE1A5xiE>VJAV+b^g+EC{%Vl^&xb<`Z~*Sg<Qtu25ah
z7}tGeFwqtg6|_Jchtaiy7A=gA0PbFLa`4EG(1mKJ*YYGesVK6s(9>15G&P|z2`pBw
z34X>elNE!FoiW~U5mW1^T`C0G-pZ>5kt)m6*-902rcGf>T+LC_$kYK_w_F}*k>Y&i
zlKVoH;ogrVp5ijw-P3T0QSa?3TfCrGmq<R}kaEz`ykczCm=PKaScj_?agI0%Q4TwK
znN`*DklA)tgB;IpL$vt!BotUhxy|hus(PbNLZC*V`Ajl*O`FG<?$#FcnU?DE`+D1R
zk5En?%TiQ~Tt0Sw+)-6k^}^JYgrTRec&(|Io8G7}#ATtjuC7R#VviA8p4RhG!*s0a
z-WOtbTZ6v7J_^XN3F&bvfnp7fW%Nssb)ytb<Yc5JIz)_#%Ag&0v}H--YJLn89S}K~
zTm(&#<c6tQCpn?``4>!fz5qo^_b^lXPV)lp@re&IWNidef0WeC7_)63U)SZ;{@64z
zUzyx5NVV}YqbHY4Owt9-=y|UfLNmJ^m47HP!y6Lbbh<-1kdaewhUKa^V(o9wbrQ=%
ziuIG3XJ+Z;@*L4s{eaUY`94AG{c$2p?EPncd%<PyM-He)Dr(Dg@b2WLE`{^v+yIhT
z9Bp`-GQ4TrNy8?Ikq?4T8d~e`Ko1;Lg>((R4a!GjTn0IXP5{wkD~I(kgs7fA`d6iP
zCKSfQjP+corL%`)eqf(CB7(JXbInbZUh*P_8iU$b-baNqXaoc(xBM2nK@}$M<98Zo
znpY&MWfiqw!Rp>uLNWFXF9CVv1SOKME2kdf0`fK#^)>aejAkvSyBZ$D2D%hr`>HHM
zmR;k@TED?wAM{?erka`E0$fZ?b<_GT@(!-z0z7sDg>!DvW<GaD)7mMR^CCu#nbKF;
z_KHQ05ZOuhOn2TGV<o}=!U3;uJqUz4#Pg(&@No8iS0b)TVRCf6(ESB88Di##+F?|h
znLG^JogNH6bG0&?u-IjWFKm#fuezE#WDyYxRsa4**_H^KkcO@*BQ@Jd!wrIVtP@7X
z!i3lRb4CCsA18eD6uB9?=83KSO7pkQPal*}bm^RlOuO81v0Jh6al$rw^3f3J!A)yK
z62CyNPNMrzO@zo|;pJq<B^V_+XXgv1XKJ9}W5j-?vp#2GK*70&fgI3t9f8@eLPSA3
zekf~^lwT~KH2zwOF$zLcgjlHvsli6jJtiL3Y<PhPk}oH#&sK5}5M2Y5ruxCP5cwZB
zFC%Hd)(}STL+CV<;kpVddyMM~Ofm8_$hBdB`5zebiODsc+@Gp7B4Bc~v|$H+)HZ{v
zw$xW$U{}~(=JuLedKU2`CgBelZeH*uaUeV8zq&>0$O%rXDz}LTB7DNDO%5cT6MJ9O
zjmZuX#J`<b^ZE6Flfp1d3X+b#O1><#k1{)3Phbt?!xsWg9Zbetn43j#zmm9%+NRZN
z_0M#U(tac(r6|}{u#ayGBCEYpfPMMgrLL)gjG%_mR+=1xBJlEKI23d_N@WObHW0`v
zE*_<1FMEwqtyv+D7yX=Ehv=|9b#7s=EadVGec<YPtKjSeH?kU<JkL^3GAq62G6HJ+
zOn7$%Y;xst$EVbRF=3#ohs(BbS_i$Ihz+yQ*pIRdg?;O(f?Ww2Zu8UGc9@tWB3!gJ
zPh9Nak1RT5TM23lY}sug<grMJKcf_*j|{!c<QAu==EBTprdH|4Ox-x-T}ZR5>}=sd
zKk!DNj-gVqv=(~E+v!JZa+!-ltXd~!LXL@AZ+^<l2oQ975wZWhT*-H!Td8}y>u>+-
zZZ_(B-&m)BnTU|7yY<&)z;Cnh-YcF0Tg#(Xm;3(0<huKiUqL-aB{^Lm1STu(e78*<
zakaQWQ(=O3rwdyk@EZjk37KdXB>}9cZC0o~Eod3UA-Kc|U;wt}<>Mqa2q*$A!79xB
zt~ZifN0I&Hw*LKWeylSyTc<eD>1+(ojd3sSGA;mzAmavJrJJtVMX|2+AoMp3y?eA1
zDtcO+mhvVWmYuaBuqYQaS^Qp=5diXLe?nD(ch3#pM85I}wP&7}a4v`_P_9+@uD-|~
z4nciyYi}Y*fe4KHE@G(<0{GH`au|^x&gjsnse0NXkHw8&b>$alI<Ff0t(~pHR$Q$u
zkI`)=j(s5V7xdnnVSbILX*-g!%`%LExxxGJkrH*T*sl?8>?vwR@q7)XAXLG^!i}hX
zZfz<uVcp4%v;YV^=V9a}3Y-|F9ePeA<c&i$ASdSnb0I2Kn+-LnPUjn%l<$QLj#=t=
zZ6Hu+*wXa>-K|v;z=$2~A(AW@e;9ti$>}#@v`Ye!Pk`;QX~SV?HUX#%$@VSc*VfeS
z`T2pa$|++F$?l#>nIzq#BtUR{cxg8!Z`^L<nfn69VQoo#%nRqMy7r5{l)U^D(zUI=
zZU;z(q)nY9JO~aylf?9%3AlMc6`@v0@^bKC*1O}Tj%WK;0f9@NtPCivrR`7--x~aa
z16!u%e@6P3gVg(+kCD~Q)3d$C>#VeNieCBvU+FMnEo=384Kb>Wdv&H$-PX4BP*c7e
zXQ<7P0b8%3EmEOq5B*EnCo6eL5OkIuWqehwq@$+?nmSuAl?60aYvQG^3!F8a)@C0K
z@n{h;G3YD_RsnZ)PIJxmwId;^rFak!+Gju|+u4X)A%kh8Xz+Lz5=naZh(D?=G;r);
zs<8Xue{=VW1fq90y6P*Z<D`hn2-DQlS9oKefR?eo&CBkDLBU1HM-rie^}_9=VdoJN
zWF!EcK&Vhpo%H*VAn7FX$wg)yn=VG?9fGumN>58RSZ3YE;9XM)#!ugoh3*VDADF>#
zi~DK8R&!a)=?j#!kjDa$aZ1`*u*uSGS#jVBZU_wMVhw}?Uk|_J;GQ_aavQ~je=-Or
z)o6T*jgdRbSQa*V*JFq_)r^Inotho?ofXl>_OUzD$ZKGAOsT7L2U8{YLvUgU3m>P{
z0M!L(&|?z{8=G4n+s_=@sO2ym@6WS-fQ4GLS;0!p)^R>rp58Ro@24~q%11j>@G?84
ze;t9jXR>x}aoWshFi{i@SiX$%d<cCOLZ`KBDa1Q=g<j$A9vNQ+08#=JsL*Qcb;wQ7
zzw(WZ(lBE;dUlFsTIqdy4=L7{f2^lFM^!LeP8tlQqBvhx3QWc6%^W)5R8Fe6g&Mfo
zy5NFLJZKUzh}74gtVfKV%H`@`53c1om_-H$iuUIzYKg@z-z@dBCF#yaHH<$vBZ***
zf8qiHtBE`|X#2b{85r)ULnvoHxLDen*DATforFqJh={p$sCx;`4ISKX6hjVD^nUp@
zhd5hMn0#&f*zQvl(p~Hq0&3rI#c#oVlzl}zTO!_)<M*6IFV_KK2ne!Znq0qFqiunr
z*fSJKd&jFj=o?W_KQc1Bn8QCIfA~#P-it?=f?gsWK4aY5U&OsI(|E*7xx+}ToNmCF
zj_vX=e^a?!G(Kn8lhlXdGCk5&#~}@dtGefgj20XqSS$D>F+$xwsWZ=7w`~Km?po5M
zM|jH?oD>I5g?BWR28h!jP}OvGNO6U6w6Q)WHcwJzX&e)Bq$Nx}n__H6Ku}cld;zW9
zjbzZl#N41lr38MVG%hK3A)<%NBi1?X9ITj8KLKfnV<dIOd~Ah6>o|RhQ0MwG|GRn^
z73P~jg-`mR=RkK`J^g~TL={(1SqEplMLLNl4@><u7Y#$!jh?!ls5JZCg0t_-W1X3V
zh>k$8WGf9VHumQzE68M1(A%x-tnNRSa)T5Lw2b{dGR>oK`XY+*{gTXCv``h<Rz1V=
z^TQJHDwYFYV5E_A2qE?$A!<r(b3Ny;TG7(hElGz8LLu+*tqV<e>vv1ZhD23KGd1wu
z5ursaOT^j1qM*(F&lBS)2+}L7wPf1Fja;M{A6mRJ(J#Twtu6sFge;dw>FXPHAeCWt
z-C{}elsI~M+ImMzDPs7oak4>QA0-4*DyZH@$dYbJEJKT%@7^Ugk>+!WWTe~}Yuc#{
zRz;~*fG3*L_<V7f{%pZ7u`;DTgSLjsbA77LI5l)_28QZ$i(=xl8(go#-H-u_DjVye
zyC6bVs-CJ+JmIhUHOK|a3g0s{*M#ghzu^Uja(&<yiuWeh2!`YupfK(nie!I;gb1?k
zt|e2h`=B8rh<ZNK%6_~>0uhvu7MG5-sm*+7q{l@EnY2e`GF;bnppG)?1V^JU%SOK0
zZQUd>{;~I9dG*!)0Yt<V?IU7$%A6@pMS0jgKDICYh#dJ2n)UJTugYW}VQ%SpnDz0>
zTby;ojM4hp+Ye?VygnQ0^OZwvH)BNJ1^HMk_(4uEGN4Sd4l6Tq@ln`1ufi#?G<2Xf
zzyN`qeKnzn>gs7THkLb1z%Av4hKDffbvfxsylc-OYDV~aZFL4{4|!XXD(8ZS!A11m
za+`<6HLU*5*_BlbM?OhYOC$;&vWD3)0$#1$p_c3btgh;+ECMB>pztYk$uVlUXo;28
zM|ozDlX4$I6{j&u7S-<miaPK)!brcD=Q6Skw4w<D!0gC9eqqo^${piGVYC%c%tn>Y
z9PyZI!yLJs+=n!DH?k^n|29A0FbO_9NQ1Uq8W<2z7BGa>02x}gwKdapgL=;C1s#U!
zAR#pHd;W$F_tdak>)b)S<uUB+GJZV3!zx8h?Tk8aD!up0J7|i^H+fI8)ijc#qhOd{
zneoY2=q2Z-Zo@*~R!4zUGz(}Hj(PMe+1JyQkAqq_E7Ql}+yUUfC*=D13Ce_3*NlPJ
zY?3%yMjSbOI!bPApROuKRu<%c8K`-l%;Pn=@XcL;kNJH%E%^xr!`JZr<3|iHY7K@l
zClYS8pjt3hrIC9*H|@jgZoonRmj<zcBF|@5X(?TW!pi`?F}vP9CjAy_PHqlrFi}(z
z6BipY#WZj!4{t|G5YIxc^6jnPB(O<ttgrbvc-{#)$GT!?lXK?lyZE5l<<+P&xipWc
zn|Zj7bBTF`Cd5aeH|%kDs+jJkY)ZRWK|8>qoT*6sG^h@rCKRcnId^sCHbYEOVVx9x
zM-=#g6pzmcDWP85#sAx=yFaTfmd1IEwj2a((4wWNX5eJALpUMaUbmj%1C(Zy9F{)I
zWYIgI{fgp{EY_+R;nP+!9x5eu3mHgUy2|noXf+T$P+cmpB~2N(Veg~m#=dq}G<WFJ
z2jW4hnKQ{XDVIPd?ObKPOVYYKi!(<YmCzEax_)_zydFCaPFnGYNtQZucaa>wIBe6x
zn5l^@M#Vj;a%-sZ@;?F(bm|gCz5CKVjjL`ePEa8!qVB(8zJx7v8+v7DbDDS2C*c4`
zSP~j~8F~9{Dz7P+N?BQzW_4eap8+kMe<H8IHLT})R?6&isPp(f0S?iIXPNAH{1SpX
zE@X74y-E^|AsLJp-8(4eoHVsixvZ?S_;b)ys8+bJ56?2JpWJlki)6=7eTHu)SYdtS
zj!R_cB*k|za<aF`F|Cx!A-2N5Cp`&rG6}G8Gqlt%yg5EdUcYqZEhA8B!&mw_m?vG$
zp?p|ab4CMgCdtW8AS1-)fu0d<FM=St7qf?TGJdvmg-|rX{K`CF*rcJUi-(UxM3}+D
z#dp4B06z826!RfWzkhEP$iCLBS&p)D%!qhLSgTmQ%KYloZ(7r}6dfgVgWM)IDUf!R
zj%ecU>I+NZZD^^=1(4`8X3Hn{S}Wz?ZSmhw^SphUfP~p%HOyCTL9`R~5Z(80g&{+h
zNnzId2wIYV?lA&wH}IOdYgDLuGt1^Ivy<t1AoBemu7>@etfc+VuN@?sc!hS=sWV4}
zGqE*xa&|N^u=$g;Gqgm6V`O3^W+48P<l&)L_OLghmp3qb`*1X|btYy4Wh&AunmF0H
zI2xHafr^N@JBukf8#tSQ(qc-i#H@cxh$(RrbH1hc_&}FBJDWJ#g7W1VnHh-R!e1Gp
zcDBx-v!cwzOe}w{5@jJ~V*4w_M9ld2V(M~+mL^7TB_(V?tT1s9gZ|&x`16&KnGr<t
zjp_e50lJp?FGl}Xf*Evz`5#NLfKL3W@{K)5J0m3%XDxd9Ponh7ChpFE(G~hD68<Za
z&>;pDl`%24Fc7kH*8<UEAZBM`AZB4;)gh)A1_=Uc9VcSY%K-jVBj#x5Vh>{YPy7A(
zLa#)x>}X)?WdGKVMjrISO7x#hTrG@D6vc!<l$8w(oj^sDoE=?^od0TTCt?mhzJKhP
zH`0Ia7*PvrP@B?=T7w$?lZla?u?fAjiLIHlIWapM+uLP-?^pNC3RNAsWd?Mgs-#zY
zB7y?0mnb7x5`|%unN)1r<(}rSa;X-{xF0OiOC9TVLFPgUQ!%O$+e__ydw0IQQx*!9
zm<O%FQ*$^^a6klXle-#iD_Btmd1>Kzs33Z=IOw2#!><NSC75slhz;NozvdO0#u7t+
zZBb!+!vXDMP!#a&_m3M<Mx6nU`zu<OGH7)pH*1RaIFWRm3l<utB1o5rpBA*47Dt`T
zDC!ETuQ6fg(j(P!mpZc<%tY1o2?p~&eC<foH)yt}j;FGZLt+Km7Ms9*yOrCEiZz{M
z%!l|r4<?7kj@7k6lNKy2@xz59YRHH~d510vHds3$)`Ekjz<Gvdp?uZ)1UhG7Wp`6v
zd8R{n<(qxmSgEtrYw)`M10Qy#T-#IDT#2nq=mnvJjSN;EsmMc*l_Wt5(=2(AygE}F
z*+h<RC6;qozQmkPF1cX8Mfm)O8;?5o5W^aL?#uF$LGD^^f5YLZ7(w~`WH6GFa10m`
zBMVE<=SH=IZ~9iojJkWpEX}QQDQV*Rt)aqVD1FQoL^THKUlFmA%5)}vp12afN+{Mz
z^uul5`;znLa@Qc>El6~<_(NI0Bb_WlAiACRkt9y%Dd5B>5>qovG18w3W6=W#_MW-E
z%g*dHZw(^_>uyczrBM9@HQm^dvJ;5~E$6FTFe8E0KuPOsuc@jO8L%L1CCZnR9VB&=
zF<N(7M-Q3t!`(St5%dO42~&gj*GFb+hElBICAF|Hl*Pay^7>4N;UicjM6bX(<sS14
zM(qpF;aSvQm<p8GdS2l3HizzCk>%>po3jd5)V2~;2$XIN4pVS%gRQaWP*u>XQ3KEM
zgyU!q553yXoYM78VF+3>s}fet8j-s)KDvlJom}rQK%0gBTGD?;omQMNW$Tdx`hL&>
z=i%XsF_9x=QKt+uD=FzBX-(whub-HpI4-OGy=k#|j7)E!y@gRCV<qZZ>}prhokdvp
zTri)E=ca!%FSKI%jYZ|-fzeGB;1Pc6lcUzZfVej-_zz8Tu{CnGu(N$rRbpoPPj)s2
z7PfB+&InR$5!*M_(jtC?m;Xfol@PLX0f8u}JRK;X?hSV6KnO_320|!0PEfw+-^hcC
zF#d&dp!g3)g5p0|2a4H2<P|~jA6Ns$e~|MHasE;L-|*^x$u!e{V_KN;jU5o}zrq_n
zF}$7OU}s<jQU0$nC|8!4@xRve#@_#+rhi5EuUa|T*;qjG(fybBe^u*SWBd<l{V(+X
zPX9k-{a3mFB<ow#|G!`BTSxytR_niN|9_J8ZCL#eYW-Jq|LWI&9AE#vrau<`?=}6+
z{QuD}CVC}v17izYGmvc*wzIZ#RI)cPGWkzS`1kC;6&AL$b9_@SW;O<5W)@aDM&`eg
zY)o`aZ*~{tdw^UREn<+>H2L!ZI`?;l1F6v;lg|e_{y~lYcnW`K?T>l>n|nd8<YMUj
z=I;FQ8R*q4jNgnY69c`tiG`Ut$n#-g_+PUuWZ~>2Z{jFyXJc<?`{pBY(ErUke-c*y
zn{Ac=`3H{nc5l}D51)TG){KlG?f6G?{kr(N0YH%yl@JAhfq?<^L4SbPO@O9|yM-wL
zAR_~y2Hl|w1Au}61H9d0{dRM&Bm~5t_|J{L3MhZ2-@YiI0^V*Y1|^Yt6yE)38gveH
zHZ&IisE&PI1_%Q{vt4K)6bv*pG%PF(90EEb0z5nd&O0<@bOKx=LIPZTd}4CC_r#<h
z$?);1*gt$^U}RxoA$rfr%fZA$$IQa?Mg$BN78U^>0UHq!n~4OUgz0~LymkUmVE|{~
zlMrB}0B}?=2vo4wUH}n@Qz%eNyfwz(4lr;CNGKpQ3@jWxs6ZVG02~Yg0vr+o3JMY=
zE|?D}9{`C8^^Sy55QwH=08Q$E&J+-z2SX-Q-G!kzbw<u?=oknKhlz!agG)jAp6UZN
z3o9Et2Pc>CClOIGaS2H!WffI5bq!4;V-r&|a|=r+XBSsDcMs2?;E>R;&*2dXiAl*{
zQc}~>zvdSdek&?2Dg9AXTUX!E*wozJ)7#fSFgP?kJu^Euzp(goX>)6P=hyDu{=wn-
z#pTuY&F$U&!<$@S0EoZD`VYzen_Q?MxxgVIAt0gO<N^bC0|f|FNGK9U;5$JDXaff{
zQl<bHbfNgX>MmF^X2mlML&qsNOmdb@it{(o{z&$}Cs^SBEy?~vu>X{634j0^nBb@o
zr~rPz{YwN@F8Kc+{y*3T)FOduIO<seCz$?ZUo!^>mvtm_gcwzwkC`r2n=x<XS$<^q
z)B%*g*16Cl9zIANxBiId#rE!eA^lMd_!>d=KL`m_6j|LOc-(0I=%%2$s-m%!n9wnq
zF$qMMO!^axHKCY)j%GBQAhUqUMzkQs7f(QpVPuZsf@=g4GV0hSo((@L&TKb**;(e^
zW8~4G35wMOY?@a|wA?w^nO2v4af>?>CkFQkrNL7?Gg>BL^vHB{a}vP!!SM8<+ahK}
zFFH&ujYW^o<YIaj0Wke<k9F$n?AYQ%_~z+xixiIG3V_1b79Hlz`Vt>+n?2KdxPYi=
zLQy$`3D#C$1X=Wkn{J0!fHd+e07HOKL&PPe9VUH7RSUTeYiJ<tm%bm@)LBROv2bs?
z^DS`uw`2eBNc3u=)L8HH33RY71)Y@e7d$IItnV==+t!DpDo^oQSqyUXO=xyZQMF89
zhsu+X&I%#`^#84(YF$oOhZhn!jrm1*k`|x_Nc4y0oBMeu)mdZZ$dZ!rHChgKGX01L
zdV{K>;8FHlz=;<LKCLH@r?hJWpEVpB4+FSA&&l4VPU_NRnRlD0Q~JI$5sdm`Z%f53
z*R6w2MXlG#4Z9GtS|#l?lT!rXVglz*vj1gdOdXRwHg1qXy!eIC+;zzqnF;Zf%yE>n
z&y|nLyg#09qD?&;C17cipi7AyAf^d8yhfy(QK%+WpX<WrQHyn7?n3Ru(_Z|<giIph
zRZ&s>$ca5zUiOUMQlij9C<5@FvfH%L-DPt>CW4rCDPJy=iu@48&dB13G|H%_QlEkm
z2R0`TKdFYg<p(veh5nHY=IB=4-W;Nfs)E-}xNXvIY5sz#sI<WpBTWDbUE$vct=buS
zyYDu~J1+0>&O;^^3a#$+dd##NO_L)8vK;23J&A6tHBD1#^K`0<$&u$l?X}R=oYhGY
z>|pdw9sfn;kPq_g=lAb0Q+bJZEA+HOygoI`SE*9H<sQHmAEp;Pc=}WXe{*mts^7*<
z48I9Y9W*O!>};_ZV`PCM4!-zVm93J~q4(@x`dpVH(iCaiGI0U!-y=Qr{mrV-uYSaO
z+*>*~>NIX;CsMJ1g%_*$E`?;8_+I5qXO!-Lc?B3Jw^ql|NdDp(QykYF{K=oJ-MY-0
zSr+rvpJxg5b2Tq#6S?V<LqcS=X#(#M(|*xk6RZcCPCYJG(Ye35<u=u{wL+GrO3=YB
z$=AT!RSXM&zhSnj3(nTQCSvzmil3;1JuJ*L2V<HbEh?*}-lfRJxPD<#%xk3ZoP>^%
z!V{(uU0f-GwitZsV`{!{B_!lu;fCd3I?ygSld$3Lp+0uzAO)eozd?|q9)!fv_)k$6
zoZY2S36^V#g*sMTQEaN=Y2?<%SA@ImL1(u;yzfOK)*N8J+@Ow*t;7!vYzq)-<$Kf4
zo+>Pz&#%r#5tD>Jl`VOH*gV&-p%De&fqL`O@KweL7sY%W;@6s;Rq*Tqxd_~mtWt$|
z?qjmn4%YVsGCEC>qs@xY!s*p+qyx}`)BZerT2oyu4H<T%f)m?1g@w)e{`nYLrE++^
znFyhJdz!8b)D3V&jk$2Q^S^H;VTK96W_T574B3*V!>p0=nV3MLYO%wV=T)n)vW->F
zJ@(2&r`@%5E>WCBld3OjEz4q~Ct!ES`JB(HMINo8&++iqootRG+5e~_&XKcxupC}?
zH_3<>t=k;q)|l3vJ^`F(i+T}h0`93J4tR7nC0f~wnMy8cr3{EVY}=E^Cc#9|<^Np(
zfW3#P@bc>(W1Eqyp*%*{kqG6)_xF2Ptl~d|4pY;fhZsgqMuo=SNWX=7Vzxq$IPdR`
z>0sRT=Mr~*bh9~+&#q&rm*e@svaDrCx`0|{h7WFW^9A&Bh7m?=Ug$yqnGk84s=_k5
z9*MnEzm?pG%uilR?#71hMf^!<j7m*0$`nq-DTzLbT&+*eQVwx20&?>?Wj*!X<~>j2
z1rP@kc?Y5Dq^9vT0h##n&^krEP{pT8JXCijv<4S>@RNqAJ7rn<AT!}b?#3j}K|n)#
zbFu~)4j;3TK<v#;=%oL~tWxkJRPb>QF9hI0A9JO)@Ed;s>{9+jbU;{_ghdW=0zE=y
zawBMQVL=UZ5F?4WpJvZ5e92S8J#z@(0Mlf};Ya{q<=>%$?zHny7pW;oZvZ<dZ>ZnH
zwK%vqL`jw8)JHVQ=>%SX89H3i6S<;I0lWv=BrdxYbeMMxTrMTo7CqmlPN=DNVM^Bm
zbDQ{_{gHP4>GwB$lF?l&ELip$8}ylnZ_3-4m~r5P##}KarG2>NfnuRop)-3H*o0vf
zE(-N$f~7L_NsQ?UOB<+}*SYXgs5mUkMUOm)l%5FFb^T}+N=->C%dHyYqEukCb%G45
z@tS8@=g2o9h6hXP;t#6HE8m7;999D-A!@bI%<LFA?+a+hq_{ALt_mJ4kjN%9(_-Ww
zCxPnz<53=0l}_^0?H>4*R{2u-P>)(>ckI5k%W>qYgkSwRL~4oH&|NCU`r6Ui&FwxK
zjtyUuM*SN2UZqq$6Ffv_lpn8(<o`|P(KAXOdL&qX<N9lh9DzGQ>&M9ngdcJUC->YO
zvDlsO*ZB(_oB;|V=>HTH{Urb{EXi#f&x|f|C_U((3CW>W2=5Law)}Y`H&cQ(zfT#p
z#)GxQ-*Ud^An>o!5btogt|aQ3X$;9*5(?XDX^b(-g}Zt1OW|(kJ1={Y<%dJxFq=->
zd$*Vi#qe_JRww9TZ5oL&a#z+nR<JgJLJr<k%p!-udX*uS)9+6?dyHU8zPB6imt`eb
zRGTnB&(Wv2U;<{8MAUNWt5SurA0#Eg`~;YUx{19N^r=>%vw?5+g!@IuWA#g_Vf0id
zD|un5JU{1g7!jcOh^lDMC~N+`#i#V%UT(fjc3IXOV(iJR;A8oP1a7Ug2LcmK=k+S7
z4CQ?yf`L)$O3c^xm~*y*6nWOK+pdS3wyhtKIv|$EE}8=sr5(Td^XNuo-^`U)71cT7
z<i*FuPsTaO|67%ZE_)(tsSm|VawRmk-d<{R98rpr2<xZW<wxYbu#$fc=pObw*%$L(
zD5W^w1LqVs8o=N6=hZ#xWt*=)8GMfqrdHb~NgxSEN{H-v=_Q9`$zJ+HM~9#&tL>=i
z>^OGu_$jwVnUD(Xg={uMx*jRV{faehvja!W@BL3Z801WvhxxE@Yot6|#__J{*3yf4
z8@em51fV-#64;^Vb2^L2Bv|A2k?7~iMf2(v**k#Sp^Vdf`tJr!W|M{?n3ddl+M5y<
zPiK3UkW4zp6LSbHwe!r2wcin{pP<3yuPRT4o2*uG@xr0UMPaXRtC-GiLkVw8vbQ<L
zWjqVhDeAro<Xc-ZFbd!_e`&!8UdEmi+(P8qWUn1M{nAf8{;mGJ+9S~4Gl3>CLtv)L
zcFh5MiEn=b=+A|G?89YUIvRFfg?86VVk5fr$zB$wv+_~1I&^w2eU)LDgPKv7b6d43
zVHfCKD<}(lor|RSZ+uHc8}LqDy->ev2k&0bg?xS1Mmx!E9^%}{#1+Gqk1xk+)w%GD
zL`<t~rOy>bsYcdjl*N6)`b`@VhppV;GS&sp8*-Pqh*G>Hvi%A<n#<Wscv1{*RMw6k
zUhqDp9Ft7YP(s<aQ_S1u&D-gBp%w(wPy3V4*XdCTl=@NU5!UVpC8{T^w2cJLo)60d
zHx^b?N-E$9q(-F^wTp%Q0XuVd^X5vyEMWVXZc;lrsm^1(NqXb)dnDqPm*-{L-7rg@
zYAPEQ!pCYw;~d*<0Ox2^p3Z?frspWOzAv?`=q6<PgM7)dpWb~!y?a0JPbFcB)BbaP
z1U&Z$>D02VRO-fu+MX+T>xasU#bg4f$xtpl7gHdm-c5LgvG$o2c)w_P>cf1w8rTD&
zIT-y_hSjnKSyKo_)P+jFi1+b1a(9Bc=V*%SWPH&I+YVV)QR7Wj0nhRN$T|k-723fm
zg!mox`1+vt*f~8@L39;=jQ~*nw?H%Ri)<m~U{$h)K@o~Vl*_-ZE2AgijnB}t2{a>O
zNvJo6XgoXiZwPWic;}>plTI~5;g1nZmuF{VyDW~c#$R$IQVtW|?z$ll?S|N^1W_GO
z6oS|1u!fYq`-#-IU_rAlIP6G((CwUXwl<up(xK0h%VqNicZg?4_;1Wp>8#Hq)bD79
zbcdan3K0R$U($Ol6;^1n-%%c26|eU?@;1c5_OGal1WH+_a3@9ei|twoOHc2lo2-OE
zq6wf|+i1zpD!^qjtJN-QeRf{Ve>n`$ZV0<V|0FR87Qi}ZM+F{}1V%Z|EH6Qn5w9fs
zi}qHH-mAQ>Zn#n$s#*a3njZPM+2VY5PRmQK#?IOWqk;9j(S7DUqI6>eRI-mb#BMD9
z5}qEW&KdYfI6AT~e=6&^TA{bm%=Cz6wLS2_&^rD%$k6T9{u56B9lPa$vQQY4cQm57
z>z_9ooY&tyoxp5Ay#hi2cJACgYW6tvbb_C`46vFomJcu|S)aUOsh_RNGQ=Bdf2hmr
zgLPG!BC;CKO|M8BULq^3L;y}IdNNX{@M5l~Nj(&(_da@5dP&T@uSsL)bot%8vmg_?
z;uI?=fN0)O0;!hezO}^dvg*(7NHa6VkXv2FdKXM6RXim&Se6LVNh^W)C^wDqxtJ2s
zW2e%7J#M#cjJCW*YV(Gg!Qqxpc|Y9onc7!Coh1z}n|<ewJaqX1n-DigGCFb+n&!Je
zAmBjuhaBBJ<GMNi%-To%_<4V(Iw|F*S`&wC&Po|pZYvWiK5Kg@H#ACxfN|cVw8kGZ
z&WqWiBHQGN0vMoKDw-AXeVDKYA?aCL$b39&OLwXXhdw8;V4CjB$%#}5`6sO%!oWKE
zPbuQVIrb5Nd)=F#<)^0ger;&U{2_u`*$fR{cIa#U8$pezIfVxQ(!YaN+GI6}aN~FW
zjxisu+|S@qNz~&87s~?meIl?U#QD3l@bCA90Gp?2E{to8**n+m-wTQbMX*avV|eex
z*&Ug+*uOV<ciM{|3lO-*m-X2Byq^eWGG5@^y0r5v8Mb~gHA>VF8j(j~6yt8*EjXJi
z?8W*qQT%Qsn^^!S$0z|4ub*0c(^Zt%9%$vH>8`7Ck%W-0CB<Wrg2u2xLKF>9nbDW1
z^K$05&zqQU;bbXBz(?{;?lo_qA(-aAR<vpg(x@{A#NSUsvzH!}VG+BQrrS(aXW=rh
zr|+{`J!%-(GiH5j;&xw?C-8L5MYgWHIQdPSblC(}USYFc(^@~&U0qyjqspy=In5b6
zjt2bhZBa%(VYJi0$pYr2nntJajx27P%C3-WlWeKFN`O(jJ)`1NVyznD7ihBdWh;L_
zk8GGRfDC;y`ul7LjU|zpwDr*q<E`TLwHC}p_Yxz0o{B7SkQ2;pp}a2LbZ^h7-4XS1
z`XGDJxcvKfYabff_vR<UiCy?}S{bRv^YBu`9-ojasUUVuu?=|-Xk@pRD9)bS7WLNJ
zcBX2~N=y3%UI7EKdm2gqq)}?EPfR~j@91E$h_Rd=7J}5KUjYajN?od7?r^BJE^~qF
zS~}WY7mOPV{B|FsOSG0+%-(H8T+?s^;uuVz$*-$;W?A}YSo1yaf9?VI&U2B4rjEbh
zY%eh|YL(}u#viDAVjQjs;*Z3L*nQ&E_kjYaW}|+zjp9>euu5&8GO%jwuEJN-UMWM?
z{ANJts_u*qwzss}idi{Dk1!r|Hs)IIsZ;AZ#T4WEpqB0aZO^xTxagNE&J)da?ZBf*
z6chcbF{C_Hi<SdiGK4?1bF@1C)W;HVD%*<AQYOpqT3luIRN%iRfs}-x$Hi>L83dR>
zWJVl?R@gb}DAdGR<($)2H#P_a@v{P?><F#%Qt&r$s~f)~ct&JTBdAjuOu|J4EEfXY
ziTYX^n&)ZV%yur({kH4&RQ6v1*ajCof|h`LO*Xp$*1dmrDgO>tYBk;jlQ$M_o;5Kh
z2Dn59^R4CxalBRs>ktQfvS+?I*6y@~mv@uEiXAE1JYz?7WX(;w)x!3AlzK`Hj)4kS
zO04=esuN8dE-&2O>k7`=3SC-Bid6_UbwB5aN$9T!NG9Q^<Jw?3o(sa4$;+8=drzDt
z1I2)SE(aa>MwP*h+gw<UP>*Bnm=@EN3#L}%lMTY_soLNeWDHI7q~QtyLN6ysO`GJg
zGT<Hr&XEpLs5c+2&9sFPJdOX@GYv$aEwI5d@>WwoYaL_1UY+6nw<!TEludyv3}TYz
z>}0@j&~+Ym{yR^32s5SXexIYbbEyHsL~gf!C>JGdXw~0{j!GI*6_yA;NVLRb{kB=a
zChq)XT0<8`_k)d1Ylmh@p(0Fp%kr~9_fkPhB>3*5-&K5$MFPIrh;Tjjh}Ox*b(r@@
ziP|onRsFn#QdVVN$1R3t)PQSZzYq_Vf4M>>fiSH#{G+!|#}oH}Wz3W&oHRyW(W%RB
zlXYYg781o?1mMM%0^1P9d8)%2jv%_3xe#$cNZ&r==f?gZ4Y^Z>k9x&A!{57~rykN4
z)G!Q>nTXAo#R+Qg8m-UD`njdJ7yTcHyYuAgJZkkYNzA(v)|7n@`K*0eQi#y4_q4IU
zy#m_JA$%qHJ+Pe;>e{Tl&|e6f0|Ztk0(3Rr$5`*=ct{95C^;j4rm<*Zg@ILkycK!+
z?$__nt<I8`zHPZ<%TYE|r~@BM#ci6ld5X9H&|iV8g@yx}@WY>@&$g>f^r5E#8y!r@
z4pH~}`0jxk%QL2wmAH|;6ejDchR%SKxt`_uvCJ11pD1+iFcy$bJ@?kTeE_cpASH)*
zxsYEjCG>_^{yZmpZc#zvLh<y%SGDFc_<}}>Jbagn5V=Zud0gQc)6IjXRdaOSXM%4M
zdYr4K=1>?q>0Ob-HrDj8kZI*hkp4jy-aA+x_^UkIGb(d0y$#3QG^%<-RV0An9?mPe
zZiX&<LBT|D%l~~ko0t8_iW*~CLH%M}^_bha#g3+EfgemI{T9nm!NCDd6#{I(Lna7m
z!4eKtYo&zNn_|@B)uvLZ_`gO}338H8{nPRp+Rv_=j79xdKz0n)b=9}4NXFa)1{*w~
zTnHk``yxr3=DK`O$&T^%AN6%Q!qN?jf}{E`NnjY2ZM&U#tiT4N=bCTc`Q%Q0y7iM7
zX!;Npwwec9G>WOm0@%9f-`VO>KRI1Q>`Ql1BrZqv&eMm6L0TiRt8qGQ{b-I&Y&rd&
zi`4HUVl&A|noJiwnHDCwd-9822A}x4Xx$P*GTbJ-i!&F=NMqW>>%J1n&;3a)*WIJ9
zKGqK6VoC^X%-*vqf&`_rd>=VIKk-!SLq%}o7lxia!PsGcE}F+6=QHdHxRtFYJ(ML~
zIZlQeu$Z~YgN;b*2F|h#QFTmtbBNDV74$Z?(Am8)o(rnc_=6BpTZ!`S=}OfC4=Pmb
z;2yb%-;CL$OXh#%*)dTJXm90MIl~yr3fx;bADOAoWqgy!MaI8dHy0VtaD)bM5tVek
zB=4x4yaMpa1UFmM$o3u${e9}|-2e3ou#`1e^JhZQ^PKVavgt~Ns|*F0)yG0?h$4(j
zV374f`f(B*lcow$g^!$h;Id2vF@u7G07Nex8P%cSae5JN)GZ2Y?NKt>DeA0BfVZJv
znj?+o@FCt*O+He;KPM5?0A*IWj(W9C?uqt-9~C+3QgLN6cDq(Gp0*2FV@VyN<cyqu
zTkn3bGvtLi*x{jnf@SjYp%|*eOm4Y{%T=>oeBi+Rk82$zhNrL)$QKr{w2GmdXbCgW
zU~yUBZ;p_$4YD7Cz+cE*7NeadG=NNy(Vk=tZOH5%H~|@;!;uNjA`UzlGKfO6gXRJ(
zRETVc>7u6AV3MBe+g!NHRKu)jK5qT$?V{<j!W}n74DDZ@bG<!C`D^}U5sTm^zOq~o
za3Pf&8a(BJYk}q3gKG*gB$ueMg=Ss)@e;diZZ71L(3#%W?y(`;QTiR}*j{k=H|>#k
zbr`K{gh}63UGP007RLYCRR7(g{9~+?&MqQqY@&vvD2Bis72Atz^MTwSmd804Oe5)+
z1oSNKZAYl4On>2K>dA@sKvLqRxCHq-ah^xDfQ9LG81naBt|ZG8Q?Y#G!~B8*1al!_
zKP!03UCK!DfqIt7$V8IbAg5Xra0!p+*Kn5&T6lltBqLHvKQNf$Df1v(e$pz7%D%tq
zz_WBD=JWtM=$wX~>b_u_j*FiPVCKh;R==MNx>sY|`rO(gQ8cjD=L+PfUd%nANuMQU
z-&scOX9fW(njKDpvEB*FdX^_t?jv2c-eV8z;Nh2TIVaNdFYxzp$JWX^?mk*27ncH1
zl|_3xA367FM)}wdRiiEJjHe--Zj+%64y`7ClfatY^xW2?>8hP0Yo2XnMbD)y*9qk*
zA=eo~G+jT)L&r_YeG&dxMFPMy>;>x_WAcZi`ubl2hI$zg8WKO^_7wnjH)}c1#&FWt
zOKq6uG(?s+koOYW4tlbDr)I0CSolVQ+fO_C!JTaAyqBD4fRCOLqkh3{nVK7)Oo=|o
zPTROV^KQyfZej<FBUz3vWil-?UIH1+A((q<pY!tu=oP=^%fT0nSvU%vP~m25Hs#OS
zR1(LBVhRDR6i#&L6EL7H{lEHC+VF@l4GoM0>Dq1Up>!Fmp6of`hX5QUSPvQ!7-F$^
z&h>b_b>?U*Ta{TJ*^x1l40IOq9GUFBwgaxm01oMud35>3QufA<h3)ckf|@V*=d7zS
z)%Xo5N@Hl=6}#eEL|IN6yHV+Yhnk+H;#o2k)xqU>Ej1^PcTm?=*mtyBaSaX46u&dl
z)!mku(c*>$srffJ`Ok1~G$ys7ykkI1(A*8}r<&eIw^7-%dr@MPQ?f1>TBA3)aP>F8
zl#6%<HS3pL8tNec`l>!9mk=Y*Q#te1PZAYk248h_1{c*Q(*ftP5!v*wfZ#~=n0|1P
zig<m`=xIXOfqIUI^vj2YEz`!PFJ_z;*wy?>3jca0qH=bEk<ss}@q*T<Hl^6Y^fWx|
zGoX3j)9EHO(>L$<4lvGHRjBJ%xBLxeQB)!qM*mB{L1-M1K=4?L4bz@zEg#sm5z(hX
zYnXycU30}cxoCrVFAwygg23u)r@$pBO2GlT;d`ki%arIGvYTp!Yl`x0WJ6ir9sb%q
z$B*cZ3kuNwt%z>ZQvtBT`)MFA0YOkEEEj5HsO-<buFr9*{WJbs;x6m@6W8}gk@f7J
zozIB^1gkM&MF*-Vol_)afn(z`PVMi{T$<T$l0yv)Mn7{Ac{Xg*I_mJPF(Zh~8AC0)
z?lyDKp(%iVOUQO#a!GP5K<HEGlkYo4$vA^iAIJUOB7E@K3izBMK4%=p(KWo+Sd!E0
z9tI8cXP^6m!`!`xQ{m2*JPcZ_YB^^PgADL@3ef2fDXuvF%H?V5&)yw?W5}%DFy_LI
zeSDL9(%<0l8J72m_m{FGAQuU3fRHHc1r_g>d#C9lHBgIld%E3(3>w(}{oJ#)nPA53
z7iu-xSyhtpD?oi+NdWQRZL|DiUR5q1l5T4|Rs$Z4l*ih;wcMxmM^^7wfR0;WtAo#`
zMfOkFFT<Omcj5c1?8u&ghg!)H*_mvM&X-4wQFE@iHdO!4U&1bxUzpRVAjWO14Li<H
zs?meJL&PVhTPoOwX(*No)W9Hj7q-5X!ge?5{n8mt;0$K*{#6Vi9pckT7+C!>T0%bE
zfIUt<7Bxz!#~u|2r8Iw%fXJn$1kxQ}@-!V3v%8uH)!UHMpr0NeSD^arALhod{DX!_
z3nP_Nq-N}@-hH4D2N+gIWxC}ez5=k0dU|lBFEaH%sS^8z$4cJ+*z%yWCA3hlY5FL^
zSsB!y{jurXwE89-zM@?69gM^#5GXgBzPtagwza`mK|8;uo5v@=qJpw<TaLN6h;hL}
zPej?J{O$QbpnPdb1+mcje*2h@ri0Exnk0kM7s@mB94Xqi!q3SCSNL6+E4kXxtT6~;
zi`^HtUh>OURy6kuZLHNdO;nz@=@<mx2iyUyMsd;>Yu>46CdiRb|FuVn#Zn>ZL!?qa
zOB+Mk5XTjiiSGhU5G#CLmCM7vok8GCM5$lNByDZq-R_jnaUejXD(0@cbIP%SnjU(p
zxav%Tm@QD1<9v#Lr0J5OMLQzd*lQ}B5QN=%Qvg!*ZEXwqx}Ph8fPg^WJ^<*w-(`p;
zQ2v8mL$_5ZcZFL9E<VrkW)96Tv8VOfIBooJTehuY6}rp3b@L@|EXcR{9um-*7kqX9
zen%BebA`8XMGf{gU%%(9D&L!?l(OY(zfoM^NMg{;QoD7P#GO<Hwu=SJ&jM0TEJvrw
z+gxOoL}|9^UnX{yR;80Wb~PdIIZv$ME+&TpL2qYY5d6CIf3f!#P;qr>zIQ<(2^t9Q
zL4p(z+@*lv!KLuvR=9f#2<{%7;O=e-Zo%DyThO2Z@_zMpPtSC}(lhtFYwntRSF=_v
z_NjBKpw2n9_p_hp_y2D$Wkz#&>3qjeAar2U^W740rK@y-`zPG`64gU;v1f@!4WY++
z%vSvh0N{?ze1iv};?ME3UR;i!7hmlk;3k3!tO^#|0aJ9eoS1tP?o}Stg5D|^9_mqu
zRF?o{RQ4?zpcNqw<I1WBTm~1%Bx^;ok(7k0qNS6ZiMu$?6*<-?yFJ&D->HzV>OhJJ
z<IfIV(v>w&drx&D(QtlnWT1N;28lYpo9`TPh8Fi;PaXSyEm2!*f7?fb*Gf@-{<Wbk
zbF!(a0AQM5@L{L!s!H{Y$f8DX&))!o#39chGZR06f1LGf8Z$b9DAp)62h;l~IGQa&
z+rUJ~dp^rr-)VT$blo2AW6Z8KcTil=l)S7SeTfiv4%CZyb5?MgN~KL3nTxfZc9#gh
zk-Sbj>5rbT9F`ObrN4hG^g+mO8{rn?r|aiG?ZW@4fQZ`YLq6x8yFA#NIPA1YPfs=9
zP<H$QxYh}|buQ(}-VA-#NE5pq<2ZgX>MMr+ST>aE#SsXmu-X*2dGKW3iaO!EgXCBd
z#*Q0NcnW&LCmP&w;Xgn%{d@iI(CkCF^X0i7zZycQ0+(O{tGem|b6WTwbS=9rwor(+
zOUq9Ozz}U#K#M5JZTH;N0pb_hROkD8ewyf?NqcEck*^v^o$Ybhy0@@UH6JX2g){Ba
zW_XUAdZ<ir$UJS+&h=4lQ(J3QrsSnlxC5KVz>ap}KdqSj(dYj!JqK|~PcIYJT!PY>
zw?IhorQ|8!XfP?u#fniyzgI?`@gtm_?$r_Eb-OhulX?HO3@Z<RL3h|VgtgiJ&HTtG
zY#~|ioTG^C8V@tRcp-Q(BadokTCHBTLdMhq%Nb1K^q8R46hF4ZOA`snvKB#V4mi#Y
z<&;ty{_Iw{JLAcU?%=FjK8*K#4aQ%EaqEVSrN#AObS7}uc?Mf<8cZix;2iIvrnW9M
zwagyYX*5`_b@gN~Xih0!YY2%15Mj{NMv#D?c;~G|L>3;89#h{(VuD6k0h#3n@<gy{
zFv~r|l(#ZIu)rb<tab!Hp}r^Yb-H0j-Ggv<FbS(7V-q<EsM#KyW*ty~-K3mQODa6g
za9F)3P=yOU#MkD;7$u1wj*Rz|qZdM75E~6lm8VC=5*njG2{y2^cd<9&0cw8MMgmp%
z;9P_HG*{S3k(qv}%+LV0o7K-Dn~N`BsUzdPAps8x=kL=B)~|iABOFH%)?j?Xsy`Iu
z)tcK=PUUu5FE2|I{}Zs|H>KU@i;67v$9uNRCUJVlM=)oDp?Bwb3h<6mtek7aSBgbx
z7^l2e%Aas%n_slRqW}+KaeS*SVjV@cU~Sx0BxgEBI~!U28472{kLy#tM2Pz{6dVO=
z6jDXf;$q7jWM-|MF#4IP{}Q$H=X1ONjQRn@I$>dYjd%Y7z-y=#D|*b8t;|mbvs|Ci
zXsA9B`Q-ic?%^S=ps6`+YEv-6Q-?86FmC2+Aeho{Q33x){@|)WjKrzVjS~XeE3xQ?
zEfKWa)O>5@<;BB<s8_1W&sKTa-}vfI+|PThu}|At$b*jB+^kScJXScGuka8im*ta~
zR%S_~2>4kev%3{#@RsElXqkto7`#Z@t{Y^U2uUr0ue4+~r%j`YX2=Ym>k#1ZHN}il
zJ>)ewww{K6qnklVCmXJ-xsxZe<8H`5nphXdS)r}TEN}UmZ4FqDH;dU(=CF?1z9$s*
z$O<S_!x=#tLvhw_B4q2KUm|Z}*rG^8C=r4>7Y5lx&f(oiC^XrNh+Vh{JF6U0&^Con
z7DAg1gFIHgp>l5;u-?YGm&mhJyw7ev*xf>UWI#NP@uL$d0Uj(l$1q;hiU@l7z8jw&
z8KT|Jn2@DSnE8E{<39z5esh2Qw;s<Y2p9WoyTCJm<ehQnuhgnvhwn0+&=_!tsQqyK
z0g+oLWKMeuTwLL2tcQ0J(82v2Oya6zJJw1%Utt_No8o9G6xMg|<E2Hu&JMO~7kA}K
z7WBOEe?gb5rB#-Zu@U1qRL;ZzoV-+u(234AuU-wa)0g5Upyb_%2E9_hO|7y<c^xiu
zoNLmZ8{l}w-LC@1l^3cToRX&zWFMlvD&GIc>D+IpcmFqSi~h3s$0C)#DA@kzfa)Ba
zy#E5#{{prD3snC{0RLa0I^sWpGyb*jf2;@qPA_wUk?{?+40za~?Bj1<>^2<xJl`Z8
zd>C{YlJm+y1ANY{db__A{b9%Mm^OlZ5RRSdPd?C>AB9(0SoZVq;qn3|Q0VaYvCm@k
zWrW6gFt*|n`kd3U-@E#<8j)2$<($mp&%t0~^89vU`)R(5)@O^k86WviD_MUSjj1IO
zcOE8O;zRFW{sQ>vOZQNWn2GPn<tsUaZox=1p+2;+Y5?xDF~5`YQby6o&l3qOT^65K
zFcOgJ(5gbLgY)!>Wq_whz=^F2y8ng03~_m+zE@8$%d8vr1#iL_RrK}NP}=JotXPY*
zc9xSuS)F`26{a4ZmRMVA%P6x?!lC#E{Ha3&)Y;4L7v1KR-=g0!F$-artu5F8RFNb{
zAciNxxh1XFJiL|bjH?JX1%eKlB9q;pMGm1C-{2wq*Mk{|2^`P`8q8^G5B%Vw=M(Mq
zWk96Fy2aL**Ra4LIu5%ZD504BMnQRNm49%SS5LZg_+@3@zR>D?VL2ZPc!CPRn)NzC
zC4FNlp4AQG7aT`j8fRuf`}V|1Ji1|O!KxcGE*FE+x}$Jm(5!0=yTWp9oFEl7;~CGI
zC<$P*UH_xbV}N@y@^{3OLHxK4PP@jZc+YIqX)&-GEcy&sqc?dPWQO8?s;&l^c{umR
zbs;XZG=Hb2b2q6Hwgk4e)g;RUOu*QF{FO=uXFpc7@1nE$wjuiYM={O<%PW%A&38%_
z(9yyk*8n*2V^8_s#%xlir4a%l77XFuHh6jK?QUt>HoajU=y)gE-C2_TCLH=Q2m-)B
z`_eM;QP(i09wtBnxNVeKqh~nO@b5xx;OTJRv<TSnl|L4+)M#eX&i(ldV0x`RZZgj)
zx<HHUnYS{~@;5xwZ}}z$_}7HP`Z_gV|8O3WRO`aKW*;;q#C!IG#v#|x;I+*u4P^nq
zAz6gzQ@a@GW4P#b2;UEgLXih9eUzz-ry~D2Vuy(S%YF<U0>n2ir^@;U5`~MlQBFNU
zL^)VGqF4oq+Gf$TWXH<LVs!a(T}i3IUe9&h-Gi_2O^3H5=X6iv3QUQXJWv||{8SgR
zPPg>c+_tRwXjy7berM%h^{dfMvjjAEGTi2B-sR(T!YFMkMzFRMurj+!Y?)mcC+aK-
zJcLf}1>nGt#hYR~lGjUR%iPmCvbYOa5TZ>ONTKM~?gY6Zt$*P)eiCFDXzq1<DJr*h
z6%*7LpOPZe`7}$D5lecSyY-^q;;yDAa<fW5kE48I81I!e;JiY&J8dk2DEVTjS6Z}P
zqC<*(COXr5j>Q+5ta@F01nSZ?rNMBx7TiAFCxRT`wc9bRZCVF(>-IDI;S~<p{N0)U
z&6Qw0fBjvqra$sz0Iifmwn$f3-rXS<9JJ#cq_Wl#(?jtzM!H7!4WhnG<Om3hdfe<L
z2g^e8);ILwQNz>%NWCWVDH_yx)^OYqool1BX!~(Lv@F{9sAS^Bvdx;|U5}H@(VNaG
zqm*7Y{e1AtHR%q3mGu&GV^v``3&Q8^t+>ll5yHvs>0{caII*SPJ#r~%d9@fB(ZR7&
z8k>m^D{(Mi4i?B@8IgI?zmpzG-2gXhN}u0^86D9b>uZ9h>xlTZQxQK|g0>f7uv{Pc
z=VUP&z<|A?B|_)RvpS`BWM>Qu1AAsCOe$Z~`EA84t7W$5-7NsRMr1ks=>3-Mq5bJn
zY^ZRdz<Puaj;)!O>gs!&k!F*R51^E{PpmISYKc@8Ik3)J9**X@+xWS$2_#US4t7z@
zuIt`94IOI;y&tZ62@`>1XlYaC!JG8q8i$q5)VWn|=K}$@S1+hv=j*{u^l!IBSMBY+
z&hE>!%PR{SGkN4m@r_Qw&nEu>9R9mn9Ky%kgLvy7<phrB9i1lop#4|ax8lo+0d)0=
z`JwF?S65Y_TWJ2Mt2_6K7RCoa_FPAmQhnx62RXS<NdWzZ&d&N4oS}GOVQ5WL^YiW)
zYX9u1THDzGkYcee8JR|XEPw|-_Drb-BY4~t+i!*Mu6|5ytSTvF&T|+CU^S4hAYLry
zvP$1e%)$67Ao5ej;crv*W6r%a^m%0(laZk6`kyU?N3E>|^eYnIP>3;Ja!dEzuyva}
zS!Z>CbM!7tZdok>+Rs8_()Ly4;?{*`L7Hpnt@q)L@n?wFM_>Y8pAI+`2WgnGXa+r{
z=A5c9I6^lhHJ>vSoE7HmJ@({HVuHzTQN!3n-WwZ#Uw^o05?}31`o7-#NFq$rr&wKy
zE4g|*Kdn>Kn!GPTx*;=ImEHNuDupMd@nUROTpsZfVVCQlBrfo$J=D70Nv@^@`@0t?
z?~i@~R4Cs<x^F~iI}C#~*O-a5V^sEqkJc`QUt(h|p)z5Jf$8sa;DcXP(8k%}vJ#RU
zwbB&~E4ThtrCz<ejyNjP!&lAs0y~uzPIIx5l>+b+>T0Fi^Wy_Sr1slWDQw~#r1>e=
zWEL>bqBN2Q6de>LXX5Hkr8OY7Fr#>*(|b72im$$BYq>7BZY~cM3j*#MfpM&r3j5o{
z%h(Pj%@=i-WoZl2w}qLy59A;|6$$hB#vnm1RVCCZ7?rm-$VT=eR!{>y^+jv*3c`Sm
z=0sgXEP1JuAdX6=uqo`Da(TNynD~a^wa$s+TS}c0sK>#5iZMVK)3V_EYaZF>X2Q*-
zykW@GPdQLLxd6*mD}%$Nr<a8hO^Hk=rbPM8EKE%O`NjKI*Cd|VFW=-=>D}Z!=?<e?
zAd6p1Dgk6YZxqKc2#M3KeQCGI0_0iaV0wrt*46=WmC7E2$v%ZY5F(wYKa#LiB5;7)
zgGKVzm)Z|y)`&^?@$yQ#RN2I;EShFcOpS}IROZ7+=Y_b7Zeg-QuXWduH)<|w;MsJB
z+-zngjH<tA9|G7Ue_w0;>twv*E!F``1=Q^te5}duc8LfRCftw?njX{FITZTzP>Uge
zt#dtJ8)YjsyB(PY`p`5@Jz*1XX*#IUzjB2?Y4syz$yQa8Gzmttpp2sKUy+X1t}5$z
z<KX11dm(AhM_Z}XpkYg~{AERUFGCr5*{a5t6w6~buf6#q!O^&=WW0b2hct*xsx!&p
z;)P4P)|HSkjQ>$rIqsO&EQTg7+d4-2oI74OH%$E*oBH(t9@L8IMPs-S16flAWYVUh
z;mOaj2^$)wr|^u>(!%<Hn}1%{I5+n+wHQs)8ZdlliYt|CNOA9rsUg}_JW_K|QnW`G
z7s9BOTfnJi{A~_WRH)<fTpJ&bDq{ZEn^D#Jy66SNXDEDSZ+bBhou@_W5fUmxsirHj
zJ}-*%RS}-21hCRi!nskOiYA=Mc(_^c{e1I?@jNTWdmB`G?xaocBXoUMt%x$*W{s;H
z?D!ulx)d*``!g#>NnU7~K5oC0_ysUOSnl;ZQu#l|n@kd}T4w8~=2QR%;KENlsn`R_
z`@uWzgQ>#^)5A8WCr5ge@3-ow>jw=@Uu*%`fhCot!Up|2leCITasa=No5Vh`Ceae(
zMrsEsHyrJDW?R$3rNM2^*MbfclFhtt)V*lme4q<6zYD$LLRjw~o&*sbC+Ka9Ft$#+
zme_JXakQZE+jZTBM^|)HCl}5$hMRS#wm;pa#Hyo1;=tsXt5I16_PZFkJ;7>oHJ758
z1&OuwJr3&^dsEfHZM-40S|Q`MtZpTuE<ZGc)i&w+zOXMX<Y=3bH5FO{q5Xa=JUEr<
z`I+-hKILkexvd?nvZM<Bh5WHO>sNgaBqFKg)zA4j^oE^IRx;+HwrurHQM$3K@Lb~C
z=`UU3!y2u@C@DQtIx9g^67VHk2OO25XRCFTb)EPF6545gc|LL6?GDUNm+MO~VHEv>
zrqYl&NF*fK$3pg`+`XL+i4{x9(IC*Cr_Ox&VSoDy$+*RyP_p=FLV<j>S|$|@k$-Ov
zCgPr3)KQn#eW9!So{`x$Snbbb7Wmxd)SD+nvY*}2v)`lvu0EmIPCd{=cM3<+Q&<<t
z;+ER9u8Lp1;K3DHi6r12<<4nrY=|aYAYiMB_d?J~ZZ)md6&SA%Xj$t}0O%zP`cjN$
zE%?8n^cIHlzG>3l=5?>y<saizH=N#UXNIq>9zxcj9gp9*ygQ8V<p{=Vsl(M5Dqe`Z
z0!$-c=z<|5cS>*4rl!9Y%>pH@h+WAD2N3J$H@+!Rc`p|kV#!s4bFiGc$2Deq<e?NM
zWQZR4E%rHw#o{A)R3;f&(2uri@)Nd_?ULd-8w4%Tn9xa<MHTCQDlhm=;(B>s=4XJ@
z)S{DLlfcO2aaneq#WQ73tUfr|<`|6cccc{j`1-82awEwF9!RITDAzuvAyop*qBJ%(
zp|^01>bes&X>QX_rsf@Wpg3NYkHE%u8hrx&<G}a^n`5huos(?^8mV+iH%o(PqV{su
zi+!E4^rWFX&SOkUF9|n?CCEN(1HsQuc-ZQdH!88|5z9bpE9V?3h7Jfsi1nAnjphB~
zBwM09o`oVM+rugwFFM%4cxg{rq9>lR%!IGi*2}V-5$+D4<kq!DUr2<WfyvSuyo;ra
zbT`0j-e`dDloSVHi8Cg80-q9i1haB}0f<gDdtD`<9kQ(~>w}0YW9$ZMs(9w=OSpXH
z<RNsh9bHNfIGFH9DPOPkZc&Vg(1MvgwGiL1OY(PbcMRlEw6N9QzVX0Cu04B9L=!O!
z^|MF=>{q(%o7P&U$3=s9y47pKZ2fm+fg(xGpADVoOG>+kf%G4S(Or=ykju=!7=`lZ
zj0SIS@8;vur!=q+6sS`A;QARf=RAFBQ|OK5Ssd?bTpmIVyK`Q^nYpgCSAjlqy7o1D
zK6^L9O^l?BvQ{7@&LbTeKXL;rf25YB{t)`rhGJU-L`ifoFW>0KCc|e$MTOQ;=21H!
z7sg|*O=PBF3CxS%Gq!WW7vD};N}lsrm8bRZaiJ;je4Tckw;6HO;E6$yfAQQu{4x`*
z%clPP84?4KYyzcmV}{JXpK$}GGBoZpEBQh@M#<RviwRX7t^&Zqx{3EGkXD+A7^PXC
zVzL3#bM!tHECifti_%07`)Ch3jf44u2zxYDb@YUIRW|h(IZP@5o;UrkuzPGx?0xN@
zk<u;ox*@#_ts<++^<x~t9|(EH^5hP-R`j?68JqbXQ~kG~Q1kSCdEZ)l5DbJ!?06^*
zyrQlyn-C%0pC^R6x<6CqEw!VWPJk;&ChM)x#7I_jw&tmuXs2z-H7=eD1s*7CuY54S
z7w%>NxKdYH6O=jH5$iE+1u!61e2bGXu1RPQoQq<7;+CH4uWe-NDOBh%%(vNOYs9u>
zd*YFUjx$mpCb-+M9je#Y#%E(*1E;a#l--VWRccG4+Ruk_u=&q>yPxDJZ9-V?Al%du
z`d%;j(6qIDF<rBSrnZ|sb|W3zfD~5=n-v7QE_KZs`(?z`Fkzr#s6usglHa$c52%tV
zpSiO)8Z0l1dly&hoey*fH&P{{Gcf^TUdKFH!`Vl;&G*_@O7-;=S}fV0^u2hMMZ+A!
z_o{Gr$IM$;0px^u2%n%Blgp{Bx<&RnCGUL7Y=&zH;}!7%g$IPZgf~o6G*5;vg+T@~
zHpj5I^Mr*7)UjUxxR*r@Gq$gmIHDYhWu@=sCw@}J3^(gszl(5^e3!S2&)1kl>c+>v
z!v0X!|HqaAMKt)XW_(oJB(~)Hz{PS?&j(Q`Z4oQscYLpDl{9oKHF~SBh+b$G!N+iA
zb9c3j465P5QpOLVj~r7-%FdBjIhcZa<g~30N^9Jio@TVweH`pe*rQojLT|3mLo;wl
zu`orZ&MTV41lS_@HTx}rlkJS$Dc7{@%S>hdVR%=gaM?aCx4!1sG0RtM@*HLNs&kTn
zsYlzB_pyV+nTuYUt8<O5nN11K_82){(VdqFp9NP&ab@lVL+FK>8Y;s?3LG2679S6f
zQL>TyHB1&zjdTQj6CHH)JY_@BFVX;mllA(X^0t+Qke}3&Q2Fv@a5uhxGTm2>=p-qA
z?8S>_%#5ZA?f{u0zHy%kl+4B|q)4?dny5t;EXNHUgP)1)ZYwDbU`vAu5+E9i=ud<5
za3b{Xm5nneP{47(5lk#F7Q>xr?(v~r`z~cFr&bSv^)rgV{M%w7Xn~&K<rA-Yl!WBz
zW+zg2u(vW&WfLz+pgi<~foR2xq|T~{znPcN>C`SAy!kPFQzOWPK~4B6zT-<>%R11B
z-C_<(VsyVYeN^?6`wyqD9S5%6+uD!|?C=%jElAenrbh!%Ul$#^#kkC!^)Dj`7b8|?
z8#a+doZr_2V8YO(+n|C#qwCDF-R>Tib*p28k7g#eMW2gfzBe)VZYOB@(N9yi`)@cW
zrlQ3Q!pp?tGwSnhwTt?)_5yhEoB@~;zaxy%La>S^=BKmp+M@NbmSk#LK8kZ*?sghi
zS>5`U!PQ&!Hbpxa2*7GCghOl>0?{>`xCKqRxeDG7J$#Wa*X7z*jrSJup<~ArXu&7K
zQZo9H)*nMDZgVh?1<tqWk|hGCxtq{~#e2U;L$jx;HNwC&J46-soHhuAl}^I@i2dI%
zqOGEM1)Z?xGT}&^CiTZS6fq=IWn}(x+XANx-GT3v>Cp|yeVr-AZ-&U|y~y*oaxTl9
z%J7*KnW7@g=vKl%Fh>O=!dMT|6p~<zWa#s>Y5O$BLtRVF2imd0+I&4^=V|R@nj&J9
zasU+ub2Fkr)P!Sotj;*SdJZGUY5EGGpC<|fbYYZ1hDRjogz@!J?5YBWU&8@M#<Nr|
z#G;HPk=Nxd(r?nfXL9%|E)4o3_o6Lm(kE!9dEXH_fky)4pXNLj^0mN7<6gPAAmE{m
zDezfwOo&c8_7gbA&fJJ-b1`W%)I=@5gH=8Mb32rd?;A;JOScAqo>Z~e`|WxE`A@e}
zR?|Im))A57&HP7w0GAtr>zbimMOl%6q$*pAnSKs@X~eSprb;$rENmyn6rQ+x-6Cp^
zcW_eOyI!94$jP13dY@UpsOC$ycX7F;?l&8u2F*xs&Dr_`T7<;MlUx{mlLUcwYm>cP
z+}?uX+_6T|ov?MA=$PzP&QrVbYy=EmUntqg%K~a08H8xmyRcKuN;?@OV4wMcJCj`@
zO^G$&$S}@?DKIzEP-BRl7OQ!y5%lsUt^F8khkn4;jR!T1Izq&F+j*_p)gs=z>*+mg
z`@22~pbpnl6_h$_gOhJ1w2Qk(0~dv|5W^ynkO%ONmE@xDZiuUec_F+-10I7&O#nKm
zgmC*?#>5tiLKy$2&_*2Wr~2Y?0VM?A9{H;>j}9n_^?IB)%C<33PeS{>=12ERa*+NW
z!27#4u>I^)v~7J9`aQ?)>d=bZU<bQ?L1BSyS#K1S&vgKlC02!9*6LQ~TJVLoDS@KY
ziv)1v(GU6dT}sX*1X!Q|@5NhW1}(cz_58xlUv}lnoSImUON_nKIX>pjWZeWz>Qgbd
z1<Q~rBPTZoZ4pNy3{Y2B=J@g)m;#@*nffnij8sSsmY8^w9PbJC8NI#DmTjps^S)%x
zc8RV>m>93nIXJuPy0OS1KYZ-Wt?f+^UvG<Zk}mM5EZJOmxYTuhE=6QV9~mQds&5ds
zm5Eb-CC#C$5dX%B=c_QA0|T(0ttRum1@W<lY4F|RB*JIzRHH2x9uIR6?<tH=WypGN
zg1i1xP@WTk*&&=X?DfWcW00=~FpT>Y5z8dAl`WGjOJk1K4wvw2v-WTJ_lq)hcbFh8
zQ||dU!}ItD4GUM6@6{Civ(zuSQ=EmKtunLK9;5D=OlZiD(4f?IFA!pW2MB6L$Cy@N
zcCQYk>I%x@A>@>iG6@V;hTJCZX3ZT@u{vx!aH_8>L7_-#GvD5AN(@@3jh%^}Z0ko0
z1cC`)J2D$$C)F%hh~c!xC`%b@1MZx>X6q5(;Pm{sE*fA~mE9S-7z|;u1WHZX+R<>>
z-p!xd9Wu^)(egUv$GsVe_7JvG)vB(MQ{{QrzP^Sph-69n?YmDnwjAL6Q)rrMk5h_c
zrOX|zkJDP}F94CGZb-@P@fHbJI9Xc@W-K3&d*}BsdMP@a%1j32)2+N>jO-)}-pfs8
zLZW@8UjUWuG)iJG^Xx)C#$wNP+(^&5_0wpDX#h;4F#&e61+Txe^itUY3;FYBXc(bQ
zz<17Xel7uE)Z_v#MUNHS$O|YO48W|$PPfeCNo_Qqr<DPi$3imO&m&DKa_f#SLv)+m
zY%sRd8eEHY4?d6bQ-ns$!MKw`(pzLcbNxg=kLvKKG!WLbNR&l;mADeGV5=)<w}DN(
z^w|;od<1upj50ARY21=0q2l-<o}IFj?>KE@wyL0ddb=eN*i?P0<wrdCvaLCdn2_0N
zBzgv6sk;pKlppD)q#%}O>KkByf3uYS!}Bj6BC$8hL8}Jay>i7DKqvNiNr3&T9_C0L
z>dt-7W#v1K(81d3*nSzI@RlO$XR8A{o!e&jK_NPA-UU%o>jC(K{it4m;j{FU>P6l!
z0HJKVtB;<(cCZ1&WMe+QQ`N<T?w&XC)FQ@hitBQi?QV?$8WCR-Zaq%HWCnE`GwoCM
zB$9}mE_7Ny=7SZUi*fdDVZ1NFztos5SS#KsTE(-vQHBKFmaf|RNW0@=02XsFtbc>L
z|BZ#94dCSNb*$1fMD|6_(Um92Bq_dA!I$nt!SvvXp-e4Xs(R$YzpM<SC1}L4zk}&1
zBRdLj$KypB(o^cZY}N)p?{~i(s@P|t;&{vzf~{=kwnRUZWhjcg%)xlVLLV=V(Cc@V
zXTPJ%pnu0-sJ8<9$_f~e%dtRcJqMG%lsS*zaGZFqo{{JrjNuIz7#4d@%c04+1s*|c
zF^)0^9E3Jutn$y>U;8Hy$nC*3vpnPmTA4)1{9^+1`dDvF$gt%SzU|AnMajc$8mOC6
zXov?z3oLar7gd&8TDYO!!jcuy8Uun#UhqVoH|gTTdxzSX4f+azRd(X@3;+i-cE%N1
zzR<}j=exe^wxW^av%AoKQ1INh0&{tfd91x%ng)R0bo#MJ-0o-klfb3&L)l-|+5St?
z>!<}h(^5?}O%kBE6D6HCTFr55jb#x0@(_G<i?^EAy-Tl+`tCZ8zg2NFKG%`go93EX
zk(i(pOKLSpep^Lo-|klrNc<<sPP`2of7tQev}F#ez4L<tyzy7Gh1E(*a+W1hdxe&`
z>v;XF&#N+|;ILL7eZ=GXm9lPR`1f~c$0BsK*imhF<8O>RLsW}@0lZor0AS4^Xrd+%
zHLKR%Ai>q9n@?>DXfp?SG0^r!WKqZ!vEd%B#%7Jp82Obwk!XOO2T@W5N0#LP*{`@D
zYX+f4rd|hkj2|Rl&W_L07P`7epZiW9=>AmW`q)M}2Lp}brsje<>j@|gW|K;m2QVlM
zU7rJoP^%hjSHhH!L#038rN+pO2NT;B;>zLVqVA}yuS_6$xom8rIqx!D<>GlZ;ITHN
z&tzR0oS+13k36?U+KbU?u;Tr2yWLFQ2k$e?$S-51)SqRub3JOlp8A%?r=i}w1TUEu
z=GH93XiOOSHe2k;JfhJ_-855NM_38im$?wSZ>Lou8QMLpUqx=oJ3du7;oX^e=3CJk
ze_53+{2hyloGG9YsM3`5^KkI#!_ht_0S>M532>xeX{yuhz#wX)iZY)kPHP=8h|n<6
z#R&fPN33aDcf|cliq_uV3{)%U8G9M?oQ1+2Q;#`gXs(-0X@|Tuv9Est2vi}$Ud-+#
zu>>p)KB32gl!g!AX|sW2{fCUwn(_mw;_~n6mXeC(t<v~kAhNH)7}2c^aBE(6%@~&~
zTSG_`&Q$x=Q)}^@ByIJqW8qn1_V=P5RrL5HmR^ZEF`;Q1%EoWwqzf5<(!E|mtG!6R
z@e(-1^ciRc8=PD^*-Jhl7YinQm20va>Pv_DIG?%pr|7y~BY2Z3qnNjp)qSgL@{M^Z
zCK8Yvyyjsu8*Mtg#kX7C4lP+eNa4A`kh-+<<*$em)RVE<OkLIF$KxS7b3OnQMAcV3
zb&7)JZ_OVlp3S7_*k;roa>#J}0%(&yMCK73nAv_Dv59flN!QF~XQp9QHzWZZQYRB{
zXe!1meN5gZI>-3{x<=Au$?TCRyz>qimQsH{YChsO^^uqADFoIH<Fc<Ttc6?bbMWjr
z>~^}JbARnT$N%;fgZK&2CH|oS-MNjIUksO@Q~e$YH!$8w;2sjmNnkus!q4@m-zh5(
zU-zR%=n)nE6+wET1b)4T9!xOp&K=xLuymY}Z-I;UBaYQ4liiel*XY6+M)32|D2XKp
ztaUyhroB|r735~8L0|S<jaEc1BFxi#+r0g#mn37pH8EW4o74++Wt7W#ncg@yLyy7I
zcw%i?%`xF0^USqECLxA?4FyF8&C0ZUFJau&iN%P@Qc7NHH3+b-y&~>-w6Q$UbOH3h
zdW69s4-diZLaO>Ld$7p>Uzr;rj((11h5*G?YKeigqZKpQiG#mre{+2>n(h9HN&o{8
zG@$dk5bg_P`clU478u{q-Q&yUKB))Z=IcW8`T=Pcf^wJje#Qp{X5CeBwV!1q3zQN;
zTqJ8n1pt@6Jm&Cnaq6v8psTbil64eAC>P9eKu1BSR<|^60*h&n)E8d3H!pDJn#>$r
z+$Y30SXSt4Q>c>@_d+{1=x1~|B^unV;Z}IP92`#67@Z2N05~u8Wq_cxOgY6833KgO
zuIP@`(f*-T9&@ZBL%FM~qHmcKvI{(D8u!ev`2REv{jCxL(Hj&9^~U%bSJUHoRh4gJ
zL8#-}v0}=}WytL_ZqRntrbnphWYxs6llM7FsmK~|sYM<D=ymNEE@6!w)Uw#_;~mvC
zH(|w>F3FevPU?VaUjXp*xxR6PKCj4aj(b}Qf#%B=lPagdl%=JBX(bNhsG(VcrZl75
z(J)`A8{hKnnXn~7@Ep%b=UX6I+UuB+m>Pf!K4ZczgBQ-2Fi2q6ttOG3G*{Sh1`SYD
zZWpQ(iTUMjAgA2cq&L}%lXdTLgLt7s!BT&1X+{1zV7R&$i|9u9Xuvgnyvh287oiEn
zzvSDdj4I}Nj;@(X8@(WDSMCQ<Ndca3T~-?G9(oT&&g0hCQk3_9{By98r#|NSA&G#1
zxTd7tl<C%0WqMDRfaOW1{Raeki3LdZB$(ocqN8wmQ_L;Ak^?bqr(I&J%<8z(I4H68
zAqO}ls&lcD#sE~ljnKH~{LH#SljZwPpl&0|_0m^Cd*Ket&vD-uiWUyIy8i`0P?Qdg
zNPNxwWQZ($|C^73JxdtJVB5@fRkoA$7xx?GZsTZobz%P;%u`b6+ojeXX8R9$LMC(7
zIzP|1WR`YSLjf8q4uI0$?d_{vm>}l!P2``cxIJ7}90;(17iHF4n@$Q`c8vw<82DA(
z@B@}(LZ|FjF_^XXl7&I^n&<rmB~0Z;wx18xN*<0r<iaRvYv1f_ySWK3?56&c%IvL~
zrG+e{l#RMG{;aTo;!}?HO2`-MW}hqxEV#bpuC#-GL$i$*QftM!-j*aQmh}r@58l7e
zH;L;OsM8xefX1&XL}a*S<timIV4!B1_qzT=b_xzRKPr0G+o}O&hVUSI1nL<`KG`U^
z?NXA{E6smhH0mq#30?#`bX2ah0V4xk<|-)`vBYlhEArnMJAU%daMt@7ys0|Lb~KJZ
z#ULgCoOT1?A&68J4QvkyWFTTfYNi)+LqF13;@1^Z*)nex^5;pSu^^bhO;_@4DjSd5
z;`#E~^CLAr3(YD>sLJcQxzg}&=1-@|hrX_5#RMqTzh>)@De8Z!diaDQI2)r{@I#XS
zJGoSKDk?Y(V7{f+9=xAbioIGgx4EtyngXt5>Mtnjb68GlY;a0zTDsJL2F1ORIJJXU
z0z>*RwQX&t6+=#yM&7l8hRRHOTYHYYzBJL*2JVO*YhC_n?(=Us(7*lGh!9+kK=&7}
zuOJI-F1=Jnt?+oI+i^~|-!yfCLm^!Wn&d?Ui#3Ix!IzoNHk^+q^sN~kpzrWH>%17!
zi?j#?6M4<<*PN3Ko(4do`6>kU)y5**Gb{?R5nE#LUr)=-e{9ynu;4F0!t{_dH*rJF
z)&ZRCwkabZdwzA+P75v~8DQFL`m&cB{(V%*00hEM@>lB)E{_<0roW~gVJ`TbpH6lJ
z?o1>7_MCpnzoCwlip)Y7`r$Y1#edYwe((76zp@V`51v_;o~x*W<lFI@Om~=1f+-ai
z4heolK6NBDqcvy<tgswR@XWc{{t=_|RS0!++K6iUWaWd_LqW`Hm9`Q2RqjR;!USB}
zm0hF6-?)c<B+rzJ_SG<3ZnAxfm8QkF1~+n@6&Y&f#T(mo!Zs%82EV-`Y-)6+-Wc+T
zm}pMyj93KO%zS5%L5OtlW#np;N@hfN5oQ3Y9!mZkvGV|FJv)M8h9^nW7qZV)e4!ln
z_@1aTRMz^M$GtHQ#XneDwH_hW)yUb=_?4*>5BY{5M6G~R<O3yTPnul~0*Ti$xMej6
zKhFYfS&P=z-+vU~{FFYf_>?8EwbfquW#jh{jmt{u-Y?ZFx=+SOX>$y{AQA<^feJ}G
zWE8LoB#3(nTIOUtdq(&LWtbm5?qUQVSs>%KH^g=`n@IN{nIoaTH2^VN=kB}e%*&Zm
z)0WIIb0K&=rQ|=cBK)nQ2ACI@8)qsJtQPBg<h0kfRPluZl3w?i|8))%55e45>SBjS
z!I?H9V<Wvoe5%cNkN&(+1&pfmCsV1vO{xBOfB&jNRtpfoF(}!fB+_Ate5R*YJCs=&
zv+xVtheYCFsl7DmuSbvoesJ5%`u5<yUJ1sxvcr{0w>FAFY<KQi6G%{eiaC`W>7d}H
zn6g>HbisBe6BHsj&Yy$1lWZz8_Zk7ApIw(dUqzzch}Q+Bn&Q!-_PbtW$qM^{vTQ{k
z&myfK9tIj<VwOZu9ukCik`T47Gc#FnO4ShLUM5w_;W+BqU$glrfF7H|TZ(P=_CwV?
z{^af3KDV2^(R<Z)U9d9t_Z+NQS3%iV$fdDU2+7ywr_T-pCczkZSU(%~e#|w|3f!+Q
z@FoMg=<_tCDmHmr5f#LbX;daGDggF<<pZvpTC}aST@;S%sB;ZI`sau}velsHsBu#G
z2&V=LB_6|TYp+0Z;$}3MTyT9krK`K}cE4^I@yg9h7|W5)>Rgl8JRv4bQ5-9v?%!rk
z|GQV^zwPxtPDAJ+;ZjoMGhe3}axg7@6w1Nr1lQtO?`NE$OHqAoU0nB*V~XIxADXs!
z0M{~}w4m9D=eC^EZI#$M6_NnG=Gj-3pXT)jd#1US;}vD;k7WP<D|`NsHT`Gv)c*tf
z-u<Z*;_uh%_#fce&%?#`FVFs8wGjW3Li|4_g*fy2Zyxjh+j`gkEC27!Xn5f|nqd=e
zPV$##INz_;v0pa};B)8Xcz7cZ4<2~40O;`Q9l=i}S?J-L(s+*W3Z~<)oxeQ%xnEtG
z$yEB_TYY4IY5r{|`~cOdUo&9v6y#qUzi#~HwbK9bHh*pYy5~<G_|#pX$a1@$g<lQ!
z;b#60!|Fe9{^I4Y(MmA)7cc*&IT(|QPHRf=mF<ffPZK8WQ-rv?@Zmo%_5Gn>hVi`j
zqFHM)wF58;_tI~9Oz~~6S~MFZrm>eZSy<@@d(a|X-YEbybL*)m8=k64%kzi#a|{@5
zwqX)(1oc<tOZxnPHF=`=B*WkyuzSM$Kv*A9mArj$wkga$H9t1P6a#y>)-~qIh#3My
zq!c^`4$G}CoML?uD#*Ck$doQd?J6#c1_%y!V`Trg)B5kH1nrMux;%Wp0jZe=I`g{P
zG*^QHLcSa{Jcv=gkI1Ym=zX{;RKsYb*1~>6_xQ~ESS9;jgAJRqS?CwQ)(pZf9z>($
zmT6C^lc2<@4)^KmPTYhAx%FxAPtx_<m^aO>(MCw2_cYNm<KqLB9ywUl;op4Z&-S#=
zW)peUjx)869iP!#6wS5pG-g7aC8#J06abQcdIkUeWBl6{#y1}8-&1bZVllEZHTkRp
zeum8anSnM72C>+n1j<*`$-B2QbZrPc*MIBZj*MA9;#|Vu9XH8-QQ^hxrLH+2V2Q`-
zL8XkEt#sDeV{ww?|JG->$>lBTf_As4V=}or+OcgOrhD}bahdaSxlb$uaHQR2gA{$)
zp5F*Z0J}X9FB{uM(3W*c7P1#FwG+CHcGRwH@^dh;XbrKZoetPhxVem%^|%i#?VPrK
z^-U(38}0;E_2N9+Kb|P6Kl|CBP!RqJLF4hYQ}YUlS9$Vb2(OsWWH1Lit>WQT*-DA!
zF(t(oex|F1qZxnj?#pnoSuM!SXhD~04oBbs5WmkOrR)4QnB%pP>D$%?xGQJiq>k5a
zkY~<1cg2c_u*QJYf5pXLr+)gRN|)fQ?Rx<DtFM9$+{2%xQgIZ7*QA*5N<Y={l&j5`
z@(sIqx{A_UwL?qb*l{~~sh(*qt)aU)>K&xuma_YxXeohKZu)t0g1zXQA8K5Ki(B8Z
zrGy~bMeBWLmdmg)21IYIW1U+t;^_~~p@zB$6Cw6wR3^E|z+*kdFz4$}&+9)%gK9hV
zkBHPJCXmLhqZo~Wz(YpOMJY`-z$M-}Q=%RPxT{E-0UJyVnXqkX3JY1Yw#zFJqbpC3
zCBwQZawG7*x8mz2sdL;JLJB8r065g%8l#8iJq~tVm$t?PeD&tsS*~<^Tso=0GtNSQ
zafIM|)H7aG!>N3ck6$}f>5XH_TfQ2i^^>6FOmi71W99MDqW!Q}4WAS1X7Owav$56K
zvFyTS`@Fma6S9-}VyDu1#>X{$N_%F?>u?$^i)&5{|EZ+*Fj#XB77LMLf@=`|{VoRQ
z`u~p8K$f>hYJ%W&p%KPQp_K|IXu9|^ZE4SJ)HLd~N1}Km9K8kazs0Q2Boh;IIBf}g
zH!n(?wF?s(k-yb0)~mT!A0pPXljNsa>PYDBq|j%bH8MpDZ_^O}iq?>~9NK`M*&{SB
zr=j=-Lm7oOxtr8K=v1>Hkf#^_kZJUbxO^=5Ky^)~=JQjyeLO;$6{29XIr>m6qHkGE
zQ&W^UT+Up{uDw)#E{ov-6b!OjUZEZ*$vOf*Gg$t#ln|469;s$`8DV93vC|ZlXY?J!
z(1l;)RC-tuousB_vvjP~E{%t<yT)}U67#Mg^zt4Zsy(0NVf;k)W<ck>!oAz8DkMPo
z8_ke&P@JOJF95V3)?>?F{%k`i2|5#8$0myLa~FWKCcCdc;6ATR;rkD|8=md_CBxz9
zi^-2GS!=;9!++d^{axBa`6(5ag11HYqrkLMJ@p|Sp{mapE$43_*co9+itcRBFelz?
zGVl2&`rr!cB-+>JcH7Uv<QM|JZ8+4=up1O@x1I-$@+_@?yIzP={N_@mqq`CjoaU5I
z0dva%bW?Xqt32@KcZd(VJL>r9pddx2g`ZHJHIZHCU~%d-NKe_JWv42~saf*G{sJ%&
zZwDPCO<!rbX(^`He#u8rnB=NGd#$5M%Ww)N*N-%^1*39<daL~V<e>=BbP}KY*vAI=
zh`A6{y}9DP#Ji~UnXKvS_9TIwFDnLuH<e`#38zX&tn9XKau6ASLl3Tb@|6c;#vwPw
z?k@(s&Si5wUV7xKR)%0;o4nOkvL^|^$Ac3rH|IQE%nyoF(nOM}VtjDFU#34Cwf;@s
zSkL9lp`d#ALfOsLjXX$2lDSJ-t<TFKtmtK2Q7#5oRj}I?7eYtQVqcMNIS%VAzKQM!
z?sTcV1P$9gd(JFI)~79kV7Pa6tNn--iAgNz^+LxCb6H%RI@kWH9wYwDvXlv63!WNV
zqnM)TzsgVGp2LulZm?(=3>*7~mbG+1?DQlD-HPz7vqFnmQ_xDn=X_`^%%{6_+==yL
zJo|b$AbeR>HpRWb7A&!R^7E`5ipk6@Rkf;c4e|&nL4=VP>5$6gAAJ|G?hV%;`i9JI
zH}IimtGEOv1b6L+E<F}t(}(Y|URt&fQ7G#Qg9-^zKwI{QoXRV&(pwzCWQkAwhnXIy
z!?%T?U-w!?ZKo@K0r-ensk`4)$-sU4`(?3}ue`p?-fPrHv>BR#;=q(FaRFk7jG*fC
zPXRH(Bukd&o}1Zjn`&@spR&U7uenBLhMa$lNaRoyQPpJiu><NSOK!84cooj2Q{SHb
z#kn845fj96%r$tjCVJT<m>Hui44PBL**->Wu6i;!c^8M!e|T(cOaP;9m<~{&{cu0)
zFi?-?B;%F_m6Jt;5e@=&bJ`of8!urxQ!t%_4j_)N^CK%2#By&7s`8fxDgg4OOCP>4
zc{f*hxLhZD<P+aXs?NXEuNjJUi-qctZeV>#F!f!TK;DZ??9<4a8Djd2W530Y97Oo2
zx~BDBGEpt=CXobCb**1(ibf)ulw6sD6wTCLQ4^Mf&2yd=9iKONUN#VzWYkrKJX=5H
zGExy;H1aNu=L-MwZMZAnPFTg~yE;AL(_o|dV<cSM$W-=0R1la#P*Ta=X3o8CnIAdV
z>3xhzc)&l{-2FD#7B<<So2_@y`#6w6N`GKbW>Jzm+E5dISJv~&FKtG2@kI5_PPE7J
zI`^Thx0{T53ace>rqcD^+jNjBt)tMI?U3#1vu%>^=}Oqvyhf%iEq7ku5Zhv~7kuNF
zhtV4lHes~qSh+4EhJ$`4gXQB`9^HaBJWXxxdpmZiWj|?Zp!bf^;*rMftrv6p99P;-
z2>hb}uXmv=&rJj{Pz>1R^MnWlnAN_l5_UMxwa%oZF{j#&<NCCeLsu0|tD{j8la~i?
zaxnFE`=2YgsWPVGQ6{pu>#%fnOnz*B*Vz*f{c?$o(eY5ib=U7C-Qbm!J78~J&9D^C
zmJ8!_C3gZneQzWC1Ff|6XSyTdhwm>@4ZJimWo`3}r(viim5Ta6#ZT>4y%I)yfsqp^
zw{u*Jof=8lg_s^qeLL^hmqthtWoTmLPOmaW(B;MFh8hyzVOyX70wB3Nbp-OefwAe~
z+Jl6@Bdva0;sUC@uD@5<tf6Fjrr?EVN36bwKX|Kz^yR%fN^5|qCy5Cl4KNWlLguBp
zlNtXI$v<j*jMEHo#h=+1qYd5OuCxRi%)ilG(C|FfCFD8NT?+UDG*0RHNSEcXw<Ff)
zkj3<?KYu05M0J!dB8;;95G~;ta;#laE{p5XU;F0Nk@YQq$rRNV9zr4aB9Uu_zNqr>
zc2>i1IjV;;KX=iN5|RX(C9tER^=(~3vsyu@LAxB5gR>r@b(3=CbOr?SwXMJN6Nr5P
zUQoW*@n@rTLKZk^|1jgP`6^km*-WgDZD6+_2`;^1tmsOzQ@XLPCWaXLB|+6v2CE&g
zX$q8$M{J2{kir36Z`1f^vLwtKwv-`O<?@Jz+PZ}bmdmTmfr0T1fSF6E_mQUThOF9s
zIp<pukJD~kOZQWEC;`_U>CV6zrQ8qZH(w%K_V9N<;Q$hPQYIo#0eqC=48ZRFUk5Tg
z<$vUWt2A$A3qz`BUxl0XFfl%e{<zxVKFvn$W%qcU(r=m?_EQ~U#lve*ciG-3m_BWv
zL@N$~R#UHOZtnU$2msBRaSgtIiPLG$2Un^d5^tuQ#9A#-Si&!x>h8w0P0N_~ysEoz
z&OJO3_K|FOMeEFTx!Iy1@NE$=GS{Khn+$R-{IcUT{6735r7jb#MB=+Lsx-8$;Q@mF
zVuSkS2Omk$5;O5vRuIXkS)=@CG2#gOJslmp<O})Wm|IK4pv<zjhmIdnF;?bl7eBw4
z-i&;Q=C9Z3|1lRSqZosvHQ?JPh66C}d*Wh>SDfzLT;-~mt4FEra|TNeIcEw0wV8T=
zg%xGimFiR<mH!z39)1(uL9=bqZICow<wU691nQ_CS1t4vo_+uut%%`+*Uy$pzYh8>
ztgPg;zR0{&SyJFF<<Ab!hCkPgy9%km`{GNDc^^s4`cfS_(iLoL!0K5{@F`3C$Vkw|
z&C6#N4Q^?Y_1MKyS{cEWm9`XXQ$t1?M9bm3R8w;b27>ln1I;I@Se#(;6@;iIY6?)U
z<b|>NnATOFp_P}-b;3(i*1R^dwEeUDpT_J<Fr2&<o^O1@&~YEAJ$cKRz-5*E>pQ2>
z`J5{L^RsyQUP(&oD)<^Z^C5dim~*mAH3{K`aPKzDo5!iG@RiAOd@j&pW|9OT@F*>>
z=wNqGRb6eeF7BHpaJIhf1$n+)IH7-93&ZEnWN7iDZaR{Ocxq`PPOJOCSLzM&=^VAJ
z9-~fihi-G9bBMe8k==2nFzlWRMf!r$nycZq{A=QYz<31!B<N$Ocx52w!ra?XAx<OD
z0tLIXVvJ87SYlTNQ+Aq%C(tC5?d0xPITeDVH)w~jWArtch+cc6xBO7W%CpH918cO^
z^Y_^Hn-Q}Krv#{HeMbK{1peI=4^?-AC4m1!T!z0ssQON+!@-MuM4Iz87O%6u3kPQ$
zm~?={Q`eKbV`K3P$4Yqywpah;XRGI~RtgxwQWxGolu<SE4kwlkWX3*&GzlX*$9+o)
z8hhA}!oT>!JqN!X*YjB0V{0qfyVEw6Dtb2n%!CuV*Ezn<Tu;;(duujU2yyBmM-Yo7
z(IsWs?8Swk)gvg*5pB=0JTGn1IWeDRNy9+K-}@oCVpTG*OW7)0c}*_2x+%&BFA$}5
zX5wjo##fVX<>7rYnL#$!U*l~<<7$j<TWP*ZVcA>i0e9a!e)i<V#yI0^vNH6QW|FOG
zNNs~y{$Y*o{@kS8I-7yBmLJy~e*C0u%xKmOg1s8Z>70qI#HcmDT^*YQE1<<zP*;pA
zZiR0ROA2anYyeFSVGIXknajl8;iu%T@VBOTZn@)Q<#}imS~!mi&}$*Rkp2RffPJk0
zAX_YxdMD{j!Brpa)*fi*{&{`4zGBDQ6P;qOLUoqaBwNk)8=FZVz-N7cW-Y#v)#b?J
zf!(&4IW<eSNW3!~FmRM`J0}dlLu2?gaq}M!NShB&P5RGggJyci%mY{48{C&wJyPmt
zk_C+@o2d``2u(aUob<zr$cADL$K|@q{A2UmkRn|zrqA~weB67j$;mFOu2(2T3M<N}
z@A7nm+TLKGt(#$jaI`2WJLH>9QU#1#iM(345Hz(kwN^<_;sW(nPokc-g%~@owdM^s
zBCB6?`NU1Z_*mSQKoIs~Kwa@8w61QNWfngX;5MIqTX^ao>Q-`RVw85N&)<F|ldl%5
zjsqsBsOSX+9i(N_VDzTZCQ%0=d~NB|NKtEQG9-tI>HumS5d1?59Q<CYVMvM1JM_5@
zs0TQyXpzO}>^(@^*xBMS3^8K@WbFxm&^no9=FHYE!USD`;mLF4X_hOZ@70J95u-ok
zU;}S!Jn7~dY$~y6G`k+8Zy{Opc|L|AgY(DA7>uEfenoH*uz~aM=YxL}NO|*iV?HTN
za4nN*=Hz=#f1mdYpvr&)MTVl`^3?xwciM;C$Yv`!!tnv_mrXjDtgFdzX9x;zu}J=j
z%~%6>-W3Yej|)1kYUDf#P-^?;3re4>AOALL%%Vv9l4Zg0R}sNSD?0<OsAUc~;9L9s
zCC~Hly$8V(X+NM8fQg@bg3Dh$*1;K!o7{DcKMd6jE0#MP^z(ZU5g%Llyl>rJ6Uae!
z319Evy97b1g>YFgm2=?Ab!9}44%+gvG*?)o1!=l)-BNWsgo<P~7no#)@=oBAlnsN)
z+Ze|30)Vif{xNY&xU_W}cvT?9@J$I64zTHAZyp>_)5{L0;F_|aFCl{{F>lOVqOY~c
z+S1Nlq2SWcWhnH&i~Mz=6hliA+ijjst2>Qga0Q3!lf)Xm3KzB9F5E6$(<}gDHW`v+
zqa<7!a96h}J-Ydh*L>=%(sz&+LTONN9w?o>w7Ya~PHoO0ntfTt@~0_1RB4*m8<U|%
z6X6G!zBUT9%1S}88J_RYpveWOs5BJ-Z^-}-syJ^lh;-3#dZ&$_#%3!d@>TuL#SW!q
zNNr6QrG&1Nk8Bc81eB^7D7Y><1rzQ@g;h==lr9xV>f(^VWR49~pyy(5Je&Qu93Oj{
zNwSo}uH#|EFSj1k04v%^gn}7Pm@b;`R*tzVxm(_j1`yY+bpFg#mTe&i*+Z)(!d`}B
z?c|@U0ncM6Gkzu_EG+M<b$kVwaObxw3}%QE1z-!!P>`QzrIxMd>J0jf`N^V8>pC2s
z%a#ts1r(vc7}h+juV%xB29GrBS4nhjdH7ZpH~ZVt1xLKw6qbEU>ZqTgRiTdpJg-~0
z8abK4TZk=3ZK<sGR3q8(t(16iuBAJECm@b_#qV`g<d^^p11m!D1G`|Kx*_g-*UQG5
zdp;W;cKxKjfX;M`7oP0%Y1Ti;j-19g09<hK6njgM^Ux85m#8o-J%>BO=tZ#B=C~f(
zVcs;!8Rn-R`1&Fa!Sc34Yb{aMgaIe~WU)_n+nUR@ZE<9Fn@>Y)|3CKL0<5a#{TJVx
zmKFhNknZl>ptN+Sl2V)QP(nbuTS~emHVvY5NQZ<d-QA^k;W_7fJoS6-_y3FkeeQGB
z<;<G3*37KgGxN@R=ktEv^0fT2cC1EB<f3lC0q5OvYHK4KrQMO~anF0cFQN+>oKoff
zvU-BEmU%wnhE4@InOwErh+rMNLX!!G2P>mL$2&4ip5>9(JWhC#?(+n%c#}w7{MALY
zGJ@;Oe&QU7I?+zdq;pQCL!{MCt$%TvwAF0gSchglS3oDo$X!p#TAtoSq;cxu8lne8
z%q_HM!VW0{O6)l$;I`;k7e@3Q)DJA^O6$ip5qkIJNvG28M!S|jUwr7qta8+gTj%y2
zG#%qXy<FJ_u<jd-=36(|I((}<zqTIc=Ea{;$L5<4r68&|u<NKj@X4Ta#%OV;o3EYH
z)<_?Ii@wRu1tFfQPD_Fv4&9+ua1y*J8=pcq9uzXJ+rD>~!PaUP<q*veU{(0YI<q-{
zfZot0G^H9D_Xysvlk+CW12+qvM1%hfTgn37;j`$6zH|qjx4h@rkqw=k^UKNlYg92x
zV`VAIT+Ymj^zc2Re7f72^7Y-y>q^VkJzJl@@iISbcP(8y3tC42Tt?{eEc`AUeijr!
z98A-=+wjV>{+r8*VVX1W1^O!f^`nyG#nz|8WvNH{)qY6weTY6vmISqLWj^@!fwB<X
zTZh9V**yc+tzJ($%}Gp-(JDu^gub<FF6d`n&~<gQH>B4`w37oZHxuKgx>h<Fue?I0
zNZq4GSQ{;7O(365T#=e0?e>gQ9f#UHU=i*~-pQx|owz{cU5mAnib%Y=pCg*~SI~^~
zMOOk)*Zv6}h|vXBUrs1!a59&VDBO)SM}(<qga;vGl3g#BlW7Q=6T1tyjHAb6Cq5C;
zymODd-hIdlbV5ydIhKsYyyj~anO3AX_a^$ejguy;?ZPLBy}$+pcSaZ|IJ<zRcDzf%
zPlaYqOSLL{MR1~i|HCcYJ`-W`b@y)ECv^-LRQZ)>PM#V-7X5cS8cZ%VP;88Yl-L96
z4*_on0_RiAV*6p;Wa7~Sd4TB%qR-s}b0H6JF)r-9i92{l4gWZW0b4J>TuY1%1u=Az
zN+hbnqlZVnNmFDU08vLH0UR{Uwt#!9&k24#X8e>bvkT=(qB#qN<*f2Gd1>ftG)cl*
z0nQb9qW9#f=kLDdd}_{_Ll)YBHLQ&MtU&t}i*U0e<4lKKJn4DS0|J%}iS(*x^S;9K
zli(uv^4*C>3Arh8lQ$mtqv%d?`Y)3XEi<^sQ#pFQryLfprg>17zfAhFFnhg`ebLA}
z#jz-ZkA|FN(JMCDcov@5pz;a8)OXl@nu%#!9!AEn16!|$5OnjbYi6#yKDX1KqAP(g
zgOgp*5nXHLk_^dMJxjd`!HJj2i5gMxGtE;=qD6M6^fm!e0?9b7{!Lr*HaaZx&+S@5
zkL*a#<JujB4?b^f6>c);sgvPTZ7LORg@O(S$5rEE6*&M$&NP2r?G$&C>L?N_idXpT
zi-mWU<b9GF!-937I~6FVV=ao6xnsDHfhUYco7HU3(D4aHrKtC@Ouhl2tW7U@|8@r+
ztHrH8m>cq&IQ?;=lQ7m#`BF>Os5O`IIG<{@ow>o86%DP!qxvhpNXe~4K`zw|4ukOO
z&&g9o<R-QCS2SLcr(43ti(ZgcMa5{r`O?JpK)$)1iC2gnxK`E1z{dC9!b!l-E#-N#
z9Ir^Qay5ls94)R^W2}WM1imglom43`&F-s7?tiY+y#OHzD)JJ|@jQ|l=!}$xS)O}!
z+}G62b(|YwF8PqYt0*S8mhqUK(Go9mY!5=NzE1S!OE-#IbRSBmm0|x%U!irkD7t&B
znwM9$KKAxLVHsi6gR$70%?7bRFVYU<g@lk*UwQ!2>@=TA&@qrPUe#6P@K!C|y9hmH
z3xc<kWg2Df!InDhF*)<;SvYF8&BNXJV-+2;2b0E?BfGYF-qcn~Q-EsSZLuJ<>s@JH
zIQhYz=>Xpgk3p^r6Y_MA8+j*=`o=`_EviJZkix=0t0aCqhc7ty!A|PZq&T$+z2wv(
z2uLuanl1DG@>LI3eE@x3YR?sVsj!PNey1iPc^S|o5|-vI<SDlo@>CjG+I~tvv2o^k
zMGcF5^0}+@=Da*uiIJrrpp{TY;tOk*;+B8HN{P`PDN@o;^R98+767_(M=DAw!NG)G
z97oSX)u|Uictf2J!h=Ww|BE|Sj{<~EvPx>PKbG`GBd^Tj4G|J)2}QU)!D1F2aUDyz
zFag{&Hdc+4IyVtKwV0?4x21};hVN*__S3tPYrBvx4f!-tL9nH&qg#-0$2nrWk}Rb>
z@=lyG0Jnw<5<di-blVH6)X%Aq+b!ua!S`&MQRCgBUN64f^6BSeYKbOrorg11@zeV9
z1)$_^-gj7zJSjo?&`fkB{|={hUx|WjVs_ore_mH=YY?ymVqCb>X<`VQ+f|)K6>1=5
zvcZ~LDGuu?Tn2GKNZ2Bs34&i%<kI9d9BQvb88WTrOtuyuxVsIP>xofB04{`Ao?Ufs
z{ToF3&(FoYsLqYQyqNRXq3pQ~0z_d+n{~y$bj0p8i*Vxk5py}peDJx_Ss=(qE;ff%
zc}-X?c~MzNi)Z2U<0n!tdEG!?0-YbytiumIeH(ezt($9$+_%`&BBT`h{<MIw9CdVU
z8!%Zufpmj=I^EQSBPpFlK7tIKwJbr~Ow@XB6sXHKG;P*gEMI=^Mk=&+W5UeX4qm17
z<=?yaxYSR4a1BuVqJ2Sr%P#OWTnnREKPU)J(ee75izkZ|RCILHeK<Kgyvd}31TYDD
z&N`*lu4_|;tU{jnfyd-5jRgFJcW#Jg;820-e$1z8j$P{)U4~5c2Zn`7IPj#+3@h#U
z&)0XK=N?^nYVlfta{o@>1E}AAaOqoIeQiuZIkwv4{_=`{m`JFBc*h|e{YAf$+)=15
zK%#+ie)-{3V0~JzxqwjPbC*qQ>r06y0|gJ%Tp3fx0e-y0bRj2`qU&||;pQg?YU-0~
z88h9`ykz6MOFgbnoM!hcgsH3XsR0O<$Oa%W`~ZRNCOclv?V~%T0`sZX>mgMt7Yx{z
zM*cL_VnHTYeE=T7c^MCStY|NNJ~f03C*DKtLl3`cQNi~FG2U0()JbLT$!;Td(U3^V
z+74e=>R^gh?Sa0n_IJ?4MJDf%(T?IRoxZ_Qe%j7U;gXtYK_6i6dV|{tuZiI)m?U-o
zJ1CTlQm^D>bbA+G{66Q7F#d75pdHSZ8?UO)$Kubuc647t@@VunnV4S>!~JQg@$)w-
zn5V3L#z4}6{7Xat>Lt^MnN;gW=I82xvJgcq%2vAynRAuWWEUOoY3&O~jE$j(z?W!Y
zYckZVudjW4t;|J`I^q->?v%0mcCSoVJi)2vYVuoz#Yh_;#|DJVV5zXyJ;UzQmuHAg
zJaqRPwWVs_xM1i5dZ{kala5Etro-@_{A950Zzy9C!9iuq4k5PS=o=xe`1Mr%)WJ%`
zDh9>@zL;sRsYZ4g76gX{4P{0w2ogPf>Vek>=dhRPeC(Ze8%~c-D~k~LA%Ooszru~m
zyu6Mo4&(l&xD7qtF%Q?69(#g9+FL^XRFhuQr2T+Ku38CzgloY20@CIbdgj5(Qn6rd
z=~}xG1n}wGbzA~``W4jTvVMc>pF9C_Mcp3l^lqMwZ^(0}cQ#TFKN-d9NKnSgR#@z3
zq}tRZPl^Xq@dI@F(=*(k-bDaAn`53wTS}9q&(a@9%K<#<rS57fdM1Pq0Ean*<k&og
z&fh^O^C2{MsAH0sNc)K{3Qx-S7G2AD2203ZAKD&r?nUj9Wk!E<c|ZKm(2PHA>_dP-
zar)w=?pVBxd32@bVV4qxYXwx_C(GAss?QL9d@y}6>~Sqs%!inan|HdGk<-_QahTxu
zSy-G0{9a;2y^hVcdxjA?Po#NM&>E0K7h5UyZZfoYKNYV<Dl%|mSoe*oa>4va0Ds$B
zrJKsLRZ#vx>!e8@voyuzpf;7sh9^YswKKgF!ux0BN4=ObmzP@UR)(zOY;WA}UKf%W
z#k){#K}dPC7l{$aq%S3!3mpqvWXmbHbjMkz`#;OV&KZf}$r3UpL9CX)p?MJh^oBp_
zzy7yd<+o?FYQoZ*6m)0w$ZQ|_1^}Gj6H>nRz><R;%Svok(Am)!q4%(t%n25(VgPM!
z)z>kb^&8e25W~7wa$5MT-&Yk?&3?1U<#I-La;#8WoSKj-Z?m~5iEww`mxqSev$G~q
ziz8TSEA?pSVO!jL=aKfr@XUnDaV={rbWXTAlF21OUp{BxL)fX6c>kD;w91mBaix1;
zt?q3ZjYh)Rz#3iFjwTzG2UIEk3;VKpW#SE8oqfA$%q>SF0?`g^q$<-UOG;}DriA_-
zWLXy>2nu*TA)=<jX_17QoxDw9oL}?SeS4ujS^{eYf}F^_YZ8ws57u%iV+9!WaXJVh
zr3(Q!FuC=OQgNdC&-^y6b#)%ezg`|j!cSR{x7K--&(SR+s9E;$+O<Qkt-&_IVh-xt
z6wS4zU3YhJX~0n4Eep+2NwiHIFQ#;JBj1v1ZcYZ5GER#&b33tuc^8hNm$sQ|Q7g=B
zy!=}`LVX>{TeT@b7t34Z!<tv5Z}`~pu)#!q{hzH{{=8LndrniAwtdF&nCp<=RwTkp
zWUXr!alhbG&8+<TiAeUNDoTVWswl=U<PHSs;VZ|lU$~W=8ym1*kXa|j3w{<lyg!E5
zC+*>agBQ6dsWy&O2$1HRKZ6c*WUBtFcm3AO{)<Cg94x#IKoVZ7oif~iY)Rvh8=pM<
zx_r)=?Bb(`k37Pm-e8=>cC6I<g!r%ifF^z_QBATCF@ZSRUOKzXs{vOcpn%l6v-$o3
zxk76Y6I6A>dG_S%9J{Fc7ky{3Jhy_bMkuUL`^&r@mki&dgC0Ut(pTKzF+KUi9b4%#
zm4=b*#I29QMaxH%DbIZ1V>Kbl$E)RG@#=K$!0;nX?JM=PiGdX7M&$5h8{MP6TN?42
z9+{_W%aCd8Sxekwy&g^5bBFh35nP6m-zchN!Wh4@BUjXCVfxD^QdL6MMvDxGR`Qhw
zy0dUk++VTPlb~jc4BQo)F{o+*=6NE=t~y=YDFjm=CQR#h+gq+jY4+mLN_mn#i%jl2
zGg?hS1nSRN7p%L44G!?vlvDNe?jXnXUDU4x!$3L*UphQ^wDZPn?%WmYr%u^fXZm=D
z`sgD<tfS|xe&SZ*{~vlV^lg_C;GiCh#?R2h!$w-Et#lokweHe%n6$dqoomTFXP%a|
zJ@{zk_%*6t|LHfL<#A=it+4fS+qHx;4U_oT!qVqT*?Z>`$<305?{lRKB<Umhmyhtq
zf|18W6JjJmp1dIkPn5?>(fKT<IO#vMLWU-*U|1o#C+jhk?qy5Qds3(lc)9_W{C)@S
z-67`i?QZJoXFPcSg_GxRljwggGzPY(I|+b~Yz`@4r!HIt_(gMY|Gy+kg8msS&P~Bi
z@qZ9+K07bh{{xHvKd?9m1Oorx1{N3mFBY7Cw+Q&JP5Tp~=s!1`{o~f`zr8g5kLvL|
z4$|Q-sO^8@z&qJ+0gU&^EkgXa2pfR(zVk<5IDipXJ&**j<f;dMb4lg*OxSSaK`QdH
zz<yh^%CC9;1}+Ek_(Sv-ny&IU8*{3@O5&*?IYyW2#%Q?2!?z;r!KqU<`_EpnsW}Up
z=Dw(r02Ilf_W;4Z#br?11NnYgW2z#vrK6Hmtw5f1Qzo71%7{pWh{H0F@rc27<{g*%
zosJ+2J~u=6YwOn&!gR$OTqvu7rb7=2d$Q0487<3fSpeEoaq8#Wx$4|Mf7Sko{{>6-
zdy50&hoPTX?6c}~|FZVnFM~~QU@=L6op+w3C2K%2#{-*XYL~?VCDbh(3bSf&vUqTW
zq~+&xFU_h~{SGFaQi7Evnr0wewNQ3KMk!BIHH+W0YX1uJa`S-SL`Cv%kQ7=qH?&vk
zN&Pi-9$0U)P`R}<zacf()g1vO`%lQ7%wOn_S{fpb!?b5)rd7n-sGo6z`$i0Si@y5k
zDVm`X)_=Zv-^1N#=XWB1<s_iQA_XDn&B8Peooe3Sxrq-XV|UolIntSfI8Z+pIe=iR
zNd1i`20ux-zk!UE_b;3U-7}m-sFP3Aj(T{lumQnq7W-<TrGJP0Ww(ve7plV33PV8g
z&@MvThv?=j%jf#sx^8rvstP6(i;Zg!7uw>0+17;X3)pHdmp7B+CUJN$7T<e~y1{+J
zt+#CuHblyJnrX(IL~=9ds9@NXNGdwlAD#fvsw9PR%ZqJa%(#p9t>qcJt9<2E>Bg%>
z%>fygofmXE=(kO7-MjA)=?+)z&9!J@T&HISYcC)K_S0OGcsY9u(nEqoUX?#I*uVT}
zRn?Om?J(Lz%BF8!)Y*IhWjXWN(liiZCB^~!I)#Fc4i=agj-S?PZuV)VxAQEMQST#8
zRK{*81*T$jFO#+=oCr7CIO%XQ`hpg#%|}>9=cu!sW_(52WBEcQBJ*?<QACPhTbcK~
z$>FWK4EQPM@cO04+yO+`p_Wv{G^q?gJb0RgH1ZP!;O{%3!I*PXnd-8*HKF;g7wT@a
z&{w-J_{|cgQeW?Of*dAW$y*zuK5`H&emiowbSY0#+<)xY5z8Jc;c-!qw-GG!R{J`(
z-JK+0GSk7qu0RJJ^EC(&<fvl7^fYpjU`hY!wlbH-Bt~%;g(BcuVAJ!BfiA+#=70|q
zCq`j7{7Mr%1T$#D=*TQO+VvPIleC>%CRzxi0QQg|!(e^ctX^~iW(_ql1LJUfj?DL^
z2-b+5+lltb;b*)K&`q4Y<Su@B{z6Gz<i&{EH4~}{NfP4-c|6hPj%n0(0X2D&9|WT@
zOky0WP&Sxis>~oMDiH+n!Q=#dJ-V<3*4E)PS!x-RaaQ&@iiyIi9b7YKIP%$Z7b<m=
zqI1*a6yCDg>4FZ@?LIW-`vW9*gn=P&j~*l)bU$8eF`627K$G9nfwF?!KRpeYZ(i$s
z4)UU6*^Td~sfrlFe{On9u>Z9%@A8NJ%P$kPyzFn*GC!@0l|l*W5==q}KS#n9_Hu7|
zdDs@nJdhX_XoGL4FxemShXVet8ITkp<pw>AT<i4ki6h=ft-3=+!Ow(WQEi=1*)62?
zF!R_|!ivRl!WjcQooCQdqsckIan$(SNvZlQizsx5?+u!lCVjbi6p)6r$8>Ta+L0*5
zh6E!sU4t?-QDk~#?{qh9l&Seuy7Oe2pLQ&`n*$0KyhZg$yrVZ%j{b@at>|71&LgNh
zb9Z_~oHV_~%?-P3o8eP?hnrcdP}YLn5Dq;$L2Mz{tb5LMLBd7+{6%r^YY!zt53rgB
zC0$S7?!dHjFM4(@nxmcyc`o`2V|BKoR@J;?LeG$|6Ow+YU+n%+D^ZO^xx^t_J`nkc
zXG?G;&5DfGAHNs1L0HSja$)ukOOi7@g-aGz+%tbY%T&S~i{*zwngo<N70$PO`g_;o
zGR;j1#M!SgF~tH)I#?-UC*Ax_87Of|u}mO$e^xpE-55X$3d`#)wR)=~Wp8S$l(lF0
z9h7yhIIG~#Qx#v5)lIt9-%4zWGq11YcDG$hBEWDYf4VoKt*Y-`Ocq+=R9$WLtL1)A
zEdx8gOjMbJ!lnAsCYKAmH4Rpw#j;>x5b`^52oa;lho{d*vs*%m!(4+jQR_FYxZ~l3
z)NIQFSE?VxAp={!Eqbz}&2=>6F>@L|d~$(1@iUD3PHkl(4;8AbB03_et#Y#G#qVuL
z)D{AK-J{x{e205_L$IgBiJ(F)UK&hp^C_oWnwn~F5!Xo9;kp%r_e)7Nr&JPG!k?Y_
z6nCzcoKnbu)8>mmxu=(tv~w0x?_0o=iQQ@E68U(3L??1*=ZToN!br}ju=3*FS1Sf{
zo>OB)@pX=mD<x(fg;4jg!aSj$VJj`2?Yro5;ls!&|6!XNkOv9VxmEP922NW<1Vx{T
z`seJw3n~|u^i&bmrlROsyU=Q|BtonWffD)SAUuipx*SF_iwcBZi;-|W#0Ic9<2TmE
zD=8UwN+l}4u?uO`!{n`}TL(nV409(j3tU*k3U_Pzncsb&2dB<C=`G&mqOqNUwltjA
zSz!W_6JODAq7snhTy0?jn)0#P;)8P&WfGg>&}Cf)boZVe2>Q5UhA$FRjLsx)ZJgra
zeLx%aNY`*qUTlvb(n)h?#|X5B>I2F`mdA@oP2s1l+}q7*cK(zM@u1M`2tluW-62ED
zzol@6+Hc`n-ad|Q>`O0ec7K`+^q_KloOh}!ryH*wYM4^uV1<PmpL*}`Aeu3KOVSk~
zhYC5L9*#wk*MCI^)6va5-$(l6D_dc|&<f$B!LQx;5Q2hF8?ghktBdT8k|60j9<&{f
zoN<HJQWLgs1YXQPn&YcKF4nTj|4I`ZE3L1Q#OIpGzw2>}Hw+V|tm7p9NH&lUJ~B3Z
zdD8P8gq3HH)3M(((icdoh#J#V@uX&aHJRi!;+DG0XLzNlr-|F<#1ju|3Kx0+26x6c
zuh&PKq`sCg+<I*JMI7tO>4N0`u)Hp_!&u)bw@y42_b(x7BJ%^Z0&E{L(WFMpOPu{O
zzv(dS>=}NfiI<0mLeN|~os=y{)_JXkp@)=#GXOLU4FfU+CmUV@vViB*P90x>z_&<E
zkqd@j&*wM7qu)mi0|#iBjzA3|D-F0{$4RAQXQ~QM=<*g0J{?k8bopI=NrK`t8P<4)
zK_>`-U{}tAu{_2URdywqbdGyspOCXce_Iy@QFj{Imb=LVb;k||4QHt6m}*#$mvY{V
zFkJBL^n9mtTF#zQvvVYpv8SeDOrCsR&zI(bNBgY_Mbv2Awz(Oe!^i^px@aE_txuIs
z?_FD$%lHO>7K?SJY3e^L;hCkkxM!iKyDWw&#8>Sa=K}ly0!w0ub90t->K49D;kG_G
z#8Y49TN?Gi4wNZaeV87l@9sU(p53UTs)`GTZl!`Qgu_)e>q3~3GRqW*;3&Oi$$YTR
z$(U$=UjQNEqW($8^!k-)jEa;z2^d1wzMrT0MeZ3hIHq^Cg%zr<gN=H!%k*+TS2NW$
z9bMYqVjQV$)4Ehav2RgQN*~y2WX-PV84e(NSJ|mlFmKS)(`6_WKEdL+v|ZcxG#q=*
zo>7F1PLnVu#3U{iw3}l;!W>v7G$lqJYRmoESWj=((dP4akc;3UyBASw5?Y5)lX3Ol
z1Mq+yfvJf2s;?kZnc$GxtH-&VRVPT5$4VG9rK8OOO!Z45B^a=}{qd3RdRT65Ai_<%
z2JFczEgZeFyx5ni=Ck!KxZt8CEIbka{FGeQt6F{<j~S-o(a&9JXmS_PS#v%I`m9lT
zWpBB9Q&`Qe2It}o5}Lo5`*Yi)*p2#f@ocfx;KP|~Zbf^lTF}m{fwFPB><7HA#yyw$
zprThEQXd3~2HuX_sq)t8Im<dlEVY2#2JWT}NUDA{ZkAu79P%(9+{e?_X6ISQKeSHF
z?HQBW8ZY-KSL+<v6kI9YasWlrN;HrhZ9&LIRMh5m<xM<TYBn`xW-XDje#dS6VbO|+
zC-NF@^%MmSio>s}WCM`o@4?M<14frV0XA_QZlcXo#*MKCMX)#&(ADP%N5F-$*)u2t
z4tpdwDyFq6hNxs<cqt*upm-MAXuDm$ZZ9WiBGpmpXI|cT*u>IG+HS>}<g;#!$?O)s
z7+i%eud#G894gD&qHPS2cIw#2aq1K^BW%Nf$rQW~Iub*|^@Mx9otB@b4W4vSR7E+S
zMMpB3Pc6&{mpJ%1R<WHqb%LCD849<AG6tv49_H{7mRWFPXbm7KvDgcO4IVebg8c5*
zNlFxQ1a>-3y~EKYjx~V|JU!_I$=;+FLONt^(Vv%SWUw5uW7JgaXTWmImeLetKv!zx
zMAaTfmPKF(^fZ!4QU#Dx`K!BUS=3h=Zq;?6G&Hh9JUK2VZZ_Z;;dAk;t#J(oaSx}#
zkzpcG$!QurA1jClVLBmrdWFC!7Likk*ds4dr(38=Z5bvWRQa?bGRT(_r!2JUQB%Hy
z#-}d^*s<tXdHQ2n9NZCNA053i#w!XhRBhZZ$!fqEP=(r0U2c53>ozP8V&~QvA;-n1
zV-H_YVJ91D*|M>0mz@ImuE+Loe1)gFFXjBm3J{(`?HHjs6=zDv*Cj72wByT5>0pfr
zk{s`!C_OZ^E8U35-E^GcV7{MVrvf27zWt1<i3bdtfBK%n+akYn&CN=Yq@JQk_<?TN
z@s<)_`3Ly(ogXx?E@+2k1TsrN2bf`IH;#QxQ;KCENMQ0sE;E~!OVr%59Z(e1oW;3k
zf(bLQc`#?@LU@ve#@j%TrEFno-;t^7D3ix4C55QL{HVabC+iM%9K|WA6~Qh-M1!EJ
z9rZgxf(`BaLoW*O>+8aM17dxF@m2Xk@Kp~-5=&cKvEbv_PRD){Oo8PX_0nszf{%Wp
z#?d5rF3Uv26T&)1xou^HI;33?H3)G9Ut&Xyf4FH}7kzCyHE~X~AfE(NKk<`y2bymv
z+PkeY)9eT<q=&a-M~5Yl`;ZBDAVkG(Z)dLAp6EV5!Kf3ynoz%>rcqFQ?cZHt1<ET@
za5#0-R>TyX!pfPsd8ZRS-;jiiiY2$>BC|_V^@4(QEwp{VGaFVPV{0sGzSj&OBgF1s
zi;!ldTl=6s&27?-Y>PS|FLS@~xp$!OCbPXRaXDg^uW-oT>50(qijUuw6Tj*JY|rIJ
zY+X9t2(_{Mqg46Hi|_&s_S##>RyS;I7sUHZjiR!azk`(ed_{;QqnT0ZYM+WGG|;A4
zd=>R>4i6v4=VL+7knFGm?P=NVXs*uT`IJPWWT8%d#O1ZTBeehggf%{#Wayf*8dqj-
zIccugp2>Fp9%AA!rjpGtLeGlBgRMrGA(aEtYS*@d4ik9_WtWhX9(H{=NMH%z`QMA6
z)pK763DV~08IVD2eP_G-TvfyOlxNC)OEGpOCtU#S%M-N-rIdM4Sr0FuoC0*{b$qln
z8>4gM^3V7<;Ma(F^6f@TquDs*X%@>9{U;^_@y_t6pwNp9s6dBl2xPGVO%`^~*@)il
zUOUm&#OZa(wR}`p!&Rk=#p=Rq5EdGQxHe`8uPAm?B~z~Kg*X0m&S9MpRsv{CWy6h+
z?~FKK!UW#;MY@dwUS5+&EZ%1TR3v;YGde%3Qaet>8R3{_X81lSY6alI_dK-=Q|<K0
zeZIn9o1)#yFVNFgLHG|-r1xV@b))yHDEX?9m=^G#%6RbO9@pWNY*To5Rdg50T0{}c
zw$kdCNiU?yz_2hR*0RObqOkA=sy;SIe}?mAoBehq%}McIc+zY>g*!SdCJ`B?3ri`@
zz7qcWN-xkvleuExL3V<hbOr{<EdxJse4V`=6CWb_0)U+@-AHTIHT%zl8FeG&3p|UH
zg3y+&bp}k%1EIZ(PwKjc=OCr^-N3G>y<>AZ9~VB$c$~7{2JJWF`VVJ36SPT)1Ka(C
z5kBC>8k^&uot<Zl&z}M|_JW!b`5T{b9#I<$d^AkctgeNl@r&j8idVcZC|8h~?#|sU
z^BMK+N06oN7JlahUkTDis>?OD0qq2cT$w3kvW%3H&9;-o@g-SM?KMZCXtqi1`gc$+
z8}d9L;CxtDKcbKDuD`$V&DUX{sQ|i8d5IbmS-Sm`-Iv0tUq=@*rYyX@$H8WkyQeoR
zbr)2MoL&=62J|Vyvmpo(bLy^VP!eNdDDt~9UA_3a@TMNn<uJSs)!Ud=Wp;N|p;S``
zEQ<L#^rj5(Hp3(b+E!wHO6C21R?mqDxruy>?2xw&%K*t>fn`5y?ierc5``AW0hWyB
zTWgroJfxjvGrxs)aByY@yVY(FM2Ij+7jnGPQsd?LNjAPnG$Y7Y7&A0=bzWXCgd#z%
zkwX;=29Vve(EL$Jhl)9g46xmk-EY+z-pLD&IAA-h00@A+;Y%9NttR<YU01|U0n0o!
zg9;=mfzwj2bXDjmSxi7TPlRYq*P1chfG29?-cAMlT>@ok>S3h$bSEY*{`k;O{&w74
z_l6!%BBkR{(>p%AJH+$WRK*q>9mf%%WVWTS_xMOPh%!47NG+E)B)pU-O$qlM#IOg!
zHLt9Rsgs2gn=Ze0(pak+tyndU97lq;wK~4wcuhi-XGU%3K%vwejJyT8@BEQ=unj>b
zo~(h?+LMmimkqA!t2vJ6YDE%97uMrpxR3<1PYTL{o1OM1@qvU(Jha}mhz-4^B3h6A
zFU_NSv`DTCaKV$}MIAjT7r`)@Q9YToao?`yBl-FX=;I<HkI79LO%bb(B*D^87D*P!
z)XMPJZUuTnc?SAo8X*lsFz;2*sT%h$9U*+dtUFZHa@feCub1!^SAB7Q1CRb;adNwY
zKdT5zl$iank8YILA9qrLSn+&6KCU9Tj7k}a60j^w_0oG3-MlS(smk*?ZU$FX0@Y7<
zC!U-}Z%fv}nlOwDJ7tp|UbvAd7^Z6N`hGRFqvkbHZ%Aj#XQCjvDOidjOCPd`BuK$3
zh@_fKH`lTj?rvpVZ}Db3b*xV@+!rBUuVI9wheI!OeaRN9biSx03dv%7IJ^}AFqZ4A
zUYh1fu2J^eSOQs?wS+jlGUN7$x{fq;W@N!|RXN`N7du)kyC~r#$_O$qy#0cCLW-;B
z3@>aT5FhI-Nfi#dXlaDgawoO1gj~0Kt=k75LC(5TynenQTq{p~t#IOxOTu5L02X^s
zT)PHJ4n5_$+3Xo}7C+#dX{bYpe)fZ#1EVRmS9RoKs|U|<RM_r=%=V-{lvUbp(TOAA
z%jy?u6F^z>XDLHE71->A8I?;ax=JU;t13Q>y0gtAb0R}!Z5`3$Bcv6q8I)2wZ^`-*
zN(=9z23Qiz3%GTjdL;*Bt<T5zP?*JGFH8x{O!Te4!>;LNQDF;-)PH(l0Bx<v@AH?j
z#YQ4Td_|arW@m@Yy#8u!v}!l2zNR7KQ$@n2g1kea9=BfXE^`IlDqcvDd!}MD%+zar
z>KOoRA!&rQgbSqvDdp*gsmh|8k>EMRav2H~owpzq*A}e??$JFo!1r5Hpp}2dhge2O
z-ln)B`t=kr#IoP2-DnOti%^ZWLXv)j5Pnj6Bi1=%-Dy8EK%F4nCzLuh3{cM#yrS1M
zJO)QBTVXg;yneoOj_#$VIddeRP=5Ci+gW#OMP~V`U)wFOEPv+3L>)2!5fg!B7k_w=
z;f;RXMl{saP#x(K;z)%Y%NTeQ5~gDY6X^NuH&f$m&0}r(Z34wo&<R3>MMN;N@N{H}
zBGR%K!S-SOytGy6r9*Yqef$X8uO3kX2<o+UX-NopBJZ;>5M?kSHYCqAH0S003QKT0
z!AmYWp}5hzDpy~0Q&_1msT@KOK^E<#Eqy{9h}A<dn%yY$)Ae44o^8u$Syb<8zC=?I
ztK(O(y48BU(dicO-K~@xuac}?P>rGy;v>HWUuq#5rZ1(vnpJ8b*)?uXJt6`zSTb0E
zf5rf4ASQzWYWmi5-S^y|$ws-zmOt^4xbZCLdfwpySPs#{G6!3Zq$e=n<8)rZ2oQ#m
z`Yn}jvh*cNV!0%*^`NgPOvf<Gv%)EsMw50_p70K&e^o}pXt^$P-0vu$e}2m3i665Z
zJRm?<rdl&<Dqj#+>lCMH%Y&we#Hm2f5>5}F2eL$lQ<WCqP~x|&Uu>wEpqckhK;pXy
zBbtFFlrPQ|#9S&P9Ja3_?dY!tnmL>{P16{2akQ0TXkK0M_U@Vu^$)}M@<c5}Ku^#@
zqKgo&+mw;b;4Y+{$QX`f?x=R7X$;F$HpkZWA$gP%@2F}G8k>ic@eU)4J2-{of=&ra
zF;w}Sqc2*YAS0DoUcThBTxk@x6;S<rpjA;*Gj4Mf1E^aF4RK>KI^VqZE}1(kLu=|W
z3D1=uf(iC3=!pnqFT3^>Db(O6Hoc_zG7s22n6&qL<QN2j$;_yqMOwGhMr0=}IRt3}
z#2h|YPL^&(yIeRi6_O-c!RN)}I_IIg55`<A;PL-y>ZA6j;y&mStOh?3x4^H;i>FWN
zPzf9qJTSz_eGsPwgE*uh`Y`15BIXqb3lIQOue6RYw(_puVTOBtzkStpT$fIz-oEY!
zQ@*?QWrK_iqv&xf0Ykc=wz`^BDTefzOx@v^?KVw(yR>O8&Io~8e(LC7pPDKo^-@G?
zv#2=LDrT*XHeBrq>U2#j&Q0`drLwggp(`U^<QTu){v6ZkRC~7{vxu^)%HO*5C4Pj=
zo+<(M^@_OdwK9Tcxy*woxEcz&-PMMv2^O0ZI^Wtm#QqsvdPIg5CNCI5`OxUU-GdN4
zLZpssBblNreDX$d2r%<0sZY;}F(HZ3*Q_q6u5sPOJQW7KYav}J60V{W>^GF?`s>ZM
zE%7hyscSZp;432~AMIKl56`ybPkNUFKp>2wFOs~O%y0@FIhZhjQ9f$2C9Y!znhq;b
zu!2C#qA}cOeU?ss-&FWJWhz*SQvemM_I}oooEa|YX7A?odZQ*@87yV-25=A~C0;a3
z;P-4^6!0LcBt3ePV*7j+U5e)0mP6`M)Ged@*qI5L;~LNLz2|$j?+=%<vQYPH{Iqsn
znIs+aq?fT}K6^l7Lx|UkXC^scAh5)fQYd~E7HGB!2-W^`PyAT(#&}!vulMv+9%X-7
zeoqH8J9j)F`@EBe#;i&LKc@vTVdEE)TRY4`9m~JSiR60A_Hn4}nU#I6>o+HU9NYK&
zTe@eWR&bSF1b(s88#<L;Q(RQ~kG!+{u2MnV{hjV}_ks-t_GG}Z7jBu!6J9-!`I<)T
zgZ;EvTAv+p#L~kXl~KKG2x4~+hqF>J*LIE&h!wVr^V8Tazs^ff;9|2{5w=_8uCBh@
z6x2wuBR)GfjEuF!bU-sY({<&0k%`>m7=J-+Y5L4jo}-sG<*y4TeCLgqL=nwpXPOWN
zuhSS%jEPZ=l$O8=-chF~U$C%u<#e~1w=1_b%yb)Ivhl2DO+^}A^HGAD*?}zYCEBhw
zlyS<iQQZrHXIRFzcAo1vb!1jJDm=+Ty{s9#Nk)4H&vF#)0aq;}t!RRdTsX9(>D(zM
z5VtyHBAt#`eWCg=aA-aY^$lT$@Jo<M<b>)Mm8MbFCFR-cqciY^Zjx?Zvjc7z0eqbP
zU{vA5LiboLRU-Z*3z2FF5gB#sIO_v2l<d(Q8i}d8DE@tWtLNT{1Fy8onaX3e1M4+W
z-P8{#>Gc@{DSbJ;#Y=j&@WSn2tGhyK6tx*l?5{;dJSWM+D|U{j%XW3%YAO`0Tk9!J
zjxt%Sq$l+mC2)f+^!i4^ACP*GxE6VHo~7Dma;E}5v0lVzALI7y4Zi8_BQ_yfo6Mb{
z`-1C$`O-BDGlP(-KG0MjY87d@ptd4}Xgj^9omhwTMV_AcEVEtRwD~^6)oDk_7_Bes
z{AVTeDnj1mA%K%*QEy^gSlq)t0Cf~w4E2Qko_j452BYa*g%Cz%x!2`Xc=ewN;{Rlb
z3(m=A-Ekc7>|@%v5mJbtcwFu%G&Mkk{^5&ZRs6Azl+zOxnrQ`U*`_3wT3`IbOa3+d
zeY#pNP1_>-;V#_9qFn`TndU&*RYLP+tqk`7wbU!s6nQ)ie&EwCZZ3!K@|gOPsBqp=
zRUE0o<yk}(wE$j0!90IY9IY?+Qo@8f{~Aw&1w7baptQ2eML!&Kq9~t=IZZDHa^Lp#
zw9%(kVMiaviHHs@3nD&ERUu8+Y&X)6rNd+>2x>Jhb-W`|#Ma<$ZgAXE3wpnKV?<@g
zWVai9;hz2@qVSP<X<>9Xe}cP)Q`E97B@%rEcLQQ3n>VjMu$dI+d}#2SrSMcnext28
z#mnBaWX%aDDeZ!86~!Lx9oDlL+wJZrPKw0%MdbwGf)2_PuFhgD6~Qmjv#hK0y%^Md
z@JY-3RJms%9o#L#M65oJQC0Pj5I!HEewnW@L7rX?CPXcuZKhq2n3KuLGDh?Ys&HmG
z4ogeH+Q*TtKzC)`?r!)9;{7K%@n?(&U3K#8{Cj%-Y0BCcM3<Z&HigPK=U0d+vX-?z
zCTzVxcd2wTzck(DBA=r^&BA)<FU<sdE9J<*sw|)IQ~-Cvxz3Tqx20NQWtrL2H4BTq
zxwbJ%uU=kxq5V4u!*%A-*>})0ZM!jaWOUEe0*4l1sRN0U6m`<>%CclCd)Z84za0oh
z;xOQpKuG&2VCHr7g$3Moe`s`7X`pNmPr4I#q9UoNyVDWoqA$IbvCOxDtk)@bjCOQ9
z0oAgzYIfd0(~F4;>dC;}Z1i1LJ}edi^JCJjZXUgNbyi9DL9yk_<vv2n$g&;>Sn4s<
zp(9kanquDQtzu+iax1p1zy#VaTtOXN!qHm@AXab99R9NExm^KGj|wXkS}Y{q$9(D2
zD*)H{1jBtg_Mj`9UL*Qso5}BuugC(=j^M=02<JeVp4EmZ?DLr!J?nUX$P<FcWKM=p
zbzW^+(!<NbZd9_}TPLr`{rNXEh55>Qb2epG-Z`&fWv*$*D<hX~8nOhEA?c`R0}FW-
zqeV0wC|^x~4f-0-SK>hsou3-Z@&>U~)!T=Fql}g%7WXZ|l2@}gWL$?!?^=v_ZkD7f
zVdI_m0u9#SO5y+r=eD!hE25wz68nJ=VMdN2^cO@kX^S>|IN7`rMD$%;sYkl>@GY5(
z!u|^Tv$4_&-GFWAwOyf=MWG+G+lgFF|04}ZRMs@*tZ#eUtFkx$Cl%nI&F#U|oCM~P
z5KfhV=NiXufjrbx$4v6C7kLPb)+N1X4Wsre^ka!HoC{ujmTQUzoLrr3^=AsM+*Rrc
zswXvnzjGq15BEW8j=y{w`_adk#P?J@9fq4@ty#<a;uPr4S?IMv8g?m|W4)pYJPk8^
z@EPs}Lq6O^A;c6Xi*I@%{D7mf+CbfL91l2{vqvpE=Q5lO;c+P`(i5Ik2B9fQ912r}
zeGHI7iS~yFvChWum@<VnHB%OE4rc>Z0(4Wu<!KWg#&B;)arGEXh5>KJ7U@q7)>ikF
zOl7f4eC@5uzGbnLP5Xf&{WG5qIiU8nl}ig`-EpJ>(No`>$qjSBqC6&L7?y>X87c8!
zg2@%%)$ue1%(m88d5C*h!*&s0G^vFpvn}>_!Fy^%NDK=3%e`hVm)7L-<V+cb7?afA
zVm7|fZhi-u!wh(7K!b`)>u;L#YlY=YMe9oY9Y4mCGh$hF|AVUO4_j8i$M5ZVrKN{R
z_@cFkE*~ihk0Ib;AQQ>Du9YIq>^LovW46U@=RpsOka4qE+hz9KDI8vJX;$f_nHC>A
zUMRE;puEy;&AG{3#C_I1&`d5j44V)>;go4}(*F($GrE#*nKAW{sAl$lh`>u@M&kSt
z<Q6;Mac)G>OVDY$Nfe8+3~5K@+{pk{h>pYBs_)@0$MwuicbXY)ooOX9S;(j(BpOY8
zs{wmGIbfO(u(m<4cjo`bxLWvY&2MY3EB8lIng4*}=U<SYf4ZlP1z8nJrCZw!Psv9d
zT)3xGql`SJsdai8&M_E{D!9zPqpdp_9y52*)P*HLx;4~{vDo$2p<u4o*cLl(w8bou
zX_mhi+W!e)gYVJWK>!CLHxvw@zl0*MGv3U{IMzXHIW?jP%Aj0Q*c=`nHqL<OL?5DQ
zeCii$$ad5dg|E$$k1?%0B)Z`U&Jk2CUCWpqV@1`rAVf(xZ#K}Ky5=U)i8kL`G^yM$
z&TaSG9&p^uM4s5m*Ht&eUZIFS@dOPvSEequ?^N}DB4<~&E*K-}&nqz@XOS+Qjjz;m
zr|Pu@l`&~+PAO^_1=IktVUgeeJ800H(P^}iCskl)pqNwAQIyfajwdtzxg{|I>N<Bg
zusz4^do+jvqM7KE$mFz_lI`@k-w|~WxtiCWckID?(gTCc5MdL`{ON(ReqVd0wHs;W
zG}V^aRdbr;*MDY$|FfO|@o$i6n6hLH_%feL7<#B+P^HDshSo+XuPMko0uUxi0WNq2
ziBr|bNvgF{n0jRpQ%83Fn2l6`jr-^A_WzFfig7Nsy`*Q<%WFN0MBW1>=PkCmSU!RG
z-}Xg=nyUaBcsNXyEUY)N2h}YAnv0XDtaSY#I%n1D;0C^1RhzQiH7{o)^;m(g(^F&4
zXdwa3@672V$gb;%u`4oUC9*@au~fP1{)2~@{%>E@;CtQUx|^^J*2vWJSEi1A%oqiz
z6>^F_Tu+BUV%*K}x}G8}bINVSBOc;D0u`bY*H7{vI)*v9Hzh^iTkQt;-`!0*PS4u<
zSVv{<`_=jiQN6I3dNU_jQ_Lxy_v}1C9h+YH6J8r&)suL6G(SUH5^j1{svzI7=Mvn~
z6oLB!MK~3-@dB1re}I6a&tXAD;$w9dCQ7k!X%K_$J!%35ZyBl-xi``;9M&ug4b3Nf
z2jw}s@j1=QnPo`1iU1UOo`W!#GDDHh_NVuv1F}kI{)B4#i~jBJrlkMLG=TU0UU0-T
zfty;W87{a4IfTc{nSt~;P08dVr-7SF>Ih(!!$SPJj?|hr6r{gardvH;!3>Y47yOEE
z9@TR~jvj7hvdG6%yuKmkV2rjnR!P;e=Jy?#-+_#O)sy{Q|Nrkzt5Jqik-tTl{!Jtq
z5E;7kVEVi_fU@*25kKIuf5!Cl{29})Z1l{GP1(rN%+{HL6S%0#rfTM7=i+E$<|Hi4
zCh6`ht>$dxYzD-o)p#gwA6Erp0u%zbF;P+AQD<i}M_b^!G6y#|#qIIS1t~jQXCPII
zi-Pk<5}On^1t;&1=cPC)IM{!^O&f4JZDw+Nrz{kx3MW4W@b9*eAEz8#92B?5PYJ-Y
zT)$NGmpiz5fI@$%*RKh@K*EoLZmZ{LXQF22tjDJONQzC}%-#8yd?kJf9{v)@8c+bY
zDwvsC8cEo>>j9;)Q}A)JQ*g8M7*Mc11YQJa9VejNU#c(dXy;-NRQLyh{*MzjH8yoe
zBU>l?+jcbZV0);>_Q=fD(!@+vS^~&f-N@JpxJAv`(Z$61m$r7I5V$pFFtauNVPWx0
z*1s8BNLktdZOSHP12p_2GZQ;gGq%TOw$Gd`fLd|>Yy;u`x-7xKZiXE<XfNg*3ot3d
z?d~RbVAe>G+E6ZV*+iXSoiQ-wX3yodvE;S<{=8dJL*fn`Z?v&qZPb^wlkNu|S`%S9
zqGd+*E|_9hB4B^CO95^vzXZ4%c4fSq`J{XFS1qR8yzDP*Y2{=Q1@BCF;}yMpZ^Po~
zI=bhxd-m0zI#%I5e@Axw2UiqI=kl`fg_QYc7uXCcc~$*`E{0Q*8Xt}cW39)DG79pd
zRHY8a17C#`2RH_g=i7s%jPl?|vW1hQFTD^kX}R`dAz*Qj3EW{vI_}3AhNmf_RH@K>
zc>5A~cfxwY6E8O#lFm@PetN~}3i`=y4mrX{g`Wz9Ls?30_CMU07t;+IoD!%r!|gXe
zcYD;Y-g?qpcrz|GqLu8ue#T1s{@eVnbDdDXLs)}?QjSbD;|r7w$01eeAUtf@?k=^(
zYqD*mhf(8cJRb>lVr`PAaMRU@(|XZ3_2S2q@wXyB__;pGibu0njh-WdiZj}(VW=Ld
zPbYpECfC1wpp7WdSfS>asxP{7Bw^03|Dn<jyFdhLSB-}o>Kl87)-oh|B~5wPh_l6n
z{&Y6&#qva2a}jN9fcxGjX~F3?Ss(Zg?Y`~8;St6;H~Wh|NrNg<5Pt8G=NC~ZA7l%r
zh>h|PKyD;H*x?rx3V6Q{lE%sPvA%;xBaP8_DX5L=f&QQktJs@!*I2LnLyn0+JL%{5
zEZS!_d~w3xH1D);RG6cUL3Pd_sqfLMb<<VjZq>#_z-b3)t(+L^Tu%Dbq^W<WW3bvi
zdL~p!Ih3m;Pk2_r-<-8?1Gk|Nb47hopEf{}9!}=3bXuskJB~E{s><Q<MRT8z#(k{x
zS5p<<8>kHO4UU8|4;sKUxC!*pCPGYnpTW6=x@fw2X6l~?`=OFes_KMh<?x8J98i%4
z1-4oHbuHCR<uEoC76oXnWn&!~R=hr%6;nR6^PSgMw&Nc|!pYgu%n17P=->lJ<iAFT
zi>-;XrJe2V@T1^jdt?U%*b{CCEe9}IC2enqiyp=8l<_wQaEFAQi>;{>a6c<>o%MDq
zVFhLnR$gG%VHE(bOZ}QXa0>_5kEsX<f6R8kwDM!t0>U3N5)l5Fh=B0cuZHUFEb?=u
zZy)~KN^}0H(hoUqs{>3fzZ^g%9<tx2@bj_r044u11g<JlaQv~P+v@!rCH+&rzmzJ#
z$IA`0H|rn5zbW-cWBePX{vp5L^8eSn{?pYzzw3{t|DP}Q_J{s2EA^k+|1a<Qv%8kx
z`sI&q|MxxUpYr|Xcm274{iURzZT)@~>Ax1FW?^J%Y5NS2cMt7s>>SnXjZDmbmu_so
zUjFswhjw<3w}Xp|mz{!(n}?Ny>z61mCoAWzECtpRfXLIM03@N=kN<$Q-(CzDX1Bu}
zSXBI>bN`U-zcu}BF>Jps8Q9cZjGb@Ke~5iHElblM0+O9g#?11W1+bdoX8-#tOISKP
zDVsSyw1e8)+1{>u_}PAyxQ`yH|0-`~0h#Y;Z+9z!f7J6g0W84J&-1ec{{HFvR}i+G
zl&lm83<iUq0)Ii@7eKm_?v~~tkb(k;5wKT70U^SJ0c!%lat%1-;NgFSw{egP_Al|<
z6P3Fl1RwySnC&V!zsGOy1A&9GK%mN~@3WwXAQU7dWF$lsWMt$!cTiB#aWT-*(9j8S
z?qT7Q5|NXU5|NNlP_xoeP%=}IkkIonFtY=zGH!A@0TF&qVOB0~&f84DckbLlM?)vT
zz#!m!K=R<$uJGUc^Z#7d{NHAU_-F0$|J3=vwmpASYyTS|!ar$<{+rXm7XJwgdN~G*
zTdy>sp#Og(KLY^%6)$>Pm%8#n{9aFYRk?LR)jm?C1G6<ZEqgm%_zu~(V{rd_s7OCv
z5Unf8f|RvlcYA8!_6H8Mf+p0HGAzJFM(qcXPGhV9&(ucaV<yxTDuX3}R*Ukg?J_XE
zTHgPhunn)jijxB!MfBvv=VM^=a8+`*VsbhBx4TS)-jIV{I06FOnf~En<lsP);d|$*
z*wP4EBX`e*6l>E4{HJ*@p~ZrG0X1RgZzo?x-5e8egTL0Kl+Wt!lk7i6$|O!eRT}iX
zVG2(qe2|cnKuD@csQonHf0Gs}HYpB*h*y{IuBSS_q@&6~@@RkP%YONrsWx^9m1Umg
zm-?>zhgn#n>TXws-Rq1RZ<}nOu;lNcHVn^WL7rw5kUtOnO)3nd7(N=AY#h8ermL^F
z^wDojY+heooG=nD)+f-5tS(Pl*eFGuYNo+?Y?M%*TK+YFTDgaj-r3QruG}Pc2FaI2
zkC8En0qkl}%D)ZL*0p$4fsFzJOJX-%#{%pRn8X0^Wr`H3`Zx8N<9-oZlD8IGbmN-l
zl?zuin0-yA!Vxv#52F+LRY_ijB!xpf>ftVct^lxr_}9<-PrbH8lgMShz&UZ!UU;-l
zQYH2H3Ffsx;Qhf#Sr73=JTdx9z{xA%3pidymfN2{CVfa!CDz*H@&3-om6>k)3OjOF
zv$ewtbJ(bjGk{B$tPT=Jhr3UA#N9oXs29av*qepLG3kwY$_sUQ=CoFfUd6dkxuYNG
zHD6#V+SP|_YaRG@zq4#^a1??9AT`N+Bt1{MU)D_4Ny^J~&0kM?x)Z(6|IGaO9w}ZW
zgv>}jb@N*C>$LOdDSYNJJ(mZqhu=XFj>#v#YtsLt38cc(Q)95qX?ga6jnC324Wd)5
zphiYwu;?7pMS7H`*aC!D6lXtN*X*@~#uN6}mEE*Wy^qB2-UY$WWQAapA)~3(ysU-?
z0CcK|ael+7iIWuC83~H>j+n@vqrQj|7I3hEHw;*K{c{4}rg8ix975MCLK6XquuoVK
z>%x0O43Ool%qYp-(Ad>+M)+`lkNo%47T1e4cOs(Uhli19CNrWa@l^Z;@OrGjA=QnS
z6PZD%_quhD?|jp^5DfJVLVN9K0pBL9^L3+!q7LPbUoe$0z`5W0>cB9yF*1T0gV2qN
zl&Q^yn2`Hwnq?W8(OvY7FXPBhwv&HRy6OtDMv20TBrUOsU(J07eI|o#Ib3s()byCx
z8}6zi=_=kA%~D2AuxKTDH$^jRn`T04`aIy;dX(D^L1$x_E*(yZ*Kvq)_u6m6U@&3f
ze5XumLZutRq1sPW{2K7S8xA!IVaIh|!b2<P^<|RNm~Vh9e%z#vEvSrWIo$YFoxMmu
zZdjv3wLg}fKAN#JTp7`Nl!)ZC^bX56d&r=*>nar|6b~c42qT#AI19bsM{LO4Tpz^v
z8J5qU-18AStBZ(K_@cYHyglCn4ntD%O$gj+r&ADNh54K~Wh0B~vcBGS=R+uFrxL)~
z$yyTvPWUj%WC67=@3HS6_ngYp*9!rw!&NhHPtwFByKsY7xu%Eg;;0pINo>2x)5Q&w
z$~<r^>hOzmoWdi80#c8jNrbD1-~Es0l=6UfGNLs7X7o+X5tJkHe4jvT_xm3ltVYvj
z&!@|x7u2rSu9+rmj@_B3Uvj(*_X~vggWxywys@027O1X!)YHyR&T*Lmvs&a~l_6D7
zuq3<_N+euK>r3B{Q{u3p0uG;8B6!Ag?-6_qgs@JVy>(BnfAoQdTDO`m-}=1X(rEQ@
z5QD(06F^0}QogO>wSFoYEl`pLPb0VpW&}e>6sv}oi^EsyJJrRoY)<r?s2@+Bp>uMa
z$zEz{dE_D2>xUPX$y+qPg7X{o6-L$>uBc(Epgg5)xGERWyd)Z^$f`I-gE~;xG(GOR
z_7^NV!CRWA-%re52rH{&B>OzHyJ{FNa)SYK`e9=5^VZ^@;uaQ-9^)_>=+(Wr<BF_i
zF|(UY<CsTHYNSkEqU0ZF4nRxPtm4NNUV(K|Iq)D8N?#g)RtgWbe$Ht=&rupa4<e<A
z2-11=^oH@U|0T&u>A5U2spa-EhzoMZQ4^vOC9qg!XVPA>mQdm(Bfl@Gp;r(S^Um2J
zvIfk|IMC;IQl_08_+Fs2Oh6|au?xc@z*k_=mv*+^>7&e60z4_>+a!e7qiNxV@3T<Y
zu=~<LOD!IB9iLh0-$X8$%rd^lhcB#nqLcHqe@=^PsfC9JgYZxQv?F>9{>rVEj_$}w
zR%~xs^Y_~w(w?*X`N=K@j{M#&o1ug48)GM(Am};m;3eVzV(+b^;@Y-#-zpq}1cG~T
zcMVR02X`m9yGtk_SOp0#!GgQH1=rvjEVu@DU3ad%&pvyvy|dmq=iSrZyZ5&HM>S^6
znzM>lHRl+8^#1kVm*^>~Ourd3%DD8?m~&5BH__noW4@=Ymem9&xHTemsUhKAHG7aK
zhW1-ZdkG~E)QXnb6nds*1#6+Qh!?XEN>l`iH%d7CMKD<MLNB&PK&r;#hcLy}oLDwu
zpB(z=LzvimL1Hkzp_Hk~J#a=4PE`p~Fn)oTX&Fe3MuHf<PG$WEtP>zf85XFtMpkvU
z1G;S5#WI{hw-Pe^T4Jhwh&Uyi%<ZDYse@m^TfHEuXZEk7@^hZQctPts{fS-@!1{)b
z%7FlQ`l!ARPLqE3R=G`&xA<*_TXXvSFkOXQlES^7tkbziaap$S4>kY{*o%fU?>W~k
z^4*+qSJPG{y_~(n!LYCK?w2zb!*+u4U1A=5u|JWsAEI}GLD0#ym=%c|jgdzzG^cWm
z_96>IZla95wlWII1SO0Ogi~LP1iDid?t9tr7L_nLA4jnc^onz<D4)|eKbJuiJ4#rI
z4yGF2<xP03bdfTHJcOPG!cHRwh!fxtgv6!#Q5l`r$p@s1rCTc8Gmpk$x=N=|XCWUc
z_muky!vPq#?pxE<i!2mXaUCV=ch9wMOTe$s<zqz>Auk)q+E-o$Xc-9A+NpNOEo39c
z5pCe_P);_}O<ApfX^oI07*iG{W2<E7mF`4Dh;1PwxpAv~6uU&i<2mOYMLglXA>8;=
z&L~hGeU+w!Xm_$suTVQ$6werFv$Q4wU6y40CJ1jlTOY&wGBYmqqSx3MFlGns@xPO2
z-^hr6ETBv^cwY-9qb$Jw67^tmTJ*^x%~X}Dp9aFvh`xEJruz1+XD?G6-1}CP5E<A$
zZJ$(7o&6%kFk$q)3{|VH<*kqc!-s1DV|GdB&G%|nxLvg8PO;u`qJ&dTNbXVA_}h}?
zNBwm&lx8|b#q`Gg5##V(vN;?^LE1W{<##?MQfVjfR$amhVtv-k;#@xNJ87m$h@*t5
zA7)tMQNfI;@3~d#1SLNUig^l|4&J2>|8hEqt5CgBH5zvC^PYG-t12NA8+^)4@TyO@
zoSQpYETr{;IPI;p9f&pP3aAIdsfBxjP(VL(V@Ny2_{h}}qOXm!Q<fMaPE~R?8RAAj
zQd<G!G^wi&!ZYUT_l@~p1cKLDD4n8%x#SQbG_5m!+R4BiN+EcVD*MCt!et2deK?5L
zs??@s7A{~~388xVO(Pt%GKr026;qEXsU`)TQ?*-Ykdhz^eK1A4Z<4-UX>i17LB^HL
z#R6w?;Y%XC$t;Ak{CVh-go$1s+*;UyzRBV-6{glgWTUnw6jYkn@=y&=s?udTusKtx
znE1J-qtISU7~dlcZi*md$Ho<=!jJ|>x^zG*uTP_1VXOwxCDsx)>C~=r#+R47k&H`L
zkIcz{ToTRas0P(Sk@2fTGExQBZ0+_GH$U@Y!HVIREY!PS)a$^rZQX0j9~bu$*QU=}
zz6#s)tm<E5R+NAZr9Fid=Vv)`P@v!kI@tG)T7XcOyK~4%Q?ODCR#~0^3y^RiVHj9a
z(xfojS$h(I#z95rPTJn3aXp$A%VXT7!Wyc_>J^`)qu2pmOH^|p1T*rL)tYz>Jqcs#
z^usQTWOrvavUx_Aipza9I1BArq?v|O?QCC)z#swZN@H$=SH+8<*_IZ|H%^5BFT_cx
zXp>mgSaAB`g(&KquGBIn3$$ZRF$2i72dHQr!Y_hng*p61j)+C>%ZTrTBr?uLQh2P#
z<mchOb@n^WQB9|02bzdWO$x}%KU*A@c$<|NSSyLPY4@s?kFB+5l3ca-sgEV57y{gV
z^prd)^z>)Nk*nzNwi1|_Bpa#H?AW3Vk3X!vEa+R>K$1iruz7UX1qkTxjlcyUbULtR
zFiDNl5YnNBBWO%d?QPa3A*g#=i7{l9Q$5>U%aFDKN4VX$aVB6-UFm4@OzNAI4#*A4
z6xLtzVzBIu5(Iviky8?JFpjp(mW5{=7Kv(8o#eGQNR@v~Ob6OJquUs=s<yELA+w?9
z=_Ct+7WfnYFf$MH?JLbKsYQL_7C;*WdcH6+9&}B(GurqQq_N(03Y}}IA7o;=4&+bC
z4`Hl`i;dNJk^BOvA_20#@qTnY)ZtIzb-&YW_4L!w<=Zm3zO6{7bdF_2@8>?wKxxPG
zmTx@5g}iFoP^T1PfEOov@2&nZ&)h%&rM%^X8kj?-Da6XW5k?eVJk)Yil}dk}3(OQ7
z5J6f;GTli@yO7erJfhxo+o=acc1?s*pFRQO%eObVY-J>CH1jifiLo+`QFJf@@<J@w
z^`J63CJIV8X9g{pzGQ>sf*c$`=WI6h+8T#_Fh_3YO*I!~cB<UXhtuPJa!p<{&V^+H
z&~cC`DWnu#-o*M`vUnvsg|xJsC#sE+Ry!e?*o_O+?JYbY>BVim+>_YR@J>s)m#WwY
z#L&NxN@U3TCF_~Soa~v15So7Ch1bhhshdWrZ_?*7>c%_XgcY>&XCc^dd=68}cP=r|
zTyHY)Q>J>~u4OpXrUSQRX1?XENwndIj#=PuDgJ}YE`+oXB6tNAnD`gE4@#->OsJAG
z<L(%EiF+*6&O*d5-n`wpB1;(mM=x(kw!!H`t`edf5pHAWp+bh~or@B3e|pDeYDqC~
zlKoE%+i{pGPU-mvhAtRvi^;-yVY?jL&D3#|vlvm#8WSAU0w>}1jQ$wq1{w(4(Zyz>
z{Kx~h#xt{zBhfd81Z7R}Atq6bm{eA7G~wQK+Yn}n0*ZlZR{NR4T(GiaqYAyStrC3U
z=Q!y(A~eZRRJwSW+;KQe1KhNL15K*<Dx4U5#04_ay5eGS(};0c7Dz^ygz+oF+ZKIA
zR^1ju88knz8)Yu*AZRY&CI-&?aUQJ^kCqFZ@Q-!&f7~{hIti7H<nm%QRa}vIiK3Y2
zIaB-IZCW`aV$EOY?qfHhDGwplb4i$BfI`7)PbA>?N}b?CyEbTpr1)EgDOL(*gAbt#
zTOyV_L^=ya$-&oty*#V~mV}W}u3gef!yMfG3Et)i?b;HC$JWs*qPvX=a$QK!1b`fM
za-X?(Iy+);4F=`~>e6s9Tx_}o%oa&KKmYEX3SuTdTgS&A(`n6t#UB6Qx~djbAPaJS
zks7%1Qjwr&>exw1F?JIO;IYd#9FpTQyfxxY*iF6^^Te1-Ib-Nyc(+2Ajs%quSi`uE
z8}>Xj*1+f=%z=A8IIA(12H(^e&5xZatp1F~`<nZFEWCsCWork>0cbg)_|+1&jdLxr
zbX&95m>nO^WubMnblz~uYEI6{?xhrIdVe0ks?IZDIzr?o8$jS-92Ml<(3M+si;|1O
z8Rd^0gS#i35vC2~f5?wxQ8W=HF^Pc&YP+x3_}68-!ts?3KiWOtaqORkyi!Zhi_1t*
zkx?BXC+w-4c@u{8zWqYzEc`^|6{w1pm;Z84MVEX@&ICt81}KtK!p6PvKp8kHM<NY@
zaAVFG!j$G9=z;axqRL2?yL0W46&ksxbt{gPu7Lnv>-sr{>`?3EIQ#d0{xTy+4yz<M
z{AV5jYYE%4j-udbwr5t3<5)O8vA%?P^jv4lL~Rl9kVC~a(ruSy*Gig5pp9azOav)c
zb#Q+y)XnMXZp=b0do^ND9Q#V`x~V1xCNq8}5-o5UU;4fqD(^hgKzAe$vY?)LW2*W|
zk^AF@*ZZ-oV%SW6$WvWK5p`Q|oPa{%(B~+dZ<l4>2b##lxn9aID#4-zT>rFG!3Lh=
z{QQc{LTSe!E%gJ7crH@&%LDy%Yov+~=d37+Kl)doIjS~G6ZO>;i_+3UGwB9);M)v&
z=?}oI0dGSW!Nx$gR<t6Y!zJrP(5sd(-;)m^q3**sPA6V}9kWnFD#<io$W=1Np>1q(
zPM?M5HIHqgmB=14eMJ4!5#CaUm#Ho#ucdCfBLZz2Tux*}lWKk}->4z2b+4{;U&afx
z*E;iy*(+Kjo2xLyGr8|@8;?5Cn>`&PKyYWrbJ&GOtvBOtt!mx#G-3DDw}=5u!pIDZ
zxNfCTX9e7>8P@(BdI5P;h2tFPqe6y<AOp~hv>M0ml^(4klZCYVCn=+;lM5M^HuGxK
zie%)9!i(Mh@035;dwtv1+Cuf~(WQw1E^=&7Qe{I&{}3IF6&4$t_nn#;SS8rAS1S?D
zWpD>aON`3&(6*utWY(T(pCoL)#I0LPiRf<a^#z>+k!wp`8nBBLBR|+vr`o`A7BA+|
zLK#anBNzmdYX<vfP?}HY4XM3T70Z|59;*m@8g<yI`>@!8FkZ#%R+{eBp#vt1fIRBv
zT_^TJ4$i*hP^_cRcI_8say}!my%7*a1jd232QPzt>j(JH?TohlwcfL~?t#yW%qW@q
zAY|7s;T{?f6h+U<&yWngeS9ab1`9u~Ich!9!PNbfcY<Z`&2pn?ebH-aa*8&T(bpc#
zUesWI->MmSvkP9g8XytPLbv|a4Gio9VIh&P&V(J?n|ylp?LPEv+Ke|HtTijPwKC7!
zfY2|6_S^>t2@Ip4QP0w5I#?YM%zfoISp0zjKP-zx));2$5AQ_*(-U~(2~i?VVTPIV
zGPD{!yNJg_2)#&H>8=~m03~~`8^_E2;!Tt?fNUbtJ*=5XUrtaH2fa9O7KZ@jcGi%)
zK<3)3{$ob_hl5FDN2d{xwmD3Vo4lAEbDz)5&nUnLfbQ%Kh!_RbkZ1jjjR}Lh@hdNP
zuVvW&z1Mb&oQk_07f9GWCW?{nI~Vjme7he1A(t@Eaa&jdq~1`cOY|Y7>QJp!v#1yK
z8CwTsYK@=Q)`#UEc}^Kz?a~*^W5X|kvBABb(dwypy9h1~mI8i^DCv~7QTkCb`QyJN
z{^=`18uHt&f^EIz#@1~TOK>&ka9`Zc9@kX`9RUFMXErzKuW^Z?$p$@zBdwYrq&6bO
z`JiwrPIFwltsaG?5+pd`PfY6&q3^7*(&woWON=o2tv{qs^IETVtNEhDrrC3pPnUC@
z8WLFcu%i=zLH{@}gKj2D;+>T$?%WQ8bK`a+fuJ8{OZ3Zn%+BRe8;6cpMXecon+_{j
ze1L9V{4;zw4l3i8X%sS-42z`Y0AniXqsCWHJM5ytC2(=QW)YJBr}DltZTvt#kd)m?
z6lQ6tp4OA4^7O!LqJy0g?3Hhw)NPXRFkI*E5&2lUWps7@vcVs`3au4XC3ae^UX9iP
z{oH7w3Z~5y$~{LOcG8ot2rv8LWflKiAT`fp<D+MeakV|*l?I-;nLVR>;B@{HBI{&8
zsGwlupmWMSTbZHi8)LyYrqlh)H5CkSS)%<wLW&M&j_ANa6r7vVC^Z<ORT2jrsc%(f
zQjBKoa@`jK#TxHW?lQZsWn8|9JEJ3T0y$6;Q@~%oIwn-UKiXQ6?X0n&7{VFrR|vyT
zv5;RZtwEQvm{mpr)?2T36NQftnpQ$2@hcU)HdEUI?=t90_(>kBaCD#-R2DC|M=~#O
zkZO>7ndtkqK3|Ze2oO~UY+CSbHyY$~P^CAg>9Os|$7u4}8=v>aIy!0JxprCN8m*;7
zZ;4|2zApF_D^Ds0%ZC$6a?z<IMTubOeM}83v1t*ZTW&&3y5?F_pgSdm(T{~fONb+S
zY#ZF!s$R6P+#lCAhn{;L>tpo?rGmaC@;WrL2T9a@h@{bjPMLPx*AWgj2pU5e_SwAL
zBf2XO4Fx=hoJ7KD$#?Jp15G^jMz^Yz_==(2w7GAo3H=LojN$!KpE76vgM91XbFtvE
zwMYHhNKtA38Gi6nM&02nxBa)!#SrNcPk1*{{JHWt<%ZfsV`CA9orbI|40FxljoF-O
z^7D7(ZoRLcRlTvlw3noLc_50-bpwao7RsGF6+ehjC9#Hw@Z1X_PH7qy>6;=*31M<M
zh=6ICA>s1jZP=GSKo%VQBdQ!TKZLFmZk1h#5!V6R`h{$i9$F|lzzr(8sdlYT9q79y
zlC=5Os$Ej|coLxHrHsBj^19?E^;|;>kdeAScd32c@A=bf>G$jD-yd}k0ww8WD%zig
zRUL0ip>(j{nOh}nypTQ1)ggx&3EqpEG886_j-0cteC#C1*uQ~<4R3cJ+2`s&?oTwQ
z#sf9Fi^BTV!J!U1Yrn5uJ(6$wuKM!<;U>(HgR0Emzsz5z+BC<%qFrj4PT4IWKNEka
zZe}txl(UU^AtHOb-+T<diR99kSZn-L`ogGa3}vHW8<{Kt<i}-kjxJ3W%-51_3Lf+R
z@?}}9P>X7)<McoW?Y>_f77B8Gw;&j!?qK&Jyru~!A9(>7D9apkv3$l61Rbf^k&>Q8
zwO{lL9vTF-X{W+$@>*w|aN?o#0POin$y(!%G}sZ78&}(g<;?gv=q{Cp4Kk%IDZ0G5
zwyp$P=^Ix{E^u*8ADQJ~;Sr|&JG$Xd@?xCDfyRRC1q~MSUz$@vaSGL1&sWvYU_e$T
z_z_M})6EKdI+&8B>cNiH=?J)w83`m^$V$Y{jKW7x0?(v;!eJN!WoYw2PKGbsqAPdD
z;9K*|Qa)H9Hu!HDQ0wDG_`d!Pgp5!*DV0zky%j<FRB&4E{&$YkFVY2ns0zC}bEz<e
zISx6a0g~IR3dH>at;t!*PPbb<pE;!sOAzz~4bO0ziztXWVv*PHlKgr1u1$W@dPoUH
z=7o)$tr4k$&@Qa=Zy>FPnpsn<w^=(e8405Y?aWxmf#bI+2eb}dO~~7^>mI2Z%#^X;
z6kU|ZVd7!|6T1KC|N5VNT}WiDATo7wk8Ak5Rt~Xb|E*@Lm{TJo`K@$C)Ox~d3Uws3
z`T3BPiJ_=3$LR&(l1=^(=^}3gZ1%q2Km|a<Vy?h<&V?_H1jh6eOhY%Y0qXV^W^riK
z8S&ufZ($1ZG~EzL@zQ2PI3OEk>W#|LZ`}YMm)y0E{%m&$Ci~c$x-GNlWSN*?m}bRf
z{;V#c*)V#BuEkk)2ksQ}F!B3C{{#?+fYOJ!F>Y^=egNjZTBUe2K1~@U=8YpA496mY
zl!}0|AmrWYCm(eS_j8WcIN4Z8F5od4zLdUcgz8a2chgkp#WSnnq=z+Swy7@EV&0;N
zcT?Qo-R#p^jnMcBlbFHF0m$=b*4e3SXnl=6+NwSrcZZ#X(EW2IDZVK;8-mAW4)}cO
zO{B`wfn6l6=(6BP2>ut_9j`3<>>n4Kzi+E7ER9&Wko2}j5>Hf`y77@ip-|BEG00ax
zR5_MJK+l2Uv|S?2i1N;2d@ffXlhMH-<-m{xMoRVq1KwA;{p9?kowSUfk$>=KmP~Qr
zC-Hkh{2K_cE)F+Y;SEGO)?10*5JA6*nsQ6bgX74&HE~?$B?~yX(Ue82%PaUyeOVO2
zvMubqis22~eo&<-k8y=IE@NU_PSoD{vJf#=`6qZ~Mz>Q+P<Mad!;<3I!JylEAK3gg
z<kf}FXaY7ejYXJUB83;?w098T`fIh7?=#KIxdqyTW~@PDgTR1Wsh&spgQ8isc5B?y
z8`CHJnNH*P@!%r2BHek($>q0Itj7H?tv`AVv4U#^3H6G&a!tOGx%v0ZJzFOuu-3U{
zAEp_0bgrtnEO&;`G`BRiHF_PHdzrZ;TJY0+`Nfhz_J8u0`@^vEUwE%S6OVwfUSR>^
z)ctS%gh1a>K`+y%M$cT+cLNNLR`E*k;T1<mx~|up(ti4bg#;<>=Pxd~fZpzZzC`}*
z`TvvG&oAele<`y52lLdQ9bfy4b?EPF(f`cjPkR9VqK*GwMo`Dj!O8!B6?K1+-u+)i
z-T!Hdy8r#S{{PUg`Ck}>|KASY|NZB!|L%kW<@@{k;~&=|P#syIt{erlp+?cI13@le
zyZRSlR1WlSjM%@Q&iA)(;`f7cp#RXi`uB7H`a%DG<loNt=f8O<ilXu$YM|fGqx1TS
z!kmpmiZBh=UU5se!T(u>abyIq<>=S0!H6d=aEG!!dckU&jZFQ$#7N`WquO>PY!}|$
ziAO}3@$0}*beSt`@R+iGNX{x_34JVA@{n#h9zJ(^k*CVJyo3pr^NR;caww=HJCjP~
z&7wYmVq^ZFPHMlN?C9VwaLk2x&a`~z4>v7;1GU0~)U(U|%?u*!8=NvyaM;G(5cr-G
zf=06IRlDv=x=eL_0~Q5mXeKLS9q7wUvE2ITVA65k3lS;BW7UsA92MxNEWLwU-<u}>
z#D#Dc7PQ(YFGVz6Vh{bHzz=lh|K98NEBgcWMG}T<Wn*T$mAB5)KuRi4!p^^owXjI(
zw0Lu)%oX=5+L5{X4`=Mkv860@T<AWx2+V#xM|D@GrSfBE(5?CiMu)2POPzH|(QZDm
zsnaZU7U;g<>(${lLh%WNMAia|DuuZBSugC!5drjyB-A%XP(gXR1U+l&AAa$F{Pq87
zyMQ6l2s9`J&xLYv4Dw{5UlCXaJaxDE7N=$*6epqG=N{M|sqs|6y1)$sXT{L-nRIns
z*fUpz_XvoxKbg3apqQj%pZpdkr%O%l(V9L7BEW;3R`5=~>Db+O+GoBb^KKT1L@^V9
z2J*g7@R68;4sRn++o<YHzpM<F<mJr+xd_u$h2CRsSWd^LIT?7Z%2y3L+3|HBQQOph
zZEc=5i68xJvLOD*7(YQCg}m*-*G*~((CJRjmSO}=why44GY?!f*h`sw2l@s<g|(Bm
zSP0Pt+yzBePp$z;fn5;6E(a7X8Nz-lMn=*e*U}2i2ez80v3m&!`!~d85;X6T@(-sW
z7Wdx|l=q{sz(~=wU-+^x;#qAArj9aJ)=-+SiZ6lwcT7Qn?H>EA1{%{H$D_7hFByD3
zp+EAc{#BnC52FCsF_$d7kMu8Qzh|zErCUp59TG0cv)NrC@`_(tVPul!c+sNy#WIOq
zTc<47=|f~qEf@g(H+y_2i-}(;84$aT$3ooG51VjpvU{TeNJrEBwu5CJPBm<EWb92}
z7`aT=IhfT!!CTLfND5)0t>Y?4+Hcs)J&81NgabegDzO&PI?bs;Y|i@x;0<Wr`$=RR
zO*$$eK)9A;W5fM3xWn{W%$RoxQC}=lB_iL5eqDG@K-5Z5Y5{J>b%XQ-G4VN&fPNaZ
z<ytR-IA!pa`q-dt4!?vm4M?59J^vgW1V=~4-mmjX8!t*i1)IeZCLu8*RzLkGA<vGT
z{>%k;$aj58)Sw$HrX<rC3{F%G@0r;O!4lClQub6(LB7@Ot6->v+dBt}g5`5cwCK@B
z#pj*~ye*0HBDR8?FZopb+8Aplxe4Gn;0QX_tL^Oz5-(A6kPIM#-I^yy1=-0@-)+@R
z2{csj-<!0VJ(ut!u(R2DnJV4=Ms*9d38(WWu5+)5fVwh(q7g1`UI^snhU23;>EIeZ
zOEiya+wFYEc2)dWcMv7Iynl7&z;W(=+d92{R(iOiaa`*tTu|pmigDs`RdA=j-w-Rj
z`MTsc&}GTog(7liUN7-@N7d2&Ku&-tsY%`gkB6E~0d>c4)-Fb#w?}X*FUEINF--CF
zFKR|8E3MU0@aLEa(mOclA(Og#vYTrp*9<^vPoZrsfUu8w<94P~_%f$JJ3%_p@}xh?
zrZC;4she$=GxZCTfYw(**DQ41o(0A4rgC|3bfcojz4EKWNTV4MlAh~Xr|2cI<c&;}
z3?0O7xhHP-QZO%7Yq-Eg>mukMZ|1B`GHfUm(ZmShJp%^%WiO0Ol2M%*ai6eIQ=V{#
z#u3poV5T&zNNp@lt<(<B)rkE#^$^1a;h<(o@?jwc?opw81}T0%g@!+<>#7rwJFUm{
z*}4lb>6RY7vM^Fu%2=54&W_%BW>&iOS)*JT;@`L$NK?@#CmVg@f4$E*bQwuuM{zLZ
zu1#rg664JVcw>M@v;S!%`lsixhTYAU({5Z+_fRqJ3xxE1F_K#4mUYprpp1lGUxsna
z+WKj#FwT~TCX!(9Sgnuo8jDqc05#;ZmlL*?9hEL*mAB4DFYE~CbyPaMg|4WH8Ot)f
zUQ7@j<1|oivR`~&4g#s+C0<=)1vzr$q)nivyDWT}{t#_1%cAf~2>~Pg=pA$<c199Z
zuKRjYc;dMg)z&R{neV7G3%P5An~;RkI91}dwjkuKZe;;*`+0ZE^6**?h(%=BRH|z(
z#>8kR@^mU84hq?*AS1kTnuOpQH!kE9<h@(bWgO6|fSuOZXnrZg`Gaf1VaIF<gLMJI
z=O^38m}}}r<&J+~DB`*CnoziUgfIbWTZve8Y^WmVSD>4u@zDjNrPuRo?`KYrf@c%x
zKj_c+ep<rz{aKCc_hE^#=>#vt_-p39<SDNhK^Oh5^s!!Wa9Yxw463yZZ7ibLfx*mL
z;YPz$POTCyTben9>6_~@{TFFK!5Kd#g}ToCm2NL;CdZFhqpAi*$R_=>l18Is6wF48
z68x|ywNFEee)ap^vfElfOJ4Qyt#nGW(JY5PW4t9`c9C|fVee*BwDGuP6nta}2qEa#
z>)ee&h+BwlO4D|mR#+B(-`WuK6k!dQ^_&YbeNiU;Xm<Mur9IG>#u5Blmbpx<0}INu
z%@QYY-QKzklswCm$3ie)%;=a}=^1D-Z6xxe1J^J721;?;LXvUhFOJ)fq-^!2r_Iw2
z?2YW(JrR#Eby8=s&5k`5%(?ETfZ)7KU8^QSBqpq$<#plcW#@w}w??*Op;%C>N2CaU
zn(rkcXjdNN0`sMk(5G_AZ{3VYFz1X>3;}78pfr_Q>K?I5y|mf1#E^9uAkrD7gIRht
z*=ZZ|0UgTbS$bb4E0AD<4KK8xpM*P{2=0L@QkJ=yl^OgPJkx|TdZ-0&d)J5Kg>en4
zy<PEZ-w6&M_0wprn$D?iP;#`{-JWE&Ta6nA;T{@bu19iTjCik8<WllnTq8i(3xCg7
zU@QG`MlY*)wp&(i<G7d>h8>df8>qPs%3tAp;t`lJ+{w1Wd$-u|LN4He@~GlmBogG&
ztPyQvVnbXd$wP<2x682Cir^8383kBeV5xFfeAln)0~N7cxY)wQ@SBeTIo!gk+{&Mh
z1YyM*v($S0h(QtREqp1InsMc?2s0$fiP1SlF^f&eFNEk~z~t^Qf%hTss?AL)#x&f6
zh}htOb*<7Bic*BrG(T$*bPncbs+i)`y29_TsdHl$L}QYot<66cFn#K8f0u?X0diNO
zU^1~9c&E1g@sI(hk$l2d{_DV~E}476<}IKJP^z1p{h<`AFrvv~BEy`PhUz43YNp{Z
zR5FZRLno@Ra+8H_qMv~?mF^z+$T-X`gy1JWLmpwK-$2=LLMD54dm&vetSM@R5afW<
z!XOFUilSun20HR49?Z1|^;+{K7~X+xMC;`rY>(HbO~Qf!4p4_v%(db=U`_+ffB!nK
z|69nNombxwyTA4_+(RG@EJ`|e@<UHo1ic$9uA6!x;IqNuRj|2(U%`8_op?oP>Vv+m
zPOMQlOKYneyh!6Yh7F!s-gkNJ+&DTkU*C3a6cI33F0J2sN+Fx$`Sem(qnvU9>eUOf
zm2)1(2B=L0y(YU)9Q93K{e)erl^k1|32U4v7?=Y3CLQO?DSJ}IH?4WzB%;M4DvOe)
za8<Hf=7novF1ZyaYG@?FK9Z=5?cfBgr5hezu{6M5PgLymS}fZzTR2elX$Yp5h>~cO
ztbZh7*k%-8EcoP@UB`6vEphRFgL|u)CBda|-$bk3_czcOH}|f$Z-i7LHr$>lT5Fr<
z5tvC@sQBn}i3~%so}KCxcarb6K&(DnPM+zCVUy8`J!)tcHh4agf<Mm+A46|0(^sb&
zu&35pdkLyv%FHT{?e5_m!8pc&z#>!$8oH4d<qg{win=jq-H#)FQ&VeD;;x&nZ0x4Q
z5eb&~7unHY(G_a5l|-K#jSQ;!#4-UJr%OYG8yx{fG=EFIdg^AP(0lsD^Ah*SE1Phh
z;U-1cg>)Oot2<u}rzXn@an!UEz9IuwI{P<cE~3~KTd7*IRaB%5SDy>INvgR%J4I|x
zJIOf|%55dE|0H>|CYtkSH__}W0~xqBB+6HTvV7E7-53<cbwnp)cGFPCPiCW?ie<Lx
z7{Po$4)h;*77RlVhow!Hyladhg8M%tPT?2BH5P{z^BHsMPM&g&yNAU3%dD4gy6`v5
zg4H0epqmwfZmu;y!?NHmDIh%dg0upCoUY#s@w)Mp`XsMR4kD)TdNl1|__`B?N{r`v
zk~Fqd_7^QFgG7mYza8hxZE|F?T~ew0_*C%h(tmW^S9qoAMP2{7v>=5pLfYp#zk4mY
zbn%h6BoLtc+Uf(^#zf>|)mt;YqJ{1{`yYGRbFQ7BoeV}SA66ct$7EMvi6+_n;SS>Y
zw0<|Iz+0a24lSGtpLtIPzId^pvhTgEYrY((uC~`Ci<S$}?{LT{KgBg(L>wX)G#u+Q
z_ZqFPwQQi*shy;OH;LJw5`us7;i5eI?AC6Y$tV$!LDJ;q^cm1oCV`y0S_{@^pmGil
zZg4jDadjzvXx_xv4XPdPn}nk?wM1>NMO$RDB(b_WWypG?fc*dMB-FR6R;AS*$&Bi`
z59Zt7E!ScGQ0$?pix_spHnMODxSN}PrBLT!w>Iy(5xXWH5S4K5A7Y#P^%N})({F=Z
z@LqF@&>+Rc#x~HF>+1^OZZC$@I-!?4G;grp4432xcizvRkA&b+d221b19=Jfv4CW0
z5F|P}3It%4YGYPypWG0Wx5BQn;9A<DGuo$VZoL-TH_>mWS$u=zY<>gP$+<?1w@a;5
zaL<)3^j*lNMC|bEfG$WBMH|~$23_6VpUF=GTs-os+r_E_J<^o*Hd&$pbu$i0g~?Lu
z(VF!~G4t5($dHhQ9yb7Nm!w%;<=B8n-wHr;w#$0mNLFT}k5bRjV7_1Kn;F=RB7Tzt
z+Fmpqk+LKr9Yudy!VLHIq>;k&zh=RAmY0lk7!>L9!mgjo)(sJFBq;=OB-1>ec4dU*
zdd>a(xz9n@UWdmH^D0QaY_;+AeFsWZAVhF%z14*`WQ&@QrPQy|rW?&q=oDt`FoUGU
zuH-4NvT?81x6Av_J>)gN_=-%}<wY1Oh<OobV~Dap+Kk!E3L-bpDb`1MqC{!2AIFK3
z2G|k9Fo&_LyAeIp^>J_1=z&<Q6hQf@Z9gy|t<L%_)bcWzX>?xlV}o_&T4L7_;ZRrO
z^wku%ihQqn&G#yMxjM4fBy9BFpyuI0=)f^#9Lq5wGW<9yt6UnYxVwc4j2cKd;t}U+
zbt5JDdcg+TY;>@JlWw|U+_sA@ZS{wjBZNn}geMC{J(lx8MCuXBlzDIG5{%Ll>&Hl`
z*5Ec{N=1@4xN{&%nx0)5+xLUa!;+w^iZ2ohJ48AYx;EdiTi=b}v0l$Y$u7LP<A@FB
z?in9L8Mq|}>ih2Jud&Zi5eD}erkSY2llX+#wNJix<D7G{uL2fJ>5U(X#6_80pgE;h
z=DHRri<-p7<=up}DQ}50(3q#OQkr8M_e9CKZ|3CuSbI1^(5j87HH6ORE=OGI4#;qS
zLUnXTZ>ZjipN?aWo#AX={4$5Ho-S-itJDxj<}T{5Q>L=$;(|{y4{#qBMyRy4!rf{4
zaAx+Z!{7CM4C&`DcPAlrRcj0)4~CmbW;Ku7v_({1Yy)X&wYK!5G*TDqE+Px}Q#A^%
znkNz!8K+3}h_JlB;xCUyewT<Fr4GYGvI|>uQu}=2#(joE_(+DdE_3}eiHK^$<K__w
zx?|hHt0^@bI4v0t-JmGC8J5FTgjhCbDRE0!LOTLn!9^?+#_<drV#_k7fG|o#gu08z
zciHaRlB-fM<<z^D&L+>!y_MjXUyZmP^3a#F8FLtUL-lp_s++)AJWOrqjGw37R?n#`
zl6`ae0aq5|mKwUAu{6M{gv_g=-(I|Vpcq|FYPR_+x@Y~$9d{Ic%4huB8}9B}jU)mr
znd=f8iHXuOTJ1GsNr6<*g?eq&Es1`0DStVFbbj@8L4)&XK-BD2dGY9F4z1NtYEI!G
zKI=<~QO;D*Hf6}_+ZAnN&dEf>+HY&5oLf;@*Cn&f5z4v2&k1}8Yqkf-IR>)mvru^4
z8#oM^s_3QXZlvC7<T{gxZ)GFbSP)-w<0O_BB1_=oX!ZIy`R#P9=u~ArBTR44eygMl
zwBWeRHc!J}NI>1emEdB_YB^#j|6!w7v$?4@bpi65>?D@3n?JC1>nfmbd|D=WEK<#J
zpZqBpDO!Gf)FHAWWj*I-Ulxrj^%Gr?+7)@VS_sO+jz{@JM6+}fyjyuH6yF1Bxr+5W
z*dww@)|vB-K*l+{O2A7mUX)6MC4X|eAjykQ(&(V9F5C;6h48TYzBO*J+=zDSgMY_T
z<!z#-r69MNX|2t~a+tULEz2J{o3VmJLMnn09VJGTNQWnP()4Yf;#cl`dlY_t#L1dc
z=6T_tzR9}`E|^vvL?9*j1ZU%gjkpoBeT@@?PtjP_7lJN27Vs`}FeMzIQg7012>;B<
zRg#@2^)k)#pvXG-ozT_UORJ;ocm`<?*G=^i+1r-^J<(8$V0+`3y!o|Gp<S4($n5UC
zCib+TZg%3-mGkcu#$P>Md(}5<ShqCv-NnHKG!Sa21VP7UMC#x)i+R1>L^(``yg}p;
zD(1***F7o~Mvwv>%y#;FdrENyDetPn{Yo7O0c5@PYNrRwa*|0vhUvZXfky3Zx!dRH
zi}@5y5xOj-2dUB#nk1i3zBMlrM1p<Kyi@N)dauSi*Y2}A7?6lEyV@OvfHdkavxLf`
zxUM&E?t)w8Cvri+S00j?rycfEqXQ)i<#t%sOkM%ezquUEM<@x3W%H7#DB?GeCn4zI
zh?~*xl_Gt|;`d;cwpQrghUH1eG!NGIR-$b)AZYVRZL~9{i|xrFsRG_U1q<7$)^uk3
z`&SQ@es0N8Flw@EpMv$pQag;Dp$;e^un=C#b{Z=zS3(BCWj5Rt2~nuH$Q8~sb{d>Q
z9l^G5ZrL~A&xGi&GuAv=cG)H$S?Rg<8_3c{vyNbZ31~CfTY&HSXwG+A6KDzRp%`Fm
zH%VnT1OaOt=~6OSM_5?yWVomXHIm7K$fK__mDRb0s$P=&c2CykAlFv@WV1okO%#uy
zSoNS9hij(pUGC{j;mP77lij^!EVQ%hH0CvKTqKdyDvL2TV?m=j*(a>qFBz<UIp^RF
z+G(fg#?4u~59e&!P}&t@pKX2B9(T%;07odmNW6s-b707~)JM@2gD7DP50dy*iBu8#
z8oP+%am%RtBp-;OyR$q~fmNkMq=yvA+%jE%QBj56EdHBG5cQbyWUD(tZlFXg55tf%
z68GZ5&5%F~74$IhZ7Y@(0_0VT;d8c#UN_t=GS~I;{^H!ht_b&XeTPY(cNB*Ve)Ol-
z%C8wH4?$4S-bTuTSUj~4>Wth$y%&#U0r^FKe~~VOSGzR~f1i~|16RsBuMFu~v3<a{
zp>(2SLPq!nC6%&&k@$j(jQtv91G?@jw+^o+eOREa6%qT2uJn+x*WcVM>TXFMiT_ps
z4P<BaWcGN^SaL|42%G0xZB^BaOHTB;0;Z|#Mem826Ccm_$m$^h=)N!e`bEpMU_os-
zW)z247U~pyPaK@q52w9eExD4%SDa~gCfvIo>|zv%(i1WlI0+sO1!2-n49@`iDUqN1
zuCAHZPGjMsPE+G2ds<848KVAZh1MaSn=w%aI|kh3+9N*uKnS<(PAz9)tbAV6cHI%R
zmFCY5-}buU6|2eQk!fpxSC09g3;+tZCHByNCj*p)wn&%7@tEnUM=Dds{c-dYzS%QW
zeJ#?uP&W#F<7G`7lSC5hr_Wo9d$GBb@e(A8-k?3#`+2Q!%G<?!>&NBI%8qYfK}~_R
zygqGd_P9@q-n*<RiOfAP7_y+#Xy~@Se3^b68VaZB{d#Lu+d+Rg_Uz|n(PsqPt@s<X
zBtZA2Ed4)a^N9?w8+e7OY9de{*4xfzt?Z8AUP{A8q@F5<&XZC{DezLnavm5AQysfU
zfnk)#0VuDwtgLp36o46*aJRcmHj^MSCF2otQGH#kr}Khjvf-X~wzjNnZF;=5Dui(e
zXkFNAxNSh?-)Pgu3PHhe8hMQ$IHT~B{7_r7c|m6Yi~Ah^NgVQuCWHE8Acv4Of4(ZN
z6amiAaA7zEJXFO`uKuImzD@<77oU#{j8IUJ_fzT{IcrgIJ8u0wry?O)k{3l)UBwHY
z41?vqGo*onU164xt1EnyTFdWIL{=jpZ=(8v?wuIbTBy@~FPwWZVcCT{&oj2S4Mr<<
z0r~oJo)vDbf!|~dWkLIMJ88I^7_-F&jZ(&l2su^LYD-~ed}FXH6)OvV`4rF&EIQba
zQ$NjSeU;2p{N0cjAQ#*#V|HkjXib{1WoA}7E>`xVk}jh*WGYQ<(=3Ek%ncrZvGm5r
z+~x@fyH!`0D|ddtTV)>@J5l^mhFxGo8vC}$ca}7Rta&2e6)x#i4MN46E<3YO{!Cin
zCX6&8Qtzhn!IXT&+cyIhDS1ULKu@#FJ@ejLJ)9R6t$(tu`3UjzrUE|DN!zWx>)coN
zqKXa!j<5-PE~XGlK3Z8XEu7#syZt?bUoC`W?`o1uT91yA7S%}<kJ(MK*|YnN7>V^m
z)OVM(OxeCbWXj}$R#sKbmDSgSo0VCJvMWZa+UNRG#KAIfQFWO<Wu>+zVxvylar#0%
z9~j(@MX?SDt0kpJ)zB5KJw6<5Hr}d*NI82Jnon*{+ewtOFFj|-_zd`(aMNuiAVbKK
zeUuQtP~b3WJ+u{TRXUA`(bT;2!5YMT_9A*<y1u-I;?@*%pAM!|T)ooAwVYM2F;!HB
zKy(KmvCOSGCBZiLd3vcP+*~GiZRNK@U8=M*OPH(77gm)7JGYzfGMajIrY@U$O4r`M
zoh2FhHmQfbd{)@UaM@N{(^rJQplARnyY+RR-E3pMG)DoDt8Lmm)=7*JzT54PhN;L1
z6#{-^ewtR!Uh9%|qO2?vH<0n42|-l00fkkKou7-PVjR7acT~<<{;-GOB%YU6)pa3V
z7V_J3w$6mpr_;hSMVH&jzt+)6M1ovHr!VnD&PKTe00#tF)@M4pVpv^YNYLfbx0}d9
zlo=%)HHpG^_6)utfe}+?Aq0eSb!rW;(7w4c{HP7$?S}6OjfgPSb$j(XA%dI65WYg&
z+?KX)(6@|z8<Q<^0I0SeU=^{!oA3GWxr5bEzl_3L!*!uXdxN6vCZx!fkX#wmE+}e%
zYO6iY3&yDVvhez34@Z=%V=eE9UDoW+a4$N6_~qUwFewTK!9JzeR=SEiHup3-Ibyv@
zK;B}gF;Tn}Cki`ewz*9Bk=1Uv@jmAZHrN|q1E5JSD!JmkKf9Goea$w212|neNr1hG
ztY|@+B4sx}6soI{nZBI^iE@n~8VLDmc?pWAj(bde>(`br^xV0K*@iIgLARFiP3!<Z
zsNlu*-7KJ~LHVaqi;$2dd%>Up2S{Hcciw5%?Os>uF?rA!@uD6w8z37btyxYfO|Heu
zg=5<?W&rAPP4{$19E*9Mi0OP?8?BDuBt!_~=c=h^h1yiNZ7!O}*&h`u&k4@S`3wN-
z(2YaFvY&n0>69PVG&mzB9#h(1o}2FE02WE2qfxtF@8xwQBqXd%l>qw#<=9a^9d`0j
zMLxk95w!W{ABt^h{f&@PZ}t1oDD8)Vd5(fSsT|oSn}^0hU?{YHPdw$uaVC#z4hs$_
z^<(YYQJj7=K@2BUQed(8iJzua6a~T#G>*Q|YfEU2kKuf7OAOeVNPm*lp++gVkubZ-
z@RcNwluG@El-;eIIysIPmLwbxE<V_iG*(+A7on&8QpU(AP=WxJ3ztLqDQ_<3wqutt
zIyL**t!Sxki7GAM6L52JFCJ#X2p$wP&I%DAvQ3m&KGufS_958la=@6e?)_P{B5E2M
z=p8K!`ckS7a26Qqd(@fy_;Akci`=f3Rkn9(V>vNK+c?+p!l<em{P@wS(;dBb+YSbI
z|BMU`JK6sH2X%RY8-a?BD0_gxet{#rCgMuF^^IR)x<D_RFh^N8${ocI^?MV_zDD2%
zxg!WzCKKY+j?s|;oz26v**I^wp2j)~$1hr{4sXfuZH^<@hLwL-jpFY$`$pAC;?(Of
zXf4J^Oju~dcdMBPaz?W?a7RzRE|X`KwlZH@lEl!NfBGhAG;$rjx2O3~3B^GG^!Uyz
zT`NLg)ICTqM&v~6TN^C%loo@H4)8qKK0I&I9J3yu;+CNnjr}?TjHR8PA=A6#x0p3_
z8eQkJC7v!lQ#$*YT|Kp^?w=H2ALw6wUF%$bh7hjB1YKNrkR(BzY=!9Rf44DS@YLat
zeaqJ02J(qN`8X`9O6hp0SF%fLHY~^V!5uXB&RAAQ6yqAf<!x&nFHt4CC^6OSY(3T|
zef{Rtro1J=^P>><N{?P02gJwhJGpRsqRcg%NvmdTjVdOmfT(Q<|Dp9>;@}|2w~40R
zb(WTwu&gJvCH2mw_xWOP{!lT$I#ueehtemxLcLcfsG<0G<M7u1N$rS$f2eohqJf^M
zE(XPxRGAO9tZA^K0ps@lvq2_S<K~aYeT$)OKA}N?-=p=wCw_YV)h|G~Cg*I<-dlsG
zF<*|CB;H9MV?ev>0w=imVZ1q}69K|^NU^`ocyT7vcbtr#u*)7@+cAn~h4alaG4Yf>
z{*o5DyViIX0DF`Q3Q&F<<YIECYGL{1lO7W0RRr1${ASVBbOL529xC|YSU%(OU4uZM
zQj{eu%8^9IYBfX1=uZ@pHOi2#QM<#c1O<#T&O`Sa1#?IOW2XvZ{Hg8ml~|o9)5AK1
z{(zJt<7raB^9yEk6*G&oViwNuAmz~3rtvrLI2oD0W@KTY@PAP9;rPhUlwUM?EDneY
zCJ3P^XG4T&hSwRMNt`!Ot(pYHym{hoXxEz9;YZ}NXB;Wa!q5oq)6l55YquSoN9p-`
zTFZgTApO)?rN@j7o@R}@L|Q#Fsc%rfB&vBh$UBh=BfL6$lhWZbx^PZ7K5<Ezx38=Z
zl2#oc=>_@0)&Crtv|rDgx*odGU^Z|Urqe~b^5RJ7f2XDTATv{WV2y>%4+{WCaD83Y
zKW82m)Yrxhv+<*Dq^0hZwwjcEU-nx6Ii#o_BZhcUDwF56<z;F-x>@sLX^IYZf=h&`
z?QkrvQ2Nu+eK{XNd0VS;&R!MR&4V~LN<3)9)1r-j5TO3d*leUT+-X{*m@IZ3@#QF;
zUZv;hCPLjBW11<5=b)PtqJ%nEb;4~Wa(=?#_c&4#%HtYlfR6W9D-vKILP2OnlHwe*
zJuyr`hwUBV^}F&g=7bI9hJ|)F5}Or45|0AwC!*S0aJu9m=i{0viEw)PW4<t6fwIoH
zHM>w(F)>za;brOk#l2g!txfG6REw)Fd{FaoEeq{)<WC4Xhjme8eTMa8D|MN_r}MD-
z7LigEhb0Ug1(QV)4o>gDn^E>-H3upmueV29{ZTVogDeFICSB!zmN4xFj`qZG?^<2U
zbOpqSQI-vfXVGQ~5`i`8G08zzjTand0s;OG7r4aADVP8bJ#9u_Rl26FU3a#D)O%z5
zGB5Ut3MmNXc9U>2IJnCmk&>hkhOXKK7A{7mH2?v*e>N|WTRJ%=Xy4|APP4F>nyri|
zo-R}Gq>@nakU=X0B2NE;zFv5A^6gdGnxoDf<?;PE0$KE>iNo6BEC|L}B;9w`iz@X>
z1WK43`sO(SVv6Iv_&s`ltL?9wvwrpU>l)xr<H^q*=KD9bAXHfm^pa?~A##B)>Jqx;
zK)!xY)+7oqulQx2_DT)m4!V^m-(f`V^Q7*v;*CJQka%XHzYzbl^!1YrzVDnamBAZI
zA;y5l>5!NTIpWvBz95FtrVZq*8c7hVEonaFm4on0BL2RGp_V4y&*qqE_nK*oH|0nC
zWFzh|L{&m`(LKj-KlfroZ{$I)9&%Iy-^e#<xjPM(<(=ryi9aE4a2NTn`+SbKTzi~k
z>n7c<jHadeM+-viU*-n?7N(ge0%6&o*SP-p8e{1RYxw3kcUa8HY;_}gLDglsLOd2w
z!Sho^FM<DNeJaVe%w{9I@caxJ1>|JFxaxp^6h-u`N!X1K?i%MH)vmiWMPn&)9FRM#
z;NXP5o#C&<<I<Df{Z0%0h9_yXi}9WgCL@pB(=<4A$Ok$!S)ta`DT5MOyIpRL_3AZ+
zbmP16Id0pAB&T7tN=iY1AM)m1)b<G`Evi83O2`WbQ<#Sy_X-i*x&4!lQ4N(<aE8r<
zt*g}v-M83wm~)_1GBZ;*a(PDw&*9Zsz9`#(ds>{?jDW#y&@Ug!n#@n@;37v=O6^aT
z4!O)iBQRkmGKzSyP~|2>EE7)Q*K?^eKjol0g0!X}NzV;0En_+F44CLJ^2AVKhGW2K
z;5HQrrIv=%ETMaKlyBc@LT3ydQ7~uQ{cb|b^ZK%{+~ZylW@aKr0Jt>nb$Mfb8_n3Y
zrE1ne{41R3|C+WMJaZ$^tMNpZ@bsPd7?a<S4#uBC!u`xwdje&RnH!^z>o?H)LBC}d
zdZ^ARs^(qX@}d+)BLCwm?ClG!Yt5+@Hcoz9SJDKXKmNA=*=yjxa-6S9D@SQem8R+^
zDeEi4&<sJS@1o&A)es1l_Sgq{>g_Bbxi$f<+{6?mQ?<tXF1q`g5uLyE5S>2qVoV^2
z^k+q#XIp3wn(PW(;A*(CB<ByD-KQ6)cKLah1F^wzH!%Pk0zF5QVtZ8r|CV#G<jx=+
zz7=h&2P~vJ``N|<`NLof`T1<E>)v`ICBaKIpK(OIathr8p0Vf4oYEki<@uFXBd;I`
zhel(h<k|d9{RA?R`e~X+g+x_DbtV<tk|v2I%)SKNRSA{T+9rYEEJBt<8XEem*-hoo
zGL%yBcDv{{B0VEW(%;Nvm^dt9h5MFi>Q{QQ$kGj)>grzkC$k`L+(eqM94u%=n~pd+
zVV$NWuDAO1W@7tJG3O@?WeBeP2nNZWKI>1?xUaP#xFME)biYSA(=c~u>H(-U3aM5s
zG*)KDqy1DFm%k=l{AX13|DPOllOKx=+e=9q6;XhY0#x(~9eN_&yOwlY!v&2@cJLj(
zsU4mQ=t_!MXi*~0$YI{iVM>53d76sqHuN8x9fYFf5X$F7Z(2(BWU8-L1SY<Bo-Lks
zbUu`f0x^Pm`^{pZUU8V~mUuznIqoAHW8BPp@|$Q4m+c?;pMC?uQCdFSJ=F~Z8B`Y5
z^6~**UXE{4itNKC=Y{nD(8l)%8SVdX`+I?S391KJBS_NyWs@BUq>Lkg5CA_17^wfU
zQ)NBwZ=1@{-(PRc{l%ejP+8D#-+v7o{~dn)@2uy)0EhoOJrcM9cLLz?7tS&3f5sz$
zRgxb#<1b`nR!P7%f&F(RIN*;U$qrZ}@cf&#0yjD5U(n=$*}#8bsK5h!KJfVK=l{35
zBXIo%md^U$<Bq`2_D2ZxA6V*tYLCFq$M=`>{sxfuOsmsfv|p5b?uWPPKQF}r(eU{S
z3V_TvW!b6ZRs{<8_!(h;cRG+RFKyhlxWo{s$+gwiQ6`FEgi!ErI*;l#S|sX488B(i
zCC#`>DEvr|`KpKz!8WwI(=;2a)fr}-%49(s^dmFocF^czTQ9Dp<?hYP)o35FM9pYs
z>Zv6mE3ez1rYDPokX9I+9R1+#@u+8=wr^h2PT0tm6{*rEVKBodN%nm6GAz@m*-CqR
zJg_S?7qTS3GcRi`6N$l0Jd^qnw%8p9u_H};4f&&`a605_F4mKy=?x>8ZtYFB#_Pts
z40?{+*&71f5o_Mppa&CBDlFMb8mchP&Xy8xjRZee@3nIw^z$Tk?q>B(9Ybin*4moQ
zgYN2t*K^^tb0-6I3BQ~655r7O2S?t)1y8z9Vl*O|s0Q+Zw}Z1qjc$o&LVbq|7x`+v
z)puC7#m|R}r+XvfyYO~YJ8rp?iBOxtMJ2qfom9%Lr5fYSfp!L&zG!`;+m-C;LUnX-
zTp6Mv9M7y?7S0S|y)5E}B1uHn5?vv9RbpYp^h=VSxuGORy}#4YANfGZikEwgn!P$D
z&#Q|d&%YMvoUm=tDg(D;%JHb?n6dqt^0ZjLRT3Oa)^%Ux`79=VWEF$Acr3XE?n&<@
zc`GWe-hp4@r-@rTZrZWT=#})xi`dGEMeFwx<h&tdW5cJ(Y3&c-k8qgoi&QLMT-4s^
z=pn|5i}cBW{F_T@cGztd-<__!^O6_ZTqV`Q>n*w8M>xJep4s<i|4K)R@6l!8@k5$G
zXC|H51Iw9yHsQCRFFn2+n~EH(tJKtCo6s)(KqJC{EG)>6G}O_#$B|Q6Dhst{G-?~M
zOj)iPwf(<tfq2iyuldB(m5fue6-wbB879)8w6fM>UuEG+GwF)p+ZfhHl^cq3F)vEq
z=$7L0#7_slPe6J-^MblAOOty1H4@z}X#!WEK>|LGMzk!Un1d{ro^6h<6`bEn1d^Wp
zmA;Kwh=753gC&n@<L~dGgxJ9gyaysRdk9uK{?LmP(jaH+xa<#EUE-Rn@R#z;jx($9
z>mSw~!u1U0Wp$~P3V)Ji1!OEA!+I*e1yN+eHq-gocd9;iZy*fOeMMYBWXdS{taS1t
zDGkRX-j`0F{WHs||9kw(BeF`hG{PFZO7RY@IIb)bqjHbS(TR`7Rd-O10nBu4(9~BU
z!af+Eb4pwg@%lG56GEbVf3ZF9XSC($8w~_!x_d&ap`oXZ#GZog?oQHa>?bR%W;(Tg
zw61CJo{AIn_}`qv>I10i7f&)cX&$uJ_HF+Mdv6(4*VeQPLI{wM1cJL{<L<5jHtw#$
z-GVzr@Qq}n!QI^*lHl&aA-F?uhhA{bxvzciC!@RX9ivC*2U&Z`oHeUzK2@`pJbcl0
zHFbS!TNm$77j0CYc;7Q<Av4)y;Dfcx-Wx4-NdEYhHM>a$ymc`V>TE!;;QKtS0ab|r
z<6I@aO+Z>`GAt}(Gs{H-j!1~oO-i(}ToEJXlh%(u2GqSyE_(%nZBuWi3>H{j&by$D
zF|C#N`>%PT&_--eUi+Kjpq#jPi8k7c5?}~$P0iZ#UC><_TfNXl2>)P^@HzeCmzTVY
zy5V0UnTxGHDIjCPy?7n)!F%3%g_-_IocTM)Ai@4OAksk5VhgXmVwLBY)GWX&4ex|~
zLP7~f*IDZ-q=e$2A~VC9G3^hh5fG06`|%VF&<dVFp(0wOA>K+)nnDR2yhK?YBs@jk
zPm+INHM}GM(N}B1@VnDw5M;n{@(l-mT4QHeNtYyh5g$RfTt?jegeTQcocprd5T(ga
zH2ShTW5VqH+8<k($t%Npl;8Tqu?Rs45p>9+<8@D+fHN?Yv3UINOav-6Uypbhj}R%Q
z+dOF?Fn*K2`io_SiY=Hgx&p-*_Ve>*{{FJEq%f0h5)qTtusnEpl)JUqS-&I|Tz67r
z#y6CzVhcf^Egh7QqCPN?+Et}H_ELh`NbMd*)?`nTApol@wqV{Y?k|!5R7GroOCST+
zJ(E8}jx4S3I87?EFDsp)7eB`bW`Zy9$f(s6dOAi+;L`Lg^vN>W*)Z-jsb!bP_)?S@
zArGl%`x3cv&R{o@jX)dE8=%=Ua}mVnxyqmfD1Uf_Iq)+q@7MEEZl{L>W`y$YnE9*~
zU5BUx-9&q+n9t=AoWd0YUzGmf9vc^(UU5!)<uzOGLl{0gn$?WL$sP2AHt7Xk;0+U#
zd|s)Hc!K^&Jaf1QeUR#Fr`nBXRJRC80hd!!p=2&&*5=pUXZ@Ko@%DA_t3|T*#^Ogo
zgCoD*Fst%Y@-B-jGiJDNP)xgZg9~U`ho7In+<4msTe^d7wybk=hnnCc`q>fI{VUcp
z3;1lP!g$ClHmaAsJ8<~9f<DdP!lrM$3}#X1C7uNJ?lrNSaLH>cnz>{mOvk=XvhDwH
z>{gpQY&HF)(9IrFUf*1w;{E2reP9EAP)*0L)O=s<BEPrd$F%NJ)sDMG2rXAc;<7_I
z8JERg$(0MffN4F0^T*Tz9mSgx*bRRJ^zXuPqWcF5yF_Q=WgCh7O1_)NHdrmcf(Id2
z#EMGC{{JR%`nNPB|L?N2u>Z-*!u}^K3;Um}EbNc0EI`8am?Q!J13WMP!qW11@n3Jl
z{zXOcC!57TQp^0`Woda7?|;zKf6nId-{nhrRL1|H*MHaU|BM^vQT6}(d;NPBlmCV-
z|MRi_JN^GNcgmw%{SU_a@7n#ZSy~>~4S)6Ye~hJt?Q#9_Pb@8u3zWY^dt5X<tZn`m
zMT-i+_wu+`&cMb5SkW*F8#tOgo*^tEDhmeFzICuLu%;Kbvo@Ah`Gdp>;G6+CU2Nal
zI$AuEIz9Y;xI)y#(a6E#y_1~-Fr)uia)?<tI68@#8#s`#vM|aR{JG7{%=HH;j3Wu>
zA5icQONz&9|3%`zwf@-IqqzT(hyXX6nd3uul1>KJ7DjJv&8$sGm>AzW8a)uoa5J$3
z6ghuv)3Y!GJTwOH|74}%WZ`0b>tgl?TMZj0H>0e9+aEtzIDkVBBUJx`zlM{EiBaP(
zE*o|xfPv<bh=+ug6=0Gxu~8#o<9s|__>r@Y9=Mw1p)V2^CZ-4aI<5z59iTOU+Qx(h
z(7+=&{-Xx|9<Tocn*rQ9O#fggxqoXYNn>Ex7EbQ;5`T`{*Z}Am=pA6q1E_!=)~hOZ
zs<svn<XnI(f38&@!^oov{y6Dz6QJGVc$8I_<nNC^?E1@EfA2j1BGX^2Kq8W&4+1=l
zi{%fhzK5TTz-|>gaY<2GgZGSoNzEv#4iFLo>IM!fyE_8Pm9#ar1LSxNB#&DBn_mFC
zAL9z6IxvvO6aGP|kJtPqsJw%*i38xcRDbnEO#;v>npptrQ+KMj0QH{<utVAT{d;Q@
zn+L!7$3^dBKYum#zwGljNgf6J%R~P~5C32A(8uz|zi8m!p$aqeKX|MBU*7sZK@^V1
z90A~i`VSF>lk;zg@|aZoqv;>6{TFHfjlKR}y8nkv#m4;iV8X%v=pTQA3EO`OCTx$V
z{{<$19{w{hdDPNB4*sF0^bdpo&uF@sSpabU6Wdt1IsV%`U4Ku@WolsU2*i?qTMT07
z;^cVb$@^P1XgAqi1w$RLHp+f5sYfu1_1<>_l1gF8VjuanCqt~md>kjUliqg3d>M;9
zp$;5I1;H<*qoL}A?+629fKe6COjSkCWX}lwOsx_+4{R7>O<c#=6r9-STQ{1z#+<Jg
zv}<Q93sPF7`F)WeNxS~bKVJ!YdU}xjg?H8dIu!?|s=B)QY{u&J^wf~^fe-BA-!~W-
ztmltgFfc-{`p`c&VVn^E|J@I|_^)&lAfjKf=yJd=_x-xVHb}r+xoYIQQu{ja&3r5v
zA(Ri`Cl8H03$n|Y<Wpx2JIuOJtnJwQOec2FZ5+^M=3!p2**&}JREDu|m||E94>Jt+
zrwN`ou*?O=Lp|D8!A{x)fGwE3{yOr5ZnS?}SJJjlYMgV@`Ckqm^}!vSmvGUc!RTLn
z13hxVJ<ZaFc<DrMVl?^APE6nrGTJv;VJ4$@`l`=^sPBWB#6!j-jTy@GC>B|-+FcH+
zwM%Y98B^5O%<U$ctF*B=Yl@C_%=cTTY1c6(X=kXc>IhsrY4m3qQy?zaHhhfSnzJf>
zF_AUnmAbVx)fCwLPOrF|_UF#UKNU--VvaTL?d?qx>&|ao_suvi&Mr*%p{XKq^x(ZH
zI5v-%Eh)L$TaKV?3m?|jsVSkWn9bbTeh&k41^b8pk&w*LA8mT$pI%1ZU}~z;MLyl`
zyS-13&gUS;R1-7_u2sIF)Cl`E$uwx_=B6g0E(;n_o+hp2P?BKXaaCQo>b#3;>a|uY
z6V@6;(=5Db?91=IxgACFSzZ;nUqe!pqSaWQxNuyd3uunq`_&y<p%N>b33l;x(j`QO
z5wiPBe}0t`9fei3wPTHMwmz~F2%J814ex#aN~*bhca^jMb4Bl%rhN<eC&$E~(@hcw
zC81kt-xr;eFPPJAOMyYV*XN_Q^e&0QiF&)9xdrJ;&HT*nsC4&$*HoQLm*@JPT-4;0
zCp0&<dvC|m3c^!dOFFd8ix771dj~q)xg3h0?N{cQCFRR<-=yy)H><(UPEqWI-zD#`
z++Dym7OVT12J5`FkmetP4IE)$kl@?Yixn1|8=bk)iFz2x3<@Gl8u4GwGdh7+OcuHw
zpJ*Irs@A(8wBd=UiDU*Xwkci}w@xRzBNbM=V^5sutco@_hsp_XMWRK_*QzeU<I-9Q
zWfrB+c2<^*map5@QqP!2=|sdNo8P_zUh(fU+HZ{~ALL=lsjm(uzw`1yMi;B;!OS9=
zRL)Obc9@IbNO-jD$P!@L=Ne=Wlj6e%CllxnANEnDiMp|$ng0cahivUNadYFW9h~$A
z-O-R$D+?oJ0J17&@9lLLQS#f$C|*4^xpX0Amin&f+1ggYP)SQY1G8X&gBGH-S^PAa
z_~4O_FtUC8L6(j>vG_L8_BXV6>&PdLx*<1&J*ySefr4o#1BA`7G+6n4BU=t!Fdyy^
z{_?VZ5aqsa1V%8i&c+OUaF^?#ULM!_Pg=oBS5;GzyXE>IJ$EI8Phwyb=1G$#4^KRk
zS1-c7dssqi30m&giErLDHTCZ9Em*1KFesJbh9|ATlU=?C_wGn*f_zI1frnLMzp7pN
z>HW66;f^`G*{%*9Z58C$s!srKJN%P@GQ6FW+iK65;1aejE&+F_4<JbKb;`hkWYwKc
z#nJk3vVWkJqbWd}s}jXbVdrN(U+iD!Q&JerNcglth}7eO&^pDro7>t_i9BWNX;vx*
zS*=YkNRK2JGhW<4W9?O9*IKO(j8ny{Y%?Rw9&%*&EHQ=h5b3H1!odvVmq|QW1_pM-
z5URtt(6ZA?i-8ndcXwmx3?VCq^gh1@rqc-FG3dC^VY*S$|F8(*PFc_?tFGpK<GGgF
z+<MLBF*Yf<b?%<nc4T}!vg?P|YR6QoO2T(6jH}zD-VMAfkmq}+?SJm;;o&7ND=X!y
z0Uj2ydC)XoaRkzG=U0^M0r;+#=pK_H-iD>lgw}a%J44zt1^*L|nlTNJ+rmkf{dp@C
z#urx57vXZoFHV^pXjsG#+_^`TIzXQGPB(3^Fq^ObayIkw?y`P;3fYZ}`BYb`EZt#&
z*ovhav{>;qcW4*!RQXF_UTH{~A<m_|Ng1K!dP~|xwl+SMQgq}bY0DKCCb?6<>Sz(g
zFKX1ggrYU8Wl@>N6Hy${dx~%dU?}p!4A?^=n7kT%tTQ0y%6DI0SM$3*hh3|YpNIaR
zN87vMq0!psJgP_}R~_rwukWrxr<ATRtW7txhv6$lT}Yems@a<bcfe?$$}{Br!>te1
z=<KD9Ike!wsdiDU_cH93Qf0`kZz<CqlI9R1MXx!`%pdi_)bf)CS;thfUDSTLk64H@
zDnn6p!^8a7LI&nP=U?;Rf#(RveLy8Kf~_S{DM{3h`5f1b*?M+Dbr&@RGM7{K(mJYj
z#&WB2Irw@}S?)`fPaNNgd)G~`VCn5uM~mybgqf5#((+ydL__JHlz-nnS0k-Uib4r*
zB01EzgJ$eE-tj)i^Rx<R%T14W)im`uY-&52YP>NM_{lz5u9$x)pIP62Q=k)uwT0%e
z8Hhes-z?94^H#rtSUsdr&*YT`y?E{#fc_qESkH}Z=vBjF<gFZ2RQRGX{J~g$ert$=
zZg?1x*f>d^tW+Cf3WKMO^|0p-cJo7xgjXlFZg<VypRS*0NA_=zFJJxe&`i$IeRKBq
z!K2}Vb5*$|{hwPCSd>~=*bL@(duhDZR!9~vtzOL{R3z%v)Y0xP-fkPAvXbbJ0Bo=&
zZKR^0nOR+SWg+wKDSk%*saCk$H2_~HFm$7$c~<Ufzv&pWS*UZkKAj8;u2iU3bOnow
zcbQ8kb_-tP)IdFTj8Kw&R=1Nq7?@@w*jUTyI0|`+MYYCfDA2EmAEI)aT@~LJ9E|=`
zoLX?NaRu;=$j(l&a(vK}(+ZEHy0V*JwA5xghR7$nn4Yv>*K>(@5RI?MqNPH%U#ue4
zPZ7UlB9XboM}mRr3?spck4xNHUM15ngn<z@n8U)w!I2O*v$aKwcx@TUxjt?zhUIsW
z<v|q2PZ!H!iPD(YoJ(AfV0z6@2ceCM)u$>7Hd0ztfPrD6zOscP`5XSgUI}Pozpz#K
zQf31Oyc?@w%=iom>FQ!7LeKZs+11t6z-pgbDW0vI?kD4IhW4lA&44LLS;WlF<<y?7
z?YFTR0-WsUh9{3rYR=5gCLa)%)!U>fC5`V$*&;ms0uCqSN61wBfR&|W;o#t4Vv;@e
zikV&?%5QSKKA^I(cz@@5HQCVt{+8gJTTsr@Koxz{$KepMNkOH;C0S%={7$w|rc*^~
z?7K#5I-p5E!;YghvdBPW0*uBt-`>|dZXoNPxbDwUTUUpLg)tN26jGg{Y8cDgJ32bL
zxSSmwjUMw`dU|>q=-%Bn@lv8k1lfAs;+-jaHY*PSDv9c4<2ymZLGL_y1u*Ll6R&P;
zwDMYC@QMVOX5~WWvGmiP!oxqef&$M`zrbd=nO(d)(?YzzJ8!jHXrfkG6Z^cSK}NWB
zvNeYI3a`rbV4-DlcW<va)l;QjI7K%K_3NH1+u4^*pWKbIu(_4XDP1KDwRx3^#Z=bU
z@T3<3K*G3br<0r<*u?I3H|GxIPKLGF_*#jWqXj>=Z`MbnJ9F(vL%*2yz_Lf&7Dn9^
z5M8S9pOi>7=|~>_wj{R^I4YJpD)rgbi^5V##8=fj$xCNxtS>35&Tc8zHP{rlRCs0!
zb;3UE#J97asoGlZfCQot`y3B&0;WCf?v_a3=0uk10&!YT36-js=;R+!Qt|WOx=pI6
zTAX3?&t=APMlbXN@3u-3h`k1!k}_Hfn5ehsAK`9U(;lsH@>zx=@n$WKEK;2Tw^u}D
zQ)xnjlu_EQ7uol0)l_ylMyhA)NMSM(QT!=VZe(}Vnmsm6x6s&Us~5x^e6+R}R{mO+
zVjZ9!TfjLSD{Cn8ECVgOhlVyd_I7vk?gZ~HGD}~vGsv`ql9Q9SI-1;%H{Dc^hUeGs
zw-TbGF~Zo-7x@b^bfH?87s5x9BOL=UD?#lv09vS$n#w~&#KbxaR12RLy(jSz0cPQ5
zYF;;0MYkfcmxi)3sTzL5&MIa+aGTgU!mHrNyc9gT*nC?(*_jtuOToroNkmt(Fyocd
z^z%i~n#1LIzOsd5S2&^Y1TitOJmz2myL28>7@Trhcezm)G9KGz<&Z#B&r|K1Y{7lS
zY!(aSw?W?pvf9#?9eTHlw%OW<rwxsV^+psk#hz;{eY<+F{A=hUhYbaEuEn`<;cTkJ
zymZj6C#*!iL5*<f9mOXO%P@twNUqY}P|xaY>aYs2iA8-`q!w_AcCFmXaL($gabgKI
zJ^h5z{pIn-p+1YV%y$s({ncb^MP;Rbf##Iw_>YF09bEO@jI$71GdS8gv|Tc=fviK(
zT`}cV59rnX1Dpss70}QM5?tz`_0&yG`OHIge!fc?iN@2Ql?yaF_>oU`=wRa0R8=mv
zP)Zzb@I!MIN-ExfH?z94!`oy<h9;>V)3<hXzTjy$oc1<9KmXQ?0PZEA5{t1McQ?1;
z7Y(qs7K+Il;;xAYPO|E?)t}}vdoho?)}EV1hKH0G!SN7eoX__j(MQFetldA^6c*IX
z`u=Ds9E^7xLs4M4TT3Y`tHNI~eRwk=#GGW8>MyvP6+?sg)uHH`dg?T+$#1m#L4ud7
z=nni1aj)N-qnBHZQ51Lq!1Cm$1;N)(;0ol;gVy_$ccpr*@5tq~v=T%nRa8{Wi|QlJ
z^Fmy}!WN;0Tl9@HrS{}&GVY&iV4tT&P+=e$q+VN>JpdXE!ZVAJr0GgYviWdA^n$@0
zEq9+JwnK;P%`-|j(PX-zI(6;as_9L2FB7buiw(j1_~>Xdl>w*Rx6TRi@uyuV*l>hn
z<=rzgGqnpI5RR?0iUaoZH6r%r?H(Y2j3oC_wQ)Ge3+1@#_EomrW5e<5fBmCdX-lcz
z*?YTg&o*%kOsunPBDB%oq;;}Znh{-j6jbkmB$@i^9`g-37Dy6{NpA-Pd#mI3Qd_pt
zgju*=qs!jCi>!)Dv`4j^oSb!9bdVJfE=#8kbT_M_tV};~X=&**!YdB|1t1|IR~nTO
z5%P@tiZ6CjJOo_Ob*n=Kb~DccR^d7N60a^=UtZyhTt9d#lUETvJ%Cde8!>{XC2m%g
zAi;Mu+`0@6b6ecPy3(VcEO+|C%H+dZ8wPu0Xtz#Bv!zv!va+-5ox-ld0-KxpF_d@#
z83IJWBK7hGxtks)N=szru^9e6m^oYn1c0uY)bQ}|I=#-ggIAMhT|HD3v_=wqYB~Cy
ze-*`#2}0fKE9jbgz7k>lsFmR14|s(ymPkZI)Lb(B^Ji>Wgl@Ivgc{Kg_Gz3C;4{wC
ziiSkZmKx}KhIWiaXE|3Q;Bz^w-yeJNcw#pXf)HF^n(LbG)Y3*<l~BB6{t+MH9Sp!z
z{m3c3k`c~&Z><FA&6_3S`-?7oyC!IS{B$Fdcm&aH0>yj59z?8Z->UA!gN=FnpaseX
z$v1NRPCX?(udhRaQNVm)D!Q?U4_<}cxt*zm3Q7&C1eI7L;HQ_6Pa6z{y7iK63hF~|
zMb(^V6zRTO>MzCZPYlnHoaH~H2_IUN6cm2%mn|K<#7#(v&0!XiHITjmp9pSaEZJsX
z+AH}J5Za_6egnbhf)@zBY>_rFzhMio>i>RHKxvpt3Wu4mf=5iu=tBD-2)Vef&O=~X
zaCKDa#FZ-4in=KTt5?EeD-x}d%i}Sbqi;T=Q_^s-stoU#4%(M`wzrgOk4sy3_C%>0
zz^UMQ03cw5B(1D=q9vmw$ryE;x|OuHX8}tSa|PQO!*Lq&(-t@+cHE}I!#&Z+Qs|!1
zdQy2SdQR#WZqIh6pyu2#Wi|i<BzHjJDJ=*p(G3#3IzU__j{y)jy<}PYt9`r3K|zC3
z{M3E(x3rI7B;mVCo!Y0oBLmMy#-4SbSU^`0Idy?=ivX^{R&&?lFX}KIswc|HxB>w}
zqD?W*FC;cN$S$b^*h*pW)rifrksU59vncIE@-uffrpIaw6BQ6k71bTYkntr!TP>%t
zn=>X}Z@6{eX^(@>?pc{c^`YMBWekh_-`_f+2%qrVn(0~Dv~`Es{&6)y!BIk{Tx`^q
zMiHMJ)@6sd>Gw1E468WQ0A)$FP1d4cNGgE0C0s<B&q_FsoFcwaN%Gj#nbjdZvoINc
zEGOVRDW*qSqv2s>+h{R5R8RGl2G!dfzBx?plOSD}fj5<UB2d=-ZaBxhO_)avcaZt|
zICU+=+Xt87adDQRoV<v(r&zMgmD1R@K*Z;C@nvZt_IbvOxRC%yNjcHvj91B#LeJ?c
za@l(Dn<M!}&k02Z{dLC|{6=xghgjgUi;DWk#*`n+f=r4tTC=ReDb8zLRvWtnM?Zf}
zn?gEj=`+JMhC;56)XIn8NdQ@h6h{;Ssx@Tad~ztEoxwCrwS91<&8&JkBF`3K$oyQ*
zz~#lQyd>G)PtV{ItxiI~uYvGnn2IW7tPgF7QcYgRv7v7GMRJ0Uotk7Bxx)Cc{wEQr
z2GgVNo6OII-3CY-Ly7M<qGma!GYVrz#9b~b&s;)n*>&}yJZQQX-yU*5%g@7Rb(CBx
zY|#+!?T3j$AuH)1k_RcVWbLwk9=tkpaQ9lbLgr26O`*0nlS`lIN3aV-=S^#hJ=iJp
zZ4jC`nc6fvu3_qD;L;D&qb24;Uc+mm5@Aa=@mz+RLA8h|{I#?yPCYFCLEPFS8l>3Q
zGO!>R$auI41%I5Aj#J#*pog@ctYgVAHTa<oi;<Zkw^i+l7p2oJtG~b0BKV$OhF*tv
zWJt$Si~4D6%D#n6ZM+!fER&!g@7tCq;|Z~4ekKxBcB5ZDBn~w2O~pJfwkv6*CN@0w
zSq<Zb;kYPTk2h^Zu&qW8+8{X?1;1Uu>ccx=82$?w4#(9d(sJ^e-WDaB8RNe8T!!Zf
zeaB|?;de6z<4G$kXY<#^v!Fnb%Se}SIj39YSwdWLvfQ)_Ldo7i<_C!cu8yr!=7kPi
zR8>RHEW|3dUeabHZ@RIPS>x@gBaK!=p8kMsTOxWw-)6sxiCB&oBQh{BZvF|MV&B(}
zwnz*F7uR-!#F6pcZ}kf<0!&z-sGm+<WS(hciFssw!S}-Q-MPv4=y$rD+y4I37Q%S9
zQ#j&_9PhO04aBA|B*oe{lNe~7x9PL>qBB`WtHO;q6<j!Hh<!JG69?BRvUyDQP_Lua
zo+NI^c3XcM6Yf{v_v^`|uw8SK787=B1^L@a8CeUoeiQO@9glm-OdS%VKP}aKy?iVE
zB)Reu<ugS`<t0bLO#gC@NG!vtMkq>d|JKqx6=h%SPhAecQ>iB|=?zM}l_aqGa!M(o
zIy^SXo3<uOP%M=fP&QP3==Mn!_L2Ayt`~T@t7LYkf!@{_tDgRsnagh9pqv?X>(%Wy
zrEc;`Pev_w1E2$(9PrH78i~%Xt_VDKsa>@^v=!a{U^GFw5}%7Aoo)-f0BfkKf7ouG
z(QQb5`-|CBh2EvD5wfolPoyDA&R4uBkf$&~K?+Ec_AB}`zJd2~qN6s_A08xveH4f0
z#E^v647$^dUC@4qIQuA$JBd@5Tj4_?VE$L-*X(|0e0fBP;6PHBop%x6wX(vVL$cmz
zXWi0CYSvUW)CD_jZMU#Vvu3Zj6?v2W3V%5bn$^M#cQowYtsqNIF007lSdTgRc#=m0
zR7Zs9o~lS-#R}F#!Qr>&!e!nqe~JRH5B2ik#E#TleOidxtc*rlDUpZ_ty+JzmTY>D
zO8Dz#uDqv8FwkvSe#k@bW9Q{J?H4=b{pnA4!`X}>WX1y_rdxg0KOti~)07JFi<N80
z4`m3&*E(;g_j$483HhT$9d<H4XFy`|;YCL`KY|@jJh+o>=^f;UJ7cg-=}*404bAUT
zmQFX?-v4+gR=X~|t6e|hL%O^9ZUZY=%5}fTS9?f1I5g+uSdI1B$OJ`vcfrw>bkBDG
z(7uqtr)xlIZnM!Ty~|kYW_pLW_a+4De*o-R4P@0Da>j36JsE!`0O=kXw-Uf;=>x5v
zSF2<7JEoRxj;qhwl`N}|mVXInNj&{*<BMREhJDgA4C~{#>U-ib9|NzFRQSI695jPU
zIDX`~Xx)4m#o$qQu&_Q6w?62~nK|UxeE#FJ0k3Ge#27{otTB{6%wb(Yletvp3t7HQ
zi#qBu^qee9*c&!}9l_mF!(`R}2V!n&Br*HXmdLk_;K(8q$pR~>1m1U?VtO<g9$Abo
zZ{nzl5MxGssC9L%s}E``*9}buoH6O2b4u-}_V$GqDAn_rb^TPRiOu2g(EXx=?ZMG{
zj2={ybF!U55gR6jnKRy@OPk)w*Pxrz!WS+EiDs`>BtLmBs!L-`YGeSI1$GO?-`M_c
z=r;g`gaXDFst~u4j^6PnAd9(gAY5-vsA?nlZu7FZ*SLq-GvG+wShZZ@xUn_7lAxq$
z*u&5LH$}FM|K7#^ltzpcR?^oz`wyEfo~`9CX|A)^HT|p=HP=j!mY_cwJLe)zS>{)|
zFd9+lMQZZKW#kGX!w3{IZ;P8*^t?+pi3uV3w)HJPtnWQ(>;~RnMOW&EVM!WZ1J$Ps
za7)Fd5FLc=EtEhAOVXj8Af#vV7}*nKrxediU9Z)SDTzc=nS$}%_x#&9l@cq%XBSA*
z+E>|w1^yJz{n{>_SJ$2XQG7~91g6q;qBAdAXKc^=EL-*>SB&C2hg>Y@Nk`=w@E7lN
z;@a~Gwp5SDHwecl`F{c&Im$2Wc#IDxR~2i!Gpang>X~np#?fe=0W<lT@I5sdL~nf`
z4~U<d=vX1Ps$#@GjShTjd}lZ0G14QI66+K{5eWOjT2#ip)rQUVp`u1sB6>vI>GL1}
zQ}FEA4Y%Sg01z#$=d&A4he|=&h%Hf%8XLfoW8Ut+1*;?hhV(&?*pK;(Ky~lI=P6o=
zTLdr3k8?0L%nA+$kM6Jr%Q`h*YQO&x$|}CDSC!e}D;vMg6hf}lR#!33aa0F5rOk<M
z#jp`epHQQ6X*{0D#Go^N7dPRgR?96Eeyn9`U8NCaiyeM?GxaV0Cb~ZHOhx6G?@<E(
z*qURkO~Xo;uI6gEwpHEAw%fDr@nFPv9(hz)lBk-mL1&kRf(RjCn$<l3uBp(mn`Fpd
zWJcfWfyH!b`a=s06ES+uX*2@*^DJlM(O3qe5o3a3v%BIbhZ_-d#d;Dj=>QXD^<hP^
zT%DJmN&>i#yx393&odBFoq%KStfw_RLNJph8rfT;<{y4-!mcf&@Y8EI&@Hls6+cR2
zWs9l~;rCtyLmGAs`PlkGxj~h3$m*}7PP5{`9B?^zmJ^-*9eJE<ZkfjoiivsAhP(Iy
zJ$s6hwW%m#<C+rhR_3}B8jy@`lHL}7!Em*96vVy64bGasdXeE7OJzULcW|*B7+$%u
zYuY~LMd-NN$Tibc_J+N^MPrIES$Dp?V3zH<pkgYhr*o-}A$&!ND9OiTf-j~q>j~^f
za1Heg16k@WMErf>&KK=Nqq7la4nk5s6!7jci?*M&!IRU<No!09A4Us=AY^NSxb|n6
zYwe;=2hII}FSq)0Wj^2r)<h#Iir@aPB&a9dicH*y&c@33-^m<=@<v(wN=TBl>aqA%
zSB8vHUxy0DW%d@ma~&1-4<>z8$`zfjjd(NKRxM2fxfdMjF6Z^?+u<oGTDV35glSt>
zj}+doZ-izKFF|IpdqCAk<x^(mDQ-Xr-kq-O077s-2t__3{E#|UhaObU!y=#0{WFSG
zx-yGSURIJdD)YxGPsy+5sNNFETFhiMiOD*bj=nyfN)_AN?9mjWMS3&p8T!bb&8GWF
z<=7vmfl9`rE;CruxYA1=_>J?7>c_8U<07_y-|)wuYB^BToW+sno2vDh{H|7VF_{^N
z{2e$RsbA6C=ZMet)3Jk3HUG<^*s-*Gk@r(zx}6YOfUTxR?o=hP!toiSz~NUz_fKpp
zL|u7qyZpmSUz)gcpO`3NJkR;SJ8BNam|hxsD(J4Ijp3B-iKg^jUZuRd_ht~jzm5MG
z-VWvi7${}!Wr{kTW<}T~3$B>W;y1Z#e;WcOqkNefGJ&qsHZ3M3$@hm$e#W}pvDq^g
zgVjc&4j@OEVz|J{0onHr$!?%c>ZEj`ab7VfmUAcxV`B2-)O_Av+-*(LB!kcXZMA+)
z5@RwCT_n5~7KiZtHl!dur+SU0hEg+Hu6)H;`+WO!7iGSre7xpEs%Wi5;HEdZQfTt)
zS7Gv3Wy#b~wdN$<i(gd;uw?Xx@t2TMzJtx)tlAKz9bGKyF8@eA7qyHD&~zhNrAm^t
zF?*8EtwX+srZWCcY3M1vTK?N>pL1VK>YcC0-9e{2eA_t<YOxvfAW2otxQt#dV$I4=
z&JmGv1(PBkeOE%6446mK(3CdNTVKZm(Rah;a}`M`^e86J^ktI6yYXf-_<K)Hl^iJ;
z$e}v?o{gJwx%c{5_4j?<je#!?rm@TIbJa|HAwRyCX29=4cY8t9Qp$M#{Jx*&qv4$8
z>?D>c3pB`dJHi>|xtPdVIMc<H3De}i)`2@?L{D`=U%}y(IATQYD7JeE>~S0N<+nl|
z%#zC&SUvE$Ms%iJ-USOIzz1*@V(U&jdrRmd8ABTnZhoNEGsq#0GQ@WChx;Wtr8LI(
zwu>P3U29*kmo`>>Xzj8K_hQkicN?VS(IVqya=xaYVE@$Oa`6>^yg&)XWS{PI+fV?p
zU0XMhSlkFj+C-^b-VEJelPrI@Eiro}4sv&u?O3&OS=^(OQg|JWtsq_bY{S1?hl_rb
zAYL<J{?b?c+$YW@n<;Gh(-^JpgHV2{&OGGrw!TJnzhzfgZjN%-31PVAKNI0=jXat|
zh%H{uUl$O?gcMQl@8YQHRm6mOH(!W{c<@hj$kOn~5O}tJ04GqrA##5=n+jxZqGlD>
zL&0$_IZt^^9Q8o3217fFTjn^g^CI%$wy9=j&>7XY$FjVNh+24^+K0c34DNf7H2mD+
z{t71gN^shXcqHz!k%e%qlkSq$0TR)b-j?$73aVtlx@=kzSGk+>B_YYNhSpbW0!0yF
zO=I<AP+U?<FIyK7cv*D?a%DM=%V{B57q?dtI)^=h;uY<|hzrUnEtmxobEPfP_3eV6
zIYQTqt=kNNwTTym%-=W}V0sFatGo|#a(FAgnAZR<xWG$v%?Hw%g^HKsG%=mNJ4Gt%
z`7Xo-lWj3G=v9g<hapx_jE%_fKFp(id0rB>?PE}-k=0ZT^ML`qv;iqoF@+e{V6b9D
z!IL$VEFgc@%qKlIl*r#aa&ofplBZW%dmb~1V)nIFV>)1^Uu^A&_grqZd|+WOZbzcH
zd#nfKxXdev9s{rc-0sENe8;pNapB}~#dxJ!jFLv?gmL*Lht|q)-3~|mkNsy@7IRtf
z6We+@E_CIDKq!|?^+sRTAZwW3VrA1>_3m13Hj2?(kWL)C+=9iDr*6Hj$U4SX+NzdP
zbw$Q<yG?MhG&u@hlRR0DJ*7oi9yf$yKq>pQw`Vbq&ieDVhaE3D2`fmOc_wey&DE<Y
z=czM)F%pZfQj9AYO6D9P(l&e<u`1o`_2wQ7Gq{`%#XPmsb9m3)Tv);5p(!U)WM}_D
zH@3YoZ+C+Dd&t-hwV+8_tZ|3RqEq+w7F?waVpMhab`A|?a5c$QS_i1!S#~3pX~k5$
zLh~aH7WdB^n^B$`scqn%;oJ;wQEr56`8c_U4KypPNZfg2&zTO6Q$s}qGUroSsSd?U
z8+D6SUI|jO^`X)$r=}CB!3hE@&2=}?oVnYCnCoPou3Bbuyqk~f<d`<()!KJ389?F~
zKB<vLu6+j6D12H7KJ#ehua;<dR&hf!Y4xdi#-oo}yP2q@D=N{TH_a0!)SO-OD_HCu
zm9>rHushC%p2OMXpmXp_Y233qQjIKB<u4?d;GBJKjL>^GwBXeR%#w`6WC4*(FajZs
z@!}l5CvUFUf-9B4<w~DPr=|ju`s#9y6Ge*7E<XNXoWdTEmh7s$2U3I>-C@<zwT|dl
z^)lbl9GCTQKllC=G}{5Fd`pz?-}H4Uxp%1FpXJ|>D^^t_@|YAxXJat<rd1xz>(|az
zCh`a(IV{}WiM>>i83Spr4tQ%utGYsT4w@yh7x(lp<SK6u3be_zGh?)i0%W3O)mLNd
z3}_~Ov3g#HmlhT>4(y>ovgXTA{Y@J}*C#81#iZKr+;HbWmF@C|))<>XZh>&UkezMz
zr;w~K82-WDE{Ozq^eiVyM?8kCqzN{W70ym)9S*da1z*r7r?)y~S;@jnB{uCKyQe;$
zmQbf(yY3*spDs>Su-TczWScY#-l14LNyNr=qKnfDX@|wKs9=Ju%6;7+^hTsq7t9iE
z7(jn7UM69bO;dXLvC>??*+H8`bM^Y5>A;r+>{LIdou_Ra<Xzr(^E6iCs2goQjyl&*
zLpHRjj(WgXe!sq&x*wN+(%DDNr_m8vHaJJNRhL`6)`WX7+z2Eod?kbk$+C_`k5&hD
zxid!=f~iqxwj$e0C^k4;?@ot1Ksz*AKlqO2?IgDAD!PWPkN{-ShP*6O-aj0!%Jah~
z_m|)PF@#-K_-?#oaO>VRVx!_3(Pj2MkpB6GP64;0hC!HnA$`<lPcabbv3lZXwxy4)
zdDO(#Qqq_wZJHXuGrtk)Dol)B7Bz=BS^<3L7l`agX~eIm<VG@tTCY>!GVHL<qgUA{
zbuh&m*!?zzrH?c$ndOQ#Tl3ePTY4YCRsL**u6T}AT7or(b}`^!X0tIm5mdmUty~1$
zqXP<93}@W7TIN;VKUN2cX{OCp9T?W71Y#}do7gRlWo@ia4{{?c^=?+D^1*XQ>^hXX
zz8c1*=d;QXvZ;=QqA%J^HT5kqVW-u})ajFWI|eBERS7sy%=t?ry1m11(GoT1<7HA|
zsR&Gl<Mfer_i;yb&I$LulV#b8a+FGCp0>jyNufbBF(yU8tyBOWH?hKPU*vO{{3?|>
z)j@>N^~DBwt}EMLg%4l$_(p%I(!v6&IQs}&RT1Zm`7~Z8nkaWc>{r_M2gVCFaRjzU
zjJKLWr}R0WTy-2nMz<$kR4_ZoT)sN7$!;FgwX)G<71UP8$~SpEe;l6bK?EPG1XNin
zR!U;>UVdZ6!z)-@>N+i_DvZ^*8xTY5=}?{wVsQ0%VG1>VZ#K!tU3nEeJ1UMyK}~#P
z%q_5d1qtug{(0Gtds$fXZfZC`^_-Bs=o|X7f;GRQJ2zLWKuorIZFlA(<!IwIB{&<Q
zd3PStqGUX%-JpXFG2Y~2jJ%KYyTd3;guN?E!XaKWnKY>JD~=m1hRCtIp7ycoe@z1~
z^fi+gaRHBaJk*yuEOVy3yWKU#<>xl==#PMjYD=B6SX!DEy|$54Da(=r1hR(+#ZL)6
zqfG-xR#glf0*^3snUzgVD@qALs+wB;J;zC#(@62g%6WFo*D{2p8w&!%KV8FY!F$RQ
zGip!AxHr6MM-5g5Zdy#`);~`l0d2WN^D};Ckf$TRQrd9d27LO(Y-H)z+!U?zAO;4f
zz@DQ@GwS_&nxozn6^?}fdXhGT9=-$O$H{B}bwW945WQ?<o~Km6FWHwi;t^_GA>z$=
zhq4!xh$uc_<j8>{2dPLU$4$ExO^X1W5QtaN5{q;U{(caCmY5eb-Sh}xS1W6JL!aTv
zKtVY5MsR@pdSPghwEOU7B0{ySNihxN`4XAAjwXlUUaS$vE$S~bQp#-fg_4yy)3=Sk
z7<2nmSn>5!vspmlE#@O}dL`WjKE$(oBs>-|p#(|y4BnF-M~f^><~C};*YSc%RA2)~
z1##|RgI5pfVKX?z1k(>CiPcO|Yf&)XyAKvAA*Jn|;P`y^tVxcOi@A)5_8HsGUo0U=
zERE4~jKI!(SFJ(jo(B&6?tT|3l?dEz-7QLMML4UB(c{shT~jU9qgPt_v_}t2tBBnV
z3boIdg%JpFc+Bh&85;T5b9ib=sXf*bD~-Ii#Ripx;;u!NJd-vz%dI)XJGl3$&Bwp3
zwk=mKQb*N_YwY$a_GW8meyUd6?qt~(hd8Sg|KuUrZ6=_i>Hb0wN~;i^_6(t-^?NEj
z#oI0CCC}yd>doA4(%kB$w@<2%Wt}9^5B_t3#iR|sjcwmUSkEWw(#m9=<W5<BGJAsG
zor`@J7Q;0|Sv&^XmJ-(#jDLd3sgLGXK1U+bQ*s^&*;SpM8E&7(aeKQ^*a}{4p+wf?
ziD&6)27oxPZ^&e!gPL)_Z%CYR0t`>Aqy+mYPVG)ab!I}Edv6PUvlHNZym{Lz-6p#w
znOv`dRt0~Pii1|Y@Upxb+l}7K^br=ndTV22^XAQ)tjsT%y@#tldl6|e>vx}QK{%hN
z8g06Lq2nRGGrhRK=h));XBdaXG?}H8HI)XW50gzOLfQpY13;gLBs?;5=GQEbTBtUn
z(<PdKFR}X8si`G^pb8;GJ1Svz`BPMo*!+p_g2k~ks^ECRiA$_E8+#(zvZ}XkZBkqP
z1je8QIcON;_4_qP#kti6iFU&azowxA(R_VbIm&hRxN7&zwy4FuMc&(^kFpbmRmcmF
zlq9B>z4jcqaf};nt&psrml?<P9JPfPijKqiapP)Sa}_Ctyhx61m#5_Os_}#up7<C`
zLX*Z;)Ja~35-<7hk(zn$o*;%{m(s&i9dYFX_$tQ7G$QDKkP>9IVxPBZ_$k1$vl-z8
z<HmA4FFvJ<hAyKGzY?@BmTDmDLb<H7E#8?GB0k^ljx@VJOehL{(Ry_mFaEh?vF~*#
zHCnRs`OwdR)Pk0p?5DoZS88fL(3mwFsD&W<w^is^tc+bRfsqC1u7u|&=sQ>zfqd!~
zq>Bm$A$K6Ig!9L9Y6o~w?x4RPfuz>2A`~cxgjN)^bF!R=U5=)Bb!D8hmz(SCSQFqx
z$b^7q5oh=uii7mwm%02Tg38$3QAOWvpoi;7U5zp9C510Lb#vVhMTw}`5$n!!Uyo*I
z8EvefWS*<uX`=Jm-5I2o6r#MP4KH~~;oV}T*U{1O0*j7>&_72w-dNeh*D6`TgBq00
z@I5W9LGNDo`{`wiArz;a^4>Ys$-Y$hv>Jso`n;EI04_d_2`r}L6$V4okt?uj4KMdC
z&s5EBan0|$*@C=mM*nMh&i?U_i0Us$1~Z7#(*XlV+UdrctRS&@ghhnBZq8Ua&ZNkF
zap?6_+EV>z`kkWA2E}c-ifjIluRU85I(3j;-(BzJs~oe{UbP;!<>Jp#dZ*p1<-Hgq
zODL3Md7NT)`CcLfIaIms&wX_3P(Q9}vLY^j-~2u{2^OeWHbfKE2_`<ABjFCS@f{R<
z$u(cs&%RsQ!!5g^>dxsBjX>nHR$ihY84_CG{(UH$La2|(X<7J$lE`|yPbmbH4KZG@
z3<e!eY9iA*l-$2#o3H!ieUv!sebmZ~Alc_cr9_4WAG=6`jIBR**vDbi_!Jir{7HyJ
zmdrI5#V=|c_8H{&xbf-ux^!KZvgx%-w8&XCO6Dp(r&on8*}H<9*jUgsyl#;zdmc+6
zbv8R6*TM*sg5IdNW2tcg^Wn^@DCHnpvd=(_4}<M4o$|!53bCU;3QxzZ)Sxd4rs*aY
zH>*DUZa<YMB2;sN_6L`gZZM4ImGw2aQk)^b9V9f%D*7)kGP#dU2b%44Mfq@&?<<Zx
zs@~0maIw{WXglvu78tmnd%MIAoPdVr6Petu_IxGjqnZrVa>&4)v$BKW&UfnyC`Z!d
z9ZVN}d59d5b{Tm@zu-rnY$<o&yVb8_;JRy$mhpFfEb)a`$U0&n4C%L!ArHsYk2;$0
zy1qlrq$xlQdHPZ4D=4+DQc>+}*X1^*7#^pHYHkPZWyJ!$Uvvp>n}1rJ`74VuyOVmx
zNu#lAaL_2p8K9RgaWKx+Z>M)YDp5{W$+hS^pF{>#f01`auOwOhmbZ_UkR>!E*MSN#
zOi-cGIvLIqT0&WTmQNQ=b~Ze<0)K0>_0E>d#e*6O`jH9fdq+v>Y4YK-#4?Yj7?#P{
z=P7Hn2Csg#75AN?MTkzuN<%N+vbiZ!hG@YdODYW7yE9~f>lu%JFRvOKR*YRwM7tp<
z<A#VH7+zPdf__!*z@O^pxJuJrzarY@PLEQZb8}KyLAEg0@3+VZ{BRPkTlZ>kfL~ed
z^_Vi54^qQ!XunPf97cYVj2}qbe_-FwQuBMxZujKJtRZh3Br3G0Ph|D5aebDmoo3YZ
zSn7Sl<`S6agy7zgy$dm~W#io}jXO-SF3?cH4e{a~fx&{(Rd=5+#K)<iOf8E?@s8+R
zH*X3QvhaThG`ljUIyy^N+QODOTbId|&`F>bfsTzyM=w`osX6pK&YI?A5<SbMn%fHN
z0?jX|dG)}zQ^k6mE3|L#s(ty(=-=L_E_XOO9ckJ%dp+MdHYqASo7sK!UYdd^y@oCh
zw6aHms9OZ)zcPz7B2BS8S$KEVA0tl~kaiTG=tnzZaB-36Xnm9UTY2IFn%F8os6Tpc
z-jS%7;hNrSK0>RvsQAeW&M!I2h`7|mMKdw5kvOvt{!So}y&NNfr}VuhSBCy}#-SQ2
z7?>t2<UOcy@062!7Ug#5Wr`<ZLR8-62tije?C-G`+CM(APGGR1SMdsG5fy@xyf)_n
zBNbfRf9mojj}Ok&S{&Y0eY@8!78Bs|B0wcWqUbK%AX;^KoRc=|a8HyY2>T6%?uG(-
zwBk=yt*maVv2~F4wjY-p#?Y*Vdl3uRDSG$-lIu#SzP{=Ev4(+dT-?VB#XtPmt^UV$
z>nQ)r@_{|M?)jxp_JHSfyQL!E3(`=a@G1rm8P9f1t8-wJCY{VFnBN-xY|f0l1&uB2
z?R*s4y{bW*oVdh!PX5`j&sdX=9}F^W=hr1yeTomU-KBEY;HbA7FRVJ(OH?4=agna{
zK8yQ>8m3MxUD|4EARo><OFrEq-uu3m)BhU0+_E1jZ=|~4%*seg|Gw>Px5%|H@*^lF
zj#|MOfzh8!>VxdhckVUKZTwk?A6=eeJb&_Wb^qieu?)%=wnAKD%%x@3g2Vx*%XsOW
zjpZcEJaMj$c}oQ{wW)SK@Gr^dFw6Vxqot(ojltfE)nF)<P3*Kdn&6}XIQ+ywg{V*l
zHMvS_k=bHJy0bJ`h~ysB5XB`j!aEYx{A0Uu>she(k2||%R7=}A%q|O)l>U|4Gl>9%
z<+s~Vz&DUiD9k$J*8NiQeh7LS_K8r15Z7K(_nDb#cH2L9^RP+M(Z(~r^H_#|VH!f#
z5##GQ^g=Xee5bLL*3G(&a5HK2fPph9B2HcU$ptwj=ckC;m}pInFyuB!9CYl4e3E!>
z^=5tH+@$0}N{PI>@o*;;xk)XdqHl)J5|_dUK0d_tiw-V780qTLmYZ&w3YNnpu(g4<
z(ljvrNyvERum!6CO}TxNAj!%XosiI~yeux{t#W+)A=0uJ4Ou8r`5Kuuq6D~TinHR&
zcr~?E7}JZ)bMt(nx9A6===O>g+{hrt8y+gfc0qd2$ty7ggozvp0&Z}Ab$zm%HhMwF
zmzQ7~WzjC*dGP^0hdvJt<?};>l7x3S=JbVCjNCi}eqYd22zM0a_$2tR^I7jvA=gt0
ziOWAy@5Y{9Bb+E(LSC~ct&<1Uu5@VrT+z~8%_u{TkL@%6khV0ZLz=~7+cPm0lvkb+
zk#l4~u@taJ@BD+4pEM)_p(n~-dG)J#f9WobeGtrNNaC?X1OojtU8;}}W9fRKnMiY)
zxk;8$6%K`%Ic1!=6npPpe$KYWdaA1CE6s>BL!l{wf_?-<k}nKoaiGH|<bW%>HM}x0
z!N9!QrF|QU>rk@1T_b$0=kPl}5u3X{=5m0)V+mU?vnxN0Lt!*XFDd|pTB>>bb<4Mj
z;E>Qm^G1|!@<S(TN@AH_N?2Xw%rmrG&Xd%hWO24!2|m&-*OLw)%wO;8PHRc5HH=wp
zZ$dT$5W7Ygcgm^nTTD!T$0PAY?Kr0SZRrueIt@2jU^>xKTU}FI99CK_YTcs`LbO0y
z$HJiu#VHR>1S!`Cw;#g36g%s$+{fEh@#_5~BTuEc`|D*)ms5_O@{%ni?MT(jwNz-$
zPVX(rtF@Xpz5tf+iZjV5@*5yDh!ZuIACeLJ>pn})^VUKyolsnLbM`dwmgMv?vDHKo
zfDu%0Kn_Fu#Ml+XJx)tnM(=$jkm9ocVRHS6rLJnIh_g@hQ!P~IWw`ep2`|CR*$F`|
z*~CftjdJfq(f4tJQQ3PNq&y<+J!I##XFv@pv2W(?1062>CoTJnaS~`!oX`=`{eAW)
zBqF|a^V;+Wby*+{p4j5<s~cb&y|%|?1|MC!*>cA|jIa~g$31aNkM46-;m8|Bk#Z)%
zG})+GTSaqE$Bu6oR7qMSBI6f%d6kE+S~E%;-Oa5<Yv^o@CH#EInm;JI2Y0s5e|mE#
zN}f0;O?zdbtSUHQ8)+%gu!nHvoIrCuqAhdZY%Z!S8hFuAFE;f9i>BUd^xMQFpG=Ze
z^r0i8fI`+%-L?hF7Atnvr_dM>*br*Bg}M9-20?n3Yg_AW!VB`|AY#_$YPFph@51Wr
zqp)R8gpWda&k{IkN=L5=+o7V8rBzoSQ$Y95W#mLzCAaPMSLwRAhfJfT>@RX_reSd_
zfM%t&j)F<yaOhTc;KNB*LK&5wSmbfe8)ZXzdP-@CoFkf;12kX0?q-6=$j{>85?OT@
zwb17F7$sR1hHyvl(*`5vD|a=HGu+xvxTfAS&cFyAndrnVv%WOOVUY?E4Q_BvXSFwm
z`JBu9IZoKa>qC$v9oDgQH8(u>{B*7MtR;fveFn)Fdnw0|{(+w2jrUbJk+4#9XxLAy
z(F0M}ZpRCKiYE-Cl7nR4=j)y=PE-n9{Ej=Aq-dxJ?)p48_H?n9RYXh@s`+Xtxa|oJ
z-DHaU&s$P4_BkObBonpL<<oJiPf>6iIM1@Y58s`T>29oo`|D6Bn$ghF6iQmg`ehmw
zaTA_mzMPsD>3p72y;B?~FW&8VMz>tPb%yO~!g%jP&qI$Y0ZJkxLUEDwOG?oqfr~y=
z%z7`n8Dl0*fc5QHe3AyXh(~lDHB^F)jV<efYnW{d{Y^P6*;^8kvERRckEUX0hTa-D
zv2AHMWgjZqmz0VTwn=agNdMY9`qpoHU8)B8sYNef>dVuc8RVwI;~Z%<A97zTPPsaI
zpw@x^QrNI|Qdsd7oUs>1ybiWF0s1$6yvDUMo`vpd*n!`wIyz1I-=B;sGRa?wOIX85
zzV055j)#7XJhQ|08k?P!=D|u1Ql}zACeb%ZTJ6-VeoxASALrG$9PG$`=w_TmJcT?9
z!fy!;1vMh@Du&2CH18o;g!G1AZ5&l03l9y_Lqg`_T>{maeM^!#(z8OTu<diXOg{S2
zT#xkgilzXM&iSjKu~GBe>Wp)KgKep;ns-zAA#QvQ^CyNMPH<l-k&tF~f8AzU{s4nO
zYve;5aq?bKQwVDLY#y;^A*c~A?HRyYOHy53?ah4e82v3SHaapK+_$`Xf-a6yCM@V&
zs&~9%>V$Atr@uv-E46&szpAsy@im<7JuW(!<CGIk9F9pt60x;O)}uX-E=@v}eTp^K
z{qt|mV|O2$%r=_c5acHxor=@G2}QS+UOKb)ewXOVU<W29;CsQDh+h}v=j(TTW&-3|
z!Fr70j$X2SL>?l2_Hc2`ipJ*tjP`6WpJQBQH>^fKi?PFk3&_eftVynI)%o|{+!}lk
zN;^jj7RCQrA`*Y2t0y>GZZ1n7V;7{i^X)|L@RyB_b%0!xzuqmuhQeDuB7d=Sh0zW6
zAUQD+=xzzkvH}c3q4DA<Er>aaBKQMHRmmtA);WH&D6m28b@vCA!=trb6v2f1E#HL1
zM9HoU!WdDU_BhjD@i+aMOSR;@sKR##ec<vl#^A2ZEdRaCEP0#ZP!_z96W=9S0;F&H
zs=6bCS<cKt&@pnXV1L5Q;)&0nLSTKSg+$6<<KD^}<ugsEZ{;g1C&YcY(8Z}I$<w6^
zfTtk&0^8FY>92GpI4~fIm;rxU9gf!8`_TgE)V^<&e-l`NK>Shm^j=4ZyQ}A(d=Y4T
z%i7CD6}i|&8V;vlhKEeo`qtwlFYW5;3V03UOZca<*N|6EVbv-v`LYM}VY}TYv8}s@
zJ!=79Zk@Z+5xEq*c8u<q$jdIM2W2hPgWj5`w=r#@pgeujj4jpNa(~7fJ(bHZPfiHQ
zDUjNK6OIoIf3DJg_Q-ov=6e759y}X^8X-=enYg?8I;x;XMym5Iw0r`!-5%zp+K}vR
zSQg@I3F=p8p%W%&ri)4i-lAFV{5cZ1fw$j-`G=(KEk^H~FHCeew<2Bx>xGe(I`PPP
zEUa5Dw)S5M$574{+=W$pIJ(LFl$*U)g6Lf(p8IRW8uIoP#kN-+!lN5p5-p)0K-c0|
z^Nz(ql`uF(DZ~?Vl>7lgPH2#h<hk^ty8nx*w~ngnd)~)YknZldbW3-0;Ue8#0@5KJ
z0!laBOG-D=Ez%-LcQ;6Pcm9sA_xH2b@9(qLnb~_!JoC&xbJ(|>yyZPUHzD1vAnsE<
z{!yymI&il=x4lq3A=-<>{U<cbs6{RGWP4po2<z$(3s(;BnyC#<Fpq&ZQKsqY=j&y^
z1JL@}_npTte7aDxRIWFXL$+SZWbnUC8;-K<c;BHI9FXMo$o+|-`~CRKL(9o~Jm3?)
z>ZY(<+N^l{RQz@cL-MA@74%tEl4=a^Q`3^l_DH6%F9z`EXdwc3T7rm+$A$191g~a|
zwlt4QE;*#8HStv$eL|c#Ib7)=fiApBCKp4VW20Tge<GDKCi`x|oIS<Hoz*0d;Q($z
z675riftZpNJ*MPh?93p-d!zX9Z~FI3QwZN*4gWZyA`cg`wZg=fLpyb&30`WuClab^
ze|D3EXUX;wA!!yVO1S)n0i!9>*etsy?8bhDHZ#VLVwBpj74}JihrV)fU#X*Xd-%9S
zBjG)g{PfSBtew5q+=GVgGqvxiZ@!`_baizp5OPQ&2e@hC?dfW2|JiU{4}YD^*_?D}
z;ER)~X5x45o@&W{=2gE|HcaExBcFXprN@~G3Nl%tk9`&W=|_lBb=}tAcY_Vc$#ytw
zn6e8i?$VerOtcJhwP`g;pLbtr_wC5pkkqYEU@one`9gRR40LhI%I}MOkU+{hzU3VC
zMi~x`v=0MLe=tYt8h)p?z1f{Yc|)90TUSf_q|$CYHaRIRM1apD#++56!kd~np1J<3
zS!37j-en~vmtekoV>~&X#q=nyGh!TXp4A?d^~l|$N02Nht%u5OY2U;@K(`G0>_vZ4
zjUqm=fqa>qKAY{YG%0L!D44bIPZ`RrGEd-ctzW(IqB^Yf4CTE)@lrUDdZtPeVEy?~
zFI+bKg%}u#btD;>4gx{S1svOc*q;TXyx!?w4*q1H*b#<kM;G;ZT}!X`i)#!C5panz
zWy1IS-I&QvU@h^DN%=sSuu=vas;dDWt5P$4rYuR=Xo~aB_cP<(nxB2Ir8jqs_6d0*
zNvO`ZDe3zJ0Drhljjud!?PTXab`<;!c4HkV-Dk4Lwb2!;-J^6v@e{kb$Whqq4jQlI
zZZ!jkc1ASe2?|{I!13Lxj$o)^aMKmed3>$x4uWTx`4dl}g`)X?8E#i*K?<^2Ev{)-
zL1UghjDMMb6@jjjaQdD`7!oi?g#~F33_7NU*KOOMt>HwlAD|(Ks&is#2w>M0=X*Ia
zaeE$m@$z5$!vio`zmM#9r)+gUu!FaGEwNM{zI~V}p}KEPbQj3*iZ>k<ImoS%oPdi`
z?s*T@mw{Mlk=75)-4O~lluxv`H(hqhJ5JTyKLpIkr71j<y0?nm<K2w^38fYGFAWQX
z&E~0EPXZS3T?RDXb47}Ubi2|HSiqlsd^Fr%ly_U^2JE@qJ9Tk8h=yLWB1GiGwl-)=
zLxp``fC_xlzi4jaklN4Kw&^eO8C7Q={!w88B0**}4Ac-64+KmeUA(4_k?S3@`|-$k
z8j?{I7x$@kt;NdcjTlTwZvb8hO$Q$z-&HJ)_hs@03I5GQ4P$&k3a?H1EA#9*TPDdq
z!R))2bSV*{Wx)sSGF^CZ1p;lTy2Xhz=2>vI<c}p}ynZww`_qa3*ah%LfB*61y@RrJ
zWW9zAls%FW<}!7556|MiFcm5i(g{!m@sfi*10>jA@BVfTh`%kI`8_hetpj)E{iLPh
zJ7C@tDYc0DX7K`U;T&!eHE==U#IrT>q*Cd`fsw%DEL0Y;q1q<UM|eN5;aFqQVGQ|V
zFGJ;NgaI2<cJ|mYm~Z1h)zn<s|Ecyd^F~K?+(#rBI3J5Dk#tVzU-mFguE;~133);7
z)Q|WT$7PYbat94pmdtIYx6~sEgXmN2)L^q}b{u#v>)gb{fYm3$Pn{Q5qj+TVeEQjo
zCM5H@JszZY&ko@%2BipcVN>%JM3O9@a$eD7bJnF$Z62x|XDConL{k_7RzLIo%^vXU
z%)V0{&j^M`wAu|CgTWAD9`6#rU>e|{=75Yo(PbwKfRfm_NMOZopW;x(%dx>L3+HKB
zAUMz2?`O4Y4Eq{}w7iv;m_qUa*MPx(%@XmC9$PC0M<F0?u_nvG>m)3s9~2j|%r$Hi
zj%N<z!{u?}+rIS<ky@8{Eq2p_E2JX!?Ww=Q^$c*IxQpW-HclB|dGK+WT$~SM3^1a+
zevGmj7@eorWL#DGc6Y&PD4UAYivIqX$f-s<(COwD=j$9;Mj?)HV!67k?&|B1G&n(A
zOw-2Su9~jWh2gT!Ml#N!d|!w7RU)#%Y19}ViCuYQisnPj&jjJib+h8wN+SR@HF-bL
zI3+TButn`tVc^jp2Y5E+aa*jEdy55#(^Eu?iH=$Rq%$a@r&qTC`!bzrW_yqPsBS)F
zv64O4jJG4$oBO4H&F(B!@@7wx-elK>8V^NYF88fF?{0O2=~9OXFV@M5<8>zbG!iw`
zsiMS9Pn(rnvx4BGu|ctzl)7w0p7oN1$~!D1nTXyEKQ7>d(s@@~Tib3>P0oj$(=4$W
z>W^Oh06Zh!B`b_TUoPNsDm9cwoyp@kK1-G8YvLd2hDB0Om+eI=%o(S*EjsuwTD7b$
zZizLxNfkSme9mt)L&_t>W#TFliuNiURTUPku{64@TY3Win$Gu&x+m5PvbMK<a|XfW
zGN0M4W>hgqGCj|*9@asmo>c+SZl#x*G_i4f(pUd-*xov2HEb9Bt@DAWu&-EIiDRSr
z6;Km&&0dx1U^Ce-|7>_C6PCi-H1cz0h;28!gmU{8Yb5Rdpf4Rm&r+ONy9aZcCYsst
z{GcwXe|0L|5x(~7vM0ybmBDP0G&m+V{1cuaJ;}vD9d*`DP-n9%Ge7iFfcxVomwR`g
zAm44!7#X!y?aL2+B`mJXSBL>RXjI5=h}%QMLJ420;m-n>bA@Ffxs2t~#|0LH2mCVY
zuW1a9J7Se1;>6Qjx2hP`IfGkv9*6D~5vXfYs>R}5IHvYmQe>s#1f;Py7-7p=V3B^r
zAou-6x~ziUZ{zQI=ttU@OAmzY=cp}755EV;_wAZ0F>>w8h~m?Jp+&OjBiSWkiP$|A
ze1NC!O1WtNe5eTyCgf^OCyfl@I$(8ctRz!(Dud&HC*_kBO-0n>kv{B#jpLg#d?Ms`
zqj*9v$2%d>EfA>)7<ASquwIXcS8mjk5tkSEK8J#Z$+8G1<`0gVuc>R`hUaWGHUaOS
z_~cT14|$Q4po%ZYs^v{aDj62`s1ynOWwyGI0AG^JQ@f#b^N>uxn++etskiw22{jYh
zRV?GkUnJU;2va1|8Vkoe_s^kjoJ(d8)nSf7BkDwYDj2#uv2U~{Bhm#d{z$}MXm9*u
z1~bZ}+<C(KQUTiJol}_>J~h;jzeJFn*e5<=pR?hrb9%e|<6P&zGA9DOK8w6OvB5KP
zcJ&yyL-YCci2+I_Y_VeBnSwh0Q=ltdmx$i(p{sqDEcNLxYoj@}1<faqW@!~@pD}wg
zdM&^#Uqar>ce6%r-kgOO-<VcYe6CswyR_y8x<}EwM`C5p9dS8E_Pkg)4=e2MMKtCk
zXGOhwSaV`xD20+hxV9FO`d~nQnru(mWIFFUDjRtFMN)l^-KHX8o6c*0D)E=?XSc6}
zuE~T?gg4Ls%W)J*kAzPzK?zH#9ZjDIz0L??`eT)BvhH_RY|1^jZnB>`<&db2Vi9mK
zdpi5&sKL#GZ+CeR=4uBd05yj;ig8s=I!#6A*ur>)#H4(F^}lDtOHN9H#!C5V(O&^7
zr&~+UmT0LDHcM{lYY%ufPJNez0@Z+vbcnwvRXnvdeXwHooVwZFjU6`Rxn(ehO02*L
zTf`OL;+{v2yb4qF`{DjczkWEJdpNuK=o#$rRw9v^7)rA)Cj$+qi$DxIJRqrx2c0w(
z?(>A^y|b=1$NE4376U5Wtd*e7`<mCsH){WfFus?Jn?c3OswZ0EvE@Dv)jl?sX$;=C
z_l`6(Yc)Q4IR`4>R>XTa31A@=*o^~a@mQf-Fi!T!Gwnj9;>%k6;T)c8GO2%RkiuEe
z6mwV(nMeG^DGz^ta#i-xTVjVs^G+ga^4ME!Oa#)WX>EN!m7sXLa)JY)(CCBVG4c^C
zV82@RHs6ahA_iS;ypiPFd(FTit6`zj6`$qQAn$QrOcQidGLxcJ8RStK|0jfEZHNau
z!swzk>~a+mp38_y269M~J~9gQV1^Yu58?<bk#HPtfuvJd#n7H!dQx6Cfkj(G6F1Xq
z!1H@*Q_(}5f^Vm5emBnXp_gsF6S6qW^d%Hj^)q;nI<#H;_B^4LNCx^FoBFJq0&OOJ
z!xkhTC#c7n2J!|=J+0cBZ}Qrq^GsTUaD+|6P)Oy(+NUHat+r#5$EmUR#Icbq@=Coc
zHEaCxSbRIJRzXFlLc}Epb~>fSYe4k}aTR=vf2;5g9(Lnl_nMYYBO$<fo&7Yc<+Xxx
z%}#$f5QKuBmu3*1a4<hMJ7iQ2fFTSFqOY9{hN<?xqJF>u*S3^KFkG8fuY4|^in>fq
zz|Uu@U5Bn$-8UR-VhXxf_^0z^h9|PdER`Ql^nJ&@veCgSwu+&Xng{@4_2EtHRY`pd
zemKJl#$O$I5$li1Z$Z_mmDVWmZd*E#KYbS3^B?bgsE324(&3ih-gmqwEvkCN55b;r
zd2wIw9NLs<w0f|=;iyfGTKfe3(;D<60KTPg?`ZjNmL<{$EbvEO5r(Wez_>0?=dkw7
z_`v|c7U|#v-OO7&FtzwEP3G_(Bo;zR6BkZsE~yy)gj6Ocx%iv54E3x7pkS7S9B1pk
z)Wc?>{?-$ou=j`I#GfnXVFfJTEJ7F49)<U(Sxh?BUyhjZz>FKalZL5@tscxi$8wbU
z9p_tZIuVHZ(b>M(zh0VIcTwex(enDf4^XY^wPhW_%xqk*m=6@G4m9JFHk5YoSk|e(
zUfCF9hFw;tgSOQ-`+ohbJh4{EypxnO_IBa`V{io|t)D+v-fz0VCDD{L$`u&0*jMBF
z<gkxxhorYulP7=sBH^QDnKuxVpKl+zGn~c`*b_B@-a=bAoiHDmB1!zMyq-rC=xDk@
z9YefZzMgMcUjBr;256pwk-R^}F`SvA&08PFQ?ygjs}%q|;aRzH_K&8&Hg|&>Grxy!
zo|%#?y?c4nKiiu=2~;r!{ZG$=_q^t|0;hc}W#DfU`O`jX1cC4OU!l-G`A<p-mT{qb
zXo4XYuMaoS-t4K7yW1M4j65HQGn#K<lGaae;I%0)b+~E7C&CR|lreqgx2>y8I`86M
z()3J7Pi(!r$@i|?#$KHW!S=^qmiK`VB9eysb+vVC`1t7u#-mOaQ_*f+JPUbG+M^l-
z@B>A|V3%3><kxHJkuPQ4<Qn9%KATEvc6N5Es#trBQ`l9iVHNIgFtqw8OHsMuFb4xS
zGq3d5dJXKDMFW^zjkVVv6}~={<_Rwv4_bV(rJ{AYs-u_)V8rUv<lz#?@+;PAvm4=#
z3k9gD8SR4+O%=&0<Vu(`VYRd}Z^#QD+ib%JIkLYZuDsOot?t+T40Ym?0W$#}Lb#Yc
zr$$(F%05{C^q<bG3NOC&sdFtY+=5wgZx!}=<mzW7yGPg6>AuKp8?iR_2+=KIz3?c`
zH@;tzj}xK6W++pU<F5r8Bx$*k(HJ=xu`Rz^>XbEtTbpZ_eAJSMS_IgKQI|yLofs~u
z<j{i^!~8NNsYKt$=xf{1tV{en`_sxnA*l6N4hyYFfS+!jpHBB|gN?KXSNBnd)W6mo
z_cvz~ZVEHCMc)wboqm-aFK>LSEBoI`<X{o=k18TWvH}+$QTHU#KA?e%F0yb`_O3qK
z={ViDPMuqSCs@UxF4!8Ny>qH(r;vb^uSQEjbAhQED`jN{FYPWsT?FW>T)&-fIH(gu
zQ8BBXWH5Xxi=p5WbUh^|(CN2bswtegmwdP(f`INJL6vD&ZN>UjT11$g$uf-XcBuOq
z&&+g=(&fiy{MBpZL~!=DSo7Q$Q{1!sQlyB_o~|)V3JK>%`LAh<7f(K)7rTmq1BQX(
zB^<GfDlX<1dQ^3)6ckzo7rt}hQ&&)wm<7_Ix4AgN7;j(RlUSz=XKJZz3B6q7M$i(U
z+_eiJX{ln%p{&?_wQri(s<yhbs#X?1eOzpqcGKKeXZTG?)SC4$7(tjSencz1X!}Or
z`U`_F&!5a4aN6wZir!`R`~|j;fW6Omik!D;0P?X7x+y)y>sZ~?pmjN=$OLAWzk$f=
z6Z&7wdoncEgv<SA#dM2-QVbNa7pZPuy^&kLYhAJTey9E#%&sy%<10u%r^}vZ_L}~B
zeP?7;uUi~9!JR@p^FMWqcOyYYIxIPmM&1)9oGpJ%V>c&4gXtKWstWaYCpH~C4AUC<
zhGkWj!I!Hj@LVo27*SkKKfVj`Ww#&$k^MKO$~RY$wsn-ez7ugHvU;78iU3ALl`YAR
z)lzIV<_nqC7;fVRw|b!o8Hw|=mH~_E^W$yI1d8Dzc3D-R%@*y&Ri#olg20&5S5dRz
z!;zkzb$JqEPk1Qx$jzxIz9j+@eU6soEom;bzFIMUFaCM#^;Bogju%(Sc-yl53mdQJ
z3{yzL@gROHp-Y~05Pb*6Qlcc4QCY5T!$%VU&x{v0<(`v#2T&wORtL$~f;)BJq#VWL
z#C4z$dA(@2*NBzZq`RzVQW*8IB9UfTu!JHSv7ffL=fTR3Mp>s<Z_2LhPi22|gHQaW
zHJ>|YAjmhgIfCKhzFxXs)GyEa$lu7|#M1RC@<Sw=GtKk2)N)Ggka&uKo}6Y@BQ=X5
zI*g}NNkmmdmEu0nlAMBz+*POPOMwzJ@O%CXwXWruf2B4Cz~cgJT_RZ=fR4tC@yZR{
zR$(GLLGu@4X*&+d`LwZbQ^$|=eXaa>o5_YR4ey<rt+T%4_hxKiGY~dp?vAaswQ2gO
zFs7JX(7aUAQ4qPZc_GKpAVp#0^`C5Dt88D{r!_<ZELg^5z4ixN%D>-DS6t0PH_QzH
zyY0U>Zo=F<vqCE!J(wLgVg<Sv4JFn^e}nvKpB(Dkzz2#LD_s~o<_}I_RlT5}6Hb%C
z0cGtiO+CKL(FT#Gi;o#sOGd*pLUVq@|E*<u7`U8BThxsDVdPWN{4Z?po4h)pv*Ua<
zE1W1uWMr1e<o~ly(m#gfsctOkeKB4{Mu9e*?YeTcld~>+fR=Ax#QXKXoKs4c642m3
zNRj3H->I^O4(8eSl9L~f2f>hDFE)Oqh9w+1IjC=bcmPS!VLG_7pm)``&Hu^gV5XuV
zUsVoZ#G6~7)Xo=6sset8UXH9Vv*w?ukxVf5r^VhMPl90|>sM9`D*v8<?8A<QfCKn5
zqH0J8weG?M?rx7xdtR?TotHJOg5q0S`BeXQGPCsp3zjXqiTzy+e==>R#^Q2oh9PCY
z{+I`wY=vj<*q$_OHs_ed#e_v{r=ZAVT5Ax}FxGw-OaW{=pv6Le&amY}4O@?kS!nR~
z4889BgMfJ5#69W!eX{|3;;L!yc`?re7TbvuvUWevXs?-5_0c0FB;?So{^EwWZ=GYO
zSbS3pq=?PchHyFuLMxj5m!=obqf1$SWY+dj;;G?bZe_Uzw&8JSHH&*K>kN!Hd)jx;
zZd>Ig6oFv@Wj}zSX@$J6!$BQc51B8=C}~}9JjSMLM>N;oE4@cJ(NKhb{n*TA3-<R4
zRmd>WJnxD^ArOObWFxYignr0@{QdwBF8b8aRP**C!{KjQ;bNo2&qzHj1nQ$lQ!uSJ
zCectbbf^#_v#uw`%nzPw*2qZb8O?EgzIO4!`Ov|$6H!sb5{h!tknk-1RidZ->xkO@
zfi!@}7IZ?VO->}JntaFSzhR_)x70$XW_oa{(1L-kfPW@rSa%gPYiApA>@rQ*suhJE
zH7HHCg8rwDSY=4w`^-h4XutBz*^0E787)cfps!(H3fYQBSl2e=-lG;^+E%BXh}u<N
zZ*1k^kerjmN16Uu`!cd`M>?3Hy)DQ(_{l&6%cT{#i6c8mT~h}0g}fw5P?>AH{(04m
zB;xAe1Jq210%nN)0psG7Cbp_B_?wE?D^Sssd~a8=)T4Gfz2n#I%#Lh4<d`Q%W-Q#E
zC7*}H5K)vV+=U)@3Ok0394Q&qpCkT;rA}2{0hd{PB4w06S>h0qhBv)MK<6l;l2x_n
zW(ipTCPJh*a9C+OA?>UlT?A3qraUvCI1j?zALWGzADbNAFmFm1zrlj|l8_U)UxWcg
z^)!>{)+Zxy?ok}afC(|VskSd7&76?D0co(WC;#lNQw2ItSzDt)h-Ii;iqK>G>Cm##
z)?@%)y$0tiX6f+88AdTyuekNpr|XV6H`@oy6ss3gPP>Vek4|@X#V`KNk%l%j$MpW9
zIuj^6AgCF;I(nBO?I$4c`6*Okjc#_Ds<Yd%fkfdM{#ZxaSCP~D4@$>s<tdeMDc8?D
z6V8t1;KNU-{*G1iFRA9LDW>}FcaPN)pg58OL78O+)yo(PzD~0LX3RfeaEt)8oqI@m
zA3{ZZ+wvw-2C3<BDemM>ZGdHzT@8npCe}H%c6GfA)%_C5+s^<XO}|;|63QD!=k&4M
zd31Aoze`Q5Bh5;n2u%3E1ID=8`_UN-Q?8w@@5H#~)?rJmelOhHsKkR&;Vway1VmWh
zw9t9tw#!Uctn*-scs6UD{AF~|`%Bi43%{=|wv`ne7luZM4Mp8nu74_clk{`@4hBtF
z;QPNEp^zNfi#M6DOMN&IGOOIQeAgLCtd+B?a?;8i7YZ_GcXcBVQX}!H$4U9CvAKJT
zbjCx0M!(zLOf-@2tPXItr2LM+jfi{ZwpGc;$}lo-&B?r(@3;$2s3zG~KRsUq{r71W
zqDr5I1iu!oZ`9XIN>r#mADL-3@iLZlk2)$m?ng*EALb}J2nL-ahvVh}++!C4;)G0)
zxq2#~AIVtkVWw=4sc!yOa81gHjTRb7Mdw;SA|XreUoB?3)ulT-C9<94fc-?v{?`bO
zC_XcNCn^5DRn7;GBx1o9A!79^euIsKw*YdKQfF*OaZJhZbo2UaYA(>wcKsuv_B|s_
zi$vT#Z6Z(5I^~{R!?MJ|RcTDwmK)>80gB+_?0b^0>_byzw47kjsxBuDWTMfg&3Tgm
z9bh9U)LYN!JZgiI%edTj;2%Qm42tXLKN)``{M4N0WjfT5L&gZJ2GqLq6Ir(b++gfx
zzz-59MFZ8P9l0uG5uA+E5QR4j_`X`g`&fOg1J@RLLjTnJ`YTaRZhK90`O2ZednK^}
zf&Obh-WH3=m@5)@#Ux=~V!$EH6EZTt6j;&nvN~KD8{bjT>I?)httz?UdG{<yQ-hyf
zH$!2wU30DrC)`CE3_?607c_A#a@PpX10nPoHm#rn4&);CuHwGX6`ysRR+|O(qouF$
zS9Y!KdRxy|MWm>s-AAtd?TRAIxNg5tohI_fyt;=^82mS+W1r-nD1_tK`})&<FV$f#
zk{elGb)l5KIM&U(P1A`?D}+?-`ljVnVeGyfW=81X>a|q8q_~cFo;2-LUMx3=crG`@
z3Q&zK*e)(F{P?lpvFOma;M(|Od5G$QDk$&^8VD2#2MVO33PR&jd5w&P^;-mYJO1RD
zs2vOTUaL^O-yo@V^uEIQhg2cgVd;~_$&+JnY@uLby>%^Ppl?~gaq~y$N5^I})^4me
zzLar7m!=<OE38pVH6Nk0YAsAMT-#Kc;N}b^i+adtUCCjG7F<V`c@Eb~lqb7suoKh>
zSN+tnm3&+3-ip-dN{9Ur<xyOQt#fpE#ifj$tQmg+BMPNZ@JXT_M#<RWn>^@jG4KuJ
zyv0ZEq5lE1%W=;C#$i-?VLn4qgC0W$6t0%2P<mYY4fg#tw}D^s;J)VdEzL)^u!8=W
zHnwQt_qR@ETszd2Cx%u~+R!AAT{f;gx3aFJmmIae;gpw)Yn$ezB%l5(?BN1!v<|gs
zHsB-wL-M}{7QMFyfR=4r4{{9AMk}5Ok5Bt$tDU1`l{f~z=zx-VutVfiUrPBO!YLmr
z;{22M$ai#{2+b?pI~He_RG+eE750WZ|L7TFM^H_n)ej~AIpan*HitH<K+IY5<8A*e
zE)H2k{bQYme8P?H32c5!aBaWJ1j8~!144R0qX7qI6Iz@>wmt|w6t%74?ti{ww}XGg
z$^U9O^T6+z5O4H(;Yee7{3eXpsWMUNQb$kpPo>E5!aT_BA|<k#lm;T`;&I0BAd46@
zzpJQobXw|!_auncxY3LQpKEHSKY!|#*n9oFq~!pu31j6-(Ms6$!;y_bb1voEp>{l+
zX@qonS6rGyZIi^7{Wrrj5>v{}2}kJzI;$fhj3+v*ZY$Bu6PVgLU->~LWK5wB`NM0=
zUM&MRabm?L)oWf?;?7?ScyYDJk(N;$VzTy$Plc)ECF4b|>~Ob%pEghAdj=2ci0&C2
zh&5L5FpQm(?bL#h-omlf(T#5_n@tb8Hm(HcC#MaT#$?mDf7N!VJaORLxmD97D9M^F
zz2NRnB(C}>L(Ads>wZv(Zf6vw@N(597@&a<+dS6!$umKr@l62DFY}*fFsLkWjz(J-
z-J;Pft8FHK_aO&c_oM;4+CK-z##C)>nJ@KDzq~IjRl@CFfpWAypz(u>hk{UzLS`>@
zWc-hTZ};PZE>@#OFVls)6fRiCoZbc^MS(+VIn9@FVni47`M2RvF=T9Unu;T2sr|@V
zhDH3Rvb5hvwdQJ6CHWU)E@N4t-E%D-9JZ}-xC-3n^Lal58Xl{sh@tp3{L_$)2K~Ri
ziwIthCWzqmDF1fg^q6Dtmw6f(`^{(nMGVzHmE43qcGJ83tAR_aFEJSQ_{x%hub@}n
zk)TAoPtXt}g6bf3-^9zIX7~%mGi_bHlr7GWvajmYZTv}c$JU$|YvT{!*<iFTo~~=x
zX<r<)B!%tPV;fwwr7);NG5qOzbp-`dWRZ|oL7!yHl3b~ui2OR-%4Rz@qP6X01-SJc
z`a#HT?kJ}ZIrPz>yF-qrrt-AMy$$JAeaxRu{Euv!5O_|D9LpIOmLd~`zLU??4Y6RD
z%%S5{=EDBpQz273_h5~>g)0vTT3KnDhcjFNnx}1ZbBR>I(qswfFZ`mUJh%BFN<__H
zw?suL6K*VQU0Utg8kf%smG)<k**s4Z8uAan06Pa|><jUIgfb_UvS*I&6K~6~!A#Vn
zEbhVI{u*j4)k-Y@A3n)W>OXxpzw|F;ev6MIIU~vbwm{By93ks0%qMCt6^<Z#yfpow
z#h}u`4$ake>ULux=dXwSrSS?YjIkgE?utGs%fQ$`j+UF<{F`>mU`rbDE(;>)O;loR
z!&lFZBetE1SfYbO$BR46(cdQjg#!&zlw}m18qO$M*5NA6W&G|p@-UH(c0|JFOCPzL
zQBQn$4VD7>fcsk#yNa+S&ki)B8+<?CzXdMH3i&jBBn4m|i)g)i8^X{&jnxxOQqQ?k
z0-JIi3`;Yu>xiYA<LdVGvfUrWOKLW6qds4vCfhW$p#3oW`pN767fBDM;?RT(mD^ty
zMPIB=UF#Z`N>RfcL(1l|z|YdAAUz`H0G~oR;g%#dYTmnz(nd2o(Zlu^@UH&e@5tNS
zvz52Igrt(ef6nttuiO8mzqmF|51Vbo>9)$2IWl@~>FVHwB|wZ<OI&q}KQ_kDU1|Xj
z4L%E;|HZV1dZd(e((NAn+oHp)UHFrpMXM#_XD<=|*^w_DxZX>I_a=lBad*jvuf<t}
z6+ipYy0IAkp=Ud%e1qGs^Fo$rQ4*gff>!yo^3Vu(ym-^%@U*lcqxg#(R8aOpvhu<g
z@W#_h7E81|Kc04vP{%V=>2b(Mfo5KpmLJ+$V|P?dy|_>8`91ovx^sdc!a#PxAQE)1
zljx-pBzH?NFFcLD64Pt?Dq+eQt{K1bLm9yx=HCcf!Eb8wp9e2Kxm0a_j-N>FtgPJ&
zu`zrbL3IzzOG6FZWU|t>WVlOoXLBK}1G`}Le;2h2L-!~*ukRZZb&u#>aLV3nha%R2
zjSt^+Seus<yF>-I2UFPs2a;b$1Gq98!aGb_`LPOTe3#ts<>kR*s0xxY>Qw0v*yPyp
z=Fjh{1I$G%>q_%}REB=Rulo-Nxcs%EsPF*cCVPrj)7@#n0;B&4o*owa(GcuphdeLU
zR@NsJwWL>d_a_x@zk+rnuAaf4UP)}YSaV&spl!?H8U9iWUXA8+l%Xd37tak&qF*8@
zVu6k!*MYX+X(L|qk<;vN)R;=jUot}fHnEK`%UND@Gm>)m3~q!*I-y}1XuiC+2#~*&
zu$*u{h>BPd`lI>sW@c0pi$s%ID?em#Z+H8z3YXEU{O#1g?KdcBo*SX~0U~ZNz}cR|
z`Qgr+MOuPdZ@a#&iqjCc-p7eJCDcFrimo!MzVVWuDTjkLoaC<oEp`18ZYx7vr$k`^
z17$Pv0wFhzf)-QPOa~hbF_W0L!tm7|rok>)0?VMV&SD_#=>Ayez~hv9#ez3#D${3x
z{&F!u=0rmU8naX>R#XW_aI7q>>3dadd!NvHHNIG|vE96xu<Bfl`7ki_E_ntUKCikj
zbwTwsO9TNfX`z#UOlj;`SFO}yYcj1<jHQ4vFI$9c3yU_8N`#B*8^H7Mk9LfzdYAK+
zB<beL6A(8A`3S&nH=3Kv7pGnI8VKaq8%xsEsh`MJWi-%bNa(Ls(*@b@F5kx$uD*|U
zRwonu(x~v23MER>q5bVjLa%8Kh}<s3G0<b+a!maxUrk{Zi<BY`h8XByiS*$?5uKF)
ztl!X5*2t)io2Q^%+viuLSC9*_edK(6!htLY3;r-%%u}8tI10DOOp$Jyp#-2-2r!<k
zBz~bgU}D6f%_4bF*twxom#x7@S6_}X{5L4Oy$$m}!4u+>h|BOtB!A{yfI#Cz4ywB8
z1n`lIu8Z^TP8gL(=X!Z9>r5iE)@Rxcb^gMuZi?Co=K>poy#hX&bCVCT(32M(&+Ss<
zN(VlqJKx@-u?lpvMUwpMAI`#NS2>%=31F_?7D7XZciN~;?ouYo@mexw(6_br|J&(r
ze~5SK!@oxfT~9MHCwS>{$YV_CRQ*CH!|qPwv5Yn7Xi0x=i=OboiPNj@QN9$D?>_Ds
z%e<QPm=1Gt`e+{O^W&$`nsqm2jQ(bFLqN=2k@h&_B!9jkd7{XNg&f+43U^LE;2I+9
z<rcgi7>_`YiR0}_zxg{V<j+UU87Rz~iUfp&xub7VOP)hKdAZp}ds%Rl-VF?No1L?4
z682X~i^IX!2qP?ROEkCKbR6W>Q{+K75m~v&<TuCdE}HX7#L}6&GDzn8Hqxja%~T;M
zQHC!p`<zOddbRu>3VAo*u4UFS8f)<S>s^2975IV8J`f9ay8zoyM-#J_>5Ya!JG*)-
z-PplfT2=-OWPasG@aBV!zvbhyl*)88tyLbAYwO>BU88$O<U6=Klk=3#>^@9u{s0-Q
z?t9iDMuj}@Ea(VViIs3nQyg6=O_1l5ns(EfGvjn5ZH&nN9(54d(aa9I@Li7K=9B{#
z=ilQXcF4H(NqS5N&jMXd*WyHz-^+?3=(W6h7y9mXBZ59)-+B3+kuB2uMej6~tHd7(
z?9f-2A$1#}#a##QdO!LEOi`LykU$IW<lje4k*95{(bi?u)W?!<IlN|%#KQud=<JU3
zbOdlBSQyOW|K7R&A;%q~v?ZOg&lK8UT)>IwRR=NYeNPfai@adyw689wEv~|p9;^7r
zw6V@z-Uq5EI4Nmt^`F6-i~eMmkpOlqMJM@}@C6YX#MsS30@$%J5H>tEHp^h?xq#iQ
z<@9!;co6tppo-TgJJ^_7zS7ahT)%;==x^V&d_@MUx-ffx(HqPdh}h3BKnsbHZv3Z4
zK{Zwg80R-jRp8AIC(*&qRm+>*p8xFuD6iNDp}IGHXSKITV5lN)CCv${_Mo52Hjx{6
z1d_Y%g7)wvWP)vtxO_OeaHGX1l<)RM+a};D`H0HEFwxh3YLYY51pjmH>|WDy$~|2c
zw+#b0HXKQ;nrfboH(Hu<xP0c&myB84<2Ul$y`UH-+BV1p{yyg)5N}gWl$q*;Gi84w
zccwt*bS2l47^0uie{bw&qv5<JmUJM9Ac&v}*WG3O7JPKpj%I2JrIqh3<QISEYqY7|
z;|_Hv!&7&F_H3665wB`bxJ6lzB}R)Dh*w>map-1F$ExGa$33v3qSxrsI$nRR=|Zwi
z$CR?N+SwXR0h~b+EBI@O4c(sWZx0_-={bE`4iIsB%U4-yQNDKItJ(R2rgFC~=n1}h
zg}*E-tVp&@FbL*zq3`PV@M{{uya9kHk|sJ>e%&9SxEC91Mm}gg1Kv|Uh|OEnNP*#T
zBeRohnZ~e|o|~vmBigEy#9OD5B2CCmOku78X%o7Px^96s^=g=B)YKtsKX9H;APkuW
zoy{1O-2;@oI9=9rN?lV#^8oLsO04Gje{YOVveeoemONGF=zltCo-?4ef<~{*k~B%@
zomKs+;HAJQJV=q&He8&6w8qs*RpRzvp~&MXH}Fg-Mj2)<H^<o;AA~ui%`*4a5k5Zs
z(d96EibA2gEPOjICqwXU28Og!2AtrxN@OOtI%zC;Y+^W=-mUV<LqPxscRxrl+~RU&
zrtb*mj<P*PwyrDZ2BRG+`J{A%_Axun9*TfsW3OIzM?ie$uOdHS36p0i*bI3a;K~`w
zgq9?CUz~l>?A&uPNZH0w{8sDKee*sne{2>%_zkw+X4$dfsj(=6`S1|is1(02yd)M>
zEmP>tO6JUF|H*QZet|5lIYzY(1h-YOv4D9HAKFYHpBs-5Hr+j^lEw~W3k4>F%Rdah
zVjX7wN&9yD#QCA)<>-;4S#bMIUKtX0he)}X8rEJ(g%{ojTI{7=YXi>7wn+~cZFw6D
zUQ(uK>KDk%Q$Kyjv5kyXz3vHdLkw2Ly!l}EJ{AtT??)YzV#G_fwF#z<iLjWknh2z-
zhnR&hZqn}r%xXZCFq%)iuorAbI$74kG?~jsm}O)vpp#J=YvjIw6z`>El>bSQT^vL{
zrD)*u_V-(2RZr+PKk7lCYho;jJ^RLobvK$R28tfmm^N_hw@JrL2FY)xwQ{LcU4H3k
zB3eFlUsMF};N$Vp$B|Co(zo?n#CcKStT5-N7)q~Iil7>0=KIK8QnEm*@Kn_jP0JeT
zHBsB;rKf>lt_r*$7(?Nwu*F+-h*J30xBa;2GIIAY8gD&3c_tVq;sQ*i#3b2w)r}x@
z-MK*3h2>qCEVVqD(MuNPKn;odr_EmzWHZy)jO(?1Zv76Y?Z7EzUMhM>@(cJ`aD7Wp
zC%x38LJCx+`My`4Ci2+1`9}CM2(}+t?z82lNEy4C6OR@Yt(**eBno1R5O4lrViC(f
zV~zjb|H>n`5dv$GEL_4^NfUkz_<S^oJ&N_HAMs?-{M}6d@7lE)n2wH)(_HoIdsg6-
zzVESSzBy4l*lIcuzF<=qW%SlhlQ29tP502itYd|JlszC|4zN(;lQS?6tC|q<X{u3>
z%JM(on<j*3Xh2~Q_c8fc-`D*sWeGz}v>yKd$@>qQ1M;*X{}8xc{;Pjci(2Cw586up
zi~>-XZ=qoll*liIIjH&4&3dK@6Uevmz-B1*lx-WvBxuzj!>aa6f2W^7l+VHGen@o3
z?XyH|=QhsVVKlQvF%l-4SVG;FP?pl9Y80na3NZxpy~N8+=|(=3{~|hTWR2zNF^?sM
z;o&-%dPCf}Y=NXr1*aiBU)dHBT?g!f$`W9qAsF^pM#rjN7Qc`w#-l*fxvCdUKToZW
zxYIGtxTzKw_mkYa+5zLfIdF`c9T-#kL;TgrU-Ng#BWsG{ZW{kvQ76br^NVLOQ`ca@
zOu>fw{)Y+~h8sw@RSp09n1d4c(TERaay?^;y6B$v7z_Fb9ffJM76yL78r&x+P0jPY
zpP0OroI5c8Zu*|AMjLE)^dMM#68NZhI2MV+e~ZPFSJ(Gr{NsPi8<){SlxpO*{onE&
zyx%NrNb`qKySTD6WF3bKem5zZPSq@JbE(p2o1T9XJ9!~%^SsKwoE|l8ltYN(;aVX2
z+0e6`JLPU(!<U6oB~5nVr31kke$!9J!CFm|h!=eAw|9qMD24E_y6rKvBJ}nW{MJmu
zji9N(5RKgx?qmm8xPqe<y_;}8!oQ>JR8Io^1Va}r43GJGK~8%^mb~0D3`{*(O4#xP
zf@QA_%G{sS_&W{36RA>_j%0Ud27s9FSkTLx7}2$Mqm4My%$K9YUZxSSJ2Q?ml{$R*
z2<YYOgP*Cr=rV5RSWGlYJp^7dxPPE_UrXS#+=8z0y=zrX!X@#Qp|)dF&0=4-&UKvu
zda`V&quslrcv?qNh2KgU3qZieX-m0lS`uG<`#kaiErA^5hhvycYO8%mV~EX!(rpdB
z3YuPf`i#~ig<N)OtX3hx&t==U^}*H_<v(Y*#9mW|`glLG^!|k;yXp-$K(2#e2n2g|
zO|Tx2{F@)z7rgK#>A@<CsM5|h1?WZl$Kmfo;GiIhXo3=5b@+CV*C$87RzW^K!0i%*
zQXzr3?)TyK_2tqZCGM6vl#I#cWg_H4Gz~V{i$Ef*2y|B{hd&)y0gpXV+7iK#tW>gn
z+I85@7AToU;C^;|eIFTEEfq~54{>ZoURmrlWrYF;vAZ4e@5KwK44p0Q(fzkw^?xDm
zYMF4KF=T3MAp}4kee1D{59=PxjXuM!s-p;;qCa+CT&@zTQ7?-r{M%0i;8?RL2wQDl
z6;W$rT<2-l8doFT7Rc>F>R)P78tv)y?TBVVN?v{F3I5x6tKvlwZs7z?lay~zP>)gA
z$DcdXHA$O(r^ByBL>};2JcQSWGG)i*Air@_YvAV;I<WCx3c4>teK`M5kc+(|UTfDs
zK|rM2{qawGG22PmHQ`^Zu#M?K52|xbxgkUmOqmr6eL@>I!-?_lVA<n%cmZOzu~PYX
zbGEI=>F>+*apIMAeR0zS-&~3=EMH;BEkN{56eUa)uyC<Tz&PvszkfYE5}|dJkd_BJ
z>LLYFt>=UMky_z~xK#W#M(tvRXGpFkjdt79!h^0N7*^ELWGMayP{C*i+@g5M(xw{v
zcKu)nzUj-+ECpfGHo?j<+Mu}oPC1PkaT0|`h9e-kZ(x$e)`#GMuSZn?*7l#=Dsb}k
zX3)(YClLj%;Wl4Ou<s!VDKItkeIaw?mlL?UA?`TG)g%1-eD{_z$81<7%kaQWWS>@4
z!2-vGX697t+Epfw3(v4wmj|zEWWhtvM72XqS~kO*L5J<q8rsjzwom|<*XWj^%V@KU
z5!bkO+W`r1Qqt0KO*8;4v<j(1s7xx?+S(eR?BFHi-C0MNI)P11*qU~o-s9a$&0LLr
zUjd<~sOyuUMLrX7WpnXGJ?l+BiCU!1PNvV^|LG@Hx-xa{9F;w}kWA@_@u#z;%pAjD
zQ_mXa_IbD^yX86%$daz=NnEXY-OHjfp<ZmT_%lBA)*aw5@$cL?+!8+IG&lQng`$sH
zeWgOG#rQMF<wcolDTUFVt0zw6pQF4XQ$wk5L8YG1N4~9Zv56snNZlYsqTH<{a>lo9
zt#GI&KhZz^_4m&%C$>u&H<h){5cX4k!Y)BDwyvgJV)z2cx45~}_n8QdqiGrI?Q&MC
zWRwh+%oj%4lwU2K_OLzn{3@x9rlv&TrU9<{AQNr>Rpz#`EuGv?KbRDCOd>K&KTi>B
zT(=Ez;OYo2Ac}Ko;^`y8J#(P5<P_jm-{as<Ud-QZ7u)<KXy)V$`Ui+#eIv#W1Jh^P
zeO%J#Jk~7>+26PB%2g~|k)Ra}mnSvR{}5PNBvIzb#cm!$u2!_Y5^no;r#2F&#=zpq
zl-KKpGKiBKn$$kX0{X5H`o9-a;DmvD0;YlJQzJK>0A{m=ZsW~SqYwi*$yFkNES|G<
z)NVR?W}N=W;2~`)jqG+`xQa5?mrVHR4xHm8<-aR@s<9rQ*XREy73p3aiS9#V-&RuL
z=|I^U!I#d|1fAukF}4VNYba765>`z#xajlhfHm|iTYrp_H)3V9=2xsNbw9R%Zh_B(
zf^pf%ikKv4A>ZgIJAK1;Fhtx#{1muzm`5r%`x1@Z2QSXAF{u&bst1mqV}q$O4!&-D
zg%;}i#-v4=Ls^oaMmq-#LIO#2*RpescFg)|Aje4EJ61t%r)nqlc>h-Oz#b!Isiz6N
z$1I{YJ&nu$=0TLQjQ3s#q_C0a#Xl@J?lBe6V~hkuA$uYChaPJ92FMitZYl@$Ah5c8
z@E194K>dko`flm`=MlRb=?Bz*wd5NMM=2g&eRE~rF(oU}862zr{c^i5sIL-fY-_un
z615F&EN7$Gx|J~tw)()vqIBWRYTGg+zIsBY<4e^$O)>*30y&JK*bPJNPv&q0@?MPc
z2%fLeLJfTATXIOT3=j|yaBy&P{#7g%<O6j0UMZ=TC{ml)a<+{4=ObYGtN==rtUsxJ
z<o01S`PYz3|CI0S{85Tx%Vy7|qp5HeiRsbt1sQvcXxL%WpA`J9tMU(4ZCHV@@`8Qq
zUE6QENOqY7$4R(Y-3Orme~TmgujzCJfV=Q4fFn6I<{ZR=wz^8LH<lS5#N|?Ii?%LF
zKJSI7WPjYY8bTy!>|-QI(s#G2*$@3ESu<)=-j)A3TwF>Nt3<vsj*TNm5w$c&2Lb~M
zfS_y!^X7r>Bl2*B#cD{>dwigEw1o7INJE2L6?xg`A0}6^oOtKeJ6C`gI#Ac@!?g?y
zV6!Xa&3>BBSIb>wDAXoOj4q%ITvDotw~c!U9@!vDa9b)z=iF;79daSCEKyIEM`i^8
zU(`y+muv>Vw5-?-7s%o-K33Qm*TgGFc%o?eh)QJjO2*uD3;;FnmIMf(tnl-R$N57m
z>MC$2es&QU(tDc|UO8{Z7|A}V;dUAEDWdOyq&DB?il1+oSgf7oZ^7`-J@PL)CJYMF
zRgiAICkmJ?b(?}wY-r}9!KIp|-Ju6-#$P(CRW6*#-CD3;N1<#%c3R6_n(HhgWwgaa
z=lH@D&avUg!7%REE24bjHme?Lu}gX*bXVGPT=)6(5xu*zaC+M_z4VwrdOrVphb8aw
zB23CrebLnWi6q3$`PslD9Pg1frtorm?GbOK-2pMZ1|oGM!|;>P^c`!0DrE$>khN~A
zwnvguN-sKb3vrSRX8o4Z5E_SMHd7$gsavx0*PHlZMYHcBThVHQ%}-4{&$!RC3bv6A
z#;Z&b3ta=0yz+SqWl-r4N~{pOa-K5Bska_7>II1gus0X{LNDn=gkS>jrOE(d4hKF`
z_8bh1Gog>udGspCEa6`b>59!5g<?5)ai;Uv-bu0HMy@Dqj1IfIlGr{S?u0h|+oObF
zK8Owo@qYAeS7(c<a3aylI1vg03nOMZ+;=>fC?)<v#X=u2YW>`Ln#vdAb#NY+>#ZGc
z#q;p`tJ>P}Yb9?04z&swX>=|XjKPH9d;*C+O_+hbTGoa~*+WT|#Z2>YVvgKRNm41(
zcD5&!JWQPbG;U99!`jsDM}IqOvv-6$5o~_c%{YIOiR~(J^x;fy0ax5kMU3vLctC#s
zyWyo+e}mB#webFLPyQ9LKcl(@AET3$`n6rQqXFiVx{CI;#b$2_mHA=eKp1i7CHJhi
z>-a(H=>$oSx)7W&Mu;37U_S&x9$lF1B!3;0JNhe{tU1>?x3F+)cRW8?*#DtS-qlgv
zeJ}s#M@q><+@$wfIBaRdVvGq#^Mih>agZ%}n+ar_b*l*g%1sL{RNhIi*IWY#9%R?w
zGac7%&3x7i(7gO%I7c(I#~5_8*Rv#x_2X0Va=;%}2S2ix@d;+Nt(_FPOVx8S6wruk
z?vCofM87I{+2btM^_7J5uW|7^EB!K!-r8Gy7^?`T`Cn(PdaQF1br`4@RcW!i9kG4C
z&9Lrxenvn2gjAz!j<Q$RAaxMej68SOBLuS9?*Zwt|7e{L+YLmq=>i%nI4BnLYeio=
zRq1~YIehRZYmRX4b|GLYOWi&Qoj&!#bJ9in5ayN~-tuc?QoQ*!+e9h^K|o7`xqu$f
z{`_jUM+VlllK5Zcn@*&pO6@?JUf*}O)dW;f&L+Q1(JpFPP@Dand!+~jd4G1dCu5{v
zPH*7&1Jq?`;l-NYM_1S<;yDmHn9DX>#uHR#w#1)w`crm7l~$0E7TKGwb5&`GMH3?Y
ziylZD<2q=^s6^4mm0DiI|Ck4iW8L2%on$R1N6ZV~9p-kicl=+4)v1A5qV?74c&}Go
zw=)=0*4WflXseD&i`5`Xj{Xxv8~F#3wEQ75QFstl>Z8{N4M5V{`o1sWZ1>C5O=Cp|
zewl0NHx9x4YC(1PQyH&k;OYec4Q{8zan2_C7&=gYgWSPf7INhIU|Dj<&6{yvP`@6v
ze0W??N4f_EplPQz8*Gf1+H!X~Md)F>`Od=ow&VA(<U0ZtDZc_2F!D0%zt`&}{kEm5
zrP9sXntvL~f3Tz~!#Vx~g;Q`{_iw<4=S*t%iTcc9nTce<>Y}uT@k1{Fi2Yag4BkxY
zBhKP)V!^)=@F)DM&42>ZQ>5r{^ntMHC!%aK*??ginj@3*6X)K(IfwK3uQ}Vmfu&G9
zxH0tKhG}VOVz+B>6qo``CZ?^FTX#1r_qF|Yi*`p`8Q(YD^^EN@gAG^P0o#cSIBE}Q
zXE`$5X>$^q9C0rU;3#Vq6<OfzXbr#Ng0CSYC`wv0vL3<y-3(c#&4%^(Psw($Pnc^k
z#T|g;DV}Iqkkp-gQa7g4&n+4B3w9^cqEKw)=6-<LnF;gk0x$0`WT=fs3e*fS8@GLz
z&){a!qm{O=TBLSosAQT4h~L+3-(TII<T7%dG&S&a6&^I%odjJN^$6kY5n65{(W${5
zg(O6B8q!Blc}Pw#W1q(IG=7=2E>!HxA~~3{ZhO$O`ti{0hk5aMKQZzNTfTf8CE@fL
z;LyhiUv-xPYTy7#8c?E=Fd@bmr*Zqi!Y7avv8TtSh1wR}@Mak=7}Ax%nP#94L|W?r
zIwgl+1qp7bR*-C&8*8Tv24_PYN&BqhQsUcVp5kA}gf^`G#f8tp->bSl9}#{#x7^t<
zX}N&g3a$EgQQ#?WXbNuKA3CEVfB8%IM#Swvt~_QKXdyLnYb<8p^q)J=-$pvZUJAXO
z_kPW(2M#XD0t1Ceze+H*2|`?Te9m<ko_sQZ+TzkLLgd9giV<L~=(kYER7fPTP-_Ls
z@N5ca>_?5At$65RfJ6G&Gmk&v#68B`p?veG<h~fq3N%Ftm;xB{=Ue_sSsPaoUvF=@
zcdWp@f@oa-LtNkLKDw>-;WR9zl$GHpdcTgTKTMcQ!tZ`0qIe^_p<*$z3^L!7YD^)A
z{>E5|wM6qnc6w450YjhixgOi`B<+diui?Eq(xhDgf;9w6Kl;2c|6-JOZ*H)E<3#y5
zW~qEd3cEb9?7j-8uKoNeOR%G*>mIwG1^L-=L1mvZhJAR$o<;Z%1(4>2r^#>ijl!p|
zie-8N&2?+>yL66u{;YAinzOQ%kFBQv7OO0BDy2w&zxRMMuek~-1J|O0frCgc`3gR*
z<PF<I9O%><*FDZUn&NH`r1Fl{IhkY;#SBO+^dD~Zfj$m(QCg_H?9!h@1ih0u1y}q$
zJ3cfh!WPds$tIp{)f)mc$sYbFv%*xV2I<>;W);rR%%P-FZ{DvR!I{G$GkH>`lZHI6
zp%QN4bm>}bj_cpg>^E%j`kDN82FStn12|V)fhPn>djigxIqI~J+pn^>e(PQ(hLetW
zSp{5KJkLu{!5&4hWzP}M`R6IxIN)x_n?+vQ>z!h>H7A2xLl46QI5RZx##dJhzBuk8
zy}LGt4zdi^G|aZ*03&P8r{J6)e$C02J`CoHUj@n*s$-|;H^~=1Jtns|gdSGpo+ytE
z*_F152X=KMeJBI&=>&S#2?nb{O<jtDIM8{--%_9Vc1%n!HoX`T-pVh0W=;q#Mj*&F
z5PTE&q)RI)woIzKzwUBMtkf~Z&4;uk_Kq)pYTMuk0~0WK;O2OF<Lryf)~+w=%F7Zx
zjO(m-ap+FAxeB904ve*E0WK*Gr+jl_pPnYyk!lz>g+guh_IY5JZn185j%T5_WBnsE
zSy4AY?rd|Qw!+;B%W*CVjKjQ1r*rb~Q(#N&Mj=1}i7|u_D|*iLDQ5LD5B*2U{{B8X
zDc`BruWOxzOa5}S6!ti_DPQANfhEZ5g}3w9zodKT2GT#Q?vVnmiR{yZPF&Jcfo=XG
zoZmV7Df2gR_>_`lb%uI#UB;($Ooc+wC!&K4n&!E5Fiuv3H~@7SRA!X^KgPZ~tjZ_q
zR*`Oy76lL8N_QVpx?8%WyQNdQq`SKtN$GBBkdQ8Exa05p?(^I~@A;Dl4)f0Jy=U#U
z);nY5<hj2?WLOl<ukr4k5SH~S2XA-fE$-*px2O&v4Y#M;CrBK^f3!l;rFRK!v<NG7
zym@FZ6Gg<lW{eixLhhBZy|Wk({WT%UjdJw@;AY`(ml6nxi$a*cMDrCxt(~?}NV*ct
zjkU3mQols4&eR^jQ#g2436?_78$K&G2duS14yr`Ll`E*@-q^&hc3kV|UG<ZUeLQ}z
z9~88A-@S5mpu93lF<WqJN<6pPHBso+^zM$un#Q$p05`vcK2d+}dw~|NOWD<3Fj`FT
zAH&Ex0&7L*1NGAJ278@pM;GeQ$DEkzeqoV*f+#rzAxxo<bG0v4ui0q=-*?kiIb4^@
z-i;CmM*ET8_GB6@#VDN<`DMm_%YT@gxZxr86Qu@YBd1Qp;V6MI1ATGc<nOrMlPn2?
zh<y3KFn7~dLavPxer291PfrQAR6RIMInf|BX|yaa2i@Ic=tJMFU+)7xhMMSIl%Gqt
z%#c3{3~iMNW9K>(;xMnvNxMfwoZb6Z{WAdH(lc#hk_&(H(j?cnEfA<%?ny>z%8<$b
zmCr~Gv#k07W<*4?#;UTmHUP>vMjJXbx!nEk@spZ0<hJgz#E?+N((5!e{t0*{yh%gQ
zatU;3!vapiYR9MPgf)o1|FueLY8atLj8!8?4Lvx*vj_Z;EiSom)QTo~wo_i{6r5)b
zuOn5B;vu{}_3QA{&zGQ!GEgwzn%9z6C)XDgR*MrjB3|LR_$aQm=kf~au`29IjBA<y
z=RJ!srYPs7^5|EWfkmouIRa_2amO~y4Va$Av){La#-J?Td=KGHknB-seWCBqr$wG!
zN3|x8u>6W_|306b|9b`{P5<ns^t(!LXQqeA`cs6)Zju$f$L+H}_-nBOKUNTWq#?9k
zlsiAO1;+3Ic#<@8=e9m{E>PnVVghxZxnT}w$(Z?EZ29CBsKT>hCG0YvjGgy7xoc%%
zb(MpeZDND#ZSF=+6(7lBWK8gchYW%T_+nTIf53-_Ao4VVChn?<L1i3qxh<FAK3{FR
za>^2JQf&7DrE}ZUj{jodyZF6a{z=ZzajhJ>&dA-N;;}|fqIbTJ-!IG{5r8RHWrnf@
zhUY$8Sd_H2f%gw|by=52Tkmnxp+U!``d`YKUhQ`%{L~@C>fHfmM11k7(KLKzW=5wT
zTB9WwNa}Ej<gVpo#i9DRr^?WWo6t~59|ycq!nArI?nfjryzW^0kJ<c+YRJ6Z-FrOw
zjBmm{^_rBB18oKqhTYaYAGf2itn$V)P{#S4Xl`C=hku~VcRoeCEL_0*KpETLkT^%a
z`I*nl@@hd*YVGYG8)3li{Myy|X0}t`eS95bUqCjm=INjzSrD9vy5^D~#WumMhBYpa
zx}5Q^1nY7(bC%Y5mx}hHgP?S``vYut<v2IEO--B~d9?%6*Jvu;t$U4`HFDl(woFt$
z+DZQ=Y3MNpk*0}6pCE4j3ia;7k_R*9i$`&1Mqr15{^qXDJ_GZP@~cNQ9F%0T5I(^#
zIqwZo+kTEQUyu9CSl}w?sIJTue4A*yr<1Ce4zo0<#Z`27sH%0e@J5y%c~2}9KJ$TJ
zpnxv6LT%Fw9@36Ie}uiM^KgG_Dh`&3eT~FPB;YbBjmb8pH2NZk?A2YIrGJFec+$^u
zFPn#Lm!LtN_a5J5Ev^={&zDoP#p~*-U>+l|V5{Ntsymw*B5%Kq>#X@wZ|8zWc|2VU
z{+If!WGa!ih1W-4C+*VXNU)()&~H7c^wLj2>dRgaVw=uixKQIx@sVrKlQHgk^$g#T
zjW*@|EY0k4LNX*!mKd(iQI28s-SODkt!VG(<~84K$#|T-V6I}?e2pAKj(mGlDSO9b
zZoK*7Vv!WlfNn&FK1mG2?|Oq!i2zclhix6o+eA9_JDR-GFnB=6|MBN%JK04Yw%mKC
z{B&S=(TnB#$W~?6Oc}HQ_^Fn?&^(XNdBfSC$^}}|Ix2aP-Rf_qT)vCb??$y8Q1}eq
zc6VMf+1hlQ`6HXZl}M^=w5HsOI=&3AxyNrg&OC9qRrq_Orja<@3gB<IW$D`tn5nJ1
zY_`uffd%y~HjJT9ZT!P=&HbN2`?psZLK>n%=+k&gOWYwao*Xf(T3#J@=Va;~O^q9y
zSDGeGrCOgulsGmoP#3OPVQeQ-GqewdWAqlifn`v{XfG~*nLh^7A7tF0gz4Lh-!6D>
z{a8mh2ua*=AK|U#l3yrGlun;7@9~qi03CMs_Il^0jSkV|7k<jypMc#laJihN`{8jF
z;YLU*$7PtmF%Zwh1JzUr&f~QItlv-1r5M5Rx~HVY(!Z2a+7MpLfl7>f*X{zn7ds0|
zl*1GYe@k{kd{RofxIL2lNFBRgZ5pUttUNsYVu@|G^K<Ag%bqQT0|9&>k_gJxBT6;!
zR}UqUh`!g;#w`_&1q{U5RdI5r=5eXgi~tcs*jFFjojizLQ?8M{Vu#@Il4m<7q5+~N
zIRz^ODL<MzRyc%@jy(H+s9aKvmCN{9Tk`v8Sr}stS6HD#*o|-_q3Av}@i3kD(zyr>
z3h%9e!KBKng3Ge)CdjOTkfOMmV+yP~L7&ze1BwaAHzAtXD&$ZI(i<MN0iUm$+ra;G
zNtwy^(I{<h63mS>q8@ps?!Hi9-XJhMhI<RECaUDt+>T8Z1M!+-vYTb-^r<9%V{RGA
zEw_8bydAOoWt{}krRe$4uuT`PB3<k%b!<_zD#8Q=sb}g|P5)up&GPtLvhoCm5T+J%
z9T*THa*s=po*4G5Xxy`c1QRwu0kH9O@Gh{=EAVPue}*+lW-jYwHdl{jnbax-R#th&
zjc=&Fune*9Ah7_<g^0T1J-RQo{C3>Ckm=ViTe7If8M4n32=#k02SIuqMZSR6{5c+&
z;yng->j7FI=fAfhO+K=AY$O|NzWx?6yl=)b#f5QG{a&${K!?ceyOj^yVmy~_Dtpc<
zcfkC4Xa1zW%`qwm?>no}^u6K?!V?{JT~RAhSXfPRGL+$q>y+0a^}eaOy51gV6ivE|
zo&e~i1X*Sa_zS;Wh9GSxT8&w??7X7|{OBEUc6CJJ9v}MxQ}|g?GzOz{sEZPYRfxKf
zj|DZU1t^hegt|mWDM=hOM!3^f|Bk<Z5x!~m%9hoF73ptAuc%~1zv!c3D8*lFHp;|a
zB9xR61`GQ43Rsvz^70T!h=Gti4NOkaSDbv8XnV)#)dK|#^d}7X`70uX<!S`)6Bdl|
zL-1LyLAh8R8Ee7`{Dtp_6tuV<ygH1q7~mt~>vB$tZIFNvs^4(SbcmuJ2@TDlFwZt_
zw!`fsR7vP#TPDI06P*(Z$3e)fGxgzH9BE=d4=87smgVQ^Z8CM*w51W`X=hE?Z<Py*
zh6{(4a^QSv(#&A(v}a0r8^0;23M&MwsG><%YsuHNFZD&cbY;WHr5(L8nxB$uDp?sp
zC>qZfZ;`&)cyZTLxr^q>JXzBeY3p19BkV7oopxz__b7gnR4E0!kL#|W7=2BLUBAE4
znquYf=2+uvlQ_NDZ_{({5g}okvgygC(+xhh$~Fg0bH9FJ6Gs+)coHJVMpjHF9u6Z#
ze92Iw@q?1OR^CaVYV@yo|L>T0w%Vnv-H!y}M2u1hOrfEn;*rGqEqIl_lzJvc8X?9R
zRrT$jpX?2^!xlJ?xco>B;$CaLf8tO+B0P+&^S+fRU6D**he}{?E{A{b5XXcyf5D*0
zItWGL_0B$fIC*=m_JJ`<`9<mMU+(@UXQzVo^gsQVUcq&!)>b;^=u|8@^b{fK*aNaC
z`nwdWs!1DlyNG^u@HYoo)=UoRvB$eCWzU~1s)enktDdiosR;U{gp*~w-{J#N4fFfI
z|HMwu;ypZZ&fD@+|LxY3L|`ssD*;F47}lldtl>p&a>qX4lQDM&4HAZzuzf4I3Gu=h
zT!p6W)pqy&0@hu~f|Y#rFd!jiU-4-CHcKFHPhP_~ZAHwVi9{kt+>;@N{VgT9u9U@$
z@N={us`=k<)5vaVV;^f*Ur2TngHhi|KN=0+P8Yy_8(HX>!YG0GE=a@p#vR6Q0NX4E
zVk_s2bM$to!ozr*O_J^URfLv$FuL<cmmx0WPiuBS01O#XP*8q-3(h5O;^+GA?2&q^
z;n+a6^n5>c<hgq$j!1fbE6JIQ?l*vE7Smm{F}HVb$!<wf$oG6Zt<SfGce|U577?zF
zfOe`CIpooGc?9%azX4W?#k3Epp8Z}WlH{|nA>?@rZ)Kfl_m!U{aF{vX0SCAdI&iU&
z<MfBwKz?dFv;3kd{#=Wb^k4?9(*Isvy7+0$5)4h^=hyV%P-n*T51*gBBwHj_S7L6~
z*OJo5Nfh2=i6(+v2y!XW3ilM9771OXahb^^G0`64mTy`w#L9R*a;fugD2}|Gxb-wb
zUpf!CBEs{oG9{x_rn#r@ql%&trxXOiH~d)CNJRr<pjluv?1en{eb?+mPgLNLE|pI1
z=;MCJq}QRWq-5BF2gbuJq@$xFB&AZORUIYiabUE5v~1L==*&;WR6W%beH*D@4tt}0
z!$|HozzpW92`6iXTf}@X-m@ua?R+j}^md~Y>0;coe*LhC1dYn3PMuT(++t_MvgJ&9
zFEp;kN6zzgKeaR$8wcm|sBw*12c@{@xBZG?`roz1npLxn#8LrW?*c9P#Bs-bOxPH6
zkl~X2`5?&^ap)d{X0fM*hiRoP_6kjQ?FdeA6I@SQWBgfaAR!U{cr?*df2P=%hC1b3
zXcy=yyc-8CCWTij3+bZV$<hd}YR{CBH1l#EE&AWe^!Ka`SoWzvKdouMO>XRh2ocQd
zru@998&iJl<{t^$FL{qR^|FYua#XJ3(_bdEMyAW=CDrB&j!|PSJYTLvBh^{^KLHta
zIqWmsQFd2x$}cqfoxo!K1&bx2{j&6EEN4Hv?)l6nwH%*`lP6N`y&fD=*tTlgXK~ji
z;<7j2-)O2)UFv;+pr@}wq-mwSn>n>kr`-)^;t+WX=t4*(5OTwGw31a5-YUgl_eT2Y
z65kem=%r;^SFLy+)dq{)LCNLKVNd$*a|X&EoFnD<Jr$U-<Tq+Bmj;SA!+anh>C3b}
zZow@4eUX_H8U9>`mG=syUUb$cJ;~c)#sju)9PDmX+}kyJd?_jNE-k`qHVu@!r<5l`
zUAE<v9iIaU2S@ktw#{s+O>>=`3o99hRS6L9jJJacN6)bZc`!3=q;PrxUtVWtXG<s2
z@BeDF@%9ea>s_~RHZd{D6_3*G@N}b-)lpY3%FAQ5T^D@)I!={%?DJ@hDp)4WPDcz$
zjS=Q{Nyt(@X5t%}RWmLh|KGf{KXxb&pygE(Xi=SnjqHm^yI!-)ap@%%r+Tryd&_u8
z&q0ZQ*e~k1p?sfPQb45UtSj5+riT%zbteXy?dITJwL@SC`8_$4aaa#8&d*^@zQRBE
z1Y^J*+dDZuUQWvue)<j$<<i#wU8`PpdTQ(2>U_N1?0UAj+U}v(;wZVW5g8c?g+e1{
zh-2W*Vqngs>=NRKN0cA69a|k&7pnzQWp^;>Y-Aq$JnxoxYJbr6#Y<@)`on^1hHl5s
ziBj6Tzl^7$tU!LJ5NO*4joY^X1^`w$UEzEi1mH<(&4^11S-X~kXHPxa+9K=jGcYs+
zn*fb1esE85NeQ>>X%-?rkJF#{*x1-ArJnI9fhY9<Xe5ciJ0k4=JEWLAXbG!fjKg>Y
zmRSt1e*Nv`6`li!R(Q=o+&KD2Yug9}%S3Z)4fbgl`Q1DREhBw5{aU=@>r0&JSy5o<
zK(Ut5k;43SQFB=FYSW2}hb<ZH%IHqfiB|K-AV3xLyR&#2Outjera;Lj?bsiupkNCh
zi^N}H)G6YSB@WNyrk-C-rzo>j+`7f;r-<ybJqT2gvcsOfmNhLXC=gpvkC%OsH2k=6
z_$(?=9@S&@y@C?dHx#f;uX)2W-+VZUxLntdPS{iN6H(9i6~r$sD~tHj+tsy^8^sSf
zOHL+u>Vaz^h!;a&tTi3~d-O|^mxU$n+13~DdvdY_Iw7BXU4A}AXi`E#y~rq5u^))&
zAD>7<UD3=|D(3?}-QLhXIO=Ez=5-@a(^wvRj;}G@I*(Q4Zh3VF87FGT|G3ogcXUNv
zaT4V%2_OHoKC^je{I7JDNLjvamu`=!CA=0tDdm#v$X7X>W7<AD-RRl+8FlijQBFYt
z3$l)b-6ntat+qFf&x8HFkaBMK<Ycvz!$93D7#(_cc4tL0!~Tf<$y|ice{a2mK{GmU
zbi>&XfNu##B^2J&)YN<kMyVUf^u7xvhADi<<4hll01w|FGD?9W@mjeuC&$l6&B>|0
zv(ra0cD*BFW=2_0Pw#_)J0!RV2^}3B17p{I?S>ofnC~f^EHw*?9FTy&Dpt?6<)l66
z7VI}VC8HW{V(Cd!rqiPQ>_h5*z_3%np1G=+r0r(;M{M|(HC#LA$SK0E&~iNMq;fyw
zFhp7q5}Q-iX4~g89mrSioQ?NM4O0m7C3zqY*Bi=P%{&&Jm_)3NSi|BG=0#lR6C+1S
z>jci=CSA!Oxs>#QU!fvYWCb|qH_I|xYklR#!vNN4eW<N53NhAIb%bkdDBn^`qSt^y
zo5$e4$<pieK4E3HTB?hWk7vh|w!1wyroL!g^L`y;x;K#>O~uczt4NhV%9*1=G&4J^
zp{eP8)`u@I0xm4dXcrR`<Ma4S?n36-9}$6Sq2}!T*JaI1+}Rf{lgA}8uP{IV+Z&F^
zyz%cWg>7wVdGj+fUzsv_-4vxrL~Fo2<yF;WF*xV$pIuiVbjo`-P8xI(%e;g%Z+DBj
z`)t~%qIkFSIRx&<<294d(lqyYQ*3F@TW^RZcZH{Y*6w^2!`Ym_NXIm??WSy+7Tt)<
zYTROhIMpR#ny_TvyS$8V{U9E)kw`lIxZ#}g@#&h`f(!fI&zogdsButmml)yWUw*m_
ztMYDSpA-=}Pg_d7KcC>7h3Yw>#p722BQe&^kn@`r>0sVn)w|FErIa4YOWHGzQ7Z&D
z)TV^gJso6IoZVMpuc{?BiS+gLd0dVIM)ReUq)HyZ$Ld!DpU;!FXvV>OB?*^3Y|2C|
zcQLF`cszWo%Slkn)&6ww8iawKp7iXOO(!_Sn$P)=pjQZE5azcwBmSmNi{s?PL{&`<
zWC@WSxz+prsIki*p|G&9wW&!kCnMt>%~5wCN~Y&|=#OJrLeK{j;6JcxQgQwVBIth;
zS|aB(p8u(HTQckvxb36AnhYQFs@<8R_dQ{^uF-A#dwiJCSkRECbk6f(;xWp{Q2RLV
zt)rLnks-sh6#3r-GQU$LC9Mnc^NyVTl-ax5xi<_C<503v&fMD`Ojx(L7p<r*)Cw<Q
zsrWQw%h3rNC8Uk<l;s>^IM}{@8VWbCr+JqyClOAKWfka1ZXAPS497+zbci|Jwu?_;
z9x1OGqVg_YTbKHl$zvZ+V{Jhac4w+ojmc~xF)1l-$3;>S36atX5hNBG8t$AP5)=*|
zUU3*lnClCBYr&4wC<_aVknPlzuFFZcM$s7AA8NdCTw>ypI9U~>00=ob`Nxmd44FE_
zo?c!XcZga}9~l{~XNnXc=mL&=@^2WX1_mS+RzqJ?g;hxu&$WF^PEB2FbDLgW%~*qM
zh+OSWzVeLzV!hmib<WAdBeFa;hQ8_X_waqIU_ckO=}7X+vzM>n%k(-k@-PUQjjsO8
zV$x7k@2zzTq)AI<3U7jl?qU8TM#s9FS;*74e4mXg&37J*mU!Wf<&k^<Dj!1T7n9}l
zJbXqtGmreHntw18{~rQY#|)pjU94;?N>S>QL8W!<h%dVG#~^>YF71T%AU9ry2;C`n
zUN_}l-d(j*-66^Ax9frXw&HskV*RwV)M7^mT@~<-v~fOX{8B6qc(BDGMxlt`v0dW-
zq5{i{hva!N%AwQhTwYQ_#N&K8Q>^6V<OH5d(gX+Lr?Q$_q1YSmr`z4j-{>dcN$j@=
zTy%vruViIqF`suceg3?;au`ivQw{<GLiDRbxlD9!!Tk-N=SM0kD)IBL<0&Z}?^@4i
z)%1j}P*G8T5PfDMhLi0Qs79T7?|O6kJ_=f+PA{R+@P$w=qut|L*z;(q9uor*G3&2v
z3M-PLqLR`^VOSWl<im06$<E04S22(G*CJ_`hR%o+kifIUd=`IX$%7Q_x-kRvJ)>GO
z9S$v*?gajdfW2lao`Qrt$1GE1Axzj}ALE!^we}6EGjF)OH;fEY2GVy!P0%@xj%bNB
zRsrmnxE&s>;20~3afsX3=jPep^@rNB-uA{Oq8c?m<~c6rIi`|+;vXLZIP(O#4~=*2
zO6V+pUHGs@LE+)xoDSyD9;!R4)rwqBR;!R0-Oe_l5(-L6{v$pQXThDoYX7V8{_hcw
z*#zm7j_X!G+QMw7&+~Ml+^@w&U!>UVps}&BXz%TTn7jUsKum-fM+b*i_bb}L7bD3Z
zUrmLgW^g$Kp_;Y1oMe3aHX2W(^jS$m<NNl*+Vc}a5DF2>x!IR5sESA)QSbPMF0vsI
z$QUve9bM^L#zQUW0V8K8l;8Uy`Pl#J8xyba&`<*1d-QAlBu1S*vdl)CHFS@j?(TKf
zygSJK)j^}%#ZGc^^6^UR{oS3Pp{ADB^oo)Rz{O&pyveta2U#ec+xwRF0@^s6b0#5;
zi%$z&qEqw_H8xANZT5Bux1>j}9)1Q0w0)X~5sHrV6$~%inoCbx$r5HtTUB4+;Hjqn
z!QLap@s6CD$Jb>6sWppxPiRLeH&1+b%m0w&VtSQ;m3j%nVj#7lEzh6E&5+Z3Wl^)$
z*PE7CFh=@;l#>bPz$khE@blZ$@3SVRd9Si%+wQNDs&#K2Ndh?IKk-YbKpBR~PO5eN
zhJWXcG;#dNOx1>WGiZO)zmL1CEcDR5^rEP0|LHWSC%x9Pq*>Y5jvsE^CZCr(bKQ%I
zA-zwF1i%$M4Pgt!V8L7LY6*=X_3hh>T_&BD%dIGZzu@{zqCX7{Xu}_3%s(|){Svds
z!^87F?LyG=d1P}cD=45e`oP8(=*+~rvZ4OetiF#|TvUW;=i%Xz!5a_lrpdyL7PTNO
zi;czRq~V0I3pSrF5RwHATMhoq3-)~#h~Q%<PGn~0`r6LwiifvOJ&{PQubzXZOV=$!
z>`0$zM-6FwQj!SrjiJ{T{ij#(s98y;*K0mKy0+u>mh<SuoF5n&(F3p)>FMd$O;)S)
z{l9%fTb8wb7Ti4Pd}gd7s(GcE_kq@ucI>_+)ST~GO5?qoqrLy{2R$1fIUg7M`dh~u
zd{M>6Tm62vDqNv?@mbnGCbo$_=4`9qn5DqMbA)68q=-}WFbp+iRVkNHlYaJMyvBy(
z!9dsy7pnJx+~nH|cvy0@X=z)8f47}Xea*tjd&Ij=Dvr=|@i)yAVaD=XyQ8EO()VAA
zNxns#bF&j6mFxTD9GHO5A%ml&Ge{Ad<z2Yr)~?XxR`TNyly7*;M}d{DV?*(WTn~c~
zZ|gBN)Gt#JdZtcZ80PWph{`8m^nnlaiCvvM+vEE9b0j}MzY%^64_-KwePwz1^K0fb
zceuK#(D&}Hu7XA&0Uv%(Gn|8d0TW>@z)*;w7_3Q4YpaOSWDfKaJCQ*PKD1X<LbhZV
zM{~vA-X0XA#DV;fkPt*<)ZU3NS)IR^@B%dQe0^UgC@Lz#&;XPhbW^3@BZ#|CLCoul
z`L{d$du%MT^P#%^8-E6FZf+1RbO2ipM87XpE%!?UKdF-Lwalw|p_zyH?&q(tT!GJ4
zT2_g5QNrf|Yh`$4%`BD)i>x8?p*)9b8E4#MO}Jr%5&q)>A@v5o1J&lk*v7fKN&lHc
zLohm_bM{RxQDSW56_>fX=?)X$f^m*0_`5JzBpZqB#4O6gwkL8>4q!gB&@_EmB0JgX
zu!Y1LSMIRkTE#FX#ZgPPmE-JtQH|O<_?H!rp3mI(ksOZe(&5P>tN4j7<jEzuA5@-t
zQ00=XT<8YjcKQBsXb*{wZ3o+e42920c75+|*COZj0n<2J;;Y>uX_~D{U2lu#U=3vL
z*AmTVyC@+dFKUCv?cJ+6+ze;Nx!)RHOy0UGa?bdcU_?QeEIDK@Ey{;r=7W%<=VjO6
z!(kmF!o#<0SCj7&sI^p7lpMNydjm#FZx>UGN=jlk%GQJaEnQ8A!fSXmB3K%K1Pu7=
zyi7#;^Si49OkbFNiZos~Z0v8}z8$O)-%H&*T#T>zJbQ)epy$2CV$VtNenn0}p>VPw
zo6ZgLq~h{7Y2%kg7!N}OgFkPczXk*cn;IMYhI>6;%?}L?b#--B&5w+fb=c9q3wc9C
z)bOj&5)QCb116^IbVBQv|L86S3EBtiYZm2KMt_TjD_+U5pVa9+HeQ@W9AsC0CUBi&
zziR*`aXTkqC?)b^!bf4je_m(L5U3w9UuVMk;f}QYI|KEU@HQ5M6wSA{=D$BXL`#sg
z@7u%)eX5P5TesqopJWlZ6Nay1h=nXcGFMzBdy)SLD$+`GZRa_X4NKCJ(a2lrXAaCm
z(h->wgoh^8*Hz+Y>oQywoGEB_ixpz4{^+QD*~4}E=-n>ZTrbtbTd}KpGx>IIo;DpP
z0dryoMXYh443@o#_KRPOVlXSY_p_>c-j6HNkQx;=4xOI+>yw^>mKJWGv8t*M3;9`D
zKRUyILJr=n?Y#JSIF%pX*2eoN8ZA4tSZ#>V4!aN_>f&<kUd@1A!Y$0HY-(8>5ERsX
z1&D>ps_(MU7d5Y*nB6b;!25)amkW3OXFXC<Qm+uuVcl%{HjcC}=5%bm!y_Uiy>~|g
zO$NG2o}ccSdgsfv5bS)O=hyPS5wa9$HCppd(-9I90*HZ%{g#-Rv@<D5&1*{s!H$7}
zp_=K{t1M_(KksIk85Q?tUAjhKz>kv%?p4~D=4>W=zjW+F89jnOcSX!a-{4;^U8^^?
zDQa(_7z}Z8GHD?E_ZrL@R?}4Q9Cl{x@;sSb$R;5T>@-GMNfE_&w^g5j%eMHMymevb
z^NUb#rOYf))jT%ZF4c%af1VNBn`ro_aTA_a^vSEKAvnoKF<v#NiqWd-EY36w){Q-C
zGV=)#B3QcdN~vGD(qj)%y)y8kpW6HVRrK{q10Sx8z}spG`+dQp;(1!6>w1F;$e@Y4
zyE`y|2tD5HiUwNpJMK+n@Ok*bogQuikv0J7HEDW;W&See5FxLt?a6ApgM*&e+n6{q
zDk=nui|xE2KrP6B3-8WWjWJ6E&8xp8QY-%p)*GQf*#Cz}ff%%-f-0H0oj3+A?Z9W!
zMNJa!b&mlI$_z^0k+#FT|F)c(j<bH?!u&a$B%`+zeKx|R+g9XLKZT%8ScBq-%|4j(
z4hLLB>JN|BVz(vsSjxmPph!c`!U^Q65UdFZv}Xtfe?y!q^X;;I)c5K#W%(+NlHa#X
zJ}VW0f9CB|bql^qQ1b9m3NTYoUoT3V26Mg{E_xF!w5F$|Sb~^i0n!K{3#&`3-Cl&%
z;^XxyN$6SrEOu%jhCcDXcZzz0)${scDkZMlqQx~D6Ci`~v%|mqiZ{yUO6Zv($}WK<
ze3%;2s9*m0hhNiu5Wp64Uo84u`BwUGm|;v(W9R#lsAuVQLV9FDwD*%@kM*j`@wVEP
zl&=$V5lVhWo9P4`yqR8Euw+AJZSS}aton1NZlo`!bZzVmsXP=kCerO+CKUUh#j~Eq
z!+d|E*@0Vo5Mmhf!}k>esqx`Lbx=^yL>@v{cFHAKPynvs4ir>X*;!bWl$2WSe@i{4
z;<<s}dQBz!l?S}|A!zc{ArS&v;tJ^`M!><bTW*Ilb+`sMgn05qnHkqEg1rr*{g0{u
zFcHSZjx(*Kqy*H#2J3bSPA9J|_ZSGh{lm>!x9(+}^C*R%V0fFuE;@PS8;)a8BaM(%
zODwU;!`mJZ8>c}YiboJkwtKit%}Ts%rIbk#HQ}l#E+%BN!1*Eug<7q)5n7L_UXC<S
znZ@L6%-Jy^93U%ELGKeHTtwfwwn?gA@hWMjK7IvkW6n@_U2M|&@XPP3$Agh_al~oZ
zhneIHzF+twY8#^J>%yDwN#};%AFt-RYW)njCk>7MN!Q0l&qJO4fju={qo1ZHfpZo9
zzn9@ip058Pzd=lS3u2n@XJQU9*L`Td55AvLh%QOvBod#31nF?+YTqzb9wM3-Ly4ZT
zgykHWCAJ$Sa=yev{e+3wJA~XUGLitg6W@JPU=Czz-nas6DDE00&JRVh`chZZucz77
zU60|>aeHcIK5T<4Z+d(ZS;kSn;-kaPr+4Ao_RU~YAMdVmDYtKTQf+AIQiDi%xVX^X
za>wWSll$4vb(+V3b{#<^4;>ZV`VM;9lwDu%l*#WU(IDgxPx$7I(l>}AMGMsX_IDBh
z5ZImcIOWbCg-d}w)grts$$pfg8!T;B5I_RF`ylJcIRpV_pDWW4!NaC7GB%Exh3cP(
zlX$^WOkjMHkoYQ6bZzzW^d~eNPiP0UmAtN}f+Zw{M_EvIX?q1fL2;gqhA6whW$Q-m
z&*J1rfC6UImc7Wt6zcu&-SRUmPH{R7+L$Sh;VrbdqXqNx5=^KQMN&xNR-b5zv2{7#
z3Oe5HOp(53M(wayv2VR{VYU$ivp{_sPAaixuaetE4_eZ}s^m*D&B6Rur)U?~>_QEx
z-G%{Gc_pQ|v@~LTeDS+sR|z?}V4cfEfwyMw#l^*yUa1C7R#ATv7M79<gDv^R4%<Qy
z;$TDuIIwkQa@if%4WX=XA@l8MKni_X>qzef-TEyCzF7<q)wZK2bH%kZHDfuSKNy<N
zw5`0zG^kde!N=kK@hF1kman9)F45m{{$jpC<Z=uYe19cBzRuhux28n9aM(Cpz6n-(
zt%5?tM4HRXOMb7u%l+x^6_NC@2??_;jXPMPn}R5!0Bh^&t}ZR5?6;8mfl^6+Hp5$~
zDckeQ)0%Qn)UCw)Kx0A@m3|wG<A;%&I*)qZ;8=gHSzkxRCrle-a)TIK8diL_3m5vt
zZPl3bcALVp%t&o+iEKSWYnkak<YqB84F7+8A`F!``0oObm#?{CWdXILj*Wr3?e#fd
z!9lbHJlU(lh9%A$;*~L0-Y4ruIEdEA#02E|9@La)R|zt(8Lrfs$2pK)PQn3RpA>ZL
z$y^RQ_a~h`M@wu42Y!fRS<sz))~nY1AdWURj2uzj#5ztq6^%Rh(@#zmjWuQZp&SEZ
z=i+=e7Ft34LlBzyW643tU@Rnw9Zo?dCozhC$oh|6bO;p2sP9v~MuxJ2%5S!U`!}>%
z^}WIAOoE!(=-W_*A}XVx_!NtZi+oW<krj|_HZ+_$xaJ%akUsKy&hOTyfXdKUeectF
zFFAZs#Cfrrmw>n@B7XCo%@XfB8Hy0*VqsR+3vcvyyaWu2CSW#xEdEAQ%5Kq>q)dIf
zPARbGd-NYK_uohyDzj|unb~!BOJPGr-=3R^DOO^`bJZaJ#_U-?W7yNn>?0nHoo5td
zltP4PJJl+l%Zlf&bl`8)&yE%m4=MwEY^dq`A3qya_4gl^S@Y%c?hnM+#`h&$$la=E
zjMNBbsx~Nwth#8wIW!`-5lIvxFkt`p6hB$|5?sGG%zuRYWsFL@t(ux&a*O~7<SXxp
zPvhA1Jg6l6o_}VFk&%%b8X7*$!-3Nr1jFNvVUFII^fgyeex-T@7}=eK1oMhF%AOiq
z(C@)2WzK3uhH+Z!NN!4Q^rB`u;;dG-&@x4QEA^|q-w%uEc3wQmRX0FK2+3_%ORR@_
z!x%^6&%u+!;bS-F>V77ta}@Pxn>==ETgWOZxKG8Io#ss_bT={}ZUIb&?9MAS_{;At
zi1Ma)%qcK(yIxdBmj*VmdLE$|@q+FiU374|XnE6H2L~u<9{LU*{e6Akh}fihH-gdM
zTI51w18HHI?KVZR@MvcL6A@~lO;kK=W2F~VwaCSr4SEl3oS<>EJ7^m9Vb_4Oo?Kqm
z{=;wx^bQj}dz-i5^{b_&rHkEhpX(KugoK2AJ7icPauiJ?)10y_qC6TpUmacL(ay#X
zxy*bm@f|yc_wqV}GU3$OogUOGf0~@!XpC*en6uP~ElQd-&b~BRzct`ShZW*RGmBw>
z6Fsk*Zivi@Cf|-XmVDrLb#vQ4I4A|5q9Ou9LO!oMWvS=R3|=>wMu1nu2X_F&{F5cB
zv3Ie~eERFlR|K~%Y4P#ohx!4I3&WWiz?Pq$J{p&tk-=!o{vppFn~)Ij9-|_`=rpvn
z8Ju=N8^B5phuzm8*VcF4jE#$H*6v#CM)K4g9v(I{G~`O-p!^wxEi5dYcc%v87*=SB
zux>y#>&UI{6%Ba+K3CVMPYIrIVy)Xr`!)Z=sYs33RB!0(e0|0vn;0M@oHn~I`C@l1
zE`s-=Bmt58k)3^K(P?+=tr<p-xCfZMBnB;2MMdEwT3TAo7Dt-A9^C;$b92cl2YdSh
znN%^xrxvG!bq&gWTRZ;ehYJ?dF^RS(iF;5Ti?Xr=8BFKOwKzghi8<wym)fi(dLSss
z$bSzP0YDUIyf4i39%6x&UYK?V)Z{xp_h_dMfG)*;2NR(aGQ&QP1I>#x9SJ;*EzSCf
zzBnAx*ksGj++m;l>mBo`bf2A4WB4`eCZ0@zarR#%T7}V=V85sE#tMN7zsA?Bo#<U^
zuzK@_QLjULlgff5{F9W_`PEf<S(zh{@V4XRR4cS;7SDY<pYBjBWa-y8`@%1Eu20qw
z>WMk5(?Ua$nqxuyP_+UtGNoJwDh+5808K^o(EyfJdu3%se;$ZR+0{gE-&&cNz!QFw
zkr{m7($bPE_N}C3Q>UHCoTq+S({!$9rE%`j-tMn^F4SF4&KTEJ;>7Xy@89R==chp2
z3MPSpfmweF&@0W%%#4eRyOdq3jt+u$znTT@K7&>R0RaIqzvoT6*t!sz2M|`kwq!d4
zsP)>Kn)UC2dc+^S*i-ocQH=R%P!+sB4=a=m$54}BRk|oTs=R^$nnH&+k9KjhIdI&1
za%%7TUIT%&!1UKa)p6afK)I3xVxumi*L3qNW;i_~BW3D@E)l0~R$AKfoB6O&SqTYD
z_RET#9GJgTtq-K8nwmtc{d>W6QBXixG|FZ2=e3&lh2c)QUY(!+9tQUyf-8zP8V6{U
z3NH61{r?QY8Gq#kk}jst<sMcqIQMmsk=dVc26NN4YraxXZ_RaOeaL2|rM|<?SnGxq
zbQp22k1s(JFm~e{`*RG=?SCZ_)Ha%Y7QfgZm4H!M$*DCqGRn)#t8<Jf-TV3aSj+<L
zxTc2LmfH83a$P{;(<fAL0CK*mIgdyXYD^Qfn}niiwphm4*!YHFZ*MPJMqiGOno4v)
zb0L~=b8v7FQ^5If0kkkxCj?;X_KmWj4e>XAW<z6j?UC<XYno?~n-|^B9%{F4aAN~9
zBMMlZg&28uYD#CHB)YjsW;H9;;nKCxqM$G&UYC=Ad66Jak*hTsg&`yJZyn3vlNR{9
zKV3Lc1`%OxbvkJ9d49U<5A6*3`4TK`hy8El!Q~7x&FSgs;>6nc7oA(x5@ycALp8?3
zML1K9uA99`A^!D3yrk<_<iQ|VZ1>lPMD7OTs3alU<3P0$hK(=;X{1=<DK9?(v@g2+
zZsWAguuYY_H53m`E0JTVhlo>CLhs4Xl9wB!ynsa&gg>7FX*e-C8I#AP-Gmu=b?wvM
z?N?p>F)#X!*us{%zWz(M^<BWW>nDYWqb#Sfn2eDAcG;gY2Ew+6h76O2Kc2azW!%dn
zP)AllR{!gXQ)dXF&sckcydHm1`MY;UQxBm0<seaPuR<7>78ive+CI^vAf;QHn?I_0
z09y}i3_7Tw6r97EKPQ!!|29@A*(VG->VKOG;z!551$UoWx>MIHyqBadPdu$mwvL$?
zf<p)x1^r4FB{|reT_>`HIBZq}OpZXd?r$;JtaYHJl1BWDBI$HGNCcHMAwk7!i{o!9
zyA?ABM<)mx(@ZaTH{CX~<USqZ@PLB!7~6nRq7^Ys7e()I&xf0jD+s<tMbr`s^73Cf
z>voF^X!L=2P=&8q2oSSY!<S;4!|;H))|?yzQBhG4g8u&g*~J+14Da6y)sWm;H4M<b
zbBAQi1ukusT9QAE!tcAlv%O9fl9R(={-<JaSTDawCuT80rB3zr74q5&cda@AEt#?j
zrl3S<c=+ox*QJF8LE^ePwMt!ZFmeD7FB#b@dTi@4@u+uzrN{VNq1&E<Q&e1xl&a@-
zHJd=E7N3xiEx2&_tC4Au?k?&@V19mn6;%kcLFh!H0M@oaEdDnrZ~5+6x<BG~m~cRf
znwWG~+JHPg^}&n4{!-W0rtL@aPk|63fCZJ}#?<wUXNj<K3)r1p1Rm<4cwBP#bXZ4<
zh`($qqeFy+smti251mNykiLnSrN<&ssFrI&mn5iU)4s}B$Ww5d0PqJoHsv+WHmmg?
zz5qo>eEWv0aC3DqpAGBebme|Vk3Du(ZJYI->Xg2ON;y7~7>h+-`di&u-UFUF&<SWR
zuM%o%YX|ECZzi%uve{&rUMj!+>t|4=-E6;5r9Uy&SYIEABmaWa=vRZ)1hWr_JwaGf
z1rZUEUMFiBn{udMA=7L+`JK7fjc|8vNY~gp2jJ*vZEX}3CJ{(HUv&QN?iP-P8H2%C
z0+FD)c9Yem+r&3;jbR)2_461|d3Cq9_Se@1gAjUu{zT#355Xe)%oLAA6{yrxi#ki%
znwFj}=|jDUV~59m%8BCP=l%TXk}(nlpVaH~IS%{v{?CSE^Ku*FmAl_bdd?@S>7G<%
zo91X}Xn`RqDMSzD$?R53H)mVzZEfo|WO+b@x;{P2#&mPL1&X}>E;tuMC>kAB(@6SE
z#)DFLPL@x=bA8YRE;WBsef!hwIJ8k(jnJEkCVYfpO9wUQ8)mq6lkNMuSFMiwFjmSX
z#%!QzSpWf$e;@YrcrRj!oU-}u-Mgv-;P${&H<6GSh#?bKt<*(B`!LX8I-cQf9%<HY
zV`^%wPh(P45<g<Z_JD^pkEXa++u(;TuY`+6$Q(dK^+{e?IiQ`?5fsSbL<awGj8xJn
z3=#kwdN72>H!N(H>X0h_0)YX}YQC<HHFT!!iJ@U&Y6yEWiWc@QmC9hoOm8o&ckQVt
z;<9v*#uNCn%iixSrm^VaA#vYzbhNeq=v`@otA6LhJ$VOf`!D{`A?^bjw844>IcKKT
z#bzKnHTB^57(K@8{_mti_cv}JQ(@!VC93LSa;T}Dkt{3|54J6}`8<0wH{vehJk}8r
z5up=&_<Ff}cY7<7&V2>q2>fV0iXIQ;K|xm58YBaDF@Ya&wtWEjPx?&O$wqatjyI~L
z=}2I+&a1MgW$nlt+a!iXc{{as;CpP1L?{!XB+2C6`EO}6HeZR@MRNY98^Z;*OPQ=d
zB#t-xMl*ABJ4~k_k55=Dj9yaM0Qq$@uX*-~&PxK<JDmKj;H7L2;OQ;I(m}x%c^$9Y
z&D8lI3_lz*3i!*WCpI=VVDIo14$%fJ^b2R8yAy9ZJ3Ggg@M`9TWF3@IF2;d+*;N_U
zc07bZXyW!~hQ@spkZ12>%HTx2@AJelGE%%B?^IM(=l+{RG2H<QQE~B}+()2E%BFE*
zWk@ysBo$Fr#pB{3WDxvlzb!EYfu=!o@FiQ)1bp7{_xZu0tlvUtax&-c`m@`fo}O%r
zM@L2^^p2LB9f}K0R@>ZOQ+_gA!vJS*_>n3C18P1Z@gyyG+TVxWfM1#7I?j(5j)k!o
zBvNBMPhKq-$-@;;?ky0ljjNiJG~x6`(WkAxytM~{wP8eF9NrH%Ko!zkPYVEC_9Y&E
zU{Vskd3nsBl{!7%RbqZJnPw+uWO60KpZ$HWyD+e2a-?=TQT=Wl0MfTab|r&7P$(t1
zyzW?geIBpWLi}kJJ+j?FrP*$7!)Mec0C=oUXV9|HC_q<H5xX~=)sDY|v#lOaLrv{^
ztNGtEif=T;^3gnK90Q4SzCvg4OFM70xH%vzFM9!jD`+4gfmmGI*5>*A^thoK&@1r#
z5VwtW<{E1%1BC`kX@Hc=1LAtzD2UQ-6R=R9Ka;wB-VeS$`ju28fn}NvATP1L9Oh%b
ztZ&_(uT|f4No6HQrrr7W;7rlV-A;yT*!j8r=^kLxgouaqYE952*J_MWW>g(ju)vO(
zC-N0<QPHpVf!x@+wft#q_4^MMg?6up7T>I|O;lmSog)lgA9^U}?PXXRnH{XEXINzz
zTwc4#h%mTJYun2xvDiy1C@X8r$H)ldVF?8Y(83IP`J8y^j`NHz^IXHYymapXouSEC
z8jHi&_vT%$&hHKx({iJ&V|CA-9uIU5ItQKK;a7Nf(7uO7gobum197N|&-2Fii_LP8
z>Fuk_FO%b<*XMHrrVK)`lQ$eoU@x|+=zxM4aR}A_q}^b6y52?NeY2te7Q~U**6vY`
z6fP1Go5l9}IwlE;oXSjx*S$esn8LU-n0rMInoHl%v_FW)Yn>R?r^8Io2bA$0(5ZQ9
zO^N`#Bvup<A}cLUEc1)V_y@8KO1JgS+Uqe_tiv}N_JiAladrr2t|5KMaIUhns%0Vf
zj62Z&{{Bsxf9#fY;8bgRg-)wQ#(l|D$rEn^?bPgSJ#gt=QZNEUhFAHI!s6|H&%@IS
zEQU&-rnH5jCECy-(7m8O`t5nNAUEH2?fjjW(tB@=9V>rZj|}E)u}WX$?i92UzW{Bi
zbYofBE>^2V7z9p?fBm*$4DO=xtBR2|l1zs+qcO+$b82KbFY1*v!o$p<Hohj#2flla
z2DbyD<|%PtBA6`oygAhu6%7F3s-g4Yd>DkV;L4}u4=>Mya~L%15Yrhiy-i0`>YWZ$
z9UB|IEDHNyPlyMCqtRU$8Av8NeSFGs#p?hE{>J3tiz#9|t^IU?3?kr`df{;(4ShdR
z9M{2pFX2R~%*I0{Dy7TI%ZEot{(gRWg8)k~boB9hKQu`t(1MYmprIXszO>$AmcHG2
zuruJ<B##C^9LMLr^01~C3R(->*Jr%(pw$U8@A88ac)B6;R^4uLdV09!gsu0>T=)m`
zW{UlF_LyUMDJg^SmcF`T`O*r=<%i|%e>40Uxzg-Fb*lllO%$GpUaZ}G4z{5fNkGOG
zhbh~@j`;odt_sIzDv15(;m{Khk1$ZVt;e>rvnxW|F#Owk($RB*kLwDIT#ETpw|94%
zka5(W51@u${Qb+QQA4izlr0iGMGrsi_V<wR6H*H?>m*Mj=m|chaaMycQ)DsXPGZsn
zkkz5t{`dL1nm{I(={aiYz;f(g@>%Uobe~$ePkT)ZN{cj)pL?`WWaU22tFUWp=>K)k
zs&B}bLuY^$)FlMc!a|7(-KYcD{?A_Fe*_!1izfbyEHY%T__#P0>t)VAXQ0;;Hp+gM
z%xr&pxR5Ij3(&5K4hvYfm@8Y1elOzvI6ETpI)B0dnHjfu*6ZHZ-hOH?arMoI&tEU0
z2A-b0<!cDbD=SfNc_?KN>qA;%D^RIvNhp|YIv@39hjQ;9z0IiZiyP9;HZ!uy6I;!I
zPc4Pb^3QywTqnBVs_LK&+6<vv+e@alQ$IzOn9J9tmDoYv8JAjd42HhqvbkFH*kRay
zSieAl%*qAwnGNXeZ_j^E60M6L?d-r)SYFcZblVH4hgg4udEkY@Cg{--fY#4ZNRm|*
zQ2WNiiGAPv|IoKNv=9c-tOK-Z10rD$_sc>RNKpN;G_E1ey@t1(wt_bHmyF<%L*c7{
zgDi*<3!HgFOk8$70i241NN><~=EFf_YyTC&m+~zU=8=V>V(cp7?Fg44k~(z97uWY<
zgqnOeGlTbT9%JpV)tt4pKl;U+D~%PC*tJ~~UV$fcsJc8}add2~AN($`fw&in_x1OS
ziHb_BC2Vks9wd=4Mc(QIbPF1F&<gMc9D(+wJoDuPfr3sDd=5D!<sTigb%2dP=}`)r
z4&mu<TbJa=a=r;Kf3<dB-SdM@$LGd!qy19!K${2y-uta9yb&|3M)wId#({XzGjIO-
zgTZpy&|(&A!ild;r<8T|5r*pTdH2UVrw3YN-{Rup{r!E_L86`y5e=ZcPt9xiRaI3j
z)mt9O0G>CH-3mx5w3;J7e~RCfB?`%;C1Y14^?!S#*XBAYZ?8$Q7#%iZIz}k{gO(ZN
z5P8u^H^FqjGb=!FE8igF0&oMWMe<#$28sH}a1-SQ&MW~(>s|i(Qi+T@7LRGC7>W7>
zVnM;d!L}uoP-r-on`(?`h6?#WZ5X*aRFgzC#%+D($dDaBsQIon?w#oYH{GX^RF3i0
zRerkRhcQKhf`PA*qGy|ZlN5Uk)w$qcNUnKoIs^6BPhHHYJraxm$~!j2fEG_oOpKk*
zPrx}$8caKznH4dY>oUL_i%W_{5PbZy@L>s$E>d!`6aspAsN|oy@>av2=u6%=r`hx=
zy13uR(^&8VRgB(+G?7?1=$qZGxtN;pdZ{MQnbjgGC@26>3N>^BlLYn)3BFqv)Le3A
zX8WOdns)aqKi9*x&Sx-Nqx>c+aQ{Y?i*FVUIv~(mzezCwx)TaM<1(NkPARS40|(>z
zj$%J{8js5upvw|S(NIxPE~XSJb@_OBc$N~&=S25r4LUkH;`21?EmTUBv#mDN^uly;
z7lH01<tGg&iOhyR91q|B#;oOcbK`2HLYdCY!t(0XEB#cX7*G}cz{lU;m=c>^S>sh8
zI+$S6i~YEKQ`1H5cPTfGbVhOIbY+-ePwQ-IYKlIHby;=$+1n>X5LAuy;BQPI#xx<{
zO_q4Ng>dx|6c7<_opZU_5*51yfE1Si=e__sS_Vf3CsLyc_OTSUVR_hhlPM&T#9V%m
z2@whgL{^XeseBq@tN_EXg@py+Zc}sxT`nNaL^l=}7cu)3^_&B7z@WBH%OBa3f+?R?
zq>vh&tZ<1oH#c2%P;n>J)|wp(%N@EtJR>}RCJc%ZqCLMD7bsk*tE&Sw-rxN^ngB0F
zuwB0==xpofG<J0s*e$>wQPUeZgM^4^>+B2?&?NwUk-Gu5bf7IJBjag|=Y$koTrro<
z?{x>Nasc^qhZj%xzeW}G2MMZf{TE_#OMvjCRjWW%a=P48C_Z=rs2{M{I4|$}Xqj!6
zJ(A5F+{}j7QlzC9_hAhgCvI}n>zo(~f<SBw<U#&f+R;XahI0A}I-j3B%x}O50Cz#^
z(17YBxN;A|2N0H$O<1o<pJst2Q?G;G5q5K$oqIO=4JP{W@$o)4=q=CmcC_we&{-00
zp3lzDV=Lsg_x6e+UcY<Cs1qwW*kreLUJNnzH_vx<cP}X#05%%Pub{bbkeC;PfWD34
z&=zi^!_1Ngz%M$i?!F6wgmedh@oJrH;Xr_EGrLQbi~spS%g5@yL5T%c4ukA6&DUTh
zOzyAH*zrTfo*9?93=iB5!qCeg06Qf6Md*S(VUfumMywSuWP1;BOEry+Bs|V#uD@5a
zg#6WX-Hbs^i+)k3c?|l|0J>xVNnhK4#Ri#w2MlwXD^hbI@C^IAyHr{hc(KUOKysnB
z_haP1RxMMXOmP_e{_r)p1iXvD`clt%9uUK}KKbD##GJNYGX!}t7LxhBxB~&{c>o;L
zlYd}o8p8Xj-hts#K6r<WLUZocrfO?5j7$lbkb08gOC`Wk6p$K9K>YwU)zZ{t=5(--
z2xi)1wxrehh#S~4Jx<z#$;0s&a?E5>SbdGYPWZcOx4W~6QbYpBq-3cQW;BrX$5J_L
z0FikLrh9<L5&SkLf)op|x&p8_#yz($Athz+;LwBA*H{8rPtx<`kA`_};QN<^dw@k&
zy=v$wVY{Ro&^%FE?3Q!%%Vq$@fteUj=SebJ2cegzs8E`U5{em{_IoJd^%?{SYY6DX
z1VludCCW6sP9HyhbbT1h6tL+NPW@LIcQnRo*^&~Mv}sEo-iC6U9Y`63;edu0@J03i
zH~MI4YjoS)Ibo3`<K`!V3vH=|0EGFtJNE0>8}UF7;>(NbUquRJUqDhJ{EGfw!-9e<
z*;?Zk6HUO$?epZh1P~U2bER%OEniP1nCu#TdFuASbhe9MleuDNnUey)OCZf9r;%@Y
zrV335ekkOM31Jwx0Mx@-XE96r1x&EEs5f9vg~6^Lbo8&=D^vf-%S(*$1_rn!SIe~;
z!LD|)Sc_ol-!HljMB2RT9Xw>PsRl39?Ll8of2qK`dMZcOOuM}dJ!Ad=AQWm34$@2~
zG-oaH8zAq<#();#nIYn@ENK}{j>E4zA?FNGw6k-8&VVHG(L{C+NMVYxvI+>WB}v6o
zD|C8$eJLy`So-w~93b;Ozv^e?yOqupjwhBos??+ut-Z5_b`OrDO-gP~&UJp_53ym|
z)dqnfX^DwJn7oDypa)<-=J{q7q<ML9A<_-n;Iz{fmo;br5R(U&+wmB|$m0wZg1;~l
zTs^JGCRA$t8}J8IrGSrl5Hbj)w{76hY^h;uB8(=oC?!%CFe6a&&i3~8IUO&5ckBXy
zYk(!HB@7n8Cw2(valHXHUXVXC7{U<*VWL*7NMSAk8a_LcY-DrMg9t*F-fY171%&vP
zadB}0NP~oggsBbWrxb1{3*an-d|7SND0ScsKO*sYm<-GRNL2>8ko;SFSfA9<*lZI2
z__o98#~kkejL}d9wRiwLAjA{kBF%Iq84r*9!^1D_rt)er|Co;fE)4URxwNso6`nwd
z|5w34(E@VxVGi&BI&h#en!2O$PU^dR63WB6IY0OX++yL(jks0Nwn@fOHhE1YD48)b
zkZ+Hsa#T;|OXJ&x;62>{JAjcIpvZosFtvRR-UhfJ#P;se9M%B;7m$vI4ZmBVl@2_-
zzFtcPv0zp4f0%m<uqxMXU04wi0h5sKZYD4ZX{5Wm8-z(rKte+4M!LJDL68thX-Vlu
zq)U_#@P8(*wg2^<Z|$?sxA#8h+Fml>ch0w-9OEAM7~^?~x)!&xsI?Wpec#P6MhMq}
z;cT@S5Wo~mBQI(MLT0SYR)M6cYyxAi#ga{2{3YP1DQ1B9i<buI?oPZ+q#GLit?hWx
zO&7qHA&ikP;^Yzugc8r!*>zlDu>IH6vbd}UUSI&zHP9C$1Li2Vq85Z?=S9ljZbY!Q
zQo(B=v*O;}yMyXZ+h6%Lajw2k8m_K^jIV5cesLBSmL6u_<->!6%9@(6$jE|RU}mZh
zdCJSnmjUsrD0Jz+%cxl%{mtMR$a5L>>eY*{U;4l>%9IxNt^VEfyjGxwMJ8q%1re1C
zG=6ipxCdA%6<Sp$0Jnl=L`&66ao=^ma|N%G2nh`ZDO>^QzG=Wl_zEmL8ih1qZjkep
z<RHd+BYYE~7WV-r0w_4}y}0Yf@XaO=+<FQCS!eqLVC?5PR*v3@UpHTT|AG#JJqJ`V
zndW9kDJJ~%KGCp{-d&wLmJMLW0tg)Q2`G2-wCL#Qr6nWtQxG7Nb;SZWJP;Y9Y4~Dd
zY)o~Z8bBATsmkxp8Kip)ExOsFC{p5cPw51m-ion4xRF7+1AR92Ao=4rnxCA~;iN(f
z{rzEr@fMbrlf|mHn*hCxfbBIkHQCe<48b=PgW|#ihcC&<O~-QV^s%mIJsXsajja;2
zMBZd52=b`0khf|C%CbM<tjX)l2|$R>l1NZfoJ<;N5*hV10AF_2*8C$>3l&1TD0X*u
zY2}iH8-)uffXC-@RF+kllRO2G(xLiCRi-FpW_e+F=<V(8{@glZ!q{2&K!J+**H%|=
zb(LihnV|Q^+bfH#c1J{byslCj`gRAqyuO}WKnDbN6(Mg1BSP>%g2>W?I1A8}q(a_I
z@`U*KAr3x1J`a&DPe1Fz$OSt$ft(x6&@F9a7!9A&kgE1KLv-=F;k`J05XdAd`f^0t
zm7bn{;}d0poN0+~QdL#e^78VQz0gBupgEYC<^98npWt5F$)XXpJb(T?9JrYA?L%=X
z6Ip2$Gt@{R;^GKAsiV73uBw(7;AD@xvbuWsL70#JKKTPTy?re`ev9gx<(=tI+>ZEB
z>U_bNYo3z%vlxN&zHfBu5ULhfKNF0?14Cfc$;Q#$Y@rm*vM=A}0eN@;S=$k&w44I4
z1Q3QyybEBdsPWlNI)TT?ijYDo8P^ZAL(o(l>O!MS*z55!UAK>s#I<E7ruoy(Ls|gR
zS$x&IDDJYf&{7q>a_|<EMW|#1j6ju-1vGiJ!L+$62%G!5u^~02iwrV*4ETVow6wI8
zob5@1oB@l7QR59=to1*j-+arF<72qL+t#@BmNpt3FxRh_MgePIxC>CV7Wvn3abq0o
zJ9`C-+CJ{@T&UKrD%0_&qawbBT#JvuDwbg-Fhe6ktGgK%{QUgD`~{vb2)4)zy`re7
zDB#IdS(K*4BN#!aAE`E%g~4Dgr1*E@AC@HyUE6*-!G)hPGJ*x4jo^Sh`ymY3_s0B;
zzlUo$bLGs9eqY}k;GGY^(<r$ST@IsD0>($7BCE!mDPUaDyPKHgKqAR;l2cO9FSS}c
zT!KYpt~N#}%{j#wM$vgKnQoR!g+2PUICWQ3LrYY24`cp{u77Ek<htGP))!wTCMHU}
zbc&FDKtf_rs6cG~91gEt&%`9**TNr+4h0i5o9UevuXrfPU^2k54oKrj@Kw*l3HEl_
zo=nvpJklRMncD%{N(Hhv+bH+D*q9v`vY^ShMl<q2y_)$Q>=%w#&-?pX=LIsqs}0t0
z{#F*iQp^XZnP&)k5&-EO<~Brvx!AH-y`ST29cZ@tx`u=Mc7@<0mxGxW+1{wv&(a-#
z7aH{UuK@KtyI!wzDkw7_(^>a-PtEeV7Q8e(IOV3z>1A|h(49|C&szxpJRhCt8g2d-
zh<IbhIK9770QgM0cMa=)pV<x95(gIF9|sFM7+(MGV(`gMtP%5XN5VHNL$BxF^<{EF
z>QsMTgK5h5uRcoa-f<mB5HRA@dHS>y41alEV-UnXClnGD^$CDQn1r*KmDL6aQ@DkM
z#Dty6{uDSfcBX3p({7yJe;8qk*~ajiK1r|1l^FtgL>NUXbOrLiahY|@=n6J1xPeYr
zxQPVf69(dU=Uyi#yPqB`-@&56yNf74I_1d72mqNx>?ZyrNx<*s;^t;pZ5A?;6icgE
zu2Gh<o;fu;%WXYs28c}%Ugz^;Cz<!CTmpo~IgEff1#LRPTr+?&0UKN}dkGSbbiU?z
zt>TG+)<GxhPh<_EzKf2&1)`E)y}CuB%*x6t79-Ey1e~cw1}%*eBEYBy_7CzzLR_5L
zXQ!f@H-7?MuCfV4bNQnl&<WTo_5gdlb21-WL>F~x&jH@kS*rnnd{+n9aDi2TaqnJt
zz&&EV@j^w?se8y!*aAN}Ik}HJcl&6*tRs1_21b`<9Pld`0dS$A5ff;ymvC=;&5u3_
zQ9c3??-%$P@PC87y&=KDR*Uaj8bim|yLW(_kb<V}1IQ1Ol#`P)GFk#sqd%T8C>kAy
z0R*#;dWo~Q0}&{AwuY5#G){P3P(DPD&CPj$_9<2S`YAJrZLZ2RXe0@=ZJuKEs4yuh
zX|*HB^zh!RPW2?SVIs&>Mq0Ko$0_D=Se1w%sDmvEmVWW0_eB&88v~b#PC4hPl2SBU
zGOu%iGEVVe#57=Yu<AaH(o+Fuxq$`A&|{O@_MXFhjb)Sy+t%;Sd<;A(Amm^GI7^*V
zD}jyzqX+mTRiZw>wbR8518pLE%XlV>@!=lOzyXeB;qh$(b?-yfj)0ycyT4IB#dbe@
z5X=p1B48#H5fitbu3%U%z0t0bQ;yMz9oz%ij%8{wA38c-wSO}@{5Al1kocF%FfuUq
zRfEwmFc{TK#(|$P>%>Y<ZUA_D6w~?QV`8%TJwVQtmLEU?#fG94p~$T7Pbrc)4(|X>
zMbF1aKnN1Ey2SHYWkG7G)8;6(**f53Jt|&<Ovwii96&y+xkk2C9_&R3qTl^B%k|ol
z&0RHMUcMZez<-k{T&U)bT?}Nt<m}<$A!wYfMNSa7NGcm`5gr|C`+bG5gxehd&@e4i
zhWu_!#D!Whg;-W@q#Pj5lv?#GLP0F<5U}yV(th#1j&uwRGNnJ`Li(B=R|Ag%-feR4
z0pw!d7bilJMID146Z2H(>x$r3nR7wU2QcY)fRw~j%zbk+uDAyzFX#jBh>*o(bSOZ~
zyaB8HeQFF59KyClAchHqFrXQKQJyL&)vN$C1@><1j?#i!-a9#}YWV>13t(us^M#3R
zEN9L7s@S?nNZJ}>6&v_b$OeNrasfuofWBQyr4OLj)DmUHY-%&R+2kJP0*l|Om)mZJ
z3PTch0RUu$TCoZ!M0#4<Fri_cO+3>*0t)aF60Mt3%JH&z=eQvcoPu5^SOVw|yoq%+
zHNXm8cx>n;l51~;gvPW9Vln~3v1XuN0ratQ$*K|3g6F=#?c2ALmYlxyE!P4oA{ULW
z3t%<%GM&0m97dUn_kf;XYu2u@sCa*F)BBp5n3y=bRc$4AaQ|}|;nvMW2S-N{JF@!W
zGC1~2$Ijq~fT2ZlLO8&{s7x8)R49ccI4!pa&|!2v8_v3kZ;zeuhX0=3RHaDE>DKpx
z`tS!DfOE+S6V)Z30ioMUbm(Urxl!R7G2|fdOIbX4eRnsaBrBCgHD-9J0T%~(yuy(5
z$Kz$dL<02=xcv_D<AO^4x6iag$Fjw47msjWc7uFWikA{d8D{X7PJ2+JU+3Gbp>sfd
zcs!NIXXwV}1M(jQ`M4?!hfxFgcC;ImVysrc#4d^uwFU4Psx5~#SZ;uGw$yeD(qzSJ
ze(5C^QF8-d?3ONwbpl)k%0mOsU9LEBGiyLHgXsr+8N&T<M%?+Mx3ZdxdoEI?sK6+7
z-^@=O79cx$8tm(pvje69@e|jl^6k&s=03df`7v#&Gv6eMkt`LDPW2gt2La3G*Tq+%
z4eVudcaXd-{;MrV-T3&&P5vMp2DKucI)<aDW_J&doKLT?SajcpQ{e3uvSX?q3vY`+
zsi9B}f{cU&iLry?s?f;D&l_{Abdn<g;t&S`!?(~0r-k8>Oi)T^v1(yXyo9VmlT|T4
zKmYJ&V^5IZ7<_j1bH8If$4-)?4$KnZgIFF0DUKWbz(TZ|9zGt?tpfvh)E!VU=$qU|
zA8ybQKt8UF(xId@fbJS)Ns)}Oe;y^{g@;kwcpS;X$hZy6Tx!M}oBj{hWP)kTEI|4d
zxNn(i;k{iTbO}n5f$z*uMLI|?ry}gZa+qTNDZ_V*qHvvbii8v?qy$+T3ia39)a>jJ
zzla-)iHg=XKF&Kfl2WH)dMr0ERb^^yLrF<VkMzt9pvpaBe<6X;1{Q@nqc)^DjB3et
zg)cw4zpSkd^8$GzzsPw=s*?%<qzadHdM=@F7y$#BY7s*AJuo6LGw~<GxkDMIaHR`t
znE-D1<@RF`Mi4Cy?b=~br`l-%z#S#0C0;_qI*q=4Aekcu3y*0gUpg_KL7ie-A{mj1
zOB{gGxd-^G&a-d)Ib4}EhyaduedcFG1Li>MIf&!g&moONr{VDDDNs8;5VgjaO)Zz_
zfnwvbkdQ~53WTR<0!2O5eP$gT2^gtDaHD8OjP9{d4;HpB=11!YgB0VW9CRH~o4aNe
z;eftkMSpx*4it&;2~ffb>C2@R??q1&4ZVw<a8$IcoDg1lA|enca0P7h9OWFSc6euz
zN<MlqIf2*4*RMfui3ek9V~{~WNruD;Lww32_o5dT^D;2A#gRRdX%d*Uo1a(7={*Ct
zAqQMY9U34HAMLxuAA-dB@CSL1$QwAhde}~|MNuCHYkmauWRpr+k1+dYJlWG|Gz1Y!
zbph53spET5V&EnAy}&>MAoAo8Dt#tUWhd%PpDW&#3hf#R{||h!>QrmGz(`dtrI>9n
z$8P)hF-X+tW7W^|?TW&@JS8+ZpXhKlgj%zY@V1n=%-|ld&5Jyth}hT$>oFR5PNz#U
z1t97Qd~eKWV>`}#6~%l3OOirAJUJ;$=<DbZ!=1*iVN_cK?o-JKf{8{K+v;~#<RGe(
zCY|1>)=@&i$Sk1m2HOhNjm>RcU=LP40rWIqqvr{DdvdX=Wap=v(1F$vy*eA5_^!#x
z$4$dM0Ps&00gKv(@a=0AgcnFh_*;rpF3$7y0sJ}Q;3w9Kay{Juuj|$_gfG13UU+_s
z9O!_8c>wD9?#m0Ye;EZhM|#-F2A)e`wydqK4ORx|1q%dNa|1`MTHYxG6`5wKsNANw
zS0UC1p7x?L{U!~YeygOQoi8}p$VjsQdY$l^K)-Z_Jst@*&IKIOC>0{M1`Jp_gB($^
zp3W|y-X5w{oAm;j799xO*_BBhbYZOiZ{etZ8uc`Zwd4UfS@R$WC)D>YdRwnL!&l&V
z9NQq8T4?pj6y<Z>LCb~79I9{0w}b=J27)yW2;SllHANOma&o`!$WZe4IstCPQr6Fa
zuh9V8A2kN)*!5|fxjyAH*?kyzF%e)_jDYYbm``;jXXJC)LM|<=hs!<mRi{!F&Z>&(
zGbfc62j;8HEf90BYC~nNIw8m2Br2YhU#66eWCz@jTD;$9tycL#>`Oj5h<us_kvvwj
zpFZ7xM9;|?=L$$>QCjSnZ>##WnKZBY+eF!9EG2HFJYU(}mB%GA`HEN!LvqlTB0__#
zMOU~`mf+@(I1AU{FQrUsU}}LtQ!G&c9GOJ*^!4)tC*mT}JJ`hUpL?kSazN}M1tldg
zKtTwO0>l`&9IDX(sFi#Uw31r6D8nO5DFubdu&~?{3mK`qy%IPYC2G>SgpJ=~Ujiqg
zSdh)tnT<k-ik_LJ<yenmguPRmLr5;SAQMJc94{Y_;|g*>2e!@kVkxZjxPrVq9zMR%
z#)yy*KXsgn+=;LX71)+JrwzqKY`^eLuEsXrQDN=F%{m*PI^$3tstUz1IL*{r6U-Cc
zRdq6N3~hGV#+4__f3Ig^Lg5<U(Vawb3o#3X8*l7pFzHo9eFkWEi;JQlNq;j~qX;L3
z*jGb`cWcyYRqhrHlUM)Yl4vD8toaaC8dD43kxqyK0@^Y!7DV{%)E=`d=cd};5~IY@
zSm%n7V5|gGT#C=VDDt9x|8HPeKMld07u+%pj*6f8y6V|gl3M^*!waPMcw^8q6U{Co
zC3U*=>OSye0k$X&L`kJ|+DVD$#HhUsB_}O|#Mz*=N9b5PWf!b%SG0DIu;@1yrD;?&
zG`Q^pdS-BA4Z!@^@>Qm=)@Rd@P?}UMhWE;^vwLeokpUZ7CGmAoxO7|gxu<8UmKVU5
zYOJ}+Sj8E7s2S*hWZWAEG1Vn14{{k)1T+gurFOp^LV+WME&X{Q$U&ZZV*4=gdJJyP
zj<)^sH^2cLptN234o3pLg(j3$g@?k|DCTTJ8I~4+pED8_I0XWzQ&jO-lSDl#tXKdj
z&kRwEQBV?SzoiRYbV{=;(fIOt^f+4Yz1*KZs9{1mjyM8=aBt!2MT_qNEVqK!SXe#9
z9WRwQ`R>R6Ib}*2P>&mqfT?T&-1M#EQh>h1n=GZo5i5oe%v0IMm#%4xBUP!qf(WKm
zgG~2Ty6Tb@bh(O#;Vq&x0C+V|k#J~)aeV;h!(ImTWOb=34{vpURA@7N^HCSS`;l2X
zpG)U6kKC@pyJ8F~`QZggQt@jLDRCt0uTM2D0x%x;y~!qHXOAgRNXns5cZz4k!N<ma
z+6zWl=r={+u7Hgs<pB1e!1n-{Wm|UT-%F&7&CP~{<k_1bILWcGl%i(2ClSygP620N
z0|u6hCkH*uc5}4Qj5%YE*&<zXut+(UWROmRT?<5@0L7kKO_anld4XLtz}FXwm$q;0
ziv2tr<X$AJ1ksd~l95lNN<qGsO3?j31`)8{e08We14j?oO}l%-vCmvpOQb@jht%%I
zkm5*xV+S&tp??`T22k&l$d%<tESIqe3SQMs8sysLH%z?|>&%-BId)RS7h+%tO?wnP
zeH*z~g0m;j_b8V_1bALJ8{PV?8uNAJS^(4ldI=H(;#?mddLD0(bH!}+ngegYA|2!*
z9iyQ9)a1rozRXxbVPOZ!h!SZo14NTMk_@NV?J^RWpjPlqB}bo$7wa4WfGCi=>9_8Y
ztH^>eSxNz{AaQ*+Z8Oz@@=-}!5DyHHDgA>P$xhB0p4bpSi<q(m9`iozqNKGovlOx$
zlv2yy-rno#F*9CCQevY3m?@YLo<|DGpvxK?AdG^Uud=~81m+bOJv1xRm3rz)PCsiK
ziyJ_&BQQb41EYv|DHwz4iE#prL#a4TeqEe>8x{62+yWwO0hSdG+>dWg)Cu1qYqC(&
z2Vm2M2b^b#co5UtvH=QE4z(OWgz-CwSl|>INy(2O>g?H=(t{oQ*|!l+z~@RmC3v_1
z18I1{OIuqELm_LGXeNmlBNIi+9ZFsdlxrdIEoArJpFd_{)+a#4LU8W5NRt=HHN3jY
z>1nTfL*J!Y%>5nGK4nd{YH9S&Dh)@*M#c%h2X9(Wttsg{DFJ~DZ(+XFLj{iG{y2I{
z8dZ@0U2iiXbE*lOEKipcNBGckzs=all*e%^{Jc1%QG8X)mcX;Nzpuy}=WZ!_`jONp
z{3>;!9TM;m(Fw-Dqed`4Q&;T(B7^|%fi_oXo5)m6Z;`n0`erfZ%d9Lik1AAWucZ$+
z(Lrw@)|XNUh`h;6nUs(K1%Yf<qhMVmf)V6lBvl=q!Oo!}h1z=H7Ms`zd?Djp#6A4L
z9U8-#QUvU->YK8_e;vy*DJ6R+j3R0BX^Cc*M!goT^gE!CL)4_DrGNbRvGE+O)Fz~a
z?aL2l?P_Fl5a#0{YuT4y^~7$56Ax}6^4d5QuU4W1_`dV~K=NrGG#fCfz#&nhR{Ti5
zA<$@ZzNubIgOZW4UplDIqzU-wsMvkA*7WZ%5i^&}zJK9q%`>C;sB-+A%X-uwH7MAR
zD32J;tQvSH3DPZjM{bNtVRDYtzRlH`yy|e;U2M&C-INFM2Bvqr6crU|{l0;S$<f4*
z;K?7BMCXIKVVuh1l!sETU;1#+V`fmqmTJ}HrbRzVgu#jy#`~rAJCwVDuiFclBt_gw
zY7q+ItL`6oCt?}N7aa-acOE?xGl^}yjR-H$K3mg__N~j`JOhJtZIXqSHppTFBe5(C
zSg-{1vdR#FcP#*_eIvE~{O0WPWD%?rRbiNDk@~si^`LUj<advcZtMtmzT8c68X8n)
z=nN-#2|P#(vW8!cf{+Afcvg&LD(mM}tRxdxRTY&i!J;baUofQCRawubt4-3vJytP$
z#r7Gv_|CKo71DusMs(@)=TF;A*0*6|Kydxssrdv12+cFxryC$Xx$aY;fz8!BNZo?4
z*xK~d(KJEfcQ_x`>a52kq;>$r&Xx&Kf>ea4RS27W$`C*kS(om~Z3YTJG$ihp^?)u;
z!JME|14s_C4&DYN9DHZ$;iWJ?M|)Z6i0qgYd4!*G1ps^oJw?5oQXupxqHTCXkPQVH
zIhtE7VoyIdTA}t0)j(!;EC@5!lf%z~;tygl%dwgR2J1K{>Hzrc-@gFP&Q{$z8}SaA
zn*_B00L#SY=2ILNjkE~AO38`}1eCv$&swTNZmOWPw)oHcl#w)tW%Uc9p21Wg7epi*
zvuko|J?cK7E{ct;tgOob8yGA?JcS#3Y;2Fqq8H3G!f0rt_ONXuLKp)CXUdv4Akg2S
z68<J#BPnp#WvdO_SNgK~uu*JOEv;=hmC{c318zyY73Br_5$?TQ4iJgGoP_pLr%09`
z6QJ2U%sr2YiRBrMt>F~~hta^(6tsYf#Y2FPpBzLOWdF!@^L-BqVcF0sa(KEHBs?p|
z2(W8VkN%`~B%pkjeBU>!j0>N<xH$ZH+>Im84$b1q3&X|;Pnidf;t{7H(YKuTb6V%*
zceaFyz<gmwA+SliLsf`^bq7t3{l=ry^YhmMqRxC419-u2KsSeKJZ`}_A4WB*1imhi
z3Q~m0sRMh*6{EDG(&VcX?=9f3@4NfTY!58`<rH?ec>7jOs?>LMDn#tb9xl2H2%^!$
zM!f_8U84v+e%1{TJ7wiz`6IVUY5+7WHB1o}*-(CJIjV#MY;dXUW!!3VFDqJrqw)<>
za^X^|A8usV#W4FBlN*f;U*UOJ-x0a(7>X*dzxiM|DLFZrj%=m{n8dw-VMPak;{}wf
zSj`&6BF(D;-7QLmK##-tB$aL>VoqCB%LEPmg%UKycHq)g5<gD41kpiY)dYW@235$X
zt#yXDM^tTJj_>998546_MKeA4{s0fJ=N2sV(Hn~7mtw%Z4YJIhz&8L{Cn}0Wz+L<@
z)|j`(w$|~z+djz=jV@oZa9}pJ|3i^dPY@Ty&w0cEgxaX(=kf7z5ZsmhEvvqbyD3Di
zSXe|xrVoIV{4iEVc9^=AI*hcB$7Z|;WWxmp1f2c+`3oom0NQmy00VF}gD}7RexD*k
zJ_?GL0=sN-F;uj)g+OlsOY`yAfxI!GWT<5mfM2{uuN$x1X*k+cjMXza2N?_mVMmbB
z2{?#LKuWxm@3Ba%f^go`fa3cD!1d0lldFU6m+0u&7|`F-z$Kf!%`CPvQ&$RNUI7-b
zhl9I?o0*w?I6$md34kbqp8+AFcIlMe1lMeEWp;Ua`8$H8kKo3FoI~2Yq*R=owIC*g
zVF-laoN4;8d<H@8AbH7xvmi5b5m2TBq>f3VAAvp6nHCAkg>w7$ET3dJmk}TlgpH(u
zEVIseVg+pqm)`&hD)QFHM@Q#?z5?rHaeydra2W_7=^+|p$Ls*{>Vy%ouV3c~T4fre
z(*S}95Ie&gUNelBUpd5afZ%JR;^m!wYT5_Nb_ZnTk#bmsJH%?e(W(LgT;%=<OxHdz
zpZg&83Ha|nJKpKSesJx`SW8YzO9K&7AjV+xDEGJfLY(j)92skCYam;8F3#Nd5AVpI
zK9m34S9Ho>x3)1AZr(*Vw>NW!JDD5XUGKt7Z0@47L%G3HQP;ct{H$v34(6;%#+HZ=
zCv$r^83Y_uVO24AhPgPInmdD2#Gk_@RpG{PbFeR|%0<R?eTJkeFBvalPf!qC3Wu9J
z*@NRs?A%Z?#NY1&5-@u>_*H^~48nPRl>{dl<oY111cZ#8?e}k7WE|If;7(eKCN}1#
zh-;+nK%pQ!WZ*xdK-XW{dAP|Ce}6pH31+Hl4%cN>dMd%H23%&pPZIsT5&ON7)+YnE
z6|uL6!JTzM$*wO3+v~f6?R6EvO|J_8w(N+TgY9)QfGx)#->-`gzF$`!Y_BV!PsS>5
zZf0#P3VW^#&SN9vhOm)wvT=dm#X!wLb2^i;Lw{F5(h26`0P1z!;MZSRRaw=XjP0Er
z5DjnY&MKzL`qbRj+SFV{QWTU~&Dg{loT3VMaxsPf?i_G8c0s|vb|vE8e|DvW^)t{5
ztP;;aS3Na1g_)VN%9-0+!mU6VICv4){W%n#r?qI$+RsWnc=0&PXG#*{o8V~(K||_v
zOlb^O<#rkl%DP`G*Za(_m%6y&>gN!qxc;*l3wX#4<?)?+ZWgZVJJaUJ<wZx1d?iyO
zo1Q;lt+Yu?NP;1?8XOhK9PQN<+b36ET?Lm0$BtIhd2gODpgc~d(~#3h(b2+(Sw$Lt
z_w+gT+_{DMP=Lfi*h7VWSr;|#&G+dU9g<X*C|Y^yI#s*9w0!k=991ZTHk_u#^ov!f
z&Wz38qtW1Zbt)}v<EwtXM)b`xNmlr08$=Sn#B2Q1qef)e;-m{4)jsBZ4Po24uy|;B
z3f@70PeZ0H{~>3&|D*T5E*s`;z8;I^4=CRpj9zXJqcFwsou&oeB2}A8TQv_XQ+>qp
z(<?pjmO8i3{bhEPg?RS>{r!&CsWaH+LXfjlYmHmM{F6^#u!aPFY?@lVq-deaRH@BJ
zw~MY)qHc3Qb~T7;S{vq)T0M8gWGP<aDHDDY;Hm94)HeMLojdp07-h=v4`e63=i5mu
z3k09y))(*#Fx_S<&SagA9;Hsn(EE82`xTD#%JcQA4MbW#Tg2}qFW5Hzfuu$q8!9`-
zJfuY>BjYM~jw?064Aq^nC7IpE)H)LT;f*2KHei9TcR<tfVb0tk<QB>p<5F1b4|+u{
z&Sv?-ox#+N<I7%ltdT-EpK&zdtBQ_;U0qlB#Vr#0f(x<4JX8lxInN8~M6{1ol&h@?
z)6eg1@>RAahHpKCvPx)ATkI9&JJHHAzj~&}t;$OhmSin8GsR4yxGcf3h?bmG&zh3w
zikVS+|JCi`pS9|3tUQyGHBSN$TvFOOu__~!yy9>18Tl!);bUgqM*B+FoW~QZz=wj@
z(0PyHc@(k5l-0}0oiC@`El)~K23@L`nI?zPo|`PEe4u!WbD*S^*ru*-j60vOCXAhr
z64&8#=5~<uDO5VnAa27g5A#acx+5N=u(FAR`AV>4%fkhA@sU<Wrhc*RF3ky1gJeJb
zb4&k~5NqVg%MA5U-3c1sND>s;c!^3cE6Ro$y%bWp6ke&3B4j1+*_^5%h7{M*mt$_U
z9awVi59bQ%<MzZ8z1%$ORa5*sWNPfcwjJqH#qF!e^qs8Hu03g#9J}#F?yOvIAl()@
zx|A=Hm+5X9l6K*VOg`=#0X5eiw^rx(w+PwFc=!1=XV<-&gY-h>ZAa@<a4u35%MBC`
z&3;*QeG+HHgKMq3nNu-KP(P~Pk<NW@rlwM}%^Rm8uIo1)_XG77^)0drCCgZgyDn`9
z-ZRHdeSOWH_IwTf+Y@;Hm`&a?j4H{21-P+$?4t*Vc*kGO?=f@eRaUiPC>YbfXKA_-
zI~Q?BASjD({qEg)O31tk8Oz5QQ8pwGbWgIKP3Fs=;5E2=XPqxS*?#%UjlO#5NGUug
z?Q7fw^;><5nj?|_kco5#Ekly#9ig_D6Fu%7Hw#&<*FV3)<!S0taENd=dCj^i#a{1s
z>**vKP7xwmdbHO*F+qJC*Wo^qjMINPhQEoa>CrJn9D2C77m3B*kXc|%No$oeL$pB2
zBY)`Qp=(ie-iWL%>Ul@g*^)s`zN-`^CcMev!R<34_chL{hyuG(9kAq@-IFm)%==Go
zXNB5m%Z5DELyP8^_4ll!acFn;|Md~+Q_G@~HFAFt^rgl4`Y(*G_k`|VkwHCLOPh-c
zD6ND-9+APdvp8v2S7-Cmw&c4)?0GA2-z26Vx_#Pai5NE(YK|LPY#w$F{wM*9zxM}q
zr~lABx*-=OcR+aaN|}FH?F9YM=rwovn$114wU<#A+-jqiT|4`jUvf)s?GXF&*%&u0
zd>*_vphE1cZSw&e_LeIplrqQm_P6`aH)zU`il%?u*fF9t+O?QSJ-b&mBUB~wV`)v*
zRXJj?6#uiLpRV6A0q*Ri5Xz5d1MCJl(=H!L_HmJrcyGHm(Nv<|-<(7q(q++a%35%#
zC?0=V7sTQU-e&ap$w|JxK6G#EhS&YO!-O1fZ$+i7-L)b>miI`kMN)Xymta{%d_!$F
zliLQfZu;{g%Z<igl<anQ>kr>TZ#kI^+!Wi?pU*vK%=Lg~DaJ4t(lxf}dgLaI%?fXj
zETbCQG!`m!9l-?(C!dYq!Ollh;a3aV_0(-1(r7y>J*8tl)DOO?K<}%j6D0LWwmkMb
z9rGGqlK5=F!j^tY_~m(j?XR0pY#fjN9r%Gzx&NlKT<lHZ)-Zd7RwUzKeG0QPwzfy;
zQg)z2#qAMF3}|h{_kaEaXNbaF0Kfs~vw-6)96)Ka00qs0P`NC;;JCyex4%~Xf2erw
zKU6&TA1a>v4;9b-hl=OER`Cef@E1kPjkx%43k~^S3N6NtC=P%JzyA<WfDQ2r4>ubZ
zsQEu`!BGXWe^lw$#rqeY^zYsG_fvVfp_~Bnu>8aJ@1BZijDO*&|8T!Q@BeRg{r8Uk
zQ(X~F|Ns7}h>remd+NW}{{K{0#IX7op8D_I_jkYkb$tEPlMvYS&nNu>&9CPdgjLnb
z*v#7A62MR~*fW@us)Mnq`Tq|(|2+K1$zm{=6Jm04K-tJRIJsEZIeza#AuJFC#07Q%
zFfVk;06;ar{s6!IIpF~0{KGi-$7%Fe(?iw81dgyi<gD#&fx%%4*9BmolZ6wQ0}yr=
zPAC}{1j53>NyfneW#Q!nHy2d}1=l2F1K?PKjFVO5Pc$uZEkXcRR#%Zm{Lxv#;SSDx
ztgOaP&#hfqU{02-2rLcZWQVY@K_O61dW22zS71%X4*564{(p4!Uq!=TG5FuqiJc2b
zCt&vQLRg?sG7cVY77lh`@bG{i|2yi$#>)bd381`xQzwr9xK4k)`mc5RtBn4eI&pEb
z@PI0DaIvrf(~g&og^iaC0_q9n_^(yT!OVgc!VbELosF9d@;6oD{NGjSpRfK)m98zk
zKWD|?REZtR#R5!I2pg0I!a)WF8*VZP7>C>lP4q91JU2Ht7H4;RV+UvR>+z@hd!G;d
z2IXYu;$Y`sV|BB(wPuBKuz~%*sVUb#*YrPe)4w(VLht?W8i1XXg$)?x+}tdjhylvM
z0*qyLFlu@KQ!QX=4YzVJVKIf-u_`&i5Jo+-yrP1LH0$-}-&7d-&lUcU@A23A{Z)ni
zO$T$Z0olR}Om|M84X-C4kTk%MXMrG)!oS?Z|8d#=7YW1tU#;I?mF3^nj~z@yb_Aut
z0$2=A_Umzs=s(W?H~q)+U#;I?)$-rekDUut43JBlJS<Q!J~??28+IrdwEw;R2X}$P
zoUDzXIkN)d%$yYgNLek-?aiHx5kM93-P!tYhA;2GQ2qbYNBnhk{}n#`P3v#~>C3@E
z#?A$0VF#ld2xtfx>s(+i|4(Qg2;2YV*7+;s`P<gvVgc0MA7cl~4O)lif6mx}u>W6f
zoxh?k1i1T?W@Y`esjk^oR!wU&Ko)WXEF@)aZD|F#U<g7e{|!(SwT3$@nLCNW>>ObB
z2-cW~RT}V}P7W{x`AB8~W{)|ms=TqYEs(Ik`Okl!orFN4?2vz?CwJ0=bX9w=8l=(4
z{rne1Hd`3fqZvkJH|7^AeM3={Qtnm`he=_HBa3)o;mj|<H7Dmi!CNHOxykKWFr<^P
zoOq;9n?LcNOg9lKSSYLyG&@_Yb1*R6_#gX^%yt8=?&3m|*kM$&iHSP#?k1wB!pg=r
zj`K$lO47OIyC)yN*PiL~l-gR#x)fYjM2VlrqM#~D=V)OF*Q<t1>TG`<Qjn4Av9FRn
zRy;&}eGdAq^tJ<5UPjK)3HtX<>K#47TbM|QBUx-#+Iv#?dyhA9=(NQHRntYT&&NhJ
zWsRDfR9$IqUsYn6z(+>>Ds0U5eV$o?;ncv9C+gp>_tm_<f{gJ4<MmE7&Jxb`P7nG4
z`t^<(%5#+Koob}!>xbUZzG-k>Gv7BiDz1+RYy8Kzp%S)avzqGlimE`aIN;au^`ZC9
z87X2B_<d`?@&b!G*708|%0}9EEM`uYB9SYC_w7EmV#}1fClhgCmB4OYpP3q9m({wz
zP?k2S`iS3eC$649Rgl-LdNy_|^>>+$hw(5Mb5yYjy0+pD8dS$RleToPi|rd{rMoBR
z&My2;87<b-=Fvp`b+g={XFq~}^5K?zr+_w=!T7cKJ@xg2q(ZWTbirbq;x~R*pN?rp
zBca$-k$&=yu~Yx2ob!b1)5xgds2yMDr2AY%qo05J`3~1YZSm3dk-VV%LM#p4^cqg-
zY~{7qR9WH|HY+t@5{0TSgsxAR!nR2vfU?Jk;Ao1%3_i+@*qu;67hQ>Exsc6XbLTOf
z97@=ly*~2>*M~E*>%IT(UICrkg*v>6NfWyzM@I-peN})?+jFD(wmLKN#JgzN2Ar?f
znt@@9fM7;KLJ!OI?R%0}T{(PDoNh-*h`k`yFUfuXfw8k~d>E}ys=wL5^|-Aiyt>p|
z`i`D}KnX|wR2Fn`ZgFyZsx8Yl@ltk(*ZhDp9J6RBZbzhRe8$>%{VQ{Qh+S4ijvno;
z(G9q#VP@w_&21YPX-P}E61;oi(_;l8y;lWQG0v!;>KoV^8ok_>M~KNqW^07hC&$r-
zhWN%N+)b^Qr*``D3g@%y8J?L?Y}(kUS<15vk4~P*%JnoGc(1k1r}b@<^CqY8@%fkE
zFsLF?=c>y(9)GgG=z8(>Q_!z{n261<Pnjn^=ia|YItJ{{sO%r{iA{7`YKr<uF3cuh
zoP2vZ6MtgQV(UG?EEJaAR>1eF+ON%oFaI0papy(3+}C|}eJ=!BTC0Y<qDUvW<xsPf
z;{$5O;$yNuv--fVPQsR~#eWT7oqYWk<w<-QpB<hG#zqjF;_C#m$A<r)=fV-wmJ0Jr
zq_^?D+Ko-|5fNy!KUOjqG4>_A+)B&S?@r+7J>tR*({#FOV-<Q^vGsGfrSWG6cZ}pc
z_VEe!idzO%xO@WZ;~{~I-j5b0CW^m*TKoq4Y(+?*k}fMQs;)mz&B$!(@zjjCUpwVk
z&^fLAbiaK1ywUrs;@9Sk*9XGA>V0X%brE42$4_8imNja47>}&Qi+`lnTZLB+T1q6P
zr2I_Xa#|b;E=L`s)c7e{JhX~yY+~xXy*s)^Kyi#NBC}yP>0F!3mpcAoWTLmMSWh6u
zAU#Fs#gab%CnJ-VQTD?hq8X?L<el~><!@Pif^7?sk6erPBDa3n7o#nEvvL2N@b|yR
z@^vCFCn_DEU}vdhbmEmrfE1(6L-Fr}u_U-b&IP4n?PCm9_ghSJi-+jVhd1Ek;j-n)
zAyH3w*9+9X#=$vj>(HO*OsYk38TgI9_pE>W^;qfIa<419%At$a<(K;>Y)mK<b@lRu
zJ~ZX;d8t0e>f^q9Irs(l#6{M=;MAGI$7iV7pkU)xOPQ}ieOT1c3yv1!+m8)2b3-p*
z%P$x?y<7gyjjh0dOz&wt$IB7FrAtR$PTm$l>N9`f*YfMz{YY}p;vpt3s}#J#7NWUI
zf`)YqrF7NNL-}Aa`IA6{n4cbfEaZ_}1NK}6u5arT5}{skGN`wwbaV&}BHAnh=8Lhl
z$40v|oZOONA|Y$HId1<f5b~ZqSU^V=VHt_3EEa*FmJh8I2z~x;&#K+Xr}kBoVCvk%
zld+txNDWQp^M!1aKc4vUntx6WkD~3YVWv~&4=3ignzl3__&?k)y!RA3u9B|mWXzWQ
zIODjYf^fS9DL*MvLfnf8Dkdy*<guxfxp#Ryub`=!hC$HClaG0Vp*i-#&IZ?DhPKPD
z^gBP={*+akg-rA)iQ6V@GMR@+%cC(S!sWCjwY-ISLYpR>FA+=6Q9V0fG#c_%!p<}o
z+dj5uxygkm%BEw#mBcV3s=@PBB=?gyay$hzD{40uz6q8%%djYjc#>Q&m`tQeRS5SE
z4+d;8!RSMthep0uT_Q=t7`5VjK^t_?xb^0&DlNY_Ex)&6Ph-0XjZZ9or@Dtw`D4_I
zr+C|()<6dh3bA?Yy>wiM;$Wpoi?vM!mx7S&?ve1K3T33?2YGMK5*0T2OJEG_k=$HM
z9$ina8hU>ov!mL22G~3FEiGT11KAD+*1NO^s_LEYT??U%vo^EE11+LxU>SH*i}ukm
z)du6RH)Lx#w-XAE5-eYp>o3^D2B3iok_IU)2Pz>gFUtxE_1w7(wewa=bp|4)X}jo^
zUeIje6e-Nmb}OZCGov&kWy9<j&cE*aH&ZzO2*~JwdJxNJXx{K|UKy^hK-X{;nC+rp
zz%Wx;PDuqFfVDf0-NTrqGuMKX7{4yx)jA;>L-RD)1EuvA&?Re|5-#2K_YTk-8T3U9
z>4zAVj`$p<0wi+ze(EAWM{$>0#-Yns#I!3-kb%gt&^DJ#gp$ZBH@|GN2%6Mj%vZa}
z#Wjtj4^dmRV9GlPeHmYe{7j4*^O)h$0lEN3Y>K9{%5xN0QIu)H#VicPS2OzddrkPo
zB1k(Err%S>m~0Q<+UdUukJqQY|1~7!LoTvG+vwQcau(I<&HKtW>|4I$qI-)vMjl-)
zQLRMUSCfjkPOAi}VX`hi>X8mYhv(8FeUPdi9ifj$@>&)srC%~5HNWIPXvx_W%DnI0
zblOVLz)z^6UoMO_mM5H>jB;u=)Er&T8tYS`G&ypwQR3P8EqtjTD^EXP;C;D68`{%4
zVKP8t(VE7((0v@*MCvVg@xyTB*YNxSe_AUs$&}q54uOtNVqk_$5#gie93yIzDc2$@
zh9w*`iaJd+j|U&)I(Of4xN%uAeeztO{f?(S)bf&a^NR+`2se(jBIz#LJ<cPCTpstv
zcV8?YMz9b0*pe^C9OnH%_ugFp_{`))RUc_|dfi~T&d0E(Gy3W47Nom)Bj03Q$loFz
zpdWoW32n;s)?LM+Gnc!YdizvGAI(N-Y#g(^nW0Xt=bdKJ_Ll!b0}c5~OAq$qEQ_RU
z-zK~ePO>F;2Z!9*et?}z+TPBc!=R0IejwN{EZ3=GaCY55xmNkYtk=L#WolQ3vmmRH
zDDRem2+65|^4Vn*lCs$3>7GVeI<8uxX{pF%q_c+2&}c#A+-uzmqs=WT_RE-yH$kLO
z`HXX>#*`Fi37H=`c{MtpY1<kM1Q$_Zqm1aMF*t;r5fMc!w?m#;R#ZATJv+N_j5Ua9
z;j!P?l5<g(y8XMqR+`LX49E|3c{Ae^cqK^etyc*%MY4ho`~n%sx%)yCd=3IVzHG>M
zF+`K_<kN;dnNfM8GY~mL-D5g+Td(5uGO?rI#Ha1U(IaX>OPg7b0ESmF+6FW9-d!{W
znptaxPLW%q(f7b`Uh?b$shy$ULYu#6SD4=Z;_M}fD#u*r|2|S)Ccw)hpz$qk_0t@c
znBm!S)<l&<oGX7Rr_cOVMdsShrWaOeN5|*lD2t5mFT78iG*?0sPFyV}dpn$-Ox8%h
zEz`u?nNvQQ|9N*CHBvLL8E?3%F1t~94e7c)9lu<@_TQrc%AbGOaDl)WqGVqD6Zf&b
z0mIrbBane2lPnKkqaQEl(MBB;?|!)`EG#YNH7_t@4aZUqJ4-x*l2c$+JkW|Y*Q&hO
zFO7e>yNSa|!2I@rN@4!PVQ90P^WJUeViv4-zo(d9!Uhf<^*y-bV7yRH!1t>Id6v>>
z#R{WJ?KbuNr>_k2!}-@`1L&;4%Xr^@25(a#kkG}Bk^cC(Z{Sm^sf8#OHLuitqOsVJ
z_o2OM`XSx)+&z7ar^MSESj4uBB^XqmZwoaY?jHAdOPGBD&k@?sXZ+p^S;AQp`M~)8
zMU8PP(S(ztyy~khNKEki8q=96i$wLFY|R6)z(-L<$-|S`wzTKw^s?a1(CaSnws;5J
z)eOn|MZ?lw*?u<qQu6M5v-rmm?<xvgB8k<^i^DGRRQaMd*jX-Cdm+!)%rhQ9j)A1v
za3zokR7;fZex_IffTQit?Xf_d+*Ki;4DVJ~-OQf26W4A*`p7Ml7_{FVKVjI~Q5@Q&
zKpazg(hc!%vyiNt8}t#1`liMZt@%dI=fT|Np|{}Sq1X%BsxeYQ@j{J{WP=8H+=o9X
z=znn%3{o??xLl1)hRnYDv`GFsS@d~X*M;nC1D$)(RP}fCEB2R@6?D^ng|owqYWSGF
zRh9n57LG&vJvRsHU1RRL#XV0$`#rPHmv5()Bz?1Kr|8MtGU+85FMLiklxW9e>wbVf
zZokvw9blCf8u$3tML^2RqHyO)ZP^)CMec1WhL;So0&m`W3{IY<J)z$Fc2S4B$)6)W
z1VC3vNS+t|odV7At25&lwfHggC*L_63HxdToe4bx(xd9vw6xme+*`uSN@F>vBGkDT
z<Omy^l=OM3Z&Z-oN7`o;^om;C#B1PD)`#HNaBv21ln9-jpi|*2yKwmz3Y|aclJ`Li
z*|jnFrfFQy(91K5<`TRC?XH;3r~%rq*QY1*fi_ih_!v90_PRJ3a)IXD)divVJAn1H
zkWrL$S@5gpfyOW+dEs!sP=nL^qHBCeBcOaGB{5`k!jrM0ufvuPf+&ex#l~rCE6df<
zT?>x*U*6t6sx6I?(20Op{F%=QccMu9G#Kbk(65dqyKvfdWqv;So=yZMdfP<Yqy2*a
zqX_2$>5G)(HD#k|amxGL_|1CxBT83pJM*ua(eA!?DwNQ$gqV7CPW79<!eGN=#G>fT
zakLnWn4#`co*I1()Eu?Y_VUMNHT1Wsnt0mUQ14UxLJcCAy;*rD^Kw$MncUsgx%}G1
zS00h~)E>uRe_-snwaCa~1b{=ID8@d=$9OULNU&CD^gX9N961)rP%a#LR@74%1KT<h
zP`<(Egcqqlysvti&W`pIKixfU%>UMIQ#wkUAwE8-B`MCvo1(Jg{4_o^zjPHK*~VS(
z?zL#2-gooUo0IDWLq0#xhkpjVT4c!)eYb8g%(k^4!>M4fM`%J`gN#L?CQoGKAwCEb
zdLZD+T=FiXS3U5SVtu^kuvKl`YYZOs90277C{OPEdLVF>gkw5+C^jA+_4IcQhJH23
z)h^aXMCLVmr3Nav4N8B)!g{1#t7@z`r}_m46Ux~9Hgw}yaz*&DkR12Ard6fs45`_7
zxLz_z>!HP=(R_NzS+eSGnNA=oA5^0WBZ}t!_ng4_Z&rR8uZS&3BRD?{EYemsQ`y>c
zmVAFN>q?+&_-Sq=+r%TMsP$)GkJ9w`^ZAdN6olJeLnNAQ+dr&JE;yb#89c;17U1p{
zQcFi?YgtjVOkE7%@JA2WVs5PG5v2M0jIn~Pva%>GwY&JyBu%%*{o`;qcMFB6rMV)|
zLw(0b+ShZwqC%=wJDu;`Ur9C~upX!twa1RCc>6~^Da2i@5Lkmy$yqtSCknyNZB2uC
z6IuG9<DduM1iV8O8`~9#Tlx6X;O{rqw^3{rKaGyWjOh~-2?&_kO5GVQ@>#r`m13jV
zvSmWL)b?o1sSwu$U~8JXvl|Yj7I626z^XoG_I@EFWUs0!)=3grIKClpdY9BYQqb|@
zidAT;TfEDy-f2LNtwtu%MwH6gbzG3wdwW&J*rV%k9ThGVWAdeyvhoG}$!@r_L}*T{
z#hix0LyhQ3VEo<P76Y*SWer60S2mCb&E=@28qLX$IUd>%@qY{d9U<3F8V^I77zNz?
zqR*a80WSLE++A?!%41yoU@=j-;qQ!2+c;Y@IwD`F{ojfrNCkZNH?}ZYD@Nq=gckLD
zgP)W!no~c|)mR4_g93EipT|W+Wms9SZFsArfw!NBoRs_acNN7%*);7ZBo-T+QSD6P
zF%?8c#KRoPs|glrNIp@boJam>|7EjoWXK0qnHnj9*(bPuADxy=&-mFMqnQPv5qM#i
zi5Hfs%NN^vrWPtTVV6T$uf=@Og7iYmworWLltVN?CezElV2KtRMp|K1t@UL!m*@LU
z0pCFI+OIR`{(}bthbT6vBe^ot_`SCPh(L2kAt3i48KNDR%l2rhUkMcr#3&fM)h~;N
z^LcyY=J9cFnI_xRn_*p?rze^;0=u8x9uO<55<R;mkf&vFQ;_80at~we-Mh{u3<__#
zN~^Shd8Phsj8-S*6vnLKhdsnaE3l_5+db~^A^zLC-<6cf$a4l;W1`E7?^kFApIZyA
z28nkn<RfuT1WcTH&)3)EomH=TrSEya?_nJFnRi)Xv$=Ced}W1HF`=!Wx(JsM#{8Dk
z^#OT>@DAF77@R5k;*I32jU8#8@H5higYa;xtK*x6f_Vlk+P}VT&CDFO>?YN<4=h@4
zV@LtKxh4Yc`ZbwoaA^x7T9pl0KfZ%U*tW0u`^v;*M2AMH-V2J4FA8%9l^4)t0!Te^
zr<jG30q0H&;~1%b*4X?}b&k${v90Z$W+Zz5_`;8unXUB>PAb_Wa=W{I2Jd+;D_@mX
zw5}^A6Gi@P(;L6P-kj3&LLi-U$`t=$HPi;{7rGa(h(4`mCM#|ByXpD`Cdf2Xwq_Q8
z?$C%?J#AGuhgv#^>Ye=u$;9)0Tk_+K!=IL4$41)Ic!wM<4tBvgA@9=4=wk+^X$;zo
zAfNcRI*TzkMmTEfaUAaeJ^SQtjXYY5ezuwA(o0}5C|4+H<Ll6@2B<$Qs6DilASoYD
zh<o4>`NP^&@em^ka-{XKTgKenE}ID8w@9eZ`Ku*Aljdx}tC6qBlp^GQA)e#ynbG+p
z{s!441sV_@UsU;*u}|Lc&&ht2P<RWq-P$<#6(}Lrl2p9E2N<SU)Fx9>C|^FUC$Ui6
z@!CTIMfXhJa$C!9TrAuX7VAb)wQFX&HIa9GPy$jK`5GbnEAGdifIZVH3Xsc(FBCbl
zC>lAA1u|9PV>EnDwh7b^euU!aVo;o}-_4)5jHI^`s+MnVpBuUP&AcnSsSeX+gj}5~
zJe<wY(p)ZzPCJ|lK>g@;Ml}tMFXl^S)VKww85(Lq^i1@EMV8^u8eSRJ?x1)_TedCs
zWg`Z3G~t)0C%r)EZW-@iX+?A`_zomL-XO{G!Sx70|8?>5_$Y+E#Ydq)tc>DUx|r%K
zzp}eqXeN9Fkb<CBjIuHMS)O#Kn5tp6#4Ru%RsHojtBo@B{@`e0uxRvZu6RonAViQe
zMWVbW=8hG(-`AnHh<>rfA+{iV^MbmX+>rL^!lMN*o5Tl{UAq*a*am(|s}2W?&oNYn
zN~MF=PMPUIC*L#hv$1yl;*th}UMFdf^`siqKE#_0Ff1E7+4rM2Agsb5xIwmP;gbTY
zM=9YFmpU7}GG~ijfh1asE25&`P?{7-a3j^f3^I<_)t!Cyd6<5+u^p|zpmf-=ZF9kr
z_OLloYZr$|V16qgxkY>Pr6R@bp_KHSU#HH4ii-vO-WvwdgJ6jQ-Zja|ap%f!Pki`b
zJJa(~LWZ7A4P(E2cGpI3pG4MkqTwFA`GHQ8h&4O0mXP@F>LsCQ?pQvL7;<p{;CZM0
z-sgWq_b+d;8#TJ0{RphZOm9Low7Y~u1BKQvuq==Jk5)bG?VrV**XN+gT%D@|dPooB
z8#5#2G@^cS25p9=pU@lho)#sGwxpWsRO_+UW{og)ygIa;`nLE<68ia0_d3#AAEQz?
z5|CLf#XUaUnVeJdR<fHm$LYjaw;SbNxME!G+f)eWB^t1}|L`|8GsD3rQTCy9uS}iv
z_;r2(9}yOkWFx1zM=;nvUF>0f)qY?k1M^&oEzdfVr4ej8E@L9SIM4Nbp0uP|+Jdy4
z65D3DUbQvZ79>ReI$nl<_#}y|7mGs06Qwao<6-tw%_u5>h?4pR&-sd<-L44cbF5u$
zHH5xFE%Uj0nAYN_lUqXKYPoyJw?g1hfAOY_ehK#mK)5QN0q8zbJ@Q0kxbu9b>nL0I
z=;-P1S@onHnfMAj76hU6Gx+yJfuYMpzoK>!NGf|$Ao^jiJy)6e#GiPZpu!;pt?6XB
z@D)~v)e-_;Qo3nUB|p(iE5Wsh<eE#&!Yz3F*4&g*yk(w9Kwmwf(cKre5n-$CoNH<M
zk<oB)b9ajTbc&#2n2l44kd$+Al~!N-6pI2NWo|a0XQ8gG@WtR)#|CW`X_!$R?*5^#
zRKSSm3Yal10za-hm9ASll-xW|f_CqGi;N~4=!@nbFXb#;1d`~Z1)yH`tui4;NlIYB
z*3;~_Un0v`Hv`G!(VJ8Q45N1f98-&exgN)0RTw$(8rL)C-|?=yQ*3187!!YZ|MKFt
zpC+D`?&1BTqOzeB{C7X>H`Np$|6D{jZg2)sq~n|AuH}Ah=lv;9W;6#CGi_`~E-d8(
z`ufxYifhIq+$t15HOpn<9kr^NZS1WO5!KExKJYBhkX&T^&Iloh=mr>^%%kW$NtI9A
z(I2{s!@yXLt*0)-=^Jt~jnyvBdK5M4*_AOKm+*<yu|#B`7>nnz+T`a@d)S9J_qPcJ
zP*8vpg>K>$E!Rba45UyVIN{V;8`qbv*%k9{c?>Jv`z3`kK6S3o(_vJu?&f|FTG3X4
zmT?(#oF}`e-M4vPkTXT!OU_F%wcv2_@Znbbg^D|P#ZBVqFNZIkRgX+r-S+((&i5C$
zlgsZ0T5s;Tx*VTv1`!jKela#z-Ky(Nq}3sCk^g)ZGWk9j5YUjys_L1?1O|q&r+JLO
z&Q@=?UUovqA1lzMo_l;NbRi1qTGSfs^Ow?rI@VQ&Oe=Q#_t3P6nt#bB%C>@`Ffzsx
zKfeQE#gL|}*JK+XmkG>R8NYd)l{~%r*$Y0#mV5i@TvKs_XS61Q>_|OEkB`?1u;LK|
zbJJ!yC6ihcWcW3=pzS(@yqFsJJgMtL+}HB5ykyIHoo6H<r8rmdR9A-k=BvTbqSoaX
z=RET2ICuQAn@WG>JAbt3-n+QC-0L_47R$@1s&{i+;Qe;ZY2P&~;m&0SNxY*Dibc3I
z(9fEZL1E{Ob)W6Fv&J^1p!XI_f1dl`V;)CTsZZ)$U8M~#wi%+>SoAHv?_1u1m7nmd
zR3_(?Jrk5C$9~Y{D@1<O?qs-O@m`ZE^#mQc<azPax5~M3>|Ag5-{9TOV5ryBde^j!
zyeF|~bV;nfQ!ls0H<M%wtA6~Eo1aOG9N1b>gPs9>GNmN$zo6Mh+Pt!{PhQ@tug-g<
zG&%O=?g^HaX<<+o3RT`5;~R>O>c=4ghEf~*<8@d#&>*zHJ(icK9|d_cTCL$WJb8_r
zC3(-kpQGPY{urs}E!ePbh>pPK21x>!mHPc3d|My7*b`iGq*9tVnAP-Sya1{FhPJds
z7hZ&dS0;%MuqK?iZI{hA2GP|%5~;?H8r-u#%2ObSSdYsPh2vZHIkYCcDZDUS!9nY<
zG|FickIfKVxu}uX%1}0`Fk(v%+MhIiJB2%>;LozB3rBa3aFdcx){8*TM_@w4Rb|Au
zA-DAXn>ajEnHh_D@Sgq17{xf9mPij1dp$Kgi0#?c7qjQ@9nkp^L{Ia;otr2pIJthr
z89ZH02i_pZ|Bt4xjEl1Q{-)JMLU55TL1IB#x*H@FmImqW?w0P7?q=z3=>`Gm?(VMV
z^8Wpw7rf<j&CEIH8|UoI68&;P<erLpadAnxYCTQI?BMzJIo7=#IYaH_2>40TMq94u
z5^0~%;cJ+vC*9R~531b#^z2Kr3^r^*-_$0YXAxmY3hNbd_A;hJro@*ooDe~%9*0rh
zyLtCPF9xb<G3Xn>TNz)Pb$G;_sGbtG(n@@BT9rBkCs@K9R$BXt;6A*1#Ta=B*4^EX
ziD+E2f1ESi#@RypM-t+pq>h2dl&)BU`g-&pOIQK)0b+Kml&d5h0q%SiEh0kcMOUDy
zn|J%SisBi&*pY>e_!Jo1ZjZkYEcNNb`uNPN!bATs-mlC{M{D_T*uESO{QZ=ibh1Q*
zy#%<+S1Vq6rc|z`Vw%D^*C|^aHF0b&o+F-^ths+GD{UeDYrH4YDbCy4wxb<A!B!vs
z;5cnLS55JdlT}|YwNq{-1`8@{W&N%f90|I|7g~sl{&~pdl4Sg|wuibBN})~Ndv~SI
z=d~F7`=hH3moWIke-bdUw5ZUdiHZZv0ck26sO5t)Bx^F@--3X)+kzVT&G~b6R<rtj
zH>PKx!VHbAY5-<&s}Nc{9dNvV%8N25YRc#p64Neg2+*{gg+1Q19v#fAhg^VmGCS0V
zq_@$@Wr>-bYRZ$+8%}fEU4LxryA9p5h+saq<c7>idR)|!x&Czyz|`bk_}3mtuPbew
z=<nrf_BT%0w8mQ(gssO#`;zQ(A1j2~Yg3@(xgrZqc2n1t{2FHoJ<hFlKY1;$Tt3dm
z)_S)M9xabdCyhvlz1RLhyc>B3g6Ns5$b1vtHdvJUx3C1QN-R1^Mc6FUB4_z0`YvH=
z&eOO7N}NRwyX~Gp?oaP~AXxX@S9!_LK)Wd4b6IdJF-lsbc3Ie>p)THFy+e3rG3<&2
zX9sNlxuBt(>TccTzIY`~Ea%s(3Uov!!`O0)>C61ux(3%5+oozw=fWm9oS)yOW=4d)
zN{XAdtJYf(2H^06j>;N*HTriBfmX=hO(WiBSwgY){Ng6A32p&BMvV$8k<CR2X5eR8
zlMO%rDTFJUKv?(oaXps7Rfxw=^qQwDldg<5fDSO$C&I)v#kq^Ohz;3Rf4V?7p*Vm(
zAyY4xUn9}ilenZnSiWc*@t280pF%$2b9nDnMuVT<sGnzfOGx7n&BTe{_0cUfGu+z;
zMDdXoOpeF{%dz<nT?f?Ku@QjwbDjE#va9=ctQ~MrRB9=K^u?jl5RJ@{xwQCCIezmQ
zrst;o--DAI^P59@GHCcES_~>pjckC$^mS_D2w3V5qE-f*OljagjO0(fb(If#Tm&w{
zty%2lDR*uqHmq-c(R&F|K+ufzsa~{vJXPJac%CO(ecvz4QPkUT#Qy=5%Xd!pfQ!i$
zmz!RZeS=xnWR~S6VVdHt&(hLn8)k!$e~YyP6pHGL@Ghf#T=v4T#hEK(O48Hq5Uo6j
zt~rQBI|oyXjLZ}}=4UN7uYFTntl!EV%Pza<^QtE!)U2SV*h=63d*Vr>ZThIn!?l2D
zHgp%4APZ%j`}#mta~u6yf-K~rN%aN&C`hdz@LI6xWi&cPcQ1Vx*yU>LgC6NAoRMjv
z=FnKX-j@XZ%)t5Cp{0}GCam|eyC?QTW7`=X6@#_AUq8j@2coENA|s3E)A~SPpVEJ(
zaAhW<ZF_I!RtJ}N3928*91i4aKA$)IVpeEksz0i8)G(%=Ui<no@dsW#E>LesyCCvq
zbrB)`E}}X}{>9WU-_5phdM<^D49?{lJg;?0zMO54>11MRt#x4A$Ws0Z=7lokwh{uI
zC8WXa+gN9WMWz)P4`)t(h~OBbS<?iMc-Twu>;xy&>_-9{JJ5;x#LSP?f}j*rQ<6Al
z?@-aGfvui0*tmyHAFg(=Ve!X!I|3bW0Rqx2B<RPfAo*18>$;+zX-MS)4J7?CyJp%K
zS_pT+`IYbRb41x@3fi2pthZWzwKJHi(SH<LZ&IJ6$|!hy)4Xk1dLl4ElD&|}s^wG4
zx7o`k^j}-`;8sw|kdZRS9ni}<8RizfcS{S;_cdKTF0tD6&8O6c-&;@TO<Aj=B=PEr
z|F<3AmAN>c;p6;q)aYd9x<xEI>x|>-`vbXebE-=lnPQi;lB!WaK<h5?ZIh$R)k!?h
zte<(vP0LvQE$d)GaUne;;3{7j3w}S~MNzlE{QgGEr{dhY{bFQ+x2WY$W%Ta|W!~!A
zcWePFZmy)Y0_*R*>Q3*c8UmB*%VS!F7EF!iyQ3--cimvH91G&rpYxIwSkd9NCBprN
z?a39Pky;lnlz?RUD~p=l_oUAtd-(QEOuanH;0}0K(&YBYD0QW^q5Ty<P6Eex1;hi(
zjLq%-mId*{ou<i0ynpK(05s@dYq8oTz*5Nz<72X3Dm9lzad<`i^ZYb`YUk>`mnga8
zP*J8<=hT!2OPA;BLeIC*Z*%p<u_0r@(k;>-LX4<n-(FIrF<{;K0LkgF(k9K&N`4H&
z@O-M=y6C%Z=Qo0CC7y~>lNAZXSKLKn^h3Hmw#S#$?Aa<CTQz)b-5zZs{jDV1<Tb|a
zU%JodmDnea%$iNME&#&DkvfNpr03O&8&%Jd3arV6z1Z6Ga$AnXeO<JD@FP;V1TNuA
zs1f^1c-%i#vgv{>)YnV9W<!-_NSmKy;{b>BOOJRp#g8b0`DAGkXw>?To_v!p2O4TB
z8)~oGR2wc$x4*lg?)P`+4Y6#*uh2{1cI0Ih+<4t(RaDD#ds$ct1dgArFj#0mQ{xdv
zS-<-slyM@G@p2H`FsAMPXXSni;|-K!ZrS!?vU~^Sp2B@wLvH5oCe-XIv)SROOj)cv
zbHW&%CRn5^q}QR=7$0x2E-6KDpyl^)=QF@CMkBTOnDSv{DXkfX&wNQKMb+EADotF>
zcYo+n0A>T+fZ(sBV*QuUM@O~LX`?)fV&_z(ERB2JZT^%6du8F%XRUgU_frtuuZD+R
z`P0pM=<V9n+Nqq&i@P}Z*)+vVz|9o>fog!heslMZ8$&4P*Zf>UtGmL~j3y^Lkycdw
zkBQrNVJFjMIn+c1oO6G(v&veZSz>kszbli!bl*5MkYL(y1=IRv85m>6q-ahpcx<7)
zIE?IbH8!8lHc8WDK?_TfxGrdDDOml2`C97NX?x(0m9zA<-a(x~I|7)wyU{<<2lu6?
zYiqNUPu3TEAD!$z&zV%+FP9+!%j>@eO+7Y|^nY^Iskb=v{MC0U{G`(#esJs3@4{xX
zy(81m60!%TebnR`mGoQk#UONN*S>zTm37$ayyphO>)jiH?oNawuSiKWjntToD~Zp(
z9OaC)wKPNopN|i=clWXjn%y4*_Ql*R-Z~x{Kc0rBm5-r+*Z~}b9JCvb16_5CUGVFj
z(y0dV5nrMzA-Yt;$#Si>N>;Cn5nadWtI)A@jawVl1_YoX9@zT4%0hVsLXsAzs;W#F
zM+}XzGXw<y%gy<!)u~@8pVocXW7Vd`T8UFRqi#y*{3eOtLqo2FyzRg4OI0g^;J3V=
zQ*qeyJqf@N+J3Wo!dC92<^}VqoUlNK{m4j^u3+-IE5O!ujJ|1G71(Rt4;kIjqZLM8
zONY&{u!Qos?+%_;-;=a)+O{t<b&%9L0A=Om_guw-Qp^fn@Fj@Ft$A>+2=M3;m>BCS
z+ioVEu07jgLKD8bAD>?ceo845%E41tR~nYZajYo|?hW1mCw~$HSk)JWhjD&0JY46#
zUJcyca^MBJJy?Wyoc{M&VJ9;MX<{YSUV{DV;ANxTeL7nTShtps4gr2{7|*9o@Gm>P
zgm+|Bw7~QVez}00DjY)*(3x9*TsO%}oDmOMcuPlbIT#9`${jrK=xHu2`j4&GbwDsR
z?5Z2Y%lwmRrbz9&tuRSZ!VNc%lbNmvFG?<!M}YAbtolAdX3!D%MI)fCy@>DBR$+1P
zTYX}pJN8Djwh7*@o7ge;++U4q4(Q?vc1%VJ3zI&$vXgVwti`vE`ALC~$+fCha^~`&
zO%o;7rQ%q&Gc5({Z{-0lfi{r~u_vjvb8b-lPw~B3>mNo7`>kXgT0YE`JBkXrLr>l7
zz#OZ1@wQ=A3GJbKi)wN*HBn(JkS<&A-}m#IYfjk@yjaEn?uo9rHE9;+4K*d2<4_mA
zY~C3N?emSs$;Z@;=B*3yXoWL&MAv<%G@h9HFgD^acll<%u=dn{LKJB2zs4ph4>KvX
zh6uevhtVWT;<3F&%T}}Tn*=3`AEhZ0Ta~@l%F6cb-5U^Y1=^o9iJ$*^G`B!ZUuG=o
zSd(iL#pkD`oxC2eJ2OZohsyC6BwVkWdLds+%gQ78#MP=k$p)yaP-Ed@n?Y=5&LYCw
zE}ln{gK?#u>i2u*KHi*wrV;uesR)R&_I0(-H_|<a`rSSVA0g76?M8(qJ4>aFcZL%-
zk?qlm;`^cS13!;wTcM}gXIqAtYQ{c_=1}ad&Z@Xxh`EzXt;ZeW&JN%7@^8`~GReU3
znPE;E=f&^YA>nO2QZRrnv&`iJDkm6J^IseF3oNdk&8D8QxllZQEb{31IRSU-$kMd<
zmX&{asF(xh7UDaD_-sngy2c8PK8%7Y?Q3+^jt##&d0me_ln3WOJOHD?NJ6`<*sdo;
z@HAOB=(My!2;{BQe}qm*730atMUyXKvj<yB#}ChZFC#3HtkAcTvjIpkNn>W7Kjscc
zM-N9^jJAh4%Nn~7k4RRwc!}#UK2?U<b@zjTJ-&e^TA^L8o!;gcDZ}UjHubALshPg7
z#hx{U{K<Jyoce=gEU1mS)&U;m;^H%*0f&yMFSkiqSxFL;*!Q;zdXW<fZrgrCbg`0N
zSp53d@2nVRO*5dkCwRG{@rD;VZq8v>t>aOl!?CKSWnI<CXDP|ifDkOE{Zn~nX{Fs8
zBV2s$wD_~1%9U2FlS|wyV##iB%cQC;@~xtP9U5fV{&6rYuw>H2QtB-wrO=8>Q@yDN
z{Shn0M?wuB0rE0fZp=ORgi08PGC4IQXKf3nXZ*VCqq0*~q^dJ*S=Q&^#7|ta1yjmU
z*k`}bDRJ>drkRn36=In-$h()FXKq8N?5@q5N~+}+cX`@0+DW{oA;FnkJ;e)4toAw<
z!X<hpf(|8DaN?wdTOi>2=<GBLp{=C{vmt{qqb^G$$ibd&g^DKs$?3q4RFDAz5>HQF
z?|t#r2a$dH(`5Wg>9Oxe795G=Cmmpcb>}_rWgHd98Oa6d{IiMFqObxibQm0i<K!A&
zTvJ=~E#XKtC%J>stDuL74d@Pa`YEzFIczm;GzNnCy*x0t4Sm<*y#(1u3|VtyvYfnk
zEEpe^bG)2b)M6ZXUI$@e!7`l-rh?s=a&&}LYea*CML&G_OL90HjSwY~D{e3l(Bu|I
zRkC|<rzP9m-t=}<vaFoV-ahl_1lWn&YWwTCBXMCQ0|UqUZxhq4?M?PmB^X<ODFfz&
zF?#08m-p3FmCNY0Ev#zKZgOmp`zge*4&D>Hnq`)69sTIsY!H@U+*n5~H1HNfq2RAU
z`V_CB-rQ<=U%gi$A&peNSjnB{g$)ys&lwh%iTr(UxRFpG$4ST$?(YZ523?RmpPcAH
znqIuT+zLKjd?vu<;lrFKw;g;go{oR-Y1cL=_}@Mx0sG)I7(GVZWt#uUC8zt|KR!0V
zNcl`3w>;0~(ScLSxB(&Hol^<5ZM*u{lvemBxABqTh_Mp}4Y^47`yhiBvJR|mt#s%@
zvfz#uAOAsGj5`@+k)>5=OM5Ig4=IhuSB=Ka<l>fzclmzKq5<3N<%bAQoG;3%&;nPx
z_?G(V=eyRxN*jFz!KG2dqfs$))!@K4sM10qug*(M);@Yb)sp+0)xEzO_fdj3eNmdi
z^vI+7P+(6M4G0oLL%f~_IDgquowdYv{KcdS;xMfPYFacV4-l!rnvG4RnVk!&2%7v>
zLGkFf7rPPWZSg@6=Yr}T5E3ZwXbXL)K>K_-M9G!56plS}uD;oY*8roKQ+&PGHzARr
zbbPJ!1VX0)j}(~tF7_i*D@j5)F5MT#3)jB-$V4g<yIB~z?6>L3yyLngWQ&iuc1xPc
zP_|WlL`m$4&bp0@SNXiR#Ai0s1Q5D#c3fn}j^7I<M_nmDU!F=F-9B1fP6Wj1tAY*C
zEtyykEg)J)Q^)4}3-jYoH3xSOfpQV6vtuRWSL!y%&MMXZ#ww7cqE`o=MByrpdbANA
zun=IUvXZW+V@}jyB30*~Gg&JXuaOwHeU<mjy5+ItSh_@m8+@I(UcoY5Ybu&u=v_WU
zWcAG34Gz<8dXj`y;0lWqJ#fw%8QJy>6fgowEAzYe70sH0Cn&rpf$*5M)y06MoABpB
z;nyV%_2pKS5xvQj#5v)oI-lnlXA^XbObX%*Zf0Di@7`DsGpWp0_7pq}uX>)Lq_Iep
zlqXZ0>qfCe+SHZ5XrT$4k*Iv32^|`y6!o4@xO4LY{I6HL>+?qQZm%mN18+wf5Cp^V
zPZU%UW;)Y6WVh$Ihp_Bn9RvOEdEF5561dwvzq7M$I(SA=$6eJ*W6B&Ox~enY>KT5z
z6ZtQE10a}F#DgX1fj2{}QvM@9I1we#w{pKLWp97V)aI<HM;oDJ=>FXeeqHP|7`3J~
zJVr#Qre(=whkfRtWI?h#i;QBudpwO6?39>3b7Y<{Dx>bI9YjNK>Rt<Nt;F}-sVi>d
zYgR(p393jQVQb8&OEV-smMO=Gb8t{ir~CBTa(6dM3TENr<b2Yj+fi#@zf0}S1ROaY
zZ<_NL!S6m5&OEnZ7pEZ>$VW$;5I#GIl}K1J-nS}NKY7M>A}DDgYN(ILQAOKaZkj=L
zw+d{>zZxkGV*cn(RyVL$HnM2iz=2v`MBEOdAr$`eHzvP%2v2|S63Yhp#9m9oCsXXe
zdy5Wor8ClABB>!2a$x{Z@1~U4^&5<=M_x}X)!!f_Ce31Bld=5*%3KYYcZ+bTr^wAi
zU@2#7_uRw9a<^v%B?&5NpF9u#319v^=I=bu_l=2FO;)LgoAPdSc4IybZTv+2?p|J*
z-4prkxWpHtW4G?UZzL!VwK;6nC4P89^3*PDY&Q)_zX#6dkI<|?n6-!gsj7-gQk9K3
z%2+l@PX10etnlbkrN#^I*C~y*Ie+A!36U;MP4#lB7Bxj^_=Fobx1)c{sw}+t@s7Y2
zIm)+)$(&&H1B+>dW8Zv<wF1Hj7?<u^*^42L_W16(XKLvvtSUf6$m*|qvYAUOYZv&x
zKo<wfV44ACsjz05M9v|iW@V#iaMv*HE>Xwr>!$YO?lRHVj!+GEa<UCjZc>t&<a5Bi
zlzfG)$kS{djf>u33q(zWhnXMa?cI@|U(!4#8{*TSMx%O^`^~BEk@si4Qqn1IE?Ni1
zlqVKo8QwFAphPIt;Ial!Q!$R6%Qrgi`=1Hel3F#@Q}Zf9p>WqMbroZ~yC=>ferl1C
z-pDQU_{}fh4FKU;n&yA~j#Y4XQ>=@FJ<dM)h$K_>Mc;EXIk)(y((N*tJhzg8udD{Z
za2WP2ys>qOKe9x-C*Q&DMgvHFwn5pgnhOsz^2NCB$bDkdYuQt2grqP!zsdJuE^S<P
zuK(?XHVm#6Y>X7+sKfegfP08=QM`n6wzs_Fbl=43fQ!y<T#r&;13uUIM{+}>_ssYD
zvC-FuW-m8dy1GAH8tJVtju4*qP{~aLyF&t0;ZPKjLNLD8oqc`x+JJYdBkW+BpoIPe
zJ=W|P`{>ZvKFi_<Iy&%wkX8n1Q<FOs$`Kh6;lvE(jCK++?lvNMu{ZFRoF;pA+YvA&
zu6hTrVCZ`p`F<)iMRD_{a1<gaP+Y94sxwG+^6}?xdBx4@2|c5(XOG8~7znjc7F~I6
z2Xh3%o~~IMl8<QonGg^OiTL}OQzw32Z9o(fd2t@UamNR0qCDu!md*&-4oU`6)&}W2
z=&9_^5IBhBnGsdzZY?a8Sc#RL*OjI;)x3N2b$4hkacT5DS9hMZh9_ax21s6{jlTBS
zDYQ%M0nHZ?_az`9iqt^smGK(pR|H`yCB&z}_`K3K%SX9TWONO|*f=`))Y!R6(dC7~
z3|xoE@Pu#Qn(rz;*oP{aF+7%QDt*L~WQVP2tE14=!cgR2(O=>Yl`z>N<EoYr;4Nz=
zkL@4y3=K^t@b5dacY-}>m}-)w2Z&abtAKQHaHYLmx@l*C%4@OIK-pR(d{+^j2FLhP
z+Qh6Ig%rM3-yK1yuiUI$1fSm)cpLvYyYiit@1y7KDk5wz+IOd12QBX{HX^((D8(H|
z?3zH5SalD(WDu5S#W~Q3LVW_BBjH7rz;UghaMyr$HTK31Ru9dJBw3FzmlfAzTytsM
zTq+AqWKek0x@JD^;YffO|JJE={qomlE00b6z`$-&bS&N8>*dc=-wyXiBwXQlx>11K
z!H?UTm)lRR<hO-8-2En8O}I=jgrogrNk=zG9c`9SptntBkfn+|dC6^u>}!fI<NA86
zQ**ty<p=fNTjgcsc!+#qW{RrEcy?mo))wHPgJe`(<|@tvgQhJAv0d&h9(eviHkIcp
zh}1>cFDam4e1Y0~WHf*?j41qvGoyWVNNyRWE^f7%I85SoqwjheR!U3tp|V2vdAm*+
ziB~;g%};MglH(77O;A(EuiVnGgbd-jX6doC-(8vNE!ANd*;ZzgXIsKE^>WI5%XD7I
z%h^E%A3KFxF;9eErcZ{!0wIQ<fcnf@Tw<iv!4r!V2cbCo1EhgGZT(Rf0gx4T1G!sy
z`a$nx{a!U!f_7G0k(xv_I=Ro@K;iaoorN$q1%*-lqDVR`zMIntLg1h3(dY1Z<})bc
zG_uSsqO0oHS0)FUzvtmktUm9bw{9Vi7c9gg;3Z;Z+94OB0~!BS^^JE(FVt<-<KST|
zBmCzBTZ&ok@WCn$b>+r}3_zd(ouDYp>ta=6Xck^Yn%(gmk5o$<fE!v<N5TYn?;r>!
zwLh%<gMht?@jI@+3=fl&_>mNu8f*AO$UQdf5=EP8OvEA#0pv7bgP?S1?2Yo}qYD@<
z9P$rky7>V)kVR<A9J_XViRj@IllO~5i>soX_iBqPF2j4$_oo|YLnzOY@M{3fCX9D{
zQ^zNs0oty_eW3mF^ZWD+Lq^8P{(-{84C=_hrxOcqf20Y#RTY>(mOOJv$d_l&#*cnV
z2M}brUP50<s2V<8UX7RQj?-0$_Wc%cN)RXx1N$^K(V;anGhx2huLdE6pVDTe(6UxW
zN1MO(wVyLG;P2viuw<rrI6prQ$lrEH363)@{{{vifn~?tR}VFzc%(Cio6c-bsvs}%
zT_a(M0OH^pVCC|rbvNZ!ff1#8r)lAfCpd!U=1<%yw{x!F0HK6UVuRfCqjb1OnfdZd
z$RA6L8!qssfD<C_jc#B8OXcrAuwwDxgdO($ayno5^yeOzuouPYNl2Q9qmj8k&L7lZ
zAwVav5*;s~+)lW9jcSmUWFzodc%#9gU>z&ra&dVpONdFHJ@5Ys*zAUkpv`d2cf9j{
zb)DNm(5m@9G_+Fu*oH}b&ij)XtV5@grgqw)Ipq_K-<;N-j^6ak%%6p1O1$fsAV~@T
z@S7CM9<IgFa}!tqUno!llBth%O-}$Z0RMA#xglirS6Tq6EwZyTK&1Q?t9vHpIB#>f
z5j*Ft2YIm8sQw|wIs^t6qpXDNCyr||wG6Vyz?D=F4Qyd5$&|ir_|3yfJoOs)m#J$5
zMd`@I2h=IF;CP7i93lo@y}R#ng8j#wjSII00WWVnx<&+fcn<V?aqIcC%04(U-161<
zIF(99s)B6Y{bS|UN|4TKU%Udfs72Xn%YswlHaw3A7>ql+z;v`yV25mQBmVJMOMRZ6
z$*3_;er^sT?AP>+ur{WGlTDa<GQy$A_=MEFM%>RsGMR3$y<`#fD~y{CAyh_8M$)@8
z>g&7?fGO8o@$C94mhpOdIz+tY-dPL@efTmhRBBv*E<_DU+9Cf34UfHGoz#+T;}7j1
zHfM4=nDysjPC<?DDp<@gxcBd7XAGl-Y`lZxV<~wL8N(w{jxvkemX~^VR56;irjkC|
z=v2(Es;i>C;zlPE1j5%I21isGQ&;C?YCCW_2}o4vg8DEoueHDSUp4_m^~XaKdmA#2
zpoA#uB*IsZrw7>Co72-rev$#P8vtO~2HsmBJW^LvP7#Ong1yzLhe!?}fwB?e84@$|
zA$6n(v%+76ewQ@;Ze#>r+|8-LJ-L67y{jj6bR>uA#Kq*PKBD4!>QOJ<A2x)bC9ZfS
zBSk=1!@`qSTKpBu;ELZ!N$sR*``6V|b)yxWmDm~WVWB1bSDOf+<N7X~c-hr-yBhEK
zm?+-3==}}`ctO_I8Wt1h*YSz`M;{TjTuPM7Iulj(gyFAS;Sv2+!4Gemwv+y32PQTT
zuqk;%1f&Zs^^?9UsWw+jE-$?uXe|0+K3w(imyvn@@m_M_u}=t0Ieb5w&jERlbg8?$
z8KvS-zH#wTb@wakFvS@eS<k2dW|g71Zwi1RX9EX{u|jKQ%<|t-OFb|fFNh<-QDbb3
z+rEaRya+u{ANX#Hja_RM!4N?Z{%*{=Pa-1x2IxI4&`%<(Im!dZ33t*oLn_W~$s+Gb
z|5k0~<z9?3nEe;vaxekCN*JY!#%9QEJYE8<rr|Td12cL}>qCU@Gh#sJd+I}ICm43(
zD6@E+kNv?(D&r#rOJ44l?zK)<E?E~tKZnz0Q{cQ<ONc00vnF<@nvxIFERr%vvkHse
zpcq?DD7?OYShYd&l~a@IjID(6^Cj|Tz%$UgSgFtKC;2M<oi19|DB)Q|a|7;yOlL}}
z2o{|5Hy&+&vZ)G!c!Cw*8l>1DC-`wkxu=*l9-QKk8EcS9MwtX`H#F~E`D7<-yX@Tj
z9FEl`Q?m*TM?2k#3-||#&zm5mX$?%NQxR&7=$cmjWK6p+miqp|kW;nfN>sV&Lkq9R
zaFOEuy)T~b-&%OeGY5)1g!LHbw+$<|C^c93eAub1>_t|3lh>(Ya|E>ImKUlTcOOnR
zGUWlp=j?h)^+Gkp84u7iw#If~ZakZQ4v3?mB(fNV;P<8<U!7o_e3g+H8vKLdTL~tG
zXT5elka-6sjQ6L5hoKn_b|wR=I;5oe6LK&lO5zkIWbPANOgrIT-72K0g`Rz7IJgXA
z;<;_P7!vv?<8Bim-tWVL`bJBI)<BViimb;;vf=7j_%OJ%DhJnMZE-{t1{v<`;&OFn
z7Nxd2LhdV-QTdD>xhM%ZIGm{pT{H%#p>#QIOUuK%3$v~v-Vp83jfT-1MYW~TPm{gw
zGT0k{WAQ81iUdsh>$@%eh_sQhrQ_1E@>Q>?hG<b><-6wmBwQ?O7n6Kfw<l9K$Z-Kv
zamza<b6tt0IiHIoOd#DprdBCl9wO1zz%9i_L{fe5U;$x5C58q7J>a95h4J!`*w&hc
ztL*EAWeg=#wpsOaOn_O9LjPOcFNpVBUqq}g+j3oCfO6?>8H~|&33Y+7Z2eEEgaN^R
zB<DbWcD#PkGVz1PrLm*UF(qv1>kh=k>_M|tqxL%c%$mO}D`HniZUIB_3x6z-M*#O&
zfa6C`SX1F?v%glAS4Nq%fqOfL=plIn;k@<gUC)F69(7aaYq^|x^eW3-294a{>gvyW
zT%(8>qpXy?G-zvOQa2+l%T!0cwJ~{aNfNaf{T$#;o2r_ghiz`}pcSzpWlD$SJp8Fl
z7jkiQbPe(F@arHm{*_l=FYtLnp(}@-w2Ey~!3P(~#O^a}q5qJ?4knO2aXpusYM{{I
z%j?`-K3p_EmztO2nzaUcB7G^GV|uHFA1qS5yZ;?Vx9mJlUy|*cskR#+7m-<HhRuP8
z_tB`}@^5}}85<mm=U!sGz5T8odW}au2p>O}08i%UEA&nZM>Fh9pE5PCEX!`)lmcOE
z`K`g25aNSXA`aiLY91Cx*G+9@;gz8*Iofn#xw&-#s$W&l09Iv<JRY{IruyeRUq|=-
zjjr10xHs1A!tWN{wY%_TOPb<(R;h-lcs!F@J!OAUjZWFjkno(}I(MVUG@CXVxmc)H
zg;Y_Q*2vCayTDgjQ-(7uUK(C5&(7A#_E~K);tL^ibpOD`eH1Vnjw-EC7mofEy)vUQ
zFZ}3o6L2+M!!lZCZH<iYcV<%TINThZYe_?&mNtmDpZL<4_gzhx-raqim8YAHi`hxP
z0yyjg!FnQ^)4~_Pz5|W>i-EzwQ65Ub(-{&+Nk|!2)EOqkV#PSSUv2LPyvI@ATCGaN
z?*}+(KJoGEFK2?7-}^o+7Y%HoR&PEXK}N!H_?BVFoXb}?6EXa6Mc`xMt{eVN1b)PX
zlS00%Q`3@M3(En*l$5Cy$=PKUnTJ=!j=s^4Z}j!65TpP|hx*L<F{YG-vWE+53F953
z&tKefc$0$ZU;hkc>=%I5|A{UV9|`O7h~+IA933w8{k*mc0;`t*G#ZrOYbxS*emBRh
z7rBe`nwp78)2t+AQqJ}H0eW;TrvIa@DVnTV6eQNJLj9hWhv5TjZXLYmzrZ)C^KkB3
z+dvx`0Vv)N=GLgF7%5m0`H2NKk!J&&;Ru)?_^D|yx+7FQC1s_}^y!(htw>huX(dp$
zYxec&z}2Xui3{gA6CkZCL8*?EKy+4*tCRx1?xIz~I?dKpj+)?F2&*l9T(SGD*NcAZ
zB99Xzhlg$d=w=BG>c5jj=mZNS6Mx3sSQ=N{-R-iQTcjY=Skp+dt1QzR8DCuv4M@Ro
z(=UWMzm}5v;`zafpBT2VG)&;-{%{o$QT@@#$o1><mmW$A301riE3LU#$mCx5jPlMK
zqem@$T^!dx+F5137XO9Pd3+v^lo;&K{X3(;V+zEl8~Lb)Ahw#uBIie5E4NzxZ5kJ+
z16w=4=xS_bDNwM8X)^ALdUE?&rn&-@YWog*R3TPG!=_ZfruO$Io!e2FoF;`os1448
z4<ICHKQ*nRpxvU(^Zi<kx191gps*E!Mt_Wo{VPhq(F&~maNwkL`Jni{s(S>P?bSl{
zoXTLTjNmp1?hP(adS(t08_Rx7v=-OR;a`D_kaSpfg7Rru>H6M4px;vNzSQ`;4SfR-
z^?kw%OIu=1;;W7DULA7K`MzU4oZ9$r!hUM%IEG=DPrY`93`{m>B}eqoWCG*uIKV_L
zYlFFZ1Pt7PDPSFvD164%P01lp`3em3CHGm>RSLJl6*ewKHCdqIX;<D7UM_tYxba9a
zqtt&B_gb|yU7;H+pS!h(^P-Ede1gr!@~NcUEja~PFp|DT!0hR=XRF(>L`Qs_ZH3s`
z-y!yYI);K#1756IQIR;l&uK2MQ0&)Ef>P%eh^f?Hrgy8N(r31(sJpf?WGUR2N>6qz
z3yMEbMzS2on{n3cDk>=K=h?=$r`w;yXX0SUzZ|BEM}h<}e4IrS&!<0sGpb0}F~6EV
zoT5C~u|v4=6g*4@OG@-8C}Yh^;NE(F9NrOF1);I;0I09(=T#<9OFyh96}o@pB!Doy
zK}_%sD8J3j87<X>Ec|$^<DzdV$$F9|gu20)283WxJtBg^XEMZ(hP<)28Iae5Qa1@Z
zSyOsDD=UAlR~zv+cHZ+WhDNV#%AL^t)Blp*fSr1FL;ZpI+`Br1y*#-YtIt8PCDz*(
zS?cTXnWHjD7&XDBLZP-*tG?Q%whYf*Ig{NeS*~L>PgwjRJcm<MQ1`>w`|wVnXHnDU
zc0~9tfrTf^x_o_B$wAU&53qGB4f0H`95yxg&->m1DTKNyRUbm0zu?oC7(>}00!-kt
zPF<R|+dG_r{O&veBVAB}bkadY172Y@&A!DBHGxF-)TcQCoI1-9^3wR5jSCdAas2#p
zlacN8fVrdw5|LWem_j|}ZSx-e&)-D~g<wkLhSVCoeONmCvEf&cD#DuwXmA07^or*p
z$0cco87m$l)WN|TwA1>A)gES1C)NU)@Tvy}3Bb;pPBDwfA`^cxkOK`95ShF@wgCn_
zK;H>J3rQy|e}G%F_js6k-qZZ9;1j?eY6tkDPM#0lYvgsg9fyhPeC#Z9X{+yX@RgFh
z^nT67^tG4#YAh7hfLB#19>Z37(gO@x7pcdA;m6xl2&0O5XDkzS{Or{Y3G^nQv&xt0
z&Vltj_$vP&R|A-8anM@s?Q~;~6&;5}aFjE#A=v&6H8e<iElJL;QLCz6p?QQR`o?L#
z&CA67B4+6gP&KZfufkjEdyf5&Pqgn}PDs2#sa7J5Gsns&+V3%h%7AyZxC{2I+asH#
z103e(jPEY%u7-X#rOC+N%+EEnj%ny-=^+k_(yxO!V%Wa`K3Kpsd{6nXB4Nhwm(;sd
z$S}_mce_B<Z0Y^u^&*q^0nL_<Oho^0^^-YiptQ#VvN0+-)??N0dUNRme{$wDC!juT
z15tL;7zDRKtT@;_iw{FP?ZC(o*N&D};o2doPhU<?Pvj??)LbKjqM0XVptF}iHH&f!
zu;FMWC4)O{z$%6#t``p!U^&3W2W(S)7kmk3sgS)gnX88db`rk+4Ck=UHI;P3{o}va
zXY>o9g(YHN-zJL$W-^t^b<p%<OL{(ATmi0TnBT#aAKTB=uiM!;Hpp~j(fhL3XC;hv
z_IV1A*0Sk3_t9gG<R#^=q|mOPZ^}z&7Iq@wTO?oxP7MU_w)Ouj2$>r2$uw_NeXD8%
z1f$xIMXlgEk}j%?bmD+qp_Y@8o9Qp=t8q959H(tboB3tpHhKpj{+=~QxR%p7D0_0Y
zLv=*(>UqMyDd_$pal8gk5s<gWV8J^S?jQWzUMyZ&sl*_!T&x9UQUYDO+DhHcL;dx2
z#p=f1&l)sF4<I5ExhSPDy6QPxQY1VJ`fC5(Odr^3FxgBM0$AM)NZgtLvJM#h(g-t`
znz$E`+bRf@hUtXjB&Y&k+sYiQpRCVWpoP18jxT3;QfPlY9-=<IUyzQ>pY5SJ?Md@k
zWBCWqv6;5W?NaiMzO=ruIzemiA0idmjWibGR3Q3%;11VB2R6?mm)TkGk*guaV%ji%
z!3_$(pKgL)o#(!U4G0j*cz!1Q^o*YZ3XVbD3Gu8g^VMg1*P5iCKFd0I)*5UJ&MAg;
z>H@MUClpn6-o<tmM1g-a`F%qp>dDL{>%EMOM#zQkc19pS-WR#xf@UNUy-IwTY$o;P
zqX&g5XFrxr&l@Du&XT6L$|OhvGkc@-Dv)#cAuvD%$ob2GzNK(cr+L<CK+k7YEh)Z@
z-~6=yfEmCp7?Llfg`h;B{msGPx}xH};PZ!teQfq^hA2pFE3e7t#Au0JNppQnf|EGH
zhTlMGzc>&N$avx~w)L|zbnv``8~0rfUe(v9ZsxzPcPJ~}<+lf+nz%So2OCwYha*C%
z)qf${!iD)~8mcKObW+pO8D~N)UFNHczfN~v&kE>$Rb*rdPUc;HRT>0ILLwb$M@=Ug
z%{`)YTpd&dzJJx?mH*qk`=)j;CN$K))^<3VxqHX9KJFOV4wkdGq_V1KVM7^`|1cQP
zw3=^_pt*#ljd7cjn6ItNBcl_PD{E~_sNWJ00NTgIJuWOFzb4sqYB(vPerMuh&L$v<
z<KFh$Q|;)NUeaw`Npk>@1EFr$f^nz%-h_$+Y=Ao$7=a#Ejg4dgJc&dJv}ST}Jb=>-
zbMa6=V01P?#{T$oksvDvLom`R#YQy3*t=I}HNh_?UUb)(KMqKz9t9uTAopur95sqk
zeyCkd!0vjARpW0e`Yp1Wfsg~uqg4XVjl(o8Q}nts!W`+zZ|9>`KUBu=ww3=yTo9xI
z;<FmUx_?<EZL>U}f#fZ#EvAn8mh5ACgotb;*N1HY?r%(r`Q|68H$ZH{x?B4()1upH
zht&JNuc!Z#Gv?JbIU03^;maQ;+I4qlONUhbypNPst=It7<mwOq{ucR=m(B*Z8JhG=
z3-6YX@h?3bs6!vX!}kxz5TG*xBq-R~tHy(e^nNt2prX<f&q9WJW=9Uz@lHRaAVVm*
z^mgN7LQa^}UnmFhVV+FccwFbEu|XvNzmf-he2Lu-$Z<kbHM6HP8G5AIfJ)xJj9#|3
zzN_>NJ}~wT0V+@pRFFA)v1e^Wfvpb-;7q_qmN0d$diCON<?5lMW7T)w7LuF{%!3d=
z&8k`>gD6W1Hk1`!6Mu1a{M|?@-r0XzctvABKtLciA}er$hDcJAcB@54i3Btkwzf8N
zj|^0d;k_|K>cs$o|DdBdmZ6<BMRcn#%lNaG>JRE^z$_BwNt?Wr4NqTb2q3zEV&>#9
zs^KH{GhC2JszAYresS=ea`x4uFF7kt4j^_|$KxxmdCYak`M9wk-=LGvS|f`oo<Hwb
ztxQAc%0C77X`7Z>d0rG>J(>MnB~3ta(@C;3t6(T=i`e7%2=zvn80q|5UCd*<uK@JQ
zrESt{nrJmXf<Dox{_;~`DTR&G@ITxh{Ei+6<PFsx(8q;5vKNXM_!5q09b-NYfT^dB
z`nu$4VJG}vS`w06&0#j`K>3VXBMt)ZD=2;XL}sI<3jW8?)<MVn?u`ij_on!C83U{O
zJJ}1kdy~h1*j}RtY5!uA2}0jB2~JOGkav@tT<ZDEdP@xj#xUTah5OZ~;U7PJP8Wlf
z&1qC$UPaK*07797<-WQlO;cWH`aYy8{qu$(2<q-iMJCM{f&NNw0VtE>)^kfO2@Q?b
z_p5d1y@8k(w7YM-69OTL;`)Fz7jQ<aT_r2@z<hF;rn<BHQnM-y8?R$Ld45Kg>QS;P
zrQI{mQ0;B_-0_(C##bff%*?Lp-nCCV?tg6a-&z(Mg%kz(j5AR!)sIw_9FvjXk&dT`
z^LQZbA_NQ3s3viN_4|h93@zFl+H3O-e~9s;g?H*-UFA<HXGPefshI2mQxo@37TGzx
zzICLwRP|d+S!irLc#{Jh2=JJw&Tf0+9e0lJvUp$IBQ=vjE>lMF>EwCE2<S}-TZsLF
zS&?{~wh43!B!kBo`J0ON4{<CM;*?*3X@(}ScTR;Ao8{fFVn4K>X6FP@Zzs~mZ9b9E
zk-}`>>H+YH|2Jin4!FuMM^*-@P(DBna|1S7GnW$#4q?d^s9-!bv+6eG)I2hUg1NYZ
z+xJ_cW%@S7e$3+uQbp7cAGX;%TuAtVUp%O0AL}!GrZ2NJp1r$@jR@eJ9J66&rg30%
zXjXUqt85<vnA?KD>vZj&k{HFKg`s<iX7%jd-93Wb`5#sfR+ii?EV3>l$L2rqz-^A>
z$m?G@boIWf;gOxDlTYB+Nif>)Og1y&>+dx=9#RzQ1;5@8;nCnv1xni+SFXd){Z<-T
zg=I_U*O*de4xUeiVif5zFlVY{vkyTKjkVY=XZr9BhC#%C4&lEX>cP<P5h0JK8}7Kp
zSI(sxM34Th@fOmKiAiae`GJMM$o%3&SoQAyI~_xkKZ<5y^9Nq`wV8>iGvf-K{ou`V
zg^CH>ue6PIu6onMu`V~SZlT{5_*T)}6iFvb1Db+uhMK#gk_Q2dFWl=x=VV`m<uc7#
z0L|Y39#;XoS{r7XWPCrZx5T+e<yxfB^M3f!Q(!71(A+9lxSl=#$3jn?t7BW{T?nM3
z4>!q$NSDB^wFHr#tPm$9OtH3Jaxv+rBI>FhQ*by(;@AIQ6@V29#%BMLoVvs<MSsUS
zLwpk=S53OUptz&v`tzA8XDL6dF5gJd@?s{EQ=I|m@x%|ynCwWbT1!sf47e?beU_yw
z4;INHN4-c{X{kADmk>r}dMh^6HQjuzwe_<rq*dm^-?)^=fCMz@i7Ef#om`;IMvm+w
zN7a3k0?X+jCdJJl*0M|d2R@)T-wJb?Uh;c@A>a#E+MahnfsRP2yORmARQ%QB-SnTn
zhqqPsI6`DuRvc(VNBoJ_Wd9!?&oH2*QLhc8tF32I$xGnqWf$p9-Q4{7@qU{<C(Y{5
zW2{GGLSJlWsHr;&iay!8vSt?a1Z`&gmH2e>RP|`{eH{y{lijMO#bXPfL=T6A=)91@
zUAI5)wocPtzi;OzIiDXhAQ)or&Ld35&C5YcDeo#5xGIF)QT*+=e2p#(@L1Urxa`YG
zYvNj}%`pkBFBRQ;z{izL7|+j$Qt|P_Uu~A~cL?)#PEeU}-WH1U@@G_!W&NZ4@1WLD
z_ik_v@;m8^LZck(4<f0kc9m3BwNa#)5*XaWvFpa43ve3|YJLH|0H(a2*QS?fl&CB>
zRlb9AR`bt~SaW}aX5@t{SE@03tM}3CTuM-hygXVPpMIY!G7HMR(!dN5if0%O*xC9+
zc*tlQfQz!B^i8lfl_$6-KfBSSe}k_<Vq3}Wh80y(o}yF(UJDmYFqCvBsbYXWE70>d
zc~9!G3#@Bwe^xC?eKmcP!4I%k=d_IK_p+jR_a8PjwIvsRc_#cvm4Rv&Qr_VENt7ja
zmlGG3YHYlw49pxNzZ<IQ1|T+hj^ZN}x0&OcaP)J~C2%!itm$#=O`W@~Rm?rty0W1_
zV^K2)^AGQa_oJl!E9E&%KaLumJd|vOHGvsD%D{d(IS(z#;G2XG`tNBfQE(go&8*ni
z2~G^(B1MvxloOm?zAME-9LQ<%ossv!)P-dZGV)NExV=%X{27kHj2yT@0%LTrJMzV8
zy?mZWb#ZXe+Sv&+h))MqV#PWofWOL}7p4DadHkbWXJk-P**anWrn~p{2i7N)WiSw<
z*isLCX9pOLMe)Ft*{#Vq0*R)Xzn?8`3xmKc<igU%MoAqq^XIYOC{mc@>Q}c&|GO)A
zbYN;lY$WU_kEe00i>R15YJgbik8VFJgIW(~y^WWTJa~eljkX38ANg?3AkbL@O+;2(
zgmUhX?R+Y4&`b=siYoDEuW`yn4fx{O?R!cU$_&?v<4Ki(=!9%-f4K&Pmi?J|b+S6x
znv<>Ewo-Zq@S!;MJZP1~E|oMX)&H6}|7!BO91Kn-p(<Xq`+dtXGcW`JG>TxgaMbVu
zuYr+<9!6X)C}!My^_BK~QQoMIHO2kn5_V-74NY7`q|ELKBu$Hg$3y?CyWP3qe}H(N
z(kuRryqZNtD@a<m>)dw#rv69*o^>YF2Aqc+y8$P+t3j5R{hnt^4;SZceVK9<BdQCM
z-JPD-bV(D!M>`zgbR2<PC(65LoyqSw>-tqyj}X{<sTC~OAOwv29V`^QtSqOsv)LSZ
z!HrRnAz{A7d>`e35gKw>l9_o}LTAvZypUZugZxfZ1m^?h0Sfi~RMX$Q^-)~~c#Cq2
z6X37*=Kjg9jcOqNXVuo*VT#zzC)e#)B*;cFvA<m8MmaNtCshib?|OSyb_)_^#nmY4
zey|%*(YfTy-#YJIoJK)z{WLo1fkB#RE;VXDM0TE*p6h(PnF?uIqnjx?E$Eq_)%&((
z>_**@-u?W(8;kd#qJRLy>%RU=<epMOqVIVDs)=q=uO2oCZt~NPsfk+Zw?&$rT#Gl$
zUu9~gmsYOb9!$iW>}RgG>fQ}xJg?!rXN`uqGf$$PXc@SbQzh26q-a;VdiB@c_h$u6
zJ?RW>+9Cfq*dF|Xn25G?qgAi`aDXrXMs~ZA*HYu>_fRx#u>IR&8=)cD4*EK~GI-@;
z-Vc5hgsmo6+XbKm<CyPo6lU}-q~t1__o^{|{R`!VwBLSDfoLE;9$2kVWUwCeI4X%R
zL`3jHoxI#1$pv*yid6eI1O_IpW^U3!w4*Z#v43F6O~u#Mi)J4}k2STG9vX9Kb#xms
zg9AjbujeP*A8Xt#isO&6lkEy1Y8WM0>yj{ehT_==gIgR&5@;T6l*qffEON^i%jf*K
z(SAZ(<Vsd|<b}5_YZDenn+9Z?A$iMMM`J%t)QWeWtSB%poG4Iw88`KF%{zU$p>%g7
zS*-AlzWn{*MJ#8Syi-u=>!FvxrEe}<xANw^&FInIzK5mdu6jdK5-<r`2<KTB#E5@o
z@cM6Ebe4CLakR=;EZ^`8te3~q%W5i#Dd3(u4IB~{VIL_bz1FD#26mPw^Md=n`Q$|`
z-P&ndedY5*qJ@ZdSG?#-K5~|g-QA1Zb@CX<6fa->XKoo<Vpwd`gWu6%WMyaf_LKYf
zsnnETo1pVIXF~(G`u;t*qs3=G&ZDapxj<yl+~>0W-(D*dHB-+IPLzR#%bDzLzf9uv
zY#>@ZXJaE%3mn*+pl4F3we)VIP;LayW@r2608w52#O!_AC5s47Bgc+bbCOd__-05`
z%1mAQ3C!4xlbH)EHC5u-p)ygtyKYRfg=W9~U7JCyp1k*HXt2m9yWqES=9bh%4T&yi
zbd65^M<1kf|HR}Y_VsCtZ0QrZa4JrEY)_T*i=WIT*p`Y4wf?F3J(FUw<zEc#Dv2ce
zYjo3xWH$g#{zj8;DZYgp>Vq?#3}5qu0P;2kqHpW6(dC(Jxc_u<GYxiqI@f;Lj6mmv
z8bki(XY9Qnx{Ry(zwG%447W=ut@5<hN?7ts;L+n&ax`A)TscLW_6FXx;+YY;x|<)m
z^v}N^o7${lNz3!QQRH4|`J=dFgBA^WzyAX_aWo#S=;x0HG4c0@CEQO#atl_P<qtOQ
z4=EGpMi`G(GvA)A(0!gSROIQN1=5KB#g)5lfCS4B$CEkL0^HZR%*U;e-Im2;{%4uV
zuk>AV;89df6X>n)Q0SiexXZNE6Cv0Z*((atU_jR>q#0-|y35n6*8Sx$dk<3;=YyvE
zOa66z|D?yr&BuTp*=?<Zq33(|%VUc{%xNCO3fWKf&?wDPKHH!c|HUC*HjHC)&!?qs
z8yLUWpyRL5(U)!-I{#<M{S9u$xbd1u{geR0N1-Go80q1Y`|Rj=)}r$u7^}$sA&+k`
z)YNmyvUf8-QP$|7tu)EC=Iie)53E*q10Rh}l1-2$o(v4LC@0{Xg#6jZzbqoU*Uuid
zjox@=ye{^G4BIPBgeplxNAm$;BH3@zK`T{_mwVdz1JH1cqQQ{uXU(4i?WrXmhguJ}
zQB4oR?TBmzjP)91ibhk^b!J5bj8JEP|I2ak@$>nP7ai)`)eT&KF>|u;rTWk+O$mg`
zn~Ui#1v2RAt43e--Z%(rzsD~J&Ctru+^&b6W6WQkLt3?0Ea_UlQ}d=WGto57AJ>c`
zw8`W;FZP~|i`mT!F@G7rbh?2@$?49EusvZ>Dk|S=i1S;rJzduuiewqmMw!MlJO}0r
zHEUftDE^lTL%X&ChJg~s%V>2&v1r{Svn+ibJ#i6#Y53*%w|(dx?$YklzQxGNOLm&`
zpS;IOwX-li{N+}HPZDBP8oO}dcHL)hm;@0?{`BdDzod2wE#_aHiwtkgG|5CISxz~>
z{9_`)kxN4R&3Y%~N|CA!<IVpn3v-(W*&q8Lf8-Aygy@0<^t~v0`;Nn3Ry26q);I7K
zH>qYEGB&Rywb!;3m`^R@^CdpcX>Rva@6)ES*t`by8}jDsGHyACcD5IdGmioPDo#dy
zW{L7tBf7FS@Z8pQcR<*pR^?(X_LtU~Z?Twi&rpG2UVytChbU|n!xp(dj2RllI?W;M
zJBt%Y&c5iM?@{d##1Vibdc26g(!idZ{oS&B^#M%n-uki5OVIH{UF9FCZ>ze28E0p-
zkJtDM1oFCeep!ZIUybU3N@A&7?z;d*N`c_eyr=I%WG$O~c&_iR!m_;i?4AH>CfuiG
zYMfy>Yt?xDe<@JfD0JD*CbB(27GrIFt?){hN%%9m?%D9mAm_+&lB#3D+aImB_;c&%
zhn1WB?<ZJxGz1$&%4lDXvl#6}6<tgimV!nSvI?$0H|;=v;fuXjQ<a+IQrZwcmV8_u
ziT)p;ejEkw>t{vam}l%x55-O&VUA)yg%K+G04Z#KHDTyu@AZ}OLPM~0RzvA*y=dGv
zK!moe-61yL<esl(Y@s!_OOqhFC;eFTiJh7tDAnUK;CB1}P=R@0BTF>>U~lXx*!bOw
z3e}HBo?pw<Ev>VoE@UZ&@rs=Rl$)T+uL;%_TE>mP8JJ_vh32oe-B^BUaiuv1d>cfe
zC1@&ti@tuM^MBO~yC2*gQxVldP%3Rm`GuXXVjfJ=pGOmEU@&27ar0^jdHo8p(YL};
z5yo9V&Xh{H4MLS3lA+?JVaNrLRK`s(s&Ve&;g1|%#8SO<w>lN=|7|mVqwmD`4Z4Od
z%dk|(z@W_Zvv{)N!*aK|wvf&1Pd@@d?`(U-K~`gEk(Q_w(1s%4CeO$8e9g~~H)&qq
zDWDH*Vqq#RZ95bO1mNXX)1AH1$!OC5WoLdgWD55@w>-PNjoap1gxPG5SvfUtKs|I>
z=I_dP3KC9=LSt5B+a9E`xZJ9>S3><@(T4+?^6XdlC!=3rv+lS0<1lL}7**D^0S^(%
z|9vi3qmVFOv96ohr$k>Mo~jr!*iWyo?qd3!W|z%?ScqPDbD097-wYY}%~w|H%sIxC
zs<8#iOHqHgFk%h=b8&3pUKw$IzAa$b9MtCTsqljK|7d#)s5qLoZ4g2N2_9SqcV}?d
z;1Jw`1qOn<1`WXj4DRmk?iLu_-QC@to%eaaclZ2f_uK#9v%6=`O!stG-`!PpSM}Z1
zbzN6cs*AOe#(zqA2>WnnK!bU#qt|^rXTth$bDIf)5j(J=ke3^3)40C=x`0rziQIas
zpv~v?>lAI-7`zn`5${4#6;c`VoJQ4DlX@G$?YER}2=G6}5D#to(b`?UREno6?X(mt
z)7YFJmS2FdO-6_ec@#@GTa6>wOyY{PELj0J&Z)-}?-_4za}6?06*OA{!odNkd;f8%
zF5Rpw{q*CMA^47`tJ+@9(>Nv!jkG@tq?+=kXM3tn5i7%H<qi^G`kJBL%2ciN-W{mi
z+Z$;u$NYQlW?@=gER#Af-+HUXhds!ze|O|b7UknE!NZ{L{7&wZYc0W8{5?d6-Y`$L
z$-%(0BVJ7{t#95g#i5svO;uZkyo%7?k!(D|+e+SP1$7h+=D&KW1zoboH3D+Nd9*;R
zV+~HB*vSbCtQ8W>NQNJj@!F+}%}Lcm=_R^ZTFtfA#=-f#%${y%xcFTI44n8zMU*G<
z2qqvCJwBQZcZHw-)!)WC1_Y`GTM*YV1T;}`oM3)SvPpwlqlg=dSwfwZ3Juu%L$#_e
zc(yvpA@x#5sDjTDLlBm1YB5Pe5n)EauxCNCH*^gxej0l2l-=-?pKm%=hQ3*7vv{oX
z_p>9hn)SW<#DPM(x6?%AhF671WQAo8p0rs-`lp$!*DV9zW}>)~F<q{Tn8itoVCQ*Q
zvRM4$eU2{Qx$bRm*gwu?I2#D8wJFRi4c`^7_4OM?q3k3s9S^moR*2NL?>Z){9z6TJ
zehd!Tb(y0cW2bBkGD2xreS4gs5m+8aumn6iu+d8)I0-Y~tVJ@s*P}PoK(3)=%)Rg8
zhL1qFLdFL9(Gf98-YEpkCWp^>VZnWHbhTe2vBuIR?+HCs4d2l9IzE4QXR^0He|_&e
zT5sm_Iy~Fu8>4CJE>==Vof=&LGwRyrsbFzO@;1BR3F%93xqonIcb)oSV%qSuh`qA5
zwbD+<^~7c$%ZMGyEikKWn3yW7++xwIQj~?}(vByIUrgURWV55{YZWr`6mBWsYrM*!
z>C`r%%$6)b+EV1ETPd^NI#*HTcJXEJ6P{LTLnl$xRYReuYu?Cmn$O<0a}p)L;vF<f
z-m@wFlc1;vYGm=YDWI%6Zy()Vnw|-PZ72nX`KWC-t@)&Fg$q7Rp%wePiIWrpm7(Y^
zHa5+y#pfkWtJDO7M^;?Pwm@mE)0<HEu}`J-_kC@W98f+B2NnifAr8oL-__egPIa5Q
zMQfCE7bV+*gPt>wW4V5%-fs1|7jGk<>np1er1xsQ$D0O?;`nqLSCSF6ggL{5s=7Z-
z=O=zzH){#oY=+m<5Mac0bHuWN^eTKGDYmI@cgUwb=^|Y4>DHixK^VA6V<{f_O<Uq@
zK?sEM3=K)Qfop;TSPld|tf;WwN6js?2nP6eB>)n7qGM>7%Q|@)F#yojbMbVx?=uq>
z>%Eo3Rhm||GORLERfj&nm_D<{qTG7qQrg#56s8`7=RjU>cYCq*`%mYu^Y=r)_G!yq
zjEfnqt+&u-#}eTfq!2Vntam+)d*5SdvekP|2o8aq=Thbvrsg(79|Jyyg_Gfy416#j
z$Z$1yN`7wpXv^qfgjD3}PXCj!ICa`X>-HG-r@qF$8~)>d;}`uR4<p_C_!0Z3yN@nN
zW%QbX?~{h372~<MOoCFVE3#yaBq2u=rPg(`p#l1TbcLmLTE6%fxYT}aH&hQpHP#o{
zBOn?$q3N>xsddtO6~mIgt)V{U)yG5|+0&NDTBJ*?tD$Rm$3jFdaaSIso}fLib=7tk
zJRJ78#4SUeu^gkdG#6Ub1^AAaq<gx3fS;Cvsi}(>lhDG*DncC=Ko^eP6B*M@r?!*Q
z1hzVrv7eQTXU%^cK?}K2Df-SmMHtRf6}d<kmo*@>ZAFzrPc}L@`js&X@3GNef-#24
z!TCm3j*KSruDR~ET}Xw2K}ZN0dWlgB+M}!biCTk*GVW|TIt7*J8DSigT0@)JCiS7$
zSEQYh9vO9UmcM+Q^qI}u3y&G|f?7Vz?XdkL9P<C~&o9h3v{!(i+CNfP|IsIh%1Ztt
zRQGS~p#Nyr{l8W&l5#No$)fNtt@?i{%tAl^(tZ11Q8N1PwTpo4K#u>Dc9E;+lq4ZU
zvA%{3Ga5ZJto~R}BM}y{HmrKkj4nmREsLqfinb({ttAnK;`p;PsnQ^^2^A$-<R=+`
z<S!9wUy;DFxo9gX*Jx`d?!X9dpI;z3SJQ)|DaSFvI{-|z@jx_{Ts))p{|75KU!^xT
zH!L(~!y~EWQt5|<a=@$OrKKrka`AXxc-p_W8gI2VCA1s9#4~nPulcL~ajz&%A(xB)
zKBIZW+t)#Vl;0msr3Gt?40zL;RfwXJt86A@?Zb_8Ry7{LU`zsOpZzulo3tTC^TyvW
zOYdsUwvhg>Zd{@r9)(q5p@7J3paCkYZHgJS^SAGJc1fj8!vK&@lJ_G;+Oy$tRH1rt
ziugU@OtM#{p{>(0@l1mo+@n~Se@c{RQR1mWC2mO0H}T%;O9>g}a6<Vk9_(A|{<D5>
z577E|A{63_Ew``Q+*<LBI14X>NFe93YMV+%ZL3QZkC7y!lN_7DYMTm1?NSHAx+m6L
z21y&tbu?5c$;>}jP_liG`2Reo)m#unni4EaNz^l0wcxfkD4K>xr$Bd(e&%^l+dheH
zx4*S_cBgT9e(ko5a2FI1t<ayfSDluUuNIfj`qZ59Q3+c{gR_{Ad7<6Q{+H~Z>Unjq
zK@tQ_6k3{}P34$i8ethk`~%vb1F@bRk6vewpRl7`%Lb}?5)r<vd&}%1GrXKDFF3cT
zmiF2!tm<lB1n?@#Fwx^=c3-u9nG5G^(=U7?U)*#|>-G7p@$jN7vYOK3mG@Hgoyk@u
zlv6f4SsMfmYA&seI;0f(kncrpCBWEM4mT1V3!5~~8&(cUQF#^THv|)7#vM1y3Gozz
z_!RZG8Zv>^#NfI=&KoZ6(gvclxY3&$EF*6CMncim-(|76@Z&kKpOKyK_2f$X>|4mU
zG$-G`;1X)FFtTcBjq4a|mp}b1hs|Md4Qm`+0Vl-YSY-Cwc2mz}Puk)>W%+;b@SkMv
z5uPMux{_Ve`zUyuEC_6U6AmeH5>|MU?d{N>n9Ru~(2*`VZwxK$ZvA?$8;!)^lU%sy
zu{||7h+&)0s`dgb`>l<EBT$>DVYk8aNsd}wI9%hIN^y~9C*p8Tp@PXiEnI){%&H4_
z)`g;WAbT$1j3K{t&_lv7Y>TBY1c-n$6-wXEbdv^~iQD$OW-{)`3APz=F0VdBGJagv
zXEpUznUM46&?Q7}#Sqp1%t{_9WJpTwKtR`k{j<r>V{Ah;r9mR5?&D<%mIL)JnE|ha
zwMho)xbkHi-g9*`8oi0!c#%BUJRko$#37F>K8wa}_+!R<29p5I=w|q4H68TGcm4@G
zE@B*g&BQ;e&*1^NxYviA?cXeX#<1K!98AV}z%+gIpmb?Z+R&nDxO*vGCl}@M)~+5B
zeUwF3K^_V!!R)G96N-}jkk|U@cME*%*0U{TyRgP)BYg88-h!r`nEQSK$n58CAUGkc
zPJgurZEAe+oj;DcVZ-}2VAk@4m;z@k=vi4RYb{W3c0=nENOIrQyx_*_H%Ne@^PBU}
zwC`%vwdI$F7>1=sQ31OUkX=}TG>1e{f-{fz8S@rKt|zJbMy{0~NMKSnL=TJM55Dt<
zj||0=qB3EKvQ07mH~r|458Iydb{`Daf}m$`Bfpx&4s{gCuKo;`L)9{IKs<^XDP<5*
zQeJI|T-AD_k7&`yA`kB;Wa^iM4hFkz7||hYYHZAJA&#n*=DIFR^7Pfc<EOnUj_p=a
z{}mZM7?dd~q4I{hJY&zymypI_<`NPBdhVqqqe_ZY=Q(}*v%?j8olxkcVlr0s+Ld7*
zFhF<b#W<&k4iO4c4g)1$5fm0u?`4tt(0qRl3I_=c88ic9)Omz@T?o&|FEOwc3y*n6
zConP8H4oimr?lbR-`yu{53$mpdi{+df)tq*#B%@X5bYcUdbjbN{gxjf0C3ZFE?ifa
zarDN}dw^?Dg8gL$HzqeejpoBZ5qQG>w|xW8D;c*j6J(~$Z?tg1TXT{*9zn+T+#2ii
zr<#~>+X~TGiUWZ>%-Yl^S{2>aw?nv{4WwsEfy!i*=Sskb4=VDjSncp#qN(Z<9n#o2
zMjyb!Zo{i)&!QliK^hZrz!r;8%p~{eWl#w8$eK)9<Q(#<fXqO?uM!0~24R~IQ=DDE
zbZ({WB+3KczTG4<_Hu1fiHBIL5RibrEX$>4J_aAcIYYs-P%U@6ZDn{|xJSEb)33ga
z*=dq9R(!ASUGVN4o+@Aqjqrk@KQ?!7{w}|WYm07DA}Cf6@{>cJxkXMx=EzA^ljWBt
zuYQ<z>r&wLSbGrXVuBpgJ<^I_Xl=mIHE?X+STaH&K+{s*AxuhROFfcKX6#~4q85X5
zJhH;A)fjEPdM^<ybHSpL)W5@$l<I}Ft_mh0P|XUrG;h4`_zc&Ouv>x8mB#ay*QI&L
zid`3F5VpbI$~j^`U?aoFJ-}Q4l%T)4@?qSXcZ*sSC3i3#B>llF_aR`Bg-tL7(3u0c
z6QmADgt3XTW#rWPH3YoOTtoKZ|0;?!jNiM(kg|)%l1kpIrRV_W7dh=)^U9JA?Y_mB
z78&g0={tLpQ|aAfQPBL{*@kz=dcB7(7usEb+=1>0%Q6U#oieVwAnG-)HD_txTP0-&
zY)N=ECV*5_9FIRLNVFAJB#Ij}`kuudK3G};ELmiurm~ewljz^^;HsK)!)<BQjyl3_
zbj;*ao$jxXy7Oriy<OxM{54NXlU;L83{1vp+TuyU*@a$3-R|><ysQP-QhX{AAx(5-
zPYH#oS5tIwmZvYYmt`2CSjjQV99B2<!kA~7m?LiqM&HMhLxP3b)AjfuWP=#FBO&dq
zn*l$iL(wg-i<A8T=>wnuhm2{EXkdo&7mv3eJc-`*=HraKtP!{hTgmSU*4P}UUDpXa
z7Fw2)-*=>|%9Un@F&e+%L14hWtXtt*8wY4}C3BlIqMyjT!!`2-zAXZbCN8Ejmwim{
zIorK<-H(s1EVC+dQ^USa?<#E0QwMKV-be#6D3v1<w~{ZeFS9?beZ@7Kv2AYH=aY^T
zge;MeyKec;Q=z6gZoHep%@T%djN2U2`s-AHEkJ%0S4r!s{m17^Vnp#}zv=*u5PK=R
z+Vmr}fW56Pn#syvzm>~f;|Q=f3EzY_0RcxWyRS|o$=?gnjw@-*(1Jf+i88|%ch|&u
zZUPtaL-y|25^HkozN)u?H`aGusBfC7A-Y$5-TjS%<7=>~vPF%Uob;#M;s7MkCj@^4
z${QOZ=R$HA8@)H#H`lM6L{g*SuZ?M~+Y|`DI9u_*fo?*Cw6QTEJ*IpLCjR!|?e}cM
zz``KONP#1&$u$Zl$f)x5CPq;!vv?+whd9Ap3W{B+e>5BV-{a3$gz6v2dYPOtJArbo
zKmr0a-U6u%yABj7-9wu9F&(YR>=YRY-dvksUM~!DvljPf=^6+wdiYIzGi*s?7ZnVD
z3HZv@po2v_0J(`CD2-U4Z<7woXb#O~xvQmkaCMDA^Rm(lFpy46J)jz!vYS$y1BL)e
z*~`*YlrW?n0l&tLDJuOdaL=JvXk)dEJ;exxyr+Mg8F4It_d|e;ct8H9QqgWQ16Jje
z?5#$12i)9u{DE`irWiq8#Di59)NHd;tLN+IH}Y~S;Iz!%sHDdXJTAEAda~%hhYRl@
znbgh57a`v<@h!o_QU#pd_m0Py{sh=`VU<7E`p<ww5Yz+>qfGU`QGZ~r_S=t6!v}^i
zjb=HV$-@3noWT~z3BwUVz(Mty6s!g2VO^`zLpdC=pmkp%uXE1q#>m*MzH!ZEb7_Jr
zebko|c^Q@J`S*}aO=<`BY~H_Xm4kbXx=OEjFm8^+bgPZF^=RYvVO~V)dae)*RIXG9
zCPyDvDHwWW;S55yT#0QuMVRKk22vjQHg<-by(PuZ=n=!~cy4{9d04@+5E?27eSe0E
z+Mzg_KLMi*UbVGd={=5@K;TEY5u-Z<v2{d`SAeE5qfzsS{K}4-C!C}maw}#BZ00ie
zhX>%5<zd?g@W`85hwNE-&E*}jN+iJJ%dlZ=rt#NcPh0cYzHK~?mY~?n(uzD5Gf5Q(
zT{KxD3}wE3^9<3q^(rjwL+Bk>2~|N~olQG9Bn%~x=-Z;B|H@zt$RJSJ9=9&s*@9?|
zC=%eunXvYBc-CTy!4l8h2}#cLm!dvC{>J^pF5*y{_%rFi-#)c3F`L-1tM>Z_-V=uN
zCRM033K<ooX1^z0EP1H!0MDspPj=nC<}=tAcbj#Nt|jGHw58B5qyfqthX?!NFz&A)
zx!lIX>bCT)*N3|)%?3>_xp~|7P2PX$Y!lLh9H&$)lmULl5!#o`{PLoD)4VvFQQAE4
zsG*T!=jg6id5KZ}fEJqZA=yH)Co`C7UApc_>j2RCgxB_uzcX8Um2<$x;DnxmhM)Gw
zz0igqWc@j2?j`EidZT_v<(}f08>Zl3P?h0hY;E|a_{R$0%be@lf|R8jXtn9|Xf1!n
z0|iL08rPJ8w(#apq;^^Aj^3NX#zt;UL~fZezvTRx%e+O!RY#*s*}x+V5FqBv@<doI
zmZ*#?TM>B~f3VYyGE}3iH3ELCs>mrh3bZf~4wYG+I4Ge}L-rUtXC?RLm|dSGqt{kn
zTZT7Cmku7H;%a=L3_00b=kaTr9!NFs8Uz;pgrlSM$sU)ga{};-_(Bv3CWTcy(XwMU
zJp=r$0zvDwI8FG;yaEc_<=S?zpC8+#-0Z9xa8ANWs&|WM-|h&gX1yKVSG(b18mSI?
zD85l1Px<O{KVxx<mLaa@F;u;vj-)-1e7C_(sI1!Ii8K7>X_)VI4DbW*Zzk-CiLd2u
zHBwzie*6tNSyd{ysg4O@YaDWc)<8j&&SSDcUy`iF{q^wUBbu9?0G0yV=b`kQmX)Ee
znB^wKT2k{6k&7O6uUfg0zo7Q;&4`17Z%dTX9`w%cz|+Z4ikA+r;l-oxs^eYy#rS@M
z?;+pO`2-Vb?QGEocw81y>I&#CFV4o>glOGA{X(%vKFkCOVxm_KITLzb%58mNaRsYq
z+2OnCT52dJKHawF7OJD@_1hD})J+LZ`HYi4U#E!&A}2D8#SC3KZM7m2{ocet>iAKI
z2lzn>CArNH9i>$TEU`#|c+fnOnR~-1x9%xuueUHB87&Z+R?~x5I|t%<;4}6}xwjba
zyx=Nj>=JTO*wTm6KnU-Pq^z|4u$bEvw}T?cepp-s`~-tgASmuv)94%P$zQEUzdSO;
zhVK>#txWf&=LtZ}`~CarrcgL~fp$_2Yjlqiks*xOka@-H-}>w-;|rq7w#hq22{j8?
zkUwV|9=-{syMUmNZ=Lo6txoU4`5|CC{0l~HzskTXOp=1XN+2aT$jan3odm&3dn8`=
z-M6sd646&hhf>w4-}}psP+hr8x`J-i>8N}{vuKbK#k(e#iXbFdZF+D?-Xv1<NEq8G
z3KO;4DRV2m3Rf?XwgC?R2R8DC8x%fruwa#(AW92yO_U6nE8tgURy0TFPI;W_ta>`w
z`NCh-d=XU45z4b$<`Yr<hzwweI5Tf#K3x5y5f(m%J|%_@(IVuq9=qbo9|F=9@dVDc
zn(eZoowvW|y!9dHH(uJrQxnhmpwa|ooGDp{93J7<9+5|a0+q8a@f|Oe&BhQj=k&bp
zEZ6bmrK$T|ZhCs0>g|to5JD<Pa9FO(so@~szj&f3fv&@+DKeVLI=&VhTrV*PgECWt
z;fYt!Su1EhLyCFadC?BpBRVsa&*jK#qL%O=nM8-8j|o$5CRl9xYp_hgn0-}-_%+=P
zY`$s5GmqCTNFlkIV1)WZDJw~Ei2z<HJAJldOp9jBMgN~NGWUk8D9~DP(HGvdS>cE@
z&CvUB9!>URmlJ6OHE8IPSw(eE_Nt>V`xjV?Lh?$J0yje>Tcv-AKSU;AYzbfpB5RW!
zz(EDIX$+jpY$z#GMuNPSwRWrz@O`)|1g=XT_tz1Pg}*V(p=(%dYt=nA>a^W|AG8hQ
z{KI#sdRE_sX0?r{gD$?R)N-iGBJ}ZiLx}P)$sH#k`GxCSuNdzwVqnEP{d>$aQfzY`
zW><fG`OhUwGO)s8J%3LYOe_I#kQ?cW%f3q^qk@`B^{6|b@#o;p`E+?&CvJh82$&Z%
zL>-5PNZ-YNpxKR`+LuD${IoFJJp|9MWgfcee0u%-?f^U99jrBWb7)x{Z(aK*z+u=)
zxBW4-0;E9z))AGP8Lb>ss`r!>-Y;tWOT`5N(gxX3Uv#Rpn?6afJ_gi$zf#45*r>&J
z)>zA-JW6<(B)#tVUULSz))?TvnF|=3v%VhIZ`_Q&>nTw`Way*t<`Wz7(ame`hUV`#
zFFer%Z`_mT-c9a?8)jSVJ50=2*?H{_Wiyjps|e5!jJPA5--jMuF?Pv<($BH9;FW;`
z^^=@PKX|Giga-Npe8oQ4IdsiVk@<LK7iUNTeXinlxZxFwDqb2`+M!<npcth{+e>)^
z+D{Oatj;Z3)hC+kU-JC|%1_=)q>w+p{qGK=iGK7n97^;AK{9hd$^d>TbS^pj!LD@d
zB9NNt8!NGGB}3(7Q~_pt4)%8K(d7umV<~N(%ZJLAGQ|hWy}OPn<NT~>_3bqbdt_a2
zDX}%#+w-O0-!Q<q<1{X{)ZBO_A9q0Gqv)7{R(=4ur-%K{rRCX8dSfTmPOV=IHmP%5
zGIPBh^XRddya8eS7LFCQFwi<B|Ej4AkYI-scKbZeXiu^umu-)nFBN^psU4GA{pwJ4
zYDrws?U1*Lcb_iKOe7SpDT_qM;}W<lqH>rhU`IYZP76FBwu%B}B1=+hvp1(O%5xHe
z8EKPy@p3}9@EqTaFSkJ2;%h11oFG?R+?l@d$pXLhtw8%MY>}0{+QPCt%^|}&P(*Zu
zEFXk(GssU?d{p=op|+Q)h0<kk{o|?%8(0+EiDXm2@xl>VrW45Z&YijxC^%gT!{jlJ
zilatEm9mZJi5IdQ)}Ks0*u5zJ^UQ8UIiHgn3`$eO<zkU0EWSK(-9Ak^BbjI<<?TC^
zBAV6C`pREVpeLnngU)dBO$tEeiQ@#W8Zk{+me*SAnsJu$yyL$LKa+z1_usIZTx2}^
z+%fEqZ_(Pq-m!;V#&w9Gt+JDAxGzyd)<Kl9T0`DrFolmSp5HO##&gWTUBTPM9l1VW
z*kPZUnoR>aZfOeMa!l{QYr;EOb|Fy&Vago-Z`3Cj{=BE|fQS@b9fX(oY34NI*hh7$
zZ7m>@UJy|ii0{ig@RAr>zmg*;C4Zdn!RoVNKi6Q@Cd2iQ=T3{=1>2yA!LtE}NH;Kp
zl&2{-zisBIQIXxo{n|nS!qH0KdkD%__w_|zRp@mP14~(serrZ9J5v~7?va@fbT(q(
zoac6`#O=z5u4SPl*AKLNKxuMXBwBXW#Tu(RglP-b+>K!qhirC<9p)J40N{c|-fuJ3
zzkJ2s`ktUOfWfuc>lnt=ym8ESx1of6zdaCiO2h?`94V8!X7}p$I_!H-xcvK36ae!|
zM|5pl7shiaVSl`MdUu}^dc@=Nvx2~Gjd)E|`3e8TLT&(f-WnDbW1zT|8l)-!Van|P
zZFY4Utw<(pSbpvWwu%F-!?8&Gz0f&hM=SRG0@D3{;`Y}OnZUU2{a_p#yrBme-Dfrp
z;3~}8iF$;&dhtlNCAPk3AA3-$`*Zo8(iXeJRRpI~H;~A~<p8PsnfA>%S9M)*B_Q23
z)FqT^2aiLQV(yf8Pa&e$@tOTaD)(jfxl2D%yYNBzycs=Z2X8v4m76ck1R2hgxkrk!
z%Jzf-76RsXU4+|dP5n9W=!Vlm>`_flqadrmFEfPObb<U}kXMkA(5}g^oHk`FA$yrj
zJ-s%+S-5hj0dp@g9#bwo1jK{sUEjY>q}mKq&*r@i?3TJAo6qOOvdC{P4(L%Bbm7%N
z7TL}JgG-S`=~D8&z~>d_mQ>>tH2y#1df)l;w!|zaW~ym)G9`i&cx`XeqU(@*r$EkR
z1vpA7k`a2c#@>o)llvj7^8u#kbkdZLYIoin-YCv|%psKtbEwJ)L+xrTewPah6Z_#k
z(jh5ZKLk`N7rQ0tmxz8s6^TYD;{<ZV{{}s|kvpyOa)Ve;MxBOe-5s7CUAOp8X*BnV
z!_rOT2}mO7JI=~?H>+mk#_SjM(TktZ_?te^>wg3LvV$^;oR>ET{yH4PN<i?$Di!3K
zuq^gX3`-ELlR-+StPU)vTv<N4a5Bbk6-BiiDt5^E5*`QMyl4$S;sYZ&!@i=N)g2mc
zR=WqT&7D)O_*Jz44U@!0)@%5M!<c0SjE^%{;AZ5~HOMAMMRaUrMu6uMLAbW^2Byt_
z9hPy6of>vLS?e73X|O)7I1a}1!aGMDpr8D_n?w_gYzsY2(&Z)p1aUU%7=NjJb;@|z
zMzZKeFTwP1^K5L;n~~X5LA|-4R(A0eK&8DUA~j$r_VS|RZ8Z2$s_HUTs3h2*z7(zV
zq@P-%REFXgm>cbg{H<tX$j{WP+vFfp8mtEvQ%g<{<V5tlf=flmGd7$8_(e=mT?~bJ
zb5ccjJ<fcL$&>D`a<B}pmXVZOuhdm2$Nzg65Tu^7o44X|tZsn+Mr;UM!KZrK;{g{E
zO;8c6R&hy_yhMjo8$zAH2U|e&Ne`Cms5v2mVDtl(AY@*IBd=EwCkRcPoHx)^)7HWM
z&I?gH=;u3jn0YRUtkaV(cb|+Q$Io$ssR&jX9(Zem*>B?Skr)z;(eowz`q_!|?5~}F
z(3n_Rm?C_mbV+-wprPevz3+XvcocE(_F3k+5Rrh<Z7BQ%saAB!u(xn6v^(I1FD98d
z8F?)-yM3=h&bulr+;&e0RhJOOFR457x8^ljg5G~lqygFAIzHm9u3^vVJ96s<a{yZ2
z+8E;Hr8hVjK*4%#^O3UGH>8LaW4~u``!tXQ@!rJ!c<nEAzQwW&^nC(X1vZFz2{h!@
zlQuiFbowwe2MXqCtmE+jL>fx8Y=4&0fVYyBtRU7l1M?eRw(077I&^e|dw8uujlmak
z+rjnA)b}e-yR%`^JwUGfb<~|zC*9(70{hsHN-R5CG^THLcjV<*t$etIf^Ec1`{3lU
zs|dtZ?23+dy$oTScepkvU{Ms~EqZ}B!qC|QTIef^EPc~Y4Rz1$=N6>{zu92s`5>A2
z)phYdFby5SLE&5PY7|+`dEj@DTn`P3yct4BU!Vidd_TuVIxqGOD?@T3D$HW;CM&7u
zH;DfftMkCW?$;YP;oNPa_g3kUzZ}a?`9dPGkg`x8@=3<N$t(y!{&Yznc1Bmpj(<vu
z(6{8mto;ITWo*yQu(3h+;I~Ad*y;xGyJ*E+d}R6B!CxY@Vv)&nyIkImo1O8c=;|8Q
zsQ6nDs2A5vHdIRvRZkfZ5r~{5QqGWBB3=_A(c?BNJGcw2$GU#E9d8dP=jewi$)t?Z
z=s-|VxNCI(N>3$}qrurn{z>eUeQD|Rn{jt98NcnGd<Kq}sgwuW)>hPZ<MZr2v$c&%
z$R;*>42D#fqT-PqvKqsLmnOI=MOYjeLR_#<)3MRMH~V$pB^|SxrsTvCnO@9(fltEv
z7fneuqX1LhMq^)&sG-kimcwNpolk}RZrdv05@zt|V_Qy}!SUa;r{f_szURwjE!P*R
z_mI&PRblGQC|AtcHo4{DwXdCu=nVGA0(f*x8j0Ldy>`eRG8Oh?X%}?0r0i%bmXV>W
z@4%u8{m0!GMs7lnGu$|m@RN1|49YVs1>}Zmk=lDFd)uDnla9x;d*4r+Lelxfs3p};
z(x+AC_<brFsVyZuCWCUf<{fqDo7BISQFhnvCn@K=GPsR>wzNks7U$nO%msG9R(=G>
zN$u~o?^bF5rl>WbY#0A^bYi`Z_e6=n2e;vtlefbfZKr#2XtnO)m7HyJ{n>)9ZRi=n
z0D0Wr>aD9`4&&DIP{yvfwHd>)`5Nh>YO{?jVN(&g@XIm(ZapX2uS0m9HRKB?-pYY4
zUaV1U6HYMp$!B5y*UsZ&q|X3u-&C6hJVN;73DW+tC`*gaDfn$D0JutYwyW=8Oh&LR
z)kfpl$|Fq5oo&jIDKfWo-Qz~SU5Dh6stPXGg!lq&2QN&F%1w{x-me>8Zrn!~?<U@v
z#s{^dzbiyDE3kO(2!HS!2p}+?LHYA&6*jwO8vq6kk=a%9A=B7mjMQ`o*bGOyHX;DO
zIRAGsfT_+XnEn$4^4sZ6ZFDHRmyltv%^mBC-0c^Tc-4EzJ8ePdYSt_#YwPBv_xWRL
zFgI7B%j6Z5?<5r?e@d=_95R>@RYdu0%Ra&1&3o&gpG)GH^qyN5h)HnJdvzAMh6)x9
zFQMjPPBdJwX8r(i&Tyg<^Sj)XJ&OQc)jixNadDL7$2V7;8tgP<=A9Yp8whnx(Ri8F
zl2@iL%mwc#B9R|Z2r@VzdyZG7|A;b={NNa-@cVGSbL>Frllxzshz3vi>;`|(`R9`Z
z={>iIk{w5?b*-lRFkBet@)d>23VSG><?<6N`Tk9hZ|&LylmXOakurfR*b$l0tw}nq
zvAk-d5A2oLIwyV?fN}6(R5}1G41C|X*#xKXdjG-k7^O^U(#3A0piK#`;{2=IQ@tH>
zU#ty>)Yd&}f(a@#$c(w>gh9z3yVuqE`uehQeJ`cMU--EE%<lNhezZq0b+F|WuRe>T
z_Lb$Pl~KuC$!zuE^ESQOOmE#y4C+rflX20-%H?8!-zc=%JH@2Sb^-4E0$B?cMpYkP
z+@|FZ%Qoh-ocMC*_X~y(22I%X-+`r8?z%13J{2Lhw;dr0@SQcgg1#{!7!l|8rCyW4
zr(_wt<J1X6X##1{?te~P&PM{Gsgyq?C(cuq9gCC<|CpDWkfQ$`QDa7(>&&b1RjjGm
z+JY=q2~6~wpP}{pl(<)u%o8`YofF_EOM_x+h60z+O_e$djWQkQNs6226xF7j|6Z(x
zJ<`1NlUlsyC^k0q{FxS2I}81AHtX7BBlBp|jk*-wOt{H_MdC@ARDV@-leGhDMp(1f
zvvt`$Ns{Hv3(xFBnq|8vf=S2Z@30In`$N~iym+%jtbOUNO%ULRHcnJ{@d5|~k$!-r
zoz|86T$PR%t3OI@3<z^`B%=Hk2(~z=f!$s^GbL!V47G_4tUmhTEafgl_JrEP{>N2w
zuFWE~cFkijTqPnaju(E1Idjei^{F*o1PtuY^3NfAey#(X20i20huuhM)U0jnr+*&1
z=u<_p33`O>cr8#J!*yb5`@8L#KDAun=bRtluwcGLK9K*Z&H2q8zF3ETy;Tb=m##0j
zGBJQ{iQ(92^QhT(S2hAHO!)OHdPr!EI&w>PoNGa%uppviT0phBRRREK9MDs83ZPJz
zaIo4_XR*EruUtH1X?*-HzW0a5hvj6T@TYk}#<X8tC$~dul_09e?_=Mq*)I-W1Zki-
zhEOkrpkl?a;(6fX`Ua*@VBM_79v-sD-pwhuQHy6ckH)b79~?`fqg-GTXOf;RCZsbB
z)p!C*{EZ2z8d9{!=Z#?Yt`of4bs{h6@7Kvo+27w{P#elXRze<dU|}j$^}caPQQ=pu
zFbCXywaRWe8qqAsd*R@syBXO!w^!S<@d9I4`e+t>tT*}E%~Os%0*T8+D)y)SvnFY5
ziw)^yX?AMG7;Zk{N%>-Lbw?PbwXGmKcxp2(OI0yHsjC?1#tms23w%>JD)4=Z<J6fc
zE9Bk>1ig<sqid5)l~tsK7k>Z^+?9<1?H)0IE?PF!AhY<}3lI>@wTZ&V{Z{Lw82#94
z4FJLRVWVFBxI84s=&fQn*q-__{M1^YZ|I6#;m#~WAlruLMor@=I;L{78k6xN$8vac
z?76ihuFt~R`fTwb?(XQF_K;5}u+Rx*qvSi6BP&%f2-nQ-{fMQi)#aqNK1EwYC<vzS
zKwRCC-`=2)$|i&GvW|;lq3|i7PdL3*nNT0ctVRw^Mgw<q45ea<Gf<PRiA6P9?u}Fq
zOlA4N!UNAV{gHk_5D!-w9dbVDMOTWL#-Z@w+0YOU>ZQ?i$_Y+Ynl1aFAiXTaZ46^8
zGkU8iNJJO6);6c`c6pVeep68__(Ee_82X9^k!_g*r?XVN<#^O@oFrdhBD1_mrjnC%
zEIJ!Ay?uhr7ld60gasv&g2ne~%qNCt#Og*I*s@jHtO0Pt{NJY&6nK~V{J%imTaK<P
zW0b&&ZKJ!hcsi$CD&LTuWy>Ai%IM1q$r;+LFrpqDCcls89Qw9~1qqtxR}_7~;b>!2
zW1F0vkiJ_~^23uN5BzP7vSPQedZf6&oi3p)tv2K8f#d<*(Cj<vn^}8C5%L!sls<Vg
zON8UaisA65BEE}2%W1|JDM_&2*juhr+0QQlA9$=(ToIVxN~a^9!T|iKq<4I3wXa7q
zld0bjmCEn@E3Q?^-cTT&u%mC~9&R~0!bs3A98ldN5vVa<8wuf#;SB`mlDH!4nQjU_
zI(he#YW`AaDu;~q+wb9W!YGZ59ToI+gDeTEYKF&6AEiwwAVkrNaHWH}=X*~r*>WK|
zQ?AU_#E?ujzKT2eg(r0w2xlfywtn~lla`lwrooqY5mx<dknyPNEe_v8KkTc002VH=
zZuQCBuaFxVuyiu7=x@?I?8BqAbE}Eb6kOzRJU2}{*ng4n=2riFGCGox0<6YzY80=S
z{{!&TFmt(XwL$NH$McH%`z$l@Wwb>Uu8&)wJJJN4MUe%^sgsDRVbZ33H=R-us2ohd
zV}rhw@N7`G`?}6Fkf9`1E+FdM4-eKOITK9$X_JaJH8F`iS_o1A$N4F_iZRCi%@Bs9
zUsIvpm$bhlFo_jZ!y)ckp`mg_Ci)bi%+X~fr$yvWGzcZG_=W=y#{F^tnJuQ0h(HMm
zxBQ`MFAg1K4irsBAQUok{8Rs{Ui|L`Lp~vGG6)7ITU20gpWXDCm-yX|d&v5h@plWJ
z?PFTwC9hCDHi**#q^m)zCY)0u530@Gde6y}h5|?kWoN8+TzlPaA|u8}T9Os2{seAo
zK2xN7EulU#%0S(3KR;67R(c24yCa_>>m@XYeQD*@NX^esW|L7$2`fe{pqn52sTVv9
z)cZpfJSAO|w9>;(?eUlw6!e6g$3u)&;KRlLe)YvF?$NQWmzT#^UVe>k>8Yhg@vg%2
z0oGEPNh!>6#HySU7BW0yBqtfi$K>Yt{CPV=dYh0t?4#e$Ve8?WFG(P2@6Y{$v&Wzj
z@J5<|<cy2ByT%`3W$%6JL0!A0*x9IHsZ&MYW9+Z@d$BpPY(_XUN;bm7$@;l;Mr!N#
zIP7v9dFyx_q6OmVURKXxERfrj%+)l<<9NC@ggl(?IXgkcT|5mPj;{@r!A$3}O~<h`
zl@(RxB5iw^V}TFT%_<R07CuB>qgUz3xqOZ01E^;xE%(<mq_$LMSdix3QZE6O43`w{
zC3|<7ZXjN|pPD-)u|1LnLh@r=(QQJ+EC#uH4e^Efbhh3TkuC|OrPYEE{7|@X-0Psb
z1^EK>vWeimJRg2w$i3LO*2LH;g1&j;i#(1>5n948e|4tj9ZmTeYE7klw6s6A8@x~?
zYtB<|;A<o2G8pOSM+2F+Dopj;b!FMfE{8H~|6qem8H1G{#BIDbp%N_YOs}Z#b35LZ
zJUtzSIa|Zhb||lCza<^+(eO=m7a7=f#T3QS`UHS5biTV$^g#(-t%Q?RWO{&h!oR&C
z*zOH1ir|vvY!ZWU9lOvlm9FvzE$}N^5=c$UBhb7m#YD3~G^Fm@yWs*&ideCY$B5rt
zP01lS1L*B8zZq}O3nd$(M+TcLSk~!bs9?6wq$<NwZsDnkjU;sg)wX^*2;f{w8tkPT
zP1Od90!eKH9j?fR0POyCnmPHs)cN7ZUe?rc+_SHj7E0wS_1wKu!W^ShO<twUqq^gr
z`k$MyD0p4?Y6Ex&Eqhq$e~;YtP1=V!yN6A`1iL4mZ_T4F-c%X?K8KGhCP+PEnzs7m
zl4A@`>V766<JPY6GygUmsvZS!C40A&%3Z9fJG3|z>ObRky|6q^bnxONIPhjf+mbyx
zG#mDgfQF)O9f8r15g!60FOsveHSiqz%ySAa(cG)ED4(2yBR=9jHF$|HvYV(UU(xIB
zdYzbz=7A0<sejL}l8y*rUQz+I+irH8zMkwjpAhYt-fH^NyY_vCM)heXZr4k!tf;lE
zlnQ*{lX$W^%ZSfY`>i}K^9XU3YU8M!$X+m10)MrMjTxhPe_+mj4v??GPg`(Yt{;+t
zRIOdLXFkX0Mud3mEBU;H2H{8K&yZRvBv6_A#eQFr$OCU3*49nC*M*B((DHT~dhj3c
z!0XdQ8q@WL0^A&Q2``z9LtL7b9X|RG^Klk@uwsLhc0W(Vnk9?WN}b#{R)Mz$8tD0~
z)KpQqJIp6|`A2tC0g}YU-<~Q-%+eOVKrYNU7iYMPkS|bmIOu=n0{p)1+j83SD1tRb
z3eGUkoN}&#09dw+Azn5J^>&2S?ym%Z7@$*Y`%-8lynOh2IeNF^$Xc)YWUTeEEkaD&
z)nwUkrYyEu>aKga-6n|3Wvsg6_o!o?Mh>3s&hILy{ZZ%>yyDX3X5+2kV^_eBtL3ZD
z@ZrQ4YRsWN#$bhyNWWa=O@K_)l5Z@Y&s&cvXD%9r#fpuQPUq|v20-;X+ff?$yNlkz
zmwUVQ^;)1d(J9%ITD_?o{p~lq#sexkNU?o?z478cy(dpPGCuX^ZmwyQDOxk+Z)WCi
zxMFOOeE+&Y2;mn2wq=SAk_MJ%qyD~WMQjue8bRHOXqTq6=92-`j6=}fksphy-_>kT
zcf@j^v=&i0%EmOI+%V8?F?bgfS+_q8WJ<eQXqG)c8#{>hnv}8iy|N#WqG2f+;rY>_
zgAQ%G%{|Sj<89rM7ZSu)UN<(K(j=NuI@qL~Jwc-bEaau&Fyo(du9<W|ULK`Dq4TB2
z8*vmsKID906ss;IS=aEgdO%JBO?C5S`vKjGRL`7@;+}-7fIjv1ApW(2yQshYlt$-X
zam3XFtn?f$o{d~GMqp9ZTBgWhhhrF9oO)e?0>Zq-%ANKlhag5rD@iTFz&Rs|a4n0c
zQIECcRVnR*4)PctNx7xz-5h?iq%6j%RccC3NG2{p&p^`F?)bAk^`5jLvxEQCxKLGU
zw8ZXw>9eZRzS%{;8qBk7&4)&n1qe6(npyWpl*NGwE^10%Nb#p1lM0Dj^EMPtW|3-N
zxn-Hwf3>bLrIR{ZX=uUpyws2EBcf8g5nG;me(wm#_4%`L=CXf?c7i%8`JwV1q|-a-
z;8}Q=$L8Y;aa}a;3N783IEqZsj{~TN;8ph`(1?kP0N$jH<cPK1$}o7ARW8)B8zl;X
z2_Kc47-|3>l6grb3K;VPhi$ZE1W|<tmd0nB$CuhBfsncvS)t<}`0}A#9S9@!wT4);
z#wXx_Kol%D2w_e&bXE({g5Q3l@g0`0O21DJKhkAj!3icKZ-EJz>C;g#JPP(1$8{Dx
z!mS<`X%PT94V&Wm<Ju^s5C%Hoa^i(*k#PtR5%f6pK6K2hy?a-LY2voQu64Ld!jNb}
zU#%v}fTdXTNJpBNimWvZ@jDS1R?EtW;{6n0sjEeZIi{E@uNYy;k&OB_flYdMzPgfY
z*v)vA(B?1<@kcALqYds?9goZEK*APy+#Y|~vDh(Q@YsFmirc4g7>HnaqFf<yLG0Qd
zP%CPRU|++KvtU957M%$-TN-vet|jDSR+9h9t!jbD|GpuyVci(q^btv9>dBnz@>uQ#
zUs8fK$PJ{Xv5Chq*r{zCy_G+{K?OU>nX6Vjq@*)BY$_hlyGt3L)Fv|fo+*F&<H^KD
zPir{hcR_HKD9iY5(v};ilU5_vkX+L5oK@M~WI?Un#GJ+KDjX`hhDHYX@U)%=q^@}j
z09>zFd{sIf*itP1H3f)K??=tzHn<<P@S%VW0tj$6LZ13kXe{irlo&Y!?b|#Op>r=U
zoXikeC!&$`3U@`1Qfhy&!}J3jv3E1*V6-s#==<$W6`=38IJ^fqAa<9b*wI{m`GIzx
zM>4aSVr|e&4rOfEqZbo-zVyLsM}gwQLa2F&KT50#N1OemQ!N(GoW*hlI+hLtT@BN6
zVYO?d&SU120SuD6WGE(k^X4p0=oKej_n%NvdbtyM)piquD!In|?LwtJ<TI5o-fmA>
zDF2Xx;qDbo4|+}91&z@q#{;n9HpK0MdZ_?v@3k}g7nG46u*fD<zkO*lQG6i_{wQ0N
z@Ou+61DV`2q_A@p1B(Hj^0WVNxsJunXwt$`+6z1X+|b>X05|*4@af3t^AE1vdXgY=
zxO~Uh(x;0br#7>92)am;Hy<_A?tb^uT!R!b>Vcf6KSQ{=R2L(}Ckmdu1&_R!|5^|E
z5L&GRD`J*DK53(eT?#+i2QoQa#9m(Z!?;HAn^eojrSWe>2~l3T`#_Zjs=i3h)?X_B
zFkr;&UC62TN&Dm=9HSlParaF5F=}VDVuYkt5ib+RWu867>D9NC*d=|xq#|SqW70xs
zeJn+6^o(IOTK_iVEm{8S?4&N`0MZZ#%JJZKW5<Jd!{huB5f-IDpHLHx39<TVuRgm8
zxL6x`)5<tr`9{ZKk1;Zrmv|wD2E*@<55{mDquXO-qn!4SaU~wpq})SzM?u6>s#)--
zZ_}G(qTPWJ@_B)3e?4L;5cV6Ce@w>FUI?`5KupjADCw>ix`+j$b=eD>YuJ9jI4YhK
zeO@Ei+z;skEtO4iOm^g=MX%M_W3+#fI_8!@a?kaHS61XMDZ`q%%hB3?*2WH3&cC&>
zFVoKuAWOqH{|Vv?g5-6lLRLs{>XC5?x!z<)*uIU-s4NXT&j%!$w6nae9I4XbK7Guc
zM#DkKdf3}UF^&q&3)*sOIh0kl?&-6B^T;g|lhAODz&{awvrBONX3BXoM{+-&@$oyZ
z&WSbXRPoJ4`!T-q^VbfJMe4WHN-yiXoiIw_49*hI0Uew+Rzs)JqF26x*t8tU+&!_c
z8(+TTS97hFncUq6OWx_MKj!8W*&<u4*II<5b#Xi%DoAlK5h1yQXe7ul!s1t2zACNc
z?H*aE@b1);w`Secx=i=pMDL%*e4DHPqlWlD0~d7$bQoD<#QrzvAYLel=YK#4DH)oY
zuqYWinAkXxvO#yMu&9_g+B!RcOdJISSR~w>q*R>@olKzXQmUM!od2YdQspK6mmw7)
zAr?tn8z<;aNp?~;j(>Jaa*(od{d)~a%Kcxdv=ogjOhEriC}RyhA&?hH`mgW5O(;3o
zf>ceMbXb(WNV2GzxH<hhk=VZ%;{RUA=#fItreI=hZYXB!_HQ&2HybM{2P-EOlOztk
z3TO@-p;rF|Q&M4(a<Fx_gP!gm1k^u&SX5cm91Lw7?f#Wl&`%a|RhBO%F6JN;6)7>O
zWi>-1M`#jNCkJPc)4$8Zk(BKppwj=2t@_9Ie@gGaM>uitvI_~JApQrM<(66&Yi+qC
zf(%(Bd?u=Obqz>WyxaFv(Mo1^s`X<g2Kl!&ntUag<<mLDxjiYUnfqa09VLpQ@mGfL
zfaXo+!hMB#&wjT1{1VH1JLx?gr66qC%zE5=3Z~K{fhV{33hCHeApyO58HHH*$cVF{
z3VHE3LuG>x%ISJL_^HaFMkR(-Q|&G9@-$($({FqJNV?^a1+Ql&@8PQ?zkknCgasQX
z0?KSw(2PsmWssBNLRUzR-bH(P=HIvjB}dsP2P=)_BXhnX&nyY>mvCKm(Ei2oo$daN
z<^Fja(^o=EfRAG}R^eWx#=RE-ymIAh$h{0)FI;HpM@X-uYnA^Im#yG>AA6Ox0XGvw
z!lPI7b#<j$p-8yF5Ur2(3E0}{qZHO;cywQv6I|pm#8l#2(n-ypeB2CojBc}Ed%VIc
zO3U?!NnPr)N>Lv}e<hN=1nuej-(COfSznA*0yJ5qGh|wd)LS2Pmpvj)#IAhW$ZpfW
z8mVi265+6hwZC!W8W-Xk*;VG@DBBGdR9!?ZODx$2LJXp@e#sADoqU~4@jhA8_}IJc
zVYPnW*eD=K;>*ZvRP(F$J_ZvH{0dh#)z8kA^GN{~Ud*Xu;hh(fj1m1*1~OBtsfYJs
zjrv^G1H24nhQ?jRE$j2UAx%W7|LbP=kE{R3asH>EJTB<$4HI)yGbd6m9;jzq**d7&
z8G=my4|n}vgW#zu7&=-)8^pg`_5Y2#vT|_zkN$d+{4n=boe+X{@L9!Hf;=g^3*7=6
zg`;1UElxd3Y&RA@9fyE8AX_wtETB4BCP}qW4N7!Dm@R`DT4$mC>UURj>$)<xILdq1
zWZTwu^Lnl?yllDlbbR13GABgy=T-4duUN0;R@lqG<5&CZ<56EY;oDTfz2d;n#d>QU
zD2?aa>k5N<@pQY_Qx2Gq{<!p8oka?lHN8fu)-xY-re2H7%jFOYbj#sP3GR`pqCmD@
zx3(WVii*d<WS-UI)tE3%(8MQ@fyW8w%gg;%L<H|{Vwj1~%Z+A2(C$b|0^iPlPQ;Jd
z(KP;!m)oV85=~sRz3L(6r`;4=o2DI{{O9W_MXM^Wr&~`y(JmM+BRg$v{A6M4#ad6l
z2;o<^@C1G9J7bY~2CjCSm1YKwQnUHWtnh~m2nqXp2!6Bc+1D*!1ma52*+Pw!bu2_u
zqe6eBHd))sSY+SVymqc!PtN1@`GF?YPRyZVFT>~cX3l_xj?R3pd_{&P?fSe2ZHbIV
zJ`o-KW!9-&x2amc%^m*j>8wNX!*7D~LhYI#C<Gk8lEMJI4%^VoBsGE{sOjE!E3S+m
zNyax!hT_AC_-{jK(nU&m**hRau7GL~YXqOuer6jyGTwNTqp1c+ws9^DpS>GX%yPC=
zRDJqKi(O@wPMt+u71HBI5J|L%5-;%t3=+y{-+;E=dZ#a^#WYk<q>I;amvUpxWyxAK
zU*3G7+GHe|^Q`^JCbw@hjKh2q<|A?gU8eWrK>=@cTI9(n&ld6%l#F8BPUvx;qB|TG
zIP(b28O2%8f$_!UqzZw7*NytmpG{%c22#?C|Ee+)Ay1wqGL|Iuef-CpBdM|FCdU&u
z0U|cz;FkchB3jZ`2wg#{QNGjb!(I)pyAj0(ya=^IvOl%c$zq-N%cFo8;8Z%8E*Kv7
zk7$)p{O_QN9H`e^#KNf+spKhU2rY#A{|*&=*^W_ELYp;AfF3$tW7rco5JSVy;*{@n
zJU{VMp>jqHi+_^#nl-r_I^EfQBLLSaS~*7&h;iU^-i0t4^vP4=Y_&~+TWr%jc=I<B
zGp!ty`lG=9a)4&YHUgg=-rND>I_PUHD!++M1hJsL`#~Q12LTy^4}T4fXqj8j#ZWzu
zt03g#7h8}BJYcqZiAEzE4sygGOsP;`HcQ7dTT}09o5$UIr}a+Qul&x3YE$F{`%E}Q
z)mXaz=4Ib$Rm6#>JAB@9*>s_<ERR*9jjuo~=<XD~2h+3>NQjqDVWQ+FfcH+WDj;7W
zg^$F@cm-F_>GSUSDF4~|>IU&Q(<zh>(){ngXbp>yZAOL@aQ?RCUUka>NMn4=M!1|U
z)1i7qe%uUY$;HUD6XHT4;){hJ3~3?cvYJ0$sHp-&osDL1x8~IOXx6-QM$<rWC}tiz
zMSomk{k8^;WHQCJT}FenDx2_b*>Mc&t}G<`4!dE$Uf&~9gz`vA`^G`S32}+aF(myz
zh5r@A@ymwV0bJIM2{{V6tY-%Evzv~39u^f;y0XElsD|zT#Z-JoQWp6u%SZBaJBoJW
z0XxU%?d7h2=?9SZqhqS)P&|{o?L_tm!7n&i(eHO>b!-F7znFqWi*be?5wp#f>dSP4
zIM%2&3Y83@g4`TjHp|j)e?u_Mk6=dke4|{2XyL&VM8pS|QCD3?50^VFu4j^(Bg#j^
z-@mlkV`bU}6;ymswp)}<H>!?o{xiTf3?cRi<7&s!$*;0qYkxXzLBry~Nyn+R0i<0?
zD(oX5S+o6bw0#9w6<Zf5A>C36(hU-a6Lf=!bST~3(jg$-pdbhW(kU%SH%N)nAPv$D
zN+bFP(0eb}TmSdo`+(t`b!N`$*n6)%!!>~Ujhd2@n_|CnvM(1`8^jncN}!=Eo|wG9
zJ~~KG)emQ{F(4!^*8awI*I;?x7KXm2wH!f0F<faoQ}%&3?8Ag@<CMRp<CN2AbE$%D
zn22KYv$VS;uW#Wgn`N}y*6$N_31MbFGm8w$OmPfo%p*sp))k$MMxt)Ic6Vd9CX<W%
z%b*-zVG%<NeiU9qmovHh3KhPj2!Ro5Eq`7Lxn+=e)m&6M>2m)KFMabIxtC@_Co368
z9u_{d{>cK5ENZ?S^Yo)rR*?C>pmk}(d3L-YwhKhrAjVmxBgjkwj^L5rhSh6#=VqZk
zN^$$4A<Vg0RUJD)&w%5@na4uiy+j{Xxaq5DT05(@YSxQr(N!Yl2I^=;@h19FzvI|O
zp(@9mWFt4P4(9L`A1`S8qjCClnp&0u&$_sQ+lD8W=Kf$0@XaMLDn!r#uN1YbqQbfj
z`qTr@s04@T%^g(JqDHP653o{LRs;2?w=J#a{pk`^=+}paT8%QQ4yPX}n}$H5kEAtW
zk!IA7MT`vFc;nuEoN=AU;_hklu#8TBii@zkSvk`lF?-F5CaDCgRn;_uazZ6Bi60R`
zrBDM}*sIga{{<6k5~J(-l9i!CCa=?4lSfR1K?+EwtD&wXVjG*WJ66L$b)h)1T#gxC
zOLAcoAzGbK%yP6Spxy{?1yk^uGoy$CuH9YFmo7*(M>PlImSCdWJV_<o0XPr54#rFg
zK@!OR=$*Vg{5~`lciZVmcP)Vss<Iw~+_o2pMY{H20r0&FP&a%qXQqdqh+1a=CWn)V
z#aySR$lcd(fC+HnR1V&Um^<{urHQSyc6T{7Lw!Gg{>+#=70&2pv=1j9<)|BD3*Cje
z4LxVoqz+IakfOGs&!^hE)|*tUqT+l_4d`2)6^>(M#w&Ukpx1?D`y3~L`ze1szu&8G
zn9&mP+6;<D3)8&(?SOf259F#A>vTkt-M4#MIq#20cZR;3CzYX;ib?Q+0mmpLhAJ;e
zJb1x1v@Q>bDt73qxO{NrA^zxZf+oZv%qu`%1WA)@*&Fi_N&2aUn=_K4q3!0)b_>td
zwSm}%J|{VRpb~lkGDJR*Q0gzz=DQt3aMz`5jB#Ph0A*z$3&ULwCh={_1Fx`7MSgdO
zZCQ$d!d&wREE?(TC6-3x<7p}kHy5WxavVt^l-vyL>_<acR~?<@4dE?Q6c<E!uKL72
zZsLHed+|BCj>=ehSCAn~lbX^VF4B6F&VzOa!||QQbcQ;%q~_Omr)iZxgiJaWOZbJX
z5Q^YehV)j?NAfx1TQWX1EHil@&8Pz;szBKnoJc9RVS%Nj`g0&7mn7yqIjWoJQp%B{
zJdLjx{ZMc|OY>6`auFzQ6^4F9U8r_gPI#uM5pO-^ztO3BKNnqCYimh2j-f`IAu9Dd
z;8iZu2NaLL+G(E|r+W7_Ehj%>`&`-gHTlt2b<`p4S0w}j@n&7;{z63&f`#R4f`oNI
zts}~2n&JuDak3V4`)o9>Mllv;uhA9PT|*fvFLV~8K|b@AC`!KR=q;R7Nq?>{V-M{9
zJEIKT?>wYUC^+N=`7Io+{ZTtlmtVS&8xn9T#f7HzN$}pGC8!DNf0Ujk7SyirG)$>w
zFx5TO-WKjn_FA45L40)2abKEPPO|$eY}_Tik8gNI+?QUM)_mn^S8N&lRNm|xT)T)m
zyhuEPMIku3Ch52z%2JfN)6t8cQ&hb~qII+J7NV+&lZYTeO}*mv>x;f7oa@hJc;Mz=
z_U18y(9<bFn#)q{MaVEwL^9`U>ha@4sJrDIKi%=Qxu}yeMatXaArEiTcPjbOGT}*N
z5#AP#VCcEybNZI?VPTSZ$Su=8y6EZo8|d%f5>aoPKBSb%tciQAA3Mg#co;)z$DEBT
z_2L`gQPi@eVoF%5($doG^?ORsfx@#wVcw@qv4k=wEBh%dI`5NxKb@nR&+Wi;-9|5<
z*74Q`3ar#<33vr<x`i>u7)pKY@{lRz9JyiX&5s=Sl@2WYls+80Gm{2<8DnA!;$@Ic
zxTYFQw6>V45FAl}EaOPXg^ljMGvM!(C9{BhtkARrvmU>@_f>*0tFlnDGBP65G1?wI
zb;~Pbl8n9FYK(Jj(1Ven@h<&-=RKY@h2+%Jh$tFKPD}!P205b+B36x+NQ9RV0&RZv
zrU54uzp+jk+a_;#DQvNv6t9}svY03nF|82el1X|(Bp*bH#W=~+!|hRP@>+}lj(c}j
zNZr^O87GTs)?RfoeBo{f3Wgi#+EDx<*LzwIt68{**)a`0n6J@b2JPlPINX}bLwVuU
zMHkoiy@TSh|2-SVkz(B^!8eP;4DK1m2lBmbf6vyJ3lz-j#K~X6j(0E&kB#{U%`?;c
zSQP8v(?zarPE>Y8-U+_}@;uoa`l9@xgrttTBt0LfY91iSLokJ^>|eZa3&(T?88KS8
zeY)ZLNIRXHn^KRA1I5N@vRAyh@|GDv?!GZ9JjR`|mt8a72xP+WA72{w&El=S+M9NJ
zCwpc<OZ(!<7ou19o<H<fRC~M2mHPBfnt8m%f?-=IAqxaKg7wHV26dthqH@|P)x9Q0
zpoxKp7I@6KT;}VmY`~Z_iXP)O)8Khq0(cxPwGw6&tD$%aLPoHCWmOxLEUz3@Gzf1o
z9%QIT<`rI@dA}7MkBORbFDeASnQ^P`i<}#d+XsWYQpF8!YHutE_hX9kq?W0-E5oRk
zrB#w1E_kX$l~Gn3i)K#cuKVFa>N-`Q-H8!8C<=|WTMbSu4@yV$^s5v$TzqwmhKOmK
zggQ1hubu?oExHeaWBG`}Tmm_uGT0SYd7Cin&2|_Ai&qn2h)aIbmy4FXHx4{P8}@><
zBk;@F>0z;Q4t8=QP?{>y#vNtnyBdh3)8>)ADf|p?LGiG9QdQ^m8So+DDwl!3q;`Xc
z+X9c_ZWKC6{x$Bmr~_J65A}h{yUk*ID~urtIS-*HDtf&EDNtd6S(ch6JtaIJD4hA}
z2>H|ZW3yW*>c>_mDs2~DGGheVvMO3J_W^ZaVj*E;0HI*!L)k=_#LEpRg{B_OXH{S@
zmAH`J{;|{$FH|s8TAQe1wzX8tLoWBlk{hE}h-0`<kjk9_{U~kb&>$ppV3^0tK{dgv
z__;flW!}qaI|$r{L*uR_7%84JO$UM_OVy`fBUz0o)8=Tw(xSNCLL9YI2V{Cpepa=T
z*>09%8G-l)0{bA{?5t>Jj6DwX=r7B?m9{Tr*uDz5?oiryv^I$yOKH<~zYopDTN$f$
zv0bpdd57~G5Ydc{f#<bU+;D<D`*Obk7n({rdl)T`he9np;x5_LCFPy3TKvK7;gi(t
z7{oZ6tfih8t-MDv$3)9Q)PzF&=|Kz5^X(DmxnU+dm2#I<z#T#;DWVH<-O(fJuSA!6
zt@ffm81EJ7Bav{FvhDP|x)`1Idq)onHO3B0sgZH3!X{NRCx(V3Rr=nZ%EY5_al@_o
zxKT^Kf5~M_E?}16dC<jf!Jg7v5FH)e`50fqp!F`12qV{&<Fo18Uug(z1Z;rTWTM!z
zgqPtOSR6|7t4mskwBP;wmvd6-C73#uAU=8t9=)G)R%fWnUr_zcbj10T!2hG^i2o0F
z2;$`6;<`E^@iy*}%1{Sk$F{9Lv0I4V{T5|mk>*O32=k3OMa$em5?O0h5N)!rY~J%*
zhF0#GZ!E6M(h9^3iL>O~hgdL15X!bmD$HlNj~j$R3H#EC{9=Mc;Ny%N>%om<XJ^jl
za+_Tg^(bDaiF_`CV29=D7sq&KLbO{L_#EFb@Mrf%$vX!U7HPbYb57my{r46$v0{yd
z4O57v^8Livx$S&cl)Q<37L$}PKOH6tl6%{x^~Jr(rhjfwx6K?FI&v0G^==od>(i6~
z?K=mR?)wEKJy}%W3T|TM9mTBOS%Gb*?52PXSS%kGy7a}<;=<}VaR=MsY$qio;aD(=
zY^GX;(o)jA@Je`5kXn|!Z;foq4^tj3uQ4K0J*vg`j-AWVlw0Y8-p7$F2-vSGaKHO;
zLlzf{{`2G1&+p!F@9t$ZDzlf|V4XlPi;sJ{c)+`{8CPG_lH%`)_g%7z|MbZXugoc5
zr1<d9_jy7BTVwldQt`cn3C8?%Ifl00n1{BVp{;n~Y?hZOyCf0&O!a<Y_t6zNb*&zl
zCiLGkU<#l~_r89hJJiXc;qE_aDM9<5Y0Bm9hY?j14eSu5yYPJ-BB`W~Xt4r2ls5$)
zKkM<2+dJgn#Xrrq0WG-E2nKHj2g7H#s7sc;v$U)f%A~J+YMh)XOb~smya$?JQdiW9
z{0VuMMT<0PXP%}u=TV>yasM+Y)Pi5iaWrdtAJyRGNCB;6^;K?l9Itn(B$_nbbQGe=
zDt&?xJwNXoGFE)%C-(_XjP=@<udfh!No<~wl=SvolX<9zEU{33=9em@mpc{4p?NHV
zc!qC0)D3z!TpA3)*C?VY)s?{xV+m^6lIpX9N#Q0XCdpKev$|r`+6}=y6fUxY=SA-*
z>sM~2!bLPqLm6?}pdDA8@jOA_x_QlJ<x2~Jdlywi@&pyGiEp;upblZ8Y$xB>FT0N4
zt_5w!?H_3HN<NZqvctSb9*avsN=8=wmS9+>Q$?{5m$2<wOQzL^)<gzAdR2v+Px<{j
z#Wv1RZnpAOlNNU4#0g<~Q7y$s*H<@g!~`Ecsu^2f-$Rzp>kd;$r~lSz94tPJL6KTK
zdr#0)mdIu;@a^m7vVxE6B8(5bE%)acvpOZ>8FZf4wv;b@MWRY?26s<V*4Y#YIV)l%
zNNr^GhGy~FSoNH;C-HsRMSHOHa1I}*#j^b~7QEmq@gb&CTk0YT+c4G-bg{3RYsDk3
zZ%`L-1%Yzrox`ICKZzp+sYcdz>jux%HZ|N4NNQ}6527F5GVI84JKB&Fq<>9I)yNM)
z?@+n_)V^jE#`U0ZK)n?YT#@U4%Cc^q{Y47unK^*D5~|2P%+{(l?eI90YtZPiWgS}I
zHv_m^TX@xI<s`XcpVCA8W0X7Z#1;)h{Jim4v~2lA^>7CUGFJo8+{_x1!YJ3o!toPD
z)96j9_2QgP+RyIW8mo8fQRkwuH^;1e&{I@vd16;2(D|(Ck@`NL%7m$NaANB9X{wnI
z1B6yl<4)_e&BW5C8lIkK$;{mg2DnG;na&@|_ji3cWV7~|ZY`AY#Lpx@PlmMIr?^YH
zzf;DIcu)TnoVfRbU%hLMdHPE<_k_Fw{PlOOGXA@bIbI18?^bh6MB|IySltt>N+@Il
zgf_)SY<E5Q0~1gS@ie%|GMF0G=6R68Y3OKa?92IQWe-i_RmJ<C+l=4CW{&o+_f(OM
z@DLq}N2Pj5DI#C-O!%u2yC~b%^3oFZ;Vd%S2i-o;7l-8=s=Xh$P@_(m)?<*Sb!kcq
z`ovdOS7u+5n%NSX9GsS9^iBtzW~(P1G2(xjJkyb2BK-hDC4D0jS=LtT7OqA7iY|PF
z$ajgv-}xySN;!`By=@y!2r&(~qzw`le~&iYn(qd}%N+ypckzebaGa>ixRU*i4k!i~
z{Su=$v+<daM!>`nrR(YKOPe>vPS*!9MdgrBpK@(T8cR(RZN4r8=U|CeY_U9u8Cco?
z0rgd){7Vb|dZXNpoKL(Z_l~IoitoORp6w2c4}Gvnol{t}lJjZ@)Y>4n%<wo4O)hib
zQ24`{$66K5m{LIUWXDc?V&9}^4k4GqC=CIYS(Wb0P~W6?F9|^!LPqP!60*%#1$gZ5
z>)x!^$6&@=QVTB<Z<I{jr{Nv1FyL!g=vm=3#n-i5rZ@DmY(~U={W{v#15tyOIItG2
z4QOsUBRDO-#=s#rpEp-wCtMP)-0nLI5m!whjE2)$WSv`JdhQ+Lae}++Z_5s&)t)el
zh}8WK79-j9U$OcaNLqhJ4reD@fOF6~KV3N|FtnPL#<+{Q6*PKd0V3Gkl7?^KhjnxM
z9Z!;N%rX+|ss1%A;)(|(lrKId#L<dT4;Kz~cXwkx!`iSZh~$eOKzGIQ$uhLRs}z6m
z?7?bZv<TwbGDPYG@11P|AKUY9DB_Q*l8m`fHb_@U!pU^@8KORh#Cq7qn3QGX2=7$g
zQ+86NTLB)Y?rt0X3bT=PKU8BQ8pi#EF6!eVKb}Wc16%ecg_-A3h!I6yyQy6h-L2?Y
z%f;$dE<)Y<q3Lgx9ry7maM~z_T`08sglINb(4${IC}ESNf6M{PU$k;~V*$Ypf|nQ}
z3I%W7z1tW1l7*~n<py<<zot?13qCT*48O9<dY&m0$FHx)%_U5*zC7&4!>g`(Pcc}R
za`^b=o+W0GOH|w=R-*mp`gdUd1*gTQvT`9`{Bu5T%KO~^D!X%ra=JkLZ0@@?u5O@X
zK&QLB=g^sL^qx^}o>)qVd5Y<gE!}kA_rIxdz<;c8%pI+a8CA}YOu$m1y|KQdxvh;j
zu&T)@&dbTc2?cR-0KXjEU>+6@9tI8$2H-SVTcdwD16Z~+axyfwXZ-dZP8QA_&JfNX
z&KAxA_%nfXge!uhgi`=syTBO(@bg#Za5g{SoNyd)AUN1hVr>6!zT&)4`h9`&d<phS
zVJGk8Xk~6=?7)7<+}^<vSPi;d!IjYm-T-wLa5}Jv`^RrM|NOUaxDed8?=1zD9vzP5
zlK@K~I9}i=3ovDL!LmM_74TjP&Kgc1&J=)B!ifW~9DzSdI1zy1^UvJ?p_l`Ilo!{6
zfiup(K?%5u5>67h#^ypq=U15nVzL3C&cLhlZ@2?oeJ;ZD^Upswgfjy^u>mlhLu>&W
zjNz;RG5%P;2Ju`jL5mwZIBHU!n}8C#nz{z?A6O;6xOb%-u(}Kg;bMLID&Oazf8d+*
zf_Bvl4&{L3Iscb5*k8c@P9i_4{UMdVqZM>X_*G&R?2Vm&)7THn|3YKupnsqo{I44O
z3myC&osi4L#J{BTa?$?>o!sZbzbfH#&_B=#`5|Gm3r$o0u$aHM0HMF55&Bn({~^l@
z@v;A)@ka~8AFcmNd&IBS551uAcWW1ble=(i2fzjd;2r?Sb9*%eXm<px{yYGjTgRoR
z{BGUn_TvaxwJq?e<==6^b<rL1C-eQm#f5M$xd6h}pCx?>`UgqFF1V1nkn_3sIsVa`
zfY%}bU6-Eb2vBMWeC~A4$KO%P{SWfK5b;k+fAo9&E^i?0{<BSRT~I0tVAqFJfl~%(
z`x#x%CGPll6kYoFU&-!=xPDS}Va<QEQXm`rGez9LP^5A}-MOD%`1;?`1md`q#b3D2
zr5Kcc5_KWYt3;iH{z0KUzYwLx2w0;CoCF{-r3)RN$5jVFllFiv&oz1(X|<UCmR!!i
zwZBUl{YLIZ<hn}kl`QnD7lDBEcb-N5s8@jC`v8gOj`7z?;U7o^U*@#Gl<cK#UN}7G
zc@DT5lrBO4AXyOHWy1RtsS-ejI?rrQ7e4f_vV>fQoxddTGVuQ(@Rz*A$?-3F2?TeU
zneGGHH3kX~CxEI;ou3yX=YDGlz#aZp&9HwU?NY2iNV_=is+!M1|0ui5)T06<ZGAwu
zmH=Vu7erD0Ejir(;3U5a2XrN+xIHvxmp8CbcC==fql9omf0R5B5C{ysv~o1IXTM_w
z6n)~xhPFn=>@vnSrjBNmTpZl6bGCl()_Sk~{t0c@{bD~Hy)KukMI>|9v90iMTcg|W
z-L0*h>uSeuV!R`#b$7Qg8K0Pf7Uk*JUl;v~Mrk+O?J3f^<n1rqd0oRMsV4%_v}y+j
zu2FaB-Nvh)@Xp*V3DMd?yx14pZ^I7BC~l51jh4&iSj|(T59YE#v~x%Z`C_V!-2`A7
zZeL*$*yvd{;e(8|?ap7VNlC2+S1atEj~dRy53UyNl0VddHt)Eb8H6pz+N5{bZjuc1
z3hI$&L5jT*2o*tOcS}>>&W1jyOIO@&vJigReD(oulDW?eff@VSjTy$E5ne=pHj<|^
zux>tnD<6&$e4<$@TzzqNpNv7_ac04A3hOp@Si$Y$lF<;X=L?A4Z)k~Sar4aK=RT6x
ziak)@%PU*u@wS|`TewlZ7}7WAymEx0iuhu~hvPcI^}a#`f0RVc3ZzVpG?bUyBv{?v
zxT>y=U()&$>fQ=jcWP~uSZk^vG!-Hyd$UAZi@?T_yb<#bX2pr&$Uhc;O#j4cZte)7
z?dHm++fAJ)PsXW?P@H{0Y!`6Pw+M1a`4b-D{6i!yFwy4`Ym_N75tT4aww-%s;V3k3
z5L{=$3cUGaoO>Tg9%>`zz3HZdmJzXL)J8a&sbnCIkaEH)z$c`q=@bQ^LLTx+jED>r
z`B9s5Oy1;2olZqKu;^@6cT^{3%u!x=9_SqX=4pKn%p5Wg{h04Vwn6pXW1ei7{L?`s
zf(Uq;^z#h`9~?1w56aX2#}s4icX`TLPmp3!S-s4I`Gq|dn+vg%`cgS*n?><`N7@V(
zLkh0NqdhfWxpTKiytNq*{rxo*R4gWx;%7G$^-<E$sGio(Y6J^sr@@sP<Kr4<LZe3U
zY68%2_cA`v!e>Pj0~YQucNmd#t9@owq#tqgAq-K9A=WEZ`IC6xpAc!fvrF>6iSgru
zeElipkzkU2alA12QB2n&O14=)`;6y9Oaq^r<uX<f>!^t;mJWFXH7Kn*VAT426?kYE
z?-hixD2Dp28DHIBR!6jD^2l0|l#$dOkkAQpWRJXu(Gz+UK~IE#d@EOJL}^pqfy_<V
zgS-l}&6_0eo*Z*Jj)Lci8RMP=8J)ET?`{5IOYeZ<6|<YU{pvSPG9o=V_jn%?HyJz&
zYaMLA-r|E_jx?l~4ZJFgQxI`oe2#qQ<LvP*#uaME3EJ-;>GP%E+=#Xf9Ec))8ac36
zi`?x;a*82@s4JZyu>CryM1IIh8qr$aPnk2hm*++_n#YHzXMG@U{qi<)#wy$)1bxH_
ztW0lavnEoc=Os&O(k>!|L%!vGeqi4CPLi+07OeF(2Bc9?d(>%(n~7Tp;3i>RS|WgH
zq@7ytU@O*gkH|u#0pbv(doybHkcDV0noKq0Uu0z#-t#oXqPEISL#Owt1xQ#g9kRmp
zwj2_AXL@5-K@7NcEuI`YbPXII2+Msu;`jLZcu0qLK!TZ=QAE7H?apIwCu>QMDCol$
zCH%QQHABkv70jD>WK;D4!!%VQk5@=yz*Z3_8;k<d6ZYYPI|aG$H=sHWJ|%6-V>s<v
z{<P)qyw_GvS^Zek>2?%TJf(XAead8A;MWO3$|;^Gq?JRay?qj^O)oyPA?A;67^Bx9
zbG{yOmS<))EU&DS=KGB8u9vr&%xjakDJa}9a$T45wif+H$kz;{qZ?@O9zG&n0|>Jk
z>R$?W*;pw#Ff}#c>R31i5fILjvqq4fZCxM9Mio=T*nD07WcIdYDxK@JEDvI*C>*t*
ztKN_+t0?mbzAo+Uqf`S7W-8Yq0yi8M2`myTbImBUYb`e{5?stB@s<YrNU1SO+j>0w
zMfwIRfM!s%L`ZlAp#ap^nc#)$F7V(T7xO_-3&QP9)B;iVn>!ODpR1d$F*Li6<kreW
zYM7`#Uwx3cm%MuSowq?+gtp~?cFTZ0Bj1oP9YIyvpdl8I@VXuGs~!^Wh4{HQK66BA
zxat_+H0bYJ(|(`DNfqg{rAH%6f*Ej^F(OrS7G#XjskQRsxtQ(eiYP@^lfS&S#mSP@
zXy7Q$Iz?CDhsBf4sGwtlk^t=>S@E;qVbe#fnvb_)(#Vh=vz{njQk&OaQnI?17obms
zS}!|BFrdXh3KGgDc49&_3AbZ%$!73t=41(sq-GU~U<DPW62-srMmTbw^&=K${mf<w
z4_knwWY`9kf^(1=w7MhkI2708<-vm{<FC>TbCH(D`>fn87lK2f^c6k*vyAbot<GeF
ze6qdq_C~U2&P?#mOxgy>DMscW*<{MomZ(DB4n~^inzVFhVlsitr*1AS-$hpVQh>Ci
z(Gxfqr)cPliDh)BJs~S)!J%g$cezh+4WrCh?D0yxCaFb)bk`fdtr|~ftZ5#4cT|kj
zWObev?5g%~GIR3XT^<dZLE6l-608m?ALFI9_;d|-w&g)JxRI^`YzQc5kI9rlH(pV*
z-K8Vxqtdqay-jOhg<CW?rj1|8Tgr?H^pLwZg-_Af<Ftiu6zG4$sD66bXA_QwI>*C!
zmxO3w#pf(FfH!-D?K4mI7F)I*;v+d}=RN(%gY_G$WM$h6+hm)g{r0yC`=eaKhdYDP
zppge*HoGL6(tG^}KIBSSZ|Cuh+RE>vQt+^{j?s*YM&t?dJBk%HQC{QxQjXpW3d}ik
zXT-C(Rj#+^oiM)>@B(B#3^sAQ*FI>=Dd_tpwP{e&HHC2&%I(50*#qA&HW(c>p|P}D
z;A1bZiN%WVXdx&3e5_A01DVHtTyM6DGlCDXgH15PBYwnv+*a?wn)w?hWY?2m;X=bL
zG_OHX9%Sr=Qt4I(95$sd92)5_9WaTXLJSL1xgQ$Lm^I?@a3)L7({#Y#Ncr613?j8Z
zXVtZbvqyZQHe`NN8s<X)qO#RoBj{j2ZS4BS{d`U7yP}~G?~X`iPw!wA)#4DzxF3nb
z;&T^)GUisx9xluvL~H-e>{8sphiajV3gHQ@c+b-5k0H#4$!Gh;DV|T45XYBV%Op*o
z=)9as68N?-e6rDRbGl@3SeU=RKi2vsk=VA-wqY`<{*;D4XzNVJ;h;I}4*m-1%+UZb
zQV1UHj3Pb(+{VCAYtWZe4PlDn$2E^?j#v5X-nWQ>ELJ5+_~k_j2q=1B*a5Y~yN}S9
zLq><&T9-(}F-aq{Ng{ie=-|m%4&F|dvAf^=HW9}@Z8hpJdyhgmSwJuRmLf&|aoUnG
zTG;J<-DTsn`<@dAa@H<~3!VmY@+<twIwwzx*izny3d_7vJ<-u0Xq#@pbi;l*%Y1-(
z5J9GQ<EuDVZ#tq7$fs~Fs>1DBHt|ktj%wNX3;6uXew)0xO0HhP7g-bWq8*PiT#u&Q
z)S^=R1AyIgGfv1os)tjzp-J11sw8J#?oAP&kiYY+{GwMeO;ME<Sms$dHFat;;W<;t
zJiDSjAXcMU_9V6}T&3kMV4CCLBy|P)6!?7A?2@5}$!SQ&uibsp(q$@AWt>G`eBzJi
zqpC9U$k865SMAHr`oPyuuUaTOV)UjflxWPCX!zJ6M5$tS!cpRr-NJ-aT`gj?jc*$T
z&F+46ZnvwJ>IVr=DPAb%zBRc|@e@bp`7KYLv%M@1RS7Q<9Oh^W)~;Tis6maX27-4k
z={5~|cN?Z7Lg=;;_u!FLKlpz2!AWcyCt*A0_vJY9F(87#=l7n~01SO&sX~|%yAIuM
z^bLl6M8MYFs{P<S{N!GbQtMbl#{sz~u@iQwcgCS?mrD@adad6_aYjbPSR&;6KEjim
ze)&_HPjqcY*OxN(8QwdeJ>!a-*xx@fIjnJO7=y8oaIJ41Z+*rYdB49jcCzN=;Ps`z
zt@^mi&H4E2v3nRw;_}3o_5F^-P08ZKse`X$HDmmbr}?$F1$ByT6Q<tnLuq4rza3nQ
zhT4f+1ZeJl67+5BQ(JL^bO@w0GuX6Zw=S%_<L<0Kp3cK#o~m$>7?xF7txX@mSJ^7c
z?YVZpCvDuSct-0bgVy&mp?rpk;b)1%w#C!B0%`(UXG^ct>+9XiYP7TqlheE+ANMNG
z6bR}-8^5*)d7X@5FX9NEA{1pj|JKl^T&psX?p)@M6^ykun!5?AYv1OovM<%h(Os@O
zL|5BgU(RpDNI63*`li=3@o7eNVTMBLnE8zp+6Le3xZxDA?{ObN&6!Ybn0(Oo>)Thy
zIp4pIr4%Lrm>L7`e_@wT{<^tcoX~j8{_SY&@#OSuYX&w(sMwC%=O;T)%ehug-ietk
zqVcaC$Y1~3;jIDO*;wG9_uJl}>f^KJ{G~zN?;CSEm>Db<`)_C;eA9&-Xj&_FjO5A3
zV>ty?VROXfaPQ5j<i1x|Xf9l9t?s++Tj#KgPvnn}xkTJ}5cb&5-~KC>;lgS4U@wYW
z5+xS{WlDdYcJX(&_g|0ab-&FP0~{>xKRy!Ekwrd=%{%MSkOVfeZEQKkIUPEkF_%L_
zP<Tmk*2a~NE9l|HQUo<`8dKgp4B@Fv+LUHl6#O#hRpx1)xp!(WbeQb?d{desYnCtR
z8JXs5L7x6Y3;(pF6-&>jEAo0QO1G)(;G3;xPX+71XKRP5y4v*&X$$-Tui{X-__V5<
zX1Kae8{`^}INf$l_VsH9TKQ!~J{#;uU8_$d5DYm*pWDgrTE{0v{79Ev*F43dbw=xU
zFxnM(<0IQZ{U}p>gOoK_zOJld>UZ|zgY8dgs;+u*)CXAJZ18(hp;4p4?{YigJ`XCE
zZ=u!R#dDv@Q_br#WvXeoD}TL%S7IunZSszXlEv4vp`G&MeMLWOi>>?UU2#>1Rx9(1
z!XA-(pgG;vMh4qLul3wK)ex@%PY}m`SDduIJedV5Yrl~b9Vu508ny!`wm|coeBapj
zO7h9@T4PB$r?LK!{JWZ}hgsaW2X&vgNmte<c41n+<qL><T*h#_0y?$1wtB6%&%@?n
z{#5g<Gs3iF(s6TP#iwqZ<?9{XlPv?0@4biRU8@D{y18C%*H=H<etC^Q2ZKBCMQo<W
zBhm!pEXKjPTie&vi}SOp3^4@kixTpv{L&5)UX-kU73gv)LJ*mbcq?}I_2gU5$d;oz
zviZ;2$fJInTc3}Q-`e;t5q;IBb*+5bpo8mK1T$&pkOlL-%s=x)n#;dtw=JV0AD=|1
zmRCqIQf}OC9grj(ce20Q`29<9K|<qq!}iQY=gmy*)5jx{T;?p3#jJPctSr4IHEwk@
zj*pmdYusQ7kg8qss=r+hwPOO-HftX`(t}Utmg8>@@8y%$at$UYlc-AtB!wRXzjeT`
z)}g&Xr;|EfpUCxK#=QFk9*XJuJq$!1tl93EA9Kd~BOdO}xd|q{IM5R{MbMm+2<UXX
zW)Xjo(i>ibsirz*TR-%Wjh*uzF|XilXz!B59ptZ~W`Wm~w`#k*Sv%e@YmUXZm||wA
zw>|2cjpI#OZX_m0W||jR&_@b-H@`yU2r_DV?o_%gp=H7SE^Ldu=3@w(Gm`cOkz=X+
zHHL@#p{bnTax_Q$Kg?ttQXd!59+&i;hLaAwBV`Vp{$gB8h1HG!bg_A^qWhU!Ns1gK
zXhNESI}kccfO6=!t*&hGHYS|oftSnKwt`tEv(6AD_l=smdV$6|bMnUOX{$uZbz-gO
z0yEk+HVF)M`$zrH*tv+=dy9)kxY$SD4}M8Z+!y^+WgBDFH}6`@f1>2&o0*QYcxxL@
zRjYl!nefEb?MwO?zio1%?xqfGJ%9hLO|4sn4O_A9+*Y#CqSkiB``zoi_Hwj562&U3
z!^56T?Tn;a;Y^Hg@}4M(*<{>#m){=Kv9@GpxOA<`pkBIDtEGh7jwyBF(9FFEIV73-
zrg>4}a(0(vJCWo>`*dP#MO!z1Hlj7QgdJiXPZCm{A#l4av52wGtp|o}x|$%;v0}4G
z=O>X&{Q4C;BRY35Qrq{%O*R%E7Cl5SV0LxnR*vr|v0~R2dTD)U;+Eyp6}0O!$>D7>
zQ7g})J~Bl|QS9x9ZgX1g8^6V|7NKojDq37RBTL-g%2(C_zC?PooMzu}@u^taJF-$>
zdn8#&otTp(^;geoCVc#*hqd!Jlq0yFy@98xRgKutf$9{!YYWbmoGHazEcM#9uhqr5
zE<PUlaWql6y_L)+G7%vN{TrE0Y?5B^zJ{4SV>dePW<8(YJ+j;xXFUf-t!l)%X|f&Y
zjn8spWIioS6BPnCLsF>w8EDXyX}64M?Lrs!CTWBByZ16tK`(7u2D(RxSu}O6G@qv5
zcj>x+Cx7=rtOH?wr1O_2J}mrc0!i>mdMj&)^Mzaql&e$Y1xz1XnG(&34J3%MXRwnE
zZiKJCbepXh`hHR$Odr$|^Jx3td)2xpRFLU6hjeE=bNBTU#S~@23fMSjM)w4#a}Lws
z3f4Ed>WPxYls(8g)}rAYBjDGg;o0qki6d-rn>KF0xM5nHUEk#X+QAcj*eZgDprL0V
z`X1&feAsQ)wE%6sJa)Lm6N#7eVC3w0l_<3AOv3C!Jzu80_UrgEQb=;~`@G3Y$p*}F
zqiV_o5^t@7pmc|Io&M3L6%2K`oqn}}Ap_=;q1!APngkLOub48YW6>l~>Sy(hq>~w~
z*vpbPZkew8RdiS1(2N|f$Rw(ux0px=HfSb~EA?^fUyouq+2Jm{m2JP{O!wx-G=!Zq
zEt51%hH<qC4L^XtDwyH!B1i0eB}+)U33E$LbdHXJFAI;ks!;f1wRyv;JzG1WWZ4Lz
zgkovf^!cYoqFp3$TH#jKcmbQnBhOxntgW}Y#yNl*ZZ&yIv3khD9ZF0E8z-MA4RPyh
zW-L{=MtrD_|Gaf)UD{&K5}}HHwVfLO9xv`1BDXy5(wu&EWDKG!V`w_PchKdROQmqk
z6}TnaXE?BB^DkP8DH0G$Z;7svy}%9iD?lQir+c&;UEgU`f<=cYFc;zS(gBoqtI1G`
zHSlmTjGuKaP;=It$)fE9&c?OH$<UsS{yC!S?L3JBwAs>3QfZlBIx=p3@&rlM#P$U(
z88_bX_H;%I1lQYcYZCMXNf4Z>LK9z0*Cv<tw>!Cm2>4on#NUc%K6U4!M|E;>1ify4
zKu{5<F7F&k!LSk6S#@KS2(ii%!(whc6pl@NiDLyRnJ7I_c_023&8(xGYh<_qoXe|J
zqM}07S<yA^7YYOL_bHm((m8}~MYXD6&dLE|#cR?wFEoro%QD8!SzD$Z?4K*Cyp{cF
zC{tNNQAI`;J<r8hv`dP;>PG2^j{c(;`$ndWyo-**NnuPWIcxW%ZxrRD&K9w(wwJia
z&Y7DZcVkK4Ahbla#+C#*)((&at}}~PuHO@MFFlo5CH4`LOqE!SJ)3KJ>PYXwERCTc
zH+p&}PQA&gW`oeFi)D?ZSWQFNz7pf^mjRYSvTFPX7Fuka&*Ln#%=6lZZx2@uD808o
zlupn^{&vfUC|1>(2t#au0r|j~shDHGY?iIeep|+lXfl>aGAS<}$t8>Mx@q^I*M}I$
zOj1b(N0}q=>Cwi$chnzLFjKV6t&OTr>E5#=K8_R)>t@MeL=_t#W;rmnTXf?%o2;dN
z{d%fcX#ZK;nT(v!<eEdeV8I^IxC?Cbn_l~}eDTs@`E!T7McL9V?k9qe`nVr`wa?e&
zT^Xqkr<CVj8#%^|Tr9KEHs5W=!Vvzlz0S?EF;X3kUn?+5y=!Xwq^#$55$6c4PX%Rf
zXuaWU<$Tqty_d0`>h{q^(^7}4X`~s96wXVLM3VGPP`MK`I-_uXwYO^A{hkOeCsA9H
zjMv*vZNGmAGTjzB12tO4_PwuA={2ku-)Mc8_{7AGwnM%q11XHZ_%%J9a@&VES3#Kv
zrZUnnirNgiOf(S2Hev$iB-%UjiaQUfSgI5nJC~jZzeksg)Nqo_M|9IMMq;q7tIgm!
zdxpf25uLH0AeF-W#4Dv~_=&_}3U^+VPLK4qe@2A8+^2({>7=6tVR)DF41wO#x+5c{
zc{7oDmEC<NNk$$YeIA23fk`I4Gc(9$!YkaA_zo*ehK>G&H($g;zkGU@xnp)l)mvI{
zWK=Wp)}TZaH)Z3*xpV>jlc-!aXHjQLNqCxxtm|#Q*k><o*A3T8@@`Ap$T{mQA$lfa
z-8T%a<$udyrdh}LR)_I}#YDWp%bcSa?FTN|YSUteuNJ018DwuQzD4Q}PoD8)-!ftU
z8aPER<-=V}G^ri8d-lyg0L^R28s<bAHfZ$Lt;LQ?n;u+~#mz3KD15)r8z#Sth8}O%
zM@aB4cg#io<`X7o^-vaUyy~e)XO~ckx3#{+LVeu@U7hC4={E%$Pjx2yjbd<f?oqXq
zd3}0&yn*6p#NVHj<C#2&(lZU(tFIr$Em>i>Gh;n>j9FZfNKwr0tXn@lnWSrioy{9*
zzmiY!ILyZFBWvqSviT}vv|Fiak})T_P0)O0=hq#%p0B1vXZ^J}__LvJbRL(}f?5=s
z@gDEp>AB&hX6<F_up5{Y+;eJl{LTOQz@en@J3AfD<?K_JL%O<7c!V>Ss<TIr&aTN6
zGYL=}(2_T>y9&RX-*i8lGb!UcedzG!+w^h%9#ijv`iJ8@v}w7Sy@f3C&#CjRXNOG-
zE~6=_q`u!Knt<Ipg}u}BDYjGmwP*bp3wSXH8+_|uOxHIX<l-B?O(o{POWe<hTh5Fo
zCZB{8<24ocI$RdZ)@`gE^V)MPJ6?G{TKD;6(Y>A~wzh2hrLGkhN@L^k^~X3~jSg;J
zZ`|x2>a*|p^9$4#e<~#gMwI$RyY?<`vfaHL9Ns^mnAED9v^r$&ZD^2FXo)_Y)@ocj
z4SMNMA1fG;;Qtahv?T$DYYD(X{QTg5erN*@-5PgOM2%Uq<<sbUtWSt6UcK4_4XJFN
zRAXan8140_3L4P{HcO-obA1+=RM{89_ETdv$5d)<)^^#PE#({Lv!+LHcs!qH+H|~s
zvj5~jD5yA7PFh{4j`h8ObSWci3d?HVJ@J)qVeV(DNJwT!1IZqiCPybp{9GRVh2P7S
zz6*e=g}x0%w>_DArjTbh^SCqnd%XO2eZp#TNeVS7VTKeu_T_Ic-S6;|KBJk4>ou3d
zIg}*EVE=6VxU=|Xtiac!HnHmM#c!VSg0!Xfm~+;^{xXJb8XSf?H>M|Y5q#W8vdI)5
zke)3EfnTd+YjL+Q;xS?HR4yJ-5?4<*l1F=MQQSPSpR2oe9R4IJv7dskKjkY2TNd8f
z*XQg=QZY8H^&_&Gp9A-;nTi%FVpL<=q89@`yig*+w|@EDt089pwK>yfD1yB7Gp1IL
z)c|b86)C1r0oF|qCUo{wOVe%+&2F}y7fRWoCt3zd<1#mwrFYw2KhB;B>9>_yA+9{V
zKRvOZq(8ORMAt?AuDR!tPz1X;4u0*~?b1mK6U<f*ri4d)P-Dp~qk3y0g6)oDPFK7}
zmM<tM^EiCWryJ_Y_)C~$5I&yF3f+@W-x1ng5XN5g68v1c``HG?d2#=VpDUB{>@xap
zwoZ=h3Rb`-dTZb=5$vK?wuYAMqQHG5fIB?=y0Ccxy0{!@B7(7TL12`eJZwB%FiIXM
z8wg6t!@&mS)TaDH9|PpP3F8k4%^e)sWq^a6leK}dy}6AkyPU0!@s-t6=pPr!0UH0q
zC9pqT@=t8ReqHOn0R3PK1ma<Xa#2D-Y+#@Xj1vT7;{j8GAlz&)t}BdPED!&Iv42GQ
zOV1A{?6(D4O-fEEgpC_U34^c!=qO>}b0JcKz)&_W*cFuAf9$~cZ-TkF#le5165#XC
zO#m05AA;co1Ow<FP&*9h$%63kuyOGK_cP&U<GHGFo<A;r|BH$7{3@6W&`(`JKx|NM
zpc{{q4Y<Jy7dIO>2(UK@8w|Ly$?rA>zG(FMZ%l$O`f9GUUtNNJ!w9`V0_I|a@lb-e
zxY$6P0FxZQp!{Rs$v-m*=D28N_@jwmf_}=C3&sX4?sIYhMgXORKmqvzb_aM8*Ppoj
zW52_{q5Q>l&yD$qBLW8Z10@F((B*F^Il*jK><xU;m-8Rx3i_ql3VZ?j!6Xy{J2z@h
zK&~J_lU!_oEuV{(<0?i#*jJ*(KR6W_^ozM(f_`ce@FJkw39uj@pi>0sWncq>6&MQe
z$aBSbu5?QLi&#MyJs4Lsc>(&VNeG1PLS_&^mw+39xWH_Xt4h4+9r}+V<vbV0Ra3nH
z{lo_N0}w?(fMmgdTLMu7!2MS!|9fl%bN&(=FF`+0f&t&-JdcJKC?OEQe4u}c@;6%p
z?lbf+GUfaw1b{C<Ke+^uK!95!U5JwMhhRB50Qp|E9Ppp=%s*KU_?LD&@CE1xO5j29
zfaw5H=fXQU!N5Bp>;QR=8*;^R!2d%G0RI{TE<is}LbyR}kl#>3!N9w}L<vCuvwvJR
zq+T)B3(!w4|D%6E{s;4c{AxZIpr0r?L2N+$g>nL=0)!F@WDxLqa0lv$t4?y!6n1gV
zznc%_SM#|5{Xhu=L<vLzFek^klfbxn&Yc7ds1(e7MVHY3!F&K~__JSLfPSFlytG(O
z5Z7<!3RDkQIfR}I>JJ?Ldx--E($SSm{>i|(ehtPKpdS>1A#8x}{w#4|+)y^4JDd{+
znETbx$n`%&W3FGL@dfAyN<bzcE^bOLPBs`2jnDHB@Q(+~#(kxz0s|Gre`b>VS0*n&
zKQO|;K+SL-Hh`vQpr!-5#kttHu44OhJ@ro_y=>*Z;!PKzAJ{m7jcAwXxGBLvc?9C&
zX5#=~F)<*y|99Sg>F5Ik1>%p(H7Oz7KtXhg4u~R9z?^{w&c8zWTao$ij&#{yc$LB5
zw4q4}1F9Gh;Hun|Fep$U1LY~;%U2n^ax1TYiTanf-nxSA_fl08zy#O}CneYU5eWY0
z^%p1QdAxvLjrte68ZK1u?+jk<@3@NcH<f4t-T<%#RNsJP0g(cj0R!dc0@B^pK=D6R
zx8Pr^+e^?7oB4t9!V7+41cA8OKz}qF$p28>LVhi7FF`*Lf??+_KnViIHh@&2fI|S*
z1L6V!74KD)zv=x_s{bCHApb)yg#4NdFF`-41VJxKMIaslg#Z_j>Y<e2^NQojFarYq
z%^&_%^$;NR|0!Z$f_`G;hOmLpBLo`=@M#z@@B-2jASBQg)kA<&{;LB+E{45VYPU<!
zPmDaAY(U5b2AAjK8X*1xl`+Q^9sHiE|D8R^FR>SL0s4Uq#K8f?S-^wYIC%g>Kn(}D
z)_FL*%HIEw0wKSqz)R2%i~vUvpgacx1q2vhfPu33yq<!cSB+Pgyx5O&am~Loc`+++
zRieL76acdk7dU^Sg#9hb|6$k;xfr8gMft~JJ2$`&;Nq8~Q!r3k0F?y?Fuu6T<L`<0
z-+8>;+49E_1-StIw3-VXm-t{n*ns>czW-j80@d^{esKZ%f$s-{AP!(I1Wb9!;RW_9
zN(3f<{<B*E(dW;OcmcY?;17J49R7k2`ac9j=&u3s5_FZpf7T$B<G)K3`s-N#5_E;Z
zf7T!rNId^lpwM6UDPDrEGWgFLg#HiXeCV&^{7cXk2LD-uu;2amUtI!rQ7K>X!AsCj
zd?27apo9T#2;v0h%dVsd7%+8oMUelV$6$Z<)_=c*>wlP1<@$9>^%C@h8&0680*3fM
z<}4sU`N0h&XkcFEN}<K|d$I+la?Xd17eV5@9R8;t=!I+leg@}7`Fr^ua#5<lE((4Q
zpu#(cLV<6FU7W*tQJS4YKtM?Z1#&V_CtcKwz_|bnc0Owf+;AAE41Z7nyO<XF9T#vd
zuoVdk19HN7<#GQ1B11v}%Aqih^TEeCMxZh{f5i=h0nqbL&f^mYFy#om2a0N7+;(yH
zxrcCFR1tu0{xKF^0xyIKSk~X~Yyp7*Hhye#0rC?M7|eMdbbrp4yC!XTpt+I`zuBn=
z({xqTb=H!Nk#`w(l^SbS&~7JwV%52>IZekG!dg5w&ltJ6K}F>geC;lRe-`}pk>{d|
z{%>~}HPeLA+-|4M%%~<H)d-~GqSq-IL`+wMX*$qiM!mj|HqIP+>8|!n?oT>=5Lljc
zDE>qlj*ISt;-?~8cxSK_{LB)!#W{C#Q+NK>@lr#~43$v=j??zxlL(8@#t~bwTobhN
zFJd&UmgLX1Ek!$9C<!WDT=%onPAlt97)3GCrnu`p`gm~Fo{BuI$9Y<Li&>>3!xl+P
zQ9(|<!Nl>CY2!$yup4^X#+&@neLney@1l63^BgAAZ6wQW!E15MO#v9GjV6T-@Aaln
zVtc6^cUb*%DV`KeaI9(iA>-B<vzSxZVMSWKPVpj6>ptuABEg$ByZzXv>wDCpf2>Eq
z5PWcE9DAT?)M2p@x97n+)AplRtnj3%wv_2l<MomZpcLUx*S-|zuuWx+Q|l$f`esYX
z-UVqyF63ToSG*e#z5;Yv9O^$x^T%u5$Visp^fz+p?(vQ$oY|W&3ziXkKg;{X*8`S0
z1+UJ^#BODw5ez{+_ONfGfn-)a^QCSGDhEk$;>;2B?|HQx#-crfx8)s|*^~sH2IHxl
zuF-dpzaCGM`_=Y9*SkVXTT)l_#RBWP1o{h-&FI)v>{*vMZ%erDj9keB%5e#&^a+12
zkQwn{pfeLkxd7Zl*0r7aX!F!f`P_tK^sFw6zB}r8N{qOlu4(ChxIX1Vn@x0|gRdXe
z(EAXY8*MIv5pW{rZHu8HLYD>=twxRz_6qu{Tpl?V`hasZJ@n=`!GK*f#uRr1q}KRF
z8C3MBmy#hZ+(>HOLQINr3Lk_p4ts9hDL+gR=+H`#b}(mqpH&TsTN?O>Hz(g&y5uvy
zJ|3Rz@rc<ktf8lnpXMYl?Qs%J<^di%^B~ihL;+NWwBNh;h&&d&od#cxfYm3T0dJ!4
z73;ZDn%z>ILIck5V6n%k%h>nA)$QZ@=x;brrAuuS7L1Y1Dto-0!5IDS>O^(#`rJ(j
z#$9Mw%8;j@1h4ND-+Qe;#KebMLbT$6;`G7BkXP~JVWcPS=5;+ni#Re-R(PDSMo$dG
zC#iMcHOH5{Dql)}pSXTBhPQ)szsEW^x|(p7O~qc~5r#(REz#KT%qLga{6@HX`i*Ss
zlJPU4dkZu5gK$hr-#i)h(_}Iz(|S^wTyto!7VcsNL@N?;W>D|w-_ON{rpYY``<WqW
z1hx0mJ6>Pbiazx~aQ-Zb{jC8*0n0*|wQn>eu%CG$VVKxt?`-Rl5c~0S<Q(lYV$zvJ
z-!4NF;h_Se@HV009Y18so5ILX=|pI~)qT0WsnOKD?a_jJh~QM<SKuw+*5EMVuz+3n
zV{oi+3)3N!Pk3o5AHSQsb|a=83$XxB?OF-&Xbf+xCL|8Fh#A^uPVZ;et*$xSiwkp6
zJ4&(nwwM(*&L7ZJYFE1SzNvN5E_3KOu-2@!nudS`E9@NoEYCV?-dH{;)~Gf(-szha
zew<QPX*xDQH>HjJ2zc_Ure#@Sp51PG;7QGR0Q8IceFZ(_WEJH5_OX5<FK(J~-bGV4
zHbFk<WD!758yGa|b1iu9Y%La6UvsZ;n0F{?#BiUZ#sc93yh~$aGd!anK{yftR-u-d
zL!7qq<`yAITJ~mW^hX_zLHb30730JyWkc_Qg3qF9{K^IOuc|-H^qBK^E;-}KP-~#<
zKiH=w;(XmrdaEhK@5|mzZdOwY|AuGqjA|1jnrPK}kFhO@5bnH>LEnepbFo)e>vkFw
zNSLOQWZk<H^MS@gGe?1qi)!hSK!R69JI$z_C#+U;y}3R~gSqmfh{4A^XG@;K_PO}a
z_3^^)4jN4oX`xcsx-<OdZ$~Sg$Ml(>ES<nk(a@D+>j@*f2+Ydl&7XEK4=H6nSBdrw
zBv3#MjO7tQLOmN+ey}jcRL&_Hps<C)=FeL?IQXFC**B(p1<E1#t6k!Kq9PbH6>4Yo
zH*+y?*PTP_m!<0wQzaxclJ5}QAx~43p`=s0Y5BB=L=USRS(%EU04a|b9@^{u64}6!
z^G1-~KJBa(-m9FQ4_(6f?)RF8sP`w)1f-@uyx>hua!d?BUBbU<+t?%WDDKRN1L126
zzPrlPm=}ryQc#krcNSAtLBsjUZ4rSw3f4-QPQxS$mQUUc%bgs&5?L0h&F2IMJDYDz
zrY1PZ+>xl3d<BoiwHmYj2+KyXE7-#B6}5tlhU#eV@Ln{z9^+0SaPv}Z+2WkyQ8@L8
zSMAzjdreaA8Szq~BeDi}g5D|IynBy)1ML$s3;(?$gY2wZv0<$C1L?EO{M>h99H6R&
zA)0er%O}Fi1+dc+_s+R%ljJPRdmLH;?$IKIdQ^5d;&YS5Bamma;meD<^wyEfXg4e;
zx0v5&#`g1_zRc9hLXviomesl+Yd8{Y5iw!tbZ{v8#3HEgrWFozK2MGb+yeTBZ-E9+
zr?XQO$XeUkWe1L{$w#K}K_&jj2bL#f7L^&gPeI`*0}3XA8?S|;C-Ec-v_^%l;c)mj
z=oHBpP#(BCP3;U5iuGAKlc~yQ<4>!()~;8t!LNuk@yw=)bd)E+2j$Xs(z}K>j-mCg
zW%@t~pVr=g`|bMkdkWsg6JQTxyJMNsKw<W~tkOe-I2jBzCZp~TUJ2xr1-@XD>-``Y
zp_%b1l>0L-GCOVGdX$KXY2&jh8lHZE+}t4ah<coIV%(9vnlloV)*EL%GRbcOSrmNn
z-&%7(vuHM4oyQ^d*s`|lL%#K9!z52IrxU0c6xlwRSyKzab`EyzYc%z5Q`2W0Fg7jY
z@SyOp9+~2Rloe0>?;z)j6*}W6)lMZ%`1y!yV>u%4xj)g;mDJfG13mm8q9)Fn($2#e
zUI=fGw2EuBW+Auk%@!l~lnxd9s~CmbUabhJfOPY?QO!dae@*2TWT7_%TDLQ{w^srp
zgfj+)%>1*Qed+P3hN7YoaYM)uoib^{i5uK|Nm>__0=DcUcC<}#`A|^Rd|ngPb}VhD
z<2F21x7|@FMe4g7-@#gv!qFm%H`nFpM5)7K7Ml=0;+hMqE$44J;^kD?)a9_dF@zfV
z+{#}=sQzA*j-68V4i-^^53js8Hqm%19F{?uZ;69C8YPZ+n4y`ERup_^s|rb;gkGj9
zGe?bx4}%@z5N;4!n$I(u)a1|gdVXY8aWu0JpROYMhjgOCztI$zX`^F^n4v>Ar{B$|
zo#FKf7#kL8QKjmW85e(Uq>8Q+O|C#ECC5US4)%YyPMaUIVWY6AzQutVoUss%GU_df
z)H^kEyN!5C4=d>bycgz2l>7l#4XhGt)H-d3dvA31u*F&Iu!1avgEBEgMabKR4L%F&
zX=oJ&`F@g#<P)K~)kOGIl~R>MN*~Gf(Wh_~s^;01sk3^-Pa~L0caYYuU&rhU2*4&7
zaSpG_hSkcv=e`!(|EL`+nkcLn)GBkzT`Coz8x5cNUSN<Bzb_)JCM_=6FwlGZ)*|-N
zDLuy12ZQ*G<#@K4I7h%!)a`uM-Y&k%5%1YgPswF~Z93zR!m1%LYGx&Zx7KL9%S9<)
zE2LjHDkHpG8kL9WPm+eZlufx1{4QHr)DQinf$>$vb&~tS@53xgi9K>W1bu8uD;ck)
z^4(=vs~)-?LG;F9a@Jc5%SUHLNlf@PZI)41v#OgXT76Zvk1EU_+}U}j_q`r%meegw
zMfQy6&Ef75=xz6DJC$)VMaQ%uA%yOEe&LR(juJMN1KZz2P9Kv{v0QJFT6n0dJvWBD
z4sS{Jd5RTYbu<ulcz`*mr%o!%2wNnU+?Xyzw{x1{E&(ORh|2^^bT3{@h`KM{t6-)N
zMG-WwRgbh=c>Nw1=-P<+N=X|Qzp^f~_0yovQ|*-d)V%Y2_06>*amN-b@s81(nSIWr
z>QmYXHQLA#CP;1Su{Y9LKQgL5Exae@j<SNpupH=Z8ZgLoL^U^i9i>zSBP57wX<3Ct
z%lBT}JPyOl+j=;9jtAGB(h%jYD>r<Xc^F(2>MKj0`o6F~w<WEsFvL3}!1M{FPF@85
znjUf7dX|pAp8!y}3W<7hEZUeMQ9T8#DfsXPC#`>#d^LeJGg?43-z+Vgwm9kkywd=k
zbsi>CrO@RDNuZl0(9cg14a2`>^M43C#~4w9F2Ig$+qP}nwr$(CZQFb^^TxJq+t$8s
zvsq>Fr&5(vC!M-Ub*gTkd)RF%?c#Fsxvmb9Sq>;uas>{6ktU~}<YfXD*RP`XK#DwO
zD0hh=k1AX|cdX4$aKk+%XRh--pkVz^TSSf>+ABQnP(3n*8nB&-2xIiJHhYG)LBT~V
zWQZ1}pAwubf`t2kKyb-G0=+)lwA)(kX&)Q4qkOMUl<>PM*Cymr-q!Aul-y|4`|W5R
z8jD2-ufhh%R#eZ7S5M#95`);+T|xQWY=4;CWPh@+E`0F2sQ8n0Gx1038s659$?&;t
zypnO5KG*tj@VOJNjX#_54L_U0HO<rFuctT0Uod>Hm<|oyEX(3`llG0fVENjpzhzev
zZ;Y?c32n@?;;%DqjW?B**V>k3+Em+PZ0FzC%(jVZWN(eLpSagYQ1H7lm>Z%y#Mi)P
zMpvA1ZE#bplYOs*V&iM)JJzgf<7*fGtVykms*&KDp_9C8SQo|9%HK7X4u{v`H<Avc
zwNbFA&`vowf_lo@_~|iar+a6xX4lxrwMDa<EjNd`P-kP!Hh69OsF|yc(o1Hd_d7Lt
z-b*JP`qro^Pgp@67^O8hX4)v##LLFqH$pN(soBQKpZ%-<M&dF{+^LZi9Y-JS*+9)i
zUc}2IzKpl8z$rx2f6WBFh<9axL$f%jU^oSL!W;&tjkZzO0@6xN^Wc^fydjJ$2-j&G
zj)Q$H`{3&IM`wq4m#{o>FropmRyd!KYYu5nPP^g-0Q}gX5-$p>*i9i{k;S%k;tu5>
zb!ldiq4cJO#S_?O2mg1c*qT(H<G|x+)my>)-x&t!5F6JU4o4so6*ezh`<mPWL^Hyx
za>DGBM0odXjUftV%zz=`HEce%L?FrtbUad?=Tex8;XR3r#s!VKH~0DU?=neRh>Iz{
zf0VF9oI=Y~a1jK9=#k#^Yb5@g-mba=KJ0ruq7c6*eL?Y^Cm3#^e3imRbe?Fuz;y1J
zt_#w3;B4_`$O3#L(zQ^0q4&I>3$+*4H^Okyph7R`c0gQVv(UHVQ%T6NU!OD19ZzIl
z_<Yw~?A#*<7a#|G5%*kiz&qy`*9%k!deNp~NW?Fk3%tQgf%P0-pEvYTegIzMSlGur
z$VQl(@b&cEFNcB?SnjY_hClwCBMNT-o;e%=DiIKdKtvwA&w@l?3I&WP5LgR=R<<?V
z=|C$$V!~*~;Qm_7xxj4(SG*S|{R+2OXAeJRQC4X9RC4-jtn3M*fxTXy%{cp~X)zH2
zl<kY|&@7}gfS)yiD5X#eok*vFuAlQpMs9P!iN!{eIoq&^_;{xYub+@NBWItYx_$fV
zDyK=_Il@P@{fx%uX00hMZa{s^Ujf=AV-nz~V0o|wDNLrUT^7J1i!A8bWhnU?30}F-
zGQ4zS_Fkl0ZlSzzI}r@qSKm<m5iJaCmn1k4As7ZHpOvRg+~;RFAa)IIt{Q+Nv(~Lw
z$YgSFP|^aU6uVeOa*{?E3ooOPbqg72$IuWEu`vj$<aq<8tO4lQlZZ8;f-#$k$;sBU
zXPqV;yxP{IL`#D_g{Z?K=jk#MsWEbM#m<e2jEu~kw=nKY?^rNzQ#*A$|C_AeHQ&kg
z+Le3!TA%CI`%(6ERy+^4?WFPSjmEZu-j+Z7?G~=Q+je?dYIBxMG!xkzjC0JczR#?K
zrKs&Eus7DYf6Q$QJ>SkQfVv<*)^aq*PKC7vpy-*lWAH@15Nmu(W~I@A@-zR^pCdP=
zCxV$0w6cO6rlh!(QuFYTguOJQ+mTt1q$<9frsT7Rw#6WE26yYknMWpHycoKg1~!`U
zsoOFxVKe%t?YLNt{;aC0)z(tT9P$%H%b;QZwphPPNq-cXDbF9g{}DC)lyh_YHPJqG
zEjB$t^y~6}2bC3VvYR6Z11&;GF^e@qi7H4y;8X$wR|aG`PErI6(FksU3k^MSU`Bvt
zF{Y(plmi7QE!)`_e@CwzVs`-R6?`Ns-N(;<VU*XcBKDf%d0w@*QCS$pPj1}}MR-)s
zpvN;!UO!lTQn8|1xfBi_q)-pi!ljx*vqL)#UMGSYU$9ng{W&K2N06Xl4+&d8nqv`Q
zXOl<8dZoE5cT~8V)_JOKYJXw!^c+5aKTpT+F!ZV053$MuuZNjNzdNJo>Ghoc4V%VS
z=9a_OlCIi#N{=$lyZWmg{>Sb%)sq%K^E!0J^ki{5<x%eQ=hR#9kXyi=KJccj$iiB|
zrzOg*lt7bU&SwHfUx-psuqSU~x>Y0s3#)oMs7ir~i;F6fNJ}z*B&;vnhy33K!^VQ&
z?7FYK-XFpge|fzo%K}4_L^{uXug#`u=)3T6|E|S%lCm4zZuCX@7v0ZM?JMmGXr1^t
zxO#sRW5@9f{jA$ymeWF#B14ps$U;Ly$rS3uh{Ul(pC;#=qzorofhab01br72&<W6i
z60|%1Vu$;+d$q%)p4>0yj*~fb@|>CR`ZxW1b3QHCkGX+F{8%*qE&k(A!Dn5*Czal_
zoQRi%cW5&nJOzLcDNxGP0`?^s(1thy^o$6LD1i8T^E1R-sS98GsEA4Le+Hw9RuN%_
zi+f)pV}Sv%pxJ~xI^<Fsh;<OAxP-g!S*IEJ)su;gBkb8-=e>A%S64j#-rG+4ddNfn
zVw%ml?S}cZy!p;%UqkuEsNa;2>eu&{ysFZ8<MqL9c;olO$+j;m|L={ag~m}f8rN@=
zZjv}<!;5sHQ6@XK(LkpLB&oUr9j8F!fjSGqK1-5$)i;Ld9zeJs80Q%O_!~gA>~B20
zw;tFGKgB+bFAxqV8iPcT7oy+1CHWjq(J@46*@mhks8SjZ0Xas3Yl7a?sJtYZC`Q~!
zVn;r*12emOzS;b#t^oFm_m$=pNhEJLe{#7#v@dRkIpbLw{=L;omq_8cd+JQ&7E6VL
z7|B;BIfcY{J|jOqY2u?z0%$l8H_@WxD1}U>KGZ1!P*M+G0wkO44rds=kU=wn(CH9u
z()iIkjN9)=%3COVv|qf#<9BAJ*;4mRnV_GAav%Ic>|I!;$9iFcs8*_T08IjeauYv7
zlqjLTAe;eH9RHC|6puidsyu>%-K?3g+&PI}**DPWG}FNJII!4YTvpXQ1i7xGz@(QX
zSCTNN)H=~I5amBLwx%gG)t5i;+n;<s4PBRk@UNZxJ_XtS+&!i?^F^`K=L6nTX>z{G
z-vqzUi@v_{hXLT#_ybYqA0K)9zaQfRinAh?=ON}U`(fj<rl#3srLJ!{95KR?5zS&1
zr=Sb{{Zgb-5%JIt!w}khh^8U_-Zsl0uQ0=@J4(^69A4<dd{6InW!YZdZ|^8KmRfn%
znwFnHR}9hqIO#?Rw|$fBwmufQD^H630;1CjJEcZe_{ZD0P)&7U`R@D@6$p(h4FtX#
zdUA1ubExR3sAN~hV#yt{$eF<kO3i0F(JjH2-(9R@6l{@st^Cq?4n%D>L-YKe+eVwy
z^)a|z9pS&GH&rJi$7s6W7omspHgwgyzY9Yb?s%93ndi~!n%LmZcWp}#I?_QSZ5Jb^
z+wFB!Sl`2R)y)iDl;Y|}<5805Se=d4r)|fcp~dm`m0D^gGd3WSWq~BS-8J(OWszdh
zVP;6H85B?!thfWc;=VqXkrsGG{2LHky|g0r1-KkKbtNR%0l5ippLZ0B+)jYu{8t6|
zLt)u;+wS?VWI2j$^D9k-C{MLl`@8XRKR<>arRPn5Yehx1KO<fezgLN_dke-T@B)A-
z(QzF@$p-Z%4ROW|FUYncu|3y6zx?;%CN=p0%$d1CH`m5{Ne%Y<er5#a!h#5dV#v@c
zphg5!sF{dTAYl+_6V71g_#&noGm42(K{#aujmM4;p*h|=;`|RAyXeGvhJI5#@8mgR
z{clS34^sk0-&Rk513kZ&N<lIhf8p;a4E4~;*nRuHZeWRMr4}{-_Q*064YcII-+IE2
zav0EHkPp3bVDm$})BK}X1K73Xw)wVCzf+<LZozm^3EVLkBbF*4h(axd#gwd+6cshB
z;ucWR@sFTVLMcJ)L>Mur_k(J{2I;{gl8^?fhQzF&KgKlr^-5VM;$cfeO^LTFDvv@z
z*IB)Ox?LaP)ouJeePvjFv(GGeU27U>>G{^JH0H9S(JLP|N3z24owV9{yPl=L0!s;X
zwX@WETFL9L_pg28jD5X4M)1D+eIH{?xpW&>nkYP-vwR)pF858ZSKY7$Kg9xyl{nvD
zQ63WzhqZ-U8KKezD_s}`=^mo_a7tuI0_>K<s9V<TX(~HSSs?AqdE$t)TsVj|lE=Ky
z*{7-F>#q3M)REBC7Tle8tGv}R-X9Y+Wle`^JFTMFxUi*dGZCGhrdRcqAB)+)4;-4W
z=O&9ai|O&3Sn>V7N6w;R;U>Q}eh#)}v#Xk~4;#V=JU^%te5mFyaxn1mOC1;s@+T%4
zIma?16sR+scX#U70x6B{q7e@nc<!TZH-BxdHP=_n+pax*flv5H!%hyIw8c887GKP?
zg4bC&*W50v>xN+KcQ)9b7OfwyIy<+qCy*z_yJ$?rEB_UjnN8A)NHa7_Az%-Mh8AL$
zM1h>qBLVG}WzpQBkzz-NIWn6MVY^_?nbLnaL9hAi-Edmg-!wPZ)SvAQ9$!b*mvxp?
zHt?axEKGjFeu}>4WW=8+>Kja<`n19hw{8=zk2|ULyCA4NNn5{-pv!qNR*9qC-R0BZ
z_E$4uL#TimvcwF4z7i{L=wU1Z0tH^BjNtef;*Ytdo7}T$W+Dg70VCs6FV3F;Cnc5L
zmsPup`t|RtwJ_xDiL%>rGz=~(Xzi){KNmj_BbQDu|F|*!s`ryq_1Rsgi?$QdPk%PP
zdm$M~`M^qR<?Am{Yg^1x)Amyr)F6=RcQ`Ukif9md4KO@H8trly)M@1op<_cjl5`V`
zj)Cmb6X`mdFMl-3+z8Vy{y)_hV}EOK%dBl<tE0PBOE2T$Yx!De(kYJn0|Slglxi~5
zceU1742&vDy5kN8R=z;W&KAVmzdox{L`C_jv=lnb7;}eupWVl@h<=I0<{+_LfWBln
zP?1X<=Ep=qBh`*x&l7VjxaSknMPgp>Pk$FeqSf#<jW)N9_h-v!=_oE+h5`cx=9qLl
zf8K#(GrwQ|blWZ&x7O;YJz8bNf6;d)H|;`}+bt_cA!yObx^9rjxeO<M>iy`1{1SlC
z{Nzv$JWgn9{j)ux18j8d57CY|My@#c<DgT3!v%qKlL^1e5QOhP<<f&Wu|J-k>))L(
zQtJ)gi~WSE-%~q%spsQ7hVJlx>>N05m*yYR<3G{?+in4(>{3fmyhUR6OF#tjC2Cvf
zf`1Of&B20w)j(R-?pq?Ns5v>+AZdVXr-ESFp}`^j<R+bbO?Ye8F%7q02JAU=(6mwd
zFK<s;lXEq{KR#Ygf!2CjS<O1!MB&4*-dxS>&z7!a(!{2Dx~x70%OK)Qp!Lo9I?QE@
z=2o;@8UP{*tMQ%d<(z-@d)*pcm<S_ht8EIu%%qAylWg?Uizw_CaeaMKjM)9~z{OhA
zV0Tu#&$|^IU40FMieA4%x?DaLha^R!zb>0T?OZ>wDUn;-Y862({8fQ$PV!KERuSr0
zNTpJb0E4UhY|@mz(LRlbcmDqADzaL2)^6)==&G_}@6O-V4P|xg#5E<wO?!LWid$HC
z+2Wug^7?w#*3!23^yTrode+v~vh)28JF8+0A3wsV28!RdZFI2*(Gp0n(1${bAVf+4
z2m7bfp0zl=IujF*)}dAsHJ43+%yBNK`NL7Sq?jXtTFM?z8^t^yyGWiv1p13gYaGq}
zNUSEKr(I3J?ag-uWk0CXNg}6Mte~8=fQnsp*TqKo0o;4fUd(^)2Ii(yd&XB$uAH+?
z3mz67wTg?)b2ozZ!=tXAqFWq2qo>p1Xw~X^P<Sl9qZc|@DHL8>z=w*hkxWQL)a$6~
zx;(V7RaXpro?7Wp7c0M9k?I)jUSE^za8%<rrl(VF=q$+A|5v^u+hn=mQ0NE^1bSGZ
zMJGZ7R9{?<@h5?nUk&P@LRaq<zJY9~L>+sep>A-&*#J@5<pM=b&bXLTNh}`{j&}DJ
z=xb6CUPUIcz!yPP$mA@l*s^p1A4%FNe(DvxhlDSMqc?)}>(ubNEO0YC`{n7#NY=)S
z@Dzj1AZrF3`>#N)-^ZUYnN!^AO8-YSGxZYjCA(lu;XZ?&ftYk-QO=<#!DgdjLBfP!
z1@Oa1bVoA(w{dU2blzcnzi>S{ocX83c0fP_iI9#*&#{lgAr$Krp~!)A3QvMa^kX_C
zI<RS+wURm71)0SF-UWOHp}4YpVDvnyzYgM`jSZvk^5^pL2O2+%$*=k5x7`X6hi4`~
zsPtixv-kc1r{pdv0webC@DzCI2;N3-U=fc|2JTVBTsYXsu!zPvTc~IVY?l(;5Q4=B
z4W%bpLvOHT>IOWKnmLp7aI9>x?j@Q^DUK5Z8!-t>@r!yV?KWll9YdwG*0a!1KIpTP
zvgUAd`bWRKS%j~7oJ-S7$W7*oJWRzf)(%2ALXRhvObZK2D#{qVg(_ebCDMO9tCWAV
z#|AXp6AISmx2AAOEaDx&DUTz`GK(Q{|EZ}vh?i0Fb(8cS#5SL0C+(V8uWWX@*Z;aJ
zp4v`cz`f*DQBX`9L!UAeH(FkhC_5=_2MjZ5sBdJZuGih#RA}er_ly*_9$j=iNf<?X
z@82Ux$lP5&Q2Txge{nia2C{-hr}ERp1Adz*!eNPVs<}3fbIonmy6j(YRS%TTs{Bb(
zso6qB4Jkb&@ytQ0*Mw-sIjh|*mOPQE(FAWx<~Qb^M;dMNpZYmpTs5U>J*n-mwiq?0
zj``}m8mW27^04J`-RSz2tBD0hmQgQtIeNL$Kl+~JKccFjXZt$4J6KSi&Rl6_U((KY
z<rN(U_NBY6RE|40Z+<iep|0z(27itBOrhBo{qQ}pYsghvYZulCK}t>{qB)=Fi@2`X
z(xKCljr5*z*HM)QdfQZAZs6XVx0b?EW^E<SY(4+#oUXUU`Pom4YD(n(Lq-lB7n`<x
z{Q}dDv%49S%)ALDjqPgXT$`A6RCB&jl;TnBcDz3v_EJl`f(mQ7QacX4%FcS*U_NcX
zyPB<3wMZ`^OT9OMDEo7f*T@K7g00`Gids;B$q5Ao+1D>fhq(h*gE3(IeJZ!F@Xs^F
zH=x-OVE^0C&5_-eY+syrG^^;iG;%(B=5U+0x#u|a$(IV`A;{3AKM3QuThtgP#z2KL
z0dfc+7T3-=reOz{|Bg^@8dqwLFG47OdKm2ghxBsx)#gWYq4+EReD?=N{869hVNUcV
zuip8EDT=!+I1@PC=W{%`Kb@`b^OE-|-JL*c&mn)va19v>%cE$R(%gf{{9Kd+1^a!*
zoo=sS;?)D3u3!@~>wNT0-Jrn%*)x$*kPWK=M7X0<7Ea^u?Gk=mtG1u-+n#-!k*r&~
zYvA@6&K;QUZ_eWT*7K6TXK)p+rDdGe!DEYnA)EmjJJP=V1S(Wan_-a8NnOg8SU||E
zk~qCC$VxyRG&+RrumJKfj%X;w3>L}OFvdaAfWWt0W~^WFH#ogvg}m_;?n>*Zng$iw
zjXU1kve}Q1nYN~6G2P0RR`<(aR!?ZQZ-@7VQFvD)TO&vF!q&iHP)IRfr?u{fmHCmd
zYhAL>#^-ZZFkM3>AFk1`VcN3|qh{O7u_+BN3yQ7@gi>k(o>$=6=)hBt6cG#c@DLF2
z+30A|0(-JzO^5mc(lnRmU)^+#?>G2r{7ofA*7>E$S4EEcAvXS=&bQNoaPs%xA+nr4
zoUxYM8;n1Hi5vHV>d3gurP#N{6j|ya(EQ#1jM;#%Lndw}9~Iem=|oPD4re4r@9z74
zM#Zo1xI9)PBiG|KvEria$bNC!3Cc@a#;aD>o>9_jwh?4%rS+Un*~-ec=dLIBylxZA
z%8Kv!E-bml4qCYAESgzuFWa)X-4aoA0z5bb@&E|}p8;U1y^%T&Z<8J#%-%Z`jADI}
z%>!S&?syicK341|pW!-;-2mep*jmT1CP2X^fl^}tqF(8mF(;l+iCVWf%x8U`$~!Wh
z{0wd8z5aa@MYF+p{&y`Qc+X#g3<|8lE^C$r7MZBeVxRuRas|mL%eYC=4`#LvwAk+b
z3U_}>I3J(1kEA;#W{7jo=pd9l!y%ZF*pcHaOPgy~cRb7Nly}Tu?Ts}`exKL;#;W+(
z!++8{^hWQ&*?{kn-ySPrkAp{K8b~g(naI|+8JG{;e7;67Ur(YXKF@+o0d3GWE)q`g
zoxyNfmG=|r?t`g-G?HKL=d%y=m!KmJwn)4J999d6TYxLk73<wrsP(&dv+JRutj@MK
zoP?YtFkp-ZAth4+$WSWdRM89pMFd45MGZ0;28u&P+Pa5hsZib5HP6et6%IGg)sFhY
zx_;v?T-&OazJ0ykW_5jSYto?hDG7mBYrNn0&L#i;>v4K+uJg-|b$#pVx_SL{Bsn@`
ztS<%4eF-VzjS(gnor*%)8DDcvPdz)g_waZbFaB&|BRfqHd8zdz67MMKYMEK;njW#2
zTa0)ZS@G*a)0-)}y4eiJQqAPcBmGrQhQ^jE%AVbGxqIxx{I&1{=cr_-N1+H6?ui^8
zvR`0aSy~u!Nq@r@1y*&38>~JkIPhapApRKPMgMGcjzJ31(a{x6o0?Zp!f7ym&);u+
znV`FH;tUW-kMVdoNxJaM3>m7#h$FQrA1M)}j<7&E87a(`rGB`AU%L!2H0j8F*QRv^
zPLoa>n6pokQ*%(@Z=0|_)EhT3LU|TURRY-@XU|ZH+vDpqiumYwcp8d?$xzIFr=D<!
zGc<`()@|}&Y_@HftUqQB7l*7J9~fL5oDHsfILsz+!%du*ko&9T<8!sdw;=Ds6AcVk
zd;7(fe_DKok4Hk{e4F(<wnDa3LA7-C0+{sC6YE<j-6AoopM8pL?KMZBe;Z4lr$O%*
zy%&jem+I&?h;*%FbGn>m-o~e9AjcOWWCqYBB=WzNHzy|Bc}@8ZB~#rtX5hSgRZ5wb
zXZ13#PSdCUBGM>RZ=*IHm8LT0HXo)UR}DmGEt({U>J=X5Ea$n!`B{ZCQmOYQEtND`
zG}&BhH57@?g}%O1*b|^L-=TGJa3jaO6V#_-3}dO=6qXy|$6Y@N&zbN$A<gWHZ8}%C
ze?>gJko^wJ4SHDJ)F8O3(sB3cku`GHv8P?vwEGfXrzUeUg<^Z}*uoUoku<Walr}ml
z3wQoIxz3wcr>A#~bk-E7a_3h$QqnnK9jYiB^V9ZZ=^N?{@zz>UG?hg(r$x|4$V|YJ
z4Tv)p_6J|b;<{+#sTjLx*Egku&SKCt_3ZHPS?*s=!AC4$@SW|(mXcha>O5?Te`j}k
zU35|t^j;Xn>}lS!I<z}{{efq{Wm;kGxC7=jo?M-WUhxW%HK$)Tyr1Q}>V&`ZJA{I(
zX6AVX)tjvS>^7=k7(ZYkiFVFOm6(Vy6*4k}L2`11iAM~g3`Efrqtt@gTmw$h`nty$
zBwvf9VT*J;^=v{Y;ZR@v{V6~hCmIFsF}<LagZpa0VH+f2d`2T5w_^+QT|b1yIhj=R
zjn_;(?6DV#yV4l!(&mti_|*}Pz26{)*gu7?%V~FYOhdLIJ^jYw9}CJySwVw*JHLp(
zf{nShLbE`Xls|3QH5i?lq(`kY`dshrAEt7C&e`jJ2L;qWsqdVt+p+T44~&lo#?Oc6
z+X1helylqLHN=Bfu3iJ5|JHZ7HB^b^&8V}!Jk8fi%bIuYHl5VpEG2N-djisxEXj_x
zbF$fJLFn#2oW@+Qrl*^y?QG#XJvX*REG+~a-bN;tMnil5L?udoI`lXXPO#6p#z5yM
z7ix%RAK9T>QX<Q@AAbh6P%2frhYP>GB)3V%VE4%$>gcycQVWqW+$GXLOufEl6?6w7
z!yJ9Cr<IV<*<=m9t{a(gT+hQhPyEKHM&?!<5o7&@+~v5y+OLmuIj2XVc^$W%mcE`~
z%jvnm<XqwRx|)YP=)MU#0Db>E=91p%enw|3lQV@BSs@dK$)z6wPM<M%)n7}}mXXz#
zBxCZqHhcf>e$!a)YJUHDH5vxTRq792PQV3PeZ#)@jZPPkM+Ucr;srHJz;E|FyuK55
z(SzTN41CfbzfB<EY5E<J`k*&x{apUT!R|D%)BB8Ls6Mtn*_5$THT9y@YAxbttK(^n
z^ka3{y7CtHtxpHJ5m{=l?(X-kBq{DAbV}G`_VD2e3g~ESq2WE^%-DV}RXVp;HhQD|
zHmnvJ?iB*Xi#o}#$$WdT^AbY&X~*}w(qp=Eg(^AG9oeaS+@BYTT5AcZY<vV9vBmWC
zE+;Fc(eN7`dV#z4um<CIDCFYwy5HGPYJGxAE#9RK?-LTkr)dG77pM)3;c61Ig~gLi
zEkxz#JIay<rcT+}NYa2me2cg*jx1>=k5FT?#z-x~?A?`jJ!q-j@OGV*)xO?!#eH>*
zUAg^PHKKldDbuOx9b>u*USq!AZY}GL4zbH_JI9{Gs*Qh-A^UuY8QssSY<D6n>pMWd
z`kzktvm^{++c1j_=jPH}dn+SZnc?MnSXpy|5x<g1`jjk{jt*K$1XX6XMoR}z#MZ;@
z+t*%EON%Ht|Lxsvhf{<!-k&C6&4w!5B(BFyVCEE1>kTqcQ##++#jKodssL=sGvo~3
z$b@X{)^updW4j<$s_6pukvT#)4+}_R3XXwza-5Lmu&$O&UJdtxI-k>LQ|7o;krLZ%
z4sTt(kBt$fq_FD0U+it8f6gt;SGlE?SqRg8mpf-u)7Z{q6#e!&_AQSBMjWdD<p^&{
zno%(iFR<(sN6p^`TIZGCDyeq5o9;?y(^hV*qD=XpBJz=V3!8O-N1}5r8<iLRZ>nM}
zxR)34CEL;2N;)ph_@a4ltUYyUaNfXsorM5c<KmJzq-dXl6pI;p^9k7gWpKrCWBT(7
zRVOioZjDGkS#=|$f8PvWYj|rNIr_LeX?WUts6K9k-1ngp&lX<N)1zE*Z)*$0Kg{K9
zBo#x{Tm)l96^dysc0om*)O#e@**oPMLU$}v<~joVSaA2iM(IK9dBcL*8IKD0XnCt_
zGqpDGCjC;nUKaDxZ(lby8`H1Eypdl{j_P#ix389BW}+N&4Aa*eY-f_<(M#%kTVc5q
zf<tQ|voJfCy}6Yy>aW>UrmmFMLF$QT{f|+SP;vC}YGfvH-RJ9BxGP?!nl@^kTXI^D
zf^n?{w58~nCWc>gN>y${Y4C<#@$k33kMIJbZ+t%=dU#1uy?edPws+Z2c6#Oco~Nwn
z$L*=_peeLjoqtv$Y?Ik<nzH@g%2|8pcX%~8P5pn%a#xokWl04gVK1e;q}f==P^x#O
zD4*GHIuSqb*h+*5_k3%Dx=pZ(;2ny+k|_aW!oD!9k@&_3K-Bf9*S{pymrptqufpvV
zL-#3RLj%9d)lO=>daXZBICacS6g8xT6$|FCsB*CAZS%4CZbjt%F}Aby^RLQk{m8Os
zWAglKyfw(&PHY?v07CoE79rB0MW#^k?Z`_lO_fOwe$2N~7-z8!hkUjoh)(!k&(gKN
zSM~uPub;JEs+2VxT}%`^s8KDdnu?=^abp3U?YmB1SB_4f^D5L?o!h^wKfJD$w8+3d
z%T|VHW-`)iDEEKn@kj-^k9&EI@bJ|t2sP-=IN1IB??oXaqvKp@I9F*5?2%OsO>7cJ
zX5^H41|m9NA&5^7#SnH-gKil(M%e6X_km8Fsx{~g{+2SLdB@D7SuIu%gsK$M!mOfM
z>c#>-D!N^ts{M<+Pr0V5e-ryGMvoO0q@30GK~M*uASta6_qtKw>)62pR<=0Lt;E2>
z&$s2ITw7^RQr*&p@<f(vSVl%!?Xqk2#Ng3`1sxS^Izj?r5d~2>m4<}wU^FqY09at%
zZsE0s`+>&qs}72zuJX9;OOftqr=DIx-$yd_Ba~XOK%{Ib0Rnoe9B5!x|E#EBuliTo
zn1PDdY-QUEGw=C1Z~5ki{A-D^dwWIGS;e?wUES7aiA8&&C>Z{g*d6|p%5M|0p~)wL
zfsy{MXQBJRoi7C6u;ia01oHZ+u$-FMeM3ZY@zbAuksf>P-Ky>w)6pz6eD^picxbB3
z^n+{;cKhz$gA(^5$<387+*bOila|_tl#5omn4Jg5jgwV)ztpyC|2%(V?TI=TQxRRJ
z@~fb`KeDW@)RV%sg6sYjRXZB!ai}jBIT1OPRDh0<v=K35kpE0Uo83OGiu<lB^Q>-y
zoSo2r@K|=pFR*x0E#bL6sKkHP3Ht{bAY1tWtHbB`aWr^BBm+|4;O3Lm`qZ4l|0tju
zaOq0GFV2GS@mzqMg0hFs41nwl_05D6Hle9TzC?j#(DV_Wq^+SP5ap5h>3;2^Y2i83
z?6h`JD@sR^(gX5+uqK3kS(RiOhGCjz{EvoF=<BgvK#d_}IAF2Kp5~e+m{wiGudjci
zU&>n5x^?J+VXtP{wo{Go+P#0@zkhW_8bIBq#umv2%f@ZPhjqexiMcrLOAKc1WR0%<
zdnsamCcePn>9N>;Q*^!d^#0*DYTN9KzuzF=q<038vm~QoU|*bhieWHj7qaF*FEVlq
z8j^t1GtQf-Z(!*4)5RE@2*ZcD2of0{S4e?i%T(I=i@9Yk1)a#wWiJ&?@Eb=tUC>eJ
zG^9|LO=%n0-ME&LRy5Bsmy!l6tzE>FV<4qnD8!>tkG$I^!U8@mS}PH6H4npr%t{0b
zC1!;JqEMjn<_34PT4?o_5|<~oSa=MD0?+Asm&fm-2K-xzwNqVKV>a|HWlnH3CAhe^
z3_%ZyGl{DTSue&>h7?yJCg0zMHyzf^S#gqx1&seu;$6}Lvd2XsY;I+bL3MIzx95Mp
zXUQQ1Oj-d*NU=bI`iiAD@m8pWBnZX3J0P|v_2YAxsFRuFqmv2WZpT%UFQn=4<?{B$
zLhp^5vkG!f^}{2#km<8KEKILH;5Z@=pI;o3Bfg0@)t-B~F2<o8t`dNT7pO23+yu1m
z!P_DMYKx_327F`^ID(|eG$97scPMMBbQn(qGvzg4U*<F$*T6(}?kHgok{xrL#}`Bu
zU=GYs1#U3s#QY1A5!x3Gh(QZ#ums$9=GlVg0loqG1qD#dU;Nz%K2Nw;3=kt%xY%c|
zh<|5(26e{|fN}K$cPD>_*8tcEF{n@K0G$W^MdJtO!RZJ1CfW@MdFST?r|1Lt3;M?N
z7tRM5o<NEra9)r^1h9t8DkL)PEWlU-#sMc1SdJjFkCFtGBiIDMJQ1?blLWpa00m$*
zNo%jO1hFHu1)x3IZSTDVxg+}tsGg`gU=<-DtA_kCkUep_?{h3a5IzC8FMRy6&pxTW
zke7T+QsOnyxet8|bT9e{>2CChd5?ZfKd?UWvTrxRKEOWdHlUZBA7Gbwj`@hM6P`x^
z452JzprTBO3dD>8*hU;TV7rpLA?Slh>brs;X%3(&<jI2zZ-ELbt2B}Yk|Wg&=yXo9
z5Bm&mPuGS0PR|SNjXRLM2*@5Vg%|<>BnVs)QY8YS4)SkMPCckTF;xx~2ri^{=p_QG
zq^OdCbs}<uTnX?Ea$rf3c9Av&vmZ>(WJM-}NG1sx<ifiQ?9r@|86Z0YY@e(QvIBTC
zKu7dBnBEW#klsW<EaNPb0k8}`Fz;;lSPk$_pAEpy^pzNp8GJMNLOp|l>s0krbssoJ
z1J?<IGuB_<a8LM-cushq)J`a$+(0}tKLlt5wCEusks%{T`n%`@bVBnUj26lrf}OYu
zk8lt2qg$lgpeMZh>OS+K+y3^`A$$P+_A#L~B5@*d4I|=4z_!#5pe|{jz;Dv=zVjrP
zJsC+phr$w!4izOh9l}n?uUe26noe54&vYO9&gg~|DZyr?NEE3As#4{^^2r%unl2Jx
z?1b%hkXUV|v;s$IXz2#qkaxrxUdTpRJ^KNOw*K1#K8&@kH-x*xL7<UQ+M^i%A$}tE
zFXSK0PGdB&DGee5n1>KVV$r`#&^06ZJ7w*P1U=%ngs7(H07}8A9>oTM^lL$4-I!ja
zA-C}o6_P;2_b^K?a3jUIKWM)JSNFO;=ocOFuw%F*#Gzb63{*Y;cH#tf`&hoY_Ur`w
zoZCC=S@L2-o6kF9Dv+8jWXp5+r+kXAokIRXjt^$m0u?}@&FP~T>w?bio|Q+ojlRt@
zsvR-)N47ta?PJqE)^1<$-t+~9|Ho=;?Eh82_5a7wm>F6BSNzO3Y0_$t0YUWj1B!N0
z^iJAMiy>^uvM-viViDe<q>;M9l#m+w*Oyc_tBq2{V9@YpDtDfuDqFX>q;x(y(*ru~
zMF|r4(S-@;3=ynSVxS^nE06N)ea#Z7^T;ieHYWChTq%vk%e8+!E3~f7h-&~gGA|Z@
zcv(Q*-u1GB*YX>Xm_piENmWJQ7K+#HqY^t%jL>!)0zKVLvn$7b*l=E8u@91MkQ}i2
zh9g4`R$yW+H!98BoeD@wXBg8A*ZXHDvtTBdD{islA%&sDw}1wA$SqI<u|h&<+d1^8
zdS^Iq+h_r8d0jL1{%Ip`nPRhG7<gg(7tIsNgtJY3sh8$QCgV^BbSA~vI~(R4ukvn)
z?dN2HgVt~3ll!UqFHJ;#jAIw%Eg{Wty)ab=4<@f={&BDTKvT2b|0N9nGf?~gpaMBK
zI2rys70ANI%>KXWul_>?BCmFyahjWvOeUdwg4!@61)B=&yn=2HMB5nmachi>B)RvE
zh=JL-0UJiHIYMX$BqU@fu!x2Uz{o<fK!%oSX0zEYuvliFY?oOsl3vRKNWbzk6B{Ry
z)!+JFUwZF8-hVNhf6w;jJBuYDib1^h#ftuzsyar(^E0+!6Y4;>uAI<m%gbC(#uKDu
z#2{qObz6nfT{;hXg-n$fZZEGa?mUCI>k|o$J0m=7uIzOFp(RQ}MxY9MSvZSR(~hQM
z=K}cn!69e`ruxc<fdu;!RLI+yZ!OZVrU?A;z$koU&E1}E8=48qg~0W|EtLG$ZTrFd
z&3|7N_U3w8rEPX?Efc5vW)qS}2j(5*-R_~@(yag`Brm$=eObv{Zms`;DEw+K_*ND1
zG;TOp?ma<g|Cs)whI%aeo{RQh8d*5)>h*fQJH3tnTI=1ZMvtNrNk}osX<955l(_tT
z|7`ySLoslXS3=f0Ob+Er0Gb9_M?8u@m{ZJnChoP32vke!!M_#85!zCugH(7}NcIG7
zowppLsQZUFcHx|}3#rLZikg~=j)f#_)(X&tY@~=oE)e=8wKZnqn5u?jn4uqk?u6U|
z==r-uT?xO&*P7cGvVD;3if)d{qOflGBZwxbm=Nq+;4`CxHeoB$_bCHJ17HCv0mK2>
zfM$>%Y(!|?;m@xqZqc4G_%g98Q2IKZQv~${&p1$4<TDpCU(p4~QM?whZh^Xl4HcNL
zlTw5p7W{?DRh;{)Fl#I1n*)}M==cM;QC9@@!X<TdFi&R(6r~_1vW^qYw630*KL#BS
zc3l3-+r3gq=LWnnJW>qi27Q+hj5P+E0_)1w27Eo@FXnwWdP7}PpW!btUX**K)RMg&
zesRu$cq&S9sYuKjsB&fG!v6@kK#gHy!Y9t0kxg+l>GW$(@<2HOJocT{*7{rHd1w1H
z#}B>g@iB^QxIO#TWr-nwXvq^>e$SFOvgm>J-v&?qe2%VXf&OT8CyFeO`JvYT`;6c1
zp&wAw=52}58<F*>c9MeUDC3HIea8GR%nhvos1^Kabjv$&&l>!uL1$zEAc0I6ae%C0
zKCz=owPFfUh8Fh%0ykc*ak0Yl8aUL3=0xGE#Q?XaSwiYh>lS6fIk^*{6zjcrtf-M*
z(eQQn!Y|sTfiHyXFZH_@f8(Njb)XWUCEzLWv7}3L?+*t2KpiGT`Nu6Bm$>YxnMOg2
zgvqdSd@K|20$<#wM%25lu%MseCp91Bo-MPMKyGLT2p7Kqwuq-+4Bja_G3_<2qtU-%
zPHUwVsj)qi__9}~Et+32*+ICqdoRU&_00?B>qehqDWv{we4qxYFqHt<XXe*V`LhuR
zm;A&yzguoR-@R!ZAs@YUvCF~obGMcxg<x(7v?lI=IU=rCc<0<0M)^1vn*lbz&7K*{
z7X7O;kH|~+y>jpWeYyX$_Pyl3$KD;mXAv6w5n5}b^DqAFzW+5czx2q^eE>44kNJ_k
zo&~02Uwl923ZGm&hlz1NO(665JTih{EV!kGGAXf6ikwXap%sA71<PkVe?pKYvStdX
zJEnKkI<^mRp3LFFSYSnqVH}5OR4C&b2)Vh~%|MXE`>t+weXe(3_v-sy_XpSUPprzU
z%lzM8aef6B=CJdq(uGD_3`o9n27$Q(C?$A)fWDC`0LuVn!0!Mfz$Ad@fJ^{PfMmeN
z(gNdVQv+u>p$l|IR3Qx#N9j}gLFNdA@c?+4UjZ@zuK=n6&oJL{`aA<r0n$x0plL`X
z0$u>1Es+Z315krOX!aIRCHm|H&)8UY!Fs3z^EaU&Qyk0I#o_S196sA>Uv1aBEe#IC
zo-bGGG`Dx#t&XGgJ|FUKOm%am(`jxj?5%Ar?V2>P(OH-1YnqqnGW3;oRTcHv>~C@w
z)=7s&Pb|B8+o`bND|;S8ztnCsELe_e!)mz5u((m*<w`ZqX_zl^%A&M<Elq6=1vB*Q
zA(5Ztl9OX;N(`(WS-i1m0)WK&H3Ss3NK@37mM@@i)fy44s%|MIug{BZon|B-s;zU)
zh*L&VqK6(V8sDU+N%PDEPeqR5#3vDqhzfU5*g}S3(t8(yax_59lNINnTWjas$Dl2r
zmREenEO|I<sDWbs82T@P%ykQ3j5yWRtyDI*X+{;Y6D7*sS7C`!3iYP6bGc4)<tigm
zvZStZJ;~Xi4jeg|(n`Q=4&ZnR);DW}ZLbbk+6%&WKulmG-Ni)_BJU`wszWPtx^h=p
z&mzh^)Ow~BR?ew$*9glcYe-wr)|P<c(iSMeWO0gj5B(Spk{#h7XlphK(Xa!f8|5yY
z{}akpdS*zt{w#=qaUr%KSQwU~C6}{9yXyVRs4sbEZEZqpeOXSDiZD1u#VP?&2Aaw2
z<cv?2Fj+>x1M1FIIV(70`e=sEd&0i-8wv+BE7jnQ01HzouQfzeD&24b@LF#7S>P(j
zb;SyTJW9r_ZXFkt)xC6s0s7s;@jh5YMbhCAJ;n^^HrG{vUZcY8dx}wKu+cIlhH^1j
zmseJ$y|jh}P6xZ94~ZyWE-XXU8U~qCP>jthLs*0mW)B#!a*+@hmyy1i2N^OYl1>M$
zOL+$r_o)J@PCvS;nI|tpXy*2=8<x2l6!$2OAs7;cGhjzLE5J!e6*_uB*LupeCa`ZL
zMLU(Ef!ZoUdCK7sjdv<gEXitIS)gV0oyZ)wUK`q?Veh$Rfn+D6J`99uq=4fw0Rkh3
z3R~nMc2I&=Q!}U0gS3}iUE;(6Z6`#su4UP_=oEm<yN}5V2?m-@MF`~%S~jt27m62(
zUcvkYPYn%{T?KLibrV!m1v0t3ILo;BqZM8zYJn(FMwUDXW)ps%MQaEr-ArMO0bf#>
zVR2)}lQf7}sE4<{1;*|WLRLLTqN?Ek<)r(WPQ6jEy2fS5mMiCDbbp>fi_#_l57mZ7
z4$M}z+Nwc9EO=~G^0p!8Aw!OPrXq54H~Wy%pc4e7184r(GaNTxlo^Ctf=0a_mCUN3
zUA@em9>U2@J2Fqlb}EoOQ}~F^1nPwhj;`3i#W@DUvT4Dgq_7zrwXRTDZ3}b>8e&#G
zXza)k!x}Slq+`|%P70!^1FM`YF%jonpkv;8R-;w{>OUWTo`b{VYRhJ&!!nm9{gooz
z5*|LS2x^Szn#oX(WaO7!t2!s_<EF%(S|V?063C^ZdMRQE7ry6UM8LQJlMC3>U&)X%
z2d={_*1rNJRq}XlQ+uGnoIouVZUjw@@`QFKL?o&iv`4_lGOyhXTr<B8urnfRg$oF$
z8wPx!0vw{TGRLzE1syn}Z@v;))fPyqOqep41d1SutD4yI500b{Zl2^=Dv0UUCf3^)
zt87k>K&7azaZU67Lop1^G#M+0lm!=?;;9392}w;A1r;An19+d+S2b{Ek(;ctI&pzG
znm$)(h!-k3SRe+|;zGFOeWoh?B$Z4xBu%VWB~h+ULa{Lp1gq4X)a1Mh@sEv^HpWQQ
ziTn>6$<--pGDL;GHl~(HZV9+F9{{NhoGn0Tv*z21qh?So&~H?@=pBhXk4R({9+D9y
zEr|V_M{$%{A}w2wlo;B$ymL^BVp(R3B)!!WlwJz1GM=<J8My&-?9_FY+O*pm#>q`H
zf@YZ_6t~9}QGw8zO+EBIEOZAZSRcXxb``9Co}((J!6vfPCasi9D++Fvx<*65yq1t7
zNH`W5VkBb^p(9P<hC~r}65<SENKcr{ry}%$&@UD7!h-SsX+bc@omES_u<#F!JU3)M
zH<KWw(jvWZLxq)HZq>oCMwA`dyJDVDquEx+6yN-;kf-8JdMdJwn-`N4StVZ&6Z~7x
z<n_I+_zy6!VWQW}mP=g|D^Bdc%q}+~0i8};V$GJwFqd{TVtcj>4-RW91k=AL#0o~c
z01jw@Az%_N`-eutNl=z6dq=VwA#TY^jwddmRfd82pLxqN*|M^H0JE+Rl;}CYc5M@F
zE7?mz2(-JGn4CH_^Jq2A@D+nPnL(+Y8I=)uCuzMXO{W+AjnP}Eyx0*lM8VWO3ssD1
zVF_VvxHKK)$e1s91a~xzdSC&lRB$taGb|On>6?cmVOrc*@IK&&{Y^hJ6yK5~iawk~
zop~;RpKG1nwZH#xs*Ts6wO%)WlB70uzngKddOZILt&^p)0%$DQeWo90{P#=@VvL5U
zuA|L02$gp!If)B4uQ9KaHL|W2-e>8?#m2LOH0d*vu4E2IUXGwVVN8@<PV9z9yW-A9
zUHQf~P0^;!(mC27A2t4<9W>g+uuJ8_{Ei+N;~1yGVDp+8@?nC9LneTCrPLq%Fy7w1
z`a$M-ne#W|F>+`P;I*-hOhZgG4oR3Ue{8!%A=E%oyn>Cw#dqt%@j~c-Y@~BFg<hvD
z!lePKI>wP+N^tF7juO9APZ)<(wE)Bu%t<cZiR`Rv9`Ul_Av^2knMZ`uV1@A6s<I|b
zvFL!+8r>1JTy7m+5E0GupoLw>@O}X4GU43g;+NLB9QHj!6nyz7@Sk+dYkWkw^5#a=
zEC(x(Hp5rt{ig~S_Z`{d$Cv(YmeV^Ko|ui_o0g@cJE8HrJtR*|u?8$?B*GfOyiLX1
zieO+O7P+#;$)~v{`wr0|AP`ukpuX;H?4;Gznn}k;>*lSmEF1P-uF0X-S(is&=H_=*
zN-00nvTXgt^_f?*l=~ML_FkUl>DQ9p0r&E*cT>ygx4IVfw;2}pUKZLg{Uo+P{hX^4
zZlrA=?*nQd?WAmbS%*j3d%M4!Ka_td|K_$2|M9fG0@%ekJwYFaeSl@O`^&d6P0#Y@
z{SRjST^gp{U$-#$UV}f``TWz<t0J<;)x}0{Rb!$)rX|_+5k67$>>ck+g8=;YRoVH}
z81I||%088QC+5(P??@Er@{W(9T<rEM>+eE6+4OUp?>;-zr?PlQ=CW>&Wf1K7*f%H5
z&0w<lkIc<U?^7uHe8kH$<>hJjmX*qHODTeUl=DO6L!0j%YwO*PVUh7!V-I0tmOds*
zYeOHE$v_@~*({BI6C&*OtEP{Co*)UIzd|tloGP;8Asip~38naGEREg<Ajb*!p~>fU
zD5gQ>ry$?s|MZ0fVA%%w%LWYZ9wgUP?kBBd#fG-t*$_*At;JrI)BH0uw2S3l=@!eV
zE$xA*99rMEQP5zca>yx`ovAT&Mu`oJfd<n!LW~yLJy9B|jO($=Fjd|+AwbJxLEaN%
zkC%rS%8<dqANtN%8J{;S=xSc5q|&<_;LeOud4&_lDK`#AT3w*6z!`RrHo3mqp(Ltb
zY^key#|b8c>e7$bSKu0D@}}*ejzz?X5&OQ>X-Go1P^ix3xQrVASb&zH+YT6+S3Xp0
z%HL4SDoM(`zTB5ULQo<=o#aqN0A)+|694-t2$-(=vt9uo=6#ni<ZpCc(czstCT>tK
zMPmyrZ+Ko%R`Hqm84p(wzW{txDYHf_Td{p{_&l8pg07Ic0tHscN2>)nSEwK13cfhH
zDNQ-xEOEdokr$qry_k2wDe)Q3-d22EK{?<fst<B}L`U?TAqL?-e~=uI8Tm#?8_*6o
zfj^*ENRiW0%*VV}3#iqr;IDW<Qu`cz1K>$7{uxIcp73l#*RXpz)JLyghGidlaEe6i
zmjZz=FekM}eo_f)i&ucam@$n3t=vG2m5Hhbl_w*siwUY@OwKe3>Orh?S=kQNX%3N%
zwCwSP{q=#GL;wWW@efQr@+rU<87iR^ek(2__t|le`r!ex86(gjNjVv57zj9J2qjqe
z+Nq<ysFszKn3$9m)@k=QH{09FA|od!<46uppF2OcL>bkYw*u=zjyj_i+%RcR5=2B0
zreJmv`Ynfv6FVPUr*@!QN8J9$_tVam07h#kzv1sdPsGEVigE%%z*m{UaLJoTv065&
zg}R(kK#hp8k&cm34jT)wLs@BA83}d>C6uUxf`kM`1W{TENx6~}We*hTskDW(k`bJw
zWD)8Cs1Jq(yU$bZ*1*Tb)Pv>xBhvUBy_g=IZfft{Q&Z_^6*8@6-D~tCkTaWBwY(k0
zH@vWB+l032$4%2We^dIkNf|CPT}HC0bZg0w1~=2K4tcpXIBxUxD7CelT4vW}@vdCs
z3>>AFDvSH=t2#S3{BZ?Y1r=4A&Q1NlZ|z_1os%p<JQW&HUoTinAVnOAKUAcg1O%+K
zOX&%Fdj$Y{VwMaDO||ED$fAmx)!NQZTI%MasJxCHkr_R^{cKmhY*w;Uk?}h9`u9Bt
zUf2BivA2<2(W)Jh2k}5(mP|hap%MOS4hV!$rYdld5|WZ+V8SX|5)uLclF4Le_LJk<
zTj@ly3OC<%mm!zEGh)v}IUl%kL68m$qvb{E;<>Kh)2>K&;vGAKlxL@7*munk!#zm(
zb5IXuYsyLvQohSZx-Mt4@N_<%1teaDw9gEs;{qNB(s`iLk61<!8la_;`9eOr$Yagn
z@WS!F2GP}X*#?|Dr<kc(RNQ=p_A&R@lTz}tMRO?Z)@nL6<0AK9f7%(e=7UsE&%Ap(
zbOd11`Ygoda!J%?-iPB+7!5Le479BpY86n#X}2r(fH}Gb!nM4L)JVw2(jk<MFcIY8
zB&Bku7&eYH#4>=$N?^056tIn!%YK-pt6pj7^BJpurw+1ucukq<!o^PMC8n*ZbFi?o
z+X+ewY8{}MPti|*UkrX?!odBTUjOxZEVAq{eI{Fx!dY>3l19mC-dD6%?X&Q%m@bpM
zW`V%v2}S%yV1q}k=_oX~R}``W2<{x{NRz&<lB~(+od(;Z*4f>7{DL34Aqd$ZJ@)5C
zl1Fy+k*C%%^T=HY?u<Zw3!nn`06rwxB;bf*K0@ggjo8lR5dG>`&{9q78*G_$FMKXD
z4u^+F7EWANvj)T4llsa&yO^TXv!vT;H9iNI+nT9mwb|^)+%(V1abs`fGqf4JIVOad
zWe5%TlRX)NA|e7xIy%nz-0<;HCD0V9D(bD650ZdH>}91;wx6qyAcP#CAacgA3hJbM
z)ox<jcg;65vUiyM_4I#A^U~Y5-F-C|7RW#4a@IT@8yg+Ik*8zh;ZgJ_^!1gS+5Bf_
zL}eCgjzyPWrmpfhx6!$=^R^S){x&;kx$iX5HgCw$NXy&un`9f$PG9JEIGBPRlYyE*
zDk{Y^B9iHgRjMEiRI<3Sjz-;+5fIZ6QxXU>spkwC{1?)`IlPnSTQiy1nTeB$J+YIC
zZQHhOTNB&1ZQHhOTl<;s{oUQWxQl0>eyZ!#DV#cQRsT`lU2nmL=~7sEi#TDs^(*o+
zYxUydUg~}BzrG9;9R7~1%g)Kuv8;(sld=UgK%}?Z&x6Y@tRfAYX9t`v>-(z1$!}<z
z^i`ZcA^@v8QX`|ZeFz~od?hp~D6Ou(`Feeug7H|SyojUCzO+_CuEj=EHSyDw>;k}e
z84Ck`yH}(^!B>O=&oS4r+)}`r^B|*cC@pmU&SVd*yg))Ex3&TuFKBZl^9!2c<ylBN
znVvY6x13Y$5j0!uJ}6&j=GNc;5<jq&)jp~e&!Rxe(C*^{qN++HjLKS)p#fQH;eZKk
zd_yhI<Thiit*QM?K%LL%3TdM(or2!}kB;}{`psb6(wdhZ`*LEsj;cJpxud??$tl6Q
zeJxYv?Pt;HYSs$f(Btq!Bbe|_J@;h};}i%s22U#VX-o26w>bI)`V^S+Qc-nK%#aqI
zqchh|I(Ab~3zXixscl>GRFBdec@r6@K$Y|oI-7gzA&!;0ASyA=Nf`c3dg8vW95kc?
zW@iN6c0hNqwS!bRxw$AyL)iG?k;4K}O+%d$hr*U?E))KoSD|}H9Xng@?y<KfB!`mo
z^ULB>T9i{h$wC!HN<$$zVNM}K+3CEe;X+$xql3meTh&1*OWR%2*JH(yr4`HZ5yScj
zYArbOvofd>#_=cC9E_72`jz3Pt`?FrNGBTdC{WOI-g>?U+aEok!wh!iWDcJtsSLXn
zsSH2ag3GC7u5In$+!9dQ%-@bdtCb6{0kk-13nViKyg*)8LNEn9Y>8;HfqLigKG#V#
zUQ$-)>(2o~_C~=_+60<O?kt(w3VJLB7@bL(v8kDvsj+{e)Rr12!pdq<kQ?(SRdlpI
zhu>d|loqjDt&L`V%o~jRfYt@Njdbac69s?m`xmD9sT9nv4GH_sW5V6&K|V($?K3E-
zV_;;{ik@ZP6Gi0dobe~cplQZw6AVVWbW!-Ni0Ey0-TgvyK7+H}LTWluVuND0^>cf}
zsLscxT@yf-K1yZX;K|Lc?%6K8A$+{9rq!mAhApV;(E!Gnzk85%lV;7uqUksW#={iK
za%#et#Qmj+RAf%mW2krrUALsJiL2Fj^}xE6dWw6IdXzdB7=FrvVo+&r_mR2%c@mB=
ze(^@mW>4v9E9z>#UcqfMnLG$$>G*c$DO8BfQbAdzV)#3;(#+Fj=Fin~G3S&2(<kyh
z2H-`*CX)XL#+yQaiO(S}Nsr)%(>x6^To81vthiD}>ZnxnDa-ZEiZ2UJc`@tl+(ndU
z^UPK0Rl4SwrT;`I-GjS@IYRvI($6c4dU#J;gRdBxN!6u}wkpeNkH<e06yQl*O+|W}
zz)?TD5bV+eNaWiH(1o90UnEH$A!$}<8gh+S41hyB3#4bpv;<6GiA5`yp#FFsKMXJ^
zb8Mbq4i30;yOwr-o^&-!X|Z=6JdskJX)jxG&A52&_xm9c9$^-6QZ7SftZ^7=!+uw~
zxTv_kQ~bfskDLqMqp=SuzSP5$3^yj#n|c)?ps7DAlsbY&IJ!3!W$%l8HJrIO6p9@Y
z@cm{eau?#V8ziJj4X#l{s3k7<3BBEAgh>cpm6r>9MlAjhCAY2LIamaG1#m?Ngc^gK
z#UKU)tEheM?hjsTV0z|K_T`6AUZ#gDGa5;lpY3RkrwzhQonOU<m;P7hUhhd*AeArU
z8*_87K1|f+D=|TjzPmh2s<(SFTaxC~rmRsnd1JT(#~%Dw2+RqV)M*zkww6~xXYApD
z0f-h}kfQH)TKa&cN%xgboyd7EZDhPE+pJh^K3BKel?VL`=9){wNKWisI-Sp&agWTK
zphqf<O?<bO^Akh!SL!<4Ye6^HZg4bQzU+o}o^0v40EcV$sYn~CssDaQt1)-CNoA#*
z<yzyCvL=NJr}d;616SWKYwwyXH}?;F_cP^!sZ}ETKG8Sc5YinI??AZ;({|(lun$*v
zz0i8XoN^w>9#+I6T^T$q?%U!s_1_WK*80L;bK;M~srAiLonfTkvHN4rgfG)$FSs=l
zF<f>#c4HTjRh2BBhZ*}CWZC1$6cobBQZS|5=H25v(^tnTbz@>f1}Ri&enR7Q6VI?h
zgwS$6c-~{b<9xo@6HbB<QsBb0jn+hI1~RCCypaid8V?P_iP4G{%@)|n^icjnHj`t}
zN{6;Vn(Xrm>gRIoBPu^o*uwf=sY!@Cy<>5AvFCG7wg&F;*uuTj-ec$A<HRd28)ieL
zuUtHMnHrj(Pp5>{9YI}?*${tM$?^KQC~1wGrF}QOme$zmS$_Hw-`Eq>`f+h`68^MA
z^?N#Xoa|J(?NXy&jYjX$;lp&C!@Pm(4AVAGFwOd$HqCi`x!q)J;%(ya&01`|Mn|kV
z4dN2-HRNV($=pG)h>rb0oK9VBa`;KZ{ATf>&1hn^Vf~?>iT<VrLAl~J+5npdr<k&%
z$}dRgZ9=lGvT0216L_2+Sc$v`8If6Zz=Y9^2|A0lXAFk`C$6OQ8;AP(uPGw2gAR%y
zoO6n2*w2L%?I*>bYU!t=1TJ2|ed$eYYY(4Hg`<wUBvwc1R5a(NuH`Ho&n_dav!`%@
zO`kkC-GrW)r4!I0cpcfnu>EQ8?QmWtP*6e%o_sRVS<QcjM%2Yh6%jp=)AjbB5wI*>
zhu~5nGsYuU^u@miV(cDSSzo<BwsPDhhI1Bq&v`Gj7nOOgl^2&vI-X^!rpUHmuy<6@
zq+7J7uC$J<x0ZoE5hbM1lb(&PUY3=tG&vl;rag{E60?az>xoO2AO*gNH!Z0=HWU;*
zaV#pf&X-GH0kQQC#&J3<sU!BuSjT+h&ovNM;CI0G-hpPbc}w1R);nJZOXnu<yvT9q
zuHch2c=PuJ(!3J#2aQC#t?_+%KK13k@Y<fsm{P_yGv!o(>-_Hcno(KlEXBaxhGgiS
z-TVy9t~sD{)$?cA9if^q|GM{=3V-{cn`qgwFRMbg0ioAxWX#r2hm2lhIru*w_c(8a
zVh0z=PbM7tC(g~tUBQ$PM|#}ZXdG+W{vDy5wB1RHqGPegtMOKD(TK_2JUrNBQb24$
z5`C|lD3xjK+ONgaJHJO>K5tXcbc4^lw?i%2HLVqQbr0>AM%S*svF}tGGz-&qp<-{1
zDUJ*6X5dvw;O#4B^G1@ScqEjw7%4`O)wu8CME-J+YOV&R=?(Wf5gWArT6OGq++TXO
z40GF;{K5W%C6^R~aEtURj-?px75MT9s0nRYtnU|Ho=!Uloiv|@;P*M&FDNblrOvb4
z&cj=9jhOunc1}WEMC`h#T?~f}JhK6yjUlc+vXI!$>}YuG9>hq$2en+UPuC^*f$kle
zR|JY+-GHVu@J?x*o*Kxy%<qciZd9JQhmlLojhY9ach^S)!6@uJb(+<U9^;ezx_B`s
zg*y88`^6(EDPFQrW^VzZn@%W4Me$8gLNlHXv6T(ZQAM?HK9YgpFyQ<;#SV<ol$2D+
z@ZWaTiH--0<5tBc!t1CfG`wm}7Xlx<yUja4f8S=*IxfuAz=1KP9nl+(rax{pjNz!O
z0Fl;-6AjKM|Kz42ncIw3@-k}}qi~e0@3JD_VV0Jll$0oyBD)l+9yc)`&g|;4>QNG>
ze1?1Y*5RkNLWB%a<fZKyGHaz!2H3Wc;k7nplAk_}!e54beauTQ_`8Cf^F{@OGH_2b
z=_AVkzR;O#e$~rDv{ERpZQn$FEwtbyiS)=ZF=Z`WfBuu$94(Q0Vu^gZf@O;+Xj_p5
zx0rI(si3O!YxFU<7KG*sp>laG0ZV%(^O5wfaP(a2CVdeIa4ez-XIN*F50Z8C?4+M{
z$yFyD5QhAX=N@&!{{929y;S9u*;Oixr>d$a+M-4KEEq~%tMStPp>V*_(`M3YvZZIa
z<EKB3!^E-MPS48)Kj%wE-EGpAHe=rKOq{ec(>>Kd)}Y9^@Ki0nS#-+mA3<N@(R-eC
z#Ok9EG)xH;Eb&<(77g-Qc07`c+c-i1i4Sg51GZ{vp6n4d=jx`5hwGzZM@wCOXBDdm
zlKi|?=A7YT*T}#op8!_-<pGGH;9AxF(v#+jlEO>(L`Dya`|r9VxcE=<iVgy<Ci5y`
zJ#XMp4E)NsCI~%29Uh=mNJc5a)3&0_!Jy}W-nQFZcN3Kjxyuc#uq*f@DsQK7*Ud&!
zv`W^w6py2#{4`0m#pE`FNmzZwt@J`{QBJ<2o9vdh%2ng_r`67t1NW+xGWZwNt76F$
zmul0&^$W|CgO^0)Gl^@w5TkuVLE2e9A{gcpe?Jr_T(CxcYgMP}Cbarl@t?1ya`&k+
ztq5F>C5JnPt*Y(LHlz9FsmD=5)U{h`uBD@6XNW_C3kNGt=fD@s8cgF^zE?bq*atlM
z_u)|rYYe317oE`D>~h2Q=vSfF_mKtYn+;z+tw!86yA2m7!H=ZN(C~@#@W_!8W2am*
z9?H&*dz`9C_kr1MpSgIB60#7-l`>OCn_kve2&gcMhlUA-?ULciKbOS60MxUqa3oK@
zeybZqnAi<O{pzO1(HSn{ohPb$-#D+N@n{AyS)o&CE;7R<DWsSZ5GW(M(oLAqsH;MN
zyZqsHu^Hw*HZ<)c!{Mp1M+mm6Orypu*7%gEPp!grxp}tM-nh%|b&+ossz}6;HtuG<
zJ`J^}DBQQNf2W1I-hgIGCY*Qf{u*%f5r;X$FR$Us_T8qHg{X4V3(NPF1&a{$+P1rV
z9SnWRVWs8LadQ*MJ^}-hP*^@EyH&z5Xc=)eU1!f@pjS{Uk0PUL6^^1c<#cyQ_-1fO
zWfyBQj=hmzCZdp9FHF-q9^X>f!u9k<O+95}S-s01o2`*TU2uj#^8wwo-YK;_B!uc?
z-LnHeg=RdXfvy<n0iWWniYct_+}qlLX%Q2+K=A!Ua`gn5$Z0fQh6xgdLslt=S(IaT
z)J;OKvvgSGK-p2<Wd(x^w}^&)gUwh+_O>7b-u|Gr9rAX=ku~pZ=MiiTYnR5j9?g)3
z{v_p)eP~4@Nl4D+qq8xF)h@3v@)t+14jZQo=GrsQnh1DHOSR)`M64iATHHy{Gb&Ff
zU(F*{F<2ct`$3u0kiv%RD>a68O`Drau$+#R8_u;ivqo8?FnPRO_7Cp9=C|C4h?_Aw
znx#`1aBAu%?zWO_ojI_!kKIB)@t_up!^x8jBowA5v`l+A0j$>B!=BPoe-`DS%647`
z7N<a^bIruGHVbP(lb21axDt1og*ED=gag6Pu6hzV<Liz#lCU4oK}@m$KBkGJETS$+
zZD{k|P{TUL)b`3?9m#WvHjV<5m7lhfu)i+9t{yu<IKl``>)VW(O_bS4kr78#%YG`e
zRoL--_q7w4{pE)!`YnwTDFX5vEc2jGq-0_@bGoI4e)?orUxuZ}QJtsr<WV0~eO{NF
z6S4@`TU*;x=M!hKwBXdagtM}vqobnpf|aS8_^n0Z%_~AKw^`>t+CV@;(Sl_YRaiPn
z2@Uq|i26N&pck*yIols+$00hp>8UjglsbWR0_?0mLY;I2cz=f;>A^Ug8yfnRu}(8N
ztHziD4j9=S3<{45LvM;Ng=`zmE1PxX**pnz##x2RZ7heIMX*UKqq--K8-u*U@<Ht=
zv&OSHmGf4&fkQ7UER2OP^XtQGOswQkXTwy8OO@R*Mb#)#b7bVsbrc>b6mQVCa57FV
zrAA+CBMwSQ0WDM<qF4|FHe?^|fZ_9$UqAQIG{#yvpvg>I*W6Zb>k?2rKTibJm`Qn#
z9Y-1HWxY-8fE5TOj!1qodX~-myvrBG)x$@BAyT$KoIO6*afb=SVU~s$kr}Gn!D7)H
ziB#s}i*!uB_|dU#I1ztl?t_I}yYqeylm)^8(Lmfcd=PgTTYZ}rAn{vZ-W&1Q3DbS_
zcVC;ExA9^($3U^ZO0D?QYVldb?I5v70%MkH#wCNdF$2aNQNB_@a?+*W;!j?@shbK(
z_aA!KL<UW6KVTn-uGyC_Oe?KN-j=s4pqAdq>H>cq+`N8xz-4Z?bOGM%aqIn=RUi-+
ziYQSDenrw_awIj7#{yHE!}^sZ6Wq&ZL-y>Xaie9?xg7gef0R=d@!BF@{yAjaA}+q!
z8}zub9-N4lJRlwU#M37(1%j#b`l4mwo}ua-u}Xr&Ge<EUmF59O_xz(ar86KD>VcR?
zp*tzs^Wg%wnX~yI<7{bbU}9ZMhG@g3;?RBq;+eNHW*q;}!C<__;ZU@A3H04883Fas
zXj@n%Yyn)(9|J|+>PD!stW3uqMD#~Q*5GDRvjm8@qI{~WA6MdUQN6I-@!;x&tzS|b
zgBqQ>S4x?<`D0um7()>*PEXG(M?aP+#iW0(2>uC~#8)@Cji+J@pMh$Dy5dyV{=P5i
zt_9AZH+X~WcsTNXLic<d+axDKM0zu>)=g{V7IdpOL_cEU*2rS}JDR~IS_QQDi4?rG
z2;46Um{nMX!D#$i7_~Y+=>3BEh6aJ*)z#^-4bMOLjGkiDNtn>;*NZ{QBvT{3M(J@I
zt8f?ODKe-|1KY9}7%7xn+9$;lu;RF~72}!g$*60}^Q&qd9KYQ+*6*1D30G`IODab`
zEh;Ly{FM!SjQJF+bAK}xvktS(RSBf1;ht+}?O~%_T;)_}r=JqGU9-HyC!7bM|8(4^
zC{K&UGk%}Ae6k^C&CrhCtwzb{{jCnPH&Ru%o3^y#CtJ}293_zs%ASWBCM5F3$V6Yg
zINlcLd-H5EC==<qP3h?`;5kKRme^2#F_}2r7)IPFD2s$-a@NoE$aL@SCe}-E%_2cN
zVHS&rR2VWf875F65jPC=n(zIM4m6Lr6I$F$9g+lWE+49ll&03Zvxe@EBEO|hd^LC2
zM0TS?XQIRIfI2w5j}{o#GGdBfxnF`Ad1G{mqy(v?E^?oG30>1#Z#%g}Q^xi7@%vu5
z{9@ti8>QHZ>9foo<=rzG`?_=U@Q7PejlTR{dnA@CI9)y3_|bGKT{+TXJrjj_%__35
zXv6}EO0q*Hi|XcFAq)6JV8h|lx^QcUlBG0xIXZk8)#D(4-g=0RKh_#%IWsdb;k!`h
zamSgJg1uj2&KoLp&#ber>Xm<bLZMWn%Eca?>4UqviU~W9v{1d<QRU;8CYx=z{&mG6
zS#|F2^X_~@3H)YwTv4)pcoWci3rL$4fRYJ4pY%6srU+-S)5iR~`f*#^wBu&h^p9(r
zH!tDN&G|QPlRrG#SCgk{P|a9(ylZ`>RMd!AeZLH&-es|Gl~)Bo$;ytBMi*G=6v<`V
zT*elhf*$dp=_xH(<s1avZFo?E<{}8ibVZ9OS{WSJ_Zv|Z?y)R=_8Q<@zK4bRo@ZXT
z!sl5Dk_#F-Q9Bt3*x$S#pCht|^XOtrZS#5gH}DLi{lo##ix1z5U9{8u&f&@>Bd^UV
z`8tcl3Lk6|CS4U#RT<G=bRPB&m*sWjprjQ>hnNN&vyQH=-U7wjsr;>wdEdLtPuq*-
z<IfB1s5Lsnbc~c3gA$P*x^_M6cMt{NKRHn7D$}V|iA@j^>O1nIadc@4nblJJ5+2fz
zJW-L+NVA-}IafS{g_%ca`J#f{7_<Eng3=O~{4ty*{(=K#vvSHPC<uiYwjsB-VRkkl
zH@9K+L)PW;*5Z;Ig^Qm<A2zg|-5VWvpE@2n!NT|liaY&N<J3-|9yl-s<MWe~U~gj%
znu)i;qpL_IDM!1j=oFtC>wJhYfL|?mvHE-?Ba^Tz9-KlcuEi@5po$g2l89Rwe0SR`
zB*zdACUMmlYMZ$5^!lFkNz@52Sq?R&tfU;J6iCSyM5SbZJ)K?)vQd$qu1z);R&uB%
zRNr2ao(0?(P*-F5jJ*~KsH{)<Oh(E|;Jz}TF?xGvs+hzs!@;p|=~Q0$IL{+(`B62Z
z7{#T?4Xtl#Z7ELT;ZfiT<#1v&lQ-)ACJ79pk))~awSsvWov;47A@N>%CJ;-^r6$Z_
z2!sP{hEc9?A#(prQH*>`<&RIRs!<otU28<a5i?hA8LTH7q=|q@LsLT9jGMbv&|DiC
zjQ<QCyHobI%|(4}`u6(tZF*ne<+3=BzdT}}V3yneiXoS;aLHYHAR;??Mek=Llkd-2
zkE`8gTEm;0(byTuNMkpHp$8=eqeU<wr~Q4jAMD1ljahHQ(>1%Hf)mor`Ic7H)L~l5
z>C@;ZQp8iZE^p-b{+X}9MTpDR<?Y%=5^R+Bed9BZ^t*vC4jggO%{jr%mPBsIR~VN%
z5-kWVJhu~(@QJOgWH8685VwpljCYg;nVfue=jRb1ocq5A=lF;PlGbsciHbAZg1`A9
z?!I`sQuTPj%<h`?4q*zpi#IU^zfmr0anskDcn7xC+AEv(*f{r^2*``M$tT;(86vM-
zq+ub)UL0grlHra%3}21O&L2{=W4_;}651!CxV9ZpjlhlGJWfU_<iDtOFB{x2UqROL
z4;t?mies>|e|z_l8d@*qwTdj()56&Dk1B;uUo8z=^&33R)ZTJ#?`5i?3}I*I;P4TS
zJ-yqgP2>BuXNFrgxuu07r>u!J`jIjE46n0^<0N8k`GE9mXKqH@eXv>n8OG^Y2+O|Q
zZi+B|O0oNha9a4(|17JPN$vDkqO_o2@@9RVnXnainEoGu`twxnnldK(Tu0-9_YD}r
z!E=JJUj8GY<uzf{z@VwfWWh%+IvZP6hx^)a5NTKq6-<<d`)|&tNZSc*?w7Ic(zl6+
z?rw)P1*oxHoDhLKOG4JmWN-yPg24Fh^Hr1!QFVVUWUlH+Ua*DrhbED{<6C+9kC_~C
z^<6ksq=$`jhjWA32K{1kb@gF3|CBg?Q4uo12U5$X{}nf^a?K|I>*ogIfzpI+G<-v8
z@rAz>=<G4v(bplo0gos}4U#p@k;fJ|WgAMA(a&qm`zZM3qekjU2~p5VXCCsPs!3@$
z(3~aMNe7)rp6&H#u`89MdPltS`5;T_*=8w`>ppjg{t}z{Gq7%*eT=IUwId$uY2Z*|
zDrztCJm@B49T(`zsPsevLwmw=fa94kB9B}qk>+}GJ3au|if+}Ys2R*MTB5wYKhR>I
zt9OPCs|C@dHKxj#{Rue38QoFRV{A0X1iaxGGgP(<-Rz}ct|q<alO+khebb%9R@6#M
z``f&u<+Xt5NGPw6^Shd^xr~fCegcEcj|lM7f+8oOBHe`k;;cbssUfawQYV<H*nL|X
zCy<MI_)>2q9<C}sk05;Uf(XF!tDcbw2*pNlhuyUHeUV>`v}|}V9jr~AFn8hY*HYIq
zE9#<bdK2w3ehHO(kG~@?ohC$S*q`z-?7I=mh;jK~eS{-OZv>>VWWUDw{7SwpFa)X)
zGMHPrmkD^VyhZv-k&M{YgtG(9)<Q}SW!;lZp%YE<ym22bisX-Jdn`^)O-*xg7~Vyf
zrwHy{6g_VvoSWZJ!tLfaJA?kGA^j)=_!=xdIAE5#J=nxse`1<izFgtoYOp-S!C_#O
z6Vz5=ArgWH#$hm*z!rLX(jhu4{2wi|J;e>|f^^D@%%`h_4|<-_ChH!cQid}6OX(eL
zs#_!8EReezP6k?S9(X<6gk@9>R1D{vsoQdPV+!9D@7{d}U+^dj{4QxZB%|(<<PPv5
zH!N5A?b04~#7}%}-ddPUO`kH%uQ;5wM=`422QW-Kz__>q!)NK;?>I#@*SFKu>FFEW
z*&qWeFfi1&+5rnf4cwm^+!_fo4SD#i&AP!n)^yWlAhQD^f-3YQCuw>}%e%4@eR+BL
z5n||b9Gxirjudm{8%-z=w~fG&d9=!>h@L4T%DuM>KH3#B>*N8uqe#zO?XOUi+guao
zgv`b(<(zpk`s{U~qg6uvRQVS=r#<{1<G*=7;iN(_dLNOzj3z=Zbrww*-J{?U5E!=Z
z#_+Z%oT#;PF=zNnD9$a%XxZh>p>jxj8=ZQ|>;>|6J{gZR+r8kEmW6*d+=<?yKE1rq
z+*D#=_nXVaNfz|O7%79#Tl?KhVV;T=2R$4Rc3mRU@Ha**{4_t~TH-)eOF~pF?RJ%T
zelA$-06SrCLpo}GN#gTll%N-0+uB=wp=!Ql%Uk7wWJG~UkDhDcMF*d+j=w-NMOln*
zp+&&~4N?ufBfDEAQ*F|Rz^^B$$l%eZ?=tk~1?N0YZkaP7>npG?Ucj=K$D5}CtqBBW
z1Fz{(f-72d)>@B_p}lsvT0~QWog7ZkG;9gC*Z2@iGNh7KxAP`m(TL?YGI=drg1Cdw
z!V;@uT}D>KK-K7~$VDs<+Qz%93a+2`#pV#rXpp1+9$q6sot1NWiFlbnD@JWr84WDi
zMXP|+*9Ur)9nKa8oaTkLeD82I1kDJ=r?j{z$6v+9v!CFjUXlIPD}>;6plorEhzy&-
ziNFP0u*O%?=My%h7o#BLS?Dt&qO%DePwUaY={f-orPXdQapp1t`KYEUE|6un_k6Xd
zO?wZKDT^`f2^sch__@C05B!)4hNK)ULkx=<u`{DtgT`~~7Or<cb~UZ&6S)d<nTBcx
zbMn;XkCI+_eR_F*KHCNmFP-g>d5%^F(iR%dVrIdeBrKE+)3G=Dt+>1h&RZdpB1sEN
zjC_jgR`1c>tQ}@3)b$B)?kf}D9&0`7Dh=$bA4faiIoa8I7)JbEXmw;!>w`%U;>XqF
za2CCyYj7dYs83)OTbju>a_6u?EIm2H^Wxm*=wD+Qn8y)HxU>J{zlQ*Q;_Q10Y#?oS
zTz8su$6xWm+r1&|x@v&xE9Ht3sVjG5nIdt=S>myA@dVdIMvg<do1dugogB-W=#!J8
zjg{yGV0@xS=P~7P%~xstxcqr>`2D~H_u>NkD8f_Jz|zCZ7<5{eD;atD-KLG#TF3tK
zFgl(MC-l-vENR7eag)oQf6twsA<@tPc7Y5N#|#eSEnqTQU!I{K=$u0nf44RZUyoJ=
zh1QwWb5MPkDn&ol?Mn8x^iZ`{wa!LN-cD()X|Jo-K{tjoX%Mp}Wf*;ew_ux#B7X4z
zMX#YQyr96Bo_Ud!W~!Sf9#t>f30V@`=s5`L$W})RQdpWH6v8S%0`8HhOM(*c7V7|e
ze6bKl7F-IVb?(4}o_Dx+rlI{{clH1+EpJWuSK>#dA)}eDnwhx4eD8dGpls!xMSNXf
z#-B<K`IPp@frE=EXTiksFqj*W8z+f!X>xS3N874j9)vX%#6<oc!p*>JFCDHQpS86g
z4~HcbWqfEqkC_n4Ln6u{84;)uU?`3<A!#F^8=RR#R8^4{)sZd|RI%mOvnrGDp+;|=
zBd!~rpu_8)lqQ`<!T?7P5REb4#WWA`vUv9jGrmnP)1s9`Has}+{M^VvUaw4MAD}Z2
z-0}%9Q8kwG*K`>;+vVfhHG`_W(!fRHT<*{46uE!w*vjmZwoI}9JbsbTWIq#kTHH;x
znq~TtGdXb{krQK&VthrjrJ--BKdWY2?3$pi)Si$a>6-EoR(`2+O7#;>ucN^{&wo~i
zWC6d`uG9t!R}*F<<J@w5p8(2pkvbL|ZH0B>V_lb>_I{{?370tzWR<n%ChGA!=AP$q
z&5gdy6Y6%z%KS-fg<Muu7S7PM@z^}!Oe2Abg9&erB`?#cf!)oz@!{2W2#=K~@u%CZ
zgQ~q1DVN>e5Bw;TFfg%YEctj9iiZKaPz7ZHhkD+^`p6WzLU}46IQZ0`02&RNlE-;a
z&VChW{KCfq1E%QfrqmlG5pj^ajAgr2`EN;ND9=W}A_|XfQh9}=5B)O^2V5*^(G~^t
z1`@fyBrQfJ79%dYqd+iB!V3k>cdFsr2iU_a2bdSZLC>(R%W$|`zFner!@&?)-c+qi
zZ_iY)#$K^lYmztHEsASxq;}}~x!XCoe1>*5hgC57veV<Z7?#}5WL>6deW7)~ULl-y
zQ7n5Z8C5^}qk)r1j8}zg8@mlUtZ8p-dbt_O&2G0$PS7-*{Taz^qo{SWU(Zn7SXt<x
zbCG?!S&fiPdNY5cK`@+8KOa>=|E|!~(q4J&J6Ju9O0?{>bsPUi$Wjnm5!r@<zdQ)0
zkA`KmdfNzj_waC#06TS%M)k>F1k4I$3Z#>AWQPWonIMOYxf=ViL*`X&CheYYrY}kF
z><!j9Y65vvG4ct%r_%I7u}h%lj1>1!dD0VuVP8CO8M~5}ww~~7hJ*7w0Si@jzYAx*
z(Xhnz;{^u~Y2Mx<>$cT0q18=f{P<K4YL2EiE(qD-^^q~uRby!GIZu!^;cQVo;_JFn
zXuyNZYBFt6`wFb>Y5DrPA9NHSYZzxn_1ZS}WQA!RillG9{n)k{Kz=0;KJ|0=>L=nQ
za&JanE`!aH?v4~_yJNKX;rAUWOjjR^J#(-4O<+@%-#1{#0chQ8pkJG->mjmj>*RU9
zE%q`TeMOA%nv5zR2RntvPEEo|Ki|hbqCA>Lww=KT*w-k8R|z=iQy~;J)$Om(%(t{r
zm%jEFP-Nd~?pIccabXuMCPr2sPh*o$^UNvy>6`F!SRSXbtT8)IE)IxiACLdUyml><
zViwcXHfg<FikuY$yX%d?d$zwmCxH~A!Px~RjjvVwxtY9Lj&9JA?4)}lauvTTFn2UN
zk44I=s9_6>%&fR<ZznLvqcDD31F1`uwklR&1xw!R*Ys;do3RjfQ`7MnT<k`yKco!s
zc!|@|ZIrTFnO!lnva%53uUFml7~7ltk-mI)ucJ3RDqpegrBU;8RP)+e)B<(c8BUmz
zM)Txa<zmRcnprA)Ip{U@u^u@ihOd!zon8VoIgO$3GB^`2>n`p<LddQ6GERo?8XKfc
zJ8y4bWJ8o1@+()(nR7$I+bW-sM5yCU!*h<IZ8bOZX4pb4L5qGC=_+BPmcBqwx+oTK
zV#IqM(ijp_Qr5Fc%hGKbsp-xBLLS#bR8<A+_{yVNm-GaZd0F;7c*HW7&e&Kxnh7{6
z<ye?NFHUN&vl!ECWPOrw3qHXf@yCo~lvWT6gv&(}0^78wN~&N>ysb_0>ZSZTQ?ML+
z8*#EDdKZFsCXO+>Oh!xtt7-jPLr*F;94|BPFK?^4>uWqNEcMjGAG9o>=L?TDjoq~a
zqFx8Yf?DfPT&aod-5P;jYw}V25$EUV_8{MNkAqTrmFZfzd{d51f{Oka7Q&0TO8TqY
zHWm7xY$rBkn2z9jIoj@tNAi~H7l>!3COsYBipT}BAUNAjfNM@s;x@NatA1~uoT%E>
z$#YZx6=BTioVr1J{^@s#Hdmo-17u=U8Pm&RG&_<Z=CbarkKpQTLSrnEiM7Gi9__fj
zIUT^jt>KK;0W&{u^QJ=AhU4&io=I3ZSS8J*sov9iV-_B&GcfgJpYOc7E|G4566Qut
zOTj&x;g{}0f$w0_Snpm|@uZs4=~3oNs)h<mKv@L(7@<ZI6WJLk;$Gks{6T;~Iov%=
zMJG3VP8(}?=oF09o)p8@jh(JdSRw4>vO3Zq_|7>Fhd<*B%hgzzmDZ>O*Drn)Eki2A
zZ-^?Fg~rh-9iGwaoFU*?@&r7SNT$}2RwGET&?y|W&E(QD;<%skFO!cZPn3>k0+FM0
ziU1bYwYZ(x=@$*@yP1K}eM5+&rcAAbADhhwE|Tj?1>!t+f%}ry*4?wCREoe{hUQ{O
zNs2))#!2omZ&MS}>Yh%zUx7UV{SVgGuU30S(hh>ltJaJ)fKT=Ua$IdIi9&PBhz$vq
zp(<%u+1bH23?SY08!S6d%Jho)Hb-S0lESFuG%klUqm)}~D8}|Tu=1s+Zo2kT%ezO5
ze}w6iMj>uB<dbl^-B@x>%Gv8#ZiurO`uW@o#Zn41geNeu^tlKLjD9x{@?mANp{OHC
zF2}<ZMJh^+R3@#a;b{MGo@uM5snZd*o>e}#*ZD+8UBtc&_g7k?n~D^UM>uQ|Vy?`)
zaFp^G`Gmb+=^pkGMAJ1k-p0(%YotdtMZyDRc+w8AVV4TDxhqnc!(T5~J-n<8@*Fst
zW6@HjyS?}zA)z_NVnw`MEj309CatVU>8fIh8Hq=+@UWVBZF0B6ZSgwh*pq2tW7o5N
z@$=LiJ>=#pr$ab-qIXac85BC#JDXDo|G^n&moCn6b|lP_d3NSjE($!mz;M$t%zOXy
zHuq+Radt^<vN$7V(jlKF@-AbvAcsB2BilNA>LR97`}|qYT56@Ij3Rh*E8S@idhSHA
zvm(dJSzktGuFtYeJIoYn#bdJll7obO%vs-~5`3>E*lXGSD`c$Qv%}U^J=2`V+FjS&
z%iO`<#j`vbjAY^a?H-8k!`zeeq9S5L5q2IbC=^dwyj2qM%taGlu}h8n;a)oxjf294
zWn}Yl>|(MC?L0c$XFl|ig<E5Toq=wD;7FRnVJm~$RK*13C0Cc*y1#2xG4~u>j<Io1
zhSg{P7=8n7GYBP_qY~vyces|b4-UQIQs0B3ryD0${W{*)OK{fonbq_QN%m8J?9<nX
zcHfcH`w{NV6@;`$4@n(Djoyj=)bv>ap7UYuUOD>8yF}HIuUB<JmiXr_a7QP2dD=kN
z*N{hZW`wQp(Fsd}gv8)r$N-}r+I}<IsW!;#fpW&iy6%%>q}c|;X`?ZVfl;M*q2$N-
zYlLjqF7b(}=YZEIv8QQiCxLdXW$Vi+bAs90!bDu)9v9qODj#3!BNI;!F;Tu@mXo!g
z>zVd}0)w?_CYx|P=Fv&InI$#fv^PCk&nf<4&GF<1gZ+1$%McuU*@m)0`|qoBn)NM^
zl|~yh)@do>HZQ<+?~)TSaV?NDb1jft?{6)TqtA`VGjv>NI}X+Y235>xJIQ2?OdZ*T
zwu|Kfe|)3cjtI)<4hiDF4PATFqT#!q=iFf3gu{=I3$j*|d5ts4a}uKE5Y9a;9S{gG
zBiwZCL#w=(>EBv63VTV4cs+>R=jvam(ijE`U5%A%VHVIuUU2g8-@fo}DK>5MSf3}G
z4@wdlG}rFz6^2h%g(!noJzP(%&J2ot8j5g9Bk&S;KlFQ(Dzy-~F03#=9GC9z_sl#x
zJ$ug+wh0%Uj@mvAUrx$68oI9&E446@6h1tv7!G(dD!I}SMqVy+Gbp|6Ocv8Ml}}>S
zjAE-=bs?6ebLVF;GAgN}%(=;Pg$?aPJE~KUVl&7FB;THZiBXcA3Qk#zWTk}V%G{-p
zc@_AB8J>Ocb-<?>aoUuunZJme%gZTeSZLI5{B{(q%(W|Cr!eN1H@HqWtUO_$7mSF(
z3d@x{VHd81uZt~yxJ)I#P0O#fdFo|pBFzwkhf~cG3A|N442_7>^j2_pt4zB{ZET6D
z+0BGVS*PbGX_84<r=^OM#fz)P83*gpApQ+==Kmr)di5G=y)tnQ8CD?Mmy;7zm!vj=
ztmZT{ZG+;w<`6gQdRo1$?70;4SoY_7D~v@6!u2Rnx24TkES-Llb2FjbQ~XX(L{w(6
zFzn?_k-EleeXY{U%?n1ZOh~N{!H{SyV!yNruA?q37wHt5BeOv~1t~dx=O8`$xN-z9
zaOcikpP_9}&OP><m_prV9h_3~TY)>#J*U#zol@tzQeyg8Rotp|v9IHlpEg9`RfMm(
z_@~<QB1&`PZdKrwxUaeSryGREoB5Kayc?BA6okgwP1Y7f*4o{Y(fgDz__)uHdJ&(h
zX=NAG$?Y8Yr(LRNS&zu$?<j1%mGqh8zl^xM;gov&EPUX7`Uq0U<m<H@wC+n-Wi&{v
zkgk4ok$lSO(S*iX#A^VqumYx8;1l`TCx>!JIfqnWCE)WJKM6{T#ES$^(x@1k_^!ON
zDy-tQ+%^CgSZ0n^>SpvNsaw{u`f1bY)q=OM_d1X1w1v$vOSf>4ECb$`GVjM%U-b>n
zy1%;(!etNV^B4N#`!80<zj8Cg)Am+Scn~)R7$zLX$J}w&AiFn6yVbiiU|F01)F#=<
z_`pC#kIm3wRA`<EL#y)`u+nr`cqn#9e^+<No$_U{qdQ|nXJFg2>nYw+&`Up4yK<X<
z5d6i`J-Su8e9BwKt*+VBYLzj}Ula+&<3C}Qwq(Jo?!9KTMrw325%wAx%}8OcG*b>%
zhY9=}Xz<*?X~an#D6{@;aC511+N<g#8TIElHa)fy`}+o6zbyj-JN6ShjuXcv>)|u|
z(Ab|Yi08b$A(oz_r|m9~I#26w3+Ekm+>X$Wt4H@M={7jvhaO&79xO=dM;rg<zBiaz
z2HJ0Db{a=5xayggo_}G&4F!o0!YJeombTz~%kjz1Z-POn**u#FHJjxY#OrGkC1YgF
zLC8ma{KTgkVKF1kr|K6kKP9r`%AQm1uTfnhlSnB4lRDnfIQ~HfV-k7vl?)dbbyUft
z?WQ>43&o1bjK{GU$B_U6+2=i)+XqA}NLqczi=ouFOrai85g(Uqpf|(lncMla0{*iU
z{IklpwG~*oFRta<iTK^I7w{QeJWCD8JL7u%n@cWu!*$p32I!NT>;)Co>$(Gqm$+qp
z!|!>ptQpMSKrM(ozD@}2X*G)bC1CPw9Bv;TWc+ENFQdf+PjSr~o|x1tOVVvH531E8
z(}{Q`wYv$<3O_p-xt=>kn~vxsex~*%umiJCPBBN$sUel^=Wi}j?v8itS~f>Y(BE93
z$&%Q2CT#X$ru+O6<{31T5ICxWa9;Sbj22gbofT-^ZpgkH=5lmz@?}Ik3XhXwo>;?f
z?BE_2Bz_HP_}oGc8^f+k3ZVF;0nwU4bkpu`Fq-`U%Hsu!xCH4op~~$#s>88_@Id5*
zezpaQ$P|zU<_eVp_QC|pJDR@p(H3GPc=Egl-lhSjeT4_=lX%9c2DgRm>jLv}x1rf)
zx?i)p0dN0)?e9XWcO(Cd(!Z^IljidU;r;E|o`bjRHCP0AECfV{dpDRCig3p#Pn1!I
zSoYf=!W*ABkssTPvEV9#^gu_^0G<XS1RD@jpmm|&z)#UQ-JbafHr%S<E?}-e*Qm8%
z*UaA(;;;=MwmA)Gw)6HyyN5<>yNgCtyY=@mj;91~*~zp?s(`!_+M%DH_NltBVxEAt
ziE)8HlRdsYTkl)@bm46QbqaMtyfXk#Mq}-RZ$r`h()wd0??i-r2Tr8@(QOm~rsF<{
zQw_2ggg%&Y!$Y|@;=;R<b`w-f141~~&j95Um*UW^Dgm{fG0xi^oIC`CtE&b64#y5b
zR}KU7P@P$hV1lH*i1z(a|B=S#h~bf;Kjbi6Cqf`xpfBY6*nnMkkRC<%j^4ER?wDS-
z!h6CqMF8>T4;STKQYfJaBHmb_0}WBCb?bP>Z<o;yK$o!&z*+>V&@L?oVB5eWK-+Ib
zc>1wSKcaz0Kz?ku@AHn(ZfC@1_~6EI1gVVZj_8tcQv;X8{fzqw;-T?Pj+Bkc6^E;X
z$QA0^a0I2>JN6ywn)JtZ^}aiKc(np03L!y)7%tFz_C0TN9Y5eR#|F74DBZ08tnkh!
z{s}%AZXyoIyWc(W^F7K5mJj!gEPi<$F$0f^5fQ^T3xYJTWvxT-W&T7+R>eeUR?S2h
zR&6E7W)&s)X1+(T?J7gs(OU0eo=?Li18xFJ<9*s%yOf`1(e5KhAeFNne$|)it#}}<
zEcZJ74@7RXyKcYT@YaAFg66cptHXNh)A~nVhd<LxSp4Qz@$x$Eet(8m-NU^`7>54}
zs3LS9?}<o}R3YHC*BKm+!P<kB+v|Dve}TBw^)m7j>VzGVz~heohKAhT>PtzM@9s<C
z%fU^MO^h+#kCt7atizSMQ+4w+{PQcsJAm0r2d*~wWtay`E;(URAnBL@O$$@gx5+)4
zz1{joeT%e^wn_5^0|io!3nGwii*_s5<dFT4G~b|-0W*G_AbP`LF~L#Lvc0|Wj()Xg
z>=;!`sOgrzH8^Rw!^0Z>ljgfu1@M2u62*c?i}w#%mpUG;Ivyh<gC<~qo;n^qBP&3l
zqf^IYrl<caF{lInIHCuHfAlQscyw$mc=RmHc=XJSc#L!mcudUn|GNNag8^XRZz+1l
zzb!H_0#b}@e<fysVV1v!8CU`KnCJmE82~XIATR@Ju;MW>0n)5=cq{-r^eg~7On+No
zWCOIoz=p@l!thTHGb<||GXotUhY8>TGaaBV8zUgF;4w4)&0)b~Wn}^6G6O8rGc)~_
z0G9tvV)@%QMnLJmUI45!0lZ>jp#AIbzv%$Ip=ShG2iRkx1$YjSSy@>A_5_cKh4yb5
zKzpnJTp0eA0N7@r|C>vP$H>Zv$MQF3WB!+ybaa0`W@ZNXu8H^0u>3OsEDWswZ7cxz
z0}}s!0mc9z{|o<rwEvIj1M2(>`G0Bya0kQy%mMfROT7QK@IPbyxBmYcXF$&13xMu_
zz5h-Be_{-1;s1#5e?;`JoBx{K^tAtu=6@D5(F4Z%|04Wn_5Vjb^FO}-o0bVMlL1)$
z2hD$9nh7vl{#BCh-$(xM)Ad(J&-yoJ`e)w%MH7Gl8|&Xk74W?LovD9&{dXh*W#|F3
z|3CQ8_*eOl{~R1N{~HdtzdM%yD+e3{0~0Ode^1Q^;DAfkP)s>yds0$XR9@=5axAl2
zsHiYMDXlEkDtq!2wH9rKT7i`GBRYYM3Ge~swF${KLEPIOj2Pdg7efF3?RNUAqloK4
z(m}!D9K#l4eTXaqhQIWP`)ITDv;b4)o~?7_;%c7lX}#|K%lq?=#@ICAJD=KfOpb%~
z)OmNV^AYLy)(6;qc&ijiL#MM&TU2}xpv|Xq_WGv~y|Zfs#NBh5oe|F^+=FY)Ri%Ic
zgzl$hMd3%>Seo`WADQbjnWNSZ2<zrk4D+)e9^6qfSH2+O?p5=}SsvW+A5Z}tBQhED
z68tN8?GOk*f4i-}Fu%!Pf2szw%R?kRUUUe1%kdnaq6RpO@ViIhmV6-wwYs2>+6O=3
zsBz4E`Jm<O0cDA~8=SP}QAFj!c6+CLaNAw%#*9~Op80(a?Ra7lG}Ak}PhPzrO<gWJ
zzO$Da1EU3Wme+C%?^Zc!e%8q4+;_zXFK6+uxb@@R_`01hiF5OdxPo`0#Rr>+m*EVo
ziT>H;n)f}GVb?3T`gII{%;SYP#z-&St=ylkcet~bjJ~Hy=ARe6maU_TF-BX<8S;YS
zU&?uZ|76Acb5}_z*;_pZNu=7B|Jf6{8fLr$RNmwRpE1zKOyX!1=D5iv8EqQ$j+(Is
zzw7%AqZb-xiK=w4UV;NDG=m+o#s_wqY-Q|YE{<9y$OG+-8QVMRyg#lms>ctEWYqZ{
zMm)!YiC44NS^JZgdkr^g&ZNw(NBq{4D7e0?)4QQU>}5nwz<EG40KNEB!O=g(-j_Vl
zIiY#c1GWQo0(Ja!{1F4EA@RZS@e*L@KFET<eg<Mt-Ok;brdsRM7e~hMz95<fIRwS$
z?Ibi@8N8G6t|buuNxUOOqdF3ntk=X@z0d0|>p_{yIbphrG)b??yHOVI;r<9NXW7wK
zT<X+WC%LvaUuv+)ef^UqgfuRUckX8cL`?CJn!Q=|J<2=c=eQN4pLhFhnplRn#>)dP
zX4IE9PWA)w+6SPwFEsbG--d_XWzfAL*lwnHxS8;Pzp!c<pyP**VsNXnk^N=gpGx$v
zZI91bF@v{bztIfvLSjIE8+ntjXex|cQ~KO%%7N?SX_JoqD0RHhh$?6Ff-W$BtiyHv
zb;1I)o`nCF`c1mr?{03F3$7Dv;QFlefOeUZHOwJd(9Gk9iyp3=Qs+fQq4p`hr})Q!
zR^c%K#5?G$1cZ+o{AcTzc2|BWY*17_LV_ZY;Y{|8IHOL|3u_j>^4u5t^Pnf@eqJ`x
zXBORg=*2CfR6+F_t@@BkA&8kDtvDM#I6_f$bK`yEdE?F;o|)gLB8)fd{N1UXL7b09
zp$XVmbfaB4b&A3rtMVNY^2~EC`4d{Vf?E4zoZ06&d$A9sVq0r3=r=}-^1FtVzZNvL
zom~zb&smnO3GVw6aSy#!e7b(}eZ4{wyq#NCg>X$On&u%_p9h@hzr(+5*ztO$y-)2=
z+@^ET&<v~ZSKKnYwzczbl3(iWIg2xWAPdv!oM8K%gZ2Z{4NLL;MD;&63YkMx!`}e2
zjY1wC0o@&w;G5=4PVmF%RU${ku2qIM@Td%^hU|gZF(Z%ec=ngW^TY|N2E~WoQG;CK
z7vX8<g`Pmf@w=wwyM^qJ3gv}&OfjX~{(`&d(~I5$yUEjw&I)>71IWNP&i!~@SoqRe
z==&^u9I(2evp#LcLa?{&_?z&$IJ4HiF2U&gPtp1~deJb5T}rUGYj|AHz&eTkR=ZMR
zyr0I1Ti<|J<5&pUK1qGLa6Yz)g){%yy#)RlP8D7xK1+NUb~E&>^;+?<kYOgm(ozmk
z)fRTE7f}{XEBIda(0o!AWe4ZlkA!T{o*R28=>w7WP0BRadV(f!(mM(Qz0&qvR)L%8
zi!NfT_8P!Q^C3Hb<q9j52H>T<AwzHY$ztR9rP>aml`J$I@`69WZ0B#hGQB;{(bb)?
zD;25X8iutGf8KFhPAQ=OT<v0pKae3<cc<KUwch?^@#I$)tD&J{;_qaTkBwn@H2<ys
z8nYyDX&N=#H(aK;(l&y93|jtf?#|fy#5v>QQrqU`W4(K&sPJ%#YvJmahLVTe6-+Kf
z+q*&2PZgMUOg~yCtYUmJ>%u8R5T-~f)VlraoT^BwMG6>a_AT$Y#1R9%V(K`N-seZY
zAT#4NS*Kx`S8L!?n+W*T(Ff2GANRfH7ArG5H&FB|+K*$(u)JW}zWy72%9jsN3LE%1
zO!B>~Xs`n0S5PTvt+q@9`WT-z2?pF!`Cq%nF(EjxYndr8b;)@4sC^c}Ew<RL7#TuW
ze)Vp|FsNsBA``>-$oT89J2;S81+|Uvl05-!el2MA@|@!|gtggAKb+8$gXyGNa6nqj
z$*4szLJW8cc6;@++ph19iVwo!1{dv%dBu2{KJ-QSyG|*zBNO6FV(G<89OM_bSpsUw
z$0?v!sK?iq`OvYD<s)kDw;_6ERT+PU+Zub2I$aGSwI#dL;H=Zx4xn_cZ)|G0UFjc*
zIq#iK;s=!*(s_<+`UC<`B+!}Q0kM?#cJt7~f`X9qi__N=`TLmjz#)TqhT%!c$cVU}
zki-#SN$t{B4V06c@AkFts(z$7suc+pI<;R1MtSaR#7qfXn+OZf0XBl>%wCLZqD5_b
zbEjw|ALASn0;S<zc!1RtXZ+m-B?6q6A5#o{oE~h4Xg-yd*BL4zj5ZVb1G|odrqM9G
zS!WdUS;Ibt{QCDeh+#)Lgfxdu!)S4A32f=dRTn==QeEvjVAI9~y%Kd2opZwHz}(4$
zlbg-<?o8Io+N%1+)7K%()0+C)+BtWe4LZb636r5h*7>-I*HjTv471oA)S7aC0nP!Q
zCU6jS=1_LJ?x`SG8u03^mmVwCj6-U=Z#-GeMK6hs#xytsAl4&W^N*e}gYTnv5n*f~
z^q%>!S#b`B>jB;<A+Dc47s#Z<v`K9$vJM49ANX=gU?6w=(2N;gH0)zVcZkGAQYTa_
z!-x(+?7~;Zo~zN+$JM~o+N6yRpDShz`1ZD`)UivVnPk>eEj7a;Z8voBbL_#W?6E;U
zE5{Z}=5v)HT^yABE4hkycPn~^WhU3q*G_C+)_=q7gP+@j%*6O<_gjM6tbMCz$+o~?
z!Qd5agtB_@fj|b9nn^EG*@sp2UlF^;gJR3%a2@Kf4CEV9x02s_0&XvJaGQF5#_=e=
zN5gH7I^J3yy?ds37ThkEQN%<Sg%DEEj?+&t2$KK(OI}7n+6dcAYhMY=rh<ytt~@4@
z_j*IJYYqP(kDNH4Riq%z!j~w{d=iZ;6lI5CddrON+Taj{{)JBp=Dv{&gA6i}CDZ7c
zYzbw&pvr2i&P9WG4?N*dwrg!)QKR79`hNQ)Zln66n_qAxg(uC~VO2wF6H7mb6V^65
z_)l#EE+{3?D5-c`h41kSWj6CMu-Nnq0&4uQZeh_k963>B>k25ymdJE*nY8$#rg#Ur
z<S6;=Wzi@pztYDl`Q&$L&uC)x*$}pVqy9O_xSjLKw~wv~jyz0GnJSv^>Ez7FaDVl)
zdVAi|Iybkw9h&$vh1KB6pZ5{qjE)S-(Iq)V?^DP!V9%sh2(4Ba9}?2w7gF@ClU2zP
z<+>wcfKvH+#DaX8N607*%m??)F6jv-=D#rZ4ltqwZJYMkwr$%pXKdTHZS#z6+xE;E
z+qP}nJMXu<`Tk8d`8S>JYSdGybW+`&uKRlKoOWSbscA|hNx4cergrbdx`*$u5RhD@
z2OqBpT(6J7zUwOur#imeCwh*qHrnMZkkzsDRXavKu&tt?N6m62XjcWv@#(}fK>3`~
z`@vqIANCVS<n$xnQZb=3XL0f3+mM7Di)e75di7uPh7%3=*>MOS1ArwORyy+N2`eW5
z(Svz}2;?eQ<jp_SEFVp!uum>f?FzVx8wc4y1A5-;rp9nfl;(dPiyk8L09PjF*NOWj
z?-d5+0ADE;_cs-D$(c~M4TK+^t<}ZTDYX5$6+rhd8E5z8F+4|UFlS_Av!3m*|KjSM
z-jnGzRz8AW&WPc(%#V!o3{U{@Ax}<iQtzgZ03<T$aai4kq(CAgQvHRAt}cJ;7B8~n
zOJ0}VzbY4IN*5<Xvg~%Vt8Z&fr5_m?_VOH-K(Gkifnva{y#I2`OLx^MiZj3sAqk+H
zA7Q0a*M@bi*15T6Ie^}1JnQI4WYQqf@)SKphQ1y907!90MFT%uuZMI&Sxh467#1LO
z6n{9uaVLe7SXo^@&A8=K5n_p+nm|(gHd^q_XO}<x)D0pxZy*Kjq-Msj+ZulA$jGMk
zM7L`w?RPbMXw$saxpvl^vP{%1tZIFr)XSqagx)xrTCQ8qTcS9(2`J&uqrE2Jzyv5A
zm{jmLgYF1dmwj%Q?0WXDTuEFL81Bxo#7<*a<U>3PE$@fUPMdMrt0J}9qg6(X_Xt#I
zAh$fnXV2nrje04=rOM>2bu|m!&*diZJHW2}kftF?)geYt1(65ueX;Te<gjtghO3f!
z@RZa8)`-k!VLuJu3!wH?$okXqAUv#euI6mqM-aYO+edTfXxr!c?hZT=U8dIv&7Rtw
z9LvG$-d2Q<un$590ACBBjkVZCsV8Y_ZX=qk7LCtlg*?ShfckN(x3f{rRBrr4rZThT
z7eN0>!GZ`U7woHCpvtQLEaHv$?oycV*puMUoR2M%3?80bQ^J-Sz%NT#2*#y8Q+wP?
z_YVw-)@yV4EDNfT2}ObOd!$PUR$qh)_6gU`Zxv(u*}J3W8iE+wGwZce4;Ci$mGHZ=
zICN2dT-F3~sgXhLZ%)@9>RE1urV60(`@<j(y4Zw$j{0q_>Xa#TX2qKRhq^k2r}6m(
z+d?B=O?i=Cp{bhcI0utsLk96U)g$UGG0m!+l}AB^n0i7VQj`eol2gXp;SisKSy)~d
zKC>MV&j9ve?X}5atsC;2mhoThJz&kyQKYKL>E@5GE2lL_>-|ft=xk<8&Q4XUlf}FC
z%#u>MNzuYa@{&@!j7UAJ@yVyh$&#qyMQgbkZLniDED+<L@ODIw@>-bU1c2gA5Xln5
zvn7FuJh^SmuA<8Dw?yC_&`|?S9+xT)4*T0x#(zyhM{IiDcNmW+A>ZBL&Ub(z#m7%Y
z9$yvT-XsI$&*Ylavb$K)K=$#v^parnTJfkV3Fd|o&Wb1w+%3Ue=wbzD7`d%-#WkmL
zGEMHl)x-u~omVOC%RU<;K;<QMPEuqSW6@;9jg5_nE!SjR*Qyw;c7WdH6;TPj<ub7i
zy0;RaHB!mVZScEIQdojvkE|>pxrIU<Xn^hgM4rSmzZva5c!B4XBV|u*Vp{Jz{vuGg
zi($XCu*<YMhFBYjpKh1fMqE3C4+u#0mT=~^yh(q@t$3s(w32ohr2|$<|532U5$B|k
z>(3hK39VNJtfft=uOuE_TpGM~*yjf}8{IDmChVq`5|s2#%|&N*`tsO!Uy&f1&3!nL
zHvaU0o<<@EC@BYmF3$bly%7_Y{L>>eJh~`*l`i7#baqW_R_EAbw|+5+48IE2<OjA3
z9au^QXhhbVrkC)H)c_@6Q482MiK^8~BebljL$Dx*$o5A2uV9?{SGrE|==okCV-pg*
zY*}yN75&-^j~(Uf<XjYj+|gRm*e`2Fv5+)fC+Xq?^RnqIU0zoz*4Un|N}q3oqW=SV
zD}!KC!F)z~{Bf{&605XcL1HDE6q4`KsK<m4Vbq9i%@~q86hpGAf0v(~UNg~!9Qtn~
z{jfSJ0~*A6E})8gmMi|TR8c@|S=-F@eX;M($O*c2S_HY<qj2Ka=I9hR)U%f)G2QwO
z$9CMTFvmac&H|uAbT`$^fTyWUepp-pL4I*iyh;B;SZbe#kxM@mZ4Kx)NMwR)o4hfY
z8@c#q-zlU!PPgUg89N5euA}P4rPl94w&6nXa|s{d>vPr$dtK{VPV=&SX@@he63pVz
zY1WBfa|$XCXDLSZ6|Za+b`Q!2x$;yhdBpNWEW^usX^~Z88OKGVo``qr`bIK%e*071
z$eZI}`ZaM&`@>p@1M6{+Ycaoxa`mveZDTRU*saZGk<JO;gqT;NlXwnU7EZ$?W{fVM
z`>3Ctd_G^80mYWahZU#Z9alBFeGOS^&I1cTlLxxV9N?m<U&<iR-5r&*;({SoY3wNf
zA4-k7(|N=b7zN5W*SvBLv`9&w$(sw;v544m5fw<Av*tqc0(bdLr;fz7qG?GKcp4?#
zCK{;izy?o1y=p&w@e5g3QtGhg_`k)b;vpqdmIC-S)^`T2W;b8xXm>afDpe7XB3Z|`
zBNt<X<efGJy$p|1*^|Smqcmg?xPH+ti>Ya}NM;#Rrv>SHj?Ct6*71jWj2XM$sYpYW
zZ6C-@;i^d#>dfHewhqPSaPLPe{&8+GIgYX-*6L>UMgnl_Bq<FHi}CX2Nnk1Yhg6!r
z1*Ap3zl=p}`s0Jv*SVfSTN@22im3}9$kjWmUA$t;nvT12=ihbD(gZzS7!W9}Cr%F}
zIWy^BgE@DZ#p>Cst_O#Q;qC4Rm*Hb$*sY{7@pq>gM9-7T=$QCvpayABG6aoDDirLa
zG-JKxqcka+OzQXKq#s9JBMrpgLa!Q@9h<UdY52a~Z$G`R63*I;C@~|iXdlX+dVZEX
zCwC`dg3oXl^Hdw8nHpA|{;<?*bWz&NQaeHf(#rhM?Nu}4+oWGmJ0E8+Z0<b}ucIcw
zU?gK7wLjg5U);u~`Gxh5PM>?;cJi&N+d?~2voG}KJ3>_VIRP(v^DhRx=}MJvSFt4Y
zY`2B1hl`fkgq5t~`+3Qc!W<CTZ}S=H7B^{<nNw%wG0*8yPm7u!3PV#bUP<WtmLPiZ
z=zXav=px#P829T7GiXcSyaO0KfvAf3W`y;CN@S)lpMv;IQa}SQS$~MepcTkum$m)o
z|Ghtvo0!%N+}xy1BpnFz<oM^sMD@73AC9_{F!>rAzcwGl|GKPpxcq!qYt>e~a&@tK
zXRjlC$#%=;Ktw6loL@*U$DwEJ)YdLI9NBYF-h!TAkc`~xs+IgXH*dwumYRuqem5-t
z8}!OGMI7yIx5M9de({Mq8Yyw>0=DwTg=MHraT)-t@p-g`jo|M`@N&$wza5-F2z@h)
zH_HV?<`>+-{%kBq!wh#l<*arfKTP?)S&|&q+jCa@E%U?m6KYsg0ypL9X6mF?a*f)~
zln76=uT;#A;{kwO1FSc^ME1_)2inkrerz<trDuw3Q+XZP-hfb0E*&_fo%%W9P8Se&
z*ruI=?8*@$=%kdRVtIbx{y5BTj+=gGZD5s|I;t~f2bj&k?@@qg{z}tZ4LV^|4L6H`
zrbB&glbP|7sASiDG+cvCf6;T-^5GiqOY(vDj#*%I1oDpXmIsK<-Z(v6i~=MHfY8up
zzWGiyT-{(f-(Zr6Hu(@C1MON<r)kY|L#cj2gSU+#UaCAjT&F+1w!lY6t*)^G(praM
zo@s8qu{Hq?_H5K1E7x?l?%hqS<k_Qbv`mL3Jy>-VDD}a-P`!P)32UTfyR`=MN&=93
z0^r+j-HD`f8MD3Q8rF1^S8m}psyBT73en7+Q#9&yNH$!VLLAY6)0fOSR@2CB>#oGo
zZ+59=9bO^`aQJs5{xW)f!^Z36c~hxKagdOM#p>ucvJ5AnzALJ`Qx(q;VOEfCBOm1o
zjthPF<LJ=~dG^|4?pi*@<NBfpcxH>ZQItyrxloQ_l+0|~`##imw;j24yS3C>NgKfi
z21*QI7h%LYHsN-c#oH|XMGze9P?z1(Ic{+x4$@R80M-n($}ri%zF*PJE}N9rxDhD$
z=3w%wHTC<(sokC=H8R-Ae}SVLh<D%<w`t3NdhS8q8fTR)q>;;e6*!sqHDYGd_rnZA
zTOQ31a2)EfDjCSvPw<T7DqG^mA~YwqmsC{m!CQFX_=K2+M2NU!5{6S!pcpohNDO(9
zU7-Xx2YXs9OYO=|J{`;Wsl<%$bq@Zk{e$;9*<4v2LhM*-$M_MQ9)|})Ccz2xFeVL4
zgYEK;b@5hh%mS9FNWH=0^t^m9+*>`##ewHee}y$M`tI%v*H&$%SATBkbZf|GU?7q=
z$12RU<eln-={9lJO~_@+TxbR<yl6|rVLQ8!NS%kOKs-6UD!;-GmcaB(S8SOn;KlDK
zUhF&M7q)w2ja*TE54l@fQOB=&?)3F>-)AAzK)nvbD6rhmTuxO+FUHU+K{hW8U(hps
zECwE#I8l7&=f-Plvf!RKt}qq<5Kuw8i-U<BC1Pqw;inbvw}3hugio3FSS_kWLBpSR
z-P0^pzRo-{{w{a>7oQ_P7F9fwF^Ej{UW$&E)9t>e>*SQb=DjfT#}`><lX}kpfQJVA
zS&2Q$NC<X1g^7k_K#?!AApbz>S%RUVDv)z*zL63{-I48Rd!-f7*`<Mf%gKtGGrPd=
z*;J~gc<8Pr621H9{m>=76;UG|B(p2pVWr6oTepih(`tvA0wTz3ed8GhqQ5t%#oEh@
zZDqR#EfntYnGOytC`j$;5f1~&HVqqht--|DSvJGXHZL6xroLUK{22+{9j~-#PM%vB
z^z5FIbPlduR>~foA444`x%{noP}+<EyYuebXULl=S%QVx)2E0%5x>=UuMmHzr0&lt
z>deD#EhAnXJ+)Vr>wK#Zazyw=`=N7fHjrU$RUpLR%9_3*RCCOOx{vy%F|CUZIxdWi
zD5u=)*8VsIo1FpAQe!0kvool+?zYmcKkTkWx}w<E$7a@mwF8T~^0fRoI@RzM;|X2k
zgdwXY_9PYf&LDWC1bKF=aUrTHey=9y2(iq=Itn@WQUSh#o9^O0ff@`xUo<{<^bF1)
zT`q!b*aA1}N*hVdGPdkNCr-zX7s|V(jsXD~+Kr7aJMdTv&zELPxw{D8ODbCU<|ddu
z{WHBAo*0Jq1ITZ7bp#bXKj4!pRUG3;rzgsmC#~$8x8C#^FsC4C8%uNUFM`SZRp$Z+
zvgaz2n^9iH+b)P&)psNDhslcXqC~EF3ldxikczTXOi07_s<YYq?;Z~~X%mY$R&{Jc
zv+wr<_Yu}^pb=E>l_b1Rj)M2Z%e}9(H$>u5KC#CNqM&OnPTCq1!0Vh0D-C+~RJ}dT
z6iQwpv@O&5GtB+m>7pmwNKG4ui(++J82r;K4IG#du-fx$UIrTNN_e+orJ+1kY?_jJ
z9fK}oB`AL8BdOaV%}k_M8;>B7x0r!5zH0*Cff&zD$WY+KMu%5}I1}B)@a2T=^_o;;
zcah5>CtVXQMK#8Lk=&*dDJqs3H&F2QD3gY}h~-!L;Uk;#M-Awd2{R+)FWee|X^!~6
zTv(lg0pHuQ=o8!W+g@HsyAi4`?V0|2HJntj$c3Q#WHEpVms(|c<WT(DoLkN-v^`Lk
zb;sj$XRV!Zj$PB+>Uj!Rc5*Szu3i~B19P}j+_9I_&JWp2=MX&u$1dE?u0CJMS>#5I
zQy)Qz#72m!T9Z&Vh|ISs{64<CPXa5sj%9{~jG2RHOctnNS)Ihns;YGgO6bYvhUrZy
z2QzmjsVv$q5?ei=p95Q4Jv@_4Ow|fbUqsW%(>=%UW)U^MsS9E=p{r`&xPJic-zuAu
zWr4esXqJ_yK`e`y1CAqtENhSQ##cT{cW_rnUN|}h=Y*+tp*T97-6s|tbJ-VDpKh<T
zu=Y3MoCpR#K%@BrVMkh7(ujxD5A@dsoNe)5_i_X9fp&>y{{?!ybBO<Hg&IBOu5h}H
z<aJ07*mraWSlw2t>d)v1=R0Ya>Zh1XCb(=2YSZhVoC2nc5+~rrvf|Tb*2^lH*L5uA
zUQ`AKz`@`CI%O_bJhTdD*(x<*#J&_?uH5q6v~p|;G&VG(d?I4N+ql@;F)XlYS)s0I
zc+5d|Xqeuf5TW1dq`S%zUvzlROkduGY)<>sX69K%Q|9fZ`iJtE@3}U0ZKj5^*sKRf
z2>8>}V}c4KrOHyAD9H0DK*SAFby}RUnlZC&$Wnh~<>lBUwe7q98XMgO#^O{QVQ-}e
zh+F54rQZ&1c!x+-ZzCLL1w_S1wxYx2RAG@s%pe|LQl(cjkGBTXrpPzaFIyT;@pAe}
zEk=Ml2lib#wPp9^a{FxiOCUSH1MPRnXU+ElZ)R29EBqzE(G&?dXLNKJ>l1mSAd%pH
z)s3fdr9eZ1PpZ`jrI-QffT&j$_?%CeUrx8Ci>5_WahdEKalw=`s-IpzfOIX^?*6oA
zVOv54V;dms%w`DJgYc62L2$5HTJI>$rt252|1JXNur@x-$Oou+uk}3dA!A#rV8zwk
z4`a&wXw2R2`B_l0Q+-t6ymf;Mg!g0iRP+YVeIj_$$VKs4MoqXGT-n{S<}HkS0YZ$G
zaEUb_c0#;=r}Cg1%`7^4S3A-PDz(_=Cyfnr6f@uIV?8o8-<Bt(hwyM}y0#~!2X;BJ
zXpfi7kv}tXR>42&FiPe*k>uWv?k3!?XCnDsgQHI)iSQJ2pzp~n+P_siuG}+@VU_RQ
zDcpjWakZ6fLyuuq?gQi4#V+8~?wRD>a#FC%4<VEJMAe=YYPiXc=UiG&?rc9@&s#wd
z5p!9CBRw>D%8s;keI_jnGk+7EAhvEC;PMQ@fe*svxbe$(J|N4;9B{UBvu`i5#JkNj
z!N46q1O?GLlSL22BJKdr2MZ0pRRIEWdj3r%EeruIJn92&oUu@{8;H?WEuUXZl_juH
zCE|g6U6m(BYDTh|5M6Hwx6w$c9F7RZ!O_v}yd-Vl5&x_rWo#L_U;>B9#)an9g2{<H
zdT-gkjtg^A*D3+)xC|c_-7rz?;UBR$Wfxp=!o_w#P)o%oaRMpXV&Wu$l5~2*f$(Z}
z1`er^vMghdbRB(Pa!}CEnV9DAdTpBOVa9M<m*%q}U&(Z4g;cGbz;+RaJ}--t+2})Q
z;ha>hB%E>La$DSsyjY^#PQJwyX&(XJ6{FaCGDnGj3Wk->EtGGp-Sv-cX`sphMJ^)9
z0sh_gQg=V!Zn-rzXJ;o&5N$m4yzT8izX%I_{AI}u_zwRjI~I-JX3cBR2sex{F*HQ5
ztuj&NkFAuu<jKi&eNF=@4LZ02OJ%co9pgU23Y$+qoKlVFgTuCaamQNREOk57Sl*TM
z;7!jf_RjHz8&24aCWxc75;z7|3ga&!v0ZL}$kz(CuAV5Tcu-PrqBel|nOS7>s;FgL
zHvDVB>BOUoF5UdBF1Y4Dmi^Rm5qM6_orp}UTUt{quriNwYV%|&n1UI`eG-GXl^NiW
z3Ep85uu<f+k39v5F@4mGU7H|q%cXh(!}$XT{hihoAQJ@-8%%E%DI;rZL-g!VaL34L
zrj*x75~{fh-b-W92T(>%X$inVQ0kp^tbme2k)U3q`&?H2G4S?Okt>PhM`$S1mX$@C
z<lqJ1wS63}N$e3cV9T=lDOt&W4(YxqYBz{xzC0G?P)oc&f2NHX)~P_fz#e9;P<sd3
zxnRtALEA#UX_~2G1+C6Qv?Fy;3G^O+_ms?7MF)})LL)q~LaxvdFs&jVoj$ICDWy_4
zv2yjB#cSgtP&IJ_m0o$61J;Pfm{-476L;*p*29oTow(+v3fDx=mnR*|F5in@_7P}%
zDl6W(SbIL%aW>gaqC=7HjGHs(cf3}5$h-YN4zNle<@1<ukG~pk@UxAjs|-4+F;ZNi
z;>V#VYh2cGRd%?jcl!m6mRl}7Ax)c~cuR1}^XfAc(fI!!p4{j-ZJn4F0()UUfQ9IH
z8;i?_M2I58b$^f^PXHrR>Z(_X5nskpG+vY>xpxPlm1^2cDQfg03@4l3-c62Y|0cS?
z;A~HfpJT8sVxK`WMoA9~_L${j;QAD>JjwcSAkANS?FRUC$dgq0ky8!0u=k2AqYAq#
zCqaP>J2{i{H(%FeK?FIg$!f+<;_zUYU7&ao8X7~*AN{%9YYo}a%y_@A*{!nITohCN
z7yMFZk;k6j#!5ObP)}2nt3f$b&F?V7#0jZS`_gLr1hb;aTe$FiH*Dh_Ry}al48BRb
z7e%`~9X8C%X;nC3fv#O0{+CsCPzs0Lyk+B&tZ?0Sv{<`5Z|&fk5km$nQNo6ub%<xm
z;10htkEPcOes&mum|**g@OGJA?WPUTnjh@W5>RHer_tymK%Rm^yB(IEmd@u!P>pNL
zWzS10(m2K|ugmt>i68}N9}8~nNHX4qiavYUvul+LQ8DGg@KPJCw5w4%@=0xx$NdVm
zSxf2d%_nNgR(4(JJKp|fwo1}edfbIgMzmMzYEE0LYshr=G4ra>C>I9fN~`HyFneT|
z)D^v#eFhHb<c&(ZBI!6pF|Zhr#9#4VdPwOWW!>birn(sFD74cgB#T7xr@?9wZ<-?u
zQD-ZlT5@=Hw4(}c)?4>qla*Cl<DBXINev~d1K38?Npy-kjnuXA8eofW9&WqbvT_Tg
z61H75AUG|uU#xw+If3H@NI~<<(CfWVeVx1BrINsHRQ8=JT@pM7#<SmeViv-#&XyW(
zvzHCvKV!Sgn9xpSI5VS6xk@_-kEv|31@x;xwWKY5zn;h~xTZNk9%F+Dchf;DLNXPP
zMd8vAR%6)CEZ-jF{zJgyQr*lcDZw)oJX&6&dmR}1d~$u9Q9r!0`)oza@TwF^_U`LR
zF?fvC4lWnoxY9~YJq@k{gJENrrf8#Aro7a2PYq%GOr-)S)9FMlNhr^SRO{VHGL*Lh
zy^=<)j!cj!(ly4ERcl@&uvAtb{)0tsm)S@2%}ea7&(B2NT!9xElIWSES^WAno8&tU
ztCCFJe(66W&7|5j!q{ptytb_)2abEBS_RZtSF)>D;n(KBPrpoyh+(F5^(KjeCB~{S
z-)LvK$XyHVuu}Jz#6$CyT{X54ntD!BBU{JQNvOj9BBp>$_}6C8HZIqIc+M}mSzl-W
zT;O-eNHgN`bq&o#gc~(^=nQE@rF7C_i2(uRk_rZ33ju4W2KYEV;?YwT>Nl+~;Avqd
zq!^ATzA)c9+IdSEERUc*43|@mFkc9IPHA3W^4t7P{RfSND_I|f>J^q#XR5U0ELIH?
z^4)+iUHxhXG^^d@Wh;t*4JPq4Pm0p3{3Yn<x2MgiCKA0%Z9-4+hJ5h~cDZSFn8}*O
z3fTQp#5U}5qb;`C-D5T*4GNA2aXE|t9X4ispd^$vp+cHERkPJJ$`d|`mD=k`?#X(p
zCE6N`XybxMIVmMyT&WX2`Yi#BEWi-Bdfwv~ceJT^;h3EDexxwRaHl<ZG(jp~YBxiF
zGsE`XDgTO_k)lrM`nmfJPH_}|z#)cmI4!A|N$&p?C={UI`L(#&5ZY>+y_&PGSE|m}
ztZZvkH+UK1tkfTANNij8)3Ky<t*J*-QDUvdz(V3#h|U`qV=^+YKZPQgL*@f6gd#~&
zP!{=&3eAHX&7;9U5(djl=l78m7x|7V-3sA76T%N+kpH}$oE}HxCi1a>+%abOP5W{F
z9AyoNXG~z$VV+IV{=`<`E+E8!{^_~3If<5o-+3D*=j7mJ<K#KdjDdOgRp8~};Uanc
z41!O>`X1FiD_Qw)sx|Xu+VyxmN*6LvK+@}eVyBt{m#~5z@!$}1+}ggw-(uWnd<Fo0
zA)k+ZL!aMp+}7A%12K*KnQX2J>H07&$#MDWjjwCc!x%#i1yNri?p*8OyJ5_w`#hXG
zeeC<_n+dnz46J<LTmB^N32B~&maWGRTt(;1Dn<scjC=Omj_CXu`p0`q#~E%#)5&HK
zr<chv*WA#6ZEb+<{xZAW6$W>gX%!CD_IPzG?Xk#K<5>e2<+|rF*up+=hqT}D(!fB4
zg3$BwYeRhlyTcmVr;hLQ=%Ds_1|Hq|Ev9`|7|Zv5s&8(4x;UFTpfqrP3r;*2zd!mk
z_j&qZ6keY?ynP4l+v7L>%gH_DKB0fjy#MfiH{Pj@{2=}X(S^~5oCbXXf9PxMo9KfN
z+{iD?N6yziEuU?l4VkUXU+Do><KTn$dGi}lLl#4%m)1Uhg>kTI;P9}xCmX1Ftm(Oa
z_om`W7Snze*l*_ZC3$shZPVKNn#&pEZRX9D0WZ-@@1`W<NEvGGBp}a0RhdJWiR?k4
z|J#GSgRo<HfP2S#ceAf@m+>0XM!TJcXbNO=Xv$_v7xJ~|{Mvl`w8q=mlhL(2jNZ7>
z$WA0kf`CZuMLajEcz9~V;Ro)G(LUGj<I(G+)7H;GKAp_z8{G!c#@7RXxA-~qR(ne1
z19o?k-KGcd*Jl$L%@3#dX2M`bPU51v1y+z_sz$@g-qRoA5I*ZU;bSy13vE<cYgjC0
z44;!%oNUaFgI9Pu{>R=kEqKce0_oXVt;Z~6!N_>{funSUx7K~yfeWDfD^f>^E(*aF
z_k*pJ*LeDau@ro?&OUBIx8d@Oz7*6kkM@8#CT{nUV2vE)x4muj?ZZa~jmPVgM+ih8
z6WQ4K;2TicSg-P%Uu~PQ!|l_7snNtdwMrP7;hlPt&#$U4o=l~&Ou=KqwW&i!Np!3U
ztE8ACLlRyYx3vCWU0r)SC8e}9Rr*s1c%ieBTLnw)T1GRVp;gJ=Y?R{4DS5+MljM47
zLT71%;VO$+`eT}g60#x<qMm^rv97~f6!hdkl!s)a^%hGRby6ZGVq~?lOsO#zHVfTQ
zkywiA`FMvZmq~dEI`X{G{Cxb-D$<Q)Yq=n<ltOcdVYO1DcWbY65SL1H4vVuZ3HL0-
zd1!~e<|0Uph?MDqJ&jIdan^j*oJx3(f2iG5SmV+xl*%do13{*VnNt01eunNm47R5^
z7S*b;&UiV?6dTI`s%$5a!aCF+x*^ht;WoPA=KNFEV)cd1f7H&jZzL*NCgWT6QYqW^
z)?>?72^K174OSMm#$jR=wnEB7WYE|hN-*ydiY1h9N}e>7rH2%}*;HAhIW)5iW8Qq}
zltp-n7Ho2{X3Ul4f>fCSg{&EqS^08BOqB{1XK6H7-UIX2nS#%co`oe=6~ZPmold<?
z)*WYc*qRdb7o?g>QIbmK{8U&ODl0ZAur!b)1vNqew{3>D<|+Bus2!jxr80FOA+D~$
zRPhIc!I(WFGzE!J<w#-j3B4!SBjQam<Q=CXc?rUxT|{&KDFPc{ywQsWauN|3MIhSH
zmkn~D9#r;lc|PFlsbfI;$b@?fBRW<{mdgPz1kY`dx}13;5_oNF)@W<H%gg-p{C>>(
z`P1I{MDD)^)8fg0Ma_D3R~X+b{NWy$z-`DbWBbftFG=}Rwh%GMsS&oQn#=;umu=5F
zGQu`8N@uCDc}%`%@uWCsEt?st_-V*qwFTE;cZcH1<Hbgi>@Q|VxK@prOw6kTmWa;(
zfNUUf0<FdNb+c395-aLEG3-z+3j%uLu1nWF`cRh`30DD0-;m&(+2p_^QJ+2A@?QX*
zD?Ro^c;uV6hbJ2vMM+4-p@Xhl_UeQXOUl}Zd*S254bJ+f0G+noxeW(JF>>ckIL?kC
zOpr~a1rUc1%?j7*{LxYvkBeowY&73Ft<Soy?-f10S~7czGLpuf@%*@2v;t;(Ui1Mq
ztRyUD-bTG-m&g%b?=$;;F&zQRq$z|oH{xubO9@6T1NZ~|ezw8$c8ycs=4Pc5{Ml4-
zB|1_uX{v!V;u0~+FtUmxX}D}WIckvyB|k?Kl?sGMZt#43#|sL&%NyFEVV+<2Zk+Fe
z%Bm>d`R{jL5z`UuSq>m<j9~+7CRU3vIxFmy7DtKg-P{1j#k_h?U5U{blj6!tB~379
zGH1@9P!_UioI;}B7tHt{<OdW!D(qujOuKb-^fZlwm@ux9LG#ru_3JZOiPi3ACI1Fm
z?uB(z2VXn$dV5%f<e*O@NGJUfJ@<~^zzj4bU!;~la~`11lJrCswy?U66uNj@lgW~J
zo1w)8iMA3B@bn8<J6cw0edKF5-%-wn4{%$Zx9flD<ei)yO$==Qzmj>z|1FvSMMM67
zkpY%pxa1e4_^<7Mm;X`S|KAoi#{VhbXJh=|V*lTm{}T8AcWqW?mj7$)|E%$=%>RW-
zm<d?^1IDnj{uflj#Q7gM<Ue2q6U+Ys27a&n=gcpF@{8>JCzAPh|3B!&FDk&o@_!-%
z|Eci*fCw-!u(SR*BEZ1F#PNS30#`ne?pkWUh=5wdp-4-WP73YWxMuZMg7sQGt=4w7
zdt~yED;9Z7HHYj!1MV}y%qE!tq?9u?&Cv8S23m%sGw0qU!G)#=3S50C6Rvbc3R=w2
z{#msYa8M`|_xK&Z>2}S3At>&(b{IPgg<jX~Cpix{KYl4(5X8ZfyJAs+)px>T^Uk;U
zh+LGR>(c?k1kSfRxjztsuNXY|Icd0h=I^#Q&=7Z?!S_kXr70fT8lNV?IJE;_FLNQ~
z8e^lp3c;7Uucj83$M1!?)*t0Kgr%_gqy6=Vuf`5%KW*6S-w=*hjAa!6s@>t6LS9B)
ztzF+b!g@*^`W}=Z(!tD*$%p-JGyVPq&IWWMBz%~S)M`<<1Nb3ty9s&u+Iq9Sb%Q_D
z3Z1^5AW|KoAE&0M%@iUCPW(u0)6<Zle>?8qa|3jH?Rnr6wK;l(^(eUS_X*t(>*WE>
zQOgJA&@TRjZuTsDPmwEeE2iF`h%oR`@#7exdy~J5o|8iy`2lR-jOLc|9%;{WOGH0@
zm0=zcl6#MAqZ27=7t9dVAnX9zA-ct2hq`sH;>PA9v<TWL<cO1?Ib^*u?E4PoUqakH
zF9R+HzAmX;k5nQ7L8{(D{J?@<nq1QbQa*m;u>=BG!i}HNw^OoYP|6iTXu9pC&QZL0
zx5!)%S;TuazE8H+7DA|ng!(J_P6q+KsvLsTWt_J1U<vjJ+oTUy-b$ZWoBw4d^ec%4
zuEK*S<n?a6kUX%_+gN#S_W4hN-Tv3kv(ezy&%?ptILN{SB3<}l?Sy@EB~zwTui;hy
zW4*hj_R8vi6<;BK5&!zPQk2wMG`JHufg|Qm^awtZ*Arby^lW~zf~)I$J1eN0KZNM4
z1-w?2Ke1r<dxV1gCK<n_bbmYadLcfToTu<fjS{fxg5rmKZ)E<P=js6=sd%lK?$e^o
z5Bv~s)Mu@ku?Awj>+j}kWm({b;KvS*@%M!u|3$VP8_a+8+l2nps_(`JP{s888#bN2
z>4W$kgD1ACf2+CsT5hkC)hA0`XDaCjYUw2D4c3NRp4XRi!*r@s!nkdpgLpJXE*}Dp
z5P6f<9YH0Jo)FcC9lZOpkkgADAKfsamH%Tct2s{%H1`FHYADip<}zB(*jn@TbhxYd
z_b$AD)hzk>HvWqLJ-Up9Wblj|Ql*di3}>GQtmQP7!JE^TBS+!b7b6+xeq0xUcK-IY
zaws6sNWw-4>7btrK{}(z6k@W(qRmM>BD7{|?ePtD8Cx?yV}wNw40}br+}rKR-LpTm
z-rIf97N{oatw_;7GR4FQ&^yge+-K-^<fVQ!rGlOwNw=Wi`POF@EzOwB8?7a|Sw0n=
zYRo|w^nCf^3hu=E{&^pls`bXszwrUPAK39ymwdk3*a3iTTwWqDb~U(z-Dgg~CM_j3
zBY_*!7`sY<NSH(4LD1x&7^Xp9*HlN_0eF#288X|wHrDXQx)w+onzDzlr->Qp3N>L2
z;Gr{)xeqhxU!fyB5xNf91aHT16dbi2J&21Htnrtg_LCMO`?ePy1xeu?eTW<Lc2gMT
z8=Z)g6D0cp|7``ZXZ*?((+}~Qnnubt<fBwQ<sbGneLal_x8N{a3aMR)7y3j3_TDyZ
zJNP(1_yNL1f?GfCvDL5vMGyM*OC8N`#-W!6n)Y-YXafx(mXn?ShC`Z7xdxiM-Y=Mm
zgpFUWd!m<laA&-s2<_U=NCSxPv-Ph@j1W>9Z$cw+t+s+<nQ#$41^bG2aA{I%RAi>k
zrL`p9*VN1+T46#K+c`DAd_WXIt!W$wk(H`}NziSR<X=1pd|>7A7JAQY@XXc=9x4o+
zZ<65~T+Udv9ap?&Y<y7LNVw>?Y2wSHx0thMRBsGC4tnJ_T6rXow`tV)E+_L<uKwJE
znJ4L-1MWAYgG6dW?yiBz!}fT9o<O95h1*WE)rNT{#T-g9F;Q3kKZf(MY0pl!Af6m_
zkM>#50Jz$rq8{5ipAn;s9RIxBkB*=3lT)7K9V0_-)IALC-H*bb9VbR1ni7l&LOx^`
zJONE9dubem2y>qjFmx>_^#q7drt8ToO^%M8PR3^d@%kS;9tS(1qL$T~AW-=s=L<)-
z9q@K|#PznUo0AfuDu*lqImFftE`e@p2JON5Eiv!%Q4a;+4PqX4!-NlC5EZ<tHwr}G
za>NE{1tEC@;oaD|nu-!GY#-OfA$gNh>7S|yLh<UZZf9?y$VRL5>)`URZOZVk1j6!Y
zv|x!bg%6lyfjGh5SPr=f{)k*T<6!}YRPJ2^#j5!qiifkD(hB2ng$9fS+>7;@QX3Kr
zf)b(LDp(&X+=jk3Lo5HO7u4_rM1>@Mt$I|LRlRRvc~DKKEN3EZmWNUeLq!YOLh4nu
zVA+hS=2BP;N1aFXrgQ-x=0$X{sc!3d3aehp<qQgwb8^$kS##J}TtpwP>0*1zN!61G
zxn*7?aTqLh678)4pak#(JS75hR)9l;Qp_h%hd?DQDhO6ah(j&=TcChNFpA7lB&buT
zNeavTwQ51zN2H=|;-XnBm)WDQdS7hF`Lo|T+|7#gA;FxJn-w*^yfRZF+RjZ!&+~*5
zWP{>M1fWGIpLbkAK_j$j6_p`Lfk+i1q#4~2+{iVMzn<}TAV}=beE@vNJG$HTRx`*7
z-E~atm&=dV%lrDwmh*zmtex=_>vZ21a7!&cuOT63p8W#i9%)o@K_L+ZX1+7vC$q3%
z5<xOQ0yYAPdbZlxi6s#u9S)NNS2?~GwG>D6I>=1(9F~=gx<cR`>)o1ft(`ret_FTx
z>g$B2P<|g*BV$MB@OqW+%EKpPy5CQFU%<jZ08+Msu2tO{cMQuA85b_r(T-b@ukjtg
zCqMHiYD^aGqc-Z0umu+-O~xDu8}JwYnc<f`yTBh&=Ub|GLKu2cNvcA{UBP;SB?3uw
zNE3d2I$i$tD4f*zHqp3)y76F_J&c%&eyB<!MeJrt5!Ltw%YLk((?g4NS+W!hV|KES
z2a)~J=s9d+!}z35;-~!a7|B=oSRLKoh3$jwP9^w#UN<4arxOxO$J*{tKOk?I!W@=Q
zFrXR(XfAQelfzr{yx;WKnP{47B8KcxR=ULln5+g=625f)Uqr(Sv_@sYw+m4+L`3#&
zO$SNj#=IyoZq!k9TWNHsvzdjR-mr<Px#c7HHcEGJPkj+_GDh2sB$2PKt3I%OOjhX~
zWfgV(sLg9yKw!Dm%jS?Dk10$l-BPAC5$jo32E+@naY~;OpUlc|2^sZ#l7VqZsMh0z
zko6QO>=FPODM+v*wT#!N{!$&iphaQBlIx-3PM|)uVF~FeB=Y;^EEiO`ti6<ZKzXz-
z!wd?{A<5?Li4e|^v-}^%3)e5(N_j3Bo7~afNAaQYeXhHgdCORhY^41iaaElz_O*g1
z)r+_1o;FLD0gEec7?ql|pWOG&WTn$K#O`o_IX$2gl_6!Xp)5H=Al3UgJjF$V5z3iF
zvN80`0h7Qi4**kwNZY2fiwcJklmimsWlhN`gT)$8F8)UiRQ*_#0?UJlc-%saHKFtT
z6^sVjDr*CgMb$!C#XVHBIy$QDg{{*lEmo3~OUbZ0-i?!nFb;COoYu~o2jJ(1V(Zno
zM@w6LYF53o9h&*X-b17Cuf9O9mI6-~moui?CcRW3e0)&zb<4}<E16ihX_zMVa*0cG
z4eSWk>}{-og~ijw3tSg8$xmFFCi1G7e->Ar%bFV$)hg#x!?-rDpU)i&xh$Iyd_e%Z
z(f#o)2^2!{-$3#{aUej!Y^Gz0_JPuS+QhwwLHPmtmtp>}px}ZgJvJ$W6f<d#qX#P@
z@Q3CjN13VK&!1ZA^_VVq6s}yw-0g#mW;>{9m=um_+cAaWuFJ03u(RM;HmAdckI=0w
zsVTdtQz@Z#+@HI?+#;6f1;gus@hKB<ult~85$JN+JgESN{!Ia@4osjVR!@Q5<}XYU
zr(LW-xc*Cn2;K*9QSrjIaVhg<>nGO?=v4mT@xfgUdnDTZV?kqE!w!OD!er%LRevX6
znQXU-Oi4$5`X^6eu|j6GzESR~%IF@XQGRoh7hHHNby(2;Sc3-S*`&Rg5EY9|KN#K;
zimzKdho72`Nj6Dd+5eSQfdNc;6~LJo&Z;V6pt>5Dw}UFA;a`D|C6pgW%1W!1UO<xu
z+w#iqH@<xz4(uPfBXf*um3<oRz=1W%%3`2)6M$HhUCOD`DoKCoSyjUfl;A3)azk@*
z<15$~7g&#=7+hER5ecc?TlI75wu-_r1Gz0Jce&{bzYH%7??P{(&H8pU!vGq?UGF`S
zV>yvU(fL;)CYHl_EgH|!*Q2C>4U74+Xwk0eL3a=~I3Hkh!kKq##xw1Il;_5w@v49r
zqSdfBy~2*)bh}toln7GAB5#@+I<#(&_d4f_bYy6aj1XM{IZLO#D*Da=)#t|l+LGcp
zdc8pjTWR8?92Mxs!m0Jd91Wj8m{>;W-85I?wP#z^0Qpc~8#5|rTbtz3pw^ppOC773
znf=tSH*gy_j5>YJIynv1Dz6Y5?PoN*mxW}a!}gP}1_|1@1&=JKDP|pDc9o3}B6fme
zhEg$>Bv%w={t{!1jdyH{>Xb{<vbQGhrG|Yqhin--P^5T9+$+I9p6?#_GW@<BB?fEI
z!RQHs(5(qeK^aPd=c@toD7(eIxx%N?x)dQDL7hR0ZNWsyDqw(yr5E6{SyK|OxBFun
z&1kO;6RVVQ<Ic9C*|c#cI=`M<7H7Pd-aHs7vzPL80Ct!eA{7e<xze|@GC`f!^9ZIh
z6eH7c)AMFw6p6eX+a`e98{qROK|X%2zH5TPNhT%B)Y_$7-C@KOXWUpgdEH4&PF+Bm
zza!5BP<48Wf7Um3j-Z>UAP)r}z&mQ~ouyuuW-n?i>+R=TXunb!<Cv=4^cH`e<ou`H
z^0itA<wdECIl)AC;WGCj(fH_%UQC=NhvM}23Sh2}?CDGU4n2$m8bHUnC^qPs9NhdV
zoBRpuY90`g?2)jw!Me}R@|Tx&y59X24I0+9<!T)W%7X^On;zlNk4GW~`c|>r1nomT
zw7_>S1Ij=i1+wSDU=>^YHjbzu?(jGUsuH3Ar~rwp{Z*DkL;;C_RNJ(GEm~~%p$jjP
zM#r11qJzB4>b_bws6dnF5oteOv0~FJwWar%I3i;JrWqfXSaMxJ&~@M(vFlJx1=gSt
z%Mp$c%m-)!$x*#!L!y;e9jOG1hz7;Yf%}&f`j>zmun5_8j0FdECP^z}rc))t3kw|a
zI0?h@mK!M<*`cOXo0^&4ZOJ4v<2+B#Xi2P?8;}KrjoAh2h_iyLbeVhn`GEMeLGJcd
z_agZ%@{l4CLPOh1=~n997ofj<0=U7Khtr<?knRIO##+@c#An8uIGvc#?5du8*7ag%
zCZq6k(mA#F8Yz;|^h82-3NGV#-FJNOKURHScvmfB$ho=6Gi6GS$EMwK^Iv-pq5bf7
z84KL8G8CEIE#O#06%CAMlN#F#dM#i=^O{(2%Y&^98I)WPrBVBMg^ZW|S!=FCbk&R!
zE4zqxv25@-&o4QY?aHXVnmW8Zi1wmf-(US(O)YD0dS9J^$*&FX6ftMdoi;6g|CleE
zH-VsBp(ROvpxLaq0ywWn`KUrTj`HAV?joNvx~rf?vzm>+0E;=bRV=E^sfk_QPN$LT
zKU&&9)S__p)K_#aTU%Wwjy;)*&?<9V(pGaXRY2ifzl&*vcYCUlFk!F;0vLN#1Jf@)
zfwfl`t)AwS4v5098+A10{4~P=>ie>k@)1M0Y;CzbZEZb)RW8Y!Hk*p<ExYA(o|QPA
z*uJGo=G2mS;Qm8n@kPC55;s9nYzLs!+DJ0~EXwJoML(yT%_fD^F&i5a29cH@Vn8%S
zG;c2hIT>fhSMX-0ms7zk+Z`xc%oYgVxG9t5HTk>7O@44ZQQOAT28XvT(~>HWa{ETN
zOB^=G+(CvhK2@%l;Q+awrQWEDaHMT{us_S6?cm_(IH%5zV0n6N>reA=)#b2jhxhsc
z54YL2vf_KZ*^X_?;TCh2j_aHA+EZ(mbKS0M<}<mZfai>Fj+O5093>(!!<SxY_I-=R
z3ye*Eib<aTAzQ%e>|}cM3@aaF&xtFe;=ie?+kr&uMCwMXW5mmyX~@f4&y-mWbFcZn
z?f7xUzU}^T)yk8NxW`&|A#gHW3*-vsLK&h-%0H#L+gPug?vGsA=^rI%!K<nq0cofl
zdM3E18BA4Jfj|15QKfJMZ*1x14l777e!Ylp<<@;Md8Agc*_Q8Wb~2~Rcfjnb*rk2L
z98udJVQs_g%ggrX4;vdh`!Yf-JInAv!^@7(%ZG%m&&B2@Ec@hC7wZLa2YdY1{-282
z(~KLJEAuKtK0^)*J8pUChEByThMd#-<t8tW9{Pk$r6umRKS>9~cmQkB0kP)VC&U0P
zBcpk<(wAc{(CgB9-y^P&ABQIYh!pphIIO5QT+0_x+iET(Zjtl1#ixGLx}IWb%zUBm
z77PFTliRBY`X?6HW=buuI$b#p(?)~Dq>XBwsSVR1NQZf=#oS1R8Np3&as!d3ku4VC
z-j^fn-*(2mekZq-z9lw=Pam7M&_QZfWo>?~J$^7{5YVGRHI1T4LSGVkK!=#fbe%HE
z6ym=lBDy|Q6@P511+8kmLH4NV;_dIM=1ucvtchy=NP$ntN5*Ty(@qGTJg+SxxjIeM
zUIWe?3RKug$vg7vp5u%UNbOlPcp)wDEwR-88>kR9^@TkWXS>X6uf3C9i$<T?g6fy9
z;1C%u#=}uG%w!xo{ubU<B~Qg3hn)+9FWz?~pYS%}pEa=&IvDUCgu&2kZeGY(H;N|=
z^7hY6?i=C7=>*=a8$fHSj`ub{9iH?MSrIB_7!2%i7D|Q|fXtu-6I<8@lV|jal`7GO
zF2<JkGDd?AF*3=UcuJ;B6L|LyBGFuWEcTU`5AqLg*FnQhu>&7o+`_D)L1h~kma#z*
z*yd@jFeI-{x>Qz|apdRrbU37R-yEet>l(cYy7k5F8l8z{o9+~uNhMFx4ISonOScx>
zDp*(ypmlqb9o7M#=};!WBi;0BBc|=F%Z5%E1DmzGdLi<t(73JmHMPMWp!eee^NW?2
zh~=5AGFQ}waQo{BSk$d7o1s6QN4z(DTS(l$tQ<nqeoCfYJ1rlhuRao_mm_?yyU3cm
z%Yb>{DP(21aYk>8LVaB`6>L%Ifrv(zsy``_62WL;+r!BWk5hVLQ+LlBz9#0^Slf#N
zX6=rnwm~~ZEQSc=zxY6fNx?YoVTx<dE4`oO#3sMCqdyIwUv#%CfndIJ-$*-Ss#A7>
z{sehDZ7HoW7si#!z*+DsxYlkQ-0&+)FW8?MSS!~9g&)y<^C2VAg-K7eW3pb!d{t~X
ze|TIAiVme0++b2*WOPgat#*;h+>49o%7)Z2Ww%1M;t_Yg^Yhn-(Q<^1*$TtTQ{Q<K
z0RsCxRi^qq4B(r-27Ch!Z*z#ztUJbVWDukfPR#TK->_d_+4}3OZlD^Y?qbP&(#s}T
zUL(BkdC$foPT8%+`rn%dHuN>><*_h8G5P`~K$W8T(=`<#Z#Qh|>Zq@tMLQ@|;%EV{
zS|VAX6oSXD55<fHb#llKhWjAbK;~j3_5=`1bn&r<JkbAu5f^V9DCGR}ek+>4a-mjR
zV0nXVPQF-aX7}%IZ?LH=8k}HgI|`JM8Mqpe!5}F0S`g*wIBASFRPfc?hjavH$W3>{
zbN<2Eg_%-|Zu5&@D?NhABc`%n{7(}L37al9185iPoQ<$%SK^UV%S?(|%t)<k`^2nU
zUN5dfDV$ufZ0vHbt5>**Q&VExxY><HMhgo@MvZ}AAG!R@oc5+ODasQ|o13IURF8SX
zyI>NZ$`D8UQHgUkPPAP1-dOI2Z1+kzIao^no;OEn!(Mjgm;c2kj@oc<!ge0g>;C&|
zVnm3Z=+xl$!!TRrIk5e8jFM<7JAHV)mB^)LdZYWo{c_!WR{fF7PJX33Ve7}CS;b6!
za0FOc!n1l_wPW^(p^4jFH?yRP*i<ntq{Gx&ylwoTsREYFvm<X_=T7~K)j6!7T3KnY
zK9yV?N|Zfhws)b;6^K5Gd`rylMhR8eeBRBuGzJGKR!n$9qy-PGRpq`<{94Nw_-AD<
z-ZGT$CD8}@w2@|`6v4$tw$ji541mc`A&#~oPS!Qds7k%xAk$54F-u<4`{;--(y~{%
zQ>vtLYbWrl-0y*PT~f*>nh!^uccY<4Rm_n3{>PPfns<zIP>+Z)l_hw(rduq{><^^(
zrgcsir}M?hS_kb(=+DLqat(*?Zf|(Rt*l;KF)2<&t9<^ShwYafLZF=t9^d`j*SiA=
zG9?DcL3{x9$ubKap6n1~Bc88eLkqPB;m+SVE%vjoXCKy@HaR;R94Jex@g4Liyi+@7
zj{()LnZ0GzCAD=gPd6`41@e$780U9_XldrVfDHeF?t*_4W7<jMoj3%_X3v>N=}rKc
z=`+(h1y(0?^ewSzIH4`}>8WZh!BaU*tw<Z+LM0VcjYE~xG|f6pb|TT!Wb7R<`JN&c
zYnC6ms3?^>GPZJ+a=FMaQs$dCQbIF``t3NU?AZ`;uh|>F#+v85yOnDi0FhVly*&Cb
z$2g^PL6?|@UyN86e?|?zR21!o%<OYVM<qxRAokqm>~vmr+Cr9_&3+xx+2VoDI~rUn
zuiD^!!hAZpM!R)z2WgvXc$LJ~oAYZM?Idt_tw8yn_qflzH$zuBGhroVX35V>|Me$q
zy*+PrZft~xuH*FO4Pe5Io05^sW~HTNLBc=2ygWU<z{|G-I)!WL^8nuy5lvg!kmAbt
zQL6pzL)BfktiW9s_RjY0n$SE}<#4vC38kxU9R6&5u5h>Ezqr+WT$Xf`sv=TU8WA(F
z4z$Ad<vvwGh^GyLP16NXQUXI}ZIL7D8gva`OkK_}6rdWgD!PvskDV@rQq}bF&79V>
zRQ0{uE<n;XfKXr3^in^rS0A4e2%D&-nd8-U6IaYDt76L(RAoJamfYjJ#tV1}0lI;9
zfUdThS&VnJ*~I-lg8Wfc6A(2OfpwM@C#n)`P{JnI1N!xDP#$f9o$XCs4X(A1B#yaJ
z&Q?XWh-f34+Pb;$)ysUS&E9;fPpjUrypjIGs4W7U$pZ8!54*GVzq{{)N+veM3O}!-
zZRwY?))R&UQL4_iaku~KpF<uYC+@q>d3no^O>Q?DK^Oc8<%99wyVLkbVF~<WM!GIO
zO$;*Hp$uvwLikwLHmF`e%=;6=@qsEQS3QQab@Vi}*rEu(GrzHw?=&WQz38&vSn@6`
zLo%n|Ql<lS74Y2$+)#kLC&nsDl?l=cv6}*(WVA1)kH~CIfLCqjo5i2g5&Z>k(eyQv
z?}KSM+B?-f0Uwt}lRN(oL>1sq?6>u%E_2L6_-He{XNpuD|6p-;i&W73o)fHLjCE-d
zXHRrtI}ry9Bn8wf_3_-5gylW9YQpo`4;{Amj1)8i;Cu<n^<GAD3i?`{N;k{=^gkFo
z#~4qdwokWv+MKp++xE0=oBy_L+qP}nwrykD*m>UFZ1Qfh*-h$0C3ULm<o;5X`&{Sx
zy-!Vaz9EM*J)Pe>ih53h1~6=<cx~|OH(YAb*v3w7X-}jkTjC%!77E+VgH5kg7&@m{
zt>a=0nNk0_X#jN2Yb$6Isw8XuHL-16C~~E0_6pV}PCZtksPLZ0L$lfdHf4XonX5~9
z+XUj)6(#Z>@HPd)cwrrYDA`92pVc@L*(Y~0Dn(3lU@$ar<<b;h-LZRF#^VZsQx#UO
zroOai6BAaf=Tg3UdSXq#Tdyl6(;QN>h~RyAm^qo<Og>(&>3*Am4$K)E0NT}^C%n_*
zx%j=8SVAlQJhle@&X4sQCbCMOcAPQTx%3&xU;1cj$6eVcJf8Q|X(vP9zslB25?wix
zv_DNkO=o_q#4+Tm#j<qsTtRKW(ULVbm%Cfo1Y<|#_sjXfbacGftak)*Lof85DhTf+
z^F}z>by8`$n`GYl2@LA#=5wn1#q^40yQFTuv$z#+$OLjAQPk|s?zQFfS<^6GI*l$$
zcq&@^5!Zw;Ow_uRh4Hw#P>+7-*x2vXvGAaXDJ&Llkx_jEuKaNTpb``SHUz2Z;WT64
z-d~9}ETkB}p;gdQzARhJdNpDu{=$K^&6Qkmvyrq~K?7s8gs(W3IS@UKIs)xik9H=c
zT3P|l9S(vm*Ox86A#FSyekI4aX{2N1xQ5!F!TE6l+Xz~WPQ$%r@?KFkj>m@XxCK+d
zl{-^CcX=_plUvJTX(Q;RdKs?5naTSOZ6~6qu810uGNujq%b%=@g(_IlOLcdeDl$q)
z27vBbgh<O>_(d1rM(mPw%|n&%LfO4ba~F>*ZGQh2fTXKOqcYL7@$6EESqwab8-xDN
zSwDPME89J>BD5!p^IB5SAXJnwCqSm|M7fXB=p7Iipb2$=86Ljm?3K-5;<yGnH|faU
zZ1FnNQJU-I2D)4L<N``pDoWhv%K0>V!0Q1K9E5BEC3BnV@4Q{ObeS9{%&3sBnm>1r
z3T<<Nh9l>#s@~dfHm?U%1+dH<4$wyb#e1AX$!)<r^6)5{&;?>a$#?0n=UR*=718D=
zk<^cQBp^!M%Q6F|c0;6KSYAgYN#d6UI-4J0FGUjyy^LH>CN-|_ipVtNt?3v7jk7%L
z!<B-0rJ{KvA<$1UStr=AGK0=%6w~q7!*waEr}N9Fl^e0o%sD4*a`|f6vU{3NZK@Dv
zl(?CewmIG{qxP(n!In~|F&<1ki*$UCLa_Ks#+ef88f>e92`tPXz-bA~lrEB9dr$St
z)R{JcOV-$h2kr}0V_uqw{IyO@aFZR!98J%#h#K)GDCtNhLPq{maionbi?gv<AyhcX
z`(?!~Apdjh&c3;%sjUb(H#+zR0RRafbQ630@mK`bi#XSu{sFy~sebcnHG>arWXBUW
z9K#pB2vG_phCTq<fAA@)12m+mQyi%h7#@Dmi0iEPMjk4z_1wK==HF}K3He0&Y|DTi
zaMhm-BayPs@~Jago94kR+&=QlmdBJ7Ly(s_U=MG={lHn(er)|{rCjA?$*ljE*irye
zRPnJPlGCq~o3QgXiax_ki6{z(h_Cp(W#5yYs0&fnIjP(nTqs7SVp;_~(U^DWQjvE0
z%qOly$<k7Ckys&*v=aQppbusXB>v1l+Ls+qnDKf}norF4eD|RQ-5I9^7N`rL?vx26
z)p0jTURK;TrPofZl3cH3#q^0uySjZkjK9DA$Gfvz7F{-EIW3SDP)L41q&w;Cw?^W`
zrC4+@Ch8_NPud1P(MS&8$UqBrB$qg<rO&{eryrp2K0Re8=#T4=?xtKGdmgop_o`?r
zY)CBbFVIspMGUf3Q&1;mPi+45%DUQ=^2a|FJcuSomb98J916X6T^#&gh>ZJAEWx*v
zsIAmhb7UE3D{_%&U3i)bd4iMJ%|92<B#(|R@yWQEDuQI;pCq%n?Qn{HX3f@fZM@O&
zCM@FmGRUwEbQ1iLPME-ni%Iczx-{M+uxMRWDeB;l7Act=^D|2KWwZb$%#Vu4$x%-i
zR&=VBM<c}R7N&@dr&}aLxE6SUF@ajh;|^{>#TBE6I`%Rq@T_jq_A}<7ONx}oyg`{5
zeuei^rbq>{P91B<aRBD=&ip8r9~mQg?~p%{4{uYYh?I|1r%4(f%e6=hO&Pxa=ilBk
z43tkPARjsn2Jl6MbqTctoj=x~Oo&n>xrWNqq20U7K_4Td#(Q<y`0T$ApO+g=dQqt3
zJ|*lTensEikG~O1@;Q7h001uGx9OQ_dfI7vIC`?O4>d-UlQ7&M{#Lc!EBvw<Z{#Ot
zpenlG8|TMHpYj08b~oq@=b2gB$FIXi$?x`|&+*~#qt(x=8S;_Sl-K@YcXMuMfT<Vb
z5kg1>_87m*a-U`##N7^g*KVq|c1BL_p+;vv7$sOB+~`Rv`N^s5{qlESZ%vDV%clsy
z=dH}w^j%_1@30n5JfL~NZ&CV_qqmELFFgxakEeeYzZ=<O(ed-4o}|~+HiL&WB_EkD
zHxqV>{^9Y?G(I<tpR=`#BU}3cAtpC#fAh<WUD;YH`S=n&!84}+H3=*H?ce^77<xrN
z(-QIoM`?zE@pj3xMA-DR^J7S~1ji}|daJ$T#WS^!$}gU4nb(v9XG<>+)6X%P1O?x$
z+o;=<(Nxzv5DsaS$VHe*>F?rm%%9H#W&ONHwKHQ-k;Fx5zvcKuFdGb8uNYstoIOuu
zy+f_T@SLF3-QC7gPyJgwA73wXik;rlQsRCD*n1n$uWMdU4~X~u^2FGe*t<@T_swoE
zKn60k`}Tk#z#<&-;?FGIlngofmlsj`7M8*H84vO3f)K#){p<;hpP7%{8Q=viQ6HzB
zm90y(lt%+BG$jifc=ovTS$=e|^|XcR%WI#_55)^yn981aA#P)i_w?=26jm~i$`z8*
zPZlJPs3QDqI+33R0F!cHf!f6tl%ycvyCi+33pd@LDa2z5B|F<~)5_~TeA*`Ia+v>4
z&G&(X7U)PJ<8P99IVshf8R(?qI%o!3X7UpKCT%8p$_e_Gh3a+|n&gDG<9}k1*5CgN
zG|j^WRZS+JKp*%G$f2w{yo2)xAYPmV`;_Jl!*&t+{w&J<Z0NFD=K0#y{)Q8vY>jn7
zxbocXxP4Lhl?r4Dg56uvEBMN$BdiFz$BJ+#H`h|Sr^D)fD(+l{wlgCXOP<HSa6+*(
zBTy@T_QD!xrGUYK6!f79TT1D27?(<UO-`xz%bGmT7SH3|yPf?HBhePY?sahK`BW!Z
zlDah8JI)fs*$Yqb4@NPJoF|`TyVy~5(=({f2<4YsUMs#&XE2%?J@usDAjr~{za7T6
z#=3-jR_eZ%P#{h4FIK4GlQLx_99hLO%q$BU6oFXXHiWtF)0L;K&JswX>dSVySB*ao
z6GAO6TQt40f;lCMPGCNXR>r<rG1%gr7Vel71GR-S$WM|K9YD@OCDKjw{KtK$UHE>t
zo~wcnofa&Tw^3^1etZ_T%vZls!$*e8wO{hU&ae@%P)e1h<s+^_7x4DZfW{{A-f<wK
zF$44-*hf9&A-`OJEZyovK;<`Od(ZMso@)}NtogA7k9Nhb@@*t`CH?uLj3k|K^?M~u
zEW-!dB<|CKH!!KJ5>bD^k`j8+L*4V{Ke5i$3wC7$Dhr!P<=)uEulqSZnab7k5N8xM
zOH7C>No9ykDd~b**I$(6xkP)j3p?1somXKxWQ&sfhgdw`sM;>%ej?K<b9erwdKnFU
zhZJBC5!re$arScXbB7LHXLozX863P1V;ZMv%H#-yq~9NNhW(HXsNr~#3cDqLO%|N}
z0M)SOd;VWA#s3I@|KHdi6BFBi$MkgK`Yi_;U_)+wpm8^a9TYbP3Bbg}2t%t2*YW!5
zlb7n&#2nz=KgMm(^g*zXetw4R#!I}dvZD$3$1mZDVOKB**6E9oJaDQ}On^r&+MM53
z5}}2dwyGV6%AEtzRlpxc4sa}jw=3^?Z-ToL1RHFFwp|w;c4-Me_k!qz2rsZ;;d#z<
zf)Q8=$D(jG2RouK{nPnJSFsRd+UfBPn%FrYi{q296_ml%;Fg0c(axs$y$jz3cYoJ+
zdU4i&xY^>}T=P1dd(O&x5FYTjLvl()=;QLOqIyXvbM>P9lPvsSs`VeH^8ce+jQ=Fd
z{~K~-`{(v%`0v%b`Ug2GjV_<1r+bpR$J0(m8c&K%Hg^$hA~Qi)<2ZFUU>N>$vp@^B
z{KuQVHW!z$$C588f^06-*bLqfHzzRJ3@akA!C{eNN?m00%o4|6G_8o0fSf_5-_5KR
zBJk7oa{QyaJGpziikZvFe43Tc$?RyF7!3phM2P`4x5~44&8p)tZ4A@^*4`iU+h%)p
z=EIFypc>zA=1hIjYEnx=54`P)94O=x-I-puo=86(O1>AHfU(Z)Z9^3MMA-*FZxTY@
z-CELYbAaCGYk0P<rk5Z_BOvO@BHef7{gE-Z*I4v68G&Zr#PFLP=;7p82iFF>@|Fv%
zFY+i?bKb?owPnX1Zi1X}u%)1}HQEM!6KJN%{-efRYu;VF?h?iiT@E-&#o6`mlh~sH
z;RLUCucwOHi{DENJ_yStkfI&rj+)IgFDw1d-|v8>i&>}jF|oGW?JjZ--tU#<%APA;
z&&GYGZKo-+vUi*~%WGjchEX8B=kG(;kjWY85JBHG$0)0O1Xy`6)Ieb4S75(a;1+3x
z33c#wSfaBb&mln70%oB#H%);Ukz$2G0f8bJPGG|sf%Y5jGDy&hg)>{#<OvVW4$qRr
zM6(M(0s=W&to{kjf$Emh*pf<;Znfm}&VXa)!T|I$(~k`S=YV<s#ZB`Jc(k`d*9C?J
z`YcbPRCla2S>9Xl9l2S@&v+>0@iSC#_CcR;g85tJd1!wZlI|(~u`6^x&~7k=V`%?}
z{1?6O>t+ifyhE5U&Nn&GIwtR{&F0-7!KTlTHLOsMAwu|FGQ=>vB|<EM9=nnMIqm@Q
zzb^demjCl~Uk`HNu<q=?p7`nt3YcxR`Db|k&-i^`Xs_*F-gd;Ga0U%GAix@0SzDPl
z8ETGnV``rK6fopPF^o4u`gM><^VP4hf>M1f&)-`=a#26*{xG9d|24n=^|GJUz7R&&
z*G=SSLwKblf?xLNC%F29sG=T}O#<XJ!N+sp-F4Yf1+`YjS^?A7Uj6QAzO%rVvclQ$
zDyPuue{-D+Zoiez!uS|OW+?E#o(r>YbskQXem<k&^bkZq^}NqPi|cC@+a!QHNy2u$
zV0jAjKKUK7$scYT)1c>I7tMgCGI&ojS6}sjo~KpxPUf-epL{ph<4>*s`5T%WIxi!B
zPtxnPIE^2OW?w&=HM8kaeUND@^7d2!w$>Pb7fyB;^4v5mAg})OoDG1O^&Hi0RuMk{
zXgSwSl~lz#7^mYh3gl#^Wacbb3;HPh6ClbN&6S`7BIF7FWGGKODq=Hj(CLHy#%{o(
zUs13YRGD_EFNw;C{FYRTv6whz4gZ(&yIf4#0x@3FrB1(2WPIbqUt(9D3c_|Al#x?X
z!d_{?TXO9}qi@APU*1YdVs0gmwMqlU3U*oF@m(~;RbInf-vsuKlE{!AL^(|2{DQIw
zRzOx(o?nq0AR8-2$*JfanDEb;2ZMTIZLU?;B=f?)q;>TyU=c2K!Byk}UZhF7TwEcL
zy%AMVuGD17+)~`dUN^s@$Ar<rY3TQyN9~^9RA-Z;XJ7-oBbEWPS*<3chRg1oJUkKY
z{EbbQq^Vxk-?cQ-m_G-{X7b_m6RgM`3kqB%oIf+7UxtL;gBe<9Q3AJFfDPgkY~2T8
z(}mw9SHI&d1($`QZK{M^q-h~(u7OBSMqbnx)%<KP8{<Xb0Smyg&R6X<R`WumoKwx+
z%mvQJw1R9f(Rv<ESV-^@*0fgtP*My<8GnaC$v!o)lu|h@FTp$=O}39L8b5=Ns2&VS
zc_qDC<iK-|36vk@P!|4$NK;HR^rl`xvIH(1LUn}g<}T`lMWS`a-1n9$`+00AyU8Ga
z$l?XTGv0mFRha*&t?AI76i?h9XntugHa`Mo*;CMD{3nR&-+FuqSSW&~z4a!DiY<;;
zXSUq!NEqyOn!<a@hmkvWlhy!1rw(v1MEk$^D-uVv)TivkEPA}8x!9MANj|0d-&sGB
ztj5LcH06pCD?fr;n2GGuNf{-xe>bs&`uFQ?EW0Vt6JxpQv1Q}afFQMzvN#kINu9be
z9lx^t`3o8qEY-2Hf~4VWlou{Pr-VT3QJWc>sh)P-PpwWceVJ;d?Jbpm^f2Zx=KNLQ
z>shHn_KMVTB$_sR4e}+PNa%|bY9ruYB+CIhS@^yR-$aiI!mw~?(B?{)Tk;h5L>k1l
zs`>JT##XT4!Zz{ddY#7168WH0v!D4lA$#rTu_6ezO%&1WkaZ~15-JwqL!|k013vmQ
zs=Y*7@8KC3TCI$EES(?;3pWW@3B4%T=d}UGo>q16N~o3}q=3UGpY$yb$C@8M;Mo{#
ztuF&9bDcW2x!hQ$A7E_Tzf`FJI<tqUXtTWVJxyq(><T+h0??;&MM0CYx0qkR$)((*
z#B)gbj5&lW^8OofQKwqsB4Lt8jvF8kPK>&GGM(n?TA|3GsW@OftOCDB^6;6feK;Me
zv`J%A$>s5fk@l+?0PqDjZXlp2VX^A(^)r}M+(Pf~qmwD1mVcN-SU=<wAKzyl=KwV>
zf}(4>Rfml|7yVW3-~Ld3e=8X+mjHVoq*xC7Qu3|nJ<v<tO`r&Mq7rY&@KS9R#gt!9
zim6iJQo>3_A;D$+NOED&O%t|S7<-z?FYtVWKU$;!f8^blr3vAX`%{21M=0he6lEm+
z3>{~#%~2R+MI%j%z+A4%ZWHw+&BTM>WtUXu!m2GyK8l)yT#X`(+!4l5Ft5V-rTL*1
z4!U<}P!#1@-P<PZGmz@oY%(JM3HL@_n!J4v=`r-Gs7k?OqN;TUm%D3H51JN(KFon3
z;o?`DyiG*dQp>MdkMd~xxD<6?&3_=>0$N762{1X4zCu%*5v}EA((8lj>(Gc8|35XP
zf_{k}urvipFlqo97q$XsCA~Zgcp_(iu5Iel%6Khd<ST6vvx`tc`^DMa1`S*SuOHP?
zc?>B$j)yXLESJz{0yF>FK)eJDrf|yyqQ|qBc9M|b=oJB-erErQxl(DzbO0w#&^Qlz
zUxZ-<lx$7d@p3MRNpPia&&u0x@vn2haFVlS^+kGVQ<l<v1%I0Qxnh|G4z&9z)NZU%
zCo?|JJSS$Tnpkba-2z*13;b<T{X?R=!el#C<Zt1a4wmIZ)E7MR_WI)iQhqr}yNr8W
z7<g++JZ9GLxe2}!{!pH}hTaAg`RFGR^+dDix?b42cvZbWWZdwA&%oA3xBUttz_4SC
z`V*mR(dQHe7`Vo_uq-TY$G;TS(xnDPhj&1=5ogB#ekXJb|3fs@;~iMCOcY4%LanSq
zQH(~0kwJ;2yTz@L*EjDNiA?Mo3*}L%S>=^acr^;eAq-Xps0j6gudibB2o&Siu|eO!
zcWh$o(dEB!Jkii9hI8O97rWH=nXix-3}2oby$2J#K`AYC%gzm2_jEbK?vaL!!pM=p
z&kPazJ%;{-c_M;K46j9;iCLfEv_xJDal7ijDMxLG>4aq>g5#`*#`|){uFJN-?xL!?
zFmHmSORzU=s=Na&;1s<O`hqQZGR~g2?0?3_1K$4W$~qZygP&>)JjfqtC2sq8O8K#7
zY7Huv0o{X6+?P<Xby${Sd0Qc8g2rhZ0p+wDPFk;xP(}m)8XU_h@9`e-HDL-kHI;)d
zlG%lokTUB#*BnJ^hjV(-#ICF{TgwD_=G&av&WzcLE%44O`7>Ng8li;;2E1#!2);&!
z)&fn0OgJxiIIc5l40s|u1rN2OZp;TRIYzs~4m4twwuX}fqN+{}!=T9xtFB?R1NkZI
z1^!n&w<WazsPHYM_!8Jf<(kcMDQ-yQ1kP4R0&;pWh0BKw)D_@e;i5Qy2fhRIArBFM
z=b*>!mB1@S^?Xpy;f|adv&-PW1$*V>Bco~PnofRn7RJT$)q!6t;LFBWA=j+f>Ye}O
z(W5&phY=Q|-%W=SS;jDNc1Ed$bbSsQequT!y)g(zeObWwze-{We>0{=c$Al=Mr<h`
zl@OsboFt$P5g5MIDLd){YG7CS$4SG2DgFrBLO(xXjF7uSJ3ya2r7arC#%hY6VK&YA
z)x*_;$Cq~*g?$<YFrS*_7`P)U6i&!haGe@6(M!*ckp>42i%^nnMvk8t5~+Pyb~S(!
z7tXgCEH3dX;b(fdRn+NQt}gvmT3?fJvM0EDa8tRk2XxS9Wgp^ZYBJ~C2u`Bucfs9L
z5P?#U*oxpAq8A9f=vxnOPe-2#TZ?J?Q5c5jKC(vEJFAbpZ7O5RIdFDM%+w)t4yqha
zvo6}>Yx%Xo#T#sQD)eYA#}q%8d4V4qQT4j3`*u#W8hX{hDox&X8{ODji#`$330#Sx
z?lWWte%#>b@{gzv*$(lBfn-J+z-J2k#u%_~PfHnpmB~;Wo9wUH$}D19!m?nJtH`-j
z-0N6+C1QOnap=jW7x43n{!~)F<IBvW8wQ{W!a=u`)$7Ps%->eev#*{-T0N;}LS5yR
zrdw9+Grmbg)2kdJa}L)0gzEgTcYdREewVs@Np*fBbJM3rP728}FL8vA9>KHb6pVla
z5>$SgRQN|L@U50t&&E{)s)ehuV&#osV#iB8l`fSC_YvPvFY3vVYv@`I!fTL`;nY?M
zFrn4tL;!5s-M`1t_b)c9V-tK4|27en4!(_6v!)-RrqkSg?uML`8#7%Ac_5z~79j|+
zIuLmiXnWJH_To?O2EKjt<sZJk2t{hzVy9O<_;P9AknbnBSpOZ}PiS7d;FT|E@i1#U
z;sDqoq;3Trwb+jXNHPaxZTA}N|4LZ>!P0<zOS*LNO9V)lz_)ThS?R_&8CKZ~w*{qE
zoWhAn=%`}_7sYA4QN-to+RR|3EXkhu7pca>Bv>|qXHoqjEU&oturE_aPpKK!Wbw=%
zt@f?u8{&q~jbFM`z3hxN{4q6p<{vw{qq~%be6<g*4wWVvYTtd=I*<EX{`o5%w*@{$
z#EHVKibQCf&x#+uA)q7as`Z9a!}_beNXk`$Vz_)qRmbwHo@Lde73I<3vY9j%z^o7c
z?8X)WN{UjYROzaLosAS=*p7vrh*GVxKxIYRFe*BB<gc@$@P=d?0eQLByvv2GMH4rd
z)V&g+(Kv!v^gEBV@)+5swnc|qi(7|VSF470d85gFL)#`~ri}Sa(P_LxBwMz09*V01
zR7Z@&L&oKPmW!DMLY4(27M#^4kLLtG^1%#dUO}5zip#3t53^Ocp>;sXil#|bC1pYM
zjQmJ@2CB}a>*rU{7FJ)Dklj$LMkT&ZQX`xC>!|>wAL2`h7EM;oroA+QKt@tOyt!p7
zWvQ8=9P&LcwN$g|nC2n#M-<8e74~H>88rN8O>N&*2jY}_^91f6=CA#TQ2d!nI0{`>
z+-SoN$@DRP2`&M(g_@9e(5B2pzaOCXh#Kp({<ouw_da)4`#D?XKug)S4Iqv-ERHe1
z3N{rLVP9vKr5A1-#$xg@<C-9!r=W@^4plWiHh<@4`v6NPu%k-n<3%KGI9y^?jf>!9
zQcy`1YUw`^%?;W$OSeu1QsIe}PqEGgLcNYvR6Bo@)d3?@P<~1_qmm$&+(BN&N~nv1
zW*95^TuTmL)cK9iy?HVa8#|h7{rod9QgGQ}f+mL<q^93XjeqIAtX>^^0`xsck!A1e
zA5)ezYH5-#Y`$!8^@wlw4%o_j@VT%wzrJt*X5MXoY^~xpE<iyMYHF=I%BnMGT2)%V
zt*EqSsVfg{c-Nj-56I|z;#!x7HZ2cqcq#bHT9#P~Q&^f`Kb+;U%QDdmL0Kq~QbiGW
zLz^{1ZlEr(Fzwj&j5sNPa`Yr?{1RxEsXRpJsH!Qe_ldNNw37z->u}N1YccIwaY(pB
zMInq!C7bs^90wQPANhHCdAoUeG`lx~9d0cfC$5Hvt#a3kSRQ6mE0QlDMB!H;f8`=5
zKTR5;Nx)>mM4(+Eb`;T>sVS_&=WAHHu%WZwB6=mvo>M<uBW@*Zp3?zt5vvlGEod8V
z5LGJ?%N{2~Mk^517Hcm4E@xJ>s@#^N8Cf41#SFrC^vxfu->x04ova<s+4uAO__TXu
z9vVSvMdwI(AYomoII0m3<L6xKOmi;;t{g+6bfCN=rxEt2>`BI<Xd(~MwdB|sPEBl0
zjb^NakR7m6RaH}o6R}%XOt6#N$SEd3B*u`0N0`bxia5$RDiUw7;6+zi%!9FRXYfs%
zPP_aWFr7A~g(Gt&|4Yg!oIpFCNJ8FB60S-Me5HjzgkQK^N!uV;S>8%Ux}t?y-AZ<L
zQSJ2#QnOsc<r@vkb>}nHi`j*`fJ8b37)lQ#`ouciCGLrQWfuXi8GLma`apu@2u*HS
zfOuT+;DZ20-<6!6;@&I2i-{z#(Lya`J{>V1EN0ZyIb<b<`<OtnQoOH_-6I&8CG7Ek
zB|Jb6BYI@2)qJ~H!-9Phh>g^51!C^zxp4T4j869JC3^=i6^7&CIO02~f(~#`1+mbG
z<>3L;4Fg40zJ;f2&02a21fF*h*$NV-)E|*n*>geg4u9Y<^b%SkG8>i}$kIgP5mkKX
z@bqW^W<P*i2luc~f(b6yM}Q}faDZC_-qI(+0qaQszLIe2z(d-_bv^Vo8!@UuoEG-W
zb?}k_LG@pTE3DqepF1{2LtePKX!@y%Ge`{f{^)tovgBXRcQ70hDT(QOBTpon0Y>{l
zf93@l1Qni!&#cluD7(ZT3cnbjlzanNyu_<Q@edX~F?rB=15-|fxp_oZ;t}o^FoL27
z%n4DuaYhFD+|pSLEU~J7>q)PLuIAtmd<}^}6p9!6hUo2+=A&4>^KFHpg&M@A)N?s-
zujK*J`Ag@+74HVFN^)TiADJ68v3alq@B`$^Bl)JA%tK+w20_}uZ-(dHz+(<^$-|ZR
zmTk~OQpfa$SLa&D)Zx?w@io5*QnukB$0(R0GVTtf>qCbbU`#UgAzS!?#3fLm;shT!
zHVHjKyym0KO`m4xDWbsmHQV6uM%iSM5H9p#rusdCk%Vf`cs*&K?Fm1@V)~@bM>jcc
za{Et>NkUxDSe?9`WSvUiqwl!7P@@CS8PGfQ;k?jq1~t4va)RW>Qdxjm5n$xP)xyaO
zTld${1ZX#QqBfZ8Z=yM*4Z`%@z&+E&tk)tPjue>!X~;6ia7l!8$!nG1nh)oH2$j|z
zuV@XTD8wr3N@%{{#p%;QrR^bV`saJ#J~~ly0?Z`IC<%(!3f1a9Ex2-4jM{SXO6(fp
ztP}0UKKtkD^J7DLqCx~Bb=i<c4On@i3hx-lg|H*WnHGe1L?_u7yJ><k(}t+j;jB!6
zU5Gaad<K1}tB_rw?7|X<*&I?^Y|bL`Qw#pgUoKmvLR?b?A!EQ9&HJL|v+Kk>I~%Ek
zU+!uC>aRYdZ|!&K8WZ1RbVJh(;UCbmhwxjQKPg^&hkojSJ5q&pP24_mctCYe=@OQG
z6u`xZIK#LobZ&~LE5T;4&KEzUe)3M4<g!P<9;{2gHmf}>C;p`{A8C-#rr;&<*wrGz
zExB3axjz-&NIVNM)58lU%qhH!*<o~cpU*FDCcawmB});^)bs?=^kDnckBfmPCbxwk
zHwbfAVR=~^gp9<yBSZ2Of}F*QpB|=GI&O@W9%FzO!f8ww!nPorqOUIZKK3&1eB=Ru
zd5g!o8R)qt;3xiI<Pm~5bW6t}^(DejkbCo;6TtLnOhi*;uO7--nGbJDpXsYhb1^t8
zsXW8V$`3sQ;uX*{IG+mtu))?EzcKCAm#gEi^ZlhvfwU#m%%G)CqfL&3ri-kL(t}!U
z7}zGVMa7q2Z!TtKYLQ}bvv4)vRm)kpY_4^O)+74?X`_5Pw0(hj_0kjRGkm>*L&j?u
zVo%B&aXB#6F^oM%gTn}CV=}p3edT&U1V~sP!zLL@X_zd0X%u_syd-KoHyY}%GW@Lt
z#x9(6Rd73}^fW6(W>JhEN3<V$gtReyaaSHe@U1(BT7$AX3iDYhFIr0$_(bV~+e!B>
zU54D6P<WvFj=UCR#DW`hP|@SR6CUg%ow^Xchb|VWH=u~~k{hme;3k*Ho)CY9Frexd
zEBR#iYTX6u8jU`VN|>uFMm<iq<uIFoWFJr!WcxrIH%|B<j@)1QE&Gg^E|uuef?7dt
z^l!@|QDY*1oyaeHY)Rm0651z{>|wlR9E0DlIou$<ftA0A@t(3B8Nk%`px1<89OSI}
zwvS$hU`*RkUOtqBV;^v}y#8#3`MA(i`(y6;zdgB^4K&~5LGXV9b=f0&--+GLb?WOh
z#nKFU`(oYXqTUHY+a?5c7DL%+#h!|yIq&JV0cm~Vy!@Qnui6p$2HzrM*Hj5oAh#Wv
z<^#Ky5Zv@)GtvaTrF}qcm8_G0<7I?H3Dm*ah|>GQ&<!%5>R9tbR?PRb+t<nmsBYSu
z>4uZ~FtI=x|Mf+5&yjD#vdQ_^(-2Jy;=aY5H(47jb`$$E%zUq*j@6BFVSi&PQX6R-
z1U~Os_;?4!$J~MPvdcQp5ao|O1e8tN5j5X6$|s#O^mz8C0qNyh@($ugT-GoIf$<aM
zI+p-1AnuOpCcEcsdOnp9WpvP=cqyjz%t0O4ci16w2qNF>OmPF>#(3LJ2sikAKD0nJ
z;g@v5q{FE<s*Abkmgl>EP#dVXYhhiO$J~zL7?JJK3`%Rg+B&(=3;SDU?0?$n|J60W
z+l6glNW1GX8PSRY->n;VJH!a5NAc-(J=`{B+2I{In5;&=r9ZsSafI42w7lP03~LQ9
zcbl~wVL%zA@iGFxJLW~Wm43W8Z35b@gSkg*!O+u{X4H;p?V{^KuU63W-5vgzV|63G
z9BiF=xesvy4MFTNwHtQ*!FBsrk6XFT`)D`*ofVfuS~DO$G$an7-htM+!;8*)N-qnZ
zK=!uNJ@>B2ZlK=bm($z5m)B=E(4C>q^-X(JJGId}pJ(#C&mR>O$2WS6d)ij*^}UA|
zB|z9W^sa&CyE=vlp^$HLs$fA1`7_ekip9{VysUq`p#(u{;$+_yQ&tV1mUsES^53S$
z<-NhCeOR$Vo)jNMq76036wrC1YdzV%E0=bi90g#{4BqK*r19T-QPFTY@uIKD^e2Iz
zw+XT0v5BR_lZ2S!KOVNrgzpT-!a`2+vKY*^EMtIabKD<eYNH@;C&QUj%E(bao(97w
z;k#eYlmle^1*=TmSvf)H&>9GF6c|TU^WhD5?k)RI;Y#ESQOP$bd))iXpB71>Rm%MF
z4)G%%ky&|i<RY0lc_#YLZ}PO&IMt-c)MXgFC0m)v>G>u+D-!TCgwoKT8C<QMVr>Og
zB_*)&G^WJV057Fdwsajc9@gS49nC?{W?(&}Si{<<GX;a4N^zp~-tliwCg~Uve*Czs
zLKV#m%s)i)FOYTIh~eL2GlL>6n1xvm7+<{U=81pCSr}bGT{L6a-&TNWCnW{3X7v5G
zw<}l4p((p|H8?p-nudN4)c&C+h%U0JJF=~(@0_>-Was!kJ6Zn<8Qr&Tj+IM|tjgm5
z%zvVq<dcYR#`Hp+aMXQzVAKyrkMjQ-0M36%qv5KVQc2h4T;gM^ENCjCGJ{Q&_};j(
z)>Bz7)4&zBODL$6I;yxYgto)=$nJf@1<H^cE`Q}hdQ~60EzOXGK9iwbe=k@%Sc(r2
zx9SSgK7dc_97BGDe$jl)KEiu>JYk=@O^luL=uRieV960fz&64=OgA^x$Pse%54D&o
z7&SJeqZrw8D`Qg0pl+b%;j?RWh3eT^Nn162@es04bPqhWZCiX~cAIP5u5{C5;I+bg
z>M`-y_8RgWc*=hpyfWHZybC_R#XBWoT3<TLkp?MQzcDGHZp>xlKXxDfj5y_e^vwAe
z1xDI`Q>Q`8=^~!KtZ3{iUny#q3acueqSK&78|*S}%*O3!?8k{M(Xx({KRbcKa|0W@
ze&mq3Jrv!N3212i!y;G;dglfm>6CMuH)z~BiNluZ-KkTLuo1f!yUzMt-H_@Ab*yp*
zSKhM3!bR<TS=Q1zMW^je*YjC|d6Jtmi?VEyd_$-mwG6@fy0oS&xj<z%m)t&72`5Ly
zS{c5-OS&(W#_vlCQkA90K1;8)6%u8PA1)dq)yP@BV9lT!Fr#e|ArgDVtdhZlnLPCS
zLm!n<z&^5ZY<%d-6lz@q)oHyQ=@}m|@L*lxfYul!G6hFNFE!^-3rO$cOPx(MWl?$f
z@+&e)YnYg1zW#l@Q*T7IAcl^HD)HMz=qf!yxN`iHoY5fKOim+<HYcBG_Uf}LDybyB
zDE2cLhur&y^G&0D2JK~Ca2AL1^2%)Bg4wF3CB}(Md{eca)iLq`I$N)YvTU6>|1n@L
z7298AH`0fKFG^QK;=9JfL}^SSD)U!9z;T#ND#|?xbz$Mj;asg`S2T8kV!4DHM=a}F
zq!ZL^)6kXS21ya(<=42m3~>@yR19q4{C-&6$I;TjHVGr4zr1wJOj_Skx(wTljC3O=
z55#&OZ2Vb$w*^Oa8b>obD{IPH?W&QX731y$Y!7sM+S)6=8V+^I@Hu8zgiPo5AweMg
zfdm`I8BM5vs*^|o`LT%ZQoPQRi{6yDcFi00yri<YM*D<w1~bDKd5^<~p)TNH2#ygF
z*od@TIa}DO?;7}|w&HP86l%oUi1A*qxE`|v1HDh%2%Uym*W{D&4U<#k88eq$5Akp`
zsn3gvABfsPsr;}Prf5DJlsJlS;%ACj`j+_suN8k=VW#?M{Enf%%-Gk3RFHn~$O=$V
zzmR59RTLsKbT#306r4y^pFzy{@YWM0MOCUY(7s!m{UL()rSNye$x;%?QieiCb}s+t
zS}R9j8to=^{}}BijLu28HkI3u)Kw%TC#;7&{}zrr=V`V^L*5Snb8eby&rV$H06E3~
z=q)YC>_Pm}bDtVDi%l=fTWCUg`gu8-8T8KF%X4evi6ymwvgGRz5)k==2P7rI|I!Bf
z=_`?JS6gA9TFv&N>EXEg+d1`%LalS<Q{ub7fSuOvq$j?1oQ@^w_itq`YM4=U_lXlU
zI@&9`OKNEkED2d#2V13^%5^)<p={Bk_RrNnc1OLsubT7XZyDMe6VwaEch}>BRcO^Z
z1Jl%tbb>k6#>Tqwa+WD&Bo@r#XeK5mirK1WK{&!O<l-~uGf0kPsSc-^dzF4nM5&Y}
zPFm~w>+wvgszNO1=T2ynCv@To?VU*CmJt`BlvK>5q0CAxDJi03u8>zYxMzx~0J$L>
zsc54d4qvPvS~+0qufBo=6kvH=Mc$&5+`e^2)rwB#ZB0)0-nAqv7qs7~na~seL<nNR
z>%^1ci$uwTbqLy?v@*6j)YxKs14$z=F-R@<`mGxNchQL3hnhUs`o~la5!@A%3%TT?
zP53ujIjN<SIGFQ3$htY{`5n?^zn!5fI?ra>BVlL{QqyYJ8{2l>8bj!R1#4&=@BCXM
zb|7~&9|vs*UlhybRp#4FH=xW?mE~Li>72IvHeIU7h`@B0zR7a(wsMF0+L^oHvkG72
zoY|O6c~n+<t$>Uzl97?=YMu2pQ{v+1#4a3NM{R+;TI%N|t3r|BSNRdy6mw*`=(Ae#
zUoC6=hgX-7nFl`GayLKrRv$E)%tng*)Uoph&I}AWengAOL>cb!?E`qvGj2T}z#I;L
z^}Lew4q%e<J@OJuiw7!~=JJme<q|J!J*gU$a40QS{MGtw-xxl?sz-Rg@}!kpk6DZ9
z_cR<(XNsR<o1M@h0H5yPVm(o5Cp@e^&s=27#3w<R<6Qu9&*CZcCi?*0zTZNyDGk#Q
zF2y*-VAo{dcrQv!V4D`hi^c=RE@1VbfpvFlw@BiJ;D*}xxI(>x=$zg$JpJ16>c@0n
zsA;92;Leaz%pC`inLoa|#4C)4^USD+XUjD7>i;2|9?wiT|1rGP>N}*k;i>HKEb%n;
zF!j*oy-mma6MIr_F)f@Rp1?~h-SRm!f?<R`g65+S3ZF9kaoq=Up8$3&Dl|4C%Qk*e
z4>iK59_P3M1x*+(+J#LIM#n>UT1O%{G#)<+oN%kROsqxLx_i=j`)qs5eO`O>?&_@K
ztn#<^yY?m>i504u7#A+zfF%OHz(^lWO9CRW$HV6A2C<PV=q1$N95%A!qY0C^(U>hK
zEo177_DhZdUr@O$=1PNe9b=ujxzxdO_7@}d<J<eu5#Bj%-(Sj?Les;)Z;=IBg{xxg
zv7&-ZH|z4$&9j;82(!cl)<6*A5AKMGq4B6?!GgB@H|6O9r-l!xY88}mJXDxO@Tct7
zeVzm2S+deZ{hG!=*ZVs9HDaSdWBEE&(E?4fK+x~G0!y$0#(gPOEY&M4$+{wS?j`{n
zbS@efrLMjw&QS=vFVTwm<R=ZJi7+kGy?2fuE!uM`!>PQ_sBFCtN>^_BCY37}orHK=
zEys>}Zl%3$%=H$KGg${RB?y6^mPTT*KmIW-DvMC?jVD0CdHQ|vLMW69>6WDH5QiSp
zu1HUmME=D>(!w{nPSE<K0+tl<<m<VGXX!?<T6+{&t|*y84`*nQj`gFFiuRc>;2^qn
zr+O1|mURlRCy%4L6S2~x6iNLZaO}53SnNwe5z!`+%VYMam-G1)DAAjFn3=GVBVP4W
zk_qWgbJB{zj$8(_am*0~zZ;uiakJhG8#Snrk&<)A7QyJafiFUxO6Jqb<N~~ve4%O(
z6#sy3vva5~j|FI^(W{0wZMJR7_)cFh<ITrdbk$rhV-$^Z>U|q74i9X62J-X<jYh1P
z;5OAX`b2zmZUIO0mt@Z{M+77dCDyT7o9Qm2|D~!-`ZMu+MQ4I`!ly~^jp_&N$7yDT
z%5A|gm-__sPIM8VM}meMCd`Vhb~mpPiEf{dXlCPJEFCt1v6J8FE&QPShLj#XS;$2E
z!_~PV^W<EjPI?~S5AWz(|1(oUS`PqN76JR<y@4w5a)JoEyjd;WlJ5@pii-&qJD0J}
zLY*sD#;M5X1nPvyQF543QIt_u397Q6#Qzl#VZtM{9SecmAMK*BZ7dpj)l&>Y9|2qB
z3|hVPyGdHV8{A56=%qAWERUF!KC(^SO}euGo!+Q!*0bB|x*k~jeO{ia#+fWYVK-XC
zW+nvRx?UoOKHaU={5O)0x9xHPH}bUivHNzvKUwcBla!E>^K(Wz7dxsg!PCdjmy1DL
ztNAe<pHD8X7o_M;^h)qZRN?uK=Ew|e{ZhC9B`*Sp#X+9&>aO|n7d3%BMi9vZ;>i>v
zl|NKrXV`RT9BmfhJ}lKOguGPJCTGH$0+Q}E{nQ*z-^VSe3aiGrQNe*3UKNKRI<I!#
zT>iB;iNE9z4l&4<fcH`>IxZ05D3$U_y%>2EHS`fthg2TjJ+v=kmYz@8?j-&m=Xc@O
zX}d6vG~Mvv+*pJSY2Ay*AtMI4fk9Ip&zL!(!x+ire%NT#@*Q#&q>+18nqLpw2sWI2
zeo^h5QUlFXo~L6T5{{7Y&Z)YReE8C5-MBu+h-7Wm*JOoI7DWz6uwjW*BmKnhN(0Ba
zhMh*qovU4pHkKiy6~hzS0P_c0)>|V!^B%cRtMi<*T_!yko)gaTu01aWkH|N>s!`q{
z1gJrV4)2uPyIBso$2@-b4?V`J;3{(Fs#25el;Np76BRGo#+8O(OoM0lXK?%nul@({
z%e1?+<GjBgQ@sqe5q28g15kLx-(a&>gp}!>3TaJ&UFp5qj|DF4;vE;jFfNU^vb#RJ
z)O<6iFh4<qIE;GTtTAa_cnP1%dIvS}oINyA<GA~=FaR=b#|gJquhLJysfmAPI8X2`
zx+rLw&O5)mh(4(Rx;}}Zz=6sDjn|67fBU?^)T{+vTe+P^Eg>Uu5`-P<N^#c&Jq-QZ
zot{SDQ?aStX2-@R%6YpNl#LKLz!!{-<aCKHtbzon_(7nH=6k>AZ1LCb-I15vVYUY2
zu^dq5yWICk4!-|}P9HTMd9wKdud;NCmo?80E?bXhl(jBj)y3;i=gUO1BNIvYEu2P+
zq&XY2##D{O2jiW?OHV_l_{uvhUqQ1zIrxlhu<Py;HUym7(BWep`U0b6&YnQW-uLo#
zO%89iF%tTir(S327AayGhmKy6uV|$9mJ@D^2$vvN58YZb_J+0EblM&A+BobY@c$jw
z`+2^X<L7n8|89_kw!Q8;vn@G&bzZgU9`C&E)&Cr*W&A0ub*WW5J-;Of<fFUrn2TUr
z8o1;p!pIT)4nZbO=(9nWFqD(OPWgrSC-n|YeR3_G9RY>aoKTj4(vXlkCkx_aeN@sS
zl2-snSP?occTo{oF)In=q<vJP+)qfk$XCVC^<V}p(|jFH^)L=I)43ufoP-=MO=}9T
zXgL=&opC-+*OI0AB<P=sI(O!@m?zEewd$ma+LnkI*-37Lv&+jp@%)k*gLiG&q(_~6
zWC562m=3yvZGOOW?Cv{HT;flp=%0;$R4yhF)0J{WTzod448Z?w&;Sr{ghO}=0?fLu
zT$85-$IA`u?Q2oMH=nmgyge02gQ;>K$5j<m0lBnTA*rBN871pNg@ktuNG^Hoh;FLh
zmPn+`xER(i!x|gmnbf>mPxI7ZiidxutRM2jK;<oDNWv1LCAxLc5~p2LG=dWQ;Fc)h
zzQny+E-**fSoX`Q1((n*YwYC0>Z=5GqhP?nfW!Hf1)l~Inh^S(A|hkaN*>+|SK$N*
zdgrQp(l-w|v4#&<_p0N$|E2#4WsD{z?sNur``}hCeNc}@C&|kq8P%;3FNv@mu$Pg<
zZB{@j!g*j_Q%atCdEHK|evx@|OCLh(MT0udQMdYJ04QKjy-b&tp}e$2Qi1!c8%6&k
zA_&E`Y|uC7)z^YjcRlFKkG<~GR*$-_J0ImPGat=)fsi2@?-p!i<|EDsGgS+pNaFy}
zMdZ)XPs2dZFLn`DQmB6eRd+uI%FyE8)e+2c6A0UO{Ps}9!fCXR#1bWaxUkrG)!7t!
zuqge99YI+*Y!uZnEdXB0D#mHTFVj3olftO)59-$gO<TW8Ik-5Yi}8~0l<&suJCyB3
zWF^URrxmZt6~FQBO8Z8v^N;0EobMX{1wRnDJ0tdCYV)4;RF<sA;xnJz6gQC0!L(|S
zO6QujSdriPS$Rx^TXR_Gkb#3T4?zyvp94a7G<^>XzwRARcp|ti$VGHyC?WWdxNvj|
zrdKj1SaxSNRMR+nt2!<YomzC^7&n3>2ZPk}!P}?mO!VT`>}7mBk`;t4XC%Vs*gZQ{
zb=#Ee!`r!Vs0G>R_k{f$7+K7$VZC!vKvGnNhQFa;o;o508kh#@vN0A)T=_EnY*2@v
zd%7WT8v9>WMQt$!O-?E7hy}8Obs!6(OM>J^5pzYgz%5xt7U?2LO1P=bj^w4Wf?tI#
zi}J>j<gy~=)<0}OG6B`#EvTG@T+2-OR17?f+pKU5&m-8#ugD~HOCs2erVpbTJc1Ae
zc3C$-K5vacTSenwYQ~mR=&2NP-11Btt`#7ng#z;wX;4pK>0Ibc(+S`Sq?3Eiq88&6
z(_ssV3S9CRzEeOLX}h?Dqz&samME=FrKbk!#CYRin9+pl10JrSIhTlBA$={Dx9Z!B
zON?8L8wQq#b+T{W_D*$!UUi2ekdAer2!bQ^@N86L>;I@^=Zl&YYqk<PE2IX-(FS7{
zZ0VSK`0g~CVn^@m`UL?n(#hXkhOFsMT)3*eRS$(Ad=&cRX|x}~_%K-vBRI?dj8@9i
z!;O`V!t47qt?b+xylEy1suTZgR2KIW%UAM$rCk$D;zFGTqT(ruMaA^7M^-K?mNO;`
zFi8QB0afYJ;2OSQnNz?@6d=^17o;~3W6H1OIj#E^N$#b|P|;8Yw(G-pv<cZNN`9$>
zomr~MG({fFntxPL+%NW1Ed(^#4EeKJUWHSZucjQOs{>MfZwe<}DH{e=H958q=c!d|
z?2e<^*KmrD7kPJvNzHgrCAsU_XJvd`-#T_SOYC|`@W9?OZJlcd*mrtPYtGGuV?tD%
z?Xh(~hDiyDV)|Fq!T82d{&^xOCYq)-Gm#L7luH!pHH*GODb}s)5FCt~;}e7Cc}Wrr
zhdF$u>Le*e?ss8<FiZM4$p?gH5&?vYoHFtl2VJOzx(WqM$^{@v#Pw#_2Ap(c5h2V#
z5Txp;K;jkw@1u!B?|Mf&mxI3K{B*tuKYdJi-u;FyKas!x$vqS3@3#Z_&DT%82&YE>
z0wBe+{k|MNn{FWq#bOVO2W0HFaQr2AY$|I(?f|b~>$N8#Z3ebU++*qO>>{|b7Y9D^
zNV6Ng#C!e7k!-!6`X>d)kZO8u_X><HPxBnDPSYcQ#pB&hFy88@-{fiBO0eHof5=e{
zC&PqwTRx@FQ};DO^WxZ8Ai?S`hZI~V<|m4Xs#lWQmye&QC?bTRQ)v6Z-S&7~B>N4}
zT=+8e1O-v9Od)H-rK7|d;$Qug>KdI_|4#iq(W+|Yuyxoa(Zg@!BVu7=LG+e23YVzS
z#9+H)ljXN8G$1lW=8qu3!V&7)gS2{`qszAdQ<CnNlL5y<05Qs6+%<$CU;k5>e69+S
z-FA3^?c;SCYq#$3aUaQ5qucgVGkMzvi1R*beBlLLc6#=@{jA=;-m*V0^LuVGFVf??
zT~s5$`=``_62t<bF~1J4@7T+hmY8ncN+}8Ezz@|&LOY`Kx+uioM9iM~y+%F7py2Ki
z2}FB_x5X+sAhh3JuxTPnTMNMZzzn$pH3tB>R!RN)yGb`)#N;Dr@H>EFK*LI0SgPkw
z+#71Kk-Ek-CYN-qmUnGEmW46$hX64>qYQ3|gb0+t-z^DXm#xA8aT)mckvq88e$Ltp
ze(pC;OEb?(ZZ+_39iW(DQgXS7y6K8M{EM@bHF$4L6KCsSV)X5qc;2+IiY1LeP)U3G
zK!tn);u)S!8Lqz77a)#zr^TMhn6`3a$P?-~Ry=a(0Dh|?_8WpS0U*y=k|i%QA^$AB
zD({B^C5iV}g1GcKiY4%zI1vur9s1X{P$TR#Qd}@lx-fu1Vpz8?18Ed&mc7=Lna5il
zvINXOy4H~_M#3oU^awYcWQL7SRol~XHK8J-Yv}a3x!Lmy23_V9y%nsQ%##UKPOCoF
z`)J+l1Lk~2bmyw%yqJ1{U`ts;szdd#8uCOO*3)q_U1^FRv_xu4rI8`IMqWn6iKr7p
zrkmp5aRnP1)Fkn+2u1D?ImSsIuVV6_(2n3H=Kl6aGOtFcTP6b~sX>g>*S%=QF4hnu
zO46WEp+Q2jqn7-4w?bfKp+M9G2&f|@41eV;4o<>smVyW~BxPBSMUd1)qnm>8@npBc
z^gPdYC_S5w<)Ji1P9K7h*5R5+#`>hq5J?2z($HT8Su;|cBphWbaW07@*)>U-3d0V>
zxa9MR5bH(v_hLF&uB&@sP7!_7R+L~cQJXqTHqAmKsxWTsVc1&uDSR~<Rb8w`)9@-G
z)SlF)W+t%I@7@FwzQ#ZCM%uV?yt!Vo-uZV}FPaame^bFTJ9W)bYb-ju?|^*#-2JvM
zH?cRccd*x2p9SV`hGa)3G({YTG~G&$Ateb1CUjjzVjvp#2ZU2Xs}QL1rF~?Y+aI50
z3^WW>OdO9(uKomj;_AurDY|ES(-N5mE5hQM7;?n0PVF&f@dw4AjoM4N9)r~H&D+zU
zA{bB>^-aYm_7^g7t{=Drj+!54S!ARczJ8RI9ZUU+mxS9!f`u3x1g@=$4>yVFWOVuA
zxR*L$-}{WI*WG5j8WNr{I4w(bj@g&xO@jxIbO444{<7ftAGDowm@L7v;Lq4IXKdTH
zea5zJ+qP}nwr%^2ZQDEdzWerJcfZ|#_Ur7d%&5q!jOwcHs?Nw?B;rV4OErkMb59lK
z3_?;E9L^mwLYW>OjA|^drV`Y}GD2*u$}aGo;cx`_5zoad*y0HB(hEw*g(x{Xy;|Tk
zROvpKI%lj{w`?Ah9bvaf>|+A{ao?{R8wO7%*-mG29ml}q&{U*)q8PWK%eFVFw-dHc
zdu^qJ?>nme?Pr7&fyZMIu1jeD_VtXY0a|V37V;hN0Jg%3UR?(egQq$ji@p)_%=o_c
z*(|dz>Q#_8cVl{xmn=i_+!B_{b>|A_byln0VkRcl)pJw(X4|)jmm{*Eg-)#6VCc_W
zsbK+asYghKn2*!lq#RfYxJkt<7v6Rb8+5_<jRm=4icnc5VoPa8MBr9=UWN>aQ$XD^
zm%IRzVmm;*(d55J;v{`8O^W2OtU3m}QkMWKeQwREIRa~>3)On!tPOrL^F`<N0YxCy
z!YE0P6dys%V>*>K{IUXjJ$`$IMi=7THUpZVb)22r8qWlH05o^>Jj2BVP?qedWSo6m
zHl?B$368*+ZrXKQKlI~VeH8O`a`I^5LhR}7K>+`Hku02|$@6Kiu|Ck^TwU2p)H2G$
z@HshBt-j1cn?Ma~{rPbTH&ltw_0k8YqpHzp*4W`ZmM!y|75(eg@h)wfp=-lMHg#+=
z-m|Rq3Umy$XhzwRz$ri|2P}gt@lh7}6rJ;)v|OB9s02aj44?52H&cuh8JiI;Yw$qG
zD5}6RX|c4VX~MqL<;y(%soAAx8OnnKtw^!QHWkj*-udD?_!J$#uH%^_+<NU4`fxX8
zmung7!o=w>-PUI6NU6mrdbyNgp~s3b12#M3zY0HhZ*CdwdpI&o3=>6p^apQ?VIupf
zO!!f(#`;9Rf^QH<VX;pTEi!J6gUdv5?YWUYz_xiu4f<9BKEBAdQ2Q$em7b>djNh+;
zcHmwBPk&Lq1NQxn7@NlA@m16DXlSwI9MEl8<>#ROUVRR~X60FK*~H|DwlsKw>B{w0
zr!k)kkRu7FJ7P;X)4J^Q;6`&Jn&F?JpMjXEz-cfcyKC~2y3TT?4#cqWQ(}Oa2gyt+
zQxGDTNU%La0v(FfbqEk5om=82gi+1?wL3C^W{7z-{K5)*-EUk%N1_UQY0pxpQ4v%K
z5xyQydwM4?q<rEKw+yRNUx4^vkE;E&;gBS$??;)n!6~bm^ZsY0$?A*hIqNisD*J6X
z7-VfSxhiQ&BP|Q!b)2~BAVR?u36ZHL(u3>^nl7=EseZ4(W9>Y_EddWWkj#|2RyutS
zc{sFs)^*|nZ31@-(*WXS2I>^QD&9q@*DyZv&jhO|Q0nlU|DnAT+s&UHM_(nt3ZO{X
zj=Ww|UnnL6H$P)4+S$m%IrTE#Qg@oCda6^2_4iKtZQraEc$orP=gRq7iP*tY(^Vry
zE@}p%r~c58kEv29yWgR<B~J+*&fb>Mfk%H1h&|DY_qL%sGo$)8G2Cq~qPz}#aO~cR
zThMup%JDJJimnOSuZeKWy>f`j{(h&v3`smlg(oHau|RV<$#wG=f>?thVV!NEog&Ls
z+CoGdJpb$1RV7uEVNQgP=~l7x`|k+xT1#KFjmJgki&q@K7Xc6KRxV=6Z?H^cd9XLq
z$P=g>g&KUT_~4wAby%`B|4`gMO|Pjj%;FMUUIuQj0kNmcxCFvpy`#}4UUdGK$pp5b
zo=6{mRV21>v?uWX3s!jj5gcYcDUP7f??6Osh>t@vR&T_TZof#5;+r0=5hQPqp3^%A
zC%U(lvyZ)vpU7wluD5L*{W`Lud_JD?sY;@6P-lNmSY^aTupzYkSnN~mQDeh|y^%!C
z3iD_bMFUKc+kve_IA^UL3EL653v4#qEFS+hrBwBtHoJ8VtQbKGt}h}>2<h=1$NoZ+
zTio(5M{!?!j5FsP-Q+v)H)4&9v~1JA?dP;UrP6Uo($H>*rgx!L1zW5)GoWg`O|D?E
z?mR<z3P-6RRH15a*7R#kAvOOy7zUwVZ<<b$l^RQ;$*fxh(LcRq%3g_H_E0JJ#U1or
z$u;TAx+c)w#<D5NJ+-(kX)=9(&F*dUwsqg>`DJ&;eL15U!@Z_m)$Egb@uf<xBu=qg
z+%bOh{3u?gsJ9&ZsTgWV=aNIs`55(N-d4FM-=yBZO-foqOZd(kKaWd_^^SM(ch~go
zRlMi{5^F~Wupzq9neyUhu6~)m3Ak0Ju|);FNX;}I?8r&}GRWMc34HQ(`?scm$@C<b
zu>RyhM&j<rJYxx4@F(?-p{h*vifxL??7+qBjlVng5Hbj9uKM;`NW0fOn#QtM1!QaP
zVBd1|yH%NdS9f)=MG^hel+Bxh+1*B~wwWRmo!S_-qY|Chn79QK35EKQ(j3Vsrcdi!
z?-)S_P5l@O*#7-6TfR0*t(JEP`I@{TM>!N40&GNG(j8!NMgN!fVV3;{>J_GDF-?z`
z0lS(;v%8YSv(=`b;o$YJ6#jFYje7faMBH}Qsn6|p`@^SXzDX-)JDQXP2?jWK`yhX;
z>5;-W7@cnW`;cZ@ODAKBZpY%^58|5<C@vKfS8n#t*(Ert3AXCO*}IwIfrEVl;C3nX
zGX-Sj`PA+&Uo0D&!4^SYAjO<f*G;?p0JO%p1fUtCor6W0@t9NCqjqhX8|Y3-O6<v!
zZTiv{N)p2Qrjf+PpN46csu`Yc8?FFVi(95&yY|q<{e3sEED-fx06_I!qePr^Ss>K`
zSiP3Fk;i>GQ8dZ+0a^Dp?XSyk55@hRtz^WU8g?y8Qx;#Ne}{_?)xRIXSlAiU5|g~V
zmo{}Q8m-sbxJ#4(GtXoxswStb?vXU^HZ4@Q201j&1)nRoUyIIBbdx~<M7^;?HUajl
z^R?VfEX18SHfesiqoWiQ%1h1D;>A-4vg?X1XRw3vRXe?Ued&<Efs&O=6hE#CU=p~a
zn14(G0WB2v<sB}51vBsqp`*B?D>vfe#Q{5@1!q^cMgrMHmZ72Tn9M+EG?y1YeI-$U
zzie7_kMAl%LWKo25mwevcNBLz*RmEqNvi$T>+~#*H_7qw(lv{4drP*}({_C0Z%R%o
zi5a}po6FYWwiSu8X!`8{KPJ_GvQSG$qW7SeI5$+gP;y6i9G?YTLJAj>hF0(I#SHtc
z>fxrD{<h{?#!cU00fFnsbjg;Td9{2bOs(0v?3Z&@8pJN)m|gs455*waGhZQ`Fjl%;
zA&EUjHToKBXZ3`qiP@Nab=SIa6<huyKuyA2n2Yw3nGn-qMXoamq?MY<A^lG0)XAQO
zh@9HrkYZh(anCWVdIU#iJPZ%>t&rPoC@kJ}uxTPzV*2bojq`K})>szvil_~hqvq20
zkYO%)9>(w)_Nvad=Gl_%f$6RyqO;^;4ZZI{-wkho1=%o`55=ivhq@2R#vXxT2<Ye8
zw3seKL@zEbMuPqd2>5(UGiWL7`m0J<GW-RCfvVn=b6moWWc$pS#Gv{&Yz2)469PGi
zKYuln0W?vROcanj_|k|>@<@@($cWxaR9StU{rC(E#*|vAV)0HjmQAcJQYLwmMyeX?
z;7Vo;5hw@kh^iT;9f;#c{PY%?P55LE>!!vTxc$aM-rxhiwD|eu!n4z3pp<24%P<eG
zlCCrf;h}I^*?0OCZ9r7giF&#bjoY(^rQw-{5;u9ddE+Xwab?%J1w4ljPHGQa-$&7$
z$-@No^pgyMB+tX7B_G><|LCD}*-pORV2u7oNL?WEMzmVw=9(kh91V!w?CG#S=TGcH
zt!Fd`T97Jb9%Hds)}l0tP1i8Ym5tR+3_%nQJ5th9q$Fa#3o%`QS~|N7j1f$Q85|dM
zn+Yxtov-fo5qfgeR1R3Y{$qf&&Jr$*d|f5y9in?P4~eco7s{-v?gPb?s-hZ$H=w31
z@D$S-V?jE0X0EmF(y0_l$hqphj+wfTMP73tkeu>hi2x8|Q3ld`F#>}y&@(`o10bme
zjg|KB0AXV>&=m&P?0O-+_Wl4p<1UkukhPQU@u|uN9e`-ko-7lVdX>7!q){zYd19r!
zm?OAyW2LImSPx)f1!)Z)koecBqkxdH=g@VL0l}t7d`JFsTP;O6Ik5@9!oy^m4wH-Y
zUD9i{=8x>U=Ooc!m&*BUeXTOT3gzlfbGh-M^v*Ot%4DiePdR+kJ_SzP?_Q7!o|m6t
zMbT@WOG+)*{bSlkZO_9i2@iLg%`S(k79-P<r=`P<_~B?uy+!4_&m>=9WXr}w89bce
zxI`vl114(qdv*nLCiKu^d-4)8WiP)}%ggfO_y)z%MK(W<IYJ@eDBK)5;#>hX1Ewl7
z`;~T2pXxtof^n&CAAvrX$&@tZ)7wn*a@+IPVl-!2>E>#=K~>a1o32^EZKgc#B`D_}
zK8&c#_-Ws{&Lx*yplR0TP#yjjZ;L$|dqtL~=RaB-tJTq-i%B1|SLjMVRFW^z(!9H7
zZ|J0bIAbKCN6d;kUUyq})&$?zqAfOewp3BaP*Svo{r!+HyR2sM6vV_aAMmnjt>W_O
zZm~S3EN8uVQ09<N<C@N(Z>CjGg&ZQFLu_xVLTZSZMyuB-3yj^$ZC?@&<lEoaFr{O)
zCV;}A(NAu*coqY`elQ@1uw-E^Ir9{Y{iXU62DZ23n#$4xH(^g&Cu>X+6D8@>zia0<
zsM>vyHm!UHk-;oiv)7}dtldhIbPA5j!O09Z{H58=&CxWQWpN!kd<QSu=um`mns~!l
z>w5ceZ2^@}qV$LY`ZukyxpYUue%h7IT}{T$P6>vs^|XZGHFc$O&!u<NmM!yDT&1=)
zpK^MkCQ-xf2+09qc^dLzlaNME8}=z%axZ>5T~VoAvZ;N?Uy>pVMQrOYh>qxv<i-!!
zBz0;BicUB%E7UUv&i>Ztu-`&cLz9rE`3&P1e)VGyZ-9!4EkS^s$T(e^q*O;wFedYc
zf`+z%xgO6_kN!}+x2mRSqhf255=6kR3jTt{g_dD=#$HfiLo`M$0{^kh?EYWFHQiln
zB8@1Q!cy*0-_sS%<ls3VWBy5|0PR{#&UA~EVB*x6(&c5%yLwHDwxOhpj>=*y%|wan
zRy`?;ZLX<v*mg@zhEpn+XGM7uCpo3Cm1V_=6rI^@%|-*fhTN{DL%3D-G@Aq3s)L4Z
z!=L|SIyw;byq1o#`;#NuiCPRD+Fd*Id*(81YqOWLEhTJZT)5hG-3+|j=u;h9u>f#4
z;9!&=Q+}P>HL6{QjqqR&xrpF~GC<{{3Z=O!Mli^s@5a!%rmjuU5f{hk5sB!b4U?C~
zU*IL{BfSSD)=icS`xv94kCa6BNFG_`Q!y_%6wkYHL`f^4zLSg2V^KaedTtauS}a&4
zWvAZEiR%mxl{-JZ>Nrs=4wbo;xogNO1z0^I-A88DnD~Hn12lO^J~LE3b)3|+cfPEO
zA1qO$747-Uj8Gd~*vNJP9go?N(%8)JyMFb6(c|jI)Qz;Fej_kYer&6D>&H&X{Bex1
zey5y9!Z%BLZ&msDPL-aFonaFF9x1bkK05j4Z@<|Ns?IK8e}nt;<4F3Rom#-2{@+ti
z;KA*Wg2c$N=}#LUKk00luc|K<z>M?<bnzb8E!?{xkpYri8xiZM&KI9F>6tmKssBjd
zuma#tjN2jyn3uaT5OXn#wGWI)^zK5gpzNvFEN&=OCL$SJtQC<EAgL=Eo#;)qjF&ej
z!3{>hi_^`lZcyzjOtS}<u+t9%X);c_9mi`VF>wp;IT>65t~tvGYKk{wQrQ_w!+}EE
z{UkObG}|Cj*`nMg6X0Y!ug4@d{GZ)ZQd`rE-7|s{sRq01`Ug;ntN^saunJp}_ao#p
z3m=GysAS(1ht#ukD9uG<m=2~l!&n%d^dX=#Qxpc1d42y$V)Ij7$JTDm2yB@?>xp`x
zo{`heOp@{5*PQeqm*D~2AFW7y{&hW_LVlg4v?V%XdUIF#fmdbmfG=5N)QFy?M8XrH
zV)n|MI3wpBdS{i`6MWNO`32QbO*}o0YBGF>T$syXUXtOzoS$XNSx7l&{YDADl9w?@
zH+33P0KYT#Wk1>zpG?-D#?uJ(hu;D(=3{u!=|tVV5qthp_6>B^rhxd8Ph!Uu>cCo*
zRbZOp?;Er2X4-kB@3KvBb4`ddlBo-8qZ{#G$PeY6kll^ZwR18PkL_rR2UJXx=yqAZ
zr(OFu4R20A9l#If#;B^3GvhTQ+eSqDv9<HOn3Zho1@$)#^p$y>4~x$>|LGuql>S>3
zHbHyZ5=Of(v?yn1R?+g=3QahZmigQG0?OP=%)Be`sWu#Os{+OnzOTtoRY)&_1_$CQ
z7nrPd9CaT~>N@N$xN0$Dcg#)7DV(W9CDX5_SivKkmkVqzR#^Klj+3Dz*?aZWpr+og
zm}R*yQI8dr^--nZ5geOkjS*?^d{!EcLqDlOrnOB1rA_KTTdQIlP8X&ZM3^+x1~Y8g
zo41k7jL{h&>ZW-`AVf6O2wlFhWT;^wPkt4Fxkc1)ja;)P;ja0pxzCt@N!K68k+fng
zjg-i8<Wh`ZGwk25wA()Hj0=j&;`6B3ozwyhcN2J&Ua#1M)OUMKuj_0jo`1*P9(w-f
zEx2SjlF4ttePubYyq-SJX2~mq&*dIJ9?m+UrOl_%7(Nrzk|X~q|B9hJ;CkDvh;NZd
zx-qqy3GyzqPlVCB>F5m{srSRxcyl86rX-pP;)3cBGJXvNOBp9@6!^$%%>9FwYRD-1
zUTY7s=WZBMdF<_S(jnQF(!UKV21D*`5lCO)xlJSq0;)$_4bMt=8F`?QxQU`>jG&`2
ztW)5<6Q9lUiubf!lsKv%VAw+LPHh|1IP9`C8cmk1VSqS}O`z~$#Tb<hr*5+`6Lopg
zXlAJoC#sU*)_*Q{vgGdG?5?XI?FdoAp)@U>>Qq$1vXm&egsAkUe8D~LD|M#xv5pPq
z;AC>zAM7yXT#Jyu@j(787_Qls^3Lh@+icr<rOpp4weUJ4J>Jgz(Y-)ssX^>@;CtI4
zg(KKbSw{E@T~LjfVmm=)H!0T*&U`|8uxj%}DI<1(-K}SxT2y$&*~sv1z5ue0c%0`^
zWNXbb?4D`!P^bj>xex#P4O^G3f;l{rzmfc2*F$t&9U@_Itu0413H?KLF|Om`Qkz9M
zM|7zdOVkt_YwmAkqT5t54Lfl~%Qm$?j(!}o%@LM+fLBhJNkpVRQ9xHq%2Wv*=^m0%
zl(0!L;Y@_L^R;ubu$tS`eWa*$nF@Pf@1}of%P644nR<z)v(X0g-CD|V<Oz2tBU)fj
zs6*liLCxR0Xd%-ZJf%!ZYgz^9BA;xVOA{M@DtB=71J04D<gTs6CiReq<jS}&ZrB3E
zsW^5<^flL~Rf>;AOpZDD%QApSNe-jTJf4Ql5v>Ec=O#0q!7c}vg=WVV@b{_??Dn5$
z?icK}rJnUO6{&BcaQ#xbp65Rj1+2}W41v$o@Oi)H-$*NK+eMmH?+VWz^x0P>grH3f
z2Z72FN=yV5R|eW1hUKuic7sv^NT+P2pW{vtqN^Mt4ID9UI`~xx3}94s=abGa_U$3D
z9{m~!H;Qm0rfuTE=E7VYOCvC2Pvl0{Ucb~g-i1pfadS?H&JCDW6@pzr7Ab`)&F8PP
zW;FMy;2%A<!0w_M3g4VY0lRrz^K&dfg^t(#<4cz68A9eLq$RS9AyW-b@;Q{1>BjX~
zLec_uN=F|9kjj)eLQr~JBuz47^Y+#IU8CGLGW$&@MM-;g{HeNGYch$j-QI>Jrppp8
zgeOsM(^5P=N2rXG>5m?`ocQx<0=4$c^6+Ma^vsa&E01?;l|bgv)S5%%U|*r^nW5G|
z;zurz-;P6R9xpNzl8qq<Y0@S#gM@`KZr6-FY35wqj*6by&8)&aQwQ2_h-FZ0rxxZ_
z`bKWm;wZU9Ho6JC-?WV&0zm4EW45VyQgqo<cw?3*vVI#~OvaTU3((Ti?{A@)Ek272
znJ0JdmfAzx=c3+nZVyP8RMZON#<E0iQdB*+1^TGDWf32YGa<~S7Re>)52-MtOJsel
zmOCXe(93A5-L=k7#IlcT>v<(+35dG&BIL!4IL{`NRjko46BG)aPcY1{+wGQ`Kp<MH
z6qUSM#+UdEMq_c<hbm>DXgp}rZdhtqF5j<b+XOZrOE*;R(`{<o1OqHo_1>{kIiwdY
zKq*hPv{!C4Z%}M2FtKz=k7C(uRyVY-Y(2c<3=iS9M|%1CXjap2+WwVl)@^mF@y2LS
zag$1+cAWIJC}+9!We>2&$exK_XCA4>giXNc+vg{T>WtQDxtRm+2)3z<wovDRYQqhp
zp#1AJ0aIx}RZC^uWS(rMPU0{~#aNYlwQhBVcS=_^TbrfZZr)Pqo0Kd3tR6h!giiGU
zvtW+?Wp>0o9j(CR=2!u}sea8CJVSzd*?w*3P@+u5yHie^!{Zsc+9x$Bku+p3!_k+1
zA60!>-C$n;u)bDQiGWdUVAT)44Qh^|QFo57NrusF)`rjNIb<a2o~}A;)`E?h&C11^
zX>6e#(iADR3;>ERnjzCLYv8oaARu8-jNJ$i5QSbn_P0A~i9@wjmr&Pg!e&hm+^$JV
zfTN#r5U_twR4Bk=_Jwq)z(jiec~Y8t5p9O>V(BIOkKbjz^+Gi(7}dEz+2wLp^jHE-
z{gy+P6pNWmSSxI;V88WpIMa^loAU3sUKsup%AIwO`e=B-1eV4?<p|NWmb|5h_Ue7~
zBRcnBU&vE17p|WPU5L$b!^K0J2V7DUW^jbz^Y*O7oN(a4fH}ol4SM&E98(HayX?`T
z5O{gZs#D;)6~|BV+>rf-jL_?%CdO1Q?G`!B>&a%A3)ww91EP#OogyehNc|FK@U{c3
zsaX}SC1>vi2W0lqBW#l;TrY!pbu3h1Nn*BeE9@cmrSl7?#5kG0<-fdv0HHdTy?{nj
zF)Ut6)eU93%wEoK(hQ(0f6M(iMZzqd*~>zoG0A0j+0nN0i~M$VTHkrqbAObEf~OY}
zti{G-93zHxo1my);$jg-NkaktO90DxS67YA^7_EnZpN_L`Z!l_KqwPrO*4Tyg@REU
z+8Tmk;a0r4lY;^<f7P3pD+U5J*91$u<tsBz<rl(e%T+tuh3_|_p5ItdmcNrHj}#@8
z9(lsjwM@fv$8hpO6Iu7i_cV_<fx4v99gSF(Hzw|*SSrXxv9ML{o~6<c5oj|e1^Oq<
zTjk~qQX3KzjH^;ZFvSX*?c(tn5YzaqNT_UQ{;Ug-@5;JBw$ix6(o$u%Nf4yQ`~+o?
zCeH}0F(n%A;;MOGyR3FKgYC(hyi-)tv1)VUur!i5d(@!}&FQOnP?D3fswM@>T`Faw
zal}NMfLS*7NVhyXm;yi&c-|7jK4ej;KzDx-_0i)R2z0{;)R2Ialz~H#dDui|z$}%<
z_}Nnbg*|;mLy?hJndX?F7p%TS(0nE=_3Myl83#;Ns=+HVw5k!l2336d1w-Y^Sohl9
ze7bh3PHBCRJ$=f^!7Ufn1P<*{5Ct2v#v}>~bMgd@@$r3Q1vEKFo!GfD>Kzk^m)ZxX
z26dvuM%0>pS-C-JpG4Q+^>+2r1Lou<C}h~tOzmJ-SW@RiG4Q9PI|0a$bQob^D$h>M
zv1hBX!v^8e@|&rE$%WSA+mqKY-ig+SOnC(s12e9u%>EZK_xSMTf|k=_SBdJayseTi
z@lR%NpS&;m&)Y4HyRMc48^HB{<eM7yw>lipTZui~+ldh;mtEA`9va=x&7Rj*uglim
z&%>TCQEq|LTN?LWEk_<UQ{OXN>2O>+7>*)B`#rPw#mo;MW-#1(^NhxOUSR`ah+(gT
zA$DDW8=xGxJu;}D<>(!(Pg+WOUuhscGCm!{Mx#CGDv2w!0MFCkg9pb%-%!>Ib}PhQ
z-gqZ5JI|kLJ%{F&&I>L~EY}((%*spxtml}_T=_N{``1&zU}CsIx~$!KgS#0B-}ML)
zKEz+t!cp7sJwOjxx12wK$sVYO&0Cv*FX<1p*_k07kk9ltSmI*oQpE7`ZiOr$?wagh
zT8cG5UR~~82r9f4-QaJ;k2<;ES>TDP1d@T_=_f=i{5fKo0H0-pD||lOw=lCU#MVGw
z9(&HS+Z!e1bzUh~oW-T@)mUTXb#Wiy2Qf&ZDEuiR%KSN4>=5o&2L`#kJP7_*k&b*e
zk&gU2oOd9^JoXEtQ@K5C_E0Ap&pt9?mpvxm+~Mut@9#c<ZzW@u2r@V_TScW~9SPsE
z`eP>XwEo_wzld;EIcJp?fmVuP$?6u}${O>F8Op?F70M>~e8?VwW_cwyq29U|KXY;a
zaD%_)Enf5U#=9$qm3+mz13n~__2_+FmmTx>#JWQ~Oq8YY_r$w{JXDme@O?8rw&zDv
z6u^<y&22~XK)<;Z%=meGKm2s_P2XKItY~BbmBRcj|AFBo>4B6_C+oEkVFC?ZFwDdp
zJvW^_;WaXTR5Rrj{Upc4`v<JT2uE&yv^$wYckg;+H|vArAruzH&GDonjP0R+Ax6ye
zw79B}n}Y)cXfPAW4fWP$XSl=WjpW3|g9BC-T_NttMehxN5-~zM{LwLjnY%Kw!p}+?
zO<os9NeUUxVYf7>lq(~Nl=L#VH`p}V6gYX@Y7cSf8`>S(5y_UH3-oP{5GR!_3HW^=
znxGQE67QsmImS|rIVPn!L&O`kbe@ni@ECjmliEh_MtCwqY6IxyaECJ6Lec~LRuM`S
zb9&F<Z#Q@qklVBf9m|`@AvEWAT~?wXgLntG_clxR8`BF-ZYQ+|YUO_680Zpe9@V!y
zR&<xwtofJC|A9~Xk9h6><dbUP(P`kZva)I6;W7RMpl7E0DKN2V;IXl><FPU@{M49#
z*rLqLEI%dIA62HGu<y)2Vd{UD|B$f!wE1UaR`!1{vHe5;N8{)Hqw({7@)HK05s&d_
zd;g}%M91_4E2aBs{$Jz5|09(Azq<6(ji1W@pVz+(``P-BQU6c-_)jMPOaI4^f0q7j
z`7dMtDfus^{`1)X(<D|V)}Ph?cOWby`+oz#dMQZR1kgcu-Ba3Mz#<d*&5MDT#VYUB
z!bM#&UGnug{vpiegZq4T*od}X5-QYP^S)-MkIpcrSeP2!Fj+`qkF2zIG}1kZE(UH6
z5iG#=o+&TJqQ&u=Xg<M49eXuH%bRlCWS1M<K$V0o_=#LleD-8}+$vKHxNrib(OJa)
z_F8w{lMBMlFcW5=%pWBHC;YA2*sGaO-2iz%<*H6_9PY~f*bDsn;h9=rp?ea6AejfZ
zBZO0(D$J=_2h`p^rlb1a{y_zuUvv=q)M+bmBH=0U89FYjnhH&vSLDK<bAKn|IOG)w
z0slhpp0|-(vdkb`mXk?%%A9Wu71AA)FNa7d+b}fHn~_T?eF*rC0YO%uLB~mB0vG#7
zLIm-{<$m3nWc^0w7G3W!A^%Q=-b2(VPIjg>l-FD3#)hKQL6EELLRhgktSMi*qDnO0
zG&2;cTO?+3lD#!|JAedVtM1EzpOfEtw&a#7W_ZVN-Xb@yabVRn6-RRWG1=&u6%FLW
z@BeTK{o@q)fA~b{=$YyNC*qf#m7e*(0g^Agpj;J~nr{9cPckwlOiD7OiNX4%WlAF0
z$4inV1WKZfjwOf!C;Vm_5JX_;^Y!J*Z=yh`z{CE1tF#^d*`#RmZinCrd`77Ln4j`e
z-SpNX0DoR1yH^mSDt7hP!&rb%-J_`L^NKe@EvD<~uf=uO)0Rb!;GL20FJF)_$iHXh
z78;ZBpBQ+_0i~`5)o+>UZJ5BMv4i-(tWBkrCMF#)YqlW=tAW6^ysx-qZj&e9eIWU)
zMQN?DEB>x@em<-d|GHfK`Wr3Lk;P>*&^{IQtKB1|s?Bz(zynL!aver@%VBr?nx<pd
zBxVz?7W)$F`s&OQ&a3X}5Or=F_(5ek2=d(FN}_hd|FE)JX)}eoXM-7j!Ut6KZX^lS
zrBR33LkqSyVf|OJc2oIhJ#(9`8}>P^pvQ6f^wm>;PBIl>{2O?(hH^{I*}>SPqOTsI
z@8z)tjrKNoJ9@jFca8iz^Et-~RmS@`iLZn`Jy{!{!OwZla!YXAPb}ug>+8E9vijb4
zxj^Pqaq@iaqiyf#d`23*uet~X2H)$PUPkCWZ>@9VHWmgZ&#RX`?B|5BWXJ^qMSq39
z8V$tgs>{gX=KCn&tAb6;$p^#nm}QR3(#F|8mZOV~z8lY$l&(_2#8tupo<3(jiV@|+
zW0Ji90raa!a{1*H4QnI;cZj$%X8Bg8^mri+u%<ewWL4G&b{m{2V>jMrnF02DI#(TN
zqDPGLOEdL321f#`TT7N3nzDOZ<dgB_L%aid_HhPX4(m&)cP40PyAa!jdh$~==I~o|
z$9RsD>)*%(wj{FGelR8HuB;m`R*}L=penX90T0is_)p$do93<QoPe)BG1&UsD{Hq)
zwOF_1H4X1B=I>U$ybhh^o7)#@|B2MQyA9u{(p$Ctg!WHxuO3$^X^gjI+nLxOHcZ}f
ziM~9J=YyHymkkA<ud<(62PXTxuy!vwzHi>Pp6dqx3tQL*Nbz|9c5tL;TKbcN3u&O?
zQsF#^+T>hNYRBWpmTy2!$7Zi@8_*@Pjn|ngK`-W1Rh=Ba_sWlxJibSShr#g=<5iw+
z?It))ll|{)7^dUTnf)Snlq<fAZw}5!RM+N$mnjh!gvO5D=!VBU?bj*KQccolL9A}b
zGvwTlhWFV;KKOGBO2wQg+{YiJpYs599YYT;4qMUIThE1O57d^82lO^DoymsyZOxLG
zFe}1$nh91n`c2aI6ZpTt#s3YMJPq3l^0Shlmvw+JbZz^I?SHTK@6s320LVDeNdZN|
zs}Te}qwKFJ=y`t>xY~L|Vsk5h$y8_rS3QhLzu%Ps6}mMJQTJz(7RD0BrjCiP%zjYz
zbLA&Y71{KZ3~yk+DwYzbRWjm5e4@1(+qgFzLZU)oa8s~wUQSdWJP<N{JOB>_yP*-}
zRz;xb5Ej;dU&gYlkp)k3^?{{n2|yUqpo-%=WXXc2od=P}qN9xCJ5=n1OlAZ@j{Yz?
zAk$hP0Y*EeQbE&Ppce4&vGWiZwvi1-p}>`5S*1#YJ3Rt1MmtnWgF17-onoIA@^J3H
zBvJv~zyi~B4GSp3VGTaEjqs>w;0twTcf#>rf4dn2+tK*i0Y%cOQpQTaGcDnl@N}6r
zz`izur%&_qkcJx%$|A86R!0zgYTZ{5j4o^sZDI0z1AV4PfZwSQT=REg99FR*SpZ8t
zj%pB;it*!w=axZ?X23x6YxsG>3eJMY(u#EcI&_F_VFb1!yps;jwF0=CFbvMcj$g4p
z)9$gpOpj2hQFogQKh#2{Ifr(l(z5T{6NW+s);MmNv@qv#vA)lAz%ZUkXD{e(E^ZgE
z&g7w{YwFkGDXMH7RJowOLqa)_a7%D-sNYo6FP>mG(;o)L=bqA&X(}Sppgt-TN|=ky
zT`^-eN1lNnC50W5TTpKi5UVg#h}5q#b*30@6LjE$R0T;fwR_|0^Dj-xPe%G<Eh~?q
z;lWP>M}cwbn9v4!&Ej*`KkNW(l9g}2FPgcDBX6Ifp_TtityjqtYL)nquACwv%f{+$
ztcUDv+`=gs-Uk0jgP)&P#AiS`^fkAgT!1jH#a}p8H9n|HkH)otXQw9$CXIQdiNNxV
zv0!eF{O1&8-v+r>t|8A}7BBZeob5NG_#~z)Qv6++Ks>U3Mt~qs6V%@e-B)ydSyXlW
z<)CB(Awerr0}iCH(g}3u;0z6F9;CHg%*0Qrs;=NbjJje*0OVez*$iF2C_jqXp>%Ei
zOzG5K^m7V#_SrN#Pi2k`j&MpOHw#Qied=Kti+GvkhZtB(OdXI_DiUJR!cjaVsi^!Y
z=>9g%xOL>!vx_NQaGP`1fq9lOGz7uR2zBIcY#4VxmldZbGDHjv)Lh680n2my`NL|X
zK$`4^>Iw8=h9)aPisgA>USZs`Gs}YYz{ap9RZ(;G8V4BgaAdbE{Wv7V^fVeDot*)v
z=tF6B=#Ux*3QoJ$eRHkEJrdiU*#Relr{L)`WyJ;J+em86^{55`OS^-rOX(t$)9}$P
zi*1zk@9J7L`med5ih%O~YPA7VGC?cKlUS9;1+(QU+u#=G`Z8++g@MnrN)0)QIF`Xp
zxcj<C|K)a?x+}vFHajHO=Tj>!8e*w@n1Njmj=`tj(L4->m2B+Z?j*orJFezPZ9(*=
zdKSLi<Cjf+r4;71zAOdIR!x1ml=WAp<))h}Wl+K<sqTyINl0TC?y%>ekaw8r8t3L9
zEFM7>W?9aY_*jB%d3KUgSf+?iScnAl=BcDCOv29mDv^y%La=nK&3C34O(xo4*!{Xc
zqy%-^<(8m<$NUDK4HWBZ+)FhIjw$`t6;tS%G7E?LeHqyvn~K~;8z_pSW*YfE<<NGA
z`7V|1ja(vD>Ou*n>kIQ3qG<6sC7M<$1C}VCDh~G}+vk1sToW2go^S(+!3!d`bF=c(
z5omm}BYSs4JHC63TS>>-&k6ACB)1dI+>&6p_VeVK2v8^&W5@ehtW~6?e8iN9?2@Wa
z2C5<d`GrLca!$RnS+IK1ekaQUW}rHZbx-vL#0qqaEt0)<c8nvhsV|(KmI0Kxvj-2=
z54AHGav*B95#AKa_PdjL(v`+LK-45QzY=->z%r`bRpRB?VPHn%RVof0WaNgzQgd~%
zK)zYCX5Df^Q$~e+QgFtzioJ-yBe_5Hltj6)isfLF6;xgKPxh5q>wl)(Z8aQAqH>fU
z7{Hyf+^kIvQ0&|V2DkK)Pod#zA5f<LUb(wh1FH<`JIjg{wyBA6je|$4l$tr|lk_L7
z5B^-q4G}-AP|FG<a(uE#m7m{p%ZeiM-yxDxfhQ=U7!a3b9#weX*PAEYt#dV7C>P~?
zwDvgDy=vi`^$YX!7*X<Vzq#caYt$SmN|s}U>5T&-V!&pKkcxUvdZW^>*ig6>Mlsc;
z$)nCAmy%+Pf|Hetw8PnB5>5W}zwAvel$#8d(|cL|rV-^Ybbt*JeBj^*Qad4*lwGle
zp*R-jms6t%qp533&}QeLYghkkZp&?kHT32TjUy62M882f!4%A7n5t(I)W6rc%x8#Q
zIm?HkPM5LQq_#;JPc7BOu*P7+f3EE*?r3dhcDWC(vY?E8G1cF`-1U-3%}6cUq$ma@
zt)82mUYBd|)US=68?scM>Yv2<20@oaQ%pEJHzVwL^hb29k`Nu&i}Ot>4ujpeN+63S
z|2$0fSft8H)u5R9{TJ@0uaZbEs;TcGa8qT%V|^T>9hzAAncx&p;}YVk0TBtoL;j3$
z@f9g|m%G~P1Q)X?wa0AynftIKo4q;9H2f24+RH}*V`@*XQw;npZ-J?t6u`BvD5`a?
zGJK5WpEyj78ffRBb$`bS0rP_weeK^R0sUbMc7qrN@p#V4l%RF#XOv~#4EZavUCNci
z2YzKkVu(hqQiBI!D=L)WM1K%}aa(4tukmt(KhNN8Y0wDoYX^p|Bxpl&(1T5ibf6=x
z%W?~zo|=~I=Z0jKu<lky=jLNNgpxEMal6loa_0(jTL@muS}yu&RB9O#l+!mo04L=N
zaaw6fW3y$wFtICC$}+9N=bTyojUz>C4j;N;SZ+-Mes)ScwX=M{(8B<TSXHA#AgY2F
z-NM&b63t+&7d_gQQ#M+V@&7&NxW^br(UG9%|GO&i&li0v0+JVg*J+a^3sSz|hiO3H
z=#JWHdnv$^l`=G2{)Js$Bunp!?h+LT%7<o%U{UdmEUVP#ER}XJdL(SR+q{00zWt2(
zh$FU{4PTThjzwg*qolAG7wVk(>15P}Q*M!uW6F!x3R`cY-wewG-7oMLU>ZR>&vpT|
z(3R*b$EvWh0llo#Nf?6L`5%Y*{!!``zcerOuFK4a@}W;q>CPPb()0_pOO1&t;)hdx
z_A666XIfSj{Y*NX?5Gu(=EBex-Wag)Luin)q(2JWQszEOp^=RFF%gq)t6a>=1atC+
zZUf^%B`6)k;g3kCgwA>ouX0IKG#DxM0<y|`+EHn<%>5~d4XXUKMUcy`phGO0&2<6(
zV1Uo~+#=lA)5}GzpL6(I=8t+8gKKj*Foun!Moo_45?tiKEr=ZX3v%ulSyucem|LEO
zVcp<+uR>Rv%hjX$oLz_*g!g?e&>ZJabej)+H7fjbd)!TxT?&RW(@dno`7$G=;W$LU
z`}OE6YZEkMhEI4hXfTal)Cj{avs&95c}+Y_9)@8>^98#goCvb7q~CrqCQVI*joFeD
zV_)EV+hqjC)dp7SmGDN2xzTTtmS05Q|Gk;cS4l{qcIv>JPvN<zQiseh2+iA)f*}m?
zk2X$P>`*=DDbve|C<?8Mc5|vv*&0rT;M7x8jiB{-0SB3KyemxOzH68qS~ycf(?)ke
z_Aqj$(3H3k_HW7&&VZ}~iC_|h=}^iitzMC8W{U2qLLo1MnSug=wgk8UAw_o+y2fkB
z`aFn8rNct}`bkKLUucNI`u(@5H}9#oR3unY5V#|OaQgjmsW;w|Y*>iin24}LfdTZp
zQK>iXlCIPwod}59(GWiZKFL*dMC(w9bpru563Z}%m7%~XdObZ!)!&Hz^m)uAje~*1
zskT3p5<(#w3<T69$-*JBQ)Pc6-WmAwkeC|ytK%5WA4htRpO8kv(z|EQ8ARe3JrIh-
z(7o{#grIW|p4gyyh0L{}dj-t#i@?#nAr#OuwCBvBqIpHnnV@6qKA4H*7&^igbVXhn
zKk$p->N=7ZtfO~j&sjxU8!=6uJfm+8o-{>j2PkIqtgj7-W1@G3&%s2}7&V5?iHMjm
zs85*^6p1Nems6w$p72)+aiQ8l><xut!rSn5!r8IzErizKrRzd`!}B0N-Q@f{`(%Ye
z?)}=U2nB-=`R#PF6r%0*zsq8M4aoS<5gKX!BLkp*qUiSs9f5cIBftSshw!r>FA6!7
zCBYuz@i0HcjovU787wY(M*(ELkKU7t0x;1K5haUq1VwACr(g3b@NfjMWgGQ!PB&$F
zC2S(`<x~`?9L{q#SBL6z91y4cLa-fDxkgD>@bQUK@#Vlt$w|zASnZvSJy<0L9`KY2
zvLjl7<$$j7xue;}H>qd#w>eYIb8e1kV>RuJ%58^|%xb!#?~1nh>&;=}lTZoUOr6SY
z=gy+e=R0V{>ZDq2LE?=j;vAiez_YY6mWxZOWV$*@rXIBh8!2dA5I6ir`+y5y75A=o
zt`4pM30=5bvN{0VQrE|=fC#}iuxpXwO9CHw26OPcBTDyhgfg^r_pSz-p9y;~*SDaG
z=iu5dy~y)@XdzwX^C+_3aasUURQIZns)3pBDq1IQ>81BrgkBFfUM0V=uLOo^52mjb
zBwRl$PZ2Y}NZXuLWiFKqw1pp!uW#Qz-pVPgsSELWA|F2|joFj8_)i6evOALBX+#wJ
zTOzLOpD^PV>@?vIw-2Wc9#iZlPQF_mQV2@kB=SAI5@{p;IuU{u$J|7;-M=-?AC!ww
zA+Ac2x1@IXYWU+@?xnZ6jUL+{+ee{^1F@pkK_Ija(IFq=ji(T3`yY1HevsYi;LGeJ
z8$?9V<zk<dA-Z9@tA~5C1GmBsvJ4J|N`*SWTeSU(P=_(;3yQz_q>#^!HsUxp%LFM<
z)-e*~_H!?!#k1c~tX$FW!e!6eOf2n!x@=+^@BVQ~^--cY4pT-FaRadb%kSMtCG3X7
zqA0;wUS2237xlo;QdpJ=hw)ob^{lvAli9>rO9j6qn<8!@PethQP)-}=ut@GI@-E(#
z(<TvB_%We?D}`|%sb8t0P7)9MRJ$6j#PS$R6N4t)Z-7<wfJ1tQxOQj`AA|128LJ8w
zb;LKqpm(TlfA7TB?xK~AqY||)2oseMh+9e^J35;L57iEz;r9akWhkf+Ht;AHL&$XG
zWYpdLC;<i~1Mm2ZsPyA?EGvDB*uf<kuK?_PTAZMqcdX~+i`~KE7uhSC0rmd9Kn@Q8
z-1;xMN4G9H063}dlU~Aa_OVk@sBT)`?VgyM2hO2)5~FaBvQ`t_Jt6wy$<a`9Qb{xg
zNCC;Tc$_46=rK95WzyV1_<B+6jc|lKj>K{Va4$VxR1!JlLpHn<i_94jP!FNFXmFgF
z)RPomBw&`wZKJl&<444-OiAz0X7%(p<pGV%N;JtY6VOEjj8TOv3F)T^7mx4{mu<1a
zjw?k8m#xxxoOVItO*=%|v6*9!Pu9&*2@UJcPZ8<u-IJFY*sV1^ae-sOn90<Gr*9Qr
zu~^dBbsOUOM2|5z>qDXut2H<8cdJ29i=vC~rK$r6d6B!@?$|$a+qvTXAraj`iO3Hy
zccXV@cM$flX;YTQ&sK^O9%BOY{>Kz$6j6;kjvBs%8Dl8TY%BOB5$~U5+XB|QFfD{7
zhnP>6@)Ai(BK3&iZfRJ$Ni~Ec27j~(!QPl0jheiMhP=m}@0p-Iycy%DD=c4oq8+~@
zXm95w-j(7zx0A%0Jf7AsB71!247r%I)FNb1T|c9w&`L-mpW!o?-$Uwb*=T2YU4OaE
z`kkp8`{0p=+>OAkE_}{-3)wEeb-SpJ)L#>}tbcEb3VBP9-dvw5Np!W+-Ssw_efe!t
zoJMV$n>~X{u3W1se9rKSWv++0apuvqBquShXgCnDB<@w6c&tB~pT5<Kzj>=t2hm68
z;2syr#8ec?xU-p=mWXpi=f%aKq`Vv!tYTVqH<YpWpBoFgy7{&-UJM5L*pRM;QI~z8
zY<w$z8^aABpG66^Ly9&5bg%`#Jc-oXC3wsyO9{2Z-l|_5VhoODd}J6~?WI@|JZAh^
zBZ=<{16Qz&X2_^oG^Va2ezhFd&)Bk9Kw5K+fq}Fd&>><Ei3o1H$Q<0rEaW1m*AX8_
zLTnf;3`;=BN{HvPQg-k&3k&9R*@^y@W7v%L2mx9kQo(pf(F9KmUJwi?rJf)fe;0~h
zh7Z9wq*!{`@%ruSlE4H?cN1J#MyG^(fO<xIc<tZu;6n5X8R-$`hj!RW<Y$<Q_17_B
z%92%0iJWBSW@z9U<{Hb}=w(Sy>adop(?b=VhpjT$TmR%9+T#fxND(1Gv+zwqm+hvc
z7Z>o=uanOjk24l|6q*eP5lEmHz80j?uh5q#7vcs0{3a@@8zo;AGdHhDU5sS(Sc#IS
zEHB4}g>pzfAm2uF^{>R}6P`J;HB|Nqif|K%4yp!{#gu<g)hAJuvJJx19w=B*p4Gx5
zzu0)GvH;Az`uj_EcH~*E{Nw>Pr))52QL7mIp!um}ZTEXTn$9-1(k?G(SKFGCxVBbD
zyv~X;<xnH^sC!_jSK|^IF4&1fWe(#LPj@&&Kha6?bbDEG$2-yh;{*TYw|JPFrjI#h
zs1%#|{O5Di>v(F1fpfTE*&s33W^T%Clci;KF>i*stiTk^_~3Amm3X`AX70s^yxP71
zv0SCd_Br9K6mLpp2^9QN-FM98A=w<BT5D}nc|aszsHY!W<crB5Fa8(29r!KV-Zgv-
zK92AQ+AYu^Vdym>j>re~9{ylJs106s^b5uwWhgBnECCMRMwlzmp7S8x9?IbPZ&pGY
zAx&R2AxmLPeoG-PusQ%c7JYPm_<XRsU-Y<gF{Q$a6BB|tAv`c%2pEvqaM)1TFkVO;
z2=2U&Y>s3P8MB;uErK?Ipn_lmv_aa?mvCLb-Dw^yP4ONaPsV1mWo=AdPWonn^0M+S
z^Uej=1vCVAXP*T#1XhBpAsz!+f~z5|z^P%FgD?ac1()&$XVG0Id9nPz)2$a*7Pj%_
zXTTgTI&77kpEM|-Uq}fUx{Wsl%_Mo}J>`d&yL9(T{KDLKEeixU_~~M<uxxIXcHoBH
z=Y-?F_7D_fyaa7W&~ye~+<Yc5x!zeVW}s;YB5oL4gU4igabCm}zY$%LSn@_lcNmmm
z_X6gcY7xz#%u#x&QsGc*ZBW#q;Cch*lz({Uofp?+(j%e41%Fd^1YILN!<=2bioP@I
zQk-UExHmx>=C&PM;Y~cCKEO7>U}|3zuZ6uM<^nW1D}5hpp^z*w{b`D`fK&z~RRaww
zM#!pymR8>ZS7BoKNFVyKOML*-ceSS!a`)R$#iP83l_I@CHR>+mZm3VqtvR=WHf@PF
zpn3p2A44!jvOm`=o?UujTd_^-s4YjBwR&atF<+Z&yx;G2{6^@tXLK!T29#dmt4F$O
z16p^bTVYv#6K(*>#O^hGEuW0&VZQMod&7FrTmOvNT91TQjX1s1M|a;o6ceQo?R2XF
zK>Z=`1dy1vCo&0UZbhXTz<%bwFu|VgL{8qlY6o^h-CK20+by_pT=lQ`@}VQ`j&Xm}
z8m$O}VmySZ4Px7|Z|nhUYU$;X4{Sc)g^!0CIPuvn^J!uOOof}A^ZfOo8L!Ai>dNv;
zYnt{=mJFw#XxR64`|2Ay82El^=Zg0JjlUrb+&_T3^E>i4T$(Q>E4sFzC%}F)Tz20J
zwlEafM|^AzE>!bs`IVxuMGptc#^p**d`9(ff<0??4NPkYPG7?p^agMY{&sLtmaD&Y
zK+Yb{3;b0P4%YdYH>xk(X;5N48qwagZ=E+-srN(E9RKpQZspSG6a6cyYx?%k{YGdT
z!OwsJDqzXyUF{pUbux#TlnAdfccoGk6_HPGIN0srcH{%(hs$o-`W@*D+){_APt6vq
zd{uL4gUE8UW|+$w?XJoVBo|!BF3iRnZQu0^!I#_D@9(m5sUkVJav$)Ra>!lIn}CJS
zGUbYPMX7QyMJUCfGb}2Z-i~tg>5-0dnCpp%6PxaG7t^ltjK$+J!H<#;)U)-vHStN0
z2io%ONpC`B5E=ZnN^3vO(GUVMKhQO+`@{q3>5vcTfR<XKZCO>*QChjA<UYf)Q4=X?
zX-785E-pn_6ptH)*Yg(82x?{hXOG{L0Z051GhC{cUyo2*1oG+(Ss4sjwA5oLjEL-2
zs_M!|l-Z+e^0l#}>z2+Ls^U~MP{P*XB+RrE+0;ZzQZ6V)MW^e4+cjm^sXX}Pl(E;7
z&1O}cqba0Jp6@A#2QFo0SDo{jOffUGEKDVa$TA;WgZ<Dtp9dGG3ujQ8uk3AzOcmEX
zJ_lY)Sw3pTZwsireGq_oZd!sl1>-w3MhS})GSp%TkIQLhW;`wU^dU3$CZDCt2cR`G
z?*)J5J9^(=m_)+}!zRhe(+b&>&1ny-mhT+<Mr6+7xI!+5lzT?5I0F^$UUTQK5~y|@
zT*(^Vsg!$w9=8bir8%oG{|XOsv(+XI9slsXff{M=Kp{ki;~C-_)-{V3+T<m|oWeuH
zbR?Uy%V`em#kB%knTkKKI{0leG}-D0b8MC^s0cUDMV(a_-HSWi+_TC1vbFwjj3L}{
z&sR<}6t<V2cX!qpyC)BvJdzkMvBpKU#L0iB(0si##`%SaScfa8IT%kLt)Ei}XN<iU
z#o!k9G<A78{vj^236PB3tD*`IN)DMiD%5H+&Y&IIzq!d?HxxBLIuJ`Bxlgc3B28;b
z*J@IqjSG}oPL$Lx{HD$uNe$O``(pMF34$vCPf9`vCj3k_tn=q3@SQo%RlkSF5aE0Z
z&|>3isS^55AlYt5@G8);46^!6lDD%vgvaQK7vL#z45vk)s$wH1jYDK?kdQbzl4vs6
zaiv!<kPv%BmQhK%qu4T>x%J{uN+3wWhCxf9K&6G_nqB$)KwhH!46DAwkeNAJvbduh
zrwkNQGFoRZLacg=5A4z8X(3^ql8wLdEiWc<dD#bxuB>AhaX;3^J1&8Sg}#unS3;6{
zp66zTlko-!rUAP$nM+SiKl-z-bY9!m;Jjqadm;UEH%XJFHd1uM;S^Kn<M^jJUBRS0
zs#DHt`P?zVA=GJM@M})xS$C4ey-;vy2&Kp5cMNVfND1d;fCP=I<?Sm)%<Rcr?40ac
zE#kV_blUaEY?GXFhFKw4(L>#E5=!&^#R9tyJ<hnLv$kDFQE~m{uxQIV5^fut-8jhq
z0Hr`$zpLshJZl|go~#+wX{kQn$f|6Y!>Dq23qG30r}8y(@-!cmdGi|Ui$;F9u+FLX
zdW}|Rbv=1Rp6jveN)m7vt)Sl#itj>p$b#vz%RYgh@R?n<lU=oTgUE}Y8DvAku3CfM
za%|G1?6l)*woP!4NPts5hEw%KC2x^0avHL;@ieg9qQ&LrEjX^?GE6);lNBX-W^-N%
z3Ey+zmZr(>8(viwWlXle`P#C5SJ!(@?y3rI;Z!->=$&%KGvO^=?VD@(XTQ9efA`Q2
zo?k>)uoLrW6OPpji)3FK$VT%_0EfZk&gB#6D02)GQXQ$gYDsz#@6dYz*EgNlcwEQx
z^L?D=6N~2%U9m$xtKDz;8+pQwCp`6J$j9b{C)_rEEocQM1n!!VR}3o7lJ6V&afxIx
z=arEl$shqzQ8qcroL@Y0Nr`0E8b+RYgzGQw^p41Eim~ZdW}CbxmT>obO*yjv#Df9Z
zP3yAd3i9jP_7g3*LiqNhT7k3nKn1Ll%N_PSuQ!j+`%LzF!RX{X;MrB1kqJgW@Bhpw
z8x6Z^je56Ww_Ea@nVA90r=_I<Z{BeYPM)qDBAqFl|H(D<u}DNh5Iy*QZ;@;K?29ob
zDT%z+j<dJZ^DJiP<P>tEXruAqEn|z!=|<Y3aroetJ#!sWWrIu#XHA}$Ar<A0=1)C0
zt88juq17AMxMRyEd0or=NGKN3P!v^PvAAGHpg6~_Pp*@%_4qP$-inL1RZdAAXq=s6
z;CGD7U9+}vNjA-Yi}8L=z_&doKBq2!Os(2gtA1G9Pk1@e<g{g2%muarzTkJVt)Fm@
zXJq8?IUmRw{e;N2q=!u=<E~oMu<n2}$-?`5rFnyCEHW(nu{8I2I*p6@^Jwy;fiZu6
z1UETh(V#hv?t-zLHkQ<E<C7X+$`xrQ_!W<OLg_<Zt+u$j#y#iasm(VmEULlepH<*G
zZ}FTaq0r|vs(&}rJ4silKauOn5%eDIGQKF!Hv7h9D{q@^O8P}XS=I#OHQ%}F^2AJc
zrj4G3x%@P)Rs9sWp-DFBfh6%sMpDXSO2Iik?hrF~)n+{`_7jk;j%cDA*1fMM?rQ6S
z49_PAi{GMCrQ4s*i%s>{n1k%a^l9O=>|;CqC}8$+YKp36NVz%Z8{F>X{3(g%%&d`j
z+-W9Fdg;Sn^=#f(VAtIzW~Tbw6NZbkEt-M?y)`?Z6nS&fadve525=fT<1+vcTBu<e
zIi1rgvWDk*;7D!-yunwicWZQd&F<VHiI@+4dWdFLPx^_MF|$4vwT)_M&g4ZtnLftc
zT-EM<ald;0mks=*f4TX?4>4LB$X)6(?$3}6^H@(-NKQ5-==J;wv#fW_G;2<nz;o<P
z=~%JFNB^-~E^1@uPpphsnM|4!D@`=znV*xSs44cGOqX7T2Vja_qY*Q+aQ(F#d_P`)
z89C>?`RghgrG1-vMjk(Z*6KWR<kEXyU$CSs_~`44=+ESD<e%eygm*ZNL9T4m05Q{_
zU+L&i=6Ca|-CC_)M;}KHy@{(u&+lR!8l9*Mk+jIm(>Yt^iZu0YBL(CQild`&#pn?K
zGgS_!;V%HXQzQ)b4^=sk1{Jbkwxy(`YSlWe?x9+3YC?+BrZs9HY%~h#w4YC&Gr##(
zdfFh3D%~6`A^0#AknztuxGOXoZI0Wgv5~_j=ZuCEueCHd(*;|0pm;LbJVi}()m3?E
z>S@!onX_BTP_EnP%AEQC+8@FhM@I)nNC!!_Ywf)KPFb4(7T$6vBqv;-k}|<`Js-Pe
zjzynDyhZ9v&ccqE)neudZpcW!p(EieFYjOZ#D-T6yfe~L&~@Iri`TE8zf3X{e9+nV
z-|T$}cw5z(_C0rztbMaAuUc%&@*+#NWLw^?c#WNyB#skjCyK4uN^HxKWE(?(KpU1c
zEM*8ZY;9>+!qSq21c&}jY25%VbtwbQz)#ac7nX(uTG|ef_<Fu`?v>*#r0vYKfBzXj
zUm~4*?^(X{e#?2!x%T6KkMXDZPxw>(dvxACKMB9~tKWaH>H8R!eZU4D0X@>FYJa)`
zl3GfFM3x}i-I$OfPqLc80twU(jV2?V!2bj7544|$3qx>WMO>r~1hCLwQ<y7OE}W`g
z+2u}ErKVZJi=61&6&&Kqt8$X$OlvEvGFg2!=)+ulO=%la2lj&Ht^;gNazU_MgjmiA
zmV1Z`g5~P{79FJD#DpXkYq_^EA&F8Y8f6knW}<eeRD$*1_B^kBUVAL%C|qDK#nD}d
z6TTiawZ}E3XEY@Sd{1f2s;!!OWqo5xdYXA@NdfwjPx4u5Wu{5P)z->#TkDzXTzf(G
z{P)*C2KEzmq*PQo)k0#)L~Q4$3<=3h@;wQxVrOEaS-tZjHcay?DcwE&xaK|m2`r{b
zbp;SV#J2hw<7)M|P(qXBLpxu8b>8rvz?;W@%U6$GJ2qUsd12F9$i}D>tr|zlZ*}tD
z<R9Z7;CInK=AY$XMix}~4$9ctanEu5z8I|h6VNu5x?JFV`Xt!v#4T-tRaKpogi)k+
zsMYCd1Vq3PHUb#J^K6~wSIjgwVeQ>IvRCj{;OrBJmpRuFo>AFq*R@Y&gfI7)($lOX
zT~73xPlCA0D!WlZx3+S+%-UwAAlF`B*^W#AG5A4<DWu<!+=8UcJ)r;nl<WYasL!an
z(o+2N8p!-_N^XRw_mZd5{gm`mct~3EFVb(&xkAh7a?0RWKvOBYR(hM1mQwDuTf{#2
z*XcLuOrZ~`r}N9tt0wF)Zin7JqSO#2#r{-E&*<OrQ-xBn^E=W4<)qP=p_ybDF-9(o
zSaDIrR8WEvXGJX9z5DJZx2)|NX|+7W|8Q|v$D&0`>srl2ul?-Kqw8wJ_pW{OE%fa7
zpW5^EbK{-6aU@(b{WZ|{cVNdyu^kUny6Lz4K81QdrPJzY8K+A$855brcl;Wo#YoFw
zLz`eKz*+8{MyV5r3gkHn<k(4-O0;r3qDiRK&4dapfg=Bb5_VZ(C^HNt(Ix3<9gOo%
z|9PW0p$DB;Y+j(NSc{$79?zJ}?05r@;54>HT~jYEy)0X2Fc`a<veCLi#B>zcjY{PA
z(+Z=#06m|QR9Mh-NE0q6R;4-m4K65Hl^!PkAZ2I#fi}`_a+eFXgs0#SN%$egZ7oy7
zc1W(KJk*$9S!ALfHWn8bKAhrmjU%t$qOfH@oRuY0K5R-+e5%OIQfP~kGEEusBolZ;
zF%Su-UuDYGYD;q6S%rledve|H!w%Vl@zYnl?e)05?nxXdx(Yo$bD#i$lOC`8q(}Q3
z_vA@<c+!oRt8+Os_ypcXGnivvKrCzq<YhbZGE~ZpeEwha)0GlV#-CcwCvXyFdOrW>
z{4^DOBh?ASekxfk(`OY=ed5kalwo#}Rs7c4M{=je>5l5}v>bD18RUh9a$R;2oj}S{
zFVf`{OYNe*FL{S~g;KzdCylb;?4M>yPPc&AjEqJjGg1;s>XBr#Ua2%6Ne5QoaZhTG
z;n^Ud$%H(^WCfCdJdl(n!j{%BtBgu*M)F@>(%f9)ZEp68->8cH>FFZ&r_3v@<>jsT
z|J29v66`Lw^MUQ#!EVzhV5gG`+^CxJ_>&!FWwvT5kZy$ZnuoR3jIG#gwifBBIv}R7
zXY_vH@eoQ+K0qrexLKmf<O5hsavVMz?8BtU08qm}sm)ggVd!8OT!~!Q(muq%rqV}0
zsVMN;mL4uBGFlx9G^{X}ws!N!?y%)+Rp~Z4-!FF+C-?ov>#}Dm_?>c#cS#V{+-c9x
zvnse{#nQq8f8Y<lD{CrEOv_=4i)FcsNBH~rO=|)sok3n)EG>3a%}2%jul-9h92snJ
zu{>w>6=)3I+q=jrAv<*NI`*qD>m(}8uaX+121@niM27mZ9Hx4*?xYR_4-1(Mh_%nl
z*nG%F&aw<PDUu++x30+JwXJVIbF-Y;$xoKoSJdXMZ(}wn*61=Tmo4P)DrR3y^seqi
z9Yv7alKBo+#Y_T!uENYK%bu3u+?AH3FS|PtZ}xTE14=E7>H#N*Py7!fD<&|hgOGjL
z;hOa1n+C%>q#~e5GIORO9=9RAF*V)NR9&&^>V>tfCgtI02FA93aL4u)E=}RQ1pXzo
zT+TIgc$c@=7N=DCz1<`A26}W&Rc^Mop?uxaj>hs@7HJ;7`yu{&->!VK#pSe1iWzxn
zdD>+yxeNPmySqNr+w_$?*W--McQ7BrtdfB{WI!@BrKz<l^DeoTvfs@lQDi0+b$BXv
ziQubxvTN~~kf^-DAkL8%=isBix%|Mg!Lb`Z_;T$~^&0;)r7NtL@h?8nps8=)_~6=#
z28+L`p}a`<-q<w{-}MBlc_zbNuRVR+!W^t7eLK2#zW=h8hxqrt)YqF<y}WC*6QBP9
zi;%MiC^MDkH&R;4Y);b~<t?&PdaYKkNfUIf^SZTDC&>~!SuSc^TH-Tg`9LeL)F!nN
zAI7|!ex~c|D_+r!JgTXx$;-4_clc0I693bcsp?*5cjI?LJ+C&j(b6WQO)psz{P!Tg
zbxXH-Yr_)I#60~Gn+=F9r0V?Erl$GzFlmo5H&^X`poXF(Wl0u`ElsPC%Oww}i9>)t
z;KNoNYT%JNuXqET!^H}`n5=oM$y>|7CQ6!xU4W5F&D_ie0Ww{VBTEhYM2F9dL8j4h
zjWshV#bC_Lwau%GEwZ??(^E|{`TFk0x)QfJw^@=QOUx*5wm0gnDTXAICe2$kuX)R5
z1=UWSMJnIa+Yqm&wp4CJKUj3fH4crz?J2l?ojSv{Xt?git7}q}2lFL~m7T6->vyg#
zOH<^x74$8$G|u1Fxa;~_{7_SIC5}Rf46dHLk}AS+k@rZxMyJ)<Y^nzmwR9R|&H4&O
z{0O`VuprR^OFffi$kvtkd>6!rRLC;Oi^|IMkal3c5G^eI%$f(*@H=dll#Db>MTT!d
zxO~Bq6uVp>38SnB_@CeMO4P30r}-NHx|&~)zLAAe7u>kDryw`eVfM9Du3A>T@y^^x
z_NI`6bAR@#Y&TLoG7|N)2HxWD_%TGm?b9b%J*S~GR2F62Z+RhMocUnC@dav}IYknQ
zxSPP6T;L{nJ7K1=6-rB7G~t}hzj(c2?BVXwy_-EB@D&*~Yy8!#s?4_P)m6>wd?q8R
zM=g(k_Rx}*51qP;Kfpgue|^^*JL_w|@WY$$`NeIGt+%`cC_V$Z4E9-69+mpE*=9?#
zk25EqR>?b+W)g*7Vg0}yA<i3pXPE*xNLiWBtHvukE7Qo8xwTz2XWp&qsLWES6O~eP
zy5-7%DoMHZ<Ev|EykE2wT9Oi!DjA2mx@b<ONRSz=u1tE;VfELUT*-|$Hu$<aa<O&y
zPJhVdv3p?sv-(w(3?fLWqA5lu<tX>_-s2u1L<lJDqzAOqW$A3CHdt3+WvqOV*P#y|
zL?5E5sXx%_-L!h@54&-HXo}0D?}8m{!afF3?#bdJ1FU>|*==ZQ_ioH0_o6H=j|r1K
zuJQm)Nm$B#{Fqn|33X3zW4F=AcJJofcN4yM6NKJgSP7b`3V((qF)<^<Y|ishHAge*
zQ*3N;b8U`Wt?(RG6<c8ugd`1s>P<jCkb9DB%dr~goZe$-VV%p8z<QBGJF?8&xzETE
zev;l)ruW$-dkafarIOSV7hhCVlFZSpgw+nbw&{yK6FoYbWf=x>DPA|<S5%tJa>*q{
z=&p6+>-v7&hpyrb8m+}&cV=&uKPyq?_p9|;{%RDRf6tD=^pE+BKYohd`y{z-Rj{|a
zfPbK>F595?`!$BFx+<oty4I%G)_=(7o;rnI{}6Dye)<%<hx-=9;XG>LKAm!$O?)~f
zfhw*TXH9-tPRhI@##Z)1!8j{9r2E)ttUh8JXJz{nj>?{8wUmNQpz8hvzV6k+2VjHT
zK*qd)6*<Ck;DdR)crXt+D+}B$C({9WD$_-mijjd2=;@iWEV`6gLDQ@(&0C064aihp
zUVojx6{8_#%9gb+E?#v@z}t5HzFvD>MR`Vg#fqw%Z@=Q|>h5L-ySQ$lDJd(>AgNcM
z?x}6bOH?*7ER)}nPt(4J91YWO{dcdr?&uAToKmmkA6>DfKIPSkA0Bl!Eib^nuo-qa
zw=z>yAyt1sp;VdxhXeNfcVOb${Zxh%=I_^7j+(9MM>DLH5-F9s1h_}uimk=b4ypQ2
zClO|)*eQSlk-Tio!sm2^nx5wdb}xDi)GJ!DbfvGhe48TKl9!&*>Mu~3OY->|hdV`z
zOujNCX<_Ekh%RY!8I_-;=cTFa{!TZ4U0p+_MhS-3+Wa->n@uZx8L~zNSo0oA&IZ{>
zC<*>$5x-KNKuI``MKr^(REpc1ti_J2J?;?#0~rw_v2hRc(wRRn>NBOxOZ3hM(AAIe
zKj(k_IRELMJ%Ee~SYnXVQ7u&K{^p}C<E+x3QhBt(>N{F)b@?+ASY6IhyEW@*w$*S{
z4^Q`NkLvIjI!eJ8Nri%de+UIa9*r0g@~#kbNAM0cCpAEp5~(9##6W0?+>ycDkpb@b
zwjz*hFEEt!c9vKQROmKsMp>4=YW~u$&Yb(xv$LGpN`8s5uqZv(mH6BAY-2)pu9}ak
zG!WT3^y<B-_H0L*iqq9e7nr*?gxr;*n>$UZ>1<t{G$+$gw`leHb$R?v4IQ3Tc}<Np
zJ1e())21T+Q;RD*O_i1|sj1-<x)eRlpsW20@-&h<K*tUl`2PYrM#^%~sM9ImH^8J1
z$zk{TzRXHiau4|s7$to^qpU^?N*2`B<ZQm|%z8DmgMZ%Xukz=I+n8Iln~dc>i}<_h
z+3p1A<;zef{?)ufek*$ecxf@!N_qFYj~0)!nuGSF)bbZHaah(LNd>1o2t0y2q9yK$
zj~WT~6%_<`3@ssx4(y+JFCjIFt0*~Pc3{lGFc<Jkun&Wpl=3Qlc3!c$$+NV*HLrDv
zRHokWoxag)SAOM(LzhK!WJZHhkx`V*@Lxk$skIB1Rt7u#1*WQon%=53mpy~N)z_BM
zsxiAP^E|C?Q(%!P-zdHLi%&ds`}A{ROOm7Wvid}QvR1~_GbK&y@|QGtR}638S-*Kx
zYooQcbtTU8+W>(sK)^<oQ8|aKDnsrIR0d3SzvqZS$V_s;MDRK<o;FOHrWuiULgIB`
zMg)|!mL-&wDHGU)niUfe+f{#ExuLvK&F{@`Ezo){U%lEjdVSu4zO6eKEFAM*{*p@H
zy0|LXSzl<XX>^A}%bEB1f17%ipKdC!TO{>+P|6;5-kPek$G;IcaQ}5{ymo67Ur{#T
zT+--WIlTQ#_5GoFBah&WbL;deCdj@+IVk%xs?_(BO(u2nI4eC!IULzXIGBrQJ=hLv
zaYD6=ikD$-3M3D2PY69FVS;HN!+O`OSY9|ftdNgBkyzbe(xj(n=<lmQohJUtjdE22
z<Ebd>Z~9)vVy|gvz`in{mb9A^)p?bRO8GFqHou|FX{c^&S%|f>!MboS;Jk`z-`~;E
z`7HDA)Mc#9uY}+%K4NlG&8(#wbUc)m^;J8vTd>=yluGH8{;b<guJv5)POc+MCiX(l
zR>EI7$Bh4hqj_t&8UD&j%?vhy&qSu`fT~$)PUNq(9Iex*nUYLtdR>Oap4^^S?{9JW
znq6j{s(M9}uf&(>$@45_G8C!VZhuE<nj<^g1T}L~7dRXJjn16vBu#3PUMWS2>Xq{=
z%d4_W?5<^uSz$~ss9%qM)wy+LxlN4}>7HCqO{XWRXw8jXi(-pCX>$G1f`sIPrk=(f
z53fi|Nb}|w*LN1_jOn_H`j*;a<>Jw0KC4nc;Y1QcUh5#v34AB_EAC#ZmdblN({jA5
z;@e<MALV4FhpcLaA_04<2$H+bT}PHngZN_;gqDf1iGOAy5wlt`O?t`8F;)sZn7j&l
zSDkKBo2^-m`Fe}a)!tN}y=b*f0dxEa<saf-8#=hHIXOx7BZb`Ab@ly+d)FRb`~1sN
z^lbCU+D4-zv#3yS);Vi)_1(+bug~r5Sd{g=k@J7{9J~IjuZ}fbUsYQea&CF$(5}r@
zh~I!7)bH7uI1fkQ?&<g0T=p0_uggD7DOvpid_*@6-%Rpx={Q@wUw?dt1;dgEJF7Su
z)|bV}Fg|`Q(`jjPw7%?@$47p0)3r}*F8n=TIehIE(dd<{H#<$J43)i(tT(Uv#;<PV
zzso;MZ|8r@Pk)G{C>g1)`XwMe4%xSWoupE!yu(mTKBR(`7WT`a#4`yeq*m^P#7c?F
zv(D>9SP^vLkr^UKW*;XN#jZIsapjg$*e{s3^^~INdYVOSU71BkuU*S%EoF7|-PzW8
zbwMOEKG;Ec?wi~ab{u%F;h;<^p(%Epc@N0q^T#K>=TX}ghj=Oa^bq<KeLVFZo%|%7
zJoVm_ge#+uxh3@1fVnF6(G+PvLvJ5NA3ymdJlTsh+$QD=gxw~<Sit#d3N{=ksJdg5
z+SxSM0F6$sU>{APk3IP$U-9IVs1n8ix+^sS)Adlz{@kP@6T_zGvAX2MB1S<?cybCS
zTshejS!UZrra37|IiWDiCgf%U9C+y2<r|2vdlJ4cIBq-lhgL4~N?U4bbSEViHSyXe
zSE5X&;cIW<*Q;f+L{}5Pu(8mX=xRiV(RJu?w3}0CGAb9JdAz;Ss%dM(Wfb0x^z~=v
zF)wf6e>BX0y}i<+ZEMq}S1n?ek@A@}%pT&IC)hLGbL3rZ%RVg-H2r?cmOsv>97^$O
zjgI$Wp)l_+oHl}U?pJnd@Or_%#9<~YVdVmo6&FiFWKVkIZ({A@n^^9GH?g{N-^4O^
z%U$b`tv$ij#h+NLP;?9o)LeUL{S7B}EgFQSv#PD^)~{T%-MO%{B$2&*WU*JZP;+`&
zyWgp4S3JTkYRVit^~k<Yu1reiA8cD$<ud%+#LF+5%4^Cq@h=hsLw%i12gXsckJ96C
zGiSb(g_X}{&3<*tM6#9Qk|s=6nM?`Ghna9wJQ<jhKuu@fx#}+XiI2FgfR?^4U$i;a
zGS7djzNW@9f0Ze<yo%4L$ur6dx{zrxvzF^@%Te=ZD$27vYg~L^M{T+so2j-H{b7ke
zU(>DxOFRX^_-*!kgohUR4N@5_W)c+VqID2dKu5R4`RL>p4_+zG<)lxsiZgF8j?;f)
z74&_Nplc5Czv16|mjCtBPXiW3uyeyFmlag4KgaG(N?;4iCn|E>6E3TBqR?uY0HQvT
zVbxFQax@d#95g}Y%p#&Q{v1EWOfs1W>5NlRG>eGNKEs6li*Sa?EKvK3jY)w+Rn4{%
zEqX!c4Bf(iXP+gn(CO0fMvb?$V9O^pHhZy#uT+<=xeqPhU+DGaXt`x=OhKllwfOBH
z&s&$Bn$ESgC3NlLALF++Eh$S^w6$^B*~Q(6<xe)0rzBe?ZEcGAkD^UzM__@CYs1;1
z9@uykW}Zl89!S(`mE+7O2lY9aZ$fEb!X+pt-hg4sxdZjrwl&q}Y+H8vBQ5hY{u@i0
zmX%%K!F*=8CaHEqCx1mdyTi7zA8l#J*}k6dVt)l*=b);nBEQT&;m{ci-?yaW5GsL4
zV#^N75)!y+*f^HW&`qzH^F_Kj`y^8=&@5iy&z@xp3`Q?fF)8ZTDCXsLRxJqKyL|CV
zsix;h^s4Rm@t-`{HDYz>%iFd5EoiHH*`nyT0{;5^%UWB@+<N-{y7?I`{@VGWKwp<5
zox5?{{wKc0?|Cs>dw6+fo`q{;w9Vcn&EG;fHw|pERd=)x;-7oD6Xth6%rBYBr?L(u
zD9!Jil3;l*+i#yz;yH2?Qp!!pB%Ty3WAPJANBn3rTRhr4_e7KSF?VZf8+H604`_z=
zcVBhGeMs@(>a7JE*6_d9ELjve5U8J58N9kB?VAm`cJKrGdiI9?eNXH~>n3wk>y!B1
zEuJOKhxj+P4Tj2Z+CB!B-!pxJd4y9?W-51|0)Wqc+Mw4fL08K`%4|-bW`rg89Lgx_
zC{RYh74Uf`QPIcLM4ni??RpI%i>+mO&L@0mQ!@WAgO<|j;1?#YSU(cZ2^G-X(o}EP
zcK-Az-*jzPMbm8pJ3PVu9#Ctc7VfXFZ+w>dBUQ_s@+)11g?ZEERJMUtdoX$#85aC3
z1-wKqlS!u4v%KVD<j_HqDUd_RQjnRvzFDW6G+~Djf+n1SVK+GIY_qJ4F1KoJu;a_F
zDPHDZwW6_Uh1aMK)~#x4a#uRbE@#uVISpGv#?o|qdTOpaYgxmp<?{;X6=f7~ilJ>C
zi%ZMhWtTJQDHS`=A9u75d-bIq-oS?3_U~-Ic5$FMG4bU>UDZ`rZTZ*6G(&lnHyFrH
zuU>LA<IX#pEyhV__8l0LV!lhVg4+jsv&#-scGj@poCmwHWWU;JN_?F$rM{Qv%*)Tu
zdltB$jkWk?Lf9lHD@m><76Txl02vXVF-;xwOnHu7G>W7b9CwnCiW5U0YEF{Z=d~9v
z?Az73IFOTVy80@V|0DjjyMKFSnffh-YMg)SXR9XHPQGoU@9!!%wp5hP?^(XFYuR15
zuW1hUtGM>h_DT1kgnRD3vbp#Q$GiOYpG*0T=&Sn2ztngu%yifE3tW)>F|eM*LkSZ3
z5eA90#XCiQuxB>yX}opnm$dCx+D1#Ko}>NSY5&x7+i@SXlnc^3fzc@U(?^8%Cj}a_
zi~T7r#c!w>Y8Ucxw=t_l-c#t;N{`&mp_jQ|avyTcQyk*PnLi&$fcDQaf2I;J9s2W0
z?Vs1K-EwjkBibdr4a=vG39r789^!u2^c`G?qsR(X`Ly&1c;`54KL9Hf4c~qP!@P!m
zgZ_vZ^d@zJIt?0-6O^?e$?lUn{Ql8>@|*>07yH`jS6DY5ivE|K=<T~;PZhq4e~Y|p
zgZ0qG)Nws9591+<fi0;7)@ZNrat-r0#7XU0oWqXZM(Z5>mlgcY4z!x<nW{K=uwsg|
zFQiv-LDC*?XsLMn$qM0+*CcghNgX~k@|%iNijT@a;(~nT;lq_Yw0)YdW}MtZl#0?F
zq@)Tt!%}Xq8`v-&SF1NkvnidHvYR9{3cUSpGXEz3BC0^yX}^0Lea1h?SGKpeqbXR0
zLTDO_{5(9L9KM=<K<raWo!=)cku2_$bCl`P@83F+hO$uw|04fp^1IL{>OkMc9U=a1
z{w|)u-9L|i#C_z_0<8akk39AHLRbB-kuRSwjB-Z4@;_Zz(<S7J{}1Gwb22CYRPtF|
zxa|LeIAI5NzB~il+w(;xS$csyCgm=WL}}(Fq)gg+3Ar^v^>>l<g#5oj4$R4%%*mYm
zBSa>dlR5cslIuX8xwObL<s(GCEr_CVPX1H!(45T4oXp9b%*p>lc~|MZKvpaJl{fq)
z`71WgJ1-$0{&R^s<!_cpL4J8@(X7`T#iC6VW&b}$bhgixUfm-X$PaaI>Q4V9(GUJb
zIsW-#@cxrY_unjU8Qw}v{tI$b;&Cj-EK#2P$4FA<-%%Qqx@O52XXO9P$tC3Hb22A$
zGADB~Cv!3<e`opZzb>Y1(`9oqCv!3<b22A$GADB~Cv!3<b22A$GADB~Cv!3<b22CY
zQ-%DADK}jy{;??)bJNTpo>Ei&<cc9zJ<K1)GNO{%L9t9zYW4=P%up_Ng;-{1)#0Fg
zr&yLyN$eA1SxSwt-xtdXR6a|KWf|4Xm5XJ$RL)%^mKD@;Wn3R6m8<-PSdQz=%;+5d
zD|jB2L@XmpscI0*G$mCn63Yx_Qk9Ekc2*q@%FD&Fgi@$Bh-E2Nr5X^+36xQ#63a3w
zOZBK&mNVN`FNtLZRiIlemX%bZ?kcgYLO;;mCYIGynPDktf>@b|vrrar7Rmz7LRr9B
zD6_Nb2sjI60cW8s;4G8{oQ1N0vrrar7Rmz7LRr9BD62$oz-0mFb}B@5QxPgmMdANO
zDn?nMG)xVF^ux0t)dP1n!m~km=7RD{C=bK;02QFd;CmSA2jM$L^+EmUtlIcq7(Q|T
zL8u*rPbXaWkb1a(+ylN7fckixA*dM=+jfh+1jXwB>3aySBQSOd?!`pyHV{4V_*jpA
zG9%n7u9wgPw+=(;{5u<G=HQ&Uj`wvjbcV=v547oqYbU`A_ctPr>zo<q{2AbxS*cNi
zQ#X8G9J5hzrnq)D%w`zI!t=Zs_P9+yDdj@_Jopa6vkf9H7wcWn;XjM*+1`6dPrYyt
z+cpX{B4k!E_zV;5FPuSq^b7T>n&ks{Cc<1|WV|7QW`OiB%(VyZjFOp$;r{2%RPepP
zIbIbcb_t80!mNbyFkB6hPb<+jZq0Z<xMn}pxRj4#3(bSl*m=FjBP1lEjQt1eyMZ7r
z*tU;gK15YgZjezTE@F#w{m(_l9Dq787zbn1OVAmD9>(BqeEv}=<9Td^XTzX5ZW$m^
zcLlW<wD3?xR59iI93O8W7{!r~+f<leKZuoJ&`Y$A&iH^31;TtT?6s2g8z3HpTL;M4
z0fJKpwCW{#VmmJ<Jw?S)tyCHGR{?Tv>v-Gq`xH<P5X(h~e*{UqM5xVwRebl7>tSMR
zp|*hd5b@9unJJzh)-5br0r%BPM(Lf2i?}~`!kq~5p-!>xD8Y3z^mD%MePSfy7z>iQ
z_mJ^0)`PQhaYT&$LU<-*SE06$XL~NtLGWQ5FJXd0FDdni*+^(RJ4$*;TVXB0vl48M
z^|(@;Kb~)Fmc}E*W@98rjEFr(31;0FXd^^`;9Y0)1@5zv;LuC%1jv~2HbVa~VlUwd
z9vLIsPx=pvRu^guz8C_mgu4-9p$Lg#!JkJ+d5qMJ5q+?Z&YAq&Pg?X59r3J%s0xTU
zT&(xmNX7LC&18qORuFQUfSZ8lSsgZuS$1&7hC*}&W@VEYX*(#kijNc)bC*zWfausy
zW+@=sIg=sdcE)xY66YyA86a_hdl5Wa$bbRx#(1qk>TK+UL_Bb<=!_>tX1G95^te%S
zx0}p2NTR8aU=|>&g%IhpQ5=Tx@aj5y#fXwP>z|d6H;{5*)|^8Goekn@6kpSVq}70!
zq0g@PLJamm1ck)7jU&}H6A>5BEJ8M)9f{o}|IS|d;;|Et7d+~SXg{2Jt;G8U?{Ln-
z>#WB_K(h;xx-h1q#7A-F?3qC&N~|JeXTirKMB`y%&)J%vMN7!w!b)-0V}j>pAOV9x
z%0b#}B3Q(zbN&4M5r#=?Awr{+uzGjH-Lp2SoHbhHoRz>zyeB@Fi{-~4tY_zrJW4#X
zPh10petJb*f}|J00|vyXnw>SeNq!#`SN<qqhwUGRUe3j&Ma;RIXSHsC+5$%rV&bpM
z=EI`qf*<vh2#m+U&<w9UyA}$%2Si)MePwXgDlO!w7>V40nHCtQAu)$WXY!oDkV3>3
zXERhBCt*bj5gUg^ekb%kNLI1g87M%aDITu_q9=uBR;Un(5bLb<ayAzKef$SU%2^I{
z@m|k97kk@l^%NERtnH!hNH`ka7_&BnBSYaxAQlP_x~%j1`>l~sZ(l5GjRd2?$Vjlq
zHB+_H+7KAn5D5jW?SWWdN3eIeKM+|Sj6|WSwaisfAwI@W<26H3Yrq<d1bTu4fyicS
zc%wDe7o0VEZzMcCgl~6;2ZjQJp<vY29v%$G#)g8{)`388Xt38B80@ijh9Y5WC)^zk
zM>a>}z4ry8)(yenptUC$4fPHNd#oG75o>s`Fxnl#w<EzoPrQd%*xDZsZ?^V^!#&o~
zK6pA52@S@eO(15C4g_H4(a@DJzO^+*Ivxo|#;m~+s2CjzbjQ0Hii80!j26^v3<Y|_
zgMohX1nS2^-Eh$t2u1osgTW}d4Jd{-LNO8qb@~D4kzoIrH5!YA2Ya2MNT@$(?F&aj
zSB3{;(0EpLK`Gn^)>D{5a9{{@13gI(n}b%k52}H=0Q6u4&<%jTG2AFT9K&D_M1%b!
zcv_u(U<^Fx?$8j72^Rz5Xv_*=hq{A-4fxg|u}%o|2z5s>NT3D2(;prUM!EyhptZX%
z5D9e0!03XC!y9^rgIGZrbPT!x6*mMiqR=c9fijE$SO)up1HnP?DzG$Iu%|Etw#7<y
zVMEMvmf@&q#O}Zl0hw3_+r=6NfWdjKLt%i!N%SJXL<(n=nz2;0FFf4egH?(4V|NC`
zBf*~GZn1Ns7C88De~bVV6dedO7<9y}n}*@BfO)*Za1<LfYV8Sk50jZxl4cPh2&|()
zJjz*r3|=8N9u39%AOfIHFX$4CVE_gK_#SqR?oe>B8}5${YzX&@nzVrDZYGZ##v-A9
zu*5~&d>A?hjQYb-Y=a>P)DVe6=pF1&P#Gj44IUK>1_tnh;1y6W7R5dnwgy52LE=zY
zCkUiaGzLD1y>~D;DmYLeLhL#KAcb&1g@(YYI4%V(Tz#?FP^H^FIy&kah{v-F!q^R!
z!o88eP~VulJGK$rIy$Q=xyIGb<^Y($P=AOZJs5LYSB8fHqA~0WU}W$Kd>g9@F&&5n
zoz|XEbf`Zt<`m-*W((N^TZ_0FOmZL;!*RV~OmJRs??K!bJOD3>#0wko2+j*r5=1z7
z3V0aKf6&&6+r$R|Kp<qh`(|l23WGvW^$&x+&uSkY1fR<d<w3-5z(~(jhaN7aEvZhD
z7WiijOd>=<d;lRv#lETt0=Xd=B?gHBvN$-*82ISuV1GE!b1r%TG2cb-biyQAK!zBC
zB+(PZ)5LZ9g8f71A__~w;Fwq)8-%2g@PE|yHQ-TQ)!O^aoHPIDWHN*hAcXt@A%rjq
zA>?OBX2MUg#ya!{ij;EG8e3YCo3_Zu?Zq0~s9Z!ZR<2^j8rw**m#<&sVv9)I&{Cy;
zl-3q$i+)(KVx>qcPd^?X%zW?uopUCW5WSBl&t{*!X05f?T6^ua*WTyMu=duQZiUQa
zasf8rbGklt^Nnz%>KpIu-XzaxN$Os#OxZ4%ILRbh1?qNoB0Aa?jx>it8gR4?W5qX3
zhCet~{G1$$?+T3-KOAQGE60kTo6GR~$BI909Pm*Sda|oCd>zFkVf_0gGM?-pW!hO%
z?XDrs?t0SZzJpxtzLQ+%{v7Et7nAj7JGm>;MD7KLZ-C;V3|<f0cufGWY2XzFuNLrH
z3|=1tuj|3<R`A*YUS9#P9pLZ-P(0@3rQKlTH4(fjz^e|t7J}C`;I#t0J_TMI!0Yqi
z^-b`42pk>*#dA(xuIU-PW`fr|@LB|39|y0M;B^alZ3M4-!0X%KwHq7`g5p>PuZuQb
zlfkPByc)slBjD8mUN?Z(XTa-o;Pvm|)el}j0tfmdpl>;OxnH*NnhIVm;MERZYryMP
z@VW!Mz7Adwf!6?dy#QV(!QnjYqf>wP`H3&^+n!I7<@oyu{u1Q3J@h|g^%nSio3?D(
zvU8nBCm!Q?cRU_H?b9@$yN6z+r^n^dbbr@Qw_EcYeSLjCL-TpNx3H_5HHuFc)9aE!
zG2(-u_j)z2KO7DZq3B-C@DC9(6x21J5sk+g-*`M4)QQg+EG`bytGIa3@7Mgn!w@l3
zNX;&!0U=MWr9%2O&9C<;DQP!1?UGXZ4NwUww{)|X5VQD_$E^jKoZvyt;v@Vo)g$k8
zpXLdq5J5sZ*h!hG&>@}pmC#h`KtKzGelvJu&$aBYo&Cjoelr;GY5`wJ*Ejd#+P2Ya
z5U+;`9zPw>w1AMDithGno?v&+mYy#Yh#&Ak{D23<(WSlYS{e-)u7Iz&vrj5uXdw@4
zU1CPdq5x%?>Y<Z4)nn;e&=W3BYf&6#pq_YIk9bcYs0GZ^r#lCi#h1me#@{0RLD<tl
zuNL$}5k0mD!nD_W4bAISnz#Z+R!wx;!**QM4*rHk_pobG169SJRt3yVFK;njLRoYP
zXx>n2km)j*V`xEdfzpL4Q;=hjpmz|e`1l2V27^H@Xi06H`h>h%D6J0^VouBzik}W@
zuAtJ0sua*Xp=i9l=U%E($V*jX7Lh7Jr=Z~qrgdUUSyHJWHLFhKU+Q%Fo+@Ta#ljd<
zyJAeUut7q0&6sMYrJ3Q=vF?WMhLyP5ced}ms^F@EHvGj4PRC5YW(K`j1L<EYCet(O
z?-tVB<M(QQ-}yT_*>%BmX{IsAFRJh0pgW{_t%mTD;x7#9bZkzZJh`2*Y$0@J9`3RK
zQAi6hy)H9YuxV$3yzcfHuCVmiOKd1cumJ5Wb%9ynLas+MeR@H(vr`Dv*%>X+8MFs?
z1tESF0E0QUT*K%Z?&?~%{uXhce<#0hq4$Y9KC`}xrn>8@XzKgvs^az=H>}SNWgG)8
zPshy&J+a^c{}GM;uizgcC)~%e{Jl*+)E*>5u1n}T4)}S^<GMw&$e+-D5v5AYcl{L{
zmZN-8E7dNtx>7%h_`MhDhJ_lD*jNSJ?OLsC2Vmf%@1l4NH>MA0?G#V<a6G^|G@aF5
zDEH8NcQMD;KEdGk+3p7zpWWIq(f&2(OUPfT!4f2Z-M-e5OvdAI4O;1W?q$mo32hnC
zmL=%(*)UR;C<&2VsF*|@Vyep8L#7ZdNiCmC{8*2)<#APekQ4CFJY+h%0}(M8m6x@L
z!@E?Q#GDz3{x@Tf_GyIFGWp5)xX@!XY@a93{Lre`&u9&x1A<XWk+kn!xsW`R`6#Od
zW7<j5sO4>=31h&0w8dy6mB!VKzbZxtqlpQe*`iJ`9;=<W;`4+kvjtt5C<#%=Z1-gX
zZzADygWgvPUUfzn<Xgw^TWt7JpO~?M-6sWp%-GE81k<IL$tmTENuErnZR}q5SI6*o
zaG0@+@or)F{j8qPbluB!7QCch^KCV=t+Ai+tP(oaiqEP9UX_r)$kw;Cm86sRNg+o<
z*d%G|KWVdb(pKByi41tv2sBZf4O_&0rGQ6m^2j3lpKp^rX@j+h5)isp2pYQ|)(ZS3
zqHX6TZC)$-m$l?wVH{#LAGagQFJ)miH(pe_wivIeajFx2(h@UHv3i&v8fTQv?R0fg
zF5?`TZCp(Iq0p^P@UY8XC-k@Lp!`sL)*|XU8;@9qEtMY%omL5$@<ZdY!w*-Hv_G-A
zheSHl4{1FHKh&^4bNv}XXc>aua%cM=agUur(3owYXQK@fNi!B1i-^lO2aCJCW!@FY
zKA!SkB5~ha{s6fvI4+n+9tlnk7LvWegP|GZQFFk2K`TUjjj*k5gp4EF1w!cm22A6u
ztSvH$$6t(H9=nqi<LRm~m~Q9Ldf{*mnRJ<Tn66kCXdPjTaV}fI(x2y2$!u*4e|%*_
zy@+5BI)T9p@W=5T^q$k&<3m`lQ&@k8PK$EJb}vc_`|+W-VAfqUB|+&%=|Mpku0tq&
zu>Z$VZ$sIRwVdLBkE@e^=Grf4x<pyW*LzhT;QWSmq5ya3l#pYa?fy8*QIsP>uGe^>
zvZo*ywl!qvq=26kbm%M9ZEM@hSy7)BpVRgQ0oy6c1^)S8_X;?MU}(_B<BW|r#UDU_
zKGc_moRa@3A(!NTE{i;uY`CX{t}oi|&xy~^3R_$h?aMa0<7v6H4%ilnfE)AD4Vhfv
z<qnIn*n)f5&9zB<PVrzvy-th==D({Q<s?5_p}8R&#{pg#?InJ0q;+$cMtPy_Iwj~R
z?r~AmG2ep%{avt&UM2YL;WD_^a(SsgxqDGB6{Uj1S!t*L1)%Uj_@iqlr*jpe6!SAF
zCNuS^ZGwI?N&#;(9Xq)#+<Bs`MxlNj^A5Ngya(KrKF<4LG^@Qo<9Oa<?keHaklEe9
zb<+`#84db&S5Te2Cusk)UuH*IFXc9-aP0t>huy;t?m=OP?ZRfgynh$?YiGqAqxLQr
z`tB9;OX>r-yIbfI;<_+-!n}6xwCNHO`fjl4UMR)@eYrOYnl5f9Hs&bqxbT+(F;CA5
zSx)oucO4Zv91-#!68h}rKIb|o#(ycVnLTL#2Sh)oI1grD__TXF_c<!N(Z~CBCj|}l
z&wg%yeFOIcs<R&Ad=2QM9}xQP6yr_pOved6r29k-TkCN_cUIu_^SR;1JayA?J16Xd
zaisesBh2yj)!a^cp%|A7!lsnp0GG{ul<TblU!yrTT??Tn0UrXM<fNE8=t~0+a+%|3
zKJFvhWic;a6gH){Mt>S?pf&J)ng$)Qd*a(gIv1mymqvXHW1wN4Xz*9J@K>f2ts6zX
zMfg4)SE}<m?swEK?mppH4Z<%P#GF6C+f+B^`y2Q@`<%v@eJGdtSWtg<f13Llg;SlV
z53)IZPQ;oD(RZit|NTPWLasBLKU;X*V*NuGx~#E6=SrQ>lg5s%sGsI-xo#m3D5d|W
z4xszB{Y29az`8E&5bWdD4r_;ru05+gOAPHf?K$GnUeaD7UhNg_72?xg*Ip-n?G5b>
z?3@c-MI`7dcFiT0`ziNRq*Q-cKSRp&cl7tjjrv7{kXw!E#tgF2m}AT#n~b@}T=K6*
zy)mEMWi(=UwAuKmaSgfK_$T8!a*yXv-Z1&HcO0^iAF?duJ(`7RGlHRDm3Ce5VDLR{
zQ>Y>|OWSKk%^GdMtTXGieUbXeeC@}`hq11|Jc!-t4CDpRqy|H+SnFlBA!|R~&wgxi
zI2?+@0s)ry5c4|oTf}dE+uTL2HXk-0A=j9D&0mmfttnQN++`iMUL{}4J(+uo^hJsz
zv&au3@yPAuN9KO*F)M<^0<*xPr^vXhI#FLM>P}Iw7j>_w?-%tRQ9mYX%t-5Pl(VA!
zA+K|EQAb3LK6C0(FxoljD+jYZ=Vp|3;`6&uFw1jxi#EI`=T#Jpe9jP(CU{#I8F(0;
z8omZ)HBna>wBa6c|4owh3hxy)yefP$%jdrqHM}|3m7NE@a($vMw!tuCa$CfGhp2Bu
z*<{1HThu#5{U8cvVD6Km{hFxXML`6}9pdc>^p2FGL`7Tl6KNOqZK4)^MZP3z^b^5M
zji8Un&rn_z@Ha&*#w#MmYn&LbaTu?0n91YnMH}Na?k1FOl&_)e67WY*5E;iELphVy
zbKD1_zQpUid=x~(ye5>Tc`Nd|1pE$BZx{6gqQ*GoVT|&Qi}pLBzR2tGVr<5TQL0g5
zqJ6cfSBm;(Q6s92zXwJ1GamhnM<3%4iZ*l@k3Pl={`ugaUz}f^55D=U^4I2XL=9W!
zW4!VQ^N;19B91HnFT9>0{9;0psLMqSpPdj#fzM9pBEzl;!VVL9P)6`w_Ibak4`skl
zpulG*2ssL1mx7}7=LN8RL0dtxU=8YZ1$Px}DR`h@Z^6NW!GaS77`Fm3UK4#2ize1h
zY?_E@KM^*Z2ze$#o{6y4MA&K~=Hf*7%tZL*Bt-N{u+Jp;#U$WOf^8<j&nCgQCS4$8
za%6JFWcbfy%!SGD&&jagWZ+C5M8Vvf44*56zZJsQ3gK&o*mV~o4iqBl7xtlGP84E3
z6kePXn1U5;3g*`o$Ug;RIRz`_6!4k?UQ?j^6s$x=k)nzs^jCyAQ3RQaHWhuX2!2`w
zA1#7hMZhb<_)ZO=fZtTazNxUw)HakP%FR=Ir{0IUZ|Y-H5znSV*QuvbAZIb;ES^*x
zFUI&4WBiISUd7P47;~W*V^WMcR(u=<@uL`hlt8DFXvvBa#HSMIUV?d90^AbtEqS}t
zSBkMIh3!fa14}zm5NAuFQz>*Rg-)gL#nOvqfilQhHoa_78Su)w%kD1QR`y`oK-ptu
z;8O<q$}qpmF}KRg%Q3UcmzJ-<zmTsSv9Nr5`R;PW&vNKp4&Nw;Z%l(<Oshq~_)f#z
zn6`P^)@eJZJunU9JMDQC=sOK#I~`fc^vdb+>CksNbeIl(r(@Ngj(Ia3zBe7Qemcf@
zh6kl+M%4`TK4TRM^qc{InE`$?Fn?#jS7r=V<W<1`E8zbX7?+Cfin}YIYsDiKgB6Gy
z6_`sE;8$^Bre`MloeBA7LcW=>%S_m1ChRs7<2Vz#%zU2v=}hpgMBkN|UzO;)68M$S
zy%Mrk-ci|837X3O%6*lX*OjnkCH!O7Boz4BEX2-PV%?YpJI&fY>#<p|=PdZ`ED;Z@
zEEM=g6=bP`+*ROR1;49;Z&vN8daMfgRq)L!#Oc}ar`b^y*lM;|$7Ta>HrB}5EUwQ!
zH5+)<hz-?ZPFBNDt1&OCw^lz`Jy88*HF#CS2Gy`>HS9kJ@qP~Eom0W_=RoE;9dmA*
z1N+Xof6kscSa;{VIOokdkay09Q9T-o7Dub2u_)qSv@^Osx;c7Zv@g0l3Y$frkDiRe
z2GPqkfg1Qz4g9_a>wQgI&B_}1Kn?V&L2RtqS+lza_%*O`4d!Rf`I^hM0p5RYF$!d;
zh23glvs$dtwU5>wss(N>aBIQ8_TpUE+%U(V3q9x7&AoPRr)X~!HO77JgL4neW&eHT
z+*5PkrF9*tgT3p@>uT%bti7g=*|F}Py6vpqT?e`9o~}Dyhxt{9`85ymd0u$l^m#0A
znb$TC^LO6O^ES`h%G!PN9>u>$=Dj%&_L=u#eW-pCh1XZq!|wI%^{eXFvi8RM-unCM
z`|5Ys@8|N>!)NQEPyIXf7w5a?`{w6!_<Rw^=eLPEDe5(%hP?C9_x$biADF*){=WHx
z0)9-?V!fFEuBgSj*Z?1HKpbtT6zv94BNjHm)(waq4LvCE=LXDy2E?)k_;dsKHXv>{
zh`8NI3uJACoQ;)@LdwQu<BCQ^gT^}=Szgd6vX92z#;uJziMkqhH?q8_abM#B0Xaq-
zSL17qZ#Q0Q9BP7JHbt6>o3Q3KA$~Q%-<pz5Ynq^c(_KwlnlSg9`kVGOLH{Q3XoB8N
z?>1d(hMvuN&85xJ=9cF6=2gwGfAhxX-e$zx=Dz0L&9Hy-!RDjQh*iy}n%`}PeVRq?
z6AQ&A#V`+I=re{sV{NfyY)x!k?5^0B*f(SSF~r^&`imWlor=M}G0-kRe+v*x7L+cC
zE@)ZMz5ual0s343KU~ne;6A%<3;Gs3x&Se40epD@#(n{OYyoV$-~)P?+Tv-+YbkAM
zXlZF_Z=vU>q-AZ(`j*X{wq<L}&X(OR`&*u7t>Y~$e{4~iP|FADep<wsw?ej7$ksZk
zwW1aN*oxe$74f~bvvqwd<J-EmRg6w6;%zI&rWI>%E5^O`1cRPw1>ZP{TXFbKyfWSp
zUlK<&iQgvsietRv814AB`2F!c@kiq#Ux+g~<3|<V2~i9GjK3}FvvI6?2|W=ZS+7Ka
zs6{+WREZk#G65e>EJ;9~#7&9r#Kr{n4hdL1(TDPAVt)cMB#tLwsl>Si)}%JX#kTx5
zXwt@JL0d!Hl5|gP9c?$Yb+bE`)3)t!O49aV+du~7$>H+0z1W86-iBz<2E7)tEO(*M
zZDFyftL+pEV+*fd$g;?VH)pp&=CyFc!h2Y~-2r`I7~WoW$Fk3bY%~_0P*CPQ3qM#y
z7Fml51<*xuh>krVA@`D9WD@qTb4eXL4{vAZ;U8n?;n!d%x`KS1?Li-BC*ki0z85&4
zl?3yGd0KO@F1SF8(erR^NoW@Kqx&Pxk!I{cHJ4boQa@%DUv(1n8?^LPdwHB_Zoady
z0L@oWS<}m7YdI-l^%UJ}B|#me&u5d^ywfu<_B^7<26a-7*3w)Dggi+m52sJ_q5o)_
z*iEIbAEr-ci_0HID+%5*Lr;LeNpa}m1U<c>C${QJQ}CxGnN~=uUKI~|5(-;Lse|m;
zN7;m)xugI{UCv_vU0EpQ({7EPXr;tnP+5FTxL2@aO!0edRDQP&<Ck#olQyYfIQxY+
z&`Ung&|Kn7A;V7qP0^_I7eG~?u3cGhrN!wegy_g5<0Eht{z1<vA(15{o{TXHyDy9g
z7uKLpK(foJe8%nro?*RWzNgL&qn9yx6~oz)r4MPn-mLR!T~?{&yypAdEc2S4c`I(|
zF&zN3SdHm`gG=^#n0>rQo@IZ>e0KFa<cJNE=E`%N`9+p!GL{+doR4Fl$(sAr-!SVd
zn3TEbB|Ns-DR8*#AtoP%Vz-RF1CV4v)9=0G9ox2T+qP}*c&EqOv2AO|wr$(C?K}HC
z@4XS9BEHBt5&x>L%&zWpy6bdDR{oN-Dw=N{&XLxW{Iz{MVUdfc_5^D<M!ZN}Nam1r
zjwwXz;((2wit)n{uM0d1<gHb4-XW^+E<bd*bC-U+3{{e5)$@K<vXaU$Edl|EDvgS8
znK{R?9yf;$16q2;IvP3n8yEV=8-=DFKHe75$Pc!R4iMFFsL#Uou<u@p)$u~py=*1)
zPZowwyp~;xz)|Gf)aWK1@{Oso_oLg%R%jS@SDpL8;gx<X!N>Q+E)~K?r7|_Dj?#%g
zF8odA&qdckR9EU!ptZ#8jC&~gO>0tbXr_I@!Yj$N7ZSmU!`BNXk-baFwRwfNfaT@F
zLrIKRx2FZP(Y*+)97*tS%$J+o&U3B_I|%+nXAAE`2cA78e?c|?(>JwsG;LcK=#JW;
zgm?#eg6doF%~eaJ_B|)2d2d?s3mB(Flvh~oAaX@63Jv)olk^7K=*cgdFYlxrjKPBc
zby(e`xV&yv%;kJ{)GoX+ovUGC39KAG)VI#1c8alT8l$HrzDg%xJy@u(VFcZbqo^a3
z{%Ow)N!G)MEa=qiahWsSN=!+T3`mp7ex`6HOVqREaczx#9RtaH7tMtp4Sr<EZW^{(
z!$Qlm->Bw$tZ^UYjL3h?+so4pY>0~SS0Sf7P?Ire^al_%1TF%-IyC-7EUGvlnnOf7
za^lsCzI*188Pk~z4{1+RY+#_e26)xcJ}Ul2#q5+`@_zemc&*fyIH+r6r$CtkPZwU6
zHoITP?zzoa>ewp}yss_GN%J+iFW7}HbV2lXz{E@X&c;h$H&YErEqF=9Dhs(TrS&KC
zlNp#YgNL;Rl4@ucR=7}O79S@UpX@cCaWcAyxnmMA$GpwAGc0sE_saAvtf9P~y?YKx
z2{*N&bF{wEbgMee?jUBf7>XJ_x5dPbmVG~YR$v?{w#TTkXDPHjE!e3Giym^ArZKfA
zbvW@?`5V1fI{~BhEa9m6$E9GP#U&2z42Gg(f4pUQGd2z|;xG}_7Ciy(J+oCJbre~P
zewjSJhd#ipmF6n(TPiC9wYvOO<r(P->qSk6>+hIyGLv|cjAaz(t6UYUj&;q^@Nnf*
z>2GU&U8E?+^CNqd83`^H3I-Jz6WX_Uf!GJ~KL0nthDVUsumTAVij*gMDYMlPFC_z&
zYE+t~q-d$KLI)$qz!;Arbq1^^cqshQLVGN-jp=ZJ?Qad8b_OYXo~?!kE*xW;C%w{m
zA@w%^^)Do(qYMn~lQEl#+ME1Ype<7Q#=8VFLyJaH3%2fd5`7D{sG~h%k3~nU6E^Bq
z&*@S3Dbhry)hXZEvj@Jy^o`_0Zj!YCcj%Kpk8yePDoV5@HT_{+*j#X}sgmnP17YN%
zcoH{<%%Q|K@5@&8?ZOsoV~5Us@7hv0W}E3#YIpZhvXi=YvRNBh%9!=*s>|xJP0W*a
z1&fn6F*ktpoRAd~*|o;-F8qX5qFb2*<<dILSdB?Oyps|fw>p_W*2&&oY(zXIuFDb`
z%1%_l;(at8i_Y|IBJ|F(o>~UOde1l#RPIkdJ0a0Lh<f?!L{rCEkEA#$9HvPnUy%^=
ziD?cm2wkHDnC0ydU#0z`HZ&I>Xn8N^A?DgxDVLnawV2%M#}aKHWQO|BTaLEc=pvHd
zLPL^RpZ2zzAwyAZ3wVxv;?&`$e)i>?2C}t9u}xZ*aWM;~%{7FV*9DlE`M30rs!xIh
z&n>$loGiCbB{gU`qNE0*Hr5qVo5#sr5_SEOFrt*K%y4<sBRs1gVHquvPK7xPe@W<~
zD~wz5$Aa~aWABAY6EKfS4oM`+Bt+Vwztu4fA65uUF(X@ZFJx9plYCfZE)9&JY3LPl
z1l4iIAI&pEedV;95}_|R8`q(?Nhe=%>YxK_xSHP&1?khJd1~Q)dAlyPHKL82enN+E
zQlk7Mm0afMJ+q81#zoU5qCMs7C-M)%>rdThGQzfvsK<uBM>tQ@GC9goQ*}3zT0+e%
zpih^d<q7ZHO1Ij!{^Mj4#D0#zRzyrqnrxo0W6T9$Y}}Pd<29Q2Yw(CGuYP!aeIE0Q
zG$CrWQWD$`NE0_t>E%1vc2^iiGT9Q#-6}iMSet(ycIxMCY!A8UL|5FLM}u7ZtJ1Uk
zBifV+I)^WT-i$zUgxQ4cHh@X40^y#ASr)3T_IMf*J*nv^Xa4W5Oz;b5dNS2lord*u
z<r$i`{N(snUX8V;#z80A8U$Er1a6$tDftjh=GfyM`}B!U<AK3?zI##f=b}Xv?PT!~
zy9e1yI%&L#B3oz3kRI6_l6z|$%IEs`PEkf0$DAz1J84GYAlL$%w}B0$k<c4{g2gzY
zKXbG^#6VewkCB>2@sfPT7_vt03#0FLu_49IYH&?7v79W})@V%!bAEh`Q!REaLYp>q
zbR9|~PMs97ul7o1aLFN)M9#!Uy}v$B=4OH4wFTtCgc>s-RL~l@2+vu<kJjT>5y29(
zYk2Rdp%aVsEb&HG@Qv%&4^PIYCsQkY!{-{06#3%o=FB3<4EbkraN?odx(PYAa7&tb
zdQ)uF_at)n1|+3i`C~Qt^pl&aDRdLCNbWmst?;nH^TV3%1x+;{Wlgxmec+>j5?ib<
z(OZB^8Op*mXnuxk%5Sm0WQ2f4+!<XKXGO4HvL19uzG!;>TF&ZHtHcYx>o*@td;lNN
zjaZg1o(#uPyU5rxj5WSDD9mF^JX+<M9YE!Ou`wd$iLLk%ejI*&(su71Tlt*o5pl<6
zDHJtcul%qyL}`<ny6!S&BW<+$L4wRoQvQV!>+Gd@QafA#Sk0j@!K*@!or>dz{D3Pp
z#a2#5g|DVc3$okK!us=&Aj{V2&lr~LqDk3|N;9POtNSLk9$^Xs@WKwNJX4+~1vQow
zTSPpnw@_uujUKw1jbv&;%p9mChPXq{C^LBPlyYYjc#$=d)d4qw*(2uB+@PF{!^TCj
zc%|iz6&FzkL*4QAm=nSdYkPC)BXovS!@&QRyJa!DEBl^i;R8I8m2+P@h2$8F6YW1{
z`Zk(n2$m*pgTzdK$JspI{H6(>B2TL5X~Hu%Oa>&0l+JFRfvJYr;;r47c|ZtJ$+I(5
zNzvfcuZILz>NZxRje|!9X*1-lS*`<s_38@EaNNcrxJ|~P+2z2LAHfOefXvPEusnYf
zViW9bVjPS=VICG-)8iBdE>7r0!$i`-4A#5t2q}n>s|bNnjwIuKGukpDkHcmLY*sh%
z<GjFd`*}?Br8B=y)p=kMHcrhNV-TmB_is(DD^gaj48}xF<rywIyylN|Wt}b0dGnWz
zyN=c<PNVDDo(T-naQ3t8*R8qBmA$hCq<`?(7C2P}lrNBKy=x+POj*`HNiuC}#a)5H
z#KdlDjV|^rZ$e_8dC1O!`>eHnmjisojguOSm_Sqah7i3ZU?SBnYhLMaovUubIS5tz
zUN)?8jR_n_BiOSuIC5z?F!g%nShJLjIa(j6Y&g^bUn8I1M|#+zTxYq(X1e{}HE!I?
zkoX<`_ZjBy;?4|vkRja*pV|@HZ5X^aasvc4ncy7sox1~~e50FIM)lfa-F#B#@-Kal
z?8q7<r7_kiu3EoFAK?O078;+TnH+^Nr!fAAb(K!s)&};TkHv)d0>pCkB>!YXnGdZW
zEPGt-b%n>EDt9Kv2Ni3ZLDf~w<A1m^J>nY2jE7p*T`+K^;?`xT<Zp4ye<bHaBo3L}
zPn%ks_BGcg_+X21nHSudoTl7dRL5(Uo@-`oXn};uWp@9qTc|>^GzCz}P4Tn5>bkhP
zNVewcmwlSRpD=zdGH6~J>wpiehf&f6AUU09Q{qOIe$Kv8aHe;10n59@m4AQH@F;5<
zvBxqp$da?J4rpnEwW~RdJXc2L#1SVc;B;N}D`-^^7{Ow#((ySlg07uj{pKS)X@rRv
zi0=Y6U1K*Lu5$bNd*C{0_gpap0_7-Q2OUlKVk(2#V|cMFkaNoPf|grG6b<0dpAaR%
zgiWWE0-NtywCO(f+yBSNN~(j9ml%#?!B%#eLOZzl%Q<>j-CkXUjkI@ClDz1T$E#oC
z&h>75Ei!*U_v^}H)iYQ`0iwn&YYJUfrbkA7>O4$K)AdnYBd(D&hDBaDDpcmB7=u;u
zLsG6n35_V(XNo;u2B0HML}}b+(d=@1m$>h4!8l4ChuiY%0xqKkOgTisBtyg3jAu>;
zvvc8c=KP~l5U36H0b)LFB&x*>Z@8U=Cn+u`^ljBNey3Azc#eER-mh=6f!Y04C>%6n
z2HABKf<7xG6|^39WEVYh2Pv2DIug${7<=J)4M8SR4qCl$^X4M^wCh>CVdNWoPt@t5
zx+wOhBK$3VL~vO=O4+U^q8{fd=UWAF$VpPR@!696h}4G;+{rMJj|gv;rt_8f+|Vb<
zSf(P27g57f9P$l^#anBfQc)JV1%5J4LAp!LcCHt$yeC{clTJ%*>aACf-{zM6OMmw4
zzVOwmL;GsM?bPEt36=FD!h2>ql4noLxXoc4`BsD%H_IhUZwgMn%lvzv;}Po=<`k*@
zaND9szsIb{q{j;SE$ZuEPi)WNcd$>`PhlWl1&k8tdWcrwbP#S}4-j!+e9-WKB~W$<
z{GWXI?+hwftz^#X-}O!xreicMD;chsqf^n#LsZROwARY=IRSFwaz2x}8Fd<CWGyVo
zVm`3L8}Mg*C*E<*>+)^cgm(G;6oh{qIvjX!FxWBJ5vp!*_qhAbJ(r&8Zm_#pyd{AL
zG525~v92(#Vi4XIQ0MO4r+7)W?h=kHnIHk};$h<lhfqp}-hJ=jr7Uh~+#@t)<5a||
zi!j#UskR*k9RhzVj29W1hPdo-$!9Wx9r1`*V~ZrH)f~_%=iw~)djxQ1v$}OS%Xwv;
zFGw#iZjRy4;E}UGGd_84&dBePV~krFaUIK<)DY)1Y|F`#Y6dlg9U~qF0Z44Ek#=#`
z>y|bIh<o09-f`yVOs=svgCK(-G0*i!>?Xh;U|38u<?Lr8CUpxoq(O&5PK2pOdTjLZ
zMACIEObflDVHFOG97;y08ej$Ut)p5}qY|7qEi6|^)=6JoL}lYhWmHGev6byu){lYN
zVvF*cm?v}xGd-v$bfwg#!qUoa_e|>_o1}*>-c>D3M^tyn$Yd#HQjPIKyAJ#(8}^^w
zK0TfsPq0o?1z)zG)#t&7#H#!TDt1%$kT>{{JaQqN>Rs4A{9yX{AB7a_4xRD=R}R-+
zV4nJ5Tlxv_5CQxk&*9$LFG$-%KYUvDtyQ4&LA6wb!_RUDa4c>pEf9myzJO`%)V0<t
z>zj2gcZjig{(O0dQ9sGQ{`mBkGQ8)8hLK~mW7yorxTf0Wlh(G>`SX2h-Fx=@cW-b9
z|A{svkWC5qoW#MxRm`q?x=@t2j@Y=2GNM9cGLg|7)<T{=N~8qy2<1V_RVBCfCpVNq
zZlub~<2t53jw6qLoS*GRpA=1->+ClUcU@!jr&o0|Ia?e2SnDAIH$|J3O>fzxX-ArA
z*<kLVGm%dMZTR*4;1|Zo_~zAjzPxbDK;7O%20p;&1P1(I=P|yzKh7b$vp~-&fjsc5
z1-s!u9_xL*ek?Ql;sTv(0=+RC@PnKm`QoBhhx_tEEZ_NdbQ{=>8xSjf*X$o_32X3}
z&Wd`3nfdf5=U3Xv#!5}C%WAk1Y(u)xwCMBTk?$$H40_VBPD<&x&RTG1Jw)H(WtVT6
zd{;WrvD2Jl{6c(JJZs;-3WVU7`k9Xh`j9O6A)m7ee2Ojbah1~vHm{BPKppnl9Pr9e
z(gl19r(h5AfTiGrI&b~``4;_PqTr&G)q)a}JsR=x1#p%$1-xbpez+)XL7l##Jd}lL
zNeX(+I)Tr-`2$Ex*nMBg6nxO;&jdPR72kNeKqkL{C&_*5AUU!@C%b@;n7doTcQCTt
z+9mI{-nSUok8D`@*>eZl+f3*KzVNQ6-rQ1neSLtgJuucUIL|0+;NH$Eik<kzR(%BC
zFzcg39@{MV!yl(a-lDUN&NzKKAKEdxoU(Y%NW0jdU>Q5FFj*JNJ`dNGIq?hmjOd=o
zDkwi)%P7qASzUyn11f%$V^kw`O92HujNAWaPvOp4gF1f9o~}S{^+Iml0N>~&EjLf`
z+7I6kp9dU1E#sD(^TmKY>OvZ~yZJvg^}KOicjC9(gI-<vJ{9+PB=2riJ@XVi^Q3@1
zdJSN=YM5?DYaD!vms@g*SDYZjF)zb(Q~gMX)F2ILsX#X<MmH>uUzb~C6d6gL@m8*n
z&i{Z~wZCzl6t2FT`dHAgUuh3vakppYhsE7J)N#_C9E&A&Z%WBdy$+CgOCTSADC9?)
z!SV2{pt|M!y`*@s$G`OVXD-8FF{bAym?zTq94w!@-_*tS9piP2@Qu6Tjl1BD`(_V0
zc)H%zm!3&1=YUS;-O=!Zd&MCva+FA%^5=w(GJv?L^vDHE_XFQ|3Uc6x^-1$Pw|f@o
z4J>jJCHhA1$L1HzW_V8<ayviNCcFO=({-ohjW;p$RlMf!ui2h9uy%gPkF{-R-Rq!2
z_wPky8+u7b({hu>vq$!*`Ks0_%0ur~S8gawg8F$Km=C(goc7rh)YA?uJ33&>Hf*Xa
zESmO^f^S-gd)tapzGd&Q>b~Nzs$*^9%i31qdd~MttgFv+t!?3*_N{|Mn0sH#pcDN0
zteYR`T_5V5TxThre53FXyW0o(*&gz}uhKr%{Kr@3C+Zz;w~xIG^Yff<HrPA-_8r}}
z56X424tvVc4<Gri4W6ry))(@#IvDS9mo|5x>nEVEAJ_aKS>nzghD+RLEpKp6SkE#~
zc1MbEF-Gqjek(**Kl{AlmJ_?Pz|N_HbW5wLahIn+JlLws{CLF;r_`3~aF@w_c|n#@
zyR%BG%_+_?q0%eA+38jI$`KNY)`*{cf`(Wn7#T+8x7YCyZh@anj#<U9`Ojz%^j>(i
zNm{3>ulc}k4ZRh)dZl0Z5OxiIyy@hGzm|S4Mf5I(wr$=eEB*>_mOsp*|J8<S_p|Pa
z$P0;qXp5V73n!>5d$mWG=odT1`j2Njx8$haC-41p95=XfrzVI_Oh#0@%s#U=luI2;
zW^6l^E^N;$lxFznq^Mr@$nf(~CoO(^_jYtz`)l%~DbKmvqg!*B%XKH3r8iW^j;9FO
z=Qq?D$EmvZYjyue_8eGWwx6pA7P6vmQF$L7dAwy7g!!0>c&1{dnHJO++A#TM%~PqY
zuG!3IsL`*8zw1xGqNPoSrs=HEgFUKS6!`hoCML#S-wyRVlE-tmk<CWm8$=z6{U71)
zlZ|=bC}<~QFfZ^=;QV?iDhf}owCS!$$8bG+X9cY(R=22Dx2RXQI4x+W54(2_HF(g`
zqJM^Cmv0HT>!<NDue#}K>uR68*Ytc!O!1?1Icz6Gc<=Sl<G+(pc(ZAuTwpFqElCa1
z!%B}qP?i0_V?=x>1B2Iy@2Ns~H_$%&tH{ec#K&Aw2(=W)%v|;q_XXeWIl}q>fr#b&
z--y`%P?p(P|527X7}>vp%Z&fX${ZX_|Io_cKxNi%8ZrUnH;`DHfRmZ^pXOlwhAp$P
z{Ez-Oj`N=jBL~|zoSBuCgMgKp^FM9&@5h)q|2ys<j+vGD-#8`)=I?&?f0ShcrvLh|
za}uz!|MOvHVELW`3(G$|Gcz;izv+DAiNBA3?f=l3|BwELI<vF<$A^{mJ01(eKMXVr
z+rQXMod068u(5qVCkrF%zvKT#bp8*-`TqwYmzAB54;ISF+0oR{78dGvhL_TqT>t|@
z`yDD@QE&)`F$I#4lqIplv2fwX4<mX?CC1?x5Fh=z^a9uls}YU-%oW`9o4egkhKb>K
z^tDnXF*SBEZH(4@)$Ye%77;_nMcAxa2;QpN4_R0J2P_fOXhoOpET*4j)NuKk%T{G=
zyz9R|>lO^KoeavJ+MSzu9zV$I4a6F6Y9lO5bn}P)`ux>0;2T{Ga(yt%Sd{z59;X8g
zfQ;nKREiCcslghVAg*U4wK1jL>`d`6I!dxt-d=^^iNxK+Bg82bTq+^VTVG3PYLW9q
zG4nDM9FYjmq7k_ZAmz^Atw8mo5P&Tm?|hUAODR{9dsOEvR#;$S(H!n^<0y~<?I0zE
zw1pT6TMZKs^1?-4<gI(?t>dzJ3EzMk;@+hIN6)MF&G*zNnZ^KfNjf9vo&g=`-@T9<
zh)Ddr6a3zjN2IE!78TGae=y=%%u5)5jks?;)LCun5A^On|G!_atc?HrdgbAv7qhf+
zHg%*IvoUlw6)`ooH!=MmiZKB*BO@or|3MnFvvDx7{9jMtH`3Vi8)^Ks>XFS{)lPPW
zZYYOwOCUUqfFwi`7at?S0M^@=LO=<LFhCB3q*9K6DgsQU5}s2bLKE#jYT}OpoUon$
zpa{JvKGV45iJF~wS|RZfGrs=C$4v12((rKB!QZvXZC(4g8vU^K-^B8BNO&U=lu(ge
z7v-X(gQ2EM3v#17#Wjwn*CI6h2RtGwd=Zk{BRb24_bDE4NX0%~k;6f1?aJ0pc4`2&
zqA6^w<+|$9hUY;>&6Ft8#GCK22kl*Y`eA06z8CWAD+UjMkIE>CG)mx0SP_rw>vb_b
z>vdyVnSx)wK)%hcXPqpW`o>&D*F|J9>MY&PO7b#95Ig;c*Tw7MBH2tS8=~@()SI0}
zYiH_AyLqS{x7>bbnQ70inh)Fu+QpLON0R6St<g8OD&+4HqNtCYW2M<0@}>Uz79029
zeJAPKAQD^mViMWWuR_n#Y(6iuZn-4U=?1LVID&~i&v?uBOu1~nj(NEwEq|018tc65
zLf2YlE(g!^1o4J$Rb!nfHA1~T2no{g*BmY=+yd}|-$9l8jA)6$si<_4C&k}OPU{Jj
zvvs1^R0@k8u_u#vVdrW3s95R{W0l<a)syiI(}EtSau_?9?CyZzrX=q!sa38$LA@?C
z-d98GK=%gu-Kf!DF#8v>^CrTF<a(fW(9W&nCtN_cij0#9@1Pd;{7T?!c8cpPV4pp&
zL$9I`KeKOXw$K9KF2VXBBfx9v72<Pox!T7_K@a>LX1F#7mbsgs$G);`>R>DPVcT+>
zwU%bq0(EucI+C~q+g0x?_N&}S=>Cg6Gcp|d)OB|3>tye|fcmrU?fpsuYNqJUG(-p`
z4<U#`<m<Gb?b)Nt8v9dGY4dHVhE;?RN-?BU+Gzi(!S`Z+>q~bH=|2Pib^njg|5JYP
z72p(6e@FQq;lpYK@4bW;NbwzM=F8;v!Dk8WomUC3P;=Le>Z1(+U81%wX8e_v^-8f&
zQl-@KN@iPqI1R2zj$~2Z%J}?5u$hbb$0m2}CDOTV{0=Kj$ZKeYub|T=-X~d<6KaEh
zPV69=Mf$~Un7gOXA|Jpwp=4}9T#X(1DVOKXVasBgPmAM8dA|g{CYO7G)~X^|WGMC$
zY{Jc6Woz<+cKMoIP{;Q@FPvTO((AQ%T-wqveM|ZO{mQ-uNc?#yuBV`@b14XioFoiy
zb?tF13#JXQ{F{0ZS>mWHh!(ah>XS}OL(@*ITBEc|5|gGU<|U)&_}JpQEN5BEl8PG&
zs^q)q_tXcsPL?Rr9Y<sjR74k8XhX84)n^)<L1<sset&U)b(>fE&_zCe7tNgf{ds@J
zf5CCHvc|o+d2>0S|CNl>$;&vW{)Gd&hco%o3anF7BF0986Uv)rwAZ;QEoO!WJLUje
zjOczD`;uSs%NY?BIT19Sy5uwt(%JIi$>J6zjmwrxSIvV{N2Kmk^Q=p57hl~=m&_jQ
zHrL{3oSydzOo#LO0I4xO6`rz+1_PB(0k^%#<uCQg>htpPh}=oSlQiRBt2y%t`vv`h
z&&|sz79J`lBEM56tQgZ(M1P6(QD>?~rjggp<ePAj%M2%)nj=%CO_o{QTa^55RT#==
z#j;urO5<XwdQU6Do1UMtWNK3rB5mnwkSacOZ|N<4s7_Tri|K0X)vR)t!+4Pt^UjQo
z7U1K>{ZYYW=^b=v7^ziQY&G&8H9838yo73oeTB1SGlnat+sI(j7js9#rQNEr%VB2n
z+vq@4H-iR|VKjcXyyTvQo4NoDFII7dWlEX3p|X~UmA8Y>tvNU{`z&LR(7c6w)U;&!
zw=qVvW&4l~OujCyxv+J8<&GDwwZ}P2@eC^86layCTBbP5!Wd(oE=E&teDnS9S#?oo
zJmyZjg_nxbynLn;D_JydE;Y-n&Qh17rlPU>PCTz4W6XcCxc@TTYjM;7TK~*EkG^uj
zYljlDaw2n<b-$-&Ce{|+U`-giByk_P>CA-`N}gO#*Qd{QP*T3~`S2I2Sm83CW}IZr
zs+fBz59CyGoTj)sxz$#Z>MzG}w~yiB{<2XwQDH#FbMP*D(MgYGo;R%ota45<S;jgM
z0r%!lEEq^;B0FIb#~(7zGrGad3p~J3O!1TtFW{X$55m2P*6Qu@TK(B5b@7x7`Ee8)
zO<!&awV1+mo*h3v%E5_lWx=2R)U1=0frCkOc%z}b%rQxUeh8;O+b7PgeU1gXF3x&w
ztNbDA{>umOT?Uzzmuo_>XO6FdPGQkP?r-uvk9tQi`)E>U9qmo>NYY~+?a_9moX9Pn
z@z;<CP3n<%-Z^AV;vM~Mbzs-bI*vtE)@6AXSDjqhxdqH?BVZ7$P~a`a$+jnh+|@Hv
z;-Cjd9#xFm-*J)CRI>#7i30PP5mS(NiWnh53~cT%&*LDZU;SoM)~C<FeG2W7AK*P(
zDuJ-mAFSeDXTP<Y=h6QbY%$2~=W?0J?I|WRH<t~1z3R@7)8i6HD^=V`Tv^`FM^3qk
z%k$--rn?JV+Qdk6^!lN#HnIifhDN;jlqou)YDcOo$J(|c*OT+g6XvtWl^cU*Yq88^
zX1>mv_RjAc=+j3_4yQ;hI&0HH8jD(i1sI3R12kcNmu$ItyLu|IAjPb;zTuL(j0$6x
zreKKEb3Z?(NIz$u<)cRmKeUhXMt?5poJ!uDOx*d#oB=v*K<Jn{*$M`MyG2B{qMJ7d
zhDSMHatTd_S;)q1A2eko&MxE)$0jh&?v0&tT7CzO5RX_}PsX_f>o0PR7&^Q&Hu3#4
z-WW!QKlfzKw@)u`F)bDeirF4M-fuuP;bwCbDp$99WMytHXQ(fKa9G(0?!>TWtsz;`
z_ik}bDSZ$8I7Pvnl)In&*#qGHWEm{bJ76CJf|dyzf0yAL8&Y$HQH3LG?`YzlAXekk
z7<{OWn^Jorl`9%7<Zmqg;(X5sr&8sCQbAGTZpLlL_5>}T1ujVM@vauIgI;Y!K-cak
z)@$q2Ged@-b7Vk=S3)t~7r4T1yZB?z<|00DcxLK)L;M%W={7`i9^&xWmDk`bRJda`
zoqP6|p-U4j(-zzZD+2%)Ml*nrJC-TGJfQG1?Q$7(<pX!K!~(=Ed`TB!*m*7f)_OZA
zJtWl30fe)rV2l7lcrqwKnpb>0|7V|E2lr^!R@9ADt{&Cy4c`HRDUshTA+-opA(oZx
z=5RNM?dExg=lJh-2_}_15l~bCt-zJ^tLaDRjbER-8;cB<Te^B4o}Yp>N-30ZS{Kt&
z&LX%dY~|$>DI*bf)*_#+wZ9la<}!eqI1GpblI<C|T6@ByM)O1qF+?CO+SCdZc}x;9
zY=$p(5!+ii`{i4iAvj+j!{1=$;Z2J%ymbib2VKNP4xbV=2RP~h{k$5!9uYlDF6L1q
z#kDfAvUUu?(aFS3>px%N_PMI9a5YNd%2gmDP6h5`H6oK1rbm#aOGVcu&brfMlMPX?
zf`p$iQ*&2d-b898{Eg4wcBKBSCfHdQ)YFV$BU_h{x&(_KCgY)mBOZm%6w_(7A;;gM
zpQCdQs{C;yFO(7pN8bS`kbozFcZ0T&KnxIY2yv0g!aNRg^O(XVm=bXR68#;EpD4Fm
zb-Q%nct=bj{i3AMiIdO;C*gz7kF|{Q&aU*B47+=kSGV#GUysQ-`E+M~U3<Q5e}iWU
z<`aLFuQY5-F%_^5!3kBnl>XVCAN=Gj-2E8bjtJ>k`;J*fO`qam0?Hg(+9M+)<gE$r
zlZHQtJKf_0&53f$aMh>X8G!FE<A1(aanG|K&(Sa_bO||X{sM%#`%c-JJSgDmL0kxl
zSM?2q){94x|4pgzcf<*2TmD)%0eE4kBbBmbV<wYx9<c%cgRwKQ2{4DlXv+5av&@)0
zcV2R+pa7$DK%0bH^N2Tx^YO;TGc!x2!2G!7m2GG8*HLQ|B(3n+LRh`6R%+%}0Eh|m
z(C2}53|j0UBCi2%dsNjUm1tHi(75DexH9*w9Pv^}{lGAy(p$@-e54MgzNB(Dju#CG
zJw#K0ef(D|kv>!>*gME|g_taLGpr}ETs#_qmahd+-he>;j~QSQXarjDya9|3SQ^Ag
z0T$qypCb8w7NB{EAz}nruz~eW-vUGtL<AOrRbU<?1dG7Rw*)9}P+<PYcW0#E^nVRD
z5bi%_N8tR=Kz6`;VFR&$Hu?SfuUr#o{wLWGu>NCde$&4HF_=K`Z!;q}7g+YI1~Ftu
zaQ&~u{@GT;XKN6843UQ);!W@hE5eWPA#e{eV^8qv_uZMjwM-h7#0xR3C=dngIPZ%C
zwogGI4#a8ouZa?q-&5LzHM#vW-E{k4+qJs?5ois<2EW=1ym9Jh6<c*b!1wyc4p6T#
z0gu4%f07Xa@1KMbAxFp*a0Z^SB;fKp2F*hbkpr2r1hvD~AnNo|@IEWtX{zCZ&T44S
zgk|1$d|i$iJ5C|AFQgOx^likvMQh!NkuDvMVOJ_5oXIBC9x*i36IWEx1dj5h++NuR
z+nz9ZiF5vG!{#X7Z6EN_u7IB>l3JX?p6-?opSrMd=*=9>B+F>acEN_rRN5x{K~|Yq
zc>8k?XJAI?3G2kMAlvr_K~T$TU+O!_leU{vJ47CcemY;?B747@nDbl#ZY{I#a>CG5
z%^OlVB3>?^sQr>tBNH!+bJ77uCn1#;j(m>SgI~`1H?uf5VA;<Xqo|F5AKcBa$<H1F
z-S4iaWgDituVl>6JCl!O`2gq_8V7Fq2q6U_ka~QIMR;elc(v`ulxU1<RD2@ry0?82
zcHU4rE7&fj{UVNdYT`v9&5K%pR{!XQ|N4asj`-ak&`rux4ZczElH!k<?FlR(hamPd
zQ*lkuwg{ZCz{#JZCn-${bM4>vwXzRfe*~M`HB*(iAwW!XTrkS=_hK*w=cl_R<f@Em
z`Vp$~HLSELr0IB=6)O$1ppulF`*P$TYq)|=45N^vzAJ!$e3EmZ?c5YC<_qAA92+*Y
zPB;)e8B!#3wwtn*Mjr{QD*y^SXKQGU8}<nA2bdnY*f_;FE=MkEa$fD0J617z&zf<U
zCUgPi`5QZ~^@n(FG_E?Q7LGDbEG9XdXDQ#5(TM_p+K}6Y%Y05>Xo~2cB?n2dXhWmK
zJ+t7;VMi2ehyou=nVhY>b&y$*1bhrHj`#HV8Diu2+B`h8ilY>M)+%OwLpt_oR_+Pm
z@qh`-Lk{VP0lU!pKy4ee@BtG=nsuS1W;~y+%eXE2!PIl56qHmdvCT<Ss;;mEH+Zuh
zXG(V?QP!#~y~)44UQVTes6$THjg>x|=84it$HUOlqEpO+EXK-HygHw>;$hVb#w8ua
zrGaJMhmwcuwqKfQic5X(wN|Eo-&YI5XGYtUpAd~lsIMxkiVFfs3ndE~_wvHas5%KP
zVTvlNC{EsO(#(Y)?zyHW>jCne<Ii)aWxP!z^s0|H6nKi7HPPZyiuz6RmOF}xb0!_L
zN{a9edw;O<IFYOpNWtc54hke$Gsq8=V69Q6$3SraCrScSXo9JK^onSEr<GIY&$x<I
z)toKUa^nS^$EP`y1dmP#jC?DON5@&}GqB3ywaO<;G5^3ab2)P4#GNsyU~Ha6MB$8M
zjK@DEN{J~HUS~+<GA0@5pKt(kIjoqw?AGS)y^@!<tPiw`H`02DdCM;xm)n4|0~Tpg
zv-d5t^DTA9R{ix2OxY$=x%Eej%;eMST6*ioHFI4eJrA+dvSTNRGf{Q#Wey@*E6wi@
zp{^vnObY8t^E%t=3o6h3Yt*u_%PRM=xu|0^7e;EN#yWFbWZ05NYex4qOdZRWI_5FT
zZmlq%SY33{sH*V!sR`QLQ(zg_Q_}SGvkdGp$w2^H%l8GQr5#W;xG0Bzp8~nZco7X&
z610=F$<nK*Oq2`WnV6On>TPTQR`hF#HnsU&tD17=y3$IetI8Y=tMT7|GGxM_MsMCv
zST}0K&18rqs;h599H<4{aO%gS`4BgFBR4cO&?ZTyTQe#)kedS<%<ok+jM$7z2EzdJ
zksR9Ofr?9}4Qh|iZ_~&Ah~=tq<W6*l{tzzMcDiPpmZvOE?y43Sa*|{EzYyfFzq6M1
zo+CGT_Fl*B&++02ujnf_+=GBZ&;BNCCfs{QOzVP{BwzxSiXRcx(HZMrw&qPnOs-E7
zA%B<r7A`bS_e=oXH9}gs(DI%AG|@iWu}L8v^62NLn80XOHq)sDEE$<SpM5#Aai85g
zMd{+4yVT5hovnaD&eOxW`X~O3*mOl(L<7Z)v<0V_mdtUcdkNE8Gf|(-<|tgy!!o&c
z#VRn_trY#-GhcCQ_-eL^kMjvc=B4cxyqz_^*G`&XIW;S$jO{cSu_2KaN%%yq`qgIX
z>m6wQ3A5U5^vg{;XUu}=8!(yIa8CGz^cC#P_z~0ioNd(f=9c%N_X_3(1pNssKTebe
z^D$PE%7=2L)bS-)J52@Ew_%4RokIoy$(}Oo^K$0@$oS~~0uzhOUz^c~OjJz$s{VpG
znAS4Q1dP9uR1|-V?`NNNtv=%_Lb$zbU3bZ*s>nyr8dr}Dye2><$SuuEtq#Wx(8s(X
zek6F(WUz<aoDxQ`#9Ezzx5jc=kar9lX+#2KKIVPGzq~|@X-xZy%1(sUhRJR!q?&y)
zzGz$hA7}1Ts^0G@)hX5%s67imv!AmEY2PW$j3Jf{S@k>2x7of+*^yJ=JVpeHlNo1l
zIz!+Vp&xIZ(0jJ{aLVSKV7zc94YDLbMtAkZzc6*mv`Y~)tbuE+iKUMOpq><dI3skv
z;6BW?(L@#2O0VEFW_<(&-?_hs{FXRJtfG{8!F<0cfO#%SY@F45MeUFgPYL_nDF36|
zhgJ=-EU&@;bRGSoj~eb6d|SwrWA^&$rT<F)#y_;d9n>i<ZOQ5RM&UAt_sZHK=aV~i
zj8a#}Xv60b=&r&4mZJTFz!%hX4cjcrPe&I1uv@KEl0dygMNe&C>h{-qT-IzW)wc7^
zGx|;MhX23^tOhyslerWMuLD-GCJ}C??D#|W734#45dIZ^%&hjxIAP$N&XwOIgLg>J
z5Z^Fqkl!yFap)E*qScq)Kilr;>E?~`74an!;`^QU`wI1;Q(+W|K!YA2?j;8FE8b)o
zsWT-aj!Kw@F)7~|o8B%+&!3#axg@{Vzm}HvvrNq!y>@%Hxd$DngK&a;9TIPOW)Cez
zE;kUwCG~#)3_<Jvz$@<wzdo)qbl}hIrz)s+P6BHS*F-Hdi(fvlL9;e%s#84=wjuTi
zu{D%808as7?{h_`-<xwswm+nPrt)Ur3XrGw@$2e|zCo_$QRE1cjzB>kkZ}lV26D1M
z9qWhs?b@+S85N>F@U{!$09=+-0;9H?P&i6v+Ey#>F8YxL;N{bv7q-fN4Fibr8t)dN
z%n>MOonXzu&EZdw-B~zAx;MPA-kB$%v4tFTJ6^nRG2NP+_w;y@M=K}QA(u$>T@XVt
zyBXpZ8LNAW74`<ZgeF{6(`J!V*C1KWqDH@8`d<_aJ>t-^I}m=|1$*DtZrynwV0trl
zOk!!)*6Ob3bPms1qRZvGy#Lsm8nuL{-)VRs@?UQI&D9zHyDRk4e5rPzK0MO^y+&IT
zQCqI+29z<gA_wUUt&)K{eVC6_L!gQB?)c5;t*N*?#akZ#6;F*Q0e{=`G#6H5X6V@V
zk(p~k<!gvz15_@bEQ84!@rqh93jn)pK30Pt@8GXv&L!mWO%|IFEpixDdQJFb_bPcL
z${Tfv>%3~ya@TMl(%+vNfa{~n$6a>2J3%^6aYH19P)sV+Ts~pTAbnY0U}cFvoT=b=
zSbXFC#DBZG-a6M8pa=3ugEcSKBzF~oM<BiD;d`1*w}E}!A^n2y;sSp;RK)k{kJA7j
z8}jDJrBhrg<zC=DomxniCEmF}y+<H=jSexxHe*QE%Z)y+8uxFIEZR}|;W5<x$kiIP
z6du5mRj2^{Xu~#nbGt2rmY=+~(}8cykKe!fjB?aZ^U8^9oIz?N=ET?u_My@@RhQcV
zOZ^+-Ol*QDW0muse4INjvS+}P<K);9M6QBw`Q8Hnzxjym3Om%I&|@{Tq<`X=7K|vS
z413lKbzW$cQI5K+e*L6iR_w?Y_|3EvJD--2$~Stz9kgt=ul0fH(!%Ly9*lmK<LOX3
zF=|9m6EVaCL76I2$hF6^`JD(k(C2NeQ4`AkrXGszwKOAr&s-i_NyDg45uT7Znml-A
zATa=eXi{{`oADxZh^O~=DDLkw=*oClv|{Z*!D}-|H`7R10CQ-Z&Z9CZ2x<HUxilGU
zf&%63BeR59>s<a^K6A|Wpr|>^jGLJHiS%p&-wU|_0AUP&JGCM%(eC6RpCaPuj4u5z
zIX6_EO7Q4HY7!$^&xlp+2KHcY9I|pC>zv653w5fDiK8-o7e2lE)7g#=AJnC(Kj~U8
zAf=G|qeHiuQI>B-f$8Mg;Vrb=ytbpZWw!%7MO}+&gzs7}z`7t^RaHfm0f;(C2IDc;
zqxY&tI_VZ(Bh!9JAnr;o>D&W<u`z^h#zq5MZ0QuNY+lu{cwX5sOp-N-wQRyrdY;sf
zObLuRXobcMl^M;DoEA+GZNhG>E^#%mB6uTjE%1;bOUM7=8g^qxdr10<H3P!}!`Ji?
zc_ws#hO4MqgXvtmo<R+zXeeA!thFKLAew^`d1fH5>$&$yk{}U%+1g9_#q&Y6Ryh$b
z6}Lh6S9oIVf*fN~fKAAIv{npKyJ?*_+Gk8rGis#-bY7DlcKtR<hDc%IBNDe-6f1){
z8GZdw6dl%pyQhwf5wJa9d#RJ%MOf#8x~|Ugy{uRE$AK^p&F?S4E$Ys(uXp9GsNl+k
zq=;bBR>KhyUdnLumC9Z96};`Wmnez41taqJ{^dg1z-LCARpS>KFA5zkWT(iTXk?Hf
zdKNO*3<{cUBNZVT&y3XY(Gyg3*jC4fT!_YGb^gQj(~lQ)`=*c7kBEbhMfT@#b-tU-
zdoM~_&2hDx;NRqVoci)^%(!b!KWl8!(|$pVs+73S4o4oEn4Y;vR*pRG!N#BejBQ7T
zCElp{88X0w4bO-9<M@J)B)Z0=1b$)f1*7(6oWc=<m*RNmFKKOXJZyY*OuSr#b2RWL
z--{6GOor9R6?Lo?zUyDHr;`QtmCIUNbM6W5iZ$ZwT0Z-Rd9fw(`Qmx5%CM|#2L1!8
z;KCI2bTMslahZK&*~G=aAs36u&(KBY6;opEBR=MUiB~O=dR|HCUCCRf7{qvmnEDB%
zosiM+T}3V_#We|8ZZ@zhc}n5ks36kfuGf0QEfSkRWoYuA7IGbEQt2lpVURI-nUKbG
z*-1haTY9Waj(!t1(KTcwv4dzjEL_{E@%@*oE$6FNY<-_TH>{JAw7`0Q%6wp<i?!w<
z_O+#EHxRaIw#VyDSp1}k=1mB$^5ltTIJ=|ZvF#q(U*2S%28CK10NSpgsvTg(;Xf`@
zYN@MzU0(?UVVTHw1Khtd-|_u^2jJz#?JBSg<Wor1RwUaF&5>*$$4TDKGd|vp{6Su(
zosY@Fvt>e$!qZ}rGI`zfxlY8!aKl&6IHURy)^AqMvcmq=InhHGH9!SZYz#Pw<h1eL
zm*u>oEJhhf!?R~Kg_oa_2yy6_4?Vhk-nil6<D29pAtqbQ8!%<qE!Ctga%~S&F*m<}
zGvB!tGJ~ZSIIk29jGPvqkFda*VaoIBZ$-=`rdj>@^plUsE<{YLoI7dg@tSp`g}6AI
z)#t~NLSL^kT{$?JK}LF9e)t_0+Nd_xz}s4f<-iR_e>#z<hq?`8rSOA7Trj9#4PYUQ
zK}RZozi_@NvNX92Gd+tRBYn|CPC_6oQDsi^zT`b`CLbD_sg5a=<J><;<_O&T1i4LZ
zLWkjP(gik}re*%AlHT8x|Ks=M6J1rO^D{NKrc7~}XRG&JxRw2Cxolk(U_8;q;H5nP
znwUoCCFsgw0q{bKnyk6t=hc~Bz5^RSW_~c-F}v@h|G_uuivkeN_AGXBSw9V&sGqr)
zKC|9!sGbU9?MuVW!eykf(-2BuHS17rNHLxW{RRt!_Wh0;R+v~hC3^rmgWhdnXh@J1
z{2^P1d^RME5DnYR!&TZH)A&<@O<7)MRFx(cU?L-E@^DVUMj9r1aw7?*$V;P*j}&i>
zte7pbI7@EgoVbhE4?bY-lBSukV(6L<hUZ}2rko76WSYB92wRL*O`#F*7hm}&Xh>IS
zR3;#TY0>v0FAb&mPleElmXS~%lsH`M8`zpCrD>Go))@Yq_f=asttl9d(NMeR)|FBt
zo%?;^5qI+WeyO_WD<Pgb^9t;_czV}WsaE|i@s{P5IKF+%QU6ECG?_>VuK1Xvmr7%r
zZ5v6ArX`%Z6H+)bImtZvxV%C*vUqW&)JB2bPi5R0CK_X7ro8i{{qhYnGq}Nmj6}S(
ztjT(3H!k_;C_$9f4OhEl#S$-Llq_gL&aohwKZ-{F4*O~QLzqAF9CQX@^Nzi<9#!^i
zc_Ujy)#X}^C*>jZOjmrK1!n?RNJ*Du@t+D-xhd`}x8AzEZ=)t#7C7M!PMCvY85^c9
z)?1!NQzS5n;kPpZU}jc@qJUD-fq0g_a?SN5;iM*7sVvjXAZ;3w1egpwwb~SES>@`h
zBFOf_YZTgX+`&GSvcdhE@SN@po1>`_jovs(3Ji#T0m+DU)fl0M<OGajC(qU%_z+|e
z<Ck_n{IU-H=SFYFYQ0r9h#L=to3fpV5-jFsz^@ATP*cfgsj;x@#87IbiXstZD;Xmh
zD_3V%C0D6OgR#JB>IBTiRKq2TCJO0zE#*hC%Z4weUG&GT)vYG+FhMfe!LmyUO4l4W
znNU)gQC3Z3li@)X`B2juCOu=*_Cu0=v7N{6`u&xX$*|G`I9cSBTZAVBJ`&EdxxqXe
zxODAK1p>RdWZO;xyMz4;a(SBml^AuT5dFX_NE|9s5NKV}!C1pU2I_vpQjTDl0mng<
zNW4PI9*U@@p<&@Jg@jvE*v<+0&ZD&#jjhf$pT4}6lAX>boVL|$@23C|*tem(`7XHo
zaUHJ|-G|dQo6Ymkt4<B)jf&#W-M87NgMNR#apkN=PK_M~fXY-g)nD0PWwgSfE3_#H
zCQ7O_g)2so#Nk#mGU6uZx2&%Sym@4Kj6(Gy_2R#1dvRwlXV7y+uB*F^o^~*j$8h;-
zFM1L5oc|I&e}*Q56USQH;S>OqTWHn%#3@knbcw;b`Nc*u>BUxUqt{dGH3SY<(g70u
zd*y`6d#&4X+>*A}82*sNqK8kZX5khZN~Pr>1M|<k^d1tl#KHnrgYJd;4c=NwV@dX3
zsWz(YfZzE0yRz-(9@lL2ulu27<Sr<rL3RpaS-Rt*J*5d)Rs7KCJzADqT1gd;S(W~{
z>Dz1O1@<cd-T@;cMn<-Ds%C{>>)N(z;j#@{<!sXM9#U?pvJPf-k`!S@tGg--W_3`8
zwUA}ifr8qqC>UafhG=zQRe^Wyr=ZY*c*b(En@2}HQvu%DZ;4yq)HPDDs8J_7`9E7X
zo>DYq*HkVdandadpXISN!w~~|M(%3E^#x-jUkOX`YP2+YKhAFRmf}<eG%)&)pSgHB
z=MqeHi)~n3c5k+dQ`A#jDex(ZRR&h~uCD0P)C>~g@WW*OoEUv?(@?fSN-u|Bbq&+I
z@vp{36Cc1#Z(dfTd(I1jr90GGR<Q3682J)(lhG(G9s2Ua!$wv6+f0lU6F8JerKKT5
zNyE8{n}`z=8wvyoD=ah;BgkuSVndnv#Y>6T?O1qi7J=_{PbQ*7+h)FvQFXg<*!HO$
zsEn=8e7&B!_Uyy(NP4ZPs=j`;+0?bNc`nT~Vv{P#BuO^XP67(-Zes}PR~-b36GG7j
z<_~QE4JwLONTUWynKZBUG<p~(QeHA&i1y$lT865PVk_G~E2?sM!2L7<I_>@8mC5kb
z`F7a(bjka)?)3HLGzA0kRT23owjKL?yJ|(;PZLJfYl3G634BpN%0L6(U}Ma5O7&jH
zZkHvGQ1+xe?)der9Xrb{%<I0*K_8hLd@$n9KMA8ooE77^1cCL(wdnM<oa#7kxrASj
zFp6&4icSZ66U?MguOHlp=(`ZG4vC~|#@Mh?C(0nHjDXCK0{!7}WOQlj8>&OS8^RNZ
z8g(vmZ|asuX72uvS?Jxd*|ukryQt?ZXK<fEPohs%rV12wN%p;p@rhkEONc{l*t=^Z
zGrwkqR-h`gXT|1NOj0%}=0HrNvVM<(K+T>2Oi>)7g>-i;Y)m;6Qu0SeyV$9c$TWEW
zIFZHSyTptYjZ$F`8Ww1>0Rg0rWV2}+LDnG@5XtR%K2<UZa6j-U@JI1H(7d(>7ZGD-
zkW`^izLyF<N2203pAao5c}NC-3bomYfFewrm;x!RKEgk1(_-Mp#4=Q|=(iKY>ya|z
z5-9-sU!X<=W`#_DdU##Zdev^Jy5t0U=gtgJve-aj#MUDrO4+J^QmGWEl!&MvB3nQ<
zF(P7<XI~tL4;{32jk)kuWn4<Q&A;$Ca^VFd-=4q;VLzlMr`p`eT1VOYr-EaqiuuA_
zC=2cdeJTgT_z8mIozidQ^;4!G=CI|IVn#MxipUc}-W-=K7JHR^h`u4OdGrYTJlcXd
z&AC~RU^`pEYh*EKHqDg%)hyubk!}MU1b2lkkdAd1rJ*@t8(A<qM${dFHt+}4>P{&Z
zjX@s{Gn0tMoHZ_r^N=<F(NwCpFl}SOK0OAT3BGR1Vb-16podV$zHrp;eySo`{=Nwy
z&~3!)-!l=#&_K#5&{oRSP`=(&5#xeK8a+nT#Fr~2vIs&LJ|3<i7tLdhIabEr=TNFz
zcnQpvI%ZKh!NoqMRt_wUsjd&NEKp6e%&`Yro+ZUWlbc(2cxB!30xB^RQaC2;CE0NW
zbWMxmBf!m0{RH=)RbhlIPzwqqXP!zZQTgRd(w+}Bd9F5Kkm_G7n73p%3@reO1a#L9
zF=y8aI`mA)F(Thj<pXB7BMLFOlYVXCP5;I41?3Iz^JnUM%Rm?g{dVs~_1)|faXfax
zWT5b(21wN+-&vM`4OH_f=ZBrB>&3=Bp@Of{>x4b_aXz{jqUS33@G%SlF?6EvM!wwE
z#3kAz##0I=(2ZG7uZK~V>aG-a*KF%(OY91f4Wk{)meJbo^7;Dt=K0Do^DWynX!hKp
z(=(48zCAr_=%!MZ>j0cYNSp~}Ob@G7&U)4B+Q-;O<|iFL`nAu&kT>U|C}D5Q*aPDh
zfW+<M4!tKj%19a0OnXZl#^^6dN(?Yha85dmN^wkpFZPe>AI_^{YM*93NyHW?t)kjx
zycG`?)`|ng!Ehf3edO6ZL0eo6<}W+X2DYV*>TTNE;J#iv>{>oPUqN?Uh@9uZWnOq@
zK(T#N4Zg-6IJ|fOm~gb}tLkMlT87jKl_;6%vLGa#FTZi!m(N?9FO4pnmi5siZ~P4X
z{zFX+56J93U!a#^rS@OT;knkDf102_5L1iGy(}&-L&NpGAG1K%BQkg!|At?<fbsKG
zmwLo5q<Sa2%URQ>bGN(aO8P?}mxF5rJ1PmDwgZ$aOfyWuyOyh$C3sMDcgV-dMMIG}
zMrfN0tegT`dU~O2EnHG?(SmhsD;pzfbnNwLyC^Z$Ld)9OpV`CHJFq`8jvYh9!rlMA
z0Peu=a1ZrUHJ!RmoKs}P!e=ZU+n~;CK^nMQ{}%vJK(4<W*x{f8K_V^?oRbp3j2=~E
zlt~5v@>Wi)i#y02<6h?&ZqxEG3WUqUI2?3dF166*xJ1{5EUVUy<0S&1TLG{DDKlUZ
zWX-Z@fW#dsBG5`*o<N-4_8?t`!(prSl2m~#FZ<iuozFenzGm?e?HBK?m^OQU-5=jx
zTsLWK<gqhs@1)<~^y_zX2W@;<`!lM4xGk98M^B8jju=17$dc7~eE+A+KVj~apx4!*
zJ=Sjj6NN97FhG5sKz*H__&j!A;dR_9({+XK8Q%#T+ob8{>7lmpLgRefg5aXU1tlx}
z8~t|%jkYjZvGej3lB@H)K4o%fa`@TMvtgzq)Dd1ES|9#6^l_MrOU0&0C=#wUDd9$`
z(KISFGQ8L{FTC1xV`z)%*3f;@{iX*(c7Tf}F2seslGo%4@u9F}VvxJVqk03%G7nnj
z+3&%gUObP=0V`w#3X_9!N*y#c77?>F&KFRKZ><@%qaCOV9Yn{Ffd0;?zFL_<Olh&`
z`Lf@Q+^XHJxEuLc)K``t>yi%w6KzCaS~COaEqx=a^Nmwm4^ir%Hr*#OQ9cKGd<7xm
z9r3fN_~}f(B7PcHyNv150bK~>k>ej0CcrF{|K+f~HU#2<3`#E_wvp27s>N1o3fO8T
z{cR!VK2yy`IA^MrJmhV!{odYpTNY|K2TOxZRiP@7<2cjEP(%1W>A{dhrP>Cla)ItD
z)76ChRVme%C>75+9c~w+YZmbuj7I_A{!QC=4xOO%{JnkCx-TC@4&>&wllC=h){je+
z3_=HAyMAjw^^Epc?H%-C?#|7tCoAJ}Y-K}QRzJG@`T76+y{Y4>s!**`keL6Yr*B*P
z!4iasewBbt>VY}T1EUTn#B!#bZ5Ee{o#GCW=Makv7#j1GAiCW?W*u2?QK>3%d;pbG
zBygM*t+crrFUOsD2WGHWNI#MtmC3D#FpP@MsMB?jjGi~@bk-)->F^U4jw-^Z(MMVn
z^HXgi^W6FK-wh-G2{%}lY0xSTnU7F35&)IKXz1W<ns2ajOn3*qgWqG`V_5<OSFyX%
zo%k+h54)du&kbBcAYijySc!Nq<>HE{C^wE8!_9zR(ijI2<$%s-$PEYNhNHXjT-Cq<
zrvzRD=7jCVSCaU4GV}%ptz$ZwkC;;o!*nBqDy^eC>5u4B0E$7EABP$M%X*Q4!uU#6
zEJp}=`Po+uICj0DfvK~pRNT{d5q0%_A9Gzo6vvLp87%QQz=&x8Hd8u~AZ8{-0au_>
z!3;;hhV)r999@eRpuy>XvAy3t&kO~M2a-#J<XS;;0`<Ed=)!N+VTP(>5@KkErFn*B
zXf|^kz{mkz)z1Vto-7B5;Q^WP3<63hG88Kbs0G%Y1t1yV-2hxMB}h+UH{6@U?#BU}
z1t>tG?kCYjlRE#1NV+ZqhVsg%0dZzB81+ow<#VTrEw$AW8|tpLX7Q(RIoE?Vq7L}p
zaox!rA7DjR>`I?OvrrqFf~KcG!Hek0>7#hmh2Ny_f+vsb|BNYPhEZXv5_PBxd7qHW
z`dz;9Ib(Cj75q{D$SPKQ8@w~3^SukA8>6>-f9AW-*OT+2@1-0g$C;ci&g+VCMb0+w
zO1u%@$34!yz!{%W-jlIEQdwawF-6pPnG#V$g^+mt%Cg9X2#z%Pi7hKPTa=-GMEPa^
zLH{>?#_umdNlJyY1a)9)dQi=^)(6!b84{0A33lV_9%p!?Nh%?0JA9_+@UfmljS{G#
zst!Y5MN}vf3r%hL#{EVN_`V+?zG`+UM&Bd_DeW+Ow-I$q76s?H(MN7H$vwxt%uTz!
z$wk8ld;(w#JNi<Da>p~J)5J#r<RA_c5n!%vA@y}Gp3%d@2|wy+>l;wY2msq0zcMXy
zbp)s4ZJ>~G7(m)AXQpsRikPw(7%s91(hiq9NNgF$g+sbMtEsNZ04GH7hST999_5<q
zDm1S@{>E!ZyBq180`0E`nWx9zo4WVumY?7Ly9v$98mFPz)qjoDw2qoEIw>3QpUd{%
z(YEDDt$XV&6LM?3LPNvh%`<=6m|GCYojiJo_J*y}6RR82QW>p@%mb-R2W_YaZSYbb
ztCL$TZMHVoLdzoCBG($vYVU5m+xUX~g6CcN9na_7=fdaq&z<Kv`yl%u=Xl$ASA(a`
zxX8#4w$-?5JoHL-rDYSl(Xz$+fbD))kL?MUXx2w5M=|S@-=Ub3CUVA`r|7xGs+f8a
zLrE}(HmiYBp(>?9Ju0~a#;O+#4+9?s+&m&@QIJZQ$bl(133#9{hYvcuzSiNF_FYm<
zeett>apIOsosMU`%8+N)kd73}I^Z1ot_#a$He)Cl4yHo;oB66qi`J}L(mdaZ9PzU!
zKG*(+Tz$`dg1@MoI&J4ePw$(xEb-gtP!uu1-wN(07<C%R?n)h_=2AP<Qd=9>CbijG
zTrHlq+&%nV;(1YAp4XX&2h)miuv77lr$-sbJ4bo%5=94zJYx+$qP}JWZ??ctNbVxD
zDT+u?w#7pEw&$TdIVgDjt#ucB*m16|sV`mkslGB~aJY`FMz*S^Mcg83k!`+fzGqP`
zmud?JtFj#gMD7OG0ZZgXXwF=zeK-8j%qO+)wC4`rguH26V$_XSZr-rq>P`D*wV@aw
z3^VfHf#nO!ADXcA*Y`g8%l**b;n3e0=!%2NMZfBy<o@$&gQ4~=aj)r4`2qHR=_&Ck
zQ@2kL9B3>a!!<~g@*Xff!9C%7QF_Vvj&#y^p8wip%C+P=RhT7C)ofKP&S#vjIccY^
z4SDr?ZgxYCf2snkwKbdD&DiX*5hV14H%CE9o4zsO4`endLPeQ8UYf~0xq7Zzz@&7M
zuwxmTJ;!Dv@qUcK<{{#a7<ej(63&bgCGzIvEz8@V$K+Xpf@-oTAn>e3h<|UVqL0KI
zIy`Ejqu!(DSs(#3<RSJ-$1(M39VpqL5m3WM8UvMVS#w45!vj^p)ackpzkq@dY_+7(
zhutJQctn(j>BZr}dOgsu?KH6>DgE(g6{KV)4>gm=n^h22hC%~viN^u10sc<vE=^bu
z5mE5~fF+{i6dlwdls$u(-1uAMss8+c_P1LWA;+72$i}5r`lc&K%#6{iTCS+8Lug83
z?=K(U`61{+TzgS_dd+QP(KR=&8#(e?UH7%gOuN2{B+x{4?n?hAKeic7%PTgTIxCm}
z0{%dkqa;q!DjJDL(z7gWj<$l9q84cC63cn(dAn_hDd`$gm@EO{>}o6=Rq~ZF?UuHI
z-8L9ahGL^BW_G!qr6walJ5PjYz~j0G=mTxG>OwkVFlO??;*0`>3o?0yl2HWFnWG#3
zIV_oQ`IZ>T%u*>)1cQ_Jc)8*tL)7OXlTP${eZK7#r~;;7w<=M|NYLgjzc~BOX6K*W
zC#Q922Gb8LOqS~ptO1nXIQ`*=MWdn*U&dKG5OzS;Y?!uSj#3@^ytOQHEGk$~G(Wy5
z0rQqhxmlNc!1h<c6wVHyyDA8D5QhV>SlAtx=JIMZT<{mREUhW9o7NtCXU$xMp7~uT
z;)gANYP<H&KVP`HeZjWP3+LV35F6yo3%V-8vw!}`<J;dx2ITwAt_x$1_AajL*=EK!
zKX~^qfAQ<CyFpmD1I}#&yXK+}t8oj;N3~=i<q>Fv^-t&<B=W3_jo?=6LMw|9wmYmg
zJMF;8LWJt4c~O!aPRT`41}Q3tY9OK<5K+H~M4!Hw?g~Ye9iA=^UhetIgTM4359Nru
zocgqdx?N86l@mF=?)r@2frDf>oZ#qOwxruXf)D#Zrfz+S73wlO?Z^u2YaVu5gH}a1
z_Z&Gy4{v_@%6*gk+L^%Qp$$uu8nDUqC;P`P-@HA&6Ia|nvuf0qjp;9-)1cdWB!a#N
z!BZ=HD3R<sSfzSZY!-2+cu+hhzAk<#viV}WxK8YX6D-Z~6re+};;O#aKvO9UsF~$>
zM#4O-8M-b8BMRdcvi-W$55V}qoG)#ZfIVLkC*ct&6XKwqL0;ww#Ap}38_z_)dmlz<
z3yjbledp(21Pgz7#AMaIg6bM?slwATZRcX*eC~ksjP#QDiuArDO{LpuY~np)12;ps
zfn%Q#KVtfr3(UVcb|ODfn9r?YwlY6w_OW}pz5HH5%4cj`oQbo=TrpoPButG=BP(6p
z`;bJMV+<_Ak&q^`vmw!vWMI1Sk5nI<5Nh*z#LqKfLlkvVh-}GujrBjy!YqAXLq6Bx
zff+?`?tlv+<6$MJc3|IvbnT_XVo;%UWKZ0x)QVJQr)I$azXf^GI5bn+g>KRQkM{4I
z+1?B1&<)y8(zDTrTeL?8;!5<<oT~3AsYPTg*=81ZvIp5??Cb28nQf$XY!{qhp-VJC
z5;}^gfl;Hp%zunpcAT<dDA`@3on+>KRdiANRS$1>+h+<31*V%pilE4&gi)5yWtP*Y
ziIq2-IHSP;%RWX?7o|@V;3RO9|29n|L)2&nLv`3>GG3f5M)Va}%<s<@Vxj(X!eoRz
zfEa?`n;t=DX3{Wi+L_4Y+Hu##LFw4r-c0SCl#lZsnK!6;!(lC-iSB!B<iZU<CUay8
zV1d2RQxhP^-RjuS&>7)t``1q9Mf@3yZC=(Z;x@U(-r{QW?8bYzJ;H8dxA->xBm05)
zw($&mhWpGU-!Hs^|G+&jykKOn7q)O41lmf>vt)1+5jhy%QOo<<bC&1e9CMKR9@OhN
zchM1f(UOXb<oSSq7I_#%%qU9P6&rMmayqDRC=xBW%w|rxHNEd2NYP&Y;&$!pTT$T7
zrAv41TDtU39NLPwTeTOz{D=1ZhW-bB@xTLJ`yP0J^!GOH8fG{2R|c%NS1lW4A8W@p
zg|0Q#+LfG9^f=Qv`>336a>N#{<st?=$A6O}fEn=VV2>x5Pjwj#vc>EU3clrl@2o{;
zvn49aI_NVjr#gwR#$TW5XGc?C9So#=dZ4?RC1=3Sk+ow!H~(_XM<P?4gkKO?3#=kW
zVBe))--fv4ql<eG)-Lq4Zl450?AkVe?oAu7TCf?0w)tx9Pg+_#r@hxOJ^eY|bL8Q>
zkKBLHeo)9w6h+tQeSbhL+|44<jHa^l+3Q(4VQV!nG%vR?l4vpJ8}W8yzY*6PCmFG^
z8?RK0cpmhd#++10iLzKOE*BZmx6ZcThUeJU*$&uVw=p)EiXvL?KgOM?3t{B7*7u;?
zOc+wfW$Jt`<!w4mc{11+w7a%4BZ>~H@t}Ju`DzP^(WxBNrbjAeB;n?DCAXq3qR}Il
zjB0P2F=ptHDG4UJd&#J(f0qq^Nc#u$u^jYShCUYK=hS1Il?w|ox78iqW834{9otnb
z@{R@vwmoI)F~1o6Bz)d<F2og?rkm!Ob{Tft?ho}C`Qc$TGAg<tbaix-ZIfeT=;nx6
z6CKSp7{;3>SsH>PLVPF^i`E#cg2c~N72!FFwTeNHDP|0XLSa4<QcJEiu5zq)-cWRX
z@n+|S;=RsY#gBy^3!6I8cK24#UBwR;A1vYAL6;f~D=sxRU&(i&k6g%=6oSnK+Y4}k
z>hUWDz7l;q46Iyp2`Vo^i4s(j7c7^NoCFG`Lv67>6Xzq<XKWnViSfo)brX?a05qdB
z;j9JG<EMxxu#d_*>#8_}IOIanP<5~&I1RPAuSSd9=a7WlnDGTexX^Ag;zHjX1~CnV
zhGrk~HQ4$3bPE0vC^Ya+b>tA&;wuEb1-mnONO!$PNcq%}{79zk_333b2M(5)P<5yw
zw8wNu=y>R@5El#?O$_6sviOin5~O&<U0RQ_U|KJS3KZR&>h}RdKpB53(~dgPSBR#N
ztb11(y{6p-H4##qC<e`8zG5)xf=h*4yOM4d?(0_Jrf#*WMsX8Qhg&Tuf&}hsap&t!
z1;#zyr-Jpi_)xR2--olE*Bz|-i^MynI!NTxicB#hjI2jA<0<HXS1J=-5$S(f6%Dp}
zOCcm6hcBKm)fyefT5@>UNPMh+J!Gh*vRmYBU=K6iQh<+2EE<dG-clgB-*c9dAO0ea
zhjQezExqbTH3bgmIPH;HYv2Fm{kICWudQ=hmz4){qv+YT*0W!}mqv;Bl<9@JiGb5#
zZ5-Be*R4ml-BvMdM7}GW=ggfyzH#I2Zycn^51f34cd~cGns7ob3Q#};q$0~;^LTTc
zh4(rs5AAYNZkyeK+&1h$9$Mrj-smCXwovXa_dz$^4%sm`?e0d*VIbm!xKU0LB@H~;
zXb=-pf&#`d2do6tPzybD)NPyYtat2p9B|O>j!wr8$Lo%-94zIK9RWwVgK>C$tGWg}
zJdFoyz)lPSJJCZq`i~KZ>xGQJRX*z_rln7hu7;|o0jgV*7I^8wy3VlGq07`wghm|D
z)^Js_s=$hGJZ6aH#>RW*{$#?9wFYthdgNoGr?hD|#dCAsFHTM#U9k(jcIwT0wJjhY
z1K@+1Oq4R99M#Dd`VtCH5{@JSNdUy1JOa567z4h+%3cO>v_TLgqXAGcw$VP(Cxxg|
z!;1zZ==@iz3%IaEu?7d_H55?A289}I*hGn$srs0NOh)})2GOlBh!PP;NyNhl-HYp6
zH94xyKuL^25=D#=2ghP9@y@88+(N}*$~TppR1;%zyM3}$FHO?756e{pgKG^;Ju``+
znO<BDz@<~Q7^^50fT=|zukkp@+)IQW_cZmTz=Ef|6ZHUbQq}>KtVa+bXo9Zt@f2aK
z`V<JFpxsT}VRis{PioUp?4`kO&Mf~P1vQXf`p=J#c9oXmJb=O=Q!_BpDIiljQXaDv
zvdB&>g~td?-UW1?CkaOq)&=KqCQNLw0@Ro>8O)rFDLcp5F+<bD&uwo9Gue#}fKV)^
zgt?FkILn>wPMS~>-Ht>RovGMz^AsoX^Via<$E%QNgKiX4MXZ-F!lYzFwUnA$tz`FL
z9LKYI7|*!6iEPrkP~!296;1M4V3mETL`E_w<M$>0UpU~+I%mwvC)IdR1~ncGYJ8ZH
zsowstK>lAHqGdD)UYQUamTbHLE3nIUFC;ckJqy}$1PUZS4CQP%jXh`wG4o~`h-OT8
zjG2M&pGRSB%gBO}GuAavp6DG>HFvfb1aHRwynuUBbBBhkADFIfyBM_%(x<7hYM4Oj
zEUKp4A;bAPsHQ=${#Qty*(D^9TBkbjgcHom=wtRxctTwD(i8?p5ukQ}K>rRv9lwXD
z1I&qsv)_>*0_@OZ+6jL;fgEtHq@9Di%);-YFWEnU@;5&zQV26Fg~%5M!Xk5;{X`NG
zZbt~cEj8;~H9jNT&UAt8_ZpfW&Tf0kda#2Y@OYLW5*Qnli4YF!QPH5UN%ZA!XRn&m
zRPEV|-^FNn@aH*o#e+#SnKqSP$PNJnMcJ#Np3CKS0y={WIsUGly9>$^yLP;M&Ad5t
zR?@$?<++xtde=A2UhwTO_PNHPL#Xdx@2-N2tx0QjjT0^oN27PGoHJ+MH81bjl_)FN
zEl(IyH-UZb+hGf4H?8lzs^vMd&(Vtt(4DvnB<OjXqUogo=XiLpIK-lae3sI8^GRI7
zomvEa0CnZeHO!~~|7(~}zkQ1>xpWPp{)gvKS}$t2bdB&oTq96l_X-2oJcSD6|KS>$
z`tm7xw0@0D&8KEEvzUp%=wYRy@~Ie=plYakY78}rnnBH>7EsHml`!@%saGw$rg_@5
zD_U3mWKiAm>k3QSuZ~O@YZOMQ3?;x{ZXi-u5{Z=5(XF{kxkHvcxf93VuwupB`3)o1
z-dJ6^bg|7fr3G_?hqb_8X!guJ-^?2q&z!mVMtXioG8dPXMMLwc#Gg*oCQiJ5f~@?B
zL_&W3gnYtU3y1Ova(MY$uZI$uTz)lE{|A--qdrK$5e|{x{)}aFdp37x^MTL#AC!NX
z|M2r4mJ5Dx|AFVxZ<Q-b`5h$rI$4pdh>#<#rV?JiNmf)O@f4D#edGkb`C{Glqvc9v
zrCu4mL_X85AnDhn@*U)ez7vviC~5B|lNBF90qur^7IL#6LxP^JOjM=E!qKksas>yn
zl{6j>J|maCr<5yYaB%4-bM;pRusrP)wHO8HCAyaQR3r1Yk$PK@PIKndm`e10(1#Lz
ze<Io?2Lo1k(@Hqxbbu@Z+9~Z6tbKv!N$?l?o3;+1<$A62AOHLZSd%OI&oE<{p_HA3
z)~Gq`$$+~Ypd{^f2mL1lmU0WWIPQ~7zWb<d`ca+LqC~2XY&4N)>0dQ4<8Y`9S1H4=
zeSpn59WFe1?a{TvMsz;4dd<-_^&{3jwffqQWjA!J=wOC6ZhLjZ&97~1Y~1$R%^P0b
z)_7t2eZRctzWeU^<$a{z_0ZYpnL(71D(az(<cDGm0@)tJMqUt%(27II2q+<$06l}Y
z{2>Vq0ZL`2!OoFyEIqHCDVo$WcgWs%Yd)aqpi&&M_7|@j+uwijdpP8q&=i!xeBo#G
zvg0noqnrCbWUm5iUQUgqCaH0&!yy|2(UBwbMwiDZsy1fH%g@7kRCzR9TA?WMVWkeQ
zXcgk6EKLMcpCn>QrYm2qBqB;C`bZphvad2pyx#U<bjJ1P$iwa+pfs|mWu!);l**uF
z#yr_wGt*9}ldK$utJXPNfA-eT*1x*530ngB+KXmch_tTQb>+N=elogt!^!=t57r~c
zkZWd+n%7um!;_niEtovF+8Q1@t+Hv`HN%<wCBIrasOq}z4cdy;N49p<lud}n8ma?>
zf3)w)xp&_%)oTw}Cto){?5@6IwDz&P%w{ehHa1!|w!#-`TARwod#wj~<Wf;;wmOtA
za2H@9;0a*f<M!Yf*eFcJa&sNAK;8}zOkSbWA1fD-MaU;Vz>=F+;B+#fQX}mvWkg+d
zthI?gT{@}0N?nDL`q!<9T9PQHKrCvN3y8OeaXN#pjHelqjYuLKwx!~}eSg-vpL`J2
z-u~+Hp4*o+gfCddWW0U<=M&QN@L}H-sdZznpE(rmd}G<7k6uNS(W}R=s9m`8&?`S0
zGh<tF#mDIO<Ed0hN{{j<F;7j<H$PRS`lBN92M{vn%d#;LFqfOL*~*u?XbT32O4T2)
zukTBeF|ACHA??e4-AF0Jq+J;wl|H<TlWo#YmNqS_yJAgKNIQva`BRqMGNtCaWpf+j
z^xAp1Obpa5`C03QqipXR)iZ_$9Tii4{P6NjYlL4wYZPjv8iCg2C;=1;6d}?cGqemP
zN^=5%Y@3S6uK?8_*IShY1%sA-08NuW=#%>@|7+W@JKIF-rBB&1?PT$|D{I<r93LR9
z&1+t`zNvialDP%pwiR2a4_UpeeN2&dGCuC=+9@|oi2Of#;H<i0Om*w9Y)>xC%v7nG
zbIWAt6`5au)IGrkfHwt+!%RSu*aAT?Fa-!x_37jA4<*tc9+y8n4s)a`2wW!V3|1wr
zLvYv8qv`geB$VVr)z@FsuTa^@_x*53iRw^?-z}hP1SCn6zzYFUa)_d|2q6gol!O6>
zNe=Q~?^KBEMJ$R&9tSdCeB?1L3dA=|XM}kCIG|V%Vfwf=nMfrQo8mI{>?W23h^01p
z<oG6G4IHJ2jnN|{iFcRXuicd1uf2z2cnVsZz7C&AC)wWg0(@6`!oc1uGYqUuj2f@z
zjLv~+%|!EPI*?bMr{>Xlc}0$3xdmCgKpmgtB?BwbT$niGFj)8cO0XsM$%}S2nUG<g
zz$`)fHcMvfG-EmF0Yi};Q0yQZrrLKS4$PW$&+3NJtL_~UQCpJb)9VW&^(~c^)71zw
z?2ey&_x`?FZ4<V?zG3sLw>6}{xO(%nNYT_EPf6W8C0aOXC6V(@zy%lTa$cqmzh1gU
z!mR_6j%FmC6?0S<a}8OpbRkD6hKQMFz?OlSIrxB<GRk_noI%o;<ow^H%q-M?rQNQ5
zf^yIVG()=zy{?tBy%&z5M>GRzWB;dkr5?2ySFHw#idHyKhFQ}NiIDQg3?|^TWU1)W
zZBF`C;*$fe?9P0(k<jH>8C_*XKWA7WT52uy#1>Att|?aGH^6g@BW9R9c?|n6U&1pa
zb?X8~|M@5j=(Ng~flimIGDe1|zz5iJ;1e{<ViFQ!kOYn)H1@=CqT4CRPLLQF(Ci@L
z<&a|aHto}`TXiyip_gtrg#_(9-1}Bwi&KHGDAYtXw<<6?fTPifC`KaAYK00`RCuC<
z@nI)T)TYEz;N~dBPt-;<!vm9GTESHHWfz~MzWRXffab8I!=NTrnMH?q2!d9JJ9D<C
zS}zT(;z|vAP@FNdzOB~hpLoSQrRvJ5_4X+8S}arRBO|H{f$Dn`9lzbS?(u7@(SqSg
z|FEkXe)fOa`x3w=s;%Lf$z<)C?v%8hQ0SVrlZI}9rJ*}?O}bE!C2dj~Xp@?x6c7a}
zs}B(bWK{tb6hRbF1VqGF79YsIxB&_xqM|-!aU=Za-kG#1i{Ja-|M|Z6{;yn`oV%U<
zo^$VHCW*^odxRtoimzToJ?2dLOrz-+*e5xx-zD<--}9Foo|2wiJt0plm+?6lVge=d
znuK#OZ$+q2#dZD@5lTc95&Ulj&_`(-tcyTmURIV^Mhd*WJZVpwjzaf*iG)JYP|<7=
z)lW27M8%3Ak5E3oa#Bu$Yl;J48c{7K#es59fsa_^jXnn4N(ltC2=)<M(UaQPIok(<
zuE1Qr?dTcoq!Gyw1(0KUw+q|b<)j7@lw1?y-iYsdQ${PrN>V9+t%q0%o)k?Vbsi{m
z#^gDVkmpG8Y@vs@zt0ddqSVLV+e5T%8*OtQR>UWyL>)WRCnY{E6g`!?6y%!^@_7@X
zgodcdVxoO|DJ7CPB@@I3Wd+Sgw+?DjNS`Q1!)ho^H1e@6K0e;~(+G{sdk5tpdI~n;
z^onv5zr<YGRgTVpf%rjQ?gquENI@X~G$^E1Qkfit?;?i12mb~;5GDE6<bf4_s{Tm{
z{<S6MofDK%>7}ZrPAw&DUXhZT8ywfax7iw3G*%n-xYapOF!yg1B}$Es)(9!eS^A?$
zGC-wMcvDM4B72W7j2@!x>l?A4F4J0_CKk{n`1ymkzoVzoGYBOyEKB99l*<X_Yf3ng
zmMa%1S1W1mp+#k64<&je5OxwuUmOy`1KtK<y<i&}PB~r|c<@^aIBUR>Q{xJkBe2{c
zHG05}3zM2;{$@-_c{pOGrqR<foVVL2{q3pp^2JA|>?A3*^IWL4G<{TVR8-E0)RGp4
zY}x;A`I6Jq=bl?${`$7W2a6)hrjItw(f2ksA-_a+TL^YT-x-va6)aUU3RaP&n6ID}
ziYWgm#*g)*{P3MLelmY#B!V}R+xFQXX+K<m?m32qiJ!2<%dsz7qEV4c$@T(Av~EgO
z!tkP=1*2LAJCjJ}slpK%jAu}Af;KLAcrI;|c?8!!xxH!0rv;&L5puztTchQXslBtO
z3~d=4B^3&}XEx}5z^Mm{Dq^urAaLDlFC)Al?h*Wh@aVI@EfNVqBQ2A_trWD3F39m4
zw@$~>fWnjb=P3EIR516}B1(H}H`PYluDpBY<`s^z;0vHEB?_`4V2KFS0t!E;11xv|
zWi@vf3<Jc`Ef8D9VyTSY!BYpa98nk55nX7?mkNq*og~jYl|b+4*G@I>SNp)PVu`w}
zXtaThCCP9qYE>**9=jlxVq)3YtXM&8Y<#$qd%7(!2rnnteD&A`Wc|RkVu(nvnLy%!
z0%|gr{1&&N=ba1Q<eY=0phL%feGDv_LM^9Mv5q-Q20wY>*@}{-=NFuYgyFmxI$>x|
zbE!&IW*wv~D#!{Vm!Eg4q6Q_0RX_7l+vHQv4BNO#Q&btFpHe+yPL0Ydgz+E4+i?=M
zV^&tMD2kR!VJ*m_@C7_Mfo}rFi9sr*BpxzIMo5Y6DD84;<n`Eowd>=KOW|m^1|Jef
zLVT(qH~cn_JnM`kIEv(3Kv4kaO)14uA`o8e76VD_#2O9<ZToE=KZpJSbNN_SzC=t*
z#Ze+*fJh{i2x%!Tl)_Gk640<E6MzH(v{Xr}X^NnIVP{5rNu$&rB;f(4E)Ri+lp@6<
znT!^YoFtuFwGmg*Rt@bS$UU>r^Mw$iLz4V5d>@%bw|lqG62gW8eO8ZFSjUI)N+jrB
za=5cE`Gs?>^R1_ypMkt7<UwcZt)b*MPdi`XJ@yyiUI?dx9;0beOPA9Ww{N7y0+$AM
z06&6`#nI^-ykY~<BQ#pt_Iv9UUL!&j;d>BmS*1aNsex1m4XK~>q=_DZULqe4anwW+
zIaWjt5EY521QDr#EGCon9Pdw>{7H>}wm;R&pY(_S-jD_4telef2&6r{g}x$x(9BjZ
zPpL1T#oGOF`nsB&j34wu8vqPk)!<v^fk84;kmirf9!Ww$-1>(Lf(0S8Smy2V7WtI(
zSNb+_(RrJ8{z^WzL*^}$2+2`B6WFvq)WF~72~vJPi2i8XtzEs-k`j8jEbhP-ugXf9
zBq4`LNQoo_;<h+Suz&{7$O7$2$5Uhu1zSe<MK8F^Q#tDb_sM9zU)8vjN@B$V!%InD
za=U=^b$;zE762QC)VAN|Q5$Ym5cpcgcl39#{?Wc$pOui_r=Sm&rp#AT>3#)%RH1<E
zCm?0Y@^I39AI@I@N0HjcBN9a);T;z`ke6dM`r~!pdC8;C9oz2*%a?pOYvxDG^!jCI
zrqB9lnU0K3C>fiXX)ad94H=g;a9pv9(mZi?X_;=>$Ft{rw4}Uz$(bi=rdP+uSI-z;
z^H^nUOx1K(Zpi|T1`xgAJgAT<eH9F37J))R^bYcO*I*C|n|^))vcUU>O>T+ui3aC)
z?rEXU4Z%`!fq7|$TGx-!l-04Shf62dO^=O=e;_HKYyfQ&2l!=H)TdWITBDIk#ZIGO
zuJeYkZ&s8#M<SqI++GE486t^j%1R0j42%l!3kdM@^@|7ykSWP%c{CLrO{kM1xb;I4
za+nqD7ZBm=<Ad&^-WCy&7!b(F65V8I$6*W7x}0|kmo&*q=n<ZHyt<KGaIWPyE`j*D
z9S{h1Vv&(@fA>94WZ+VYHq0>$QuOQ@_<(g-npv~Mx&C>LF4Vgx6CLV5Kffwe6|0H&
zt<Wx`Z9cuyVq!*W5=K`S2GfDYV&|owOKN;2QX%Ecpw<a!ab}DvT|`kA@N+#^>1gn|
zG-6Ixp)Hz>?v<dF^i(PoE4|6F-eiq8=}ky{C5$9XLQ5q68H6$|t(TJfPLMRD3MKkY
zDQ|&y--w=x3R0nnj))FHaeQ-Mj|ilY5Z)W*8C>o~pZ#IL?IP0%w73EGg;8jc^V@wa
zJO0mQDA#vYpac~h)UokHpBhG!#*n6=F$Jki92+pKVEFtfM?c%T#*^R4o#*8hrDfHD
zeX9pmO^vG>6Vb0S)x6^TbtU=4$Wh)A>Rxb?6$i<K`ZKk2b0*Yi)^76DX0dVpVWB;I
z0%8MIOPkcADszJ3_Ur>~#X@}j5%1=svV4xvBrS@vq6g5$G7*V{6(H;1YKNG5c*Ly_
zM&NIe;+`yd)cK0wedh+joGVv;Z$&pGgV)UfOmgr!jhBZ^>LvAvl1T$(a5OEDl2SP>
z5`)G?QRwMYiA4S!oC3rI11AGQ>H&ELV$NoXREFaYayB^BjL<+9fsKCW$rNq<@xI^H
z;0mB5`WO%bCsi(#dii-DAl1$s=WotDGVO@BpEn3&!TWqF<+phdr1tW=eG>itFTDb|
zmcULw20ybAg`U|Ei_m>|0*a*3ms^nmpb~V&lN1voAtmTYxb3b4c-HMAMTqY$sB-D9
z;~@+3U1tHAM@mSF%m%K`QHz{4<hom9oxRZ+3+Hvn!%<!f@b9yId|XWgWy#<)BqpgW
zlBBl_Hi4<)$@ml)opSpJs)hPHDfUVyD{s#vq+3XWg6&aC3c@uUFnmv47l?`P3F1~P
zA>K?NUU6Rvb&zTi9^z(F20T+Fz%$`rDqo%;&UO~E7deJ6^T>_lMk<MF{;!Ilw_p#Q
z3T3)5TDU-XN_a}NL-duzPb!x#mfn)yl5O%h?->VWKPp~>x)$$3D3^Uk`I>x9a=rY7
zUvDTo{C@EJ!QbS6HefiE4}zM52`KAP2{He#E6Y2|u79}v{_j_MD4_K3F5RWO{0mCu
zpHi&drMvtamN`A1@3E`9beHbZUAjwm=`P)+yL6ZC(p|dCKT<*ubeDhIa<sd2m;dPU
z+kbcInbY&3yUN6#)9x$^siIZ!PFO4yUD%6ZFNTYt3=LlhWlw|#m;Mpi5kn$&GAYaw
z=38Z~(x5!rOWG?EN?WfDk%~w|<lf#2C@cCT_o;@m8JFw->JsJozqa&@ioIt^?=Ib?
zyL6ZC(p~-&3x2><FZh~3Pa~j5E<$k&g3<Rsh4L^(NT`)OEFdzeM|qeg0;u^sEF?mx
zw|Q6u@JSvP6UGia5`v|E<zXrDxKPH!Uf#5Dk?W~f(#wA-50iv2;6)y$2s+>r4-1Hh
zfYUro6S9EwJS-$U1FrM1i0}*~d00%SJMc(|5dY_RSW4sudhxKAm<rSZW|9^FEpNqW
z9;S&<MFob1&@NL<;$fNyQdlr7f_9N&J`dA`pJF<O#Yk_8H9QQuQap`e3AB4E-sEAL
z2vMxZuoQ$0r@rN3PX8Rt>7Ro+{c|v<e-7sK&%vDjIao~8D&FH^PX8Rt>7RqWyaU2|
zV;FrSymx#K4}<LS=@|BacE9+MJWLbe@tm$a5pVI6c^LSKZ^p1UwEM;{=V6*q#LvU9
zFUB*OhY=obyg$-m{F^)sI*jLZ5`g(Rz{7|iPR2lJ4~W0O!!*IfpT=+y8n2>w7>&Py
z;T~wbO2NZ0UgeA7p2()E0Xz&gRk0Wj!!}*R!^ozb?IQ4a9S@`Nc^K}EY^rMDVX&#{
zK@7(s{ivq$Fz83+z;FV_vxtWgp4kYNxanWwrhkc>ToO0AJl)27x{ddAvx}$8E-w=d
zkxa0Jnn(kvf@mO2@Lo(<2@Cu=h>1iqZpnt44Pf+YfOaz;kqA9nq7e#1=%8&3%y$rW
zTr<JD2}X~HS0f(j1*HILwa{uJTA`&BW48cZ7o8%&J`r%Y03HVLS^=k-r~|kT;AZHv
zb<kltxMv9sKv5mFG(v@WFaU;T7|Q@%1JFS@>WFdt*uhY1fL7Gof`94NB@@zv8Ozal
zUw-PbE*N4E)N7#!wHmM{y2{AmvGS5Km`V%u)?s;3tsZc<!dx3}X@OBjtT_h%)-Gp{
zLf{E$$BgG$ux|U~`6fKVL^J_Sq-i65Wq6KUV;S6ThZdy2<_-&X%7OYF_}9Q2VI2B>
zZZwjU$R&jV^MpLWh$$kT$6@K}|0(aeb7a3RR0ly%BbFrwMw&72)((xu5>;4B_6|wX
z03-5*P8<mxIEsP0f32^}@L#vT|EKu--G0z%r5x-DtuWRC`apK9hhpYsO2E3e0zYO<
zp#=9dV9zrEZe*J>>`6A<W5(-953ilFBb}*<3?dyYdbh75xm$pjW-KKqZ$0MMf$dj=
zb<E%uIT7oW(}APIuCCFj&5FGXX&>=q!u%QWxMtq|D!kS$m`*e1nwwk4V=?h{1IFEq
zC1`?C4%~-k)nYze_Po<J4t@sbDYm;>>N{jmb<{h3<8JLV<GK;%)DaFm$F&|gx~dL3
zcS^untQG624zHYh>8zEP)Qs0uBVIkOm42u0(VRvM#lZO3F1~ti>>LmOtZv;`g3CW`
z*!Nxj=JNe}N#LS>H(&kTd;m$p$>PBDT(QxHSI<Q3Kj<5YEI48q?kg$hdxkDv#jSiR
z|H?_l!6*hb^N|Q~>$0W`4;qQ$-5>cV7qu<CWje9DR*0F`vJJ;ZGhV|E-nMAJ;>r^B
zcr`a-DP3Cb>eVW283TrmydT^h(e7LcF*vd#Ir|YwP)s-?BO2py+%jRS8lVknXbg;S
z^(FB*N8J%6WBJwF87=G`8gp^;f0Kv)!MM*T?!Zyx!oh@fc=K3j<Lu<}8WYZ$jeNf7
z^zJ{(WiGG0e?CK&E9+P}cDFqOXK&66OgybI*q1H5-Bnm-8=q&m*n=X20qdW$v&)Y;
zziQ?qI7b2H0WPmtIy}WdbmsFr<Jq5Jiw+GLu<TYo=eXjB5x2B}Zn!nlxyNB}R%_&a
zCdS3%{cTI2Jk}+ngSBJbbY#R?rm;($xm(6Rz=xx+8P9W#zISv}-4Wefy1#Qa(i#^}
z-Q{-i)w$d1Ts57U$Ynznj`vnfrM{zXa`O-rw>S&g0b6H=<+!fJyqNe*)zV>0_gKQ&
zFp0Og9j}tc4jx?Vx~nh!<63pk@SK$HnWn2xcWR;)>#^ydw4*B<pxvN_*Nn-H10#M#
zG&}V&7Dm*$<qpRm#A7b<8?gkgjMuMA6gL1yD~>q#-d}V3KUbFN)Q>BZb?V4HrgWWU
z$FYL5RV^>~y>qVNJ{GlgNN>kpYQfmKRm5dp_l*9}dVnjx6%e_&x0J|(S~X-#9d0Rv
zHneZmK~ELba-cN_TB2cup6`#wma4{ir~t-Q;vC1}(7|g7)N62z%Oe<EN3|g^z65Zg
zxw*tpOd}Vt>G4P%#$61pMev@>k3%!Ep`{Y)2+qe*kfT=uv$)ejA)gaDPAj0D>5#B1
zSB03Oi?d>==>UHL->U`Og%}^=AJNala7hQBc|50DtRsXO;mih(if|3JR>FH3jMHQK
zS}ZfiZ3&hk5BfP-axn*paw0D)HxB8iitj--Li`m$>6A)~bx?r0>C|5~yq5vz2!B5G
zR^SX#3Nv%Cq<XBqTwZ5L+9F)*l!~)dHkJly73m-c;9~g8@6eqNzjD0j+^}`kT{Z6S
z9L33}<zKV0=1Osmvqm<qRbX48UKMXk9hT!xs@2#NbMYuGmRR56A$iz)IqqHF$<Ziv
z<AkG!Z0pX8%d43`SR))3SAQjM>AUrUw64WEL44^uDBllr;>%1jt4?Do8ca;F)nau_
zY&J33R$H^xW^kCTmPAI|*vRP2V;UTGMrX2{Y~xMFM8?ajz*K89wKAp6CQAjHQ)HNE
zZE-M-)-mQfrq0?t(T3(Q2tBJ|qR?BKiqRPwn;Vz{gQd<|Hx60{TN^A)L5tCjC{;9=
z?M$OPo_ec|8Dy?)G}jp#8J;4Hu>w)XZf&vEnc%J7(Q2@nm==rCWMdpij=~D2$XsW#
z*iHQzyUE0unrcl(qshoLa;=QfWUsTCn~{K+hSB6Om>cbhTALXN0R;o&uo;Y|CWCDp
zW39ixc3h49xS4~jjYcM>*j#6`BEDj)Og1~Bk(S76@Q4I7qPU_17gk-4&CqJLjA2Ua
z>w!NefzerO%@(G_T-RW2G}u*4nZaQ**O?8B-hidFGwO`=<PI{7y`{Oi(G2p{TP==6
zrpDUBG#Mr`Eg-1_=@d0H4l7e<GZ`Ev6=O8pn?cVi#$Yir%{DXi*1-@Hyc_IHv&q(E
zb~pfC?L@3km)0E60m`?zfO<qhh2F8&JJ@NqS&c1q4i$sE1?H;I92Wr)thE6obK|TP
zh?*^RjV(sxS}yLbmd1%pj5(IGp4)K1@JHNo#zy+Ine0e!$WWaG(DV*m{jmmO%s|Cq
zYC=}EnSrX&+G=UE8jM}FXW&!?P6pywffT&9IGVv(jV2@}8q;8EZ0@R2SU{GE{BUFt
zKnVJ2FxQ%a=R_|r<XZLC#zrf40bbQArq*BwUagi6hjtkurorK8?w6EgvLv>e$C;Z=
zMzbN&Y8#V;YDqA16z}@6U|#GNcElJ$bMLi!uQhg@A5nxxoIq+GYXzB*YE0uzjj;Z(
zy1TAsq{=Sq*~_a88OM&70LTq0Fu|-bHUntJsAB4Eur|Sa>KY8TF(5P2G3W{`3ey>D
zEv!ikQi=h`6PKU;<5D1o40gK}Ru1wHqqVN32`pmZmc6+VG!ug`c9E9R^8w{VEau2)
zLJ@(p!#&3_t!769YIbus74L3{dsla(8T^!^jd0nxkO~y=B0>_Xm?o>yT#w#OSRu_V
zAdtNQFIYfb+kzIa9X0Y^0um;H;C2(lXuyDM&1>nNta1w;NOFsY*ED9LwZYo-2Qs2%
z&|<RyGbW6{XobLwc^PY}bGRI=(~rUVjb^;?`f)C9sI`tax#b0je`v{Ie$XOo?(`JC
z%idrBscKDKR<6NKP#Yp|cYrgY&<l%~Tj+nJ0JPo;av6PTUPZN5m&+9DnKE5zRbftU
z4il}_Lp@r>R2NnhlvY+SFhZv-si<K}^B8SO4Kt*$BuB;M4lUE=>h(;ijwviID=N%|
zw!)I^qRO1Yl6+<m%quB{^iv2c5s+4tGKe6LsxViNkQL|ZvJ0T99aLCUSW%;5@(L?T
z5Y9Y6s%6Twx{AW=$|9|fDXY|#mFjbWd=8*3DJ;p;0hQe1+>(k!pbBkFZWUA*eSx;9
z2$RxQ0{1%1Uv_C(jjk}ipn@qVEy~G-#zDEjnRZZ7E=LN4$}Z9t7OR*XZLu~#7tbmM
zEIK@l=dQXS7dHVtE&R`}C@d{OVq}+=ROq0p0x5MB9aF0d^|>lWt1Hwah2-f<0X<SD
z%qYc3U|vZshXtvc>0%KWg6fs}+)iF{a<xT(OOGbI5AOQOQ3;$h#^Cb?I?1^AIEWU~
z3(hcCy0q5g6OwyN9-ixPZxhTG>=LvKcEjuIUB>=dpHg*SkMF)7-+ev)fBt$r_nB(<
zHTnO{Yx11+y06Q3UzhK`F5i7!{?2ILeQmz$wRx9zy06c7U!U*3KL2laeg2+jT=QLL
zT(k>B5obK!W5J2HOK(2j-`H^uao{-G_1t>}-p3I)VD=SgckiphGu&GW@cVdte(u^+
zhU1J4$1E-~O}sDnT{xg4=uEmlolU3G>2wzT06m1x=rXh7o+tPr=$%wUd)HPJ*P20|
zE;L9vaX}CXQM;?wto#mJz}@|SJDNB{-1{HNzsI1b0gR27F+3b(=ip5E>!r0dSyW8+
zL|dbZ$+wxtshA>z!=kkrYE{hLedszRHv;45o)>_>s6coR;K)UJv29UOku+{*!OWXp
zq?lUO*5_BYt<MdLB-K8wmq<E#W&vpu&=e^o*oQ>2co9jHZD|xquc~7WtjeuLu_k<~
zf*62HDNZa_ywyV+kxW!r^&JRlCVl*oId2zkuD(6Fbb3t872PNwdj28JG3_I31{enC
zytHIho5ygrjlRgX2@b9jP$Wh9YY0LZuPZm7%ZeL&7(W;hkI?cGkRHJ2qiPRUDiT!E
zB7dq<ul8r<s3P%~RU7OLX3H3d)uQ%gy-}msU#v43o2(Y2I-FIY7MXuw=Z6h$pT?+T
z*eKL5@Q-lsH<}o|c?`aiT$ZgxA9t~Ac(7NphV9Fys?(FxHS92`_Jyj3ud<Fmg+IPu
zaG!p*jqK&FDA2#4jr1X)T}HK$B(ZMYf&lhH=#V(itf<f*GndI$lnvReU9@S{ydPub
zl~UV{+H1*z!ykzcUAyIIAEi!DaLo2)PEs%IHTZtqr~T>5N4{?S`&a4fXKg+2-GA1G
z`U$lQ%ht67yf*7xbMW>4%Yv4BW%{1owt3C3shNvvvOdy16z(x_*n5w?le$;&(>H%P
zxBu^lt$XF+Q>fQ`wm<p7iOB5(S~k6qyX5$W%gZ)}O!I$}eWdi!i=#6wuN0k)J-TYz
zvi9jK>Q^4x^R4fbyA5yD_#K~7wd|3U{1;6Y)mIBI%!xbsm*JCRkBoc!+L(_Ht$hFT
z=$(qzZz<YX<(skQ_3YEateO3CWbfV#ek-TGaG_`G+VzjTFQ){sM4oRWYe6F;S%1)F
zSRdMxm5C(a;zFTVAYj8#qc<Hy2fUlzylto8_>5w&(XU3*N8XUl)yvpi)Geno*#R%~
zW7Ay*@wlJCg4h7mFZ2g*NzO>gh)-eJjMOwX0-=edL)f6H0bl+;>fxQeh@oHDO5V7p
zbVMe9`TYjA3Jr*$OW9&}$g09s`7?9*k8SE~jfqVzT8VYmrljU^X4IO*e`;n=0#?9j
z!DYc)M<H)bVAB%V)I=D>4s(f0l5`1M#13}VS!!k`PolN8^<E@Qwm-z}U_BAf{v>&u
zrdZ;R1uH=A_tl*0DGe_kyRzxa9iFQ%Cw^yp^6>1J9{aq{)YQ-C>h#x`%fSQs#1_5t
zZt16W3A^Rqr`}&N`0G)F{ggewncw>3;_KUfI@9#nk`GpvJN5*H365lcM)dgp*wZ7{
z1$xOgWtA@WqB3NUHQm^BI!k&vVv@>N;(y(DsPC`GMVWs+;uV{FW8>vdjtv@p>C3E}
z->rQ)ZOdbIdzyXMG=6*Dd!yg3&)x}q<Hy;JhHXpegPOI)zh!M29q{_u#+=6d$+`5o
z^{=%r_q=fNnQ?~(Z<pqMx})jh#ir@byifNY3S`Ho<V{*A+|YDn(YBwS7oR9jTFK0%
zvW9!#*tUrt^zx4jJ`WkV|6#+?D&M#Fd$Vn#W(ajdxiBX)c<PF{=fUobZqsJ}Dd8iT
zWx4PX+u5(PT44~_)?9C{GdN6)w#Ct4wV53gI|3cNrm?BX5Z=^jDA1*F)f80y9}mR;
zRmi*IZ7iEO=8g7?x1>bTvx_#|tZMi==ffj=hnBwl;`qr;xjRl|Jhk<O@Lzs0wS5tM
z{K;E6D<xl<79LhrPT&2A<YHp{OWOF5ZEJ>D3X8@Ciq9Q8{@$GMhZcSC#*`sjH%UG`
zH2d_p;6?ow9*!FL?U&BJORG<XjVQdaIWF<kjGZ+Df1bZJ{xQej;<xnAyZA$1;hy07
ziuV<}!rCiqt8G7z+1`igdv3(q7Z;CwC1&aer#3%(N$^J9iOm5A_8fRTN>(#P{OxV;
zt5f|_iu~5TqZ@vG?Z=<}#iOA0^o;ydzT5YGwf?)mj7<<eIC|gKxZx`!6{B)5h6aRN
z(+>8~OdUI?`1!H*brTkzVvj72aD_a`ujF~wS0v?=c_9AIcET;*-7^mL=$H^d(VpQl
z*k7XESvH|%J<v=a8ljoV`gE*)AuE8lE)nk3jjDrBezoSo+J<8fESf+3%;DuB?f<V4
z4*V9p7MzujLa7PKn*Xi{cOTpicG@$DX@;J*n4R_%JMGC1<s=H&Y17yTT%;&6NPS;Y
zWkU*+jMh4PQg)d>$!MxKv@|*r8yrn+R>yRT?HiuVgu(U_ZFtdEa}?ejbC-A~LQT(K
zd@^-hf=gt=?uxl!;84f+)upiyw+GFb@K)K0UcXD9+0eG+=f1b&R7)27UHW48k=+Xq
zT<rVuC)3{gG@N+nc#3t?r705^w_c)7{{5Q|KPU<BX;|~#u*lFK=Dt*0o;yZzao~XP
zg+H@1f)8Y5tv%ztMHzeP#ntAyy%rvFEWNxsKl?}hhIf5f^AzXtK1`F<aN@l9)JHZ#
zWu7_yf$~q*W)!@iW@r+h)%Q5K?zG{(-4`Bv#pkneD;A%AB&PheIfKhzSl)PGTg2c{
zZ}ZD%KHc-^vBKu{Z*JUa%dZRmb?xajYiEA@l5ft6x;HnQ=ZN;_&72f6@T<LH%2N;j
zLPaL*)gIg(R&*ff``1@a`MuYW!pALv7uQZ1Km3?};({5^oI3u=1NM}k2E1CaS(iWd
z-Sz&9kI$2Tyll+K<hj44%{caH%Z!z?_6@6^@!syUUQf<lk?_sd?~X*Dcw?mb*B63l
za$WB+_9MlmZ=MrYEtz!limv!ttFUy&z7IYAKJV%v=`pYI7bB}C^omM-=WnmiwX6^O
zV#de$n%XB{cz=Or)I;G}o0gj1@AcInWuM0t38Ozso1>i*7vyusFkn%`Xx;ax^Ovof
zn)O}awARd(pXozF%fiwZuL!T#_(x|1PneZ@xa6;+HeY`rU;pN3mp<_@JP>#0Db?Y$
zVVPNj)GtK%O5Uqlxx4p>+`VIzWX-lVTI#AU+qP}nwz_O~*|u%lwrv|-wrzja@80`-
z=Z<mC9ryPgD<fA-tT`i|j31dJBj#gwnp%q(4VL}vGM(qT8~nzVa2IBJo=p0b8U5?i
zVO-l$vxIaFg~E4(2h#eD7pv;3z1f+Qp(5=O1A?=IQ1XYl>=Prn&7At3oO}Icr>~{L
zk&wnlxG@d#l|?#ZL>9qIPV)3Fk$g2Q7zi~~2ak98`^!F<%Jd&nUHkPS_cF!Ru5zHF
z*NqSH9rM8jxrV%p^|&qr>8)7bW0ts-+5_P66I?LmQtiqVkCN$B+Ar<9^Rmu^`K*;!
z@%hBL(X11Hd+$ry@hr4OuE+Sl1ZQg*Rta7X?rvXQ{9E4z2MmrXWJeYotAo6n%dZxJ
zX%jHZ$`n1lT3U%;ibW;%PBpqct#1SMmZP%^-K$1rQ@5!exX}?GRXdq}Wx%l{(@?lp
zHE#9LYF~7A-Lw|i2k&iiG&uJ8oO+fcgSYPL{ESA|>1rLgJDwQ#E`Pjcn~;Gx=cRdW
zp67W@z?eN#*XcrIXMQ~U$gV=-qG~O`{CKC>TGnx=87}`Me0af}o-7|Zwm&WOt>=CD
zwfwt%-~F)@M>YP@aO$O(<GjX5RAO_bU}78ph}VP0=tNrCbul#ft-=~od!XsZ^p#vS
zA79y;U$Ma2K27lKHi;>NV!`%hzbR%ZqHD{<3FOf%ivnbuCjbXSPqypAyoDjqEe_Z;
zPV<tN{S+PL`CX(Y>BGuNCz)RK-a?`v@3{*(Hy_Vlnk_9cLl&3K?obBYmm$iO-N~tg
zBj|Yy1HydV8;rq8DUz$zVuJy#6z7Bt=W}sI8jmHBvx}U=qeZ$D%Sv!=dP0l5lOxkw
zFeZxiENhB%q)3rKwTayW@y8sa+K3E!tEGU9$QBbSk1uBzgnc|Y{}mk*Go#z3ahtN$
zZS^V(eSZ@;apsZLt&rO7?e?d71@;%lS}3P;LHrjIv`}p)dsP4B=nZ!9MZ!LkV-N>=
zg}?0D@$gvd>6Z?u{#r?Ua|6<!Ifa_Sz=STxO-jgl<KlDAat*hE`d7QrsZLv`wXh;o
z)=#6s)#U(tSz{5Up^m%NQsrDyYe>o<Yb-Yd$4Mh;f#;TclR6ry6&@k}D^7&Rj<%h>
zS?OCXK=#`l6@lhF^Mm+sM$smikqk~<)!Wz){pi>8yIpitPwha${xPlYFo<UB&D((y
z9T=@{Q!O)e`m39ahp}pni1B91tWKMkGCE5^a`y5v+iRH%#q6*9$7bBeh^$y=(^J1E
z)GwPm$TOPukyLGmqIc&yC&~3Xi}J?`?o}S=K2ospv)@?`ROw1gh1Fbo`?S+sUozs3
zH;rA0M1%%77@1!S4g9B~EGG9B*B%FMO-d|Y^ON6oTPm<j+_fq0G=67QspHCW)f3Su
zpS0hWJKD=gZn^Kjt$SgTnt^p-VA*+S$c@C<UOiiQZOIjcC$AmdSA1`Id6-XnwfNK%
zxD7{4*ACObcp3``Bs{|5<Xv@>_<ZRvla;CZQb<|Wprl6gwAs_dq^9k@`J9d0%zwA%
z%2?P8XC4@44LhAfGEsSAVDktp*TlUi!}wAje;(99{cu=#I?bh?{vi0Y-Rtmyraga}
z)vDLPrABk}`j~ESewNhEGV`J>*lh<!-8&CI-s3bWT3s9t`fB?+0()DeU443;oeA_O
zMAiQMbhyfnqR8^1cU%?Z&D5}Q+Xys!Jk+7g0GnP;`@~Ra@qQ1oEku8_{sB9ZQ32zT
zZ~Q<~Zy~Ak)0S@83fS9&m+`I=VD~89{<qia9cilT*<fs=*5l4}B_tMqL<RQEN~7ek
z^qJeKqT8?9+WZ#JXSt@Z%4gn-RA;66QIuhE)42jMRghSk5!(AGJZ3y<Ch}b8pMZWo
za0xkNcz`HK!Ucp1RQECk;)-w}zH*Ub@*ssma$~?tj;o$s%~#!?N10F4R}-D~#)lJE
z6PZ_?*`rLOOkoVq&f;~n47$9?U$#Ck#6DZm*HyKc_3c}oCCGk1uSoYd!4T}+0bo|J
z#6}`N?JBqIZoe!Wc?Ewic@ET@U>9%&KmG=ydFB4zKPBM$ViJ0XE-E=r1)a$HglJ4S
zTF6U@1&>Oq#HC%$Sx$ccYtD$0frUA86I5kK&39S8Nt0cnK_(A)i-VWhF39!}P=47d
z*SYDScR-pRHHx&AjJe7b?91a&x!%R)HN4@Ocopu_cy`E%;p-T@J$1mK3C@YM)h-m)
zjeGZ|waRl*APKp8UPIJ)s_ICNTk;{7+!3kbLk$1ckVuPjblu7=^<3H6UDp}lW`G%;
zkc#$e@esoo?UdC<*fX}uX-8xT<XG4Y6V8F442izcNi-l=deI&nRdP4RXWPBU%#|oA
zqMk9S_XBcpTMtxyj%^AE0UI!-IzX}wj97$GHswi~nu>Yoylvhw9<cH{3D%M}bw;0h
z?)Qcu=pnUG`>||sqd$hE$OhzkFMgk9IQF5M-tX3unOjtC3JLZ-!MzvXfHBKkC}+ra
zrcN`-98VD0`dB>gAZjQas<sOhA^AI+!Oc~x%;i;^+k~HAgk5;BK8Zo{8zkx8ifHr-
z?3mHhbo~?lQll|KP#D|VDe~>;ajKQIN`_TsjOU{9g3LdyWtPu*TLo((Z5KyAn*8#X
z^4x0d_Y6Et&oy36vg6_q1t`kQtC?o;H&kE!)ZInUrX7D;(ZuUFLWQR%WNzGn_MQ8?
z@9*#2d9^(#FB*B7GgjWgsS~w$7#KWbE3Ij*6&`44O;m}zUYPH{Y%#|L6k07&Vtc4Q
zP4P79y<8QsAi(iyBlwJni;0O{K@<^Pmy@@ed`{YZssgco9Cm$Lwtj)9#hhQ_cf|zt
z`=_XChMOfMeEir1^~KhjLlK%G{(X#;n=2AHscknrRI~9gE}fE%jE`mBs71Z!q<ltX
zCA=QHi5FT7b#DJ?DjP2rS@xzzO)GJj*aT@IFJd5#f$haI1h*<>&Mve*jQ^?!sFu<J
z{H$-Wlhi$IP?e{jCzape!}!^xPi<1N-#@;z!-^KQ!MIwLVZV=iS8&y+wK3G^rbGLS
znYWoq8#nolg}zHkdx6ag=Y)Tmm@sHSfTJJ~V>3x-&m#3S0=Aki68BBQ+G%ZAi(a{W
z$KWzQysvI>1DAl=q!CUF!Cy8X08>!j&)_5+Lty>kL~{_&R4ZQxFugJTW8jrGbhtI4
z*~JZb#=KO+msaDVU???D=l8))PCu*Y95X59$Drt}Q?i99WWN!L;eBW^9BfL&k<ESq
zyC>k&^k`mOw7A|eFFlp2S-tl}b~|EG8Sg9FW(NZsR*uI~j<|w0E@YLeGw<Z}XIL#t
zZgp?`#uy;d%s~<~E<zgsiCEnm?7^Lf`RAAr_x`@*8u=cUMW!q7Z9pZqkA##AP8I#1
zoKPpV*xVT7?mTV|r-t9<4%=$!sIx-@D@kSv-H)~KFTz(WA~auxEzt2x<(A^IiE1M3
zMr*LOHRYhFW-3+K-Jrw6#r3c5T<%C3%7we)Y#-OGzL;CcE$}`kc=ZHZS8J2`Ue7)5
zbEI*<>n_&Q{)C7t&BdWy8m+*SYI5a7@FMf1>!?Se0{};mn|8|<<A!chCoBRi0+Wf}
zRtrfOL?5QK;CTXb$#s3=ztJ6$MneXF%FciC&3h5FJx$D{6TohT*tPrOc`W^&`*>i1
z^w?UXQ!PU?<pfKXBPt{lB%<68PWv!ax|!P4qXj<jJUzyrpS;=Xc&m$BbwxeW+Cb7h
z8v4Dgf&+__#Ga|*Lr;qDDX@vgeMGM;*@(t*a<WFY9*L&12o4fiBm1mty6hp7CIi+F
z^g?Am#Hdm;Hm$Nwsuj|D7^q{d0+9;dwQs=|AG%j!sIT9kjFQ1&FIn|G9%SPn$-Ra+
zUw&$o-f&WjHCvx;%0-w)$hN>P1!Q2D)~bO8BopoyNT4KK>X&+iC<$3X>S!<5@BkT_
z{FZ2vLs1nL<S4I0%qD7GIfRJFKagcoLY>#uO)DsmH$(EUzgsiQ(s=NUUg)Hl?Jh%x
zJ&OQrRZC*bz?*Rgs$<|Ii<(rH3uTyVams~2=Wx=}2>R1vF!oqDIhK;?a0q`um9jWF
z?AfVrVO%j@zJ1$%F+Zx+v}LFBGx-&jf=a4Xf{PDhM^7DQ6L4+85z@SB3`cWA+PrON
zM?7-lENotxs^u0Zkt^gT(G9BwJRW;&Xb=HAcs;Aqg?s>A8j;q&dU30zU67l$af<Z9
z$vmve?Qke_`M1lKH-eJtp==g^ZUy(-oH9?4i2%{;!^T}M8+(C>PTx@!K_FV7U{tb5
zj7*KaZ8%ysmt7DEFNAE69Sx)ScpX*2nH@^d{dyIY*wuVlWW`tSE$182!{sI!`G_nw
z$q)Oux|o!Pw+McX1<)!0qw)Sws^=dSm+=!JVtBiD*3VlN^WIQ$bnK(Q9KN7>#$~>M
zQ_hpN;K<8yVP39a0Dh3Y%b1Mt!O8Q1<b3k<ZkG37Hc9}2)*E~LrYrvZgE@ZGwoGZS
zOXUJf^XcQ^UICUN4vqB(Q$!+lF#bUk6Djefo>Y|a!poohmosjnU^ZV;cnje&z@(O+
zwoHRU{m#Q7YOWV+S1<g*RH2ph=Q@wDOQG)`phScemKJzmsLnv1k!|@xXbBc1!ZMZ0
zBc2ouaoNI<b@`~BUtBuO2TeFGMs4AHY7fuKX<E5%_fK3>6};o&qKXpf*lMH13XTQB
zV!e&C=OYCJijK%2>jw`v?gNoUj8o{T=P6W2M`yPS8KV1D^lNAu<jg^W-($(#tdlIq
zjmFxV5`i!}P;=1wh!8s4jpJw<#~pIt+wau&?a1F*I2x_JN<3n2J>ojwsmEZN*5=)U
zNBK|f%0CW{U;3v}U4FHvv7Wib!LKDLEVG5cL7`*&;OGB=Eh*@J;`)%Bae({O=acif
z!}}<F0|t60?P<qOh{G~&knwh~`9YMQfB?}sT|Q2&lcGMkcM=+b>JJF711kl95-ijK
zMqun4VuqLJ8*zlXy!ce97#@`5Am@BnCxIp4m?wr!^z4|DM{rp$u!8|-`__#I=l!|&
z$TB@cY&>gW(OhqTg>gY&iGVTP9H8d}DhiXR=(y6(?C%}+lL0D4NJl}x(cgt|>EgJ5
z0u8YxwTMmFwUc<04d^w;wP8+v2_A(*)X~mwfozfN_`;$UWUTDzK&XXjib!Jpqi&40
z1$LmAv=BpJB<r=dz~>AaDLPF2w>S8S4*~C-^`XWv{C&HA_9T5qChF-0f><?FR+TNK
z)p<-QDpk0ayIM4|!%h`3OM`le&Iq@m&J?wacN8g~#l)N|TDIK=8x-Xs7g1Dm_f)M@
zP=+6F8<kI|0UV|>`f>>0v6NyKz?p~(@hsZ~U2a@vRZE)LU5Q>M)hpO?-I?S@dO?Xm
zpBtLVZ5zAPfsw%<PgC-FYnF)@ED3*G{Cy8f`S>PsHqd;it7J~4(ig@wri~)Tu)sUV
zW<MErES`tO&+BF{;UWrr*m5!vj6eJNVPnn5R$yB<#AQ|G2+3(F1ZiWE*>5GJYW066
z(73(Jz?hc{d3flAwo{oe4+Ym6)2J7Zk=_!}cBAOjjnR5VyHJv2683wcMQ{T+x09K*
zXuXXgEPmp{F=*FgI&IFUWL8wE*JxBvo)~@*r7wxUPVwptI2eg2Ul*oqzrfl$L?4L_
z&7uIG<q7@?#9axSRDX_N2@eq7G-WccCV+qGS1-D6wm(75QaNGWPaqSa3YRwSm0Pyn
zw<t=xZ9DBvvs%AU|3DVdmZRJr0(lpj4WAG^B6GsyH#SV!=zG9*Ip0wGUznpA{=*#o
z57Rj-^FQ3;-z4Fze<{!D>Axw;85#e!ztT5h_+RPYwr`5@Z{G92?c2uqmyDc&;XmzL
z!}R?u3q3s^>pzj0nEn%!={w3_&%ZXd?`YrsZ*S(m-YkEK%$XVgZD(Qq>+`Mo7jgRE
zEUe$u>i^33mpuL7z3<3>WBqOa8X4<9MC&a7me~G1j(_`nM`2_Bo(J1EsrvWm|1~qV
zf6e9F#{OS%8R@?V!c6xM9`xT&{L|IK`v=B<xY1de8UHVK-<iLK|9<@&w}0#YjoY{M
zH|yW_-+lk5{$Gs#qyNV6-~Iot{cHUP$$ugBU*7+D>Oc2?W&7{%bpN3B*Zx=gud)9J
z!@qaB|BlbUy#AjvXZlwD1HwO_>bLN}UH^*xH_QK*_P<5>|EK<+74_fO6Z3yi{eNX~
zXJBPuW&gh{?hM~N?*9Xe`{3epYU|VGo2~QRAH(F0cJ;cSkRS+n;6iqHK=ijT^x*?U
zL_3u}{6B=%aJhcTU(1&*P-s+|makg^QAML5l_3jT?$j%n=M0*J>w1T25Wi)%wqqh#
zd}V)lZCzbeZ#5rXrm{2s{jJ_=GL`DyPs0b$;|C6#P9i%^E0%3zK@E@^+$c@0%1rxW
z>3W9d6UGZP(@4Ec9iO%7y-VW*iq2>MkU?x;P2t7G2q3osqN{XBqd{-7+k)r}+&~6!
z>v;DkO_SH#Y!%n0qMINpEg1Q+uL7FQ%bV&c+j+6>Ku|RmM*pQ5;2=pjTr=(H%!CW%
zdPNwZ>sonoJlU|4`K{&yVL!RbTytr{3ONmMsx`$!l~rfaDW~H>-Uqb}ASg<+uEkT`
z63!Cz`5kkpdq1s-I9iD39Zk3Fr|uY7lS<`d>Ot3*wM9dcSC###Pj9|A4qeE>)wPc7
z^V{Q{z3`0mV4n7YiBGGO<r7f}($3Y|mzV!fZffuvEJNldK{z1zAYd>gd-NrspX#R_
zAZ*s$s~|wf0n`HcFxwGF>sPukjb|X{3)cyN-UvZMFg>@FO5kwjq$(0@NeNGNHWls^
z<O@;6@aSSxDE;Y&sE>7t0zoAB2-GM1fuDd?0p5T$(>O_Y7j(ckAo}F`*!nVg)&%;D
zyACR!U3(C;J%V4z(TC(MU!1Spx78zG(*Q=;Pg^IV*?!Dlh<9J@fLjppUZ&`uw19tB
z1qLjIu3)JpArxZ#kP7gAkUxKc8ERTi!&3J@{pfwUBDDC&@*cy7*CZNDCNTE9=-Ww*
z|G)hIhwipF%(IE!6fCtMgu?X|sNpy)^}qbzb%HJlw<x|qZ<GhN@V)$=F?O@P;ARne
zn)|7yV2s|8S^{vvPz_w!=1}igdo8^Hmg=t*Dt$B_H^^x)hwfj!lzm@6%9KZvVVO^#
z%B|1eJ}5p4ukXB-QG&i2KK#AkyB3@IU3PF?pIN`oyvW6Rzf^K$`K_<6EbxtqPlE_#
z_^<-hQpoDLo`REJyczH3y35{hHM0Jhw^%PM%r79V&}<QxACh9Rdp&|6;~)G2VZ+Z{
zND?^^Bz;V#ffl-y4t>^<R40WVW}yuPS4NMLkRzN(K@U5p$AgU#wu7JVmE}L)B40&b
z#wop5onEne+)=&XoC%d`p7Ny4-DoocWvRQH&YK%FVAQf28*&%YAbC0X$p(9#D&=cp
z_X4$%S;Qo#M<(nHkJz+O&B2I5^{CKaVEZDj6kj)aZ>~3Nu6QlBKi&5}FgICW!kEYZ
zRI9fc-Ylw8YqaQocC_06F=k!R(Pqtex-GD4wLBjzG!RKfLXE_dSEKV#`<%h|2lfZm
zX6nga-by}8<ht~tF_CE^q+}%Ip0q}+M5!M&ojGg3@I4~)?Op7$m5$r}t>Dsq6(<=~
zQ&){nndYv{M2?^mc{ZLeCQXsPkgO;&#lE$hu$N&6OA248K!(krR`w`97E6(hM9f&c
zMCfUfNJ~autn8L2@ms-+aZa1oDW(kju8b&<Nqljv<?Jx9JiW}O1tvdtPIc87jRB1V
zN`xp<j9k5o7@Np~N*+8-nY+#3Zz@87oV}~eoOn%jkVvkj<MdF)Xl&v2l%jYU6N^C&
zKasj?72=r!$%4<wSHclPSh3V}(X8OblG!GBf&Q7#MNlbVRT@ySyt>g<rL&J2?3<+y
zU`n&laKmQIHaEn7jq+^y!P?Po$9&i$6uE!LF|N+h-1AC>R??pz3;1oW!&Z97mhAyl
zNq^D-+$7(^?*T&HUs1x6*i!lCq4A|mU3wl!eD;mP<E{@f?RsfH06C?p4|8-h)_GV)
zK&BC?s`AM-k)fD&<=4TfSnzcNa^`AX!bY>v$TrL@imGMsU>cs(;iizLY`R40c|=+2
zQoed0(XBL}zgz(dIC1IIPFk4yoC?%VlD?tfPlWTrhF$E`>SN0EzFkw5=~qgDtmF8;
zY+wzat9zZdB_>jHCfD7g9x-E5?xLUzaSd>g1b+XNP*cjAth@+=#oG~l|LSms?BAhe
zdO@fD<Rxo|+s7VC-OJ<F*Vyvt#s>W0(v{K4#3>gMJ{h<_DH=i>vBa7TCv>TE4JBot
zNN_dml273nkTScE43HV;%Z?&d#16mmTd6m-88=iJ9gRDcu&pJIn~Iub$v3sv=aX_0
z8phnF#1WAk?aV1L4k~brRZ3XNJ#f63%H*3x8aWyy8iU04kZMojTrikiI=AW-nVgay
z6zU;25cDbXfj!3!UK8FzOd#yZ=&p*H8yWlc1hZL~8lM9Y5zsZb7M9!#C4LxhPfhg8
zGCAd>=*^Ull+;NK`Fz*ekU)B)<3MzP^Ho2J>D`-|4ea0Zm}{|7Y+=WMoO~3|qwPrN
z5zU6AEwz@wkYQ>%f+=RO7KC$Tj*h@#SQ*yn+gXCyzO}(vUFBEGE4huzVPJ>5C$9(Q
zsjlvVUY?`JklOrHBvC-7<nTQ5T8hn}&g*IXVGVCE_xk7cCvihvDZ$Dt?%JudBpStn
z#+bP}F{M}q05ga(VlGGGR;woH&(g{cjoc*7Jdgzrt-2^a$%LV^foCBvM*?(N7;1T*
zO#;$Z*NXYUGXdMnaD;UZOL+BhoSqP1a|0}n8aYi`%&`60TQpZH>iiOT73AJD?h(!)
zz9TgSkqm6ML#9R!2?1;a!y{|Q0yFR_IJ>$wIazFpsTDWqfV)9dp1nQ@<B5tZEJ(Jl
zGy>5dz=vk?A)D@Ej#3j-w6F{5rY*)C8h92=NSx4}X~awN_&kNP&FkYT#S1SgF12&T
zV#k%SrNBe&emgY*$*_%c3aYrW>WJv)eX)KLrcQCoCj#j$a!h)vi&Y$26T$ZrP6l>O
z7B(y*0rB(X)LZPB64f!3YGqzZH=H8Av^=~bC(i{t>N}~X(b|Q$>1_C5DAZLs96%Cw
z-4x*R{)&)U;D<GxMD(P=)^pBgV3|mA_IZaplAeo|BfW6`mtr02+NI1REgqKhbp2n&
zQU$1pkVL23dbUz^LI{P=kI<C%Zzx9(NW=64j1rqOE$KatC7Mg+R5ldhe!e(K#!ZKG
zmlv~^{?RI&(IzVadL|tR=4K-B^qW$qH+qAGYWR!OQ5G>jPU$$=8<`APiw*{)#uj~f
z1oLF^j`M8rny`qOgv}u&q>lwhl$4qj6+k8m*<CztnHjt3iMz7MkKgGnU}T58daTnG
zXY+W|=hqJnrj+#_ah7@FV?|GN-8zA$Y*vC<0tgNx1&y&bt4-e#)q=8#0d?##Iaeq3
zz}lOhV}S>izw6f6tGspa8|J!}(a77j%o^7h5Kl}+LQ4ZK1W;STN)qd>UqchTg|;TO
zk=$FvkwwNYXl|SjAW-N5QU0*aVaw-<rq}m=*i)^7a9}dMqgg1)9`t3am%&!%79vxO
zqtbZrADHtIs1K`+rOuK{FCF^PAur`BunD469iehy*lCNpNYW?L@ddK9nA)LtW37fL
z1q#sc6vb8NCGlI9O|o`bs=Y5X40}$#y=Z*|+8A{*&}(}KXdRDi&_hmWej4$B=JcME
ztNL;ev`<`U3_1ej)z!%!xWy}$9*2GGh2sMK$I>l+MgR?)jx@0pDxtOL`gCn@vdC2A
z13^(aykN*0{1ahUSV**6N}US0E&XnP2&p>ca<siLQM4M5&-hT+Y6}Qu@(&X@gAlB-
z)WCY<Uc&*AfyfY8LUSb^m3lN==3TFWdh_rJBjHPOq1Ac<=MwcJKj9zdwd~p%i#aHq
zw6H^Wp^AI1ivetF$82lY@HeNq$TuE6+b-7Als{|Nb!ph-vPO+|2^i(CiBk~VepUqd
zfHm2n=in*G#FxafyPJ4>_x8WB<NOq5_nZEmwE`F=G|}W|2OK%e_zZQJn>{msgW}dU
zuTSaH@~-eoV=1~GJu>Tj;}+ZnTz(>6WG^)ihbyuk?Sgazl^2vXa0a6Nd%n3>V$08C
zfZ=;8!QTL6@h{9Z(;=T^ui(o{qiY3;on(JY=`9tk^o?_@F>d}{zIoW;NQp@2DxrK{
ze%JfNAyg)QH6|!RCs%|z&_dz2dzm8Pf5s2`)X4Vj2wnSU-?3unUb4tO?2D1EEva!u
zqW%3wHo>rlQ=|O@*nZT4+k)qTmM+J*K?{VW!!;97gqRwogoPk*M?5p^TF^nsi)8qf
z<X4N>q>*uB+^D*YF`iJ~{}>U7#LewizDqv2Gyg>Rh)nAaX~n$jzkdl?13nP31Uo0x
zr_s-fz5w!Us_))Fq->u3DB*-HdI>QKiHFC8XC*K{_DaJvdswlJ>9kP;8$tJtUU)yc
zE`izqfwQU;3QATx!27CSK<BFFwjyivbqH27g7BsFgg1!rWrZ1|ANm!P9;+YfJ?kh|
zJFwLTK%Vq$!b0$E+}9|e3!MvgdcfT#PwR*gJ5X`sHa~G&n0qKGJo7DHu7~W}O||3=
zg~ABHSR()fg}Dv5wycj1D2koQCt45@C>J|6MxY?hr4%RQ6QDt`i;s~Ka2Ka9BM{oJ
z;u%C(<rfGRKQ==!C&lLlKZup{Mu-!O#VXt!2@qf|SE-+}YdWFpcA;{^rF_T}=`_95
zz6XD!=MF<tlw@EKZ?!$NWF5EX1WOzo7ZgRM8oIED<HQ+Qvw@SryLIu0$wS8;;D^}-
zQ2AaanDDT8S^N?B<}sY8KekGWRHW|U|7unLE|8Ml4E}-|HvC;DWjYcEFM;dP5H9Wz
zRT^#W)5Nz>8hSkKJ}U?K@^mSu;q``}42-MpXz5cZ-$gTYQo7&!pkD2`JHxFFtJ19{
z_Av={a?ePfMBm-H5776Aw-0A;#lWA%aSjJ(s**(gB!_R|xAVGtI`DSO`mL8jylYIp
z|3%T}<ko4GapkcSCXd5ydvyE66Lv+oc?Vm1*DpS5Idhs0f04&tI=>SIH+CmVbZjnP
zxx3d(d@#0LLAa^VUzM4buDZzzq-o5h-n22|OfoDtN%Jl`LcppXV_qX{-b>-+HmaiJ
zF0`$Rt(caXrbEJqBL&N?0oFP#;9*^Ia21(Kkw+rF+wc7|`N!?T@Pf?B*QsWy{K53i
zEc1^;rWWZb6-WG3&in}zhqkC=j%4(Z%oyM!2fY?yom78)f~r|;T|%d;VO8`o*)IqQ
z3{^pkBGnj-13t)wiKGPkc~5!$9x&csReriU0cop11+0=#bA$PWJif@hOgmvuFns49
zwJN!1H?xIJJQDg9=8^H8`m4|ZD@*GqhA+Yvmr$U+v94OF`4c=#DhySpoN+}$7%c!%
zJPzFuM)s5UuOlPHS*NP-N)SpMD1vPwt;wBXtJ0q$Q!1(#B-wRTYTfCWlAkKvk&E^Q
z<$k?sWTlEPd<5A9xM@?x6xlHOOnboMM^SfHp?3#*-uq|M4EytE__7D%^JmIDS#(`d
zkDrUm*dK4@T<Qo7<y;r)3cs`ZT}R@Ri*F5swrj@h*%$|05^5>J#{;RXlg9+?dJ5}W
z#G=L&oPCLTd+pp|$1hH$uv&Ze!tPRJNn9sLV@a<)b1Mnp@G=ejsOwiODoAu3G4<o^
zh8^#)&V^_k8$b}Rn%|}CH4kgCrVIFWG!y4`%i}nPCSIcs)hk>hs>^_lm6w;CC*s!1
z2n_b=7rPG2bdR3N3Bz6|9NnjX5f*t2>ng7360ZE(3-8Kr4PlNpu}7|L4f~c4ZccyY
zq`fA*!eA?bP?`+h$(S?ctg`H|cM&N5Vo+(^q1PU!IKts-vjjH4D@pM{4nvP+h9<{V
zm`by;s3n1PS4}mwtH9WEyu@EakS0)B^FW4EbFzAC+%Cz|LH=qW)LpEzNbTD*%lXI#
zY*MDQvsZp&@WC*exyLAk!u8^8(5SG|msAiTJ&PDRH9hC+up?86GET%=?jJdoP|wuP
z)_os-^R^<WHf+ZedU8Tol}_S!k5OD_Zbaf1u-Yx0!QoP?Rhz{b_|X&(U?v+C3&#oP
z<AT1D7T<FI2PAQ3uucf)Dpc!FJjDrMQ3c=kF|^YDvQwyuGTLUF{1y+g;rcteiC|qG
zkUO9W$QF;)%O{(Lk^E(3*N7mBXcff+@hA)^pBk__c>4fSy!3B!JiJH1-C(M#EZn!4
z#`+Ae2yMnRJ|C-Vrc_wjelM4(j6v-+Jyr)mPG#j@{~Cl#e@8TO6aC2xI%rzJvn#^Y
zTAGnyQ92bKAkmm7JGCF~c=2rdxV`3T{n-857Clg{Eh*$RXqQCEgH?g*Hh$g6VcQHX
z7)dd56+BL==UFQT*KFs9UYMM)A|t*i9Z>^3@SkXJh!nag?>}*T5chFDU}9{O;Ost;
zs(h(`0n6&!8+eO?ci3=`_4Es|3Bb$%L~VlN!hxxeD({l<hLhI{jvV-brhsYc!lH{0
zDOS@_vd}w$FxW;Nu$=<K`DVHZ@2Fmb(u12AL=R30us{WYw`9?{1Y&KsO>eDh1z{k{
zNdo7BXrlC2@BDB{d>*NHsMSS)08wwjcfy*}W1HYJ%kIg#il;rM=uokrzT4AB*5&V9
zpKO5c_H`D686}Mu$Ho>zoeyrY;0tDi87@aY%<sAyp4}2LSQnSRCwpT2M15aQ^V%Ra
zK<Oa#dIo<-$|5%YRVwsnlzN}})<i=b^F;Qzeckgt{{5<PvgMR=hoS}13s47+lbl3x
z6xk3*U3@9FeH3{Ar)#2z*ti+Bme{n-T}$D5^)p0A(cQ&+WuL|l%w+J!!$xqM_wt8u
z1zr<MQ6Bet#N5|UoW8`1p!{nNZPINm0yx6jZDM%ZRDSCL3*^>#E{UdCQgPI&TME}?
zj{x5b`&;J08foT*P!n>eyxUoe>lrs(&lsG+Gv!y+SLEcugWdj1v8n_}yKwn3$90kp
zC!4El_IG)nAU7H~7D!mgwE&%;)8Hn|Bp1{!x-Kv2*_pTwpBL80+FPBioO2t<?qCep
zIQme1CM^mHUFg+6T)J8F2d#n)1_Ii~@%=~C?9k1NVyW;XN9`8&tqdVue<gmP*TFX4
zgV})^u_z`}+{tmosmzSJXo;^KD{!rC2(}+v++SrRG1{rn_+t)49$~bwSfe-ltxb@x
zkFRU4x39Bbepi+0@O`>&?Z}3`-g^=G)cC~z1pWlpOS+D`&Kk;Tm8e%iT=WPNEvWrN
z{-^X0=!M9I%!REMqQ(*b3Shh2*G_cCzV%^QE4B?P^?v<L=`nJ{k%;L5i9&-?TVzEs
zYb?zl9knWS!QmKjo1~}ws_bIed3y&a*4TxZlNukG=;$KxqwKzl1m2&;FP<$;sy6!v
z7>Me^)232z_@`Gd@mn_i&j3#d9le=&8lUhk@vJdac4<7l()^Uq$h;~gV6;zoT>aF<
zti3QA(7MkW&43p!2(Azx0@UPqI(GCPcv%4|N&_G0)`5Zd(KZ-QH*U}LW(P-oT5@74
zePGx2>&U=?<;?y}==;JP*|76Wb3YQi<34Z!3wU<|99kloY&4lnj?ZkUu@0cGS)L>l
zKFK_T(nqq9i(*|{fUW%+YnYrEE8tFFw&GiRVmU_kWb>RgN0G0!CsgxQQv-RdNG~tB
zkHN-TL)G^9##^B-^g-Q1s9iRqz5GNW)1uwq?e6uf`I3v0MD(h3@sAw=o|D^(dnZ<=
zcYIfVwQxAWLh*ao&@>^4u7h6nimZq%Ic)uuygs>iTwhBD-#eDq`(^nPdVcxDp{@1m
zL)hvF9?T<RnNbam@;s343oHf3s1~~Ri|?%KC-*rp9j0t|u}xz3HdL|=SWb;Q!bM2r
zUedOkY|p|9B0Vg9Psa*D3e?-SZ6(0uAAn7Rz;~Qwcfy8Qvs$BHnok}J{UQrMZ(jse
z!e92B-3i@^Ul1(YSz8gG#*~-o#WqJZov^2pE)GFJZ-i|q6m4Yeg|i|jz;+lv-^pR1
zG(M4hEi*;fgj+pL4c#2r1$*J)rW3PhGl{%ZIBq=16Y#0M{70|#w9}W9g28cacQ8Xa
zuOq2?mho=DR=Vls<WMo`gTQlwC&$wGJcC6%`(^n<<}T{wRm<p)MehpJW&`%KIlLjp
zrn+?w&uvO8y(|lJ=+Sg`@ACt{sT;#$qbUoO2m(3`>Ck>Z^4Rg>EXbDy&4uoo-5|l(
zO1y)Njgf>MZ4C>$75jjZ5hR5b_HJM6$#h6-sQXW2Z<A4uvn#~#TDs6h)+7I6vo_Ql
z0uv?`c|RvpA2<<reA@iob=-9|0{^Q&@fT^Qww6EqrI7_6^HA6M#3UxZkb%xv>!&Ya
z1P1UXj>k_VkjNXvPMKj_q=q&He`VY`MeZzzeW-fJfKRKwo5F+<<F%i0RvNDX{-HR!
z7|7FL7!t~Z40_Ya`T~}ZaR@>E!LfWj<mo@FnSFA4hbx|e1_afKPl4o|s^p|uk+INk
zm#zu>TV&!KA1$9mZxXnFU-TMci~NEKTfr~{qy7O*Z8c!if)VMz6~?9MS|oGxOR(RJ
zj5G4JFCP_@6|4;6h<pJ25sN=@`<_uB_nKt~AY>-mq5LUv=qn|D?=aAzc{L+2{Ih1<
zypRzM-z0rfY_(^=9B<yRZ(-mR4|kehl#bnxvCMvmy5LGIdq)g|W$sPOxL(48zyvcA
zgQZ}^oR2A0ZS2%7sDrMED|ERc|19#Xl2!wxbLlu|(Ja~MSDMyIg>-r%J*9h^eXdh(
zEwPpSP~SPp5rkXkA;%u{l4IIiWBMQ}_V1ZxrfT%9hQs?1C7K}~V8j$TZpRqpH%yHn
zdVcTFBT~j#BTX%pcJ0Sk5vX~@nQ+q|2cYK(h6{V}vPSF5#asKesZvE`vF)dpDEag<
z#}mdx9A}mnJrlzLgtcEofdX;J4&W*w^_La?(wC^0x=*mln{aHA<b;?>WrhOC1$yL6
zWDN?j#S6}61(vhOk(rTlqt9n6;v+&0gc|VS=x_tNbjRm(#iFh6Rl>%M+5SR?@X^Di
zb$seRsoAEu%4#vGX47z`L&=;y*(Am&C=qp`UOH7Oj0QWkmCM3O%2aHRBR9y-S6+4p
zW2D)?8gJ)7-6F2{cXe2toQ5}#FE4qX??2i{WZS(x4ykY)P&^pCsWtY%nHs8RhoA=f
zs4C`X>Tikk#LOY%0`RNCaSBH^IifC_0^iNsVtI1*yhjnaO+m&^e(2}z*YGb^`EHQn
zog29`In_xb&C=!9MjNG@SJ5o~qAqLAYudLojt~!5tfU<#8foeN!IBghNel-ro2WFx
za*1DP2wfBD1#u<xen{6NupXaeln95pwg)`}hj$~gex7j5T!(iYDn9L?WlTFd&F;4e
zz<l|z%7k$-BA*`yak8v+Fl_GqZvDydIaEu@JsrWj9%4Z@7*ZrW*?Qsw$)mcoCR9&I
zEzQ_J!ICgpgpfVrCNDIWNz<|_GN}(`&1&J;PPMYjQj6S{??5|dI5HN;l?RPo113Dm
zI|VYv3-mJ417t+St<=**cab_{*sSO%p#HmYcAtyXD4#oW%BHxUgT)CX*chhYw16?@
zxxB*st=-t5J#BK>Sdlb-I2wM)^bz8X*}akWSlhvjG{vt-V_ZP7`~dU~+N^o)X?1Jd
zb=s9>xM8b>GO`UiPbQ!Jd`#TA)y|;`D@sl)Dx9%>kj%5fzAkblZ=lwH6kMuCMnF1<
zXKiD~q8-z%sl9b-Cd(1ZlWOhOkhT+@2u{4#vhIScfBr+iE$!9;_5v2Cu2yiP06IHU
z#DEN$H~dQG{>7+xnl+<tHI-YpZi<0Pw^f{Lps(jz@0%3uEHh8?oU~1Nvie3|G#pbo
zxD|)KRVUnkh7Y5vS8Lq5a<z0Zt(0>N*@-%@$f3T?eIBuWSw){xC^1j`z{ur8*i<fG
zBofQ_O(h?jF3jpQ!hwnD{rb=EFjJMy>86a;$DYV$E-|NK&zE+CBO2G;R#B7IY_GPC
zhJ&6m!K+Dimn;9BhWu*pcZUzfHyq6^E-dO=nn#}O&?GdxBR64igC;@ar{x`>z9y-j
zfFBGqCUlz8D$Tugbp02*8^Bt9eD%|oc7}*Gk_2l3aZA$`0kv;;snNV~(=;{7x=dWu
zSwn*vUfLd;+5|WdNOXpe^ip&tY2}j^lOBy1c599@)*6Qdw)}1uhmD8GCl-@C_iLHa
z>Chtat9=&WQ=%#OHQoTV$u*)PjOeRI3`W;c-1qb&MR$hW5~MjU#_qW7HBWVr?$va<
zzkes7NGjRYcoGFRcygGzJI+0x7Fa!%*Y?Zy(7uvaz1C}0bbHUki6$V4)<ws17obh!
zgBcpg8m=(0NnDDfsctuzYl9zsspsU9f@bgzHqDyJH#WAEKBTA$#jvesnu_orMvW*K
z3%U60dXm&H`ZU!W#4es<j1V?j%<|bc_3w<yQI8dBDDivYGnT#c>G6Xb2U2k$8P%EQ
zXe2h31WRF{)zeW+_9mZIpWVKj?wnpkZz{Q{w{5K8XtnEp2Hn$K4Np_?cG$?AQKz@@
zdR(lcwJzTDa2zBhMi=l3C2uX*jaxN~b84qxnpt_+l)R}rV;_IMdphxq^23ttrcMGp
zzYkOW#!b=pjxO&kpQ;cCfx|}^XA&3KLez{4h>4(1Bg(xBWf2!Bm!vz33t&OdbZo=P
z5;C152FDjSjSJurC)!Q<rDRggI>~57&u!*5OZ00<%eX80==`{@r(mbSuXn_Z9wTis
zn4Kqc&t(c~!SJwgUAqkx);hs55xVDY1SlBVBVlTl_$O#<wn+d!`XST}W1@sd9!?dM
zyaG{rqg~bS0G561(hb8x%V><r6GERbr93R7Om$o$nF~YDV;IQ<OF8MQ>hts7ymb>Q
zl7z6UaPye{qagdjrZ|KLxpr$)AJJ*!Mh*J&((0Fq+GfH>gb2i8BLh9~WX^(sy|)KS
zWu3TOe1hl<>$phbqL~DQ_;XtNgEl%`1qAO%k*+;z29OL+=??AwRI>!imBA<)-QD<H
zCOI`VxadNbD=`IA%8Bb$Hq6}g=`9g&x2wBpXUAxw$ms9yPp$Vim!dQFU&rqP4nNNq
z&XB#LY?aHi#OC5y^kosuiUu~CNJE(vca#3au1vDHt%F~J`e`sTG9x4pZcmNatg6Q_
zYq)M+FlhcX#dZOakDj5(<&Z-8OzAhkL{GN;gyUd8+!I+@q_a|<Ps>35==)$P=8kom
zxlcQhE|$qYtbrQanS(-qY-s35v1lWv-q2^x&yJs8S*SBA57eMiVfQB*STQ;nr9T9w
zM;l`yiK&$TMOB5u8rJdL>6s$<pa%m4Gs}-Hj(fO5wE7eqt4qu)Ftr<EH0=DsAgiOZ
zG@Q<yBV4OidYS{sEe-7cs9B7O0XI{T<J@yT2a6bl{-m3+uhc!nwKfO(c*^WL17wpr
zPvx??&A5w0L)d@1ySP_1ZrNzCA3;@jJL;1<4R<k!5}n?;f%)3N^gAe7up1QpM1u?R
z&E%b8&sVtFfxC0(78;e?71i_Oy=t_1!_((DY2#(}_jG&=TG!r8z9vEZIKxSC_j#AK
z%2VrBr;YPI5sf#2h6d=+q!(R?#{#9DTg8AN2DoSjYO4_=efkaC0g~OuiLrk=U)BhW
z#%X=h@f1SpiD9WNqon83m;@IZHoKS5yYyTDhD>-cMhZBzdWv*egPn-7QzFy6HYN4G
zMq_2$k4F`=)Ju(Jj17%ti?+Q~mdb(49iJA+nThM#{)~8e?MB~F=0PcWI#TwbsXTcU
z`gj2Zly+TFZQQrfGb3Kt&U4agEu{-lBD}ovzQ)Xr!KwA#qTb{DFwTup=_8poJyy5L
z&QmsP1%mYPI*fW0lgg>x`tO2(=7NspNo^HUEJ`XQ@$8fmZ_c1@IGMY(b<LfbQD8F6
zx_NzW5f-eRK90~a9&xN`4KyqO;hFqLFajX{F9eH46UHWPRpyE(#>F{7QpGmCMG(77
zLJgmQr;70+<9Yd+*xeqjx`^Rw6+|bUmXU95^Yku#eQmes^=2<!ty)Lb-VaUAZL%Nt
zkFeQZXCAZr?Ic=vEBUOO*GRk@b-F79dIr2Nijs;?sNJyH+QQ}|61V&HYguoHl|jXR
zv%!~JUD#I&j6>s6JKzf+*r#iN<M@m_H-e5GIQ|g!QvGB*4<z=S!}3m=B;~~#QHN@d
z7q8tth7DsPV~KiADHTH|?Us`I!1c+Dl9HrePHu57QQUVSCDKlR1`!3`O~c=2R|{Ef
zO(`v_;T@}ylRik@b%1b{9cewLTI;KwABp4D>tqoa#YrH2W1xWaz|+6UlmUUn@$iZ8
z;{gDXK=3Uk8YmKO$(xjCW#v=|8>SC#7RpLIH7b`XuXg4h3(avGWr^djyr(*y833)`
z9^N{yIzP4TQaHcrv%fxhkJdrTv<3mwfovg+%tKdyX-sEsVq%ui(DRI1eLQK^RYzZT
zavDc@&VQBXKQ}+W2!;sS^IQDNVTMTvyB)Rfce9rWj_Yt;i0u6gnwTDE1nYosO<Jya
zQV5=gA*adIGeKe(iD5K$jGPM!0IHZf-Zv$FNY7XZ3opk?HlCk7WMRpREX}ZbrqbW%
z2YcWLzBd)+x^nTO7Q+92QSB3OKG8{7t7K#+0ot&~2|%7SG!#d!dSUiEpz)e9q(BA_
z{1C$}dteN|U^C4&&Q7|iy<~l{bL8m)-w0qGZBw#n$ZWIz03F6@z`RPlklV=XRBjgZ
zPTw<{w@0amam^G1W5k~72Oq%7UET!ge3WqWEE>Y}l!#nJoG|<`CT^M;Kf^)V3$ZU0
z3D2trNGKw*DHieZ9Hkg2BmR&Xcgk5P2xoF{3uFIbI5^5wDxbDMCe5KF9{#7GvTX~c
z7G6`Q_b#ql4<-mZ>sQ!Yy34gCO}Rk<Qq;k&F`m(JKO#;yW<(EoH?L54)s5i9=gv(|
zw)&6Ws|Cj@ehXb4Cj_$O;RykSxW0_R2|xAfpLF#(Vswy3Oai^==!3$$#!?W8@Cj-a
z;7fS3OxsCOOVQbGug&EuPn#%jw-?O@I-9kY!)1wU&dil7>`oe6>zYwm8*MN6G~_Nh
z2iKgqSk`}VC%C)sNCn{zc{{Jrxqcs1t|d`jbw1qAOmwZnTCcv2z@KzPn<t?f#sAr1
z3?yyfxxPFhxxUb0s4bAmdig=hO*Wcmcfv&9%T#-Z5I>)go<Dhu0nO8I32Z{AHj5Rp
zN+LI%j&w&4&8%>In6B{VA)|#~-x3(HjaRP}>T=q>`BwbQF4#v9)L$sZ5}qt2h1zYf
z6l?!|x`oU(ZzQ4i68%x^H1hFBsr0XM&qjCQQ_v^r&CW~iP5;N>4PM)?rO(<g$!?>L
z5A8R3PF9wc96a^S)gWS~s@oay>oa?h-!5S?XX!DicyC=Gt54!}vU<OQ8o)0a1a*ei
z%n<*W-K2z=8P+T&OL|_IT{p&|6i8~s(3tVNEg&reDGmrpd9o8W0Q|}+0nB?J7Oc`k
zb%8MwuuSB*-x>AM*-4(lUD|F7oB$ka0PO_?n%v_@z6<z1He(A$7NCe+&_WjP#`7$K
z_iT*FsXoyo)nO%d$r<&Bb#c1WOa`2Wz4>**sKUc~t7iyW=ly!tc|;P{q4ImXP*M8j
zu>A|P6(h@6o#0W0dZ2RmJHQ>7!57Ax;CcMYsDZ4lFfW_^=Zv<EH{RA00a10}M;<)!
zp85oE+6NDp{DJr*f|OLpa){FX0_!7oahHw&d!uR>hG>vZr02Pd;6-tkK&4*swv38E
zW}xoTMwg-X?$2uYr=J}?R3Wl1yquBu;i9oR486uaE>JHI<W3Z;y!XY5=Xi12!lo;F
zhMCUm1San}wN)N57ySX28`pY>?H#Erh~ztQ2^4`6RdJ<;5XtiwL=crC#j_S{fs@va
zn&%k=q4pp(s{pXYV~9OYk>oyh$LdBb@9TbNaZ!X>nRNKbGAJ2rq1mv((!K)08+yT}
zL1<#;ZaH#X#(r|7Hng9o-piXEHIy6{{n)5Y!%=&6oH%4DT=w2)75TNv2PRgdj7fvq
z++T83k2an=vvGc--LBVryhiRPo<o2ENFv*4)02Ac^vX>tIrxCO13Z$<_eMgJp@j^`
z(w6fOm|6h)_xFt90vwE$Ntliq7`>YsmCSgFX{HSj5}wqu<W)byvU4jNxbifOV`;ze
zZw_ut;hWJH##MPfn7;m4$j$>NWnei4$NLwoL1)j&R#PCIkJc7Pa37qPp5nd=5+w=<
z4Qr5*MTh!|!^Go4yHlJ%kj*P6eDF945Djy55B)MUgl&!bE>|K<flOi<EGudNfVq!V
z%5OXnpB92s8yX{kK*B<mM=v;4I73Xrlz4Ce#NY_T0#3U^B+NpnucR&>C()$REcgTz
zqrfW7Iyv&g=;ZZRKlFNJUE}LRkankp<bD~0XQ&=a>7j0SZdz&gQVu&`*Yrkyhp1zK
zyI3h5Vt#K7EvZUtMoEwB{<tzgnZq(>^)S3th(zI%X`@?bZ;v7mHS=$J#5;965T=^#
zfc5Ir;UC40=8+;Cg|j~;3ae(H3EL($D!3s{F>6r%#CbjY&FK4)^fnL_O>wxf`oDiy
z4ZQm(I0WzZDD?=>h<}PFB$eJn;jsS&hu5RBz%I6E(Q~m2hIb@0qFs?+41I7eA}e9-
zrJJLgl|1i0|FX$N`av-wx)^P7q{4gIS@jr{O|UV;idXxafZ?755~B=vnO|$kvC*%$
z(kPht%XU2{H*t4$6w?IAC}<sklb2ml!<dZ9X#2z>M~RDYRDGqj2&S?$KL}mk>!gvQ
zAZ~t{H8j@DBBfA*-=KCwULg#)NC{D9un3q9dMo9(GqRuqEiY=hE0vS~`#Gz3i@|9I
zu1#`xRb1!2bnSWn?{{6%D=<zsq=varg%RWL`eDYEfa#K4ja?**=Gjt{G*U|ErH0c?
z*ZdPnwHDwI$3%RTfQO&C-t1;hsJ%}|q>2pTQcT$y>I30hA~%?)-mVPnf+fL^9naEd
zg!2GmtWr->Pt%Juw-t{LWy_du5o^qxk#7&Mm~WBU7(Dd1Ir?7lv*rxw(ju9EGK7Gv
z+eP$>Skdi@HLRRlw6uqLp|~@3-?F1tN>16Z$YKOKf7o$EiujLl_Gv{x1qnObwhg80
zu@%HvOCZO&s?JC^h7(lD$sv%RzMEm2Y0kAYAd$9G+%Af_YaHp^qx)eCK@^>C9dalP
zW5Zt-5FgPiUgJ1Uc(%}R2!HVlr@Fy8;WPeBt%GZChO44JCWq>rY1`$48OjH9#ZVql
zGu|Ign=n}2*}xzf49a~Xv{p?eA5=CDipJk1-&3r(zUduSA?c(~b`ZF*ySER7w<;eG
ztbq?e6J(P|b7oIGk1LUK;MxUbe8!ceNEYomC`ijuYdQS6F6)%-?b;t+Jw1`eYj8YQ
zf47G63$f&rxy$>FyZ-TuEQywv^DQt-@Ha_u@#JY#W$%yXOYD4Q7$^Ikxh!%@XP*XD
zXwNZEfH`Y{(Sg1K&-kE``;%9K52DVF6^(OszC*LB=BbAYJm_1I2fH(c>G>|!{qCBM
zqt}t!*aybpt6#Xp-p81?-)Ez-oLf8BwJt8NA6^n%iKMela3#YY77gpV#yBSH`4sbY
za=*lGLO(1#Mtck}O=fOI682{)q{R&GY&4EQqy?moLDaY|or35&NJqCyF@7dS;-&XX
zM}p%Q$(y5!m(v&By<3n>2wJu*Gz=2m2_HYIgjh)#Qy7t8OxH*%#P{ju__CDeZ%RrU
zO&I1Rhm(wMr4V)NbC6bEN=25ASA^llN*tB?*<_c}7n8f52xX#14Gb@G6?!PY_}Yn)
z-;q-$)+c7nNiG*UD6Fcuoj@c^q>`@=37!~d%^gsu#pC_Cr*R@dKI{d7uhaxiO24E7
zwI8E};Ow${rBAH&hj7COVqrRA6Q$)QNK=sshXyT}C8_OEDQEgW?7dTzZOyVSTDE4{
zwr$(CZQHi(S>~K&+qP}jEZbGL{(o((ecHMA+_fLh(;h7!GNVUCMnvyzjL6X=^Q&8C
zbfoM*VnlrfiL_XOT4!?=x}*=0VRx8kExCC=(sOR^sI@ue$xjOU6l-7RwKv{>eRJDr
zf6Bt!3~Sw7mcb9L{vG~uh?eVd`7>>LzRZjZ<oEO_Wjtg((#c3mJm?)`HYHV}O!8z3
z%V?W1RHH<(YWV|dl4cw`%91CtWCcH_i)t>^Z^d_1YXS<jE;9iBca&h~iOYc(dx_cO
zdl4T#PgtLDV`}pFzMSxgI;>G*C&D5A<n{2e;Ga4P(7nNt8L1ja6vttOio2g#bDxBI
zZsm7`!cr-~C3TFap#V30XDsyF>dTNfhe+Z<D3-GM7srzNQ5D8%G9d-^Q0pfK8A7A6
z`Y5dPk9exgYFjNWduvw1G)<l+7*Ew1oR9ev6e&_%;M;k%pVXd9B<%o(w!&B#D0d6O
zgZZqtD9fhFMy<2gDYA6SR?fdzLyo98miC(@=fvQUVkeH5&rP_BqRwf4N*$Il3Ip;R
zUK6rJ-WOv8--Cq*68%x_l%!oI=KyNmuf%qo%c|`|=p--1@>0rU2VneB11gQq|Bj2E
z;S;yxbANV=ehEt5zwH%niWgZKtHUe!(jD0mww=-)y+A&uI<@~@BM**O3}t{3T>Y`)
zra0(Bc}B(;iJl_4)6#}sm}acqY}%W|PTVp4Q2bo-<NO+IJVc70ati^K8#3vc(9%SB
z$N`;-AqHA{S023&waM_N5g4h;c1_*^e?fAKXm$C8O;c+KKJ(;NTfPQq=~#fz_)Kcg
zn3L?()%W0e#3Lig;$Y>%(NVH<-o!PR<Y6Sp$4vTlLK2uYo8&`W=ns)9lI(4?3RtBa
zfzM2Qg!U+WwQ(d@60MtmqW87priib-JOZj%27*$qYbJO0sEpj#?zW(76~vgQX0@Bu
zMlvlKGim7bpy25>E-Rr$vD%h9l|Xp%_qX|vih<9f`(F~vvNacSTg^saHv3f^+-xXz
zBv&aZi|-p)6Qk7-G6(Bf%Tu{(JfxhwmEhO#@o6J)J$&FiL)UD@zucuL`R2_V#vM#~
z8=fKaD8MIz_|0_csGK6&H)SX{S7dIQfj_H^t+}lsUzt>Tqv7MJPNoI~4#rZ#>G9<a
zt`UiZ$cp<(cJ*H!F~Am16AXppYPnO`EE~D$(!c?q$Pzbc(>8Ok&B*-{Y+L*9Kl!>I
zTw%5sC>5nH)H<PhWOK_zpf@ZYj-#^_nOp*<OyyUrf79ZzmCV)BxzE3L*(j0HX1iVe
z^t#%+WJ_J|te1oyhqUcK<Tw>+HNv5}Q=O!!tX;t(TfDFZ_rTzcn!ow+&Iz>nOhI_S
zwBq{#=X`&+?})ueSC=-EZiyX{adYMH%1|4}eM<L~K<49<+u_>+FS@;WT)68Ye@<P6
zd0F^M^;$NQJM|0k_1aGa57);v747x9403VIHhEG|vPKN<Kq!MeSW<x`GtjgU)ErVX
zU~QJO0izQ^qpQwks>377R=wLo+CxlD^>Qw>0Yk~<m&6Z@Kz0a^cZ<VT3yapn4cMFl
zZ>ni7p8AsNi*xNIWtWRdEJ>(!&56e|t8hbW5!j;S!A4!Ti_p}ywNzMbrO2W9pJ{0c
zbHRdFdgRbL?iWCd74qZ*-L3)M0GVJjpirc~>Ri}}T`y|Ek7~?azj)+(GBX9g^c6~;
zZGLp)>@_P_cM$p=KBYXU9nDvx>wUjhCF(9C|Dt_;R%*f5ju*{Ww;*`OhDAdT9(}my
zI4J=&*6O&}seLc#6I?>?=07uxI=Pc;xY0r0y9^NHJutKj=?BH*2fG5#3x#X?f%{w<
zlE2qm4zT$%S5NreurM9bU(TYrIqEj*76AQIN$0#2fSBABuyKdJ8LC4S@%E>Hz)wQ+
zLqDKU;h^m6<oubQc3^hqYhDxv&$kmcl$6xq5A6i|V&i(n2U?om-S5r)2<-sy*u2Iv
z<<)YvP;&3>`>>Vn(A<VvZIWr!81sWU&X<0IS@s+gl>zY%f&;&m%YxP)S|qwv<?e}v
zR0)X~D;7slpxGxZ-XuOGDpHA%+pf1>di&b$cJu^~`(4n+{`>)(27Z%@DyDM7GTS2^
zwC#1XQ*W)i+Lr-m+jBFFT?$eUNU5zxi0uSpN3(}cK%@CCTC^r%?YoQ3n=4`hhxO!1
zmnZ4-#0ed+&|gzB{hh+3kco}}K20o&<f+HiV~pr+gC4epM;;v5Cy3hxeS2n4K@_F`
zxB^<Gcnf@QGPGjlbD8r?-d#XMWznYoCaZRnq8`n4qq)M>>21Qpy`z<bhrN8SEgbsk
z{<|(=xywRS#T`ce{J7>8L4E@6m7B`YIX%!3z5>lE2sgO1W+_~NLhX?Po(j@kgXEYK
z{ETHz1+N)h;bap~SKaX^1q?!kCq6dj&NdDNu#-ieQQ?oc8u9LkQgG{)v1m6-DA}&*
zj(7R3qld+1y7YFAN5m@g>Z)*;tBMxeZQ0v&$D=G2l%yp$xu3ym`|Xr{wTqo9NKJs1
ze(>B)0F^2b?|cG|$jRl%AgZ3)p(la<5p?h!(wkBxt3ad^qAed&0V}A9z7!@%IsQss
zM+3A&j|8-jq&qplE$i}>>UHHmu;$9dXKb6n=Ic}h;>6G<z%q&r6Vr~T^TO{&<c@bq
z213>;LvP>iEeg_Tum@%V0Z-Z8BBLZLfu2AJj=v9bX*OKUw1|4-Z8&Iy<yofV*;0zn
zOi96nD>in&tbz%>ttWCrlp%jrHr*XNVd?dm9ZM{@`RT?pe;^Kp;+{QKv~oE)8gLtl
zOt<Lo23ShnGYUC-eIFiBr-&!*su74Ez&j+{sOXdX%D~cViSS8G8BBU~Y*T;`Ykbs>
zOtVwG;~Yg|%&!-a1z@CK1REdG>6J^!LDRukZDynL;8Jfpv^<bwmy6Qt)nwgnLNz7d
zb}`yB9l>K&Djn%Vx1^~Me06@YDO$K8B9uFVU3W7Agvid4*gh6*HR+xLxBSOG2x>aa
zuP6b*qhUG#CzjuF!EQkGw_2f_mjWM|w(G9^Pv<o~viBW9x>^lbViuA(WHc3osD`v!
zjf*1dkI{#HN-tT_<)7MWx~oovubo_PkJ}E%ICVFR&E1Y?O=@=4Rb3y8HqBj*UJr+n
z%Q=3#jcR_6?^X+Qv)djT#8jYXiJ!qoB8}KcG1*;+zvRtdThI5^!Lxu#LD=sl(U%gR
zU(X7Vl4M2TKMESMvNRRbZq&Cbl}^Hye(d=Ix0vOF6Uxlb-GIH>a;@6*^RRB1_py+6
zjQcNRT{ENPQ1(+~XlRViZbEU()+z`DLe=nU+B`~SJnJ73WtcIwvSc(HCvs#+F^x86
zuv0lnla3A?l?^gt8Cfe78D~yrbS!(Inx{%k;e_eXkl>8apBwrwx+sU!K`b!M3qFJO
z3q6$4ej-5t&`43W>=keeR?35-p+Z(N+m8V}WsOUDvw*WT*!{GYq5NI>Tv(~c(fW3M
zJ0wI?tZ~=8xa2H1EuV-)!h^+@j4E4MHX@xBpX)uU*ls4_yVdb9U(&Q74R4x^W~xPQ
zGc=6Zf<9A%y%!O)KD>KT{JezN@f+cavS-|>u(gO3O>T5>vNs36l>oMR#eN|gYr?$?
zLoMTmmS?NDUk2NjUkBg#v(8+!=W2R<Bwp;NtdWQ=GPmEoDN&A%wxKnf<B+@w?$r3W
zz#(8Y-|EQm9eak{5e>%{AwpdeYV{C)qLe(mlDq#UV1OlmD`&~;{Us-MK`mt?j7x-Z
zl5b5~QZOTvZ-6jGxqK>Vk3$`0^1(KtjMCii=3LvVFN0~A3k$dHSMjrKtGeG=M3>@i
zE`4oli49{OptGB)N=F=<Pt+|pZx{PmnAbe7BYO`jG4M9&>`f0nhRaX3(>e1DCf;|^
z*fFoVY8smdi7)%!T+Lvq8(qZR?iN=Mv+dRSnd%7Z<XB|TLEcz1$>NE+<tYY5zP0N$
zoBWyj`-Sn1C)djMhLi|TNSYy-!C%O!rw}tGh}<VHWAr=dZSoMGcYPk8-UbBo0m?U%
zjs6rU<3l`dmNh}#zROOvIJfGa)<j#M_-^Zm<Q7;RNjDu5C5grTRn}%jo}E^uQN}k$
z?uwQ@?ac1Qrk$bE^E;=1G+m-He=F{dVpk2aQ%K;lWHsuh&6hvmhY6uA67IQhHgIMM
zl{&4k7amh{gl%X)N))P*+$MzhN5_TeAN9=EO@bK_-Zk}eBW)!LmQz?{L{x_}!bdxK
zzD?=#KHA~sMCjCgEcnW;Uy8+E_&VPZ`5aXC`o6yusGooTD_Y(0nrJXxrS^o!?U0r7
zzKbsJiLug=_m1)SIUFz&S+F2c-dy5{QQpD^S}oJt>B~JCGH<PJTfZt^5BhZLP7~K2
z?Ou%=w<x!qG-}B$x0*)`i=8vR%YdHc528<$<d1m@co6>DsBXpR)rqjqRjC`aajJ4k
z5&{q3p(rk6ns1*l0rS4tH%4FTW^g`;i%i~7p9F2#2ib(o#pdh%=XROTkLM9pnC)a3
zk4p}PyZg*T+hzW#yW3{7FfLwddZ^3$;kZ_>7xQUXx8rG4>D|t-JFb&w<*k0*mg{My
zi%Zuem8B`89mIP%Z%q8wMsnlVMiKGi7*+}~bRgXI87D2JQn|6X1V=fb(YQiJVFz@W
zdvEwGNl>98KJ8+3+5RzYCyLk7M-dR24?X&ktP{8&A3IAE_;Gw1J%^~@?+UP%co6NH
z`tBM};<?rMu;c#j^C9axO6X<NT@{NKR#-Z@-Wc7Eu^_E%_jXvf)oVCtU9(=S7as1_
zyzNRdo=hFgxu_dWi0t@YOqX_v<mr=fd<=|V-hK=EBQdYa>c#a|2@|`pU`k1yq#nQ2
zaLq>j3t__cT#IONrM#iF{?tqur$AP#CZi7PwaS~4){x9@I#60i_}si|)k=Z0cE6p<
z<-BfcRkL2MeZN3#FEzL$ira3u7#3q+hOO26Vh){U^3rt~A@%kC;=<c{8qYwoE4Mq@
z>@t5i>Mg>vTX#K}jmg3T9sP-BrN6ZnF(<EAR5hS?NH*WuG7;rQvX9N>x?Z~$+g{N!
z8i|&99g{4q1=}*_9|hyy2G(459dj+$Bq_?>M*hdiuv96*0Ps{Mf^q^i4b35YT2#;f
z`WML)F4`))PakvFJL(wB_MlRNjLhgrkwE<_v0Tyc+}`J6sbPADGa$H8u8!|F2v=cm
z8ZJAom&ba3KNcGfPoNMBjrv_wb1lzfTy2gzbJM4D_bw84->(Iyx69{YweIF82WDpC
z4^s>U{sY0g{hnajcs|&6qq7-8Rl-kK=!Tlwj%O~g_~v?Vf(5hDX~C`CWKozAyx@+c
zMJ1{PSR%^!anZZQdwAe+yugmPW@frB5wMG-_=WAkHLn#tS`P$*MTVENGKfZDQX9IS
zGbnr2%XDo<J`}a$Jqlax!(R>wqxcJ!9kMa?#zw}(6z5_XpVY<+uMgzYXOJM$P}M(~
z;;C_jH&TwLgO);i6$K|AF8K-M`gi(%R6wMsqcI_1p?}uUP(L$<J<CAvKh&LauxOz<
z<CV$yyhe}J+fP20Gr76YN~@Z%KAvZ+F8Q6^m*#(lUIIfawVrm$i9(}o+$=<n<Bd#r
zq3F^RY_hfbObskyXe6|My8pQ)ihvzRXVmaTeKZOo$IM{p21rAdroe}0a<BnlTelB_
zte)Amy>O+mnAvlQ)CE;Iu(`f`ObpRIk0CS;e=Oc@Xej6?!y{%sQ7Ky-Id1fSEs#{e
z-xn}5F!v9Yj=;s6w-3(|jfobNl4@<q0tZs>4A!b|x3JU6Ah~*jm4a_fkz28Mc6mZ#
z)6<&s@SZQQK$&E6ax!Cy%UxmRBD0&ad|CCO0bgE3JJRvGXbZ#4jNNAJd~Hf=V&;9j
z_0mf9{g5s*6?!<Y$~f)Cz)?}MYpV@c1Uw+Yc)kLMR7c8C#Tqi%wJ;d_Bn7vv-AMuK
zQ0Nwu(|m9zQztu!eLUQM_wF(d!JB^Chv9n!reG}a#l7RuO;L>k(#^`oJOx1vrg(G-
zT*MRz=C=sxNTw|E_u`gU))d|-z6O8uB&*C;NxhgqzOpOhThQfo*x&!%E@u}RtcknX
zP}Ty}ms(*FgM!3$9C~}yu+3U*HG2LFGT=P|X_;?f<@tH(bg?~0XZv{%3(rQEx*BE@
ztt_6U(C5#BOf(d@KUBN16~CvAxizs#G@P8`P6WvKoO|K6c(iC34NmzxWx?D*8FLf%
zii)w{cvT<bH4_^;c}$p1Irue0b|7%AoMG+|p&4D{Mk9M^&F<wPa2-qSb|3DYp3&7d
zmad1M)q237z>N%o2ZXAj(32P{PR$-JRab9hsYdOhwQQP$Du!9=gXNRes3whW{veAc
zH%3Q`ZZ55w`XN-OcQf|W$PN0wGSO+OP1+9(yX5iYY?_}4VS*X43%)E0PO6?S@jjBY
z{gkjO>fvKk(3d$PWv1YF$=seLYB@rFR=;U;T!!uhH$n+!Phi(CV!yK_dv#pSl5n0j
zGFNF=7*{+~tHen2PPlW-ok#7<BRCz~_)3fhG@83N)#Rink+m-vdRKp#Eriw~%hD%q
z3vM13#$p2at8X;=II*NX<$$XvtMDPyMoy<>$ws~~LI<GV6i|U-#%6>rLB}!$bBh8L
zUabuAV}}mq*2RVk*7aQW{PZt?tJAcfgg)Mf_knmhBsrn|IEP|*TP>FM47km|?e?oy
z_FEc^C1WXu#+RN;E87juK3p|=1-O5g7(8lQDkz2Wl5vfqsh8sg9m8Mn|4Bk0Z78d2
zNnff0D(RBq-A_JY3I<b3xCJQ~>;0p>1M6DXHeJjar;O&W;1*ApcL`l4Rq7&8sebJ5
z+nhA5Kji5BF;i(R%4%L)le3*<bHG!EGi{k~mbkzK|L3tT2c<>!+#<T#1v7em$WwI>
zD<tQp(Nt)9vce2xI*9XvBJjs_TivUDhcA#Weynv=T(+1lsn;P|u*WE)s?~1M8vTN;
zY2%Dq1<Rn7Ql2Z>Z_wAs#!UB0U`xBSTqJXWBZvjE2%qL!%{<V1&}bOp0dq``+=g1t
z9@VlW376Fy6u>6=;hGc&pr-RuVQKIgw(T7LQ>78^+mH(3t@3BRF+WwSh2M>~o0oP{
zuZL=+MmfpP;Gd3+onM=y9f#fYonKEG)E=ouOTT8BGezgWW*YAKNNmt$3QxNY+QvO6
zUi-{3WeP2rV@m$M{_lfs(p6nNnJ=69YSDS~S1(!DwDhBOIrfHF?_q)A?yqi=W94Ja
z<M`j(4=G-aZ*dYY5_F!XUa0P=o~oXl+-E#m+*~|9Ilwvnf|8$Ax&2!UKP(PaPL~yL
zWzc25iY;R8YO5EUdz;6bx1aK^lCL_hT(-NPyRHkn>y7Wp-kjd|zo-4%u8(P%bDiwk
z13UyDwXDxDd|TDNS~o2gYB7UAn1gr-Cv(JBjbIZ<n}ZRF925i420yXjlrn-X(U9BG
zG{Oqe_Cu&2OIXIYyr)mtwOfv95Jtq!TXcOO6qVhm25`mdU;hvmH|r$sgDPr}=;kUH
z2EAdWJZO054_-8{lcDJIB&nVBPpFM9{HhH!Lk&6vxXT%YQLVXU1*6h<0R#Ju-jVga
zp`|R%o3alG7F0bi9{`#eSzkjCD<BkzdPd$q#XRFU58^M{H=%+}ohJ>WLPEVewTdRp
ztcEs!gMSyBFUX^<Dcc)N3xM@lnSLd6W^2eES>H>rHR|DK{|+&08ojBbAeJ6b_Rt#i
z{e-DKRZz77ePJo3!N;%QR%ggVPRO({f;<Z0mok79AZGYV;h1Y4q{c$lXXkv}g<eTg
z!73#E*NU)P(<MT9OVT`5abIVi<Vycf9r7{9nCnf5%H_WLfW`%(OsG~9`L*yRfl+H#
zPpk>5NhV;?tAN^e18lzNbvPr|!b1Q$@D|`docnjr`@g5f4E!1QS{0MWm*nAzocXo*
zCbZ!1W*AZnRSefp<Fz1*8=A0|&|~(6Pru82%s-|cm>D#mXB*@7z*rg@auf`SWBb@I
z2uplfrPvl|&7SA9k)k`{UR~=FZOs>Z$dAJuBk<<+Ws*53EA+4mFW8s56B^Bhe;de3
zo`OBzM^PoNy=r(MOOk(Z?qOpO1uePtQfB6xA(znyG$PX{)|8o96(G*^m=;;-4%S$`
zW)&Ter5cG%NSc^~&J?Ch?u%*6bY&RK;FfA3EYfR=>^~>>wJ76;ie*Aly?s#~V?JV}
zJanUJbZf$ewlYUZ^l_T6gWczOTL&&s?>WO3`fyd0W=zsYEmj00N=}mRPhyiw{H!R+
zIGxdUhufF6Q1u_1LEkx9M3lX`3~h9#rv~-TTJda0|3YQNe9K^cq|bz6K8d_jv6LBK
z%WzisGta_O_rOx)ibMG2l#$b1>%qD5E2L|DPyut$n+K~s$e;#O)1{Vk%?d*+Y+#zo
zW1i#@fw=hYS7He}T<gtyjkUJSPq<V&f8KF|T&gk1MKOb|jc0B(LwtK{oWgm>5{_-r
zdKbcyN$FC}j7RZIUy=)pTvad=fwvt9jgQCK5SI7K2AKsA30|&@bE#X>(a&ZB{7K)3
zwjq)cfI;D&;;2co)gjpu9v*4mlWk=SBh<oAE(R3iYTsaG$joh8UeF4V^tC*=S;W~z
z$&KWpe46skHz!6)^$XSnrv5@H%sFbs)}+-T(~^E2LOY8JLXQa2lF3RKsfaP6V$L*&
zlAK=H0&H!Y*{X$LYSUGU93uq?MWXr<=U9~qTNcKuB_WLB87Omt#gGApG;ITtpn^v@
zttj(s!#T@OyZ0GAi>Hqqs-bj(m|eM8DJSS;Nbkg=l7yED)mc3lsi)jKlSF0TX1>Dg
zETrXZb3tSLYpwV|QHLrN>C|=Mu4Be*E)hPW6<kY<KNugNyi$(TBPwC$y&)K7cnI&3
zTH1<#W^4Gm=W;&Ct_fxHJwv_PPn?L~W_u*t0$2lMVTRd#j}|pWGDZ`Zo;N#07UE*A
zPkJu&fnqSz(=2cd%&LZNrlW@|?3m8T8b4jnQoTZ4lFa=~>?B!wb4d4SDy`VpFqbk7
z<cM$R(^lVHS*4V?x6|<n7HlE0Bm2zzaaGkH7@JZ>XO&!rJu=LtKyX8aW5qI38-|wg
zPaniJUeC%iPj*46k$5J>GjK}=I7NpS{fO{L?Dz|NSrZszAgRpt<v5S&bu{w^Z$Y+a
zAI#2c^<S{i!4ikDxcVZO`@=6L_Po8kX$Jff(=bZn4n{xazAM4tRNh#5vFc2rX`D5M
z>#}QqkLc6)nGY!{!!V%_O)xVFhD4K-P%2Ayic4mxUKA;oK}b+iP*PqLcXl0TV<_5P
zy}ea^eZA>iUA<LhnohOm9&Y7$dE~rhS9P74n@hiZ@7#+)No<5l8T&ZP9)FHPL4lE^
z6Ru2-GE8$%Aijf}Jo&0drH|j<U@&e-i*x{$2N+3ZZU_Ndi}#^|y?+KC0c~jWM+K%2
zq`hF{SEB@&6uQ*U#>?C%W;fqi#_DmJK|hWRkVg+N4ZPU`Kx0gc&WM@A2ex*0CWaln
z0M+E_O`*mKo3IC?@dXgQ+?*jN&p(DWm5_x-wvbJ>{{y|2F97Lx5E-b5y)yyL&+Nag
zvnB=9LP5?O>=)cc<$1+3Qu;et3Go*Z2vguZhW$Z=p!jdRmRElV=VV^U6pg-0Hp(EX
zle7^*Q4W6cEJ>4;XrcRkx(k6Nh}~%<?=C43L6@Ca$2%RrshvN->|)pL0lIq3Lh`-R
zXv&J5zj*4D&B7aU$nSM1M<qE5=kPT}oOggOJA0OG<lUI>)%u`qAe!)8(EK0$@JJHk
zh(BCJS;RyLMlIP3<LTN|#j9(bX)MaH`IKP!X%eDi3G)%>!D$9i8g(I`fR`xp1#tQz
zf^qAbggh{6EKlsbg#%6Uw&mL9{Fa~#To8XD1=GFaA9nXyYl3C;3uBG2C4zd~v~Vi`
z<$`kDfq3Wm6BypLlpsX(2biCSizgry)CN=c3Zl*<E2_Ghg+Qr0Af!z1y%IWcV?cJO
z{}|X6sp-$7$K@07p9ny>uK{I-Y+XSYVg?JtgKTALG3cl<pZU`pbs%_ID@`Cc%9cnI
z%r0~jd<PPb#Fg9+XJn60AwnMt9xf=TTY(=9O1oRY9_kM|=$JtbNGy1e_bixmPLHZK
zk_iT>K^=~QG-k-CAstR+5t2@AknTSz1|n=huR#cwMZ`=3x#EwI*W)h?1eS!-k0316
zaoxQ%#lNpSDve1+nA(rPAF6v!4+w8yQ^zNTFFdl6-iLaEPq!@~UACtU63x!a22>Cl
z27HN+6g~uP;zVHQs!kl&kANw}3zOiFGi1u|3`SXqK)AM=39slMT<gsn23rJu>>yGy
zgRM)A+%N8L7?4dUTqw>&yeD7)geo+^7@BTPfM>pnZfKu@pw|P|X`l}Qt&JfVK8-j~
zBSPp;%M7@9<Ub{x2VAC0Z;uo!PcK?kHU+-A3++C!LZ848yaJVO6+MB04*(Js1(FE6
zOAUu$;?&PxD?LyH)^hB?0U1x=RcCw=3ImS*8LKs_Nc4(a6CA!3Z}KV&kii>nh4~9m
zh&7MUa1h^$QXlx2v@Uj9zY)FP0<KU2rM@9QHZDG>uaSti{x4}%LQ+Q3#KGOKGKC%9
z4UkiNV*vocyj*cta;$6XUlq~ls_b$7yZNN}Dvb=tV+cUH9vcEqxauJN;tAn=ixbg<
zEHOr6^)J+<Sq5WB!s!I@80Z+(EbpWG9t!mcgwX`%z8B@)w>74x5OT)C&;>{2xbct*
zuvP?7z#Z@^PjOw*Ww+pVs;Z9ysAA!oeDPRi{-CBJ5a|3&(njRk0NhCGa_YoF2!O_-
z&M?lPs>sq|?)g1Lt>KF3=FAv1^<Q1c5JE8d@!;IxweX~L)U03?5JG$R)^Ib?1c5i;
z76fg~*$vX79ENrA?)VyEda*%!D+S_39^tJRQj@T|zIj>wV6K$;F*}IjK$Lm@oIeQj
z8Ib#p>iQF?pj9CFP$7t;g>g{x;9^9w)xw!2D?!A~!9dXJ_=CaF;~P^gPtz(Sv2o**
z*<+OF$&nlqI?}rnemDfF1ESIQ3;v+@z&|hO5U%YL1_pcFj19P9S}og`Nl9lXN^Xv4
zA*S+w4r_rKgWofLze**%Kon*4SI)1%<4$7`mIoBnB?lS`2GQN=KotAp0>%G4k_|mk
zR@(+ZjDb-&iHDE*Bj-TS&JRY+=?@#74-i?+wK$CiL}X5Ex6OgDh(hQw9bl^1p%YDT
zYmJAQAsbf49KLtBFF_E*8rfW5nhQO+4(kYX*HMoDBJWQIRMe#mK|n4dLNNcsSl2kS
zo(n)i7FjOx3fpKTW$!Fz#~D;yA|5{CNXp^yj(8Ryk1rEGMHcKp2SC#<vY852p1>=i
z(?<qSWd<za0}x<#Qz9pa{QJa*ot)6yW9f$sp~9BdH^EC~IW$IdXF}j9aoHSWNDBPq
zda{T;6PVWc4sly!XH*pZ{QUl+4bYaPSyKKukWbDx#j>q%^eJUKJxqX@fMn+F-f}=>
z&Q)~6S#oD#f+}W8{<t!viM#^w9&Woj5a<HSlQ0xOA%vAU+0Usc=!^qM``Lt1A@Hm~
zA6R%EB7$QST2EBSL1O)s-UuS7p17I$IetDJ&n|-kGelBfq)&iP<}N7jxP7^ur}t~(
zlkJE5Q@6^Gf3)1{MyZ^%&#G^<vx;UZ)hoBjkN;@L=AZ4CpGKQL{9N5uSN~{V28~Yy
zWa?m<L|{8!ClRR+{g7!7{TQe`gNXMK!hO<#SiQLKYXP$`2;U3+Hey{7!zZJtYjkE`
z!ecGKS5)*sq1R7#u)9fsdfdA+KK0W@1dpt&(OpsTtH$6&FYs#|Im7|`7=mLEz+rSp
zq?&>l0=dv|DMWg~n0&q`f)tKX2xLCgV!*CfuU-`;L0La8>Lt3w6gEjWIlt2Alzx=R
zhvk40YHU;~Y&fd%v9O|cBMo;TII5rkv=K__gh9j_LoDo6d2}!){e1!&qsWKU*j{Nd
zK4s>WTX_+}(N(~go`GBh#JnG!PV0;zs-<`A?n6^b)EKdTVqvN^sz&PXbyCaQnDo$t
zkpea8Gq6!NX;3%p)NrK+0lg#A`ICm*4$P63{Cd{8fq5yC{wg5eq(4v$JTyA+1XO{K
z#mFpsEFtKng$Vo!qK%!1gt=)IP*epl519|lsZC+x#)<#*8pWf6#{AeGKNHH~MS$Iv
z5a-i1I-l_+g)XQknprE4V}kVP0Pgo(8#_X+A+eTeU}<jj2L?2U<cFq!i29^?mw3DJ
zn}v_6-4COLI>DnGf*baC1<gi}8a!XARYkhQWSJKqb(kwtgL{F>@-f9FK1@Y3?aZt=
zBBMKj_c4mKGf!)OKZ-i>rl0-ZHz0`87D`oaP`0(e*S$q|!*Fa;eNlB7#-&n1vyFuN
zox0Q$sXBe#a(Q1g&)+t(QgD^IV)LlK&#n=;#Ts$h1mx^R_tclCEU_5r@@cpn?~Q}$
z#$@U;+J0T{uc~R1V&~#&=?ZSmbd-{HYm<o-0?8JUx`eUYyLq`vcWI_4`uVONT?dXY
z`uti|i6(Md$eMq@L#o_ps&5(U<j}zoT~-C9$=E>m`6DCzb{N=}6Q5+X_i7JP()~;4
z2JrZdG4@nR2YISvp7#h*c2psfYBNQ>G_)TWZIyH=6ewmQjK!bU<pEN%Ch(kIe~*#T
z(Ve>@iuo5p14O1-_KTR@Ue<g^ve2UpJ~OVuND)<!agkQc4uBk82Glwnb@-khO*(2t
z#QnHAfE7b?eO;CzXus<c>$N(_BuF4!MWgcF?(xnb*|3r6Hkv!02a_yw@B8i@PYoZV
zK6L<;S1%JZMTqUG@o7*bJ>W32seh^kI^9q--lVAJWYKg)u&F<aenXFOKqDB@k|of3
zde2gcJ_0s7J|(KubtsQP93c8oF_*Z+z~JCVB#6{?bBjm~VqZNN>RXI|5Cb0vH7-N<
zR<!@Y$c0SY7+#J%<sSwg(h*zrTBe7bPsKB@4CYs?GBK~VPvhw^riFC_QWZ<eN|!7i
zTyU?lIIIt2nT_8a&0>1ZgGIJ2iu6*-mBmqZ&RtO$FC7*i9XLeEC}TL~9%JlWAq(lB
zw`Z}pJVt03zv1THe{*U*7}Zzww~8K)9(;E4G-qZD;&MBCKUFkL9LZa%v*<OZ&o7tP
zseRmUj{OkRt=yrHwZ+%2BY${3P{%Fs$P3!<bI=kb-B@k>dM7Qex}=#+<B1(_-{So-
zzll?S{`>4AbDZdVWB0NM2cwrErc+$k7U}Yyi7J4}9(IuOYg{T)_Ly#EXDQSEp5c&+
z-=Y$liheCMUU1uLB7JF(e1V7LC(rcsERD%vH;H$8e+{v>^yik`{!I^lP1Cu(E4t7?
zpm=S8c#c1fBsz@q&N5gNEBJ1Vc@PLE6{LBH|Cc-dry9Xk0@OSi!QRNrCEYFhbY-j8
z){7XgYwN5!Q<i3v&u$|6Z3Jq&OPJI>ueW~0l@*(rp92N@()gGK{iBv@3VX%6^O)=$
z^3TFSrdH(XlOK+5nVHk3Cp06Sj>H~EySD~xwAC%Rl|Q*yN)6_syTbHn*uWb8Ocmba
zF*Dl8aB-Loy|ILq7DfIk#m+oh`{+witi-w0VmoTM^yr=e=zR;{e~mx<=~29XcXY+7
z?OFV=vjqN3WIzMXV)fffO;#~IiF)}-=I~U}g-Y&WPEvDbeJ;yQjiPN>N6yJgg~vDF
zZMj}Wft1lnh!&d_iZY#^G<}0dQtA6t8jGUAxiXk)8--ni8;(kQqpg*Ic<@`QCM4CA
zlr+-2AL>9gf)nxXPvh*&j`o41v3W(M0FTlm6yq2|fhY+X{g4ou&hF27SK~eI_Adl|
zjgN17W0y2JSeUEEx$UH7l?F*uFJIdb;OD67H1w(#$aSm?&Cj`!_I?3cAf?s<>7fu5
z9`<9L>?B_D93}4TB%n=r_#yq4KfV%^r==q?lF&$*ogVYE7&LaH)niAP6_J6t;ADq;
zQ$3I3X;)qezk<P5E5L$+Amub4#Q_j>G~*3mQ;VeuHu(m%YkCTWr-93o&~le0qxg>I
z89(daQ+f7auV}h4FziN@{Or4b)l!x=t=FBaj@7PvrjMCY>AnOV1b4K6w^-5TZnW%G
zvSliGv)cESp=EFhnI#Z~x=g26rQPAm&Q~=$QQIhSFx%b+U5K1d+;F6cV0lGuWb4g7
zWn;kACv~%)2{CYsN|kC`wF4MU$m*H%ovD`p^!f=)Bw7{3dpj3<7G1BRQ{Eo79kZ3~
z#xrZb^a&HEL6XJZC^qaFt!R+exLGA@S#a&9a?V3x4?X{>?%CPG+-X<(Yy7AtGUyy;
zHLB_~%GvaO=_J-l=N*u(w<y%PTynWy_3R}e@L+o1Oi|TQ%sGve4#u*8tU_jeZq0jK
z*Zjfrm3KRcxw(v~o%uGie!=!gY=Uk2-8gB<nTxej|0SfyzoA|QXKeKwtU3qVcgcLl
z(U|wmC;}C*-N-PwW;ROGL2SsczrZcdT-R>TxXc30mb`1i^AGWMQAdqepx^tm#8S!w
zIAd$O^Hu4lb3Mc)ZMl}!j}HFnT5qD?w%*nA>>nvc(t>L?jN%xgn*HnO^G-xU=F`>f
zhiJ4e-mHWqQ~5izI%dgnRW@6uo%YbMz+4y4hfA<q$AR!&jc$c%8$R|@2?llxO;ats
zq%*y<5$cSpE-M;efxjZ|Om~}vNtmsgH!7wk6SXS3drX?G7y7NxILWLv@w9}H<ER4$
ziJ*IJ@xV;q;KR<kH`d`f7?DfP#rk3w;ty|xi}TG8dq&;F6Uu<#FK_^}d~(I{zaaqH
zC$>)3=1TUY9r^C8wRyNK?_j*Y*z>hiB)wQ{x0A0IYsTYGC55e4>g1iuUvqPFP+~MV
zp>E9c2a5e}PjeX&1fmDgZFQ7!-=`8W)HwP-uW#;$pI&Wq9BUPHnWsAD#235o%ECN-
zoKH(P;Ngc*w$)WCO-sPiTAw}*)RucI4y12mNiIfZ+Z#kR<R~0V`xlG4pmRF|gWsrl
zAy_*$?L2OjqaOC`lS0^lWH?G7E9GzNmExR`Qen8x9b}eD427Mr?L<{feUHJdpMpN<
zG;w*dfB14KxNfmEdX-8)Ot-64RTi${=HYIKY2i3^RPf^|)m0vgx+nm*5;4@E#42Xq
zrVp;=V15nP20taa#%}jIoTK{?SDud3Yk#D6!NVqKl_*zlq&ay^JS3s!BCo3s>8{kJ
z2(32Fjy&0z_YC`p<YJN|?cW?X9iT)s#u(H#?dEyjxKk@sR06Lt`>{t`j{B2R!$p1F
z87N2pB>K%>ll^Oln{ucPc(ctPPE3DVabf(3e5baX-TmmbTG#JvC(6pCbusR!q=V~6
z&N|6V<vg6qWp!r$lZ_{WY^sNuYNlauY$ylJ_xW|gS_L>Y3QkH+TI>AjHOOH;O6iV;
z#KkDG7N5=TF_7FMj_&Etow<bi4)M+GW!|aA$q2W?Th0ay5lK4{iMwrG2`~pXSbCf|
zg!7%f90PM319K79Hhb2#VuXMCk%*_*kGM4TTw%t8X$=Ri3EB>5>I7@o4ArU40HRDn
z{rFss;2m*pMmOrT54y*IXQr{{Nxo1;#7X+CPCgU&@!&R(=+%j@0bxgU7Xr;?8<GRR
z$d(~d_dh#&2MMsWu~&8iLG<({7?*Y4drPhSimAB>UM{*eb9u@NIXM`_9#Z+7eY4|Q
ziL~C%H}5^Fw>EIJJdZ6M4=y^EDiUHPj0`SUIqI#topq%bJ$NoKE^W2-<n4UkPzK{)
zX#y%+`?z_fZYB@H#;Lh)I&y+88%Og)soax~_OK4oT1)r5VKUooy}4;FwT7+ep31qk
zE6VuJJw`Y?8&G2DCgdzqm#w3=H0n2tkLQZxf8!WIky3Lvcld5DkuQZ<5q$2ygt7Ti
zWvljWV|9=bT1~gaWDVV+ZnII3{N~6oE!an^cs%U25ZSa99SU>Pz^km!?IxQ<GVug?
zzKKMscI>F8@nmZ=X>_w@|4fR`iAD5K+cv%HfINJ9IBB!TdKpjjq7|f>RO@hhi7lRI
zwZVF7Q21<Q6l)aF%4o1xjoo3H9$$X$n{0w>3<)q=Ol@&oDJq8k)A(!<(21h+2k)?|
zfv-y1ZK1{J;8o1%W9am`@cM)%m(`dunAmzP*HPeeF|$q1l<u@$nhvca#m8-9yvZDg
zxl&WX#bLCd#M^6@t6J2gnBqmpc7BN-W#6R^&Ej-poCs}V>__#RDqC}{!NMIGg=htR
z>-A}=E?owy{I*(gp&c86(7j|a`~Jo6M9qHrvGnKm^dS|xb>~_ov{yQfO%5dkmW#sM
zdUjY#wntrjx>Yo-wpvUoBh6JWLf;Jo^=&KtAUQyV1a4!l+^;eH(+$@~??0R^y?o?@
zI9(WxJ7GF6cwC|r{S)v%(_W7Rn5>4)+ZIQ}fY{aRsB&T7`ny6J1aqCQy4M-+o&}wa
zc+GoWkFMR2zyR!Zd<JwPY6u({+jx#)R&&C7?si*+nvQwD5|?`5KDxy&juV4XHLnHo
z@>y~X%=u?<VjF_`qc`UCYzpn#Rk$l#v%5IvUSF%m+d_$O(roN7mprt6`<^L}Vop#{
zWZ_k<eRI?;qcu#=$~L}Qo*QZ&S5Cn-PGzI4zRy@3%6iOv85Ty*?lP&o-m<WE@pRA3
za6ga4kxH&o(M>j*@~ZvJyGm^CPgY+r&5ij)le1H##=AgmWVKSp`Fu|&a~Y0{F}7Jx
zeD#bVFWjFUuXK$GQ&EH)b#WQwtFTfV!p^~9Yc8aEmR?QD;m{o&U|Nt$$@C6;*ye?L
zr1o0c`#PAybG)w)86|}?9RD=Is<|sIyP}=9CdO?^TGO%^$jzYED{4}wU|EYyR%R1;
z<9Lv@$kwWDwJG^C50xYo#)_*Li?pC=X4q85DQo$$_Huc`H9YXKnBnPXZ3#<@$fmrb
zn#k%-+$=B1g;Ra6uX07__k{0<c{MqVI4$yxi%p;;nA@MF*0$`GqJnn6(oJM~^xz9|
z$?>+hUOeRRv_Q2D6mE6MMwZ_7kl%2*_91!-<uE5%#Q?9G>sY9~`7xI85U4~d3%+r^
z%r{nHmAj0RnIW-RDjbWoeDv;XsWgQX1OWYOxZi)!u~Ny1P7$}1Z@C7N{WY)i*$1U_
zBs$IEh~k4#ui<tk!GeuVi-qaCmd?f|isa0Tx>M2MWWPz}-9oXSNNsUuq>h{JT(<p}
zO=TUK+fy_jp2?e|tD!8S#3A)Xy~maIwsp!&68|r40L8=Xt8zr9f)X-MDqAKJyvp6^
z=jp25bR+{V=yY={TU%Su+Y(S}KpKQ=ys1B5l)anXYJXOp)ahjz@}Cvg2ZHA*EB8|A
zrlAfFGRN7sY8xL^0Y6rZV~01Z=7(__gs8hFi*;fA(!}%-R#FarJU^tn2xp#w6chbs
z!Q7viF8gyIN-R37s1uuN+A2D3(N(4K%({8(+9-7q*jgXX8Uod{u_(75y8R73WH{VI
z!`Xcn<*!rK?j_glQoKlfG#N858?9+w>t-RZSn?;GrlA_8Gyk=@FqCb|>y9OA%e|9s
za{J6<xO|ah)2beR4P%<peZY)rSu5+hKD<#iw6q<bF4z6iR>yVj$M*R;p2<7wkg)9f
zf#<8mXqQNm&7-zwqU5E+;Zjy}jzrk8VobXrye`L9qvznqgI5hM85LTx1-MA?hDWz;
zWlq%xTA4z+_}T%c*P(ZfNtox*1KoQ5K+*Jt*62?Zpn9!{UMabawG`t`vx`-}kai^>
zRl5`zU#+B?g>lhjt|3M4_mDro3vD^&-0qL)GIl+Fdw;GjSF!~^PHiosy5?xKD9I+2
z{EADPbiA<S#3@D^>47>Uiac({pdE10IDuGm<)@qSRaP^H-ltg9U{V7`NiB)ZLW7A~
zIy%~7zq}J*2)bF*T#_`)Sg%|!%<97KU@akS^P{y{;(pM2B^&cGMs=`Ua0Z=L^UM1w
zH$TO}<TFsIz={?YGn4V7J4^dU)$ZjzYgMZe<Q)196_4i+!lFr=8-A~gKgcrNZ}>|6
z(L5i0JeHMEA;sKTxHYw-7nzj3#(AAeQnp{dOhnvRi5Pwv&qtD!T)y-;IV8V{MUFEv
zAz~+q-G}1)Vh6h=6=}t7!iM(lf~|kZ#rvna`recotX!?F!8bPQ5pUA?M&!mkZZY9v
z1(3+Xv^A^H-#KAi-Dpojf!nc}z5}ji!#)Z}5xLr!p{Y-|j2=+8Vp^d&IkMk;uAa+f
zmlSCKyqU%g5tA<Va<S%C$Vc<*Uu(#dK9B3YN-Li@G1=xJ__B5|L#}qbCAo~jxU5}q
z{mI2(SlrBj<b5?fZ$t)$ef7NWlKK&{oba>a^ZRJvt6NH#511-h?hAEwmO4Ql9h;oa
zUY+5GCa*1@^lXRA<VW9a=2D^Dl%^`ojM+Ou2FgS0G4axbhLw~hGnZ+G;F6))s8*)v
z!+jRKN*;C81&LKsMb5=$SBv@&A^5Osi-n|9yh*XfYQLn-Zq)T{dx87nZKWQiO&sWD
z^-DeH{^Q?$=WxTPXg)?w!4VrV@b2vy%2tS|$_#wc705&16?D10!LWJHjO!sbqn-k^
zD-}HnR)r~F<P04+40HB-kn0&)ExAK4qGAb)8)Le-n`-M>47qK^FTalahyc58!B^DN
zibzCYVy}Pqjq)nC@Vt!|TOyKBP?X-zy;(fP#g=hy?9}{Zw^}Wy)NX8XuWh;vk6wJQ
z<`JvHj2)AoXzKFlyX!x)gNmcxmTQPicT{^>>7RH%dm7^NHP|t(&V7?7hKjUY`*Y1)
z9&euD{%5XmP^HN`t%QZn%1us(!$sZPk&7?-vhg*8-LyU0@&Ra-e6hdH7quQ9<)&W`
zCBnf59~R%tXXh=w;H$m#W)BjIUz9^5gM!k1gZv#rI@L2#bDQ%v%(ovhyW*gAO^t;@
z30g^UH@TQZ;>*4Zcdv|*5j`8G#O<&J-MwSV-U4F~F0|4*oHYO#R)3h#!hc`??TQ5p
zDAgS7T~8nqzymcOz}KGrP=_61|6s;T#$?oAC9re^;EZ72L447n<~0mCz&-@(n%)>w
zw+ez1vt_sh?Q?*Umjtw)mC9}#Z8%|hyyoz!x{}H$B8gM;Uaf*^tea{JMbi7IcnlN8
z`_UysbZyhMbn-6u;RP8p8R6mg)w0DxurR34hRe9@0v07*;_3UN{`QnpArGq(eseYB
z%cLAnf!#30`A_?L1&^O~s_|jDR^_(xxrkbo9F&4_LgiE{N$@QxER1R7q~!gEf!vy@
zlP_j!_jFWzLQ!>w72w>K<|?W$VRIX~P}NRb5Tvnpr)*c7CT0#_;|X+DVwAXS=4wA7
ze<HKQtiD>3X5TtM0A*t^95Q6j0hexsDaj(?dwTcZ=Xk-1DNVm&9iYy!-gly;n?5mf
z({W}yxi&Nhp=8(H@b#ehR<h&2-vhH@YwmQNC4{Vs&?u#)H70R#Su>{}+YNhpb3I!3
zXXPg12t@a<FzNLZHwTt_wTP&ajmuv8)i;tJ6dytlQ;N7Nb9lASH?M(<<nGJnprd%n
zH8PT{clu3@GG%5CWhvF@fIngCknyOUG@YNkH<{0e6gABLqGQ9IR(G0~I82c{#<ZK?
z$I)&N;a*n!I;QiERy|1_(pj^2q~kfu`k98et!3+&8=G~v!~`DPXvAixLa(Ky_wy{h
zy<(=q<WQh6Zu4y`iOSPXh_W<Qg_m+<Y|Zu1C(%lvcU}daapz~;VH}9X9vr3Kwy%+p
zG9|&1@gtT9Q9k6$WxBq~$=Dz4uWGP>Qw+;k$YK+#r(d1AXyz#u4gI|0(}njBlZ4xc
zXhSX0U0JSd{Hl|Kd5mi=?uS|LF_BMj@yA#wRqSNVy<>v~w@biG=6))q;}XX|cTw=9
z>z-$I$8JTr?>Ul}nw^}Dn{3PKKf+S?7Cig&-a`YM_zLv+;8vdwDB5{_IsC@z*5%w8
zO~B-r6V+a(E{8qI?xweu&bs>|w{zV$sP4rr&I2y*P=~)EjC`L}omVu=?Nxnq&pDdY
zZCvcl^a@QL|BUqEYY3*44obiI4|If#%chr?z0w?3%_=f<7OyL4e?C-<*Sr?jA8l;D
zf#~wcP??Cy^V%7_T*4rE3b>fqRk0HotN!+U>-!)RKg}sFP>M1FX8Wc3N4sj(u)XOI
z&}Y05mg)W3jyhNUB~zi<c{rfh$QMw*W5(#;_*PENjwS{+{|t5Y7yjAb0Ak-zSIQpt
zCUi0;&IZN?&IWW!E{4v35!7X*{|)2{pNXFRzd&4Z^SRp_7+IM(;~ScoS=jOrUbS}<
z;#(N=5UR1r(975hnV4IMdpepZdCDpqd0H897!&gGa=UZ7+t}NDPlNAnV{Pli>CQuF
zU~Fe-!uj3*i%ds||2K=X6%Qd3Edvc3Jp&;x6t|<XDW{^a=)Vblk9Y{pot^DD>FC_t
z+-TjHXzd)$=omOSIOymZ=@=PlzBy=|JZzl}+-Yo`i2hxJu!)nAqlLY*g`F+_Um6Vz
z?OdFB2nq53O|Shw>9uvD{o62FBRd<q?+bJcv`ln=Df;en$~)Q_yBL``^2%5kIodhd
znL77X;mg~(nK;VZS=jzHV5DbY<^D_VKY6+7{+jcjZ1^|7{)zIxwaVD&Kb*C9akT#1
zRbwMM6KfM26I*AeZ^Ib=Hq6+F)7iq=+JyJ-XbD(5|D$7V@t1d;)&{m_JcRBv#wMl)
zF4oS3yvl#^{=+6~3*LWp{uZM9PeuPti@$I2f0+DV;^=N*|7|Y)-{yWZaSDAuk<NCG
z%64|v|EEy?uk76aK`;?Ea5mv(q-SKIVPK@8XHaIO=cH%nWa6Nr=isEL=cfB#IRBQE
zu`{+X_4xlI`A?kx-8=YgfQ-!lcSLM#{yiu%GMvJ8MlOHdTM1!a7Z(d-PIeXs4gp~S
zb{YW|4h|Y-Rv~5@0R|>^8X*=DdJa}rW)@*KmcQNnud;tR`c09rb#gYaH8T0{X!|GG
z|Bdtb{}(<`{}<-K|DQ?zr6~VTxc(<x|5610CFcKmy8b6z|5610CFcKmy8g9r{o`l<
zJNN!h5&t83=l##*-^BJi<9GW`?f+la7;OKv#t^n}vbQ$y_)c&BPCoIS9bHWRLe2fw
z@o!+xe<3XYmlcV+qKPS;l7*)UJ{t==othRt13okUH&!*BqMe=d_kbQ>kxs(a)DEA4
z<1ZlS?}4xuw*b3<Farnscd{hFBE-zh`d6~_&$$xs|C!@^22l%ZXA?&{QS0yYQrN`E
z&e-JNmqH92EKL6u?U!f8!&`X~W9X&>gcvX}gR9ms0s%n*5CtJT9s%FjKtd3a9;CdX
ztWh4+&=?6~2S~Z1c#JTRe2LH&RA~(=SVcitp+Tkm8@_B6xgq>K%PD6&`ykGn?~2Rm
zveRk$r&ms^gTrjMR2uawUwD*L1>%z2f`|A57Zt4)_*3|6jEJDn3FLO<fKI_QS%G0h
znKjkW<d1y}`cD*we2fVcc;X&pc6?G<q$I|{1L|i9WgMiq(x?~g)pwKYCih=JOW~kc
zgYz8rVe01c_^1|Qs2p^vDN&1dkuE>=bJg_#vIat@u79nhgT+P4&|tg#YK~0lAIPK=
znKZK3uD}6oA8Z4>u2SE|-EtbJjtHx|Mm<MiVcH?8eZ&#lEZ8mhl2B-pfysn;6?PdP
z-vwr1$J#<@F7CAy`<NHPf$RlJm=k68&?&mr{DbPCLaH29QmLO{jtU=z6T#O6>JvJ!
zpZPg}v_FV3>AbgEyr+%G4P-X(0<}U%i`;J@7KEuo5CcUQ`4eZ5Rz{_8Gie~lwMO0>
zB@n}2_PjEfZeW?St|+6;H7V-^nYh#z)j}dqL&DY<xC38P8?t_I(Z!&_JcC?PEi!C5
z%F?Gb@yQn9jah8;Z8!q>{IQ4Ip$3o}BJ=b>h{zy}y<!>x>TE=CHS<Y6mRGV2AB7h+
z1S2m38bt>>^OyV=gmT2*P#!dsasd^UvL+nTfd7$wH9EWG#@OeWvtVC_kZ@{My#17H
z0vDESRLAUf%!nC(7aZFL7cei9kFy`-(_$s@Mzt_<a%3M|0Xcw<#;&1^d&~b*+_gt{
zRUBbS2(K7k79kiQHPWi2a$kENR5U1tRH@i%f}~Iog@8(e0eJ{SAOV$xbAt6jsZvym
zZE9mvuwp_3!3R_mN?R&elq8me(gvIII1Q+6zr9y`es^a6?J0VW$IqGFo!!~}W_IWP
zJf8cVnwJ;t+_t%^@h4s5Uajmjr!|aw_TA^7`=or_3uCHBJzl@+Yngdvw^ls#*!-k9
z>&jPlTXo&M{=YwUdEUuhKVJ9F3x^+E__qfdI_BQltFkTq&dd*QxUJ{KGhZLvuzpx&
z{sZT>T=T|_NrxVJC(|5LTYgRRmK#T0e{K5E8<O)@=gzmsXRb8%ozBj`FK@w*HXrv^
zq-@<Yx~+6h()#wN=Z|#C@0inPa?`oue(!ufyzqtvJBJqTd2oK()YM;WS$}k0@ua4f
zxn+0Ho2IHyWVG(;U0iY5mgF6eEUHd>_PI+I9?u>-;ZMo+4P)>B$Fh(6oIIFPyefPD
z%HrzYV?QWZxbNaIi~e!hD14&3=&@z<t|(dBvU}vd{AGta4ju9qOv}IdOz+m_1y2^V
zZdtIa<V3^O&z|sGJ5JR+J$Lxoma>C`FXW6n`;C+J)4HF$@w0EHPE(a93R<&EpS<+%
zZBI>X?f835pG~8dZ$FY>->t{A!minm{rv4Y2hOw{Z7UekE7_m&yW5|9ty^JhdF{E+
zcaE!ld+U^COP_AIH>Kr;#=tL}b>+zJ>8~$eknw)X;`+n)PCAWWx*CVKhech3-tV@1
z?OmOhbq#wzZTFh?;(mpDk`~t;%xJ&*Yu9XF^kG@QtoHBk?3w%8V(Vu=#NFMP!Ur2}
zXepl6IHkSjq~m>hVnywkpUf_(FDa=1Yw@OAR-7rDyrOpYKi_H$-pc=c&Vkb{b+v`J
zoO!9_z#~0pBvpO1yfNoo_s@bcwfnwZHg(X%ir*T&hp)Y=v;V|3b2~GeD<;h7G4YXo
zZT*bY;cMROoX~a2wS6bm|K-r-gU&8FHsX_v$yZhP9Gle`9L`_ry!h#fYbXC=>y(Cx
zZ`5W#wRz`}tIvF;=;X_H;QvH;&-51_cyICf2P&t&vhqaV)~XNA4Ne|4d1^=Mj_xyG
z-SzOE?>>6!(J6Izo_X`*ZOg9e?Eaez7sh5U_|rX`RO9)_f8Kbz{o?wP#*Uh!bFcf$
zkKSp|Uy|~#9xpfDykuCxp4R7v9;@)$rgu*2`>&g7)Gvn?G>y44RsHhVuq{bTtB)QW
z@llV-vwr{5>6Z1ig}0u0w4``l!O{y)4mzH8?ps@`4j)_F(s}92-zdNQ%Hyy6ZpNrx
zso!fG)^*XZn?CIvzWm}5O^dqvU$<sb+x49#{zczxU(hwWW90{(BbRq;XuI!7>hz}J
zNBXty`S*+w&#W$KH!@b2caF_ilhrxwvtIk!{@t1UOvNRgmt=f2b>H5@fuCB`=1g5(
zIltQh`<Y%v<FC)UFy;Qey%*+gv7i6(fX}P?&#G{<4yLa<Fz~CF{<+uFxo7RYn+G&k
z?U-FLHmf7OsX6by+*&*7*;l&F+%({iRUNapUYoTc-E8^8$|s7hu(Gy6F|RbYX2;)F
z<rG~JWEG{KZ{A;&d)@Y5=M<|yY})WH8mgO-J8}EwoUho{#tltXGiHxBv&z%cn|sZE
zci5wg3od%CI=OCqZl1HZ`rO*(`;$*UzF~87%I00~EkAYr+3CwpSEem&`9WiPrTJz~
z{~ZH2CYL{#(WB{>Jp<<!Kh|7w>eX-UH)j4|Ku-3h`Ms{m%{2#B4wz6?HFN90tldM}
z-}t2a-s<(M+jIKX4amtbt#uo2zCXKat$ACwf4;urw%*yta(mbX^D5s=ezz>k9@2a7
z{*>H-M(L`$k1t!bCok!(p~V#`cc*!euiTJYb;XS1Nm)rlLj3uXij=JW8!In5|I)qZ
z%ksA$DgAa^-BM%l`_{(Y(~=(=G4S?tm18$<D;nAKX?Dlanl%kY6W;4P`0V~sJAXR8
zV?)71n+Lsi>n)}C{AzgZ_?jJgH*LGJe!{llgTB7^!>whd6K<VXy7i8~-gWoeLvCt$
zqVCk2JAb&k=jf{@Jiq(Y!#!Rso3=!ZO_r3-nw{3iKwuHzAJHGn@H1V7pTW=6|LKcA
zCSn-<C$sp!nCT<hAee0%409cXC~-McaXC}*_Dt8Yn67UsidlvV7-pJG*Rp($DT`vJ
z?>f{r%U2%l*YX38+GZO@fCw&TuWeW!?ZY-~kIt!WIDx}-U6c3WS-cP5q2sfSK+*NE
zP1B&Z*`^h+K1}7%IkipCW_IWxla5*W+z#J#nZ3Skvhn%0!!gC@z*oFq-*<Q)fye9!
zut;>wf$4J0Vt!x;4)asbumij!B(6D}&!N5n%(uzho2KoVOxN}UG6#?g0y590=`bG$
zW?94zV79~hKu*BM;wYE+6?8p|_=f4ZHuXPXo=yAkd{+^FF+Jb6xLjbfJ_5s`WAU&?
zbPl|L?P=2sFj-_hJZuHBw;<;#;xAZmwx=z}!Nq#KJz%CwY|~i4F`M_{IMiO~y7WA-
zd?Z=K4j5}IGB1|zm<F}Occ4zj>^rtk<{r9=o(I5Oi})Wf&*pvjip(kGgfV0Ll6k?Q
zYty};EFUWzAG5;AM9&h1Gt4CO0?g9cZ{i*VX4A76n4<gv3=Yk7J$n8@&UeZD0b~B6
zYzw<T-kz&WtR}@Q43}XR^($;5mt(e1&oEcPT;dz9att~zz$|*60<-D40nDN2DKJI%
zATXDnb-;YOXK+m7ueiOw;o`(1m}41;K4NPLcZQQw3=7OLcn6bnX#9e$pd%d8hm9#@
zm`82X+sG$t35=c3KF$Nmzdk|>>IZ=ZgUK8KW4s?&n0zumV6IJk4VXtc6c`9IZX2+`
zBmNv%u0i)3FgP^Tb+H>5hC_m2HV!8$r;LEfxt7QJ^>8#%T@Ox4&LJ3K(bxx=LwN=m
zwk8>$=el%_Am>pY1cs%fx}HHfC4je3KLuvdJrnpIi<R+o9~kjIOgJcwk$_niiKR5=
z5WduyBImQlToQ|F%p>uJ#scI4iTeW>0$Dab?7{e)YB`+bOxGcEpyfO(?k^hiNn8?Q
zB=!O37;fC}HHHJ9_Nxh<VT$Z&t?QFB+caE+=<)Lf7>lofset$&FxO;rg;Si(mFwEX
z|Fo_{{7++w_@Bl+;(r?RiT`N~fex7ioY*AZfSiZGlgYV+qqH27Au8t^B;J6WZyT&%
zAC%5?X-)^-z%=6LxW;VeSAh!xj<;tb?V)}EjExx^m7Wm@EF8=<X%CqJYtQr$3{W}G
zBJnQT!y!g|2$(DA(pU<*zCq4TjaejL&=`&DG)B)ljlntSn0;R{I|7^tR4y<{Y=QOy
zo8?D1I1rc6`vVI;$uU#FECI6x%n>jpV6K390><w=EbufNGvZ>733lL4fy)VY;LPE2
zf*r`W66Fw2yWCIp(H=ju_4%#y!9?8<L)^qM!48~PTu!h9F$k9v>_Cpl<%IPB?{hi9
z4%{sz%ISIWJYfzHV<yUla{%Avm|zFab}lE_fw+Xr33ec_U~=F9j>T;Y$BgrZ%L#U<
z_?Y#)_~hJ;+7s-++0OeA>_BeH<XpiHSFi*54A&LxK)#hI7uw;|^VD(!=^Vm+4%MYG
zI@<FL8W&??fW~ygxkAj4*j|JQb|ALpa)MvsK9S4C?Tz{n)&oAyb>sGix|k~_2M*v^
ze9U@YSR9K6k=_fsf*s&D-iKfZIEBjzb|5}wa=0ttSbTh;9q>Or7hm38Ys@0Cp8n4k
zVoctiU<dA&66M17NFJpzo5T#d4+PLm*QT*IbXlGk>c+>X`%pB#(in*yqkiM_taa%f
zPl&O*O^g}&G_}_wIj7e3NE{aSYx4E*X`TYzfadJlUfk0p_8Yaw*8{iGT#oRx9$!H7
z3S7t1d?xGzIZ0x_(De<HuWHPs_nmsoi2125kDG9br{X>t^~?MU@iWPf^c6C_gVOyH
zp4Q`|d3T7B_%G}i_aJo4irmd<T}AJ>!+zuTM(qjfA@I50ABp{jy12LC7+(*U`;d<V
zE4I(0_TuBycHs6pCZ{oWC#`KGJRQoB*g|6gf8WCO72D^2K<?q78yF<^4#$i;9ySNa
z<2lA`L(UY>iKF&}^#JGb_Js8i_}muw+!pxU#yqn=kh60v?tkH!3GYXkU<b}R)*j($
zJ<lfMA>Bp-2Lu+$t-?OM`1rK0js>VLySs*fE%3PwUf|;s>=5|eCOoajg4;AU7Iq(|
z$3l2o>(aMHjVY4HhjV}&J8{ff&Lw^oV&o1g)FnKv<veoV8Djh$7H?>Af6;O@W(_g=
z#-i=T*+j=2ICQS?HcQ`0LfyFE<EhP{F`>px`VIzNJPeV3AxH0)plcDnger97_C{FT
z|00a<4_to5`i<n`>lR^rf9QMk_*`Lp<US(QCA=SDaeE_7u!Hb^Bq!K`yNtxKL~?>1
z*sEMFzHXr|;r$4U$1f2k*g<$dk`vYg?;30@g!dy|!48tgM{<H4LVm8_3|YT~_rvxW
zpCgJOZ+F_C9da)f>Bi?dlq0c<#uV09%YS*EhPo^lha9_0vkh0cqrgK`%r@PJi?cYf
z4~?-mNC-GWeva4FSbNd<g!Moi!RD0keyGd%9AjbkVfZ|d%vGct_ZMwPz~VY!LR^Q(
zHa<QfKX(K^cZB@h5%?SrO}r0*&vm^3n=66O^*ukA6YOC5IbJ#lCu6QmdaoSLDc&#=
z`_OXuoB9iGDC0g9>Bh&W6=|J^#^8am_9ET5zl3rmhSL~(<Abt;I+VmQhwTaZxkGqA
zl8f6LVZwS4-jC$s_C}bn9)$NJxwyR%CfGrEKa>;la|hhb=78{iq#HjEB22J@@O~sG
z>~qus@P4@+it{-h1mgFJdVTTi&wNAY;)#8PawOl<m`makJ!VB}?m{`1FCfz;z82~d
zp4Pf}R*#J@#8`b3`o+6$;+UiMxL@h>p3BAUjdZzRDIq^cHH6TfupR=RE5u=JEW&-e
z68KyR_w7pHb5vXieQ>`*$%-H+*dg$__9146z~?#_=W@b&5Z({hL%45Ohy{3if*nGB
zj%pIFOL#x*SGaFSg^8dm*dg#aDog}j!484XbsZzKSGaFi;BGD_><=M7*YE6HSJ)o{
zpX(fq>k8{3@VOHBTnT)x1U^UM2=7<ma~;F6@d^34LO#gl1Um@thyEhmx9c~0)*j*g
zNLR3f<nfW5V26;OE4+*Ieg!)OK34*tD}m1y@?X{m?p`@2_!a72xty>+1U^^7eLISE
zd3yq%D}m3Iz~@Tfa}-DjeF%04e2yX|L04E0axbsfT-PqpxpGlgz%jlau8^PWdl0V6
z?Qn(s+{N7k*X4Gg0E^3!oISLS@P32|c9`6+P;@8sA=n}0=dQr#uE6Ig%;0?pe2(HW
zK~Atk;Bypy3A%zE0-vMeOwbk9LwG-T1wKbX9B)tHb64PVUBkzGL*R2);B!<d3A%zE
z0-w7ApX={6*jNNU*WXBRIl&Hr&r#qc=n8fSeC`T-u50+%SOh-T_2^tqu!Hclj-OEg
z!(!@y#P{JI6!;t!VzGYF9!fsrb#tMvz~`tWi|Iykf*k^%qmoR}750a~=cq0dbOpZ>
z_*~ZmC(d(dFL^@??GX4JRcJBYNKUXrxNk?{rl2d>A>6mS0-w7ApX(Z`MBBpg34D&~
zH$hIYL*R2=AIAF->=5|e75Lm0_#72>ybpoTQT!&z33dqY=O{cBbOk$v{M;4z90j+m
zJ^VbDfbso-@^e9s+kt`*L5|zu34HF6JYM^TC-Au^@VS1!q-*X8`MD?XxhL?sC%m8Q
zZ;f~#d_7RyD9FXnu+TPv&-M30tPdeS*R{V~PFN2iKi73`iMslHq2FAD7^xSH#w^$&
z@VO`Ox&Hnwv0vRD{=_iduSM$CHKs6Ek#zJ8r7ksf$oMqovo~g4`b%|DOHA&9OcND>
z<i1m5KCLgtH&_9C-$w}sIiFE3iuz94uOHBFGVw0P?n*UAYgjbql5Z%1S-32X&kH_>
zVDFs3TvAV?FKrE4%Z}0+{T3aYXJDvUW*9ykqnKiEy}II^jt|coDE^4+Y789z-+mXC
z-Zl6BS*2-x@Uwzn{B%d~zyH0C=m$CODqR?T*NbIA1+kabXXMD6CgrB}`SNRDBdyQ>
mXMCe?E1k6{^NV4P*1v0V`@(tp-}U6N!Q%@t{aJE2`2PS1PhGqK

literal 0
HcmV?d00001