tensorflow · copybara-service · Jul 1, 2026
diff --git a/setup.py b/setup.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/__init__.py b/tensorflow_recommenders/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/examples/__init__.py b/tensorflow_recommenders/examples/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/examples/movielens.py b/tensorflow_recommenders/examples/movielens.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -65,14 +65,14 @@ def evaluate(user_model: tf.keras.Model,
 
   movie_embeddings = np.concatenate(
       list(movies.batch(4096).map(
-          lambda x: movie_model({"movie_id": x["movie_id"]})
+          lambda x: movie_model({"movie_id": x["movie_id"]})  # pyrefly: ignore[not-callable]
       ).as_numpy_iterator()))
 
   precision_values = []
   recall_values = []
 
   for user_id, test_movies in test_user_to_movies.items():
-    user_embedding = user_model({"user_id": np.array([user_id])}).numpy()
+    user_embedding = user_model({"user_id": np.array([user_id])}).numpy()  # pyrefly: ignore[not-callable]
     scores = (user_embedding @ movie_embeddings.T).flatten()
 
     test_movies = np.frombuffer(test_movies, dtype=np.int32)
@@ -189,4 +189,4 @@ def sample_listwise(
       tensor_slices["movie_title"].append(sampled_movie_titles)
       tensor_slices["user_rating"].append(sampled_ratings)
 
-  return tf.data.Dataset.from_tensor_slices(tensor_slices)
+  return tf.data.Dataset.from_tensor_slices(tensor_slices)  # pyrefly: ignore[bad-argument-type]
diff --git a/tensorflow_recommenders/examples/nbtool.py b/tensorflow_recommenders/examples/nbtool.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/__init__.py b/tensorflow_recommenders/experimental/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/layers/__init__.py b/tensorflow_recommenders/experimental/layers/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/layers/embedding/__init__.py b/tensorflow_recommenders/experimental/layers/embedding/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/layers/embedding/partial_tpu_embedding.py b/tensorflow_recommenders/experimental/layers/embedding/partial_tpu_embedding.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/layers/embedding/partial_tpu_embedding_test.py b/tensorflow_recommenders/experimental/layers/embedding/partial_tpu_embedding_test.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/models/__init__.py b/tensorflow_recommenders/experimental/models/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/models/ranking.py b/tensorflow_recommenders/experimental/models/ranking.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -192,7 +192,7 @@ def compute_loss(self,
           "Got a length {len(inputs)} tuple instead: {inputs}."
       )
 
-    outputs = self(features, training=training)
+    outputs = self(features, training=training)  # pyrefly: ignore[not-callable]
 
     loss = self._task(labels, outputs, sample_weight=sample_weight)
     loss = tf.reduce_mean(loss)
@@ -212,26 +212,26 @@ def call(self, inputs: Dict[str, tf.Tensor]) -> tf.Tensor:
     dense_features = inputs["dense_features"]
     sparse_features = inputs["sparse_features"]
 
-    sparse_embeddings = self._embedding_layer(sparse_features)
+    sparse_embeddings = self._embedding_layer(sparse_features)  # pyrefly: ignore[not-callable]
     # Combine a dictionary into a vector and squeeze dimension from
     # (batch_size, 1, emb) to (batch_size, emb).
     sparse_embeddings = tf.nest.flatten(sparse_embeddings)
 
     sparse_embedding_vecs = [
         tf.squeeze(sparse_embedding) for sparse_embedding in sparse_embeddings
     ]
-    dense_embedding_vec = self._bottom_stack(dense_features)
+    dense_embedding_vec = self._bottom_stack(dense_features)  # pyrefly: ignore[not-callable]
 
     interaction_args = sparse_embedding_vecs + [dense_embedding_vec]
-    interaction_output = self._feature_interaction(interaction_args)
+    interaction_output = self._feature_interaction(interaction_args)  # pyrefly: ignore[not-callable]
     if self._concat_dense:
       feature_interaction_output = tf.concat(
           [dense_embedding_vec, interaction_output], axis=1
       )
     else:
       feature_interaction_output = interaction_output
 
-    prediction = self._top_stack(feature_interaction_output)
+    prediction = self._top_stack(feature_interaction_output)  # pyrefly: ignore[not-callable]
 
     return tf.reshape(prediction, [-1])
 

diff --git a/tensorflow_recommenders/experimental/models/ranking_test.py b/tensorflow_recommenders/experimental/models/ranking_test.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/optimizers/__init__.py b/tensorflow_recommenders/experimental/optimizers/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/optimizers/clippy_adagrad.py b/tensorflow_recommenders/experimental/optimizers/clippy_adagrad.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -175,7 +175,7 @@ def build(self, var_list: Sequence[tf.Variable]) -> None:
           self.add_variable_from_reference(
               var,
               "accumulator",
-              initial_value=initializer(shape=var.shape, dtype=var.dtype),
+              initial_value=initializer(shape=var.shape, dtype=var.dtype),  # pyrefly: ignore[not-callable]
           ))
       if self.export_clipping_factors:
         self.clipping_factors.append(
@@ -220,7 +220,7 @@ def update_step(self, grad: Union[tf.Tensor, tf.IndexedSlices],
     delta = lr * grad_values * precondition
     clipped_delta, clipping_factor = shrink_by_references(
         delta,
-        references=[variable_values, precondition],
+        references=[variable_values, precondition],  # pyrefly: ignore[bad-argument-type]
         relative_factors=[
             self.variable_relative_threshold,
             self.accumulator_relative_threshold
@@ -235,7 +235,7 @@ def update_step(self, grad: Union[tf.Tensor, tf.IndexedSlices],
         # Clip the accumulator update: this acts like clipping the gradient
         # before sending it to the optimizer. This is a good option when the
         # gradient is an outlier.
-        accumulator_update = grad_values * clipping_factor
+        accumulator_update = grad_values * clipping_factor  # pyrefly: ignore[unsupported-operation]
       else:
         # Does not clip the accumulator update: This is a good option in cases
         # where the gradient increases during training, and allows for quicker

diff --git a/tensorflow_recommenders/experimental/optimizers/clippy_adagrad_test.py b/tensorflow_recommenders/experimental/optimizers/clippy_adagrad_test.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/optimizers/composite_optimizer.py b/tensorflow_recommenders/experimental/optimizers/composite_optimizer.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/experimental/optimizers/composite_optimizer_test.py b/tensorflow_recommenders/experimental/optimizers/composite_optimizer_test.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/layers/__init__.py b/tensorflow_recommenders/layers/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/layers/blocks.py b/tensorflow_recommenders/layers/blocks.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/layers/embedding/__init__.py b/tensorflow_recommenders/layers/embedding/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/layers/embedding/tpu_embedding_layer.py b/tensorflow_recommenders/layers/embedding/tpu_embedding_layer.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/layers/embedding/tpu_embedding_layer_test.py b/tensorflow_recommenders/layers/embedding/tpu_embedding_layer_test.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/layers/factorized_top_k.py b/tensorflow_recommenders/layers/factorized_top_k.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -215,7 +215,7 @@ def index_from_dataset(self, candidates: tf.data.Dataset) -> "TopK":
     return self.index(candidates, identifiers)
 
   @abc.abstractmethod
-  def call(
+  def call(  # pyrefly: ignore[bad-override]
       self,
       queries: Union[tf.Tensor, Dict[Text, tf.Tensor]],
       k: Optional[int] = None,
@@ -284,7 +284,7 @@ def query_with_exclusions(
     k = k if k is not None else self._k
 
     adjusted_k = k + exclusions.shape[1]
-    x, y = self(queries=queries, k=adjusted_k)
+    x, y = self(queries=queries, k=adjusted_k)  # pyrefly: ignore[not-callable]
     return _exclude(x, y, exclude=exclusions, k=k)
 
   @abc.abstractmethod
@@ -416,7 +416,7 @@ def call(
       )
 
     if self.query_model is not None:
-      queries = self.query_model(queries)
+      queries = self.query_model(queries)  # pyrefly: ignore[not-callable]
 
     # Reset the element counter.
     self._counter.assign(0)
@@ -426,7 +426,7 @@ def top_scores(
     ) -> Tuple[tf.Tensor, tf.Tensor]:
       """Computes top scores and indices for a batch of candidates."""
 
-      scores = self._compute_score(queries, candidate_batch)
+      scores = self._compute_score(queries, candidate_batch)  # pyrefly: ignore[bad-argument-type]
 
       if self._handle_incomplete_batches:
         k_ = tf.math.minimum(k, tf.shape(scores)[1])
@@ -598,9 +598,9 @@ def call(
       )
 
     if self.query_model is not None:
-      queries = self.query_model(queries)
+      queries = self.query_model(queries)  # pyrefly: ignore[not-callable]
 
-    scores = self._compute_score(queries, self._candidates)
+    scores = self._compute_score(queries, self._candidates)  # pyrefly: ignore[bad-argument-type]
 
     values, indices = tf.math.top_k(scores, k=k)
 
@@ -764,7 +764,7 @@ def call(
     searcher = scann_ops.searcher_from_module(self._serialized_searcher)
 
     if self.query_model is not None:
-      queries = self.query_model(queries)
+      queries = self.query_model(queries)  # pyrefly: ignore[not-callable]
 
     if not isinstance(queries, tf.Tensor):
       raise ValueError(f"Queries must be a tensor, got {type(queries)}.")

diff --git a/tensorflow_recommenders/layers/factorized_top_k_test.py b/tensorflow_recommenders/layers/factorized_top_k_test.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -80,7 +80,7 @@ def run_save_and_restore_test(self, layer, query, num):
     for _ in range(num):
       post_serialization_results = restored(tf.constant(query))
 
-    self.assertAllEqual(post_serialization_results, pre_serialization_results)
+    self.assertAllEqual(post_serialization_results, pre_serialization_results)  # pyrefly: ignore[unbound-name]
 
   def run_top_k_test(self,
                      layer_class,
@@ -121,13 +121,13 @@ def run_top_k_test(self,
     expected_top_scores = np.take_along_axis(scores, indices, 1)
     expected_top_indices = candidate_indices[indices]
 
-    candidates = tf.data.Dataset.from_tensor_slices(candidates).batch(
+    candidates = tf.data.Dataset.from_tensor_slices(candidates).batch(  # pyrefly: ignore[bad-argument-type]
         batch_size)
 
     if indices_dtype is not None:
-      identifiers = tf.data.Dataset.from_tensor_slices(candidate_indices).batch(
+      identifiers = tf.data.Dataset.from_tensor_slices(candidate_indices).batch(  # pyrefly: ignore[bad-argument-type]
           batch_size)
-      candidates = tf.data.Dataset.zip((identifiers, candidates))
+      candidates = tf.data.Dataset.zip((identifiers, candidates))  # pyrefly: ignore[bad-argument-type]
 
     # Call twice to ensure the results are repeatable.
     for _ in range(2):
@@ -203,7 +203,7 @@ def test_scann_dataset_arg_no_identifiers(self):
 
     rng = np.random.RandomState(42)
     candidates = tf.data.Dataset.from_tensor_slices(
-        rng.normal(size=(num_candidates, 4)).astype(np.float32))
+        rng.normal(size=(num_candidates, 4)).astype(np.float32))  # pyrefly: ignore[bad-argument-type]
     query = rng.normal(size=(num_queries, 4)).astype(np.float32)
 
     scann = factorized_top_k.ScaNN()
@@ -217,9 +217,9 @@ def test_scann_dataset_arg_with_identifiers(self):
 
     rng = np.random.RandomState(42)
     candidates = tf.data.Dataset.from_tensor_slices(
-        rng.normal(size=(num_candidates, 4)).astype(np.float32))
+        rng.normal(size=(num_candidates, 4)).astype(np.float32))  # pyrefly: ignore[bad-argument-type]
     query = rng.normal(size=(num_queries, 4)).astype(np.float32)
-    identifiers = tf.data.Dataset.from_tensor_slices(np.arange(num_candidates))
+    identifiers = tf.data.Dataset.from_tensor_slices(np.arange(num_candidates))  # pyrefly: ignore[bad-argument-type]
 
     index = factorized_top_k.ScaNN()
     index.index_from_dataset(identifiers.zip(candidates).batch(100))
@@ -232,14 +232,14 @@ def test_raise_on_incorrect_input_shape(
 
     num_candidates = 100
     candidates = tf.data.Dataset.from_tensor_slices(
-        np.random.normal(size=(num_candidates, 4)).astype(np.float32))
+        np.random.normal(size=(num_candidates, 4)).astype(np.float32))  # pyrefly: ignore[bad-argument-type]
     identifiers = tf.data.Dataset.from_tensor_slices(
-        np.arange(num_candidates - 1))
+        np.arange(num_candidates - 1))  # pyrefly: ignore[bad-argument-type]
 
     with self.assertRaises(ValueError):
       index = layer_class()
       index.index_from_dataset(
-          tf.data.Dataset.zip((identifiers.batch(20), candidates.batch(100)))
+          tf.data.Dataset.zip((identifiers.batch(20), candidates.batch(100)))  # pyrefly: ignore[bad-argument-type]
       )
 
   @parameterized.parameters(test_cases())

diff --git a/tensorflow_recommenders/layers/feature_interaction/__init__.py b/tensorflow_recommenders/layers/feature_interaction/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/tensorflow_recommenders/layers/feature_interaction/dcn.py b/tensorflow_recommenders/layers/feature_interaction/dcn.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -174,14 +174,14 @@ def call(self, x0: tf.Tensor, x: Optional[tf.Tensor] = None) -> tf.Tensor:
               x0.shape[-1], x.shape[-1]))
 
     if self._projection_dim is None:
-      prod_output = self._dense(x)
+      prod_output = self._dense(x)  # pyrefly: ignore[not-callable]
     else:
-      prod_output = self._dense_v(self._dense_u(x))
+      prod_output = self._dense_v(self._dense_u(x))  # pyrefly: ignore[not-callable]
 
     prod_output = tf.cast(prod_output, self.compute_dtype)
 
     if self._diag_scale:
-      prod_output = prod_output + self._diag_scale * x
+      prod_output = prod_output + self._diag_scale * x  # pyrefly: ignore[unsupported-operation]
 
     return x0 * prod_output + x
 

diff --git a/tensorflow_recommenders/layers/feature_interaction/dcn_test.py b/tensorflow_recommenders/layers/feature_interaction/dcn_test.py
@@ -1,4 +1,4 @@
-# Copyright 2025 The TensorFlow Recommenders Authors.
+# Copyright 2026 The TensorFlow Recommenders Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.