diff --git a/.gitignore b/.gitignore
index 3eae1d6..f2ca833 100644
--- a/.gitignore
+++ b/.gitignore
@@ -136,3 +136,4 @@ dmypy.json
 test.ipynb
 dummy.py
 .DS_Store
+.cursor/
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 2dc8f14..de39ddf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -75,4 +75,4 @@ line-length = 110
 atomic = true
 profile = "black"
 skip_gitignore = true
-known_first_party = ["black", "blib2to3", "blackd", "_black_version"]
+known_first_party = ["wnb"]
diff --git a/wnb/_utils.py b/wnb/_utils.py
index cc9d534..2aa9f81 100644
--- a/wnb/_utils.py
+++ b/wnb/_utils.py
@@ -54,7 +54,6 @@ def _check_feature_names(estimator, X, *, reset):
 except ImportError:
 
     def _fit_context(*, prefer_skip_nested_validation: bool):
-
         def decorator(fit_method):
             @functools.wraps(fit_method)
             def wrapper(estimator, *args: Any, **kwargs: Any):
diff --git a/wnb/gnb.py b/wnb/gnb.py
index dcd6c98..dbc397a 100644
--- a/wnb/gnb.py
+++ b/wnb/gnb.py
@@ -114,7 +114,7 @@ class GeneralNB(_BaseNB):
     >>> X = np.array([[-1, 1], [-2, 1], [-3, 2], [1, 1], [2, 1], [3, 2]])
     >>> Y = np.array([1, 1, 1, 2, 2, 2])
     >>> from wnb import GeneralNB, Distribution as D
-    >>> clf = GeneralNB(distributions=[D.NORMAL, D.POISSON])
+    >>> clf = GeneralNB([D.NORMAL, D.POISSON])
     >>> clf.fit(X, Y)
     GeneralNB(distributions=[<Distribution.NORMAL: 'Normal'>,
                              <Distribution.POISSON: 'Poisson'>])
@@ -200,7 +200,9 @@ def _check_X_y(self, X, y) -> tuple[np.ndarray, np.ndarray]:
 
     @staticmethod
     def _find_dist(
-        feature_idx: int, feature_name: str | None, dist_mapping: dict[DistributionLike, ColumnKey]
+        feature_idx: int,
+        feature_name: str | None,
+        dist_mapping: dict[DistributionLike, ColumnKey],
     ) -> DistributionLike:
         for dist, cols in dist_mapping.items():
             cols_ = (
diff --git a/wnb/gwnb.py b/wnb/gwnb.py
index 219bdb6..9f52ebf 100644
--- a/wnb/gwnb.py
+++ b/wnb/gwnb.py
@@ -250,8 +250,7 @@ def _init_parameters(self) -> None:
             # Ensure the size of error weights matrix matches number of classes
             if error_weights.shape != (self.n_classes_, self.n_classes_):
                 raise ValueError(
-                    "The shape of error weights matrix does not match the number of classes, "
-                    "must be (n_classes, n_classes)."
+                    "The shape of error weights matrix does not match the number of classes, must be (n_classes, n_classes)."
                 )
 
             self.error_weights_ = error_weights