Fix RUF013

Author: Armavica

pymc/distributions/shape_utils.py

@@ -420,7 +420,7 @@ def get_support_shape(
     shape: Optional[Shape] = None,
     dims: Optional[Dims] = None,
     observed: Optional[Any] = None,
-    support_shape_offset: Sequence[int] = None,
+    support_shape_offset: Optional[Sequence[int]] = None,
     ndim_supp: int = 1,
 ) -> Optional[TensorVariable]:
     """Extract the support shapes from shape / dims / observed information

pymc/initial_point.py

@@ -180,7 +180,7 @@ def make_initial_point_expression(
     free_rvs: Sequence[TensorVariable],
     rvs_to_transforms: dict[TensorVariable, Transform],
     initval_strategies: dict[TensorVariable, Optional[Union[np.ndarray, Variable, str]]],
-    jitter_rvs: set[TensorVariable] = None,
+    jitter_rvs: Optional[set[TensorVariable]] = None,
     default_strategy: str = "moment",
     return_transformed: bool = False,
 ) -> list[TensorVariable]:

pymc/sampling/forward.py

@@ -345,8 +345,8 @@ def sample_prior_predictive(
     var_names: Optional[Iterable[str]] = None,
     random_seed: RandomState = None,
     return_inferencedata: bool = True,
-    idata_kwargs: dict = None,
-    compile_kwargs: dict = None,
+    idata_kwargs: Optional[dict] = None,
+    compile_kwargs: Optional[dict] = None,
 ) -> Union[InferenceData, dict[str, np.ndarray]]:
     """Generate samples from the prior predictive distribution.
 
@@ -446,8 +446,8 @@ def sample_posterior_predictive(
     return_inferencedata: bool = True,
     extend_inferencedata: bool = False,
     predictions: bool = False,
-    idata_kwargs: dict = None,
-    compile_kwargs: dict = None,
+    idata_kwargs: Optional[dict] = None,
+    compile_kwargs: Optional[dict] = None,
 ) -> Union[InferenceData, dict[str, np.ndarray]]:
     """Generate posterior predictive samples from a model given a trace.
 
@@ -681,7 +681,7 @@ def sample_posterior_predictive_w(
     random_seed: RandomState = None,
     progressbar: bool = True,
     return_inferencedata: bool = True,
-    idata_kwargs: dict = None,
+    idata_kwargs: Optional[dict] = None,
 ):
     """Generate weighted posterior predictive samples from a list of models and
     a list of traces according to a set of weights.