ran linter

Author: eodole

bayesflow/adapters/transforms/one_hot.py

@@ -10,7 +10,7 @@
 @serializable(package="bayesflow.adapters")
 class OneHot(ElementwiseTransform):
     """
-    Changes data to be one-hot encoded. 
+    Changes data to be one-hot encoded.
     """
 
     def __init__(self, num_classes: int):

bayesflow/adapters/transforms/standardize.py

@@ -11,12 +11,12 @@
 @serializable(package="bayesflow.adapters")
 class Standardize(ElementwiseTransform):
     """
-    Transform that when applied standardizes data using typical z-score standardization i.e. for some unstandardized data 
-    x the standardized version z  would be
+    Transform that when applied standardizes data using typical z-score standardization i.e. for some unstandardized
+    data x the standardized version z  would be
 
     z = (x - mean(x))/std(x)
 
-    Parameters: 
+    Parameters:
     mean: integer or float used to specify a mean if known but will be estimated from data when not provided
     std: integer or float used to specify a standard devation if known but will be estimated from data when not provided
     axis: integer representing a specific axis along which standardization should take place. By default

bayesflow/adapters/transforms/transform.py

@@ -7,6 +7,7 @@ class Transform:
     """
     Base class on which other transforms are based
     """
+
     def __call__(self, data: dict[str, np.ndarray], *, inverse: bool = False, **kwargs) -> dict[str, np.ndarray]:
         if inverse:
             return self.inverse(data, **kwargs)