cleanup: remove verbose docstrings from MDN

Author: BHARATH0153

sbi/neural_nets/estimators/mixture_density_estimator.py

@@ -356,8 +356,7 @@ def __init__(
                 evaluation, and samples are inverse transformed as:
                 x = z * scale + shift.
                 This is used for z-scoring inputs to improve numerical stability.
-            input_transform: Optional PyTorch Transform for general bijective
-                transformation of inputs. Mutually exclusive with transform_input.
+            input_transform: Optional Transform for general bijective transformation.
         """
         super().__init__(net, input_shape, condition_shape)
         self._embedding_net = (
@@ -410,19 +409,16 @@ def embedding_net(self) -> nn.Module:
 
     @property
     def has_input_transform(self) -> bool:
-        """Whether input transform is enabled (z-score or general transform)."""
         return self._input_transform is not None or self._transform_shift is not None
 
     def _transform_input(self, input: Tensor) -> Tensor:
-        """Apply input transform: z = transform(x) or z = (x - shift) / scale."""
         if self._input_transform is not None:
             return self._input_transform(input)
         if self._transform_shift is None:
             return input
         return (input - self._transform_shift) / self._transform_scale
 
     def _inverse_transform_input(self, z: Tensor) -> Tensor:
-        """Apply inverse input transform: x = transform.inv(z) or x = z * scale + shift."""
         if self._input_transform is not None:
             return self._input_transform.inv(z)
         if self._transform_shift is None:
@@ -432,11 +428,6 @@ def _inverse_transform_input(self, z: Tensor) -> Tensor:
     def _log_det_jacobian_forward(
         self, input: Tensor, transformed_input: Tensor
     ) -> Tensor:
-        """Log determinant of the forward input transform Jacobian.
-
-        For the affine z-score transform: -sum(log(scale)).
-        For a general transform: transform.log_abs_det_jacobian(x, z).sum(dim=-1).
-        """
         if self._input_transform is not None:
             jac = self._input_transform.log_abs_det_jacobian(
                 input, transformed_input