class_docstr: A distribution generated by applying a sequence of transformations to
  a base distribution/
  
  Parameters
  ----------
  base_dist : Distribution
      Base distribution
  transforms : Transformation or List
      Transformation to be applied
code: def __init__(self, base_dist, transforms, validate_args=None):
      self._base_dist = base_dist
      if isinstance(transforms, Transformation):
          transforms = [
              transforms,
          ]
      self._transforms = transforms
      event_dim = max(
          [self._base_dist.event_dim]
          + [t.event_dim for t in self._transforms]
      )
      super(TransformedDistribution, self).__init__(
          event_dim=event_dim, validate_args=validate_args
      )
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/transformed_distribution.py
imports: from ..transformation import Transformation
  from .distribution import Distribution
  from .utils import sum_right_most
  from ....None import np
label_desc: Use image node_id 1 to create a new TransformedDistribution object from inherited base classes: Distribution with example: obj = TransformedDistribution(base_dist, transforms, validate_args)