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 icdf(self, value):
      sign = np.ones_like(
          value
      )  # pylint: disable=too-many-function-args
      for t in self._transforms:
          sign = sign * t.sign
      value = sign * (value - 0.5) + 0.5  # value or (1 - value)
      samples_base = self._base_dist.icdf(value)
      for t in self._transforms:
          samples_base = t(samples_base)
      return samples_base
  
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 6 for calling the TransformedDistribution obj's underlying member method code with example usage: obj.icdf(value) and returns: samples_base