class_docstr: Conditional transforms with learnable parameters such as normalizing flows should inherit from this class rather
  than :class:`~pyro.distributions.conditional.ConditionalTransform` so they are also a subclass of
  :class:`~torch.nn.Module` and inherit all the useful methods of that class.
code: def __hash__(self):
      return super().__hash__()
  
imports: from abc import ABC, abstractmethod
  import torch
  import torch.nn
  from .torch import TransformedDistribution
  from .torch_transform import ComposeTransformModule
label_desc: Use image node_id 2 for calling the ConditionalTransformModule obj's underlying member method code with example usage: obj.__hash__() and returns: super