class_docstr: A distribution obtained by applying a sequence of transformations on top
  of a base distribution.
code: def event_dim(self):
      if self._event_dim is None:
          self._event_dim = max(
              [self.base_distribution.event_dim]
              + [t.event_dim for t in self.transforms]
          )
      assert isinstance(self._event_dim, int)
      return self._event_dim
  
imports: from typing import Any, List, Optional, Tuple
  import mxnet
  import numpy
  from mxnet import autograd
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .None import bijection
  from .distribution import Distribution, _index_tensor, getF
  from .bijection import AffineTransformation
label_desc: Use image node_id 6 for calling the TransformedDistribution obj's underlying member method code with example usage: obj.event_dim() and returns: self