class_docstr: A distribution obtained by applying a sequence of transformations on top
  of a base distribution.
code: def batch_shape(self) -> Tuple:
      if self._batch_shape is None:
          shape = (
              self.base_distribution.batch_shape
              + self.base_distribution.event_shape
          )
          self._batch_shape = shape[: len(shape) - self.event_dim]
      assert isinstance(self._batch_shape, tuple)
      return self._batch_shape
  
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 7 for calling the TransformedDistribution obj's underlying member method code with example usage: obj.batch_shape() and returns: self