class_docstr: Applies distinct bijectors to initial + transition states of a chain.
code: def _inverse(self, y):
      xs_step_axes = tf.nest.map_structure(
          lambda nd: -nd,
          self.transition_bijector.inverse_event_ndims(
              # Outputs `y` have the num_steps axis at `-inverse_min_event_ndims`.
              self.inverse_min_event_ndims
          ),
      )
      x0, xs = self._apply_batch(fn=lambda b, y: b.inverse(y), y=y)
      return (x0, move_dimensions(xs, source=0, dest=xs_step_axes))
  
imports: import functools
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.math import generic
label_desc: Use image node_id 10 for calling the _MarkovChainBijector obj's underlying member method code with example usage: obj._inverse(y) and returns: x0,  move_dimensions