class_docstr: Applies distinct bijectors to initial + transition states of a chain.
code: def _forward(self, x):
      x0, xs = x
      y = self._apply_forward_scan(fn=lambda b, x, y: y, x0=x0, xs=xs)
      return move_dimensions(y, source=0, dest=self.chain._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 9 for calling the _MarkovChainBijector obj's underlying member method code with example usage: obj._forward(x) and returns: move_dimensions