class_docstr: Applies distinct bijectors to initial + transition states of a chain.
code: def bijector_fn(self):
      return self._bijector_fn
  
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 2 for calling the _MarkovChainBijector obj's underlying member method code with example usage: obj.bijector_fn() and returns: self