class_docstr: Applies distinct bijectors to initial + transition states of a chain.
code: def _parameter_properties(cls, dtype):
      return dict(
          chain=parameter_properties.BatchedComponentProperties(),
          transition_bijector=parameter_properties.BatchedComponentProperties(
              # The transition bijector contributes no batch shape
              # beyond that from the chain itself.
              event_ndims=None
          ),
      )
  
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 6 for calling the _MarkovChainBijector obj's underlying member method code with example usage: obj._parameter_properties(cls, dtype) and returns: dict