class_docstr: Applies distinct bijectors to initial + transition states of a chain.
code: def _inverse_log_det_jacobian(self, y):
      inverse_ndims_for_one_step = tf.nest.map_structure(
          lambda nd: nd - 1, self.inverse_min_event_ndims
      )
      initial_ildj, ildjs = self._apply_batch(
          fn=lambda b, x: compute_and_maybe_broadcast_ldj(  # pylint: disable=g-long-lambda
              b,
              x,
              event_ndims=inverse_ndims_for_one_step,
              ldj_fn=lambda b: b.inverse_log_det_jacobian,
          ),
          y=y,
      )
      return initial_ildj + tf.reduce_sum(ildjs, axis=0)
  
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 12 for calling the _MarkovChainBijector obj's underlying member method code with example usage: obj._inverse_log_det_jacobian(y) and returns: unknown