code: def broadcast_to_full_batch_shape(
      time_indep, time_dep, observation=Observations(None, None)
  ):
      """Ensures that all provided Tensors have full batch shape."""
      time_indep_ranks = TimeIndependentParameters(2, 2, 1)
      time_dep_ranks = TimeDependentParameters(2, 2, 2, 1, 2, 2, 2, 1)
      observation_ranks = Observations(1, 0)
  
      batch_shape = functools.reduce(
          ps.broadcast_shape,
          tf.nest.flatten(
              tf.nest.map_structure(
                  _extract_batch_shape,
                  (time_indep, time_dep, observation),
                  # Time-dependent tensors have a num_timesteps 'sample' dimension.
                  (
                      TimeIndependentParameters(0, 0, 0),
                      TimeDependentParameters(1, 1, 1, 1, 1, 1, 1, 1),
                      Observations(1, 1),
                  ),
                  (time_indep_ranks, time_dep_ranks, observation_ranks),
              )
          ),
      )
  
      time_indep = tf.nest.map_structure(
          lambda x, r: _broadcast_to_full_batch_shape_helper(  # pylint: disable=g-long-lambda
              x, r, batch_shape=batch_shape
          ),
          time_indep,
          time_indep_ranks,
      )
  
      time_dep, observation = tf.nest.map_structure(
          lambda x, r: _broadcast_to_full_batch_shape_helper(  # pylint: disable=g-long-lambda
              x, r, batch_shape=batch_shape, sample_ndims=1
          ),
          (time_dep, observation),
          (time_dep_ranks, observation_ranks),
      )
  
      return time_indep, time_dep, observation
  
imports: import collections
  import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.math import linalg
  from tensorflow_probability.python.math.scan_associative import scan_associative
label_desc: Use image node_id 1 for calling a global function with example usage: broadcast_to_full_batch_shape(time_indep, time_dep, observation) and returns: time_indep,  time_dep,  observation