code: def unstack_nested_tensors(tensors, specs):
      """Make list of unstacked nested tensors.
  
      Args:
        tensors: Nested tensors whose first dimension is to be unstacked.
        specs: Tensor specs for tensors.
  
      Returns:
        A list of the unstacked nested tensors.
      Raises:
        ValueError: if the tensors and specs have incompatible dimensions or shapes.
      """
      unstacked_tensor_lists = []
      (
          flat_tensors,
          flat_shapes,
      ) = _flatten_and_check_shape_nested_tensors(tensors, specs)
      for tensor, shape in zip(flat_tensors, flat_shapes):
          if tensor.shape.rank == shape.rank:
              raise ValueError(
                  "Can only unstack tensors with a batch dimension."
              )
          if tensor.shape.rank == shape.rank + 1:
              unstacked_tensors = tf.unstack(tensor)
          unstacked_tensor_lists.append(unstacked_tensors)
      unstacked_tensors_zipped = zip(*unstacked_tensor_lists)
      return [
          tf.nest.pack_sequence_as(tensors, zipped)
          for zipped in unstacked_tensors_zipped
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import numbers
  from typing import Optional, Text
  from absl import logging
  import numpy
  from six.moves import zip
  import tensorflow
  from tf_agents.typing import types
  from tf_agents.utils import composite
  import wrapt
  from tensorflow.python.util import nest
label_desc: Use image node_id 19 for calling a global function with example usage: unstack_nested_tensors(tensors, specs) and returns: unknown