code: def unbatch_nested_tensors(tensors, specs=None):
      """Remove the batch dimension if needed from nested tensors using their specs.
  
      If specs is None, the first dimension of each tensor will be removed.
      If specs are provided, each tensor is compared to the corresponding spec,
      and the first dimension is removed only if the tensor was batched.
  
      Args:
        tensors: Nested list/tuple or dict of batched Tensors.
        specs: Nested list/tuple or dict of TensorSpecs, describing the shape of the
          non-batched Tensors.
  
      Returns:
        A nested non-batched version of each tensor.
      Raises:
        ValueError: if the tensors and specs have incompatible dimensions or shapes.
      """
      if specs is None:
          return tf.nest.map_structure(
              lambda x: composite.squeeze(x, 0), tensors
          )
  
      unbatched_tensors = []
      (
          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 + 1:
              tensor = composite.squeeze(tensor, 0)
          unbatched_tensors.append(tensor)
      return tf.nest.pack_sequence_as(tensors, unbatched_tensors)
  
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 17 for calling a global function with example usage: unbatch_nested_tensors(tensors, specs) and returns: tf, tf