code: def flatten_multi_batched_nested_tensors(tensors, specs):
      """Reshape tensors to contain only one batch dimension.
  
      For each tensor, it checks the number of extra dimensions beyond those in
      the spec, and reshapes tensor to have only one batch dimension.
      NOTE: Each tensor's batch dimensions must be the same.
  
      Args:
        tensors: Nested list/tuple or dict of batched Tensors or SparseTensors.
        specs: Nested list/tuple or dict of TensorSpecs, describing the shape of the
          non-batched Tensors.
  
      Returns:
        A nested version of each tensor with a single batch dimension.
        A list of the batch dimensions which were flattened.
      Raises:
        ValueError: if the tensors and specs have incompatible dimensions or shapes.
      """
      assert_same_structure(
          tensors,
          specs,
          message="Tensors and specs do not have matching structures",
      )
      flat_tensors = tf.nest.flatten(tensors)
      flat_spec_shapes = [spec_shape(s) for s in tf.nest.flatten(specs)]
      out_tensors = []
      batch_dims = []
      for i, (tensor, sp_shape) in enumerate(
          zip(flat_tensors, flat_spec_shapes)
      ):
          if i == 0:  # Set batch_dims based on first tensor.
              batch_dims = tensor.shape[
                  : tensor.shape.rank - sp_shape.rank
              ]
              if batch_dims.is_fully_defined():
                  batch_dims = batch_dims.as_list()
                  batch_prod = np.prod(batch_dims)
                  batch_dims = tf.constant(batch_dims, dtype=tf.int64)
              else:
                  batch_dims = tf.shape(tensor)[
                      : tensor.shape.rank - sp_shape.rank
                  ]
                  batch_prod = tf.reduce_prod(batch_dims)
          if not sp_shape.is_fully_defined():
              # When shape of spec is not fully defined, we do not rely on it to
              # reshape the tensor but retain the original non-batch dims of tensors.
              non_batch_dims = tf.shape(tensor)[
                  tensor.shape.rank - sp_shape.rank :
              ]
              reshaped_dims = tf.concat(
                  [[batch_prod], non_batch_dims], 0
              )
          else:
              reshaped_dims = [batch_prod] + sp_shape.as_list()
          out_tensors.append(composite.reshape(tensor, reshaped_dims))
      return tf.nest.pack_sequence_as(tensors, out_tensors), batch_dims
  
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 21 for calling a global function with example usage: flatten_multi_batched_nested_tensors(tensors, specs) and returns: tf,  batch_dims