code: def transpose_dict_list(dict_list):
      """Transpose a nested dict[list] into a list[nested dict]."""
      # 1. Unstack numpy arrays into list
      dict_list = utils.map_nested(
          _np_to_list, dict_list, dict_only=True
      )
  
      # 2. Extract the sequence length (and ensure the length is constant for all
      # elements)
      length = {
          "value": None
      }  # dict because `nonlocal` is Python3 only
  
      def update_length(elem):
          if length["value"] is None:
              length["value"] = len(elem)
          elif length["value"] != len(elem):
              raise ValueError(
                  "The length of all elements of one sequence should be the same. "
                  "Got {} != {}".format(length["value"], len(elem))
              )
          return elem
  
      utils.map_nested(update_length, dict_list, dict_only=True)
  
      # 3. Extract each individual elements
      return [
          utils.map_nested(
              lambda elem: elem[i], dict_list, dict_only=True
          )  # pylint: disable=cell-var-from-loop
          for i in range(
              length["value"]
          )  # pytype: disable=wrong-arg-types
      ]
  
imports: from __future__ import annotations
  from typing import List, Optional, Union
  import numpy
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.features import feature
  from tensorflow_datasets.core.features import features_dict
  from tensorflow_datasets.core.features import tensor_feature
  from tensorflow_datasets.core.features import top_level_feature
  from tensorflow_datasets.core.proto import feature_pb2
  from tensorflow_datasets.core.utils import py_utils
  from tensorflow_datasets.core.utils import tree_utils
  from tensorflow_datasets.core.utils import type_utils
label_desc: Use image node_id 4 for calling a global function with example usage: transpose_dict_list(dict_list) and returns: unknown, elem