code: def _create_fake_features(
      all_features: List[List[List["Features"]]],
  ) -> List["Features"]:
      """Computes default feature values.
  
      All given features should have the same type, e.g. dense or sparse.
  
      Args:
          all_features: list containing all feature values encountered in the dataset
          for an attribute.
  
      Returns:
          The default features
      """
      example_features = next(
          iter(
              [
                  list_of_features
                  for list_of_list_of_features in all_features
                  for list_of_features in list_of_list_of_features
                  if list_of_features is not None
              ]
          )
      )
  
      # create fake_features for Nones
      fake_features = []
      for _features in example_features:
          new_features = copy.deepcopy(_features)
          if _features.is_dense():
              new_features.features = np.zeros(
                  (0, _features.features.shape[-1]),
                  _features.features.dtype,
              )
          if _features.is_sparse():
              new_features.features = scipy.sparse.coo_matrix(
                  (0, _features.features.shape[-1]),
                  _features.features.dtype,
              )
          fake_features.append(new_features)
  
      return fake_features
  
imports: import typing
  import copy
  import numpy
  import scipy.sparse
  from collections import defaultdict, OrderedDict
  from typing import List, Optional, Text, Dict, Tuple, Union, Any, DefaultDict, cast
  from rasa.nlu.constants import TOKENS_NAMES
  from rasa.utils.tensorflow.model_data import Data, FeatureArray, ragged_array_to_ndarray
  from rasa.utils.tensorflow.constants import MASK, IDS
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.constants import TEXT, ENTITIES, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_GROUP, ENTITY_ATTRIBUTE_ROLE
label_desc: Use image node_id 6 for calling a global function with example usage: _create_fake_features(all_features) and returns: fake_features