code: def _feature_arrays_for_attribute(
      attribute: Text,
      absent_features: List[Any],
      attribute_to_features: Dict[Text, List[List[List["Features"]]]],
      training: bool,
      fake_features: Dict[Text, List["Features"]],
      consider_dialogue_dimension: bool,
  ) -> Dict[Text, List[FeatureArray]]:
      """Create the features for the given attribute from the all examples features.
  
      Args:
          attribute: the attribute of Message to be featurized
          absent_features: list of Nones, used as features if `attribute_to_features`
              does not contain the `attribute`
          attribute_to_features: features for every example
          training: boolean indicating whether we are currently in training or not
          fake_features: zero features
          consider_dialogue_dimension: If set to false the dialogue dimension will be
            removed from the resulting sequence features.
  
      Returns:
          A dictionary of feature type to actual features for the given attribute.
      """
      features = (
          attribute_to_features[attribute]
          if attribute in attribute_to_features
          else absent_features
      )
  
      # in case some features for a specific attribute are
      # missing, replace them with a feature vector of zeros
      if training:
          fake_features[attribute] = _create_fake_features(features)
  
      (
          attribute_masks,
          _dense_features,
          _sparse_features,
      ) = _extract_features(
          features, fake_features[attribute], attribute
      )
  
      sparse_features = {}
      dense_features = {}
  
      for key, values in _sparse_features.items():
          if consider_dialogue_dimension:
              sparse_features[key] = FeatureArray(
                  ragged_array_to_ndarray(values),
                  number_of_dimensions=4,
              )
          else:
              sparse_features[key] = FeatureArray(
                  ragged_array_to_ndarray([v[0] for v in values]),
                  number_of_dimensions=3,
              )
  
      for key, values in _dense_features.items():
          if consider_dialogue_dimension:
              dense_features[key] = FeatureArray(
                  ragged_array_to_ndarray(values),
                  number_of_dimensions=4,
              )
          else:
              dense_features[key] = FeatureArray(
                  ragged_array_to_ndarray([v[0] for v in values]),
                  number_of_dimensions=3,
              )
      attribute_to_feature_arrays = {
          MASK: [
              FeatureArray(
                  ragged_array_to_ndarray(attribute_masks),
                  number_of_dimensions=3,
              )
          ]
      }
  
      feature_types = set()
      feature_types.update(list(dense_features.keys()))
      feature_types.update(list(sparse_features.keys()))
  
      for feature_type in feature_types:
          attribute_to_feature_arrays[feature_type] = []
          if feature_type in sparse_features:
              attribute_to_feature_arrays[feature_type].append(
                  sparse_features[feature_type]
              )
          if feature_type in dense_features:
              attribute_to_feature_arrays[feature_type].append(
                  dense_features[feature_type]
              )
  
      return attribute_to_feature_arrays
  
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 8 for calling a global function with example usage: _feature_arrays_for_attribute(attribute, absent_features, attribute_to_features, training, fake_features, consider_dialogue_dimension) and returns: attribute_to_feature_arrays