code: def dummy_features(
      fill_value: int,
      units: int,
      attribute: Text,
      type: Text,
      is_sparse: bool,
  ) -> Features:
      """Create some dummy `Features` with the desired properties."""
      matrix = np.full(shape=(1, units), fill_value=fill_value)
      if is_sparse:
          matrix = scipy.sparse.coo_matrix(matrix)
      return Features(
          features=matrix,
          attribute=attribute,
          feature_type=type,
          origin="whatever",
      )
  
imports: from typing import Text
  import numpy
  import re
  import scipy.sparse
  import pytest
  from rasa.nlu.tokenizers.tokenizer import Token
  from rasa.nlu.constants import TOKENS_NAMES
  from rasa.core.featurizers.precomputation import MessageContainerForCoreFeaturization
  from rasa.core.featurizers.single_state_featurizer import SingleStateFeaturizer
  from rasa.shared.nlu.training_data.features import Features
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.constants import ACTION_TEXT, ACTION_NAME, ENTITIES, TEXT, INTENT, FEATURE_TYPE_SEQUENCE, FEATURE_TYPE_SENTENCE, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_VALUE, ENTITY_ATTRIBUTE_START, ENTITY_ATTRIBUTE_END, ENTITY_TAGS
  from rasa.shared.core.domain import Domain
  from rasa.shared.core.slots import TextSlot
  from rasa.shared.core.constants import ACTION_LISTEN_NAME, ACTIVE_LOOP, PREVIOUS_ACTION, SLOTS, ENTITY_LABEL_SEPARATOR, USER
  from rasa.utils.tensorflow.constants import SENTENCE, SEQUENCE
label_desc: Use image node_id 8 for calling a global function with example usage: dummy_features(fill_value, units, attribute, type, is_sparse) and returns: Features