class_docstr: Stores the features produced by any featurizer.
class_docstr_tok: ["Stores","the","features","produced","by","any","featurizer","."]
docstr: Checks if features are sparse or not.
  
  Returns:
      True, if features are sparse, false otherwise.
docstr_tok: ["Checks","if","features","are","sparse","or","not",".","Returns",":","True",",","if","features","are","sparse",",","false","otherwise","."]
code: def is_sparse(self) -> bool:
      """Checks if features are sparse or not.
  
      Returns:
          True, if features are sparse, false otherwise.
      """
      return isinstance(self.features, scipy.sparse.spmatrix)
  
imports: from __future__ import annotations
  from typing import Iterable, Union, Text, Optional, List, Any, Tuple, Dict, Set
  import itertools
  import numpy
  import scipy.sparse
  import rasa.shared.utils.io
  import rasa.shared.nlu.training_data.util
  from rasa.shared.nlu.constants import FEATURE_TYPE_SEQUENCE, FEATURE_TYPE_SENTENCE
label_desc: Use image node_id 4 for calling the Features obj's underlying member method code with example usage: obj.is_sparse() and returns: isinstance