class_docstr: Stores the features produced by any featurizer.
class_docstr_tok: ["Stores","the","features","produced","by","any","featurizer","."]
docstr: Checks if features are dense or not.
  
  Returns:
      True, if features are dense, false otherwise.
docstr_tok: ["Checks","if","features","are","dense","or","not",".","Returns",":","True",",","if","features","are","dense",",","false","otherwise","."]
code: def is_dense(self) -> bool:
      """Checks if features are dense or not.
  
      Returns:
          True, if features are dense, false otherwise.
      """
      return not self.is_sparse()
  
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 5 for calling the Features obj's underlying member method code with example usage: obj.is_dense() and returns: unknown