class_docstr: Stores the features produced by any featurizer.
class_docstr_tok: ["Stores","the","features","produced","by","any","featurizer","."]
docstr: Calculate a stable string fingerprint for the features.
docstr_tok: ["Calculate","a","stable","string","fingerprint","for","the","features","."]
code: def fingerprint(self) -> Text:
      """Calculate a stable string fingerprint for the features."""
      if self._cached_fingerprint is None:
          if self.is_dense():
              f_as_text = self.features.tobytes()
          else:
              f_as_text = rasa.shared.nlu.training_data.util.sparse_matrix_to_string(
                  self.features
              )
          self._cached_fingerprint = (
              rasa.shared.utils.io.deep_container_fingerprint(
                  [self.type, self.origin, self.attribute, f_as_text]
              )
          )
      return self._cached_fingerprint
  
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 11 for calling the Features obj's underlying member method code with example usage: obj.fingerprint() and returns: self