class_docstr: Stores the features produced by any featurizer.
class_docstr_tok: ["Stores","the","features","produced","by","any","featurizer","."]
code: def __str__(self) -> Text:
      return (
          f"{self.__class__.__name__}("
          f"features.shape={self.features.shape}, "
          f"is_sparse={self.is_sparse()}, "
          f"type={self.type}, "
          f"origin={self.origin}, "
          f"attribute={self.attribute})"
      )
  
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 3 for calling the Features obj's underlying member method code with example usage: obj.__str__() and returns: self, str, self, str, self, str, self, str, self, str, self, str