class_docstr: Stores the features produced by any featurizer.
class_docstr_tok: ["Stores","the","features","produced","by","any","featurizer","."]
code: def _combine_sparse_features(
      self, additional_features: Features
  ) -> None:
      from scipy.sparse import hstack
  
      if (
          self.features.shape[0]
          != additional_features.features.shape[0]
      ):
          raise ValueError(
              f"Cannot combine sparse features as sequence dimensions do not "
              f"match: {self.features.shape[0]} != "
              f"{additional_features.features.shape[0]}."
          )
  
      self.features = hstack(
          [self.features, additional_features.features]
      )
      self._cached_fingerprint = None
  
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 8 for calling the Features obj's underlying member method code with example usage: obj._combine_sparse_features(additional_features) without return types