class_docstr: Stores the features produced by any featurizer.
class_docstr_tok: ["Stores","the","features","produced","by","any","featurizer","."]
code: def _combine_dense_features(
      self, additional_features: Features
  ) -> None:
      if self.features.ndim != additional_features.features.ndim:
          raise ValueError(
              f"Cannot combine dense features as sequence dimensions do not "
              f"match: {self.features.ndim} != {additional_features.features.ndim}."
          )
      self.features = np.concatenate(
          (self.features, additional_features.features), axis=-1
      )
      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 7 for calling the Features obj's underlying member method code with example usage: obj._combine_dense_features(additional_features) without return types