class_docstr: Data object used for all RasaModels.
  
  It contains all features needed to train the models.
  'data' is a mapping of attribute name, e.g. TEXT, INTENT, etc., and feature name,
  e.g. SENTENCE, SEQUENCE, etc., to a list of feature arrays representing the actual
  features.
  'label_key' and 'label_sub_key' point to the labels inside 'data'. For
  example, if your intent labels are stored under INTENT -> IDS, 'label_key' would
  be "INTENT" and 'label_sub_key' would be "IDS".
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docstr: Concatenate features.
  
  Args:
      feature_1: Features to concatenate.
      feature_2: Features to concatenate.
  
  Returns:
      The combined features.
docstr_tok: ["Concatenate","features",".","Args",":","feature_1",":","Features","to","concatenate",".","feature_2",":","Features","to","concatenate",".","Returns",":","The","combined","features","."]
code: def _combine_features(
      feature_1: Union[np.ndarray, scipy.sparse.spmatrix],
      feature_2: Union[np.ndarray, scipy.sparse.spmatrix],
      number_of_dimensions: Optional[int] = 1,
  ) -> FeatureArray:
      """Concatenate features.
  
      Args:
          feature_1: Features to concatenate.
          feature_2: Features to concatenate.
  
      Returns:
          The combined features.
      """
      if isinstance(feature_1, scipy.sparse.spmatrix) and isinstance(
          feature_2, scipy.sparse.spmatrix
      ):
          if feature_2.shape[0] == 0:
              return FeatureArray(feature_1, number_of_dimensions)
          if feature_1.shape[0] == 0:
              return FeatureArray(feature_2, number_of_dimensions)
          return FeatureArray(
              scipy.sparse.vstack([feature_1, feature_2]),
              number_of_dimensions,
          )
      return FeatureArray(
          np.concatenate([feature_1, feature_2]),
          number_of_dimensions,
      )
  
imports: import logging
  from typing import Optional, DefaultDict, Dict, Iterable, Text, List, Tuple, Any, Union, NamedTuple, ItemsView, overload, cast
  from collections import defaultdict, OrderedDict
  import numpy
  import scipy.sparse
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 30 for calling the RasaModelData obj's underlying member method code with example usage: obj._combine_features(feature_1, feature_2, number_of_dimensions) and returns: FeatureArray, FeatureArray, FeatureArray, FeatureArray