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: Add incoming data to data.
  
  Args:
      data: The data to add.
      key_prefix: Optional key prefix to use in front of the key value.
docstr_tok: ["Add","incoming","data","to","data",".","Args",":","data",":","The","data","to","add",".","key_prefix",":","Optional","key","prefix","to","use","in","front","of","the","key","value","."]
code: def add_data(
      self, data: Data, key_prefix: Optional[Text] = None
  ) -> None:
      """Add incoming data to data.
  
      Args:
          data: The data to add.
          key_prefix: Optional key prefix to use in front of the key value.
      """
      for key, attribute_data in data.items():
          for sub_key, features in attribute_data.items():
              if key_prefix:
                  self.add_features(
                      f"{key_prefix}{key}", sub_key, features
                  )
              else:
                  self.add_features(key, sub_key, features)
  
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 15 for calling the RasaModelData obj's underlying member method code with example usage: obj.add_data(data, key_prefix) without return types