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: Filter model data by ids.
  
  Args:
      data: The data to filter
      ids: The ids
  
  Returns:
      The filtered data
docstr_tok: ["Filter","model","data","by","ids",".","Args",":","data",":","The","data","to","filter","ids",":","The","ids","Returns",":","The","filtered","data"]
code: def _data_for_ids(data: Optional[Data], ids: np.ndarray) -> Data:
      """Filter model data by ids.
  
      Args:
          data: The data to filter
          ids: The ids
  
      Returns:
          The filtered data
      """
      new_data: Data = defaultdict(lambda: defaultdict(list))
  
      if data is None:
          return new_data
  
      for key, attribute_data in data.items():
          for sub_key, features in attribute_data.items():
              for f in features:
                  new_data[key][sub_key].append(f[ids])
      return new_data
  
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 26 for calling the RasaModelData obj's underlying member method code with example usage: obj._data_for_ids(data, ids) and returns: new_data, new_data