class_docstr: Specify how to augment data before training the CRF
  
  Data augmentation essentially consists in creating additional utterances
  by combining utterance patterns and slot values
  
  Args:
      min_utterances (int, optional): Specify the minimum amount of
          utterances to generate per intent (default=200)
      capitalization_ratio (float, optional): If an entity has one or more
          capitalized values, the data augmentation will randomly capitalize
          its values with a ratio of *capitalization_ratio* (default=.2)
      add_builtin_entities_examples (bool, optional): If True, some builtin
          entity examples will be automatically added to the training data.
          Default is True.
code: def get_required_resources(self):
      return {STOP_WORDS: True}
  
imports: from __future__ import unicode_literals
  from snips_nlu.common.from_dict import FromDict
  from snips_nlu.constants import STOP_WORDS
  from snips_nlu.pipeline.configs import Config, ProcessingUnitConfig, default_features_factories
  from snips_nlu.resources import merge_required_resources
label_desc: Use image node_id 2 for calling the SlotFillerDataAugmentationConfig obj's underlying member method code with example usage: obj.get_required_resources() and returns: dict