class_docstr: Dataset used in the main NLU training API
  
  Consists of intents and entities data. This object can be built either from
  text files (:meth:`.Dataset.from_files`) or from YAML files
  (:meth:`.Dataset.from_yaml_files`).
  
  Attributes:
      language (str): language of the intents
      intents (list of :class:`.Intent`): intents data
      entities (list of :class:`.Entity`): entities data
code: def _add_missing_entities(self):
      entity_names = set(e.name for e in self.entities)
  
      # Add entities appearing only in the intents utterances
      for intent in self.intents:
          for entity_name in intent.entities_names:
              if entity_name not in entity_names:
                  entity_names.add(entity_name)
                  self.entities.append(Entity(name=entity_name))
  
file_path: snips-nlu/snips_nlu/dataset/dataset.py
imports: from __future__ import print_function, unicode_literals
  import io
  from itertools import cycle
  from snips_nlu.common.utils import unicode_string
  from snips_nlu.dataset.entity import Entity
  from snips_nlu.dataset.intent import Intent
  from snips_nlu.exceptions import DatasetFormatError
label_desc: Use image node_id 4 for calling the Dataset obj's underlying member method code with example usage: obj._add_missing_entities() without return types