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 _load_dataset_parts(cls, stream, stream_description):
      from snips_nlu.dataset.yaml_wrapper import yaml
  
      intents = []
      entities = []
      for doc in yaml.safe_load_all(stream):
          doc_type = doc.get("type")
          if doc_type == "entity":
              entities.append(Entity.from_yaml(doc))
          elif doc_type == "intent":
              intents.append(Intent.from_yaml(doc))
          else:
              raise DatasetFormatError(
                  "Invalid 'type' value in YAML file '%s': '%s'"
                  % (stream_description, doc_type)
              )
      return intents, entities
  
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 3 for calling the Dataset obj's underlying member method code with example usage: obj._load_dataset_parts(cls, stream, stream_description) and returns: intents,  entities