class_docstr: SNIPS dataset class
  
  Args:
          path (str): dataset path
          sentence_length (int, optional): max sentence length
          word_length (int, optional): max word length
class_docstr_tok: ["SNIPS","dataset","class","Args",":","path","(","str",")",":","dataset","path","sentence_length","(","int",",","optional",")",":","max","sentence","length","word_length","(","int",",","optional",")",":","max","word","length"]
docstr: returns a tuple of train/test with 3-tuple of tokens, tags, intent_type
docstr_tok: ["returns","a","tuple","of","train\/test","with","3-tuple","of","tokens",",","tags",",","intent_type"]
code: def _load_dataset(self):
      """returns a tuple of train/test with 3-tuple of tokens, tags, intent_type"""
      train_data = self._load_intents(self.train_files)
      test_data = self._load_intents(self.test_files)
      train = [
          (t, l, i)
          for i in sorted(train_data)
          for t, l in train_data[i]
      ]
      test = [
          (t, l, i) for i in sorted(test_data) for t, l in test_data[i]
      ]
      return train, test
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import json
  import os
  import sys
  import numpy
  from nlp_architect.utils.generic import pad_sentences
  from nlp_architect.utils.text import SpacyInstance, Vocabulary, character_vector_generator, word_vector_generator
label_desc: Use image node_id 2 for calling the SNIPS obj's underlying member method code with example usage: obj._load_dataset() and returns: train,  test