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"]
code: def __init__(self, path, sentence_length=30, word_length=12):
      if path is None or not os.path.isdir(path):
          print("invalid path for SNIPS dataset loader")
          sys.exit(0)
      self.dataset_root = path
      train_set_raw, test_set_raw = self._load_dataset()
      super(SNIPS, self).__init__(
          sentence_length=sentence_length, word_length=word_length
      )
      self._load_data(train_set_raw, test_set_raw)
  
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 1 to create a new SNIPS object from inherited base classes: IntentDataset with example: obj = SNIPS(path, sentence_length, word_length)