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 _load_intents(self, files):
      data = {}
      for f in sorted(files):
          fname = os.path.join(self.dataset_root, f)
          intent = f.split(os.sep)[0]
          with open(fname, encoding="utf-8", errors="ignore") as fp:
              fdata = json.load(fp)
          entries = self._parse_json([d["data"] for d in fdata[intent]])
          data[intent] = entries
      return data
  
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 3 for calling the SNIPS obj's underlying member method code with example usage: obj._load_intents(files) and returns: data