class_docstr: Reads a file from the Stanford Natural Language Inference (SNLI) dataset.  This data is
  formatted as jsonl, one json-formatted instance per line.  The keys in the data are
  "gold_label", "sentence1", and "sentence2".  We convert these keys into fields named "label",
  "premise" and "hypothesis", along with a metadata field containing the tokenized strings of the
  premise and hypothesis.
  
  Parameters
  ----------
  tokenizer : ``Tokenizer``, optional (default=``WordTokenizer()``)
      We use this ``Tokenizer`` for both the premise and the hypothesis.  See :class:`Tokenizer`.
  token_indexers : ``Dict[str, TokenIndexer]``, optional (default=``{"tokens": SingleIdTokenIndexer()}``)
      We similarly use this for both the premise and the hypothesis.  See :class:`TokenIndexer`.
code: def __init__(self, tokenizer=None, token_indexers=None, lazy=False):
      super(SnliReader, self).__init__(lazy)
      self._tokenizer = tokenizer or WordTokenizer()
      self._token_indexers = token_indexers or {
          "tokens": SingleIdTokenIndexer()
      }
  
file_path: magnitude/pymagnitude/third_party/allennlp/data/dataset_readers/snli.py
imports: from __future__ import with_statement
  from __future__ import absolute_import
  import json
  import logging
  from allennlp.common.file_utils import cached_path
  from allennlp.data.dataset_readers.dataset_reader import DatasetReader
  from allennlp.data.fields import Field, TextField, LabelField, MetadataField
  from allennlp.data.instance import Instance
  from allennlp.data.token_indexers import SingleIdTokenIndexer, TokenIndexer
  from allennlp.data.tokenizers import Tokenizer, WordTokenizer
  from io import open
label_desc: Use image node_id 1 to create a new SnliReader object from inherited base classes: DatasetReader with example: obj = SnliReader(tokenizer, token_indexers, lazy)