class_docstr: Reads a JSON-formatted SQuAD file and returns a ``Dataset`` where the ``Instances`` have four
  fields: ``question``, a ``TextField``, ``passage``, another ``TextField``, and ``span_start``
  and ``span_end``, both ``IndexFields`` into the ``passage`` ``TextField``.  We also add a
  ``MetadataField`` that stores the instance's ID, the original passage text, gold answer strings,
  and token offsets into the original passage, accessible as ``metadata['id']``,
  ``metadata['original_passage']``, ``metadata['answer_texts']`` and
  ``metadata['token_offsets']``.  This is so that we can more easily use the official SQuAD
  evaluation script to get metrics.
  
  Parameters
  ----------
  tokenizer : ``Tokenizer``, optional (default=``WordTokenizer()``)
      We use this ``Tokenizer`` for both the question and the passage.  See :class:`Tokenizer`.
      Default is ```WordTokenizer()``.
  token_indexers : ``Dict[str, TokenIndexer]``, optional
      We similarly use this for both the question and the passage.  See :class:`TokenIndexer`.
      Default is ``{"tokens": SingleIdTokenIndexer()}``.
code: def _read(self, file_path):
      # if `file_path` is a URL, redirect to the cache
      file_path = cached_path(file_path)
  
      logger.info("Reading file at %s", file_path)
      with open(file_path) as dataset_file:
          dataset_json = json.load(dataset_file)
          dataset = dataset_json["data"]
      logger.info("Reading the dataset")
      for article in dataset:
          for paragraph_json in article["paragraphs"]:
              paragraph = paragraph_json["context"]
              tokenized_paragraph = self._tokenizer.tokenize(paragraph)
  
              for question_answer in paragraph_json["qas"]:
                  question_text = (
                      question_answer["question"]
                      .strip()
                      .replace("\n", "")
                  )
                  answer_texts = [
                      answer["text"]
                      for answer in question_answer["answers"]
                  ]
                  span_starts = [
                      answer["answer_start"]
                      for answer in question_answer["answers"]
                  ]
                  span_ends = [
                      start + len(answer)
                      for start, answer in izip(
                          span_starts, answer_texts
                      )
                  ]
                  instance = self.text_to_instance(
                      question_text,
                      paragraph,
                      izip(span_starts, span_ends),
                      answer_texts,
                      tokenized_paragraph,
                  )
                  yield instance
  
file_path: magnitude/pymagnitude/third_party/allennlp/data/dataset_readers/reading_comprehension/squad.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.instance import Instance
  from allennlp.data.dataset_readers.reading_comprehension import util
  from allennlp.data.token_indexers import SingleIdTokenIndexer, TokenIndexer
  from allennlp.data.tokenizers import Token, Tokenizer, WordTokenizer
  from io import open
label_desc: Use image node_id 2 for calling the SquadReader obj's underlying member method code with example usage: obj._read(file_path) without return types