code: def test_read_from_file_ag_news_corpus_and_segments_sentences_properly(
      self, max_sequence_length
  ):
      reader = TextClassificationJsonReader(
          segment_sentences=True,
          max_sequence_length=max_sequence_length,
      )
      ag_path = (
          AllenNlpTestCase.FIXTURES_ROOT
          / "data"
          / "text_classification_json"
          / "ag_news_corpus.jsonl"
      )
      instances = list(reader.read(ag_path))
  
      splitter = SpacySentenceSplitter()
      spacy_tokenizer = get_spacy_model(
          "en_core_web_sm", parse=False, ner=False
      )
  
      text1 = (
          "Memphis Rout Still Stings for No. 14 Louisville; Coach "
          "Petrino Vows to Have Team Better Prepared. NASHVILLE, "
          "Tenn. Nov 3, 2004 - Louisville #39;s 30-point loss "
          "at home to Memphis last season is still a painful memory "
          "for the Cardinals."
      )
      instance1 = {"text": text1, "label": "2"}
      text2 = (
          "AP - Eli Manning has replaced Kurt Warner as the New York"
          " Giants' starting quarterback."
      )
      instance2 = {"text": text2, "label": "2"}
      text3 = (
          "A conference dedicated to online journalism explores the "
          "effect blogs have on news reporting. Some say they draw "
          "attention to under-reported stories. Others struggle to "
          "establish the credibility enjoyed by professionals."
      )
      instance3 = {"text": text3, "label": "4"}
  
      for instance in [instance1, instance2, instance3]:
          sentences = splitter.split_sentences(instance["text"])
          tokenized_sentences: List[List[str]] = []
          for sentence in sentences:
              tokens = [
                  token.text for token in spacy_tokenizer(sentence)
              ]
              if max_sequence_length:
                  tokens = tokens[:max_sequence_length]
              tokenized_sentences.append(tokens)
          instance["tokens"] = tokenized_sentences
  
      assert len(instances) == 3
      fields = instances[0].fields
      text = [
          [token.text for token in sentence.tokens]
          for sentence in fields["tokens"]
      ]
      assert text == instance1["tokens"]
      assert fields["label"].label == instance1["label"]
      fields = instances[1].fields
      text = [
          [token.text for token in sentence.tokens]
          for sentence in fields["tokens"]
      ]
      assert text == instance2["tokens"]
      assert fields["label"].label == instance2["label"]
      fields = instances[2].fields
      text = [
          [token.text for token in sentence.tokens]
          for sentence in fields["tokens"]
      ]
      assert text == instance3["tokens"]
      assert fields["label"].label == instance3["label"]
  
file_path: allennlp/tests/data/dataset_readers/text_classification_json_test.py
imports: import pytest
  from typing import List
  import spacy
  from allennlp.data.dataset_readers import TextClassificationJsonReader
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.data.tokenizers.sentence_splitter import SpacySentenceSplitter
  from allennlp.common.util import get_spacy_model
label_desc: Use image node_id 4 for calling the TestTextClassificationJsonReader obj's underlying member method code with example usage: obj.test_read_from_file_ag_news_corpus_and_segments_sentences_properly(max_sequence_length) without return types