class_docstr: MultiNLI dataset, from TSV.
  
  Compared to the TFDS version, this includes:
  - label2 field for binary labels, with same schema as HANS
  - genre labels, for stratified analysis
  
  The downside is that you need to download the data from
  https://gluebenchmark.com/tasks, and provide a path to the .tsv file.
class_docstr_tok: ["MultiNLI","dataset",",","from","TSV",".","Compared","to","the","TFDS","version",",","this","includes",":","-","label2","field","for","binary","labels",",","with","same","schema","as","HANS","-","genre","labels",",","for","stratified","analysis","The","downside","is","that","you","need","to","download","the","data","from","https",":","\/\/gluebenchmark.com\/tasks",",","and","provide","a","path","to","the",".tsv","file","."]
code: def save(self, examples: list[lit_types.IndexedInput], path: str):
      example_data = [ex["data"] for ex in examples]
      df = pd.DataFrame(example_data).rename(
          columns={
              "premise": "sentence1",
              "hypothesis": "sentence2",
              "label": "gold_label",
          }
      )
      with open(path, "w") as fd:
          df.to_csv(fd, sep="\t")
  
imports: from typing import Optional
  from absl import logging
  from lit_nlp.api import dataset
  from lit_nlp.api import types
  import pandas
  import tensorflow_datasets
label_desc: Use image node_id 5 for calling the MNLIDataFromTSV obj's underlying member method code with example usage: obj.save(examples, path) without return types