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 load(self, path: str):
      datapoints = self.load_datapoints(path)
      return lit_dataset.Dataset(base=self, examples=datapoints)
  
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 4 for calling the MNLIDataFromTSV obj's underlying member method code with example usage: obj.load(path) and returns: lit_dataset