class_docstr: MultiNLI classification problems.
class_docstr_tok: ["MultiNLI","classification","problems","."]
code: def generate_samples(self, data_dir, tmp_dir, dataset_split):
      mnli_dir = _maybe_download_corpora(tmp_dir)
      if dataset_split == problem.DatasetSplit.TRAIN:
          filesplit = ["train.tsv"]
      else:
          # Using dev matched as the default for eval. Can also switch this to
          # dev_mismatched.tsv
          filesplit = ["dev_matched.tsv"]
      label_list = self.class_labels(data_dir=None)
      for fs in filesplit:
          filename = os.path.join(mnli_dir, fs)
          for example in _example_generator(filename):
              yield {
                  "inputs": [example["premise"], example["hypothesis"]],
                  "label": label_list.index(example["label"]),
              }
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import zipfile
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import lm1b
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.data_generators import wiki_lm
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling the MultiNLI obj's underlying member method code with example usage: obj.generate_samples(data_dir, tmp_dir, dataset_split) without return types