class_docstr: Processor for the XNLI dataset. Adapted from
  https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/run_classifier.py#L207
docstr: See base class.
code: def get_train_examples(self, data_dir):
      """See base class."""
      lg = (
          self.language
          if self.train_language is None
          else self.train_language
      )
      lines = self._read_tsv(
          os.path.join(
              data_dir, f"XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"
          )
      )
      examples = []
      for i, line in enumerate(lines):
          if i == 0:
              continue
          guid = f"train-{i}"
          text_a = line[0]
          text_b = line[1]
          label = (
              "contradiction" if line[2] == "contradictory" else line[2]
          )
          if not isinstance(text_a, str):
              raise ValueError(
                  f"Training input {text_a} is not a string"
              )
          if not isinstance(text_b, str):
              raise ValueError(
                  f"Training input {text_b} is not a string"
              )
          if not isinstance(label, str):
              raise ValueError(
                  f"Training label {label} is not a string"
              )
          examples.append(
              InputExample(
                  guid=guid, text_a=text_a, text_b=text_b, label=label
              )
          )
      return examples
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/data/processors/xnli.py
imports: import os
  from ...utils import logging
  from .utils import DataProcessor, InputExample
label_desc: Use image node_id 2 for calling the XnliProcessor obj's underlying member method code with example usage: obj.get_train_examples(data_dir) and returns: examples