class_docstr: Cross-lingual NLI; see https://cims.nyu.edu/~sbowman/xnli/.
class_docstr_tok: ["Cross-lingual","NLI",";","see","https",":","\/\/cims.nyu.edu\/~sbowman\/xnli\/","."]
code: def _process_example(self, ex, languages: list[str]):
      # Hypothesis is stored as parallel arrays, so make a map.
      hyp_map = {
          lang.decode("utf-8"): hyp.decode("utf-8")
          for lang, hyp in zip(
              ex["hypothesis"]["language"],
              ex["hypothesis"]["translation"],
          )
      }
      for lang in languages:
          if lang not in hyp_map:
              logging.warning(
                  "Missing hypothesis (lang=%s) for premise '%s'",
                  lang,
                  ex["premise"]["lang"].decode("utf-8"),
              )
              continue
          yield {
              "premise": ex["premise"][lang].decode("utf-8"),
              "hypothesis": hyp_map[lang],
              "label": self.LABELS[ex["label"]],
              "language": lang,
          }
  
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 1 for calling the XNLIData obj's underlying member method code with example usage: obj._process_example(ex, languages) without return types