class_docstr: Processor for the MultiRC data set.
class_docstr_tok: ["Processor","for","the","MultiRC","data","set","."]
code: def get_examples(
      self, data_dir: str, split: str
  ) -> List[InputExample]:
      examples = []
      path = os.path.join(data_dir, "{}.jsonl".format(split))
      with open(path, encoding="utf8") as f:
          for line in f:
              example_json = json.loads(line)
  
              passage_idx = example_json["idx"]
              text = example_json["passage"]["text"]
              questions = example_json["passage"]["questions"]
              for question_json in questions:
                  question = question_json["question"]
                  question_idx = question_json["idx"]
                  answers = question_json["answers"]
                  for answer_json in answers:
                      label = self.get_label_id(answer_json["label"])
                      answer_idx = answer_json["idx"]
                      guid = f"{split}-p{passage_idx}-q{question_idx}-a{answer_idx}"
                      meta = {
                          "passage_idx": passage_idx,
                          "question_idx": question_idx,
                          "answer_idx": answer_idx,
                          "answer": answer_json["text"],
                      }
                      idx = [passage_idx, question_idx, answer_idx]
                      example = InputExample(
                          guid=guid,
                          text_a=text,
                          text_b=question,
                          label=label,
                          meta=meta,
                          idx=idx,
                      )
                      examples.append(example)
  
      question_indices = list(
          set(example.meta["question_idx"] for example in examples)
      )
      label_distribution = Counter(
          example.label for example in examples
      )
      logger.info(
          f"Returning {len(examples)} examples corresponding to {len(question_indices)} questions with label "
          f"distribution {list(label_distribution.items())}"
      )
      return examples
  
imports: import csv
  import json
  import os
  import random
  from abc import ABC, abstractmethod
  from collections import defaultdict, Counter
  from typing import List, Dict, Callable, Sequence
  from torch.utils.data import dataset
  from openprompt.utils.logging import logger
  from openprompt.data_utils.utils import InputExample
  from openprompt.data_utils.data_processor import DataProcessor
label_desc: Use image node_id 2 for calling the MultiRcProcessor obj's underlying member method code with example usage: obj.get_examples(data_dir, split) and returns: examples