code: def __init__(
      self,
      dataset_name,
      datapaths,
      tokenizer,
      max_seq_length,
      max_qa_length=MAX_QA_LENGTH,
  ):
      self.dataset_name = dataset_name
      print_rank_0(
          " > building RACE dataset for {}:".format(self.dataset_name)
      )
  
      string = "  > paths:"
      for path in datapaths:
          string += " " + path
      print_rank_0(string)
  
      self.samples = []
      for datapath in datapaths:
          self.samples.extend(
              process_single_datapath(
                  datapath, tokenizer, max_qa_length, max_seq_length
              )
          )
  
      print_rank_0(
          "  >> total number of samples: {}".format(len(self.samples))
      )
  
      # This indicates that each "sample" has multiple samples that
      # will collapse into batch dimension
      self.sample_multiplier = NUM_CHOICES
  
file_path: megatron-lm/tasks/race/data.py
imports: import glob
  import json
  import os
  import time
  from torch.utils.data import Dataset
  from megatron import print_rank_0
  from tasks.data_utils import build_sample
  from tasks.data_utils import build_tokens_types_paddings_from_ids
  from tasks.data_utils import clean_text
label_desc: Use image node_id 1 to create a new RaceDataset object from inherited base classes: Dataset with example: obj = RaceDataset(dataset_name, datapaths, tokenizer, max_seq_length, max_qa_length)