class_docstr: Tokenize and preprocess the dataset
  Multiple copies of the same prompt are sent sequentially.
  See compute_code for more details.
class_docstr_tok: ["Tokenize","and","preprocess","the","dataset","Multiple","copies","of","the","same","prompt","are","sent","sequentially",".","See","compute_code","for","more","details","."]
code: def __init__(self, tokenizer, dataset, n_tasks=None, n_copies=1):
      self.tokenizer = tokenizer
      self.dataset = dataset
      self.n_tasks = len(dataset) if n_tasks is None else n_tasks
      self.n_copies = n_copies
  
imports: import json
  import multiprocessing
  import os
  import re
  from collections import defaultdict
  import torch
  from datasets import load_dataset, load_metric
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from tqdm import tqdm
  import transformers
  from accelerate import Accelerator
  from accelerate.utils import set_seed
  from arguments import HumanEvalArguments
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
label_desc: Use image node_id 1 to create a new TokenizedDataset object from inherited base classes: IterableDataset with example: obj = TokenizedDataset(tokenizer, dataset, n_tasks, n_copies)