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 __iter__(self):
      prompts = []
      for task in range(self.n_tasks):
          # without strip, the model generate commented codes ...
          prompts.append(
              self.tokenizer.eos_token
              + self.dataset[task]["prompt"].strip()
          )
      outputs = self.tokenizer(
          prompts, padding=True, return_tensors="pt"
      )
      for task in range(self.n_tasks):
          for _ in range(self.n_copies):
              yield {
                  "ids": outputs.input_ids[task],
                  "task_id": task,
                  "input_len": outputs.attention_mask[task].sum(),
              }
  
imports: import json
  import multiprocessing
  import os
  import re
  from collections import defaultdict
  import torch
  from accelerate import Accelerator
  from accelerate.utils import set_seed
  from arguments import HumanEvalArguments
  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 transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
label_desc: Use image node_id 2 for calling the TokenizedDataset obj's underlying member method code with example usage: obj.__iter__() without return types