code: def complete_code(
      accelerator,
      model,
      tokenizer,
      dataloader,
      n_tasks,
      batch_size=20,
      **gen_kwargs,
  ):
      """Generate multiple codes for each task in the dataset. This function leverage accelerator to distribute
      the processing to multiple GPUs.
      dataloader, a wrapper around a TokenizeDataset objectm is supposed to send all the prompts from
      the evalution dataset to the modelm as the following:
      [p_0_0, p_0_1, ..., p_0_nc-1, p_1_0, ..., p_nt-1_nc-1]
      where nc is the number of copies of the prompt, and nt is the number of tasks.
      nc is such that num_sample = nc * batch_size
  
      Parameters
      ----------
      accelerator: Accelerator
  
      model: transformers.PreTrainedModel
          Code generation model. AutoTokenizer.from_pretrained(model_ckpt), ex model_ckpt = "lvwerra/codeparrot"
  
      tokenizer: transformers.AutoTokenizer
          The tokenizer used to train model
  
      dataloader: DataLoader
          The dataloader is a wrapper around a TokenizeDataset object. It is designed to be used with multiple GPUs.
  
      n_tasks: int
          The number of tasks in the dataset. It is used to determine the length of the output.
          Should be aligned with the number of tasks in the TokenizeDataset.
  
      batch_size: int
          num_return_sequences per copy of the prompt such that num_sample = batch_size * n_copies
  
      gen_kwargs: dict
          Keyword arguments for the generation function of the model.
  
      Returns
      -------
      code_gens: list of list of str, of length n_tasks
          List of generated codes for each task.
          Each element is a list of generated codes for each task, with length num_samples
      """
      gen_token_dict = defaultdict(
          list
      )  # dict of list of generated tokens
      for step, batch in tqdm(enumerate(dataloader)):
          with torch.no_grad():
              gen_kwargs["stopping_criteria"][0].start_length = batch[
                  "ids"
              ].shape[-1]
              generated_tokens = accelerator.unwrap_model(
                  model
              ).generate(
                  input_ids=batch["ids"][:, : batch["input_len"]],
                  num_return_sequences=batch_size,
                  **gen_kwargs,
              )
              # each task is generated batch_size times
              generated_tasks = batch["task_id"].repeat(batch_size)
              generated_tokens = accelerator.pad_across_processes(
                  generated_tokens,
                  dim=1,
                  pad_index=tokenizer.pad_token_id,
              )
  
              generated_tokens, generated_tasks = accelerator.gather(
                  (generated_tokens, generated_tasks)
              )
              generated_tokens = generated_tokens.cpu().numpy()
              generated_tasks = generated_tasks.cpu().numpy()
  
              for task, generated_tokens in zip(
                  generated_tasks, generated_tokens
              ):
                  gen_token_dict[task].append(generated_tokens)
  
      code_gens = [[] for _ in range(n_tasks)]
      for task, generated_tokens in gen_token_dict.items():
          for s in generated_tokens:
              gen_code = tokenizer.decode(
                  s,
                  skip_special_tokens=True,
                  clean_up_tokenization_spaces=True,
              )
              code_gens[task].append(remove_last_block(gen_code))
      return code_gens
  
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 a global function with example usage: complete_code(accelerator, model, tokenizer, dataloader, n_tasks, batch_size) and returns: code_gens