class_docstr: Custom `StoppingCriteria` which checks if all generated functions in the batch are completed.
class_docstr_tok: ["Custom","`","StoppingCriteria","`","which","checks","if","all","generated","functions","in","the","batch","are","completed","."]
code: def __init__(self, start_length, eof_strings, tokenizer):
      self.start_length = start_length
      self.eof_strings = eof_strings
      self.tokenizer = tokenizer
  
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 1 to create a new EndOfFunctionCriteria object from inherited base classes: StoppingCriteria with example: obj = EndOfFunctionCriteria(start_length, eof_strings, tokenizer)