code: def process_init():
      global tokenizer
      model_name = os.environ.get("MODEL_NAME", "facebook/opt-1.3b")
  
      if model_name == "EleutherAI/gpt-neox-20b":
          tokenizer = AutoTokenizer.from_pretrained(model_name)
          tokenizer.model_max_length = int(1e30)
          tokenizer.pad_token = "<|endoftext|>"
      elif model_name == "huggyllama/llama-7b":
          tokenizer = AutoTokenizer.from_pretrained(model_name)
          tokenizer.model_max_length = int(1e30)
          tokenizer.pad_token = "<|endoftext|>"
      else:
          tokenizer = AutoTokenizer.from_pretrained(model_name)
          tokenizer.add_bos_token = False
  
file_path: H2O/h2o_hf/tasks/eval_harness.py
imports: from functools import partial
  import os
  import transformers
  from lm_eval.base import LM
  from tqdm import tqdm
  import numpy
  from tasks.util import sample_batch, shrink_seq
  import multiprocessing
  import ftfy
  from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
label_desc: Use image node_id 1 for calling a global function with example usage: process_init() without return types