code: def get_tokenizer(model_name_or_path, fast_tokenizer=True):
      if "llama" in model_name_or_path:
          from transformers.models.llama import LlamaTokenizer
  
          tokenizer = LlamaTokenizer.from_pretrained(
              model_name_or_path, fast_tokenizer=fast_tokenizer
          )
          if tokenizer.pad_token is None:
              # assert tokenizer.eos_token is not None
              # tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})
              tokenizer.add_special_tokens({"pad_token": "[PAD]"})
              tokenizer.padding_side = "right"
      else:
          tokenizer = AutoTokenizer.from_pretrained(
              model_name_or_path, fast_tokenizer=fast_tokenizer
          )
          tokenizer.pad_token = tokenizer.eos_token
          # make sure tokenizer is right pad in our logic
          tokenizer.padding_side = "right"
      return tokenizer
  
imports: import os
  import torch
  import random
  import numpy
  from transformers import set_seed, AutoTokenizer
  import json
  import deepspeed
  from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
  from deepspeed.accelerator import get_accelerator
  import torch.nn
label_desc: Use image node_id 4 for calling a global function with example usage: get_tokenizer(model_name_or_path, fast_tokenizer) and returns: tokenizer