code: def get_model_and_tokenizer(config):
      model_name_or_path, quant_type = config
      bnb_config = get_4bit_config()
      if quant_type == "16bit":
          bnb_config.load_in_4bit = False
      else:
          bnb_config.bnb_4bit_quant_type = quant_type
      model = AutoModelForCausalLM.from_pretrained(
          model_name_or_path,
          quantization_config=bnb_config,
          max_memory={0: "48GB"},
          device_map="auto",
          torch_dtype=torch.bfloat16,
      ).eval()
  
      tokenizer = transformers.AutoTokenizer.from_pretrained(
          model_name_or_path
      )
  
      return model, tokenizer
  
file_path: bitsandbytes/tests/test_generation.py
imports: import pytest
  import torch
  import math
  from itertools import product
  import transformers
  from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, GenerationConfig, set_seed
label_desc: Use image node_id 2 for calling a global function with example usage: get_model_and_tokenizer(config) and returns: model,  tokenizer