code: def get_weight_data_type(data_type):
      if data_type == "fp32":
          return np.float32
      elif data_type == "fp16":
          return np.float16
      else:
          assert False, f"Invalid weight data type {data_type}"
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/faster_transformer/gpt/utils/huggingface_gpt_convert.py
imports: import argparse
  import configparser
  import os
  import sys
  from loguru import logger
  import numpy
  from transformers import GPT2Model
  from nebullvm.optional_modules.torch import torch
label_desc: Use image node_id 1 for calling a global function with example usage: get_weight_data_type(data_type) and returns: np, np