code: def load_delta(delta_path):
      delta_dict = torch.load(delta_path)
      delta_with_prefix = OrderedDict()
      for k, v in delta_dict.items():
          # CpmBeeModel -> CpmBeeForCasualLM
          if (
              k.startswith("encoder.")
              or k.startswith("input_embedding.")
              or k.startswith("position_bias.")
          ):
              delta_with_prefix["cpmbee." + k] = v
          else:
              delta_with_prefix[k] = v
      del delta_dict
      return delta_with_prefix
  
file_path: DeepKE/example/llm/CPM-Bee/src/text_generation_hf.py
imports: import torch
  from collections import OrderedDict
  from argparse import ArgumentParser
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from accelerate import dispatch_model
  from accelerate.utils import get_balanced_memory, infer_auto_device_map
label_desc: Use image node_id 2 for calling a global function with example usage: load_delta(delta_path) and returns: delta_with_prefix