code: def rename_key(orig_key):
      if "model" in orig_key:
          orig_key = orig_key.replace("model.", "")
      if "norm1" in orig_key:
          orig_key = orig_key.replace(
              "norm1", "attention.output.LayerNorm"
          )
      if "norm2" in orig_key:
          orig_key = orig_key.replace("norm2", "output.LayerNorm")
      if "norm" in orig_key:
          orig_key = orig_key.replace("norm", "LayerNorm")
      if "transformer" in orig_key:
          layer_num = orig_key.split(".")[0].split("_")[-1]
          orig_key = orig_key.replace(
              f"transformer_{layer_num}", f"encoder.layer.{layer_num}"
          )
      if "mha.attn" in orig_key:
          orig_key = orig_key.replace("mha.attn", "attention.self")
      if "mha" in orig_key:
          orig_key = orig_key.replace("mha", "attention")
      if "W_q" in orig_key:
          orig_key = orig_key.replace("W_q", "self.query")
      if "W_k" in orig_key:
          orig_key = orig_key.replace("W_k", "self.key")
      if "W_v" in orig_key:
          orig_key = orig_key.replace("W_v", "self.value")
      if "ff1" in orig_key:
          orig_key = orig_key.replace("ff1", "intermediate.dense")
      if "ff2" in orig_key:
          orig_key = orig_key.replace("ff2", "output.dense")
      if "ff" in orig_key:
          orig_key = orig_key.replace("ff", "output.dense")
      if "mlm_class" in orig_key:
          orig_key = orig_key.replace(
              "mlm.mlm_class", "cls.predictions.decoder"
          )
      if "mlm" in orig_key:
          orig_key = orig_key.replace(
              "mlm", "cls.predictions.transform"
          )
      if "cls" not in orig_key:
          orig_key = "yoso." + orig_key
  
      return orig_key
  
imports: import argparse
  import torch
  from transformers import YosoConfig, YosoForMaskedLM
label_desc: Use image node_id 1 for calling a global function with example usage: rename_key(orig_key) and returns: orig_key