code: def rename_key(old_name, num_meta4D_last_stage):
      new_name = old_name
  
      if "patch_embed" in old_name:
          _, layer, param = old_name.split(".")
  
          if layer == "0":
              new_name = old_name.replace("0", "convolution1")
          elif layer == "1":
              new_name = old_name.replace("1", "batchnorm_before")
          elif layer == "3":
              new_name = old_name.replace("3", "convolution2")
          else:
              new_name = old_name.replace("4", "batchnorm_after")
  
      if "network" in old_name and re.search(r"\d\.\d", old_name):
          two_digit_num = r"\b\d{2}\b"
          if bool(re.search(two_digit_num, old_name)):
              match = re.search(r"\d\.\d\d.", old_name).group()
          else:
              match = re.search(r"\d\.\d.", old_name).group()
          if int(match[0]) < 6:
              trimmed_name = old_name.replace(match, "")
              trimmed_name = trimmed_name.replace(
                  "network",
                  match[0] + ".meta4D_layers.blocks." + match[2:-1],
              )
              new_name = "intermediate_stages." + trimmed_name
          else:
              trimmed_name = old_name.replace(match, "")
              if int(match[2]) < num_meta4D_last_stage:
                  trimmed_name = trimmed_name.replace(
                      "network", "meta4D_layers.blocks." + match[2]
                  )
              else:
                  layer_index = str(
                      int(match[2]) - num_meta4D_last_stage
                  )
                  trimmed_name = trimmed_name.replace(
                      "network", "meta3D_layers.blocks." + layer_index
                  )
                  if "norm1" in old_name:
                      trimmed_name = trimmed_name.replace(
                          "norm1", "layernorm1"
                      )
                  elif "norm2" in old_name:
                      trimmed_name = trimmed_name.replace(
                          "norm2", "layernorm2"
                      )
                  elif "fc1" in old_name:
                      trimmed_name = trimmed_name.replace(
                          "fc1", "linear_in"
                      )
                  elif "fc2" in old_name:
                      trimmed_name = trimmed_name.replace(
                          "fc2", "linear_out"
                      )
  
              new_name = "last_stage." + trimmed_name
  
      elif "network" in old_name and re.search(r".\d.", old_name):
          new_name = old_name.replace("network", "intermediate_stages")
  
      if "fc" in new_name:
          new_name = new_name.replace("fc", "convolution")
      elif ("norm1" in new_name) and ("layernorm1" not in new_name):
          new_name = new_name.replace("norm1", "batchnorm_before")
      elif ("norm2" in new_name) and ("layernorm2" not in new_name):
          new_name = new_name.replace("norm2", "batchnorm_after")
      if "proj" in new_name:
          new_name = new_name.replace("proj", "projection")
      if "dist_head" in new_name:
          new_name = new_name.replace(
              "dist_head", "distillation_classifier"
          )
      elif "head" in new_name:
          new_name = new_name.replace("head", "classifier")
      elif "patch_embed" in new_name:
          new_name = "efficientformer." + new_name
      elif new_name == "norm.weight" or new_name == "norm.bias":
          new_name = new_name.replace("norm", "layernorm")
          new_name = "efficientformer." + new_name
      else:
          new_name = "efficientformer.encoder." + new_name
  
      return new_name
  
file_path: transformers/src/transformers/models/efficientformer/convert_efficientformer_original_pytorch_checkpoint_to_pytorch.py
imports: import argparse
  import re
  from pathlib import Path
  import requests
  import torch
  from PIL import Image
  from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
  from transformers import EfficientFormerConfig, EfficientFormerForImageClassificationWithTeacher, EfficientFormerImageProcessor
  from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
label_desc: Use image node_id 1 for calling a global function with example usage: rename_key(old_name, num_meta4D_last_stage) and returns: new_name