code: def rename_key(name):
      if "patch_embed.proj" in name:
          name = name.replace(
              "patch_embed.proj",
              "embeddings.patch_embeddings.projection",
          )
      if "patch_embed.norm" in name:
          name = name.replace("patch_embed.norm", "embeddings.norm")
      if "layers" in name:
          name = "encoder." + name
      if "attn.proj" in name:
          name = name.replace("attn.proj", "attention.output.dense")
      if "attn" in name:
          name = name.replace("attn", "attention.self")
      if "norm1" in name:
          name = name.replace("norm1", "layernorm_before")
      if "norm2" in name:
          name = name.replace("norm2", "layernorm_after")
      if "mlp.fc1" in name:
          name = name.replace("mlp.fc1", "intermediate.dense")
      if "mlp.fc2" in name:
          name = name.replace("mlp.fc2", "output.dense")
      if "q_bias" in name:
          name = name.replace("q_bias", "query.bias")
      if "k_bias" in name:
          name = name.replace("k_bias", "key.bias")
      if "v_bias" in name:
          name = name.replace("v_bias", "value.bias")
      if "cpb_mlp" in name:
          name = name.replace("cpb_mlp", "continuous_position_bias_mlp")
      if name == "norm.weight":
          name = "layernorm.weight"
      if name == "norm.bias":
          name = "layernorm.bias"
  
      if "head" in name:
          name = name.replace("head", "classifier")
      else:
          name = "swinv2." + name
  
      return name
  
file_path: transformers/src/transformers/models/swinv2/convert_swinv2_timm_to_pytorch.py
imports: import argparse
  import json
  from pathlib import Path
  import requests
  import timm
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from transformers import AutoImageProcessor, Swinv2Config, Swinv2ForImageClassification
label_desc: Use image node_id 2 for calling a global function with example usage: rename_key(name) and returns: name