code: def create_rename_keys(config, base_model=False):
      rename_keys = []
      for i in range(config.num_hidden_layers):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append(
              (
                  f"module.blocks.{i}.norm1.weight",
                  f"vit.encoder.layer.{i}.layernorm_before.weight",
              )
          )
          rename_keys.append(
              (
                  f"module.blocks.{i}.norm1.bias",
                  f"vit.encoder.layer.{i}.layernorm_before.bias",
              )
          )
          rename_keys.append(
              (
                  f"module.blocks.{i}.attn.proj.weight",
                  f"vit.encoder.layer.{i}.attention.output.dense.weight",
              )
          )
          rename_keys.append(
              (
                  f"module.blocks.{i}.attn.proj.bias",
                  f"vit.encoder.layer.{i}.attention.output.dense.bias",
              )
          )
          rename_keys.append(
              (
                  f"module.blocks.{i}.norm2.weight",
                  f"vit.encoder.layer.{i}.layernorm_after.weight",
              )
          )
          rename_keys.append(
              (
                  f"module.blocks.{i}.norm2.bias",
                  f"vit.encoder.layer.{i}.layernorm_after.bias",
              )
          )
          rename_keys.append(
              (
                  f"module.blocks.{i}.mlp.fc1.weight",
                  f"vit.encoder.layer.{i}.intermediate.dense.weight",
              )
          )
          rename_keys.append(
              (
                  f"module.blocks.{i}.mlp.fc1.bias",
                  f"vit.encoder.layer.{i}.intermediate.dense.bias",
              )
          )
          rename_keys.append(
              (
                  f"module.blocks.{i}.mlp.fc2.weight",
                  f"vit.encoder.layer.{i}.output.dense.weight",
              )
          )
          rename_keys.append(
              (
                  f"module.blocks.{i}.mlp.fc2.bias",
                  f"vit.encoder.layer.{i}.output.dense.bias",
              )
          )
  
      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("module.cls_token", "vit.embeddings.cls_token"),
              (
                  "module.patch_embed.proj.weight",
                  "vit.embeddings.patch_embeddings.projection.weight",
              ),
              (
                  "module.patch_embed.proj.bias",
                  "vit.embeddings.patch_embeddings.projection.bias",
              ),
              (
                  "module.pos_embed",
                  "vit.embeddings.position_embeddings",
              ),
          ]
      )
  
      if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ("module.norm.weight", "layernorm.weight"),
                  ("module.norm.bias", "layernorm.bias"),
              ]
          )
  
          # if just the base model, we should remove "vit" from all keys that start with "vit"
          rename_keys = [
              (pair[0], pair[1][4:])
              if pair[1].startswith("vit")
              else pair
              for pair in rename_keys
          ]
      else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("norm.weight", "vit.layernorm.weight"),
                  ("norm.bias", "vit.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]
          )
  
      return rename_keys
  
file_path: transformers/src/transformers/models/vit_msn/convert_msn_to_pytorch.py
imports: import argparse
  import json
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
  from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
label_desc: Use image node_id 1 for calling a global function with example usage: create_rename_keys(config, base_model) and returns: rename_keys