code: def convert_vit_msn_checkpoint(
      checkpoint_url, pytorch_dump_folder_path
  ):
      config = ViTMSNConfig()
      config.num_labels = 1000
  
      repo_id = "datasets/huggingface/label-files"
      filename = "imagenet-1k-id2label.json"
      id2label = json.load(
          open(hf_hub_download(repo_id, filename), "r")
      )
      id2label = {int(k): v for k, v in id2label.items()}
      config.id2label = id2label
      config.label2id = {v: k for k, v in id2label.items()}
  
      if "s16" in checkpoint_url:
          config.hidden_size = 384
          config.intermediate_size = 1536
          config.num_attention_heads = 6
      elif "l16" in checkpoint_url:
          config.hidden_size = 1024
          config.intermediate_size = 4096
          config.num_hidden_layers = 24
          config.num_attention_heads = 16
          config.hidden_dropout_prob = 0.1
      elif "b4" in checkpoint_url:
          config.patch_size = 4
      elif "l7" in checkpoint_url:
          config.patch_size = 7
          config.hidden_size = 1024
          config.intermediate_size = 4096
          config.num_hidden_layers = 24
          config.num_attention_heads = 16
          config.hidden_dropout_prob = 0.1
  
      model = ViTMSNModel(config)
  
      state_dict = torch.hub.load_state_dict_from_url(
          checkpoint_url, map_location="cpu"
      )["target_encoder"]
  
      image_processor = ViTImageProcessor(size=config.image_size)
  
      remove_projection_head(state_dict)
      rename_keys = create_rename_keys(config, base_model=True)
  
      for src, dest in rename_keys:
          rename_key(state_dict, src, dest)
      read_in_q_k_v(state_dict, config, base_model=True)
  
      model.load_state_dict(state_dict)
      model.eval()
  
      url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  
      image = Image.open(requests.get(url, stream=True).raw)
      image_processor = ViTImageProcessor(
          size=config.image_size,
          image_mean=IMAGENET_DEFAULT_MEAN,
          image_std=IMAGENET_DEFAULT_STD,
      )
      inputs = image_processor(images=image, return_tensors="pt")
  
      # forward pass
      torch.manual_seed(2)
      outputs = model(**inputs)
      last_hidden_state = outputs.last_hidden_state
  
      # The following Colab Notebook was used to generate these outputs:
      # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
      if "s16" in checkpoint_url:
          expected_slice = torch.tensor([[-1.0915, -1.4876, -1.1809]])
      elif "b16" in checkpoint_url:
          expected_slice = torch.tensor([[14.2889, -18.9045, 11.7281]])
      elif "l16" in checkpoint_url:
          expected_slice = torch.tensor([[41.5028, -22.8681, 45.6475]])
      elif "b4" in checkpoint_url:
          expected_slice = torch.tensor([[-4.3868, 5.2932, -0.4137]])
      else:
          expected_slice = torch.tensor([[-0.1792, -0.6465, 2.4263]])
  
      # verify logits
      assert torch.allclose(
          last_hidden_state[:, 0, :3], expected_slice, atol=1e-4
      )
  
      print(f"Saving model to {pytorch_dump_folder_path}")
      model.save_pretrained(pytorch_dump_folder_path)
  
      print(f"Saving image processor to {pytorch_dump_folder_path}")
      image_processor.save_pretrained(pytorch_dump_folder_path)
  
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 6 for calling a global function with example usage: convert_vit_msn_checkpoint(checkpoint_url, pytorch_dump_folder_path) without return types