code: def convert_swin_checkpoint(
      model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub
  ):
      state_dict = torch.load(checkpoint_path, map_location="cpu")[
          "model"
      ]
  
      config = get_swin_config(model_name)
      model = SwinForMaskedImageModeling(config)
      model.eval()
  
      new_state_dict = convert_state_dict(state_dict, model)
      model.load_state_dict(new_state_dict)
  
      url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  
      image_processor = ViTImageProcessor(
          size={"height": 192, "width": 192}
      )
      image = Image.open(requests.get(url, stream=True).raw)
      inputs = image_processor(images=image, return_tensors="pt")
  
      with torch.no_grad():
          outputs = model(**inputs).logits
  
      print(outputs.keys())
      print("Looks ok!")
  
      if pytorch_dump_folder_path is not None:
          print(
              f"Saving model {model_name} 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)
  
      if push_to_hub:
          print(
              f"Pushing model and image processor for {model_name} to hub"
          )
          model.push_to_hub(f"microsoft/{model_name}")
          image_processor.push_to_hub(f"microsoft/{model_name}")
  
file_path: transformers/src/transformers/models/swin/convert_swin_simmim_to_pytorch.py
imports: import argparse
  import requests
  import torch
  from PIL import Image
  from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
label_desc: Use image node_id 4 for calling a global function with example usage: convert_swin_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub) without return types