code: def convert_swinv2_checkpoint(swinv2_name, pytorch_dump_folder_path):
      timm_model = timm.create_model(swinv2_name, pretrained=True)
      timm_model.eval()
  
      config = get_swinv2_config(swinv2_name)
      model = Swinv2ForImageClassification(config)
      model.eval()
  
      new_state_dict = convert_state_dict(
          timm_model.state_dict(), model
      )
      model.load_state_dict(new_state_dict)
  
      url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  
      feature_extractor = AutoFeatureExtractor.from_pretrained(
          "microsoft/{}".format(swinv2_name.replace("_", "-"))
      )
      image = Image.open(requests.get(url, stream=True).raw)
      inputs = feature_extractor(images=image, return_tensors="pt")
  
      timm_outs = timm_model(inputs["pixel_values"])
      hf_outs = model(**inputs).logits
  
      assert torch.allclose(timm_outs, hf_outs, atol=1e-3)
  
      print(f"Saving model {swinv2_name} to {pytorch_dump_folder_path}")
      model.save_pretrained(pytorch_dump_folder_path)
  
      print(f"Saving feature extractor to {pytorch_dump_folder_path}")
      feature_extractor.save_pretrained(pytorch_dump_folder_path)
  
      model.push_to_hub(
          repo_path_or_name=Path(pytorch_dump_folder_path, swinv2_name),
          organization="nandwalritik",
          commit_message="Add model",
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/swinv2/convert_swinv2_timm_to_pytorch.py
imports: import argparse
  import json
  from pathlib import Path
  import torch
  from PIL import Image
  import requests
  import timm
  from huggingface_hub import hf_hub_download
  from transformers import AutoFeatureExtractor, Swinv2Config, Swinv2ForImageClassification
label_desc: Use image node_id 4 for calling a global function with example usage: convert_swinv2_checkpoint(swinv2_name, pytorch_dump_folder_path) without return types