code: def convert_focalnet_checkpoint(
      model_name, pytorch_dump_folder_path, push_to_hub=False
  ):
      # fmt: off
      model_name_to_url = {
          "focalnet-tiny": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth",
          "focalnet-tiny-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth",
          "focalnet-small": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth",
          "focalnet-small-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth",
          "focalnet-base": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth",
          "focalnet-base-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth",
          "focalnet-large-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth",
          "focalnet-large-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth",
          "focalnet-xlarge-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth",
          "focalnet-xlarge-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth",
      }
      # fmt: on
  
      checkpoint_url = model_name_to_url[model_name]
      print("Checkpoint URL: ", checkpoint_url)
      state_dict = torch.hub.load_state_dict_from_url(
          checkpoint_url, map_location="cpu"
      )["model"]
  
      # rename keys
      for key in state_dict.copy().keys():
          val = state_dict.pop(key)
          state_dict[rename_key(key)] = val
  
      config = get_focalnet_config(model_name)
      model = FocalNetForImageClassification(config)
      model.eval()
  
      # load state dict
      model.load_state_dict(state_dict)
  
      # verify conversion
      url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  
      processor = BitImageProcessor(
          do_resize=True,
          size={"shortest_edge": 256},
          resample=PILImageResampling.BILINEAR,
          do_center_crop=True,
          crop_size=224,
          do_normalize=True,
          image_mean=IMAGENET_DEFAULT_MEAN,
          image_std=IMAGENET_DEFAULT_STD,
      )
      image = Image.open(requests.get(url, stream=True).raw)
      inputs = processor(images=image, return_tensors="pt")
  
      image_transforms = transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
          ]
      )
  
      original_pixel_values = image_transforms(image).unsqueeze(0)
  
      # verify pixel_values
      assert torch.allclose(
          inputs.pixel_values, original_pixel_values, atol=1e-4
      )
  
      outputs = model(**inputs)
  
      predicted_class_idx = outputs.logits.argmax(-1).item()
      print(
          "Predicted class:", model.config.id2label[predicted_class_idx]
      )
  
      print("First values of logits:", outputs.logits[0, :3])
  
      if model_name == "focalnet-tiny":
          expected_slice = torch.tensor([0.2166, -0.4368, 0.2191])
      elif model_name == "focalnet-tiny-lrf":
          expected_slice = torch.tensor([1.1669, 0.0125, -0.1695])
      elif model_name == "focalnet-small":
          expected_slice = torch.tensor([0.4917, -0.0430, 0.1341])
      elif model_name == "focalnet-small-lrf":
          expected_slice = torch.tensor([-0.2588, -0.5342, -0.2331])
      elif model_name == "focalnet-base":
          expected_slice = torch.tensor([-0.1655, -0.4090, -0.1730])
      elif model_name == "focalnet-base-lrf":
          expected_slice = torch.tensor([0.5306, -0.0483, -0.3928])
      assert torch.allclose(
          outputs.logits[0, :3], expected_slice, atol=1e-4
      )
      print("Looks ok!")
  
      if pytorch_dump_folder_path is not None:
          print(
              f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}"
          )
          model.save_pretrained(pytorch_dump_folder_path)
          processor.save_pretrained(pytorch_dump_folder_path)
  
      if push_to_hub:
          print(
              f"Pushing model and processor of {model_name} to the hub..."
          )
          model.push_to_hub(f"{model_name}")
          processor.push_to_hub(f"{model_name}")
  
file_path: transformers/src/transformers/models/focalnet/convert_focalnet_to_hf_format.py
imports: import argparse
  import json
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from torchvision import transforms
  from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
  from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
label_desc: Use image node_id 3 for calling a global function with example usage: convert_focalnet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub) without return types