code: def convert_convnext_checkpoint(
      checkpoint_url, pytorch_dump_folder_path
  ):
      """
      Copy/paste/tweak model's weights to our ConvNext structure.
      """
  
      # define ConvNext configuration based on URL
      config, expected_shape = get_convnext_config(checkpoint_url)
      # load original state_dict from URL
      state_dict = torch.hub.load_state_dict_from_url(checkpoint_url)[
          "model"
      ]
      # rename keys
      for key in state_dict.copy().keys():
          val = state_dict.pop(key)
          state_dict[rename_key(key)] = val
      # add prefix to all keys expect classifier head
      for key in state_dict.copy().keys():
          val = state_dict.pop(key)
          if not key.startswith("classifier"):
              key = "convnext." + key
          state_dict[key] = val
  
      # load HuggingFace model
      model = ConvNextForImageClassification(config)
      model.load_state_dict(state_dict)
      model.eval()
  
      # Check outputs on an image, prepared by ConvNextFeatureExtractor
      size = 224 if "224" in checkpoint_url else 384
      feature_extractor = ConvNextFeatureExtractor(size=size)
      pixel_values = feature_extractor(
          images=prepare_img(), return_tensors="pt"
      ).pixel_values
  
      logits = model(pixel_values).logits
  
      # note: the logits below were obtained without center cropping
      if (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth"
      ):
          expected_logits = torch.tensor([-0.1210, -0.6605, 0.1918])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth"
      ):
          expected_logits = torch.tensor([-0.4473, -0.1847, -0.6365])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth"
      ):
          expected_logits = torch.tensor([0.4525, 0.7539, 0.0308])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth"
      ):
          expected_logits = torch.tensor([0.3561, 0.6350, -0.0384])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth"
      ):
          expected_logits = torch.tensor([0.4174, -0.0989, 0.1489])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth"
      ):
          expected_logits = torch.tensor([0.2513, -0.1349, -0.1613])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth"
      ):
          expected_logits = torch.tensor([1.2980, 0.3631, -0.1198])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth"
      ):
          expected_logits = torch.tensor([1.2963, 0.1227, 0.1723])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth"
      ):
          expected_logits = torch.tensor([1.7956, 0.8390, 0.2820])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth"
      ):
          expected_logits = torch.tensor([-0.2822, -0.0502, -0.0878])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth"
      ):
          expected_logits = torch.tensor([-0.5672, -0.0730, -0.4348])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth"
      ):
          expected_logits = torch.tensor([0.2681, 0.2365, 0.6246])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth"
      ):
          expected_logits = torch.tensor([-0.2642, 0.3931, 0.5116])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth"
      ):
          expected_logits = torch.tensor([-0.6677, -0.1873, -0.8379])
      elif (
          checkpoint_url
          == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth"
      ):
          expected_logits = torch.tensor([-0.7749, -0.2967, -0.6444])
      else:
          raise ValueError(f"Unknown URL: {checkpoint_url}")
  
      assert torch.allclose(logits[0, :3], expected_logits, atol=1e-3)
      assert logits.shape == expected_shape
  
      Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
      print(f"Saving model 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)
  
      print("Pushing model to the hub...")
      model_name = "convnext"
      if "tiny" in checkpoint_url:
          model_name += "-tiny"
      elif "small" in checkpoint_url:
          model_name += "-small"
      elif "base" in checkpoint_url:
          model_name += "-base"
      elif "xlarge" in checkpoint_url:
          model_name += "-xlarge"
      elif "large" in checkpoint_url:
          model_name += "-large"
      if "224" in checkpoint_url:
          model_name += "-224"
      elif "384" in checkpoint_url:
          model_name += "-384"
      if "22k" in checkpoint_url and "1k" not in checkpoint_url:
          model_name += "-22k"
      if "22k" in checkpoint_url and "1k" in checkpoint_url:
          model_name += "-22k-1k"
  
      model.push_to_hub(
          repo_path_or_name=Path(pytorch_dump_folder_path, model_name),
          organization="nielsr",
          commit_message="Add model",
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/convnext/convert_convnext_to_pytorch.py
imports: import argparse
  import json
  from pathlib import Path
  import torch
  from PIL import Image
  import requests
  from huggingface_hub import hf_hub_download
  from transformers import ConvNextConfig, ConvNextFeatureExtractor, ConvNextForImageClassification
  from transformers.utils import logging
label_desc: Use image node_id 4 for calling a global function with example usage: convert_convnext_checkpoint(checkpoint_url, pytorch_dump_folder_path) without return types