code: def convert_timesformer_checkpoint(
      checkpoint_url, pytorch_dump_folder_path, model_name, push_to_hub
  ):
      config = get_timesformer_config(model_name)
  
      model = TimesformerForVideoClassification(config)
  
      # download original checkpoint, hosted on Google Drive
      output = "pytorch_model.bin"
      gdown.cached_download(checkpoint_url, output, quiet=False)
      files = torch.load(output, map_location="cpu")
      if "model" in files:
          state_dict = files["model"]
      elif "module" in files:
          state_dict = files["module"]
      else:
          state_dict = files["model_state"]
      new_state_dict = convert_state_dict(state_dict, config)
  
      model.load_state_dict(new_state_dict)
      model.eval()
  
      # verify model on basic input
      image_processor = VideoMAEImageProcessor(
          image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5]
      )
      video = prepare_video()
      inputs = image_processor(video[:8], return_tensors="pt")
  
      outputs = model(**inputs)
      logits = outputs.logits
  
      model_names = [
          # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
          "timesformer-base-finetuned-k400",
          "timesformer-large-finetuned-k400",
          "timesformer-hr-finetuned-k400",
          # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
          "timesformer-base-finetuned-k600",
          "timesformer-large-finetuned-k600",
          "timesformer-hr-finetuned-k600",
          # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
          "timesformer-base-finetuned-ssv2",
          "timesformer-large-finetuned-ssv2",
          "timesformer-hr-finetuned-ssv2",
      ]
  
      # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
      if model_name == "timesformer-base-finetuned-k400":
          expected_shape = torch.Size([1, 400])
          expected_slice = torch.tensor([-0.3016, -0.7713, -0.4205])
      elif model_name == "timesformer-base-finetuned-k600":
          expected_shape = torch.Size([1, 600])
          expected_slice = torch.tensor([-0.7267, -0.7466, 3.2404])
      elif model_name == "timesformer-base-finetuned-ssv2":
          expected_shape = torch.Size([1, 174])
          expected_slice = torch.tensor([-0.9059, 0.6433, -3.1457])
      elif model_name == "timesformer-large-finetuned-k400":
          expected_shape = torch.Size([1, 400])
          expected_slice = torch.tensor([0, 0, 0])
      elif model_name == "timesformer-large-finetuned-k600":
          expected_shape = torch.Size([1, 600])
          expected_slice = torch.tensor([0, 0, 0])
      elif model_name == "timesformer-large-finetuned-ssv2":
          expected_shape = torch.Size([1, 174])
          expected_slice = torch.tensor([0, 0, 0])
      elif model_name == "timesformer-hr-finetuned-k400":
          expected_shape = torch.Size([1, 400])
          expected_slice = torch.tensor([-0.9617, -3.7311, -3.7708])
      elif model_name == "timesformer-hr-finetuned-k600":
          expected_shape = torch.Size([1, 600])
          expected_slice = torch.tensor([2.5273, 0.7127, 1.8848])
      elif model_name == "timesformer-hr-finetuned-ssv2":
          expected_shape = torch.Size([1, 174])
          expected_slice = torch.tensor([-3.6756, -0.7513, 0.7180])
      else:
          raise ValueError(
              f"Model name not supported. Should be one of {model_names}"
          )
  
      # verify logits
      assert logits.shape == expected_shape
      assert torch.allclose(logits[0, :3], expected_slice, atol=1e-4)
      print("Logits ok!")
  
      if pytorch_dump_folder_path is not None:
          print(
              f"Saving model and image processor to {pytorch_dump_folder_path}"
          )
          image_processor.save_pretrained(pytorch_dump_folder_path)
          model.save_pretrained(pytorch_dump_folder_path)
  
      if push_to_hub:
          print("Pushing to the hub...")
          model.push_to_hub(f"fcakyon/{model_name}")
  
file_path: transformers/src/transformers/models/timesformer/convert_timesformer_to_pytorch.py
imports: import argparse
  import json
  import gdown
  import numpy
  import torch
  from huggingface_hub import hf_hub_download
  from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor
label_desc: Use image node_id 5 for calling a global function with example usage: convert_timesformer_checkpoint(checkpoint_url, pytorch_dump_folder_path, model_name, push_to_hub) without return types