code: def test_performance_speed(device, dtype):
      if device.type != "cuda" or not torch.cuda.is_available():
          pytest.skip("Cuda not available in system,")
  
      print("Benchmarking warp_affine")
      for input_shape in shapes:
          for PS in PSs:
              BS = input_shape[0]
              inpt = torch.rand(input_shape).to(device)
              As = (
                  torch.eye(3)
                  .unsqueeze(0)
                  .repeat(BS, 1, 1)[:, :2, :]
                  .to(device)
              )
              As += 0.1 * torch.rand(As.size()).to(device)
              torch.cuda.synchronize(device)
              t = time()
              _ = kornia.warp_affine(inpt, As, (PS, PS))
              print(
                  f"inp={input_shape}, PS={PS}, dev={device}, {time() - t}, sec"
              )
              torch.cuda.synchronize(device)
  
imports: from time import time
  import pytest
  import torch
  import kornia
label_desc: Use image node_id 1 for calling a global function with example usage: test_performance_speed(device, dtype) without return types