code: def test_jit(self, device, dtype):
      @torch.jit.script
      def op_script(data: torch.Tensor) -> torch.Tensor:
          return kornia.geometry.transform.rot180(data)
  
      input = torch.tensor(
          [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]],
          device=device,
          dtype=dtype,
      )  # 3 x 3
  
      # Build jit trace
      op_trace = torch.jit.trace(op_script, (input,))
  
      # Create new inputs
      input = torch.tensor(
          [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [5.0, 5.0, 0.0]],
          device=device,
          dtype=dtype,
      )  # 3 x 3
  
      input = input.repeat(2, 1, 1)  # 2 x 3 x 3
  
      expected = torch.tensor(
          [[[0.0, 5.0, 5.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
          device=device,
          dtype=dtype,
      )  # 3 x 3
  
      expected = expected.repeat(2, 1, 1)
  
      actual = op_trace(input)
  
      assert_close(actual, expected)
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import assert_close
label_desc: Use image node_id 4 for calling the TestRot180 obj's underlying member method code with example usage: obj.test_jit(device, dtype) and returns: kornia