code: def test_dynamo(
      self,
      batch_size,
      align_corners,
      normalized_coordinates,
      device,
      dtype,
      torch_optimizer,
  ):
      # generate input data
      height, width = 128, 64
      eye_size = 3  # identity 3x3
  
      patch_src = torch.rand(
          batch_size, 1, height, width, device=device, dtype=dtype
      )
  
      # create base homography
      dst_homo_src = utils.create_eye_batch(
          batch_size, eye_size, device=device, dtype=dtype
      )
  
      for _ in range(self.num_tests):
          # generate homography noise
          homo_delta = torch.rand_like(dst_homo_src) * 0.3
  
          dst_homo_src_i = dst_homo_src + homo_delta
  
          # transform the points with and without jit
          patch_dst = kornia.geometry.transform.homography_warp(
              patch_src,
              dst_homo_src_i,
              (height, width),
              align_corners=align_corners,
              normalized_coordinates=normalized_coordinates,
          )
          patch_dst_optimized = torch_optimizer(
              kornia.geometry.transform.homography_warp
          )(
              patch_src,
              dst_homo_src_i,
              (height, width),
              align_corners=align_corners,
              normalized_coordinates=normalized_coordinates,
          )
  
          assert_close(
              patch_dst, patch_dst_optimized, atol=1e-4, rtol=1e-4
          )
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import assert_close
  from kornia.utils.helpers import _torch_inverse_cast
label_desc: Use image node_id 13 for calling the TestHomographyWarper obj's underlying member method code with example usage: obj.test_dynamo(batch_size, align_corners, normalized_coordinates, device, dtype, torch_optimizer) without return types