code: def test_parse_colmap_output(
      device, dtype, colmap_cameras_path, colmap_images_path
  ) -> None:
      img_names, cameras = parse_colmap_output(
          colmap_cameras_path, colmap_images_path, device, dtype
      )
      assert_close(
          cameras.fx[0],
          torch.tensor(845.5654, device=device, dtype=dtype),
      )
      assert_close(
          cameras.fy[0],
          torch.tensor(845.5654, device=device, dtype=dtype),
      )
      assert_close(
          cameras.cx[0], torch.tensor(504, device=device, dtype=dtype)
      )
      assert_close(
          cameras.cy[0], torch.tensor(378, device=device, dtype=dtype)
      )
  
      qw = 0.99967420027533338
      qx = -0.016637661896892787
      qy = 0.012113517278577941
      qz = 0.015097821353390602
      tx = 3.2407154690493751
      ty = 2.3447342819165637
      tz = -1.0631749488011808
  
      q = torch.tensor([qw, qx, qy, qz], device=device, dtype=dtype)
      R = quaternion_to_rotation_matrix(q)
      t = torch.tensor([tx, ty, tz], device=device, dtype=dtype)
  
      assert_close(R, cameras.rotation_matrix[2])
      assert_close(cameras.translation_vector[2], t.unsqueeze(-1))
  
      assert img_names[2] == "image002.png"
  
file_path: kornia/test/nerf/test_camera_utils.py
imports: import hashlib
  import urllib.request
  import pytest
  import torch
  from kornia.geometry.conversions import quaternion_to_rotation_matrix
  from kornia.nerf.camera_utils import create_spiral_path, parse_colmap_output
  from kornia.testing import assert_close
label_desc: Use image node_id 4 for calling a global function with example usage: test_parse_colmap_output(device, dtype, colmap_cameras_path, colmap_images_path) without return types