code: def test_location_to_NxN_grid() -> None:
      # create a test instance of the Location class
      test_location = test_module.Location(
          left=10,
          top=20,
          width=100,
          height=200,
          image_height=244,
          image_width=244,
          horizontal_flip=True,
          vertical_flip=False,
      )
  
      # create an instance of the class containing the function
      test_class = test_module.RandomResizedCropAndFlip(grid_size=3)
  
      # call the function with the test location
      result = test_class.location_to_NxN_grid(test_location)
  
      # create a tensor representing the expected output
      expected_output = torch.tensor(
          [
              [
                  [126.6667, 53.3333],
                  [76.6667, 53.3333],
                  [26.6667, 53.3333],
              ],
              [
                  [126.6667, 153.3333],
                  [76.6667, 153.3333],
                  [26.6667, 153.3333],
              ],
              [
                  [126.6667, 253.3333],
                  [76.6667, 253.3333],
                  [26.6667, 253.3333],
              ],
          ]
      )
      # check that the function output matches the expected output
      assert torch.allclose(result, expected_output)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_location_to_NxN_grid() without return types