code: def test_mid_to_top_level_splatting(self):
      # Check that occlusion buffers get accumulated as expected when the splatting
      # and splatted pixels are co-surface on different intersection layers.
      # This test will make best sense with accompanying Fig. 4 from "Differentiable
      # Surface Rendering via Non-differentiable Sampling" by Cole et al.
      for direction, offset in enumerate(offsets):
          if direction == 4:
              continue  # Skip self-splatting which is always co-surface.
  
          depths = torch.zeros(1, 3, 3, 3)
  
          # This is our q, the pixel splatted onto, in the center of the image.
          depths[0, 1, 1] = torch.tensor([0.71, 0.8, 1.0])
  
          # This is our p, the splatting pixel.
          depths[0, offset[0] + 1, offset[1] + 1] = torch.tensor(
              [0.5, 0.7, 0.9]
          )
  
          occlusion_layers = _compute_occlusion_layers(depths)
  
          # Check that we computed that it is the middle layer of p that is co-
          # surface with q. (1, 1) is the id of q in the depth array, and offset_id
          # is the id of p's direction w.r.t. q.
          psurfaceid_onto_q = occlusion_layers[0, 1, 1, direction]
          self.assertEqual(int(psurfaceid_onto_q), 1)
  
          # Conversely, if we swap p and q, we have a top-level splatting onto
          # mid-level. offset + 1 is the id of p, and 8-offset_id is the id of
          # q's direction w.r.t. p (e.g. if p is [-1, -1] w.r.t. q, then q is
          # [1, 1] w.r.t. p; we use the ids of these two directions in the offsets
          # array).
          qsurfaceid_onto_p = occlusion_layers[
              0, offset[0] + 1, offset[1] + 1, 8 - direction
          ]
          self.assertEqual(int(qsurfaceid_onto_p), -1)
  
imports: import unittest
  import torch
  from pytorch3d.common.compat import meshgrid_ij
  from pytorch3d.renderer.cameras import FoVPerspectiveCameras
  from pytorch3d.renderer.splatter_blend import _compute_occlusion_layers, _compute_splatted_colors_and_weights, _compute_splatting_colors_and_weights, _get_splat_kernel_normalization, _normalize_and_compose_all_layers, _offset_splats, _precompute, _prepare_pixels_and_colors
  from .common_testing import TestCaseMixin
label_desc: Use image node_id 3 for calling the TestComputeOcclusionLayers obj's underlying member method code with example usage: obj.test_mid_to_top_level_splatting() without return types