code: def test_zero_alpha_zero_weight(self):
      # Pixels with zero alpha do no splatting, but should still be splatted on.
      colors = self.colors.clone()
      colors[0, 1, 1, 0, 3] = 0.0
      splatting_colors_and_weights = (
          _compute_splatting_colors_and_weights(
              self.pixel_coords_screen,
              colors,
              sigma=0.5,
              offsets=offsets,
          )
      )
  
      # The transparent pixel should do no splatting.
      self.assertTrue(
          torch.all(splatting_colors_and_weights[0, 1, 1, 0] == 0.0)
      )
  
      # Splatting *onto* the transparent pixel should be unaffected.
      reference_weights_colors = splatting_colors_and_weights[
          0, 1, 1, 1
      ]
      for direction, offset in enumerate(offsets):
          if direction == 4:
              continue  # Ignore self-splats
          # We invert the direction to get the right (h, w, d) coordinate of each
          # pixel splatting *onto* the pixel with zero alpha.
          self.assertClose(
              splatting_colors_and_weights[
                  0, 1 + offset[0], 1 + offset[1], 0, 8 - direction
              ],
              reference_weights_colors[8 - direction],
              atol=0.001,
          )
  
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 TestComputeSplattingColorsAndWeights obj's underlying member method code with example usage: obj.test_zero_alpha_zero_weight() without return types