code: def test_crops_w(self):
      target_crops_w1 = torch.tensor(
          [
              # chennels being offset:
              # R  G  B  A  W(eight)
              [0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0],
              [1, 1, 1, 1, 1],
              [1, 1, 1, 1, 1],
              [1, 1, 1, 1, 1],
              [2, 2, 2, 2, 2],
              [2, 2, 2, 2, 2],
              [2, 2, 2, 2, 2],
          ],
          device=torch.device("cpu"),
      ).reshape(1, 1, 1, 1, 9, 5)
      self.assertClose(self.results1_cpu[1], target_crops_w1)
  
      target_crops_w_base = target_crops_w1[0, 0, 0]
      target_crops_w = torch.cat(
          [
              target_crops_w_base,
              target_crops_w_base + 1,
              target_crops_w_base + 2,
              target_crops_w_base + 3,
          ],
          dim=0,
      )  # Each w value needs an increment.
  
      # Check that we have the right shape, and (after broadcasting) it has the right
      # values. These should be repeated (tiled) for each n and k.
      self.assertClose(
          self.results_cpu[1].shape, torch.tensor([2, 3, 4, 5, 9, 5])
      )
      for n in range(2):
          for h in range(3):
              for k in range(5):
                  self.assertClose(
                      self.results_cpu[1][n, h, :, k],
                      target_crops_w,
                      atol=0,
                  )
  
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 4 for calling the TestPrecompute obj's underlying member method code with example usage: obj.test_crops_w() without return types