code: def test_background_color(self):
      # Background should always have alpha=0, and the chosen RGB.
      N, H, W = 2, 3, 4
      # Make a mask with background in the zeroth row of the first image.
      bg_mask = torch.zeros([N, H, W, 1, 1])
      bg_mask[0, :, 0] = 1
  
      bg_color = torch.tensor([0.2, 0.3, 0.4])
  
      color_layers = torch.rand((N, H, W, 4, 3)) * (1 - bg_mask)
      color_weights = torch.rand((N, H, W, 1, 3)) * (1 - bg_mask)
  
      colors = _normalize_and_compose_all_layers(
          bg_color, color_layers, color_weights
      )
  
      # Background RGB should be .2, .3, .4, and alpha should be 0.
      self.assertClose(
          torch.masked_select(colors, bg_mask.bool()[..., 0]),
          torch.tensor(
              [0.2, 0.3, 0.4, 0, 0.2, 0.3, 0.4, 0, 0.2, 0.3, 0.4, 0.0]
          ),
          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 1 for calling the TestNormalizeAndComposeAllLayers obj's underlying member method code with example usage: obj.test_background_color() without return types