code: def _add_background_color_to_images(
      pix_idxs, images, background_color
  ):
      """
      Mask pixels in images without corresponding points with a given background_color.
  
      Args:
          pix_idxs: int32 Tensor of shape (N, points_per_pixel, image_size, image_size)
              giving the indices of the nearest points at each pixel, sorted in z-order.
          images: Tensor of shape (N, 4, image_size, image_size) giving the
              accumulated features at each point, where 4 refers to a rgba feature.
          background_color: Tensor, list, or tuple with 3 or 4 values indicating the rgb/rgba
              value for the new background. Values should be in the interval [0,1].
       Returns:
          images: Tensor of shape (N, 4, image_size, image_size), where pixels with
              no nearest points have features set to the background color, and other
              pixels with accumulated features have unchanged values.
      """
      # Initialize background mask
      background_mask = pix_idxs[:, 0] < 0  # (N, H, W)
  
      # Convert background_color to an appropriate tensor and check shape
      if not torch.is_tensor(background_color):
          background_color = images.new_tensor(background_color)
  
      if background_color.ndim == 0:
          background_color = background_color.expand(images.shape[1])
  
      if background_color.ndim > 1:
          raise ValueError("Wrong shape of background_color")
  
      background_color = background_color.to(images)
  
      # add alpha channel if needed
      if background_color.shape[0] + 1 == images.shape[1]:
          alpha = images.new_ones(1)
          background_color = torch.cat([background_color, alpha])
  
      if images.shape[1] != background_color.shape[0]:
          raise ValueError(
              "Background color has %s channels not %s"
              % (background_color.shape[0], images.shape[1])
          )
  
      num_background_pixels = background_mask.sum()
  
      # permute so that features are the last dimension for masked_scatter to work
      masked_images = images.permute(0, 2, 3, 1).masked_scatter(
          background_mask[..., None],
          background_color[None, :].expand(num_background_pixels, -1),
      )
  
      return masked_images.permute(0, 3, 1, 2)
  
file_path: pytorch3d/pytorch3d/renderer/points/compositor.py
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from ..compositing import alpha_composite, norm_weighted_sum
label_desc: Use image node_id 1 for calling a global function with example usage: _add_background_color_to_images(pix_idxs, images, background_color) and returns: masked_images