code: def alpha_composite(pointsidx, alphas, pt_clds) -> torch.Tensor:
      """
      Composite features within a z-buffer using alpha compositing. Given a z-buffer
      with corresponding features and weights, these values are accumulated according
      to their weights such that features nearer in depth contribute more to the final
      feature than ones further away.
  
      Concretely this means:
          weighted_fs[b,c,i,j] = sum_k cum_alpha_k * features[c,pointsidx[b,k,i,j]]
          cum_alpha_k = alphas[b,k,i,j] * prod_l=0..k-1 (1 - alphas[b,l,i,j])
  
  
      Args:
          pt_clds: Tensor of shape (N, C, P) giving the features of each point (can use
              RGB for example).
          alphas: float32 Tensor of shape (N, points_per_pixel, image_size,
              image_size) giving the weight of each point in the z-buffer.
              Values should be in the interval [0, 1].
          pointsidx: 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.
              Concretely pointsidx[n, k, y, x] = p means that features[n, :, p] is the
              feature of the kth closest point (along the z-direction) to pixel (y, x) in
              batch element n. This is weighted by alphas[n, k, y, x].
  
      Returns:
          Combined features: Tensor of shape (N, C, image_size, image_size)
              giving the accumulated features at each point.
      """
      return _CompositeAlphaPoints.apply(pt_clds, alphas, pointsidx)
  
file_path: pytorch3d/pytorch3d/renderer/compositing.py
imports: import torch
  from pytorch3d import _C
label_desc: Use image node_id 1 for calling a global function with example usage: alpha_composite(pointsidx, alphas, pt_clds) and returns: _CompositeAlphaPoints