code: def backward(
      ctx, grad_weights, grad_weights_sum, grad_depth, grad_image
  ):
      grad_weights = grad_weights.contiguous()
      grad_weights_sum = grad_weights_sum.contiguous()
      grad_depth = grad_depth.contiguous()
      grad_image = grad_image.contiguous()
  
      (
          sigmas,
          rgbs,
          ts,
          rays,
          weights_sum,
          depth,
          image,
      ) = ctx.saved_tensors
      M, N, T_thresh, binarize = ctx.dims
  
      grad_sigmas = torch.zeros_like(sigmas)
      grad_rgbs = torch.zeros_like(rgbs)
  
      get_backend().composite_rays_train_backward(
          grad_weights,
          grad_weights_sum,
          grad_depth,
          grad_image,
          sigmas,
          rgbs,
          ts,
          rays,
          weights_sum,
          depth,
          image,
          M,
          N,
          T_thresh,
          binarize,
          grad_sigmas,
          grad_rgbs,
      )
  
      return grad_sigmas, grad_rgbs, None, None, None, None
  
imports: import torch
  from torch.autograd import Function
  from torch.cuda.amp import custom_bwd, custom_fwd
label_desc: Use image node_id 2 for calling the _composite_rays_train obj's underlying member method code with example usage: obj.backward(ctx, grad_weights, grad_weights_sum, grad_depth, grad_image) and returns: grad_sigmas,  grad_rgbs,  None,  None,  None,  None