code: def backward(ctx, grad):
      inputs, embeddings, offsets, dy_dx = ctx.saved_tensors
      B, D, C, L, S, H, gridtype, interpolation, max_level = ctx.dims
      align_corners = ctx.align_corners
  
      # grad: [B, L * C] --> [L, B, C]
      grad = grad.view(B, L, C).permute(1, 0, 2).contiguous()
  
      grad_embeddings = torch.zeros_like(embeddings)
  
      if dy_dx is not None:
          grad_inputs = torch.zeros_like(inputs, dtype=embeddings.dtype)
      else:
          grad_inputs = None
  
      _backend.grid_encode_backward(
          grad,
          inputs,
          embeddings,
          offsets,
          grad_embeddings,
          B,
          D,
          C,
          L,
          max_level,
          S,
          H,
          dy_dx,
          grad_inputs,
          gridtype,
          align_corners,
          interpolation,
      )
  
      if dy_dx is not None:
          grad_inputs = grad_inputs.to(inputs.dtype)
  
      return (
          grad_inputs,
          grad_embeddings,
          None,
          None,
          None,
          None,
          None,
          None,
          None,
          None,
      )
  
file_path: NeMo/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/gridencoder.py
imports: import math
  import _gridencoder
  import numpy
  import torch
  import torch.nn
  from torch.autograd import Function
  from torch.cuda.amp import custom_bwd, custom_fwd
label_desc: Use image node_id 2 for calling the _grid_encode obj's underlying member method code with example usage: obj.backward(ctx, grad) and returns: grad_inputs,  grad_embeddings,  None,  None,  None,  None,  None,  None,  None,  None