class_docstr: Torch Function wrapper for marching_cubes implementation.
  This function is not differentiable. An autograd wrapper is used
  to ensure an error if user tries to get gradients.
class_docstr_tok: ["Torch","Function","wrapper","for","marching_cubes","implementation",".","This","function","is","not","differentiable",".","An","autograd","wrapper","is","used","to","ensure","an","error","if","user","tries","to","get","gradients","."]
code: def backward(ctx, grad_verts, grad_faces):
      raise ValueError("marching_cubes backward is not supported")
  
file_path: pytorch3d/pytorch3d/ops/marching_cubes.py
imports: from typing import List, Optional, Tuple
  import torch
  from pytorch3d import _C
  from pytorch3d.ops.marching_cubes_data import EDGE_TO_VERTICES, FACE_TABLE, INDEX
  from pytorch3d.transforms import Translate
  from torch.autograd import Function
label_desc: Use image node_id 2 for calling the _marching_cubes obj's underlying member method code with example usage: obj.backward(ctx, grad_verts, grad_faces) without return types