code: def backward(self, dZ):
      ctx = context(dZ)
      X, Y, argX, argY = self.saved_tensors
      gidx, op, reduce_op = self.gidx, self.op, self.reduce_op
      if op != "copy_rhs":
          g_rev = gidx.reverse()
          if reduce_op == "sum":
              if op in ["mul", "div"]:
                  dX = _gspmm(g_rev, "mul", "sum", dZ, _muldiv(op, Y))[
                      0
                  ]
              elif op in ["add", "sub"]:
                  dX = _gspmm(g_rev, "copy_lhs", "sum", dZ, Y)[0]
              elif op == "copy_lhs":
                  dX = _gspmm(g_rev, "copy_lhs", "sum", dZ, None)[0]
          else:
              if op in ["mul", "div"]:
                  dX = _scatter_nd(
                      argX,
                      _muldiv(
                          op, _gather_nd(argY, _expand(Y, dZ.shape[1:]))
                      )
                      * dZ,
                      X.shape[0],
                  )
              elif op in ["add", "sub", "copy_lhs"]:
                  dX = _scatter_nd(argX, dZ, X.shape[0])
          dX = _reduce_grad(dX, X.shape)
      else:
          dX = nd.zeros_like(X)
      if op != "copy_lhs":
          if reduce_op == "sum":
              if op == "mul" and _need_reduce_last_dim(X, Y):
                  dY = _gsddmm(gidx, "dot", X, dZ)
              elif op in ["mul", "div"]:
                  dY = _gsddmm(gidx, "mul", X, dZ)
                  if op == "div":
                      dY = -dY / (Y**2)
              elif op in ["add", "sub", "copy_rhs"]:
                  dY = _gsddmm(gidx, "copy_rhs", X, _addsub(op, dZ))
          else:
              if op in ["mul", "div"]:
                  dY = _scatter_nd(
                      argY,
                      _gather_nd(argX, _expand(X, dZ.shape[1:])) * dZ,
                      Y.shape[0],
                  )
                  if op == "div":
                      dY = -dY / (Y**2)
              elif op in ["add", "sub", "copy_rhs"]:
                  dY = _scatter_nd(argY, _addsub(op, dZ), Y.shape[0])
          dY = _reduce_grad(dY, Y.shape)
      else:
          dY = nd.zeros_like(Y)
      self.saved_tensors = None
      return dX, dY
  
imports: import mxnet
  import numpy
  from mxnet import nd
  from ..._sparse_ops import _bwd_segment_cmp, _csrmask, _csrmm, _csrsum, _gsddmm, _gspmm, _scatter_add, _segment_reduce
  from ...base import ALL, dgl_warning, is_all
  from ...heterograph_index import create_unitgraph_from_csr
  from .tensor import asnumpy, context, copy_to, to_backend_ctx, zerocopy_from_numpy
label_desc: Use image node_id 3 for calling the GSpMM obj's underlying member method code with example usage: obj.backward(dZ) and returns: dX,  dY