def forward(self, graph, feat, edge_weight=None):
        r"""

        Description
        -----------
        Compute Simplifying Graph Convolution layer.

        Parameters
        ----------
        graph : DGLGraph
            The graph.
        feat : torch.Tensor
            The input feature of shape :math:`(N, D_{in})` where :math:`D_{in}`
            is size of input feature, :math:`N` is the number of nodes.
        edge_weight: torch.Tensor, optional
            edge_weight to use in the message passing process. This is equivalent to
            using weighted adjacency matrix in the equation above, and
            :math:`\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}`
            is based on :class:`dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm`.

        Returns
        -------
        torch.Tensor
            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
            is size of output feature.

        Raises
        ------
        DGLError
            If there are 0-in-degree nodes in the input graph, it will raise DGLError
            since no message will be passed to those nodes. This will cause invalid output.
            The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``.

        Note
        ----
        If ``cache`` is set to True, ``feat`` and ``graph`` should not change during
        training, or you will get wrong results.
        """
        with graph.local_scope():
            if not self._allow_zero_in_degree:
                if (graph.in_degrees() == 0).any():
                    raise DGLError(
                        "There are 0-in-degree nodes in the graph, "
                        "output for those nodes will be invalid. "
                        "This is harmful for some applications, "
                        "causing silent performance regression. "
                        "Adding self-loop on the input graph by "
                        "calling `g = dgl.add_self_loop(g)` will resolve "
                        "the issue. Setting ``allow_zero_in_degree`` "
                        "to be `True` when constructing this module will "
                        "suppress the check and let the code run."
                    )

            msg_func = fn.copy_u("h", "m")
            if edge_weight is not None:
                graph.edata["_edge_weight"] = EdgeWeightNorm("both")(
                    graph, edge_weight
                )
                msg_func = fn.u_mul_e("h", "_edge_weight", "m")

            if self._cached_h is not None:
                feat = self._cached_h
            else:
                if edge_weight is None:
                    # compute normalization
                    degs = graph.in_degrees().to(feat).clamp(min=1)
                    norm = th.pow(degs, -0.5)
                    norm = norm.to(feat.device).unsqueeze(1)
                # compute (D^-1 A^k D)^k X
                for _ in range(self._k):
                    if edge_weight is None:
                        feat = feat * norm
                    graph.ndata["h"] = feat
                    graph.update_all(msg_func, fn.sum("m", "h"))
                    feat = graph.ndata.pop("h")
                    if edge_weight is None:
                        feat = feat * norm

                if self.norm is not None:
                    feat = self.norm(feat)

                # cache feature
                if self._cached:
                    self._cached_h = feat
            return self.fc(feat)


        Description
        -----------
        Compute Simplifying Graph Convolution layer.

        Parameters
        ----------
        graph : DGLGraph
            The graph.
        feat : torch.Tensor
            The input feature of shape :math:`(N, D_{in})` where :math:`D_{in}`
            is size of input feature, :math:`N` is the number of nodes.
        edge_weight: torch.Tensor, optional
            edge_weight to use in the message passing process. This is equivalent to
            using weighted adjacency matrix in the equation above, and
            :math:`\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}`
            is based on :class:`dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm`.

        Returns
        -------
        torch.Tensor
            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
            is size of output feature.

        Raises
        ------
        DGLError
            If there are 0-in-degree nodes in the input graph, it will raise DGLError
            since no message will be passed to those nodes. This will cause invalid output.
            The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``.

        Note
        ----
        If ``cache`` is set to True, ``feat`` and ``graph`` should not change during
        training, or you will get wrong results.
        
python/dgl/nn/pytorch/conv/sgconv.py