def forward(self, graph, feat):
        r"""

        Description
        -----------
        Compute APPNP layer.

        Parameters
        ----------
        graph : DGLGraph
            The graph.
        feat : mx.NDArray
            The input feature of shape :math:`(N, *)`. :math:`N` is the
            number of nodes, and :math:`*` could be of any shape.

        Returns
        -------
        mx.NDArray
            The output feature of shape :math:`(N, *)` where :math:`*`
            should be the same as input shape.
        """
        with graph.local_scope():
            norm = mx.nd.power(
                mx.nd.clip(
                    graph.in_degrees().astype(feat.dtype),
                    a_min=1,
                    a_max=float("inf"),
                ),
                -0.5,
            )
            shp = norm.shape + (1,) * (feat.ndim - 1)
            norm = norm.reshape(shp).as_in_context(feat.context)
            feat_0 = feat
            for _ in range(self._k):
                # normalization by src node
                feat = feat * norm
                graph.ndata["h"] = feat
                graph.edata["w"] = self.edge_drop(
                    nd.ones((graph.num_edges(), 1), ctx=feat.context)
                )
                graph.update_all(fn.u_mul_e("h", "w", "m"), fn.sum("m", "h"))
                feat = graph.ndata.pop("h")
                # normalization by dst node
                feat = feat * norm
                feat = (1 - self._alpha) * feat + self._alpha * feat_0
            return feat


        Description
        -----------
        Compute APPNP layer.

        Parameters
        ----------
        graph : DGLGraph
            The graph.
        feat : mx.NDArray
            The input feature of shape :math:`(N, *)`. :math:`N` is the
            number of nodes, and :math:`*` could be of any shape.

        Returns
        -------
        mx.NDArray
            The output feature of shape :math:`(N, *)` where :math:`*`
            should be the same as input shape.
        
python/dgl/nn/mxnet/conv/appnpconv.py