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

        Description
        -----------
        Compute (Dense) Graph Convolution layer.

        Parameters
        ----------
        adj : mxnet.NDArray
            The adjacency matrix of the graph to apply Graph Convolution on, when
            applied to a unidirectional bipartite graph, ``adj`` should be of shape
            should be of shape :math:`(N_{out}, N_{in})`; when applied to a homo
            graph, ``adj`` should be of shape :math:`(N, N)`. In both cases,
            a row represents a destination node while a column represents a source
            node.
        feat : mxnet.NDArray
            The input feature.

        Returns
        -------
        mxnet.NDArray
            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
            is size of output feature.
        """
        adj = adj.astype(feat.dtype).as_in_context(feat.context)
        src_degrees = nd.clip(adj.sum(axis=0), a_min=1, a_max=float("inf"))
        dst_degrees = nd.clip(adj.sum(axis=1), a_min=1, a_max=float("inf"))
        feat_src = feat

        if self._norm == "both":
            norm_src = nd.power(src_degrees, -0.5)
            shp_src = norm_src.shape + (1,) * (feat.ndim - 1)
            norm_src = norm_src.reshape(shp_src).as_in_context(feat.context)
            feat_src = feat_src * norm_src

        if self._in_feats > self._out_feats:
            # mult W first to reduce the feature size for aggregation.
            feat_src = nd.dot(feat_src, self.weight.data(feat_src.context))
            rst = nd.dot(adj, feat_src)
        else:
            # aggregate first then mult W
            rst = nd.dot(adj, feat_src)
            rst = nd.dot(rst, self.weight.data(feat_src.context))

        if self._norm != "none":
            if self._norm == "both":
                norm_dst = nd.power(dst_degrees, -0.5)
            else:  # right
                norm_dst = 1.0 / dst_degrees
            shp_dst = norm_dst.shape + (1,) * (feat.ndim - 1)
            norm_dst = norm_dst.reshape(shp_dst).as_in_context(feat.context)
            rst = rst * norm_dst

        if self.bias is not None:
            rst = rst + self.bias.data(feat.context)

        if self._activation is not None:
            rst = self._activation(rst)

        return rst


        Description
        -----------
        Compute (Dense) Graph Convolution layer.

        Parameters
        ----------
        adj : mxnet.NDArray
            The adjacency matrix of the graph to apply Graph Convolution on, when
            applied to a unidirectional bipartite graph, ``adj`` should be of shape
            should be of shape :math:`(N_{out}, N_{in})`; when applied to a homo
            graph, ``adj`` should be of shape :math:`(N, N)`. In both cases,
            a row represents a destination node while a column represents a source
            node.
        feat : mxnet.NDArray
            The input feature.

        Returns
        -------
        mxnet.NDArray
            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
            is size of output feature.
        
python/dgl/nn/mxnet/conv/densegraphconv.py