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

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

        Parameters
        ----------
        adj : torch.Tensor
            The adjacency matrix of the graph to apply SAGE 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 : torch.Tensor or a pair of torch.Tensor
            If a torch.Tensor is given, 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.
            If a pair of torch.Tensor is given, the pair must contain two tensors of shape
            :math:`(N_{in}, D_{in})` and :math:`(N_{out}, D_{in})`.

        Returns
        -------
        torch.Tensor
            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
            is size of output feature.
        """
        check_eq_shape(feat)
        if isinstance(feat, tuple):
            feat_src = self.feat_drop(feat[0])
            feat_dst = self.feat_drop(feat[1])
        else:
            feat_src = feat_dst = self.feat_drop(feat)
        adj = adj.to(feat_src)
        in_degrees = adj.sum(dim=1, keepdim=True)
        h_neigh = (adj @ feat_src + feat_dst) / (in_degrees + 1)
        rst = self.fc(h_neigh)
        # activation
        if self.activation is not None:
            rst = self.activation(rst)
        # normalization
        if self._norm is not None:
            rst = self._norm(rst)

        return rst


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

        Parameters
        ----------
        adj : torch.Tensor
            The adjacency matrix of the graph to apply SAGE 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 : torch.Tensor or a pair of torch.Tensor
            If a torch.Tensor is given, 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.
            If a pair of torch.Tensor is given, the pair must contain two tensors of shape
            :math:`(N_{in}, D_{in})` and :math:`(N_{out}, D_{in})`.

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