def forward(self, g, inputs):
        """Forward computation

        Parameters
        ----------
        g : DGLGraph
            Input graph.
        inputs : dict[str, torch.Tensor]
            Node feature for each node type.

        Returns
        -------
        dict[str, torch.Tensor]
            New node features for each node type.
        """
        g = g.local_var()
        if self.use_weight:
            weight = self.basis() if self.use_basis else self.weight
            wdict = {
                self.rel_names[i]: {"weight": w.squeeze(0)}
                for i, w in enumerate(th.split(weight, 1, dim=0))
            }
        else:
            wdict = {}

        if g.is_block:
            inputs_src = inputs
            inputs_dst = {
                k: v[: g.number_of_dst_nodes(k)] for k, v in inputs.items()
            }
        else:
            inputs_src = inputs_dst = inputs

        hs = self.conv(g, inputs, mod_kwargs=wdict)

        def _apply(ntype, h):
            if self.self_loop:
                h = h + th.matmul(inputs_dst[ntype], self.loop_weight)
            if self.bias:
                h = h + self.h_bias
            if self.activation:
                h = self.activation(h)
            return self.dropout(h)

        return {ntype: _apply(ntype, h) for ntype, h in hs.items()}
Forward computation

        Parameters
        ----------
        g : DGLGraph
            Input graph.
        inputs : dict[str, torch.Tensor]
            Node feature for each node type.

        Returns
        -------
        dict[str, torch.Tensor]
            New node features for each node type.
        
benchmarks/benchmarks/model_speed/bench_rgcn_hetero_ns.py