def forward(self, node_ids):
        """Forward computation
        Parameters
        ----------
        node_ids : dict of Tensor
            node ids to generate embedding for.
        Returns
        -------
        tensor
            embeddings as the input of the next layer
        """
        embeds = {}
        for ntype in node_ids:
            if self.feat_name in self.g.nodes[ntype].data:
                embeds[ntype] = self.node_projs[ntype](
                    self.g.nodes[ntype]
                    .data[self.feat_name][node_ids[ntype]]
                    .to(self.dev_id)
                )
            else:
                embeds[ntype] = self.node_embeds[ntype](node_ids[ntype]).to(
                    self.dev_id
                )
        return embeds
Forward computation
        Parameters
        ----------
        node_ids : dict of Tensor
            node ids to generate embedding for.
        Returns
        -------
        tensor
            embeddings as the input of the next layer
        
examples/distributed/rgcn/node_classification.py