def forward()

in models.py [0:0]

• 30 lines of code
• 8 McCabe index (conditional complexity)

    def forward(self, z, edge_index, batch, x=None, edge_weight=None, node_id=None):
        z_emb = self.z_embedding(z)
        if z_emb.ndim == 3:  # in case z has multiple integer labels
            z_emb = z_emb.sum(dim=1)
        if self.use_feature and x is not None:
            x = torch.cat([z_emb, x.to(torch.float)], 1)
        else:
            x = z_emb
        if self.node_embedding is not None and node_id is not None:
            n_emb = self.node_embedding(node_id)
            x = torch.cat([x, n_emb], 1)
        for conv in self.convs[:-1]:
            x = conv(x, edge_index, edge_weight)
            x = F.relu(x)
            x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.convs[-1](x, edge_index, edge_weight)
        if True:  # center pooling
            _, center_indices = np.unique(batch.cpu().numpy(), return_index=True)
            x_src = x[center_indices]
            x_dst = x[center_indices + 1]
            x = (x_src * x_dst)
            x = F.relu(self.lin1(x))
            x = F.dropout(x, p=self.dropout, training=self.training)
            x = self.lin2(x)
        else:  # sum pooling
            x = global_add_pool(x, batch)
            x = F.relu(self.lin1(x))
            x = F.dropout(x, p=self.dropout, training=self.training)
            x = self.lin2(x)

        return x