def forward()

in models.py [0:0]

• 25 lines of code
• 7 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)
        xs = [x]

        for conv in self.convs:
            xs += [torch.tanh(conv(xs[-1], edge_index, edge_weight))]
        x = torch.cat(xs[1:], dim=-1)

        # Global pooling.
        x = global_sort_pool(x, batch, self.k)
        x = x.unsqueeze(1)  # [num_graphs, 1, k * hidden]
        x = F.relu(self.conv1(x))
        x = self.maxpool1d(x)
        x = F.relu(self.conv2(x))
        x = x.view(x.size(0), -1)  # [num_graphs, dense_dim]

        # MLP.
        x = F.relu(self.lin1(x))
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.lin2(x)
        return x