def METHOD_NAME(self, data):
    x, edge_index, batch = data.x, data.edge_index, data.batch
    x = F.relu(self.conv1(x, edge_index))
    x, edge_index, _, batch, _, _ = self.pool1(x, edge_index, None, batch)
    x1 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
    x = F.relu(self.conv2(x, edge_index))
    x, edge_index, _, batch, _, _ = self.pool2(x, edge_index, None, batch)
    x2 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
    x = F.relu(self.conv3(x, edge_index))
    x, edge_index, _, batch, _, _ = self.pool3(x, edge_index, None, batch)
    x3 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
    x = x1 + x2 + x3
    x = F.relu(self.lin1(x))
    x = F.dropout(x, p=self.dropout, training=self.training)
    x = F.relu(self.lin2(x))
    x = F.log_softmax(self.lin3(x), dim=-1)
    return x