def __init__()

in models.py [0:0]

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

    def __init__(self, hidden_channels, num_layers, max_z, k=0.6, train_dataset=None, 
                 dynamic_train=False, GNN=GCNConv, use_feature=False, 
                 node_embedding=None):
        super(DGCNN, self).__init__()

        self.use_feature = use_feature
        self.node_embedding = node_embedding

        if k <= 1:  # Transform percentile to number.
            if train_dataset is None:
                k = 30
            else:
                if dynamic_train:
                    sampled_train = train_dataset[:1000]
                else:
                    sampled_train = train_dataset
                num_nodes = sorted([g.num_nodes for g in sampled_train])
                k = num_nodes[int(math.ceil(k * len(num_nodes))) - 1]
                k = max(10, k)
        self.k = int(k)

        self.max_z = max_z
        self.z_embedding = Embedding(self.max_z, hidden_channels)

        self.convs = ModuleList()
        initial_channels = hidden_channels
        if self.use_feature:
            initial_channels += train_dataset.num_features
        if self.node_embedding is not None:
            initial_channels += node_embedding.embedding_dim

        self.convs.append(GNN(initial_channels, hidden_channels))
        for i in range(0, num_layers-1):
            self.convs.append(GNN(hidden_channels, hidden_channels))
        self.convs.append(GNN(hidden_channels, 1))

        conv1d_channels = [16, 32]
        total_latent_dim = hidden_channels * num_layers + 1
        conv1d_kws = [total_latent_dim, 5]
        self.conv1 = Conv1d(1, conv1d_channels[0], conv1d_kws[0],
                            conv1d_kws[0])
        self.maxpool1d = MaxPool1d(2, 2)
        self.conv2 = Conv1d(conv1d_channels[0], conv1d_channels[1],
                            conv1d_kws[1], 1)
        dense_dim = int((self.k - 2) / 2 + 1)
        dense_dim = (dense_dim - conv1d_kws[1] + 1) * conv1d_channels[1]
        self.lin1 = Linear(dense_dim, 128)
        self.lin2 = Linear(128, 1)