def __init__()

in models.py [0:0]

• 41 lines of code
• 5 McCabe index (conditional complexity)

    def __init__(self, hidden_channels, num_layers, max_z, train_dataset,
                 use_feature=False, node_embedding=None, dropout=0.5, 
                 jk=True, train_eps=False):
        super(GIN, self).__init__()
        self.use_feature = use_feature
        self.node_embedding = node_embedding
        self.max_z = max_z
        self.z_embedding = Embedding(self.max_z, hidden_channels)
        self.jk = jk

        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.conv1 = GINConv(
            Sequential(
                Linear(initial_channels, hidden_channels),
                ReLU(),
                Linear(hidden_channels, hidden_channels),
                ReLU(),
                BN(hidden_channels),
            ),
            train_eps=train_eps)
        self.convs = torch.nn.ModuleList()
        for i in range(num_layers - 1):
            self.convs.append(
                GINConv(
                    Sequential(
                        Linear(hidden_channels, hidden_channels),
                        ReLU(),
                        Linear(hidden_channels, hidden_channels),
                        ReLU(),
                        BN(hidden_channels),
                    ),
                    train_eps=train_eps))

        self.dropout = dropout
        if self.jk:
            self.lin1 = Linear(num_layers * hidden_channels, hidden_channels)
        else:
            self.lin1 = Linear(hidden_channels, hidden_channels)
        self.lin2 = Linear(hidden_channels, 1)