def __init__(self, in_features, out_features, nbaselayer,
                 withbn=True, withloop=True, activation=F.relu, dropout=True,
                 aggrmethod=None, dense=None):
        """
        The multiple layer GCN block.
        :param in_features: the input feature dimension.
        :param out_features: the hidden feature dimension.
        :param nbaselayer: the number of layers in the base block.
        :param withbn: using batch normalization in graph convolution.
        :param withloop: using self feature modeling in graph convolution.
        :param activation: the activation function, default is ReLu.
        :param dropout: the dropout ratio.
        :param aggrmethod: not applied.
        :param dense: not applied.
        """
        super(MultiLayerGCNBlock, self).__init__()
        self.model = GraphBaseBlock(in_features=in_features,
                                    out_features=out_features,
                                    nbaselayer=nbaselayer,
                                    withbn=withbn,
                                    withloop=withloop,
                                    activation=activation,
                                    dropout=dropout,
                                    dense=False,
                                    aggrmethod="nores")

        The multiple layer GCN block.
        :param in_features: the input feature dimension.
        :param out_features: the hidden feature dimension.
        :param nbaselayer: the number of layers in the base block.
        :param withbn: using batch normalization in graph convolution.
        :param withloop: using self feature modeling in graph convolution.
        :param activation: the activation function, default is ReLu.
        :param dropout: the dropout ratio.
        :param aggrmethod: not applied.
        :param dense: not applied.
        
src/layers.py