in src/model.py [0:0]
def __init__(self, in_feats: int, h_feats: int, num_classes: int) -> None:
super(GCN, self).__init__()
self.conv1 = GraphConv(in_feats, h_feats)
self.conv2 = GraphConv(h_feats, h_feats)
# the gate layer that maps node feature to outputs
self.gate_nn = nn.Linear(h_feats, num_classes)
self.gap = GlobalAttentionPooling(self.gate_nn)
# the output layer making predictions
self.output = nn.Linear(h_feats, num_classes)