4be4b134247cf79617480e5f4646dfa07bd96a4e,examples/pytorch/graphsage/experimental/train_dist.py,DistSAGE,__init__,#DistSAGE#Any#Any#Any#Any#Any#Any#,47

Before Change

class DistSAGE(SAGE):
    def __init__(self, in_feats, n_hidden, n_classes, n_layers,
                 activation, dropout):
        super(DistSAGE, self).__init__(in_feats, n_hidden, n_classes, n_layers,
                                       activation, dropout)

    def inference(self, g, x, batch_size, device):
        
        Inference with the GraphSAGE model on full neighbors (i.e. without neighbor sampling).

After Change

        for i in range(1, n_layers - 1):
            self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, "mean"))
        self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, "mean"))
        self.dropout = nn.Dropout(dropout)
        self.activation = activation

    def forward(self, blocks, x):
        h = x

Instances