7be56405f2af0f700763249da3c368976582d792,examples/tgn.py,,test,#,159

Before Change

                                dtype=torch.long, device=device)

        n_id = torch.cat([src, pos_dst, neg_dst]).unique()
        query_t = t[0]
        adj_t, n_id = sampler(n_id, t=query_t.cpu())
        assoc[n_id] = torch.arange(n_id.size(0), device=device)

        z, _ = model(n_id, query_t)

After Change

@torch.no_grad()
def test(data):
    memory.eval()
    gnn.eval()
    link_pred.eval()

    torch.manual_seed(12345)  // Ensure deterministic sampling across epochs.

Instances