7b8716403efd3cee3fd62f50d2e2e4b2183a90b6,torch_geometric/nn/functional/cheb_conv.py,,cheb_conv,#Any#Any#Any#Any#Any#,7

Before Change

    n, e, K = x.size(0), row.size(0), weight.size(0)

    if edge_attr is None:
        edge_attr = x.data.new(e).fill_(1)

    // Compute degree.
    degree = x.data.new(n).fill_(0).scatter_add_(0, row, edge_attr)
    degree = degree.pow_(-0.5)

    // Compute normalized and rescaled Laplacian.
    edge_attr *= degree[row]
    edge_attr *= degree[col]
    lap = SparseTensor(edge_index, -edge_attr, torch.Size([n, n]))

    // Convolution.
    Tx_0 = x
    output = torch.mm(Tx_0, weight[0])

After Change

    row, col = edge_index
    n, e, K = x.size(0), row.size(0), weight.size(0)

    edge_attr = x.new_full((e, ), 1) if edge_attr is None else edge_attr
    deg = degree(row, n, dtype=edge_attr.dtype, device=edge_attr.device)

    // Compute normalized and rescaled Laplacian.

Instances