def sigmoid_log_double_softmax(sim: Tensor, z0: Tensor, z1: Tensor) -> Tensor:
    """Create the log assignment matrix from logits and similarity."""
    b, m, n = sim.shape
    certainties = F.logsigmoid(z0) + F.logsigmoid(z1).transpose(1, 2)
    scores0 = F.log_softmax(sim, 2)
    scores1 = F.log_softmax(sim.transpose(-1, -2).contiguous(), 2).transpose(-1, -2)
    scores = sim.new_full((b, m + 1, n + 1), 0)
    scores[:, :m, :n] = scores0 + scores1 + certainties
    scores[:, :-1, -1] = F.logsigmoid(-z0.squeeze(-1))
    scores[:, -1, :-1] = F.logsigmoid(-z1.squeeze(-1))
    return scores
Create the log assignment matrix from logits and similarity.
kornia/feature/lightglue.py