def calculate_uncertainty(sem_seg_logits):
    """
    For each location of the prediction `sem_seg_logits` we estimate uncerainty as the
        difference between top first and top second predicted logits.

    Args:
        mask_logits (Tensor): A tensor of shape (N, C, ...), where N is the minibatch size and
            C is the number of foreground classes. The values are logits.

    Returns:
        scores (Tensor): A tensor of shape (N, 1, ...) that contains uncertainty scores with
            the most uncertain locations having the highest uncertainty score.
    """
    top2_scores = torch.topk(sem_seg_logits, k=2, dim=1)[0]
    return (top2_scores[:, 1] - top2_scores[:, 0]).unsqueeze(1)

    For each location of the prediction `sem_seg_logits` we estimate uncerainty as the
        difference between top first and top second predicted logits.

    Args:
        mask_logits (Tensor): A tensor of shape (N, C, ...), where N is the minibatch size and
            C is the number of foreground classes. The values are logits.

    Returns:
        scores (Tensor): A tensor of shape (N, 1, ...) that contains uncertainty scores with
            the most uncertain locations having the highest uncertainty score.
    
EVA-02/det/projects/PointRend/point_rend/semantic_seg.py