def __call__(self, cls_pred, gt_labels):
        """
        Args:
            cls_pred (Tensor): Predicted classification logits, shape
                [num_query, num_class].
            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).

        Returns:
            torch.Tensor: cls_cost value with weight
        """
        # Following the official DETR repo, contrary to the loss that
        # NLL is used, we approximate it in 1 - cls_score[gt_label].
        # The 1 is a constant that doesn't change the matching,
        # so it can be omitted.
        cls_score = cls_pred.softmax(-1)
        cls_cost = -cls_score[:, gt_labels]
        return cls_cost * self.weight

        Args:
            cls_pred (Tensor): Predicted classification logits, shape
                [num_query, num_class].
            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).

        Returns:
            torch.Tensor: cls_cost value with weight
        
EVA-01/seg/mmseg_custom/models/losses/match_costs.py