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
        """
        cls_pred = cls_pred.sigmoid()
        neg_cost = -(1 - cls_pred + self.eps).log() * (
                1 - self.alpha) * cls_pred.pow(self.gamma)
        pos_cost = -(cls_pred + self.eps).log() * self.alpha * (
                1 - cls_pred).pow(self.gamma)
        cls_cost = pos_cost[:, gt_labels] - neg_cost[:, 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