def update(self, preds: Tensor, target: Tensor) -> None:
        """Update metric state with new predictions and targets.

        Unlike the base class, this method accepts raw predictions without
        applying sigmoid normalization.

        Args:
            preds (Tensor): Raw predicted scores or probabilities
            target (Tensor): Ground truth binary labels (0 or 1)
        """
        preds, target, _ = BinaryPrecisionRecallCurve._binary_precision_recall_curve_format(
            preds,
            target,
            self.thresholds,
            self.ignore_index,
        )
        state = _binary_precision_recall_curve_update(preds, target, self.thresholds)
        if isinstance(state, Tensor):
            self.confmat += state
        else:
            self.preds.append(state[0])
            self.target.append(state[1])
Update metric state with new predictions and targets.

        Unlike the base class, this method accepts raw predictions without
        applying sigmoid normalization.

        Args:
            preds (Tensor): Raw predicted scores or probabilities
            target (Tensor): Ground truth binary labels (0 or 1)
        
src/anomalib/metrics/precision_recall_curve.py