def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> float:
        """
        Update metric value with accuracy for new data
        and return intermediate metric value.

        Args:
            outputs: tensor of outputs
            targets: tensor of true answers

        Returns:
            accuracy metric for outputs and targets
        """
        metric = multilabel_accuracy(
            outputs=outputs, targets=targets, threshold=self.threshold
        ).item()
        super().update(value=metric, num_samples=np.prod(targets.shape))
        return metric

        Update metric value with accuracy for new data
        and return intermediate metric value.

        Args:
            outputs: tensor of outputs
            targets: tensor of true answers

        Returns:
            accuracy metric for outputs and targets
        
catalyst/metrics/_accuracy.py