    ignore_index:
            Specifies a target value that is ignored and does not contribute to the metric calculation
        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Returns:
        (tuple): a tuple of either 2 tensors or 2 lists containing

        - precision: an 1d tensor of size (n_classes, ) with the maximum precision for the given recall level per class
        - thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class

    Example:
        >>> from torchmetrics.functional.classification import multilabel_precision_at_fixed_recall
        >>> preds = torch.tensor([[0.75, 0.05, 0.35],
        ...                       [0.45, 0.75, 0.05],
        ...                       [0.05, 0.55, 0.75],
        ...                       [0.05, 0.65, 0.05]])
        >>> target = torch.tensor([[1, 0, 1],
        ...                        [0, 0, 0],
        ...                        [0, 1, 1],
        ...                        [1, 1, 1]])
        >>> multilabel_precision_at_fixed_recall(preds, target, num_labels=3, min_recall=0.5, thresholds=None)
        (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5500, 0.3500]))
        >>> multilabel_precision_at_fixed_recall(preds, target, num_labels=3, min_recall=0.5, thresholds=5)
        (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5000, 0.2500]))

    r1