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 multiclass_precision_at_fixed_recall
        >>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
        ...                       [0.05, 0.75, 0.05, 0.05, 0.05],
        ...                       [0.05, 0.05, 0.75, 0.05, 0.05],
        ...                       [0.05, 0.05, 0.05, 0.75, 0.05]])
        >>> target = torch.tensor([0, 1, 3, 2])
        >>> multiclass_precision_at_fixed_recall(  # doctest: +NORMALIZE_WHITESPACE
        ...     preds, target, num_classes=5, min_recall=0.5, thresholds=None)
        (tensor([1.0000, 1.0000, 0.2500, 0.2500, 0.0000]),
         tensor([7.5000e-01, 7.5000e-01, 5.0000e-02, 5.0000e-02, 1.0000e+06]))
        >>> multiclass_precision_at_fixed_recall(  # doctest: +NORMALIZE_WHITESPACE
        ...     preds, target, num_classes=5, min_recall=0.5, thresholds=5)
        (tensor([1.0000, 1.0000, 0.2500, 0.2500, 0.0000]),
         tensor([7.5000e-01, 7.5000e-01, 0.0000e+00, 0.0000e+00, 1.0000e+06]))

    r1