1), will use that number of thresholds linearly spaced from
              0 to 1 as bins for the calculation.
            - If set to an ``list`` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to an 1d :class:`~torch.Tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the calculation.

        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 2 tensors containing:

        - precision: an scalar tensor with the maximum precision for the given precision level
        - threshold: an scalar tensor with the corresponding threshold level

    Example:
        >>> from torchmetrics.functional.classification import binary_precision_at_fixed_recall
        >>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
        >>> target = torch.tensor([0, 1, 1, 0])
        >>> binary_precision_at_fixed_recall(preds, target, min_recall=0.5, thresholds=None)
        (tensor(0.6667), tensor(0.5000))
        >>> binary_precision_at_fixed_recall(preds, target, min_recall=0.5, thresholds=5)
        (tensor(0.6667), tensor(0.5000))

    ©