          - If set to an `int` (larger than 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 `tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the calculation.

        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Example:
        >>> from torchmetrics.classification import MultilabelPrecisionRecallCurve
        >>> 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]])
        >>> mlprc = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=None)
        >>> precision, recall, thresholds = mlprc(preds, target)
        >>> precision  # doctest: +NORMALIZE_WHITESPACE
        [tensor([0.5000, 0.5000, 1.0000, 1.0000]), tensor([0.5000, 0.6667, 0.5000, 0.0000, 1.0000]),
         tensor([0.7500, 1.0000, 1.0000, 1.0000])]
        >>> recall  # doctest: +NORMALIZE_WHITESPACE
        [tensor([1.0000, 0.5000, 0.5000, 0.0000]), tensor([1.0000, 1.0000, 0.5000, 0.0000, 0.0000]),
         tensor([1.0000, 0.6667, 0.3333, 0.0000])]
        >>> thresholds  # doctest: +NORMALIZE_WHITESPACE
        [tensor([0.0500, 0.4500, 0.7500]), tensor([0.0500, 0.5500, 0.6500, 0.7500]), tensor([0.0500, 0.3500, 0.7500])]
        >>> mlprc = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=5)
        >>> mlprc(preds, target)  # doctest: +NORMALIZE_WHITESPACE
        (tensor([[0.5000, 0.5000, 1.0000, 1.0000, 0.0000, 1.0000],
                 [0.5000, 0.6667, 0.6667, 0.0000, 0.0000, 1.0000],
                 [0.7500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000]]),
         tensor([[1.0000, 0.5000, 0.5000, 0.5000, 0.0000, 0.0000],
                 [1.0000, 1.0000, 1.0000, 0.0000, 0.0000, 0.0000],
                 [1.0000, 0.6667, 0.3333, 0.3333, 0.0000, 0.0000]]),
         tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))

    Fr'