t to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to a 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.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example:
        >>> from torchmetrics.classification import MulticlassPrecisionRecallCurve
        >>> 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])
        >>> mcprc = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=None)
        >>> precision, recall, thresholds = mcprc(preds, target)
        >>> precision  # doctest: +NORMALIZE_WHITESPACE
        [tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]),
         tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])]
        >>> recall
        [tensor([1., 1., 0.]), tensor([1., 1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])]
        >>> thresholds
        [tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]),
         tensor(0.0500)]
        >>> mcprc = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=5)
        >>> mcprc(preds, target)  # doctest: +NORMALIZE_WHITESPACE
        (tensor([[0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
                 [0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
                 [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
                 [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
                 [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]),
         tensor([[1., 1., 1., 1., 0., 0.],
                 [1., 1., 1., 1., 0., 0.],
                 [1., 0., 0., 0., 0., 0.],
                 [1., 0., 0., 0., 0., 0.],
                 [0., 0., 0., 0., 0., 0.]]),
         tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))

    Fr'