 the metric in a non-binned but accurate version and a binned version
    that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the
    non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
    argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
    size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory).

    Note that outputted thresholds will be in reversed order to ensure that they corresponds to both fpr and tpr which
    are sorted in reversed order during their calculation, such that they are monotome increasing.

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        num_classes: Integer specifying the number of classes
        thresholds:
            Can be one of:

            - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
              all the data. Most accurate but also most memory consuming approach.
            - 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.

        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 3 tensors or 3 lists containing

        - fpr: if `thresholds=None` a list for each class is returned with an 1d tensor of size (n_thresholds+1, )
          with false positive rate values (length may differ between classes). If `thresholds` is set to something else,
          then a single 2d tensor of size (n_classes, n_thresholds+1) with false positive rate values is returned.
        - tpr: if `thresholds=None` a list for each class is returned with an 1d tensor of size (n_thresholds+1, )
          with true positive rate values (length may differ between classes). If `thresholds` is set to something else,
          then a single 2d tensor of size (n_classes, n_thresholds+1) with true positive rate values is returned.
        - thresholds: if `thresholds=None` a list for each class is returned with an 1d tensor of size (n_thresholds, )
          with decreasing threshold values (length may differ between classes). If `threshold` is set to something else,
          then a single 1d tensor of size (n_thresholds, ) is returned with shared threshold values for all classes.

    Example:
        >>> from torchmetrics.functional.classification import multiclass_roc
        >>> 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])
        >>> fpr, tpr, thresholds = multiclass_roc(
        ...    preds, target, num_classes=5, thresholds=None
        ... )
        >>> fpr  # doctest: +NORMALIZE_WHITESPACE
        [tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]),
         tensor([0.0000, 0.3333, 1.0000]), tensor([0., 1.])]
        >>> tpr
        [tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0., 0.])]
        >>> thresholds  # doctest: +NORMALIZE_WHITESPACE
        [tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.7500, 0.0500]),
         tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.0500])]
        >>> multiclass_roc(
        ...     preds, target, num_classes=5, thresholds=5
        ... )  # doctest: +NORMALIZE_WHITESPACE
        (tensor([[0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
                 [0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
                 [0.0000, 0.3333, 0.3333, 0.3333, 1.0000],
                 [0.0000, 0.3333, 0.3333, 0.3333, 1.0000],
                 [0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]),
         tensor([[0., 1., 1., 1., 1.],
                 [0., 1., 1., 1., 1.],
                 [0., 0., 0., 0., 1.],
                 [0., 0., 0., 0., 1.],
                 [0., 0., 0., 0., 0.]]),
         tensor([1.0000, 0.7500, 0.5000, 0.2500, 0.0000]))

    )