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

    Example:
        >>> from torchmetrics.functional.classification import multilabel_roc
        >>> 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]])
        >>> fpr, tpr, thresholds = multilabel_roc(
        ...    preds, target, num_labels=3, thresholds=None
        ... )
        >>> fpr  # doctest: +NORMALIZE_WHITESPACE
        [tensor([0.0000, 0.0000, 0.5000, 1.0000]),
         tensor([0.0000, 0.5000, 0.5000, 0.5000, 1.0000]),
         tensor([0., 0., 0., 1.])]
        >>> tpr  # doctest: +NORMALIZE_WHITESPACE
        [tensor([0.0000, 0.5000, 0.5000, 1.0000]),
         tensor([0.0000, 0.0000, 0.5000, 1.0000, 1.0000]),
         tensor([0.0000, 0.3333, 0.6667, 1.0000])]
        >>> thresholds  # doctest: +NORMALIZE_WHITESPACE
        [tensor([1.0000, 0.7500, 0.4500, 0.0500]),
         tensor([1.0000, 0.7500, 0.6500, 0.5500, 0.0500]),
         tensor([1.0000, 0.7500, 0.3500, 0.0500])]
        >>> multilabel_roc(
        ...     preds, target, num_labels=3, thresholds=5
        ... )  # doctest: +NORMALIZE_WHITESPACE
        (tensor([[0.0000, 0.0000, 0.0000, 0.5000, 1.0000],
                 [0.0000, 0.5000, 0.5000, 0.5000, 1.0000],
                 [0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]),
         tensor([[0.0000, 0.5000, 0.5000, 0.5000, 1.0000],
                 [0.0000, 0.0000, 1.0000, 1.0000, 1.0000],
                 [0.0000, 0.3333, 0.3333, 0.6667, 1.0000]]),
         tensor([1.0000, 0.7500, 0.5000, 0.2500, 0.0000]))

    )