ll use a binned version that uses memory of
    size :math:`\mathcal{O}(n_{thresholds})` (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
        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 3 tensors containing:

        - fpr: an 1d tensor of size (n_thresholds+1, ) with false positive rate values
        - tpr: an 1d tensor of size (n_thresholds+1, ) with true positive rate values
        - thresholds: an 1d tensor of size (n_thresholds, ) with decreasing threshold values

    Example:
        >>> from torchmetrics.functional.classification import binary_roc
        >>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
        >>> target = torch.tensor([0, 1, 1, 0])
        >>> binary_roc(preds, target, thresholds=None)  # doctest: +NORMALIZE_WHITESPACE
        (tensor([0.0000, 0.5000, 0.5000, 0.5000, 1.0000]),
         tensor([0.0000, 0.0000, 0.5000, 1.0000, 1.0000]),
         tensor([1.0000, 0.8000, 0.7000, 0.5000, 0.0000]))
        >>> binary_roc(preds, target, thresholds=5)  # doctest: +NORMALIZE_WHITESPACE
        (tensor([0.0000, 0.5000, 0.5000, 0.5000, 1.0000]),
         tensor([0., 0., 1., 1., 1.]),
         tensor([1.0000, 0.7500, 0.5000, 0.2500, 0.0000]))

    )