(int tensor): ``(N, ...)``

    Additional dimension ``...`` will be flattened into the batch dimension.

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        threshold: Threshold for transforming probability to binary (0,1) predictions
        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.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.functional.classification import binary_jaccard_index
        >>> target = tensor([1, 1, 0, 0])
        >>> preds = tensor([0, 1, 0, 0])
        >>> binary_jaccard_index(preds, target)
        tensor(0.5000)

    Example (preds is float tensor):
        >>> from torchmetrics.functional.classification import binary_jaccard_index
        >>> target = tensor([1, 1, 0, 0])
        >>> preds = tensor([0.35, 0.85, 0.48, 0.01])
        >>> binary_jaccard_index(preds, target)
        tensor(0.5000)

    r