ld be handled. Should be one of the following:

            - ``global``: Additional dimensions are flatted along the batch dimension
            - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
              The statistics in this case are calculated over the additional dimensions.

        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:
        If ``multidim_average`` is set to ``global``, the metric returns a scalar value. If ``multidim_average``
        is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value per sample.

    Example (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.functional.classification import binary_f1_score
        >>> target = tensor([0, 1, 0, 1, 0, 1])
        >>> preds = tensor([0, 0, 1, 1, 0, 1])
        >>> binary_f1_score(preds, target)
        tensor(0.6667)

    Example (preds is float tensor):
        >>> from torchmetrics.functional.classification import binary_f1_score
        >>> target = tensor([0, 1, 0, 1, 0, 1])
        >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92])
        >>> binary_f1_score(preds, target)
        tensor(0.6667)

    Example (multidim tensors):
        >>> from torchmetrics.functional.classification import binary_f1_score
        >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
        >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
        ...                 [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
        >>> binary_f1_score(preds, target, multidim_average='samplewise')
        tensor([0.5000, 0.0000])

    