``reduction!='none'`` returns float scalar tensor with average MSSSIM
      value over sample else returns tensor of shape ``(N,)`` with SSIM values per sample

    Args:
        gaussian_kernel: If ``True`` (default), a gaussian kernel is used, if false a uniform kernel is used
        kernel_size: size of the gaussian kernel
        sigma: Standard deviation of the gaussian kernel
        reduction: a method to reduce metric score over labels.

            - ``'elementwise_mean'``: takes the mean
            - ``'sum'``: takes the sum
            - ``'none'`` or ``None``: no reduction will be applied

        data_range:
            the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then
            the range is calculated as the difference and input is clamped between the values.
            The ``data_range`` must be given when ``dim`` is not None.
        k1: Parameter of structural similarity index measure.
        k2: Parameter of structural similarity index measure.
        betas: Exponent parameters for individual similarities and contrastive sensitivities returned by different image
            resolutions.
        normalize: When MultiScaleStructuralSimilarityIndexMeasure loss is used for training, it is desirable to use
            normalizes to improve the training stability. This `normalize` argument is out of scope of the original
            implementation [1], and it is adapted from https://github.com/jorge-pessoa/pytorch-msssim instead.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Return:
        Tensor with Multi-Scale SSIM score

    Raises:
        ValueError:
            If ``kernel_size`` is not an int or a Sequence of ints with size 2 or 3.
        ValueError:
            If ``betas`` is not a tuple of floats with length 2.
        ValueError:
            If ``normalize`` is neither `None`, `ReLU` nor `simple`.

    Example:
        >>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
        >>> import torch
        >>> gen = torch.manual_seed(42)
        >>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
        >>> target = preds * 0.75
        >>> ms_ssim = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
        >>> ms_ssim(preds, target)
        tensor(0.9627)

    Tr