penai/clip-vit-large-patch14"`

        data_range: The maximum value of the input tensor. For example, if the input images are in range [0, 255],
            data_range should be 255. The images are normalized by this value.
        prompts: A string, tuple of strings or nested tuple of strings. If a single string is provided, it must be one
            of the available prompts (see above). Else the input is expected to be a tuple, where each element can
            be one of two things: either a string or a tuple of strings. If a string is provided, it must be one of the
            available prompts (see above). If tuple is provided, it must be of length 2 and the first string must be a
            positive prompt and the second string must be a negative prompt.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    .. note:: If using the default `clip_iqa` model, the package `piq` must be installed. Either install with
        `pip install piq` or `pip install torchmetrics[image]`.

    Raises:
        ModuleNotFoundError:
            If transformers package is not installed or version is lower than 4.10.0
        ValueError:
            If `prompts` is a tuple and it is not of length 2
        ValueError:
            If `prompts` is a string and it is not one of the available prompts
        ValueError:
            If `prompts` is a list of strings and not all strings are one of the available prompts

    Example::
        Single prompt:

        >>> from torchmetrics.multimodal import CLIPImageQualityAssessment
        >>> import torch
        >>> _ = torch.manual_seed(42)
        >>> imgs = torch.randint(255, (2, 3, 224, 224)).float()
        >>> metric = CLIPImageQualityAssessment()
        >>> metric(imgs)
        tensor([0.8894, 0.8902])

    Example::
        Multiple prompts:

        >>> from torchmetrics.multimodal import CLIPImageQualityAssessment
        >>> import torch
        >>> _ = torch.manual_seed(42)
        >>> imgs = torch.randint(255, (2, 3, 224, 224)).float()
        >>> metric = CLIPImageQualityAssessment(prompts=("quality", "brightness"))
        >>> metric(imgs)
        {'quality': tensor([0.8894, 0.8902]), 'brightness': tensor([0.5507, 0.5208])}

    Example::
        Custom prompts. Must always be a tuple of length 2, with a positive and negative prompt.

        >>> from torchmetrics.multimodal import CLIPImageQualityAssessment
        >>> import torch
        >>> _ = torch.manual_seed(42)
        >>> imgs = torch.randint(255, (2, 3, 224, 224)).float()
        >>> metric = CLIPImageQualityAssessment(prompts=(("Super good photo.", "Super bad photo."), "brightness"))
        >>> metric(imgs)
        {'user_defined_0': tensor([0.9652, 0.9629]), 'brightness': tensor([0.5507, 0.5208])}

    F