uidelines for Efficient CNN Architecture Design
    <https://arxiv.org/abs/1807.11164>`__.

    .. note::
        Note that ``quantize = True`` returns a quantized model with 8 bit
        weights. Quantized models only support inference and run on CPUs.
        GPU inference is not yet supported.

    Args:
        weights (:class:`~torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights`, optional): The
            pretrained weights for the model. See
            :class:`~torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the download to stderr.
            Default is True.
        quantize (bool, optional): If True, return a quantized version of the model.
            Default is False.
        **kwargs: parameters passed to the ``torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/shufflenetv2.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights
        :members:

    .. autoclass:: torchvision.models.ShuffleNet_V2_X2_0_Weights
        :members:
        :noindex:
    r€