(pretrained=False, progress=True, **kwargs)
return _shufflenetv2('shufflenetv2_x1.0', pretrained, progress,
                         [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)
Constructs a ShuffleNetV2 with 1.0x output channels, as described in
    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
    <https://arxiv.org/abs/1807.11164>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
Constructs a ShuffleNetV2 with 1.0x output channels, as described in
    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
    <https://arxiv.org/abs/1807.11164>`_.
Constructs a ShuffleNetV2 with 1 . 0x output channels as described in ShuffleNet V2 : Practical Guidelines for Efficient CNN Architecture Design <https : // arxiv . org / abs / 1807 . 11164 > _ .
def shufflenet_v2_x1_0(pretrained=False, progress=True, **kwargs):
    """
    Constructs a ShuffleNetV2 with 1.0x output channels, as described in
    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
    <https://arxiv.org/abs/1807.11164>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _shufflenetv2('shufflenetv2_x1.0', pretrained, progress,
                         [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)
def shufflenet_v2_x1_0 ( pretrained = False , progress = True , * * kwargs ) : return _shufflenetv2 ( 'shufflenetv2_x1.0' , pretrained , progress , [ 4 , 8 , 4 ] , [ 24 , 116 , 232 , 464 , 1024 ] , * * kwargs )