(pretrained=False, progress=True, **kwargs)
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
                   pretrained, progress, **kwargs)
r"""ResNeXt-101 32x8d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_

    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
r"""ResNeXt-101 32x8d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
r ResNeXt - 101 32x8d model from Aggregated Residual Transformation for Deep Neural Networks <https : // arxiv . org / pdf / 1611 . 05431 . pdf > _
def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
    r"""ResNeXt-101 32x8d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_

    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
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 8
    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
                   pretrained, progress, **kwargs)
def resnext101_32x8d ( pretrained = False , progress = True , * * kwargs ) : kwargs [ 'groups' ] = 32 kwargs [ 'width_per_group' ] = 8 return _resnet ( 'resnext101_32x8d' , Bottleneck , [ 3 , 4 , 23 , 3 ] , pretrained , progress , * * kwargs )