def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
    'ResNeXt-101 32x8d model from\n    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n        progress (bool): If True, displays a progress bar of the download to stderr\n    '
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 8
    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)
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
def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
    'ResNeXt-101 32x8d model from\n    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n        progress (bool): If True, displays a progress bar of the download to stderr\n    '
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 8
    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)