(**kwargs)
return ResNet(Bottleneck, [3, 4, 23, 3], **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(**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(Bottleneck, [3, 4, 23, 3], **kwargs)
def resnext101_32x8d ( * * kwargs ) : kwargs [ "groups" ] = 32 kwargs [ "width_per_group" ] = 8 return ResNet ( Bottleneck , [ 3 , 4 , 23 , 3 ] , * * kwargs )