def swsl_resnext101_32x16d(pretrained=True, **kwargs):
    """Constructs a semi-weakly supervised ResNeXt-101 32x16 model pre-trained on 1B weakly supervised
       image dataset and finetuned on ImageNet.
       `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
       Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
    """
    model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16, **kwargs)
    return _create_resnet('swsl_resnext101_32x16d', pretrained, **model_args)
Constructs a semi-weakly supervised ResNeXt-101 32x16 model pre-trained on 1B weakly supervised
       image dataset and finetuned on ImageNet.
       `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
       Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
    
analyze/convnexts4nd/src/models/baselines/resnet_timm.py