def densenet201(pretrained=False, **kwargs):
    'Densenet-201 model from\n    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    '
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32), **kwargs)
    if pretrained:
        pattern = re.compile('^(.*denselayer\\d+\\.(?:norm|relu|conv))\\.((?:[12])\\.(?:weight|bias|running_mean|running_var))$')
        state_dict = model_zoo.load_url(model_urls['densenet201'])
        for key in list(state_dict.keys()):
            res = pattern.match(key)
            if res:
                new_key = (res.group(1) + res.group(2))
                state_dict[new_key] = state_dict[key]
                del state_dict[key]
        model.load_state_dict(state_dict)
    return model
Densenet-201 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
def densenet201(pretrained=False, **kwargs):
    'Densenet-201 model from\n    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    '
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32), **kwargs)
    if pretrained:
        pattern = re.compile('^(.*denselayer\\d+\\.(?:norm|relu|conv))\\.((?:[12])\\.(?:weight|bias|running_mean|running_var))$')
        state_dict = model_zoo.load_url(model_urls['densenet201'])
        for key in list(state_dict.keys()):
            res = pattern.match(key)
            if res:
                new_key = (res.group(1) + res.group(2))
                state_dict[new_key] = state_dict[key]
                del state_dict[key]
        model.load_state_dict(state_dict)
    return model