def vgg13(pretrained=False, **kwargs):
    """VGG 13-layer model (configuration "B")

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['B']), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg13']))
    return model
def vgg13(pretrained=False, **kwargs):
    """VGG 13-layer model (configuration "B")

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['B']), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg13']))
    return model
def vgg13 ( pretrained = False , * * kwargs ) : if pretrained : kwargs [ 'init_weights' ] = False model = VGG ( make_layers ( cfg [ 'B' ] ) , * * kwargs ) if pretrained : model . load_state_dict ( model_zoo . load_url ( model_urls [ 'vgg13' ] ) ) return model
VGG 13-layer model (configuration "B")

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
VGG 13 - layer model ( configuration B )