def get_robust_classifier(dataset, model_path, parallel=True):
    if (dataset == 'cifar10'):
        (model, _) = make_and_restore_model(arch='resnet50', dataset=CIFAR(), resume_path=model_path, parallel=parallel)
    elif (dataset == 'RestrictedImageNet'):
        (model, _) = make_and_restore_model(arch='resnet50', dataset=RestrictedImageNet(''), resume_path=model_path, parallel=parallel)
    elif (dataset == 'ImageNet'):
        (model, _) = make_and_restore_model(arch='resnet50', dataset=ImageNet(''), resume_path=model_path, parallel=parallel)
    else:
        raise NotImplementedError('Model for {} is not implemented!'.format(dataset))
    model.eval()
    return model
def get_robust_classifier(dataset, model_path, parallel=True):
    if (dataset == 'cifar10'):
        (model, _) = make_and_restore_model(arch='resnet50', dataset=CIFAR(), resume_path=model_path, parallel=parallel)
    elif (dataset == 'RestrictedImageNet'):
        (model, _) = make_and_restore_model(arch='resnet50', dataset=RestrictedImageNet(), resume_path=model_path, parallel=parallel)
    elif (dataset == 'ImageNet'):
        (model, _) = make_and_restore_model(arch='resnet50', dataset=ImageNet(), resume_path=model_path, parallel=parallel)
    else:
        raise NotImplementedError('Model for {} is not implemented!'.format(dataset))
    model.eval()
    return model