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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def fbresnet152(num_classes=1000, pretrained='imagenet'):
 """Constructs a ResNet-152 model. Args: pretrained (bool):
 If True, returns a model pre-trained on ImageNet """

    model = FBResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes)
    if pretrained is not None:
        settings = pretrained_settings['fbresnet152'][pretrained]
        assert num_classes == settings['num_classes'], \
            "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
        model.load_state_dict(model_zoo.load_url(settings['url']))
        model.input_space = settings['input_space']
        model.input_size = settings['input_size']
        model.input_range = settings['input_range']
        model.mean = settings['mean']
        model.std = settings['std']
    return model