code: def fbresnet152(num_classes=1000, pretrained='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
docs: Constructs a ResNet-152 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet