code: 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]
          if num_classes != settings["num_classes"]:
              raise AssertionError(
                  "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
  
imports: import torch.nn
  import torch.nn.functional
  import math
  import torch.utils.model_zoo
label_desc: Use image node_id 6 for calling a global function with example usage: fbresnet152(num_classes, pretrained) and returns: model