code: def nasnetalarge(pretrained="imagenet"):
      """
      pretrained NASNet
      """
  
      # both 'imagenet'&'imagenet+background' are loaded from same parameters
      model = NASNetALarge(num_classes=1001)
  
      if pretrained:
          settings = pretrained_settings["nasnetalarge"][pretrained]
          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"]
  
      if pretrained == "imagenet":
          new_last_linear = nn.Linear(
              model.last_linear.in_features, settings["num_classes"]
          )
          new_last_linear.weight.data = model.last_linear.weight.data[
              1:
          ]
          new_last_linear.bias.data = model.last_linear.bias.data[1:]
          model.last_linear = new_last_linear
  
      return model
  
imports: import torch
  import torch.nn
  import torch.utils.model_zoo
label_desc: Use image node_id 1 for calling a global function with example usage: nasnetalarge(pretrained) and returns: model