code: def evaluate():
      """
      ## Evaluate trained Cycle GAN
      """
      # Set the run UUID from the training run
      trained_run_uuid = "f73c1164184711eb9190b74249275441"
      # Create configs object
      conf = Configs()
      # Create experiment
      experiment.create(name="cycle_gan_inference")
      # Load hyper parameters set for training
      conf_dict = experiment.load_configs(trained_run_uuid)
      # Calculate configurations. We specify the generators `'generator_xy', 'generator_yx'`
      # so that it only loads those and their dependencies.
      # Configs like `device` and `img_channels` will be calculated, since these are required by
      # `generator_xy` and `generator_yx`.
      #
      # If you want other parameters like `dataset_name` you should specify them here.
      # If you specify nothing, all the configurations will be calculated, including data loaders.
      # Calculation of configurations and their dependencies will happen when you call `experiment.start`
      experiment.configs(conf, conf_dict)
      conf.initialize()
  
      # Register models for saving and loading.
      # `get_modules` gives a dictionary of `nn.Modules` in `conf`.
      # You can also specify a custom dictionary of models.
      experiment.add_pytorch_models(get_modules(conf))
      # Specify which run to load from.
      # Loading will actually happen when you call `experiment.start`
      experiment.load(trained_run_uuid)
  
      # Start the experiment
      with experiment.start():
          # Image transformations
          transforms_ = [
              transforms.ToTensor(),
              transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
          ]
  
          # Load your own data. Here we try the test set.
          # I was trying with Yosemite photos, they look awesome.
          # You can use `conf.dataset_name`, if you specified `dataset_name` as something you wanted to be calculated
          # in the call to `experiment.configs`
          dataset = ImageDataset(
              conf.dataset_name, transforms_, "train"
          )
          # Get an image from dataset
          x_image = dataset[10]["x"]
          # Display the image
          plot_image(x_image)
  
          # Evaluation mode
          conf.generator_xy.eval()
          conf.generator_yx.eval()
  
          # We don't need gradients
          with torch.no_grad():
              # Add batch dimension and move to the device we use
              data = x_image.unsqueeze(0).to(conf.device)
              generated_y = conf.generator_xy(data)
  
          # Display the generated image.
          plot_image(generated_y[0].cpu())
  
imports: import itertools
  import random
  import zipfile
  from typing import Tuple
  import torch
  import torch.nn
  import torchvision.transforms
  from PIL import Image
  from torch.utils.data import DataLoader, Dataset
  from torchvision.transforms import InterpolationMode
  from torchvision.utils import make_grid
  from labml import lab, tracker, experiment, monit
  from labml.configs import BaseConfigs
  from labml.utils.download import download_file
  from labml.utils.pytorch import get_modules
  from labml_helpers.device import DeviceConfigs
  from labml_helpers.module import Module
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate() without return types