code: def main():
      # Create experiment
      experiment.create(name="unet")
  
      # Create configurations
      configs = Configs()
  
      # Set configurations. You can override the defaults by passing the values in the dictionary.
      experiment.configs(configs, {})
  
      # Initialize
      configs.init()
  
      # Set models for saving and loading
      experiment.add_pytorch_models({"model": configs.model})
  
      # Start and run the training loop
      with experiment.start():
          configs.run()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/unet/experiment.py
imports: import numpy
  import torch
  import torch.utils.data
  import torchvision.transforms.functional
  from torch import nn
  from labml import lab, tracker, experiment, monit
  from labml.configs import BaseConfigs
  from labml_helpers.device import DeviceConfigs
  from labml_nn.unet.carvana import CarvanaDataset
  from labml_nn.unet import UNet
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types