code: def main():
      # Create configs object
      conf = Configs()
      # Create experiment
      experiment.create(name="mnist_wassertein_gp_dcgan")
      # Override configurations
      experiment.configs(
          conf,
          {
              "discriminator": "cnn",
              "generator": "cnn",
              "label_smoothing": 0.01,
              "generator_loss": "wasserstein",
              "discriminator_loss": "wasserstein",
              "discriminator_k": 5,
          },
      )
  
      # Start the experiment and run training loop
      with experiment.start():
          conf.run()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/gan/wasserstein/gradient_penalty/experiment.py
imports: import torch
  from labml import experiment, tracker
  from labml_nn.gan.wasserstein.experiment import Configs
  from labml_nn.gan.wasserstein.gradient_penalty import GradientPenalty
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types