code: def main(run_uuid: str, checkpoint: int):
      """
      Train a small model with distillation
      """
      # Load saved model
      large_model = get_saved_model(run_uuid, checkpoint)
      # Create experiment
      experiment.create(name="distillation", comment="cifar10")
      # Create configurations
      conf = Configs()
      # Set the loaded large model
      conf.large = large_model
      # Load configurations
      experiment.configs(
          conf,
          {
              "optimizer.optimizer": "Adam",
              "optimizer.learning_rate": 2.5e-4,
              "model": "_small_student_model",
          },
      )
      # Set model for saving/loading
      experiment.add_pytorch_models({"model": conf.model})
      # Start experiment from scratch
      experiment.load(None, None)
      # Start the experiment and run the training loop
      with experiment.start():
          conf.run()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/distillation/__init__.py
imports: import torch
  import torch.nn.functional
  from torch import nn
  from labml import experiment, tracker
  from labml.configs import option
  from labml_helpers.train_valid import BatchIndex
  from labml_nn.distillation.large import LargeModel
  from labml_nn.distillation.small import SmallModel
  from labml_nn.experiments.cifar10 import CIFAR10Configs
label_desc: Use image node_id 4 for calling a global function with example usage: main(run_uuid, checkpoint) without return types