code: def main():
      # Create experiment
      experiment.create(name="cifar10", comment="small model")
      # Create configurations
      conf = Configs()
      # Load configurations
      experiment.configs(
          conf,
          {
              "optimizer.optimizer": "Adam",
              "optimizer.learning_rate": 2.5e-4,
          },
      )
      # Set model for saving/loading
      experiment.add_pytorch_models({"model": conf.model})
      # Print number of parameters in the model
      logger.inspect(
          params=(
              sum(
                  p.numel()
                  for p in conf.model.parameters()
                  if p.requires_grad
              )
          )
      )
      # Start the experiment and run the training loop
      with experiment.start():
          conf.run()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/distillation/small.py
imports: import torch.nn
  from labml import experiment, logger
  from labml.configs import option
  from labml_nn.experiments.cifar10 import CIFAR10Configs, CIFAR10VGGModel
  from labml_nn.normalization.batch_norm import BatchNorm
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types