code: def main():
      # Create experiment
      experiment.create(name="resnet", comment="cifar10")
      # Create configurations
      conf = Configs()
      # Load configurations
      experiment.configs(
          conf,
          {
              "bottlenecks": [8, 16, 16],
              "n_blocks": [6, 6, 6],
              "optimizer.optimizer": "Adam",
              "optimizer.learning_rate": 2.5e-4,
              "epochs": 500,
              "train_batch_size": 256,
              "train_dataset": "cifar10_train_augmented",
              "valid_dataset": "cifar10_valid_no_augment",
          },
      )
      # Set model for saving/loading
      experiment.add_pytorch_models({"model": conf.model})
      # Start the experiment and run the training loop
      with experiment.start():
          conf.run()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/resnet/experiment.py
imports: from typing import List, Optional
  from torch import nn
  from labml import experiment
  from labml.configs import option
  from labml_nn.experiments.cifar10 import CIFAR10Configs
  from labml_nn.resnet import ResNetBase
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types