code: def main():
      # Create experiment
      experiment.create(name="hyper_lstm", comment="")
      # Create configs
      conf = Configs()
      # Load configurations
      experiment.configs(
          conf,
          # A dictionary of configurations to override
          {
              "tokenizer": "character",
              "text": "tiny_shakespeare",
              "optimizer.learning_rate": 2.5e-4,
              "optimizer.optimizer": "Adam",
              "prompt": "It is",
              "prompt_separator": "",
              "rnn_model": "hyper_lstm",
              "train_loader": "shuffled_train_loader",
              "valid_loader": "shuffled_valid_loader",
              "seq_len": 512,
              "epochs": 128,
              "batch_size": 2,
              "inner_iterations": 25,
          },
      )
  
      # Set models for saving and loading
      experiment.add_pytorch_models(get_modules(conf))
  
      # Start the experiment
      with experiment.start():
          # `TrainValidConfigs.run`
          conf.run()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/hypernetworks/experiment.py
imports: import torch
  import torch.nn
  from labml import experiment
  from labml.configs import option
  from labml.utils.pytorch import get_modules
  from labml_helpers.module import Module
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
  from labml_nn.hypernetworks.hyper_lstm import HyperLSTM
  from labml_nn.lstm import LSTM
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types