code: def main():
      # Create experiment
      experiment.create(
          name="rotary_pe_transformer", writers={"screen"}
      )
      # Create configs
      conf = Configs()
      # Override configurations
      experiment.configs(
          conf,
          {
              # No fixed positional embeddings
              "transformer.src_embed": "no_pos",
              "transformer.tgt_embed": "no_pos",
              # Encoder with RoPE
              "transformer.encoder_attn": "rotary",
              #
              "model": "rotary_pe_transformer",
              # Use character level tokenizer
              "tokenizer": "character",
              # Prompt separator is blank
              "prompt_separator": "",
              # Starting prompt for sampling
              "prompt": "It is ",
              # Use Tiny Shakespeare dataset
              "text": "tiny_shakespeare",
              # Use a context size of $256$
              "seq_len": 512,
              # Train for 32 epochs
              "epochs": 32,
              # Batch size $4$
              "batch_size": 4,
              # Switch between training and validation for $10$ times
              # per epoch
              "inner_iterations": 10,
              # Model size
              "d_model": 128,
              "transformer.ffn.d_ff": 512,
              "transformer.n_heads": 16,
              "transformer.dropout": 0.0,
              # Use [Noam optimizer](../../optimizers/noam.html)
              "optimizer.optimizer": "Noam",
              "optimizer.learning_rate": 1.0,
              "dataloader_shuffle_with_replacement": True,
          },
      )
  
      # Set models for saving and loading
      experiment.add_pytorch_models({"model": conf.model})
  
      # Start the experiment
      with experiment.start():
          # Run training
          conf.run()
  
imports: from labml import experiment
  from labml.configs import option, calculate
  from labml_nn.transformers import TransformerConfigs
  from labml_nn.transformers.basic.autoregressive_experiment import AutoregressiveTransformer, Configs
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types