code: def main():
      # Create experiment
      experiment.create(name="mlp_mixer_mlm")
      # Create configs
      conf = Configs()
      # Override configurations
      experiment.configs(
          conf,
          {
              # Batch size
              "batch_size": 64,
              # Sequence length of $32$. We use a short sequence length to train faster.
              # Otherwise MLM models take forever to train.
              "seq_len": 32,
              # Train for 1024 epochs.
              "epochs": 1024,
              # Switch between training and validation for $1$ times
              # per epoch
              "inner_iterations": 1,
              # Transformer configurations
              "d_model": 128,
              "transformer.ffn.d_ff": 256,
              "transformer.n_heads": 8,
              "transformer.n_layers": 6,
              "transformer.ffn.activation": "GELU",
              # Mixer MLP hidden layer size
              "mix_mlp.d_ff": 128,
              # Use [Noam optimizer](../../optimizers/noam.html)
              "optimizer.optimizer": "Noam",
              "optimizer.learning_rate": 1.0,
          },
      )
  
      # Set models for saving and loading
      experiment.add_pytorch_models({"model": conf.model})
  
      # Start the experiment
      with experiment.start():
          # Run training
          conf.run()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/mlp_mixer/experiment.py
imports: from labml import experiment
  from labml.configs import option
  from labml_nn.transformers import TransformerConfigs
  from labml_nn.transformers.configs import FeedForwardConfigs
  from labml_nn.transformers.mlm.experiment import TransformerMLM, Configs
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types