code: def main():
      # Create experiment
      experiment.create(name="fnet")
      # Create configs
      conf = Configs()
      # Override configurations
      experiment.configs(
          conf,
          {
              # Use world level tokenizer
              "tokenizer": "basic_english",
              # Train for $32$ epochs
              "epochs": 32,
              # Switch between training and validation for $10$ times
              # per epoch
              "inner_iterations": 10,
              # Transformer configurations (same as defaults)
              "transformer.d_model": 512,
              "transformer.ffn.d_ff": 2048,
              "transformer.n_heads": 8,
              "transformer.n_layers": 6,
              # Use [FNet](index.html) instead of self-a
              # ttention
              "transformer.encoder_attn": "fnet_mix",
              # 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/fnet/experiment.py
imports: import torch
  from torch import nn
  from labml import experiment
  from labml.configs import option
  from labml_helpers.module import Module
  from labml_nn.experiments.nlp_classification import NLPClassificationConfigs
  from labml_nn.transformers import Encoder
  from labml_nn.transformers import TransformerConfigs
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types