code: def train():
      """
      ## Create and train a small model
      """
  
      # Create an experiment
      experiment.create(name="retro_small")
  
      # GPU device
      device = torch.device("cuda:0")
  
      # Load Tiny Shakespeare dataset
      tds = TextFileDataset(
          lab.get_data_path() / "tiny_shakespeare.txt",
          list,
          url="https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt",
      )
  
      # Load [Retro dataset](dataset.html)
      train_dataset = Dataset(
          lab.get_data_path() / "retro_train_dataset.json", tds
      )
  
      # Create dataloader
      train_dl = DataLoader(
          train_dataset,
          batch_size=4,
          sampler=RandomSampler(train_dataset, replacement=True),
      )
  
      # Hyper-parameters
      chunk_len = 16
      d_model = 128
      d_ff = 512
      n_heads = 16
      d_k = 16
  
      # Create the nearest neighbor encoder
      nearest_neighbor_encoder = NearestNeighborEncoder(
          chunk_len, 6, {3}, d_model, n_heads, d_k, d_ff
      )
      # Create the model
      model = RetroModel(
          tds.n_tokens,
          d_model,
          6,
          {3, 5},
          chunk_len,
          n_heads,
          d_k,
          d_ff,
          encoder=nearest_neighbor_encoder,
      )
      # Move the model to the device
      model = model.to(device)
      # Create the optimizer
      optimizer = Noam(
          model.parameters(), lr=1.0, d_model=d_model, warmup=2_000
      )
      # Create the `Trainer`
      trainer = Trainer(device, model, train_dl, optimizer)
      # Create the `Sampler`
      sampler = Sampler(device, model, tds, chunk_len)
      #
      prompt = """Second Citizen:\nOne word, good citizens.\n\nFirst Citizen:"""
  
      # Set models for saving and loading
      experiment.add_pytorch_models(model=model)
  
      # Start the experiment
      with experiment.start():
          # Train for `32` epochs
          for epoch in monit.loop(32):
              # Train
              trainer()
              # Print a new line
              tracker.new_line()
              # Sample from the `prompt`
              logger.log(
                  [
                      (prompt.replace("\n", "\\n\n"), Text.subtle),
                      (
                          sampler.sample(prompt, 128).replace(
                              "\n", "\\n\n"
                          ),
                          Text.none,
                      ),
                  ]
              )
              # Save models
              experiment.save_checkpoint()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/retro/train.py
imports: import torch
  from torch import nn
  from torch.utils.data import DataLoader, RandomSampler
  from labml import monit, lab, tracker, experiment, logger
  from labml.logger import Text
  from labml_helpers.datasets.text import TextFileDataset
  from labml_nn.optimizers.noam import Noam
  from labml_nn.transformers.retro import model
  from labml_nn.transformers.retro.dataset import Dataset, RetroIndex
  from labml_nn.transformers.retro.model import RetroModel, NearestNeighborEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: train() without return types