code: def main():
      # Create experiment
      experiment.create(
          name="pipe_neox_biases", writers={"screen", "web_api"}
      )
  
      # Initialize configs
      conf = PipelineParallelTrainerConf()
      experiment.configs(
          conf,
          {
              "learning_rate": 3e-4,
              "is_checkpointing": False,
              "max_seq_len": 128,
              "batch_size": 64,
              "chunks": 8,
          },
      )
  
      # Start the experiment
      with experiment.start():
          # Initialize the model. Do this before the loop for cleaner logs.
          _ = conf.model
  
          # Train
          for epoch in monit.loop(conf.epochs):
              conf.train_epoch()
              tracker.new_line()
              torch.save(
                  conf.fine_tuner.state_dict(),
                  str(lab.get_data_path() / "fine_tune.pt"),
              )
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/neox/samples/finetune.py
imports: import fairscale
  import torch
  import torch.nn
  import torch.utils.data
  import torch.utils.data
  import typing
  from torch.utils.data import DataLoader, RandomSampler
  from labml import experiment, monit, tracker, lab
  from labml.configs import option
  from labml.logger import inspect
  from labml_nn.neox.utils.text_dataset import get_training_data
  from labml_nn.neox.utils.finetune import FineTuneBiases
  from labml_nn.neox.model import LayerGenerator, NeoXModule
  from labml_nn.neox.utils import balance_layers_simple
  from labml_nn.neox.utils.trainer import PipelineParallelTrainerConf
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types