code: def main(
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      workdir: str,
      writer: metric_writers.MetricWriter,
  ):
      """Main funtion for OWL-ViT training."""
  
      data_rng, rng = jax.random.split(rng)
  
      if config.checkpoint:
          # When restoring from a checkpoint, change the dataset seed to ensure that
          # the example order is new:
          train_state = checkpoints.restore_checkpoint(
              workdir, target=None
          )
          if train_state is not None:
              global_step = train_state.get("global_step", 0)
              logging.info(
                  "Folding global_step %s into dataset seed.",
                  global_step,
              )
              data_rng = jax.random.fold_in(data_rng, global_step)
  
      dataset = train_utils.get_dataset(
          config,
          data_rng,
          dataset_service_address=FLAGS.dataset_service_address,
      )
  
      trainer.train(
          rng=rng,
          config=config,
          model_cls=models.TextZeroShotDetectionModel,
          dataset=dataset,
          workdir=workdir,
          writer=writer,
      )
  
file_path: scenic/scenic/projects/owl_vit/main.py
imports: from absl import flags
  from absl import logging
  from clu import metric_writers
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import ml_collections
  from scenic import app
  from scenic.projects.owl_vit import models
  from scenic.projects.owl_vit import trainer
  from scenic.projects.owl_vit.preprocessing import input_pipeline
  from scenic.train_lib import train_utils
label_desc: Use image node_id 1 for calling a global function with example usage: main(rng, config, workdir, writer) without return types