code: def main(
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      workdir: str,
      writer: metric_writers.MetricWriter,
  ):
      """Main function for the DETR project."""
      if _BATCH_SIZE.value is not None:
          config.batch_size = _BATCH_SIZE.value
      config = resolve(config)
      config = ml_collections.FrozenConfigDict(config)
  
      model_cls = get_model_cls(config.model_name)
      data_rng, rng = jax.random.split(rng)
      dataset = train_utils.get_dataset(
          config,
          data_rng,
          dataset_service_address=FLAGS.dataset_service_address,
      )
      if _TRAIN.value:
          trainer.train_and_evaluate(
              rng=rng,
              config=config,
              model_cls=model_cls,
              dataset=dataset,
              workdir=workdir,
              writer=writer,
          )
      else:
          trainer.evaluate(
              rng=rng,
              config=config,
              model_cls=model_cls,
              dataset=dataset,
              workdir=workdir,
              writer=writer,
          )
  
file_path: scenic/scenic/projects/baselines/deformable_detr/main.py
imports: from typing import Any
  from absl import flags
  from clu import metric_writers
  import jax
  import jax.numpy
  import ml_collections
  from scenic import app
  from scenic.projects.baselines.deformable_detr import input_pipeline_detection
  from scenic.projects.baselines.deformable_detr import trainer
  from scenic.projects.baselines.deformable_detr.model import DeformableDETRModel
  from scenic.train_lib import train_utils
label_desc: Use image node_id 3 for calling a global function with example usage: main(rng, config, workdir, writer) without return types