code: def main(
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      workdir: str,
      writer: metric_writers.MetricWriter,
  ):
      """Main function for the Vid2Seq project."""
      jave_jre = JRE_BIN_JAVA
      os.environ["JRE_BIN_JAVA"] = java_jre
  
      # ensure arguments match
      config.model.decoder.num_bins = config.dataset_configs.num_bins
      config.model.decoder.tmp_only = config.dataset_configs.tmp_only
      config.model.decoder.order = config.dataset_configs.order
  
      model_cls = get_model_cls(config.model_name)
      data_rng, rng = jax.random.split(rng)
      dataset_dict = get_datasets(config, data_rng=data_rng)
  
      if config.num_training_epochs:
          trainer.train_and_eval(
              rng=rng,
              config=config,
              model_cls=model_cls,
              dataset_dict=dataset_dict,
              workdir=workdir,
              writer=writer,
          )
      else:
          trainer.eval_only(
              rng=rng,
              config=config,
              model_cls=model_cls,
              dataset_dict=dataset_dict,
              workdir=workdir,
              writer=writer,
          )
  
file_path: scenic/scenic/projects/vid2seq/main.py
imports: import os
  from typing import Any, Callable
  from absl import flags
  from clu import metric_writers
  import jax
  import jax.numpy
  import ml_collections
  from scenic import app
  from scenic.projects.vid2seq import models
  from scenic.projects.vid2seq import trainer
  from scenic.projects.vid2seq.datasets.dense_video_captioning_tfrecord_dataset import get_datasets
label_desc: Use image node_id 2 for calling a global function with example usage: main(rng, config, workdir, writer) without return types