code: def main(
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      workdir: str,
      writer: metric_writers.MetricWriter,
  ) -> None:
      """Main function for the knowledge project."""
      data_rng, rng = jax.random.split(rng)
  
      dataset = train_utils.get_dataset(
          config,
          data_rng,
          dataset_service_address=FLAGS.dataset_service_address,
      )
  
      logging.info(FLAGS.dataset_service_address)
      logging.info("workdir= %s", workdir)
      logging.info("global_num_shards= %d", jax.device_count())
      logging.info("num_shards= %d", jax.local_device_count())
      logging.info("cpu info")
      logging.info(jax.local_devices(backend="cpu"))
      logging.info(jax.local_devices(backend="cpu"))
      logging.info(
          "****************** Dataset metadata *****************"
      )
      logging.info(dataset.meta_data)
  
      if config.update_num:
          trainer_utils.update_config(config, dataset.meta_data)
      if config.model_name == "retrieval_image_captioner_soft":
          model_cls = fusion_in_decoder_soft.FIDSoftModel
          trainer.train_and_eval(
              rng=rng,
              config=config,
              model_cls=model_cls,
              dataset=dataset,
              workdir=workdir,
              writer=writer,
          )
      elif config.model_name == "knowledge_fid":
          model_cls = knowledge_fid.KnowledgeFIDModel
          kb_datasets = {}
          for kb_dataset_name, kb_dataset_config in zip(
              config.kb_dataset_names, config.kb_dataset_configs
          ):
              kb_datasets[kb_dataset_name] = train_utils.get_dataset(
                  config,
                  data_rng,
                  dataset_service_address=FLAGS.dataset_service_address,
                  dataset_name=kb_dataset_name,
                  dataset_configs=kb_dataset_config,
              )
  
          trainer_memory.train_and_eval(
              rng=rng,
              config=config,
              model_cls=model_cls,
              dataset=dataset,
              workdir=workdir,
              writer=writer,
              kb_datasets=kb_datasets,
          )
      else:
          raise ValueError(
              ("Unknown model name %s" % config.model_name)
          )
  
      return None
  
imports: from absl import flags
  from absl import logging
  from clu import metric_writers
  import jax
  import jax.numpy
  import ml_collections
  from scenic import app
  from scenic.projects.knowledge_visual_language import trainer
  from scenic.projects.knowledge_visual_language import trainer_memory
  from scenic.projects.knowledge_visual_language import trainer_utils
  from scenic.projects.knowledge_visual_language.data import cc12m_table_dataset
  from scenic.projects.knowledge_visual_language.data import vqa_dataset
  from scenic.projects.knowledge_visual_language.data import vqa_table_dataset
  from scenic.projects.knowledge_visual_language.data import web_image_text_generation_dataset
  from scenic.projects.knowledge_visual_language.data import wiki_image_text_generation_dataset
  from scenic.projects.knowledge_visual_language.data import wit_table_dataset
  from scenic.projects.knowledge_visual_language.models import fusion_in_decoder_soft
  from scenic.projects.knowledge_visual_language.models import knowledge_fid
  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) and returns: None