code: def main(
      rng: Optional[jnp.ndarray],
      config: ml_collections.ConfigDict,
      workdir: str,
      writer: metric_writers.MetricWriter,
  ) -> None:
      """Main function for the Scenic."""
      with chex.fake_pmap_and_jit(
          enable_pmap_patching=not config.get("enable_pmap_and_jit"),
          enable_jit_patching=not config.get("enable_pmap_and_jit"),
      ):
          with config.unlocked():
              config.device_memory_gbs = util.get_device_memory(
                  platform_type=FLAGS.platform_type,
                  megacore=FLAGS.megacore,
              )
              config.device_count = jax.device_count()
  
          if "lr_configs" in config:
              # TODO(sacastro): there must be a better way to check if this is the
              #   train job. Maybe some flag?
              logging.info(
                  "Logging the training batch size (%d),"
                  " so the eval jobs can know about it.",
                  config.batch_size,
              )
              writer.write_scalars(0, {"batch_size": config.batch_size})
  
          if config.get("debug_train"):
              logging.warning("DEBUG MODE IS ENABLED!")
              debug_utils.enable_jax_debugging_flags()
  
          if config.get("use_jax_compilation_cache", True) and hasattr(
              jax.devices()[0].client, "runtime_type"
          ):
              jax_cache_dir = os.path.join(
                  workdir, "jax_cache", "ttl=30d"
              )
              logging.info(
                  "JAX compilation cache path: %s", jax_cache_dir
              )
              compilation_cache.set_cache_dir(jax_cache_dir)
  
          model_cls = models.get_model_cls(config.model_name)
          assert model_cls is ImageTextModel
  
          if rng is None:
              data_rng = None
          else:
              data_rng, rng = jax.random.split(rng)
  
          if config.checkpoint and data_rng is not None:
              # When restoring from a checkpoint, change the dataset seed to ensure
              # that the example order is new. With deterministic data, this ensures
              # enough randomization and in the future with deterministic data +
              # random access, we can feed the global step to the dataset loader to
              # always continue reading the rest of the data if we resume a job that
              # was interrupted.
              checkpoint_path = checkpoints.latest_checkpoint(workdir)
              logging.info("CHECKPOINT PATH: %s", checkpoint_path)
              if checkpoint_path is not None:
                  global_step = (
                      train_utils.checkpoint_path_step(checkpoint_path)
                      or 0
                  )
                  logging.info(
                      "Folding global_step %s into dataset seed.",
                      global_step,
                  )
                  data_rng = jax.random.fold_in(data_rng, global_step)
  
          # We set some defaults for the dataset loading:
  
          if (dataset_configs := config.get("dataset_configs")) is None:
              with config.unlocked():
                  config.dataset_configs = (
                      dataset_configs
                  ) = ml_collections.ConfigDict()
  
          # Resolve the field references. This is necessary because
          # `ref_util.FieldReference` comparison operations return
          # `ml_collections.FieldReference` objects, which in turn don't support
          # `__bool__`, and make any comparison fail. This behavior makes cached
          # functions fail.
          #
          # It creates an extra copy, but it's not an issue.
          #
          # Also, this needs to be done before freezing because otherwise it doesn't
          # copy the field `_configdict` from `ml_collections.FrozenConfigDict`.
          hashable_config = config.copy_and_resolve_references()
  
          hashable_config = ml_collections.FrozenConfigDict(
              hashable_config
          )
  
          dataset = _get_dataset_cached(
              config=hashable_config,
              data_rng=data_rng,
              dataset_service_address=FLAGS.dataset_service_address,
          )
  
          if lr_configs := config.get("lr_configs"):
              total_steps = train_utils.get_num_training_steps(
                  config, dataset.meta_data
              )[0]
  
              with lr_configs.unlocked():
                  if (
                      (warmup_steps := lr_configs.get("warmup_steps"))
                      and isinstance(warmup_steps, float)
                      and 0 <= warmup_steps <= 1
                  ):
                      lr_configs.warmup_steps = (
                          warmup_steps * total_steps
                      )
  
                  if (
                      (
                          steps_per_cycle := lr_configs.get(
                              "steps_per_cycle"
                          )
                      )
                      and isinstance(steps_per_cycle, float)
                      and 0 <= steps_per_cycle <= 1
                  ):
                      lr_configs.steps_per_cycle = (
                          steps_per_cycle * total_steps
                      )
  
          trainers.get_trainer(config.trainer_name)(
              rng=rng,
              config=config,
              model_cls=model_cls,
              dataset=dataset,
              workdir=workdir,
              writer=writer,
          )
  
imports: import getpass
  import os
  from typing import Optional
  from absl import flags
  from absl import logging
  import chex
  from clu import metric_writers
  from flax.training import checkpoints
  import jax
  from jax.experimental.compilation_cache import compilation_cache
  import jax.numpy
  import ml_collections
  from scenic.common_lib import debug_utils
  from scenic.model_lib import models
  from scenic.projects.lang4video import util
  from scenic.projects.lang4video.model.image_text_model import ImageTextModel
  import scenic.projects.lang4video.opt
  import scenic.projects.lang4video.pp_ops
  from scenic.projects.lang4video.trainer import visual_text_trainer
  from scenic.projects.lang4video.trainer import visual_text_with_text_pretraining_trainer
  from scenic.projects.lang4video.trainer import zero_shot_classification_trainer
  from scenic.projects.lang4video.trainer import zero_shot_text_to_visual_retrieval_trainer
  from scenic.projects.lang4video.trainer.train_utils import get_cached_fn
  from scenic.train_lib import train_utils
  from scenic.train_lib import trainers
label_desc: Use image node_id 1 for calling a global function with example usage: main(rng, config, workdir, writer) without return types