code: def process_iterator(
      tag: str,
      item_ids: Sequence[str],
      iterator,
      rng: types.PRNGKey,
      state: model_utils.TrainState,
      step: int,
      render_fn: Any,
      summary_writer: tensorboard.SummaryWriter,
      save_dir: Optional[gpath.GPath],
      datasource: datasets.DataSource,
  ):
      """Process a dataset iterator and compute metrics."""
      save_dir = save_dir / f"{step:08d}" / tag if save_dir else None
      meters = collections.defaultdict(utils.ValueMeter)
      for i, (item_id, batch) in enumerate(zip(item_ids, iterator)):
          logging.info(
              "[%s:%d/%d] Processing %s ",
              tag,
              i + 1,
              len(item_ids),
              item_id,
          )
          if tag == "test":
              test_rng = random.PRNGKey(step)
              shape = batch["origins"][..., :1].shape
              metadata = {}
              if datasource.use_appearance_id:
                  appearance_id = random.choice(
                      test_rng, jnp.asarray(datasource.appearance_ids)
                  )
                  logging.info(
                      "\tUsing appearance_id = %d", appearance_id
                  )
                  metadata["appearance"] = jnp.full(
                      shape, fill_value=appearance_id, dtype=jnp.uint32
                  )
              if datasource.use_warp_id:
                  warp_id = random.choice(
                      test_rng, jnp.asarray(datasource.warp_ids)
                  )
                  logging.info("\tUsing warp_id = %d", warp_id)
                  metadata["warp"] = jnp.full(
                      shape, fill_value=warp_id, dtype=jnp.uint32
                  )
              if datasource.use_camera_id:
                  camera_id = random.choice(
                      test_rng, jnp.asarray(datasource.camera_ids)
                  )
                  logging.info("\tUsing camera_id = %d", camera_id)
                  metadata["camera"] = jnp.full(
                      shape, fill_value=camera_id, dtype=jnp.uint32
                  )
              if datasource.use_time:
                  timestamp = random.uniform(
                      test_rng, minval=0.0, maxval=1.0
                  )
                  logging.info("\tUsing time = %d", timestamp)
                  metadata["time"] = jnp.full(
                      shape, fill_value=timestamp, dtype=jnp.uint32
                  )
              batch["metadata"] = metadata
  
          stats = process_batch(
              batch=batch,
              rng=rng,
              state=state,
              tag=tag,
              item_id=item_id,
              step=step,
              render_fn=render_fn,
              summary_writer=summary_writer,
              save_dir=save_dir,
              datasource=datasource,
          )
          if jax.process_index() == 0:
              for k, v in stats.items():
                  meters[k].update(v)
  
      if jax.process_index() == 0:
          for meter_name, meter in meters.items():
              summary_writer.scalar(
                  tag=f"metrics-eval/{meter_name}/{tag}",
                  value=meter.reduce("mean"),
                  step=step,
              )
  
imports: import collections
  import functools
  import time
  from typing import Any, Dict, Optional, Sequence
  from absl import app
  from absl import flags
  from absl import logging
  from flax import jax_utils
  from flax import optim
  from flax.metrics import tensorboard
  from flax.training import checkpoints
  import gin
  import jax
  from jax import numpy
  from jax import random
  import tensorflow
  from nerfies import configs
  from nerfies import datasets
  from nerfies import evaluation
  from nerfies import gpath
  from nerfies import image_utils
  from nerfies import model_utils
  from nerfies import models
  from nerfies import types
  from nerfies import utils
  from nerfies import visualization
label_desc: Use image node_id 3 for calling a global function with example usage: process_iterator(tag, item_ids, iterator, rng, state, step, render_fn, summary_writer, save_dir, datasource) without return types