code: def eval_step(
      train_state: utils.OptaxTrainState,
      batch: Any,
      *,
      flax_model: nn.Module,
      debug: Optional[bool] = False,
  ) -> Any:
      """Runs a single step of evaluation."""
      variables = {
          "params": train_state.weights,
          **train_state.model_state,
      }
      (encoded_video, encoded_text) = flax_model.apply(
          variables,
          batch["inputs"].get("rgb"),
          batch["text_indices"],
          train=False,
          mutable=False,
          debug=debug,
      )
      # This function uses all_gather to fetch embeddings from all devices.
      _, encoded_video, encoded_text = utils.compute_inners(
          encoded_video, encoded_text, "batch", return_embeddings=True
      )
      return encoded_video, encoded_text
  
imports: import functools
  from typing import Any, Dict, Optional, Tuple
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from clu import platform
  from flax import jax_utils
  import flax.linen
  import jax
  import jax.example_libraries.optimizers
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.projects.verbs_in_action import utils
  from scenic.train_lib import train_utils
label_desc: Use image node_id 3 for calling a global function with example usage: eval_step(train_state, batch) and returns: encoded_video,  encoded_text