code: def eval_step(
      state: TrainState, batch: Dict[str, Array], rngs: Dict[str, Any]
  ) -> Metrics:
      """Evaluate for a single step. Model should be in deterministic mode."""
      variables = {"params": state.params}
      logits = state.apply_fn(
          variables,
          batch["token_ids"],
          batch["length"],
          deterministic=True,
          rngs=rngs,
      )
      metrics = compute_metrics(labels=batch["label"], logits=logits)
      return metrics
  
imports: from typing import Any, Callable, Dict, Iterable, Optional, Sequence, Tuple, Union
  from absl import logging
  from flax import struct
  from flax.metrics import tensorboard
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  import tensorflow
  import input_pipeline
  import models
label_desc: Use image node_id 7 for calling a global function with example usage: eval_step(state, batch, rngs) and returns: metrics