code: def get_eval_step(
      flax_model, loss_and_metrics_fn, metrics_only=False, debug=False
  ):
      """Runs a single step of training.
  
      Note that in this code, the buffer of the second argument (batch) is donated
      to the computation.
  
      Args:
        flax_model: Flax model (an instance of nn.Module).
        loss_and_metrics_fn: A function that given model predictions, a batch, and
          parameters of the model calculates the loss as well as metrics.
        metrics_only: bool; Only return metrics.
        debug: bool; Whether the debug mode is enabled during evaluation.
          `debug=True` enables model specific logging/storing some values using
          jax.host_callback.
  
      Returns:
        Eval step function which returns predictions and calculated metrics.
      """
  
      def metrics_fn(train_state, batch, predictions, task):
          _, metrics = loss_and_metrics_fn(
              predictions,
              batch,
              task=task,
              model_params=train_state.optimizer.target,
          )
  
          if metrics_only:
              return None, None, metrics
  
          # Collect necessary predictions and target information from all hosts.
          if task == "layout_denoise":
              predictions_out = {
                  "pred_logits": predictions["pred_logits"],
              }
              if "binary_pred_logits" in predictions:
                  predictions_out["binary_pred_logits"] = predictions[
                      "binary_pred_logits"
                  ]
  
          targets = {
              "label": {
                  "size": batch["label"]["size"],
                  "orig_size": batch["label"]["orig_size"],
                  "labels": batch["label"]["labels"],
                  "binary_labels": batch["label"]["binary_labels"],
              },
              "batch_mask": batch["batch_mask"],
          }
  
          predictions_out = jax.lax.all_gather(predictions_out, "batch")
          targets = jax.lax.all_gather(targets, "batch")
          return targets, predictions_out, metrics
  
      def eval_step(train_state, batch, task):
          variables = {
              "params": train_state.optimizer.target,
              **train_state.model_state,
          }
          predictions = flax_model.apply(
              variables,
              image=batch["inputs"],
              obj_mask=batch["label"]["obj_mask"],
              desc_id=batch["label"]["desc_id"],
              resource_id=batch["label"]["resource_id"],
              name_id=batch["label"]["name_id"],
              boxes=batch["label"]["boxes"],
              # clickable=batch['label']['clickable'],
              task=task,
              padding_mask=batch["padding_mask"],
              train=False,
              mutable=False,
              debug=debug,
          )
          if task == "layout_denoise":
              _pred_probs(predictions)
  
          return metrics_fn(train_state, batch, predictions, task=task)
  
      return eval_step
  
imports: import collections
  from concurrent import futures
  import functools
  import random
  import time
  from typing import Any, Dict, Tuple, Optional
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import jax_utils
  import flax.linen
  from flax.training.checkpoints import restore_checkpoint
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.baselines.detr import train_utils
  from scenic.projects.layout_denoise import train_utils
  from scenic.projects.layout_denoise.datasets import dataset
  from scenic.train_lib_deprecated import lr_schedules
  from scenic.train_lib_deprecated import pretrain_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 3 for calling a global function with example usage: get_eval_step(flax_model, loss_and_metrics_fn, metrics_only, debug) and returns: eval_step, targets,  predictions_out,  metrics, metrics_fn, None,  None,  metrics