code: def get_eval_step(
      flax_model: nn.Module,
      loss_and_metrics_fn: Callable[..., Any],
      logits_to_probs_fn: Callable[[jnp.ndarray], jnp.ndarray],
      metrics_only: bool = False,
      debug: bool = False,
  ) -> Callable[
      [train_utils.TrainState, ArrayDict], Tuple[Any, Any, Any]
  ]:
      """Runs a single step of training.
  
  
      Args:
        flax_model: Instance of model to evaluate.
        loss_and_metrics_fn: A function that given model predictions, a batch, and
          parameters of the model calculates the loss as well as metrics.
        logits_to_probs_fn: Function that takes logits and converts them to probs.
        metrics_only: 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. Also
        the buffer of the second argument (batch) is donated to the computation.
      """
  
      def metrics_fn(
          train_state: train_utils.TrainState,
          batch: ArrayDict,
          predictions: ArrayDict,
      ) -> Tuple[Any, Any, Any]:
          _, metrics = loss_and_metrics_fn(
              predictions, batch, model_params=train_state.params
          )
  
          if metrics_only:
              return None, None, metrics
  
          targets, predictions_out = prepare_coco_eval_dicts(
              batch=batch,
              predictions=predictions,
              logits_to_probs_fn=logits_to_probs_fn,
              gather=True,
          )
          return targets, predictions_out, metrics
  
      def eval_step(
          train_state: train_utils.TrainState, batch: ArrayDict
      ) -> Tuple[Any, Any, Any]:
          variables = {
              "params": train_state.params,
              **train_state.model_state,
          }
          predictions = flax_model.apply(
              variables,
              batch["inputs"],
              padding_mask=batch["padding_mask"],
              train=False,
              mutable=False,
              debug=debug,
          )
          return metrics_fn(train_state, batch, predictions)
  
      return eval_step
  
file_path: scenic/scenic/projects/baselines/deformable_detr/evaluate.py
imports: from concurrent import futures
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple
  from absl import logging
  from flax import jax_utils
  import flax.linen
  import jax
  import jax.numpy
  import numpy
  from scenic.dataset_lib.dataset_utils import Dataset
  from scenic.projects.baselines.deformable_detr.coco_eval import DeformableDetrGlobalEvaluator
  from scenic.projects.baselines.deformable_detr.coco_eval import prepare_coco_eval_dicts
  from scenic.projects.baselines.detr import train_utils
  from scenic.train_lib import train_utils
label_desc: Use image node_id 1 for calling a global function with example usage: get_eval_step(flax_model, loss_and_metrics_fn, logits_to_probs_fn, metrics_only, debug) and returns: eval_step, targets,  predictions_out,  metrics, metrics_fn, None,  None,  metrics