code: def test_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      metrics_fn: MetricFnEval,
      n_clips: int = 2,
      return_logits_and_labels: bool = False,
      softmax_logits: bool = False,
      debug: bool = False,
      learn_token_score: bool = False,
      add_boxes: bool = False,
  ) -> Union[
      Dict[str, Tuple[float, int]],
      Tuple[Dict[str, Tuple[float, int]], jnp.ndarray, jnp.ndarray],
  ]:
      """Runs a single step of testing.
  
      For multi-crop testing, we assume that num_crops consecutive entries in the
      batch are from the same example. And we average the logits over these examples
  
      We assume that the batch contains different crops of the same original
      example. Therefore, we can average all the logits of it.
      This assumption is true when local_batch_size = num_local_devices
  
      Args:
        train_state: The state of training including the current
          global_step, model_state, rng, and optimizer, and other metadata.
        batch: Dictionary with keys 'inputs', 'labels', 'batch_mask'. We assume that
          all the inputs correspond to the same original example in the test set.
          The input shapes to this function are batch['inputs'] = [num_crops, t, h,
          w, c] batch['labels'] = [num_crops, num_classes] However, for
          classification, the labels for all the crops are the same.
          batch['batch_mask'] = [num_crops]
        flax_model: A Flax model.
        metrics_fn: Metrics function for the model.
        n_clips: The number of clips to process at a time by each device. Set
          due to memory constraints.
        return_logits_and_labels: Whether return logits of the model or not.
        softmax_logits: Whether to softmax-normalise the logits before
          averaging
        debug: Whether the debug mode is enabled during evaluation.
          `debug=True` enables model specific logging/storing some values using
          jax.host_callback.
        learn_token_score: if enable learning token score. The network will have
          additional outputs of each scores for each token.
        add_boxes: if add boxes in shape batch x time x num_objs x 4
  
      Returns:
        Calculated metrics [and optionally averaged logits that are of
        shape `[1, num_classes]`].
      """
      all_logits = jnp.zeros(batch["label"].shape[1])
      num_crops = batch["inputs"].shape[0]
      variables = {
          "params": train_state.params,
          **train_state.model_state,
      }
  
      for idx in range(0, num_crops, n_clips):
          temp_input = batch["inputs"][idx : idx + n_clips]
          outputs = flax_model.apply(
              variables,
              temp_input,
              boxes=batch["bboxes"][idx : idx + n_clips]
              if add_boxes
              else None,
              rngs={"dropout": train_state.rng},
              train=False,
              mutable=False,
              debug=debug,
          )
          if learn_token_score:
              logits = outputs["logits"]
          else:
              logits = outputs
          if softmax_logits:
              logits = nn.softmax(logits, axis=-1)
          logits = jnp.sum(logits, axis=0)
          all_logits = all_logits + logits
  
      all_logits = all_logits / num_crops
      all_logits = jnp.expand_dims(all_logits, axis=0)
      batch["label"] = jnp.expand_dims(batch["label"][0], axis=0)
      batch["batch_mask"] = jnp.expand_dims(
          batch["batch_mask"][0], axis=0
      )
      metrics = metrics_fn(all_logits, batch)
      if return_logits_and_labels:
          return metrics, all_logits, batch["label"]
      return metrics
  
imports: import functools
  from typing import Any, Callable, Dict, Iterator, Optional, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries.optimizers import clip_grads
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.objectvivit import optimizer_utils
  from scenic.projects.objectvivit import train_utils
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
label_desc: Use image node_id 3 for calling a global function with example usage: test_step(train_state, batch) and returns: metrics, metrics,  all_logits,  batch