code: def test_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      metrics_fn: MetricFn,
      n_clips: int = 2,
      return_logits_and_labels: bool = False,
      softmax_logits: bool = False,
      debug: 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.
  
      Returns:
        Calculated metrics [and optionally averaged logits that are of
        shape `[1, num_classes]`].
      """
  
      all_logits = jnp.zeros(batch["label"].shape[1])
      assert (
          len(batch["batch_mask"].shape) == 1
      ), "Spatial padding is not supported in multi-crop evaluation."
  
      variables = {
          "params": train_state.optimizer.target,
          **train_state.model_state,
      }
      for modality in batch["inputs"]:
          num_crops = batch["inputs"][modality].shape[0]
      for idx in range(0, num_crops, n_clips):
          current_input = {}
          for modality in batch["inputs"]:
              current_input[modality] = batch["inputs"][modality][
                  idx : idx + n_clips
              ]
          logits = flax_model.apply(
              variables,
              current_input,
              train=False,
              mutable=False,
              debug=debug,
          )
  
          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:
          all_logits = jax.lax.all_gather(all_logits, "batch")
          labels = jax.lax.all_gather(batch["label"], "batch")
          return metrics, all_logits, labels
      return metrics
  
imports: import copy
  import functools
  from typing import Any, Callable, Dict, Optional, Tuple, 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
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.mbt import train_utils
  from scenic.projects.vivit import evaluation_lib
  from scenic.projects.vivit import train_utils
  from scenic.train_lib_deprecated import lr_schedules
  from scenic.train_lib_deprecated import optimizers
  from scenic.train_lib_deprecated import pretrain_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 4 for calling a global function with example usage: test_step(train_state, batch) and returns: metrics, metrics,  all_logits,  labels