code: def process_and_fetch_to_host(
      pred_or_tgt: Union[jnp.ndarray, Dict[str, jnp.ndarray]],
      batch_mask: jnp.ndarray,
  ) -> Union[Sequence[jnp.ndarray], Dict[str, jnp.ndarray]]:
      """Used to collect predictions and targets of the whole valid/test set.
  
      Args:
        pred_or_tgt: A jnp-array or dict of arrays, each of shape `[n_dev, bs,
          X,...,Y].
        batch_mask: A nd-array of shape `[nun_devices, bs]`, where zero values
          indicate padded examples.
  
      Returns:
        A list of length n_dev*bs of items, where each item is a dictionary with
        same keys as `pred_or_tgt` & values are normal np-arrays of shape [X,...,Y].
      """
  
      def _split_mini_batches(x):
          # Fetch to host and filter out padded examples.
          x = jax.device_get(x)[np.array(batch_mask).astype(bool)]
          # Split minibatch of examples into a list of examples.
          x_list = jnp.split(x, x.shape[0], axis=0)
          # Squeeze out the dummy dimension.
          return jax.tree_util.tree_map(
              lambda x: jnp.squeeze(x, axis=0), x_list
          )
  
      pred_or_tgt = jax.tree_util.tree_map(
          _split_mini_batches, pred_or_tgt
      )
  
      if isinstance(pred_or_tgt, list):
          # Pred_or_tgt was a single array, so just return the list:
          return pred_or_tgt
      else:
          # Pred_or_tgt was dict of arrays, so convert dict of lists to list of dicts:
          keys, values = zip(*pred_or_tgt.items())
          return [dict(zip(keys, v)) for v in zip(*values)]
  
imports: import collections.abc
  import copy
  import functools
  import os
  import re
  import time
  from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Tuple, Union
  from absl import logging
  from clu import metric_writers
  import flax
  from flax import jax_utils
  from flax import struct
  import flax.linen
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.train_lib import optimizers
  from tensorflow.io import gfile
label_desc: Use image node_id 15 for calling a global function with example usage: process_and_fetch_to_host(pred_or_tgt, batch_mask) and returns: jax, pred_or_tgt, unknown