code: def process_and_fetch_to_host(pred_or_tgt, batch_mask):
      """Used to collect predictions and targets of the whole valid/test set.
  
      Args:
        pred_or_tgt: pytree; A pytree of jnp-arrays where leaves are of shape
          `[num_devices, bs, X,...,Y]`.
        batch_mask: A nd-array of shape `[num_devices, bs]`, where zero values
          indicate padded examples.
  
      Returns:
        A list of length num_devices * bs of items, where each item is a tree with
        the same structure as `pred_or_tgt` and each leaf contains a single example.
      """
      # Fetch to host in a single call.
      pred_or_tgt, batch_mask = jax.device_get(
          (pred_or_tgt, batch_mask)
      )
      batch_mask = np.array(batch_mask).astype(bool)
  
      def _split_mini_batches(x):
          # Filter out padded examples.
          x = x[batch_mask]
          # Split minibatch of examples into a list of examples.
          x_list = np.split(x, x.shape[0], axis=0)
          # Squeeze out the dummy dimension.
          return jax.tree_util.tree_map(
              lambda x: np.squeeze(x, axis=0), x_list
          )
  
      leaves, treedef = jax.tree_util.tree_flatten(pred_or_tgt)
  
      batch_shape = batch_mask.shape
      assert all(
          [leaf.shape[:2] == batch_shape for leaf in leaves]
      ), "Inconsistent batch shapes."
  
      # Split batched leaves into lists of examples:
      leaves = list(map(_split_mini_batches, leaves))
  
      # Go from leaf-lists to list of trees:
      out = []
      if leaves:
          num_examples = np.sum(batch_mask, dtype=np.int32)
          for example_ind in range(num_examples):
              out.append(
                  treedef.unflatten(
                      [leaf[example_ind] for leaf in leaves]
                  )
              )
      return out
  
imports: import copy
  import json
  import os
  from typing import Any, Dict, Optional, Set
  from absl import logging
  from flax import core
  from flax import optim
  from flax import traverse_util
  import jax
  from jax.example_libraries import optimizers
  import jax.numpy
  import numpy
  import PIL
  import PIL.ImageDraw
  import PIL.ImageFont
  from scenic.common_lib import image_utils
  from scenic.dataset_lib.coco_dataset import coco_eval
  from scenic.model_lib.base_models import box_utils
  from scenic.train_lib_deprecated import optimizers
  from scenic.train_lib_deprecated import train_utils
  import scipy.special
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: process_and_fetch_to_host(pred_or_tgt, batch_mask) and returns: out, jax