code: def prepare_tf_data(xs):
      """Convert a input batch from tf Tensors to numpy arrays."""
      local_device_count = jax.local_device_count()
  
      def _prepare(x):
          # Use _numpy() for zero-copy conversion between TF and NumPy.
          x = x._numpy()  # pylint: disable=protected-access
  
          # reshape (host_batch_size, height, width, 3) to
          # (local_devices, device_batch_size, height, width, 3)
          return x.reshape((local_device_count, -1) + x.shape[1:])
  
      return jax.tree_util.tree_map(_prepare, xs)
  
imports: import functools
  import time
  from typing import Any
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import jax_utils
  from flax.training import checkpoints
  from flax.training import common_utils
  from flax.training import dynamic_scale
  from flax.training import train_state
  import jax
  from jax import lax
  import jax.numpy
  from jax import random
  import ml_collections
  import optax
  import tensorflow
  import tensorflow_datasets
  import input_pipeline
  import models
label_desc: Use image node_id 8 for calling a global function with example usage: prepare_tf_data(xs) and returns: jax, x