code: def prepare_data_jit(
      xs, pad: Optional[int], global_devices: np.ndarray
  ):
      """Prepare data-pipeline for jit-based backend."""
  
      if pad is not None:
          xs["mask"] = np.full(len(xs["image"]), 1.0)
  
      def _pad(x):
          if pad is not None and len(x) != pad:
              x = np.asarray(memoryview(x))
              # Append `pad - len(x)` rows of zeros.
              x = np.r_[
                  x, np.zeros((pad - len(x),) + x.shape[1:], x.dtype)
              ]
  
          return x
  
      xs = jax.tree_util.tree_map(_pad, xs)
      xs = dataset_utils.shard_jit(xs, global_devices)
      return {
          "inputs": xs["image"],
          "label": xs["label"],
          "batch_mask": xs["mask"],
      }
  
imports: import functools
  from typing import Any, Optional, Tuple
  from absl import logging
  from clu import metric_writers
  from flax import jax_utils
  import jax
  import jax.numpy
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib.big_transfer import builder
  from scenic.train_lib import train_utils
  import tensorflow_datasets
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_data_jit(xs, pad, global_devices) and returns: dict, x