code: def start_input_pipeline(
      data,
      pad=None,
      num_devices=None,
      backend="pmap",
      devices: Optional[np.ndarray] = None,
  ):
      train_iter = iter(data)
      if backend == "pmap":
          train_iter = map(
              lambda x: prepare_data(x, pad, num_devices=num_devices),
              train_iter,
          )
      elif backend == "jit":
          train_iter = map(
              lambda x: prepare_data_jit(
                  x, pad, global_devices=devices
              ),
              train_iter,
          )
      return train_iter
  
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 3 for calling a global function with example usage: start_input_pipeline(data, pad, num_devices, backend, devices) and returns: train_iter