(split)
return input_fn, record_info_dict
doc.
doc.
doc .
def get_input_fn(split):
  """doc."""
  assert split == "train"
  batch_size = FLAGS.train_batch_size

  input_fn, record_info_dict = data_utils.get_input_fn(
      tfrecord_dir=FLAGS.record_info_dir,
      split=split,
      bsz_per_host=batch_size // FLAGS.num_hosts,
      seq_len=FLAGS.seq_len,
      reuse_len=FLAGS.reuse_len,
      bi_data=FLAGS.bi_data,
      num_hosts=FLAGS.num_hosts,
      num_core_per_host=FLAGS.num_core_per_host,
      perm_size=FLAGS.perm_size,
      mask_alpha=FLAGS.mask_alpha,
      mask_beta=FLAGS.mask_beta,
      uncased=FLAGS.uncased,
      num_passes=FLAGS.num_passes,
      use_bfloat16=FLAGS.use_bfloat16,
      num_predict=FLAGS.num_predict)

  return input_fn, record_info_dict
def get_input_fn ( split ) : assert split == "train" batch_size = FLAGS . train_batch_size input_fn , record_info_dict = data_utils . get_input_fn ( tfrecord_dir = FLAGS . record_info_dir , split = split , bsz_per_host = batch_size // FLAGS . num_hosts , seq_len = FLAGS . seq_len , reuse_len = FLAGS . reuse_len , bi_data = FLAGS . bi_data , num_hosts = FLAGS . num_hosts , num_core_per_host = FLAGS . num_core_per_host , perm_size = FLAGS . perm_size , mask_alpha = FLAGS . mask_alpha , mask_beta = FLAGS . mask_beta , uncased = FLAGS . uncased , num_passes = FLAGS . num_passes , use_bfloat16 = FLAGS . use_bfloat16 , num_predict = FLAGS . num_predict ) return input_fn , record_info_dict