import tensorflow as tf

    """actual host call function."""
    step = global_step[0]
    with tf.contrib.summary.create_file_writer(
        logdir=model_dir, filename_suffix=".host_call").as_default():
      with tf.contrib.summary.always_record_summaries():
        for i, name in enumerate(metric_names):
          if reduce_fn is None:
            scalar = args[i][0]
          else:
            scalar = reduce_fn(args[i])
          with tf.contrib.summary.record_summaries_every_n_global_steps(
              100, global_step=step):
            tf.contrib.summary.scalar(prefix + name, scalar, step=step)

        return tf.contrib.summary.all_summary_ops()

  global_step_tensor = tf.reshape(tf.train.get_or_create_global_step(), [1])
  other_tensors = [tf.reshape(monitor_dict[key], [1]) for key in metric_names]

  return host_call_fn, [global_step_tensor] + other_tensors


def two_stream_loss(FLAGS, features, labels, mems, is_training):
  """Pretraining loss with two-stream attention Transformer-XL."""
