def METHOD_NAME(text, max_length):
  """Given string and length, convert to byte seq of at most max_length.
  This process mimics docqa/elmo's preprocessing:
  https://github.com/allenai/document-qa/blob/master/docqa/elmo/data.py
  Note that we make use of BOS_CHAR_ID and EOS_CHAR_ID in iterator_utils.py & 
  our usage differs from docqa/elmo.
  Args:
    text: tf.string tensor of shape []
    max_length: max number of chars for each word.
  Returns:
    A tf.int32 tensor of the byte encoded text.
  """
  byte_ids = tf.to_int32(tf.decode_raw(text, tf.uint8))
  byte_ids = byte_ids[:max_length - 2]
  padding = tf.fill([max_length - tf.shape(byte_ids)[0] - 2], PAD_CHAR_ID)
  byte_ids = tf.concat(
      [[BOW_CHAR_ID], byte_ids, [EOW_CHAR_ID], padding], axis=0)
  tf.logging.info(byte_ids)
  byte_ids = tf.reshape(byte_ids, [max_length])
  tf.logging.info(byte_ids.get_shape().as_list())
  return byte_ids + 1