docs: def translate_file(
      estimator, subtokenizer, input_file, output_file=None,
      print_all_translations=True):
    """Translate lines in file, and save to output file if specified.
  
    Args:
      estimator: tf.Estimator used to generate the translations.
      subtokenizer: Subtokenizer object for encoding and decoding source and
         translated lines.
      input_file: file containing lines to translate
      output_file: file that stores the generated translations.
      print_all_translations: If true, all translations are printed to stdout.
  
    Raises:
      ValueError: if output file is invalid.
    """
    batch_size = _DECODE_BATCH_SIZE
  
    # Read and sort inputs by length. Keep dictionary (original index-->new index
    # in sorted list) to write translations in the original order.
    sorted_inputs, sorted_keys = _get_sorted_inputs(input_file)
    num_decode_batches = (len(sorted_inputs) - 1) // batch_size + 1
  
    def input_generator():
      """Yield encoded strings from sorted_inputs."""
      for i, line in enumerate(sorted_inputs):
        if i % batch_size == 0:
          batch_num = (i // batch_size) + 1
  
          print("Decoding batch %d out of %d." % (batch_num, num_decode_batches))
        yield _encode_and_add_eos(line, subtokenizer)
  
    def input_fn():
      """Created batched dataset of encoded inputs."""
      ds = tf.data.Dataset.from_generator(
          input_generator, tf.int64, tf.TensorShape([None]))
      ds = ds.padded_batch(batch_size, [None])
      return ds
  
    translations = []
    for i, prediction in enumerate(estimator.predict(input_fn)):
      translation = _trim_and_decode(prediction["outputs"], subtokenizer)
      translations.append(translation)
  
      if print_all_translations:
        print("Translating:")
        print("\tInput: %s" % sorted_inputs[i])
        print("\tOutput: %s\n" % translation)
        print("=" * 100)
  
    # Write translations in the order they appeared in the original file.
    if output_file is not None:
      if tf.gfile.IsDirectory(output_file):
        raise ValueError("File output is a directory, will not save outputs to "
                         "file.")
      tf.logging.info("Writing to file %s" % output_file)
      with tf.gfile.Open(output_file, "w") as f:
        for index in xrange(len(sorted_keys)):
          f.write("%s\n" % translations[sorted_keys[index]])