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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def score_file(filename):
 """Score each line in a file and return the scores."""

  # Prepare model.
  hparams = create_hparams()
  encoders = registry.problem(FLAGS.problem).feature_encoders(FLAGS.data_dir)
  has_inputs = "inputs" in encoders

  # Prepare features for feeding into the model.
  if has_inputs:
    inputs_ph = tf.placeholder(dtype=tf.int32)  # Just length dimension.
    batch_inputs = tf.reshape(inputs_ph, [1, -1, 1, 1])  # Make it 4D.
  targets_ph = tf.placeholder(dtype=tf.int32)  # Just length dimension.
  batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1])  # Make it 4D.
  if has_inputs:
    features = {"inputs": batch_inputs, "targets": batch_targets}
  else:
    features = {"targets": batch_targets}

  # Prepare the model and the graph when model runs on features.
  model = registry.model(FLAGS.model)(hparams, tf.estimator.ModeKeys.EVAL)
  _, losses = model(features)
  saver = tf.train.Saver()

  with tf.Session() as sess:
    # Load weights from checkpoint.
    if FLAGS.checkpoint_path is None:
      ckpts = tf.train.get_checkpoint_state(FLAGS.output_dir)
      ckpt = ckpts.model_checkpoint_path
    else:
      ckpt = FLAGS.checkpoint_path
    saver.restore(sess, ckpt)
    # Run on each line.
    with tf.gfile.Open(filename) as f:
      lines = f.readlines()
    results = []
    for line in lines:
      tab_split = line.split("\t")
      if len(tab_split) > 2:
        raise ValueError("Each line must have at most one tab separator.")
      if len(tab_split) == 1:
        targets = tab_split[0].strip()
      else:
        targets = tab_split[1].strip()
        inputs = tab_split[0].strip()
      # Run encoders and append EOS symbol.
      targets_numpy = encoders["targets"].encode(
          targets) + [text_encoder.EOS_ID]
      if has_inputs:
        inputs_numpy = encoders["inputs"].encode(inputs) + [text_encoder.EOS_ID]
      # Prepare the feed.
      if has_inputs:
        feed = {inputs_ph: inputs_numpy, targets_ph: targets_numpy}
      else:
        feed = {targets_ph: targets_numpy}
      # Get the score.
      np_loss = sess.run(losses["training"], feed)
      results.append(np_loss)
  return results