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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 make_prediction_output_tensors(args, features, input_ops, model_fn_ops, keep_target):
 """Makes the final prediction output layer."""

  target_name = feature_transforms.get_target_name(features)
  key_names = get_key_names(features)

  outputs = {}
  outputs.update({key_name: tf.squeeze(input_ops.features[key_name])
                  for key_name in key_names})

  if is_classification_model(args.model):

    # build maps from ints to the origional categorical strings.
    class_names = read_vocab(args, target_name)
    table = tf.contrib.lookup.index_to_string_table_from_tensor(
        mapping=class_names,
        default_value='UNKNOWN')

    # Get the label of the input target.
    if keep_target:
      input_target_label = table.lookup(input_ops.features[target_name])
      outputs[PG_TARGET] = tf.squeeze(input_target_label)

    # TODO(brandondutra): get the score of the target label too.
    probabilities = model_fn_ops.predictions['probabilities']

    # if top_n == 0, this means use all the classes. We will use class names as
    # probabilities labels.
    if args.top_n == 0:
      predicted_index = tf.argmax(probabilities, axis=1)
      predicted = table.lookup(predicted_index)
      outputs.update({PG_CLASSIFICATION_FIRST_LABEL: predicted})
      probabilities_list = tf.unstack(probabilities, axis=1)
      for class_name, p in zip(class_names, probabilities_list):
        outputs[class_name] = p
    else:
      top_n = args.top_n

      # get top k labels and their scores.
      (top_k_values, top_k_indices) = tf.nn.top_k(probabilities, k=top_n)
      top_k_labels = table.lookup(tf.to_int64(top_k_indices))

      # Write the top_k values using 2*top_n columns.
      num_digits = int(math.ceil(math.log(top_n, 10)))
      if num_digits == 0:
        num_digits = 1
      for i in range(0, top_n):
        # Pad i based on the size of k. So if k = 100, i = 23 -> i = '023'. This
        # makes sorting the columns easy.
        padded_i = str(i + 1).zfill(num_digits)

        if i == 0:
          label_alias = PG_CLASSIFICATION_FIRST_LABEL
        else:
          label_alias = PG_CLASSIFICATION_LABEL_TEMPLATE % padded_i

        label_tensor_name = (tf.squeeze(
            tf.slice(top_k_labels, [0, i], [tf.shape(top_k_labels)[0], 1])))

        if i == 0:
          score_alias = PG_CLASSIFICATION_FIRST_SCORE
        else:
          score_alias = PG_CLASSIFICATION_SCORE_TEMPLATE % padded_i

        score_tensor_name = (tf.squeeze(
            tf.slice(top_k_values,
                     [0, i],
                     [tf.shape(top_k_values)[0], 1])))

        outputs.update({label_alias: label_tensor_name,
                        score_alias: score_tensor_name})

  else:
    if keep_target:
      outputs[PG_TARGET] = tf.squeeze(input_ops.features[target_name])

    scores = model_fn_ops.predictions['scores']
    outputs[PG_REGRESSION_PREDICTED_TARGET] = tf.squeeze(scores)

  return outputs