code: def _print_predictions(self, batch, gold_label_type):
      lines = []
      for sentence in batch:
          eval_line = (
              f" - Text:       {' '.join([token.text for token in sentence])}\n"
              f" - Gold-Lemma: {' '.join([token.get_label(gold_label_type).value for token in sentence])}\n"
              f" - Predicted:  {' '.join([token.get_label('predicted').value for token in sentence])}\n\n"
          )
          lines.append(eval_line)
      return lines
  
imports: import logging
  from math import inf
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn
  import flair.embeddings
  import flair.nn
  from flair.data import Dictionary, Sentence, Token
  from flair.datasets import DataLoader, FlairDatapointDataset
  from flair.training_utils import Result, store_embeddings
label_desc: Use image node_id 15 for calling the Lemmatizer obj's underlying member method code with example usage: obj._print_predictions(batch, gold_label_type) and returns: lines