class_docstr: This is a simple class of models that tags individual words in text.
code: def _print_predictions(self, batch, gold_label_type):
      lines = []
      if self.span_prediction_problem:
          for datapoint in batch:
              # all labels default to "O"
              for token in datapoint:
                  token.set_label("gold_bio", "O")
                  token.set_label("predicted_bio", "O")
  
              # set gold token-level
              for gold_label in datapoint.get_labels(gold_label_type):
                  gold_span: Span = gold_label.data_point
                  prefix = "B-"
                  for token in gold_span:
                      token.set_label(
                          "gold_bio", prefix + gold_label.value
                      )
                      prefix = "I-"
  
              # set predicted token-level
              for predicted_label in datapoint.get_labels("predicted"):
                  predicted_span: Span = predicted_label.data_point
                  prefix = "B-"
                  for token in predicted_span:
                      token.set_label(
                          "predicted_bio",
                          prefix + predicted_label.value,
                      )
                      prefix = "I-"
  
              # now print labels in CoNLL format
              for token in datapoint:
                  eval_line = (
                      f"{token.text} "
                      f"{token.get_label('gold_bio').value} "
                      f"{token.get_label('predicted_bio').value}\n"
                  )
                  lines.append(eval_line)
              lines.append("\n")
  
      else:
          for datapoint in batch:
              # print labels in CoNLL format
              for token in datapoint:
                  eval_line = (
                      f"{token.text} "
                      f"{token.get_label(gold_label_type).value} "
                      f"{token.get_label('predicted').value}\n"
                  )
                  lines.append(eval_line)
              lines.append("\n")
      return lines
  
imports: import logging
  from pathlib import Path
  from typing import Any, Dict, List, Union
  import torch
  from deprecated.sphinx import deprecated
  import flair.nn
  from flair.data import Dictionary, Sentence, Span, Token
  from flair.embeddings import TokenEmbeddings
label_desc: Use image node_id 10 for calling the TokenClassifier obj's underlying member method code with example usage: obj._print_predictions(batch, gold_label_type) and returns: lines