code: def _print_conll_report(
      results,
      accuracy,
      total_true_entities,
      total_predicted_entities,
      n_tokens,
      total_correct,
      short_report=False,
      entity_of_interest=None,
  ):
      tags = list(results.keys())
  
      s = (
          "processed {len} tokens "
          "with {tot_true} phrases; "
          "found: {tot_pred} phrases;"
          " correct: {tot_cor}.\n\n".format(
              len=n_tokens,
              tot_true=total_true_entities,
              tot_pred=total_predicted_entities,
              tot_cor=total_correct,
          )
      )
  
      s += (
          "precision:  {tot_prec:.2f}%; "
          "recall:  {tot_recall:.2f}%; "
          "FB1:  {tot_f1:.2f}\n\n".format(
              acc=accuracy,
              tot_prec=results["__total__"]["precision"],
              tot_recall=results["__total__"]["recall"],
              tot_f1=results["__total__"]["f1"],
          )
      )
  
      if not short_report:
          for tag in tags:
              if entity_of_interest is not None:
                  if entity_of_interest in tag:
                      s += (
                          "\t" + tag + ": precision:  {tot_prec:.2f}%; "
                          "recall:  {tot_recall:.2f}%; "
                          "F1:  {tot_f1:.2f} "
                          "{tot_predicted}\n\n".format(
                              tot_prec=results[tag]["precision"],
                              tot_recall=results[tag]["recall"],
                              tot_f1=results[tag]["f1"],
                              tot_predicted=results[tag]["n_pred"],
                          )
                      )
              elif tag != "__total__":
                  s += (
                      "\t" + tag + ": precision:  {tot_prec:.2f}%; "
                      "recall:  {tot_recall:.2f}%; "
                      "F1:  {tot_f1:.2f} "
                      "{tot_predicted}\n\n".format(
                          tot_prec=results[tag]["precision"],
                          tot_recall=results[tag]["recall"],
                          tot_f1=results[tag]["f1"],
                          tot_predicted=results[tag]["n_pred"],
                      )
                  )
      elif entity_of_interest is not None:
          s += (
              "\t"
              + entity_of_interest
              + ": precision:  {tot_prec:.2f}%; "
              "recall:  {tot_recall:.2f}%; "
              "F1:  {tot_f1:.2f} "
              "{tot_predicted}\n\n".format(
                  tot_prec=results[entity_of_interest]["precision"],
                  tot_recall=results[entity_of_interest]["recall"],
                  tot_f1=results[entity_of_interest]["f1"],
                  tot_predicted=results[entity_of_interest]["n_pred"],
              )
          )
      log.debug(s)
  
file_path: DeepPavlov/deeppavlov/metrics/fmeasure.py
imports: import itertools
  from collections import OrderedDict
  from itertools import chain
  from logging import getLogger
  import numpy
  from sklearn.metrics import f1_score
  from deeppavlov.core.common.metrics_registry import register_metric
label_desc: Use image node_id 3 for calling a global function with example usage: _print_conll_report(results, accuracy, total_true_entities, total_predicted_entities, n_tokens, total_correct, short_report, entity_of_interest) without return types