docstr: Call the coreference scoring perl script on the created test file
          
docstr_tok: ["Call","the","coreference","scoring","perl","script","on","the","created","test","file"]
code: def get_score(self, file_path=OUT_PATH, debug=False):
      """Call the coreference scoring perl script on the created test file"""
      print("🌋 Computing score")
      score = {}
      ident = None
      for metric_name in CONLL_METRICS:
          if debug:
              print("Computing metric:", metric_name)
          try:
              scorer_out = subprocess.check_output(
                  [
                      "perl",
                      SCORING_SCRIPT,
                      metric_name,
                      self.test_key_file,
                      file_path,
                  ],
                  stderr=subprocess.STDOUT,
                  encoding="utf-8",
              )
          except subprocess.CalledProcessError as err:
              print("Error during the scoring")
              print(err)
              print(err.output)
              raise
          if debug:
              print("scorer_out", scorer_out)
          value, ident = (
              scorer_out.split("\n")[-2],
              scorer_out.split("\n")[-1],
          )
          if debug:
              print("value", value, "identification", ident)
          NR, DR, NP, DP = [float(x) for x in value.split(" ")]
          ident_NR, ident_DR, ident_NP, ident_DP = [
              float(x) for x in ident.split(" ")
          ]
          precision = NP / DP if DP else 0
          recall = NR / DR if DR else 0
          F1 = (
              2 * precision * recall / (precision + recall)
              if precision + recall > 0
              else 0
          )
          ident_precision = ident_NP / ident_DP if ident_DP else 0
          ident_recall = ident_NR / ident_DR if ident_DR else 0
          ident_F1 = (
              2
              * ident_precision
              * ident_recall
              / (ident_precision + ident_recall)
              if ident_precision + ident_recall > 0
              else 0
          )
          score[metric_name] = (precision, recall, F1)
          ident = (
              ident_precision,
              ident_recall,
              ident_F1,
              ident_NR,
              ident_DR,
              ident_NP,
              ident_DP,
          )
      F1_conll = sum(
          [score[metric][2] for metric in CONLL_METRICS]
      ) / len(CONLL_METRICS)
      print(
          "Mention identification recall",
          ident[1],
          "<= Detected mentions",
          ident[3],
          "True mentions",
          ident[4],
      )
      print("Scores", score)
      print("F1_conll", F1_conll)
      return score, F1_conll, ident
  
imports: import os
  import subprocess
  import io
  import pickle
  import torch
  from torch.utils.data import DataLoader
  from neuralcoref.train.conllparser import FEATURES_NAMES
  from neuralcoref.train.dataset import NCBatchSampler, padder_collate
  from neuralcoref.train.compat import unicode_
label_desc: Use image node_id 10 for calling the ConllEvaluator obj's underlying member method code with example usage: obj.get_score(file_path, debug) and returns: score,  F1_conll,  ident