code: def compute_score(
      metric_data: dict,
      metric: Union[str, Scorer],
      pos_label: Optional[int] = 1,
  ) -> float:
      """
      Use sklearn to compute the score of one metric.
  
      Parameters
      ----------
      metric_data
          A dictionary with the groundtruth (Y_TRUE) and predicted values (Y_PRED, Y_PRED_PROB).
          The predicted class probabilities are required to compute the roc_auc score.
      metric
          The name of metric or the function of metric to compute.
      pos_label
          The encoded label (0 or 1) of binary classification's positive class.
  
      Returns
      -------
      Computed score.
      """
      if isinstance(metric, str) and metric in [
          OVERALL_ACCURACY,
          OVERALL_F1,
      ]:
          metric = evaluate.load("seqeval")
          warnings.filterwarnings("ignore")
          for p in metric_data[Y_TRUE]:
              if "_" in p:
                  print(p)
          for p in metric_data[Y_PRED]:
              if "_" in p:
                  print(p)
          return metric.compute(
              references=metric_data[Y_TRUE],
              predictions=metric_data[Y_PRED],
          )
  
      metric = get_metric(metric)
      if metric.name in [ROC_AUC, AVERAGE_PRECISION]:
          return metric._sign * metric(
              metric_data[Y_TRUE],
              metric_data[Y_PRED_PROB][:, pos_label],
          )
      elif metric.name in [F1]:  # only for binary classification
          return f1_score(
              metric_data[Y_TRUE],
              metric_data[Y_PRED],
              pos_label=pos_label,
          )
      else:
          try:
              return metric._sign * metric(
                  metric_data[Y_TRUE],
                  metric_data[Y_PRED],
                  y_prob=metric_data[Y_PRED_PROB],
              )
          except:
              return metric._sign * metric(
                  metric_data[Y_TRUE], metric_data[Y_PRED]
              )
  
imports: import logging
  import math
  import operator
  import warnings
  from typing import Dict, List, Optional, Tuple, Union
  import evaluate
  import numpy
  from sklearn.metrics import f1_score
  from autogluon.core.metrics import Scorer, get_metric
  from ..constants import ACCURACY, AVERAGE_PRECISION, BINARY, DIRECT_LOSS, F1, FEW_SHOT_CLASSIFICATION, IOU, MAP, MATCHING_METRICS, MATCHING_METRICS_WITHOUT_PROBLEM_TYPE, MAX, METRIC_MODE_MAP, MIN, MULTICLASS, NDCG, NER, NER_TOKEN_F1, NUMERICAL, OBJECT_DETECTION, OPEN_VOCABULARY_OBJECT_DETECTION, OVERALL_ACCURACY, OVERALL_F1, PRECISION, RECALL, REGRESSION, RETRIEVAL_METRICS, RMSE, ROC_AUC, SEMANTIC_SEGMENTATION, SPEARMANR, Y_PRED, Y_PRED_PROB, Y_TRUE
  from ..problem_types import PROBLEM_TYPES_REG
label_desc: Use image node_id 4 for calling a global function with example usage: compute_score(metric_data, metric, pos_label) and returns: metric, unknown, f1_score, unknown, unknown