code: def score(
      self,
      estimator,
      X,
      y,
      took_log_of_y=False,
      advanced_scoring=False,
      verbose=2,
      name=None,
  ):
      X, y = utils.drop_missing_y_vals(X, y, output_column=None)
  
      if isinstance(estimator, GradientBoostingRegressor):
          X = X.toarray()
  
      predictions = estimator.predict(X)
  
      if took_log_of_y:
          for idx, val in enumerate(predictions):
              predictions[idx] = math.exp(val)
  
      try:
          score = self.scoring_func(y, predictions)
      except ValueError:
          bad_val_indices = []
          for idx, val in enumerate(y):
              if (
                  str(val) in bad_vals_as_strings
                  or str(predictions[idx]) in bad_vals_as_strings
              ):
                  bad_val_indices.append(idx)
  
          predictions = [
              val
              for idx, val in enumerate(predictions)
              if idx not in bad_val_indices
          ]
          y = [
              val
              for idx, val in enumerate(y)
              if idx not in bad_val_indices
          ]
  
          print(
              "Found "
              + str(len(bad_val_indices))
              + " null or infinity values in the predicted or y values. We will ignore these, and report the score on the rest of the dataset"
          )
          score = self.scoring_func(y, predictions)
  
      if advanced_scoring == True:
          if hasattr(estimator, "name"):
              print(estimator.name)
          advanced_scoring_regressors(
              predictions, y, verbose=verbose, name=name
          )
      return -1 * score
  
imports: from collections import OrderedDict
  import math
  from auto_ml import utils
  import pandas
  from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
  from sklearn.metrics import mean_squared_error, make_scorer, brier_score_loss, accuracy_score, explained_variance_score, mean_absolute_error, median_absolute_error, r2_score, log_loss, roc_auc_score
  import numpy
  from tabulate import tabulate
label_desc: Use image node_id 3 for calling the RegressionScorer obj's underlying member method code with example usage: obj.score(estimator, X, y, took_log_of_y, advanced_scoring, verbose, name) and returns: unknown