code: def log_metrics(self):
      if is_classification(self.model) == True:
          predict, _ = self.make_predictions()
          metrics = self.classification_metrics(
              self.target_data, predict
          )
          return metrics
      else:
          predict = self.make_predictions()
          metrics = self.regression_metrics(self.target_data, predict)
          return metrics
  
file_path: explainx/lib/models/modelprocessor.py
imports: from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from typing import Dict, List, Union
  from sklearn.impute import SimpleImputer
  from explainx.lib.utils import is_classification
label_desc: Use image node_id 9 for calling the ModelProcessor obj's underlying member method code with example usage: obj.log_metrics() and returns: metrics, metrics