code: def get_metric_name(self):
      metric = self.params.get("eval_metric")
      if metric is None:
          return None
      if metric == "Logloss":
          return "logloss"
      elif metric == "AUC":
          return "auc"
      elif metric == "MultiClass":
          return "logloss"
      elif metric == "RMSE":
          return "rmse"
      elif metric == "MSE":
          return "mse"
      elif metric == "MAE":
          return "mae"
      elif metric == "MAPE":
          return "mape"
      elif metric in ["F1", "TotalF1:average=Micro"]:
          return "f1"
      elif metric == "Accuracy":
          return "accuracy"
      return metric
  
imports: import copy
  import logging
  import os
  import time
  import numpy
  import pandas
  from sklearn.base import ClassifierMixin, RegressorMixin
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.preprocessing.preprocessing_utils import PreprocessingUtils
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import CatBoostEvalMetricAveragePrecision, CatBoostEvalMetricMSE, CatBoostEvalMetricPearson, CatBoostEvalMetricSpearman, CatBoostEvalMetricUserDefined
  import catboost
  from catboost import CatBoost, CatBoostClassifier, CatBoostRegressor, Pool
label_desc: Use image node_id 10 for calling the CatBoostAlgorithm obj's underlying member method code with example usage: obj.get_metric_name() and returns: metric, None, str, str, str, str, str, str, str, str, str