code: def get_acceptable_regression_metrics() -> Set[str]:
      """Return the set of metrics that are acceptable for regression."""
      # it's fairly straightforward for regression since
      # we do not have to check the labels
      acceptable_metrics = (
          REGRESSION_ONLY_METRICS
          | UNWEIGHTED_KAPPA_METRICS
          | WEIGHTED_KAPPA_METRICS
          | CORRELATION_METRICS
      )
  
      # if there are any user-defined custom metrics registered, include them too
      user_defined_metrics = set(_CUSTOM_METRICS) - set(
          _PREDEFINED_CUSTOM_METRICS
      )
      if len(user_defined_metrics) > 0:
          acceptable_metrics.update(user_defined_metrics)
  
      return acceptable_metrics
  
imports: from __future__ import annotations
  import inspect
  import logging
  import sys
  import time
  from collections import Counter, defaultdict
  from csv import DictWriter, excel_tab
  from functools import wraps
  from importlib import import_module
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
  import joblib
  import numpy
  import scipy.sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_selection import SelectKBest
  from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_fscore_support
  from sklearn.model_selection import KFold, LeaveOneGroupOut, ShuffleSplit, StratifiedKFold
  from skll.data import FeatureSet
  from skll.metrics import _CUSTOM_METRICS, _PREDEFINED_CUSTOM_METRICS, use_score_func
  from skll.types import ComputeEvalMetricsResults, ConfusionMatrix, FeaturesetIterator, FoldMapping, IdType, IndexIterator, LabelType, PathOrStr, SparseFeatureMatrix
  from skll.utils.constants import CLASSIFICATION_ONLY_METRICS, CORRELATION_METRICS, REGRESSION_ONLY_METRICS, UNWEIGHTED_KAPPA_METRICS, WEIGHTED_KAPPA_METRICS
  from skll.version import VERSION
label_desc: Use image node_id 6 for calling a global function with example usage: get_acceptable_regression_metrics() and returns: acceptable_metrics