code: def combined_evaluators(
      test_data: pd.DataFrame, evaluators: List[EvalFnType]
  ) -> EvalReturnType:
      """
      Combine partially applies evaluation functions.
  
      Parameters
      ----------
      test_data : Pandas' DataFrame
          A Pandas' DataFrame to apply the evaluators on
  
      evaluators: List
          List of evaluator functions
  
      Returns
      ----------
      log: dict
          A log-like dictionary with the column mean
      """
      return fp.merge(e(test_data) for e in evaluators)
  
imports: import warnings
  from typing import Any, Callable, Iterable, List
  import numpy
  import pandas
  import toolz
  from pandas.util import hash_pandas_object
  from sklearn.metrics import average_precision_score, brier_score_loss, fbeta_score, log_loss, mean_absolute_error, mean_squared_error, precision_score, r2_score, recall_score, roc_auc_score
  from toolz import curry, last, first
  from scipy import optimize
  from sklearn.linear_model import LogisticRegression
  from fklearn.types import EvalFnType, EvalReturnType, PredictFnType, UncurriedEvalFnType
label_desc: Use image node_id 18 for calling a global function with example usage: combined_evaluators(test_data, evaluators) and returns: fp