code: def auc_evaluator(
      test_data: pd.DataFrame,
      prediction_column: str = "prediction",
      target_column: str = "target",
      weight_column: str = None,
      eval_name: str = None,
  ) -> EvalReturnType:
      """
      Computes the ROC AUC score, given true label and prediction scores.
  
      Parameters
      ----------
      test_data : Pandas' DataFrame
          A Pandas' DataFrame with target and prediction scores.
  
      prediction_column : Strings
          The name of the column in `test_data` with the prediction scores.
  
      target_column : String
          The name of the column in `test_data` with the binary target.
  
      weight_column : String (default=None)
          The name of the column in `test_data` with the sample weights.
  
      eval_name : String, optional (default=None)
          the name of the evaluator as it will appear in the logs.
  
      Returns
      ----------
      log: dict
          A log-like dictionary with the ROC AUC Score
      """
  
      warnings.warn(
          "The method `auc_evaluator` will be renamed to `roc_auc_evaluator` in the next major release 2.0.0."
          " Please use `roc_auc_evaluator` instead of `auc_evaluator` for Area Under the Curve of the"
          " Receiver Operating Characteristics curve."
      )
  
      return roc_auc_evaluator(
          test_data,
          prediction_column,
          target_column,
          weight_column,
          eval_name,
      )
  
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 2 for calling a global function with example usage: auc_evaluator(test_data, prediction_column, target_column, weight_column, eval_name) and returns: roc_auc_evaluator