class_docstr: Implementation of AUC metric by Fawcett et al., 2006.
  
  AUC is a ranking metric and  compares the ranking between attributions and a given ground-truth mask
  
  References:
      1) Tom Fawcett: 'An introduction to ROC analysis' "Pattern Recognition Letters" Vol 27, Issue 8, 2006
  
  Attributes:
      -  _name: The name of the metric.
      - _data_applicability: The data types that the metric implementation currently supports.
      - _models: The model types that this metric can work with.
      - score_direction: How to interpret the scores, whether higher/ lower values are considered better.
      - evaluation_category: What property/ explanation quality that this metric measures.
docstr: Implementation of custom_preprocess_batch.
  
  Parameters
  ----------
  x_batch: np.ndarray
      A np.ndarray which contains the input data that are explained.
  s_batch: np.ndarray
      A Union[np.ndarray, None] which contains segmentation masks that matches the input.
  kwargs:
      Unused.
  
  Returns
  -------
  None
docstr_tok: ["Implementation","of","custom_preprocess_batch",".","Parameters","--","--","--","--","--","x_batch",":","np.ndarray","A","np.ndarray","which","contains","the","input","data","that","are","explained",".","s_batch",":","np.ndarray","A","Union","[","np.ndarray",",","None","]","which","contains","segmentation","masks","that","matches","the","input",".","kwargs",":","Unused",".","Returns","--","--","--","-","None"]
code: def custom_preprocess(
      self,
      x_batch: np.ndarray,
      s_batch: np.ndarray,
      **kwargs,
  ) -> None:
      """
      Implementation of custom_preprocess_batch.
  
      Parameters
      ----------
      x_batch: np.ndarray
          A np.ndarray which contains the input data that are explained.
      s_batch: np.ndarray
          A Union[np.ndarray, None] which contains segmentation masks that matches the input.
      kwargs:
          Unused.
  
      Returns
      -------
      None
      """
      # Asserts.
      asserts.assert_segmentations(x_batch=x_batch, s_batch=s_batch)
  
file_path: Quantus/quantus/metrics/localisation/auc.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  from sklearn.metrics import auc, roc_curve
  from quantus.helpers import asserts, warn
  from quantus.helpers.enums import DataType, EvaluationCategory, ModelType, ScoreDirection
  from quantus.metrics.base import Metric
label_desc: Use image node_id 4 for calling the AUC obj's underlying member method code with example usage: obj.custom_preprocess(x_batch, s_batch) without return types