class_docstr: Implementation of Faithfulness Estimate by Alvares-Melis at el., 2018a and 2018b.
  
  Computes the correlations of probability drops and the relevance scores on various points,
  showing the aggregate statistics.
  
  References:
      1) David Alvarez-Melis and Tommi S. Jaakkola.: "Towards robust interpretability with self-explaining
      neural networks." NeurIPS (2018): 7786-7795.
  
  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.
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docstr: Implementation of custom_preprocess_batch.
  
  Parameters
  ----------
  x_batch: np.ndarray
      A np.ndarray which contains the input data that are explained.
  kwargs:
      Unused.
  
  Returns
  -------
  tuple
      In addition to the x_batch, y_batch, a_batch, s_batch and custom_batch,
      returning a custom preprocess batch (custom_preprocess_batch).
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code: def custom_preprocess(self, x_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.
      kwargs:
          Unused.
  
      Returns
      -------
      tuple
          In addition to the x_batch, y_batch, a_batch, s_batch and custom_batch,
          returning a custom preprocess batch (custom_preprocess_batch).
      """
      # Asserts.
      asserts.assert_features_in_step(
          features_in_step=self.features_in_step,
          input_shape=x_batch.shape[2:],
      )
  
file_path: Quantus/quantus/metrics/faithfulness/faithfulness_estimate.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  from quantus.functions.perturb_func import baseline_replacement_by_indices
  from quantus.functions.similarity_func import correlation_pearson
  from quantus.helpers import asserts, warn
  from quantus.helpers.enums import DataType, EvaluationCategory, ModelType, ScoreDirection
  from quantus.helpers.model.model_interface import ModelInterface
  from quantus.helpers.perturbation_utils import make_perturb_func
  from quantus.metrics.base import Metric
label_desc: Use image node_id 4 for calling the FaithfulnessEstimate obj's underlying member method code with example usage: obj.custom_preprocess(x_batch) without return types