class_docstr: Implementation of the Local Lipschitz Estimate (or Stability) test by Alvarez-Melis et al., 2018a, 2018b.
  
  This tests asks how consistent are the explanations for similar/neighboring examples.
  The test denotes a (weaker) empirical notion of stability based on discrete,
  finite-sample neighborhoods i.e., argmax_(||f(x) - f(x')||_2 / ||x - x'||_2)
  where f(x) is the explanation for input x and x' is the perturbed input.
  
  References:
      1) David Alvarez-Melis and Tommi S. Jaakkola. "On the robustness of interpretability methods."
      arXiv preprint arXiv:1806.08049 (2018).
  
      2) 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.
docstr: Implementation of custom_preprocess_batch.
  
  Parameters
  ----------
  kwargs:
      Unused.
  
  Returns
  -------
  None
docstr_tok: ["Implementation","of","custom_preprocess_batch",".","Parameters","--","--","--","--","--","kwargs",":","Unused",".","Returns","--","--","--","-","None"]
code: def custom_preprocess(
      self,
      **kwargs,
  ) -> None:
      """
      Implementation of custom_preprocess_batch.
  
      Parameters
      ----------
      kwargs:
          Unused.
  
      Returns
      -------
      None
      """
      # Additional explain_func assert, as the one in prepare() won't be
      # executed when a_batch != None.
      asserts.assert_explain_func(explain_func=self.explain_func)
  
file_path: Quantus/quantus/metrics/robustness/local_lipschitz_estimate.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  from quantus.functions.perturb_func import gaussian_noise, perturb_batch
  from quantus.functions.similarity_func import distance_euclidean, lipschitz_constant
  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_changed_prediction_indices_func, make_perturb_func
  from quantus.metrics.base import Metric
label_desc: Use image node_id 4 for calling the LocalLipschitzEstimate obj's underlying member method code with example usage: obj.custom_preprocess() without return types