class_docstr: Implementation of NonSensitivity by Nguyen at el., 2020.
  
  Non-sensitivity measures if zero-importance is only assigned to features, that the model is not
  functionally dependent on.
  
  References:
      1) An-phi Nguyen and María Rodríguez Martínez.: "On quantitative aspects of model
      interpretability." arXiv preprint arXiv:2007.07584 (2020).
      2) Marco Ancona et al.: "Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley
      Values Approximation." ICML (2019): 272-281.
      3) Grégoire Montavon et al.: "Methods for interpreting and
      understanding deep neural networks." Digital Signal Processing 73 (2018): 1-15.
  
  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.
  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",".","kwargs",":","Unused",".","Returns","--","--","--","-","None"]
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
      -------
      None
      """
      # Asserts.
      asserts.assert_features_in_step(
          features_in_step=self.features_in_step,
          input_shape=x_batch.shape[2:],
      )
  
file_path: Quantus/quantus/metrics/axiomatic/non_sensitivity.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.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 NonSensitivity obj's underlying member method code with example usage: obj.custom_preprocess(x_batch) without return types