docstr: The Consistency_plot has the main objective of comparing explainability methods.
  
  Because explainability methods are different from each other,
  they may not give the same explanation to the same instance.
  Then, which method should be selected?
  Answering this question is tough. This method compares methods between them
  and evaluates how close the explanations are from each other.
  The idea behind this is pretty simple: if underlying assumptions lead to similar results,
  we would be more confident in using those methods.
  If not, careful conideration should be taken in the interpretation of the explanations
  
  Parameters
  ----------
  selection: list
      Contains list of index, subset of the input DataFrame that we use
      for the compute of consitency statistics, by default None
  max_features: int, optional
      Maximum number of displayed features, by default 20
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code: def consistency_plot(self, selection=None, max_features=20):
      """
      The Consistency_plot has the main objective of comparing explainability methods.
  
      Because explainability methods are different from each other,
      they may not give the same explanation to the same instance.
      Then, which method should be selected?
      Answering this question is tough. This method compares methods between them
      and evaluates how close the explanations are from each other.
      The idea behind this is pretty simple: if underlying assumptions lead to similar results,
      we would be more confident in using those methods.
      If not, careful conideration should be taken in the interpretation of the explanations
  
      Parameters
      ----------
      selection: list
          Contains list of index, subset of the input DataFrame that we use
          for the compute of consitency statistics, by default None
      max_features: int, optional
          Maximum number of displayed features, by default 20
      """
      # Selection
      if selection is None:
          weights = [weight.values for weight in self.weights]
      elif isinstance(selection, list):
          if len(selection) == 1:
              raise ValueError("Selection must include multiple points")
          else:
              weights = [
                  weight.values[selection] for weight in self.weights
              ]
      else:
          raise ValueError("Parameter selection must be a list")
  
      all_comparisons, mean_distances = self.calculate_all_distances(
          self.methods, weights
      )
  
      (
          method_1,
          method_2,
          l2,
          index,
          backend_name_1,
          backend_name_2,
      ) = self.find_examples(mean_distances, all_comparisons, weights)
  
      self.plot_comparison(mean_distances)
      self.plot_examples(
          method_1,
          method_2,
          l2,
          index,
          backend_name_1,
          backend_name_2,
          max_features,
      )
  
imports: from category_encoders import OrdinalEncoder
  import copy
  import itertools
  import matplotlib.pyplot
  import numpy
  import pandas
  from plotly import graph_objs
  from plotly.offline import plot
  from plotly.subplots import make_subplots
  from sklearn.manifold import MDS
  from shapash import SmartExplainer
  from shapash.style.style_utils import colors_loading, select_palette, define_style
label_desc: Use image node_id 5 for calling the Consistency obj's underlying member method code with example usage: obj.consistency_plot(selection, max_features) without return types