code: def consistency_guarantees(X, y, model_generator, method_name):
      """Consistency Guarantees
      transform = "identity"
      sort_order = 1
      """
  
      # 1.0 - perfect consistency
      # 0.8 - guarantees depend on sampling
      # 0.6 - guarantees depend on approximation
      # 0.0 - no garuntees
      guarantees = {
          "linear_shap_corr": 1.0,
          "linear_shap_ind": 1.0,
          "coef": 0.0,
          "kernel_shap_1000_meanref": 0.8,
          "sampling_shap_1000": 0.8,
          "random": 0.0,
          "saabas": 0.0,
          "tree_gain": 0.0,
          "tree_shap_tree_path_dependent": 1.0,
          "tree_shap_independent_200": 1.0,
          "mean_abs_tree_shap": 1.0,
          "lime_tabular_regression_1000": 0.8,
          "lime_tabular_classification_1000": 0.8,
          "maple": 0.8,
          "tree_maple": 0.8,
          "deep_shap": 0.6,
          "expected_gradients": 0.6,
      }
  
      return None, guarantees[method_name]
  
imports: import hashlib
  import os
  import time
  import numpy
  import sklearn
  from ..None import __version__
  from .None import measures, methods
label_desc: Use image node_id 3 for calling a global function with example usage: consistency_guarantees(X, y, model_generator, method_name) and returns: None,  guarantees