code: def local_accuracy(X, y, model_generator, method_name):
      """Local Accuracy
      transform = "identity"
      sort_order = 0
      """
  
      def score_map(true, pred):
          """Computes local accuracy as the normalized standard deviation of numerical scores."""
          return np.std(pred - true) / (np.std(true) + 1e-6)
  
      def score_function(
          X_train,
          X_test,
          y_train,
          y_test,
          attr_function,
          trained_model,
          random_state,
      ):
          return measures.local_accuracy(
              X_train,
              y_train,
              X_test,
              y_test,
              attr_function(X_test),
              model_generator,
              score_map,
              trained_model,
          )
  
      return None, __score_method(
          X, y, None, model_generator, score_function, 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 2 for calling a global function with example usage: local_accuracy(X, y, model_generator, method_name) and returns: None,  __score_method, unknown, measures