code: def __score_method(
      X,
      y,
      fcounts,
      model_generator,
      score_function,
      method_name,
      nreps=10,
      test_size=100,
      cache_dir="/tmp",
  ):
      """Test an explanation method."""
  
      try:
          pickle
      except NameError:
          raise ImportError(
              "The 'dill' package could not be loaded and is needed for the benchmark!"
          )
  
      old_seed = np.random.seed()
      np.random.seed(3293)
  
      # average the method scores over several train/test splits
      method_reps = []
  
      data_hash = (
          hashlib.sha256(__toarray(X).flatten()).hexdigest()
          + hashlib.sha256(__toarray(y)).hexdigest()
      )
      for i in range(nreps):
          X_train, X_test, y_train, y_test = train_test_split(
              __toarray(X), y, test_size=test_size, random_state=i
          )
  
          # define the model we are going to explain, caching so we onlu build it once
          model_id = (
              "model_cache__v"
              + "__".join(
                  [__version__, data_hash, model_generator.__name__]
              )
              + ".pickle"
          )
          cache_file = os.path.join(cache_dir, model_id + ".pickle")
          if os.path.isfile(cache_file):
              with open(cache_file, "rb") as f:
                  model = pickle.load(f)
          else:
              model = model_generator()
              model.fit(X_train, y_train)
              with open(cache_file, "wb") as f:
                  pickle.dump(model, f)
  
          attr_key = "_".join(
              [
                  model_generator.__name__,
                  method_name,
                  str(test_size),
                  str(nreps),
                  str(i),
                  data_hash,
              ]
          )
  
          def score(attr_function):
              def cached_attr_function(X_inner):
                  if attr_key not in _attribution_cache:
                      _attribution_cache[attr_key] = attr_function(
                          X_inner
                      )
                  return _attribution_cache[attr_key]
  
              # cached_attr_function = lambda X: __check_cache(attr_function, X)
              if fcounts is None:
                  return score_function(
                      X_train,
                      X_test,
                      y_train,
                      y_test,
                      cached_attr_function,
                      model,
                      i,
                  )
              else:
                  scores = []
                  for f in fcounts:
                      scores.append(
                          score_function(
                              f,
                              X_train,
                              X_test,
                              y_train,
                              y_test,
                              cached_attr_function,
                              model,
                              i,
                          )
                      )
                  return np.array(scores)
  
          # evaluate the method (only building the attribution function if we need to)
          if attr_key not in _attribution_cache:
              method_reps.append(
                  score(getattr(methods, method_name)(model, X_train))
              )
          else:
              method_reps.append(score(None))
  
      np.random.seed(old_seed)
      return np.array(method_reps).mean(0)
  
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 39 for calling a global function with example usage: __score_method(X, y, fcounts, model_generator, score_function, method_name, nreps, test_size, cache_dir) and returns: np, _attribution_cache, score_function, np