code: def test_simple_sklearn_tuner(tmp_path):
      class SimpleSklearnTuner(base_tuner.BaseTuner):
          def run_trial(self, trial, x, y, validation_data):
              model = self.hypermodel.build(trial.hyperparameters)
              model.fit(x, y)
              x_val, y_val = validation_data
              score = model.score(x_val, y_val)
              self.save_model(trial.trial_id, model)
              return {"score": score}
  
          def save_model(self, trial_id, model, step=0):
              fname = os.path.join(
                  self.get_trial_dir(trial_id), "model.pickle"
              )
              with open(fname, "wb") as f:
                  pickle.dump(model, f)
  
          def load_model(self, trial):
              fname = os.path.join(
                  self.get_trial_dir(trial.trial_id), "model.pickle"
              )
              with open(fname, "rb") as f:
                  return pickle.load(f)
  
      def sklearn_build_fn(hp):
          c = hp.Float("c", 1e-4, 10)
          return linear_model.LogisticRegression(C=c)
  
      tuner = SimpleSklearnTuner(
          oracle=randomsearch.RandomSearchOracle(
              objective=keras_tuner.Objective("score", "max"),
              max_trials=2,
          ),
          hypermodel=sklearn_build_fn,
          directory=tmp_path,
      )
      tuner.search(
          TRAIN_INPUTS,
          TRAIN_TARGETS,
          validation_data=(VAL_INPUTS, VAL_TARGETS),
      )
      models = tuner.get_best_models(2)
      score0 = models[0].score(VAL_INPUTS, VAL_TARGETS)
      score1 = models[1].score(VAL_INPUTS, VAL_TARGETS)
      assert score0 >= score1
  
imports: import os
  import pickle
  import time
  import numpy
  import pytest
  from sklearn import linear_model
  import keras_tuner
  from keras_tuner import errors
  from keras_tuner.distribute import oracle_client
  from keras_tuner.engine import base_tuner
  from keras_tuner.test_utils import mock_distribute
  from keras_tuner.tuners import gridsearch
  from keras_tuner.tuners import randomsearch
label_desc: Use image node_id 3 for calling a global function with example usage: test_simple_sklearn_tuner(tmp_path) and returns: linear_model, dict, pickle