code: def test_sklearn_tuner_with_df(tmp_path):
      tuner = keras_tuner.SklearnTuner(
          oracle=keras_tuner.oracles.BayesianOptimizationOracle(
              objective=keras_tuner.Objective("score", "max"),
              max_trials=10,
          ),
          hypermodel=build_model,
          directory=tmp_path,
      )
  
      x = pd.DataFrame(np.random.uniform(size=(50, 10)))
      y = pd.DataFrame(np.random.randint(0, 2, size=(50,)))
      tuner.search(x, y)
  
      assert len(tuner.oracle.trials) == 10
  
imports: import numpy
  import pandas
  import pytest
  from sklearn import datasets
  from sklearn import decomposition
  from sklearn import ensemble
  from sklearn import linear_model
  from sklearn import metrics
  from sklearn import model_selection
  from sklearn import neighbors
  from sklearn import pipeline
  import keras_tuner
label_desc: Use image node_id 4 for calling a global function with example usage: test_sklearn_tuner_with_df(tmp_path) without return types