code: def test_data_shuffling_should_shuffle_data_inputs_and_expected_outputs():
      callback_fit = TapeCallbackFunction()
      callback_transform = TapeCallbackFunction()
      data_shuffler = Pipeline(
          [
              DataShuffler(seed=42, increment_seed_after_each_fit=True),
              FitTransformCallbackStep(
                  callback_transform, callback_fit
              ),
          ]
      )
      data_inputs = np.array(range(10))
      expected_outputs = np.array(range(10, 20))
  
      outputs = data_shuffler.fit_transform(
          data_inputs, expected_outputs
      )
  
      assert not np.array_equal(outputs, data_inputs)
      assert not np.array_equal(callback_fit.data[0][0], data_inputs)
      assert not np.array_equal(
          callback_fit.data[0][1], expected_outputs
      )
      assert not np.array_equal(callback_transform.data, data_inputs)
  
imports: import numpy
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.data import DataShuffler
  from neuraxle.steps.misc import TapeCallbackFunction, FitTransformCallbackStep
label_desc: Use image node_id 1 for calling a global function with example usage: test_data_shuffling_should_shuffle_data_inputs_and_expected_outputs() without return types