code: def test_handle_predict_should_handle_transform_with_initial_is_train_mode_after_predict():
      tape_fit = TapeCallbackFunction()
      tape_transform = TapeCallbackFunction()
      p = Pipeline(
          [
              TestOnlyWrapper(
                  CallbackWrapper(
                      MultiplyByN(2), tape_transform, tape_fit
                  )
              ),
              TrainOnlyWrapper(
                  CallbackWrapper(
                      MultiplyByN(4), tape_transform, tape_fit
                  )
              ),
          ]
      )
      data_container = DACT(
          data_inputs=np.array([1, 1]),
          expected_outputs=np.array([1, 1]),
      )
  
      p.handle_predict(
          data_container=data_container.copy(), context=CX()
      )
      data_container = p.handle_transform(data_container, CX())
  
      assert np.array_equal(
          data_container.data_inputs, np.array([4, 4])
      )
  
file_path: Neuraxle/testing_neuraxle/test_basestep.py
imports: import numpy
  from neuraxle.base import ExecutionContext
  from neuraxle.data_container import DataContainer
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.flow import TestOnlyWrapper, TrainOnlyWrapper
  from neuraxle.steps.misc import TapeCallbackFunction, CallbackWrapper
  from neuraxle.steps.numpy import MultiplyByN
  from testing_neuraxle.mocks.step_mocks import SomeStepWithHyperparams
label_desc: Use image node_id 4 for calling a global function with example usage: test_handle_predict_should_handle_transform_with_initial_is_train_mode_after_predict() without return types