code: def test_output_transformer_should_zip_data_input_and_expected_output_in_the_transformed_output():
      pipeline = Pipeline([MultiplyBy2OutputTransformer()])
  
      pipeline, new_data_container = pipeline.handle_fit_transform(
          DACT(
              data_inputs=[1, 2, 3],
              ids=[0, 1, 2],
              expected_outputs=[2, 3, 4],
          ),
          CX(),
      )
  
      assert new_data_container.data_inputs == [2, 4, 6]
      assert new_data_container.expected_outputs == [4, 6, 8]
  
file_path: Neuraxle/testing_neuraxle/steps/test_output_transformer_wrapper.py
imports: from typing import Any, Tuple
  from neuraxle.base import BaseTransformer
  from neuraxle.base import ExecutionContext
  from neuraxle.data_container import DataContainer
  from neuraxle.hyperparams.space import HyperparameterSamples, HyperparameterSpace
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.output_handlers import IdsAndInputAndOutputTransformerMixin
label_desc: Use image node_id 1 for calling a global function with example usage: test_output_transformer_should_zip_data_input_and_expected_output_in_the_transformed_output() without return types