code: def test_output_transformer_wrapper_should_fit_transform_with_data_inputs_and_expected_outputs():
      tape = TapeCallbackFunction()
      p = OutputTransformerWrapper(
          Pipeline([MultiplyByN(2), FitCallbackStep(tape)])
      )
      data_inputs, expected_outputs = _create_data_source((10, 10))
  
      p, data_container = p.handle_fit_transform(
          DACT(
              data_inputs=data_inputs, expected_outputs=expected_outputs
          ),
          CX(),
      )
  
      assert np.array_equal(data_container.data_inputs, data_inputs)
      assert np.array_equal(
          data_container.expected_outputs, expected_outputs * 2
      )
      assert np.array_equal(tape.data[0][0], expected_outputs * 2)
      for i in range(10):
          _first_eot_seen: EOT = tape.data[0][1]
          assert _first_eot_seen is None
  
imports: import numpy
  import pytest
  from neuraxle.base import BaseTransformer
  from neuraxle.base import ExecutionContext
  from neuraxle.base import ForceHandleMixin
  from neuraxle.data_container import EOT
  from neuraxle.data_container import DataContainer
  from neuraxle.hyperparams.space import HyperparameterSamples
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.misc import FitCallbackStep, TapeCallbackFunction
  from neuraxle.steps.numpy import MultiplyByN
  from neuraxle.steps.output_handlers import IdsInputAndOutputTransformerWrapper, OutputTransformerWrapper
label_desc: Use image node_id 2 for calling a global function with example usage: test_output_transformer_wrapper_should_fit_transform_with_data_inputs_and_expected_outputs() without return types