code: def test_input_and_output_transformer_wrapper_should_transform_with_data_inputs_and_expected_outputs():
      p = IdsInputAndOutputTransformerWrapper(
          MultiplyByNInputAndOutput(2)
      )
      data_inputs, expected_outputs = _create_data_source((10, 10))
  
      data_container = p.handle_transform(
          DACT(
              data_inputs=data_inputs, expected_outputs=expected_outputs
          ),
          CX(),
      )
  
      assert np.array_equal(data_container.data_inputs, data_inputs * 2)
      assert np.array_equal(
          data_container.expected_outputs, expected_outputs * 2
      )
  
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 6 for calling a global function with example usage: test_input_and_output_transformer_wrapper_should_transform_with_data_inputs_and_expected_outputs() without return types