code: def test_input_and_output_transformer_wrapper_should_fit_with_data_inputs_and_expected_outputs_as_data_inputs():
      tape = TapeCallbackFunction()
      p = IdsInputAndOutputTransformerWrapper(FitCallbackStep(tape))
      ids, data_inputs, expected_outputs = _create_data_source(
          (10, 10), with_ids=True
      )
  
      p.handle_fit(
          DACT(ids=ids, di=data_inputs, eo=expected_outputs), CX()
      )
  
      assert np.array_equal(tape.data[0][0][0], ids)
      assert np.array_equal(tape.data[0][0][1], data_inputs)
      assert np.array_equal(tape.data[0][0][2], expected_outputs)
  
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 4 for calling a global function with example usage: test_input_and_output_transformer_wrapper_should_fit_with_data_inputs_and_expected_outputs_as_data_inputs() without return types