code: def test_zip_data_container_should_concatenate_inner_features():
      data_inputs_3d, expected_outputs_3d = _create_data_source(
          (10, 10, 2)
      )
      data_inputs_2d, expected_outputs_2d = _create_data_source(
          (10, 10)
      )
      data_container_2d = DACT(
          data_inputs=data_inputs_2d,
          expected_outputs=expected_outputs_2d,
      )
      data_container = DACT(
          data_inputs=data_inputs_3d,
          expected_outputs=expected_outputs_3d,
      )
  
      zip_data_container = ZipDataContainer.create_from(
          data_container, data_container_2d
      )
      zip_data_container.concatenate_inner_features()
  
      assert np.array_equal(
          np.array(zip_data_container.data_inputs)[..., -1],
          data_container_2d.data_inputs,
      )
      assert np.array_equal(
          np.array(zip_data_container.expected_outputs),
          expected_outputs_3d,
      )
  
imports: import numpy
  from neuraxle.data_container import ZipDataContainer, DACT
label_desc: Use image node_id 4 for calling a global function with example usage: test_zip_data_container_should_concatenate_inner_features() without return types