code: def test_list_data_container_concat():
      # Given
      data_container = ListDataContainer(
          ids=[str(i) for i in range(100)],
          data_inputs=np.array(list(range(100))),
          expected_outputs=np.array(list(range(100, 200))),
      )
  
      # When
      data_container.extend(
          DACT(
              ids=[str(i) for i in range(100, 200)],
              data_inputs=np.array(list(range(100, 200))),
              expected_outputs=np.array(list(range(200, 300))),
          )
      )
  
      # Then
      assert np.array_equal(
          np.array(data_container.ids),
          np.array(list(range(0, 200))).astype(np.str),
      )
  
      expected_data_inputs = np.array(list(range(0, 200))).astype(
          np.int
      )
      actual_data_inputs = np.array(data_container.data_inputs).astype(
          np.int
      )
      assert np.array_equal(actual_data_inputs, expected_data_inputs)
  
      expected_expected_outputs = np.array(
          list(range(100, 300))
      ).astype(np.int)
      assert np.array_equal(
          np.array(data_container.expected_outputs).astype(np.int),
          expected_expected_outputs,
      )
  
imports: import numpy
  from neuraxle.data_container import DACT, ListDataContainer
label_desc: Use image node_id 5 for calling a global function with example usage: test_list_data_container_concat() without return types