code: def testSingleOutputs(self):
      # Tests single object and dictionaries in the DAG.
      a, b, c, d = UnitA(), UnitB(), UnitC(), UnitD()
      dag = {
          a: dag_pipeline.DagInput(Type0),
          b: a["t1"],
          c: {"A_data": a["t2"], "B_data": b},
          d: {"0": c["regular_data"], "1": b, "2": c["special_data"]},
          dag_pipeline.DagOutput("abcdz"): d,
      }
  
      dag_pipe_obj = dag_pipeline.DAGPipeline(dag)
      inputs = [Type0(1, 2, 3), Type0(-1, -2, -3), Type0(3, -3, 2)]
  
      for input_object in inputs:
          x, y, z = input_object.x, input_object.y, input_object.z
          output_dict = dag_pipe_obj.transform(input_object)
  
          self.assertEqual(list(output_dict.keys()), ["abcdz"])
          results = output_dict["abcdz"]
          self.assertLen(results, 1)
          result = results[0]
  
          # The following outputs are the result of passing the values in
          # `input_object` through the transform functions of UnitA, UnitB, UnitC,
          # and UnitD (all defined at the top of this file), connected in the way
          # defined by `dag`.
          self.assertEqual(result.a, x * 1000)
          self.assertEqual(result.b, y - 100)
          self.assertEqual(result.c, x * 1000 + z * 100)
          self.assertEqual(result.d, y - 100 - z * 100)
          self.assertEqual(result.z, z)
  
imports: import collections
  from absl.testing import absltest
  from magenta.pipelines import dag_pipeline
  from magenta.pipelines import pipeline
  from magenta.pipelines import statistics
label_desc: Use image node_id 2 for calling the DAGPipelineTest obj's underlying member method code with example usage: obj.testSingleOutputs() without return types