code: def testUnequalOutputCounts(self):
      # Tests dictionary output type where each output list has a different size.
  
      class UnitQ(pipeline.Pipeline):
          def __init__(self):
              pipeline.Pipeline.__init__(self, Type0, Type1)
  
          def transform(self, input_object):
              return [
                  Type1(x=input_object.x + i, y=input_object.y + i)
                  for i in range(input_object.z)
              ]
  
      class Partitioner(pipeline.Pipeline):
          def __init__(
              self, input_type, training_set_name, test_set_name
          ):
              self.training_set_name = training_set_name
              self.test_set_name = test_set_name
              pipeline.Pipeline.__init__(
                  self,
                  input_type,
                  {
                      training_set_name: input_type,
                      test_set_name: input_type,
                  },
              )
  
          def transform(self, input_object):
              if input_object.x < 0:
                  return {
                      self.training_set_name: [],
                      self.test_set_name: [input_object],
                  }
              return {
                  self.training_set_name: [input_object],
                  self.test_set_name: [],
              }
  
      q = UnitQ()
      partition = Partitioner(q.output_type, "training_set", "test_set")
  
      dag = {
          q: dag_pipeline.DagInput(q.input_type),
          partition: q,
          dag_pipeline.DagOutput("training_set"): partition[
              "training_set"
          ],
          dag_pipeline.DagOutput("test_set"): partition["test_set"],
      }
  
      dag_pipe_obj = dag_pipeline.DAGPipeline(dag)
      x, y, z = -3, 0, 8
      output_dict = dag_pipe_obj.transform(Type0(x, y, z))
  
      self.assertEqual(
          set(output_dict.keys()), set(["training_set", "test_set"])
      )
      training_results = output_dict["training_set"]
      test_results = output_dict["test_set"]
  
      expected_training_results = [
          Type1(x + i, y + i) for i in range(-x, z)
      ]
      expected_test_results = [
          Type1(x + i, y + i) for i in range(0, -x)
      ]
      self.assertEqual(
          set(training_results), set(expected_training_results)
      )
      self.assertEqual(set(test_results), set(expected_test_results))
  
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 4 for calling the DAGPipelineTest obj's underlying member method code with example usage: obj.testUnequalOutputCounts() and returns: unknown, dict, dict