docstr: Evaluator-only test pipeline on DataflowRunner.
docstr_tok: ["Evaluator-only","test","pipeline","on","DataflowRunner","."]
code: def testEvaluatorOnDataflowRunner(self):
      """Evaluator-only test pipeline on DataflowRunner."""
      pipeline_name = "kubeflow-evaluator-dataflow-test-{}".format(
          test_utils.random_id()
      )
      pipeline = self._create_dataflow_pipeline(
          pipeline_name,
          [
              self.raw_examples_importer,
              self.model_1_importer,
              Evaluator(
                  examples=self.raw_examples_importer.outputs["result"],
                  model=self.model_1_importer.outputs["result"],
                  feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(
                      specs=[
                          evaluator_pb2.SingleSlicingSpec(
                              column_for_slicing=["trip_start_hour"]
                          )
                      ]
                  ),
              ),
          ],
      )
      self._compile_and_run_pipeline(pipeline)
  
file_path: tfx/tfx/orchestration/kubeflow/e2e_tests/kubeflow_dataflow_integration_test.py
imports: import os
  import absl
  import tensorflow
  from tfx.components.evaluator.component import Evaluator
  from tfx.components.example_gen.csv_example_gen.component import CsvExampleGen
  from tfx.components.statistics_gen.component import StatisticsGen
  from tfx.components.transform.component import Transform
  from tfx.dsl.components.common import importer
  from tfx.orchestration import test_utils
  from tfx.orchestration.kubeflow import test_utils
  from tfx.proto import evaluator_pb2
  from tfx.types import standard_artifacts
label_desc: Use image node_id 5 for calling the KubeflowDataflowIntegrationTest obj's underlying member method code with example usage: obj.testEvaluatorOnDataflowRunner() without return types