code: def testDoSkippedModelCreation(
      self, mock_runner, mock_run_model_inference, _
  ):
      input_dict = {
          "examples": [self._examples],
          "model": [self._model],
          "model_blessing": [self._model_blessing],
      }
      output_dict = {
          "inference_result": [self._inference_result],
      }
      ai_platform_serving_args = {
          "model_name": "model_name",
          "project_id": "project_id",
      }
      # Create exe properties.
      exec_properties = {
          "data_spec": proto_utils.proto_to_json(
              bulk_inferrer_pb2.DataSpec()
          ),
          "custom_config": json_utils.dumps(
              {executor.SERVING_ARGS_KEY: ai_platform_serving_args}
          ),
      }
      mock_runner.get_service_name_and_api_version.return_value = (
          "ml",
          "v1",
      )
      mock_runner.create_model_for_aip_prediction_if_not_exist.return_value = (
          False
      )
  
      # Run executor.
      bulk_inferrer = executor.Executor(self._context)
      bulk_inferrer.Do(input_dict, output_dict, exec_properties)
  
      ai_platform_prediction_model_spec = (
          model_spec_pb2.AIPlatformPredictionModelSpec(
              project_id="project_id",
              model_name="model_name",
              version_name=self._model_version,
          )
      )
      ai_platform_prediction_model_spec.use_serialization_config = True
      inference_endpoint = model_spec_pb2.InferenceSpecType()
      inference_endpoint.ai_platform_prediction_model_spec.CopyFrom(
          ai_platform_prediction_model_spec
      )
      mock_run_model_inference.assert_called_once_with(
          mock.ANY,
          mock.ANY,
          mock.ANY,
          mock.ANY,
          mock.ANY,
          inference_endpoint,
      )
      executor_class_path = name_utils.get_full_name(
          bulk_inferrer.__class__
      )
      with telemetry_utils.scoped_labels(
          {telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}
      ):
          job_labels = telemetry_utils.make_labels_dict()
      mock_runner.deploy_model_for_aip_prediction.assert_called_once_with(
          serving_path=path_utils.serving_model_path(self._model.uri),
          model_version_name=mock.ANY,
          ai_platform_serving_args=ai_platform_serving_args,
          labels=job_labels,
          api=mock.ANY,
          skip_model_endpoint_creation=True,
          set_default=False,
      )
      mock_runner.delete_model_from_aip_if_exists.assert_called_once_with(
          model_version_name=mock.ANY,
          ai_platform_serving_args=ai_platform_serving_args,
          api=mock.ANY,
          delete_model_endpoint=False,
      )
  
imports: import hashlib
  import os
  from unittest import mock
  import tensorflow
  from tfx.extensions.google_cloud_ai_platform import constants
  from tfx.extensions.google_cloud_ai_platform.bulk_inferrer import executor
  from tfx.proto import bulk_inferrer_pb2
  from tfx.types import artifact_utils
  from tfx.types import standard_artifacts
  from tfx.utils import json_utils
  from tfx.utils import name_utils
  from tfx.utils import path_utils
  from tfx.utils import proto_utils
  from tfx.utils import telemetry_utils
  from tfx_bsl.public.proto import model_spec_pb2
label_desc: Use image node_id 3 for calling the ExecutorTest obj's underlying member method code with example usage: obj.testDoSkippedModelCreation(mock_runner, mock_run_model_inference, _) without return types