code: def testEvaluateExistingMetricsWithExportedCustomMetricsDNN(self):
      temp_eval_export_dir = self._getEvalExportDir()
      _, eval_export_dir = dnn_classifier.simple_dnn_classifier(
          None, temp_eval_export_dir
      )
  
      eval_saved_model = load.EvalSavedModel(eval_export_dir)
      example1 = self._makeExample(
          age=3.0, language="english", label=1.0
      ).SerializeToString()
  
      example2 = self._makeExample(
          age=2.0, language="chinese", label=0.0
      ).SerializeToString()
      eval_saved_model.metrics_reset_update_get_list(
          [example1, example2]
      )
  
      metric_values = eval_saved_model.get_metric_values()
      self.assertDictElementsAlmostEqual(
          metric_values,
          {
              # We don't check accuracy and AUC here because it varies from run to
              # run due to DNN initialization
              "my_mean_age": 2.5,
              "my_mean_label": 0.5,
              "my_mean_age_times_label": 1.5,
          },
      )
  
      self.assertIn("my_mean_prediction", metric_values)
      self.assertIn("prediction/mean", metric_values)
      self.assertAlmostEqual(
          metric_values["prediction/mean"],
          metric_values["my_mean_prediction"],
          places=5,
      )
  
imports: import os
  import numpy
  import tensorflow
  from tensorflow_model_analysis.eval_saved_model import encoding
  from tensorflow_model_analysis.eval_saved_model import load
  from tensorflow_model_analysis.eval_saved_model import testutil
  from tensorflow_model_analysis.eval_saved_model.example_trainers import control_dependency_estimator
  from tensorflow_model_analysis.eval_saved_model.example_trainers import csv_linear_classifier
  from tensorflow_model_analysis.eval_saved_model.example_trainers import custom_estimator
  from tensorflow_model_analysis.eval_saved_model.example_trainers import dnn_classifier
  from tensorflow_model_analysis.eval_saved_model.example_trainers import fake_multi_examples_per_input_estimator
  from tensorflow_model_analysis.eval_saved_model.example_trainers import fake_sequence_to_prediction
  from tensorflow_model_analysis.eval_saved_model.example_trainers import fixed_prediction_classifier
  from tensorflow_model_analysis.eval_saved_model.example_trainers import fixed_prediction_estimator
  from tensorflow_model_analysis.eval_saved_model.example_trainers import fixed_prediction_estimator_no_labels
  from tensorflow_model_analysis.eval_saved_model.example_trainers import linear_classifier
  from tensorflow_model_analysis.eval_saved_model.example_trainers import linear_classifier_multivalent
  from tensorflow_model_analysis.eval_saved_model.example_trainers import multi_head
  from tensorflow_model_analysis.post_export_metrics import metrics
  from tensorflow.core.example import example_pb2
label_desc: Use image node_id 27 for calling the IntegrationTest obj's underlying member method code with example usage: obj.testEvaluateExistingMetricsWithExportedCustomMetricsDNN() without return types