code: def testEvaluateExistingMetricsBasicForControlDependencyEstimator(
      self,
  ):
      temp_eval_export_dir = self._getEvalExportDir()
      (
          _,
          eval_export_dir,
      ) = control_dependency_estimator.simple_control_dependency_estimator(
          None, temp_eval_export_dir
      )
  
      eval_saved_model = load.EvalSavedModel(eval_export_dir)
      example1 = self._makeExample(
          prediction=0.9,
          label=0.0,
          fixed_float=1.0,
          fixed_string="apple",
          fixed_int=2,
          var_float=10.0,
          var_string="banana",
          var_int=20,
      ).SerializeToString()
      example2 = self._makeExample(
          prediction=0.1,
          label=0.0,
          fixed_float=5.0,
          fixed_string="avocado",
          fixed_int=6,
          var_float=50.0,
          var_string="berry",
          var_int=60,
      ).SerializeToString()
  
      eval_saved_model.metrics_reset_update_get_list(
          [example1, example2]
      )
      metric_values = eval_saved_model.get_metric_values()
  
      self.assertDictElementsAlmostEqual(
          metric_values,
          {
              "control_dependency_on_fixed_float": 1.0,
              "control_dependency_on_var_float": 10.0,
              "control_dependency_on_actual_label": 100.0,
              "control_dependency_on_var_int_label": 1000.0,
              "control_dependency_on_prediction": 10000.0,
          },
      )
  
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 7 for calling the IntegrationTest obj's underlying member method code with example usage: obj.testEvaluateExistingMetricsBasicForControlDependencyEstimator() without return types