code: def _sharedTestForPredictListMultipleExamplesPerInputModel(
      self, use_legacy, use_iterator
  ):
      temp_eval_export_dir = self._getEvalExportDir()
      if use_legacy:
          (
              _,
              eval_export_dir,
          ) = fake_multi_examples_per_input_estimator.legacy_fake_multi_examples_per_input_estimator(
              None, temp_eval_export_dir
          )
      else:
          (
              _,
              eval_export_dir,
          ) = fake_multi_examples_per_input_estimator.fake_multi_examples_per_input_estimator(
              None, temp_eval_export_dir, use_iterator
          )
  
      eval_saved_model = load.EvalSavedModel(eval_export_dir)
      fetched_list = eval_saved_model.predict_list(
          [b"0", b"1", b"3", b"0", b"2"]
      )
      self.assertEqual(6, len(fetched_list))
  
      input_index = []
      example_count = []
      labels = []
      predictions = []
      intra_input_index = []
      annotation = []
  
      def _check_and_append_feature(
          feature_name, one_fetch, feature_values
      ):
          self.assertEqual(
              (1,), one_fetch.values["features"][feature_name].shape
          )
          feature_values.append(
              one_fetch.values["features"][feature_name][0]
          )
  
      for fetched in fetched_list:
          _check_and_append_feature("input_index", fetched, input_index)
          _check_and_append_feature(
              "example_count", fetched, example_count
          )
          _check_and_append_feature(
              "intra_input_index", fetched, intra_input_index
          )
          _check_and_append_feature("annotation", fetched, annotation)
  
          self.assertAllEqual((1,), fetched.values["labels"].shape)
          labels.append(fetched.values["labels"])
  
          self.assertAllEqual((1,), fetched.values["predictions"].shape)
          predictions.append(fetched.values["predictions"])
  
      self.assertSequenceEqual([1, 3, 3, 3, 2, 2], example_count)
      self.assertSequenceEqual([1, 2, 2, 2, 4, 4], input_index)
      self.assertSequenceEqual([0, 0, 1, 2, 0, 1], intra_input_index)
      self.assertAllEqual(
          [
              b"raw_input: 1; index: 0",
              b"raw_input: 3; index: 0",
              b"raw_input: 3; index: 1",
              b"raw_input: 3; index: 2",
              b"raw_input: 2; index: 0",
              b"raw_input: 2; index: 1",
          ],
          annotation,
      )
  
      self.assertSequenceEqual([1, 2, 2, 2, 4, 4], labels)
      self.assertSequenceEqual([1, 2, 2, 2, 4, 4], predictions)
  
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 10 for calling the IntegrationTest obj's underlying member method code with example usage: obj._sharedTestForPredictListMultipleExamplesPerInputModel(use_legacy, use_iterator) without return types