code: def test_analyze_one_classification_result(self):
      self.storage_client = fake_cloud_client.FakeStorageClient(
          {
              "filename": "a1.png,1\na2.png,4\na3.png,1\na4.png,1\na5.png,2\na6.png,9"
          }
      )
      adv_batch = {
          "dataset_batch_id": "BATCH000",
          "images": {
              "a" + str(i): {"clean_image_id": "c" + str(i)}
              for i in range(1, 6)
          },
      }
      dataset_batches = image_batches.DatasetBatches(
          datastore_client=self.datastore_client,
          storage_client=self.storage_client,
          dataset_name="final",
      )
      dataset_batches._data = {
          "BATCH000": {
              "images": {
                  "c" + str(i): {"dataset_image_id": str(i)}
                  for i in range(1, 6)
              }
          },
      }
      (
          count_correctly_classified,
          count_errors,
          count_hit_target_class,
          num_images,
      ) = classification_results.analyze_one_classification_result(
          self.storage_client,
          "filename",
          adv_batch,
          dataset_batches,
          FakeDatasetMeta(),
      )
      self.assertEqual(3, count_correctly_classified)
      self.assertEqual(2, count_errors)
      self.assertEqual(1, count_hit_target_class)
      self.assertEqual(5, num_images)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import unittest
  from six import assertCountEqual
  from eval_lib import classification_results
  from eval_lib import image_batches
  from eval_lib import submissions
  from eval_lib import work_data
  from eval_lib.tests import fake_cloud_client
label_desc: Use image node_id 7 for calling the ClassificationResultsTest obj's underlying member method code with example usage: obj.test_analyze_one_classification_result() without return types