code: def analyze_one_classification_result(
      storage_client,
      file_path,
      adv_batch,
      dataset_batches,
      dataset_meta,
  ):
      """Reads and analyzes one classification result.
  
      This method reads file with classification result and counts
      how many images were classified correctly and incorrectly,
      how many times target class was hit and total number of images.
  
      Args:
        storage_client: instance of CompetitionStorageClient
        file_path: result file path
        adv_batch: AversarialBatches.data[adv_batch_id]
          adv_batch_id is stored in each ClassificationBatch entity
        dataset_batches: instance of DatasetBatches
        dataset_meta: instance of DatasetMetadata
  
      Returns:
        Tuple of (count_correctly_classified, count_errors, count_hit_target_class,
        num_images)
      """
      class_result = read_classification_results(
          storage_client, file_path
      )
      if class_result is None:
          return 0, 0, 0, 0
      adv_images = adv_batch["images"]
      dataset_batch_images = dataset_batches.data[
          adv_batch["dataset_batch_id"]
      ]["images"]
      count_correctly_classified = 0
      count_errors = 0
      count_hit_target_class = 0
      num_images = 0
      for adv_img_id, label in iteritems(class_result):
          if adv_img_id not in adv_images:
              continue
          num_images += 1
          clean_image_id = adv_images[adv_img_id]["clean_image_id"]
          dataset_image_id = dataset_batch_images[clean_image_id][
              "dataset_image_id"
          ]
          if label == dataset_meta.get_true_label(dataset_image_id):
              count_correctly_classified += 1
          else:
              count_errors += 1
          if label == dataset_meta.get_target_class(dataset_image_id):
              count_hit_target_class += 1
      return (
          count_correctly_classified,
          count_errors,
          count_hit_target_class,
          num_images,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import csv
  from io import BytesIO
  from io import StringIO
  import logging
  import os
  import pickle
  import time
  from six import iteritems
  from six import iterkeys
  from six import itervalues
  from six import PY3
label_desc: Use image node_id 2 for calling a global function with example usage: analyze_one_classification_result(storage_client, file_path, adv_batch, dataset_batches, dataset_meta) and returns: count_correctly_classified,  count_errors,  count_hit_target_class,  num_images, int,  int,  int,  int