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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)