class_docstr: Class which generates and stores classification batches.
  
  Each classification batch contains result of the classification of one
  batch of adversarial images.
class_docstr_tok: ["Class","which","generates","and","stores","classification","batches",".","Each","classification","batch","contains","result","of","the","classification","of","one","batch","of","adversarial","images","."]
docstr: Initializes ClassificationBatches.
  
  Args:
    datastore_client: instance of CompetitionDatastoreClient
    storage_client: instance of CompetitionStorageClient
    round_name: name of the round
docstr_tok: ["Initializes","ClassificationBatches",".","Args",":","datastore_client",":","instance","of","CompetitionDatastoreClient","storage_client",":","instance","of","CompetitionStorageClient","round_name",":","name","of","the","round"]
code: def __init__(self, datastore_client, storage_client, round_name):
      """Initializes ClassificationBatches.
  
      Args:
        datastore_client: instance of CompetitionDatastoreClient
        storage_client: instance of CompetitionStorageClient
        round_name: name of the round
      """
      self._datastore_client = datastore_client
      self._storage_client = storage_client
      self._round_name = round_name
      # Data is dict of dicts {CLASSIFICATION_BATCH_ID: { ... }}
      self._data = {}
  
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 1 to create a new ClassificationBatches object from inherited base classes: object with example: obj = ClassificationBatches(datastore_client, storage_client, round_name)