code: def testImbalancedZipAndRepeat(self):
      smaller_num_elements = 200
      larger_num_elements = 1000
      repetitions = 3
  
      cluster = data_service_test_base.TestCluster(num_workers=1)
      snapshot_dir = data_service_test_base.TempDir()
      dataset1 = dataset_ops.Dataset.range(smaller_num_elements)
      dataset2 = dataset_ops.Dataset.range(larger_num_elements)
      dataset = dataset_ops.Dataset.zip((dataset1, dataset2))
      dataset = dataset.repeat(repetitions)
      self.evaluate(
          distributed_save_op.distributed_save(
              dataset,
              snapshot_dir.full_path,
              cluster.dispatcher_address(),
          )
      )
      _wait_for_snapshot(snapshot_dir.full_path)
  
      dataset = dataset_ops.Dataset.load(snapshot_dir.full_path)
      expected = repetitions * (
          list(
              zip(
                  range(smaller_num_elements),
                  range(smaller_num_elements),
              )
          )
      )
      self.assertDatasetProduces(
          dataset, expected, assert_items_equal=True
      )
  
imports: import os
  import time
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.data.experimental.kernel_tests.service import test_base
  from tensorflow.python.data.experimental.ops import data_service_ops
  from tensorflow.python.data.experimental.ops import distributed_save_op
  from tensorflow.python.data.experimental.service import _pywrap_snapshot_utils
  from tensorflow.python.data.kernel_tests import checkpoint_test_base
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import errors
  from tensorflow.python.ops import array_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 9 for calling the DistributedSaveTest obj's underlying member method code with example usage: obj.testImbalancedZipAndRepeat() without return types