class_docstr: Tests for distributed save/load with the new load algorithm.
  
  TODO(b/297930782): Add fault tolerance tests.
code: def test_save_load(
      self,
      num_workers: int,
      num_elements: int,
      num_repetitions: int,
      compression: Optional[str],
  ):
      test_snapshot = TestSnapshot()
      cluster = data_service_test_base.TestCluster(
          num_workers=num_workers
      )
      dataset = dataset_ops.Dataset.range(num_elements)
      dataset = dataset.repeat(num_repetitions)
      self.evaluate(
          distributed_save_op.distributed_save(
              dataset, test_snapshot.path, cluster.dispatcher_address()
          )
      )
  
      # Unlike the old load op, v2 does not need to wait for snapshot to finish.
      dataset = load_op._load_distributed_snapshot_v2(
          test_snapshot.path
      )
      self.assertDatasetProduces(
          dataset,
          list(range(num_elements)) * num_repetitions,
          assert_items_equal=True,
      )
  
imports: import os
  import shutil
  import tempfile
  import threading
  import time
  from typing import Callable, Optional
  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.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.data.ops import load_op
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import errors
  from tensorflow.python.ops import array_ops
  from tensorflow.python.platform import googletest
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the DistributedSaveLoadTest obj's underlying member method code with example usage: obj.test_save_load(num_workers, num_elements, num_repetitions, compression) without return types