class_docstr: Verifies the local and remote workers are running and producing data.
code: def testCluster(self, num_local_workers, num_remote_workers):
      cluster = multi_process_cluster.MultiProcessCluster(
          num_local_workers=num_local_workers,
          num_remote_workers=num_remote_workers,
      )
      num_elements = 10
      num_workers = num_local_workers + num_remote_workers
      if num_workers == 0:
          return
      dataset = self.make_distributed_range_dataset(
          num_elements, cluster
      )
      self.assertDatasetProduces(
          dataset,
          num_workers * list(range(num_elements)),
          assert_items_equal=True,
      )
  
file_path: tensorflow/tensorflow/python/data/experimental/kernel_tests/service/multi_process_cluster_test.py
imports: from absl.testing import parameterized
  from tensorflow.python.data.experimental.kernel_tests.service import multi_process_cluster
  from tensorflow.python.data.experimental.kernel_tests.service import test_base
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.framework import combinations
label_desc: Use image node_id 1 for calling the MultiProcessClusterTest obj's underlying member method code with example usage: obj.testCluster(num_local_workers, num_remote_workers) without return types