class_docstr: tf.data service cluster with local and remote workers.
  
  Represents a cluster with a dispatcher, `num_local_workers` local workers, and
  `num_remote_workers` remote workers. Remote workers run in separate processes.
  This is useful to test reading from local in-process workers. For example:
  
  ```
  cluster = multi_process_cluster.MultiProcessCluster(
      num_local_workers=1, num_remote_workers=3)
  num_elements = 10
  dataset = self.make_distributed_range_dataset(
      num_elements, cluster, target_workers="LOCAL")
  self.assertDatasetProduces(dataset, list(range(num_elements)))
  ```
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code: def _start_remote_workers(self, num_workers, worker_tags=None):
      # List of (worker address, remote worker process) tuples.
      self._remote_workers = []
      for _ in range(num_workers):
          self.start_remote_worker(worker_tags)
  
imports: import tempfile
  from tensorflow.core.protobuf import data_service_pb2
  from tensorflow.core.protobuf import service_config_pb2
  from tensorflow.python.data.experimental.kernel_tests.service import test_base
  from tensorflow.python.data.experimental.service import server_lib
  from tensorflow.python.distribute import multi_process_lib
  from tensorflow.python.framework import test_util
  from tensorflow.python.platform import googletest
label_desc: Use image node_id 4 for calling the MultiProcessCluster obj's underlying member method code with example usage: obj._start_remote_workers(num_workers, worker_tags) without return types