class_docstr: Single worker fault tolerance tests.
  
  This covers the cases that ensure training can continue in a single-worker
  cluster, even if the only worker can become unavailable at some point and
  recovered (if there are multiple workers, it is possible that the training
  succeeds with the workers that did not fail). Realistically single worker
  is very rarely used, but the tests are important to ensure the correct
  behaviors.
code: def setUp(self):
      super(SingleWorkerFaultToleranceTest, self).setUp(1, 1)
  
imports: import threading
  import time
  from tensorflow.python.compat import v2_compat
  from tensorflow.python.distribute import multi_process_runner
  from tensorflow.python.distribute import multi_worker_test_base
  from tensorflow.python.distribute import parameter_server_strategy_v2
  from tensorflow.python.distribute.cluster_resolver.cluster_resolver import SimpleClusterResolver
  from tensorflow.python.distribute.coordinator import cluster_coordinator
  from tensorflow.python.distribute.coordinator import fault_tolerance_test_base
  from tensorflow.python.eager import test
  from tensorflow.python.training import coordinator
  from tensorflow.python.training import server_lib
label_desc: Use image node_id 1 for calling the SingleWorkerFaultToleranceTest obj's underlying member method code with example usage: obj.setUp() without return types