code: def testTimeoutBatchReduceDense(
      self,
      num_processes,
      implementation,
      required_gpus,
      prefer_unique_instance_key,
  ):
      if (
          required_gpus == 0
          and implementation == CommunicationImplementation.NCCL
      ):
          self.skipTest("Skip CPU + NCCL combination")
      if (
          num_processes != required_gpus
          and implementation == CommunicationImplementation.NCCL
      ):
          self.skipTest(
              "Skip NCCL combination with mismatched process and GPU "
              "count. NCCL requires physical GPUs for every process."
          )
      if (
          num_processes != required_gpus
          and implementation == CommunicationImplementation.AUTO
      ):
          self.skipTest(
              "Skip potential NCCL combination (AUTO) with mismatched "
              "process and GPU count. NCCL requires physical GPUs for "
              "every process."
          )
  
      def replica_fn():
          CollectiveReplicaLauncher._prefer_unique_instance_key = (
              prefer_unique_instance_key
          )
          collective, devices, task_id = self.make_collective(
              num_processes, required_gpus
          )
          if task_id != 0:
              return
  
          v = make_per_replica_value(1.0, devices)
          options = collective_util.Options(
              timeout_seconds=1.0, implementation=implementation
          )
  
          @def_function.function
          def batch_reduce_dense():
              return collective.batch_reduce(
                  reduce_util.ReduceOp.SUM, [(v, v), (v, v)], options
              )
  
          # The collective should time out because we only launch it on worker-0,
          # while there're two workers in total.
          with self.assertRaises(errors.DeadlineExceededError):
              batch_reduce_dense()
  
      get_global_mpr(num_processes).run(replica_fn)
  
imports: import collections
  import os
  import threading
  import time
  from absl.testing import parameterized
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.core.protobuf import tensorflow_server_pb2
  from tensorflow.python.distribute import cluster_resolver
  from tensorflow.python.distribute import collective_util
  from tensorflow.python.distribute import combinations
  from tensorflow.python.distribute import cross_device_ops
  from tensorflow.python.distribute import cross_device_utils
  from tensorflow.python.distribute import device_util
  from tensorflow.python.distribute import multi_process_runner
  from tensorflow.python.distribute import multi_worker_test_base
  from tensorflow.python.distribute import reduce_util
  from tensorflow.python.distribute import test_util
  from tensorflow.python.distribute import values
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import collective_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import math_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 20 for calling the CollectiveOpsTest obj's underlying member method code with example usage: obj.testTimeoutBatchReduceDense(num_processes, implementation, required_gpus, prefer_unique_instance_key) and returns: collective