code: def testMultiThreadedCollectiveLaunchNoInterleave(
      self,
      num_processes,
      required_gpus,
      implementation,
      prefer_unique_instance_key,
  ):
      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, _ = self.make_collective(
              num_processes, required_gpus
          )
          options = collective_util.Options(
              implementation=implementation
          )
  
          # We would like to simulate the following sequence:
          #   thread-0  device0                 device1
          #   thread-1          device0 device1
          # If the kernel launch sequence is as-is the program will deadlock since
          # NCCL requires the launch order to be same on each device.
          v0 = make_per_replica_value(1.0, devices)
          v1 = make_per_replica_value(2.0, devices)
  
          # Add a delay to collective_ops.all_reduce according to the input tensors
          # index in `sequence.`
          sequence = [
              v0.values[0],
              v1.values[0],
              v1.values[1],
              v0.values[1],
          ]
          all_reduce = collective_ops.all_reduce
  
          def delayed_all_reduce(input_tensor, *args, **kwargs):
              for idx, v in enumerate(sequence):
                  if input_tensor is v:
                      time.sleep(idx)
                      break
              return all_reduce(input_tensor, *args, **kwargs)
  
          with test.mock.patch.object(
              collective_ops, "all_reduce", delayed_all_reduce
          ):
              # We only use NCCL for batch reduce with two or more values, so we use
              # two values here.
  
              def thread_fn():
                  reduced = collective.batch_reduce(
                      reduce_util.ReduceOp.SUM,
                      [(v0, v0), (v0, v0)],
                      options,
                  )
                  self.assertAllEqual(reduced[0].values, [2.0, 2.0])
                  self.assertAllEqual(reduced[1].values, [2.0, 2.0])
  
              t = threading.Thread(target=thread_fn)
              t.start()
              reduced = collective.batch_reduce(
                  reduce_util.ReduceOp.SUM,
                  [(v1, v1), (v1, v1)],
                  options,
              )
              self.assertAllEqual(reduced[0].values, [4.0, 4.0])
              self.assertAllEqual(reduced[1].values, [4.0, 4.0])
              t.join()
  
      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 17 for calling the CollectiveOpsTest obj's underlying member method code with example usage: obj.testMultiThreadedCollectiveLaunchNoInterleave(num_processes, required_gpus, implementation, prefer_unique_instance_key) and returns: all_reduce