code: def benchmark_send(self):
      remote.connect_to_remote_host(self._cached_server_target1)
  
      x = random_ops.random_uniform((2, 2)).cpu()
  
      @def_function.function
      def remote_func(m):
          return math_ops.matmul(m, m)
  
      def func(m):
          with ops.device("job:worker/replica:0/task:0/device:CPU:0"):
              return remote_func(m)
  
      self._run(lambda: func(x))
      # NOTE(b/136184459): Force garbage collecting hanging resources before
      # subsequent calls to set_server_def, to ensure the destroy resource ops are
      # executed when their corresponding device and manager are still available.
      gc.collect()
  
imports: import gc
  import time
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import remote
  from tensorflow.python.eager import test
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.training import server_lib
label_desc: Use image node_id 3 for calling the RemoteWorkerMicroBenchmarks obj's underlying member method code with example usage: obj.benchmark_send() and returns: math_ops, remote_func