code: def _testWhile(self, num_vars, num_iterations, key_base):
      group_size = 2
      group_key = 1
      instances = [(key_base + i) for i in range(num_vars)]
      devices = ["CPU:{}".format(i) for i in range(group_size)]
  
      config = config_pb2.ConfigProto(device_count={"CPU": group_size})
      rewrite_options = config.graph_options.rewrite_options
      rewrite_options.scoped_allocator_optimization = (
          rewriter_config_pb2.RewriterConfig.ON
      )
      del rewrite_options.scoped_allocator_opts.enable_op[:]
      rewrite_options.scoped_allocator_opts.enable_op.append(
          "CollectiveReduce"
      )
  
      with self.session(config=config) as sess:
          loop_vars = []
          for device in devices:
              with ops.device(device):
                  loop_vars.append(
                      [
                          variable_v1.VariableV1((1 << i) * 1.0)
                          for i in range(num_vars)
                      ]
                  )
          # This variable controls number of iterations.
          loop_vars.append(variable_v1.VariableV1(0.0))
  
          def loop_body(dev0_tensors, dev1_tensors, loop_tensor):
              return_ops = []
              for i in range(len(devices)):
                  device = devices[i]
                  device_tensors = (
                      dev0_tensors if i == 0 else dev1_tensors
                  )
                  with ops.device(device):
                      device_collectives = []
                      for j in range(num_vars):
                          # NOTE(ayushd): we need the `cast` here to ensure that the input
                          # to `all_reduce` has an explicit device string.  We don't use
                          # `identity` because `cast` is more resilient to getting optimized
                          # away by various optimization passes.
                          input_tensor = math_ops.cast(
                              device_tensors[j], dtypes.float16
                          )
                          collective_op = collective_ops.all_reduce(
                              input_tensor,
                              group_size,
                              group_key,
                              instances[j],
                              "Add",
                              "Id",
                          )
                          output_tensor = math_ops.cast(
                              collective_op, dtypes.float32
                          )
                          device_collectives.append(output_tensor)
                      return_ops.append(device_collectives)
              return_ops.append(math_ops.add(loop_tensor, 1.0))
              return return_ops
  
          # Run until last variable exceeds number of iterations.
          loop_cond = lambda d0, d1, i: math_ops.less(i, num_iterations)
          sess.run(variables.global_variables_initializer())
          results = sess.run(
              while_loop.while_loop(loop_cond, loop_body, loop_vars)
          )
          self.assertEqual(
              results[:-1],
              [
                  [
                      ((1 << (num_iterations + v)) * 1.0)
                      for v in range(num_vars)
                  ]
                  for _ in range(group_size)
              ],
          )
  
imports: import time
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.core.protobuf import rewriter_config_pb2
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import config
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import kernels
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import collective_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variable_v1
  from tensorflow.python.ops import variables
  from tensorflow.python.ops import while_loop
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 10 for calling the CollectiveOpTest obj's underlying member method code with example usage: obj._testWhile(num_vars, num_iterations, key_base) and returns: return_ops