code: def testNamedTuple(self):
      # We include toy implementations of Scaffold and EstimatorSpec to
      # avoid a dependency on Estimator here.
  
      class Scaffold(object):
          pass
  
      class EstimatorSpec(
          collections.namedtuple(
              "EstimatorSpec", ["mode", "loss", "train_op", "scaffold"]
          )
      ):
          def __new__(cls, mode, loss, train_op, scaffold=None):
              return super(EstimatorSpec, cls).__new__(
                  cls,
                  mode=mode,
                  loss=loss,
                  train_op=train_op,
                  scaffold=scaffold or Scaffold(),
              )
  
      with context.graph_mode(), ops.Graph().as_default():
          created_estimator_specs = []
  
          for device_id in range(3):
              spec = EstimatorSpec(
                  mode=mode_keys.EstimatorModeKeys.TRAIN,
                  loss=constant_op.constant(device_id / 2),
                  train_op=array_ops.identity(
                      constant_op.constant(device_id)
                  ),
              )
              created_estimator_specs.append(spec)
  
          merged_estimator_spec = distribute_utils.regroup(
              created_estimator_specs
          )
  
          self.assertIsInstance(merged_estimator_spec, EstimatorSpec)
          self.assertEqual(
              mode_keys.EstimatorModeKeys.TRAIN,
              merged_estimator_spec.mode,
          )
          for device_id in range(3):
              self.assertEqual(
                  created_estimator_specs[device_id].loss,
                  merged_estimator_spec.loss.values[device_id],
              )
              self.assertEqual(
                  created_estimator_specs[device_id].train_op,
                  merged_estimator_spec.train_op.values[device_id],
              )
              # Scaffold is populated by `EstimatorSpec.__new__`.
              self.assertEqual(
                  created_estimator_specs[device_id].scaffold,
                  merged_estimator_spec.scaffold.values[device_id],
              )
              self.assertIsInstance(
                  created_estimator_specs[device_id].scaffold, Scaffold
              )
              # Also test that we can undo the merge using select_replica()
              self.assertEqual(
                  created_estimator_specs[device_id],
                  distribute_utils.select_replica(
                      device_id, merged_estimator_spec
                  ),
              )
  
imports: import collections
  import collections.abc
  from absl.testing import parameterized
  import wrapt
  from tensorflow.python.distribute import combinations
  from tensorflow.python.distribute import distribute_utils
  from tensorflow.python.distribute import strategy_combinations
  from tensorflow.python.distribute import values
  from tensorflow.python.eager import context
  from tensorflow.python.eager import test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variable_v1
  from tensorflow.python.saved_model.model_utils import mode_keys
label_desc: Use image node_id 10 for calling the RegroupAndSelectDeviceTest obj's underlying member method code with example usage: obj.testNamedTuple() and returns: super