class_docstr: Optimizer that implements the LAMBOptimizer as tf.train.Optimizer.
code: def _finish(self, update_ops, name_scope):
      # Update the power accumulators.
      with ops.control_dependencies(update_ops):
          beta1_power, beta2_power = self._get_beta_accumulators()
          with ops.colocate_with(beta1_power):
              update_beta1 = beta1_power.assign(
                  beta1_power * self._beta1_t,
                  use_locking=self._use_locking,
              )
              update_beta2 = beta2_power.assign(
                  beta2_power * self._beta2_t,
                  use_locking=self._use_locking,
              )
      return control_flow_ops.group(
          *update_ops + [update_beta1, update_beta2], name=name_scope
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import re
  import tensorflow.compat.v1
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import state_ops
  from tensorflow.python.training import optimizer
label_desc: Use image node_id 12 for calling the LAMBOptimizer obj's underlying member method code with example usage: obj._finish(update_ops, name_scope) and returns: control_flow_ops