class_docstr: Optimizer that implements the LAMBOptimizer as tf.train.Optimizer.
code: def _create_slots(self, var_list):
      # Create the beta1 and beta2 accumulators on the same device as the first
      # variable. Sort the var_list to make sure this device is consistent across
      # workers (these need to go on the same PS, otherwise some updates are
      # silently ignored).
      first_var = min(var_list, key=lambda x: x.name)
      self._create_non_slot_variable(
          initial_value=self._beta1,
          name="beta1_power",
          colocate_with=first_var,
      )
      self._create_non_slot_variable(
          initial_value=self._beta2,
          name="beta2_power",
          colocate_with=first_var,
      )
  
      # Create slots for the first and second moments.
      for v in var_list:
          self._zeros_slot(v, "m", self._name)
          self._zeros_slot(v, "v", self._name)
  
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 3 for calling the LAMBOptimizer obj's underlying member method code with example usage: obj._create_slots(var_list) without return types