class_docstr: Optimizer that implements the LAMBOptimizer as tf.train.Optimizer.
code: def __init__(
      self,
      learning_rate=0.001,
      beta_1=0.9,
      beta_2=0.999,
      epsilon=1e-6,
      weight_decay_rate=0.0,
      exclude_from_weight_decay=None,
      exclude_from_layer_adaptation=None,
      use_locking=False,
      name="LAMB",
      clip_by_global_norm_after_gradient_allreduce=False,
  ):
      super(LAMBOptimizer, self).__init__(use_locking, name)
      self._lr = learning_rate
      self._beta1 = beta_1
      self._beta2 = beta_2
      self._epsilon = epsilon
      self._weight_decay_rate = weight_decay_rate
      self.exclude_from_weight_decay = exclude_from_weight_decay
      self.clip_by_global_norm_after_gradient_allreduce = (
          clip_by_global_norm_after_gradient_allreduce
      )
      # exclude_from_layer_adaptation is set to exclude_from_weight_decay if the
      # arg is None.
      if exclude_from_layer_adaptation:
          self.exclude_from_layer_adaptation = (
              exclude_from_layer_adaptation
          )
      else:
          self.exclude_from_layer_adaptation = exclude_from_weight_decay
  
      # Tensor versions of the constructor arguments, created in _prepare().
      self._lr_t = None
      self._beta1_t = None
      self._beta2_t = None
      self._epsilon_t = None
      self._weight_decay_rate_t = None
  
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 1 to create a new LAMBOptimizer object from inherited base classes: optimizer with example: obj = LAMBOptimizer(learning_rate, beta_1, beta_2, epsilon, weight_decay_rate, exclude_from_weight_decay, exclude_from_layer_adaptation, use_locking, name, clip_by_global_norm_after_gradient_allreduce)