class_docstr: A basic Adam optimizer that includes "correct" L2 weight decay.
docstr: Constructs a AdamWeightDecayOptimizer.
docstr_tok: ["Constructs","a","AdamWeightDecayOptimizer","."]
code: def __init__(
      self,
      learning_rate,
      weight_decay_rate=0.0,
      beta_1=0.9,
      beta_2=0.999,
      epsilon=1e-6,
      exclude_from_weight_decay=None,
  ):
      """Constructs a AdamWeightDecayOptimizer."""
  
      self.learning_rate = learning_rate
      self.weight_decay_rate = weight_decay_rate
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.epsilon = epsilon
      self.exclude_from_weight_decay = exclude_from_weight_decay
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import re
  import gin
  from mesh_tensorflow import layers
  from mesh_tensorflow import ops_with_redefined_builtins
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new AdamWeightDecayOptimizer object from inherited base classes: Optimizer with example: obj = AdamWeightDecayOptimizer(learning_rate, weight_decay_rate, beta_1, beta_2, epsilon, exclude_from_weight_decay)