code: def __init__(
      self,
      learning_rate=0.001,
      beta1=0.9,
      beta2=0.999,
      epsilon=1e-8,
      weight_decay=0.01,
      multi_precision=False,
      grad_clip=None,
      no_weight_decay_name=None,
      one_dim_param_no_weight_decay=False,
      name=None,
      lazy_mode=False,
      **args,
  ):
      super().__init__()
      self.learning_rate = learning_rate
      self.beta1 = beta1
      self.beta2 = beta2
      self.epsilon = epsilon
      self.grad_clip = grad_clip
      self.weight_decay = 0.01 if weight_decay is None else weight_decay
      self.grad_clip = grad_clip
      self.name = name
      self.lazy_mode = lazy_mode
      self.multi_precision = multi_precision
      self.no_weight_decay_name_list = (
          no_weight_decay_name.split() if no_weight_decay_name else []
      )
      self.one_dim_param_no_weight_decay = one_dim_param_no_weight_decay
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from paddle import optimizer
label_desc: Use image node_id 1 to create a new AdamW object from inherited base classes: object with example: obj = AdamW(learning_rate, beta1, beta2, epsilon, weight_decay, multi_precision, grad_clip, no_weight_decay_name, one_dim_param_no_weight_decay, name, lazy_mode)