class_docstr: AdamW optimizer. 
class_docstr_tok: ["AdamW","optimizer","."]
code: def __call__(self, model, lr):
      params = self._collect_params(model)
      skip_list = model.backbone.no_weight_decay()
  
      decay_dict = {
          param.name: not (
              len(param.shape) == 1
              or name.endswith(".bias")
              or name in skip_list
          )
          for name, param in model.named_parameters()
      }
      self.args["n_layers"] = model.backbone.get_num_layers()
      self.args["apply_decay_param_fun"] = lambda n: decay_dict[n]
      name_dict = dict()
      for n, p in model.named_parameters():
          name_dict[p.name] = n
      self.args["name_dict"] = name_dict
  
      opt = custom_opt.AdamWDL(
          learning_rate=lr, parameters=params, **self.args
      )
      return opt
  
imports: import paddle
  from paddleseg.cvlibs import manager
  from paddleseg.utils import logger
  import paddleseg.optimizers.custom_optimizers
label_desc: Use image node_id 2 for calling the AdamWDL_CAE obj's underlying member method code with example usage: obj.__call__(model, lr) and returns: opt