code: def __call__(self, model):
      train_params = [
          param
          for param in model.parameters()
          if param.trainable is True
      ]
      opt = optim.Adadelta(
          learning_rate=self.learning_rate,
          epsilon=self.epsilon,
          rho=self.rho,
          weight_decay=self.weight_decay,
          grad_clip=self.grad_clip,
          name=self.name,
          parameters=train_params,
      )
      return opt
  
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 2 for calling the Adadelta obj's underlying member method code with example usage: obj.__call__(model) and returns: opt