parameters: (self,
                   loss,
                   startup_program=None,
                   parameter_list=None,
                   no_grad_set=None)
return_statement: return optimize_ops, scaled_params_grads
docstring: Perform optimization by minimizing the given loss.
  
          Args:
              loss (Variable): The loss Variable.
              startup_program (Program): startup_program for initializing parameters
                  in `parameter_list`.
              parameter_list (list): list of Variables to update.
              no_grad_set (set|None): set of Variables should be ignored.
  
          Returns:
              The scaled loss by scaling factor, the list of optimize ops, and a
              list of scaled parameters and gradients.
docstring_summary: Perform optimization by minimizing the given loss.
docstring_tokens: Perform optimization by minimizing the given loss .
function: def minimize(self,
                   loss,
                   startup_program=None,
                   parameter_list=None,
                   no_grad_set=None):
          """
          Perform optimization by minimizing the given loss.
  
          Args:
              loss (Variable): The loss Variable.
              startup_program (Program): startup_program for initializing parameters
                  in `parameter_list`.
              parameter_list (list): list of Variables to update.
              no_grad_set (set|None): set of Variables should be ignored.
  
          Returns:
              The scaled loss by scaling factor, the list of optimize ops, and a
              list of scaled parameters and gradients.
          """
  
          opt_dict = self._optimizer.__class__.__dict__
          if 'minimize' in opt_dict and isinstance(opt_dict['minimize'],
                                                   types.FunctionType):
              warnings.warn(
                  "The decorated optimizer has its own `minimize` method, but it will not be executed."
              )
  
          scaled_params_grads = self.backward(
              loss,
              startup_program=startup_program,
              parameter_list=parameter_list,
              no_grad_set=no_grad_set)
  
          optimize_ops = self.apply_optimize(loss, startup_program,
                                             scaled_params_grads)
  
          return optimize_ops, scaled_params_grads
function_tokens: def minimize ( self , loss , startup_program = None , parameter_list = None , no_grad_set = None ) : opt_dict = self . _optimizer . __class__ . __dict__ if 'minimize' in opt_dict and isinstance ( opt_dict [ 'minimize' ] , types . FunctionType ) : warnings . warn ( "The decorated optimizer has its own `minimize` method, but it will not be executed." ) scaled_params_grads = self . backward ( loss , startup_program = startup_program , parameter_list = parameter_list , no_grad_set = no_grad_set ) optimize_ops = self . apply_optimize ( loss , startup_program , scaled_params_grads ) return optimize_ops , scaled_params_grads