parameters: (self)
return_statement: return state_dict
docstring: Get state dict information from optimizer. It contain all the tensor used by optimizer. For Adam optimizer, contains beta1, beta2, momentum etc. If LRScheduler have been used, global_step will be include in state dict.
          If the optimizer never be called(minimize function), the state_dict is empty.
  
          Args: 
              None
  
          Returns:
              state_dict(dict) : dict contains all the Tensor used by optimizer
          
          Examples:
              .. code-block:: python
  
                  import paddle
                  emb = paddle.nn.Embedding(10, 10)
  
                  adam = paddle.optimizer.Adam(0.001, parameters=emb.parameters())
                  state_dict = adam.state_dict()
docstring_summary: Get state dict information from optimizer. It contain all the tensor used by optimizer. For Adam optimizer, contains beta1, beta2, momentum etc. If LRScheduler have been used, global_step will be include in state dict.
          If the optimizer never be called(minimize function), the state_dict is empty.
docstring_tokens: Get state dict information from optimizer . It contain all the tensor used by optimizer . For Adam optimizer contains beta1 beta2 momentum etc . If LRScheduler have been used global_step will be include in state dict . If the optimizer never be called ( minimize function ) the state_dict is empty .
function: def state_dict(self):
          '''
          Get state dict information from optimizer. It contain all the tensor used by optimizer. For Adam optimizer, contains beta1, beta2, momentum etc. If LRScheduler have been used, global_step will be include in state dict.
          If the optimizer never be called(minimize function), the state_dict is empty.
  
          Args: 
              None
  
          Returns:
              state_dict(dict) : dict contains all the Tensor used by optimizer
          
          Examples:
              .. code-block:: python
  
                  import paddle
                  emb = paddle.nn.Embedding(10, 10)
  
                  adam = paddle.optimizer.Adam(0.001, parameters=emb.parameters())
                  state_dict = adam.state_dict()
  
          '''
          state_dict = {}
          for k, v in self._accumulators.items():
              for para_name, var_tmp in v.items():
                  state_dict[var_tmp.name] = var_tmp
          # if has master weight and then save master weight
          if hasattr(self, "_master_weights"):
              if len(self._master_weights) != 0:
                  state_dict["master_weights"] = self._master_weights
          # global step if use lr decay
          if isinstance(self._learning_rate, LRScheduler):
              state_dict["LR_Scheduler"] = self._learning_rate.state_dict()
          return state_dict
function_tokens: def state_dict ( self ) : state_dict = { } for k , v in self . _accumulators . items ( ) : for para_name , var_tmp in v . items ( ) : state_dict [ var_tmp . name ] = var_tmp # if has master weight and then save master weight if hasattr ( self , "_master_weights" ) : if len ( self . _master_weights ) != 0 : state_dict [ "master_weights" ] = self . _master_weights # global step if use lr decay if isinstance ( self . _learning_rate , LRScheduler ) : state_dict [ "LR_Scheduler" ] = self . _learning_rate . state_dict ( ) return state_dict