parameters: (self)
return_statement: return super(GradScaler, self).get_incr_every_n_steps()
docstring: Return the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients.
  
          Reurns:
              int:  the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients.
          
          Examples:
              .. code-block:: python
  
                  # required: gpu,xpu
                  import paddle
                  scaler = paddle.amp.GradScaler(enable=True,
                                                 init_loss_scaling=1024,
                                                 incr_ratio=2.0,
                                                 decr_ratio=0.5,
                                                 incr_every_n_steps=1000,
                                                 decr_every_n_nan_or_inf=2,
                                                 use_dynamic_loss_scaling=True)
                  incr_every_n_steps = scaler.get_incr_every_n_steps()
                  print(incr_every_n_steps) # 1000
docstring_summary: Return the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients.
docstring_tokens: Return the num n n represent increases loss scaling every n consecutive steps with finite gradients .
function: def get_incr_every_n_steps(self):
          """
          Return the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients.
  
          Reurns:
              int:  the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients.
          
          Examples:
              .. code-block:: python
  
                  # required: gpu,xpu
                  import paddle
                  scaler = paddle.amp.GradScaler(enable=True,
                                                 init_loss_scaling=1024,
                                                 incr_ratio=2.0,
                                                 decr_ratio=0.5,
                                                 incr_every_n_steps=1000,
                                                 decr_every_n_nan_or_inf=2,
                                                 use_dynamic_loss_scaling=True)
                  incr_every_n_steps = scaler.get_incr_every_n_steps()
                  print(incr_every_n_steps) # 1000
          """
          return super(GradScaler, self).get_incr_every_n_steps()
function_tokens: def get_incr_every_n_steps ( self ) : return super ( GradScaler , self ) . get_incr_every_n_steps ( )