diff --git a/mindspore/nn/wrap/loss_scale.py b/mindspore/nn/wrap/loss_scale.py
index 9389c021f9..490ccb3f90 100644
--- a/mindspore/nn/wrap/loss_scale.py
+++ b/mindspore/nn/wrap/loss_scale.py
@@ -193,16 +193,21 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell):
         - **loss scaling value** (Tensor) -  Tensor with shape :math:`()`
 
     Examples:
+        >>> #1) when the type scale_sense is Cell:
         >>> net_with_loss = Net()
         >>> optimizer = nn.Momentum(net_with_loss.trainable_params(), learning_rate=0.1, momentum=0.9)
         >>> manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000)
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager)
         >>> train_network.set_train()
         >>>
+        >>> #2) when the type scale_sense is Tensor:
+        >>> net_with_loss = Net()
+        >>> optimizer = nn.Momentum(net_with_loss.trainable_params(), learning_rate=0.1, momentum=0.9)
         >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
         >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
         >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
-        >>> output = train_network(inputs, label, scaling_sens)
+        >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=scaling_sens)
+        >>> output = train_network(inputs, label)
     """
     def __init__(self, network, optimizer, scale_sense):
         super(TrainOneStepWithLossScaleCell, self).__init__(network, optimizer, sens=None)