diff --git a/mindspore/nn/learning_rate_schedule.py b/mindspore/nn/learning_rate_schedule.py
index b8cde1673b..181db58e44 100644
--- a/mindspore/nn/learning_rate_schedule.py
+++ b/mindspore/nn/learning_rate_schedule.py
@@ -24,10 +24,22 @@ from .._checkparam import Rel
 
 
 class LearningRateSchedule(Cell):
+    """Basic class of learning rate schedule."""
     def __init__(self):
         super(LearningRateSchedule, self).__init__()
 
     def construct(self, global_step):
+        """
+        Defines the computation to get the current learning rate.
+
+        This method should be overridden by all subclasses.
+
+        Note:
+            The output should be a Tensor of scalar.
+
+        Inputs:
+            Tensor. The current step number.
+        """
         raise NotImplementedError
 
 
diff --git a/mindspore/nn/optim/proximal_ada_grad.py b/mindspore/nn/optim/proximal_ada_grad.py
index 2ef320fd9c..616f070d32 100644
--- a/mindspore/nn/optim/proximal_ada_grad.py
+++ b/mindspore/nn/optim/proximal_ada_grad.py
@@ -24,7 +24,7 @@ _proximal_ada_grad_opt = C.MultitypeFuncGraph("proximal_ada_grad_opt")
 
 @_proximal_ada_grad_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "IndexedSlices", "Tensor",
                                  "Tensor")
-def _tensor_run_opt_with_sparse(opt, sparse_opt, learning_rate, l1, l2, gradient, weight, accum):
+def _tensor_run_opt_with_sparse(opt, sparse_opt, l1, l2, learning_rate, gradient, weight, accum):
     """Apply sparse proximal_ada_grad optimizer to the weight parameter."""
     success = True
     success = F.depend(success, sparse_opt(weight, accum, learning_rate, l1, l2, gradient.values(), gradient.indices()))