diff --git a/mindspore/ccsrc/runtime/device/gpu/cuda_env_checker.cc b/mindspore/ccsrc/runtime/device/gpu/cuda_env_checker.cc
index 59e896cdc7d..df124e7b822 100644
--- a/mindspore/ccsrc/runtime/device/gpu/cuda_env_checker.cc
+++ b/mindspore/ccsrc/runtime/device/gpu/cuda_env_checker.cc
@@ -118,7 +118,6 @@ std::pair<std::string, bool> CudaEnvChecker::IsCudaRealPath(const std::string &p
   valid_path = (end == real_path.size() - 1) ? true : ((end == real_path.size() - 2) && (real_path.back() == '/'));
   return {real_path.substr(0, end + 1), valid_path};
 }
-
 }  // namespace gpu
 }  // namespace device
 }  // namespace mindspore
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index db4360f6d3e..c9592fcf584 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -625,7 +625,21 @@ class FusedBatchNorm(Primitive):
 
 class FusedBatchNormEx(PrimitiveWithInfer):
     r"""
-    FusedBatchNormEx is an extension of FusedBatchNorm
+    FusedBatchNormEx is an extension of FusedBatchNorm, FusedBatchNormEx has one more output(output reserve)
+    than FusedBatchNorm, reserve will be used in backpropagation phase. FusedBatchNorm is a BatchNorm that
+    moving mean and moving variance will be computed instead of being loaded.
+
+    Batch Normalization is widely used in convolutional networks. This operation applies
+    Batch Normalization over input to avoid internal covariate shift as described in the
+    paper `Batch Normalization: Accelerating Deep Network Training by Reducing Internal
+    Covariate Shift <https://arxiv.org/abs/1502.03167>`_. It rescales and recenters the
+    feature using a mini-batch of data and the learned parameters which can be described
+    in the following formula.
+
+    .. math::
+        y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta
+
+    where :math:`\gamma` is scale, :math:`\beta` is bias, :math:`\epsilon` is epsilon.
 
     Args:
         mode (int): Mode of batch normalization, value is 0 or 1. Default: 0.
@@ -635,21 +649,25 @@ class FusedBatchNormEx(PrimitiveWithInfer):
             Momentum value should be [0, 1]. Default: 0.9.
 
     Inputs:
-        - **input_x** (Tensor) - Tensor of shape :math:`(N, C)`.
-        - **scale** (Tensor) - Tensor of shape :math:`(C,)`.
-        - **bias** (Tensor) - Tensor of shape :math:`(C,)`.
-        - **mean** (Tensor) - Tensor of shape :math:`(C,)`.
-        - **variance** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **input_x** (Tensor) - The input of FusedBatchNormEx, Tensor of shape :math:`(N, C)`,
+                                 data type: float16 or float32.
+        - **scale** (Tensor) - Parameter scale, same with gamma above-mentioned, Tensor of shape :math:`(C,)`,
+                               data type: float32.
+        - **bias** (Tensor) - Parameter bias, same with beta above-mentioned, Tensor of shape :math:`(C,)`,
+                              data type: float32.
+        - **mean** (Tensor) - mean value, Tensor of shape :math:`(C,)`, data type: float32.
+        - **variance** (Tensor) - variance value, Tensor of shape :math:`(C,)`, data type: float32.
 
     Outputs:
-        Tuple of 6 Tensor, the normalized input and the updated parameters.
+        Tuple of 6 Tensor, the normalized input, the updated parameters and reserve.
 
-        - **output_x** (Tensor) - The same type and shape as the `input_x`.
-        - **updated_scale** (Tensor) - Tensor of shape :math:`(C,)`.
-        - **updated_bias** (Tensor) - Tensor of shape :math:`(C,)`.
-        - **updated_moving_mean** (Tensor) - Tensor of shape :math:`(C,)`.
-        - **updated_moving_variance** (Tensor) - Tensor of shape :math:`(C,)`.
-        - **reserve** (Tensor) - Tensor of shape :math:`(C,)`.
+        - **output_x** (Tensor) - The input of FusedBatchNormEx, same type and shape as the `input_x`.
+        - **updated_scale** (Tensor) - Updated parameter scale, Tensor of shape :math:`(C,)`, data type: float32.
+        - **updated_bias** (Tensor) - Updated parameter bias, Tensor of shape :math:`(C,)`, data type: float32.
+        - **updated_moving_mean** (Tensor) - Updated mean value, Tensor of shape :math:`(C,)`, data type: float32.
+        - **updated_moving_variance** (Tensor) - Updated variance value, Tensor of shape :math:`(C,)`,
+                                                 data type: float32.
+        - **reserve** (Tensor) - reserve space, Tensor of shape :math:`(C,)`, data type: float32.
 
     Examples:
         >>> input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32)