modify BatchNorm docs

mindspore/ops/operations/nn_ops.py

@@ -1267,12 +1267,20 @@ class BatchNorm(PrimitiveWithInfer):
             Default: "NCHW".
 
     Inputs:
+        If `is_training` is False, inputs are Tensors.
         - **input_x** (Tensor) - Tensor of shape :math:`(N, C)`, with float16 or float32 data type.
         - **scale** (Tensor) - Tensor of shape :math:`(C,)`, with float16 or float32 data type.
         - **bias** (Tensor) - Tensor of shape :math:`(C,)`, has the same data type with `scale`.
         - **mean** (Tensor) - Tensor of shape :math:`(C,)`, with float16 or float32 data type.
         - **variance** (Tensor) - Tensor of shape :math:`(C,)`, has the same data type with `mean`.
 
+        If `is_training` is True, `scale`, `bias`, `mean` and `variance` are Parameters.
+        - **input_x** (Tensor) - Tensor of shape :math:`(N, C)`, with float16 or float32 data type.
+        - **scale** (Parameter) - Parameter of shape :math:`(C,)`, with float16 or float32 data type.
+        - **bias** (Parameter) - Parameter of shape :math:`(C,)`, has the same data type with `scale`.
+        - **mean** (Parameter) - Parameter of shape :math:`(C,)`, with float16 or float32 data type.
+        - **variance** (Parameter) - Parameter of shape :math:`(C,)`, has the same data type with `mean`.
+
     Outputs:
         Tuple of 5 Tensor, the normalized inputs and the updated parameters.