fix the wrong output of some operators

mindspore/nn/layer/math.py

@@ -957,7 +957,7 @@ class Moments(Cell):
         >>> output = net(x)
         >>> print(output)
         (Tensor(shape=[1, 1, 1, 4], dtype=Float32, value=
-        [[[[ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00, 4.00000000e+00]]]]),
+        [[[[ 2.00000000e+00, 3.00000000e+00, 4.00000000e+00, 5.00000000e+00]]]]),
         Tensor(shape=[1, 1, 1, 4], dtype=Float32, value=
         [[[[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]]]))
         >>> net = nn.Moments(axis=3, keep_dims=True)

mindspore/ops/operations/array_ops.py

@@ -3114,6 +3114,7 @@ class StridedSlice(PrimitiveWithInfer):
         >>> #     ]
         >>> # ]
         >>> # The final output after finishing is:
+        >>> print(output)
         [[[3], [5]]]
         >>> # another example like :
         >>> output = strided_slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1))

mindspore/ops/operations/debug_ops.py

@@ -371,6 +371,9 @@ class Print(PrimitiveWithInfer):
         - **input_x** (Union[Tensor, bool, int, float, str]) - The graph node to attach to.
           Supports multiple inputs which are separated by ','.
 
+    Outputs:
+        Tensor, has the same data type and shape as original `input_x`.
+
     Raises:
         TypeError: If `input_x` is not one of the following: Tensor, bool, int, float, str.
 
@@ -392,10 +395,12 @@ class Print(PrimitiveWithInfer):
         >>> net = PrintDemo()
         >>> result = net(x, y)
         Print Tensor x and Tensor y:
+        Tensor(shape=[2, 1], dtype=Int32, value=
         [[1]
-         [1]]
+         [1]])
+        Tensor(shape=[2, 2], dtype=Int32, value=
         [[1 1]
-         [1 1]]
+         [1 1]])
     """
 
     @prim_attr_register

mindspore/ops/operations/math_ops.py

@@ -198,10 +198,21 @@ class AssignAdd(PrimitiveWithInfer):
     and the data type is consistent with the Tensor data type involved in the operation.
     RuntimeError exception will be thrown when the data type conversion of Parameter is required.
 
+    Note:
+        Since `variable` is a data type Parameter, the data type cannot be changed,
+        so only the type of `value` is allowed to be promoted to the type of `variable`.
+        And the conversion type supported by different devices will be different,
+        it is recommended to use the same data type when using this operator.
+
     Inputs:
         - **variable** (Parameter) - The `Parameter`.
+          :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
         - **value** (Union[numbers.Number, Tensor]) - The value to be added to the `variable`.
           It must have the same shape as `variable` if it is a Tensor.
+          it is recommended to use the same data type when using this operator.
+
+    Outputs:
+        Tensor, has the same data type and shape as original `variable`.
 
     Raises:
         TypeError: If `value` is neither Number nor Tensor.
@@ -224,7 +235,7 @@ class AssignAdd(PrimitiveWithInfer):
         >>> value = Tensor(np.ones([1]).astype(np.int64)*100)
         >>> output = net(value)
         >>> print(output)
-        Parameter (name=global_step, shape=(1,), dtype=Int64, requires_grad=True)
+        [101]
     """
     __mindspore_signature__ = (
         sig.make_sig('x', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
@@ -257,10 +268,21 @@ class AssignSub(PrimitiveWithInfer):
     and the data type is consistent with the Tensor data type involved in the operation.
     RuntimeError exception will be thrown when the data type conversion of Parameter is required.
 
+    Note:
+        Since `variable` is a data type Parameter, the data type cannot be changed,
+        so only the type of `value` is allowed to be promoted to the type of `variable`.
+        And the conversion type supported by different devices will be different,
+        it is recommended to use the same data type when using this operator.
+
     Inputs:
         - **variable** (Parameter) - The `Parameter`.
+          :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
         - **value** (Union[numbers.Number, Tensor]) - The value to be subtracted from the `variable`.
           It must have the same shape as `variable` if it is a Tensor.
+          it is recommended to use the same data type when using this operator.
+
+    Outputs:
+        Tensor, has the same data type and shape as original `variable`.
 
     Raises:
         TypeError: If `value` is neither Number nor Tensor.
@@ -283,7 +305,7 @@ class AssignSub(PrimitiveWithInfer):
         >>> value = Tensor(np.ones([1]).astype(np.int32)*100)
         >>> output = net(value)
         >>> print(output)
-        Parameter (name=global_step, shape=(1,), dtype=Int32, requires_grad=True)
+        [-99]
     """
 
     __mindspore_signature__ = (

mindspore/ops/operations/other_ops.py

@@ -61,7 +61,7 @@ class Assign(Primitive):
         >>> net = Net()
         >>> output = net(x)
         >>> print(output)
-        Parameter (name=y, shape=(1,), dtype=Float32, requires_grad=True)
+        [2.]
     """
     __mindspore_signature__ = (
         sig.make_sig('variable', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),