diff --git a/mindspore/common/seed.py b/mindspore/common/seed.py
index bfed1d11ae..cf17956870 100644
--- a/mindspore/common/seed.py
+++ b/mindspore/common/seed.py
@@ -59,6 +59,13 @@ def set_seed(seed):
 
     Examples:
         >>> from mindspore.ops import composite as C
+        >>> from mindspore import Tensor
+        >>>
+        >>> # Note: (1) Please make sure the code is running in PYNATIVE MODE;
+        >>> # (2) Becasuse Composite-level ops need parameters to be Tensors, for below examples,
+        >>> # when using C.uniform operator, minval and maxval are initialised as:
+        >>> minval = Tensor(1.0, mstype.float32)
+        >>> maxval = Tensor(2.0, mstype.float32)
         >>>
         >>> # 1. If global seed is not set, numpy.random and initializer will choose a random seed:
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
@@ -86,53 +93,53 @@ def set_seed(seed):
         >>>
         >>> # 3. If neither global seed nor op seed is set, mindspore.ops.composite.random_ops and
         >>> # mindspore.nn.probability.distribution will choose a random seed:
-        >>> c1 = C.uniform((1, 4), 1.0, 2.0) # C1
-        >>> c2 = C.uniform((1, 4), 1.0, 2.0) # C2
+        >>> c1 = C.uniform((1, 4), minval, maxval) # C1
+        >>> c2 = C.uniform((1, 4), minval, maxval) # C2
         >>> Rerun the program will get different results:
-        >>> c1 = C.uniform((1, 4), 1.0, 2.0) # C3
-        >>> c2 = C.uniform((1, 4), 1.0, 2.0) # C4
+        >>> c1 = C.uniform((1, 4), minval, maxval) # C3
+        >>> c2 = C.uniform((1, 4), minval, maxval) # C4
         >>>
         >>> # 4. If global seed is set, but op seed is not set, mindspore.ops.composite.random_ops and
         >>> # mindspore.nn.probability.distribution will caculate a seed according to global seed and
         >>> # default op seed. Each call will change the default op seed, thus each call get different
         >>> # results.
         >>> set_seed(1234)
-        >>> c1 = C.uniform((1, 4), 1.0, 2.0) # C1
-        >>> c2 = C.uniform((1, 4), 1.0, 2.0) # C2
+        >>> c1 = C.uniform((1, 4), minval, maxval) # C1
+        >>> c2 = C.uniform((1, 4), minval, maxval) # C2
         >>> # Rerun the program will get the same results:
         >>> set_seed(1234)
-        >>> c1 = C.uniform((1, 4), 1.0, 2.0) # C1
-        >>> c2 = C.uniform((1, 4), 1.0, 2.0) # C2
+        >>> c1 = C.uniform((1, 4), minval, maxval) # C1
+        >>> c2 = C.uniform((1, 4), minval, maxval) # C2
         >>>
         >>> # 5. If both global seed and op seed are set, mindspore.ops.composite.random_ops and
         >>> # mindspore.nn.probability.distribution will caculate a seed according to global seed and
         >>> # op seed counter. Each call will change the op seed counter, thus each call get different
         >>> # results.
         >>> set_seed(1234)
-        >>> c1 = C.uniform((1, 4), 1.0, 2.0, seed=2) # C1
-        >>> c2 = C.uniform((1, 4), 1.0, 2.0, seed=2) # C2
+        >>> c1 = C.uniform((1, 4), minval, maxval, seed=2) # C1
+        >>> c2 = C.uniform((1, 4), minval, maxval, seed=2) # C2
         >>> Rerun the program will get the same results:
         >>> set_seed(1234)
-        >>> c1 = C.uniform((1, 4), 1.0, 2.0, seed=2) # C1
-        >>> c2 = C.uniform((1, 4), 1.0, 2.0, seed=2) # C2
+        >>> c1 = C.uniform((1, 4), minval, maxval, seed=2) # C1
+        >>> c2 = C.uniform((1, 4), minval, maxval, seed=2) # C2
         >>>
         >>> # 6. If op seed is set but global seed is not set, 0 will be used as global seed. Then
         >>> # mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution act as in
         >>> # condition 5.
-        >>> c1 = C.uniform((1, 4), 1.0, 2.0, seed=2) # C1
-        >>> c2 = C.uniform((1, 4), 1.0, 2.0, seed=2) # C2
+        >>> c1 = C.uniform((1, 4), minval, maxval, seed=2) # C1
+        >>> c2 = C.uniform((1, 4), minval, maxval, seed=2) # C2
         >>> # Rerun the program will get the same results:
-        >>> c1 = C.uniform((1, 4), 1.0, 2.0, seed=2) # C1
-        >>> c2 = C.uniform((1, 4), 1.0, 2.0, seed=2) # C2
+        >>> c1 = C.uniform((1, 4), minval, maxval, seed=2) # C1
+        >>> c2 = C.uniform((1, 4), minval, maxval, seed=2) # C2
         >>>
         >>> # 7. Recall set_seed() in the program will reset numpy seed and op seed counter of
         >>> # mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution.
         >>> set_seed(1234)
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
-        >>> c1 = C.uniform((1, 4), 1.0, 2.0, seed=2) # C1
+        >>> c1 = C.uniform((1, 4), minval, maxval, seed=2) # C1
         >>> set_seed(1234)
         >>> np_2 = np.random.normal(0, 1, [1]).astype(np.float32) # still get A1
-        >>> c2 = C.uniform((1, 4), 1.0, 2.0, seed=2) # still get C1
+        >>> c2 = C.uniform((1, 4), minval, maxval, seed=2) # still get C1
     """
     if not isinstance(seed, int):
         raise TypeError("The seed must be type of int.")
@@ -200,11 +207,13 @@ def _get_graph_seed(op_seed, kernel_name):
         So, the state of the seed regarding to this op should be recorded.
         A simple illustration should be:
           If a random op is called twice within one program, the two results should be different:
-          print(C.uniform((1, 4), seed=1))  # generates 'A1'
-          print(C.uniform((1, 4), seed=1))  # generates 'A2'
+          minval = Tensor(1.0, mstype.float32)
+          maxval = Tensor(2.0, mstype.float32)
+          print(C.uniform((1, 4), minval, maxval, seed=1))  # generates 'A1'
+          print(C.uniform((1, 4), minval, maxval, seed=1))  # generates 'A2'
           If the same program runs again, it repeat the results:
-          print(C.uniform((1, 4), seed=1))  # generates 'A1'
-          print(C.uniform((1, 4), seed=1))  # generates 'A2'
+          print(C.uniform((1, 4), minval, maxval, seed=1))  # generates 'A1'
+          print(C.uniform((1, 4), minval, maxval, seed=1))  # generates 'A2'
 
     Returns:
         Interger. The current graph-level seed.