diff --git a/mindspore/common/api.py b/mindspore/common/api.py
index 9928450f55..e84bdab726 100644
--- a/mindspore/common/api.py
+++ b/mindspore/common/api.py
@@ -235,18 +235,18 @@ def ms_function(fn=None, obj=None, input_signature=None):
         >>> from mindspore.ops import  functional as F
         ...
         >>> def tensor_add(x, y):
-        ...     z = F.tensor_add(x, y)
+        ...     z = x + y
         ...     return z
         ...
         >>> @ms_function
         ... def tensor_add_with_dec(x, y):
-        ...     z = F.tensor_add(x, y)
+        ...     z = x + y
         ...     return z
         ...
         >>> @ms_function(input_signature=(MetaTensor(mindspore.float32, (1, 1, 3, 3)),
         ...                               MetaTensor(mindspore.float32, (1, 1, 3, 3))))
         ... def tensor_add_with_sig(x, y):
-        ...     z = F.tensor_add(x, y)
+        ...     z = x + y
         ...     return z
         ...
         >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32))
diff --git a/mindspore/common/seed.py b/mindspore/common/seed.py
index f2dd9c263a..a6536701c1 100644
--- a/mindspore/common/seed.py
+++ b/mindspore/common/seed.py
@@ -78,7 +78,7 @@ def set_seed(seed):
         >>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W3
         >>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W4
         >>>
-        >>> 2. If global seed is set, numpy.random and initializer will use it:
+        >>> # 2. If global seed is set, numpy.random and initializer will use it:
         >>> set_seed(1234)
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A2
@@ -95,7 +95,7 @@ def set_seed(seed):
         >>> # mindspore.nn.probability.distribution will choose a random seed:
         >>> c1 = C.uniform((1, 4), minval, maxval) # C1
         >>> c2 = C.uniform((1, 4), minval, maxval) # C2
-        >>> Rerun the program will get different results:
+        >>> # Rerun the program will get different results:
         >>> c1 = C.uniform((1, 4), minval, maxval) # C3
         >>> c2 = C.uniform((1, 4), minval, maxval) # C4
         >>>
@@ -118,7 +118,7 @@ def set_seed(seed):
         >>> set_seed(1234)
         >>> 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:
+        >>> # Rerun the program will get the same results:
         >>> set_seed(1234)
         >>> c1 = C.uniform((1, 4), minval, maxval, seed=2) # C1
         >>> c2 = C.uniform((1, 4), minval, maxval, seed=2) # C2
@@ -235,7 +235,8 @@ def _get_graph_seed(op_seed, kernel_name):
         Interger. The current graph-level seed.
 
     Examples:
-        >>> _get_graph_seed(seed, 'normal')
+        >>> print(_get_graph_seed(0, 'normal'))
+        (0, 0)
     """
     global_seed = get_seed()
     if global_seed == 0:
diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py
index 13e871350f..ad4167a272 100644
--- a/mindspore/common/tensor.py
+++ b/mindspore/common/tensor.py
@@ -301,7 +301,7 @@ class Tensor(Tensor_):
         Reshape the tensor according to the input shape.
 
         Args:
-            shape (Union(list[int], \*int)): Dimension of the output tensor.
+            shape (Union(tuple[int], \*int)): Dimension of the output tensor.
 
         Returns:
             Tensor, has the same dimension as the input shape.