unified tensor and mindspore.type

mindspore/common/tensor.py

@@ -42,14 +42,14 @@ class Tensor(Tensor_):
 
     Examples:
         >>> # init a tensor with input data
-        >>> t1 = mindspore.Tensor(np.zeros([1, 2, 3]), mindspore.float32)
-        >>> assert isinstance(t1, mindspore.Tensor)
+        >>> t1 = Tensor(np.zeros([1, 2, 3]), mindspore.float32)
+        >>> assert isinstance(t1, Tensor)
         >>> assert t1.shape() == (1, 2, 3)
         >>> assert t1.dtype() == mindspore.float32
         >>>
         >>> # init a tensor with a float scalar
-        >>> t2 = mindspore.Tensor(0.1)
-        >>> assert isinstance(t2, mindspore.Tensor)
+        >>> t2 = Tensor(0.1)
+        >>> assert isinstance(t2, Tensor)
         >>> assert t2.dtype() == mindspore.float64
     """
 

mindspore/ops/operations/math_ops.py

@@ -1208,7 +1208,7 @@ class Acosh(PrimitiveWithInfer):
 
     Examples:
         >>> acosh = Acosh()
-        >>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), ms.float32)
+        >>> X = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32)
         >>> output = acosh(X)
     """
 
@@ -1752,7 +1752,7 @@ class Cos(PrimitiveWithInfer):
 
     Examples:
         >>> cos = P.Cos()
-        >>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), ms.float32)
+        >>> X = Tensor(np.array([0.24, 0.83, 0.31, 0.09]), mindspore.float32)
         >>> output = cos(X)
     """
 
@@ -1808,7 +1808,7 @@ class Sin(PrimitiveWithInfer):
 
     Examples:
         >>> sin = P.Sin()
-        >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), ms.float32)
+        >>> input_x = Tensor(np.array([0.62, 0.28, 0.43, 0.62]), mindspore.float32)
         >>> output = sin(input_x)
     """
 

mindspore/ops/operations/random_ops.py

@@ -45,7 +45,7 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
 
     Examples:
         >>> rnd_choice_mask = RandomChoiceWithMask()
-        >>> input_x = Tensor(np.ones(shape=[240000, 4]), ms.bool_)
+        >>> input_x = Tensor(np.ones(shape=[240000, 4]), mindspore.bool_)
         >>> output_y, output_mask = rnd_choice_mask(input_x)
     """