Updating remaining notes

mindspore/ops/operations/_inner_ops.py

@@ -132,7 +132,8 @@ class Range(PrimitiveWithInfer):
     Examples:
         >>> range = P.Range(1.0, 8.0, 2.0)
         >>> x = Tensor(np.array([1, 2, 3, 2]), mindspore.int32)
-        >>> range(x)
+        >>> output = range(x)
+        >>> print(output)
         [3, 5, 7, 5]
     """
 
@@ -524,7 +525,8 @@ class DynamicGRUV2(PrimitiveWithInfer):
         >>> init_h = Tensor(np.random.rand(8, 16).astype(np.float16))
         >>> dynamic_gru_v2 = P.DynamicGRUV2()
         >>> output = dynamic_gru_v2(x, weight_i, weight_h, bias_i, bias_h, None, init_h)
-        >>> output[0].shape
+        >>> result = output[0].shape
+        >>> print(result)
         (2, 8, 16)
     """
 

mindspore/ops/operations/array_ops.py

@@ -1072,7 +1072,8 @@ class Ones(PrimitiveWithInfer):
 
     Examples:
         >>> ones = P.Ones()
-        >>> Ones((2, 2), mindspore.float32)
+        >>> output = Ones((2, 2), mindspore.float32)
+        >>> print(output)
         [[1.0, 1.0],
          [1.0, 1.0]]
     """
@@ -1115,7 +1116,8 @@ class Zeros(PrimitiveWithInfer):
 
     Examples:
         >>> zeros = P.Zeros()
-        >>> Zeros((2, 2), mindspore.float32)
+        >>> output = Zeros((2, 2), mindspore.float32)
+        >>> print(output)
         [[0.0, 0.0],
          [0.0, 0.0]]
 
@@ -2615,7 +2617,8 @@ class Diag(PrimitiveWithInfer):
     Examples:
         >>> input_x = Tensor([1, 2, 3, 4])
         >>> diag = P.Diag()
-        >>> diag(input_x)
+        >>> output = diag(input_x)
+        >>> print(output)
         [[1, 0, 0, 0],
          [0, 2, 0, 0],
          [0, 0, 3, 0],

mindspore/ops/operations/math_ops.py

@@ -348,7 +348,8 @@ class ReduceMean(_Reduce):
         >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = P.ReduceMean(keep_dims=True)
         >>> output = op(input_x, 1)
-        >>> output.shape
+        >>> result = output.shape
+        >>> print(result)
         (3, 1, 5, 6)
     """
 
@@ -501,7 +502,8 @@ class ReduceMax(_Reduce):
         >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = P.ReduceMax(keep_dims=True)
         >>> output = op(input_x, 1)
-        >>> output.shape
+        >>> result = output.shape
+        >>> print(result)
         (3, 1, 5, 6)
     """
 
@@ -545,7 +547,8 @@ class ReduceMin(_Reduce):
         >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = P.ReduceMin(keep_dims=True)
         >>> output = op(input_x, 1)
-        >>> output.shape
+        >>> result = output.shape
+        >>> print(result)
         (3, 1, 5, 6)
     """
 
@@ -580,7 +583,8 @@ class ReduceProd(_Reduce):
         >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = P.ReduceProd(keep_dims=True)
         >>> output = op(input_x, 1)
-        >>> output.shape
+        >>> reuslt = output.shape
+        >>> print(result)
         (3, 1, 5, 6)
     """
 

mindspore/ops/operations/nn_ops.py

@@ -661,7 +661,8 @@ class FusedBatchNorm(Primitive):
         >>> input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32)
         >>> net = FusedBatchNormNet()
         >>> output = net(input_x)
-        >>> output[0].shape
+        >>> result = output[0].shape
+        >>> print(result)
         (128, 64, 32, 64)
     """
     __mindspore_signature__ = (
@@ -754,7 +755,8 @@ class FusedBatchNormEx(PrimitiveWithInfer):
         >>> input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32)
         >>> net = FusedBatchNormExNet()
         >>> output = net(input_x)
-        >>> output[0].shape
+        >>> result = output[0].shape
+        >>> print(result)
         (128, 64, 32, 64)
     """
     __mindspore_signature__ = (
@@ -1803,6 +1805,7 @@ class TopK(PrimitiveWithInfer):
         >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16)
         >>> k = 3
         >>> values, indices = topk(input_x, k)
+        >>> print((values, indices))
         ([5.0, 4.0, 3.0], [4, 3, 2])
     """
 

mindspore/ops/operations/random_ops.py

@@ -38,7 +38,8 @@ class StandardNormal(PrimitiveWithInfer):
         >>> shape = (4, 16)
         >>> stdnormal = P.StandardNormal(seed=2)
         >>> output = stdnormal(shape)
-        >>> output.shape
+        >>> result = output.shape
+        >>> print(result)
         (4, 16)
     """
 
@@ -85,7 +86,8 @@ class StandardLaplace(PrimitiveWithInfer):
         >>> shape = (4, 16)
         >>> stdlaplace = P.StandardLaplace(seed=2)
         >>> output = stdlaplace(shape)
-        >>> output.shape
+        >>> result = output.shape
+        >>> print(result)
         (4, 16)
     """
 
@@ -354,9 +356,11 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
         >>> rnd_choice_mask = P.RandomChoiceWithMask()
         >>> input_x = Tensor(np.ones(shape=[240000, 4]).astype(np.bool))
         >>> output_y, output_mask = rnd_choice_mask(input_x)
-        >>> output_y.shape
+        >>> result = output_y.shape
+        >>> print(result)
         (256, 2)
-        >>> output_mask.shape
+        >>> result = output_mask.shape
+        >>> print(reuslt)
         (256,)
     """