fix errors in notes in clesses' example

mindspore/ops/composite/random_ops.py

@@ -128,13 +128,13 @@ def uniform(shape, minval, maxval, seed=None, dtype=mstype.float32):
         The dtype is designated as the input `dtype`.
 
     Examples:
-        >>> For discrete uniform distribution, only one number is allowed for both minval and maxval:
+        >>> # For discrete uniform distribution, only one number is allowed for both minval and maxval:
         >>> shape = (4, 2)
         >>> minval = Tensor(1, mstype.int32)
         >>> maxval = Tensor(2, mstype.int32)
         >>> output = C.uniform(shape, minval, maxval, seed=5, dtype=mstype.int32)
         >>>
-        >>> For continuous uniform distribution, minval and maxval can be multi-dimentional:
+        >>> # For continuous uniform distribution, minval and maxval can be multi-dimentional:
         >>> shape = (4, 2)
         >>> minval = Tensor([1.0, 2.0], mstype.float32)
         >>> maxval = Tensor([4.0, 5.0], mstype.float32)

mindspore/ops/operations/array_ops.py

@@ -1151,8 +1151,8 @@ class Ones(PrimitiveWithInfer):
         >>> ones = ops.Ones()
         >>> output = ones((2, 2), mindspore.float32)
         >>> print(output)
-        [[1.0, 1.0],
-         [1.0, 1.0]]
+        [[1. 1.]
+         [1. 1.]]
     """
 
     @prim_attr_register
@@ -1204,8 +1204,8 @@ class Zeros(PrimitiveWithInfer):
         >>> zeros = ops.Zeros()
         >>> output = zeros((2, 2), mindspore.float32)
         >>> print(output)
-        [[0.0, 0.0],
-         [0.0, 0.0]]
+        [[0. 0.]
+         [0. 0.]]
 
     """
 
@@ -3347,7 +3347,8 @@ class ScatterSub(_ScatterOp):
         >>> scatter_sub = ops.ScatterSub()
         >>> output = scatter_sub(input_x, indices, updates)
         >>> print(output)
-        [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1.0]]
+        [[-1. -1. -1.]
+         [-1. -1. -1.]]
     """
 
 

mindspore/ops/operations/math_ops.py

@@ -1376,8 +1376,8 @@ class Rsqrt(PrimitiveWithInfer):
         >>> rsqrt = ops.Rsqrt()
         >>> output = rsqrt(input_tensor)
         >>> print(output)
-        [[0.5      0.5     ]
-         [0.333334 0.333334]]
+        [[0.5        0.5       ]
+         [0.33333334 0.33333334]]
     """
 
     @prim_attr_register
@@ -1677,7 +1677,7 @@ class Log(PrimitiveWithInfer):
         >>> log = ops.Log()
         >>> output = log(input_x)
         >>> print(output)
-        [0.        0.6931472 1.38629444]
+        [0.        0.6931472 1.3862944]
     """
 
     @prim_attr_register