!10213 fix the comment format and word usage

From: @zhunaipan
Reviewed-by: @c_34,@stsuteng
Signed-off-by: @c_34

mindspore/nn/layer/basic.py

@@ -557,15 +557,15 @@ class Pad(Cell):
         Tensor, the tensor after padding.
 
         - If `mode` is "CONSTANT", it fills the edge with 0, regardless of the values of the `input_x`.
-          If the `input_x` is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the
-          Outputs is [[0,0,0,0,0,0,0],[0,0,1,2,3,0,0],[0,0,4,5,6,0,0],[0,0,7,8,9,0,0],[0,0,0,0,0,0,0]].
+          If the `input_x` is [[1,2,3], [4,5,6], [7,8,9]] and `paddings` is [[1,1], [2,2]], then the
+          Outputs is [[0,0,0,0,0,0,0], [0,0,1,2,3,0,0], [0,0,4,5,6,0,0], [0,0,7,8,9,0,0], [0,0,0,0,0,0,0]].
         - If `mode` is "REFLECT", it uses a way of symmetrical copying throught the axis of symmetry to fill in.
-          If the `input_x` is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the
-          Outputs is [[6,5,4,5,6,5,4],[3,2,1,2,3,2,1],[6,5,4,5,6,5,4],[9,8,7,8,9,8,7],[6,5,4,5,6,5,4]].
+          If the `input_x` is [[1,2,3], [4,5,6], [7,8,9]] and `paddings` is [[1,1], [2,2]], then the
+          Outputs is [[6,5,4,5,6,5,4], [3,2,1,2,3,2,1], [6,5,4,5,6,5,4], [9,8,7,8,9,8,7], [6,5,4,5,6,5,4]].
         - If `mode` is "SYMMETRIC", the filling method is similar to the "REFLECT". It is also copied
           according to the symmetry axis, except that it includes the symmetry axis. If the `input_x`
-          is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the Outputs is
-          [[2,1,1,2,3,3,2],[2,1,1,2,3,3,2],[5,4,4,5,6,6,5],[8,7,7,8,9,9,8],[8,7,7,8,9,9,8]].
+          is [[1,2,3], [4,5,6], [7,8,9]] and `paddings` is [[1,1], [2,2]], then the Outputs is
+          [[2,1,1,2,3,3,2], [2,1,1,2,3,3,2], [5,4,4,5,6,6,5], [8,7,7,8,9,9,8], [8,7,7,8,9,9,8]].
 
     Supported Platforms:
         ``Ascend`` ``GPU``

mindspore/ops/composite/clip_ops.py

@@ -124,8 +124,8 @@ def clip_by_global_norm(x, clip_norm=1.0, use_norm=None):
         ``Ascend`` ``GPU``
 
     Examples:
-        >>> x1 = np.array([[2., 3.],[1., 2.]]).astype(np.float32)
-        >>> x2 = np.array([[1., 4.],[3., 1.]]).astype(np.float32)
+        >>> x1 = np.array([[2., 3.], [1., 2.]]).astype(np.float32)
+        >>> x2 = np.array([[1., 4.], [3., 1.]]).astype(np.float32)
         >>> input_x = (Tensor(x1), Tensor(x2))
         >>> out = clip_by_global_norm(input_x, 1.0)
         >>> print(out)

mindspore/ops/operations/math_ops.py

@@ -701,7 +701,7 @@ class MatMul(PrimitiveWithInfer):
 
     Inputs:
         - **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, C)`. If
-          `transpose_a` is True, its shape must be :math:`(N, C)` after transposing.
+          `transpose_a` is True, its shape must be :math:`(N, C)` after transpose.
         - **input_y** (Tensor) - The second tensor to be multiplied. The shape of the tensor is :math:`(C, M)`. If
           `transpose_b` is True, its shape must be :math:`(C, M)` after transpose.
 
@@ -856,7 +856,7 @@ class CumSum(PrimitiveWithInfer):
         ``Ascend`` ``GPU``
 
     Examples:
-        >>> input = Tensor(np.array([[3, 4, 6, 10],[1, 6, 7, 9],[4, 3, 8, 7],[1, 3, 7, 9]]).astype(np.float32))
+        >>> input = Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float32))
         >>> cumsum = ops.CumSum()
         >>> output = cumsum(input, 1)
         >>> print(output)

mindspore/ops/operations/nn_ops.py

@@ -3347,12 +3347,12 @@ class MirrorPad(PrimitiveWithInfer):
         Tensor, the tensor after padding.
 
         - If `mode` is "REFLECT", it uses a way of symmetrical copying through the axis of symmetry to fill in.
-          If the `input_x` is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the
-          Outputs is [[6,5,4,5,6,5,4],[3,2,1,2,3,2,1],[6,5,4,5,6,5,4],[9,8,7,8,9,8,7],[6,5,4,5,6,5,4]].
+          If the `input_x` is [[1,2,3], [4,5,6], [7,8,9]] and `paddings` is [[1,1], [2,2]], then the
+          Outputs is [[6,5,4,5,6,5,4], [3,2,1,2,3,2,1], [6,5,4,5,6,5,4], [9,8,7,8,9,8,7], [6,5,4,5,6,5,4]].
         - If `mode` is "SYMMETRIC", the filling method is similar to the "REFLECT". It is also copied
           according to the symmetry axis, except that it includes the symmetry axis. If the `input_x`
-          is [[1,2,3],[4,5,6],[7,8,9]] and `paddings` is [[1,1],[2,2]], then the Outputs is
-          [[2,1,1,2,3,3,2],[2,1,1,2,3,3,2],[5,4,4,5,6,6,5],[8,7,7,8,9,9,8],[8,7,7,8,9,9,8]].
+          is [[1,2,3], [4,5,6], [7,8,9]] and `paddings` is [[1,1], [2,2]], then the Outputs is
+          [[2,1,1,2,3,3,2], [2,1,1,2,3,3,2], [5,4,4,5,6,6,5], [8,7,7,8,9,9,8], [8,7,7,8,9,9,8]].
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -3370,7 +3370,7 @@ class MirrorPad(PrimitiveWithInfer):
         ...         return self.pad(x, paddings)
         ...
         >>> x = np.random.random(size=(2, 3)).astype(np.float32)
-        >>> paddings = Tensor([[1,1],[2,2]])
+        >>> paddings = Tensor([[1, 1], [2, 2]])
         >>> pad = Net()
         >>> output = pad(Tensor(x), paddings)
         >>> print(output.shape)

mindspore/ops/operations/other_ops.py

@@ -128,8 +128,8 @@ class BoundingBoxEncode(PrimitiveWithInfer):
         ``Ascend`` ``GPU``
 
     Examples:
-        >>> anchor_box = Tensor([[4,1,2,1],[2,2,2,3]],mindspore.float32)
-        >>> groundtruth_box = Tensor([[3,1,2,2],[1,2,1,4]],mindspore.float32)
+        >>> anchor_box = Tensor([[4, 1, 2, 1], [2, 2, 2, 3]], mindspore.float32)
+        >>> groundtruth_box = Tensor([[3, 1, 2, 2], [1, 2, 1, 4]], mindspore.float32)
         >>> boundingbox_encode = ops.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
         >>> output = boundingbox_encode(anchor_box, groundtruth_box)
         >>> print(output)
@@ -184,8 +184,8 @@ class BoundingBoxDecode(PrimitiveWithInfer):
         ``Ascend`` ``GPU``
 
     Examples:
-        >>> anchor_box = Tensor([[4,1,2,1],[2,2,2,3]],mindspore.float32)
-        >>> deltas = Tensor([[3,1,2,2],[1,2,1,4]],mindspore.float32)
+        >>> anchor_box = Tensor([[4, 1, 2, 1], [2, 2, 2, 3]], mindspore.float32)
+        >>> deltas = Tensor([[3, 1, 2, 2], [1, 2, 1, 4]], mindspore.float32)
         >>> boundingbox_decode = ops.BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
         ...                                          max_shape=(768, 1280), wh_ratio_clip=0.016)
         >>> output = boundingbox_decode(anchor_box, deltas)

mindspore/ops/operations/random_ops.py

@@ -560,7 +560,7 @@ class UniformCandidateSampler(PrimitiveWithInfer):
 
     Examples:
         >>> sampler = ops.UniformCandidateSampler(1, 3, False, 4)
-        >>> output1, output2, output3 = sampler(Tensor(np.array([[1],[3],[4],[6],[3]], dtype=np.int32)))
+        >>> output1, output2, output3 = sampler(Tensor(np.array([[1], [3], [4], [6], [3]], dtype=np.int32)))
         >>> print(output1, output2, output3)
         [1, 1, 3], [[0.75], [0.75], [0.75], [0.75], [0.75]], [0.75, 0.75, 0.75]
     """