!8718 Add labels to python files

From: @JunYuLiu
Reviewed-by: 
Signed-off-by:

mindspore/ops/operations/comm_ops.py

@@ -33,6 +33,9 @@ class ReduceOp:
         - MAX: Take the maximum.
         - MIN: Take the minimum.
         - PROD: Take the product.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU``
     """
     SUM = "sum"
     MAX = "max"
@@ -67,6 +70,9 @@ class AllReduce(PrimitiveWithInfer):
         Tensor, has the same shape of the input, i.e., :math:`(x_1, x_2, ..., x_R)`.
         The contents depend on the specified operation.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> from mindspore.communication import init
         >>> from mindspore import Tensor
@@ -243,6 +249,9 @@ class AllGather(PrimitiveWithInfer):
         Tensor. If the number of devices in the group is N,
         then the shape of output is :math:`(N, x_1, x_2, ..., x_R)`.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> import mindspore.ops.operations as P
         >>> import mindspore.nn as nn
@@ -356,6 +365,9 @@ class ReduceScatter(PrimitiveWithInfer):
         TypeError: If any of operation and group is not a string.
         ValueError: If the first dimension of the input cannot be divided by the rank size.
 
+    Supported Platforms:
+        ``GPU``
+
     Examples:
         >>> from mindspore import Tensor
         >>> from mindspore.communication import init
@@ -474,6 +486,9 @@ class Broadcast(PrimitiveWithInfer):
     Raises:
         TypeError: If root_rank is not a integer or group is not a string.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> from mindspore import Tensor
         >>> from mindspore.communication import init

mindspore/ops/operations/control_ops.py

@@ -49,6 +49,9 @@ class ControlDepend(Primitive):
     Outputs:
         This operation has no actual data output, it will be used to setup the order of relative operations.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> class Net(nn.Cell):
         ...     def __init__(self):

mindspore/ops/operations/debug_ops.py

@@ -48,6 +48,9 @@ class ScalarSummary(PrimitiveWithInfer):
         - **name** (str) - The name of the input variable, it must not be an empty string.
         - **value** (Tensor) - The value of scalar, and the shape of value must be [] or [1].
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> class SummaryDemo(nn.Cell):
         ...     def __init__(self,):
@@ -87,6 +90,9 @@ class ImageSummary(PrimitiveWithInfer):
         - **name** (str) - The name of the input variable, it must not be an empty string.
         - **value** (Tensor) - The value of image, the rank of tensor must be 4.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> class Net(nn.Cell):
         ...     def __init__(self):
@@ -125,6 +131,9 @@ class TensorSummary(PrimitiveWithInfer):
         - **name** (str) - The name of the input variable.
         - **value** (Tensor) - The value of tensor, and the rank of tensor must be greater than 0.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> class SummaryDemo(nn.Cell):
         ...     def __init__(self,):
@@ -164,6 +173,9 @@ class HistogramSummary(PrimitiveWithInfer):
         - **name** (str) - The name of the input variable.
         - **value** (Tensor) - The value of tensor, and the rank of tensor must be greater than 0.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> class SummaryDemo(nn.Cell):
         ...     def __init__(self,):
@@ -208,6 +220,9 @@ class InsertGradientOf(PrimitiveWithInfer):
     Outputs:
         Tensor, returns `input_x` directly. `InsertGradientOf` does not affect the forward result.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> def clip_gradient(dx):
         ...     ret = dx
@@ -319,6 +334,9 @@ class Print(PrimitiveWithInfer):
         - **input_x** (Union[Tensor, str]) - The graph node to attach to. The input supports
           multiple strings and tensors which are separated by ','.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> class PrintDemo(nn.Cell):
         ...     def __init__(self):

mindspore/ops/operations/image_ops.py

@@ -53,6 +53,9 @@ class CropAndResize(PrimitiveWithInfer):
     Outputs:
         A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth] with type: float32.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> class CropAndResizeNet(nn.Cell):
         ...     def __init__(self, crop_size):

mindspore/ops/operations/inner_ops.py

@@ -32,6 +32,9 @@ class ScalarCast(PrimitiveWithInfer):
     Outputs:
         Scalar. The type is the same as the python type corresponding to `input_y`.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> scalar_cast = P.ScalarCast()
         >>> output = scalar_cast(255.0, mindspore.int32)

mindspore/ops/operations/other_ops.py

@@ -37,6 +37,9 @@ class Assign(PrimitiveWithCheck):
     Outputs:
         Tensor, has the same type as original `variable`.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> class Net(nn.Cell):
         ...     def __init__(self):
@@ -120,6 +123,9 @@ class BoundingBoxEncode(PrimitiveWithInfer):
     Outputs:
         Tensor, encoded bounding boxes.
 
+    Supported Platforms:
+        ``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)
@@ -173,6 +179,9 @@ class BoundingBoxDecode(PrimitiveWithInfer):
     Outputs:
         Tensor, decoded boxes.
 
+    Supported Platforms:
+        ``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)
@@ -228,6 +237,9 @@ class CheckValid(PrimitiveWithInfer):
     Outputs:
         Tensor, with shape of (N,) and dtype of bool.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> import mindspore
         >>> import mindspore.nn as nn
@@ -297,6 +309,9 @@ class IOU(PrimitiveWithInfer):
     Raises:
         KeyError: When `mode` is not 'iou' or 'iof'.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> iou = P.IOU()
         >>> anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
@@ -344,6 +359,9 @@ class MakeRefKey(Primitive):
     Outputs:
         RefKeyType, made from the Parameter name.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> from mindspore.ops import functional as F
         >>> class Net(nn.Cell):
@@ -545,6 +563,9 @@ class PopulationCount(PrimitiveWithInfer):
     Outputs:
         Tensor, with the sam  shape as the input.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> population_count = P.PopulationCount()
         >>> x_input = Tensor([0, 1, 3], mindspore.int16)

mindspore/ops/operations/random_ops.py

@@ -34,6 +34,9 @@ class StandardNormal(PrimitiveWithInfer):
     Outputs:
         Tensor. The shape is the same as the input `shape`. The dtype is float32.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> shape = (4, 16)
         >>> stdnormal = P.StandardNormal(seed=2)
@@ -81,6 +84,9 @@ class StandardLaplace(PrimitiveWithInfer):
     Outputs:
         Tensor. The shape that the input 'shape' denotes. The dtype is float32.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> shape = (4, 16)
         >>> stdlaplace = P.StandardLaplace(seed=2)
@@ -133,6 +139,9 @@ class Gamma(PrimitiveWithInfer):
         Tensor. The shape must be the broadcasted shape of Input "shape" and shapes of alpha and beta.
         The dtype is float32.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> shape = (2, 2)
         >>> alpha = Tensor(1.0, mstype.float32)
@@ -189,6 +198,9 @@ class Poisson(PrimitiveWithInfer):
         Tensor. Its shape must be the broadcasted shape of `shape` and the shape of `mean`.
         The dtype is int32.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> shape = (4, 16)
         >>> mean = Tensor(5.0, mstype.float32)
@@ -244,6 +256,9 @@ class UniformInt(PrimitiveWithInfer):
     Outputs:
         Tensor. The shape is the same as the input 'shape', and the data type is int32.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> shape = (2, 4)
         >>> minval = Tensor(1, mstype.int32)
@@ -296,6 +311,9 @@ class UniformReal(PrimitiveWithInfer):
     Outputs:
         Tensor. The shape that the input 'shape' denotes. The dtype is float32.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> shape = (2, 2)
         >>> uniformreal = P.UniformReal(seed=2)
@@ -350,6 +368,9 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
         - **index** (Tensor) - The output shape is 2-D.
         - **mask** (Tensor) - The output shape is 1-D.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> rnd_choice_mask = P.RandomChoiceWithMask()
         >>> input_x = Tensor(np.ones(shape=[240000, 4]).astype(np.bool))
@@ -394,6 +415,9 @@ class RandomCategorical(PrimitiveWithInfer):
     Outputs:
         - **output** (Tensor) - The output Tensor with shape [batch_size, num_samples].
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> class Net(nn.Cell):
         ...   def __init__(self, num_sample):
@@ -469,6 +493,9 @@ class Multinomial(PrimitiveWithInfer):
     Outputs:
         Tensor with the same rows as input, each row has num_samples sampled indices.
 
+    Supported Platforms:
+        ``GPU``
+
     Examples:
         >>> input = Tensor([0., 9., 4., 0.], mstype.float32)
         >>> multinomial = P.Multinomial(seed=10)