fix example error

mindspore/nn/layer/pooling.py

@@ -371,7 +371,7 @@ class AvgPool1d(_PoolNd):
         self.squeeze = P.Squeeze(2)
 
     def construct(self, x):
-        _shape_check(self.shape(x))
+        x = F.depend(x, _shape_check(self.shape(x)))
         batch, channel, width = self.shape(x)
         if width == self.kernel_size[1]:
             x = self.reduce_mean(x, 2)

mindspore/ops/operations/__init__.py

@@ -82,7 +82,8 @@ from .nn_ops import (LSTM, SGD, Adam, FusedSparseAdam, FusedSparseLazyAdam, Appl
                      ApplyRMSProp, ApplyCenteredRMSProp, BasicLSTMCell, InTopK, UniformCandidateSampler)
 from . import _quant_ops
 from ._quant_ops import *
-from .other_ops import (Assign, InplaceAssign, IOU, BoundingBoxDecode, BoundingBoxEncode, PopulationCount,
+from .other_ops import (Assign, InplaceAssign, IOU, BoundingBoxDecode, BoundingBoxEncode,
+                        ConfusionMatrix, PopulationCount,
                         CheckValid, MakeRefKey, Partial, Depend, identity, CheckBprop, Push, Pull)
 from ._thor_ops import (CusBatchMatMul, CusCholeskyTrsm, CusFusedAbsMax1, CusImg2Col, CusMatMulCubeDenseLeft,
                         CusMatMulCubeFraczRightMul, CusMatMulCube, CusMatrixCombine, CusTranspose02314,
@@ -289,6 +290,7 @@ __all__ = [
     'DepthwiseConv2dNative',
     'UnsortedSegmentSum',
     'UnsortedSegmentMin',
+    'UnsortedSegmentMax',
     'UnsortedSegmentProd',
     "AllGather",
     "AllReduce",
@@ -377,6 +379,7 @@ __all__ = [
     "UniformCandidateSampler",
     "LRN",
     "Mod",
+    "ConfusionMatrix",
     "PopulationCount",
     "ParallelConcat",
     "Push",

mindspore/ops/operations/array_ops.py

@@ -2104,18 +2104,19 @@ class Slice(PrimitiveWithInfer):
     Slices a tensor in the specified shape.
 
     Inputs:
-        x (Tensor): The target tensor.
-        begin (tuple): The beginning of the slice. Only constant value is allowed.
-        size (tuple): The size of the slice. Only constant value is allowed.
+        - **x** (Tensor): The target tensor.
+        - **begin** (tuple): The beginning of the slice. Only constant value is allowed.
+        - **size** (tuple): The size of the slice. Only constant value is allowed.
 
-    Returns:
-        Tensor.
+    Outputs:
+        Tensor, the shape is : input `size`, the data type is the same as input `x`.
 
     Examples:
         >>> data = Tensor(np.array([[[1, 1, 1], [2, 2, 2]],
         >>>                         [[3, 3, 3], [4, 4, 4]],
         >>>                         [[5, 5, 5], [6, 6, 6]]]).astype(np.int32))
         >>> type = P.Slice()(data, (1, 0, 0), (1, 1, 3))
+        >>> print(type)
         [[[3 3 3]]]
     """
 

mindspore/ops/operations/nn_ops.py

@@ -5412,7 +5412,7 @@ class Dropout(PrimitiveWithInfer):
 
     Args:
         keep_prob (float): The keep rate, between 0 and 1, e.g. keep_prob = 0.9,
-          means dropping out 10% of input units.
+            means dropping out 10% of input units.
 
     Inputs:
         - **input** (Tensor) - The input tensor.
@@ -5426,9 +5426,9 @@ class Dropout(PrimitiveWithInfer):
         >>> x = Tensor((20, 16, 50, 50), mindspore.float32)
         >>> output, mask = dropout(x)
         >>> print(output)
-        [ 0.	32.	0.	0.]
+        [0. 32. 0. 0.]
         >>> print(mask)
-        [0.	1.	0.	0.]
+        [0. 1. 0. 0.]
     """
 
     @prim_attr_register

mindspore/ops/operations/other_ops.py

@@ -492,7 +492,12 @@ class ConfusionMatrix(PrimitiveWithInfer):
         >>> confusion_matrix = P.ConfusionMatrix(4)
         >>> labels = Tensor([0, 1, 1, 3], mindspore.int32)
         >>> predictions = Tensor([1, 2, 1, 3], mindspore.int32)
-        >>> confusion_matrix(labels, predictions)
+        >>> output = confusion_matrix(labels, predictions)
+        >>> print(output)
+        [[0 1 0 0
+         [0 1 1 0]
+         [0 0 0 0]
+         [0 0 0 1]]
     """
 
     @prim_attr_register