fix bug of docs

docs/api/api_python/ops/mindspore.ops.Cauchy.rst

@@ -1,17 +1,17 @@
 mindspore.ops.Cauchy
 ====================
 
-.. py:class:: mindspore.ops.Cauchy
+.. py:class:: mindspore.ops.Cauchy(size, sigma=1.0, median=0.0)
 
     从柯西分布中提取的随机数创建shape由 `size` 决定的Tensor。
 
     .. math::
-        \f(x)= \frac{1}{\pi} \frac{\sigma}{(x-median)^2 +\sigma^2}
+        f(x)= \frac{1}{\pi} \frac{\sigma}{(x-median)^2 +\sigma^2}
 
-    输入：
+    参数：
         - **size** (list[int]) - 描述输出Tensor的shape。
-        - **sigma** (float) - 位置参数，指定分布峰值的位置。默认值：1.0。
-        - **median** (float) - 尺度参数，指定半宽半最大值处的scale参数。默认值：0.0。
+        - **sigma** (float，可选) - 位置参数，指定分布峰值的位置。默认值：1.0。
+        - **median** (float，可选) - 尺度参数，指定半宽半最大值处的scale参数。默认值：0.0。
 
     输出：
         Tensor，数据类型为float32，shape由 `size` 决定的Tensor。Tensor中的数值符合柯西分布。
@@ -19,6 +19,6 @@ mindspore.ops.Cauchy
     异常：
         - **TypeError** - `sigma` 不是float。
         - **TypeError** - `median` 不是float。
-        - **ValueError** - `size` 不是list。
+        - **TypeError** - `size` 不是list。
         - **ValueError** - `size` 是空的。
         - **ValueError** - `size` 中的数值不是正数。

mindspore/python/mindspore/ops/function/nn_func.py

@@ -175,7 +175,7 @@ def adaptive_avg_pool3d(input_x, output_size):
         ValueError: If `output_size` value is not positive.
 
     Supported Platforms:
-        ``GPU``
+        ``GPU`` ``CPU``
 
     Examples:
         >>> # case 1: output_size=(3, 3, 4)

mindspore/python/mindspore/ops/operations/math_ops.py

@@ -7622,13 +7622,13 @@ class Cauchy(Primitive):
     Create a tensor of shape `size` with random numbers drawn from Cauchy distribution
 
     .. math::
-        \f(x)= \frac{1}{\pi} \frac{\sigma}{(x-median)^2 +\sigma^2}
+        f(x)= \frac{1}{\pi} \frac{\sigma}{(x-median)^2 +\sigma^2}
 
     Args:
         size (list[int]): The size of tensor.
-        sigma (float): the location parameter, specifying the location
+        sigma (float, optional): the location parameter, specifying the location
             of the peak of the distribution. Default: 1.0.
-        median (float): the scale parameter which specifies the half-width
+        median (float, optional): the scale parameter which specifies the half-width
             at half-maximum. Default: 0.0.
 
     Outputs:

mindspore/python/mindspore/ops/operations/nn_ops.py

@@ -148,43 +148,6 @@ class AdaptiveAvgPool3D(Primitive):
     r"""
     AdaptiveAvgPool3D operation.
 
-    This operator applies a 3D adaptive average pooling to an input signal composed of multiple input planes.
-    That is, for any input size, the size of the specified output is D x H x W.
-    The number of output features is equal to the number of input planes.
-
-    Suppose the last 3 dimension size of x is inD, inH, inW, the last 3 dimension size of output is outD, outH, outW.
-
-    .. math::
-        \begin{array}{ll} \\
-            \forall \quad od \in [0,outD-1], oh \in [0,outH-1], ow \in [0,outW-1]\\
-            output[od,oh,ow] = \\
-            \qquad mean(x[istartD:iendD+1,istartH:iendH+1,istartW:iendW+1])\\
-            where,\\
-            \qquad istartD= \left\lceil \frac{od * inD}{outD} \right\rceil \\
-            \qquad iendD=\left\lfloor \frac{(od+1)* inD}{outD} \right\rfloor \\
-            \qquad istartH=\left\lceil \frac{oh * inH}{outH} \right\rceil \\
-            \qquad iendH=\left\lfloor \frac{(oh+1) * inH}{outH} \right\rfloor \\
-            \qquad istartW=\left\lceil \frac{ow * inW}{outW} \right\rceil \\
-            \qquad iendW=\left\lfloor \frac{(ow+1) * inW}{outW} \right\rfloor
-        \end{array}
-
-    Args:
-        output_size (Union[int, tuple[int]]): The last 3 dimension size of output tensor, which is int or
-            triple-int tuple. The value in output_size tuple can also be None. In this case, the particular output
-            dimesion size will be set to be the same as the according input dimension.
-
-    Inputs:
-        - **x** (Tensor) - The input of AdaptiveAvgPool3D, which is a 5D or 4D tensor.
-
-    Outputs:
-        Tensor, with the same type as the `x`.
-
-    Raises:
-        TypeError: If `x` is not a tensor.
-        ValueError: If the dimension of `x` is not 4D or 5D.
-        ValueError: If the attr `output_size` is not int or triple-int tuple.
-        ValueError: If `output_size` value is negative.
-
     Refer to :func:`mindspore.ops.adaptive_avg_pool3d` for more details.
 
     Supported Platforms: