!40453 correct the errors on webpage

Merge pull request !40453 from 宦晓玲/code_docs_0816

docs/api/api_python/mindspore/mindspore.COOTensor.rst

@@ -16,7 +16,7 @@ mindspore.COOTensor
          [0, 0, 0, 0]]
 
     .. note::
-        这是一个实验特性，在未来可能会发生API的变化。目前COOTensor中相同索引的值不会进行合并。
+        这是一个实验特性，在未来可能会发生API的变化。目前COOTensor中相同索引的值不会进行合并。如果索引中包含界外值，则得出未定义结果。
 
     参数：
         - **indices** (Tensor) - 形状为 `[N, ndims]` 的二维整数张量，其中N和ndims分别表示稀疏张量中 `values` 的数量和COOTensor维度的数量。目前 `ndims` 只能为2。请确保indices的值在所给shape范围内。

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

@@ -10,9 +10,6 @@
     .. math::
         (x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)
 
-    .. note::
-        `axis` 的取值范围为 :math:`[-dims, dims - 1]` 。 `dims` 为 `input_x` 的维度长度。
-
     参数：
         - **input_x** (tuple, list) - 输入为Tensor组成的tuple或list。假设在这个tuple或list中有两个Tensor，即 `x1` 和 `x2` 。要在0轴方向上执行 `Concat` ，除0轴外，其他轴的shape都应相等，即 :math:`x1.shape[1] = x2.shape[1]，x1.shape[2] = x2.shape[2]，...，x1.shape[R] = x2.shape[R]` ，其中 :math:`R` 表示最后一个轴。
         - **axis** (int) - 表示指定的轴，取值范围是 :math:`[-R, R)` 。默认值：0。

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

@@ -16,7 +16,7 @@ mindspore.ops.log_softmax
         - **logits** (Tensor) - shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度，其数据类型为float16或float32。
         - **axis** (int) - 指定进行运算的轴。默认值：-1。
 
-    输出：
+    返回：
         Tensor，数据类型和shape与 `logits` 相同。
 
     异常：

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

@@ -1,7 +1,7 @@
 mindspore.ops.softmax
 =====================
 
-.. py::: function.ops.softmax(x, axis=-1)
+.. py:function:: mindspore.ops.softmax(x, axis=-1)
 
     Softmax函数。
 

mindspore/python/mindspore/dataset/core/config.py

@@ -686,7 +686,7 @@ def get_auto_offload():
     Returns:
         bool, Whether the automatic offload feature is enabled.
 
-    Example:
+    Examples:
         >>> # Get the global configuration of the automatic offload feature.
         >>> auto_offload = ds.config.get_auto_offload()
     """

mindspore/python/mindspore/ops/function/array_func.py

@@ -1061,7 +1061,8 @@ def slice(input_x, begin, size):
     The slice `begin` represents the offset in each dimension of `input_x`,
     The slice `size` represents the size of the output tensor.
 
-    Note that `begin` is zero-based and `size` is one-based.
+    Note:
+        `begin` is zero-based and `size` is one-based.
 
     If `size[i]` is -1, all remaining elements in dimension i are included in the slice.
     This is equivalent to setting :math:`size[i] = input_x.shape(i) - begin[i]`.

mindspore/python/mindspore/ops/function/math_func.py

@@ -2662,11 +2662,6 @@ def equal(x, y):
     r"""
     Computes the equivalence between two tensors element-wise.
 
-    Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
-    The inputs must be two tensors or one tensor and one scalar.
-    When the inputs are two tensors, the shapes of them could be broadcast.
-    When the inputs are one tensor and one scalar, the scalar could only be a constant.
-
     .. math::
 
         out_{i} =\begin{cases}
@@ -2674,6 +2669,12 @@ def equal(x, y):
             & \text{False,   if } x_{i} \ne y_{i}
             \end{cases}
 
+    Note:
+        - Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
+        - The inputs must be two tensors or one tensor and one scalar.
+        - When the inputs are two tensors, the shapes of them could be broadcast.
+        - When the inputs are one tensor and one scalar, the scalar could only be a constant.
+
     Args:
         x (Union[Tensor, Number]): The first input is a number or
             a tensor whose data type is number.
@@ -3036,12 +3037,13 @@ def minimum(x, y):
     r"""
     Computes the minimum of input tensors element-wise.
 
-    Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
-    The inputs must be two tensors or one tensor and one scalar.
-    When the inputs are two tensors, dtypes of them cannot be bool at the same time.
-    When the inputs are one tensor and one scalar, the scalar could only be a constant.
-    Shapes of them are supposed to be broadcast.
-    If one of the elements being compared is a NaN, then that element is returned.
+    Note:
+        - Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
+        - The inputs must be two tensors or one tensor and one scalar.
+        - When the inputs are two tensors, dtypes of them cannot be bool at the same time.
+        - When the inputs are one tensor and one scalar, the scalar could only be a constant.
+        - Shapes of them are supposed to be broadcast.
+        - If one of the elements being compared is a NaN, then that element is returned.
 
     .. math::
         output_i = min(x_i, y_i)

mindspore/python/mindspore/ops/function/random_func.py

@@ -347,7 +347,7 @@ def random_poisson(shape, rate, seed=None, dtype=mstype.float32):
           mindspore.dtype.float64, mindspore.dtype.float32 or mindspore.dtype.float16. Default: mindspore.dtype.float32.
 
     Returns:
-        A Tensor whose shape is `mindspore.concat([`shape`, mindspore.shape(`rate`)], axis=0)` and data type is equal to
+        A Tensor whose shape is `mindspore.concat(['shape', mindspore.shape('rate')], axis=0)` and data type is equal to
         argument `dtype`.
 
     Raises:

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

@@ -634,6 +634,7 @@ class ReduceMean(_Reduce):
         TypeError: If `keep_dims` is not a bool.
         TypeError: If `x` is not a Tensor.
         TypeError: If `axis` is not one of the following: int, tuple or list.
+        ValueError: If `axis` is out of range.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -918,6 +919,7 @@ class ReduceMax(_Reduce):
         TypeError: If `keep_dims` is not a bool.
         TypeError: If `x` is not a Tensor.
         TypeError: If `axis` is not one of the following: int, tuple or list.
+        ValueError: If `axis` is out of range.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -1004,6 +1006,7 @@ class ReduceMin(_Reduce):
         TypeError: If `keep_dims` is not a bool.
         TypeError: If `x` is not a Tensor.
         TypeError: If `axis` is not one of the following: int, tuple or list.
+        ValueError: If `axis` is out of range.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -1126,6 +1129,7 @@ class ReduceProd(_Reduce):
         TypeError: If `keep_dims` is not a bool.
         TypeError: If `x` is not a Tensor.
         TypeError: If `axis` is not one of the following: int, tuple or list.
+        ValueError: If `axis` is out of range.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``