!41937 modify format

Merge pull request !41937 from 俞涵/code_docs_0819

docs/api/api_python/dataset/mindspore.dataset.Dataset.rst

@@ -28,8 +28,8 @@
         - **drop_remainder** (bool, 可选) - 当最后一个批处理数据包含的数据条目小于 `batch_size` 时，是否将该批处理丢弃，不传递给下一个操作。默认值：False，不丢弃。
         - **num_parallel_workers** (int, 可选) - 指定 `batch` 操作的并发进程数/线程数（由参数 `python_multiprocessing` 决定当前为多进程模式或多线程模式）。
           默认值：None，使用mindspore.dataset.config中配置的线程数。
-        - **per_batch_map** (Callable[[List[numpy.ndarray], ..., List[numpy.ndarray], BatchInfo], (List[numpy.ndarray],
-          ..., List[numpy.ndarray])], 可选) - 可调用对象，以(list[numpy.ndarray], ..., list[numpy.ndarray], BatchInfo)作为输入参数，
+        - **per_batch_map** (Callable[[List[numpy.ndarray], ..., List[numpy.ndarray], BatchInfo], (List[numpy.ndarray],..., List[numpy.ndarray])], 可选) - 可调用对象，
+          以(list[numpy.ndarray], ..., list[numpy.ndarray], BatchInfo)作为输入参数，
           处理后返回(list[numpy.ndarray], list[numpy.ndarray],...)作为新的数据列。输入参数中每个list[numpy.ndarray]代表给定数据列中的一批numpy.ndarray，
           list[numpy.ndarray]的个数应与 `input_columns` 中传入列名的数量相匹配，在返回的(list[numpy.ndarray], list[numpy.ndarray], ...)中，
           list[numpy.ndarray]的个数应与输入相同，如果输出列数与输入列数不一致，则需要指定 `output_columns`。该可调用对象的最后一个输入参数始终是BatchInfo，

docs/api/api_python/nn/mindspore.nn.Adam.rst

@@ -91,6 +91,3 @@ mindspore.nn.Adam
         - **ValueError** - `loss_scale` 或 `eps` 小于或等于0。
         - **ValueError** - `beta1` 、`beta2` 不在（0.0,1.0）范围内。
         - **ValueError** - `weight_decay` 小于0。
-
-
-    .. include:: mindspore.nn.optim_target_unique_for_sparse.rst

docs/api/api_python/nn/mindspore.nn.AdamWeightDecay.rst

@@ -77,5 +77,3 @@ mindspore.nn.AdamWeightDecay
         - **ValueError** - `eps` 小于等于0。
         - **ValueError** - `beta1` 、 `beta2` 不在（0.0,1.0）范围内。
         - **ValueError** - `weight_decay` 小于0。
-
-    .. include:: mindspore.nn.optim_target_unique_for_sparse.rst

docs/api/api_python/nn/mindspore.nn.FTRL.rst

@@ -69,6 +69,3 @@ mindspore.nn.FTRL
         - **ValueError** - `lr_power` 大于0。
         - **ValueError** - `loss_scale` 小于等于0。
         - **ValueError** - `initial_accum`、`l1` 或 `l2` 小于0。
-
-
-    .. include:: mindspore.nn.optim_target_unique_for_sparse.rst

docs/api/api_python/nn/mindspore.nn.LazyAdam.rst

@@ -66,5 +66,3 @@ mindspore.nn.LazyAdam
         - **ValueError** - `loss_scale` 或 `eps` 小于或等于0。
         - **ValueError** - `beta1`、`beta2` 不在（0.0,1.0）范围内。
         - **ValueError** - `weight_decay` 小于0。
-
-    .. include:: mindspore.nn.optim_target_unique_for_sparse.rst

docs/api/api_python/nn/mindspore.nn.ProximalAdagrad.rst

@@ -62,6 +62,3 @@ mindspore.nn.ProximalAdagrad
         - **TypeError** - `weight_decay` 不是float或int。
         - **ValueError** - `loss_scale` 小于或等于0。
         - **ValueError** - `accum`、`l1`、`l2` 或 `weight_decay` 小于0。
-
-
-    .. include:: mindspore.nn.optim_target_unique_for_sparse.rst

docs/api/api_python_en/Tensor_list.rst

@@ -10,6 +10,13 @@
     :exclude-members: infer_value, infer_shape, infer_dtype, construct
     :members:
 
+{% elif fullname in ["mindspore.nn.Adam","mindspore.nn.AdamWeightDecay","mindspore.nn.FTRL","mindspore.nn.LazyAdam","mindspore.nn.ProximalAdagrad"] %}
+{{ fullname | underline }}
+
+.. autoclass:: {{ name }}
+    :exclude-members: infer_value, infer_shape, infer_dtype, target
+    :members:
+
 {% elif fullname=="mindspore.Tensor" %}
 {{ fullname | underline }}
 

mindspore/python/mindspore/common/tensor.py

@@ -5525,7 +5525,7 @@ class Tensor(Tensor_):
         The first input tensor must be not less than `3` and the second input must be not less than `2`.
 
         Args:
-            mat2 (Tensor) - The tensor to be multiplied. The shape of the tensor is :math:`(*B, C, M)`.
+            mat2 (Tensor): The tensor to be multiplied. The shape of the tensor is :math:`(*B, C, M)`.
 
         Outputs:
             Tensor, the shape of the output tensor is :math:`(*B, N, M)`.

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

@@ -5190,10 +5190,10 @@ def bmm(input_x, mat2):
     The first input tensor must be not less than `3` and the second input must be not less than `2`.
 
     Args:
-        input_x (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(*B, N, C)`,
+        input_x (Tensor): The first tensor to be multiplied. The shape of the tensor is :math:`(*B, N, C)`,
             where :math:`*B` represents the batch size which can be multidimensional, :math:`N` and :math:`C` are the
             size of the last two dimensions.
-        mat2 (Tensor) - The second tensor to be multiplied. The shape of the tensor is :math:`(*B, C, M)`.
+        mat2 (Tensor): The second tensor to be multiplied. The shape of the tensor is :math:`(*B, C, M)`.
 
     Outputs:
         Tensor, the shape of the output tensor is :math:`(*B, N, M)`.