!40586 modify the wrong format of the files

Merge pull request !40586 from 宦晓玲/code_docs_0818

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

@@ -24,3 +24,6 @@ mindspore.nn.DynamicLossScaleUpdateCell
     .. py:method:: get_loss_scale()
 
         获取当前损失缩放系数。
+
+        返回：
+            float，损失缩放系数。

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

@@ -20,3 +20,6 @@ mindspore.nn.FixedLossScaleUpdateCell
     .. py:method:: get_loss_scale()
 
         获取当前损失缩放系数。
+
+        返回：
+            float，损失缩放系数。

mindspore/python/mindspore/common/parameter.py

@@ -677,14 +677,14 @@ class Parameter(Tensor_):
             set_sliced (bool): True if the parameter is set sliced after initializing the data.
                 Default: False.
 
+        Returns:
+            Parameter, the `Parameter` after initializing data. If current `Parameter` was already initialized before,
+            returns the same initialized `Parameter`.
+
         Raises:
             RuntimeError: If it is from Initializer, and parallel mode has changed after the Initializer created.
             ValueError: If the length of the layout is less than 6.
             TypeError: If `layout` is not tuple.
-
-        Returns:
-            Parameter, the `Parameter` after initializing data. If current `Parameter` was already initialized before,
-            returns the same initialized `Parameter`.
         """
         if self.is_default_input_init and self.is_in_parallel != _is_in_parallel_mode():
             raise RuntimeError("Must set or change parallel mode before any Tensor created.")

mindspore/python/mindspore/dataset/engine/datasets.py

@@ -1101,6 +1101,9 @@ class Dataset:
                Shuffling the dataset may not be deterministic, which means the data in each split
                will be different in each epoch.
 
+        Returns:
+            tuple(Dataset), a tuple of datasets that have been split.
+
         Raises:
             RuntimeError: If get_dataset_size returns None or is not supported for this dataset.
             RuntimeError: If `sizes` is list of integers and sum of all elements in sizes does not
@@ -1110,9 +1113,6 @@ class Dataset:
             ValueError: If `sizes` is list of float and not all floats are between 0 and 1, or if the
                 floats don't sum to 1.
 
-        Returns:
-            tuple(Dataset), a tuple of datasets that have been split.
-
         Examples:
             >>> # TextFileDataset is not a mappable dataset, so this non-optimized split will be called.
             >>> # Since many datasets have shuffle on by default, set shuffle to False if split will be called!
@@ -2288,6 +2288,9 @@ class MappableDataset(SourceDataset):
                will be different in each epoch. Furthermore, if sharding occurs after split, each
                shard may not be part of the same split.
 
+        Returns:
+            tuple(Dataset), a tuple of datasets that have been split.
+
         Raises:
             RuntimeError: If get_dataset_size returns None or is not supported for this dataset.
             RuntimeError: If `sizes` is list of integers and sum of all elements in sizes does not
@@ -2297,9 +2300,6 @@ class MappableDataset(SourceDataset):
             ValueError: If `sizes` is list of float and not all floats are between 0 and 1, or if the
                 floats don't sum to 1.
 
-        Returns:
-            tuple(Dataset), a tuple of datasets that have been split.
-
         Examples:
             >>> # Since many datasets have shuffle on by default, set shuffle to False if split will be called!
             >>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir, shuffle=False)

mindspore/python/mindspore/nn/cell.py

@@ -914,8 +914,9 @@ class Cell(Cell_):
         Returns the dynamic_inputs of a cell object in one network.
 
         Returns:
-            inputs (tuple): Inputs of the Cell object.
+            inputs (tuple), Inputs of the Cell object.
-        NOTE:
+
+        Note:
             This is an experimental interface that is subject to change or deletion.
         """
 
@@ -2006,7 +2007,7 @@ class Cell(Cell_):
     def set_comm_fusion(self, fusion_type, recurse=True):
         """
         Set `comm_fusion` for all the parameters in this cell. Please refer to the description of
-         :class:`mindspore.Parameter.comm_fusion`.
+        :class:`mindspore.Parameter.comm_fusion`.
 
         Note:
             The value of attribute will be overwritten when the function is called multiply.

mindspore/python/mindspore/nn/dynamic_lr.py

@@ -358,15 +358,15 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e
         power (float): The power of polynomial. It must be greater than 0.
         update_decay_epoch (bool): If true, update `decay_epoch`. Default: False.
 
+    Returns:
+        list[float]. The size of list is `total_step`.
+
     Raises:
         TypeError: If `learning_rate` or `end_learning_rate` or `power` is not a float.
         TypeError: If `total_step` or `step_per_epoch` or `decay_epoch` is not an int.
         TypeError: If `update_decay_epoch` is not a bool.
         ValueError: If `learning_rate` or `power` is not greater than 0.
 
-    Returns:
-        list[float]. The size of list is `total_step`.
-
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 

mindspore/python/mindspore/nn/layer/activation.py

@@ -1319,13 +1319,13 @@ class HShrink(Cell):
     Outputs:
         Tensor, the same shape and data type as the input.
 
-    Supported Platforms:
-        ``Ascend`` ``CPU`` ``GPU``
-
     Raises:
         TypeError: If `lambd` is not a float.
         TypeError: If dtype of `input_x` is neither float16 nor float32.
 
+    Supported Platforms:
+        ``Ascend`` ``CPU`` ``GPU``
+
     Examples:
         >>> import mindspore
         >>> from mindspore import Tensor, nn
@@ -1368,13 +1368,13 @@ class Threshold(Cell):
     Outputs:
         Tensor, the same shape and data type as the input.
 
-    Supported Platforms:
-        ``Ascend`` ``CPU`` ``GPU``
-
     Raises:
         TypeError: If `threshold` is not a float or an int.
         TypeError: If `value` is not a float or an int.
 
+    Supported Platforms:
+        ``Ascend`` ``CPU`` ``GPU``
+
     Examples:
         >>> import mindspore
         >>> import mindspore.nn as nn

mindspore/python/mindspore/nn/layer/normalization.py

@@ -344,15 +344,15 @@ class BatchNorm1d(_BatchNorm):
     Outputs:
         Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out})`.
 
-    Supported Platforms:
-        ``Ascend`` ``GPU`` ``CPU``
-
     Raises:
         TypeError: If `num_features` is not an int.
         TypeError: If `eps` is not a float.
         ValueError: If `num_features` is less than 1.
         ValueError: If `momentum` is not in range [0, 1].
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> import mindspore.nn as nn
@@ -977,9 +977,6 @@ class InstanceNorm1d(_InstanceNorm):
         Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C, L)`. Same type and
         shape as the `x`.
 
-    Supported Platforms:
-        ``GPU``
-
     Raises:
         TypeError: If the type of `num_features` is not int.
         TypeError: If the type of `eps` is not float.
@@ -993,6 +990,9 @@ class InstanceNorm1d(_InstanceNorm):
         KeyError: If any of `gamma_init`/`beta_init` is str and the homonymous class inheriting from `Initializer` not
             exists.
 
+    Supported Platforms:
+        ``GPU``
+
     Examples:
         >>> import mindspore
         >>> import numpy as np
@@ -1067,9 +1067,6 @@ class InstanceNorm2d(_InstanceNorm):
         Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C, H, W)`. Same type and
         shape as the `x`.
 
-    Supported Platforms:
-        ``GPU``
-
     Raises:
         TypeError: If the type of `num_features` is not int.
         TypeError: If the type of `eps` is not float.
@@ -1083,6 +1080,9 @@ class InstanceNorm2d(_InstanceNorm):
         KeyError: If any of `gamma_init`/`beta_init` is str and the homonymous class inheriting from `Initializer` not
             exists.
 
+    Supported Platforms:
+        ``GPU``
+
     Examples:
         >>> import mindspore
         >>> import numpy as np
@@ -1157,9 +1157,6 @@ class InstanceNorm3d(_InstanceNorm):
         Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C, D, H, W)`. Same type and
         shape as the `x`.
 
-    Supported Platforms:
-        ``GPU``
-
     Raises:
         TypeError: If the type of `num_features` is not int.
         TypeError: If the type of `eps` is not float.
@@ -1173,6 +1170,9 @@ class InstanceNorm3d(_InstanceNorm):
         KeyError: If any of `gamma_init`/`beta_init` is str and the homonymous class inheriting from `Initializer` not
             exists.
 
+    Supported Platforms:
+        ``GPU``
+
     Examples:
         >>> import mindspore
         >>> import numpy as np

mindspore/python/mindspore/nn/layer/timedistributed.py

@@ -87,12 +87,12 @@ class TimeDistributed(Cell):
     Outputs:
         Tensor of shape :math:`(N, T, *)`
 
-    Supported Platforms:
-        ``Ascend`` ``GPU`` ``CPU``
-
     Raises:
         TypeError: If layer is not a Cell or Primitive.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> x = Tensor(np.random.random([32, 10, 3]), mindspore.float32)
         >>> dense = nn.Dense(3, 6)

mindspore/python/mindspore/nn/optim/adamax.py

@@ -73,7 +73,7 @@ class AdaMax(Optimizer):
     Note:
         If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without
         'beta' or 'gamma' in their names. Users can group parameters to change the strategy of decaying weight. When
-        parameters are grouped, each group can set `weight_decay`, if not, the `weight_decay` in optimizer will be
+        parameters are grouped, each group can set `weight_decay`. If not, the `weight_decay` in optimizer will be
         applied.
 
     Args:

mindspore/python/mindspore/nn/probability/bijector/bijector.py

@@ -243,7 +243,7 @@ class Bijector(Cell):
             *args (list): the list of positional arguments forwarded to subclasses.
             **kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
 
-        Output:
+        Returns:
             Tensor, the value of the  transformed random variable.
         """
         return self._forward(value, *args, **kwargs)
@@ -257,7 +257,7 @@ class Bijector(Cell):
             *args (list): the list of positional arguments forwarded to subclasses.
             **kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
 
-        Output:
+        Returns:
             Tensor, the value of the input random variable.
         """
         return self._inverse(value, *args, **kwargs)
@@ -271,7 +271,7 @@ class Bijector(Cell):
             *args (list): the list of positional arguments forwarded to subclasses.
             **kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
 
-        Output:
+        Returns:
             Tensor, the value of logarithm of the derivative of the forward transformation.
         """
         return self._forward_log_jacobian(value, *args, **kwargs)
@@ -285,7 +285,7 @@ class Bijector(Cell):
             *args (list): the list of positional arguments forwarded to subclasses.
             **kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
 
-        Output:
+        Returns:
             Tensor, the value of logarithm of the derivative of the inverse transformation.
         """
         return self._inverse_log_jacobian(value, *args, **kwargs)

mindspore/python/mindspore/nn/probability/bijector/gumbel_cdf.py

@@ -32,9 +32,6 @@ class GumbelCDF(Bijector):
         scale (float, list, numpy.ndarray, Tensor): The scale. Default: 1.0.
         name (str): The name of the Bijector. Default: 'GumbelCDF'.
 
-    Supported Platforms:
-        ``Ascend`` ``GPU``
-
     Note:
         `scale` must be greater than zero.
         For `inverse` and `inverse_log_jacobian`, input should be in range of (0, 1).
@@ -46,6 +43,9 @@ class GumbelCDF(Bijector):
         TypeError: When the dtype of `loc` or `scale` is not float,
                    or when the dtype of `loc` and `scale` is not same.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> import mindspore
         >>> import mindspore.nn as nn

mindspore/python/mindspore/nn/probability/bijector/power_transform.py

@@ -37,9 +37,6 @@ class PowerTransform(Bijector):
         power (float, list, numpy.ndarray, Tensor): The scale factor. Default: 0.
         name (str): The name of the bijector. Default: 'PowerTransform'.
 
-    Supported Platforms:
-        ``Ascend`` ``GPU``
-
     Note:
         The dtype of `power` must be float.
 
@@ -47,6 +44,9 @@ class PowerTransform(Bijector):
         ValueError: When `power` is less than 0 or is not known statically.
         TypeError: When the dtype of `power` is not float.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> import mindspore
         >>> import mindspore.nn as nn

mindspore/python/mindspore/nn/probability/bijector/scalar_affine.py

@@ -33,9 +33,6 @@ class ScalarAffine(Bijector):
         shift (float, list, numpy.ndarray, Tensor): The shift factor. Default: 0.0.
         name (str): The name of the bijector. Default: 'ScalarAffine'.
 
-    Supported Platforms:
-        ``Ascend`` ``GPU``
-
     Note:
         The dtype of `shift` and `scale` must be float.
         If `shift`, `scale` are passed in as numpy.ndarray or tensor, they have to have
@@ -45,6 +42,9 @@ class ScalarAffine(Bijector):
         TypeError: When the dtype of `shift` or `scale` is not float,
                    and when the dtype of `shift` and `scale` is not same.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> import mindspore
         >>> import mindspore.nn as nn

mindspore/python/mindspore/nn/probability/bijector/softplus.py

@@ -34,15 +34,15 @@ class Softplus(Bijector):
         sharpness (float, list, numpy.ndarray, Tensor): The scale factor. Default: 1.0.
         name (str): The name of the Bijector. Default: 'Softplus'.
 
-    Supported Platforms:
-        ``Ascend`` ``GPU``
-
     Note:
         The dtype of `sharpness` must be float.
 
     Raises:
         TypeError: When the dtype of the sharpness is not float.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> import mindspore
         >>> import mindspore.nn as nn

mindspore/python/mindspore/nn/probability/distribution/bernoulli.py

@@ -34,9 +34,6 @@ class Bernoulli(Distribution):
         dtype (mindspore.dtype): The type of the event samples. Default: mstype.int32.
         name (str): The name of the distribution. Default: 'Bernoulli'.
 
-    Supported Platforms:
-        ``Ascend`` ``GPU``
-
     Note:
         `probs` must be a proper probability (0 < p < 1).
         `dist_spec_args` is `probs`.
@@ -44,6 +41,9 @@ class Bernoulli(Distribution):
     Raises:
         ValueError: When p <= 0 or p >=1.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> import mindspore
         >>> import mindspore.nn as nn

mindspore/python/mindspore/nn/probability/distribution/beta.py

@@ -43,9 +43,6 @@ class Beta(Distribution):
         dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
         name (str): The name of the distribution. Default: 'Beta'.
 
-    Supported Platforms:
-        ``Ascend``
-
     Note:
         `concentration1` and `concentration0` must be greater than zero.
         `dist_spec_args` are `concentration1` and `concentration0`.
@@ -55,6 +52,9 @@ class Beta(Distribution):
         ValueError: When concentration1 <= 0 or concentration0 >=1.
         TypeError: When the input `dtype` is not a subclass of float.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> import mindspore
         >>> import mindspore.nn as nn

mindspore/python/mindspore/train/train_thor/convert_utils.py

@@ -207,7 +207,7 @@ class ConvertModelUtils:
                 will be overwritten. Default: False.
 
         Returns:
-             model (Object): High-Level API for Training.
+             model (Object), High-Level API for Training.
 
         Supported Platforms:
             ``Ascend`` ``GPU``