fix error format of api comments.

mindspore/dataset/engine/datasets.py

@@ -857,12 +857,10 @@ class Dataset:
                 original dataset.
                 If after rounding:
 
-                    - Any size equals 0, an error will occur.
-
-                    - The sum of split sizes < K, the difference will be added to the first split.
-
-                    - The sum of split sizes > K, the difference will be removed from the first large
-                      enough split such that it will have at least 1 row after removing the difference.
+                - Any size equals 0, an error will occur.
+                - The sum of split sizes < K, the difference will be added to the first split.
+                - The sum of split sizes > K, the difference will be removed from the first large
+                  enough split such that it will have at least 1 row after removing the difference.
 
             randomize (bool, optional): Determines whether or not to split the data randomly (default=True).
                 If True, the data will be randomly split. Otherwise, each split will be created with
@@ -4120,9 +4118,9 @@ class VOCDataset(MappableDataset):
 
     The generated dataset has multiple columns :
 
-        - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['label', dtype=uint32],
-          ['difficult', dtype=uint32], ['truncate', dtype=uint32]].
-        - task='Segmentation', column: [['image', dtype=uint8], ['target',dtype=uint8]].
+    - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['label', dtype=uint32],
+      ['difficult', dtype=uint32], ['truncate', dtype=uint32]].
+    - task='Segmentation', column: [['image', dtype=uint8], ['target',dtype=uint8]].
 
     This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. The table
     below shows what input arguments are allowed and their expected behavior.
@@ -4276,13 +4274,13 @@ class CocoDataset(MappableDataset):
 
     The generated dataset has multi-columns :
 
-        - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
-          ['iscrowd', dtype=uint32]].
-        - task='Stuff', column: [['image', dtype=uint8], ['segmentation',dtype=float32], ['iscrowd',dtype=uint32]].
-        - task='Keypoint', column: [['image', dtype=uint8], ['keypoints', dtype=float32],
-          ['num_keypoints', dtype=uint32]].
-        - task='Panoptic', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
-          ['iscrowd', dtype=uint32], ['area', dtype=uint32]].
+    - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
+      ['iscrowd', dtype=uint32]].
+    - task='Stuff', column: [['image', dtype=uint8], ['segmentation',dtype=float32], ['iscrowd',dtype=uint32]].
+    - task='Keypoint', column: [['image', dtype=uint8], ['keypoints', dtype=float32],
+      ['num_keypoints', dtype=uint32]].
+    - task='Panoptic', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
+      ['iscrowd', dtype=uint32], ['area', dtype=uint32]].
 
     This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. CocoDataset doesn't support
     PKSampler. The table below shows what input arguments are allowed and their expected behavior.

mindspore/nn/metrics/roc.py

@@ -163,9 +163,9 @@ class ROC(Metric):
             A tuple, composed of `fpr`, `tpr`, and `thresholds`.
 
             - **fpr** (np.array) - np.array with false positive rates. If multiclass, this is a list of such np.array,
-                one for each class.
+              one for each class.
             - **tps** (np.array) - np.array with true positive rates. If multiclass, this is a list of such np.array,
-                one for each class.
+              one for each class.
             - **thresholds** (np.array) - thresholds used for computing false- and true positive rates.
         """
         if self._is_update is False:

mindspore/nn/optim/sgd.py

@@ -44,13 +44,13 @@ class SGD(Optimizer):
 
     If nesterov is True:
 
-        .. math::
-                p_{t+1} = p_{t} - lr \ast (gradient + u \ast v_{t+1})
+    .. math::
+            p_{t+1} = p_{t} - lr \ast (gradient + u \ast v_{t+1})
 
     If nesterov is Flase:
 
-        .. math::
-                p_{t+1} = p_{t} - lr \ast v_{t+1}
+    .. math::
+            p_{t+1} = p_{t} - lr \ast v_{t+1}
 
     To be noticed, for the first step, v_{t+1} = gradient
 

mindspore/ops/operations/comm_ops.py

@@ -29,10 +29,10 @@ class ReduceOp:
 
     There are four kinds of operation options, "SUM", "MAX", "MIN", and "PROD".
 
-        - SUM: Take the sum.
-        - MAX: Take the maximum.
-        - MIN: Take the minimum.
-        - PROD: Take the product.
+    - SUM: Take the sum.
+    - MAX: Take the maximum.
+    - MIN: Take the minimum.
+    - PROD: Take the product.
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -285,7 +285,7 @@ class _HostAllGather(PrimitiveWithInfer):
 
 class ReduceScatter(PrimitiveWithInfer):
     """
-     Reduces and scatters tensors from the specified communication group.
+    Reduces and scatters tensors from the specified communication group.
 
     Note:
         The back propagation of the op is not supported yet. Stay tuned for more.