diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index d9a90fafff..80a2fb5204 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -560,9 +560,9 @@ class Dataset:
 
         Note:
             1. If count is greater than the number of element in dataset or equal to -1,
-            all the element in dataset will be taken.
+                all the element in dataset will be taken.
             2. The order of using take and batch effects. If take before batch operation,
-            then taken given number of rows, otherwise take given number of batches.
+                then taken given number of rows, otherwise take given number of batches.
 
         Args:
             count (int, optional): Number of elements to be taken from the dataset (default=-1).
@@ -590,7 +590,7 @@ class Dataset:
         # here again
         dataset_size = self.get_dataset_size()
 
-        if(dataset_size is None or dataset_size <= 0):
+        if dataset_size is None or dataset_size <= 0:
             raise RuntimeError("dataset size unknown, unable to split.")
 
         all_int = all(isinstance(item, int) for item in sizes)
@@ -640,8 +640,8 @@ class Dataset:
         Note:
             1. Dataset cannot be sharded if split is going to be called.
             2. It is strongly recommended to not shuffle the dataset, but use randomize=True instead.
-            Shuffling the dataset may not be deterministic, which means the data in each split
-            will be different in each epoch.
+                Shuffling the dataset may not be deterministic, which means the data in each split
+                will be different in each epoch.
 
         Raises:
             RuntimeError: If get_dataset_size returns None or is not supported for this dataset.
@@ -1173,6 +1173,7 @@ class SourceDataset(Dataset):
     def is_sharded(self):
         raise NotImplementedError("SourceDataset must implement is_sharded.")
 
+
 class MappableDataset(SourceDataset):
     """
     Abstract class to represent a source dataset which supports use of samplers.
@@ -1253,13 +1254,13 @@ class MappableDataset(SourceDataset):
 
         Note:
             1. Dataset should not be sharded if split is going to be called. Instead, create a
-            DistributedSampler and specify a split to shard after splitting. If dataset is
-            sharded after a split, it is strongly recommended to set the same seed in each instance
-            of execution, otherwise each shard may not be part of the same split (see Examples)
+                DistributedSampler and specify a split to shard after splitting. If dataset is
+                sharded after a split, it is strongly recommended to set the same seed in each instance
+                of execution, otherwise each shard may not be part of the same split (see Examples)
             2. It is strongly recommended to not shuffle the dataset, but use randomize=True instead.
-            Shuffling the dataset may not be deterministic, which means the data in each split
-            will be different in each epoch. Furthermore, if sharding occurs after split, each
-            shard may not be part of the same split.
+                Shuffling the dataset may not be deterministic, which means the data in each split
+                will be different in each epoch. Furthermore, if sharding occurs after split, each
+                shard may not be part of the same split.
 
         Raises:
             RuntimeError: If get_dataset_size returns None or is not supported for this dataset.