diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index dff443dc1e..1e16b5161e 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -2494,8 +2494,7 @@ class GeneratorDataset(SourceDataset):
         shuffle (bool, optional): Whether or not to perform shuffle on the dataset. Random accessible input is required.
             (default=None, expected order behavior shown in the table).
         sampler (Sampler/Iterable, optional): Object used to choose samples from the dataset. Random accessible input is
-        required.
-            (default=None, expected order behavior shown in the table).
+            required (default=None, expected order behavior shown in the table).
         num_shards (int, optional): Number of shards that the dataset should be divided into (default=None).
             This argument should be specified only when 'num_samples' is "None". Random accessible input is required.
         shard_id (int, optional): The shard ID within num_shards (default=None). This argument should be specified only
diff --git a/mindspore/dataset/transforms/vision/c_transforms.py b/mindspore/dataset/transforms/vision/c_transforms.py
index 07011b1d53..1b495ffe92 100644
--- a/mindspore/dataset/transforms/vision/c_transforms.py
+++ b/mindspore/dataset/transforms/vision/c_transforms.py
@@ -89,8 +89,8 @@ class Normalize(cde.NormalizeOp):
     Normalize the input image with respect to mean and standard deviation.
 
     Args:
-        mean (list): List of mean values for each channel, w.r.t channel order.
-        std (list): List of standard deviations for each channel, w.r.t. channel order.
+        mean (sequence): List or tuple of mean values for each channel, w.r.t channel order.
+        std (sequence): List or tuple of standard deviations for each channel, w.r.t. channel order.
     """
 
     @check_normalize_c
@@ -109,6 +109,7 @@ class RandomCrop(cde.RandomCropOp):
             If size is an int, a square crop of size (size, size) is returned.
             If size is a sequence of length 2, it should be (height, width).
         padding (int or sequence, optional): The number of pixels to pad the image (default=None).
+            If padding is not None, pad image firstly with padding values.
             If a single number is provided, it pads all borders with this value.
             If a tuple or list of 2 values are provided, it pads the (left and top)
             with the first value and (right and bottom) with the second value.
diff --git a/mindspore/dataset/transforms/vision/py_transforms.py b/mindspore/dataset/transforms/vision/py_transforms.py
index 51bea80b21..de9ce3f450 100644
--- a/mindspore/dataset/transforms/vision/py_transforms.py
+++ b/mindspore/dataset/transforms/vision/py_transforms.py
@@ -225,8 +225,8 @@ class Normalize:
     The values of the array need to be in range [0.0, 1.0].
 
     Args:
-        mean (list): List of mean values for each channel, w.r.t channel order.
-        std (list): List of standard deviations for each channel, w.r.t. channel order.
+        mean (sequence): List or tuple of mean values for each channel, w.r.t channel order.
+        std (sequence): List or tuple of standard deviations for each channel, w.r.t. channel order.
 
     Examples:
         >>> py_transforms.ComposeOp([py_transforms.Decode(),
@@ -262,6 +262,7 @@ class RandomCrop:
             If size is an int, a square crop of size (size, size) is returned.
             If size is a sequence of length 2, it should be (height, width).
         padding (int or sequence, optional): The number of pixels to pad the image (default=None).
+            If padding is not None, pad image firstly with padding values.
             If a single number is provided, it pads all borders with this value.
             If a tuple or list of 2 values are provided, it pads the (left and top)
             with the first value and (right and bottom) with the second value.