!779 Fix pylint warning for samplers.py

Merge pull request !779 from JunhanHu/sampler_pylint

mindspore/dataset/engine/samplers.py

@@ -19,8 +19,8 @@ SequentialSampler, SubsetRandomSampler, WeightedRandomSampler.
 User can also define custom sampler by extending from Sampler class.
 """
 
-import mindspore._c_dataengine as cde
 import numpy as np
+import mindspore._c_dataengine as cde
 
 
 class Sampler:
@@ -137,6 +137,7 @@ class DistributedSampler(BuiltinSampler):
         self.shard_id = shard_id
         self.shuffle = shuffle
         self.seed = 0
+        super().__init__()
 
     def create(self):
         # each time user calls create_dict_iterator() (to do repeat) sampler would get a different seed to shuffle
@@ -182,6 +183,7 @@ class PKSampler(BuiltinSampler):
         self.num_val = num_val
         self.shuffle = shuffle
         self.class_column = class_column # work for minddataset
+        super().__init__()
 
     def create(self):
         return cde.PKSampler(self.num_val, self.shuffle)
@@ -192,6 +194,7 @@ class PKSampler(BuiltinSampler):
                     but got class_column={}".format(class_column))
         return cde.MindrecordPkSampler(self.num_val, self.class_column, self.shuffle)
 
+
 class RandomSampler(BuiltinSampler):
     """
     Samples the elements randomly.
@@ -225,6 +228,7 @@ class RandomSampler(BuiltinSampler):
 
         self.replacement = replacement
         self.num_samples = num_samples
+        super().__init__()
 
     def create(self):
         # If num_samples is not specified, then call constructor #2
@@ -275,6 +279,7 @@ class SubsetRandomSampler(BuiltinSampler):
             indices = [indices]
 
         self.indices = indices
+        super().__init__()
 
     def create(self):
         return cde.SubsetRandomSampler(self.indices)
@@ -322,6 +327,7 @@ class WeightedRandomSampler(BuiltinSampler):
         self.weights = weights
         self.num_samples = num_samples
         self.replacement = replacement
+        super().__init__()
 
     def create(self):
         return cde.WeightedRandomSampler(self.weights, self.num_samples, self.replacement)