diff --git a/pytorch_lightning/trainer/supporters.py b/pytorch_lightning/trainer/supporters.py
index 2235f3f70561f..26f27ce56f1c7 100644
--- a/pytorch_lightning/trainer/supporters.py
+++ b/pytorch_lightning/trainer/supporters.py
@@ -257,7 +257,7 @@ def __init__(self, datasets: Union[Sequence, Mapping], mode: str):
         Args:
             datasets: a sequence/mapping datasets. Can be a collections of torch.utils.Dataset,
                 Iterable or even None.
-            mode: whether to use the minimum number of batches in all samples or the maximum 
+            mode: whether to use the minimum number of batches in all samples or the maximum
                 number of batches in all samples.
 
         """
@@ -307,7 +307,7 @@ def __len__(self) -> int:
 class CombinedLoader(object):
     """
     Combines different dataloaders and allows sampling in parallel.
-    
+
     Supported modes are 'min_size', which raises StopIteration after the shortest loader
     (the one with the lowest number of batches) is done, and 'max_size_cycle` which raises
     StopIteration after the longest loader (the one with most batches) is done, while cycling
diff --git a/tests/base/model_train_dataloaders.py b/tests/base/model_train_dataloaders.py
index 65873cfa8d6c4..0cc6b7e9e14db 100644
--- a/tests/base/model_train_dataloaders.py
+++ b/tests/base/model_train_dataloaders.py
@@ -42,7 +42,7 @@ def train_dataloader__multiple_mapping(self):
         """Return a mapping loaders with different lengths"""
         return {'a': self.dataloader(train=True, num_samples=100),
                 'b': self.dataloader(train=True, num_samples=50)}
-    
+
     def train_dataloader__multiple_sequence(self):
         return [self.dataloader(train=True, num_samples=100),
                 self.dataloader(train=True, num_samples=50)]
diff --git a/tests/base/model_train_steps.py b/tests/base/model_train_steps.py
index e12d004db8f98..e39dd47aa565b 100644
--- a/tests/base/model_train_steps.py
+++ b/tests/base/model_train_steps.py
@@ -174,4 +174,3 @@ def training_step__multiple_dataloaders(self, batch, batch_idx, optimizer_idx=No
             }
         )
         return output
-