diff --git a/autoPyTorch/datasets/base_dataset.py b/autoPyTorch/datasets/base_dataset.py
index 38b22f445..9c2748b8e 100644
--- a/autoPyTorch/datasets/base_dataset.py
+++ b/autoPyTorch/datasets/base_dataset.py
@@ -23,7 +23,7 @@
 )
 from autoPyTorch.utils.common import FitRequirement, hash_array_or_matrix
 
-BaseDatasetType = Union[Tuple[np.ndarray, np.ndarray], Dataset]
+BaseDatasetInputType = Union[Tuple[np.ndarray, np.ndarray], Dataset]
 
 
 def check_valid_data(data: Any) -> None:
@@ -32,10 +32,9 @@ def check_valid_data(data: Any) -> None:
             'The specified Data for Dataset must have both __getitem__ and __len__ attribute.')
 
 
-def type_check(train_tensors: BaseDatasetType, val_tensors: Optional[BaseDatasetType] = None) -> None:
-    """To avoid unexpected behavior, we use loops over indices."""
-    for i in range(len(train_tensors)):
-        check_valid_data(train_tensors[i])
+def type_check(train_tensors: BaseDatasetInputType, val_tensors: Optional[BaseDatasetInputType] = None) -> None:
+    for train_tensor in train_tensors:
+        check_valid_data(train_tensor)
     if val_tensors is not None:
         for i in range(len(val_tensors)):
             check_valid_data(val_tensors[i])
@@ -63,10 +62,10 @@ def __getitem__(self, idx: int) -> np.ndarray:
 class BaseDataset(Dataset, metaclass=ABCMeta):
     def __init__(
         self,
-        train_tensors: BaseDatasetType,
+        train_tensors: BaseDatasetInputType,
         dataset_name: Optional[str] = None,
-        val_tensors: Optional[BaseDatasetType] = None,
-        test_tensors: Optional[BaseDatasetType] = None,
+        val_tensors: Optional[BaseDatasetInputType] = None,
+        test_tensors: Optional[BaseDatasetInputType] = None,
         resampling_strategy: Union[CrossValTypes, HoldoutValTypes] = HoldoutValTypes.holdout_validation,
         resampling_strategy_args: Optional[Dict[str, Any]] = None,
         shuffle: Optional[bool] = True,
@@ -313,7 +312,7 @@ def get_dataset_for_training(self, split_id: int) -> Tuple[Dataset, Dataset]:
         return (TransformSubset(self, self.splits[split_id][0], train=True),
                 TransformSubset(self, self.splits[split_id][1], train=False))
 
-    def replace_data(self, X_train: BaseDatasetType, X_test: Optional[BaseDatasetType]) -> 'BaseDataset':
+    def replace_data(self, X_train: BaseDatasetInputType, X_test: Optional[BaseDatasetInputType]) -> 'BaseDataset':
         """
         To speed up the training of small dataset, early pre-processing of the data
         can be made on the fly by the pipeline.
diff --git a/autoPyTorch/datasets/resampling_strategy.py b/autoPyTorch/datasets/resampling_strategy.py
index 1c8fea5fd..ee9458217 100644
--- a/autoPyTorch/datasets/resampling_strategy.py
+++ b/autoPyTorch/datasets/resampling_strategy.py
@@ -150,9 +150,12 @@ def k_fold_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any)
         """
         Standard k fold cross validation.
 
-        :param indices: array of indices to be split
-        :param num_splits: number of cross validation splits
-        :return: list of tuples of training and validation indices
+        Args:
+            indices (np.ndarray): array of indices to be split
+            num_splits (int): number of cross validation splits
+
+        Returns:
+            splits (List[Tuple[List, List]]): list of tuples of training and validation indices
         """
         cv = KFold(n_splits=num_splits)
         splits = list(cv.split(indices))
@@ -163,14 +166,21 @@ def time_series_cross_validation(num_splits: int, indices: np.ndarray, **kwargs:
             -> List[Tuple[np.ndarray, np.ndarray]]:
         """
         Returns train and validation indices respecting the temporal ordering of the data.
-        Dummy example: [0, 1, 2, 3] with 3 folds yields
-            [0] [1]
-            [0, 1] [2]
-            [0, 1, 2] [3]
-
-        :param indices: array of indices to be split
-        :param num_splits: number of cross validation splits
-        :return: list of tuples of training and validation indices
+
+        Args:
+            indices (np.ndarray): array of indices to be split
+            num_splits (int): number of cross validation splits
+
+        Returns:
+            splits (List[Tuple[List, List]]): list of tuples of training and validation indices
+
+        Examples:
+            >>> indices = np.array([0, 1, 2, 3])
+            >>> CrossValFuncs.time_series_cross_validation(3, indices)
+                [([0], [1]),
+                 ([0, 1], [2]),
+                 ([0, 1, 2], [3])]
+
         """
         cv = TimeSeriesSplit(n_splits=num_splits)
         splits = list(cv.split(indices))