fix: linting issues

rul_datasets/adaption.py

@@ -2,15 +2,13 @@
 
 import warnings
 from copy import deepcopy
-from typing import List, Optional, Any, Tuple, Callable, Sequence, Union, cast
+from typing import List, Optional, Any, Tuple, Callable, Sequence, cast
 
 import numpy as np
 import pytorch_lightning as pl
 import torch
-from torch.utils.data import DataLoader, Dataset
-from torch.utils.data.dataset import ConcatDataset, TensorDataset
+from torch.utils.data import DataLoader, Dataset, ConcatDataset
 
-from rul_datasets import utils
 from rul_datasets.core import PairedRulDataset, RulDataModule, RulDataset
 
 

rul_datasets/core.py

@@ -416,7 +416,7 @@ class RulDataset(Dataset):
     def __init__(
         self,
         features: List[np.ndarray],
-        *targets: Tuple[List[np.ndarray]],
+        *targets: List[np.ndarray],
         copy_tensors: bool = False,
     ) -> None:
         """

rul_datasets/utils.py

@@ -169,7 +169,7 @@ def to_tensor(
 ) -> Tuple[List[torch.Tensor], ...]:
     dtype = torch.float32
     tensor_feats = [feature_to_tensor(f, dtype, copy) for f in features]
-    convert = torch.tensor if copy else torch.as_tensor
+    convert: Callable = torch.tensor if copy else torch.as_tensor  # type: ignore
     tensor_targets = [[convert(t, dtype=dtype) for t in target] for target in targets]
 
     return tensor_feats, *tensor_targets