diff --git a/torchvision/prototype/tv_tensors/_label.py b/torchvision/prototype/tv_tensors/_label.py
index 506c4fb2b0e..23b6142c33d 100644
--- a/torchvision/prototype/tv_tensors/_label.py
+++ b/torchvision/prototype/tv_tensors/_label.py
@@ -48,7 +48,7 @@ def to_categories(self) -> Any:
         if self.categories is None:
             raise RuntimeError("Label does not have categories")
 
-        return tree_map(lambda idx: self.categories[idx], self.tolist())
+        return tree_map(lambda idx: self.categories[idx], self.tolist())  # type: ignore[index]
 
 
 class OneHotLabel(_LabelBase):
diff --git a/torchvision/transforms/v2/_geometry.py b/torchvision/transforms/v2/_geometry.py
index e4ba3d9824b..c670ca5e523 100644
--- a/torchvision/transforms/v2/_geometry.py
+++ b/torchvision/transforms/v2/_geometry.py
@@ -1,7 +1,7 @@
 import math
 import numbers
 import warnings
-from typing import Any, Callable, cast, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
+from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
 
 import PIL.Image
 import torch
@@ -241,10 +241,8 @@ def __init__(
 
         if not isinstance(scale, Sequence):
             raise TypeError("Scale should be a sequence")
-        scale = cast(Tuple[float, float], scale)
         if not isinstance(ratio, Sequence):
             raise TypeError("Ratio should be a sequence")
-        ratio = cast(Tuple[float, float], ratio)
         if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
             warnings.warn("Scale and ratio should be of kind (min, max)")