diff --git a/references/classification/README.md b/references/classification/README.md
index 65ee416bf89..bc481f421ed 100644
--- a/references/classification/README.md
+++ b/references/classification/README.md
@@ -135,7 +135,7 @@ torchrun --nproc_per_node=8 train.py\
      --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\
      --lr-warmup-epochs=5 --lr-warmup-decay=0.1
 ```
-Here `$MODEL` is one of `regnet_x_400mf`, `regnet_x_800mf`, `regnet_x_1_6gf`, `regnet_y_400mf`, `regnet_y_800mf` and `regnet_y_1_6gf`. Please note we used learning rate 0.4 for `regent_y_400mf` to get the same Acc@1 as [the paper)(https://arxiv.org/abs/2003.13678).
+Here `$MODEL` is one of `regnet_x_400mf`, `regnet_x_800mf`, `regnet_x_1_6gf`, `regnet_y_400mf`, `regnet_y_800mf` and `regnet_y_1_6gf`. Please note we used learning rate 0.4 for `regent_y_400mf` to get the same Acc@1 as [the paper](https://arxiv.org/abs/2003.13678).
 
 #### Medium models
 ```
diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py
index 07e3d75df6d..3cf7d89de77 100644
--- a/test/test_transforms_v2.py
+++ b/test/test_transforms_v2.py
@@ -394,7 +394,15 @@ def _check_transform_sample_input_smoke(transform, input, *, adapter):
 
 
 def check_transform(transform, input, check_v1_compatibility=True, check_sample_input=True):
-    pickle.loads(pickle.dumps(transform))
+    # TODO: remove this cm once https://github.com/pytorch/vision/issues/8517
+    # and https://github.com/pytorch/pytorch/issues/130242 are resolved.
+    if isinstance(transform, (transforms.RandomResizedCrop, transforms.LinearTransformation)):
+        cm = pytest.warns(FutureWarning, match="You are using `torch.load`")
+    else:
+        cm = contextlib.nullcontext()
+
+    with cm:
+        pickle.loads(pickle.dumps(transform))
 
     output = transform(input)
     assert isinstance(output, type(input))
diff --git a/torchvision/datasets/_optical_flow.py b/torchvision/datasets/_optical_flow.py
index 40d25583942..6d8b852a5d0 100644
--- a/torchvision/datasets/_optical_flow.py
+++ b/torchvision/datasets/_optical_flow.py
@@ -43,7 +43,7 @@ def __init__(self, root: Union[str, Path], transforms: Optional[Callable] = None
     def _read_img(self, file_name: str) -> Image.Image:
         img = Image.open(file_name)
         if img.mode != "RGB":
-            img = img.convert("RGB")
+            img = img.convert("RGB")  # type: ignore[assignment]
         return img
 
     @abstractmethod
diff --git a/torchvision/datasets/_stereo_matching.py b/torchvision/datasets/_stereo_matching.py
index 6a3f563a2da..1deaab7e2f3 100644
--- a/torchvision/datasets/_stereo_matching.py
+++ b/torchvision/datasets/_stereo_matching.py
@@ -58,7 +58,7 @@ def __init__(self, root: Union[str, Path], transforms: Optional[Callable] = None
     def _read_img(self, file_path: Union[str, Path]) -> Image.Image:
         img = Image.open(file_path)
         if img.mode != "RGB":
-            img = img.convert("RGB")
+            img = img.convert("RGB")  # type: ignore [assignment]
         return img
 
     def _scan_pairs(
diff --git a/torchvision/datasets/widerface.py b/torchvision/datasets/widerface.py
index 90f80b7175b..71f4ce313c3 100644
--- a/torchvision/datasets/widerface.py
+++ b/torchvision/datasets/widerface.py
@@ -92,7 +92,7 @@ def __getitem__(self, index: int) -> Tuple[Any, Any]:
         """
 
         # stay consistent with other datasets and return a PIL Image
-        img = Image.open(self.img_info[index]["img_path"])
+        img = Image.open(self.img_info[index]["img_path"])  # type: ignore[arg-type]
 
         if self.transform is not None:
             img = self.transform(img)
diff --git a/torchvision/utils.py b/torchvision/utils.py
index 6b2d19ec3dd..33ac826e5ce 100644
--- a/torchvision/utils.py
+++ b/torchvision/utils.py
@@ -612,8 +612,8 @@ def _parse_colors(
 
     colors = [ImageColor.getrgb(color) if isinstance(color, str) else color for color in colors]
     if dtype.is_floating_point:  # [0, 255] -> [0, 1]
-        colors = [tuple(v / 255 for v in color) for color in colors]
-    return colors
+        colors = [tuple(v / 255 for v in color) for color in colors]  # type: ignore[union-attr]
+    return colors  # type: ignore[return-value]
 
 
 def _log_api_usage_once(obj: Any) -> None: