diff --git a/torchvision/prototype/transforms/_augment.py b/torchvision/prototype/transforms/_augment.py
index 3160770a09d..3618ecc285d 100644
--- a/torchvision/prototype/transforms/_augment.py
+++ b/torchvision/prototype/transforms/_augment.py
@@ -1,6 +1,7 @@
 import math
 import numbers
 import warnings
+from types import SimpleNamespace
 from typing import Any, cast, Dict, List, Optional, Tuple, Union
 
 import PIL.Image
@@ -83,14 +84,28 @@ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
             else:
                 v = torch.tensor(self.value)[:, None, None]
 
-            i = torch.randint(0, img_h - h + 1, size=(1,)).item()
-            j = torch.randint(0, img_w - w + 1, size=(1,)).item()
+            i = int(torch.randint(0, img_h - h + 1, size=(1,)))
+            j = int(torch.randint(0, img_w - w + 1, size=(1,)))
             break
         else:
             i, j, h, w, v = 0, 0, img_h, img_w, None
 
         return dict(i=i, j=j, h=h, w=w, v=v)
 
+    @staticmethod
+    def get_params(
+        image: torch.Tensor,
+        scale: Tuple[float, float],
+        ratio: Tuple[float, float],
+        value: Optional[List[float]] = None,
+    ) -> Tuple[int, int, int, int, torch.Tensor]:
+        self = SimpleNamespace(scale=scale, _log_ratio=torch.log(torch.tensor(ratio)), value=value)
+        params = RandomErasing._get_params(self, [image])  # type: ignore[arg-type]
+        v = params["v"]
+        if v is None:
+            v = image
+        return params["i"], params["j"], params["h"], params["w"], v
+
     def _transform(
         self, inpt: Union[datapoints.ImageType, datapoints.VideoType], params: Dict[str, Any]
     ) -> Union[datapoints.ImageType, datapoints.VideoType]: