Update RandomErasing value type and doc string

torchvision/transforms/v2/_augment.py

@@ -2,7 +2,7 @@
 import numbers
 import warnings
 from collections.abc import Sequence
-from typing import Any, Callable, Optional, Union
+from typing import Any, Callable, List, Literal, Optional, Union
 
 import PIL.Image
 import torch
@@ -25,8 +25,8 @@ class RandomErasing(_RandomApplyTransform):
         p (float, optional): probability that the random erasing operation will be performed.
         scale (tuple of float, optional): range of proportion of erased area against input image.
         ratio (tuple of float, optional): range of aspect ratio of erased area.
-        value (number or tuple of numbers): erasing value. Default is 0. If a single int, it is used to
-            erase all pixels. If a tuple of length 3, it is used to erase
+        value (number, str, or tuple of numbers): erasing value. Default is 0. If a single int,
+            it is used to erase all pixels. If a tuple of length 3, it is used to erase
             R, G, B channels respectively.
             If a str of 'random', erasing each pixel with random values.
         inplace (bool, optional): boolean to make this transform inplace. Default set to False.
@@ -46,6 +46,8 @@ class RandomErasing(_RandomApplyTransform):
         >>> ])
     """
 
+    value: Optional[List[float]]
+
     _v1_transform_cls = _transforms.RandomErasing
 
     def _extract_params_for_v1_transform(self) -> dict[str, Any]:
@@ -59,7 +61,7 @@ def __init__(
         p: float = 0.5,
         scale: Sequence[float] = (0.02, 0.33),
         ratio: Sequence[float] = (0.3, 3.3),
-        value: float = 0.0,
+        value: Union[float, int, Literal["random"], Sequence[Union[float, int]]] = 0.0,
         inplace: bool = False,
     ):
         super().__init__(p=p)