diff --git a/test/test_functional_tensor.py b/test/test_functional_tensor.py
index b7e27a808bf..649fc5ed1b0 100644
--- a/test/test_functional_tensor.py
+++ b/test/test_functional_tensor.py
@@ -972,7 +972,7 @@ def test_adjust_gamma(device, dtype, config, channels):
     [
         {"padding_mode": "constant", "fill": 0},
         {"padding_mode": "constant", "fill": 10},
-        {"padding_mode": "constant", "fill": 20},
+        {"padding_mode": "constant", "fill": 20.2},
         {"padding_mode": "edge"},
         {"padding_mode": "reflect"},
         {"padding_mode": "symmetric"},
diff --git a/torchvision/transforms/functional.py b/torchvision/transforms/functional.py
index 2a4a7f1b6dd..eea53a228a9 100644
--- a/torchvision/transforms/functional.py
+++ b/torchvision/transforms/functional.py
@@ -2,7 +2,7 @@
 import numbers
 import warnings
 from enum import Enum
-from typing import List, Tuple, Any, Optional
+from typing import List, Tuple, Any, Optional, Union
 
 import numpy as np
 import torch
@@ -474,7 +474,7 @@ def resize(
     return F_t.resize(img, size=output_size, interpolation=interpolation.value, antialias=antialias)
 
 
-def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor:
+def pad(img: Tensor, padding: List[int], fill: Union[int, float] = 0, padding_mode: str = "constant") -> Tensor:
     r"""Pad the given image on all sides with the given "pad" value.
     If the image is torch Tensor, it is expected
     to have [..., H, W] shape, where ... means at most 2 leading dimensions for mode reflect and symmetric,
diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
index acc8d3ae3e1..50e41647af1 100644
--- a/torchvision/transforms/functional_tensor.py
+++ b/torchvision/transforms/functional_tensor.py
@@ -1,5 +1,5 @@
 import warnings
-from typing import Optional, Tuple, List
+from typing import Optional, Tuple, List, Union
 
 import torch
 from torch import Tensor
@@ -370,7 +370,7 @@ def _parse_pad_padding(padding: List[int]) -> List[int]:
     return [pad_left, pad_right, pad_top, pad_bottom]
 
 
-def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor:
+def pad(img: Tensor, padding: List[int], fill: Union[int, float] = 0, padding_mode: str = "constant") -> Tensor:
     _assert_image_tensor(img)
 
     if not isinstance(padding, (int, tuple, list)):