diff --git a/torchvision/transforms/functional.py b/torchvision/transforms/functional.py
index 09c8bdbcfeb..c79aef6cb04 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 Any, List, Optional, Tuple, Union
+from typing import Any, List, Optional, Tuple, Union, Sequence
 
 import numpy as np
 import torch
@@ -390,7 +390,7 @@ def _compute_resized_output_size(
 
 def resize(
     img: Tensor,
-    size: List[int],
+    size: Union[int,Sequence[int]],
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     max_size: Optional[int] = None,
     antialias: Optional[Union[str, bool]] = "warn",
@@ -492,7 +492,7 @@ def resize(
     return F_t.resize(img, size=output_size, interpolation=interpolation.value, antialias=antialias)
 
 
-def pad(img: Tensor, padding: List[int], fill: Union[int, float] = 0, padding_mode: str = "constant") -> Tensor:
+def pad(img: Tensor, padding: Union[int, Sequence[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,
@@ -566,7 +566,7 @@ def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor:
     return F_t.crop(img, top, left, height, width)
 
 
-def center_crop(img: Tensor, output_size: List[int]) -> Tensor:
+def center_crop(img: Tensor, output_size: Union[int, Sequence[int]]) -> Tensor:
     """Crops the given image at the center.
     If the image is torch Tensor, it is expected
     to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
@@ -613,7 +613,7 @@ def resized_crop(
     left: int,
     height: int,
     width: int,
-    size: List[int],
+    size: Union[int, Sequence[int]],
     interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     antialias: Optional[Union[str, bool]] = "warn",
 ) -> Tensor:
@@ -782,7 +782,7 @@ def vflip(img: Tensor) -> Tensor:
     return F_t.vflip(img)
 
 
-def five_crop(img: Tensor, size: List[int]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
+def five_crop(img: Tensor, size: Union[int, Sequence[int]]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
     """Crop the given image into four corners and the central crop.
     If the image is torch Tensor, it is expected
     to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
@@ -828,7 +828,7 @@ def five_crop(img: Tensor, size: List[int]) -> Tuple[Tensor, Tensor, Tensor, Ten
 
 
 def ten_crop(
-    img: Tensor, size: List[int], vertical_flip: bool = False
+    img: Tensor, size: Union[int, Sequence[int]], vertical_flip: bool = False
 ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
     """Generate ten cropped images from the given image.
     Crop the given image into four corners and the central crop plus the