diff --git a/torchvision/prototype/transforms/functional/_color.py b/torchvision/prototype/transforms/functional/_color.py
index 2c268fa4085..0d5bdc31d3c 100644
--- a/torchvision/prototype/transforms/functional/_color.py
+++ b/torchvision/prototype/transforms/functional/_color.py
@@ -2,7 +2,7 @@
 from torchvision.prototype import features
 from torchvision.transforms import functional_pil as _FP, functional_tensor as _FT
 
-from ._meta import _rgb_to_gray, get_dimensions_image_tensor, get_num_channels_image_tensor
+from ._meta import _rgb_to_gray, convert_dtype_image_tensor, get_dimensions_image_tensor, get_num_channels_image_tensor
 
 
 def _blend(image1: torch.Tensor, image2: torch.Tensor, ratio: float) -> torch.Tensor:
@@ -257,7 +257,28 @@ def adjust_hue(inpt: features.InputTypeJIT, hue_factor: float) -> features.Input
         return adjust_hue_image_pil(inpt, hue_factor=hue_factor)
 
 
-adjust_gamma_image_tensor = _FT.adjust_gamma
+def adjust_gamma_image_tensor(image: torch.Tensor, gamma: float, gain: float = 1.0) -> torch.Tensor:
+    if not (isinstance(image, torch.Tensor)):
+        raise TypeError("Input img should be Tensor image")
+
+    if gamma < 0:
+        raise ValueError("Gamma should be a non-negative real number")
+
+    # The input image is either assumed to be at [0, 1] scale (if float) or is converted to that scale (if integer).
+    # Since the gamma is non-negative, the output remains at [0, 1] scale.
+    if not torch.is_floating_point(image):
+        output = convert_dtype_image_tensor(image, torch.float32).pow_(gamma)
+    else:
+        output = image.pow(gamma)
+
+    if gain != 1.0:
+        # The clamp operation is needed only if multiplication is performed. It's only when gain != 1, that the scale
+        # of the output can go beyond [0, 1].
+        output = output.mul_(gain).clamp_(0.0, 1.0)
+
+    return convert_dtype_image_tensor(output, image.dtype)
+
+
 adjust_gamma_image_pil = _FP.adjust_gamma