diff --git a/torchvision/prototype/transforms/functional/_color.py b/torchvision/prototype/transforms/functional/_color.py
index 68b52fff637..7bf412aaf99 100644
--- a/torchvision/prototype/transforms/functional/_color.py
+++ b/torchvision/prototype/transforms/functional/_color.py
@@ -211,7 +211,34 @@ def solarize(inpt: features.InputTypeJIT, threshold: float) -> features.InputTyp
         return solarize_image_pil(inpt, threshold=threshold)
 
 
-autocontrast_image_tensor = _FT.autocontrast
+def autocontrast_image_tensor(image: torch.Tensor) -> torch.Tensor:
+
+    if not (isinstance(image, torch.Tensor)):
+        raise TypeError("Input img should be Tensor image")
+
+    c = get_num_channels_image_tensor(image)
+
+    if c not in [1, 3]:
+        raise TypeError(f"Input image tensor permitted channel values are {[1, 3]}, but found {c}")
+
+    if image.numel() == 0:
+        # exit earlier on empty images
+        return image
+
+    bound = 1.0 if image.is_floating_point() else 255.0
+    dtype = image.dtype if torch.is_floating_point(image) else torch.float32
+
+    minimum = image.amin(dim=(-2, -1), keepdim=True).to(dtype)
+    maximum = image.amax(dim=(-2, -1), keepdim=True).to(dtype)
+
+    scale = bound / (maximum - minimum)
+    eq_idxs = maximum == minimum
+    minimum[eq_idxs] = 0.0
+    scale[eq_idxs] = 1.0
+
+    return (image - minimum).mul_(scale).clamp_(0, bound).to(image.dtype)
+
+
 autocontrast_image_pil = _FP.autocontrast