Setting `0` and `1` to `p` argument of `RandomAutocontrast()` gets the same results

🐛 Describe the bug

Setting 0 and 1 to p argument of RandomAutocontrast() gets the same results as shown below:

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandomAutocontrast

origin_data = OxfordIIITPet(
    root="data",
    transform=None
)

p0_data = OxfordIIITPet(
    root="data",
    transform=RandomAutocontrast(p=0)
)

p1_data = OxfordIIITPet(
    root="data",
    transform=RandomAutocontrast(p=1)
)

import matplotlib.pyplot as plt

def show_images1(data, main_title=None):
    plt.figure(figsize=[10, 5])
    plt.suptitle(t=main_title, y=0.8, fontsize=14)
    for i, (im, _) in zip(range(1, 6), data):
        plt.subplot(1, 5, i)
        plt.imshow(X=im)
        plt.xticks(ticks=[])
        plt.yticks(ticks=[])
    plt.tight_layout()
    plt.show()

show_images1(data=origin_data, main_title="origin_data")
show_images1(data=p0_data, main_title="p0_data")
show_images1(data=p1_data, main_title="p1_data")

I expected the results of ColorJitter() as shown below:

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import ColorJitter

origin_data = OxfordIIITPet(
    root="data",
    transform=None
)

contrast06_06_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(contrast=[0.6, 0.6])
)

contrast4_4_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(contrast=[4, 4])
)

import matplotlib.pyplot as plt

def show_images1(data, main_title=None):
    plt.figure(figsize=[10, 5])
    plt.suptitle(t=main_title, y=0.8, fontsize=14)
    for i, (im, _) in zip(range(1, 6), data):
        plt.subplot(1, 5, i)
        plt.imshow(X=im)
        plt.xticks(ticks=[])
        plt.yticks(ticks=[])
    plt.tight_layout()
    plt.show()

show_images1(data=origin_data, main_title="origin_data")
show_images1(data=contrast06_06_data, main_title="contrast06_06_data")
show_images1(data=contrast4_4_data, main_title="contrast4_4_data")

Versions

import torchvision

torchvision.__version__ # '0.20.1'

Start by reproducing the p=0 and p=1 cases for torchvision.transforms.v2.RandomAutocontrast shown in the report. Inspect the RandomAutocontrast entry point and verify the expected behavior: p=0 should leave inputs unchanged, while p=1 should apply autocontrast; add regression coverage for both cases.