Memory leak on GaussianBlur

🐛 Describe the bug

Hello. When using num_workers > 0 for dataloader and GaussianBlur BEFORE the resize function in transforms (images in dataset are of different size) a memory leak appears. The larger num_workers used, the more the leak is (I ran out of 128 GB RAM in 300 iterations with batch_size of 32 and num_workers of 16).
To reproduce (you should initialize images with array of filepaths to images):

import torch
import glob
from torchvision import transforms
from PIL import Image
class FramesDataset(torch.utils.data.Dataset):
    def __init__(self, images):
        self.images = images
        self.init_base_transform()

    def __len__(self):
        return len(self.images)

    def init_base_transform(self):
        self.tr_aug = transforms.Compose([transforms.GaussianBlur(7, (1, 5)),
                                          transforms.Resize((256, 256), antialias=True),
                                          transforms.ToTensor(),
                                          transforms.Normalize([0.5]*3, [0.5]*3) ])

    def __getitem__(self, idx):
        img = Image.open(self.images[idx]).convert('RGB')
        out = self.tr_aug(img)
        return out

dataset = TestDataset(images)
dl = torch.utils.data.DataLoader(dataset, batch_size = 16, num_workers = 8, pin_memory = False)
while True:
    for batch in dl:
        pass

Versions

torch: 1.12.0+cu116
torchvision: 0.13.0+cu116
PIL: 9.0.0
Ubuntu: 20.04.4 LTS

cc @vfdev-5 @datumbox

Start with the reported transforms.GaussianBlur-before-Resize pipeline and the torch.utils.data.DataLoader reproduction using multiple workers and differently sized images. Compare memory usage across worker counts and isolate whether GaussianBlur, Resize, or their interaction causes the growth; done means the reproduction no longer shows unbounded memory growth and the behavior is covered by a regression test.