Sure! You could start with the following code:
class MyDataset(Dataset):
    def __init__(self, train=True, transforms=None):
        self.cifar10 = datasets.CIFAR10(root='YOUR_PATH',
                                        train=train,
                                        download=False)
        self.transforms = transforms

    def __getitem__(self, index):
        x, y = self.cifar10[index]
        
        if self.transforms:
            print('Chosing transform ', y)
            x = self.transforms[y](x) # Chose class transform based on y
        
        return x, y

    def __len__(self):
        return len(self.cifar10)


class_transforms = []
for _ in range(10):
    transform = transforms.Compose([
        transforms.ColorJitter(
            brightness=0.1*torch.randn(1),
            contrast=0.1*torch.randn(1),
            saturation=0.1*torch.randn(1),
            hue=0.1*torch.randn(1)),
        transforms.ToTensor()
    ])
    class_transforms.append(transform)


dataset = MyDataset(transforms=class_transforms)
loader = DataLoader(dataset, batch_size=5, shuffle=True)
data, target = iter(loader).next()
> ('Chosing transform ', 9)
('Chosing transform ', 2)
('Chosing transform ', 5)
('Chosing transform ', 0)
('Chosing transform ', 2)
print(target)
> tensor([ 9,  2,  5,  0,  2])

I have just applied random transformations to each class. Your class_transforms will probably look a bit more complicated.