Sorry for not explaining it clearly.
The use case would be to return N transformed samples instead of one:
def __getitem__(self, index):
    x = self.data[index]
    x1 = self.transform1(x)
    x2 = self.transform2(x)
    ...
    return x1, x2, ...

Now in your DataLoader you will get a batch of samples in all transformations. You could try to return a dict of transformed samples, if you find it easier to get the right one for the models.