I suggest you to define your own dataset so that you can use only one loader while taking batches from both tensors. For example, you can use something like this:
import torch
size = 16
a = torch.randn(size,3,224,224)
b = torch.randn(size,3,224,224)

class Dataset(torch.utils.data.Dataset):
    def __init__(self, a, b):
        assert len(a)==len(b)
        self.a = a
        self.b = b
        
    def __len__(self):
        return len(self.a)
        
    def __getitem__(self, i):
        return self.a[i], self.b[i]

dataset = Dataset(a, b)
loader = torch.utils.data.DataLoader(dataset, batch_size=4)

for a, b in loader:
    print(a.shape, b.shape)

There might be better solutions though.