You should be able to iterate these DataLoaders together e.g. via zip:
datasetA = TensorDataset(torch.randn(12, 7), torch.randn(12, 11))
loaderA = DataLoader(datasetA, batch_size=3)

datasetB = TensorDataset(torch.randn(10, 13))
loaderB = DataLoader(datasetB, batch_size=5)

for (dataA, dataB), (dataC,) in zip(loaderA, loaderB):
    print(dataA.shape, dataB.shape, dataC.shape)

Also, itertools might provide you some methods, which e.g. could iterate the longest DataLoader by cycling the shorter one or by returning None instead (the standard zip would use the shortest loader).