How to use the Dataloader to load two different datasets like :

train_set = DataCustom(path=path, train=True)
train_loader = torch.utils.data.DataLoader(dataset=(train_set1, train_set2),
                                           batch_size=args.batch_size,
                                           pin_memory=True,
                                           shuffle=True,
                                           )

test_set = DataCustom(path=path, train=False)
test_loader = torch.utils.data.DataLoader(dataset=(test_set1,test_set2)
                                          batch_size=args.batch_size,
                                          pin_memory=True,
                                          shuffle=False,
                                          )

instead of writing two different dataloader like :
# load the First datasets 
train_set = DataCustom(path=path, train=True)
train_loader = torch.utils.data.DataLoader(dataset=train_set,
                                           batch_size=args.batch_size,
                                           pin_memory=True,
                                           shuffle=True,
                                           )
test_set = DataCustom(path=path, train=False)
test_loader = torch.utils.data.DataLoader(dataset=test_set,
                                          batch_size=args.batch_size,
                                          pin_memory=True,
                                          shuffle=False,
                                          )

# Load the second datasets 
train_set_2 = DataCustom(path=path_2, train=True)
train_loader_2 = torch.utils.data.DataLoader(dataset=train_set,
                                           batch_size=args.batch_size,
                                           pin_memory=True,
                                           shuffle=True,
                                           )

test_set_2 = DataCustom(path=path_2, train=False)
test_loader_2 = torch.utils.data.DataLoader(dataset=test_set,
                                          batch_size=args.batch_size,
                                          pin_memory=True,
                                          shuffle=False,
                                          )


Thanks in Advance (@ptrblck for you special thanks dude  )