It’s nice when you can share these goodies with your friends. 
n1 = np.array([1,2,3])
d1 = DataLoader( n1, batch_size=3)
t1 = torch.Tensor(n1) 
d2 = DataLoader( t1, batch_size=3)
print(d1)
print(d1.dataset)
print(type(d1.dataset))
print(d2)
print(d2.dataset)
print(type(d2.dataset))


<torch.utils.data.dataloader.DataLoader object at 0x0000024C52514588>
[1 2 3]
<class 'numpy.ndarray'>
<torch.utils.data.dataloader.DataLoader object at 0x0000024C52515A20>
tensor([1., 2., 3.])
<class 'torch.Tensor'>

As you can see, in the first case we feed with the numpy the dataset inside dataloader will be numpy, and in the second case will be torch.Tensor.
Looks like no internal conversion as I can see and I would say it is better to feed with Tensors because this way we  can relay on cuda stuff.
Any comments?

Update
Further check shows that the feed element need to be a sequence. Any kind of sequence where insde are numeric values. There is no container type checking. It can work for single int  0, or list [0,1], …
n1 = 0#[0,1] #np.array([])#[1,2,3,...])
d1 = DataLoader( n1, batch_size=3)
t1 = torch.Tensor(n1) 
d2 = DataLoader( t1, batch_size=3)
print(d1)
print(d1.dataset)
print(type(d1.dataset))
print(d2)
print(d2.dataset)
print(type(d2.dataset))