Creation

x = torch.randn(*size)              # tensor with independent N(0,1) entries
x = torch.[ones|zeros](*size)       # tensor with all 1's [or 0's]
x = torch.tensor(L)                 # create tensor from [nested] list or ndarray L
y = x.clone()                       # clone of x
with torch.no_grad():               # code wrap that stops autograd from tracking tensor history
requires_grad=True                  # arg, when set to True, tracks computation
                                    # history for future derivative calculations