You are absolutely right!
Christian S. Perone wrote a nice article about it:
http://blog.christianperone.com/2018/10/pytorch-1-0-tracing-jit-and-libtorch-c-api-to-integrate-pytorch-into-nodejs/ 56

A word of caution for those who are starting now is to be careful with the use of the tensors that can be created both from ATen and autograd,  do not mix them , the ATen will return the plain tensors (when you create them using the  at  namespace) while the autograd functions (from the  torch  namespace) will return  Variable , by adding its automatic differentiation mechanism.

So if you use:
at::Tensor test = at::rand({1,28,28})

gives you a at::Tensor without autograd functionality.
at::Tensor test = tensor::rand({1,28,28})

gives you a at::Tensor with autograd functionality.
Thanks again!