title: Pytorch in-place concatenation and conversion to tensor from numpy
body: <p>Suppose I have a list of tensors of the same size which could be concatenated along a dimension, say 0. Do any of the commands torch.cat or torch.stack or any numpy commands do the concatenation in-place? Also, if I want to convert a numpy ndarray to tensor. If I do the following, are two copies existing in the memory at any given time? I am dealing with a massive dataset so big only one copy of it can exist in the memory at any given time.</p>
  
  <pre><code># initially data is a huge ndarray
  data = torch.Tensor(data)
  </code></pre>