Pytorch beginner : tensor.new method

It seems that in the newer versions of PyTorch there are many of various new_* methods that are intended to replace this "legacy" new method.

So if you have some tensor t = torch.randn((3, 4)) then you can construct a new one with the same type and device using one of these methods, depending on your goals:

t = torch.randn((3, 4))
a = t.new_tensor([1, 2, 3])  # same type, device, new data
b = t.new_empty((3, 4))      # same type, device, non-initialized
c = t.new_zeros((2, 3))      # same type, device, filled with zeros
... 

for x in (t, a, b, c):
    print(x.type(), x.device, x.size())
# torch.FloatTensor cpu torch.Size([3, 4])
# torch.FloatTensor cpu torch.Size([3])
# torch.FloatTensor cpu torch.Size([3, 4])
# torch.FloatTensor cpu torch.Size([2, 3])

Here is a simple use-case and example using new(), since without this the utility of this function is not very clear.

Suppose you want to add Gaussian noise to a tensor (or Variable) without knowing a priori what it's datatype is.

This will create a tensor of Gaussian noise, the same shape and data type as a Variable X:

 noise_like_grad = X.data.new(X.size()).normal_(0,0.01)

This example also illustrates the usage of new(size), so that we get a tensor of same type and same size as X.

As the documentation of tensor.new() says:

Constructs a new tensor of the same data type as self tensor.

Also note:

For CUDA tensors, this method will create new tensor on the same device as this tensor.

I've found an answer. It is used to create a new tensor with the same type.