Returns a new Tensor with data as the tensor data. By default, the returned Tensor has the same torch.dtype and torch.device as this tensor.
Warning
When data is a tensor x, new_tensor() reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Therefore tensor.new_tensor(x) is equivalent to x.detach().clone() and tensor.new_tensor(x, requires_grad=True) is equivalent to x.detach().clone().requires_grad_(True). The equivalents using detach() and clone() are recommended.
data (array_like) – The returned Tensor copies data.
dtype (torch.dtype, optional) – the desired type of returned tensor. Default: if None, same torch.dtype as this tensor.
device (torch.device, optional) – the desired device of returned tensor. Default: if None, same torch.device as this tensor.
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.
layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.
pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.
Example:
>>> tensor = torch.ones((2,), dtype=torch.int8)
>>> data = [[0, 1], [2, 3]]
>>> tensor.new_tensor(data)
tensor([[ 0, 1],
[ 2, 3]], dtype=torch.int8)
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