new_tensor(data, dtype=None, device=None, requires_grad=False) → Tensor
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：
new_tensor() always copies data. If you have a Tensor data and want to avoid a copy, use torch.Tensor.requires_grad_() or torch.Tensor.detach(). If you have a numpy array and want to avoid a copy, use torch.from_numpy().
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.clone().detach() and tensor.new_tensor(x, requires_grad=True) is equivalent to x.clone().detach().requires_grad_(True). The equivalents using clone() and detach() are recommended.
Parameters：
- data (array_like) – The returned Tensor copies
data. - dtype (
torch.dtype, optional) – the desired type of returned tensor. Default: if None, sametorch.dtypeas this tensor. - device (
torch.device, optional) – the desired device of returned tensor. Default: if None, sametorch.deviceas this tensor. - requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default:
False.
Example：
代码语言：javascript复制>>> tensor = torch.ones((2,), dtype=torch.int8)
>>> data = [[0, 1], [2, 3]]
>>> tensor.new_tensor(data)
tensor([[ 0, 1],
[ 2, 3]], dtype=torch.int8)
