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# torch.Tensor.new_tensor

Tensor.new_tensor(data, *, dtype=None, device=None, requires_grad=False, layout=torch.strided, pin_memory=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`.

  Keyword Arguments:

    - 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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