Keyword Arguments:
      * dtype ("torch.dtype", optional) -- the desired data type
        of returned tensor. Default: if "None", infers data type from
        "data".
  * **device** ("torch.device", optional) -- the device of the
    constructed tensor. If None and data is a tensor then the
    device of data is used. If None and data is not a tensor then
    the result tensor is constructed on the CPU.

  * **requires_grad** (*bool**, **optional*) -- If autograd should
    record operations on the returned tensor. Default: "False".

  * **pin_memory** (*bool**, **optional*) -- If set, returned
    tensor would be allocated in the pinned memory. Works only for
    CPU tensors. Default: "False".

Example:
  >>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
  tensor([[ 0.1000,  1.2000],
          [ 2.2000,  3.1000],
          [ 4.9000,  5.2000]])

  >>> torch.tensor([0, 1])  # Type inference on data
  tensor([ 0,  1])
