returned tensor. Default: if "None", uses the current device
        for the default tensor type (see
        "torch.set_default_tensor_type()"). "device" will be the CPU
        for CPU tensor types and the current CUDA device for CUDA
        tensor types.
  * **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:
  >>> a = torch.empty_strided((2, 3), (1, 2))
  >>> a
  tensor([[8.9683e-44, 4.4842e-44, 5.1239e+07],
          [0.0000e+00, 0.0000e+00, 3.0705e-41]])
  >>> a.stride()
  (1, 2)
  >>> a.size()
  torch.Size([2, 3])
