tensor([1., 2., 3.], grad_fn=)
      >>> # Shares memory with tensor 'b', with no grad
      >>> c = torch.asarray(b)
      >>> c
      tensor([1., 2., 3.])
      >>> # Shares memory with tensor 'b', retaining autograd history
      >>> d = torch.asarray(b, requires_grad=True)
      >>> d
      tensor([1., 2., 3.], grad_fn=)
  >>> array = numpy.array([1, 2, 3])
  >>> # Shares memory with array 'array'
  >>> t1 = torch.asarray(array)
  >>> array.__array_interface__['data'][0] == t1.data_ptr()
  True
  >>> # Copies memory due to dtype mismatch
  >>> t2 = torch.asarray(array, dtype=torch.float32)
  >>> array.__array_interface__['data'][0] == t1.data_ptr()
  False
