torch.tensor
torch.tensor(data, *, dtype=None, device=None, requires_grad=False, pin_memory=False) -> Tensor
Constructs a tensor with no autograd history (also known as a "leaf
   tensor", see Autograd mechanics) by copying "data".
Warning:
 When working with tensors prefer using "torch.Tensor.clone()",
 "torch.Tensor.detach()", and "torch.Tensor.requires_grad_()" for
 readability. Letting *t* be a tensor, "torch.tensor(t)" is
 equivalent to "t.clone().detach()", and "torch.tensor(t,
 requires_grad=True)" is equivalent to
 "t.clone().detach().requires_grad_(True)".

See also:
 "torch.as_tensor()" preserves autograd history and avoids copies
 where possible. "torch.from_numpy()" creates a tensor that shares
 storage with a NumPy array.

Parameters:
      data (array_like) -- Initial data for the tensor. Can be a
      list, tuple, NumPy "ndarray", scalar, and other types.
Keyword Arguments: