Constructs a tensor with no autograd history (also known as a “leaf tensor”, see Autograd mechanics) by copying data.
>>> 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])
>>> torch.tensor([[0.11111, 0.222222, 0.3333333]],
... dtype=torch.float64,
... device=torch.device('cuda:0')) # creates a double tensor on a CUDA device
tensor([[ 0.1111, 0.2222, 0.3333]], dtype=torch.float64, device='cuda:0')
>>> torch.tensor(3.14159) # Create a zero-dimensional (scalar) tensor
tensor(3.1416)
>>> torch.tensor([]) # Create an empty tensor (of size (0,))
tensor([])
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