In chapter 4 of the book, we have this code:

```
xt = tensor([3.,4.,10.]).requires_grad_()
xt
```

with this output:

```
tensor([ 3., 4., 10.], requires_grad=True)
```

And then we try convert the vector (rank-1 tensor) to a scalar (rank-0 tensor):

```
def f(x): return (x**2).sum()
yt = f(xt)
yt
```

Output:

```
tensor(125., grad_fn=<SumBackward0>)
```

Why do we convert the vector (rank-1 tensor) to a scalar (rank-0 tensor)? Is this because a scalar value is needed to calculate gradients using `backward()`

?