# tensorplay.autograd.forward_ad.make_dual

Source: https://www.tensorplay.cn/docs/generated/tensorplay.autograd.forward_ad.make_dual.html

```python
tensorplay.autograd.forward_ad.make_dual(tensor: Tensor, tangent: Tensor, *, level: int | None = None) → Tensor
```

Associates a tensor value with its tangent to create a “dual tensor”.

The result is a new tensor aliased to tensor with tangent
attached as its forward gradient.  The tangent can be recovered with
[unpack_dual()](/docs/generated/tensorplay.autograd.forward_ad.unpack_dual.html#tensorplay.autograd.forward_ad.unpack_dual).

Given a function f whose jacobian is J, this computes the
Jacobian-vector product (jvp) between J and a given vector v:

```
>>> # xdoctest: +SKIP("Undefined variables")
>>> with dual_level():
...     inp = make_dual(x, v)
...     out = f(inp)
...     y, jvp = unpack_dual(out)
```