# tensorplay.func.jvp

Source: https://www.tensorplay.cn/docs/generated/tensorplay.func.jvp.html

```python
tensorplay.func.jvp(func: Callable, primals: Any, tangents: Any, *, strict: bool = False, has_aux: bool = False)
```

Evaluates func at primals together with its directional
derivative along tangents.

Parameters:

- func (Callable) – a Python function taking one or more tensor arguments.

- primals (Tensors) – a tuple of positional arguments to evaluate at.

- tangents (Tensors) – the direction to differentiate along. Must have the same python structure, shapes and dtypes as primals.

- strict ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – raise instead of returning zeros when the output turns out to be independent of the inputs.

- has_aux ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – whether func returns (output, aux).

Returns:

(output, jvp_out), or (output, jvp_out, aux) when has_aux.

Example

```
>>> x = tensorplay.randn(5)
>>> out, tangent_out = jvp(tensorplay.sin, (x,), (tensorplay.ones(5),))
```