# tensorplay.library.custom_op

Source: https://www.tensorplay.cn/docs/generated/tensorplay.library.custom_op.html

```python
tensorplay.library.custom_op(name: str, fn: Callable[[...], Any] | None = None, /, *, mutates_args: Sequence[str] = (), device_types: Any = None, schema: str | None = None) → Callable[[Callable[[...], Any]], CustomOpDef]
```

Example:

```
@tensorplay.library.custom_op("mylib::weighted_sum", mutates_args=())
def weighted_sum(x, weight):
    return (x * weight).sum()

# Optional extra kernels per device:
@weighted_sum.register_kernel("cuda")
def _(x, weight): ...
```

fn may also be passed positionally
(custom_op("mylib::op", my_fn, mutates_args=())), matching
operator’s default kernel for the advertised device_types (every
device when omitted).

Parameters:

- name – Qualified "namespace::name" identifier.

- fn – Operator body; omit to use the return value as a decorator.

- mutates_args – Names of arguments the kernel mutates in place. Compile-time fusion treats these as barriers regardless of the value; eager execution trusts the declaration.

- device_types – Restriction advertised to users at definition time. Kernels are selected per call from whatever was registered via CustomOpDef.register_kernel().

- schema – Optional schema string kept for introspection and [opcheck()](/docs/generated/tensorplay.library.opcheck.html#tensorplay.library.opcheck) (TensorPlay models no schema grammar).

Returns:

A decorator producing a callable CustomOpDef.