# NestedIOFunction

Source: https://www.tensorplay.cn/docs/generated/tensorplay.autograd.function.NestedIOFunction.html

```python
class tensorplay.autograd.function.NestedIOFunction
```

Kept only for import compatibility; the modern contract is to define
forward + backward on [Function](/docs/generated/tensorplay.autograd.function.Function.html#tensorplay.autograd.function.Function) directly.

```python
classmethod apply(*args, **kwargs)
```

Runs the operation and attaches gradient bookkeeping to outputs.

flat arguments computes needs_input_grad and wires next-edges
BEFORE forward; outputs are marked and attached AFTER
setup_context.  When the fused C++ apply is present the hot
path makes a single pybind crossing that also wires the backward
entry; otherwise a generic Python fallback runs.

```python
static jvp(ctx, *grad_inputs)
```

Defines a formula for computing the jacobian-vector product.

Called by forward-mode AD when at least one input of this Function
carries a tangent: grad_inputs holds the tangent of each input
(None for inputs without one), and the returned tangents are
attached to the outputs one-for-one.

```python
static setup_context(ctx, inputs, output)
```

Sets up the context object (Usage 2 above).

Parameters:

- ctx (_Context) – context object to modify in-place

- inputs ([tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple)) – inputs to forward()

- output (Any) – output of forward()

```python
static vmap(info, in_dims, *args)
```

Defines a formula for vectorizing the operation.

Not yet supported by this engine; provided for API compatibility.