# tensorplay.func.stack_module_state

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

```python
tensorplay.func.stack_module_state(models: list[Module]) → tuple[dict[str, Any], dict[str, Any]]
```

Stacks the state of several identical modules into batched tensors.

Pair the result with [functional_call()](/docs/generated/tensorplay.func.functional_call.html#tensorplay.func.functional_call) under
[vmap()](/docs/generated/tensorplay.func.vmap.html#tensorplay.func.vmap) to evaluate a whole ensemble in one call
instead of looping over the models.

All models must be the same class and in the same training mode – a mix
would make the stacked call mean two different things at once.

Returns:

(stacked_params, stacked_buffers), each keyed as the modules’
named_parameters/named_buffers are, with a new leading
dimension of length len(models).

Example

```
>>> params, buffers = stack_module_state(models)
>>> def call(p, b, x):
...     return functional_call(base_model, (p, b), (x,))
>>> vmap(call)(params, buffers, batched_x)
```