# {py:mod}`evox.problems.neuroevolution.virtual_lora_problem`

```{py:module} evox.problems.neuroevolution.virtual_lora_problem
```

```{autodoc2-docstring} evox.problems.neuroevolution.virtual_lora_problem
:allowtitles:
```

## Module Contents

### Classes

````{list-table}
:class: autosummary longtable
:align: left

* - {py:obj}`VirtualLoRAProblem <evox.problems.neuroevolution.virtual_lora_problem.VirtualLoRAProblem>`
  - ```{autodoc2-docstring} evox.problems.neuroevolution.virtual_lora_problem.VirtualLoRAProblem
    :summary:
    ```
````

### API

`````{py:class} VirtualLoRAProblem(model: torch.nn.Sequential, data_loader: torch.utils.data.DataLoader, criterion: torch.nn.Module, lora_rank: int, n_batch_per_eval: int = 1, device: torch.device | None = None, reduction: str = 'mean')
:canonical: evox.problems.neuroevolution.virtual_lora_problem.VirtualLoRAProblem

Bases: {py:obj}`evox.core.Problem`

```{autodoc2-docstring} evox.problems.neuroevolution.virtual_lora_problem.VirtualLoRAProblem
```

```{rubric} Initialization
```

```{autodoc2-docstring} evox.problems.neuroevolution.virtual_lora_problem.VirtualLoRAProblem.__init__
```

````{py:method} _virtual_forward(inputs: torch.Tensor, center_params: typing.Dict[str, torch.nn.Parameter], seeds: torch.Tensor, sigma: float, pop_size: int) -> torch.Tensor
:canonical: evox.problems.neuroevolution.virtual_lora_problem.VirtualLoRAProblem._virtual_forward

```{autodoc2-docstring} evox.problems.neuroevolution.virtual_lora_problem.VirtualLoRAProblem._virtual_forward
```

````

````{py:method} evaluate(payload) -> torch.Tensor
:canonical: evox.problems.neuroevolution.virtual_lora_problem.VirtualLoRAProblem.evaluate

```{autodoc2-docstring} evox.problems.neuroevolution.virtual_lora_problem.VirtualLoRAProblem.evaluate
```

````

`````