# concrete.ml.torch.numpy\_module.md

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## module `concrete.ml.torch.numpy_module`

A torch to numpy module.

### **Global Variables**

* **OPSET\_VERSION\_FOR\_ONNX\_EXPORT**

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/torch/numpy_module.py#L19)

### class `NumpyModule`

General interface to transform a torch.nn.Module to numpy module.

**Args:**

* `torch_model` (Union\[nn.Module, onnx.ModelProto]): A fully trained, torch model along with its parameters or the onnx graph of the model.
* `dummy_input` (Union\[torch.Tensor, Tuple\[torch.Tensor, ...]]): Sample tensors for all the module inputs, used in the ONNX export to get a simple to manipulate nn representation.
* `debug_onnx_output_file_path`: (Optional\[Union\[Path, str]], optional): An optional path to indicate where to save the ONNX file exported by torch for debug. Defaults to None.

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/torch/numpy_module.py#L32)

#### method `__init__`

```python
__init__(
    model: Union[Module, ModelProto],
    dummy_input: Optional[Tensor, Tuple[Tensor, ]] = None,
    debug_onnx_output_file_path: Optional[Path, str] = None
)
```

***

**property onnx\_model**

Get the ONNX model.

.. # noqa: DAR201

**Returns:**

* `_onnx_model` (onnx.ModelProto): the ONNX model

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/torch/numpy_module.py#L79)

#### method `forward`

```python
forward(*args: ndarray) → Union[ndarray, Tuple[ndarray, ]]
```

Apply a forward pass on args with the equivalent numpy function only.

**Args:**

* `*args`: the inputs of the forward function

**Returns:**

* `Union[numpy.ndarray, Tuple[numpy.ndarray, ...]]`: result of the forward on the given inputs


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