# concrete.ml.onnx.convert.md

[![](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/onnx/convert.py#L0)

## module `concrete.ml.onnx.convert`

ONNX conversion related code.

### **Global Variables**

* **IMPLEMENTED\_ONNX\_OPS**
* **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/onnx/convert.py#L17)

### function `get_equivalent_numpy_forward_and_onnx_model`

```python
get_equivalent_numpy_forward_and_onnx_model(
    torch_module: Module,
    dummy_input: Union[Tensor, Tuple[Tensor, ]],
    output_onnx_file: Optional[Path, str] = None
) → Tuple[Callable[, Tuple[ndarray, ]], GraphProto]
```

Get the numpy equivalent forward of the provided torch Module.

**Args:**

* `torch_module` (torch.nn.Module): the torch Module for which to get the equivalent numpy forward.
* `dummy_input` (Union\[torch.Tensor, Tuple\[torch.Tensor, ...]]): dummy inputs for ONNX export.
* `output_onnx_file` (Optional\[Union\[Path, str]]): Path to save the ONNX file to. Will use a temp file if not provided. Defaults to None.

**Returns:**

* `Tuple[Callable[..., Tuple[numpy.ndarray, ...]], onnx.GraphProto]`: The function that will execute the equivalent numpy code to the passed torch\_module and the generated 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/onnx/convert.py#L67)

### function `get_equivalent_numpy_forward`

```python
get_equivalent_numpy_forward(
    onnx_model: ModelProto,
    check_model: bool = True
) → Callable[, Tuple[ndarray, ]]
```

Get the numpy equivalent forward of the provided ONNX model.

**Args:**

* `onnx_model` (onnx.ModelProto): the ONNX model for which to get the equivalent numpy forward.
* `check_model` (bool): set to True to run the onnx checker on the model. Defaults to True.

**Raises:**

* `ValueError`: Raised if there is an unsupported ONNX operator required to convert the torch model to numpy.

**Returns:**

* `Callable[..., Tuple[numpy.ndarray, ...]]`: The function that will execute the equivalent numpy function.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.zama.org/concrete-ml/1.1/developer-guide/api/concrete.ml.onnx.convert.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.