def METHOD_NAME(
    model: torch.nn.Module,
    device: str,
    export_path: Union[str, Path],
    is_coca: bool,
    **export_kwargs,
):
    module_name = "clip_visual.onnx"
    visual_model = model.visual
    image_shape = visual_model.image_size[0]
    sample_input = torch.randn(1, 3, image_shape, image_shape, requires_grad=True)
    visual_model = visual_model.to(device)
    visual_model.eval()
    _export_onnx(
        module=visual_model,
        sample_batch=sample_input,
        file_path=export_path / module_name,
        **export_kwargs,
    )