def METHOD_NAME(weights='./yolov5s.pt',  # weights path
        img_size=(640, 640),  # image (height, width)
        batch_size=1,  # batch size
        device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        include=('torchscript', 'onnx', 'coreml'),  # include formats
        half=False,  # FP16 half-precision export
        inplace=False,  # set YOLOv5 Detect() inplace=True
        train=False,  # model.train() mode
        optimize=False,  # TorchScript: optimize for mobile
        dynamic=False,  # ONNX: dynamic axes
        simplify=False,  # ONNX: simplify model
        opset=12,  # ONNX: opset version
        ):
    t = time.time()
    include = [x.lower() for x in include]
    img_size *= 2 if len(img_size) == 1 else 1  # expand
    file = Path(weights)
    # Load PyTorch model
    device = select_device(device)
    assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
    model = attempt_load(weights, map_location=device)  # load FP32 model
    names = model.names
    # Input
    gs = int(max(model.stride))  # grid size (max stride)
    img_size = [check_img_size(x, gs) for x in img_size]  # verify img_size are gs-multiples
    img = torch.zeros(batch_size, 3, *img_size).to(device)  # image size(1,3,320,192) iDetection
    # Update model
    if half:
        img, model = img.half(), model.half()  # to FP16
    model.train() if train else model.eval()  # training mode = no Detect() layer grid construction
    for k, m in model.named_modules():
        if isinstance(m, Conv):  # assign export-friendly activations
            if isinstance(m.act, nn.Hardswish):
                m.act = Hardswish()
            elif isinstance(m.act, nn.SiLU):
                m.act = SiLU()
        elif isinstance(m, Detect):
            m.inplace = inplace
            m.onnx_dynamic = dynamic
            # m.forward = m.forward_export  # assign forward (optional)
    for _ in range(2):
        y = model(img)  # dry runs
    print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)")
    # Exports
    if 'torchscript' in include:
        export_torchscript(model, img, file, optimize)
    if 'onnx' in include:
        export_onnx(model, img, file, opset, train, dynamic, simplify)
    if 'coreml' in include:
        export_coreml(model, img, file)
    # Finish
    print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')