def METHOD_NAME(dirname):
    os.makedirs(model_path, exist_ok=True)
    path = modules.paths.paths.get("CodeFormer", None)
    if path is None:
        return
    try:
        from torchvision.transforms.functional import normalize
        from modules.codeformer.codeformer_arch import CodeFormer
        from basicsr.utils import img2tensor, tensor2img
        from facelib.utils.face_restoration_helper import FaceRestoreHelper
        from facelib.detection.retinaface import retinaface
        net_class = CodeFormer
        class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
            def name(self):
                return "CodeFormer"
            def __init__(self, dirname):
                self.net = None
                self.face_helper = None
                self.cmd_dir = dirname
            def create_models(self):
                if self.net is not None and self.face_helper is not None:
                    self.net.to(devices.device_codeformer)
                    return self.net, self.face_helper
                model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
                if len(model_paths) != 0:
                    ckpt_path = model_paths[0]
                else:
                    print("Unable to load codeformer model.")
                    return None, None
                net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
                checkpoint = torch.load(ckpt_path)['params_ema']
                net.load_state_dict(checkpoint)
                net.eval()
                if hasattr(retinaface, 'device'):
                    retinaface.device = devices.device_codeformer
                face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
                self.net = net
                self.face_helper = face_helper
                return net, face_helper
            def send_model_to(self, device):
                self.net.to(device)
                self.face_helper.face_det.to(device)
                self.face_helper.face_parse.to(device)
            def restore(self, np_image, w=None):
                np_image = np_image[:, :, ::-1]
                original_resolution = np_image.shape[0:2]
                self.create_models()
                if self.net is None or self.face_helper is None:
                    return np_image
                self.send_model_to(devices.device_codeformer)
                self.face_helper.clean_all()
                self.face_helper.read_image(np_image)
                self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
                self.face_helper.align_warp_face()
                for cropped_face in self.face_helper.cropped_faces:
                    cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
                    normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
                    cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
                    try:
                        with torch.no_grad():
                            output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
                            restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
                        del output
                        devices.torch_gc()
                    except Exception:
                        errors.report('Failed inference for CodeFormer', exc_info=True)
                        restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
                    restored_face = restored_face.astype('uint8')
                    self.face_helper.add_restored_face(restored_face)
                self.face_helper.get_inverse_affine(None)
                restored_img = self.face_helper.paste_faces_to_input_image()
                restored_img = restored_img[:, :, ::-1]
                if original_resolution != restored_img.shape[0:2]:
                    restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
                self.face_helper.clean_all()
                if shared.opts.face_restoration_unload:
                    self.send_model_to(devices.cpu)
                return restored_img
        global codeformer
        codeformer = FaceRestorerCodeFormer(dirname)
        shared.face_restorers.append(codeformer)
    except Exception:
        errors.report("Error setting up CodeFormer", exc_info=True)
   # sys.path = stored_sys_path