def METHOD_NAME(self, image, info: dict):
    # crop templates
    crop_resize_patches = [
        sample_target(
            image,
            info['init_bbox'],
            factor,
            output_sz=self.cfg.TEST.TEMPLATE_SIZE)
        for factor in self.cfg.TEST.TEMPLATE_FACTOR
    ]
    z_patch_arr, resize_factor, z_amask_arr = zip(*crop_resize_patches)
    for idx in range(len(z_patch_arr)):
        template = self.preprocessor.process(z_patch_arr[idx],
                                             z_amask_arr[idx])
        with torch.no_grad():
            self.z_dict1 = template
        self.z_dict_list.append(self.z_dict1)
    self.box_mask_z = []
    if self.cfg.MODEL.BACKBONE.CE_LOC:
        for i in range(len(self.cfg.TEST.TEMPLATE_FACTOR) * 2):
            template_bbox = self.transform_bbox_to_crop(
                info['init_bbox'], resize_factor[0],
                template.tensors.device).squeeze(1)
            self.box_mask_z.append(
                generate_mask_cond(self.cfg, 1, template.tensors.device,
                                   template_bbox))
    # init dynamic templates with static templates
    for idx in range(len(self.cfg.TEST.TEMPLATE_FACTOR)):
        self.z_dict_list.append(deepcopy(self.z_dict_list[idx]))
    # save states
    self.state = info['init_bbox']
    self.frame_id = 0