def METHOD_NAME(cls,
               ir_files: Sequence[str],
               work_dir: str,
               deploy_cfg: Any,
               log_level: int = logging.INFO,
               device: str = 'cpu',
               **kwargs) -> Sequence[str]:
    """Convert intermediate representation to given backend.
    Args:
        ir_files (Sequence[str]): The intermediate representation files.
        work_dir (str): The work directory, backend files and logs should
            be saved in this directory.
        deploy_cfg (Any): The deploy config.
        log_level (int, optional): The log level. Defaults to logging.INFO.
        device (str, optional): The device type. Defaults to 'cpu'.
    Returns:
        Sequence[str]: Backend files.
    """
    from mmdeploy.utils import (get_common_config, get_ir_config,
                                get_model_inputs, load_config)
    from .torchscript2coreml import from_torchscript, get_model_suffix
    coreml_files = []
    for model_id, torchscript_path in enumerate(ir_files):
        torchscript_name = osp.splitext(osp.split(torchscript_path)[1])[0]
        output_file_prefix = osp.join(work_dir, torchscript_name)
        deploy_cfg = load_config(deploy_cfg)[0]
        common_params = get_common_config(deploy_cfg)
        model_params = get_model_inputs(deploy_cfg)[model_id]
        final_params = common_params
        final_params.update(model_params)
        ir_config = get_ir_config(deploy_cfg)
        input_names = ir_config.get('input_names', [])
        output_names = ir_config.get('output_names', [])
        input_shapes = final_params['input_shapes']
        compute_precision = final_params.get('compute_precision',
                                             'FLOAT32')
        convert_to = deploy_cfg.backend_config.convert_to
        minimum_deployment_target = final_params.get(
            'minimum_deployment_target', None)
        skip_model_load = final_params.get('skip_model_load', False)
        from_torchscript(
            torchscript_path,
            output_file_prefix,
            input_names=input_names,
            output_names=output_names,
            input_shapes=input_shapes,
            compute_precision=compute_precision,
            convert_to=convert_to,
            minimum_deployment_target=minimum_deployment_target,
            skip_model_load=skip_model_load)
        suffix = get_model_suffix(convert_to)
        output_path = output_file_prefix + suffix
        coreml_files.append(output_path)
    return coreml_files