def METHOD_NAME(
    state_dict: Dict[str, Any], pretrained_xlm_checkpoint: str
) -> Dict[str, Any]:
    """
    Load XLM weights into a Transformer encoder or decoder model.
    Args:
        state_dict: state dict for either TransformerEncoder or
            TransformerDecoder
        pretrained_xlm_checkpoint: checkpoint to load XLM weights from
    Raises:
        AssertionError: If architecture (num layers, attention heads, etc.)
            does not match between the current Transformer encoder or
            decoder and the pretrained_xlm_checkpoint
    """
    if not os.path.exists(pretrained_xlm_checkpoint):
        raise IOError("Model file not found: {}".format(pretrained_xlm_checkpoint))
    state = checkpoint_utils.load_checkpoint_to_cpu(pretrained_xlm_checkpoint)
    xlm_state_dict = state["model"]
    for key in xlm_state_dict.keys():
        for search_key in ["embed_tokens", "embed_positions", "layers"]:
            if search_key in key:
                subkey = key[key.find(search_key) :]
                assert subkey in state_dict, (
                    "{} Transformer encoder / decoder "
                    "state_dict does not contain {}. Cannot "
                    "load {} from pretrained XLM checkpoint "
                    "{} into Transformer.".format(
                        str(state_dict.keys()), subkey, key, pretrained_xlm_checkpoint
                    )
                )
                state_dict[subkey] = xlm_state_dict[key]
    return state_dict