def METHOD_NAME(config, orig_state_dict):
    for key in orig_state_dict.copy().keys():
        val = orig_state_dict.pop(key)
        if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
            continue
        else:
            orig_state_dict[rename_key(key)] = val
    orig_state_dict["cls.predictions.bias"] = orig_state_dict["cls.predictions.decoder.bias"]
    orig_state_dict["nystromformer.embeddings.position_ids"] = (
        torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
    )
    return orig_state_dict