def METHOD_NAME(predict_ds, train_ds):
    # It copies the important configurations from the train dataset of the model
    # into the predict_ds to be used for prediction. It is needed to match the training configurations.
    if train_ds is None:
        return
    predict_ds.sample_rate = train_ds.get("sample_rate", 16000)
    cfg_name_list = [
        "int_values",
        "use_start_end_token",
        "blank_index",
        "unk_index",
        "normalize",
        "parser",
        "eos_id",
        "bos_id",
        "pad_id",
    ]
    if is_dataclass(predict_ds):
        predict_ds = OmegaConf.structured(predict_ds)
    for cfg_name in cfg_name_list:
        if hasattr(train_ds, cfg_name):
            setattr(predict_ds, cfg_name, getattr(train_ds, cfg_name))
    return predict_ds