An MLOps engineer is building a Pandas UDF that applies a language model that translates English strings into Spanish. The initial code is loading the model on every call to the UDF, which is hurting the performance of the data pipeline. The initial code is: def in_spanish_inner(df: pd.Series) -> pd.Series: model = get_translation_model(target_lang='es') return df.apply(model) in_spanish = sf.pandas_udf(in_spanish_inner, StringType()) How can the MLOps engineer change this code to reduce how many times the language model is loaded?
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