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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _single_feature_logliks_one_step(self, feature, models):
 """Get log-likelihood of models at each parameterization for given data Parameters feature : pandas.Series Percent-based values of a single feature. May contain NAs, but only non-NA values are used. Returns ------- logliks : pandas.DataFrame """

        x_non_na = feature[~feature.isnull()]
        if x_non_na.empty:
            return pd.DataFrame()
        else:
            dfs = []
            for name, model in models.items():
                df = model.single_feature_logliks(feature)
                df['Modality'] = name
                dfs.append(df)
            return pd.concat(dfs, ignore_index=True)