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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 fit(self, X, y=None):
 """Fit all transformers using X. Parameters X : iterable or array-like, depending on transformers Input data, used to fit transformers. Targets for supervised learning. Returns ------- self : FeatureUnion This estimator """

        self.transformer_list = list(self.transformer_list)
        self._validate_transformers()
        with Pool(self.n_jobs) as pool:
            transformers = pool.starmap(_fit_one_transformer,
                        ((trans, X[trans['col_pick']] if hasattr(trans, 'col_pick') else X, y) for _, trans, _ in self._iter()))
        self._update_transformer_list(transformers)
        return self