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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_transform(self, X, y=None):
 """Fits the imputer on X and return the transformed X. Parameters X : array-like, shape (n_samples, n_features) Input data, where "n_samples" is the number of samples and "n_features" is the number of features. y : ignored. Returns ------- Xt : array-like, shape (n_samples, n_features) The imputed input data. """

        self.random_state_ = getattr(self, "random_state_",
                                     check_random_state(self.random_state))

        if self.n_iter < 0:
            raise ValueError(
                "'n_iter' should be a positive integer. Got {} instead."
                .format(self.n_iter))

        if self.predictor is None:
            if self.sample_posterior:
                from sklearn.linear_model import BayesianRidge
                self._predictor = BayesianRidge()
            else:
                from sklearn.linear_model import RidgeCV
                # including a very small alpha to approximate OLS
                self._predictor = RidgeCV(alphas=np.array([1e-5, 0.1,  1, 10]))
        else:
            self._predictor = clone(self.predictor)

        if hasattr(self._predictor, 'random_state'):
            self._predictor.random_state = self.random_state_

        self._min_value = np.nan if self.min_value is None else self.min_value
        self._max_value = np.nan if self.max_value is None else self.max_value

        self.initial_imputer_ = None
        X, Xt, mask_missing_values = self._initial_imputation(X)

        if self.n_iter == 0:
            return Xt

        # order in which to impute
        # note this is probably too slow for large feature data (d > 100000)
        # and a better way would be good.
        # see: https://goo.gl/KyCNwj and subsequent comments
        ordered_idx = self._get_ordered_idx(mask_missing_values)
        self.n_features_with_missing_ = len(ordered_idx)

        abs_corr_mat = self._get_abs_corr_mat(Xt)

        # impute data
        n_samples, n_features = Xt.shape
        self.imputation_sequence_ = []
        if self.verbose > 0:
            print("[IterativeImputer] Completing matrix with shape %s"
                  % (X.shape,))
        start_t = time()
        for i_rnd in range(self.n_iter):
            if self.imputation_order == 'random':
                ordered_idx = self._get_ordered_idx(mask_missing_values)

            for feat_idx in ordered_idx:
                neighbor_feat_idx = self._get_neighbor_feat_idx(n_features,
                                                                feat_idx,
                                                                abs_corr_mat)
                Xt, predictor = self._impute_one_feature(
                    Xt, mask_missing_values, feat_idx, neighbor_feat_idx,
                    predictor=None, fit_mode=True)
                predictor_triplet = ImputerTriplet(feat_idx,
                                                   neighbor_feat_idx,
                                                   predictor)
                self.imputation_sequence_.append(predictor_triplet)

            if self.verbose > 0:
                print('[IterativeImputer] Ending imputation round '
                      '%d/%d, elapsed time %0.2f'
                      % (i_rnd + 1, self.n_iter, time() - start_t))

        Xt[~mask_missing_values] = X[~mask_missing_values]
        return Xt