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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 transform(self, X):
 """Imputes all missing values in X. Note that this is stochastic, and that if random_state is not fixed, repeated calls, or permuted input, will yield different results. Parameters X : array-like, shape = [n_samples, n_features] The input data to complete. Returns ------- Xt : array-like, shape (n_samples, n_features) The imputed input data. """

        check_is_fitted(self, 'initial_imputer_')

        X, Xt, mask_missing_values = self._initial_imputation(X)

        if self.n_iter == 0:
            return Xt

        imputations_per_round = len(self.imputation_sequence_) // self.n_iter
        i_rnd = 0
        if self.verbose > 0:
            print("[IterativeImputer] Completing matrix with shape %s"
                  % (X.shape,))
        start_t = time()
        for it, predictor_triplet in enumerate(self.imputation_sequence_):
            Xt, _ = self._impute_one_feature(
                Xt,
                mask_missing_values,
                predictor_triplet.feat_idx,
                predictor_triplet.neighbor_feat_idx,
                predictor=predictor_triplet.predictor,
                fit_mode=False
            )
            if not (it + 1) % imputations_per_round:
                if self.verbose > 1:
                    print('[IterativeImputer] Ending imputation round '
                          '%d/%d, elapsed time %0.2f'
                          % (i_rnd + 1, self.n_iter, time() - start_t))
                i_rnd += 1

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