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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 train(self, X_feat, X_seq, y, id_vec=None, n_folds=10, use_stored_folds=None, n_cores=1, train_global_model=False):
 """Train the Concise model in cross-validation. Args: X_feat: See :py:func:`concise.Concise.train` X_seq: See :py:func:`concise.Concise.train` y: See :py:func:`concise.Concise.train` id_vec: List of character id's used to differentiate the trainig samples. Returned by :py:func:`concise.prepare_data`. n_folds (int):
 Number of CV-folds to use. use_stored_folds (chr or None):
 File path to a .json file containing the fold information (as returned by :py:func:`concise.ConciseCV.get_folds`). If None, the folds are generated. n_cores (int):
 Number of CPU cores used for training. If available, GPU is used for training and this argument is ignored. train_global_model (bool):
 In addition to training the model in cross-validation, should the global model be fitted (using all the samples from :code:`(X_feat, X_seq, y)`). """

        # TODO: input check - dimensions
        self._use_stored_folds = use_stored_folds
        self._n_folds = n_folds
        self._n_rows = X_feat.shape[0]

    # TODO: - fix the get_cv_accuracy
    # save:
    # - each model
    # - each model's performance
    # - each model's predictions
    # - globally:
    #    - mean perfomance
    #    - sd performance
    #    - predictions

        self._kf = self._get_folds(self._n_rows, self._n_folds, self._use_stored_folds)

        cv_obj = {}

        if id_vec is None:
            id_vec = np.arange(1, self._n_rows + 1)

        best_val_acc_epoch_l = []
        for fold, train, test in self._kf:
            X_feat_train = X_feat[train]
            X_seq_train = X_seq[train]
            y_train = y[train]

            X_feat_test = X_feat[test]
            X_seq_test = X_seq[test]
            y_test = y[test]
            id_vec_test = id_vec[test]
            print(fold, "/", n_folds)

            # copy the object
            dc = copy.deepcopy(self._concise_model)
            dc.train(X_feat_train, X_seq_train, y_train,
                     X_feat_test, X_seq_test, y_test,
                     n_cores=n_cores
                     )

            dc._test(X_feat_test, X_seq_test, y_test, id_vec_test)
            cv_obj[fold] = dc
            best_val_acc_epoch_l.append(dc.get_accuracy()["best_val_acc_epoch"])
        self._cv_model = cv_obj

        # additionaly train the global model
        if train_global_model:
            dc = copy.deepcopy(self._concise_model)

            # overwrite n_epochs with the best average number of best epochs
            dc._param["n_epochs"] = int(np.array(best_val_acc_epoch_l).mean())
            print("tranining global model with n_epochs = " + str(dc._param["n_epochs"]))

            dc.train(X_feat, X_seq, y,
                     n_cores=n_cores
                     )
            dc._test(X_feat, X_seq, y, id_vec)
            self._concise_global_model = dc