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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 _pretrain_layer_and_gen_feed(self, layer_obj, set_params_func, train_set, validation_set, graph):
 """Pretrain a single autoencoder and encode the data for the next layer. :param layer_obj: layer model :param set_params_func: function used to set the parameters after pretraining :param train_set: training set :param validation_set: validation set :param graph: tf object for the rbm :return: encoded train data, encoded validation data """

        layer_obj.fit(train_set, train_set,
                      validation_set, validation_set, graph=graph)

        with graph.as_default():
            set_params_func(layer_obj, graph)

            next_train = layer_obj.transform(train_set, graph=graph)
            if validation_set is not None:
                next_valid = layer_obj.transform(validation_set, graph=graph)
            else:
                next_valid = None

        return next_train, next_valid