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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 _load_training_state(self, train_iter: data_io.BaseParallelSampleIter):
 """ Loads the full training state from disk. :param train_iter: training data iterator. """

        # (1) Parameters
        params_fname = os.path.join(self.training_state_dirname, C.TRAINING_STATE_PARAMS_NAME)
        self.model.load_params_from_file(params_fname)

        # (2) Optimizer states
        opt_state_fname = os.path.join(self.training_state_dirname, C.OPT_STATES_LAST)
        self.model.load_optimizer_states(opt_state_fname)

        # (3) Data Iterator
        train_iter.load_state(os.path.join(self.training_state_dirname, C.BUCKET_ITER_STATE_NAME))

        # (4) Random generators
        # RNG states: python's random and np.random provide functions for
        # storing the state, mxnet does not, but inside our code mxnet's RNG is
        # not used AFAIK
        with open(os.path.join(self.training_state_dirname, C.RNG_STATE_NAME), "rb") as fp:
            random.setstate(pickle.load(fp))
            np.random.set_state(pickle.load(fp))

        # (5) Training state
        self.state = TrainState.load(os.path.join(self.training_state_dirname, C.TRAINING_STATE_NAME))

        # (6) Learning rate scheduler
        with open(os.path.join(self.training_state_dirname, C.SCHEDULER_STATE_NAME), "rb") as fp:
            self.optimizer_config.set_lr_scheduler(pickle.load(fp))
        # initialize optimizer again
        self._initialize_optimizer()