<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def apply( self, docs=None, split=0, train=False, lfs=None, clear=True, parallelism=None, progress_bar=True, ):
 """Apply the labels of the specified candidates based on the provided LFs. :param docs: If provided, apply the LFs to all the candidates in these documents. :param split: If docs is None, apply the LFs to the candidates in this particular split. :type split: int :param train: Whether or not to update the global key set of labels and the labels of candidates. :type train: bool :param lfs: A list of lists of labeling functions to apply. Each list should correspond with the candidate_classes used to initialize the Labeler. :type lfs: list of lists :param clear: Whether or not to clear the labels table before applying these LFs. :type clear: bool :param parallelism: How many threads to use for extraction. This will override the parallelism value used to initialize the Labeler if it is provided. :type parallelism: int :param progress_bar: Whether or not to display a progress bar. The progress bar is measured per document. :type progress_bar: bool :raises ValueError: If labeling functions are not provided for each candidate class. """

        if lfs is None:
            raise ValueError("Please provide a list of labeling functions.")

        if len(lfs) != len(self.candidate_classes):
            raise ValueError("Please provide LFs for each candidate class.")

        self.lfs = lfs
        if docs:
            # Call apply on the specified docs for all splits
            split = ALL_SPLITS
            super(Labeler, self).apply(
                docs,
                split=split,
                train=train,
                lfs=self.lfs,
                clear=clear,
                parallelism=parallelism,
                progress_bar=progress_bar,
            )
            # Needed to sync the bulk operations
            self.session.commit()
        else:
            # Only grab the docs containing candidates from the given split.
            split_docs = get_docs_from_split(
                self.session, self.candidate_classes, split
            )
            super(Labeler, self).apply(
                split_docs,
                split=split,
                train=train,
                lfs=self.lfs,
                clear=clear,
                parallelism=parallelism,
                progress_bar=progress_bar,
            )
            # Needed to sync the bulk operations
            self.session.commit()