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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 apply( self, docs=None, split=0, train=False, clear=True, parallelism=None, progress_bar=True, ):
 """Apply features to the specified candidates. :param docs: If provided, apply features to all the candidates in these documents. :param split: If docs is None, apply features to the candidates in this particular split. :type split: int :param train: Whether or not to update the global key set of features and the features of candidates. :type train: bool :param clear: Whether or not to clear the features table before applying features. :type clear: bool :param parallelism: How many threads to use for extraction. This will override the parallelism value used to initialize the Featurizer 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 """

        if docs:
            # Call apply on the specified docs for all splits
            split = ALL_SPLITS
            super(Featurizer, self).apply(
                docs,
                split=split,
                train=train,
                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(Featurizer, self).apply(
                split_docs,
                split=split,
                train=train,
                clear=clear,
                parallelism=parallelism,
                progress_bar=progress_bar,
            )
            # Needed to sync the bulk operations
            self.session.commit()