How to use the medaka.features.SampleGenerator function in medaka

def _run_region(bam, region, *args, **kwargs):
        data_gen = medaka.features.SampleGenerator(
            bam, region, *args, **kwargs)
        return data_gen.samples, data_gen._quarantined

def sample_gen():
        # chain all samples whilst dispensing with generators when done
        #   (they hold the feature vector in memory until they die)
        for region in regions:
            data_gen = medaka.features.SampleGenerator(
                bam, region, model_file, rle_ref, read_fraction,
                chunk_len=chunk_len, chunk_overlap=chunk_ovlp,
                tag_name=tag_name, tag_value=tag_value,
                tag_keep_missing=tag_keep_missing,
                enable_chunking=enable_chunking)
            yield from data_gen.samples
            remainder_regions.extend(data_gen._quarantined)
    batches = medaka.common.background_generator(

def _samples_worker(args, region, feature_encoder, label_scheme):
    logger = medaka.common.get_named_logger('PrepWork')
    logger.info("Processing region {}.".format(region))
    data_gen = SampleGenerator(
        args.bam, region, feature_encoder, truth_bam=args.truth,
        label_scheme=label_scheme, truth_haplotag=args.truth_haplotag,
        chunk_len=args.chunk_len, chunk_overlap=args.chunk_ovlp)

    return list(data_gen.samples), region