Project: s41467-024-48673-8 | Function: target (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements RISER's core real-time RNA classification and selective sequencing algorithm. Processes streaming nanopore reads, performs signal preprocessing, applies deep learning classification, and makes real-time decisions to accept/reject reads based on RNA class probability thresholds.

**KEY TERMS:** {
  "target": "RISER targeting algorithm",
  "classify": "deep learning classification",
  "threshold": "probability threshold",
  "mode": "enrich/deplete mode",
  "polyA_cache": "polyA tail detection cache"
}

**CONTEXT CODE:** import time

class SequencerControl():
    def __init__(self, client, models, processor, logger, out_file):
        self.client = client
        self.models = models
        self.proc = processor
        self.logger = logger
        self.out_filename = out_file

    def target(self, mode, duration_h, threshold, unblock_duration=0.1):
        """Implements RISER's core real-time RNA classification and selective sequencing algorithm. Processes streaming nanopore reads, performs signal preprocessing, applies deep learning classification, and makes real-time decisions to accept/reject reads based on RNA class probability thresholds."""
        raise NotImplementedError("Function has been masked for testing purposes")

    def start(self):
        self.client.start_streaming_reads()
        self.logger.info('Live read stream started.')

    def finish(self):
        self.client.reset()
        self.logger.info('Client reset and live read stream ended.')

    def _hours_to_seconds(self, hours):
        return hours * 60 * 60

    def _get_read_id(self, read):
        # Support for minknow-api <= v5.*
        if hasattr(read, "number"):
            return read.number
        # Support for minknow-api >= v6.*
        else:
            return read.id

    def _write_header(self, csv_file):
        csv_file.write('batch_start,read_id,channel,sig_length,models,prob_targets,threshold,mode,decision\n')

    def _write(self, csv_file, batch_start, channel, read, sig_length,
               models, p_on_targets, threshold, mode, decision):
        csv_file.write(f'{batch_start:.0f},{read},{channel},{sig_length},'
                       f'{";".join([m.target for m in models])},'
                       f'{";".join([str(p.item()) for p in p_on_targets])},'
                       f'{threshold},{mode},{decision}\n')


Complete the function implementation replacing "raise NotImplementedError".