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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 predict_mhcii_binding(job, peptfile, allele, univ_options, mhcii_options):
 """ Predict binding for each peptide in `peptfile` to `allele` using the IEDB mhcii binding prediction tool. :param toil.fileStore.FileID peptfile: The input peptide fasta :param str allele: Allele to predict binding against :param dict univ_options: Dict of universal options used by almost all tools :param dict mhcii_options: Options specific to mhcii binding prediction :return: tuple of fsID for file containing the predictions and the predictor used :rtype: tuple(toil.fileStore.FileID, str|None) """

    work_dir = os.getcwd()
    input_files = {
        'peptfile.faa': peptfile}
    input_files = get_files_from_filestore(job, input_files, work_dir, docker=True)
    peptides = read_peptide_file(os.path.join(os.getcwd(), 'peptfile.faa'))
    parameters = [mhcii_options['pred'],
                  allele,
                  input_files['peptfile.faa']]
    if not peptides:
        return job.fileStore.writeGlobalFile(job.fileStore.getLocalTempFile()), None
    with open('/'.join([work_dir, 'predictions.tsv']), 'w') as predfile:
        docker_call(tool='mhcii', tool_parameters=parameters, work_dir=work_dir,
                    dockerhub=univ_options['dockerhub'], outfile=predfile, interactive=True,
                    tool_version=mhcii_options['version'])
    run_netmhciipan = True
    predictor = None
    with open(predfile.name, 'r') as predfile:
        for line in predfile:
            if not line.startswith('HLA'):
                continue
            if line.strip().split('\t')[5] == 'NetMHCIIpan':
                break
            # If the predictor type is sturniolo then it needs to be processed differently
            elif line.strip().split('\t')[5] == 'Sturniolo':
                predictor = 'Sturniolo'
            else:
                predictor = 'Consensus'
            run_netmhciipan = False
            break
    if run_netmhciipan:
        netmhciipan = job.addChildJobFn(predict_netmhcii_binding, peptfile, allele, univ_options,
                                        mhcii_options['netmhciipan'], disk='100M', memory='100M',
                                        cores=1)
        job.fileStore.logToMaster('Ran mhcii on %s:%s successfully'
                                  % (univ_options['patient'], allele))
        return netmhciipan.rv()
    else:
        output_file = job.fileStore.writeGlobalFile(predfile.name)
        job.fileStore.logToMaster('Ran mhcii on %s:%s successfully'
                                  % (univ_options['patient'], allele))
        return output_file, predictor