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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 cgmlst_subspecies_call(df_relatives):
 """Call Salmonella subspecies based on cgMLST results This method attempts to find the majority subspecies type within curated public genomes above a cgMLST allelic profile distance threshold. Note: ``CGMLST_SUBSPECIATION_DISTANCE_THRESHOLD`` is the cgMLST distance threshold used to determine the subspecies by cgMLST. It is set at a distance of 0.9 which translates to a cgMLST allelic similarity of 10%. A threshold of 0.9 is generous and reasonable given the congruence between subspecies designations and 10% cgMLST clusters by Adjusted Rand (~0.850) and Adjusted Wallace metrics (~0.850 both ways). Args: df_relatives (pandas.DataFrame):
 Table of genomes related by cgMLST to input genome Returns: None: if no curated public genomes found to have a cgMLST profile similarity of 10% or greater (string, float, dict):
 most common subspecies, closest related public genome distance, subspecies frequencies """


    closest_distance = df_relatives['distance'].min()

    if closest_distance > CGMLST_SUBSPECIATION_DISTANCE_THRESHOLD:
        logging.warning('Min cgMLST distance (%s) above subspeciation distance threshold (%s)',
            closest_distance,
            CGMLST_SUBSPECIATION_DISTANCE_THRESHOLD)
        return None
    else:
        df_relatives = df_relatives.loc[df_relatives.distance <= CGMLST_SUBSPECIATION_DISTANCE_THRESHOLD, :]
        df_relatives = df_relatives.sort_values('distance', ascending=True)
        logging.debug('df_relatives by cgmlst %s', df_relatives.head())
        genome_spp = genomes_to_subspecies()
        subspecies_below_threshold = [genome_spp[member_genome] if member_genome in genome_spp else None for member_genome in df_relatives.index]
        subspecies_below_threshold = filter(None, subspecies_below_threshold)
        subspecies_counter = Counter(subspecies_below_threshold)
        logging.debug('Subspecies counter: %s', subspecies_counter)
        return (subspecies_counter.most_common(1)[0][0], closest_distance, dict(subspecies_counter))