def centers_within_distance(school: Dict[str, str], centers: Dict[str, str], distance_threshold: float) -> List[Dict[str, any]]:
    """
    Return List of centers that are within given distance from school.
    If there are no centers within given distance return one that is closest
    Returned params :
            {'cscode', 'name', 'address', 'capacity', 'lat', 'long', 'distance_km'}

    """
    def center_to_dict(c, distance):
        return {'cscode': c['cscode'], 'name': c['name'], 'address': c['address'], 'capacity': c['capacity'], 'lat': c['lat'], 'long': c['long'], 'distance_km': distance}
    
    def sort_key(c):
        # intent: sort by preference score DESC then by distance_km ASC 
        # leaky abstraction - sorted requires a single numberic value for each element
        return c['distance_km'] * random.uniform(1,5) - get_pref(school['scode'], c['cscode'])*100
    
    school_lat = school.get('lat')
    school_long = school.get('long')
    if len(school_lat) == 0 or len(school_long) == 0:
        return []
    
    within_distance = []
    nearest_distance = None;
    nearest_center = None
    for c in centers: 
        distance = haversine_distance(float(school_lat), float(school_long), float(c.get('lat')), float(c.get('long')))
        if school['scode'] == c['cscode']:
            continue
        if nearest_center == None or distance < nearest_distance:
            nearest_center = c
            nearest_distance = distance

        if distance <= distance_threshold and get_pref(school['scode'], c['cscode']) > PREF_CUTOFF:
            within_distance.append(center_to_dict(c, distance))
            
    if len(within_distance) > 0:
        return sorted(within_distance, key=sort_key) 
    else: # if there are no centers within given  threshold, return one that is closest
        return [center_to_dict(nearest_center, nearest_distance)]