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):
         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 is 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 no centers within the preferred distance and above the preference cutoff,
         # return centers sorted by preference score and distance, excluding those below PREF_CUTOFF
         all_centers = [center_to_dict(c, haversine_distance(float(school_lat), float(school_long), float(c.get('lat')), float(c.get('long')))) for c in centers if school['scode'] != c['cscode']]
         qualified_centers = [c for c in all_centers if get_pref(school['scode'], c['cscode']) > PREF_CUTOFF]
         if qualified_centers:
             return sorted(qualified_centers, key=sort_key)
         else:
             return [center_to_dict(nearest_center, nearest_distance)]