Need help please with programming some search algorithms. I am using VSC. Need major help with the coding. When I run my code below I get the an error message. Need the correct outputs for the other search algorithms please. Here is the traceback

line 189, in pprint(search.depthFirstSearch('San Bernardino', 'Los Angeles', graph)) ^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'SearchAlgorithms' object has no attribute 'depthFirstSearch'. Did you mean: 'breadthFirstSearch'?

Please use the code below code and help me get the correct outputs.

from queue import Priorityoueve from pprint import pprint \# Graph of cities with connections to each city. similar to our class exercises, you can drav it on a piece of paper * with step-by-step node inspection for better understanding aph ={ 'San ardino': ['Riverside', 'Rancho Cucamonga'], 'Riverside': ['san ardino', 'Ontario', 'Pomona'], 'Rancho Cucamonga': ['San Bernardino', 'Axusa', 'Los Angeles'], 'Ontario': ['Riverside', 'Whittier', 'Los Angeles'], 'Pombna': ['Riverside', 'Whittier', 'Axusa', 'Los Angeles'], 'Whittier': ['Ontario', 'Pomona', 'Los Angeles'], 'Azusa' : ['Rancho Cucamonga', 'Pomona', 'Arcadia'], 'ArCadia': ['Azusa', 'Los Angeles'], 'Los Angeles': ['Rancho Cucamonga', 'Ontario', 'Pomona', 'Hhittier', 'Arcadia'] \} * Heights are treated as E(n) function as we studied in our class lecture which represents the bachard oost. * In the data structure below, the key represents the oost from a source to target node. For example, the first * entry shaws that there is a oost of 2 for oing from san eardino to Riverside. weights ={ ('San dino', 'Riverside'): 2, ('San ardino', 'Rancho Cucamonga'): 1, ('Riverside', 'ontario'): 1, ('Riverside', 'Pomona'): 3, ('Rancho Cucamonga', 'Los Angeles'): 5, ('Pombna', 'Los Angeles'): 2, ('ortario', 'Whittier'): 2 , ('Ontario', 'Los Angeles'): 3, ('Rancho Cucamonga', 'Azusa'): 3, ('Pomena', 'Azusa'): 2, ('Pomona', 'Whittier'): 2, ('Azusa', 'Arcadia'): 1, ('Whittier', 'Los Angeles'): 2, ('Arcadia', 'Los Angeles'): 2 3 \# heurist is the h(n) function as we studied in our class lecture which represents the forward oost. * In the data structure below, each entry represents the h(n) value. For example, the second entry \# shaws that h(Riverside) is 2 (i.e., h value as forward oost for eaching at Riverside assuming that * your ut/start city is san adino) heuristic ={ 'Sa dino': 4, 'Riverside': 2 , 'Bancho Cucamonga' : 1, 'Ontario' : 1, 'Pomona' : 3, ' Whittier': 4, 'Axysa': 3, 'Arcadia': 2, 'Los Angeles' : g \} \# Data structure to implement search algerithme. Each function below currently has one line of oode returning empty solution with empty expanded cities. vou can rempue the current return statement and \# implement your oode to complete the functions. def uniform_oost_search(self, start, poal, graph, weights): frontier = Priorit yousue() fromtier.put(le, start)) came_from ={} oost_50_ ={} came_fiom [ start] = woe oost_so_far [start] =G while not frontier.enpty(): curent = fiontier.get ( id [1] if cuent == Boal: break for nert_node in aph.neighbors(cut): new_oost = oost_so_far [curent ]+ ETaph.oost (curent, next_node) if next_node not in oost_so_far or new_oost_ oost_so_far[next_node]: oost_30_far[nest_node] = new_oost priority = new_oost frontier.put((priority, nest_node)) came_from[nest_node] = curent t came_from, oost_so_far * wo can delete the line below once you have implemented your solution above def Astar(self, start, goal, graph, weights, heuristic): frontier = Priorit youeue() fromtier.put( (g,start)) explored = set() oost_so_far ={ start:es } came_from = start: wone while not frontier.empty(): cuent_oost, cuent_node = frontier.get () explored.add(curent_node) if curent_node == goal: path =[] while cuent_node != start: path.append(curent_node) curent_node = came_fiom[curent_node] path.append(start) path.reverse() et \{ "Retued solution: " + str(path) + "Expanded cities: " + str(list(explored))\} for eghbor in raph[cuent_node]: try; neighbor_oost = wejhts [(cuent_node, nejbor )] t : neighbor_oost = weights [( neighbor, cuent_node )] nev_oost = oost_so_far[current_node] + neighbor_oost if neighbor not in explored or new_oost ]= new_oost came_from[nejghbor] = cuent_node oombined_oost = new_oost + heuristic[neighbor ] frontier.put((oombined_oost,neighbor)) et \{"Retued solution: [], Expanded cities; \{\}".formut(explored)\}