import queue def bfs(graph, initial_vertex, search_value): visited_vertices = [] bfs_queue = queue.SimpleQueue()

Here's the completed code: ``` import queue def bfs(graph, initial_vertex, search_value): visited_vertices = [] bfs_queue = queue.SimpleQueue() visited_vertices.append(initial_vertex) bfs_queue.put(initial_vertex) while not bfs_queue.empty(): current_vertex = bfs_queue.get() # Check if you found the search value if current_vertex == search_value: # Return True if you find the search value return True for adjacent_vertex in graph[current_vertex]: # Check if the adjacent vertex has been visited if adjacent_vertex not in visited_vertices: visited_vertices.append(adjacent_vertex) bfs_queue.put(adjacent_vertex) # Return False if you didn't find the search value return False print(bfs(graph, '4', '8')) ``` The code defines a bfs function that performs a breadth-first search on a graph. It takes three parameters: graph, which is a dictionary that represents the graph; initial_vertex, which is the vertex to start the search from; and search_value, which is the value to search for in the graph. It starts by creating an empty list for visited_vertices and a SimpleQueue object for bfs_queue. It adds the initial_vertex to visited_vertices and puts it into bfs_queue. Then, it enters a loop that continues until bfs_queue is empty. In each iteration of the loop, it gets the next vertex from bfs_queue and checks whether it matches the search_value. If it does, it returns True. Otherwise, it loops through all adjacent vertices of the current vertex and adds them to visited_vertices and bfs_queue if they haven't been visited before. This process continues until all vertices in the graph have been visited or the search_value has been found. Finally, it returns False if the search_value wasn't found. The code calls the bfs function with the graph, the initial vertex '4', and the search value '8'.