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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 graph_from_bbox(north, south, east, west, network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, name='unnamed', timeout=180, memory=None, max_query_area_size=50*1000*50*1000, clean_periphery=True, infrastructure='way["highway"]', custom_filter=None):
 """ Create a networkx graph from OSM data within some bounding box. Parameters north : float northern latitude of bounding box south : float southern latitude of bounding box east : float eastern longitude of bounding box west : float western longitude of bounding box network_type : string what type of street network to get simplify : bool if true, simplify the graph topology retain_all : bool if True, return the entire graph even if it is not connected truncate_by_edge : bool if True retain node if it's outside bbox but at least one of node's neighbors are within bbox name : string the name of the graph timeout : int the timeout interval for requests and to pass to API memory : int server memory allocation size for the query, in bytes. If none, server will use its default allocation size max_query_area_size : float max size for any part of the geometry, in square degrees: any polygon bigger will get divided up for multiple queries to API clean_periphery : bool if True (and simplify=True), buffer 0.5km to get a graph larger than requested, then simplify, then truncate it to requested spatial extent infrastructure : string download infrastructure of given type (default is streets (ie, 'way["highway"]') but other infrastructures may be selected like power grids (ie, 'way["power"~"line"]')) custom_filter : string a custom network filter to be used instead of the network_type presets Returns ------- networkx multidigraph """


    if clean_periphery and simplify:
        # create a new buffered bbox 0.5km around the desired one
        buffer_dist = 500
        polygon = Polygon([(west, north), (west, south), (east, south), (east, north)])
        polygon_utm, crs_utm = project_geometry(geometry=polygon)
        polygon_proj_buff = polygon_utm.buffer(buffer_dist)
        polygon_buff, _ = project_geometry(geometry=polygon_proj_buff, crs=crs_utm, to_latlong=True)
        west_buffered, south_buffered, east_buffered, north_buffered = polygon_buff.bounds

        # get the network data from OSM then create the graph
        response_jsons = osm_net_download(north=north_buffered, south=south_buffered,
                                          east=east_buffered, west=west_buffered,
                                          network_type=network_type, timeout=timeout,
                                          memory=memory, max_query_area_size=max_query_area_size,
                                          infrastructure=infrastructure, custom_filter=custom_filter)
        G_buffered = create_graph(response_jsons, name=name, retain_all=retain_all,
                                  bidirectional=network_type in settings.bidirectional_network_types)
        G = truncate_graph_bbox(G_buffered, north, south, east, west, retain_all=True, truncate_by_edge=truncate_by_edge)

        # simplify the graph topology
        G_buffered = simplify_graph(G_buffered)

        # truncate graph by desired bbox to return the graph within the bbox
        # caller wants
        G = truncate_graph_bbox(G_buffered, north, south, east, west, retain_all=retain_all, truncate_by_edge=truncate_by_edge)

        # count how many street segments in buffered graph emanate from each
        # intersection in un-buffered graph, to retain true counts for each
        # intersection, even if some of its neighbors are outside the bbox
        G.graph['streets_per_node'] = count_streets_per_node(G_buffered, nodes=G.nodes())

    else:
        # get the network data from OSM
        response_jsons = osm_net_download(north=north, south=south, east=east,
                                          west=west, network_type=network_type,
                                          timeout=timeout, memory=memory,
                                          max_query_area_size=max_query_area_size,
                                          infrastructure=infrastructure, custom_filter=custom_filter)

        # create the graph, then truncate to the bounding box
        G = create_graph(response_jsons, name=name, retain_all=retain_all,
                         bidirectional=network_type in settings.bidirectional_network_types)
        G = truncate_graph_bbox(G, north, south, east, west, retain_all=retain_all, truncate_by_edge=truncate_by_edge)

        # simplify the graph topology as the last step. don't truncate after
        # simplifying or you may have simplified out to an endpoint
        # beyond the truncation distance, in which case you will then strip out
        # your entire edge
        if simplify:
            G = simplify_graph(G)

    log('graph_from_bbox() returning graph with {:,} nodes and {:,} edges'.format(len(list(G.nodes())), len(list(G.edges()))))
    return  G