<｜begin▁of▁sentence｜>### Instruction:
Imagine you're structuring the digital landscape of a city's road network, where intersections ('A', 'B') are connected with multiple pathways having different passage weights due to varying traffic conditions or construction work, denoted numerically (e.g., 1, 2). In this digital model, stored within a GML file named "city_roads.gml," you aim to examine and display the passage weights of all the diverse pathways that link a pair of intersections using NetworkX's sophisticated MultiAdjacency view. How would you approach accessing this detailed information to accurately reflect the intricate web of your city's road network within the data architecture?
### Response:
import networkx as nx

# Create a MultiGraph
G = nx.MultiGraph()

# Add multiple edges between the same pairs of nodes
G.add_edge('A', 'B', key='first', weight=1)
G.add_edge('A', 'B', key='second', weight=2)

# Access edges using a MultiAdjacency view
for n, nbrs in G.adjacency():
    for nbr, keydict in nbrs.items():
        for key, eattr in keydict.items():
            weight = eattr['weight']
            print(f'Edge from {n} to {nbr} with key {key} has weight {weight}')
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