<｜begin▁of▁sentence｜>### Instruction:
Imagine you're on a cybersecurity operation, and you've mapped out a network infrastructure using a GML file named "network_vulnerabilities.gml". This complex map is represented as a MultiGraph in NetworkX, where nodes are systems and multiple edges indicate different types of potential security vulnerabilities between the same pair of systems. Each edge has an associated weight that represents the risk level of the vulnerability.

Now, your objective is to meticulously audit the risk levels of these vulnerabilities. To do so, you need to utilize the MultiAdjacencyView afforded by NetworkX to systematically access and catalog the weights of all the multiple edges between each pair of nodes within your graph structure. 

How would you proceed with this task to ensure that no vulnerability escapes your scrutiny, given the intricate web of multi-edged relationships in your "network_vulnerabilities.gml" graph representation?
### Response:
import networkx as nx

# Create a MultiGraph.
G = nx.MultiGraph()

# Add multiple edges between the same nodes.
G.add_edge(1, 2, key='a')
G.add_edge(1, 2, key='b', weight=2.5)
G.add_edge(2, 3, key='a')

# Access the MultiAdjacencyView object.
multi_adjacency_view = G.adj

# Display the weights for multiple edges between nodes 1 and 2.
weights = [d['weight'] for u, adj in multi_adjacency_view.items() if u == 1 for v, keydict in adj.items() if v == 2 for key, d in keydict.items()]
print('Weights between nodes 1 and 2:', weights)
<|EOT|>
