<｜begin▁of▁sentence｜>### Instruction:
Imagine you're arranging a symphonic piece, and you have a detailed score that lists multiple possible interpretations for a particular melody line played between violin 1 and violin 2. The interpretations could be seen as variations with different emphases, akin to nuances in tonal weight that you might want to consider. Similarly, in a musical network graph you've been working on, where the score has been encoded as a .gml file, you have the nodes (representing musical instruments) 1 and 2 with two distinct connecting lines (edges) characterizing different ways the melody can be passed from one to the other. These lines are inscribed with a 'weight' denoting their tonal emphasis, the first with a weight of 5, signifying a softer passage, and the second with a heavier weight of 10, for a more pronounced rendition.

Now, in the midst of orchestrating this complex composition, you're faced with the task of referencing the specific detail of the first interpretative option – the one with the softer touch, or in graph terms, the edge with the lighter weight of 5. In order to accurately review this within your network graph, how might one go about using the MultiAdjacencyView to find the tonal weight of this initial edge between our violin 1 (node 1) and violin 2 (node 2)? The answer you're seeking is akin to choosing the right bowing technique that brings out the desired dynamic in your arrangement.
### Response:
import networkx as nx

# Create a MultiGraph
G = nx.MultiGraph()
# Add multiple edges between the same two nodes
G.add_edge(1, 2, weight=5)
G.add_edge(1, 2, weight=10)
# Add an edge between two different nodes
G.add_edge(2, 3, weight=15)

# Get the MultiAdjacencyView of the graph
multiadj_view = G.adj

# Access the weight for a specific edge
edge_weight = multiadj_view[1][2][0]['weight']
print('Weight of the edge:', edge_weight)
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