<｜begin▁of▁sentence｜>### Instruction:
Imagine you're perfecting the recipe for a sumptuous three-course vegetarian feast, where each dish represents a node in a sequential dining experience – a path graph of nodes consisting of an appetizer (0), a main course (1), and a dessert (2). Now, to understand the influence each dish has on the overall dining experience, akin to the betweenness centrality in a social network, how would you compute the relative importance or 'culinary centrality' of each course? Further, once you've determined these values, how would you garnish your menu (akin to annotating a network graph) with this vital information so that you can reference or analyze it when planning future gourmet adventures?
### Response:
import networkx as nx

# Create a path graph with 3 nodes
G = nx.path_graph(3)

# Compute the betweenness centrality for the nodes
betweenness = nx.betweenness_centrality(G)

# Set the betweenness centrality as a node attribute
definition = 'The fraction of all shortest paths that pass through a given node.'
nx.set_node_attributes(G, betweenness, 'betweenness_centrality')

# Print node attributes
definition = 'The fraction of all shortest paths that pass through a given node.'
for node, attrs in G.nodes(data=True):
    bc = attrs.get('betweenness_centrality', 0)
    print(f'Node {node}: Betweenness centrality = {bc}, Definition = {definition}')
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