<｜begin▁of▁sentence｜>### Instruction:
Imagine you are overseeing the regulatory aspects of a telecommunications network project within a regulated industry, ensuring that the network's design adheres to specific performance standards. Your team has presented you with the blueprint of the network's infrastructure, which is conceptualized as a weighted undirected graph. The graph's connection data consists of various links as follows: [("A", "B", 3), ("A", "C", 2), ("B", "C", 1), ("C", "D", 4), ("D", "A", 5)], where each tuple represents a connection between two nodes with an associated weight that represents the cost or distance of that connection.

For efficiency and cost-effectiveness, we aim to modify the blueprint to form a streamlined version of our network – or to be precise, a subgraph, dubbed a spanner. The critical requirement for this spanner is that it should preserve the integrity of the distances between all network nodes, in such a manner that the maximum distance between any pair of nodes in the spanner does not exceed thrice the distance between them in the original blueprint.

Given the importance of meeting industry standards and maintaining the reliability and performance of the network, how would you instruct your team to apply NetworkX algorithms to transform the initial graph into the desired spanner that adheres to the aforementioned distance constraint? Your directive will enable them to present a revised list of connections for the spanner that complies with the regulatory requirements.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_weighted_edges_from([("A", "B", 3), ("A", "C", 2), ("B", "C", 1), ("C", "D", 4), ("D", "A", 5)])

# Generate a spanner of the graph with stretch 3
spanner_graph = nx.spanner(G, stretch=3, weight='weight')

# Print the edges of the spanner
print(spanner_graph.edges(data=True))
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