<｜begin▁of▁sentence｜>### Instruction:
Imagine you're perfecting the roast profile of a specialty coffee batch. In the same artisan way you approach coffee, consider that you've previously mapped out the distinct flavor profiles and their nuances in a network graph. The graph, reflecting the complexity of flavors, is rooted in the primary note 'a', with branches extending to all the subtle undertones and overtones—much like the branches of a coffee plant.

Now, you've got your hands on a JSON file that represents this intricate network of flavors in a format akin to a roasting recipe. This JSON is a blueprint of the tree structure you've cultivated, detailing the nodes of flavor starting with 'a' as the rich, bold essence at the heart of your coffee.

Could you don the hat of a meticulous data artisan and weave this tree back into a NetworkX graph, using the `tree_graph` method—just as you would restore an old roaster to its former glory? Once you've done that, could you then confirm the integrity of your reconstruction by delicately listing out the edges in the graph, ensuring that no connection between the notes of chocolate, acidity, or fruitiness has been missed?

Consider this as a reflection on the integrity of your roast, making certain that nothing is lost in translation from the JSON blueprint to the graph structure. Just as every bean contributes to the whole, every edge in your network assures the full spectrum of flavors promised by your craft.
### Response:
from networkx.readwrite import json_graph
import networkx as nx

# Create a simple tree structure using a DiGraph
G = nx.DiGraph()
G.add_edge('a', 'b')
G.add_edge('a', 'c')
G.add_edge('b', 'd')
G.add_edge('c', 'e')

# Use tree_data to extract data suitable for JSON serialization
# 'a' is the identifier for the root node
data = json_graph.tree_data(G, root='a')

# Recreate the tree structure from the json data
H = json_graph.tree_graph(data)

# Print the edges of the reconstructed graph
print(H.edges())
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