<｜begin▁of▁sentence｜>### Instruction:
Suppose you're grooming a quartet of playful pups named Ace, Bella, Coco, and Duke. You're looking to jazz up their style with some matching accessories. To keep track of your styling plans, you sketched out a neat linear sequence tying the four dogs together in a path, from Ace to Bella, Bella to Coco, and Coco to Duke, almost like they're following each other in a charming conga line at the dog park.

Now, picture transferring that sequence onto your computer using NetworkX to design a digital path graph that mirrors your grooming blueprint. Each pup represents a node, and each direct scurry from one to the next is an edge. 

To spruce things up and make your planning a tad more sophisticated, you fancy sharing this pawesome graph with some fellow groomers online. So, here's a thought – how about you translate this NetworkX diagram into GraphML format? It's a structured language perfect for graphs. And don't just bark out the codes; make them as neat and pretty as the fur on a meticulously groomed poodle. Assume we're saving this as "StylishPups.graphml" and kindly draft the GraphML snippet that encapsulates our doggy diagram with that meticulousness you're known for. How would you go about it?
### Response:
import networkx as nx

# Create a NetworkX graph
G = nx.path_graph(4)

# Generate GraphML lines
for line in nx.generate_graphml(G, prettyprint=True):
    print(line)
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