<｜begin▁of▁sentence｜>### Instruction:
Imagine you're at an avant-garde hair salon, where the layout is inspired by unique connections resembling patterns found in modern art. Picture this: The salon has two popular styling zones, each with 10 styling chairs (numbered 1-10 in the first zone and 11-20 in the second zone), known for their complete set of tools and resources, allowing each stylist to create masterpieces. These zones are connected by a shared, premium chair used for special collaborative styling projects, representing a bridge of creativity between the two zones.

As a renowned stylist with an eye for nuanced relationships, you're curious about the collaborative dynamics within this space. Specifically, you're considering whether stylist chair number 5 (in the first styling zone) and stylist chair number 15 (in the second styling zone) have a sort of 'back-up' connection – meaning, if the shared premium chair suddenly became unavailable (like a snap of a hair-clipper's blade), would you still be able to collaborate indirectly through at least one other styling chair (this is your 'locally 1-edge-connected' query).

You're also pondering whether there's even stronger resilience in their connection – asking if there are two unique indirect paths that could keep collaboration alive between chairs 5 and 15 without using the direct link (like having two separate sets of combs when one goes missing), which would be your 'locally 2-edge-connected' scenario.

Lastly, within the first styling zone itself, you're curious if chairs 1 and 5 have this kind of creative redundancy, where two separate pathways exist for sharing tools and ideas, making sure that an unexpected outage of one chair won't halt the flow between them.

Envision this complex network of connections as a web of hairstyles that can adapt and flourish even when a strand falls out of place, maintaining the salon's bustling atmosphere of collaboration and creativity.
### Response:
from networkx.algorithms.connectivity import is_locally_k_edge_connected
import networkx as nx

G = nx.barbell_graph(10, 0)
result = is_locally_k_edge_connected(G, 5, 15, k=1)
print('Are nodes 5 and 15 locally 1-edge-connected:', result)
result = is_locally_k_edge_connected(G, 5, 15, k=2)
print('Are nodes 5 and 15 locally 2-edge-connected:', result)
result = is_locally_k_edge_connected(G, 1, 5, k=2)
print('Are nodes 1 and 5 locally 2-edge-connected:', result)
<|EOT|>
