<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a photojournalist assigned to visually document a unique tale of twin cities, each with its distinct neighborhoods, numbering five in each city. These cities share a history, connected by a lone, storied bridge that has withstood the test of time, serving as the only passage between them. In your narrative, you aim to highlight the interconnected lives and the bridge that plays a pivotal role in their unity.

As you weave this story through your lens, you're intrigued by the idea of exploring not just the physical bridge but the metaphorical bridges within each city - the invisible bonds that hold the neighborhoods together, making them resilient, almost inseparable. Your quest is to uncover and illustrate these connections, showcasing how despite being two separate entities, the cities and their inner neighborhoods are tightly-knit communities, standing strong together through shared experiences and mutual support.

To bring this concept to life, you decide to use NetworkX, a tool not usually in a photojournalist's toolkit, to create a visual map of these connections. You're particularly interested in identifying those special links within the cities that, much like the lone physical bridge, are critical to the cities' unity. You're looking for those integral bonds within each city that, if severed, would isolate any neighborhood from the rest, revealing the underlying structure of these resilient communities.

Your task, then, is to use NetworkX to map out and identify these crucial connections within each city, akin to finding all the "invisible bridges" that are essential for the cohesion and resilience of these urban landscapes. How can you, through the lens of NetworkX, discover these vital links, ensuring your narrative captures the true essence of interconnectedness and resilience in these twin cities?
### Response:
import networkx as nx
from networkx.algorithms.connectivity.edge_kcomponents import bridge_components

# Create an undirected graph
G = nx.barbell_graph(5, 0)

# Find all bridge-connected components
components = sorted(map(sorted, bridge_components(G)))
print(components)
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