<｜begin▁of▁sentence｜>### Instruction:
Imagine we're constructing a metaphorical "community network" to keep track of juvenile offenders' interactions and support structures in a rehabilitation program. This network consists of nine individuals, each represented by a node, and they are connected in a circular pattern, much like a support circle where everyone is equally linked with their immediate peers.

Now, in our ongoing efforts to strengthen this support system, we want to identify any "unique connections" or local bridges within this circle. These would be connections that, if removed, would increase the distance between the two individuals connected by the bridge, potentially hindering effective communication and support.

Could you assist us by employing the 'local_bridges' functionality available in NetworkX? This tool will help us pinpoint these critical links in our network and understand the impact – or "span" – of removing them, which directly relates to how much longer the path would become between two individuals if that bridge were not there. By highlighting and analyzing this information, we can ensure the integrity of our community support network and take preventive measures to maintain a strong rehabilitation framework.
### Response:
import networkx as nx

# Create a cycle graph
G = nx.cycle_graph(9)

# Use the local_bridges function to find local bridges and their spans in the graph
# with_span set to True to compute the span
for bridge in nx.local_bridges(G, with_span=True):
    print(bridge)
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