<｜begin▁of▁sentence｜>### Instruction:
Imagine we're in a food science lab, and we've concocted a unique kind of molecular flavor network, modeled after the barbell-shaped structures found in some complex food molecules. Our edible model, represented in the 'flavor_network.gml' data file, mirrors a nx.barbell_graph(10, 0) with two robust clusters of flavor compounds, akin to two separate groups of 10 tightly bound atoms, and these clusters are linked by a single, delicate bond akin to a culinary bridge that merges their tastes.

Our task is to meticulously identify all such fragile bonds—these "culinary bridges" if you will—since they are crucial junctures where the flavor profile could potentially break down and change significantly with the slightest alteration. Using the tools of NetworkX, could you please assist in delicately pinpointing all these subtle connections within our flavorful construction?
### Response:
import networkx as nx

# Create a barbell graph with parameter zero
G = nx.barbell_graph(10, 0)

# Generate all bridges in the graph
all_bridges = list(nx.bridges(G))

print("All bridges in the graph:", all_bridges)
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