<｜begin▁of▁sentence｜>### Instruction:
In the workshop of digital woodworking, imagine we are crafting a complex piece that reflects the interconnectedness of various components in a furniture ensemble. Think of each joint and connection as a path in a blueprint for a grand armoire, where the design is laid out as a directional diagram with connections such as [(hinge-0, shelf-1), (shelf-1, drawer-2), (drawer-2, hinge-0), (panel-3, knob-4), (knob-4, leg-5), (leg-5, panel-3), (leg-5, hinge-0)] showcasing the flow of structural relationships.

As a master woodworker aiming to enhance the structural integrity of our design, we would like to understand the clusters of components that are tightly joined, so as to ensure that when one piece moves or fits into another, all connected parts move in harmony without falling apart. This analysis is akin to applying Kosaraju's technique for uncovering these clusters - much like revealing the hidden cores of strength within our wooden masterpiece. How might we apply this old-world joinery wisdom through digital means to our current project's structural blueprint?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(0, 1), (1, 2), (2, 0), (3, 4), (4, 5), (5, 3), (5, 0)])

# Generate strongly connected components
components = sorted(nx.kosaraju_strongly_connected_components(G), key=len, reverse=True)

# Print the strongly connected components
for component in components:
    print(component)
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