<｜begin▁of▁sentence｜>### Instruction:
Imagine you're evaluating the supply chain network for a series of retail stores you manage. In this network, nodes represent the stores, and the directed edges indicate the direction of goods movement between these stores. The structure of the goods movement is represented by the relationships: store 0 sends to store 1, store 1 to store 2, store 2 back to store 0, store 3 to store 4, store 4 to store 5, store 5 to store 3, and store 5 also sends to store 6. 

To optimize the stock replenishment process, you need to assess which clusters of stores are self-sustaining loops in terms of supply exchange, meaning that every store in such a loop can receive items from each other directly or indirectly. Could you identify these self-supporting clusters within the supply network?
### Response:
import networkx as nx

# Create a digraph
digraph = nx.DiGraph()

# Add edges to the digraph (nodes are added automatically)
digraph.add_edges_from([(0, 1), (1, 2), (2, 0), (3, 4), (4, 5), (5, 3), (5, 6)])

# Generate the strongly connected components
components = list(nx.kosaraju_strongly_connected_components(digraph))

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