<｜begin▁of▁sentence｜>### Instruction:
Imagine we're looking at a series of claims represented by a chain of connected events. There are six events in total, and we suspect that each pair of consecutive events might be related to a single cause.

Let's use the visual representation tool of a graph to better analyze these connections. Think of each event as a node in this graph. We have nodes 0 through 5, with each node connected in a line, mirroring the sequence of events (like a timeline).

To simplify our investigation, we would like to group these nodes into pairs based on their consecutive occurrence. Therefore, we will consider events 0 and 1 as one unit, events 2 and 3 as another, and events 4 and 5 as the final one. These pairs might hint at shared circumstances that could be crucial in our assessment of the situation.

We will then create a new graph that reflects this grouping, effectively combining the pairs into single nodes. This will transform our original line graph into a simpler one that's more manageable.

Could you restructure the six-node path into such a quotient graph, consolidating nodes into the pairs mentioned earlier, and then provide us with the list of edges that exist in this newly formed graph? This information will be critical in determining if there's a pattern of claims that needs further investigation.
### Response:
import networkx as nx

# Create a path graph of 6 nodes
G = nx.path_graph(6)

# Define a partition of the node set
partition = [{0, 1}, {2, 3}, {4, 5}]

# Create the quotient graph based on the given partition
M = nx.quotient_graph(G, partition, relabel=True)

# Print the edges of the quotient graph
print(list(M.edges()))
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