<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working on a feature for a social networking application where you have a sequence of user interactions modeled as a path graph with 6 nodes. For the sake of optimization, you want to simplify this graph by grouping certain users together based on their consecutive interaction patterns.

To accomplish this, you need to write a function that will merge these user nodes into collective blocks to form a new, streamlined network structure. Specifically, you want to merge user nodes 0 and 1 into a single entity, user nodes 2 and 3 into a second entity, and user nodes 4 and 5 into a third entity.

Once you've created this function utilizing the `quotient_graph` feature from the NetworkX library, apply it to the path graph and output the resulting graph's edges to review the new simplified connection structure. This reconfigured network should represent the combined interactions of the users within the same blocks. Please format the edges output in an easy-to-read manner for subsequent analysis.

As a developer, this task involves manipulating graph structures programmatically, which is a standard requirement when optimizing data processing within the backend of a software system. Remember that this task is purely about reorganizing the existing network graph data; hence, no additional details or files are required.
### Response:
import networkx as nx

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

# Define an equivalence partition
partition = [{0, 1}, {2, 3}, {4, 5}]

# Create the quotient graph
Q = nx.quotient_graph(G, partition, relabel=True)

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