<｜begin▁of▁sentence｜>### Instruction:
Imagine we are working on a community health map representing four distinct neighborhoods, labeled as 0, 1, 2, and 3. The connections between these neighborhoods are the roads we take to travel from one to another, and we've assigned a difficulty level to each road based on the distance and the resources needed to navigate it. For example, traveling from Neighborhood 0 to 1 is rated with a difficulty level of 2, whereas the road from 1 to 2 has a slightly higher difficulty level of 3, and so on until all the roads connecting these neighborhoods are assessed with the levels being 2, 3, 4, and 5 respectively.

As caring professionals, we are interested in applying a method similar to how we might cluster together patients based on their health profiles, here we will use the Louvain Community Detection Algorithm. This approach helps us to understand how the neighborhoods might best be grouped to ensure our care teams can effectively serve the community—potentially revealing the optimal structure for resource allocation and response times.

Could you take this map, use the Louvain Community Detection technique, and identify for us how the neighborhoods group together at each stage of the algorithm? Please document how the neighborhoods are clustered at each level, providing us with a clear picture of the community groupings as determined by this method. This will greatly aid us in our planning and ensure that our health services are as efficient and responsive as possible.
### Response:
import networkx as nx
import community as community_louvain

# Create a weighted graph
G = nx.Graph()
G.add_edge(0, 1, weight=2)
G.add_edge(1, 2, weight=3)
G.add_edge(2, 3, weight=4)
G.add_edge(3, 0, weight=5)

# Run Louvain Community Detection Algorithm
partition_generator = community_louvain.louvain_partitions(G, weight='weight', resolution=1.0, threshold=0.0000001, seed=None)

# Print the partitions at each level
for level, partition in enumerate(partition_generator):
    print(f'Level {level}:', partition)
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