<｜begin▁of▁sentence｜>### Instruction:
As a Dance Therapist, you aim to utilize movement and dance to enhance the emotional, cognitive, and physical well-being of your clients during therapy sessions. Now, imagine you're working with a group of individuals who are part of a community dance program. To monitor their interactions and social dynamics, you've been collecting data on their connections and interactions over time.

You've been using a specialized tool called the "TempoGrapher" which captures snapshots of the group's network structure at different intervals. Each snapshot represents the connections between participants during a particular dance session. These snapshots are stored as dynamic network graphs, allowing you to observe how the community evolves over time.

To gain deeper insights into the evolving social dynamics within the group, you're interested in identifying and tracking the formation and dissolution of subgroups or communities over time. One method you're exploring is the TILES function provided by the "CommunityDanceAnalyzer" module. This function analyzes the network snapshots and incrementally updates the detected communities as the graph evolves.

Furthermore, you're looking to store the resulting temporal clustering information in a format that allows for easy sharing and further analysis. You're interested in exporting the output of the TILES function, represented as a TemporalClustering object, into a JSON format. This would enable you to visualize and explore the community structure over time using different tools and platforms, ensuring seamless integration into your therapeutic practice.
### Response:
import networkx as nx
import dynetx as dn
from cdlib import algorithms

# Initialize a dynamic graph
G = dn.DynGraph()

# Assume we have graph snapshots for 10 timestamps
for t in range(10):
    # Generating a random graph for the current timestamp
    snapshot = nx.erdos_renyi_graph(200, 0.05)
    G.add_interactions_from(snapshot.edges(), t=t)

# Run TILES algorithm with observation interval set to 2
communities = algorithms.tiles(G, obs=2)

# Convert the TemporalClustering to JSON representation
json_repr = communities.to_json()
print(json_repr)
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