<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working on a project to create a smart grid that integrates various renewable energy sources, and you're looking to understand the resilience of the network. You decide to model the grid as a simplified network using the Erdős-Rényi random graph model. Your network has 10 nodes, representing the grid's interconnected substations, and the likelihood of any two substations being directly connected is 20%.

To get an insight into how well energy can be distributed across the grid in various configurations, you want to determine the average clustering coefficient of this network model. The clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together–a useful indicator of the energy distribution efficiency in local sub-networks of the grid.

To ensure accuracy in the face of randomness inherent in such a model, you plan to simulate the generation of this 10-node network 1000 times, effectively running 1000 trials. By doing this, you aim to approximate the average clustering coefficient, giving you a better understanding of the robustness and efficiency of this hypothetical smart grid network. How would you proceed with this task, specifically with the aspect of estimating the average clustering coefficient over numerous trials?
### Response:
import networkx as nx
from networkx.algorithms import approximation

# Create an Erdős-Rényi graph with 10 nodes and a probability of 0.2 for edge creation
G = nx.erdos_renyi_graph(10, 0.2, seed=10)

# Estimate the average clustering coefficient with 1000 trials
avg_clustering_coefficient = approximation.average_clustering(G, trials=1000, seed=10)
print(f'The approximated average clustering coefficient is: {avg_clustering_coefficient}')
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