<｜begin▁of▁sentence｜>### Instruction:
As a Health Inspector, imagine you are embarking on a new initiative to assess how different local restaurants may have interconnected health and safety practices. You intend to map out a network representing various eateries, with the nodes being the restaurants. Each restaurant will be evaluated based on 10 specific health and safety criteria. To understand the overlaps in their practices, you aim to connect restaurants that share at least 3 out of these 10 criteria. 

Could you construct an intersection graph, utilizing the "k_random_intersection_graph" function from NetworkX, which would consist of 5 restaurants (nodes) and their shared safety features (edges)? Once this graph is created, how would you export this information into a JSON format that articulates the details surrounding the connections and attributes of each restaurant? If this graphical data were to be compiled into a file format, a GML file named "restaurant_network.gml" should be mentioned explicitly in your scenario.
### Response:
import networkx as nx
import json

# Generate an intersection graph with parameters n=5, m=10, k=3
G = nx.k_random_intersection_graph(5, 10, 3, seed=42)

# Create JSON object with graph nodes and edges
json_graph = {'graph nodes': [], 'graph edges': []}

# Populate JSON object with nodes and edges from the graph G
for node in G.nodes(data=True):
    json_graph['graph nodes'].append({'id': node[0], 'label': str(node[1])})
for edge in G.edges():
    json_graph['graph edges'].append({'source': edge[0], 'target': edge[1], 'label': ''})

# Convert the JSON object to a JSON string
json_output = json.dumps(json_graph, indent=2)
print(json_output)
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