<｜begin▁of▁sentence｜>### Instruction:
Imagine you're devising a dietary network program to connect 100 individuals (nodes) with personalized nutrition plans. To mirror natural social groupings, we'll subdivide our participants into various circles (partitions) where each circle has a dietary focus, such as low-carb, vegan, or high-protein diets. Now, the average circle will have about 10 participants (mean size of partitions), but we expect some variability – some might be a bit larger, some smaller, with a variance of size set at 10.0. 

Within these dietary circles, we predict a 25% chance (p_in) that any two individuals will communicate and share tips, recipes, or support with one another, reinforcing their dietary choices. Between the different dietary circles, the chance of interaction drops to 10% (p_out), as their dietary goals differ.

Now, to construct and visualize this network, we require a "dietary-social-network.json" file, which will contain a detailed map of all our individuals and the connections between them. This file will be crucial for our tailored nutritional counseling as it will help us understand the social dynamics of our program.

For consistency and reproducibility, let's use the seed number 42 to initialize our random generation process, ensuring that we can recreate the same scenario when needed. Could you reformulate the nutritional network scenario into a digital format that reflects these parameters?
### Response:
import networkx as nx

# Parameters for the Gaussian random partition graph
n = 100  # Number of nodes
s = 10.0  # Mean size of the partitions
v = 10.0  # Variance of the size of the partitions
p_in = 0.25  # Probability of edges within a partition
p_out = 0.1  # Probability of edges between partitions
directed = False  # Whether the graph is directed
seed = 42  # Seed for the random number generator

# Generate the graph
G = nx.gaussian_random_partition_graph(n, s, v, p_in, p_out, directed, seed)

# Create JSON object
result = {'graph nodes': [], 'graph edges': []}

# Add nodes and edges to the JSON object
for node in G.nodes():
    result['graph nodes'].append({'id': node, 'label': 'Node {}'.format(node)})

for edge in G.edges():
    result['graph edges'].append({'source': edge[0], 'target': edge[1], 'label': 'Edge_{}_{}'.format(edge[0], edge[1])})

# Display the JSON object
print(result)
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