<｜begin▁of▁sentence｜>### Instruction:
Imagine you're planning to organize your organic farm into different sections or communities where each section has its own group of plants or animals. You decide to represent this plan using a farm layout model, where each section or "community" has 10 elements.

For this model, there's a higher likelihood that elements within the same section intermingle, which we'll set at a 25% chance. However, interactions between different sections are less common, occurring just 1% of the time. You're considering a layout where the sections aren't one-directional pathways, but rather areas where the movement can go back and forth, like bees pollinating various plants.

To create this model, you seek to use a tool, something akin to drawing a map of connections, known as NetworkX in the technology realm, which can provide a visual of your farm's layout. Using this tool, what would be the total count of elements (nodes) and the number of distinct sections (communities) in your farm's model?

Please keep in mind you're not actually creating the model now, merely painting a picture of how you would represent your farming community using this "map".
### Response:
import networkx as nx

# Using the random_partition_graph function
sizes = [10, 10, 10]  # Sizes of the three groups
p_in = 0.25  # Probability of creating edges within a group
p_out = 0.01  # Probability of creating edges between groups
directed = False  # Create an undirected graph
seed = 42  # Seed for the random number generator

# Create the random partition graph
G = nx.random_partition_graph(sizes, p_in, p_out, directed, seed)

# Get the partition attribute
partition = G.graph['partition']

# Display the number of nodes in the graph
print('Number of nodes in the graph:', len(G))

# Display the number of communities
print('Number of communities:', len(partition))
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