<｜begin▁of▁sentence｜>### Instruction:
As a Solar Energy Technician, you may encounter the need to analyze and understand the network structure of solar power systems within a residential or commercial setting. Utilizing network analysis techniques, such as the 'DANMF' model, can help in identifying communities within the interconnected solar energy systems. For instance, if you are working with a solar energy network comprising 100 nodes, each representing a specific component or area of the system, and there is a probability of 0.05 for any two nodes to have a direct connection, you may want to explore how to use the 'DANMF' model to detect communities within this network, and obtain both the embeddings and the memberships of the nodes. This analysis can provide valuable insights into the interrelationships and functioning of different components within the solar energy system, aiding in maintenance and optimization efforts.
### Response:
import networkx as nx
from karateclub import DANMF

# Step 1: Create a Graph
G = nx.erdos_renyi_graph(100, 0.05)

# Step 2: Apply the DANMF model with custom parameters
model = DANMF(layers=[32, 8], pre_iterations=100, iterations=100, lamb=0.01, seed=42)
model.fit(G)

# Step 3: Get the embeddings and memberships
embeddings = model.get_embedding()
memberships = model.get_memberships()

print('Embeddings:\n', embeddings)
print('Memberships:\n', memberships)
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