<｜begin▁of▁sentence｜>### Instruction:
Certainly! Let's translate this into a purchasing manager scenario:

Imagine you're the Purchasing Manager for a company that constructs supply chain networks. You need to simulate a new distribution network starting with two fully cooperative vendors. Your goal is to expand this network to include a total of 10 vendors. Each new vendor that considers joining your network is influenced by an existing partner, but there's only a 50% chance they'll establish direct trade connections with that partner's contacts. Additionally, there is a 20% chance that a vendor who is considering joining doesn't proceed with the partnership.

To ensure a consistent simulation outcome for comparison, please use a set guideline of 42 so that the results are predictable and can be replicated in subsequent analyses.

Now, picture this process resulting in a graphical map of your network, indicating all the connections between the vendors. Could you generate this supply chain network diagram using these parameters, and then provide a summary of the vendor connections (nodes and edges) for review?
### Response:
import networkx as nx

# Parameters for the model
N = 10  # Total number of nodes
n = 2   # Initial number of fully connected nodes
p = 0.5 # Probability of adding an edge during duplication
q = 0.2 # Probability of NOT adding a duplicated node
seed = 42 # Seed for random number generator

# Create the random graph using the partial duplication model
pd_graph = nx.partial_duplication_graph(N, n, p, q, seed=seed)

# Print nodes and edges of the graph
print('Nodes:', pd_graph.nodes())
print('Edges:', pd_graph.edges())
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