<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working on a risk evaluation model for our insurance firm, where we want to simulate different networks of clients and their interrelated risk factors. To test our algorithms, we need to create a test scenario where we have a network comprising 10 clients, potentially connected by 45 different risk pathways. To accomplish this, we will utilize the NetworkX library to generate a dense random network graph that represents our scenario.

For the sake of consistency in our model testing and to ensure that the risk analysis simulations can be replicated accurately during subsequent evaluations, we must initialize this network creation process with a specific random seed. Can you describe how we would proceed to design such a dense network using NetworkX, with the necessary steps to guarantee the same network configuration can be replicated using the defined random seed, as if we were documenting the process for our actuarial team to understand and apply? Just to note, should this procedure involve the generation or use of any data files such as GML files, it's crucial we accurately reference them by name for proper version control and data management.
### Response:
import networkx as nx

# Parameters
n = 10  # number of nodes
m = 45  # number of edges

# Seed for reproducibility
seed = 42

# Generate a random graph using dense_gnm_random_graph
G = nx.dense_gnm_random_graph(n, m, seed=seed)

# Print the edges of the graph
print('Graph edges:', G.edges())
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