<｜begin▁of▁sentence｜>### Instruction:
As an E-commerce Manager, I'm looking to explore a new model for understanding our customer referral network. I'd like us to construct a simulated representation that can help us understand how customers might refer other customers. Specifically, I'm asking to have a network model created with a defined number of customers (nodes) and a set number of referrals each customer can make (fixed out-degree). Additionally, I want to introduce a nuance where customers with more referrals have a higher chance of making additional referrals (preference attachment weighting factor).

To ensure accuracy and repeatability in our model, we need to use a specific initial random state (seed) for this simulation. This will help us to replicate the scenario for future analysis. It's crucial to maintain the integrity of this referral network by not allowing customers to refer themselves (no self-loops).

Could you employ NetworkX to generate this referral network? Additionally, to facilitate an easy-to-understand visual, please produce a visual representation of the resulting network, making sure to adhere to the parameters I've outlined. It will greatly enhance our strategic meetings to have this visual model that represents potential customer referral patterns. 

If the generated network can be saved as a 'referral_network.gml' file, that would streamline the process of sharing and reviewing the model with the team. Thank you for assisting with this critical project that will help shape our understanding of customer behavior.
### Response:
import networkx as nx

# Parameters for the random k-out graph
n = 5 # number of nodes
k = 2 # each node will have an out-degree of k
alpha = 0.5 # controls the attachment preference
self_loops = False # self-loops are not allowed
seed = 42 # seed for random number generation

# Create the random k-out graph
G = nx.random_k_out_graph(n, k, alpha, self_loops=self_loops, seed=seed)

# Draw the graph
import matplotlib.pyplot as plt
nx.draw(G, with_labels=True)
plt.show()
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