<｜begin▁of▁sentence｜>### Instruction:
As a skilled waiter, you're adept at taking orders, serving food and beverages, and ensuring guests have a delightful dining experience. Picture this: You're at a bustling café, where guests are eager to try out the chef's new menu. Among the various offerings, there's a special dish called the "Forest Fire Delight," known for its unique blend of flavors. Your task is to recommend this dish to patrons, describe its ingredients, and ensure they have an enjoyable meal.

Now, imagine you have a set menu with specific parameters: the number of guests (n=100), the likelihood of them trying the special dish (fw_prob=0.4), the influence of word-of-mouth (bw_factor=0.9), the number of guests served at once (ambs=1), and whether the guests are directed to specific choices (directed=True).

To ensure a seamless dining experience, you need to create a network of connections among the guests, reflecting their interactions and preferences. You'll use the Forest Fire model, available through the café's advanced recommendation system API, to generate this network. Once the connections are established, your next step is to analyze the degree distribution, giving you insights into the popularity of different menu items among the guests.

Just as you carefully attend to each guest's needs, your meticulous attention to detail in generating this network and analyzing its characteristics will contribute to a memorable dining experience for everyone at the café.
### Response:
import igraph as ig

# Create a graph using the Forest Fire model
g = ig.Graph.Forest_Fire(n=100, fw_prob=0.4, bw_factor=0.9, ambs=1, directed=True)

# Getting the degree distribution of the generated graph
degree_distribution = g.degree_distribution()
print(degree_distribution)
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