<｜begin▁of▁sentence｜>### Instruction:
As an expert in network analysis, I can certainly help with that. Given the data provided, I can offer support in generating a graph based on the forest fire model using the Forest_Fire function in the NetworkX library. This can be applied in a practical scenario where the number of vertices and the probabilities for forwarding and backward burning are known, allowing for the creation and analysis of complex networks.
### Response:
import igraph as ig

# Parameters for the forest fire model
n = 100  # Number of vertices
forward_proba = 0.4  # The probability of forwarding to a new vertex
backward_proba = 0.1  # The probability of backward burning

# Generate a graph based on the forest fire model
g = ig.Graph.Forest_Fire(n, fw_prob=forward_proba, bw_prob=backward_proba)

# Summarize the graph
g.summary()

# Plot the graph
ig.plot(g)
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