<｜begin▁of▁sentence｜>### Instruction:
As a mortgage broker, you're well-versed in juggling figures and understanding complex networks of borrowers, lenders, and financial institutions. You spend your days analyzing market trends, evaluating loan applications, and navigating through the intricate web of mortgage lending to find the best deals for your clients. Imagine these relationships as a vast and interconnected graph, where each node represents a party and the edges signify the contractual commitments existing between them.

Now, think of a situation where you want to simplify this network to better understand the underlying structure or to explain a concept to a client. You decide to create a model Ã¢ÂÂ a smaller representation of this network Ã¢ÂÂ much like you'd present a summarized loan option to a client. The goal is to capture the essence without getting bogged down in the details.

Translating this into a technical task, consider that you have a graph data file named 'littleballoffur10.gml.' You want to use a tool from the littleballoffur library, specifically the ForestFireSampler, to extract a representative subgraph. The hope is to maintain the integrity of the original network while focusing on a subset that is manageable for analysis. In this case, you want to set specific parameters such as the number of nodes you wish to highlight in your subgraph, adjust the burning probability to control the sampling process, and make sure the results are reproducible by setting a random seed.

So, the question you're looking to answer would go something like this: Can we employ the ForestFireSampler from littleballoffur to create a sample of our mortgage lending network contained within the 'littleballoffur10.gml' file, ensuring this excerpt has a pre-defined number of nodes, conforms to a given criterion of burning probability, and is generated consistently using a specific random seed?
### Response:
import networkx as nx
from littleballoffur import ForestFireSampler
G = nx.read_sparse6('littleballoffur10.sparse6')
sampler = ForestFireSampler(number_of_nodes=10, p=0.5, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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