<｜begin▁of▁sentence｜>### Instruction:
As an ecologist, I'm constantly seeking to understand the intricate web of connections within natural ecosystems. It's fascinating to observe how different species interact with one another and with their environment, forming complex networks that resemble the graphs we analyze in computational ecology. Typically, I might be out in the field, observing patterns of predation, symbiosis, or competition within a forest ecosystem, and taking samples of populations to estimate the overall health and connectivity of the habitat. However, modern ecological research often spans beyond boots-on-the-ground observations and into the realm of computational simulations. 

For instance, when I'm back in the lab, I utilize sophisticated tools to create and analyze models of these natural networks. Graph theory becomes particularly handy here, offering insights that can guide conservation efforts or predict the outcomes of environmental changes. Today, I'm working with a digital representation of a particular ecological network, which I have stored in a file named 'littleballoffur4.sparse6'. Now, to study this network's resilience and how disturbances might propagate, I'd like to simulate a natural disturbance using a computational model, similar to how a forest fire might affect a section of a forest.

For this simulation, I'll employ the ForestFireSampler, a method that mimics the stochastic and spreading nature of disturbances. I aim to sample a smaller, yet representative, subgraph from our larger network, containing a predefined amount of nodes. I'll control the sampling using a certain 'burning probability,' which in the real world might correspond to the likelihood of a tree catching fire from a neighboring burning tree. To ensure that my experiment is reproducible and that random fluctuations are consistent across trials, I'll set a random seed.

So, with that ecological mindset embedded in a computational framework, I would like to know if you could show me how to use the ForestFireSampler to sample a subgraph from the 'littleballoffur4.sparse6' file, ensuring that the resulting subgraph has a specified number of nodes. The sampling should be performed using a predetermined burning probability, while also fixing the randomness with a specific seed value. Can you guide me through this process using littleballoffur?
### Response:
import networkx as nx
from littleballoffur import ForestFireSampler
G = nx.read_sparse6('littleballoffur4.sparse6')
sampler = ForestFireSampler(number_of_nodes=10, p=0.4, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges())
print(sampler_G.nodes());
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