<｜begin▁of▁sentence｜>### Instruction:
Alright, let's get analytical here. Imagine we're working with network data that encapsulates a complex systemÃ¢ÂÂcould be social interactions, biological systems, or even a communication network. Our goal is to extract a manageable but informative subset of this dataÃ¢ÂÂa representative sample that captures the essential characteristics without the bulk.

Now, say we've got this hefty graph data sitting in a file named 'littleballoffur7.sparse6'. This file is tricky to work with in its entirety due to its size, and we want to trim it down to something more digestible. That's where some clever sampling comes into play.

Here's the task: we want to deploy a SpikyBallSamplerÃ¢ÂÂthat's a neat piece of our analytical toolkitÃ¢ÂÂto grab around 50 nodes from our original graph. We're thinking of using the 'fireball' sampling method because it's designed to capture a broad yet connected subgraph, which is just what we need for our analysis.

So put on your data science cap, because what I'm going to ask you to do is this: Can you use the SpikyBallSampler, with the 'fireball' procedure, to fetch us a subgraph from 'littleballoffur7.sparse6', aiming for about 50 nodes in size? And let's make sure we save this sampled graph in a way we can work with later, perhaps as a GML file named "sampled_graph.gml".
### Response:
import networkx as nx
from littleballoffur import SpikyBallSampler
G = nx.read_sparse6('littleballoffur7.sparse6')
sampler = SpikyBallSampler(number_of_nodes=50, sampling_probability=0.1, initial_nodes_ratio=0.1, seed=42, max_hops=100000, mode='fireball', max_visited_nodes_backlog=100, restart_hop_size=10, distrib_coeff=1.0)
sampler_G = sampler.sample(G)
print(sampler_G.edges())
print(sampler_G.nodes());
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