<｜begin▁of▁sentence｜>### Instruction:
Alright team, as we navigate through our current project analyzing network topologies, we've come across the need to simplify complex graphs for better manageability and understanding. To achieve this, we've been using tools from the littleballoffur package to handle these large structures. Specifically, we've been examining portions of the network to understand its basic properties and to test our hypotheses on a smaller scale.

We recently received a graph for analysis, encoded in a 'sparse6' format. Remember the file we discussed in our last meeting, "littleballoffur15.sparse6"? The next step in our project plan is to sample a subgraph from this large graph. We'll employ the RandomWalkSampler method, which will randomly walk through the graph and select a connected subgraph for us. This will help us in finding local patterns or anomalies that we can report back to our stakeholders.

Moving forward, I need your assistance with the technical aspect. Can we utilize the RandomWalkSampler to extract a subgraph that contains exactly 10 nodes from our graph in "littleballoffur15.sparse6"? This sampled subgraph will be crucial for the next phase of our analysis.

Remember, this request is extension-independent, so if the original data were presented in another format such as a GML file, we would be referring to that file instead, adjusting the filename as needed. For example, it would become "littleballoffur15.gml". For now, let's stick to the "sparse6" at hand. Can you handle this task?
### Response:
import networkx as nx
from littleballoffur import RandomWalkSampler
G = nx.read_sparse6('littleballoffur15.sparse6')
sampler = RandomWalkSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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