<｜begin▁of▁sentence｜>### Instruction:
Ah, imagine we're in a dance studio, right? We're choreographing a new routine, but instead of working with dancers today, we're playing with a network of steps, a graph of movements if you will. Picture this: the studio is our space, and this graph - think of it like a complex dance pattern, full of connections just like our steps in a routine. Now, our challenge is not to overload the dance floor, so we want to simplify this pattern, keeping it elegant and flowing without any tangled stepsÃ¢ÂÂa dance that consists of 10 moves only, where each step follows the last, no doubling back and creating any confusing cycles.

Our graph, it's stored in this digital format, like a choreographer's notebook, in a file called 'littleballoffur9.sparse6'. Our tool for the job, the LoopErasedRandomWalkSampler, that's like our dance assistant, helping us pick out a sequence of steps to create a smaller, neat little routine. You following me?

So, let's translate this back into our tech lingo: how do we take the graph from 'littleballoffur9.sparse6' and use this LoopErasedRandomWalkSampler to sample a connected subgraph that has 10 nodes and forms an undirected tree, meaning it has no cycles, much like planning a sequence for a small group so that each dancer moves seamlessly from one move to the next without any confusion. If we were working with a GML file to store our dance pattern, we'd say we want the subgraph in a GML file named 'dance_routine.gml'. How about that?
### Response:
import networkx as nx
from littleballoffur import LoopErasedRandomWalkSampler
G = nx.read_sparse6('littleballoffur9.sparse6')
sampler = LoopErasedRandomWalkSampler(number_of_nodes=10)
sampled_graph = sampler.sample(G);
<|EOT|>
