<｜begin▁of▁sentence｜>### Instruction:
Alright, as a Quality Assurance Manager working closely with our data science team, I constantly emphasize the importance of adhering to stringent quality control protocols. We need to guarantee that our process of sampling subgraphs maintains a high level of precision, and that each step is meticulously executed to meet our project's requirements. Part of this involves ensuring that our software tools are leveraged correctly to extract the most representative and error-free data samples from larger networks.

Our team uses the graph from the file littleballoffur12.sparse6 as part of an analysis pipeline to understand the underlying structures of complex networks. Now, when we're aiming to perform a specific type of network sampling, particularly using the NonBackTrackingRandomWalkSampler from littleballoffur, we need to be precise in our methodology to achieve reproducible and accurate results. The sampling process we're looking to implement must extract a subgraph with exactly 10 nodes, while incorporating the constraint that the random walk used to sample this subgraph does not revisit the node it just came from.

Bringing this back to the context of our quality standards, consider we are refining one of our graph analysis procedures. I need you to generate a sample subgraph using the NonBackTrackingRandomWalkSampler methodology, ensuring that our sample consists of 10 nodes and the walk does not return to the immediately previous node during sampling. For consistency and quality adherence, remember that if our sampling process involved a .gml file, the name of that file would have to be accurately specified in our notes. However, since our current file at hand is a .sparse6 format, let's proceed accordingly.
### Response:
import networkx as nx
from littleballoffur import NonBackTrackingRandomWalkSampler
G = nx.read_sparse6('littleballoffur12.sparse6')
sampler = NonBackTrackingRandomWalkSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges())
print(sampler_G.nodes())
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