<｜begin▁of▁sentence｜>### Instruction:
As a seasoned phlebotomist, you know the importance of precision and protocol in every aspect of your workÃ¢ÂÂfrom the delicate technique of venipuncture, ensuring each blood sample is cleanly and safely collected, to the meticulous labeling and storage of those samples for further analysis or transfusion. Just like the care taken in your daily tasks is crucial for accurate medical diagnosis and treatment, imagine if you had to apply that same level of precision and protocol to extract a smaller, connected "sample" from a vast "circulatory system" of data points within a graph structure.

Now, let's shift focus to our problem at hand, expressed in the unique language of your profession. Think of a graph as a network of interconnected veins and arteries in a patientÃ¢ÂÂs body. Now, youÃ¢ÂÂve been tasked with not drawing blood, but rather drawing a representative connected sub-graphÃ¢ÂÂa smaller section of this complex network. Specifically, you aim to obtain a connected sub-section that exactly comprises 10 "cells" or nodes, which in the world of graph theory, can be extracted utilizing a tool called the Metropolis-Hastings Random Walk Sampler.

But rather than working with a live patient, the "body" of data you're working with today comes from a digital file, akin to the medical records you must carefully read and understand. This file isn't a patient chart, but a 'littleballoffur9.sparse6' file containing all the data points and their connections.

So, coming back to the task at hand, rephrased in a way that resonates with your daily language and understanding, how can you utilize the Metropolis-Hastings Random Walk Sampler to meticulously extract a connected "sample" of 10 nodes from the "vascular network" detailed in the littleballoffur9.sparse6 file? If we were working with a 'gml' file format, which is another way we could potentially be receiving this data, what would be the name of that gml file that would house this graph information?
### Response:
import networkx as nx
from littleballoffur.exploration_sampling import MetropolisHastingsRandomWalkSampler
G = nx.read_sparse6('littleballoffur9.sparse6')
sampler = MetropolisHastingsRandomWalkSampler(number_of_nodes=10, seed=42, alpha=1.0)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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