<｜begin▁of▁sentence｜>### Instruction:
Imagine you're spending your days in the tranquil sanctuary of your greenhouse, where all around you vivid blossoms and lush greenery thrive under your attentive care. You've got your routine down to a fine art, from monitoring the perfect humidity levels for your orchids to ensuring just the right amount of sunlight kisses the tomatoes to coax them into plump perfection. In between your daily dance with the watering can and the soil, you've developed a keen interest in network theory, seeing the parallels between the interconnectedness of plant life and complex networks.

Just like how you'd carefully select a subset of plants for a new propagation experiment, taking cuttings from a mature plant to nurture and grow, you've been wondering about a different kind of pruning Ã¢ÂÂ in the digital realm. Specifically, you're curious about how to extract a manageable subnetwork from a larger network graph you've obtained. The graph in question, let's call it 'littleballoffur1.gml' following your greenhouse's charming tradition of whimsical monikers, is a dense intertwining of nodes and edges, mirroring the tangled vines of your passion fruits, but in need of some trimming to make sense of it all.

So, as you potter around your humid haven, mulching and mulling over this digital conundrum, consider this: How might you use a tool called BreadthFirstSearchSampler to meticulously clip away the excess and end up with a more manageable subgraph containing just 10 nodes, akin to preparing a small cutting for propagation? The task at hand is not unlike selecting seedlings for a new garden bed, ensuring a balance and diversity representative of the larger ecosystem. How would you undertake this task using the 'littleballoffur1.gml' file as your starting point?
### Response:
import networkx as nx
from littleballoffur import BreadthFirstSearchSampler
G = nx.read_sparse6('littleballoffur1.sparse6')
sampler = BreadthFirstSearchSampler(number_of_nodes=10)
sampled_graph = sampler.sample(G)
print(sampled_graph.edges(), sampled_graph.nodes())
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