<｜begin▁of▁sentence｜>### Instruction:
In the gentle craft of herbalism, where we tend to the natural world and embrace the healing powers of Mother Earth's bounty, our methods often reflect the intricate networks akin to the roots of a plant or the veins of a leaf. Just as we might concoct a tea from a blend of herbs, each chosen for their synergistic properties and strength, so too can we approach other complexities in life, such as understanding the interconnectedness in networks and systems.

Imagine, if you will, a graph as a garden, where each node is a plant, and their connections are the tender roots and shoots that bind them together. Now, suppose we've come across a sprawling, tangled garden within a vast landscape of data, recorded in the ancient script of sparse6, reminiscent of a little ball of fur - complex and interwoven. 

As an herbalist, you might wish to cultivate a smaller, yet potent, representation of this garden to understand the essence of the whole. In essence, just as you would select certain herbs for a remedy, you want to sample a subset from this great network, a subgraph with a specified number of nodes, chosen not at random, but rather through a natural flow, a diffusion process that reflects the organic spread of influence and energy in our plant world.

So, dear fellow botanist of data, how would you employ the mystical tool known as the DiffusionTreeSampler, to extract from the file littleballoffur10.sparse6, a concentrated subgraph through a diffusion process, ensuring the subgraph contains a precise count of nodes? This, much like selecting the perfect quantity of herbs for a potent tincture, must be done with intention and skill.
### Response:
import networkx as nx
from littleballoffur import DiffusionTreeSampler
G = nx.read_sparse6('littleballoffur10.sparse6')
sampler = DiffusionTreeSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
<|EOT|>
