<｜begin▁of▁sentence｜>### Instruction:
As a Music Therapist, I often use structured approaches to address various needs during therapy sessions. Imagine you're planning a therapeutic session for a group of 315 individuals, each with unique emotional and cognitive aspects. You've decided to use a special musical activity that involves creating connections between participants, akin to the interconnectedness in a network. To organize this, you've chosen to represent their interactions using a network model called the Erdos-Renyi graph. 

Now, you're faced with a challenge: you need to create smaller groups within this large network to facilitate more intimate interactions. You want to sample a subgraph with 10 individuals that still captures the essence of the larger network. Additionally, you want to identify the minimal set of musical connections that, if removed, would disconnect the subgraph entirely, simulating a powerful intervention that can break down barriers and promote individual expression within the smaller group.

To achieve this, you're considering using a specialized tool called the RandomNodeNeighborSampler, which helps in sampling subgraphs from larger networks efficiently. With this tool, you aim to select a subset of 10 individuals and pinpoint the smallest number of musical connections needed to effectively isolate this group, fostering a unique therapeutic experience tailored to their specific needs.
### Response:
import networkx as nx
from littleballoffur import RandomNodeNeighborSampler

G = nx.gnp_random_graph(n=315, p=0.25)

sampler = RandomNodeNeighborSampler(10)

sampler_G = sampler.sample(G)

result = nx.minimum_edge_cut(sampler_G)

print(result)
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