<｜begin▁of▁sentence｜>### Instruction:
Imagine you're coordinating a networking event designed to connect two separate groups within a larger community. The groups are represented by two sets of individuals: Group S, which includes individuals 1, 2, and 3; and Group T, with individuals 4 and 5. The interactions between these individuals so far have been noted as connections—think of them as collaborative opportunities or past encounters. The connections recorded are as follows: individuals 1 and 2 had an encounter, 1 and 3, 1 and 4, 2 and 3, 4 and 5, and lastly, 3 and 5.

As the planner, your job is to facilitate interactions between groups S and T and optimize the networking potential of the event. To do this effectively, you'd want to evaluate the current level of interaction or 'conductance' between these two groups based on their previous encounters. By analyzing this, you're aiming to understand how easily or frequently members from Group S are currently interacting with members from Group T.

So, here's what we need to conceptualize: How would you measure the conductance of the aforementioned connections between Group S and Group T using an analytical tool like NetworkX, to ensure that the event you're organizing leads to a balanced and fruitful exchange between the two subsets of guests? This information is not contained in a GML file, but rather is presented as a list of prior connections that should be considered in your analysis.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (1, 4), (2, 3), (4, 5), (3, 5)])

# Sets of nodes
S = {1, 2, 3}
T = {4, 5}

# Compute the conductance
cond = nx.conductance(G, S, T)
print('Conductance between set S and T:', cond)
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