<｜begin▁of▁sentence｜>### Instruction:
Imagine you are examining a set of medical imaging data that outlines how different regions of a body part, say, the brain, are connected – much like how areas of the brain might light up and interact during an MRI scan. In this scenario, your data set is a network representing various connections: for example, [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4), (4, 5)], where each node represents a distinct region and each edge signifies a measurable interaction between these regions.

In your analysis, you've identified two distinct groups of regions you're particularly interested in. These groups are like two sets of brain regions that you suspect interact differently with each other. The first set, which we can call 'Region Set S', includes regions 0, 1, and 2. The second set, 'Region Set T', consists of regions 3, 4, and 5. Now, you need to quantify the level of interaction between these two sets of brain regions, or in network terms, you wish to calculate the 'mixing expansion' between Region Sets S and T.

Using a 'network analysis' tool (akin to the sophisticated software you might use to interpret complex imaging results), such as NetworkX, could you demonstrate how to measure this 'mixing expansion' metric based on the connections you've outlined in your data? Such a metric might give insights into how these groups of brain regions interact in a patient, which could be crucial for diagnosis or understanding the underlying brain functions.
### Response:
import networkx as nx

# Example to demonstrate the use of mixing_expansion function

# Create a graph
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4), (4, 5)])

# Define two sets of nodes
S = {0, 1, 2}
T = {3, 4, 5}

# Calculate the mixing expansion between sets S and T
cut_size = nx.algorithms.mixing_expansion(G, S, T, weight=None)
print('Mixing expansion between sets S and T:', cut_size)
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