<｜begin▁of▁sentence｜>### Instruction:
As a neuropsychologist, I'm interested in understanding how different brain regions contribute to cognitive processes. Suppose we have a brain network represented as a graph, where each node corresponds to a brain region, and the edges represent connections between them. Now, let's say we have a patient with a specific condition, and we want to assess the activity or influence of a particular brain region, let's call it node 1. We can think of the "strength" of node 1 as its level of connectivity or how much it contributes to the overall network activity.

To quantify the strength of node 1, we can use the `strength()` function from the network analysis library that we're using, such as NetworkX. This function calculates the strength of a node in a graph, considering the weights assigned to its edges. For example, if we have edges connecting node 1 to nodes 0 and 2 with weights 3 and 5 respectively, and node 1 has no other connections, then the strength of node 1 would be the sum of these weights, which is 8 in this case. This measure helps us understand the importance or influence of a specific brain region within the larger network.
### Response:
from igraph import Graph, Vertex

# Create a graph with weights on edges
edges = [(0, 1), (1, 2), (2, 0)]
weights = [3, 5, 2]
g = Graph(edges=edges)
g.es['weight'] = weights

# Get the vertex with ID 1
vertex = Vertex(g, 1)

# Use strength method to get the strength of vertex 1
vertex_strength = vertex.strength(weights='weight')

# Print the strength of the vertex
print('Strength of vertex 1:', vertex_strength)
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