<｜begin▁of▁sentence｜>### Instruction:
As a prosthetist, you work diligently to design and fit orthopedic devices that enhance the mobility and quality of life for individuals facing physical challenges. Imagine you're tasked with creating a customized support system for a patient's prosthetic leg. To ensure optimal functionality, you need to assess the strength and stability of different components.

Now, let's translate this scenario to graph theory. Think of the prosthetic leg as a network, where each connection (edge) represents a crucial junction for support, and the weight of each connection signifies its importance or load-bearing capacity. For instance, consider a simplified scenario where the prosthetic leg is represented by four key points (vertices) linked by edges: (0, 1), (1, 2), (2, 3), and (3, 0). These connections have varying weights: 2.0, 1.0, 3.0, and 4.5 respectively.

Your task is akin to assessing the strength (weighted degree) of a particular junction, let's say vertex 1, within this prosthetic leg network. In this context, you'll leverage the "strength" function, analogous to an evaluation tool specific to the prosthetic field, to precisely quantify the load-bearing capacity of vertex 1, ensuring it can effectively support the patient's mobility needs.
### Response:
from igraph import Graph

# Define a weighted graph
edges = [(0, 1), (1, 2), (2, 3), (3, 0)]
weights = [2.0, 1.0, 3.0, 4.5]
g = Graph(edges=edges)
g.es['weight'] = weights

# Calculate the strength (weighted degree) of vertex 1
vertex_id = 1
vertex_strength = g.strength(vertices=vertex_id, weights='weight')

print(f'Vertex {vertex_id} strength: {vertex_strength}')
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