<｜begin▁of▁sentence｜>### Instruction:
As a medical illustrator, you've likely encountered the challenge of effectively communicating complex medical concepts to various audiences. Imagine you've been tasked with creating visualizations to explain the intricate interconnections within a network of biological processes. You've received a dataset in the form of a GML file called 'graph_net.gml', representing these interconnected relationships.

Now, your objective is to analyze this network and provide insights into the importance of individual nodes in facilitating communication within the system. To achieve this, you need to calculate Burt's constraint scores for each vertex in the network. Burt's constraint measures how dependent a node is on its connections, indicating its potential role in controlling the flow of information within the network.

To accomplish this task, you might utilize the `constraint` function from the igraph library, a powerful tool commonly employed in network analysis. By applying this function to the dataset, you can generate constraint scores for each vertex, enabling you to identify key nodes that play pivotal roles in regulating communication and influencing the overall dynamics of the biological system. Through your visualizations and illustrations, you'll effectively convey these insights, contributing to a deeper understanding of the network's structure and functionality among healthcare professionals, researchers, and students alike.
### Response:
import igraph as ig

# Read the graph from a file
g = ig.read('graph_net.gml')

# Calculate Burt's constraint scores for all vertices in the graph
constraint_scores = g.constraint(vertices=None, weights=None)

# Print the constraint scores
print(constraint_scores)
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