<｜begin▁of▁sentence｜>### Instruction:
As an immigration lawyer, you often deal with complex legal matters that require precision and attention to detail. Just like in the legal field, network analysis also involves meticulous examination and understanding of interconnected relationships. 

Imagine you're representing a diverse group of individuals seeking different types of visas to enter a new country. You're tasked with assessing the connections between these individuals, each identified with a specific category based on their unique circumstances.

Suppose you're provided with a network representation of these individuals, created using the Erdos-Renyi model. This network consists of 100 nodes representing individuals and 500 edges representing their connections. Additionally, each individual is categorized as either 'A' or 'B' based on whether their index is even or odd, respectively.

To gain insights into the social dynamics within this network, you decide to employ network analysis techniques. Specifically, you're interested in measuring the assortativity based on the assigned categories ('A' or 'B') using the assortativity_nominal function provided by the igraph API.

In essence, your goal is to understand how individuals of different categories are interconnected within the network, much like how you navigate the complexities of immigration laws to ensure your clients' interests are protected and understood.
### Response:
from igraph import Graph

# Assume 'g' is an igraph Graph instance and the 'category' attribute is set for each vertex
g = Graph.Erdos_Renyi(n=100, m=500)
g.vs['category'] = ['A' if v.index % 2 == 0 else 'B' for v in g.vs]

# Calculate the assortativity based on the 'category' attribute
assortativity = g.assortativity_nominal(types=g.vs['category'])
print('Assortativity based on categories:', assortativity)
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