<｜begin▁of▁sentence｜>### Instruction:
As a psychologist, let's delve into a scenario where understanding group dynamics plays a crucial role. Imagine you're a counselor for a tightly knit community center that hosts various clubs and activities. One of the most prominent groups is the Karate Club, known for its strong bonds and close relationships among members.

Now, let's say you're interested in analyzing the cohesion within the Karate Club to better understand its dynamics. To do so, you decide to utilize network analysis, a method commonly used in psychology to study social structures. In particular, you're interested in calculating the convergence field size, which essentially measures the extent to which individuals within the club are interconnected.

To achieve this, you plan to leverage the convergence_field_size function provided by the igraph API, a powerful tool for analyzing network data. By applying this function to the Karate Club graph, you aim to gain insights into the size of the convergence field, shedding light on the strength of relationships and the overall cohesion within the club.
### Response:
import igraph as ig

# Load or define your graph
g = ig.Graph.Famous('Zachary')

# Call convergence_field_size function on your graph
convergence_field = g.convergence_field_size()

print('Convergence field size:', convergence_field)
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