<｜begin▁of▁sentence｜>### Instruction:
As a photographer, you're often tasked with capturing intricate details within a scene to tell a compelling visual story. Imagine you're commissioned to photograph a well-known architectural marvel, the "petersen" building. In your quest to document its essence, you need to explore and highlight the intricate patterns within its design. To achieve this, you seek to identify clusters of unique features in the building's structure, akin to how vertices form clusters in a graph.

In this context, you're interested in iterating over clusters within the building's vertex cover, essentially groups of interconnected architectural elements that contribute to its overall aesthetic appeal. Your goal is to document these clusters through your lens, capturing their essence in a series of visually striking images that showcase the building's beauty and complexity.

To accomplish this, you'd leverage tools like NetworkX, a powerful library for analyzing complex networks. By utilizing NetworkX's functionality to extract and visualize clusters within the building's design, you can effectively identify key focal points and areas of interest to showcase in your photography project. Through this approach, you'll not only capture the building's physical structure but also convey its underlying architectural intricacies, creating captivating visual representations that resonate with viewers.
### Response:
import igraph as ig

# Create a graph
g = ig.Graph.Famous('petersen')

# Compute a vertex cover of the graph
vertex_cover = ig.VertexCover(g)

# Iterate over the clusters in the vertex cover and print them
for cluster in vertex_cover:
    print(cluster)
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