<｜begin▁of▁sentence｜>### Instruction:
Given a graph and a simplified example of hierarchical clustering merges (step-by-step merges of clusters), create an instance of VertexDendrogram, specifying the graph, the merges, and the optimal count for clusters. Then, cut the dendrogram at the optimal count to create a VertexClustering object. Print the resulting clusters and provide a summary of the dendrogram.
### Response:
from igraph import Graph
from igraph import VertexDendrogram

# Assume we have a graph and some hierarchical clustering data
graph = Graph.Famous('Zachary')
merges = [[0, 1], [2, 3], [1, 2], [3, 4]]  # Simplified example of clustering steps

# Create a VertexDendrogram with the given graph and merges
# Assuming the optimal cut is known to be 2 clusters
vertex_dendrogram = VertexDendrogram(graph, merges, optimal_count=2)

# Get the VertexClustering object by cutting the dendrogram
vertex_clustering = vertex_dendrogram.as_clustering()

# Print the clusters
for cluster in vertex_clustering:
    print(cluster)

# Print the summary of the dendrogram
print(vertex_dendrogram.summary())
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