<｜begin▁of▁sentence｜>### Instruction:
Given a graph object 'g' for which you wish to perform community detection, how would you use the igraph VertexDendrogram class to generate a dendrogram from a fast greedy community structure, represent it as a clustering, and then plot and summarize the dendrogram?
### Response:
import igraph as ig

# Suppose we have a graph object 'g' already created
# Compute the dendrogram using some community detection method, such as fast greedy
# Note: In practice, you would need to have an actual graph 'g' with real data
dendrogram = g.community_fastgreedy()
vertex_dendrogram = ig.VertexDendrogram(g, dendrogram.merges, optimal_count=dendrogram.optimal_count)

# Convert the dendrogram to a clustering object and select the optimal number of clusters
clustering = vertex_dendrogram.as_clustering()

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

# Plot the dendrogram
ig.plot(vertex_dendrogram, mark_groups=True)
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