<｜begin▁of▁sentence｜>### Instruction:
Given a graph with edge set [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)] and edge weights [1, 2, 1, 2, 1], can you use the VertexDendrogram object in igraph to create and plot a dendrogram, then find and print the optimal number of clusters?
### Response:
from igraph import Graph

# Assume 'edges' contains edge tuples and 'weights' contains edge weights
edges = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]
weights = [1, 2, 1, 2, 1]
g = Graph(edges=edges)
g.es['weight'] = weights

# Perform hierarchical clustering (using some community detection method, e.g., edge betweenness)
dendrogram = g.community_edge_betweenness(weights='weight').as_clustering()

# Create a VertexDendrogram object using the merges from the clustering algorithm
vertex_dendrogram = dendrogram.as_dendrogram()

# Plot the dendrogram
vertex_dendrogram.__plot__()

# Find the optimal clustering by cutting the dendrogram
optimal_clustering = vertex_dendrogram.as_clustering()
print(optimal_clustering)

# Get and print the optimal count for cluster cutting
optimal_count = vertex_dendrogram.optimal_count
print('Optimal count:', optimal_count)
<|EOT|>
