<｜begin▁of▁sentence｜>### Instruction:
Given a graph with nodes ['A', 'B', 'C'] and edges [('A', 'B', {'thickness': 1, 'weight': 3}), ('A', 'C', {'thickness': 2}), ('B', 'C', {'thickness': 3})], how can you represent the edge thicknesses as a sparse matrix using NetworkX's attr_sparse_matrix function?
### Response:
import networkx as nx
from scipy.sparse import csr_matrix

# Create a graph
G = nx.Graph()
G.add_edge('A', 'B', thickness=1, weight=3)
G.add_edge('A', 'C', thickness=2)
G.add_edge('B', 'C', thickness=3)

# Use the 'thickness' attribute for the edges to create a sparse matrix
M, nodes = nx.attr_sparse_matrix(G, edge_attr='thickness')

# Convert sparse matrix to a dense array representation
matrix_array = M.toarray()

# Print the result
print('Node order:', nodes)
print('Matrix:\n', matrix_array)
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