<｜begin▁of▁sentence｜>### Instruction:
Given a graph whose nodes have a 'value' attribute (value of node 'A' is 1, 'B' is 2, and 'C' is 3) and edges have a 'weight' attribute (('A', 'B') has weight 4, ('B', 'C') has weight 7, ('A', 'C') has weight 5), can you construct an attribute sparse matrix where rows and columns represent the node attribute 'value' and the non-zero matrix entries represent the edge attribute 'weight'?
### Response:
import networkx as nx

# Create a graph with edge attributes
g = nx.Graph()
g.add_edge('A', 'B', weight=4)
g.add_edge('B', 'C', weight=7)
g.add_edge('A', 'C', weight=5)
g.add_node('A', value=1)
g.add_node('B', value=2)
g.add_node('C', value=3)

# Generate a sparse matrix using a node attribute as the row/col
# and an edge attribute as values
M, ordering = nx.attr_sparse_matrix(g, node_attr='value', edge_attr='weight')

# Convert sparse matrix to a dense format and print
print(M.todense())
print('Ordering:', ordering)
<|EOT|>
