<｜begin▁of▁sentence｜>### Instruction:
Given a bipartite graph with two node sets U = {'A', 'B', 'C'} and V = {1, 2, 3} and edges set E = {('A', 1), ('B', 1), ('C', 2), ('A', 3)}, how can you represent this graph as a biadjacency matrix using NetworkX, with rows ordered by U and columns ordered by V?
### Response:
import networkx as nx
from scipy.sparse import csr_matrix

# Create a bipartite graph
B = nx.Graph()
# Add nodes with the bipartite attribute
B.add_nodes_from(['A', 'B', 'C'], bipartite=0)
B.add_nodes_from([1, 2, 3], bipartite=1)
# Add edges only between nodes of opposite node sets
B.add_edges_from([('A', 1), ('B', 1), ('C', 2), ('A', 3)])

row_order = ['A', 'B', 'C']
column_order = [1, 2, 3]
b_matrix = nx.bipartite.biadjacency_matrix(B, row_order=row_order, column_order=column_order)
print(b_matrix.toarray())
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