<｜begin▁of▁sentence｜>### Instruction:
Given a graph whose edge set is [('A', 'B', weight=2), ('A', 'C', weight=3), ('B', 'C', weight=1), ('C', 'D', weight=4)], can you compute the approximate current-flow betweenness centrality for nodes using NetworkX with normalization, considering edge weights, using a 'lu' solver, an absolute error tolerance of 0.01, a maximum of 1000 samples for the approximation, and a fixed random seed of 42?
### Response:
import networkx as nx
from networkx.algorithms import centrality

# Create a graph
G = nx.Graph()
# Add edges
G.add_edge('A', 'B', weight=2)
G.add_edge('A', 'C', weight=3)
G.add_edge('B', 'C', weight=1)
G.add_edge('C', 'D', weight=4)

# Compute approximate current-flow betweenness centrality
betweenness = centrality.approximate_current_flow_betweenness_centrality(
    G, normalized=True, weight='weight', dtype=float,
    solver='lu', epsilon=0.01, kmax=1000, seed=42)

print('Approximate current-flow betweenness centrality:')
for node in betweenness:
    print(f'{node}: {betweenness[node]}')
<|EOT|>
