<｜begin▁of▁sentence｜>### Instruction:
Given a path graph with 4 nodes where nodes 0 and 3 are labeled 'A' and 'B' respectively, how can we use networkx to predict the labels for the unlabeled nodes using the Harmonic function algorithm?
### Response:
import networkx as nx
from networkx.algorithms.node_classification import harmonic_function

# Create a graph
G = nx.path_graph(4)
# Add labels to nodes 0 and 3
G.nodes[0]['label'] = 'A'
G.nodes[3]['label'] = 'B'
# Show nodes with data
print(G.nodes(data=True))
# NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}})
# Show edges
print(G.edges())
# EdgeView([(0, 1), (1, 2), (2, 3)])
# Apply the Harmonic function algorithm for node classification
predicted = harmonic_function(G)
# Output the predicted labels for each node
print(predicted)
# ['A', 'A', 'B', 'B']
<|EOT|>
