<｜begin▁of▁sentence｜>### Instruction:
Given a NetworkX path graph of 4 nodes, where the first node is labeled 'A' and the last node is labeled 'B', how can we predict the labels of the unlabeled inner nodes using the Local and Global Consistency algorithm by Zhou et al. with an alpha of 0.99 and a maximum of 100 iterations?
### Response:
from networkx.algorithms import node_classification
import networkx as nx

# Create a graph
G = nx.path_graph(4)
# Assign labels to the terminal nodes
G.nodes[0]['label'] = 'A'
G.nodes[3]['label'] = 'B'
# Display nodes with their data
print(G.nodes(data=True))
# Display edges
print(G.edges())
# Apply the Local and global consistency algorithm
predicted = node_classification.local_and_global_consistency(G, alpha=0.99, max_iter=100, label_name='label')
print(predicted)
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