<｜begin▁of▁sentence｜>### Instruction:
Let's imagine we're in the middle of an engaging workshop on the practical applications of graph theory in data analysis. Our current module involves a hands-on activity where we use NetworkX, a powerful library for network analysis in Python, to deal with real-world data.

Consider we're working with a simplified corporate hierarchy represented as a path graph within our 'corporate_network.gml' file. This graph consists of four positions, where the position at one end is labeled as 'Manager (A)', and the position at the other end is labeled as 'Intern (B)'. The two positions between them are currently unnamed.

Our challenge is to utilize the 'node_classification.harmonic_function' provided by NetworkX to infer the potential titles for these unnamed positions based on the known labels 'A' and 'B'. This method will shed light on how information spreads through the corporate structure and aids us in predicting the labels for the intermediary roles.

Can you think about how to apply such a classification algorithm effectively to assist in our workshop scenario, thus bringing a theoretical concept into a tangible learning experience?
### Response:
import networkx as nx
from networkx.algorithms import node_classification

# Create a path graph
G = nx.path_graph(4)
# Assign labels to the end nodes
G.nodes[0]['label'] = 'A'
G.nodes[3]['label'] = 'B'
# Predict labels for the unlabeled nodes
predicted_labels = node_classification.harmonic_function(G)
print('Predicted labels:', predicted_labels)
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