<｜begin▁of▁sentence｜>### Instruction:
Imagine you're guiding a group of eager adventurers along a unique trail, comprised of four stunning campsites set in the wilderness. The journey starts at the famed Camp A, known for its breathtaking sunrise views, and concludes at the much-anticipated Camp B, where the night skies are unlike any other. Along the path, there are two mysterious campsites, their names lost to time and tales. Your challenge, as the guide who knows the lands and its stories, is to rediscover the names of these forgotten camps, using clues from the legends of Camp A and Camp B, ensuring the entire trail has a coherent narrative from start to end.

Could you, with your deep understanding of the trails and their stories, utilize the method of piecing together local legends and overall trail lore, much like the 'local_and_global_consistency' technique, to unveil the names of these enigmatic campsites? Let's imagine this journey is not just a path through the wilderness, but a puzzle to be solved, bridging the gap between the known and the unknown, ensuring every adventurer who walks this trail can tell the story of every campsite from start to finish.
### Response:
from networkx.algorithms import node_classification
import networkx as nx

# 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'

# Print node data
echo = G.nodes(data=True)
print(echo)

# Print edges
echo = G.edges()
print(echo)

# Predict labels for the nodes
predicted = node_classification.local_and_global_consistency(G, alpha=0.99, max_iter=1000, label_name='label')
print(predicted)
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