<｜begin▁of▁sentence｜>### Instruction:
As a seasoned physician well-versed in piecing together complex cases to diagnose a condition, you might appreciate the methodical process of solving a maze, akin to unraveling the intricacies of a challenging medical diagnosis. In this particular instance, we are examining a maze as depicted in Sedgewick's renowned 'Algorithms' textbook, a veritable labyrinth of pathways akin to the intricate network of veins and arteries within the human body.

Picture, if you will, a small-scale representation of this maze, a puzzle that could be seen as a metaphor for the diagnostic process. Our aim is to construct this network of pathways using a specialized tool called 'sedgewick_maze_graph' from the NetworkX library, a procedural instrument that allows us to generate the interconnected web of the maze’s passageways.

Upon the successful generation of this maze, much like the detailed post-diagnostic report you might present to a patient, could you elucidate the connections among the various junctions within our metaphorical 'patient' – the maze? Specifically, I am interested in an 'adjacency list' for each node within the labyrinthine structure - a comprehensive map detailing how each passageway is linked, not unlike a detailed chart mapping the symptoms to various potential conditions.

If our process were to produce any sort of data file, such as a 'gml' file, it would be paramount to note its designation. However, in this scenario, I believe our primary focus remains the construction and subsequent elucidation of the maze's intricate network.
### Response:
import networkx as nx

# Use the sedgewick_maze_graph function to generate the maze graph
maze = nx.sedgewick_maze_graph()

# Print the adjacency list of each node in the maze
for node in maze:
    print(f'Node {node}: {list(maze[node])}')
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