<｜begin▁of▁sentence｜>### Instruction:
Imagine we're exploring the intricate pathways of the respiratory system, akin to navigating a complex maze to understand airflow patterns similar to how we assess lung function in our patients. In this case, our maze is a graph structure, analogous to the labyrinthine airways of the pulmonary network. I've come across a fascinating model in Sedgewick's authoritative text on graph algorithms, where he discusses a small maze incorporating a cycle, offering a challenge reminiscent of a COPD patient's obstructed airways.

Let me rephrase our objective in terms more familiar to our field: How might we, as respiratory therapists leveraging our analytical acumen, simulate the generation and visualization of this 'sedgewick_maze_graph' using NetworkX and matplotlib within a Python environment? This exercise, similar to conducting a pulmonary function test, will allow us to better appreciate the complexity of the airway passages by examining the connectivity of this maze's pathways and nodes, as if we were tracing the flow of oxygen through bronchi and alveoli.

Now, should there be applicable data or a GML file detailing the structure of this maze, akin to a patient's medical charts or imaging, these details would be critical for creating a most accurate representation. Such visualization not only aids our understanding but can also be instrumental in strategizing treatments or interventions for our patients.
### Response:
import networkx as nx

# Use the sedgewick_maze_graph API to generate a maze graph
maze_graph = nx.sedgewick_maze_graph()

# Print the edges of the maze graph
print('Edges of the maze graph:', list(maze_graph.edges()))

# Draw the maze graph (this requires matplotlib)
import matplotlib.pyplot as plt
nx.draw(maze_graph, with_labels=True)
plt.show()
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