<｜begin▁of▁sentence｜>### Instruction:
Imagine you're in a medical scenario where you must create a complex network to map out a particular set of interactions within a healthcare system. You have chosen to model this system as a robust and intricate network, analogous to the Heawood Graph in terms of complexity.

The Heawood Graph is a well-defined structure in the realm of network theory, much like a specific protocol in medical procedures. In this case, your network will consist of 14 crucial nodes, which could represent departments or key functions in the healthcare system, connected by 21 vital pathways or relationships, mirroring the graph's edges.

Now, to mirror the properties of the Heawood Graph, your network model should be undirected, implying bidirectional communication or influence between the nodes. It must also be cubic symmetric, ensuring each node is of equal importance with three connections – akin to equal workload distribution in a medical team. Additionally, the nonplanar attribute of this network reflects the complex overlapping interactions that cannot be simplified onto a two-dimensional plane without intersections.

Furthermore, incorporating a Hamiltonian cycle into your network suggests that there is a route through which every department or node can be visited once – a desirable feature for efficiency in operations. The graphical representation of this cycle can be described using LCF notation as [5,-5]^7.

To visualize how all these elements come together, resembling the diagnostic imaging used to see within a patient's body, how would you go about constructing such a network, and what approach would you take to display the interconnected web of nodes and pathways in your healthcare system model?
### Response:
import networkx as nx

# Create the Heawood Graph using NetworkX
G = nx.heawood_graph()

# Print the number of nodes and edges
print('Number of nodes:', G.number_of_nodes())
print('Number of edges:', G.number_of_edges())

# Plot the Heawood Graph (additional libraries required - matplotlib)
import matplotlib.pyplot as plt
nx.draw(G, with_labels=True)
plt.show()
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