<｜begin▁of▁sentence｜>### Instruction:
Imagine we're sitting in a Career Exploration workshop, and one of our exercises is to analyze different pathways using network diagrams. Now, just as a mentor would check if a career path is realistic and feasible, there's a tool in NetworkX called 'is_planar' that can check if a network diagram, or graph, can be drawn on a plane without any edges crossing, which is somewhat analogous to checking the practicality of a career plan.

To make this more concrete, let's consider two different career pathways. The first pathway involves just three stages: deciding to pursue higher education (stage 0), then either applying for an apprenticeship (stage 1) or doing a vocational course (stage 2). Our second pathway is much more complex, akin to the many twists and turns in a career like those in the corporate world, where connections are rich and the environment is dynamic, represented by a completely interconnected network of five stages, where every option leads to every other option (much like a complete graph K5).

What we'd like to do is use our NetworkX tool to check if each of these career pathways can be laid out clearly without any overlapping steps (edges), in other words, to see if they're 'planar'. Could you apply 'is_planar' to our simple three-stage graph and the densely interconnected five-stage graph, to determine if they're clearly mapped without any overlaps? Remember, just like in real life, not all career paths can be laid out without complications, and in graph theory, not all graphs are planar—especially complex ones like the second pathway we're imagining.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2)])

# Check if the graph is planar
is_g_planar = nx.is_planar(G)
print('The graph is planar:', is_g_planar)

# Check the planarity of a complete graph K5
is_k5_planar = nx.is_planar(nx.complete_graph(5))
print('The complete graph K5 is planar:', is_k5_planar)
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