<｜begin▁of▁sentence｜>### Instruction:
Imagine you're behind the wheel of your big rig, and you've got a haul of 10 different cargos that need to be delivered. Now, you're planning the most efficient routes, figuring you've got an average of just one stop per route, keeping things nice and simple. You think of this as plotting points on a map, where each point is a delivery destination, and you connect them with lines that represent the potential routes.

To keep it organized, you're considering using a sort of route-planning chart. It's akin to one of those computer-generated maps that use a basic set-up, where the chances of linking any two points are as straightforward as a straight highway. This helps you avoid any unnecessary detours, saving time and fuel.

Could you sketch out a simple map like that, representing your 10 deliveries as points and connecting them with potential routes, ensuring that on average, each destination just gets one route leading into it? Just remember, we're talking theoretical here; it's a bit like drawing out a "what-if" scenario to visualize your potential day on the road.
### Response:
import networkx as nx
import matplotlib.pyplot as plt

# Define the kernel integral function
def kernel_integral(u, w, z):
    c = 1  # constant for mean expected degree
    return c * (z - w)

# Define the kernel root function
# This example assumes c = 1 for simplicity
def kernel_root(u, w, r):
    return r + w

# Number of nodes
n = 10

# Generate the random kernel graph
G = nx.random_kernel_graph(n, kernel_integral, kernel_root)

# Draw the graph
nx.draw(G, with_labels=True)
plt.show()
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