<｜begin▁of▁sentence｜>### Instruction:
Imagine you're in a pharmacy, where each aisle represents a connection in a network, and each section within the aisle is a node where specific medications are found. Now, we have 5 aisles, forming a neat, single-file line of sections from the entrance to the back of the store. This layout is quite similar to a linear path graph with 5 nodes, where customers pass through each section in order to get to the next.

We are interested in evaluating the importance of each section not just by its immediate connections (like a customer only considering the adjacent aisles he can walk to), but also by assessing the influence it has through two degrees of separation (akin to considering where a customer can get if they take an extra step from the neighboring aisles). In the language of network analysis, what we're looking for is the 'second order centrality' of each section — a measure that goes one step beyond the immediate.

With this in mind, could you help us identify which section (node) in our pharmacy has the least influence on customer flow when considering second order centrality? To clarify, we'd also like to understand the centrality values for each of the 5 sections to ensure we're properly managing our store's layout for optimal customer satisfaction.
### Response:
import networkx as nx

# Create a graph
G = nx.path_graph(5)

# Compute second order centrality
soc = nx.second_order_centrality(G)

# Output the node with the lowest second order centrality
lowest_centrality_node = sorted(soc.items(), key=lambda x: x[1])[0][0]
print('Node with lowest second order centrality:', lowest_centrality_node)

# Output the centrality values
print('Second order centrality values:\n', soc)
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