<｜begin▁of▁sentence｜>### Instruction:
Imagine you're in the midst of organizing a small networking event intended to connect people through a series of brief, targeted encounters - akin to speed networking. The room setup resembles a certain pathway model: let's say we have three key networking stations, labelled as points 0, 1, and 2. The interactions happen along the pathways that connect these stations: one path running between station 0 and 1, and another between station 1 and 2. 

As you map out the flow of the event, you're interested in understanding the possible circulation patterns among attendees. Specifically, you're considering a scenario where an attendee would engage in two consecutive networking interactions before taking a break - effectively moving within the room twice. Your task is to figure out how many different circulation patterns each participant could follow under these constraints.

Using NetworkX, a useful tool for analyzing such connection patterns in an event space, how would you go about quantifying these potential movement sequences - or in graph-theoretical terms, the number of walks of length 2 - for each pair of stations? This data will aid in optimizing the event's structure and ensuring a dynamic and engaging networking environment for all attendees.
### Response:
import networkx as nx

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

# Calculate the number of walks of a specific length
walk_length = 2
walks = nx.number_of_walks(G, walk_length)

# Output the number of walks
print(walks)

# Calculate the total number of walks
total_walks = sum(sum(targets.values()) for _, targets in walks.items())
print('Total number of walks of length', walk_length, ':', total_walks)

# Get the number of walks from a specific source node
source_node = 0
walks_from_source = sum(walks[source_node].values())
print('Total number of walks from node', source_node, 'of length', walk_length, ':', walks_from_source)
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