<｜begin▁of▁sentence｜>### Instruction:
In the intricate and precise realm of surgical procedure planning, imagine we are crafting a network representation to map out the various pathways similar to a surgeon's approach to understanding the complex human anatomy. We have a graph, akin to a detailed schematic of interconnected systems, where certain paths loop back on themselves, much like the return of blood flow to the heart. In addition, there are regular connections analogous to nerve pathways or blood vessels extending from one organ to another. 

Visualize this graph as a patient's chart, with each edge representing a potential route for surgical intervention. Among these myriad edges, we find ourselves needing to ascertain the quantity of singular pathways that, rather than leading to new territories, loop back to their origin point. 

To transpose our inquiry into clinical terms whilst maintaining its essence: How might we determine the count of these self-referential loops, using the analytical tool akin to our surgical instruments, NetworkX, within the confines of this patient's chart? This data will enable us to understand the prevalence of cyclical paths before we make our incision.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
# Add edges
G.add_edge(1, 1)
G.add_edge(1, 2)

# Count number of selfloops
selfloops = nx.number_of_selfloops(G)
print('Number of selfloops:', selfloops)
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