<｜begin▁of▁sentence｜>### Instruction:
Imagine you're at a bustling restaurant during the dinner rush, with the kitchen representing the network in question. Now, suppose the kitchen suddenly experiences some unforeseen disruptions—perhaps a couple of stovetops are out of service, or some vital equipment is on the fritz. This leaves you with various stations in the kitchen (the network) unable to collaborate as they usually would. It's as if the kitchen has been split into separate areas, each cooking its own dishes, disconnected from the rest.

In this culinary conundrum, it's crucial to identify which part of the kitchen—or in our metaphor, the largest section of the network—is still fully operational. This way, you can prioritize where the head chef (the network engineer) should focus their efforts to get the whole kitchen (the entire network) back on track, serving the full menu to the awaiting diners (the users).

To handle this efficiently, the head chef could use a valuable tool from the graspologic toolkit, specifically the 'largest_connected_component' function. This handy resource can help pinpoint the biggest cluster of cooking stations still in communication with one another, ensuring that the majority of the kitchen continues to whip up the delightful dishes that keep the patrons happily dining and the restaurant humming.

In essence, you need a way to swiftly diagnose the most robust section of your kitchen that's still flipping pans and sautéeing garlic, so that you can get those temperamental burners fixed posthaste, and return to delighting the entire dining room with every dish on the menu. That's where the 'largest_connected_component' is the perfect ingredient for success.
### Response:
import networkx as nx
from graspologic.utils import largest_connected_component

# Create a graph with two disconnected components
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 1), (4, 5)])

# Find the largest connected component
lcg = largest_connected_component(G)

# Print nodes of the largest connected component
print(lcg.nodes())
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