<｜begin▁of▁sentence｜>### Instruction:
As a floral designer, imagine you're creating a grand centerpiece for an event, but due to space constraints, you can only showcase the most stunning cluster of interlinked flowers from your collection. Similar to arranging flowers where we must find the most harmonious grouping, an ecologist seeks to pinpoint the most extensive cluster of interdependent habitats within a disrupted ecosystem – a network of flourishing life where each habitat patch is like a flower, and the pathways between them are the stems that bind them together. To assemble the most impactful conservation strategy, the ecologist needs a tool to identify the most expansive interconnected grouping of these habitats. How might they appropriately utilize the 'largest_connected_component' function from graspologic to uncover this dominant network of habitat patches and retrieve the original placement of each 'flower' in their ecological masterpiece?
### Response:
import networkx as nx
from graspologic.utils import largest_connected_component
import numpy as np

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

# Find the largest connected component
largest_cc_subgraph, node_indices = largest_connected_component(G, return_inds=True)

# Print the largest connected component and the node indices
print('Largest connected component:', largest_cc_subgraph)
print('Node indices:', node_indices)
<|EOT|>
