<｜begin▁of▁sentence｜>### Instruction:
Imagine you're investigating a chemical interaction network where nodes represent chemicals and edges represent interactions between pairs of chemicals. This network is mapped using the following interaction pairs: [(1, 2), (1, 3), (2, 3), (3, 4), (4, 5)]. Your task is to analyze the network and determine the largest subset of chemicals that show a specific pattern of interaction—each chemical should have at least two other chemicals with which it interacts directly, and there should be a direct interaction between at least one pair of its interacting partners (this subset is known as a locally (2,1)-connected subgraph in network analysis terms). 

Could you assess whether this network subset, once identified, represents the entire complex of chemical interactions as originally charted? The outcome will help understand the robustness of the interaction network and identify any critical chemicals that might be essential for maintaining the integrity of these interactions.

Please note that this scenario is a theoretical abstraction to assist in the visualization of the network analysis concept using NetworkX, no actual chemical data or GML files are provided or required for this illustration.
### Response:
import networkx as nx

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

# Find the maximum locally (k,l)-connected subgraph
k = 2  # The maximum length of paths to consider
l = 1  # The number of edge-disjoint paths

# Use the low_memory option if necessary
low_memory = False

# Use the same_as_graph option to check if the original graph is (k,l)-connected
H, is_same = nx.kl_connected_subgraph(G, k, l, low_memory=low_memory, same_as_graph=True)

# Print the result
def nodes_edges(g):
    return list(g.nodes()), list(g.edges())

print('Nodes:', nodes_edges(H)[0])
print('Edges:', nodes_edges(H)[1])
print('Is the same graph:', is_same)
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