<｜begin▁of▁sentence｜>### Instruction:
Imagine you're teaching a class in massage therapy, and you're using a network of pressure points as a learning tool. This network consists of points and pathways where each point represents a pressure point on the body, and the pathways are the potential strokes you can use to connect these points in a massage sequence. Now, the map of points and pathways you're working with today includes these connections: between point 1 and 2, point 2 and 3, point 1 and 3, and point 3 and 4.

In this session, we want to ensure that for each connection between two points, we have at least two alternative stroke pathways that we can use without overworking the same path. Think of it as making sure there's a backup stroke in case one pathway is not suitable for a client. Also, it's important that these alternative strokes don't go beyond one intermediary point, ensuring that our massage remains focused and localized.

Can you consider whether our current map provides this level of redundancy in the pathways for effectively connecting these points? Each stroke pathway should be no longer than connecting a maximum of one additional point to maintain the localized focus on the area. This way, we can be confident that each pressure point pairing consistently has a couple of diverse stroke options for an effective massage.
### Response:
import networkx as nx
from networkx.algorithms.connectivity import is_kl_connected

# Create a graph
G = nx.Graph()
G.add_edge(1, 2)
G.add_edge(2, 3)
G.add_edge(1, 3)
G.add_edge(3, 4)

# Check if the graph is locally (2, 2)-connected
result = is_kl_connected(G, k=2, l=2)
print('The graph is locally (2,2)-connected:', result)
<|EOT|>
