<｜begin▁of▁sentence｜>### Instruction:
Imagine you're exploring a beautiful national park with a series of interconnected trails, each path representing a connection from one scenic spot to another. Think of 'A' as the Alpine Meadow, 'B' as the Babbling Brook, 'C' as the Cedar Grove, and 'D' as the Dragonfly Pond. The paths between these attractions are like arrows pointing from one place to the next: you can stroll from Alpine Meadow to Babbling Brook and Cedar Grove, from Babbling Brook to Dragonfly Pond, from Cedar Grove to Dragonfly Pond, and there's even a trail looping back from Dragonfly Pond to Alpine Meadow.

Now, consider a part of our journey where we focus on the impact made by visiting just the Alpine Meadow and Babbling Brook. We want to understand their collective influence on the accessibility of the other spots within our nature trail network. How significant is their role in guiding visitors through the park's beauty? In ecological terms, we're looking to compute the group out-degree centrality for our selected spots, Alpine Meadow and Babbling Brook. This measurement will help us appreciate their shared contribution to directing the flow of eco-travelers through the network of trails, ensuring a sustainable and enlightening tour of our park's treasures. How can we calculate this aspect of our journey using our trusty trail map and observations?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add edges
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'D'), ('D', 'A')])

# Define the group of nodes for which we want to calculate the group out-degree centrality
S = ['A', 'B']

# Calculate the group out-degree centrality
centrality = nx.group_out_degree_centrality(G, S)

print('Group out-degree centrality for the group', S, 'is:', centrality)
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