<｜begin▁of▁sentence｜>### Instruction:
Imagine you're leading a community initiative to enhance connectivity between different neighborhoods in our town – neighborhoods 1 through 5. You've successfully established routes that connect neighborhood 1 to neighborhoods 2 and 3. Additionally, there's a pathway from neighborhood 2 to neighborhood 4, another from 3 to 4, and a final one connecting the heart of neighborhood 4 to the burgeoning community in neighborhood 5.

As you mobilize efforts to improve our community's network, you're curious about the influence exerted by the foundational neighborhoods in your initiative: 1 and 2. Specifically, you're interested in understanding the combined outward reach or impact these two neighborhoods have within the network you're developing. Could we quantify this collective reach or influence in terms of the direct pathways these two neighborhoods have to others within the initiative's scope? This measure could prove vital in strategizing future developments and connections within our community's improvement plan.
### Response:
import networkx as nx

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

# Add nodes to the graph
G.add_nodes_from([1, 2, 3, 4, 5])

# Add edges to the graph
G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 4), (4, 5)])

# Specify the group of nodes for which we want to find the group out-degree centrality
S = [1, 2]

# Compute the group out-degree centrality
centrality = nx.group_out_degree_centrality(G, S)
print('Group out-degree centrality:', centrality)
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