<｜begin▁of▁sentence｜>### Instruction:
Imagine you're developing a novel data visualization tool intended for observing signal flow within a complex neural network. For an initial assessment, you've digitally reconstructed a simplified neural pathway with connections analogous to a directed graph. This graph's connections, or synaptic pathways, are represented by an edge set: [(1, 2), (1, 3), (3, 4), (2, 5)].

In order to evaluate the aggregate signaling capability of a subset of this network, specifically the neurons labeled 1 and 2, you wish to calculate a parameter akin to what in network analysis is referred to as the group out-degree centrality. How would you determine this measure of collective outflow capacity for this subgroup of neurons using computational tools like NetworkX, which is a software package for complex network analysis?
### Response:
import networkx as nx

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

# Define a group of nodes
S = [1, 2]

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

print("Group out-degree centrality:", centrality)
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