<｜begin▁of▁sentence｜>### Instruction:
Imagine that you, a diligent Patent Examiner, are evaluating a submission which includes the specifications of a novel networking protocol represented as a directed graph. The patent application includes a diagram with a set of connections: (1 → 2), (1 → 3), (2 → 3), and (2 → 4). These are akin to the pathways in which information or permissions could flow within a system.

In order to assess the novelty and functionality of this system, you are particularly interested in understanding the importance of specific nodes within this network. The nodes represent components of the system for which patentability must be assessed in terms of their receiving connections. Specifically, you've zeroed in on components 2 and 3.

Can you assess the collective importance of components 2 and 3 based on the number of incoming pathways they receive together in this directed graph model? This measurement is crucial as it could indicate a potential bottleneck or a critical control point in the network, thereby affecting the system's overall efficiency and its claim to innovation. Please refer to the patent figures provided for a visual representation of the graph when considering this question.
### Response:
import networkx as nx

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

# Define a group of nodes
S = [2, 3]

# Calculate group in-degree centrality
centrality = nx.algorithms.group_in_degree_centrality(G, S)

print('Group in-degree centrality:', centrality)
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