<｜begin▁of▁sentence｜>### Instruction:
Imagine you're overseeing the well-being of a patient represented by the medical network within a hospital information system. This system is structured in a way that resembles a directed flow of procedures, where (0, 1) represents a patient referral from the general practitioner to a specialist, (1, 2) signifies the subsequent referral to a diagnostic lab, (2, 3) symbolizes the progression to a treatment facility, and (3, 0) reflects the return to the general practitioner for follow-up.

In this medical flow network, you're interested in a specific group of nodes, namely S = [1, 2], which represent the specialist and the diagnostic lab. Your goal is to assess the centrality of this group within the referral patterns – essentially measuring how integral these nodes are in relation to receiving inward connections from the rest of the network. 

Using NetworkX tools, you seek to determine the group in-degree centrality for the nodes 1 (specialist) and 2 (diagnostic lab). This quantitative measure would help you understand their importance and influence in the context of the entire network of patient referrals and potentially optimize the workflow for better patient care outcomes.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0)])

# Define the group of nodes for which we want to calculate group in-degree centrality
S = [1, 2]

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

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