<｜begin▁of▁sentence｜>### Instruction:
Certainly! Imagine we're analyzing a mechanical system with components that influence each other, the dynamic of this system can be modeled as a network where each node represents a component, and each directed, weighted edge represents the influence one component has over another with associated intensities. In our case, we have a set of relationships between components as follows: the influence from component 1 to 2 is rated at 0.5, the influence from 2 to 1 at 0.75, and from component 2 to 3 at 1.5.

Could we leverage the NetworkX tool to evaluate the overall efficiency of this system by determining the normalized global reaching centrality of the network based on the given influence weights? This measurement will help us understand which component has the greatest overall influence on the mechanical system's performance.
### Response:
import networkx as nx

# Create a weighted directed graph
G = nx.DiGraph()
G.add_weighted_edges_from([(1, 2, 0.5), (2, 1, 0.75), (2, 3, 1.5)])

# Calculate the global reaching centrality
grc = nx.global_reaching_centrality(G, weight='weight', normalized=True)

print(f'Global reaching centrality: {grc}')
<|EOT|>
