<｜begin▁of▁sentence｜>### Instruction:
Imagine you are designing an interactive display that showcases the flow of customer attention across different sections of a product package. Think of each section as a node in a network, with the pathways that customers' eyes travel across as connections between these nodes. These connections, like the seams of a package, have varying strengths represented by the thickness of the line or the width of the seam, which correlate to how likely a customer is to look from one section to another.

Now you have a network representing the flow of customer attention across the package, with the connections outlined as follows: between section 1 and 2 there's a seam of thickness 0.5, between section 1 and 3 it's 0.75, section 2 and 3 have a seam of strength 1.0, between section 3 and 4 it's 1.5, and finally, between section 4 and 2 the seam has a strength of 2.0.

To refine the design of the packaging and optimize the customer's visual journey, you want to determine the sections that play the most critical roles in this flow of attention - akin to hubs in a network. In technical terms, you're looking to calculate the approximate current-flow betweenness centrality of each node (section), using NetworkX to simulate this flow and identify these key hubs within your design. How would you compute these values using the data provided?
### Response:
import networkx as nx
import numpy as np

# Create a graph
G = nx.Graph()
G.add_weighted_edges_from([(1,2,0.5), (1,3,0.75), (2,3,1.0), (3,4,1.5), (4,2,2.0)])

# Compute the approximate current-flow betweenness centrality
betweenness = nx.approximate_current_flow_betweenness_centrality(G, normalized=True, weight='weight', dtype=np.float64, solver='lu', epsilon=0.01, kmax=1000, seed=42)

print(betweenness)
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