<｜begin▁of▁sentence｜>### Instruction:
In our ongoing project to optimize the coolant flow network within the nuclear power plant's secondary cooling loop, we are deploying two separate models, analogous to digital blueprints, to diagram the intricate pathways of the cooling system. The first model, named "Coolant_Path_Unweighted.gml", represents a simplistic and idealized schematic of four interconnected components forming a linear sequence, akin to a path graph, absent of any weightings signifying the relative capacity or flow rate between the components. 

The second model, "Coolant_Path_Weighted.gml", simulates a more complex aspect of the system. In this directed graph model, we introduced a singular weighted connection to signify a channel with a designated flow rate, specifically between component-1 and component-2, embodying the vital regulatory control necessitated by differing operational demands or heat exchange rates.

To refine our computational analysis and system diagnostics, we seek to ascertain with certainty: Does the unweighted model inherently lack any representation of flow capacities, and is the weighted connection in the second model uniquely characterized by its prescribed data attribute? More practically, can we identify if these differing characteristics in flow dynamics - unweighted versus weighted - are effectively captured in the respective models using NetworkX, and can we examine the absence or presence of such weightings in the unweighted model within a specified edge, for example, between two components? This scrutiny ensures that our simulations reflect the true physical properties of our nuclear systems and inform subsequent design or operational adjustments for enhanced performance and safety.
### Response:
import networkx as nx

# Create graphs
G_unweighted = nx.path_graph(4)
G_weighted = nx.DiGraph()
G_weighted.add_edge(1, 2, weight=1)

# Check if G_unweighted has weighted edges
is_G_unweighted_weighted = nx.is_weighted(G_unweighted)

# Check if G_weighted has weighted edges
is_G_weighted_weighted = nx.is_weighted(G_weighted)

# Check if a specific edge in G_unweighted is weighted
is_edge_weighted = nx.is_weighted(G_unweighted, (2, 3))

print('G_unweighted has weighted edges:', is_G_unweighted_weighted)
print('G_weighted has weighted edges:', is_G_weighted_weighted)
print('(2, 3) is a weighted edge in G_unweighted:', is_edge_weighted)
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