<｜begin▁of▁sentence｜>### Instruction:
In the realm of mental arithmetic, consider we're navigating a maze of abacus beads, where each movement from one bead to another carries a specific numerical cost. Imagine a sequence of beads labeled 'A', 'B', 'C', and 'D' are connected in such a way that moving from 'A' to 'B' has a cost of 3 units, from 'B' to 'C' incurs 2 units, and advancing from 'C' to 'D' requires 5 units of effort. Using the computational prowess of the path_weight method found in the mathematical tool NetworkX, could you delineate the cumulative expenditure of units necessary to traverse from the 'A' bead all the way to the 'D' bead through this configured pathway? Keep in mind, we’re not physically moving beads, but rather translating this into a hypothetical scenario for a NetworkX graph representation.
### Response:
import networkx as nx

# Create a weighted graph
G = nx.Graph()
G.add_edge('A', 'B', weight=3)
G.add_edge('B', 'C', weight=2)
G.add_edge('C', 'D', weight=5)

# Specify a path
path = ['A', 'B', 'C', 'D']

# Calculate the total weight (cost) of the specified path
path_cost = nx.path_weight(G, path, weight='weight')
print('Total path cost:', path_cost)
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