<｜begin▁of▁sentence｜>### Instruction:
Imagine you're plotting a training course for a dog agility competition where each obstacle's difficulty level is assigned a numerical value, akin to a weight. You've mapped out the sequence of obstacles 'A', 'B', 'C', 'D', 'E', with respective difficulty scores of 5, 4, 8, and 2, creating transitions from 'A' to 'B', 'B' to 'C', 'C' to 'D', and 'D' to 'E'.

In preparation for the event, you're curious about the overall challenge your dog will face when running through this specific course in the order you've planned: 'A', 'B', 'C', 'D', 'E'. How would you tally up the total difficulty value of this course using the scores assigned to each section, with the help of NetworkX, to ensure your furry athlete is ready for the big day? Please remember, while the question is framed for your profession, I'm looking for the approach in a network context, not actual dog training methods.
### Response:
import networkx as nx
from networkx import path_weight

# Create a graph
G = nx.Graph()
G.add_weighted_edges_from([ ('A', 'B', 5), ('B', 'C', 4), ('C', 'D', 8), ('D', 'E', 2) ])

# Define a path
defined_path = ['A', 'B', 'C', 'D', 'E']

# Calculate the total cost of the path using the weights
path_cost = path_weight(G, defined_path, weight='weight')
print(f'The total cost of the path is: {path_cost}')
<|EOT|>
