<｜begin▁of▁sentence｜>### Instruction:
In the context of assisting a patient who is working on developing their problem-solving skills, imagine we have a route planning activity that involves navigating through a series of checkpoints to reach a final destination. Think of these checkpoints as a metaphor for life's daily tasks. 

We've designed a map where 'Checkpoint A' connects to 'Checkpoint B' with a difficulty level of 2, 'Checkpoint B' to 'Checkpoint C' has a difficulty level of 5, 'Checkpoint C' to 'Checkpoint D' is at a difficulty level of 1, and there's a direct but more challenging path from 'Checkpoint A' to 'Checkpoint D' with a difficulty level of 10.

Our goal in this activity is to strategize and determine the path with the least overall difficulty starting from 'Checkpoint A' and reaching 'Checkpoint D'. What would be the total difficulty level on this path using the Bellman-Ford strategy, which helps us ensure that even if there are hurdles, we find a manageable way through?
### Response:
import networkx as nx

# Create a weighted graph
G = nx.DiGraph()
G.add_edge('A', 'B', weight=2)
G.add_edge('B', 'C', weight=5)
G.add_edge('C', 'D', weight=1)
G.add_edge('A', 'D', weight=10)

# Calculate the shortest path length using Bellman-Ford algorithm
length = nx.bellman_ford_path_length(G, 'A', 'D', weight='weight')
print('Shortest path length from A to D:', length)
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