<｜begin▁of▁sentence｜>### Instruction:
Imagine we're mapping out a learning pathway where each concept in massage therapy is a checkpoint ('A', 'B', 'C', 'D'), and the effort to move from one to the next is represented by the time spent or the difficulty level (with respective weights like 1, 2, 3, etc.). We have a structured plan: starting from 'A', you can progress to 'B' and 'C' with different efforts (1 and 2, respectively), moving from 'B' to 'C' takes a bit more (3), 'C' to 'D' even more so (4), and 'D' back to 'A' requires the highest effort (5).

In this teaching scenario, we're looking at various complete journeys through the concepts where every point is covered without any cycles—think of it as covering all areas in a comprehensive, yet distinct, massage program. How can we explore all the possible complete journeys, or 'spanning arborescences,' making sure we look at the least demanding routes first, gradually working up to the more challenging ones? How would we do this if our journey plans were sketched out using NetworkX, a tool like a textbook appendix that helps us map complex information?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_weighted_edges_from([
    ('A', 'B', 1),
    ('A', 'C', 2),
    ('B', 'C', 3),
    ('C', 'D', 4),
    ('D', 'A', 5)
])

# Create an iterator for all spanning arborescences in increasing cost
arborescence_iter = nx.arborescence_branching.ArborescenceIterator(G, weight='weight', minimum=True)

# Use the iterator to find and print all spanning arborescences
for arborescence in arborescence_iter:
    print(arborescence.edges(data=True))
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