<｜begin▁of▁sentence｜>### Instruction:
In the realm of game design, let's imagine you're crafting a virtual world where the terrain is mapped as a series of connections and pathways with different travel costs, representing the challenge of moving from one point to another. Within this world, you have two distinct maps – let's call them "Map Alpha" and "Map Beta."

Map Alpha contains pathways from the fortress 'A' leading to the enchanted forest 'B' (at a cost of 2 stamina points) and to the crystal caves 'C' (at a cost of 3 stamina points). Map Beta, on the other hand, charts the routes from the goblin town 'B' to the crystal caves 'C' (with a hefty toll of 5 stamina points) and a secret passage from the crystal caves 'C' back to fortress 'A' (which drains a massive 7 stamina points).

As the game designer, you want players at fortress 'A' on Map Alpha to have a merged perspective of their possible routes and threats by drawing information from both maps, essentially offering a unified view of where they can go next (the successor paths) and from where dangers might approach (the predecessor paths).

Could you fabricate a scenario where the adventurers stationed at fortress 'A' are able to discern all their strategic choices, taking into account the combined pathways and travel costs from both maps? This would mean merging the intelligence gathered on the routes from Map Alpha and Map Beta, ensuring our heroes are well-informed about their surroundings for their upcoming quests. How might we weave these intertwined paths and the associated costs of travel into their story?
### Response:
import networkx as nx

# Create two MultiDiGraphs
g1 = nx.MultiDiGraph()
g2 = nx.MultiDiGraph()

# Add some edges to the first graph
g1.add_edge('A', 'B', weight=2)
g1.add_edge('A', 'C', weight=3)

# Add some edges to the second graph
g2.add_edge('B', 'C', weight=5)
g2.add_edge('C', 'A', weight=7)

# Create UnionMultiInner representing the union of G.succ['A'] and G.pred['A']
union_inner = nx.classes.coreviews.UnionMultiInner(g1.succ['A'], g1.pred['A'])

# Access UnionMultiInner data
for nbr, edict in union_inner.items():
    for key, eattr in edict.items():
        print(f'Neighbor: {nbr}, Key: {key}, Attributes: {eattr}')
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