<｜begin▁of▁sentence｜>### Instruction:
Imagine we're choreographing a complex dance sequence that reflects the emotional journey of our dancers. We've charted every step and movement transition in a diagram, similar to a MultiDiGraph, to illustrate the current flow of the performance. However, as creativity is an ever-evolving process, we've also composed an updated sequence with additional movements that intertwine with the original choreography.

We now wish to visualize both the established routine and the new, enriched transitions in harmony, without tampering with the integrity of either set. Picture this as merging the two dance sequences and observing them in a unified form, akin to overlaying two transparent choreography diagrams.

Could we, as dance therapists seeking to understand the full range of available expressions and transitions, create a view that seamlessly combines these two intricate dance diagrams? This view should allow us to observe the interplay of all movements without altering the structured sequence of either one, thus preserving their original grace and flow. How might we approach composing such a comprehensive but non-editable representation of our collective choreographic movements?
### Response:
import networkx as nx

# Create two MultiDiGraphs
g1 = nx.MultiDiGraph()
g1.add_edge('A', 'B', key='k1', weight=1)
g1.add_edge('A', 'B', key='k2', weight=2)

# Create another MultiDiGraph with some overlapping edges
g2 = nx.MultiDiGraph()
g2.add_edge('A', 'B', key='k2', weight=3)
g2.add_edge('B', 'C', key='k1', weight=4)

# Create the union adjacency
union_adj = nx.classes.coreviews.unionmultiadjacency(g1._succ, g2._succ)

# Access the UnionAdjacency to get combined edges from both graphs
# It will print a dictionary containing all edges with their attributes
print(dict(union_adj['A']['B']))
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