<｜begin▁of▁sentence｜>### Instruction:
As a respiratory therapist, imagine you are working with a complex ventilation system composed of multiple interconnected units. Consider two distinct units, G1 and G2, each with their own configurations and pathways for air circulation. Unit G1 features a two-way valve between compartments 'a' and 'b', with settings 'k1' tuned for a flow rate of 3 liters per minute and 'k2' adjusted for a stronger flow rate of 7 liters per minute, allowing for adaptable patient support. Conversely, unit G2 has a one-way valve guiding airflow from 'b' to 'a' with setting 'k3' set to a fixed rate of 8 liters per minute for robust respiratory assistance, and another branching off from 'b' to 'c' under setting 'k4' at 5 liters per minute to cater to different therapeutic needs.

In an effort to consolidate these separate systems for a broader, more flexible treatment strategy, you need to establish a combined operational schematic that would give you read-only access to the complete valve settings and flow pathways from both G1 and G2. How would you generate this integrated diagram of valves and pathways, making sure it reflects the entire network of possible airflows, while still being able to consult the specific configuration details, such as flow rates, of any given multi-way valve?
### Response:
import networkx as nx

# Create two multidigraphs
G1 = nx.MultiDiGraph()
G1.add_edge('a', 'b', key='k1', weight=3)
G1.add_edge('a', 'b', key='k2', weight=7)

G2 = nx.MultiDiGraph()
G2.add_edge('b', 'a', key='k3', weight=8)
G2.add_edge('b', 'c', key='k4', weight=5)

# Create a union of the adjacency views of the two graphs
U = nx.classes.coreviews.UnionMultiAdjacency(G1._succ, G2._succ)

# Example usage (reading the weights for an edge)
for key, data in U['a']['b'].items():
  print(f"Edge from 'a' to 'b' with key {key} has weight {data['weight']}")
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