<｜begin▁of▁sentence｜>### Instruction:
Curious as ever about the intricate social dynamics of our feline friends, imagine you have meticulously drafted a detailed multi-layered map, akin to an intricate MultiDiGraph, charting the diverse interactions of various cats in a given colony. Each cat is denoted by a distinctive ID and known by a specific moniker, mirroring nodes in this map. The connections, like the complex relationships of our whiskered companions, can be manifold between any two cats, each distinguished by a bespoke label signifying the type of interaction—be it a friendly head-butt or a competitive spat over territory.

Now, envision you are seeking to transcribe this elaborate tapestry of cat relationships into a digital record, a JSON codex, if you will, that encapsulates the entirety of your observations. The compendium should articulate each feline actor with 'id' and 'label' for their unique identifiers and titles, respectively. Moreover, the multi-faceted interactions, akin to multi-edges within the graph, should be explicitly illustrated with 'source', 'target', and 'label' to portray the origin, recipient, and nature of each individual connection.

How might one go about enriching this digital ledger to accurately reflect the complex and multi-tiered framework of interactions that exist within this cat colony, while adhering to the structure of a MultiDiGraph? Should this information be held in a GML file, it would be apt to include its name, "feline_social_map.gml", as part of the detailing. In absence of a GML, should there be data to be integrated, it must be woven into the scenario in a manner that maintains the relational semantics of the cat congregation.
### Response:
import networkx as nx

# Create a MultiDiGraph
G = nx.MultiDiGraph()

# Add nodes with labels
G.add_node(1, label='Node 1')
G.add_node(2, label='Node 2')

# Add edges with multiple connections and labels
G.add_edge(1, 2, label='Edge 1')
G.add_edge(1, 2, label='Edge 2', relationship='alternative')

# Show multi-edges
multidiedges = [{'source': u, 'target': v, 'label': d['label']} for u, v, k, d in G.edges(keys=True, data=True) if G.number_of_edges(u, v) > 1]
graphtoJSON = {'graph nodes': [{'id': n, 'label': d['label']} for n, d in G.nodes(data=True)], 'graph edges': multidiedges}
print(graphtoJSON)
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