<｜begin▁of▁sentence｜>### Instruction:
Imagine we're analyzing the dietary interactions within a complex hospital network, where multiple connections represent different nutritional relationships between patients and various dietary interventions. This network is stored in the GML file "HospitalDietaryNetwork.gml", and is much like a MultiGraph in NetworkX, meaning for each pair of patient and intervention, there can be several distinct connections, each denoting a unique dietary consideration or therapeutic action with its specific characteristics.

Now, as a clinical dietitian tasked with optimizing these nutritional strategies, you would need to examine every single dietary edge and their unique attributes to provide informed counseling and therapy plans. How would you methodically and efficiently gather the properties from all these dietary connections utilizing the MultiAdjacencyView in NetworkX, without overlooking any crucial details of our patients' nutritional care?
### Response:
# Import the required NetworkX package
import networkx as nx

# Create a MultiGraph
G = nx.MultiGraph()

# Add multiple edges between the same pairs of nodes
G.add_edge(1, 2, key='a', weight=5)
G.add_edge(1, 2, key='b', weight=8)

# Access the MultiAdjacencyView
multi_adjacency_view = nx.classes.coreviews.MultiAdjacencyView(G._adj)

# Access edge data using the MultiAdjacencyView
for u, adjacencies in multi_adjacency_view.items():
    for v, key_dict in adjacencies.items():
        for key, edge_attr in key_dict.items():
            weight = edge_attr['weight']
            print(f'Edge from {u} to {v} with key {key} has weight {weight}')
<|EOT|>
