<｜begin▁of▁sentence｜>### Instruction:
Imagine you are working in an industrial facility, and the plant has a complex network of safety protocols, each designated by various connections and relationships. Within this facility's safety protocol network, represented by a MultiGraph data structure in NetworkX, the connections are labeled with various keys denoting different levels of safety measures. The current MultiGraph includes multiple connections between protocols, notably repeated connections between protocols 1 and 2, each with a unique identifier - imagine them as 'a', 'b', and a default with no label. Additionally, there is a connection between protocols 1 and 3.

Now, you are focused on evaluating a specific set of safety measures corresponding to identifier 'a' between protocols 1 and 2. To streamline your assessment process, you require a modified view of the network that filters out all connections except for those pertinent to your current evaluation, specifically the edges keyed with 'a'.

Your task is to utilize NetworkX tools, namely the FilterMultiInner function, to generate a tailored view of the MultiGraph. This custom view should exclusively showcase the edges keyed with 'a', enabling you to concentrate on analyzing the associated safety protocols without the distraction of irrelevant data. How would you approach this scenario using NetworkX to filter the graph accordingly?
### Response:
import networkx as nx

# Create a MultiGraph
G = nx.MultiGraph()
G.add_edge(1, 2)
G.add_edge(1, 2, key='a')
G.add_edge(1, 2, key='b')
G.add_edge(1, 3)

# Define a filter function to filter edges with a specific key
edge_filter = lambda edge: G.edges[edge]['key'] == 'a' if 'key' in G.edges[edge] else False

# Create a FilterMultiInner view that filters the graph's edges
filtered_edges = nx.classes.coreviews.FilterMultiInner(G.edges, edge_filter)

# Print the edges in the filtered view
print(list(filtered_edges))
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