<｜begin▁of▁sentence｜>### Instruction:
Imagine we are designing an eco-friendly community layout represented by a network of pathways intertwining through various green zones and buildings. In our design model, we have two directed flow diagrams detailing the circulation of pedestrians. The first diagram, 'Flow_Successors.gml,' showcases paths leading from one space to the next with connections such as from the community garden (node 1) to the green residential area (node 2), and from there to the recreational park (node 3). Conversely, the second diagram, 'Flow_Predecessors.gml,' tracks the routes leading back, such as from the residential area (node 2) to the garden (node 1), and from the park (node 3) to the residential area (node 2).

In merging these two diagrams to assess the interconnectedness of, let's say, the green residential area (node 2) in our master ecological blueprint, could you determine the total number of unique pathways that directly lead to and from this central hub when considering the inbound and outbound traffic flows depicted in both 'Flow_Successors.gml' and 'Flow_Predecessors.gml'?
### Response:
import networkx as nx

# Create two directed graphs
G_succ = nx.DiGraph()
G_pred = nx.DiGraph()

# Add edges to successor graph
G_succ.add_edge(1, 2)
G_succ.add_edge(2, 3)

# Add edges to predecessor graph (opposite direction)
G_pred.add_edge(2, 1)
G_pred.add_edge(3, 2)

# Create union adjacency view
union_adj = nx.classes.coreviews.UnionAdjacency(G_succ._succ, G_pred._pred)

# Access the UnionAdjacency for node 2
node_2_union_adj = union_adj[2]

# Print the adjacency for node 2
count_successors = len(node_2_union_adj) # Number of edges including both successors and predecessors
print('Node 2 has', count_successors, 'neighbours in the union adjacency.')
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