<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a psychologist trying to understand the intricate connections within a group therapy session, where each member's dynamics with others are like pathways of communication. You've created a diagram to visualize these dynamics, represented by a directed graph, "GroupDynamics.gml", with members 1, 2, 3, and 4. In this graph, the arrows indicate who initiates the conversation with whom, thus we have pathways from 1 to 2, 2 to 3, and 3 to 4.

To gain a holistic view of these interactions for each participant, you'd like to form a comprehensive perspective that combines who each person talks to (akin to successors) and who initiates conversations with them (akin to predecessors). In technical terms, you'd like to utilize the 'networkx' library's functionality to create a unified adjacency view that merges these two directions of communication.

Specifically for member 2, you wish to observe a combined list of interactions—both who they reach out to and who approaches them within the session. You're considering using the 'networkx.classes.coreviews.UnionAdjacency' API to craft this unified communication view.

How could this be applied in your analysis? Could you merge these interaction directions to better understand how member 2 is engaged within the group's network in "GroupDynamics.gml"? This united view would potentially unveil a deeper insight into the social exchanges of the therapy group.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add nodes and edges to the graph
G.add_edges_from([(1, 2), (2, 3), (3, 4)])

# Access the adjacency representation for the union of successors and predecessors
union_adj = nx.classes.coreviews.union_adjacency(G.succ, G.pred)

# Print the adjacency of node 2
print(union_adj[2])

# The output shows both the successors and predecessors of node 2
# Represented as a union adjacency view
<|EOT|>
