<｜begin▁of▁sentence｜>### Instruction:
Certainly. Imagine you're a mortgage broker who has developed a network representing various clients and their connections. This network is emulated through a set of paired relationships indicative of financial interactions, specifically captured as [(1, 2), (3, 4), (5, 6)]. In order to streamline your focus on potential borrowers with IDs lower than 4, you seek to utilize the 'show_nodes' filter feature within the NetworkX toolkit. You aim to cultivate a specialized perspective of your network, which crystallizes only those client nodes fitting your specified criteria. Your objective is to generate this refined subgraph view and enumerate the eligible client nodes fitting within this targeted threshold for strategic assessment. Could you propose a scenario on how one might use this 'show_nodes' functionality effectively in the network, while adhering to the current context of client ID parameters?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (3, 4), (5, 6)])

# Define a filter function
def filter_func(node):
    # Only show nodes that are less than 4
    return node < 4

# Use the filter with a subgraph view
filtered_graph = nx.subgraph_view(G, filter_node=filter_func)

# Show nodes in the filtered subgraph view
print('Nodes in filtered subgraph:', list(filtered_graph.nodes()))
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