<｜begin▁of▁sentence｜>### Instruction:
Imagine you're constructing a cognitive map to model the flow of neural information within the brain, where each node represents a distinct neural cluster and each directed edge signifies synaptic transmission from one cluster to another. You're particularly interested in tracking the connections that are two synapses distant from an initial cluster, labeled as '0'. Your synaptic map includes the following connectivity pattern: from 'Cluster 0' information flows to 'Cluster 1' and 'Cluster 2', then from 'Cluster 1' it moves on to 'Clusters 3 and 4', and similarly from 'Cluster 2' it reaches 'Clusters 5 and 6'.

In this intricate web of neural connections, could you identify which clusters are receiving signals that have traversed exactly two synaptic connections starting from 'Cluster 0'? This is crucial for understanding the propagation of neural signals and deciphering complex cognitive functions. If this connectivity data were stored in a file, it would be referenced as 'neural_network.gml' for your analysis.
### Response:
import networkx as nx

# Create a directed graph
H = nx.DiGraph()
H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])

# Find all nodes at distance 2 from node 0
nodes_at_distance_2 = nx.descendants_at_distance(H, 0, 2)

print('Nodes at distance 2 from node 0:', nodes_at_distance_2)
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