<｜begin▁of▁sentence｜>### Instruction:
Certainly! Let's reframe this in a context that might resonate with an educational psychologist:

Imagine we have two different classroom networks wherein students forge connections through collaborative activities. The first classroom's interaction patterns are represented by a set of pairings: Student1 with Student2, Student2 with Student3, and Student3 with Student1. In the second classroom, there are a different set of connections: Student4 with Student5, Student5 with Student6, and Student6 with Student4.

Each student is identified not only by their unique number but also by a 'color' attribute that signifies a certain characteristic or learning style that is relevant for pairing students in optimal learning scenarios. To understand the equivalence of these classroom dynamics and to potentially apply successful strategies across them, we're interested in uncovering all the ways these two networks are structurally similar with respect to the 'color' attribute of the students.

Utilizing a theoretical tool in network analysis known as vf2pp_all_isomorphisms, we would be looking to map out all correspondence between the two classrooms that demonstrates how students with similar characteristics are connected within their respective networks. Identifying these mappings helps us understand the possible learning dynamics that could be transferred from one classroom environment to another. How might we go about revealing all these isomorphic relationships between Classroom Network G1 and Classroom Network G2?
### Response:
import networkx as nx

# Create two graphs G1 and G2
G1 = nx.Graph()
G1.add_edges_from([(1, 2), (2, 3), (3, 1)])

G2 = nx.Graph()
G2.add_edges_from([(4, 5), (5, 6), (6, 4)])

# Use the vf2pp_all_isomorphisms function to find isomorphisms
for iso_mapping in nx.algorithms.isomorphism.vf2pp_all_isomorphisms(G1, G2, node_label='color'):
    print('Isomorphic mapping:', iso_mapping)
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