<｜begin▁of▁sentence｜>### Instruction:
In the dynamic landscape of humanitarian assistance, picture this scenario: we are deploying resources in two distinct regions, where the infrastructure systems are represented as interconnected networks. For our operational efficiency, we've modeled these systems as Graphs G1 and G2.

Graph G1 maps the resources between settlements A, B, and C (nodes {1, 2, 3}) with paths (edges) running from A to B, B to C, and C back to A, forming a closed loop of aid distribution. Each settlement is identified by a unique color code for logistical purposes: settlement A (node 1) is marked 'red', B (node 2) as 'green', and C (node 3) as 'blue', reflecting the types of aid they are prioritized to receive.

Simultaneously, we're confronted with a second region with a similar setup where settlements X, Y, and Z (nodes {4, 5, 6}) are connected in a corresponding manner, facilitating the flow of resources between them along paths X-Y, Y-Z, and Z-X. The color-coding here is analogous to the first region: 'red' for X (node 4), 'green' for Y (node 5), and 'blue' for Z (node 6).

To streamline our operation and possibly consolidate our efforts, we require an assessment: are the infrastructure networks in these two regions -- G1 and G2 -- functionally interchangeable, or isomorphic, both irrespective of and taking into account the color codes representing different types of aid? Insight into this will determine if the processes, personnel, and resources can be standardized across both regions, potentially allowing for a more seamless transfer of knowledge and assistance between the two.

Using the systems' analytical tool NetworkX's 'is_isomorphic' function, how may we ascertain the structural equivalence of these two networks under both conditions? This evaluation is vital for our strategic planning and the effective deployment of aid to the affected communities.
### Response:
import networkx as nx
from networkx.algorithms.isomorphism import is_isomorphic

G1 = nx.Graph()
G2 = nx.Graph()

G1.add_edges_from([(1, 2), (2, 3), (3, 1)])
G2.add_edges_from([(4, 5), (5, 6), (6, 4)])

# No additional node/edge matching criteria
are_isomorphic = is_isomorphic(G1, G2)

print('Are G1 and G2 isomorphic without considering attributes?', are_isomorphic)

# Considering node attributes using node_match
nm = lambda n1, n2: n1['color'] == n2['color']
G1.add_node(1, color='red')
G1.add_node(2, color='green')
G1.add_node(3, color='blue')

G2.add_node(4, color='red')
G2.add_node(5, color='green')
G2.add_node(6, color='blue')

are_isomorphic_with_node_match = is_isomorphic(G1, G2, node_match=nm)

print('Are G1 and G2 isomorphic considering node attributes?', are_isomorphic_with_node_match)
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