<｜begin▁of▁sentence｜>### Instruction:
Imagine we're using satellite imagery to analyze an ecological network where habitats are represented as nodes, and their ecological connections as edges, forming a spatial pattern akin to a quadrilateral with an intersecting diagonal. Upon analysis, we find that some habitat nodes are connected in such a way that they could disrupt the migration paths of certain species, since the pattern does not permit a continuous traverse of each connection exactly once – in other words, it's not Eulerian due to nodes with an odd number of connections.

To model a more wildlife-friendly habitat network, we need to apply techniques that could modify our current graph into an Eulerian graph. Using remote sensing data processed through NetworkX's eulerize function, how can we restructure our ecological network graph so that all species can traverse it unimpeded? 

Furthermore, to ensure that our conservation objectives are met, how might we verify the Eulerian property of the restructured graph, confirming that each species could then navigate the ecological network without retracing their steps, promoting biodiversity and ecological resilience?

Keep in mind this practical scenario requires us to consider the necessary data transformations without loss of ecological context.
### Response:
import networkx as nx

# Create a graph that is not Eulerian
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0), (0, 2)])
# Now apply the eulerize function to create a Eulerian multigraph
H = nx.eulerize(G)
# Check if the resulting graph is Eulerian
is_eulerian = nx.is_eulerian(H)
print('Is the graph Eulerian?', is_eulerian)
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