<｜begin▁of▁sentence｜>### Instruction:
In the realm of computational network analysis, a scholar might encounter a scenario where graph data arrives in heterogeneous forms—as either an instantiation of a NetworkX graph object or a sparse adjacency matrix. For coherence in subsequent analytical endeavors, there's a necessity for harmonization of these disparate data structures. Would you elucidate on the method by which a researcher can leverage the `import_graph` utility from the graspologic package to transition these varied graph representations into a consolidated format that's amenable to rigorous exploration and scrutiny? Furthermore, what constitutes the schema of the result after this transformation through `import_graph`?
### Response:
import networkx as nx
from scipy.sparse import csr_matrix
from graspologic.utils import import_graph

# Creating a synthetic network using NetworkX
G_nx = nx.erdos_renyi_graph(n=6, p=0.5, seed=42)

# Alternatively, creating a sparse matrix representation of a graph
G_sparse = csr_matrix([[0, 1, 0, 0, 1, 0],
                        [1, 0, 1, 0, 0, 0],
                        [0, 1, 0, 1, 0, 0],
                        [0, 0, 1, 0, 1, 0],
                        [1, 0, 0, 1, 0, 1],
                        [0, 0, 0, 0, 1, 0]])

# Import the NetworkX graph
G_imported_nx = import_graph(G_nx)

# Import the sparse matrix graph
G_imported_sparse = import_graph(G_sparse, copy=False)

# Display the adjacency matrix of the imported NetworkX graph
print(G_imported_nx)

# Display the adjacency matrix of the imported sparse graph
print(G_imported_sparse)
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