<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a physician, and each patient's health is represented by a unique graph, displaying interconnected systems and health indicators. These graphs have been created to represent the complex web of health interactions, similar to how we might model the spread of a contagion with an Erdős-Rényi model at a set chance of connections. Here, let's say, the rate stands at a 50% likelihood of any two health indicators being connected.

Now, to understand how these health maps compare and contrast—essentially to diagnose similarities and differences in the health conditions they represent—you're looking to perform what's akin to extracting a detailed comparative health report for each pair of patient health graphs. In network science terms, this is obtaining a pairwise omnibus embedding for each graph pair to study the minutiae and overarching structure of these health indicators.

As with a medical comparison analysis, what would emerge from this sophisticated process would be an intricate, multidimensional representation of the health graphs' relationships, showing you how they are similar or divergent in their patterns of connections. This would be akin to a diagnostic tool that examines not just individual symptoms but the entire health network of a patient, providing you with a deeper understanding of the underlying issues and potentially guiding targeted treatment strategies. 

How might you proceed with obtaining these comparative health reports, and what form would the resulting diagnostic data likely take?
### Response:
import networkx as nx
from graspologic.pipeline.embed import omnibus_embedding_pairwise

# Step 1: Create a list of graphs
A = nx.fast_gnp_random_graph(8, 0.5, seed=42)
B = nx.fast_gnp_random_graph(8, 0.5, seed=42)
C = nx.fast_gnp_random_graph(8, 0.5, seed=42)
graphs = [A, B, C]

# Step 2: Obtain the pairwise omnibus embeddings
embeddings = omnibus_embedding_pairwise(graphs=graphs, dimensions=2, elbow_cut=None, svd_solver_algorithm='randomized', svd_solver_iterations=5, svd_seed=42, weight_attribute='weight', use_laplacian=False)

# Step 3: Print the embeddings
for pair in embeddings:
    print('Embedding for graph pair:')
    print('Embedding 1:', pair[0])
    print('Embedding 2:', pair[1])
    print('\n')
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