<｜begin▁of▁sentence｜>### Instruction:
A neuroscientist is studying the functional connectivity patterns in the human brain during different cognitive tasks. They have a dataset of brain activity represented as a series of unweighted graphs, where each graph corresponds to brain activity during a particular task. These graphs were derived from fMRI scans taken over several sessions. The scientist categorizes the tasks into two classes - 'high cognitive load' and 'low cognitive load'. They wish to determine which connections in the brain (edges in the graphs) are most indicative of the cognitive load based on this labeled data. The provided code aims to perform this analysis by applying the SignalSubgraph estimator to find a subgraph that best discriminates between the two classes.
### Response:
import numpy as np
from graspologic.subgraph import SignalSubgraph

# Generate synthetic graph data
n_vertices = 6
s_samples = 3
np.random.seed(42)
graphs = np.random.rand(n_vertices, n_vertices, s_samples)

# Synthetic binary class labels
class_labels = np.array([0, 1, 0])

# Instantiate SignalSubgraph with constraints
sigsub = SignalSubgraph()

# Fit the model. Here, we specify that we want a signal-subgraph of size 5
sigsub.fit(graphs, class_labels, constraints=5)
# Get the signal subgraph
sig_edges = sigsub.fit_transform(graphs, class_labels, constraints=5)

# Print the edges of the signal subgraph
print(f'Signal subgraph edges:\n{sig_edges}')
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