<｜begin▁of▁sentence｜>### Instruction:
Given a dataset of 300 data points grouped into blobs (clusters), how can the AutoGMMCluster algorithm from graspologic be used to perform clustering and predict which cluster each data point belongs to? Also, how can this clustering result be used to create a graph in networkx where nodes represent data points, node attributes represent the cluster label, and edges connect nodes within the same cluster?
### Response:
import networkx as nx
from graspologic.cluster import AutoGMMCluster
from sklearn.datasets import make_blobs

# Generate synthetic data
X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)

# Fit the AutoGMMCluster model
auto_gmm = AutoGMMCluster(min_components=2, max_components=5)
auto_gmm.fit(X)

# Predict the cluster labels
labels = auto_gmm.predict(X)

# Construct a graph from the data and cluster labels
G = nx.Graph()
for i, label in enumerate(labels):
    G.add_node(i, cluster=label)

for i in range(len(X)):
    for j in range(len(X)):
        if labels[i] == labels[j]:
            G.add_edge(i, j)

# Number of clusters found
n_clusters = auto_gmm.n_components_
print(f'Number of clusters: {n_clusters}')
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