<｜begin▁of▁sentence｜>### Instruction:
In an in-depth study of neural connections in the Drosophila larva's mushroom body, a part of the brain responsible for learning and memory, scientists have created a connectome to understand the network structure and cell types involved. Using graph theory and neuroscience, the provided code helps analyze the connectome by loading the adjacency matrix of the connectome and corresponding cell type labels. The code then converts this matrix into a NetworkX directed graph to compute graph metrics, such as graph density, which can elucidate global properties of the neural network, and displays labels for the first 10 nodes to indicate different cell types involved in the connectome.
### Response:
import numpy as np
import networkx as nx
from graspologic.datasets import load_drosophila_right

# Load the Drosophila right connectome
adjacency_matrix, labels = load_drosophila_right(return_labels=True)

# Create a graph from the adjacency matrix
G = nx.from_numpy_array(adjacency_matrix, create_using=nx.DiGraph)

# Analyze the graph, for example, by computing its density
graph_density = nx.density(G)

print('Graph density:', graph_density)
print('Node labels:', labels[:10])  # Print labels for the first 10 nodes
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