<｜begin▁of▁sentence｜>### Instruction:
As an Adaptive Technology Specialist, envisage a scenario where we are crafting an accessible digital interface to assist researchers in visualizing complex neural networks, specifically for scholars studying the Drosophila larva mushroom body. Our current project involves utilizing the `load_drosophila_right` API function to seamlessly acquire the right side connectome adjacency matrix of the Drosophila larva mushroom body. Our goal is to translate this matrix into an interpretable format using the NetworkX library, giving us a graphical representation of the connectome.

Could we take the data gathered from this function and embody it in a NetworkX graph structure? We also aim to integrate meaningful labels into this graph, pinpointing each node with the cell type it represents. Following this, we would proceed to elegantly illustrate the graph to provide a visual tool for researchers to better understand the neural connections. In this manner, we can enrich the accessibility of complex biological data for those with various needs in the scientific community.
### Response:
import networkx as nx
from graspologic.datasets import load_drosophila_right
import matplotlib.pyplot as plt

# 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)

# Draw the graph
nx.draw(G, with_labels=True, font_weight='bold')
plt.show()
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