<｜begin▁of▁sentence｜>### Instruction:
Imagine we're setting the scene for a documentary that intricately showcases the complexities of the Drosophila larva's neural architecture, focusing specifically on the masterfully crafted network within the mushroom body of its right hemisphere. We've got access to the connectome data that maps out all the synaptic connections in that region. To bring this to life on screen, we want to translate this biological dataset into a dynamic visual using NetworkX, a powerful tool for constructing and analyzing complex networks.

Could you articulate how we might take the raw connectome data and, utilizing NetworkX's capabilities, construct a detailed graphical model? It's crucial that our depiction accurately incorporates the individual components, possibly neuronal identities or other pertinent node labels provided in the data, to enrich our visual narrative and provide our audience with a clear, insightful look into the neural pathways of this organism. This graphical representation will serve as the backbone for a pivotal segment of our documentary, offering viewers a window into the unseen intricacy of neurological connections.
### Response:
import networkx as nx
from graspologic.datasets import load_drosophila_right

# Load the right hemisphere of the Drosophila larva mushroom body connectome
adjacency_matrix, labels = load_drosophila_right(return_labels=True)

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

# Optionally, add labels to the graph nodes
for i, label in enumerate(labels):
    G.nodes[i]['label'] = label

# Now you can use G for further analysis with NetworkX functionalities
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