<｜begin▁of▁sentence｜>### Instruction:
As a game developer looking to create a neuroscience-inspired level that intricately maps the neural connections of the Drosophila larva's right mushroom body, you'd want to get your hands on a detailed connectome to base your design on. You'd start by tapping into the 'graspologic.datasets' module which conveniently offers a 'load_drosophila_right' function.

Picture this: you're scripting the initialization phase of your game's engine, and you need to spawn an in-game structure that mirrors the real-world complexity of this connectome. The task? Execute the 'load_drosophila_right' function to extract not just the adjacency matrix that represents synaptic connections between each neural cell, but also retrieve the corresponding labels that identify these cells.

Once you've loaded the matrix, think of each node and connection as potential pathways for your player to explore. For this, you'd need to translate the matrix into something more manipulable by your game's environment – a NetworkX graph. Now, turning to the NetworkX API, you'd convert this matrix and uncover the graph's architecture. How many rooms (nodes) and corridors (edges) are you dealing with?

Visualize the following output in your debug log: the count of these rooms and corridors, a crucial stat that defines the scale and complexity of the neural labyrinth awaiting your players. Can you imagine integrating this biological authenticity into your game design?
### Response:
import numpy as np
from graspologic.datasets import load_drosophila_right
import networkx as nx

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

# Convert the adjacency matrix to a NetworkX graph
G = nx.from_numpy_array(adjacency_matrix)

print(G.number_of_nodes())
print(G.number_of_edges())
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