<｜begin▁of▁sentence｜>### Instruction:
As a civil rights attorney might frame a question concerning legal document analysis, let's take a similarly meticulous approach to our inquiry about organizing and examining a neurological dataset. Suppose a researcher is meticulously delving into a fundamental component of the insect brain – the left mushroom body of a Drosophila larva, akin to scrutinizing vital legal documents for a pivotal case. This investigation hinges on meticulously reconstructed neuron data from electron microscopy, akin to carefully gathered evidence, which forms the very substrate of our network analysis.

To meticulously decode this neuronal network, like a legal expert examining contracts clause by clause, the researcher seeks to deftly utilize the Graspologic library – an analytical tool as essential to our inquiry as legal databases are to an attorney. The researcher's quest is to efficiently import the adjacency matrix, which encapsulates the neural connections, and the accompanying neuron labels into Graspologic. This is akin to cataloging evidence and testimonies in preparation for a legal proceeding.

In this scenario, could you guide the researcher on how they might employ specific Graspologic library methods or functions to efficiently load the adjacency matrix and associated labels to initiate a precise analysis of the Drosophila larva's connectome? The focus should be on maintaining the fidelity of the dataset and ensuring that the analysis is as rigorously structured as a well-prepared legal argument.
### Response:
import graspologic

# Load the left Drosophila larva mushroom body connectome
adjacency_matrix, labels = graspologic.datasets.load_drosophila_left(return_labels=True)

# Utilize the adjacency matrix and labels for analysis
print('Adjacency Matrix:\n', adjacency_matrix)
print('Labels:\n', labels)
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