<｜begin▁of▁sentence｜>### Instruction:
As a seasoned gunsmith meticulously crafts and fine-tunes firearms for precise performance, so must a neuroscientist approach the intricate task of dissecting the complex neural circuitry of the mouse brain. Picture a scenario where a neuroscientist, much like an expert in weaponry examining distinct gun models from different manufacturers, delves into the connectomes—each a detailed map of neural connections much like schematics of rifle internals—of 32 mice, their subjects divided evenly across four separate genotypes, akin to firearm brands, with each strain represented by eight meticulously age-matched specimens, comparable to models from the same production year.

The researcher's mission—much like analyzing the nuances of rifling patterns or trigger mechanisms—is to load and meticulously comb through this trove of connectomic data. To achieve this, imagine them deploying an analytical tool as specialized as any gunsmith's caliper or borescope, in this case, a software API by the name of graspologic. With it, they'll aim to examine the structural integrity and connectivity patterns of these cerebral networks, scrutinizing each ROI as a gunsmith would a barrel or chamber, and contrast the neurological frameworks across the differing strains, just as one might evaluate the unique features of rifles from different eras or origins.
### Response:
import graspologic

# Load the mice connectomes dataset
mice_data = graspologic.datasets.load_mice()

# Access the adjacency matrices of the connectomes
connectome_graphs = mice_data.graphs

# Access the labels for each mouse
mouse_labels = mice_data.labels

# Retrieve the DataFrame containing information about each ROI
roi_atlas = mice_data.atlas

# Get block assignments for each ROI
blocks = mice_data.blocks

# Access anatomical features for each ROI in each connectome
features = mice_data.features

# Subject IDs and genotypes for each connectome
participants = mice_data.participants

# Dataset meta information
meta_info = mice_data.meta

# Print out the number of subjects and number of vertices
print('Number of subjects:', meta_info['n_subjects'])
print('Number of vertices per connectome:', meta_info['n_vertices'])
<|EOT|>
