<｜begin▁of▁sentence｜>### Instruction:
A group of researchers is experimenting with mice of different genotypes and performs whole-brain diffusion magnetic resonance imaging to create connectomes. The research is focused on defining the brain connectivity patterns and differences between these genotypes. Using the 'graspologic.datasets.load_mice' API, how can you load this connectome data into Python, and then, as an example of working with such data, how would you find and print the maximum matching in a bipartite representation of the first mouse's connectome using NetworkX?
### Response:
import numpy as np
import networkx as nx
from graspologic.datasets import load_mice
from networkx.algorithms import bipartite

# Load the mouse connectome data
mice = load_mice()

# Convert the first adjacency matrix to a NetworkX graph
G = nx.from_numpy_array(mice.graphs[0])

# Since the connectomes are bipartite (two sets of nodes),
# we can use a networkx function to compute a maximum matching.
# Here, we assume that the nodes 0 to (N-1) belong to one set,
# and nodes N to (2N-1) belong to the other.
N = mice.graphs[0].shape[0] // 2
nodes_a = range(N)
nodes_b = range(N, 2*N)
matching = bipartite.maximum_matching(G, nodes_a)

# Print the matching
print(matching)
<|EOT|>
