<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working on a biomedical networking project where we've developed a hypothetical neural network graph modeled for analyzing connections between different types of neurons. In this simulation, there are neurons labeled '0' and '1' representing one neural type encoded with a 'red' tag, signifying perhaps an excitatory characteristic. Neurons '2' and '3' represent another neural type and are tagged as 'blue', potentially indicating an inhibitory nature. Synapses, or connections, exist between neuron '0' and '1', and between neuron '2' and '3'.

To understand the structural properties of our neural network model, particularly how neurons of similar or different types (excitatory or inhibitory) preferentially connect, we need to assess the network's tendency for neurons to link to others with the same color attribute, i.e., the same type. The relevant criterion for this assessment is known as the attribute assortativity coefficient.

So the challenge before us now as biomedical engineers, is to calculate the attribute assortativity coefficient for the 'color' attribute within this artificial neural network. How can we determine this assortativity measure in the given network using NetworkX to inform our analysis of this graph's neuronal connection patterns?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
# Add nodes with a 'color' attribute
G.add_nodes_from([0, 1], color="red")
G.add_nodes_from([2, 3], color="blue")
# Add edges between the nodes
G.add_edges_from([(0, 1), (2, 3)])

# Calculate the attribute assortativity coefficient for the 'color' attribute
assortativity_coefficient = nx.attribute_assortativity_coefficient(G, "color")

print('Assortativity coefficient:', assortativity_coefficient)
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