<｜begin▁of▁sentence｜>### Instruction:
Given a graph whose nodes are labeled with attributes (e.g., type 'A' or 'B') and its edge set is [(0, 1), (0, 2), (1, 3), (2, 3)], with nodes 0 and 1 having type 'A' and nodes 2 and 3 having type 'B', can you compute the normalized mixing matrix (attribute mixing dict) based on the node attribute 'type' using NetworkX?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_nodes_from([(0, {'type': 'A'}), (1, {'type': 'A'}), (2, {'type': 'B'}), (3, {'type': 'B'})])
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])

# Get the mixing matrix as a dictionary
mixing = nx.attribute_mixing_dict(G, 'type', normalized=True)

print(mixing)
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