<｜begin▁of▁sentence｜>### Instruction:
Imagine you're putting together a portfolio that captures the interconnectedness of various models and agencies based on their collaborations, each represented as a snapshot in your graph. Your photoshoots have brought forth an ensemble where every model has a distinct profile, notably their gender, setting the stage for a vivid tapestry of connections. You're now interested in creating a harmonious composition that reflects the diversity of your models, akin to a mixing matrix that considers their gender.

By translating this concept into the structure of your portfolio's graph, how might one encapsulate and quantify the intricate interplay of male and female collaborations? To put it another way, using the information captured within your project's framework, what approach would allow you to produce a balanced and normalized matrix that exhibits these gender-based interactions?

In your visual narrative, you have data akin to a 'gml' file that captures all the nuances of your professional network. How would you proceed to extract from this data a representation that doesn't just illustrate but also quantifies the collaborative dynamics between genders, wrapped in the elegance of a normalized mixing matrix?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
# Add nodes with an attribute 'gender'
G.add_node('A', gender='male')
G.add_node('B', gender='female')
G.add_node('C', gender='female')
G.add_node('D', gender='male')

# Add edges between the nodes
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'D')])

# Obtain the mixing dictionary based on the 'gender' attribute
mixing = nx.attribute_mixing_dict(G, ('gender', 'gender'), normalized=True)
print(mixing)
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