<｜begin▁of▁sentence｜>### Instruction:
As a Textile Designer, you can think of the directed graph as a visual representation of different design elements interconnected in a pattern. Suppose you have created a design with interconnected elements represented by the edges [(0, 1), (1, 2), (2, 3), (3, 0)]. Using a specialized tool called DyadCensus, you can count the number of mutual interactions or connections between these design elements to gain insight into their interrelationships, which can help in refining and improving the overall design. This understanding of interconnections is crucial in creating more cohesive and visually appealing patterns for fabrics, textiles, or apparel, considering current color, texture, and style trends for fashion or interior applications.
### Response:
import networkx as nx

# Create a directed graph
dg = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 0)])

# Compute dyad census
dyad_census = nx.algorithms.structuralholes.dyad_census(dg)

# Access an attribute of dyad census
mutual_dyads = dyad_census.mutual
print(f'Number of mutual dyads: {mutual_dyads}')
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