<｜begin▁of▁sentence｜>### Instruction:
As a nutritionist, you're likely familiar with the importance of analyzing ingredients to understand their nutritional impact. Think of a directed graph as a recipe, where each ingredient (node) connects to others through steps (edges) in the cooking process. Suppose you have a recipe with ingredients like flour, sugar, and eggs, represented as nodes connected by steps like "mix flour with sugar" (edge). Now, imagine you're using a special tool called dyadic_census, akin to a nutritional analyzer, which examines these connections to determine the recipe's composition. In this case, the recipe has flour mixed with sugar and sugar mixed with eggs. Using the dyadic_census tool, you aim to understand the overall pattern of ingredient interactions in the recipe, just as you would assess the nutritional breakdown of a dish.
### Response:
from networkx import DiGraph, dyadic_census

# Create a directed graph
g = DiGraph()
# Add edges to create a dyad
edges = [(0, 1), (1, 2)]
g.add_edges_from(edges)
# Calculate and print the dyadic census
print(dyadic_census(g))
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