<｜begin▁of▁sentence｜>### Instruction:
As an athletic trainer, imagine you're tasked with assessing the overall health and performance of a sports team. You're provided with data representing the players' interactions during training sessions. Each interaction is akin to an edge in a directed graph, detailing which player directed an action towards another. For instance, an edge like (0, 1) signifies that player 0 directed an action towards player 1 during a training session.

Now, your objective is to utilize a tool called "TriadCensus" from the NetworkX library to analyze these interactions. This tool helps you count and categorize all potential triads formed within the team. Triads here represent groupings of three players where each player is linked to the other two in some manner. By understanding the structure of these triads, you gain insights into how players interact within the team, enabling you to better tailor preventive care, treatment, and rehabilitation strategies to enhance the team's overall performance and mitigate injuries.
### Response:
import networkx as nx

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

# Compute the triad census
triad_census = nx.triad_census(g)

# Print the triad census
print(triad_census)
<|EOT|>
