<｜begin▁of▁sentence｜>### Instruction:
Imagine you're in a laboratory, much like the one you're accustomed to, but instead of analyzing blood samples, you're involved in a unique project analyzing the patterns of interaction within a network of subjects. Visualize this scenario as a complex web of interactions where you’re tracing the influence of one subject upon another. You've been provided a set of data that resembles the connections between different entities.

The data at hand outlines a directed network with specific relationships marked by arrows: from subject 1 to 2, from 2 to 3, then looping back from 3 to 1, further from 3 to 4, and then 4 connects back to 1 and 2. In this network, similar to how you'd categorize blood cells under a microscope, you're assigned to categorize the types of triads – groups of three interconnected subjects.

Your goal is to apply a method, equivalent to a test you'd run in the lab, using a function called `triadic_census` from a toolkit named NetworkX. This will allow you to determine and count all the possible triadic relationships within this network. Just like labeling blood samples, you’ll need to meticulously document the counts of each triad type that emerges from the analysis. 

Could you proceed with this task by engaging the `triadic_census` function on the provided directed network and record the resulting counts of each triad configuration? This will contribute to our understanding of the interaction patterns, giving context to our larger research initiative.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add edges to the graph (nodes are added automatically)
G.add_edges_from([(1, 2), (2, 3), (3, 1), (3, 4), (4, 1), (4, 2)])

# Determine the triadic census of the directed graph
triadic_census = nx.triadic_census(G)

# Print the counts of all triads
for key, value in triadic_census.items():
    print(f"{key}: {value}")
<|EOT|>
