<｜begin▁of▁sentence｜>### Instruction:
Imagine you're leading a workshop on the intricacies of network data structures for a group of eager professionals. You're using Python to illuminate these concepts, and you want to provide a hands-on experience with the NetworkX library. 

To make this exercise more relatable, consider a simplified corporate hierarchy represented as a graph in the 'company_hierarchy.gml' file. Within this structure, you have individuals denoted by nodes — for our purposes, let's focus on two key roles labeled as '1' for a junior employee and '2' for a senior manager, with a communication line flowing from the junior to the senior, designated by a single directed edge from node '1' to node '2'.

In order to help the participants understand the flow of communication within the hierarchy and verify the nature of this graph, can we engage the NetworkX's 'is_directed' function to ascertain whether this representation depicts a directed graph? This inquiry sets the stage for analyzing how instructions, information, and decisions might cascade through a company's structure.
### Response:
import networkx as nx

# Create a graph
G = nx.DiGraph()

# Add nodes
G.add_node(1)
G.add_node(2)

# Add a directed edge
G.add_edge(1, 2)

# Check if the graph is directed
is_directed = nx.is_directed(G)
print('Is the graph directed:', is_directed)
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