<｜begin▁of▁sentence｜>### Instruction:
Imagine we're charting the course of an innovative initiative within our organization, where various departments interact to bring fresh ideas to life. Envision a scenario where we map these interactions as a directed network, with each department represented as a node.

We're contemplating a network where some departments provide input to two others, while others contribute to only one. Specifically, we have a blueprint of connections where two departments each influence two others, another two departments each influence one, and a single department influences two others.

To strategically plan our innovation pathway, we aim to leverage the Havel-Hakimi algorithm to create a directed graph that would illustrate these interactions. We have determined the in-degree sequence to be [2, 2, 2, 1, 1] and the out-degree sequence to be [2, 2, 1, 2, 1], reflecting the influences flowing between the departments.

Using NetworkX, a tool adept at mapping out such relational landscapes, we'd like to instantiate this graph. In the context of our innovation strategy, what would be the connections between departments? By providing the edges of the generated graph, we can visualize our strategy's execution plan and identify potential hubs of creativity and exchange.
### Response:
import networkx as nx

# Define the in-degree and out-degree sequences
in_deg_sequence = [2, 2, 2, 1, 1]
out_deg_sequence = [2, 2, 1, 2, 1]

# Create a directed graph using the directed Havel-Hakimi algorithm
digraph = nx.directed_havel_hakimi_graph(in_deg_sequence, out_deg_sequence)

# Print the edges of the graph
print(digraph.edges())
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