<｜begin▁of▁sentence｜>### Instruction:
Imagine you're responsible for preparing a detailed financial ledger, where each line item, or "node," represents a transaction with two key attributes: incoming funds, resembling the in-degree, and outgoing funds, mirroring the out-degree. You have a list where each transaction's attributes align with the following: incoming fund counts being [0,1,1,2] and outgoing fund counts being [1,1,1,1]. Now, to maintain an accurate ledger, you must also adhere to a specific auditing guideline, analogous to the directed joint degree dictionary nkk={1:{1:2,2:2}}, which stipulates the interaction pattern among transactions of varying fund flows.

Given this scenario, consider how you would employ a specialized ledger-structuring tool akin to NetworkX's directed_joint_degree_graph function to organize these transactions into a comprehensive graph. This graph should faithfully represent each transaction's incoming and outgoing fund counts while strictly following the auditing requirements outlined by the joint degree patterns. How would you go about this task to ensure your ledger remains compliant and organized?
### Response:
import networkx as nx

# Define in-degrees and out-degrees
in_degrees = [0, 1, 1, 2]
out_degrees = [1, 1, 1, 1]

# Directed joint degree dictionary
directed_joint_degree = {
    1: {1: 2, 2: 2}
}

# Create a graph using the directed_joint_degree_graph function
G = nx.directed_joint_degree_graph(in_degrees, out_degrees, directed_joint_degree)

# Print the edges of the graph
print('Edges of the generated graph:', G.edges())
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