<｜begin▁of▁sentence｜>### Instruction:
Using the joint degree matrix where the entry joint_degrees[k][l] indicates the number of edges connecting nodes of degree k with nodes of degree l, can you generate a random graph that exhibits these properties? For example, if the joint degree dictionary is specified as {1: {4: 1}, 2: {2: 2, 3: 2, 4: 2}, 3: {2: 2, 4: 1}, 4: {1: 1, 2: 2, 3: 1}}, can you construct a graph using NetworkX that follows this given joint degree distribution and ensure the graph's randomness by setting a seed value?
### Response:
import networkx as nx

# Define the joint degree dictionary
joint_degrees = {
  1: {4: 1},
  2: {2: 2, 3: 2, 4: 2},
  3: {2: 2, 4: 1},
  4: {1: 1, 2: 2, 3: 1}
}

# Generate the graph
G = nx.joint_degree_graph(joint_degrees, seed=42)

# Print information about the created graph
print(nx.info(G))
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