<｜begin▁of▁sentence｜>### Instruction:
Imagine you are a forester in charge of managing a new digital forest ecosystem, where the health and growth of your forest are tracked by data in a digital simulation. You want to create a simulation of your network of trees, where trees represent nodes and the interactions between them represent edges.

In this digital forest, some trees rely on others for resources, while some provide resources to multiple trees. To accurately represent this, you have a list that details how many trees each tree depends on (in-degrees) and how many trees depend on each one (out-degrees). Specifically, your list states that one tree depends on one other, two trees each depend on two others, and two trees are independent and don't depend on any others. Similarly, two trees provide resources to no others, while each of the two remaining trees supports one other.

Furthermore, you have a detailed joint degree dictionary that describes the connections between trees of specific degrees. For instance, there are two connections between trees that are each dependent on one other tree, and there are trees that are independent but provide resources to two and one trees respectively.

With this complex set of dependencies and support systems, your task is to cultivate a digital network that represents these interactions within NetworkX. You need to simulate a random directed graph that adheres to this specific pattern of connections to understand and manage your digital forest better. How would you go about planting this digital forest using the data and NetworkX's capabilities?
### Response:
import networkx as nx

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

# Define joint degree dictionary
nkk = {1: {1: 2}, 0: {2: 1, 1: 1}}

# Create a directed graph with the joint degree
G = nx.directed_joint_degree_graph(in_degrees, out_degrees, nkk)

# Print the edges of the graph
print(list(G.edges()))
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