<｜begin▁of▁sentence｜>### Instruction:
As a game developer, imagine you're designing a new strategy game with a map consisting of various territories, each represented as a node in a graph where arrows signify troop paths between territories, and the numbers on the arrows denote the cost of moving troops along that path. The starting scenario of our game has the following troop paths: troops can be moved from Territory A to B at a cost of 2 resources, from A to C at a cost of 3, from C to B for 5, from C to D with 1, and from D back to A for 4 resources.

Now, you're interested in implementing a feature that helps to analyze potential alliances and conflicts between various territories based on their interactions. Essentially, you want to examine the relationship between the number of paths leading out of a territory (the ability of a territory to dispatch troops) and the number receiving troops (the territory being a target for others).

Using NetworkX's 'degree_mixing_dict' function, would you be able to generate a dictionary that maps these relationships, with 'out' representing the number of outgoing troop paths (dispatch power) and 'in' representing the incoming troop paths (target attractiveness)?

By doing this, you can start to strategize and balance the game by understanding how territories with similar or different dispatch and target profiles interact, making for a more engaging gameplay experience.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add edges with weights
G.add_weighted_edge('A', 'B', weight=2)
G.add_weighted_edge('A', 'C', weight=3)
G.add_weighted_edge('C', 'B', weight=5)
G.add_weighted_edge('C', 'D', weight=1)
G.add_weighted_edge('D', 'A', weight=4)

# Compute the degree mixing dict
mixing_dict = nx.degree_mixing_dict(G, x='out', y='in', weight='weight', normalized=False)
print(mixing_dict)
<|EOT|>
