## Problem Description and Formulation

The problem is an optimization problem with four variables: 'security onions', 'pen testers', 'deployed decoys', and 'security engineers'. The objective is to maximize the function:

\[ 9.05 \times \text{security onions} + 8.15 \times \text{pen testers} + 7.34 \times \text{deployed decoys} + 9.75 \times \text{security engineers} \]

subject to various constraints on power consumption and computational load.

## Constraints

The constraints can be summarized as follows:

### Power Consumption Constraints

1. Each security onion adds 2 kWh.
2. Each pen tester adds 11 kWh.
3. Each deployed decoy adds 9 kWh.
4. Each security engineer requires 6 kWh.
5. Pen testers + security engineers >= 56 kWh.
6. Pen testers + deployed decoys >= 57 kWh.
7. -10 * security onions + 10 * security engineers >= 0.
8. Security onions + security engineers <= 244 kWh.
9. Pen testers + security engineers <= 189 kWh.
10. Pen testers + deployed decoys <= 144 kWh.
11. Security onions + pen testers <= 185 kWh.
12. Deployed decoys + security engineers <= 141 kWh.
13. Total power consumption <= 141 kWh.

### Computational Load Constraints

1. Each security onion creates a load of 9 TFLOPs.
2. Each pen tester creates a load of 1 TFLOP.
3. Each deployed decoy creates a load of 3 TFLOPs.
4. Each security engineer creates a load of 10 TFLOPs.
5. Deployed decoys + security engineers <= 85 TFLOPs.
6. Pen testers + security engineers <= 56 TFLOPs.
7. Security onions + pen testers + security engineers <= 70 TFLOPs.
8. Pen testers + deployed decoys + security engineers <= 101 TFLOPs.
9. Security onions + pen testers + deployed decoys <= 114 TFLOPs.
10. Security onions + deployed decoys + security engineers <= 196 TFLOPs.
11. Total computational load <= 196 TFLOPs.

### Variable Constraints

1. All variables must be non-negative integers.

## Gurobi Code

```python
import gurobi

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define variables
    security_onions = model.addVar(name="security_onions", vtype=gurobi.GRB.INTEGER)
    pen_testers = model.addVar(name="pen_testers", vtype=gurobi.GRB.INTEGER)
    deployed_decoys = model.addVar(name="deployed_decoys", vtype=gurobi.GRB.INTEGER)
    security_engineers = model.addVar(name="security_engineers", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(9.05 * security_onions + 8.15 * pen_testers + 7.34 * deployed_decoys + 9.75 * security_engineers, gurobi.GRB.MAXIMIZE)

    # Power consumption constraints
    model.addConstr(2 * security_onions + 11 * pen_testers + 9 * deployed_decoys + 6 * security_engineers <= 141, "total_power_consumption")
    model.addConstr(pen_testers + security_engineers >= 56, "pen_testers_security_engineers_power")
    model.addConstr(pen_testers + deployed_decoys >= 57, "pen_testers_deployed_decoys_power")
    model.addConstr(-10 * security_onions + 10 * security_engineers >= 0, "security_onions_security_engineers_power")
    model.addConstr(security_onions + security_engineers <= 244, "security_onions_security_engineers_total_power")
    model.addConstr(pen_testers + security_engineers <= 189, "pen_testers_security_engineers_total_power")
    model.addConstr(pen_testers + deployed_decoys <= 144, "pen_testers_deployed_decoys_total_power")
    model.addConstr(security_onions + pen_testers <= 185, "security_onions_pen_testers_total_power")
    model.addConstr(deployed_decoys + security_engineers <= 141, "deployed_decoys_security_engineers_total_power")

    # Computational load constraints
    model.addConstr(9 * security_onions + pen_testers + 3 * deployed_decoys + 10 * security_engineers <= 196, "total_computational_load")
    model.addConstr(3 * deployed_decoys + 10 * security_engineers <= 85, "deployed_decoys_security_engineers_computational_load")
    model.addConstr(pen_testers + 10 * security_engineers <= 56, "pen_testers_security_engineers_computational_load")
    model.addConstr(9 * security_onions + pen_testers + 10 * security_engineers <= 70, "security_onions_pen_testers_security_engineers_computational_load")
    model.addConstr(pen_testers + 3 * deployed_decoys + 10 * security_engineers <= 101, "pen_testers_deployed_decoys_security_engineers_computational_load")
    model.addConstr(9 * security_onions + pen_testers + 3 * deployed_decoys <= 114, "security_onions_pen_testers_deployed_decoys_computational_load")
    model.addConstr(9 * security_onions + 3 * deployed_decoys + 10 * security_engineers <= 196, "security_onions_deployed_decoys_security_engineers_computational_load")

    # Variable bounds
    model.addConstr(security_onions >= 0, "security_onions_lower_bound")
    model.addConstr(pen_testers >= 0, "pen_testers_lower_bound")
    model.addConstr(deployed_decoys >= 0, "deployed_decoys_lower_bound")
    model.addConstr(security_engineers >= 0, "security_engineers_lower_bound")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Security Onions: {security_onions.varValue}")
        print(f"Pen Testers: {pen_testers.varValue}")
        print(f"Deployed Decoys: {deployed_decoys.varValue}")
        print(f"Security Engineers: {security_engineers.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```