## Step 1: Define the optimization problem
The problem is to maximize the objective function: $5x_0 + 8x_1 + 7x_2 + 6x_3$, where $x_0$ represents the milligrams of calcium, $x_1$ represents the milligrams of magnesium, $x_2$ represents the milligrams of vitamin B7, and $x_3$ represents the milligrams of vitamin B2.

## Step 2: List all the constraints
The constraints are as follows:
- $25x_0 \leq 162$
- $26x_0 \leq 551$
- $3x_0 \leq 557$
- $6x_1 \leq 162$
- $20x_1 \leq 551$
- $22x_1 \leq 557$
- $16x_2 \leq 162$
- $26x_2 \leq 551$
- $19x_2 \leq 557$
- $8x_3 \leq 162$
- $24x_3 \leq 551$
- $18x_3 \leq 557$
- $25x_0 + 16x_2 \geq 22$
- $25x_0 + 6x_1 \geq 18$
- $6x_1 + 16x_2 \geq 14$
- $26x_0 + 20x_1 + 26x_2 \geq 107$
- $3x_0 + 22x_1 \geq 80$
- $22x_1 + 19x_2 + 18x_3 \geq 98$
- $3x_0 + 22x_1 + 18x_3 \geq 98$
- $22x_1 + 19x_2 + 18x_3 \geq 104$
- $3x_0 + 22x_1 + 18x_3 \geq 104$
- $6x_1 + 8x_3 \leq 102$
- $25x_0 + 8x_3 \leq 149$
- $25x_0 + 16x_2 + 8x_3 \leq 43$
- $25x_0 + 6x_1 + 16x_2 \leq 84$
- $25x_0 + 6x_1 + 8x_3 \leq 96$
- $25x_0 + 6x_1 + 16x_2 + 8x_3 \leq 96$
- $20x_1 + 24x_3 \leq 276$
- $20x_1 + 26x_2 \leq 241$
- $20x_1 + 26x_2 + 24x_3 \leq 155$
- $26x_0 + 20x_1 + 24x_3 \leq 548$
- $26x_0 + 20x_1 + 26x_2 \leq 329$
- $26x_0 + 26x_2 + 24x_3 \leq 455$
- $26x_0 + 20x_1 + 26x_2 + 24x_3 \leq 455$
- $3x_0 + 19x_2 \leq 495$
- $3x_0 + 22x_1 \leq 279$
- $22x_1 + 19x_2 + 18x_3 \leq 513$
- $3x_0 + 19x_2 + 18x_3 \leq 342$
- $3x_0 + 22x_1 + 19x_2 \leq 201$
- $3x_0 + 22x_1 + 19x_2 + 18x_3 \leq 201$

## Step 3: Implement the optimization problem using Gurobi
We will use the Gurobi Python library to model and solve this optimization problem.

```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(lb=0, name="milligrams of calcium")
    x1 = model.addVar(lb=0, name="milligrams of magnesium")
    x2 = model.addVar(lb=0, name="milligrams of vitamin B7")
    x3 = model.addVar(lb=0, name="milligrams of vitamin B2")

    # Objective function
    model.setObjective(5*x0 + 8*x1 + 7*x2 + 6*x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(25*x0 <= 162)
    model.addConstr(26*x0 <= 551)
    model.addConstr(3*x0 <= 557)
    model.addConstr(6*x1 <= 162)
    model.addConstr(20*x1 <= 551)
    model.addConstr(22*x1 <= 557)
    model.addConstr(16*x2 <= 162)
    model.addConstr(26*x2 <= 551)
    model.addConstr(19*x2 <= 557)
    model.addConstr(8*x3 <= 162)
    model.addConstr(24*x3 <= 551)
    model.addConstr(18*x3 <= 557)

    model.addConstr(25*x0 + 16*x2 >= 22)
    model.addConstr(25*x0 + 6*x1 >= 18)
    model.addConstr(6*x1 + 16*x2 >= 14)
    model.addConstr(26*x0 + 20*x1 + 26*x2 >= 107)
    model.addConstr(3*x0 + 22*x1 >= 80)
    model.addConstr(22*x1 + 19*x2 + 18*x3 >= 98)
    model.addConstr(3*x0 + 22*x1 + 18*x3 >= 98)
    model.addConstr(22*x1 + 19*x2 + 18*x3 >= 104)
    model.addConstr(3*x0 + 22*x1 + 18*x3 >= 104)

    model.addConstr(6*x1 + 8*x3 <= 102)
    model.addConstr(25*x0 + 8*x3 <= 149)
    model.addConstr(25*x0 + 16*x2 + 8*x3 <= 43)
    model.addConstr(25*x0 + 6*x1 + 16*x2 <= 84)
    model.addConstr(25*x0 + 6*x1 + 8*x3 <= 96)
    model.addConstr(25*x0 + 6*x1 + 16*x2 + 8*x3 <= 96)

    model.addConstr(20*x1 + 24*x3 <= 276)
    model.addConstr(20*x1 + 26*x2 <= 241)
    model.addConstr(20*x1 + 26*x2 + 24*x3 <= 155)
    model.addConstr(26*x0 + 20*x1 + 24*x3 <= 548)
    model.addConstr(26*x0 + 20*x1 + 26*x2 <= 329)
    model.addConstr(26*x0 + 26*x2 + 24*x3 <= 455)
    model.addConstr(26*x0 + 20*x1 + 26*x2 + 24*x3 <= 455)

    model.addConstr(3*x0 + 19*x2 <= 495)
    model.addConstr(3*x0 + 22*x1 <= 279)
    model.addConstr(22*x1 + 19*x2 + 18*x3 <= 513)
    model.addConstr(3*x0 + 19*x2 + 18*x3 <= 342)
    model.addConstr(3*x0 + 22*x1 + 19*x2 <= 201)
    model.addConstr(3*x0 + 22*x1 + 19*x2 + 18*x3 <= 201)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print("Milligrams of calcium: ", x0.varValue)
        print("Milligrams of magnesium: ", x1.varValue)
        print("Milligrams of vitamin B7: ", x2.varValue)
        print("Milligrams of vitamin B2: ", x3.varValue)
        print("Objective function value: ", model.objVal)
    else:
        print("No optimal solution found.")

optimize()
```