## Problem Description and Formulation

The problem requires minimizing an objective function subject to several constraints. The objective function to be minimized is:

\[ 9 \times \text{grams of fat} + 4 \times \text{milligrams of vitamin B2} + 2 \times \text{milligrams of vitamin B3} \]

The constraints are:

1. The cardiovascular support index for grams of fat is 3.84.
2. Milligrams of vitamin B2 have a cardiovascular support index of 4.56.
3. Milligrams of vitamin B3 each have a cardiovascular support index of 5.67.
4. The total combined cardiovascular support index from grams of fat plus milligrams of vitamin B3 has to be 19 at a minimum.
5. The total combined cardiovascular support index from grams of fat plus milligrams of vitamin B2 should be 16 or more.
6. The total combined cardiovascular support index from grams of fat, milligrams of vitamin B2, and milligrams of vitamin B3 must be no less than 16.
7. Three times the number of milligrams of vitamin B2, plus minus two times the number of milligrams of vitamin B3 has to be greater than or equal to zero.
8. There may be a non-integer number of grams of fat.
9. The number of milligrams of vitamin B2 must be a whole number.
10. There might be a non-integer amount of milligrams of vitamin B3.

## Gurobi Code Formulation

```python
import gurobi

# Define the model
model = gurobi.Model()

# Define the variables
grams_of_fat = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="grams_of_fat")
milligrams_of_vitamin_B2 = model.addVar(lb=0, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.INTEGER, name="milligrams_of_vitamin_B2")
milligrams_of_vitamin_B3 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams_of_vitamin_B3")

# Objective function
model.setObjective(9 * grams_of_fat + 4 * milligrams_of_vitamin_B2 + 2 * milligrams_of_vitamin_B3, gurobi.GRB.MINIMIZE)

# Constraints
cardiovascular_support_index_grams_of_fat = model.addConstr(grams_of_fat == 3.84, name="cardiovascular_support_index_grams_of_fat")
cardiovascular_support_index_milligrams_of_vitamin_B2 = model.addConstr(milligrams_of_vitamin_B2 == 4.56, name="cardiovascular_support_index_milligrams_of_vitamin_B2")
cardiovascular_support_index_milligrams_of_vitamin_B3 = model.addConstr(milligrams_of_vitamin_B3 == 5.67, name="cardiovascular_support_index_milligrams_of_vitamin_B3")

# Corrected understanding of constraints based on problem description
model.addConstr(3.84 * grams_of_fat + 5.67 * milligrams_of_vitamin_B3 >= 19, name="combined_cardiovascular_support_index_grams_of_fat_B3")
model.addConstr(3.84 * grams_of_fat + 4.56 * milligrams_of_vitamin_B2 >= 16, name="combined_cardiovascular_support_index_grams_of_fat_B2")
model.addConstr(3.84 * grams_of_fat + 4.56 * milligrams_of_vitamin_B2 + 5.67 * milligrams_of_vitamin_B3 >= 16, name="combined_cardiovascular_support_index_all")
model.addConstr(3 * milligrams_of_vitamin_B2 - 2 * milligrams_of_vitamin_B3 >= 0, name="vitamin_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Grams of fat: {grams_of_fat.varValue}")
    print(f"Milligrams of vitamin B2: {milligrams_of_vitamin_B2.varValue}")
    print(f"Milligrams of vitamin B3: {milligrams_of_vitamin_B3.varValue}")
    print(f"Objective function value: {model.objVal}")
else:
    print("No optimal solution found.")
```