To tackle this optimization problem, we will use Gurobi, a powerful solver for linear and mixed-integer programming problems. The goal is to maximize an objective function that involves the quantities of knishes, granola bars, and fruit salads, subject to various constraints related to carbohydrate intake from these food items.

Here's how we can translate the given problem into a mathematical formulation:

### Objective Function:
Maximize \(1 \times \text{knishes} + 1 \times \text{granola bars} + 2 \times \text{fruit salads}\)

### Constraints:
1. Carbohydrates from knishes: \(16 \times \text{knishes}\)
2. Carbohydrates from granola bars: \(3 \times \text{granola bars}\)
3. Carbohydrates from fruit salads: \(19 \times \text{fruit salads}\)

- At least 20 grams of carbohydrates must come from knishes and fruit salads: \(16 \times \text{knishes} + 19 \times \text{fruit salads} \geq 20\)
- At least 15 grams of carbohydrates must come from granola bars and fruit salads: \(3 \times \text{granola bars} + 19 \times \text{fruit salads} \geq 15\)
- No more than 34 grams of carbohydrates can come from knishes and fruit salads: \(16 \times \text{knishes} + 19 \times \text{fruit salads} \leq 34\)
- At most 64 grams of carbohydrates can come from granola bars and fruit salads: \(3 \times \text{granola bars} + 19 \times \text{fruit salads} \leq 64\)
- Total carbohydrate intake cannot exceed 69 grams: \(16 \times \text{knishes} + 3 \times \text{granola bars} + 19 \times \text{fruit salads} \leq 69\)

### Gurobi Code Implementation:
We'll define the variables, objective function, and constraints using Gurobi's Python API.

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define the variables
knishes = m.addVar(vtype=GRB.CONTINUOUS, name="knishes")
granola_bars = m.addVar(vtype=GRB.CONTINUOUS, name="granola_bars")
fruit_salads = m.addVar(vtype=GRB.CONTINUOUS, name="fruit_salads")

# Define the objective function
m.setObjective(1 * knishes + 1 * granola_bars + 2 * fruit_salads, GRB.MAXIMIZE)

# Add constraints
m.addConstr(16 * knishes + 19 * fruit_salads >= 20, name="knishes_and_fruit_min")
m.addConstr(3 * granola_bars + 19 * fruit_salads >= 15, name="granola_and_fruit_min")
m.addConstr(16 * knishes + 19 * fruit_salads <= 34, name="knishes_and_fruit_max")
m.addConstr(3 * granola_bars + 19 * fruit_salads <= 64, name="granola_and_fruit_max")
m.addConstr(16 * knishes + 3 * granola_bars + 19 * fruit_salads <= 69, name="total_carb_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Knishes: {knishes.x}, Granola Bars: {granola_bars.x}, Fruit Salads: {fruit_salads.x}")
else:
    print("No optimal solution found")
```