input: def knapSack(W: int, wt: List[int], val: List[int], n: int) -> int:
      """
      Find the optimal solution to solve a knapsack problem using dynamic programming.
      Given a list of items with their values and weights, and a capacity of the knapsack,
      return the maximum value that can be achieved without exceeding the capacity.
  
      >>> knapSack(50, [10, 20, 30], [60, 100, 120], 3)
      220
      >>> knapSack(10, [5, 4, 6, 3], [10, 40, 30, 50], 4)
      90
      """
output:     
      dp = [[0 for _ in range(W + 1)] for _ in range(n + 1)]
      
      for i in range(1, n + 1):
          for w in range(1, W + 1):
              if wt[i - 1] <= w:
                  dp[i][w] = max(val[i - 1] + dp[i - 1][w - wt[i - 1]], dp[i - 1][w])
              else:
                  dp[i][w] = dp[i - 1][w]
      
      return dp[n][W]
  