input: Write a function to find the optimal solution to solve a knapsack problem using dynamic programming. Implement the knapSack function where W is the capacity of the knapsack, wt is a list of weights of the items, val is a list of values of the items, and n is the number of items. The function should return the maximum value that can be put in a knapsack of capacity W. The function should not use naive recursion but instead use a dynamic programming approach.
  
  assert knapSack(50, [10, 20, 30], [60, 100, 120], 3) == 220
  assert knapSack(10, [1, 2, 3], [6, 10, 12], 3) == 22
  assert knapSack(7, [1, 2, 3], [6, 10, 12], 3) == 16
  assert knapSack(0, [10, 20, 30], [60, 100, 120], 3) == 0
  assert knapSack(50, [], [], 0) == 0
output: def knapSack(W, wt, val, n):
      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]