input: Write a function to find the optimal solution to solve a knapsack problem using dynamic programming. The function should take in a capacity W, a list of weights wt, a list of values val, and the number of items n. It should return the maximum value that can be put in a knapsack of the given capacity.
  assert knapSack(50, [10, 20, 30], [60, 100, 120], 3) == 220
  assert knapSack(10, [1, 2, 3], [10, 20, 30], 3) == 50
  assert knapSack(0, [10, 20, 30], [60, 100, 120], 3) == 0
  assert knapSack(50, [5, 10, 15], [10, 20, 30], 3) == 60
  assert knapSack(50, [10, 20, 30], [60, 100, 120], 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]