input: Write a function to solve the 0-1 Knapsack problem using dynamic programming. The function should return the maximum value that can be obtained given a list of items with their respective values and weights, and a capacity constraint. If the list of items is empty or the capacity is zero, the function should return 0.
  def knapSack(W, wt, val, n):
    
      # Base Case 
      if n == 0 or W == 0 : 
          return 0
    
      # If weight of the nth item is more 
      # than Knapsack capacity W, then 
      # this item cannot be included in the optimal solution 
      if (wt[n-1] > W): 
          return knapSack(W, wt, val, n-1) 
    
      # return the maximum of two cases: 
      # (1) nth item included 
      # (2) not included 
      else: 
          return max(val[n-1] + knapSack(W-wt[n-1], wt, val, n-1), 
                      knapSack(W, wt, val, n-1)) 
  assert knapSack(50, [10, 20, 30], [60, 100, 120], 3) == 220
  assert knapSack(10, [1, 2, 4, 2], [3, 5, 10, 4], 4) == 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(n + 1):
          for w in range(W + 1):
              if i == 0 or w == 0:
                  dp[i][w] = 0
              elif 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]