input: Write a function to find the maximum value that can be obtained by selecting items with given values and weights, without exceeding a specified capacity, using dynamic programming for the knapsack problem. If the given list of items is empty, return 0.
  
  def knapSack(W, wt, val):
    
      # Base Case
      if len(val) == 0 or W == 0 :
          return 0
    
      # Create a DP table
      n = len(val)
      dp = [[0 for _ in range(W + 1)] for _ in range(n + 1)]
    
      # Build table dp[][] in bottom up manner
      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]
  
  assert knapSack(50, [10, 20, 30], [60, 100, 120]) == 220
  assert knapSack(0, [10, 20, 30], [60, 100, 120]) == 0
  assert knapSack(50, [], []) == 0
  assert knapSack(50, [10, 20, 30], [0, 0, 0]) == 0
output: def knapSack(W, wt, val):
      if len(val) == 0 or W == 0:
          return 0
      n = len(val)
      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]