code: def monte_carlo_pricer(paths, dt, interest, volatility):
      c0 = interest - 0.5 * volatility**2
      c1 = volatility * np.sqrt(dt)
  
      for j in range(1, paths.shape[1]):  # for all trials
          prices = paths[:, j - 1]
          # generate normally distributed random number
          noises = np.random.normal(0.0, 1.0, prices.size)
          # calculate the next batch of prices for all trials
          paths[:, j] = step(dt, prices, c0, c1, noises)
  
label_desc: Use image node_id 2 for calling a global function with example usage: monte_carlo_pricer(paths, dt, interest, volatility) without return types