code: def black_scholes(xp, s, x, t, r, v):
      sqrt_t = xp.sqrt(t)
      d1 = (xp.log(s / x) + (r + v * v / 2) * t) / (v * sqrt_t)
      d2 = d1 - v * sqrt_t
  
      def get_cumulative_normal_distribution(x):
          A1 = 0.31938153
          A2 = -0.356563782
          A3 = 1.781477937
          A4 = -1.821255978
          A5 = 1.330274429
          RSQRT2PI = 0.39894228040143267793994605993438
          W = 0.2316419
  
          k = 1 / (1 + W * xp.abs(x))
          cnd = (
              RSQRT2PI
              * xp.exp(-x * x / 2)
              * (k * (A1 + k * (A2 + k * (A3 + k * (A4 + k * A5)))))
          )
          cnd = xp.where(x > 0, 1 - cnd, cnd)
          return cnd
  
      cnd_d1 = get_cumulative_normal_distribution(d1)
      cnd_d2 = get_cumulative_normal_distribution(d2)
  
      exp_rt = xp.exp(-r * t)
      call = s * cnd_d1 - x * exp_rt * cnd_d2
      put = x * exp_rt * (1 - cnd_d2) - s * (1 - cnd_d1)
      return call, put
  
imports: import argparse
  import contextlib
  import time
  import cupy
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: black_scholes(xp, s, x, t, r, v) and returns: call,  put, cnd