code: def montecarlo_integration(xmin, xmax, numSteps, rand, randsize):
      # define any function here!
      def f(x):
          return math.sin(x)
  
      # find ymin-ymax
      ymin = f(xmin)
      ymax = ymin
      for i in range(numSteps):
          x = xmin + (xmax - xmin) * float(i) / numSteps
          y = f(x)
          if y < ymin:
              ymin = y
          if y > ymax:
              ymax = y
  
      # Monte Carlo
      rectArea = (xmax - xmin) * (ymax - ymin)
      numPoints = numSteps  # bigger the better but slower!
      ctr = 0
      for j in range(numPoints):
          x = xmin + (xmax - xmin) * rand[j % randsize]
          y = ymin + (ymax - ymin) * rand[j % randsize]
          if math.fabs(y) <= math.fabs(f(x)):
              if f(x) > 0 and y > 0 and y <= f(x):
                  ctr += 1  # area over x-axis is positive
              if f(x) < 0 and y < 0 and y >= f(x):
                  ctr -= 1  # area under x-axis is negative
  
      fnArea = rectArea * float(ctr) / numPoints
      return fnArea
  
label_desc: Use image node_id 1 for calling a global function with example usage: montecarlo_integration(xmin, xmax, numSteps, rand, randsize) and returns: fnArea, math