code: def plot_single(errors_t, fp_diff_t, max_iters, gamma, C):
      """Plot the errors and false positive rate disparity over time.
  
      :param errors_t: list of errors at each iteration
      :param fp_diff_t: list of fp rate disparity at each iteration
      :param max_iters: time horizon T of the algorithm
      :param gamma: input gamma disparity
      :param C: input C parameter - see gerryfair_classifier.py
      """
      # plot errors
      x = range(max_iters - 1)
      y_t = errors_t
      fig1 = plt.figure()
      ax1 = fig1.add_subplot(111)
      ax1.plot(x, y_t)
      plt.ylabel("average error of mixture")
      plt.xlabel("iterations")
      plt.title("error vs. time: C: {}, gamma: {}".format(C, gamma))
      plt.show()
  
      # plot fp disparity
      x = range(max_iters - 1)
      y_t = fp_diff_t
      fig2 = plt.figure()
      ax2 = fig2.add_subplot(111)
      ax2.plot(x, y_t)
      plt.ylabel("fp_diff*group_size")
      plt.xlabel("iterations")
      plt.title(
          "fp_diff*size vs. time: C: {}, gamma: {}".format(C, gamma)
      )
      ax2.plot(x, [gamma] * len(y_t))
      plt.show()
  
label_desc: Use image node_id 1 for calling a global function with example usage: plot_single(errors_t, fp_diff_t, max_iters, gamma, C) without return types