code: def plot_det(fps, fns, save_path):
      """
      This might be a good metric when you are doing a binary classification problem
        with two species and you care how it is incorrectly classifying both species.
      Given false positive and false negative rates,
        produce a DET(detection error rate) Curve.
      The false positive rate is assumed to be increasing while the false
      negative rate is assumed to be decreasing.
      """
      # pylint: disable=unused-argument, unused-variable, unused-variable, invalid-name
      axis_min = min(fps[0], fns[-1])
      fig, ax = plt.subplots()
      ax.plot(fps, fns)
      ax.yscale("log")
      ax.xscale("log")
      ticks_to_use = [
          0.001,
          0.002,
          0.005,
          0.01,
          0.02,
          0.05,
          0.1,
          0.2,
          0.5,
          1,
          2,
          5,
          10,
          20,
          50,
      ]
      ax.get_xaxis().set_major_formatter(
          matplotlib.ticker.ScalarFormatter()
      )
      ax.get_yaxis().set_major_formatter(
          matplotlib.ticker.ScalarFormatter()
      )
      ax.set_xticks(ticks_to_use)
      ax.set_yticks(ticks_to_use)
      ax.axis([0.001, 50, 0.001, 50])
      ax.savefig(save_path)
  
imports: import matplotlib
  import matplotlib.pyplot
label_desc: Use image node_id 3 for calling a global function with example usage: plot_det(fps, fns, save_path) without return types