code: def binary_classification(
      fairness_metric,
      col_name,
      metrics,
      selection_rates,
      max_selection_rate,
      fairness_threshold,
  ):
      figures = []
      # selection rate figure
      fair_selection_rate = max_selection_rate * fairness_threshold
  
      fig = plt.figure(figsize=(10, 7))
      ax1 = fig.add_subplot(1, 1, 1)
      bars = ax1.bar(metrics.index[1:], metrics["Selection Rate"][1:])
  
      ax1.spines[["right", "top", "left"]].set_visible(False)
      ax1.yaxis.set_visible(False)
      _ = ax1.bar_label(bars, padding=5)
  
      if fairness_metric == "demographic_parity_ratio":
          ax1.axhline(
              y=fair_selection_rate,
              zorder=0,
              color="grey",
              ls="--",
              lw=1.5,
          )
          _ = ax1.text(
              y=fair_selection_rate,
              x=-0.6,
              s="Fairness threshold",
              ha="center",
              fontsize=12,
              bbox=dict(facecolor="white", edgecolor="grey", ls="--"),
          )
          _ = ax1.text(
              y=1.2 * fair_selection_rate,
              x=-0.6,
              s="Fair",
              ha="center",
              fontsize=12,
          )
          _ = ax1.text(
              y=0.8 * fair_selection_rate,
              x=-0.6,
              s="Unfair",
              ha="center",
              fontsize=12,
          )
  
          ax1.axhspan(
              fairness_threshold * max_selection_rate,
              1.25 * np.max(selection_rates[1:]),
              color="green",
              alpha=0.05,
          )
          ax1.axhspan(
              0,
              fairness_threshold * max_selection_rate,
              color="red",
              alpha=0.05,
          )
  
      figures += [
          {
              "title": f"Selection Rate for {col_name}",
              "fname": f"selection_rate_{col_name}.png",
              "figure": fig,
          }
      ]
  
      fig, axes = plt.subplots(figsize=(10, 5), ncols=2, sharey=True)
      fig.tight_layout()
      bars = axes[0].barh(
          metrics.index[1:],
          metrics["False Negative Rate"][1:],
          zorder=10,
          color="tab:orange",
      )
      xmax = 1.2 * max(
          metrics["False Negative Rate"][1:].max(),
          metrics["False Positive Rate"][1:].max(),
      )
      axes[0].set_xlim(0, xmax)
      axes[0].invert_xaxis()
      axes[0].set_title("False Negative Rate")
      _ = axes[0].bar_label(bars, padding=5)
  
      bars = axes[1].barh(
          metrics.index[1:],
          metrics["False Positive Rate"][1:],
          zorder=10,
          color="tab:blue",
      )
      axes[1].tick_params(axis="y", colors="tab:orange")  # tick color
      axes[1].set_xlim(0, xmax)
      axes[1].set_title("False Positive Rate")
      _ = axes[1].bar_label(bars, padding=5)
      _ = plt.subplots_adjust(
          wspace=0, top=0.85, bottom=0.1, left=0.18, right=0.95
      )
  
      figures += [
          {
              "title": f"False Rates for {col_name}",
              "fname": f"false_rates_{col_name}.png",
              "figure": fig,
          }
      ]
  
      return figures
  
imports: import numpy
  from matplotlib import pyplot
label_desc: Use image node_id 1 for calling the FairnessPlots obj's underlying member method code with example usage: obj.binary_classification(fairness_metric, col_name, metrics, selection_rates, max_selection_rate, fairness_threshold) and returns: figures