code: def plot_sequential_feature_selection(
      metric_dict,
      figsize=None,
      kind="std_dev",
      color="blue",
      bcolor="steelblue",
      marker="o",
      alpha=0.2,
      ylabel="Performance",
      confidence_interval=0.95,
  ):
      """Plot feature selection results.
  
      Parameters
      ----------
      metric_dict : mlxtend.SequentialFeatureSelector.get_metric_dict() object
      figsize : tuple (default: None)
          Height and width of the figure
      kind : str (default: "std_dev")
          The kind of error bar or confidence interval in
          {'std_dev', 'std_err', 'ci', None}.
      color : str (default: "blue")
          Color of the lineplot (accepts any matplotlib color name)
      bcolor : str (default: "steelblue").
          Color of the error bars / confidence intervals
          (accepts any matplotlib color name).
      marker : str (default: "o")
          Marker of the line plot
          (accepts any matplotlib marker name).
      alpha : float in [0, 1] (default: 0.2)
          Transparency of the error bars / confidence intervals.
      ylabel : str (default: "Performance")
          Y-axis label.
      confidence_interval : float (default: 0.95)
          Confidence level if `kind='ci'`.
  
      Returns
      ----------
      fig : matplotlib.pyplot.figure() object
  
      Examples
      -----------
      For usage examples, please see
      https://rasbt.github.io/mlxtend/user_guide/plotting/plot_sequential_feature_selection/
  
      """
  
      allowed = {"std_dev", "std_err", "ci", None}
      if kind not in allowed:
          raise AttributeError("kind not in %s" % allowed)
  
      # fig = plt.figure()
      if figsize is not None:
          fig = plt.subplots(figsize=figsize)
      else:
          fig = plt.subplots()
  
      k_feat = sorted(metric_dict.keys())
      avg = [metric_dict[k]["avg_score"] for k in k_feat]
  
      if kind:
          upper, lower = [], []
          if kind == "ci":
              kind = "ci_bound"
  
          for k in k_feat:
              upper.append(
                  metric_dict[k]["avg_score"] + metric_dict[k][kind]
              )
              lower.append(
                  metric_dict[k]["avg_score"] - metric_dict[k][kind]
              )
  
          plt.fill_between(
              k_feat, upper, lower, alpha=alpha, color=bcolor, lw=1
          )
  
          if kind == "ci_bound":
              kind = "Confidence Interval (%d%%)" % (
                  confidence_interval * 100
              )
  
      plt.plot(k_feat, avg, color=color, marker=marker)
      plt.ylabel(ylabel)
      plt.xlabel("Number of Features")
      feature_min = len(metric_dict[k_feat[0]]["feature_idx"])
      feature_max = len(metric_dict[k_feat[-1]]["feature_idx"])
      plt.xticks(
          range(feature_min, feature_max + 1),
          range(feature_min, feature_max + 1),
      )
      return fig
  
label_desc: Use image node_id 1 for calling a global function with example usage: plot_sequential_feature_selection(metric_dict, figsize, kind, color, bcolor, marker, alpha, ylabel, confidence_interval) and returns: fig