code: def plot_pie(self):
      cnf_matrix = confusion_matrix(self.y_test, self.y_pred)
  
      FP = cnf_matrix.sum(axis=0) - np.diag(cnf_matrix)
      FN = cnf_matrix.sum(axis=1) - np.diag(cnf_matrix)
      TP = np.diag(cnf_matrix)
      TN = cnf_matrix.sum() - (FP + FN + TP)
      fp = FP.astype(float).sum()
      fn = FN.astype(float).sum()
      tp = TP.astype(float).sum()
      tn = TN.astype(float).sum()
  
      label_text = [
          "True Positive",
          "False Negative",
          "False Positive",
          "True Negative",
      ]
      labels = ["TP", "FN", "FP", "TN"]
      blue = cl.flipper()["seq"]["9"]["Blues"]
      red = cl.flipper()["seq"]["9"]["Reds"]
      colors = [blue[4], blue[1], red[1], red[4]]
  
      trace0 = go.Pie(
          labels=label_text,
          values=[tp, fn, fp, tn],
          hoverinfo="label+value+percent",
          textinfo="text+value",
          text=labels,
          sort=False,
          marker=dict(colors=colors),
      )
  
      layout = go.Layout(
          title=f"TP, TN, FP, FN",
          margin=dict(l=10, r=10, t=60, b=10),
          legend=dict(bgcolor="rgba(255,255,255,0)", orientation="h"),
      )
  
      data = [trace0]
      figure = go.Figure(data=data, layout=layout)
      return figure
  
imports: import pandas
  import numpy
  import sklearn
  from sklearn import *
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import label_binarize
  from sklearn.metrics import *
  from itertools import cycle
  import colorlover
  import plotly.figure_factory
  import plotly.graph_objects
label_desc: Use image node_id 6 for calling the performance_metrics obj's underlying member method code with example usage: obj.plot_pie() and returns: figure