code: def print_accuracy(self, xtest, ytest):
      results = self.model(xtest).detach().numpy()
      idx = np.argmax(results, axis=-1)
      results = np.zeros(results.shape)
      results[np.arange(results.shape[0]), idx] = 1
      results = results.argmax(axis=-1)
  
      numTotal = 0
      numCorrect = 0
      for i in range(0, len(results)):
          if results[i] == ytest[i]:
              numCorrect = numCorrect + 1
          numTotal = numTotal + 1
      print("Accuracy: ", numCorrect / numTotal)
      accuracy = float(numCorrect / numTotal)
  
imports: import sys
  import pandas
  import numpy
  import torch
  import torch.nn
  from torch.autograd import Function
  from torch.nn.parameter import Parameter
  import torch.nn.functional
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.model_selection import train_test_split
  from aix360.algorithms.cofrnet.utils import generate_connections
  from aix360.algorithms.cofrnet.utils import process_data
  from aix360.algorithms.cofrnet.CustomizedLinearClasses import CustomizedLinearFunction
  from aix360.algorithms.cofrnet.CustomizedLinearClasses import CustomizedLinear
  from aix360.algorithms.die import DIExplainer
label_desc: Use image node_id 3 for calling the CoFrNet_Explainer obj's underlying member method code with example usage: obj.print_accuracy(xtest, ytest) without return types