code: def variational_classifier(
      kl_inference,
      train_fname=traindat,
      test_fname=testdat,
      label_fname=label_binary_traindat,
      kernel_log_sigma=0,
      kernel_log_scale=0,
      noise_factor=1e-5,
      min_coeff_kernel=1e-2,
      max_attempt=0,
  ):
      from math import exp
  
      features_train = sg.create_features(CSVFile(train_fname))
      labels_train = BinaryLabels(CSVFile(label_fname))
  
      likelihood = LogitDVGLikelihood()
      error_eval = ErrorRateMeasure()
      mean_func = ConstMean()
      kernel_sigma = 2 * exp(2 * kernel_log_sigma)
      kernel_func = sg.create_kernel(
          "GaussianKernel", width=kernel_sigma
      )
  
      inf = kl_inference(
          kernel_func,
          features_train,
          mean_func,
          labels_train,
          likelihood,
      )
      try:
          inf.set_noise_factor(noise_factor)
          inf.set_min_coeff_kernel(min_coeff_kernel)
          inf.set_max_attempt(max_attempt)
      except:
          pass
      inf.set_scale(exp(kernel_log_scale))
      gp = GaussianProcessClassification(inf)
      gp.train()
      pred_labels_train = gp.apply_binary(features_train)
      error_train = error_eval.evaluate(pred_labels_train, labels_train)
      # print "\nInference name:%s"%inf.get_name(),
      # print "marginal likelihood:%.10f"%inf.get_negative_log_marginal_likelihood(),
      # print "Training error %.4f"%error_train
      return pred_labels_train, gp, pred_labels_train.get_labels()
  
label_desc: Use image node_id 1 for calling a global function with example usage: variational_classifier(kl_inference, train_fname, test_fname, label_fname, kernel_log_sigma, kernel_log_scale, noise_factor, min_coeff_kernel, max_attempt) and returns: pred_labels_train,  gp,  pred_labels_train