code: def _compute_final_scores(
      self,
      votes,
      scores,
      weights_are_all_positive_,
      has_proba,
      classlabels_ints,
  ):
      max_weight = 0
      if votes is not None and len(votes) > 0:
          max_class = np.argmax(votes)
          max_weight = votes[max_class]
      else:
          max_class = np.argmax(scores)
          max_weight = scores[max_class]
  
      write_additional_scores = -1
      if self._svm.atts.rho.size == 1:  # type: ignore
          label, write_additional_scores = set_score_svm(
              max_weight,
              max_class,
              has_proba,
              weights_are_all_positive_,
              classlabels_ints,
              1,
              0,
          )
      elif classlabels_ints is not None and len(classlabels_ints) > 0:
          label = classlabels_ints[max_class]
      else:
          label = max_class
  
      new_scores = write_scores(
          scores.size, scores, self._svm.atts.post_transform, write_additional_scores  # type: ignore
      )
      return label, new_scores
  
file_path: onnx/onnx/reference/ops/aionnxml/op_svm_classifier.py
imports: import numpy
  from onnx.reference.ops.aionnxml._common_classifier import compute_logistic, compute_probit, compute_softmax_zero, logistic, softmax, softmax_zero
  from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
  from onnx.reference.ops.aionnxml.op_svm_helper import SVMCommon
label_desc: Use image node_id 4 for calling the SVMClassifier obj's underlying member method code with example usage: obj._compute_final_scores(votes, scores, weights_are_all_positive_, has_proba, classlabels_ints) and returns: label,  new_scores