code: def _run_svm(
      self,
      X,
      sv,
      vector_count_,
      kernel_type_,
      class_count_,
      starting_vector_,
      coefs,
  ):
      evals = 0
  
      kernels_list = []
      for j in range(vector_count_):
          kernels_list.append(
              self._svm.kernel_dot(X, sv[j], kernel_type_)
          )
      kernels = np.array(kernels_list)
  
      votes = np.zeros((class_count_,), dtype=X.dtype)
      scores = []
      for i in range(class_count_):
          si_i = starting_vector_[i]
          class_i_sc = self._svm.atts.vectors_per_class[i]  # type: ignore
  
          for j in range(i + 1, class_count_):
              si_j = starting_vector_[j]
              class_j_sc = self._svm.atts.vectors_per_class[j]  # type: ignore
  
              s1 = np.dot(
                  coefs[j - 1, si_i : si_i + class_i_sc],
                  kernels[si_i : si_i + class_i_sc],
              )
              s2 = np.dot(
                  coefs[i, si_j : si_j + class_j_sc],
                  kernels[si_j : si_j + class_j_sc],
              )
  
              s = self._svm.atts.rho[evals] + s1 + s2  # type: ignore
              scores.append(s)
              if s > 0:
                  votes[i] += 1
              else:
                  votes[j] += 1
              evals += 1
      return votes, np.array(scores, dtype=X.dtype)
  
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 2 for calling the SVMClassifier obj's underlying member method code with example usage: obj._run_svm(X, sv, vector_count_, kernel_type_, class_count_, starting_vector_, coefs) and returns: votes,  np