code: def _run_linear(self, X, coefs, class_count_, kernel_type_):
      scores = []
      for j in range(class_count_):
          d = self._svm.kernel_dot(X, coefs[j], kernel_type_)
          score = self._svm.atts.rho[0] + d  # type: ignore
          scores.append(score)
      return 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 1 for calling the SVMClassifier obj's underlying member method code with example usage: obj._run_linear(X, coefs, class_count_, kernel_type_) and returns: np