code: def _onedal_cpu_supported(self, method_name, *data):
      class_name = self.__class__.__name__
      patching_status = PatchingConditionsChain(
          f"sklearn.svm.{class_name}.{method_name}"
      )
      if method_name == "fit":
          patching_status.and_conditions(
              [
                  (
                      self.kernel
                      in ["linear", "rbf", "poly", "sigmoid"],
                      f'Kernel is "{self.kernel}" while '
                      '"linear", "rbf", "poly" and "sigmoid" are only supported.',
                  )
              ]
          )
          return patching_status
      inference_methods = (
          ["predict"]
          if class_name.endswith("R")
          else ["predict", "predict_proba", "decision_function"]
      )
      if method_name in inference_methods:
          patching_status.and_conditions(
              [
                  (
                      hasattr(self, "_onedal_estimator"),
                      "oneDAL model was not trained.",
                  )
              ]
          )
          return patching_status
      raise RuntimeError(
          f"Unknown method {method_name} in {class_name}"
      )
  
imports: from abc import ABC
  import numpy
  from sklearn.calibration import CalibratedClassifierCV
  from sklearn.model_selection import StratifiedKFold
  from sklearn.preprocessing import LabelEncoder
  from daal4py.sklearn._utils import sklearn_check_version
  from onedal.utils import _column_or_1d
  from .._utils import PatchingConditionsChain
label_desc: Use image node_id 2 for calling the BaseSVM obj's underlying member method code with example usage: obj._onedal_cpu_supported(method_name) and returns: patching_status, patching_status