code: def _fit_proba(self, X, y, sample_weight=None, queue=None):
      params = self.get_params()
      params["probability"] = False
      params["decision_function_shape"] = "ovr"
      clf_base = self.__class__(**params)
  
      try:
          n_splits = 5
          n_jobs = (
              n_splits
              if queue is None or queue.sycl_device.is_cpu
              else 1
          )
          cv = StratifiedKFold(
              n_splits=n_splits,
              shuffle=True,
              random_state=self.random_state,
          )
          if sklearn_check_version("0.24"):
              self.clf_prob = CalibratedClassifierCV(
                  clf_base,
                  ensemble=False,
                  cv=cv,
                  method="sigmoid",
                  n_jobs=n_jobs,
              )
          else:
              self.clf_prob = CalibratedClassifierCV(
                  clf_base, cv=cv, method="sigmoid"
              )
          self.clf_prob.fit(X, y, sample_weight)
      except ValueError:
          clf_base = clf_base.fit(X, y, sample_weight)
          self.clf_prob = CalibratedClassifierCV(
              clf_base, cv="prefit", method="sigmoid"
          )
          self.clf_prob.fit(X, y, sample_weight)
  
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 BaseSVC obj's underlying member method code with example usage: obj._fit_proba(X, y, sample_weight, queue) without return types