code: def _decision_function(self, X, module, queue):
      _check_is_fitted(self)
      X = _check_array(
          X,
          dtype=[np.float64, np.float32],
          force_all_finite=False,
          accept_sparse="csr",
      )
      _check_n_features(self, X, False)
  
      if self._sparse and not sp.isspmatrix(X):
          X = sp.csr_matrix(X)
      if self._sparse:
          X.sort_indices()
  
      if (
          sp.issparse(X)
          and not self._sparse
          and not callable(self.kernel)
      ):
          raise ValueError(
              "cannot use sparse input in %r trained on dense data"
              % type(self).__name__
          )
  
      if module in [
          _backend.svm.classification,
          _backend.svm.nu_classification,
      ]:
          sv = self.support_vectors_
          if (
              not self._sparse
              and sv.size > 0
              and self._n_support.sum() != sv.shape[0]
          ):
              raise ValueError(
                  "The internal representation "
                  f"of {self.__class__.__name__} was altered"
              )
  
      policy = _get_policy(queue, X)
      params = self._get_onedal_params(X)
  
      if hasattr(self, "_onedal_model"):
          model = self._onedal_model
      else:
          model = self._create_model(module)
      result = module.infer(policy, params, model, to_table(X))
      decision_function = from_table(result.decision_function)
  
      if len(self.classes_) == 2:
          decision_function = decision_function.ravel()
  
      if (
          self.decision_function_shape == "ovr"
          and len(self.classes_) > 2
      ):
          decision_function = self._ovr_decision_function(
              decision_function < 0,
              -decision_function,
              len(self.classes_),
          )
      return decision_function
  
imports: from abc import ABCMeta, abstractmethod
  from enum import Enum
  from numbers import Number, Real
  import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator
  from daal4py.sklearn._utils import sklearn_check_version
  from onedal import _backend
  from ..common._estimator_checks import _check_is_fitted
  from ..common._mixin import ClassifierMixin, RegressorMixin
  from ..common._policy import _get_policy
  from ..datatypes import from_table, to_table
  from ..utils import _check_array, _check_n_features, _check_X_y, _column_or_1d, _validate_targets
label_desc: Use image node_id 10 for calling the BaseSVM obj's underlying member method code with example usage: obj._decision_function(X, module, queue) and returns: decision_function