code: def _predict(self, X, module, queue):
      _check_is_fitted(self)
      if self.break_ties and self.decision_function_shape == "ovo":
          raise ValueError(
              "break_ties must be False when "
              "decision_function_shape is 'ovo'"
          )
  
      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"
              )
  
      if (
          self.break_ties
          and self.decision_function_shape == "ovr"
          and len(self.classes_) > 2
      ):
          y = np.argmax(self.decision_function(X), axis=1)
      else:
          X = _check_array(
              X,
              dtype=[np.float64, np.float32],
              force_all_finite=True,
              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__
              )
  
          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))
          y = from_table(result.responses)
      return y
  
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 8 for calling the BaseSVM obj's underlying member method code with example usage: obj._predict(X, module, queue) and returns: y