code: def _get_onedal_params(self, data):
      max_iter = 10000 if self.max_iter == -1 else self.max_iter
      # TODO: remove this workaround
      # when oneDAL SVM starts support of 'n_iterations' result
      self.n_iter_ = 1 if max_iter < 1 else max_iter
      class_count = 0 if self.classes_ is None else len(self.classes_)
      return {
          "fptype": "float" if data.dtype == np.float32 else "double",
          "method": self.algorithm,
          "kernel": self.kernel,
          "c": self.C,
          "nu": self.nu,
          "epsilon": self.epsilon,
          "class_count": class_count,
          "accuracy_threshold": self.tol,
          "max_iteration_count": int(max_iter),
          "scale": self._scale_,
          "sigma": self._sigma_,
          "shift": self.coef0,
          "degree": self.degree,
          "tau": self.tau,
          "shrinking": self.shrinking,
          "cache_size": self.cache_size,
      }
  
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 5 for calling the BaseSVM obj's underlying member method code with example usage: obj._get_onedal_params(data) and returns: dict