code: def __init__(
      self,
      C=1.0,
      kernel="rbf",
      degree=3,
      gamma="scale",
      coef0=0.0,
      shrinking=True,
      probability=False,
      tol=1e-3,
      cache_size=200,
      class_weight=None,
      verbose=False,
      max_iter=-1,
      decision_function_shape="ovr",
      break_ties=False,
      random_state=None,
  ):
      super(SVC, self).__init__(
          kernel=kernel,
          degree=degree,
          gamma=gamma,
          coef0=coef0,
          tol=tol,
          C=C,
          nu=0.0,
          shrinking=shrinking,
          probability=probability,
          cache_size=cache_size,
          class_weight=class_weight,
          verbose=verbose,
          max_iter=max_iter,
          decision_function_shape=decision_function_shape,
          break_ties=break_ties,
          random_state=random_state,
      )
  
imports: from __future__ import print_function
  import warnings
  import numpy
  import sklearn.svm._base
  import sklearn.svm._classes
  from scipy import sparse
  from sklearn.calibration import CalibratedClassifierCV
  from sklearn.exceptions import NotFittedError
  from sklearn.model_selection import StratifiedKFold
  from sklearn.utils import check_random_state
  from sklearn.utils.multiclass import _ovr_decision_function
  from sklearn.utils.validation import _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted
  from sklearn import __version__
  import daal4py
  from .._utils import PatchingConditionsChain, getFPType, make2d, sklearn_check_version
label_desc: Use image node_id 1 to create a new SVC object from inherited base classes: svm_base with example: obj = SVC(C, kernel, degree, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, break_ties, random_state)