code: def __init__(
      self,
      kernel="rbf",
      degree=3,
      gamma="auto",
      coef0=0.0,
      C=1.0,
      tol=0.001,
      probability=False,
      class_weight=None,
      shrinking=False,
      cache_size=None,
      verbose=False,
      max_iter=-1,
      n_jobs=-1,
      max_mem_size=-1,
      random_state=None,
      decision_function_shape="ovo",
      gpu_id=0,
  ):
      self.decision_function_shape = decision_function_shape
      super(SVC, self).__init__(
          kernel=kernel,
          degree=degree,
          gamma=gamma,
          coef0=coef0,
          C=C,
          nu=0.0,
          epsilon=0.0,
          tol=tol,
          probability=probability,
          class_weight=class_weight,
          shrinking=shrinking,
          cache_size=cache_size,
          verbose=verbose,
          max_iter=max_iter,
          n_jobs=n_jobs,
          max_mem_size=max_mem_size,
          random_state=random_state,
          gpu_id=gpu_id,
      )
  
imports: from sklearn.base import BaseEstimator
  from sklearn.base import RegressorMixin, ClassifierMixin
  import numpy
  import scipy.sparse
  from sklearn.utils import check_X_y, column_or_1d, check_array
  from sklearn.utils.validation import _num_samples
  from ctypes import *
  from os import path, curdir
  from sys import platform
label_desc: Use image node_id 1 to create a new SVC object from inherited base classes: SvmModel, ClassifierMixin with example: obj = SVC(kernel, degree, gamma, coef0, C, tol, probability, class_weight, shrinking, cache_size, verbose, max_iter, n_jobs, max_mem_size, random_state, decision_function_shape, gpu_id)