code: def __init__(
      self,
      kernel,
      degree,
      gamma,
      coef0,
      C,
      nu,
      epsilon,
      tol,
      probability,
      class_weight,
      shrinking,
      cache_size,
      verbose,
      max_iter,
      n_jobs,
      max_mem_size,
      random_state,
      gpu_id,
  ):
      self.kernel = kernel
      self.degree = degree
      self.gamma = gamma
      self.coef0 = coef0
      self.C = C
      self.nu = nu
      self.epsilon = epsilon
      self.tol = tol
      self.probability = probability
      self.class_weight = class_weight
      self.shrinking = shrinking
      self.cache_size = cache_size
      self.verbose = verbose
      self.max_iter = max_iter
      self.n_jobs = n_jobs
      self.random_state = random_state
      self.max_mem_size = max_mem_size
      self.gpu_id = gpu_id
      self.model = None
      thundersvm.model_new.restype = c_void_p
  
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 SvmModel object from inherited base classes: ThundersvmBase with example: obj = SvmModel(kernel, degree, gamma, coef0, C, nu, epsilon, tol, probability, class_weight, shrinking, cache_size, verbose, max_iter, n_jobs, max_mem_size, random_state, gpu_id)