class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
code: def __init__(
      self,
      impl,
      kernel,
      degree,
      gamma,
      coef0,
      tol,
      C,
      nu,
      shrinking,
      probability,
      cache_size,
      class_weight,
      verbose,
      max_iter,
      decision_function_shape,
      random_state,
  ):
      self.decision_function_shape = decision_function_shape
      super(BaseSVC, self).__init__(
          impl=impl,
          kernel=kernel,
          degree=degree,
          gamma=gamma,
          coef0=coef0,
          tol=tol,
          C=C,
          nu=nu,
          epsilon=0.0,
          shrinking=shrinking,
          probability=probability,
          cache_size=cache_size,
          class_weight=class_weight,
          verbose=verbose,
          max_iter=max_iter,
          random_state=random_state,
      )
  
imports: from __future__ import print_function
  import numpy
  import scipy.sparse
  import warnings
  from abc import ABCMeta, abstractmethod
  from .None import libsvm, liblinear
  from .None import libsvm_sparse
  from ..base import BaseEstimator, ClassifierMixin
  from ..preprocessing import LabelEncoder
  from ..utils.multiclass import _ovr_decision_function
  from ..utils import check_array, check_consistent_length, check_random_state
  from ..utils import column_or_1d, check_X_y
  from ..utils import compute_class_weight, deprecated
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import check_classification_targets
  from ..externals import six
  from ..exceptions import ChangedBehaviorWarning
  from ..exceptions import ConvergenceWarning
  from ..exceptions import NotFittedError
label_desc: Use image node_id 1 to create a new BaseSVC object from inherited base classes:  with example: obj = BaseSVC(impl, kernel, degree, gamma, coef0, tol, C, nu, shrinking, probability, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state)