class_docstr: Base class for estimators that use libsvm as backing library.
  
  This implements support vector machine classification and regression.
  
  Parameter documentation is in the derived `SVC` class.
class_docstr_tok: ["Base","class","for","estimators","that","use","libsvm","as","backing","library",".","This","implements","support","vector","machine","classification","and","regression",".","Parameter","documentation","is","in","the","derived","`","SVC","`","class","."]
docstr: Weights assigned to the features when `kernel="linear"`.
  
  Returns
  -------
  ndarray of shape (n_features, n_classes)
code: def coef_(self):
      """Weights assigned to the features when `kernel="linear"`.
  
      Returns
      -------
      ndarray of shape (n_features, n_classes)
      """
      if self.kernel != "linear":
          raise AttributeError(
              "coef_ is only available when using a linear kernel"
          )
  
      coef = self._get_coef()
  
      # coef_ being a read-only property, it's better to mark the value as
      # immutable to avoid hiding potential bugs for the unsuspecting user.
      if sp.issparse(coef):
          # sparse matrix do not have global flags
          coef.data.flags.writeable = False
      else:
          # regular dense array
          coef.flags.writeable = False
      return coef
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from ..base import BaseEstimator, ClassifierMixin, _fit_context
  from ..exceptions import ConvergenceWarning, NotFittedError
  from ..preprocessing import LabelEncoder
  from ..utils import check_array, check_random_state, column_or_1d, compute_class_weight
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.extmath import safe_sparse_dot
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import _ovr_decision_function, check_classification_targets
  from ..utils.validation import _check_large_sparse, _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted
  from .None import _liblinear
  from .None import _libsvm
  from .None import _libsvm_sparse
label_desc: Use image node_id 16 for calling the BaseLibSVM obj's underlying member method code with example usage: obj.coef_() and returns: coef