class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
code: def _get_coef(self):
      if self.dual_coef_.shape[0] == 1:
          # binary classifier
          coef = safe_sparse_dot(self.dual_coef_, self.support_vectors_)
      else:
          # 1vs1 classifier
          coef = _one_vs_one_coef(
              self.dual_coef_, self._n_support, self.support_vectors_
          )
          if sp.issparse(coef[0]):
              coef = sp.vstack(coef).tocsr()
          else:
              coef = np.vstack(coef)
  
      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 10 for calling the BaseSVC obj's underlying member method code with example usage: obj._get_coef() and returns: coef