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","."]
code: def _dense_fit(
      self, X, y, sample_weight, solver_type, kernel, random_seed
  ):
      if callable(self.kernel):
          # you must store a reference to X to compute the kernel in predict
          # TODO: add keyword copy to copy on demand
          self.__Xfit = X
          X = self._compute_kernel(X)
  
          if X.shape[0] != X.shape[1]:
              raise ValueError(
                  "X.shape[0] should be equal to X.shape[1]"
              )
  
      libsvm.set_verbosity_wrap(self.verbose)
  
      # we don't pass **self.get_params() to allow subclasses to
      # add other parameters to __init__
      (
          self.support_,
          self.support_vectors_,
          self._n_support,
          self.dual_coef_,
          self.intercept_,
          self._probA,
          self._probB,
          self.fit_status_,
          self._num_iter,
      ) = libsvm.fit(
          X,
          y,
          svm_type=solver_type,
          sample_weight=sample_weight,
          # TODO(1.4): Replace "_class_weight" with "class_weight_"
          class_weight=getattr(self, "_class_weight", np.empty(0)),
          kernel=kernel,
          C=self.C,
          nu=self.nu,
          probability=self.probability,
          degree=self.degree,
          shrinking=self.shrinking,
          tol=self.tol,
          cache_size=self.cache_size,
          coef0=self.coef0,
          gamma=self._gamma,
          epsilon=self.epsilon,
          max_iter=self.max_iter,
          random_seed=random_seed,
      )
  
      self._warn_from_fit_status()
  
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 6 for calling the BaseLibSVM obj's underlying member method code with example usage: obj._dense_fit(X, y, sample_weight, solver_type, kernel, random_seed) without return types