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_predict(self, X):
      X = self._compute_kernel(X)
      if X.ndim == 1:
          X = check_array(X, order="C", accept_large_sparse=False)
  
      kernel = self.kernel
      if callable(self.kernel):
          kernel = "precomputed"
          if X.shape[1] != self.shape_fit_[0]:
              raise ValueError(
                  "X.shape[1] = %d should be equal to %d, "
                  "the number of samples at training time"
                  % (X.shape[1], self.shape_fit_[0])
              )
  
      svm_type = LIBSVM_IMPL.index(self._impl)
  
      return libsvm.predict(
          X,
          self.support_,
          self.support_vectors_,
          self._n_support,
          self._dual_coef_,
          self._intercept_,
          self._probA,
          self._probB,
          svm_type=svm_type,
          kernel=kernel,
          degree=self.degree,
          coef0=self.coef0,
          gamma=self._gamma,
          cache_size=self.cache_size,
      )
  
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 9 for calling the BaseLibSVM obj's underlying member method code with example usage: obj._dense_predict(X) and returns: libsvm