class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
docstr: Perform classification on samples in X.
  
  For an one-class model, +1 or -1 is returned.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features) or                 (n_samples_test, n_samples_train)
      For kernel="precomputed", the expected shape of X is
      (n_samples_test, n_samples_train).
  
  Returns
  -------
  y_pred : ndarray of shape (n_samples,)
      Class labels for samples in X.
code: def predict(self, X):
      """Perform classification on samples in X.
  
          For an one-class model, +1 or -1 is returned.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
                  (n_samples_test, n_samples_train)
              For kernel="precomputed", the expected shape of X is
              (n_samples_test, n_samples_train).
  
          Returns
          -------
          y_pred : ndarray of shape (n_samples,)
              Class labels for samples in X.
          """
      check_is_fitted(self)
      if self.break_ties and self.decision_function_shape == "ovo":
          raise ValueError(
              "break_ties must be False when decision_function_shape is 'ovo'"
          )
  
      if (
          self.break_ties
          and self.decision_function_shape == "ovr"
          and len(self.classes_) > 2
      ):
          y = np.argmax(self.decision_function(X), axis=1)
      else:
          y = super().predict(X)
      return self.classes_.take(np.asarray(y, dtype=np.intp))
  
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 4 for calling the BaseSVC obj's underlying member method code with example usage: obj.predict(X) and returns: self