docstr: Perform regression on samples in X.
  
  For an one-class model, +1 (inlier) or -1 (outlier) is returned.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      For kernel="precomputed", the expected shape of X is
      (n_samples_test, n_samples_train).
  
  Returns
  -------
  y_pred : ndarray of shape (n_samples,)
      The predicted values.
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code: def predict(self, X):
      """
      Perform regression on samples in X.
  
      For an one-class model, +1 (inlier) or -1 (outlier) is returned.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          For kernel="precomputed", the expected shape of X is
          (n_samples_test, n_samples_train).
  
      Returns
      -------
      y_pred : ndarray of shape (n_samples,)
          The predicted values.
      """
      if sklearn_check_version("1.0"):
          self._check_feature_names(X, reset=False)
      return dispatch(
          self,
          "predict",
          {
              "onedal": self.__class__._onedal_predict,
              "sklearn": sklearn_NuSVC.predict,
          },
          X,
      )
  
file_path: scikit-learn-intelex/sklearnex/svm/nusvc.py
imports: from sklearn.exceptions import NotFittedError
  from sklearn.svm import NuSVC
  from sklearn.utils.validation import _deprecate_positional_args
  from daal4py.sklearn._utils import control_n_jobs, run_with_n_jobs, sklearn_check_version
  from .._device_offload import dispatch, wrap_output_data
  from ._common import BaseSVC
  from onedal.svm import NuSVC
label_desc: Use image node_id 3 for calling the NuSVC obj's underlying member method code with example usage: obj.predict(X) and returns: dispatch