class_docstr: Unsupervised Outlier Detection.
  
  Estimate the support of a high-dimensional distribution.
  
  The implementation is based on libsvm.
  
  Read more in the :ref:`User Guide <svm_outlier_detection>`.
  
  Parameters
  ----------
  kernel : string, optional (default='rbf')
       Specifies the kernel type to be used in the algorithm.
       It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
       a callable.
       If none is given, 'rbf' will be used. If a callable is given it is
       used to precompute the kernel matrix.
  
  nu : float, optional
      An upper bound on the fraction of training
      errors and a lower bound of the fraction of support
      vectors. Should be in the interval (0, 1]. By default 0.5
      will be taken.
  
  degree : int, optional (default=3)
      Degree of the polynomial kernel function ('poly').
      Ignored by all other kernels.
  
  gamma : float, optional (default='auto')
      Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
      If gamma is 'auto' then 1/n_features will be used instead.
  
  coef0 : float, optional (default=0.0)
      Independent term in kernel function.
      It is only significant in 'poly' and 'sigmoid'.
  
  tol : float, optional
      Tolerance for stopping criterion.
  
  shrinking : boolean, optional
      Whether to use the shrinking heuristic.
  
  cache_size : float, optional
      Specify the size of the kernel cache (in MB).
  
  verbose : bool, default: False
      Enable verbose output. Note that this setting takes advantage of a
      per-process runtime setting in libsvm that, if enabled, may not work
      properly in a multithreaded context.
  
  max_iter : int, optional (default=-1)
      Hard limit on iterations within solver, or -1 for no limit.
  
  random_state : int seed, RandomState instance, or None (default)
      The seed of the pseudo random number generator to use when
      shuffling the data for probability estimation.
  
  Attributes
  ----------
  support_ : array-like, shape = [n_SV]
      Indices of support vectors.
  
  support_vectors_ : array-like, shape = [nSV, n_features]
      Support vectors.
  
  dual_coef_ : array, shape = [n_classes-1, n_SV]
      Coefficients of the support vectors in the decision function.
  
  coef_ : array, shape = [n_classes-1, n_features]
      Weights assigned to the features (coefficients in the primal
      problem). This is only available in the case of a linear kernel.
  
      `coef_` is readonly property derived from `dual_coef_` and
      `support_vectors_`
  
  intercept_ : array, shape = [n_classes-1]
      Constants in decision function.
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docstr: Detects the soft boundary of the set of samples X.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape (n_samples, n_features)
      Set of samples, where n_samples is the number of samples and
      n_features is the number of features.
  
  sample_weight : array-like, shape (n_samples,)
      Per-sample weights. Rescale C per sample. Higher weights
      force the classifier to put more emphasis on these points.
  
  Returns
  -------
  self : object
      Returns self.
  
  Notes
  -----
  If X is not a C-ordered contiguous array it is copied.
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code: def fit(self, X, y=None, sample_weight=None, **params):
      """
      Detects the soft boundary of the set of samples X.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape (n_samples, n_features)
          Set of samples, where n_samples is the number of samples and
          n_features is the number of features.
  
      sample_weight : array-like, shape (n_samples,)
          Per-sample weights. Rescale C per sample. Higher weights
          force the classifier to put more emphasis on these points.
  
      Returns
      -------
      self : object
          Returns self.
  
      Notes
      -----
      If X is not a C-ordered contiguous array it is copied.
  
      """
      super(OneClassSVM, self).fit(
          X,
          np.ones(_num_samples(X)),
          sample_weight=sample_weight,
          **params,
      )
      return self
  
imports: import warnings
  import numpy
  from .base import _fit_liblinear, BaseSVC, BaseLibSVM
  from ..base import BaseEstimator, RegressorMixin
  from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, LinearModel
  from ..feature_selection.from_model import _LearntSelectorMixin
  from ..utils import check_X_y
  from ..utils.validation import _num_samples
  from ..utils.multiclass import check_classification_targets
label_desc: Use image node_id 2 for calling the OneClassSVM obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) and returns: self