class_docstr: Estimator for learning linear support vector machine by coordinate
  descent in the dual.
  
  Parameters
  ----------
  loss : str, 'hinge', 'squared_hinge'
      The loss function to be used.
  
  criterion : str, 'accuracy', 'auc'
      Whether to optimize for classification accuracy or AUC.
  
  C : float
      Weight of the loss term.
  
  max_iter : int
      Maximum number of iterations to perform.
  
  tol : float
      Tolerance of the stopping criterion.
  
  shrinking : bool
      Whether to activate shrinking or not.
  
  warm_start : bool
      Whether to activate warm-start or not.
  
  permute : bool
      Whether to permute coordinates or not before cycling.
  
  callback : callable
      Callback function.
  
  n_calls : int
      Frequency with which `callback` must be called.
  
  random_state : RandomState or int
      The seed of the pseudo random number generator to use.
  
  verbose : int
      Verbosity level.
  
  Examples
  --------
  The following example demonstrates how to learn a classification
  model:
  
  >>> from sklearn.datasets import fetch_20newsgroups_vectorized
  >>> from lightning.classification import LinearSVC
  >>> bunch = fetch_20newsgroups_vectorized(subset="all")
  >>> X, y = bunch.data, bunch.target
  >>> clf = LinearSVC().fit(X, y)
  >>> accuracy = clf.score(X, y)
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code: def _get_loss(self):
      loss = {"l1": 1, "hinge": 1, "l2": 2, "squared_hinge": 2}
      return loss[self.loss]
  
file_path: lightning/lightning/impl/dual_cd.py
imports: import numpy
  from sklearn.preprocessing import add_dummy_feature
  from .base import BaseClassifier, BaseRegressor
  from .dataset_fast import get_dataset
  from .dual_cd_fast import _dual_cd
  from .dual_cd_fast import _dual_cd_auc
  from .dual_cd_fast import _dual_cd_svr
label_desc: Use image node_id 2 for calling the LinearSVC obj's underlying member method code with example usage: obj._get_loss() and returns: loss