old_contents: 
  import numpy as np
  
  from ...base import ClassifierMixin
  from .base import SparseBaseLibLinear
  from .. import _liblinear
  
  from scipy import sparse
  
  class LinearSVC(SparseBaseLibLinear, ClassifierMixin):
      """
      Linear Support Vector Classification, Sparse Version
  
      Similar to SVC with parameter kernel='linear', but uses internally
      liblinear rather than libsvm, so it has more flexibility in the
      choice of penalties and loss functions and should be faster for
      huge datasets.
  
      Parameters
      ----------
      loss : string, 'l1' or 'l2' (default 'l2')
          Specifies the loss function. With 'l1' it is the standard SVM
          loss (a.k.a. hinge Loss) while with 'l2' it is the squared loss.
          (a.k.a. squared hinge Loss)
  
      penalty : string, 'l1' or 'l2' (default 'l2')
          Specifies the norm used in the penalization. The 'l2' penalty
          is the standard used in SVC. The 'l1' leads to ``coef_``
          vectors that are sparse.
  
      dual : bool, (default True)
          Select the algorithm to either solve the dual or primal
          optimization problem.
  
      intercept_scaling : float, default: 1
          when self.fit_intercept is True, instance vector x becomes
          [x, self.intercept_scaling],
          i.e. a "synthetic" feature with constant value equals to
          intercept_scaling is appended to the instance vector.
          The intercept becomes intercept_scaling * synthetic feature weight
          Note! the synthetic feature weight is subject to l1/l2 regularization
          as all other features.
          To lessen the effect of regularization on synthetic feature weight
          (and therefore on the intercept) intercept_scaling has to be increased
  
      Attributes
      ----------
      `coef_` : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]
          Wiehgiths asigned to the features (coefficients in the primal
          problem). This is only available in the case of linear kernel.
  
      `intercept_` : array, shape = [1] if n_classes == 2 else [n_classes]
          constants in decision function
  
      Notes
      -----
      The underlying C implementation uses a random number generator to
      select features when fitting the model. It is thus not uncommon,
      to have slightly different results for the same input data. If
      that happens, try with a smaller eps parameter.
  
      See also
      --------
      SVC
  
      References
      ----------
      LIBLINEAR -- A Library for Large Linear Classification
      http://www.csie.ntu.edu.tw/~cjlin/liblinear/
  
      """
      pass
  
new_contents: 
  import numpy as np
  
  from ...base import ClassifierMixin
  from .base import SparseBaseLibLinear
  from .. import _liblinear
  
  class LinearSVC(SparseBaseLibLinear, ClassifierMixin):
      """
      Linear Support Vector Classification, Sparse Version
  
      Similar to SVC with parameter kernel='linear', but uses internally
      liblinear rather than libsvm, so it has more flexibility in the
      choice of penalties and loss functions and should be faster for
      huge datasets.
  
      Parameters
      ----------
      loss : string, 'l1' or 'l2' (default 'l2')
          Specifies the loss function. With 'l1' it is the standard SVM
          loss (a.k.a. hinge Loss) while with 'l2' it is the squared loss.
          (a.k.a. squared hinge Loss)
  
      penalty : string, 'l1' or 'l2' (default 'l2')
          Specifies the norm used in the penalization. The 'l2' penalty
          is the standard used in SVC. The 'l1' leads to ``coef_``
          vectors that are sparse.
  
      dual : bool, (default True)
          Select the algorithm to either solve the dual or primal
          optimization problem.
  
      intercept_scaling : float, default: 1
          when self.fit_intercept is True, instance vector x becomes
          [x, self.intercept_scaling],
          i.e. a "synthetic" feature with constant value equals to
          intercept_scaling is appended to the instance vector.
          The intercept becomes intercept_scaling * synthetic feature weight
          Note! the synthetic feature weight is subject to l1/l2 regularization
          as all other features.
          To lessen the effect of regularization on synthetic feature weight
          (and therefore on the intercept) intercept_scaling has to be increased
  
      Attributes
      ----------
      `coef_` : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]
          Wiehgiths asigned to the features (coefficients in the primal
          problem). This is only available in the case of linear kernel.
  
      `intercept_` : array, shape = [1] if n_classes == 2 else [n_classes]
          constants in decision function
  
      Notes
      -----
      The underlying C implementation uses a random number generator to
      select features when fitting the model. It is thus not uncommon,
      to have slightly different results for the same input data. If
      that happens, try with a smaller eps parameter.
  
      See also
      --------
      SVC
  
      References
      ----------
      LIBLINEAR -- A Library for Large Linear Classification
      http://www.csie.ntu.edu.tw/~cjlin/liblinear/
  
      """
      pass
  
subject: Remove unused imports