class_docstr: Estimator for learning kernel SVMs by Newton's method.
  
  Parameters
  ----------
  alpha : float
      Weight of the penalty term.
  
  solver : str, 'cg', 'dense'
  
  max_iter : int
      Maximum number of iterations to perform.
  
  tol : float
      Tolerance of the stopping criterion.
  
  kernel: "linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed"
      Kernel to use.  Default: "linear"
  
  degree : int, default=3
      Degree for poly, rbf and sigmoid kernels. Ignored by other kernels.
  
  gamma : float, optional
      Kernel coefficient for rbf and poly kernels. Default: 1/n_features.
      Ignored by other kernels.
  
  coef0 : float, optional
      Independent term in poly and sigmoid kernels.
      Ignored by other kernels.
  
  random_state : RandomState or int
      The seed of the pseudo random number generator to use.
  
  verbose : int
      Verbosity level.
  
  n_jobs : int
      Number of jobs to use to compute the kernel matrix.
  
  Examples
  --------
  >>> from sklearn.datasets import make_classification
  >>> from lightning.classification import KernelSVC
  >>> X, y = make_classification()
  >>> clf = KernelSVC().fit(X, y)
  >>> accuracy = clf.score(X, y)
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code: def _solve(self, A, b):
      if self.solver == "cg":
          x, info = cg(A, b, tol=self.tol)
      elif self.solver == "dense":
          x = solve(A, b, sym_pos=True)
      return x
  
file_path: lightning/lightning/impl/primal_newton.py
imports: import numpy
  from scipy.sparse.linalg import cg
  from scipy.linalg import solve
  from sklearn.utils import safe_mask
  from sklearn.preprocessing import LabelBinarizer
  from sklearn.utils import check_random_state
  from sklearn.metrics.pairwise import pairwise_kernels
  from .base import BaseClassifier
label_desc: Use image node_id 3 for calling the KernelSVC obj's underlying member method code with example usage: obj._solve(A, b) and returns: x