class_docstr: Kernelized online algorithm for learning an ordinal regression model.
  
  Parameters
  ----------
  n_iter : int
      Number of iterations to run.
  
  shuffle : boolean
      Whether to shuffle data.
  
  random_state : RandomState or int
      The seed of the pseudo random number generator to use.
  
  kernel: "linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed"
      Kernel.
      Default: "linear"
  
  degree : int, default=3
      Degree for poly 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.
  
  kernel_params : mapping of string to any, optional
      Parameters (keyword arguments) and values for kernel passed as
      callable object. Ignored by other kernels.
  
  Attributes
  ----------
  dual_coef_ : array, shape=[n_samples]
      Estimated weights.
  
  thresholds_ : array, shape=[n_classes]
      Estimated thresholds.
  
  References
  ----------
  Pranking with Ranking
  Koby Crammer, Yoram Singer
  NIPS 2001
code: def _get_kernel(self, X, Y=None):
      if callable(self.kernel):
          params = self.kernel_params or {}
      else:
          params = {
              "gamma": self.gamma,
              "degree": self.degree,
              "coef0": self.coef0,
          }
      return pairwise_kernels(
          X, Y, metric=self.kernel, filter_params=True, **params
      )
  
imports: import numpy
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.metrics.pairwise import pairwise_kernels
  from .base import BaseEstimator
  from .dataset_fast import get_dataset
  from .prank_fast import _prank_fit
  from .prank_fast import _prank_fit_kernel
  from .prank_fast import _prank_predict
label_desc: Use image node_id 3 for calling the KernelPRank obj's underlying member method code with example usage: obj._get_kernel(X, Y) and returns: pairwise_kernels