class_docstr: Covariate-Independence Regularized Least Squares (CoIRLS)
  
  Args:
      kernel (str, optional): {"linear", "rbf", "poly"}. Kernel to use. Defaults to "linear".
      kernel_kwargs (dict or None, optional): Hyperparameter for the kernel. Defaults to None.
      alpha (float, optional): Hyperparameter of the l2 (Ridge) penalty. Defaults to 1.0.
      lambda_ (float, optional): Hyperparameter of the covariate dependence.  Defaults to 1.0.
  
  Reference:
      [1] Zhou, S., 2022. Interpretable Domain-Aware Learning for Neuroimage Classification (Doctoral dissertation,
          University of Sheffield).
      [2] Zhou, S., Li, W., Cox, C.R., & Lu, H. (2020). Side Information Dependence as a Regularizer for Analyzing
          Human Brain Conditions across Cognitive Experiments. AAAI 2020, New York, USA.
docstr: Compute decision scores for data x
  
  Args:
      x (np.ndarray or tensor): Samples need decision scores, shape (n_samples, n_features)
  
  Returns:
      scores (np.ndarray): Decision scores, shape (n_samples, )
docstr_tok: ["Compute","decision","scores","for","data","x","Args",":","x","(","np.ndarray","or","tensor",")",":","Samples","need","decision","scores",",","shape","(","n_samples",",","n_features",")","Returns",":","scores","(","np.ndarray",")",":","Decision","scores",",","shape","(","n_samples",",",")"]
code: def decision_function(self, x):
      """Compute decision scores for data x
  
      Args:
          x (np.ndarray or tensor): Samples need decision scores, shape (n_samples, n_features)
  
      Returns:
          scores (np.ndarray): Decision scores, shape (n_samples, )
      """
      x = torch.as_tensor(x)
      x = torch.cat([x, torch.ones(x.shape[0], 1)], dim=1)
      krnl_x = torch.as_tensor(
          pairwise_kernels(
              x.detach().numpy(),
              self.x.detach().numpy(),
              metric=self.kernel,
              filter_params=True,
              **self.kernel_kwargs,
          ),
          dtype=torch.float,
      )
  
      return torch.mm(krnl_x, self.coef_)
  
imports: import torch
  import torch.nn
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.metrics.pairwise import pairwise_kernels
  from sklearn.preprocessing import LabelBinarizer
  from torch.linalg import multi_dot
  from torch.nn.functional import one_hot
  import kale.predict.losses
  from kale.embed.image_cnn import _Bottleneck
  from kale.pipeline.domain_adapter import BaseAdaptTrainer
label_desc: Use image node_id 4 for calling the CoIRLS obj's underlying member method code with example usage: obj.decision_function(x) and returns: torch