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.
code: def __init__(
      self, kernel="linear", kernel_kwargs=None, alpha=1.0, lambda_=1.0
  ):
      super().__init__()
      self.kernel = kernel
      self.model = None
      self.alpha = alpha
      self.lambda_ = lambda_
      if kernel_kwargs is None:
          self.kernel_kwargs = dict()
      else:
          self.kernel_kwargs = kernel_kwargs
      self.device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
      self.losses = {"ovr": [], "pred": [], "code": [], "reg": []}
      self.x = None
      self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
      self.coef_ = None
  
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 1 to create a new CoIRLS object from inherited base classes: BaseEstimator, ClassifierMixin with example: obj = CoIRLS(kernel, kernel_kwargs, alpha, lambda_)