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: Predict labels for data x
  
  Args:
      x (np.ndarray or tensor): Samples need prediction, shape (n_samples, n_features)
  
  Returns:
      y (np.ndarray): Predicted labels, shape (n_samples, )
docstr_tok: ["Predict","labels","for","data","x","Args",":","x","(","np.ndarray","or","tensor",")",":","Samples","need","prediction",",","shape","(","n_samples",",","n_features",")","Returns",":","y","(","np.ndarray",")",":","Predicted","labels",",","shape","(","n_samples",",",")"]
code: def predict(self, x):
      """Predict labels for data x
  
      Args:
          x (np.ndarray or tensor): Samples need prediction, shape (n_samples, n_features)
  
      Returns:
          y (np.ndarray): Predicted labels, shape (n_samples, )
      """
      out = self.decision_function(x)
      if self._label_binarizer.y_type_ == "binary":
          pred = self._label_binarizer.inverse_transform(
              torch.sign(out).view(-1)
          )
      else:
          pred = self._label_binarizer.inverse_transform(out)
  
      return pred
  
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 3 for calling the CoIRLS obj's underlying member method code with example usage: obj.predict(x) and returns: pred