class_docstr: Domain independent network (DIN). It is under development and will be updated with references later.
code: def __init__(
      self,
      dataset,
      feature_extractor,
      task_classifier,
      n_classes: int,
      target_domain: str,
      kernel: str = "linear",
      kernel_mul: float = 2.0,
      kernel_num: int = 5,
      **base_params,
  ):
      super().__init__(
          dataset,
          feature_extractor,
          task_classifier,
          n_classes,
          target_domain,
          **base_params,
      )
      self.kernel = kernel
      self.n_domains = len(self.domain_to_idx.values())
      self.classifier = task_classifier(self.feature_dim, n_classes)
      self._kernel_mul = kernel_mul
      self._kernel_num = kernel_num
  
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 _DINTrainer object from inherited base classes: BaseMultiSourceTrainer with example: obj = _DINTrainer(dataset, feature_extractor, task_classifier, n_classes, target_domain, kernel, kernel_mul, kernel_num)