class_docstr: Base class for all domain adaptation architectures
  
  Args:
      dataset (kale.loaddata.multi_domain): the multi-domain datasets to be used for train, validation, and tests.
      feature_extractor (torch.nn.Module): the feature extractor network
      task_classifier (torch.nn.Module): the task classifier network
      n_classes (int): number of classes
      target_domain (str): target domain name
code: def forward(self, x):
      if self.feat is not None:
          x = self.feat(x)
  
      return x
  
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 2 for calling the BaseMultiSourceTrainer obj's underlying member method code with example usage: obj.forward(x) and returns: x