class_docstr: Multiple Feature Spaces Adaptation Network (MFSAN)
  
  Reference: Zhu, Y., Zhuang, F. and Wang, D., 2019, July. Aligning domain-specific distribution and classifier
      for cross-domain classification from multiple sources. In AAAI.
      https://ojs.aaai.org/index.php/AAAI/article/view/4551
  
  Original implementation: https://github.com/easezyc/deep-transfer-learning/tree/master/MUDA/MFSAN
code: def _get_cls_output(self, x):
      return [
          self.classifiers[key](self.domain_net[key](x))
          for key in self.classifiers
      ]
  
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 MFSANTrainer obj's underlying member method code with example usage: obj._get_cls_output(x) and returns: unknown