code: def _average_cls_output(x, classifiers: nn.ModuleDict):
      cls_output = [classifiers[key](x) for key in classifiers]
  
      return torch.stack(cls_output).mean(0)
  
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 a global function with example usage: _average_cls_output(x, classifiers) and returns: torch