class_docstr: Common API for MME-based domain adaptation on video data: DAN, JAN
code: def forward(self, x):
      if self.feat is not None:
          if self.image_modality in ["rgb", "flow"]:
              if self.rgb_feat is not None:
                  x = self.rgb_feat(x)
              else:
                  x = self.flow_feat(x)
              x = x.view(x.size(0), -1)
              class_output = self.classifier(x)
              return x, class_output
  
          elif self.image_modality == "joint":
              x_rgb = self.rgb_feat(x["rgb"])
              x_flow = self.flow_feat(x["flow"])
              x_rgb = x_rgb.view(x_rgb.size(0), -1)
              x_flow = x_flow.view(x_flow.size(0), -1)
              x = torch.cat((x_rgb, x_flow), dim=1)
              class_output = self.classifier(x)
              return [x_rgb, x_flow], class_output
  
imports: import torch
  import kale.predict.losses
  from kale.loaddata.video_access import get_image_modality
  from kale.pipeline.domain_adapter import BaseMMDLike, CDANTrainer, DANNTrainer, GradReverse, Method, set_requires_grad, WDGRLTrainer
label_desc: Use image node_id 2 for calling the BaseMMDLikeVideo obj's underlying member method code with example usage: obj.forward(x) and returns: x,  class_output, list,  class_output