class_docstr: This is an implementation of WDGRL for video data.
code: def forward(self, x):
      if self.feat is not None:
          x_rgb = (
              x_flow
          ) = adversarial_output_rgb = adversarial_output_flow = None
  
          # For joint input, both two ifs are used
          if self.rgb:
              x_rgb = self.rgb_feat(x["rgb"])
              x_rgb = x_rgb.view(x_rgb.size(0), -1)
              adversarial_output_rgb = self.domain_classifier(x_rgb)
          if self.flow:
              x_flow = self.flow_feat(x["flow"])
              x_flow = x_flow.view(x_flow.size(0), -1)
              adversarial_output_flow = self.domain_classifier(x_flow)
  
          if self.rgb:
              if self.flow:  # For joint input
                  x = torch.cat((x_rgb, x_flow), dim=1)
              else:  # For rgb input
                  x = x_rgb
          else:  # For flow input
              x = x_flow
          class_output = self.classifier(x)
  
          return (
              [x_rgb, x_flow],
              class_output,
              [adversarial_output_rgb, adversarial_output_flow],
          )
  
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 WDGRLTrainerVideo obj's underlying member method code with example usage: obj.forward(x) and returns: list,  class_output,  list