class_docstr: This is an implementation of CDAN for video data.
code: def __init__(
      self,
      dataset,
      image_modality,
      feature_extractor,
      task_classifier,
      critic,
      use_entropy=False,
      use_random=False,
      random_dim=1024,
      **base_params,
  ):
      super(CDANTrainerVideo, self).__init__(
          dataset,
          feature_extractor,
          task_classifier,
          critic,
          use_entropy,
          use_random,
          random_dim,
          **base_params,
      )
      self.image_modality = image_modality
      self.rgb, self.flow = get_image_modality(image_modality)
      self.rgb_feat = self.feat["rgb"]
      self.flow_feat = self.feat["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 1 to create a new CDANTrainerVideo object from inherited base classes: CDANTrainer with example: obj = CDANTrainerVideo(dataset, image_modality, feature_extractor, task_classifier, critic, use_entropy, use_random, random_dim)