class_docstr: This is an implementation of JAN for video data.
code: def __init__(
      self,
      dataset,
      image_modality,
      feature_extractor,
      task_classifier,
      kernel_mul=(2.0, 2.0),
      kernel_num=(5, 1),
      **base_params,
  ):
      super().__init__(
          dataset,
          image_modality,
          feature_extractor,
          task_classifier,
          kernel_mul=kernel_mul,
          kernel_num=kernel_num,
          **base_params,
      )
  
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 JANTrainerVideo object from inherited base classes: BaseMMDLikeVideo with example: obj = JANTrainerVideo(dataset, image_modality, feature_extractor, task_classifier, kernel_mul, kernel_num)