class_docstr: This is an implementation of DAN for video data.
code: def _compute_mmd(self, phi_s, phi_t, y_hat, y_t_hat):
      batch_size = int(phi_s.size()[0])
      kernels = losses.gaussian_kernel(
          phi_s,
          phi_t,
          kernel_mul=self._kernel_mul,
          kernel_num=self._kernel_num,
      )
      return losses.compute_mmd_loss(kernels, batch_size)
  
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 DANTrainerVideo obj's underlying member method code with example usage: obj._compute_mmd(phi_s, phi_t, y_hat, y_t_hat) and returns: losses