class_docstr: This is an implementation of WDGRL for video data.
code: def critic_update_steps(self, batch):
      if self.current_epoch < self._init_epochs:
          return
  
      set_requires_grad(self.domain_classifier, requires_grad=True)
  
      if self.image_modality in ["rgb", "flow"]:
          if self.rgb_feat is not None:
              set_requires_grad(self.rgb_feat, requires_grad=False)
              (x_s, y_s), (x_tu, _) = batch
              with torch.no_grad():
                  h_s = self.rgb_feat(x_s).data.view(x_s.shape[0], -1)
                  h_t = self.rgb_feat(x_tu).data.view(x_tu.shape[0], -1)
          else:
              set_requires_grad(self.flow_feat, requires_grad=False)
              (x_s, y_s), (x_tu, _) = batch
              with torch.no_grad():
                  h_s = self.flow_feat(x_s).data.view(x_s.shape[0], -1)
                  h_t = self.flow_feat(x_tu).data.view(
                      x_tu.shape[0], -1
                  )
  
          for _ in range(self._k_critic):
              # gp refers to gradient penelty in Wasserstein distance.
              gp = losses.gradient_penalty(
                  self.domain_classifier, h_s, h_t
              )
  
              critic_s = self.domain_classifier(h_s)
              critic_t = self.domain_classifier(h_t)
              wasserstein_distance = (
                  critic_s.mean()
                  - (1 + self._beta_ratio) * critic_t.mean()
              )
  
              critic_cost = -wasserstein_distance + self._gamma * gp
  
              self.critic_opt.zero_grad()
              critic_cost.backward()
              self.critic_opt.step()
              if self.critic_sched:
                  self.critic_sched.step()
  
          if self.rgb_feat is not None:
              set_requires_grad(self.rgb_feat, requires_grad=True)
          else:
              set_requires_grad(self.flow_feat, requires_grad=True)
          set_requires_grad(self.domain_classifier, requires_grad=False)
  
      elif self.image_modality == "joint":
          set_requires_grad(self.rgb_feat, requires_grad=False)
          set_requires_grad(self.flow_feat, requires_grad=False)
          (
              (x_s_rgb, y_s),
              (x_s_flow, _),
              (x_tu_rgb, _),
              (x_tu_flow, _),
          ) = batch
          with torch.no_grad():
              h_s_rgb = self.rgb_feat(x_s_rgb).data.view(
                  x_s_rgb.shape[0], -1
              )
              h_t_rgb = self.rgb_feat(x_tu_rgb).data.view(
                  x_tu_rgb.shape[0], -1
              )
              h_s_flow = self.flow_feat(x_s_flow).data.view(
                  x_s_flow.shape[0], -1
              )
              h_t_flow = self.flow_feat(x_tu_flow).data.view(
                  x_tu_flow.shape[0], -1
              )
              h_s = torch.cat((h_s_rgb, h_s_flow), dim=1)
              h_t = torch.cat((h_t_rgb, h_t_flow), dim=1)
  
          # Need to improve to process rgb and flow separately in the future.
          for _ in range(self._k_critic):
              # gp_x refers to gradient penelty for the input with the modality x.
              gp_rgb = losses.gradient_penalty(
                  self.domain_classifier, h_s_rgb, h_t_rgb
              )
              gp_flow = losses.gradient_penalty(
                  self.domain_classifier, h_s_flow, h_t_flow
              )
  
              critic_s_rgb = self.domain_classifier(h_s_rgb)
              critic_s_flow = self.domain_classifier(h_s_flow)
              critic_t_rgb = self.domain_classifier(h_t_rgb)
              critic_t_flow = self.domain_classifier(h_t_flow)
              wasserstein_distance_rgb = (
                  critic_s_rgb.mean()
                  - (1 + self._beta_ratio) * critic_t_rgb.mean()
              )
              wasserstein_distance_flow = (
                  critic_s_flow.mean()
                  - (1 + self._beta_ratio) * critic_t_flow.mean()
              )
  
              critic_cost = (
                  -wasserstein_distance_rgb
                  + -wasserstein_distance_flow
                  + self._gamma * gp_rgb
                  + self._gamma * gp_flow
              ) * 0.5
  
              self.critic_opt.zero_grad()
              critic_cost.backward()
              self.critic_opt.step()
              if self.critic_sched:
                  self.critic_sched.step()
  
          set_requires_grad(self.rgb_feat, requires_grad=True)
          set_requires_grad(self.flow_feat, requires_grad=True)
          set_requires_grad(self.domain_classifier, requires_grad=False)
  
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 5 for calling the WDGRLTrainerVideo obj's underlying member method code with example usage: obj.critic_update_steps(batch) without return types