code: def forward(self, x, target=None):
      if target is None:
          return None
      loss = dict()
      x = self.convs(x)
      x = (
          F.adaptive_avg_pool3d(x, 1)
          .squeeze(-1)
          .squeeze(-1)
          .squeeze(-1)
      )
      x = self.dropout(x)
      x = self.fc(x)
  
      loss["loss_aux"] = self.loss_weight * F.cross_entropy(x, target)
      return loss
  
imports: import numpy
  import torch
  import torch.nn
  import torch.utils.checkpoint
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the AuxHead obj's underlying member method code with example usage: obj.forward(x, target) and returns: loss, None