code: def forward(self, loss_dict, embeddings, labels):
      self.reset_stats()
      sub_losses = torch.zeros(
          len(loss_dict),
          dtype=embeddings.dtype,
          device=embeddings.device,
      )
      loss_count = 0
      for loss_name, loss_info in loss_dict.items():
          input_dict = {loss_name: loss_info}
          if loss_name in self.reducers:
              loss_val = self.reducers[loss_name](
                  input_dict, embeddings, labels
              )
          else:
              loss_val = self.default_reducer(
                  input_dict, embeddings, labels
              )
          sub_losses[loss_count] = loss_val
          loss_count += 1
      return self.sub_loss_reduction(sub_losses, embeddings, labels)
  
imports: import torch
  from .base_reducer import BaseReducer
  from .mean_reducer import MeanReducer
label_desc: Use image node_id 2 for calling the MultipleReducers obj's underlying member method code with example usage: obj.forward(loss_dict, embeddings, labels) and returns: self