code: def forward(self, input, label, meta, train):
      feature = self.head_layer(input)
  
      pred = {}
      for name, pred_layer in self.pred_layers.items():
          pred[name] = pred_layer(feature)
  
      if train:
          losses = self.calculate_losses(pred, label)
          pred = self.postprocess_pred(pred)
          loss = sum(losses.values())
          return loss, pred, losses
      else:
          pred = self.postprocess_pred(pred)
          return pred
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from backbones.upsample_head import SimpleUpsampleHead
label_desc: Use image node_id 7 for calling the SimpleDetectionDecoder obj's underlying member method code with example usage: obj.forward(input, label, meta, train) and returns: loss,  pred,  losses, pred