code: def train_step_fp32(self, data):
      # Forward
      net_out = self.runner.train_forward(data)
  
      # Compute loss
      loss_list = self.runner.compute_losses(net_out, data)
      loss = self.runner.aggregate_losses(loss_list)
  
      # Backward
      loss.backward()
  
      # Update parameters
      self.optimizer.step()
      return loss, loss_list
  
file_path: PaddleSeg/contrib/PanopticSeg/paddlepanseg/core/launcher.py
imports: import abc
  import paddle
label_desc: Use image node_id 3 for calling the AMPLauncher obj's underlying member method code with example usage: obj.train_step_fp32(data) and returns: loss,  loss_list