code: def run_step_full_semisup(self, data):
      label_data_k, label_data_q, unlabel_data_k, unlabel_data_q = data
      data_merge = self.merge_data(label_data_k, label_data_q)
      loss_sup_dict = self.model.modelStudent(
          data_merge, branch="supervised"
      )
      loss_dict = {}
      for key in loss_sup_dict.keys():
          if key[:4] == "loss":
              loss_dict[key] = loss_sup_dict[key] * 1
      losses_sup = paddle.add_n(list(loss_dict.values()))
      # norm loss when using gradient accumulation
      losses_sup = losses_sup / self.cfg.optimize_rate
      losses_sup.backward()
  
      for key in loss_sup_dict.keys():
          loss_dict[key + "_pseudo"] = paddle.to_tensor([0])
      loss_dict["loss_tot"] = losses_sup
      """
          semi-supervised training after burn-in stage
          """
      if self.iter >= self.cfg.SEMISUPNET["BURN_UP_STEP"]:
          # init teacher model with burn-up weight
          if self.iter == self.cfg.SEMISUPNET["BURN_UP_STEP"]:
              print(
                  "Starting semi-supervised learning and load the teacher model."
              )
              self._update_teacher_model(keep_rate=0.00)
              # save burn-in model
              if dist.get_world_size() < 2 or dist.get_rank() == 0:
                  print("saving burn-in model.")
                  save_name = "burnIn"
                  epoch_id = self.iter // self.epoch_iter
                  save_model(
                      self.model,
                      self.optimizer,
                      self.base_path,
                      save_name,
                      epoch_id,
                  )
          # Update teacher model with EMA
          elif (self.iter + 1) % self.cfg.optimize_rate == 0:
              self._update_teacher_model(
                  keep_rate=self.cfg.SEMISUPNET["EMA_KEEP_RATE"]
              )
  
          # warm-up weight for pseudo loss
          pseudo_weight = self.cfg.SEMISUPNET["UNSUP_LOSS_WEIGHT"]
          pseudo_warmup_iter = self.cfg.SEMISUPNET[
              "PSEUDO_WARM_UP_STEPS"
          ]
          temp = self.iter - self.cfg.SEMISUPNET["BURN_UP_STEP"]
          if temp <= pseudo_warmup_iter:
              pseudo_weight *= temp / pseudo_warmup_iter
  
          # get teacher predictions on weak-augmented unlabeled data
          with paddle.no_grad():
              teacher_pred = self.model.modelTeacher(
                  unlabel_data_k, branch="semi_supervised"
              )
  
          # calculate unsupervised loss on strong-augmented unlabeled data
          loss_unsup_dict = self.model.modelStudent(
              unlabel_data_q,
              branch="semi_supervised",
              teacher_prediction=teacher_pred,
          )
  
          for key in loss_unsup_dict.keys():
              if key[-6:] == "pseudo":
                  loss_unsup_dict[key] = (
                      loss_unsup_dict[key] * pseudo_weight
                  )
          losses_unsup = paddle.add_n(list(loss_unsup_dict.values()))
          # norm loss when using gradient accumulation
          losses_unsup = losses_unsup / self.cfg.optimize_rate
          losses_unsup.backward()
  
          loss_dict.update(loss_unsup_dict)
          loss_dict["loss_tot"] += losses_unsup
      return loss_dict
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import copy
  import time
  import typing
  import numpy
  import paddle
  import paddle.nn
  import paddle.distributed
  from paddle.distributed import fleet
  from ppdet.optimizer import ModelEMA, SimpleModelEMA
  from ppdet.core.workspace import create
  from ppdet.utils.checkpoint import load_weight, load_pretrain_weight, save_model
  import ppdet.utils.stats
  from ppdet.utils import profiler
  from ppdet.modeling.ssod.utils import align_weak_strong_shape
  from .trainer import Trainer
  from ppdet.utils.logger import setup_logger
  from paddle.static import InputSpec
  from ppdet.engine.export_utils import _dump_infer_config, _prune_input_spec
label_desc: Use image node_id 5 for calling the Trainer_ARSL obj's underlying member method code with example usage: obj.run_step_full_semisup(data) and returns: loss_dict