code: def train(self, validate=False):
      self.semi_start_iters = self.cfg.get("semi_start_iters", 5000)
      Init_mark = False
      if validate:
          self.cfg["EvalDataset"] = self.cfg.EvalDataset = create(
              "EvalDataset"
          )()
  
      sync_bn = (
          getattr(self.cfg, "norm_type", None) == "sync_bn"
          and self.cfg.use_gpu
          and self._nranks > 1
      )
      if sync_bn:
          self.model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(
              self.model
          )
  
      if self.cfg.get("fleet", False):
          self.model = fleet.distributed_model(self.model)
          self.optimizer = fleet.distributed_optimizer(self.optimizer)
      elif self._nranks > 1:
          find_unused_parameters = (
              self.cfg["find_unused_parameters"]
              if "find_unused_parameters" in self.cfg
              else False
          )
          self.model = paddle.DataParallel(
              self.model, find_unused_parameters=find_unused_parameters
          )
          self.ema.model = paddle.DataParallel(
              self.ema.model,
              find_unused_parameters=find_unused_parameters,
          )
  
      self.status.update(
          {
              "epoch_id": self.start_epoch,
              "step_id": 0,
              "steps_per_epoch": len(self.loader),
              "exchange_save_model": True,
          }
      )
      # Note: exchange_save_model
      # in DenseTeacher SSOD, the teacher model will be higher, so exchange when saving pdparams
  
      self.status["batch_time"] = stats.SmoothedValue(
          self.cfg.log_iter, fmt="{avg:.4f}"
      )
      self.status["data_time"] = stats.SmoothedValue(
          self.cfg.log_iter, fmt="{avg:.4f}"
      )
      self.status["training_staus"] = stats.TrainingStats(
          self.cfg.log_iter
      )
      profiler_options = self.cfg.get("profiler_options", None)
      self._compose_callback.on_train_begin(self.status)
  
      train_cfg = self.cfg.DenseTeacher["train_cfg"]
      concat_sup_data = train_cfg.get("concat_sup_data", True)
  
      for param in self.ema.model.parameters():
          param.stop_gradient = True
  
      for epoch_id in range(self.start_epoch, self.cfg.epoch):
          self.status["mode"] = "train"
          self.status["epoch_id"] = epoch_id
          self._compose_callback.on_epoch_begin(self.status)
          self.loader.dataset_label.set_epoch(epoch_id)
          self.loader.dataset_unlabel.set_epoch(epoch_id)
          iter_tic = time.time()
          loss_dict = {
              "loss": paddle.to_tensor([0]),
              "loss_sup_sum": paddle.to_tensor([0]),
              "loss_unsup_sum": paddle.to_tensor([0]),
              "fg_sum": paddle.to_tensor([0]),
          }
          if self._nranks > 1:
              for k in self.model._layers.get_loss_keys():
                  loss_dict.update({k: paddle.to_tensor([0.0])})
              for k in self.model._layers.get_loss_keys():
                  loss_dict.update(
                      {"distill_" + k: paddle.to_tensor([0.0])}
                  )
          else:
              for k in self.model.get_loss_keys():
                  loss_dict.update({k: paddle.to_tensor([0.0])})
              for k in self.model.get_loss_keys():
                  loss_dict.update(
                      {"distill_" + k: paddle.to_tensor([0.0])}
                  )
  
          # Note: for step_id, data in enumerate(self.loader): # enumerate bug
          for step_id in range(len(self.loader)):
              data = next(self.loader)
  
              self.model.train()
              self.ema.model.eval()
              data_sup_w, data_sup_s, data_unsup_w, data_unsup_s = data
  
              self.status["data_time"].update(time.time() - iter_tic)
              self.status["step_id"] = step_id
              profiler.add_profiler_step(profiler_options)
              self._compose_callback.on_step_begin(self.status)
  
              if data_sup_w["image"].shape != data_sup_s["image"].shape:
                  data_sup_w, data_sup_s = align_weak_strong_shape(
                      data_sup_w, data_sup_s
                  )
  
              data_sup_w["epoch_id"] = epoch_id
              data_sup_s["epoch_id"] = epoch_id
              if concat_sup_data:
                  for k, v in data_sup_s.items():
                      if k in ["epoch_id"]:
                          continue
                      data_sup_s[k] = paddle.concat([v, data_sup_w[k]])
                  loss_dict_sup = self.model(data_sup_s)
              else:
                  loss_dict_sup_w = self.model(data_sup_w)
                  loss_dict_sup = self.model(data_sup_s)
                  for k, v in loss_dict_sup_w.items():
                      loss_dict_sup[k] = (loss_dict_sup[k] + v) * 0.5
  
              losses_sup = (
                  loss_dict_sup["loss"] * train_cfg["sup_weight"]
              )
              losses_sup.backward()
  
              losses = losses_sup.detach()
              loss_dict.update(loss_dict_sup)
              loss_dict.update({"loss_sup_sum": loss_dict["loss"]})
  
              curr_iter = len(self.loader) * epoch_id + step_id
              st_iter = self.semi_start_iters
              if curr_iter == st_iter:
                  logger.info("***" * 30)
                  logger.info("Semi starting ...")
                  logger.info("***" * 30)
              if curr_iter > st_iter:
                  unsup_weight = train_cfg["unsup_weight"]
                  if train_cfg["suppress"] == "linear":
                      tar_iter = st_iter * 2
                      if curr_iter <= tar_iter:
                          unsup_weight *= (
                              curr_iter - st_iter
                          ) / st_iter
                  elif train_cfg["suppress"] == "exp":
                      tar_iter = st_iter + 2000
                      if curr_iter <= tar_iter:
                          scale = np.exp((curr_iter - tar_iter) / 1000)
                          unsup_weight *= scale
                  elif train_cfg["suppress"] == "step":
                      tar_iter = st_iter * 2
                      if curr_iter <= tar_iter:
                          unsup_weight *= 0.25
                  else:
                      raise ValueError
  
                  if (
                      data_unsup_w["image"].shape
                      != data_unsup_s["image"].shape
                  ):
                      (
                          data_unsup_w,
                          data_unsup_s,
                      ) = align_weak_strong_shape(
                          data_unsup_w, data_unsup_s
                      )
  
                  data_unsup_w["epoch_id"] = epoch_id
                  data_unsup_s["epoch_id"] = epoch_id
  
                  data_unsup_s["get_data"] = True
                  student_preds = self.model(data_unsup_s)
  
                  with paddle.no_grad():
                      data_unsup_w["is_teacher"] = True
                      teacher_preds = self.ema.model(data_unsup_w)
  
                  train_cfg["curr_iter"] = curr_iter
                  train_cfg["st_iter"] = st_iter
                  if self._nranks > 1:
                      loss_dict_unsup = (
                          self.model._layers.get_ssod_loss(
                              student_preds, teacher_preds, train_cfg
                          )
                      )
                  else:
                      loss_dict_unsup = self.model.get_ssod_loss(
                          student_preds, teacher_preds, train_cfg
                      )
  
                  fg_num = loss_dict_unsup["fg_sum"]
                  del loss_dict_unsup["fg_sum"]
                  distill_weights = train_cfg["loss_weight"]
                  loss_dict_unsup = {
                      k: v * distill_weights[k]
                      for k, v in loss_dict_unsup.items()
                  }
  
                  losses_unsup = (
                      sum(
                          [
                              metrics_value
                              for metrics_value in loss_dict_unsup.values()
                          ]
                      )
                      * unsup_weight
                  )
                  losses_unsup.backward()
  
                  loss_dict.update(loss_dict_unsup)
                  loss_dict.update({"loss_unsup_sum": losses_unsup})
                  losses += losses_unsup.detach()
                  loss_dict.update({"fg_sum": fg_num})
                  loss_dict["loss"] = losses
  
              self.optimizer.step()
              curr_lr = self.optimizer.get_lr()
              self.lr.step()
              self.optimizer.clear_grad()
              self.status["learning_rate"] = curr_lr
              if self._nranks < 2 or self._local_rank == 0:
                  self.status["training_staus"].update(loss_dict)
  
              self.status["batch_time"].update(time.time() - iter_tic)
              self._compose_callback.on_step_end(self.status)
              # Note: ema_start_iters
              if self.use_ema and curr_iter == self.ema_start_iters:
                  logger.info("***" * 30)
                  logger.info("EMA starting ...")
                  logger.info("***" * 30)
                  self.ema.update(self.model, decay=0)
              elif self.use_ema and curr_iter > self.ema_start_iters:
                  self.ema.update(self.model)
              iter_tic = time.time()
  
          is_snapshot = (
              self._nranks < 2 or self._local_rank == 0
          ) and (
              (epoch_id + 1) % self.cfg.snapshot_epoch == 0
              or epoch_id == self.end_epoch - 1
          )
          if is_snapshot and self.use_ema:
              # apply ema weight on model
              weight = copy.deepcopy(self.ema.model.state_dict())
              for k, v in weight.items():
                  if paddle.is_floating_point(v):
                      weight[k].stop_gradient = True
              self.status["weight"] = weight
  
          self._compose_callback.on_epoch_end(self.status)
  
          if validate and is_snapshot:
              if not hasattr(self, "_eval_loader"):
                  # build evaluation dataset and loader
                  self._eval_dataset = self.cfg.EvalDataset
                  self._eval_batch_sampler = paddle.io.BatchSampler(
                      self._eval_dataset,
                      batch_size=self.cfg.EvalReader["batch_size"],
                  )
                  # If metric is VOC, need to be set collate_batch=False.
                  if self.cfg.metric == "VOC":
                      self.cfg["EvalReader"]["collate_batch"] = False
                  self._eval_loader = create("EvalReader")(
                      self._eval_dataset,
                      self.cfg.worker_num,
                      batch_sampler=self._eval_batch_sampler,
                  )
              # if validation in training is enabled, metrics should be re-init
              # Init_mark makes sure this code will only execute once
              if validate and Init_mark == False:
                  Init_mark = True
                  self._init_metrics(validate=validate)
                  self._reset_metrics()
  
              with paddle.no_grad():
                  self.status["save_best_model"] = True
                  self._eval_with_loader(self._eval_loader)
  
          if is_snapshot and self.use_ema:
              self.status.pop("weight")
  
      self._compose_callback.on_train_end(self.status)
  
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 4 for calling the Trainer_DenseTeacher obj's underlying member method code with example usage: obj.train(validate) without return types