code: def __call__(
      self,
      dataset_label,
      dataset_unlabel,
      worker_num,
      batch_sampler_label=None,
      batch_sampler_unlabel=None,
      return_list=False,
  ):
      # sup dataset
      self.dataset_label = dataset_label
      self.dataset_label.check_or_download_dataset()
      self.dataset_label.parse_dataset()
      self.dataset_label.set_transform(self._sample_transforms_label)
      self.dataset_label.set_kwargs(**self.kwargs)
      if batch_sampler_label is None:
          self._batch_sampler_label = DistributedBatchSampler(
              self.dataset_label,
              batch_size=self.batch_size_label,
              shuffle=self.shuffle,
              drop_last=self.drop_last,
          )
      else:
          self._batch_sampler_label = batch_sampler_label
  
      # unsup dataset
      self.dataset_unlabel = dataset_unlabel
      self.dataset_unlabel.length = self.dataset_label.__len__()
      self.dataset_unlabel.check_or_download_dataset()
      self.dataset_unlabel.parse_dataset()
      self.dataset_unlabel.set_transform(
          self._sample_transforms_unlabel
      )
      self.dataset_unlabel.set_kwargs(**self.kwargs)
      if batch_sampler_unlabel is None:
          self._batch_sampler_unlabel = DistributedBatchSampler(
              self.dataset_unlabel,
              batch_size=self.batch_size_unlabel,
              shuffle=self.shuffle,
              drop_last=self.drop_last,
          )
      else:
          self._batch_sampler_unlabel = batch_sampler_unlabel
  
      # DataLoader do not start sub-process in Windows and Mac
      # system, do not need to use shared memory
      use_shared_memory = (
          self.use_shared_memory
          and sys.platform not in ["win32", "darwin"]
      )
      # check whether shared memory size is bigger than 1G(1024M)
      if use_shared_memory:
          shm_size = _get_shared_memory_size_in_M()
          if shm_size is not None and shm_size < 1024.0:
              logger.warning(
                  "Shared memory size is less than 1G, "
                  "disable shared_memory in DataLoader"
              )
              use_shared_memory = False
  
      self.dataloader_label = DataLoader(
          dataset=self.dataset_label,
          batch_sampler=self._batch_sampler_label,
          collate_fn=self._batch_transforms_label,
          num_workers=worker_num,
          return_list=return_list,
          use_shared_memory=use_shared_memory,
      )
  
      self.dataloader_unlabel = DataLoader(
          dataset=self.dataset_unlabel,
          batch_sampler=self._batch_sampler_unlabel,
          collate_fn=self._batch_transforms_unlabel,
          num_workers=worker_num,
          return_list=return_list,
          use_shared_memory=use_shared_memory,
      )
  
      self.dataloader = CombineSSODLoader(
          self.dataloader_label, self.dataloader_unlabel
      )
      self.loader = iter(self.dataloader)
      return self
  
imports: import copy
  import os
  import traceback
  import six
  import sys
  import numpy
  import paddle
  import paddle.nn.functional
  from copy import deepcopy
  from paddle.io import DataLoader, DistributedBatchSampler
  from .utils import default_collate_fn
  from ppdet.core.workspace import register
  from .None import transform
  from .shm_utils import _get_shared_memory_size_in_M
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 2 for calling the BaseSemiDataLoader obj's underlying member method code with example usage: obj.__call__(dataset_label, dataset_unlabel, worker_num, batch_sampler_label, batch_sampler_unlabel, return_list) and returns: self