class_docstr:     Pytorch Lightning module for semi-supervised learning.
  
  Args:
      active_dataset (ActiveLearningDataset): active learning dataset
      hparams (Namespace): hyper-parameters for the module
      **kwargs (**dict): extra arguments
class_docstr_tok: ["Pytorch","Lightning","module","for","semi-supervised","learning",".","Args",":","active_dataset","(","ActiveLearningDataset",")",":","active","learning","dataset","hparams","(","Namespace",")",":","hyper-parameters","for","the","module","*","*","kwargs","(","*","*","dict",")",":","extra","arguments"]
docstr: SemiSupervisedIterator for train set.
  
  Returns:
      SemiSupervisedIterator on the train set + pool set.
docstr_tok: ["SemiSupervisedIterator","for","train","set",".","Returns",":","SemiSupervisedIterator","on","the","train","set","+","pool","set","."]
code: def train_dataloader(self) -> SemiSupervisedIterator:
      """SemiSupervisedIterator for train set.
  
      Returns:
          SemiSupervisedIterator on the train set + pool set.
      """
      return SemiSupervisedIterator(
          self.active_dataset,
          self.hparams.batch_size,
          num_steps=self.hparams.num_steps,
          p=self.hparams.p,
          num_workers=self.hparams.workers,
          shuffle=True,
      )
  
file_path: baal/baal/utils/ssl_module.py
imports: import argparse
  import typing
  from argparse import Namespace
  from typing import Dict
  import pytorch_lightning
  from baal.active import ActiveLearningDataset
  from baal.utils.ssl_iterator import SemiSupervisedIterator
label_desc: Use image node_id 5 for calling the SSLModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: SemiSupervisedIterator