old_contents: import os
  
  from metal.mmtl.dataset import BERTDataset
  
  
  def get_all_dataloaders(
      dataset_cls, bert_model, train_dev_split_prop=0.8, max_len=512, dl_kwargs={}
  ):
      """ Initializes train/dev/test dataloaders given dataset_class"""
  
      assert isinstance(dataset_cls, BERTDataset)
  
      # split train -> artificial train/dev
      train_ds = dataset_cls(split="train", bert_model=bert_model, max_len=max_len)
      train_dl, dev_dl = train_ds.get_dataloader(
          split_prop=train_dev_split_prop, **dl_kwargs
      )
  
      # treat dev -> test
      test_ds = dataset_cls(split="dev", bert_model=bert_model, max_len=max_len)
      test_dl = test_ds.get_dataloader(**dl_kwargs)
  
      return train_dl, dev_dl, test_dl
  
  
  def tsv_path_for_dataset(dataset_name, dataset_split):
      return os.path.join(
          os.environ["GLUEDATA"], "{}/{}.tsv".format(dataset_name, dataset_split)
      )
  
new_contents: import os
  
  
  def get_all_dataloaders(
      dataset_cls, bert_model, train_dev_split_prop=0.8, max_len=512, dl_kwargs={}
  ):
      """ Initializes train/dev/test dataloaders given dataset_class"""
  
      # split train -> artificial train/dev
      train_ds = dataset_cls(split="train", bert_model=bert_model, max_len=max_len)
      train_dl, dev_dl = train_ds.get_dataloader(
          split_prop=train_dev_split_prop, **dl_kwargs
      )
  
      # treat dev -> test
      test_ds = dataset_cls(split="dev", bert_model=bert_model, max_len=max_len)
      test_dl = test_ds.get_dataloader(**dl_kwargs)
  
      return train_dl, dev_dl, test_dl
  
  
  def tsv_path_for_dataset(dataset_name, dataset_split):
      return os.path.join(
          os.environ["GLUEDATA"], "{}/{}.tsv".format(dataset_name, dataset_split)
      )
  
subject: remove ciruclar dependencies in imports