docstr: Generate dataloaders for unsupervised train and eval set.
  
  Parameters
  ----------
  X_train : np.array
      Train set.
  eval_set : list of tuple
      List of eval tuple set (X, y).
  
  Returns
  -------
  train_dataloader : `torch.utils.data.Dataloader`
      Training dataloader.
  valid_dataloaders : list of `torch.utils.data.Dataloader`
      List of validation dataloaders.
docstr_tok: ["Generate","dataloaders","for","unsupervised","train","and","eval","set",".","Parameters","--","--","--","--","--","X_train",":","np.array","Train","set",".","eval_set",":","list","of","tuple","List","of","eval","tuple","set","(","X",",","y",")",".","Returns","--","--","--","-","train_dataloader",":","`","torch.utils.data.Dataloader","`","Training","dataloader",".","valid_dataloaders",":","list","of","`","torch.utils.data.Dataloader","`","List","of","validation","dataloaders","."]
code: def _construct_loaders(self, X_train, eval_set):
      """Generate dataloaders for unsupervised train and eval set.
  
      Parameters
      ----------
      X_train : np.array
          Train set.
      eval_set : list of tuple
          List of eval tuple set (X, y).
  
      Returns
      -------
      train_dataloader : `torch.utils.data.Dataloader`
          Training dataloader.
      valid_dataloaders : list of `torch.utils.data.Dataloader`
          List of validation dataloaders.
  
      """
      train_dataloader, valid_dataloaders = create_dataloaders(
          X_train,
          eval_set,
          self.updated_weights,
          self.batch_size,
          self.num_workers,
          self.drop_last,
          self.pin_memory,
      )
      return train_dataloader, valid_dataloaders
  
imports: import torch
  import numpy
  from torch.utils.data import DataLoader
  from pytorch_tabnet import tab_network
  from pytorch_tabnet.utils import create_explain_matrix, filter_weights, SparsePredictDataset, PredictDataset, check_input, create_group_matrix
  from torch.nn.utils import clip_grad_norm_
  from pytorch_tabnet.pretraining_utils import create_dataloaders, validate_eval_set
  from pytorch_tabnet.metrics import UnsupMetricContainer, check_metrics, UnsupervisedLoss
  from pytorch_tabnet.abstract_model import TabModel
  import scipy
label_desc: Use image node_id 9 for calling the TabNetPretrainer obj's underlying member method code with example usage: obj._construct_loaders(X_train, eval_set) and returns: train_dataloader,  valid_dataloaders