code: def __init__(self, X, encoders, problem_type, y=None, col_info=None):
      self.encoders = encoders
      self.col_info = col_info
  
      self.raw_data = X
  
      if y is None:
          self.targets = None
      elif problem_type == REGRESSION:
          self.targets = torch.FloatTensor(y)
      else:
          self.targets = torch.LongTensor(y)
  
      if col_info is None:
          self.columns = get_col_info(X)
      else:
          self.columns = self.col_info
  
      """must be a list of dicts, each dict is of the form {"name": col_name, "type": col_type} 
          where col_name is obtained from the df X, and col_type is CATEGORICAL, TEXT or SCALAR
           """
      self.cat_feat_origin_cards = None
      self.cont_feat_origin = None
      self.feature_encoders = None
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tab_transformer/utils.py
imports: import logging
  import torch
  from torch.utils.data import DataLoader, Dataset
  from .None import tab_transformer_encoder
  from .tab_transformer_encoder import NullEnc, WontEncodeError
label_desc: Use image node_id 1 to create a new TabTransformerDataset object from inherited base classes: Dataset with example: obj = TabTransformerDataset(X, encoders, problem_type, y, col_info)