class_docstr: This class is the Base class for pythae's dataset
  
  A ``__getitem__`` is redefined and outputs a python dictionnary
  with the keys corresponding to `data` and `labels`.
  This Class should be used for any new data sets.
class_docstr_tok: ["This","class","is","the","Base","class","for","pythae","'s","dataset","A","``","__getitem__","``","is","redefined","and","outputs","a","python","dictionnary","with","the","keys","corresponding","to","`","data","`","and","`","labels","`",".","This","Class","should","be","used","for","any","new","data","sets","."]
code: def __init__(self, data, labels):
      self.labels = labels.type(torch.float)
      self.data = data.type(torch.float)
  
file_path: benchmark_VAE/src/pythae/data/datasets.py
imports: from collections import OrderedDict
  from typing import Any, Tuple
  import torch
  from torch.utils.data import Dataset
  from torch.utils.data._utils.collate import default_collate
label_desc: Use image node_id 1 to create a new BaseDataset object from inherited base classes: Dataset with example: obj = BaseDataset(data, labels)