class_docstr: This is a basic class which preprocesses the data.
  Basically, it takes messy data, detects potential nan, bad types end convert the
  data to a type handled by the VAE models (*i.e.* `torch.Tensor`). Moreover, if the
  data does not have the same shape, a reshaping is applied and data is resized to the
  **minimal shape**.
class_docstr_tok: ["This","is","a","basic","class","which","preprocesses","the","data",".","Basically",",","it","takes","messy","data",",","detects","potential","nan",",","bad","types","end","convert","the","data","to","a","type","handled","by","the","VAE","models","(","*","i.e",".","*","`","torch.Tensor","`",")",".","Moreover",",","if","the","data","does","not","have","the","same","shape",",","a","reshaping","is","applied","and","data","is","resized","to","the","*","*","minimal","shape","*","*","."]
code: def __init__(self):
      pass
  
file_path: benchmark_VAE/src/pythae/data/preprocessors.py
imports: import logging
  from typing import Optional, Union
  import numpy
  import torch
  from typing_extensions import Literal
  from pythae.data.datasets import BaseDataset
label_desc: Use image node_id 1 to create a new DataProcessor object  with example: obj = DataProcessor()