code: def __getitem__(self, index):
      x = self.data[index]
      x = (
          x > torch.distributions.Uniform(0, 1).sample(x.shape)
      ).float()
      return DatasetOutput(data=x)
  
imports: import logging
  import os
  from typing import List
  import numpy
  import torch
  import torch.nn
  from torch.utils.data import Dataset
  from pythae.data.datasets import DatasetOutput
  from pythae.models import SVAE, AutoModel, SVAEConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseEncoder
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 3 for calling the DynBinarizedMNIST obj's underlying member method code with example usage: obj.__getitem__(index) and returns: DatasetOutput