docstr: Args:
          images: 4D numpy array
          labels: 2D or 1D numpy array
          shuffle: `bool`, should shuffle data or not
          normalization: `str` or None
                  None: no any normalization
                  divide_255: divide all pixels by 255
                  divide_256: divide all pixels by 256
                  by_channels: substract mean of every chanel and divide each
                          chanel data by it's standard deviation
docstr_tok: ["Args",":","images",":","4D","numpy","array","labels",":","2D","or","1D","numpy","array","shuffle",":","`","bool","`",",","should","shuffle","data","or","not","normalization",":","`","str","`","or","None","None",":","no","any","normalization","divide_255",":","divide","all","pixels","by","255","divide_256",":","divide","all","pixels","by","256","by_channels",":","substract","mean","of","every","chanel","and","divide","each","chanel","data","by","it","'s","standard","deviation"]
code: def __init__(self, images, labels, shuffle, normalization):
      """
      Args:
              images: 4D numpy array
              labels: 2D or 1D numpy array
              shuffle: `bool`, should shuffle data or not
              normalization: `str` or None
                      None: no any normalization
                      divide_255: divide all pixels by 255
                      divide_256: divide all pixels by 256
                      by_channels: substract mean of every chanel and divide each
                              chanel data by it's standard deviation
      """
      self._batch_counter, self.epoch_images, self.epoch_labels = (
          0,
          None,
          None,
      )
  
      self.shuffle = shuffle
      self.images = images
      self.labels = labels
      self.normalization = normalization
      self.start_new_epoch()
  
file_path: auptimizer/src/aup/Proposer/eas/data_providers/svhn.py
imports: import tempfile
  import os
  import scipy.io
  import numpy
  from ..data_providers.base_provider import ImagesDataSet, DataProvider
  from ..data_providers.downloader import download_data_url
label_desc: Use image node_id 1 to create a new SVHNDataSet object from inherited base classes: ImagesDataSet with example: obj = SVHNDataSet(images, labels, shuffle, normalization)