class_docstr: CIFAR10 image classification dataset from https://www.cs.toronto.edu/~kriz/cifar.html
  
  Each sample is an image (in 3D NDArray) with shape (32, 32, 3).
  
  Parameters
  ----------
  root : str, default $MXNET_HOME/datasets/cifar10
      Path to temp folder for storing data.
  train : bool, default True
      Whether to load the training or testing set.
  transform : function, default None
      DEPRECATED FUNCTION ARGUMENTS.
      A user defined callback that transforms each sample. For example::
  
          transform=lambda data, label: (data.astype(np.float32)/255, label)
class_docstr_tok: ["CIFAR10","image","classification","dataset","from","https",":","\/\/www.cs.toronto.edu\/~kriz\/cifar.html","Each","sample","is","an","image","(","in","3D","NDArray",")","with","shape","(","32",",","32",",","3",")",".","Parameters","--","--","--","--","--","root",":","str",",","default","$","MXNET_HOME\/datasets\/cifar10","Path","to","temp","folder","for","storing","data",".","train",":","bool",",","default","True","Whether","to","load","the","training","or","testing","set",".","transform",":","function",",","default","None","DEPRECATED","FUNCTION","ARGUMENTS",".","A","user","defined","callback","that","transforms","each","sample",".","For","example",":",":","transform=lambda","data",",","label",":","(","data.astype","(","np.float32",")","\/255",",","label",")"]
code: def _get_data(self):
      if any(
          not os.path.exists(path) or not check_sha1(path, sha1)
          for path, sha1 in (
              (os.path.join(self._root, name), sha1)
              for name, sha1 in self._train_data + self._test_data
          )
      ):
          namespace = "gluon/dataset/" + self._namespace
          filename = download(
              _get_repo_file_url(namespace, self._archive_file[0]),
              path=self._root,
              sha1_hash=self._archive_file[1],
          )
  
          with tarfile.open(filename) as tar:
              tar.extractall(self._root)
  
      if self._train:
          data_files = self._train_data
      else:
          data_files = self._test_data
      data, label = zip(
          *(
              self._read_batch(os.path.join(self._root, name))
              for name, _ in data_files
          )
      )
      data = np.concatenate(data)
      label = np.concatenate(label)
  
      array_fn = _mx_np.array if is_np_array() else nd.array
      self._data = array_fn(data, dtype=data.dtype)
      self._label = (
          array_fn(label, dtype=label.dtype) if is_np_array() else label
      )
  
imports: import os
  import gzip
  import tarfile
  import struct
  import warnings
  import numpy
  from ..None import dataset
  from ...utils import download, check_sha1, _get_repo_file_url
  from ....None import ndarray, image, recordio, base
  from ....None import numpy
  from ....util import is_np_array, default_array
  from ....base import numeric_types
label_desc: Use image node_id 3 for calling the CIFAR10 obj's underlying member method code with example usage: obj._get_data() without return types