code: def get_svhn(
      withlabel=True,
      scale=1.0,
      dtype=None,
      label_dtype=numpy.int32,
      add_extra=False,
  ):
      """Gets the SVHN dataset.
  
      `The Street View House Numbers (SVHN) dataset
      <http://ufldl.stanford.edu/housenumbers/>`_
      is a dataset similar to MNIST but composed of cropped images of house
      numbers.
      The functionality of this function is identical to the counterpart for the
      MNIST dataset (:func:`~chainer.datasets.get_mnist`),
      with the exception that there is no ``ndim`` argument.
  
      .. note::
         `SciPy <https://www.scipy.org/>`_ is required to use this feature.
  
      Args:
          withlabel (bool): If ``True``, it returns datasets with labels. In this
              case, each example is a tuple of an image and a label. Otherwise,
              the datasets only contain images.
          scale (float): Pixel value scale. If it is 1 (default), pixels are
              scaled to the interval ``[0, 1]``.
          dtype: Data type of resulting image arrays. ``chainer.config.dtype`` is
              used by default (see :ref:`configuration`).
          label_dtype: Data type of the labels.
          add_extra: Use extra training set.
  
      Returns:
          If ``add_extra`` is ``False``, a tuple of two datasets (train and
          test). Otherwise, a tuple of three datasets (train, test, and extra).
          If ``withlabel`` is ``True``, all datasets are
          :class:`~chainer.datasets.TupleDataset` instances. Otherwise, both
          datasets are arrays of images.
  
      """
      if not _scipy_available:
          raise RuntimeError("SciPy is not available: %s" % _error)
  
      train_raw = _retrieve_svhn_training()
      dtype = chainer.get_dtype(dtype)
  
      train = _preprocess_svhn(
          train_raw, withlabel, scale, dtype, label_dtype
      )
      test_raw = _retrieve_svhn_test()
      test = _preprocess_svhn(
          test_raw, withlabel, scale, dtype, label_dtype
      )
      if add_extra:
          extra_raw = _retrieve_svhn_extra()
          extra = _preprocess_svhn(
              extra_raw, withlabel, scale, dtype, label_dtype
          )
          return train, test, extra
      else:
          return train, test
  
file_path: chainer/chainer/datasets/svhn.py
imports: import os
  import numpy
  import chainer
  from chainer.dataset import download
  from chainer.datasets import tuple_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: get_svhn(withlabel, scale, dtype, label_dtype, add_extra) and returns: train,  test,  extra, train,  test