code: def get_fashion_mnist(
      withlabel=True,
      ndim=1,
      scale=1.0,
      dtype=None,
      label_dtype=numpy.int32,
      rgb_format=False,
  ):
      """Gets the Fashion-MNIST dataset.
  
      `Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist/>`_ is a
      set of fashion articles represented by grey-scale 28x28 images. In the
      original images, each pixel is represented by one-byte unsigned integer.
      This function scales the pixels to floating point values in the interval
      ``[0, scale]``.
  
      This function returns the training set and the test set of the official
      Fashion-MNIST dataset. If ``withlabel`` is ``True``, each dataset consists
      of tuples of images and labels, otherwise it only consists of images.
  
      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.
          ndim (int): Number of dimensions of each image. The shape of each image
              is determined depending on ``ndim`` as follows:
  
              - ``ndim == 1``: the shape is ``(784,)``
              - ``ndim == 2``: the shape is ``(28, 28)``
              - ``ndim == 3``: the shape is ``(1, 28, 28)``
  
          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.
          rgb_format (bool): if ``ndim == 3`` and ``rgb_format`` is ``True``, the
              image will be converted to rgb format by duplicating the channels
              so the image shape is (3, 28, 28). Default is ``False``.
  
      Returns:
          A tuple of two datasets. If ``withlabel`` is ``True``, both datasets
          are :class:`~chainer.datasets.TupleDataset` instances. Otherwise, both
          datasets are arrays of images.
  
      """
      train_raw = _retrieve_fashion_mnist_training()
      dtype = chainer.get_dtype(dtype)
  
      train = preprocess_mnist(
          train_raw,
          withlabel,
          ndim,
          scale,
          dtype,
          label_dtype,
          rgb_format,
      )
      test_raw = _retrieve_fashion_mnist_test()
      test = preprocess_mnist(
          test_raw,
          withlabel,
          ndim,
          scale,
          dtype,
          label_dtype,
          rgb_format,
      )
      return train, test
  
file_path: chainer/chainer/datasets/fashion_mnist.py
imports: import os
  import numpy
  import chainer
  from chainer.dataset import download
  from chainer.datasets._mnist_helper import make_npz
  from chainer.datasets._mnist_helper import preprocess_mnist
label_desc: Use image node_id 2 for calling a global function with example usage: get_fashion_mnist(withlabel, ndim, scale, dtype, label_dtype, rgb_format) and returns: train,  test