code: def load_data_mnist(batch_size, resize=None, num_workers=4):
      """
      Load MNIST dataset
      """
      transformer = []
      if resize:
          transformer += [data.vision.transforms.Resize(resize)]
      transformer += [data.vision.transforms.ToTensor()]
      transformer = data.vision.transforms.Compose(transformer)
      mnist_train = data.vision.MNIST(train=True)
      mnist_test = data.vision.MNIST(train=False)
      train_iter = data.DataLoader(
          mnist_train.transform_first(transformer),
          batch_size,
          shuffle=True,
          num_workers=num_workers,
      )
      test_iter = data.DataLoader(
          mnist_test.transform_first(transformer),
          batch_size,
          shuffle=False,
          num_workers=num_workers,
      )
      return train_iter, test_iter
  
file_path: mxnet/tests/nightly/estimator/test_estimator_cnn.py
imports: import os
  import sys
  import mxnet
  import numpy
  from mxnet import gluon, init, nd
  from mxnet.gluon import data
  from mxnet.gluon.contrib.estimator import estimator
  from mxnet.gluon.model_zoo import vision
  import pytest
label_desc: Use image node_id 1 for calling a global function with example usage: load_data_mnist(batch_size, resize, num_workers) and returns: train_iter,  test_iter