code: def get_data_iters(dataset, batch_size, opt):
      """get dataset iterators"""
      if dataset == "mnist":
          train_data, val_data = get_mnist_iterator(
              batch_size,
              (1, 28, 28),
              num_parts=kv.num_workers,
              part_index=kv.rank,
          )
      elif dataset == "cifar10":
          train_data, val_data = get_cifar10_iterator(
              batch_size,
              (3, 32, 32),
              num_parts=kv.num_workers,
              part_index=kv.rank,
          )
      elif dataset == "imagenet":
          shape_dim = 299 if model_name == "inceptionv3" else 224
  
          if not opt.data_dir:
              raise ValueError(
                  "Dir containing raw images in train/val is required for imagenet."
                  'Please specify "--data-dir"'
              )
  
          train_data, val_data = get_imagenet_iterator(
              opt.data_dir,
              batch_size,
              opt.num_workers,
              shape_dim,
              opt.dtype,
          )
      elif dataset == "caltech101":
          train_data, val_data = get_caltech101_iterator(
              batch_size, opt.num_workers, opt.dtype
          )
      elif dataset == "dummy":
          shape_dim = 299 if model_name == "inceptionv3" else 224
          train_data, val_data = dummy_iterator(
              batch_size, (3, shape_dim, shape_dim)
          )
      return train_data, val_data
  
file_path: mxnet/example/gluon/image_classification.py
imports: from __future__ import division
  import argparse, time, os
  import logging
  import mxnet
  from mxnet import gluon
  from mxnet import profiler
  from mxnet.gluon import nn
  from mxnet.gluon.model_zoo import vision
  from mxnet import autograd
  from mxnet.test_utils import get_mnist_iterator
  from mxnet.gluon.metric import Accuracy, TopKAccuracy, CompositeEvalMetric
  import numpy
  from data import get_cifar10_iterator, get_imagenet_iterator, get_caltech101_iterator, dummy_iterator
label_desc: Use image node_id 2 for calling a global function with example usage: get_data_iters(dataset, batch_size, opt) and returns: train_data,  val_data