code: def fake_data(
      dtype="float32",
      batch_size=32,
      height=224,
      width=224,
      depth=3,
      num_classes=1000,
  ):
      image_list = []
      label_list = []
      for _ in range(8):
          image = mx.ndarray.random.normal(
              -1, 1, shape=[1, depth, height, width], dtype=dtype
          )
          label = mx.ndarray.random.randint(0, num_classes, [1, 1])
  
          images = mx.ndarray.repeat(image, 128, axis=0)
          labels = mx.ndarray.repeat(label, 128, axis=0)
          # print(labels)
          image_list.append(images)
          label_list.append(labels)
  
      images = nd.concat(*image_list, dim=0)
      labels = nd.concat(*label_list, dim=0)
      # print(labels)
      fake_dataset = mx.gluon.data.ArrayDataset(images, labels)
  
      return mx.gluon.data.DataLoader(
          fake_dataset,
          batch_size=batch_size,
          num_workers=4,
          shuffle=True,
          last_batch="discard",
      )
  
file_path: byteps/tests/utils.py
imports: import mxnet
  import mxnet.ndarray
  import numpy
  from numba import jit
label_desc: Use image node_id 1 for calling a global function with example usage: fake_data(dtype, batch_size, height, width, depth, num_classes) and returns: mx