code: def make_dataset(images, labels, num_epochs=1, shuffle_data_seed=0):
      img = tf.data.Dataset.from_tensor_slices(images)
      lab = tf.data.Dataset.from_tensor_slices(
          np.array(labels, dtype=np.int64)
      )
      dataset = tf.data.Dataset.zip((img, lab))
      dataset = dataset.repeat(num_epochs).shuffle(
          buffer_size=10000, seed=shuffle_data_seed
      )
      return dataset
  
file_path: TensorLayer/examples/distributed_training/tutorial_cifar10_distributed_trainer.py
imports: import multiprocessing
  import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer.layers import BatchNormLayer, Conv2d, DenseLayer, FlattenLayer, InputLayer, MaxPool2d
label_desc: Use image node_id 1 for calling a global function with example usage: make_dataset(images, labels, num_epochs, shuffle_data_seed) and returns: dataset