code: def fake_image_set(batch_size, height, width, with_label=True):
      data_shape = [batch_size, height, width, 3]  # 3 channels
      images = tf.random.truncated_normal(
          data_shape,
          dtype=tf.float32,
          mean=112,
          stddev=70,
          name="fake_images",
      )
      images = tf.clip_by_value(images, 0.0, 255.0)
      images = tf.cast(images, tf.float32)
      if with_label:
          labels = tf.random.uniform(
              [batch_size],
              minval=0,
              maxval=1000 - 1,
              dtype=tf.int32,
              name="fake_labels",
          )
          ds = tf.data.Dataset.from_tensor_slices(([images], [labels]))
      else:
          ds = tf.data.Dataset.from_tensor_slices(([images]))
      ds = ds.repeat()
      return ds
  
imports: import tensorflow
  import sys
  import os
  import numpy
  from subprocess import call
  import horovod.tensorflow.keras
  from nvidia.dali.pipeline import pipeline_def
  import nvidia.dali.fn
  import nvidia.dali.types
  import nvidia.dali.tfrecord
label_desc: Use image node_id 6 for calling a global function with example usage: fake_image_set(batch_size, height, width, with_label) and returns: ds