code: def graph_model(images, reuse, is_training):
      with tf_v1.variable_scope("mnist_net", reuse=reuse):
          images = tf_v1.layers.flatten(images)
          images = tf_v1.layers.dense(
              images, HIDDEN_SIZE, activation=tf_v1.nn.relu
          )
          images = tf_v1.layers.dropout(
              images, rate=DROPOUT, training=is_training
          )
          images = tf_v1.layers.dense(
              images, NUM_CLASSES, activation=tf_v1.nn.softmax
          )
      return images
  
imports: import nvidia.dali.plugin.tf
  import os
  from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.fn
  import nvidia.dali.types
  from test_utils_tensorflow import num_available_gpus
  from shutil import rmtree
  import tensorflow
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling a global function with example usage: graph_model(images, reuse, is_training) and returns: images