code: def keras_model():
      model = tf.keras.models.Sequential(
          [
              tf.keras.layers.Input(
                  shape=(IMAGE_SIZE, IMAGE_SIZE), name="images"
              ),
              tf.keras.layers.Flatten(
                  input_shape=(IMAGE_SIZE, IMAGE_SIZE)
              ),
              tf.keras.layers.Dense(HIDDEN_SIZE, activation="relu"),
              tf.keras.layers.Dropout(DROPOUT),
              tf.keras.layers.Dense(NUM_CLASSES, activation="softmax"),
          ]
      )
      model.compile(
          optimizer="adam",
          loss="sparse_categorical_crossentropy",
          metrics=["accuracy"],
      )
  
      return model
  
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 4 for calling a global function with example usage: keras_model() and returns: model