code: def cnn(params):
      nn = tf.keras.models.Sequential(
          [
              tf.keras.layers.Flatten(input_shape=(28, 28)),
              tf.keras.layers.Dense(128, activation="relu"),
              tf.keras.layers.Dropout(0.2),
              tf.keras.layers.Dense(10),
          ]
      )
      loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
          from_logits=True
      )
  
      nn.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
      nn.fit(x_train, y_train, epochs=5)
      _, score = nn.evaluate(x=x_test, y=y_test)
  
      return score
  
label_desc: Use image node_id 1 for calling a global function with example usage: cnn(params) and returns: score