code: def network_b(batch_input_shape, classes, private=True):
      if private:
          model = tfe.keras.Sequential()
          model.add(
              tfe.keras.layers.Conv2D(
                  16,
                  5,
                  1,
                  padding="valid",
                  activation=None,
                  batch_input_shape=batch_input_shape,
                  lazy_normalization=True,
              )
          )
          model.add(tfe.keras.layers.MaxPooling2D(2))
          model.add(tfe.keras.layers.ReLU())
          model.add(
              tfe.keras.layers.Conv2D(
                  16,
                  5,
                  1,
                  padding="valid",
                  activation=None,
                  lazy_normalization=True,
              )
          )
          model.add(tfe.keras.layers.MaxPooling2D(2))
          model.add(tfe.keras.layers.ReLU())
          model.add(tfe.keras.layers.Flatten())
          model.add(
              tfe.keras.layers.Dense(
                  100, activation=None, lazy_normalization=True
              )
          )
          model.add(tfe.keras.layers.ReLU())
          model.add(
              tfe.keras.layers.Dense(
                  classes, activation=None, lazy_normalization=True
              )
          )
          return model
      else:
          model = tf.keras.Sequential()
          model.add(
              tf.keras.layers.Conv2D(
                  16,
                  5,
                  1,
                  padding="valid",
                  activation=None,
                  batch_input_shape=batch_input_shape,
              )
          )
          model.add(tf.keras.layers.MaxPooling2D(2))
          model.add(tf.keras.layers.ReLU())
          model.add(
              tf.keras.layers.Conv2D(
                  16, 5, 1, padding="valid", activation=None
              )
          )
          model.add(tf.keras.layers.MaxPooling2D(2))
          model.add(tf.keras.layers.ReLU())
          model.add(tf.keras.layers.Flatten())
          model.add(tf.keras.layers.Dense(100, activation=None))
          model.add(tf.keras.layers.ReLU())
          model.add(tf.keras.layers.Dense(classes, activation=None))
          return model
  
label_desc: Use image node_id 1 for calling a global function with example usage: network_b(batch_input_shape, classes, private) and returns: model, model